diff --git a/src/asim/calibration/resident/tour_mode_choice/coefficient_updates.csv b/src/asim/calibration/resident/tour_mode_choice/coefficient_updates.csv
deleted file mode 100644
index 5a51826e3..000000000
--- a/src/asim/calibration/resident/tour_mode_choice/coefficient_updates.csv
+++ /dev/null
@@ -1,758 +0,0 @@
-coefficient_name,value,constrain,purpose,mode,auto_suff,model_counts,target_counts,difference,percent_diff,coef_change,new_value,converged
-coef_zero,0.0,T,,,,,,,,,0.0,True
-coef_one,1.0,T,,,,,,,,,1.0,True
-coef_nest_root,1.0,T,,,,,,,,,1.0,True
-coef_nest_AUTO,0.5,T,,,,,,,,,0.5,True
-coef_nest_NONMOTORIZED,0.5,T,,,,,,,,,0.5,True
-coef_nest_MICROMOBILITY,0.5,T,,,,,,,,,0.5,True
-coef_nest_TRANSIT,0.5,T,,,,,,,,,0.5,True
-coef_nest_RIDEHAIL,0.5,T,,,,,,,,,0.5,True
-coef_nest_SCHOOL_BUS,0.5,T,,,,,,,,,0.5,True
-coef_nest_TRANSIT_WALKACCESS,0.5,T,,,,,,,,,0.5,True
-coef_nest_TRANSIT_PNRACCESS,0.5,T,,,,,,,,,0.5,True
-coef_nest_TRANSIT_KNRACCESS,0.5,T,,,,,,,,,0.5,True
-coef_nest_TRANSIT_TNCACCESS,0.5,T,,,,,,,,,0.5,True
-coef_ivt_work,-0.016,F,,,,,,,,,-0.016,True
-coef_ivt_univ,-0.016,F,,,,,,,,,-0.016,True
-coef_ivt_school,-0.01,F,,,,,,,,,-0.01,True
-coef_ivt_maint,-0.017,F,,,,,,,,,-0.017,True
-coef_ivt_disc,-0.015,F,,,,,,,,,-0.015,True
-coef_ivt_atwork,-0.032,F,,,,,,,,,-0.032,True
-coef_rel_out_work,-0.224,F,,,,,,,,,-0.224,True
-coef_rel_out_univ,-0.192,F,,,,,,,,,-0.192,True
-coef_rel_out_school,-0.12,F,,,,,,,,,-0.12,True
-coef_rel_out_maint,-0.204,F,,,,,,,,,-0.204,True
-coef_rel_out_disc,-0.18,F,,,,,,,,,-0.18,True
-coef_rel_out_atwork,-0.384,F,,,,,,,,,-0.384,True
-coef_rel_inb_work,-0.192,F,,,,,,,,,-0.192,True
-coef_rel_inb_univ,-0.192,F,,,,,,,,,-0.192,True
-coef_rel_inb_school,-0.12,F,,,,,,,,,-0.12,True
-coef_rel_inb_maint,-0.204,F,,,,,,,,,-0.204,True
-coef_rel_inb_disc,-0.18,F,,,,,,,,,-0.18,True
-coef_rel_inb_atwork,-0.384,F,,,,,,,,,-0.384,True
-coef_income_work,-0.625,F,,,,,,,,,-0.625,True
-coef_income_univ,-0.262,F,,,,,,,,,-0.262,True
-coef_income_school,-0.262,F,,,,,,,,,-0.262,True
-coef_income_maint,-0.262,F,,,,,,,,,-0.262,True
-coef_income_disc,-0.262,F,,,,,,,,,-0.262,True
-coef_income_atwork,-0.262,F,,,,,,,,,-0.262,True
-coef_walktime_work,-0.0424,F,,,,,,,,,-0.0424,True
-coef_walktime_univ,-0.0424,F,,,,,,,,,-0.0424,True
-coef_walktime_school,-0.068,F,,,,,,,,,-0.068,True
-coef_walktime_maint,-0.03995,F,,,,,,,,,-0.03995,True
-coef_walktime_disc,-0.045,F,,,,,,,,,-0.045,True
-coef_walktime_atwork,-0.0848,F,,,,,,,,,-0.0848,True
-coef_bikels_work,0.134333061,F,,,,,,,,,0.134333061,True
-coef_bikels_univ,0.134333061,F,,,,,,,,,0.134333061,True
-coef_bikels_school,0.214932897,F,,,,,,,,,0.214932897,True
-coef_bikels_maint,0.22,F,,,,,,,,,0.22,True
-coef_bikels_disc,0.22,F,,,,,,,,,0.22,True
-coef_bikels_atwork,0.23,F,,,,,,,,,0.23,True
-coef_wait_work,-0.024,F,,,,,,,,,-0.024,True
-coef_wait_univ,-0.024,F,,,,,,,,,-0.024,True
-coef_wait_school,-0.015,F,,,,,,,,,-0.015,True
-coef_wait_maint,-0.0255,F,,,,,,,,,-0.0255,True
-coef_wait_disc,-0.0225,F,,,,,,,,,-0.0225,True
-coef_wait_atwork,-0.048,F,,,,,,,,,-0.048,True
-coef_xwalk_work,-0.04,F,,,,,,,,,-0.04,True
-coef_xwalk_univ,-0.04,F,,,,,,,,,-0.04,True
-coef_xwalk_school,-0.025,F,,,,,,,,,-0.025,True
-coef_xwalk_maint,-0.0425,F,,,,,,,,,-0.0425,True
-coef_xwalk_disc,-0.0375,F,,,,,,,,,-0.0375,True
-coef_xwalk_atwork,-0.08,F,,,,,,,,,-0.08,True
-coef_xwait_work,-0.032,F,,,,,,,,,-0.032,True
-coef_xwait_univ,-0.032,F,,,,,,,,,-0.032,True
-coef_xwait_school,-0.02,F,,,,,,,,,-0.02,True
-coef_xwait_maint,-0.034,F,,,,,,,,,-0.034,True
-coef_xwait_disc,-0.03,F,,,,,,,,,-0.03,True
-coef_xwait_atwork,-0.064,F,,,,,,,,,-0.064,True
-coef_xfer_work,-0.024,F,,,,,,,,,-0.024,True
-coef_xfer_univ,-0.024,F,,,,,,,,,-0.024,True
-coef_xfer_school,-0.015,F,,,,,,,,,-0.015,True
-coef_xfer_maint,-0.0255,F,,,,,,,,,-0.0255,True
-coef_xfer_disc,-0.0225,F,,,,,,,,,-0.0225,True
-coef_xfer_atwork,-0.048,F,,,,,,,,,-0.048,True
-coef_xferdrive_work,-0.032,F,,,,,,,,,-0.032,True
-coef_xferdrive_univ,-0.032,F,,,,,,,,,-0.032,True
-coef_xferdrive_school,-0.02,F,,,,,,,,,-0.02,True
-coef_xferdrive_maint,-0.034,F,,,,,,,,,-0.034,True
-coef_xferdrive_disc,-0.03,F,,,,,,,,,-0.03,True
-coef_xferdrive_atwork,-0.064,F,,,,,,,,,-0.064,True
-coef_acctime_work,-0.032,F,,,,,,,,,-0.032,True
-coef_acctime_univ,-0.032,F,,,,,,,,,-0.032,True
-coef_acctime_school,-0.02,F,,,,,,,,,-0.02,True
-coef_acctime_maint,-0.034,F,,,,,,,,,-0.034,True
-coef_acctime_disc,-0.03,F,,,,,,,,,-0.03,True
-coef_acctime_atwork,-0.064,F,,,,,,,,,-0.064,True
-coef_oMix_nmot_work,0.210144,F,,,,,,,,,0.210144,True
-coef_oMix_wTran_work,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_nmot_work,0.002998,F,,,,,,,,,0.002998,True
-coef_oIntDen_wTran_work,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_nmot_work,0.020707,F,,,,,,,,,0.020707,True
-coef_dEmpDen_wTran_work,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_dTran_work,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr2_work,-0.21388,F,,,,,,,,,-0.21388,True
-coef_age1624_sr3p_work,-1.79023,F,,,,,,,,,-1.79023,True
-coef_age1624_nmot_work,0.303216,F,,,,,,,,,0.303216,True
-coef_age1624_tran_work,0.794718,F,,,,,,,,,0.794718,True
-coef_age4155_sr2_work,-0.30638,F,,,,,,,,,-0.30638,True
-coef_age4155_sr3p_work,-0.41025,F,,,,,,,,,-0.41025,True
-coef_age4155_nmot_work,-0.17752,F,,,,,,,,,-0.17752,True
-coef_age4155_tran_work,-0.42301,F,,,,,,,,,-0.42301,True
-coef_age5664_sr2_work,-1.02962,F,,,,,,,,,-1.02962,True
-coef_age5664_sr3p_work,-0.85641,F,,,,,,,,,-0.85641,True
-coef_age5664_nmot_work,-0.64534,F,,,,,,,,,-0.64534,True
-coef_age5664_tran_work,-0.44991,F,,,,,,,,,-0.44991,True
-coef_age65pl_sr2_work,-0.67111,F,,,,,,,,,-0.67111,True
-coef_age65pl_sr3p_work,-1.43462,F,,,,,,,,,-1.43462,True
-coef_age65pl_nmot_work,-1.45334,F,,,,,,,,,-1.45334,True
-coef_age65pl_tran_work,-1.1231,F,,,,,,,,,-1.1231,True
-coef_female_sr2_work,0.594728,F,,,,,,,,,0.594728,True
-coef_female_sr3p_work,0.848064,F,,,,,,,,,0.848064,True
-coef_female_tran_work,0.157786,F,,,,,,,,,0.157786,True
-coef_female_nmot_work,0.0,F,,,,,,,,,0.0,True
-coef_ageund16_bike_work,0.0,F,,,,,,,,,0.0,True
-coef_age1624_bike_work,0.0,F,,,,,,,,,0.0,True
-coef_age4155_bike_work,-0.73111,F,,,,,,,,,-0.73111,True
-coef_age5664_bike_work,-0.64352,F,,,,,,,,,-0.64352,True
-coef_age65pl_bike_work,-1.54867,F,,,,,,,,,-1.54867,True
-coef_female_bike_work,-1.19364,F,,,,,,,,,-1.19364,True
-coef_inc100plus_bike_work,0.641381,F,,,,,,,,,0.641381,True
-coef_LUnorm_bike_work,0.083266,F,,,,,,,,,0.083266,True
-coef_oMlCoast_bike_work,-1.42018,F,,,,,,,,,-1.42018,True
-coef_oMlCoast2p_bike_work,1.335076,F,,,,,,,,,1.335076,True
-coef_oMlCoast5p_bike_work,0.079563,F,,,,,,,,,0.079563,True
-coef_ageund16_walk_work,2.188395,F,,,,,,,,,2.188395,True
-coef_age1624_walk_work,1.430593,F,,,,,,,,,1.430593,True
-coef_age4155_walk_work,-0.43207,F,,,,,,,,,-0.43207,True
-coef_age5664_walk_work,-0.51999,F,,,,,,,,,-0.51999,True
-coef_age65pl_walk_work,-0.83162,F,,,,,,,,,-0.83162,True
-coef_female_walk_work,0.0,F,,,,,,,,,0.0,True
-coef_inc60100_walk_work,-0.20839,F,,,,,,,,,-0.20839,True
-coef_LUnorm_walk_work,0.009309,F,,,,,,,,,0.009309,True
-coef_dEmpNorm_walk_work,0.099811,F,,,,,,,,,0.099811,True
-coef_hhsize2_sr2_work,1.069642353,F,,,,,,,,,1.069642353,True
-coef_hhsize2_sr3p_work,-0.467357298,F,,,,,,,,,-0.467357298,True
-coef_hhsize3_sr2_work,1.58018418,F,,,,,,,,,1.58018418,True
-coef_hhsize3_sr3p_work,0.654631352,F,,,,,,,,,0.654631352,True
-coef_hhsize4p_sr2_work,1.688389262,F,,,,,,,,,1.688389262,True
-coef_hhsize4p_sr3p_work,1.49870326,F,,,,,,,,,1.49870326,True
-coef_oMix_nmot_univ,0.122315,F,,,,,,,,,0.122315,True
-coef_oMix_wTran_univ,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_nmot_univ,0.009072,F,,,,,,,,,0.009072,True
-coef_oIntDen_wTran_univ,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_nmot_univ,0.081786,F,,,,,,,,,0.081786,True
-coef_dEmpDen_wTran_univ,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_dTran_univ,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr2_univ,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr3p_univ,0.0,F,,,,,,,,,0.0,True
-coef_age1624_nmot_univ,0.0,F,,,,,,,,,0.0,True
-coef_age1624_tran_univ,0.461169,F,,,,,,,,,0.461169,True
-coef_age4155_sr2_univ,0.0,F,,,,,,,,,0.0,True
-coef_age4155_sr3p_univ,0.0,F,,,,,,,,,0.0,True
-coef_age4155_nmot_univ,0.0,F,,,,,,,,,0.0,True
-coef_age4155_tran_univ,0.0,F,,,,,,,,,0.0,True
-coef_age5664_sr2_univ,0.0,F,,,,,,,,,0.0,True
-coef_age5664_sr3p_univ,0.0,F,,,,,,,,,0.0,True
-coef_age5664_nmot_univ,0.0,F,,,,,,,,,0.0,True
-coef_age5664_tran_univ,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_sr2_univ,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_sr3p_univ,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_nmot_univ,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_tran_univ,0.0,F,,,,,,,,,0.0,True
-coef_female_sr2_univ,0.0,F,,,,,,,,,0.0,True
-coef_female_sr3p_univ,0.0,F,,,,,,,,,0.0,True
-coef_female_tran_univ,0.0,F,,,,,,,,,0.0,True
-coef_female_nmot_univ,0.0,F,,,,,,,,,0.0,True
-coef_ageund16_bike_univ,0.0,F,,,,,,,,,0.0,True
-coef_age1624_bike_univ,0.0,F,,,,,,,,,0.0,True
-coef_age4155_bike_univ,-0.73111,F,,,,,,,,,-0.73111,True
-coef_age5664_bike_univ,-0.64352,F,,,,,,,,,-0.64352,True
-coef_age65pl_bike_univ,-1.54867,F,,,,,,,,,-1.54867,True
-coef_female_bike_univ,-1.19364,F,,,,,,,,,-1.19364,True
-coef_inc100plus_bike_univ,0.641381,F,,,,,,,,,0.641381,True
-coef_LUnorm_bike_univ,0.083266,F,,,,,,,,,0.083266,True
-coef_oMlCoast_bike_univ,0.0,F,,,,,,,,,0.0,True
-coef_oMlCoast2p_bike_univ,0.0,F,,,,,,,,,0.0,True
-coef_oMlCoast5p_bike_univ,0.0,F,,,,,,,,,0.0,True
-coef_ageund16_walk_univ,2.188395,F,,,,,,,,,2.188395,True
-coef_age1624_walk_univ,1.430593,F,,,,,,,,,1.430593,True
-coef_age4155_walk_univ,-0.43207,F,,,,,,,,,-0.43207,True
-coef_age5664_walk_univ,-0.51999,F,,,,,,,,,-0.51999,True
-coef_age65pl_walk_univ,-0.83162,F,,,,,,,,,-0.83162,True
-coef_female_walk_univ,0.0,F,,,,,,,,,0.0,True
-coef_inc60100_walk_univ,-0.20839,F,,,,,,,,,-0.20839,True
-coef_LUnorm_walk_univ,0.009309,F,,,,,,,,,0.009309,True
-coef_dEmpNorm_walk_univ,0.099811,F,,,,,,,,,0.099811,True
-coef_hhsize2_sr2_univ,1.871179646,F,,,,,,,,,1.871179646,True
-coef_hhsize2_sr3p_univ,1.871179646,F,,,,,,,,,1.871179646,True
-coef_hhsize3_sr2_univ,1.871179646,F,,,,,,,,,1.871179646,True
-coef_hhsize3_sr3p_univ,1.871179646,F,,,,,,,,,1.871179646,True
-coef_hhsize4p_sr2_univ,2.426268851,F,,,,,,,,,2.426268851,True
-coef_hhsize4p_sr3p_univ,2.426268851,F,,,,,,,,,2.426268851,True
-coef_oMix_nmot_school,0.0,F,,,,,,,,,0.0,True
-coef_oMix_wTran_school,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_nmot_school,0.002951,F,,,,,,,,,0.002951,True
-coef_oIntDen_wTran_school,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_nmot_school,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_wTran_school,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_dTran_school,0.0,F,,,,,,,,,0.0,True
-coef_age0105_schb_school,0.0,F,,,,,,,,,0.0,True
-coef_age0105_nmot_school,-1.16217,F,,,,,,,,,-1.16217,True
-coef_age0105_tran_school,-6.49996,F,,,,,,,,,-6.49996,True
-coef_age0612_schb_school,1.448589,F,,,,,,,,,1.448589,True
-coef_age0612_nmot_school,-0.57675,F,,,,,,,,,-0.57675,True
-coef_age0612_tran_school,-4.5987,F,,,,,,,,,-4.5987,True
-coef_age1315_schb_school,1.296494,F,,,,,,,,,1.296494,True
-coef_age1315_nmot_school,0.687165,F,,,,,,,,,0.687165,True
-coef_age1315_tran_school,-1.18344,F,,,,,,,,,-1.18344,True
-coef_female_sr2_school,0.370191,F,,,,,,,,,0.370191,True
-coef_female_sr3p_school,0.326161,F,,,,,,,,,0.326161,True
-coef_female_tran_school,0.609312,F,,,,,,,,,0.609312,True
-coef_female_nmot_school,0.0,F,,,,,,,,,0.0,True
-coef_female_schb_school,0.0,F,,,,,,,,,0.0,True
-coef_ageund16_bike_school,0.0,F,,,,,,,,,0.0,True
-coef_age1624_bike_school,0.0,F,,,,,,,,,0.0,True
-coef_age4155_bike_school,-1.16978,F,,,,,,,,,-1.16978,True
-coef_age5664_bike_school,-1.02963,F,,,,,,,,,-1.02963,True
-coef_age65pl_bike_school,-2.47787,F,,,,,,,,,-2.47787,True
-coef_female_bike_school,-1.90983,F,,,,,,,,,-1.90983,True
-coef_inc100plus_bike_school,1.026209,F,,,,,,,,,1.026209,True
-coef_LUnorm_bike_school,0.133225,F,,,,,,,,,0.133225,True
-coef_oMlCoast_bike_school,-2.27229,F,,,,,,,,,-2.27229,True
-coef_oMlCoast2p_bike_school,2.136121,F,,,,,,,,,2.136121,True
-coef_oMlCoast5p_bike_school,0.127301,F,,,,,,,,,0.127301,True
-coef_ageund16_walk_school,3.371151,F,,,,,,,,,3.371151,True
-coef_age1624_walk_school,2.126636,F,,,,,,,,,2.126636,True
-coef_age4155_walk_school,-0.76078,F,,,,,,,,,-0.76078,True
-coef_age5664_walk_school,-0.90034,F,,,,,,,,,-0.90034,True
-coef_age65pl_walk_school,-1.38069,F,,,,,,,,,-1.38069,True
-coef_female_walk_school,0.0,F,,,,,,,,,0.0,True
-coef_inc60100_walk_school,-0.35622,F,,,,,,,,,-0.35622,True
-coef_LUnorm_walk_school,0.701592,F,,,,,,,,,0.701592,True
-coef_dEmpNorm_walk_school,0.191664,F,,,,,,,,,0.191664,True
-coef_hhsize2_sr2_school,0.0,F,,,,,,,,,0.0,True
-coef_hhsize2_sr3p_school,0.0,F,,,,,,,,,0.0,True
-coef_hhsize3_sr2_school,0.66814,F,,,,,,,,,0.66814,True
-coef_hhsize3_sr3p_school,0.66814,F,,,,,,,,,0.66814,True
-coef_hhsize4p_sr2_school,0.41147,F,,,,,,,,,0.41147,True
-coef_hhsize4p_sr3p_school,2.09725,F,,,,,,,,,2.09725,True
-coef_oMix_nmot_maint,0.145634,F,,,,,,,,,0.145634,True
-coef_oMix_wTran_maint,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_nmot_maint,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_wTran_maint,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_nmot_maint,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_wTran_maint,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_dTran_maint,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr2_maint,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr3p_maint,0.0,F,,,,,,,,,0.0,True
-coef_age1624_nmot_maint,0.0,F,,,,,,,,,0.0,True
-coef_age1624_tran_maint,1.621112,F,,,,,,,,,1.621112,True
-coef_age4155_sr2_maint,-0.82262,F,,,,,,,,,-0.82262,True
-coef_age4155_sr3p_maint,-1.93552,F,,,,,,,,,-1.93552,True
-coef_age4155_nmot_maint,-1.34146,F,,,,,,,,,-1.34146,True
-coef_age4155_tran_maint,-1.39312,F,,,,,,,,,-1.39312,True
-coef_age5664_sr2_maint,-0.95497,F,,,,,,,,,-0.95497,True
-coef_age5664_sr3p_maint,-2.16777,F,,,,,,,,,-2.16777,True
-coef_age5664_nmot_maint,-1.3422,F,,,,,,,,,-1.3422,True
-coef_age5664_tran_maint,-1.46184,F,,,,,,,,,-1.46184,True
-coef_age65pl_sr2_maint,-1.06222,F,,,,,,,,,-1.06222,True
-coef_age65pl_sr3p_maint,-2.1471,F,,,,,,,,,-2.1471,True
-coef_age65pl_nmot_maint,-2.32071,F,,,,,,,,,-2.32071,True
-coef_age65pl_tran_maint,-2.86495,F,,,,,,,,,-2.86495,True
-coef_female_sr2_maint,0.323867,F,,,,,,,,,0.323867,True
-coef_female_sr3p_maint,0.34258,F,,,,,,,,,0.34258,True
-coef_female_tran_maint,0.0,F,,,,,,,,,0.0,True
-coef_female_nmot_maint,0.0,F,,,,,,,,,0.0,True
-coef_ageund16_bike_maint,0.0,F,,,,,,,,,0.0,True
-coef_age1624_bike_maint,0.0,F,,,,,,,,,0.0,True
-coef_age4155_bike_maint,-0.68811,F,,,,,,,,,-0.68811,True
-coef_age5664_bike_maint,-0.60566,F,,,,,,,,,-0.60566,True
-coef_age65pl_bike_maint,-1.45757,F,,,,,,,,,-1.45757,True
-coef_female_bike_maint,-1.12343,F,,,,,,,,,-1.12343,True
-coef_inc100plus_bike_maint,0.603652,F,,,,,,,,,0.603652,True
-coef_LUnorm_bike_maint,0.078368,F,,,,,,,,,0.078368,True
-coef_oMlCoast_bike_maint,-1.33664,F,,,,,,,,,-1.33664,True
-coef_oMlCoast2p_bike_maint,1.256542,F,,,,,,,,,1.256542,True
-coef_oMlCoast5p_bike_maint,0.074883,F,,,,,,,,,0.074883,True
-coef_ageund16_walk_maint,1.98303,F,,,,,,,,,1.98303,True
-coef_age1624_walk_maint,1.250962,F,,,,,,,,,1.250962,True
-coef_age4155_walk_maint,-0.44752,F,,,,,,,,,-0.44752,True
-coef_age5664_walk_maint,-0.52961,F,,,,,,,,,-0.52961,True
-coef_age65pl_walk_maint,-0.81217,F,,,,,,,,,-0.81217,True
-coef_female_walk_maint,0.0,F,,,,,,,,,0.0,True
-coef_inc60100_walk_maint,-0.20954,F,,,,,,,,,-0.20954,True
-coef_LUnorm_walk_maint,0.412701,F,,,,,,,,,0.412701,True
-coef_dEmpNorm_walk_maint,0.112744,F,,,,,,,,,0.112744,True
-coef_hhsize2_sr2_maint,0.0,F,,,,,,,,,0.0,True
-coef_hhsize2_sr3p_maint,-1.679852226,F,,,,,,,,,-1.679852226,True
-coef_hhsize3_sr2_maint,0.488088852,F,,,,,,,,,0.488088852,True
-coef_hhsize3_sr3p_maint,-1.335043303,F,,,,,,,,,-1.335043303,True
-coef_hhsize4p_sr2_maint,0.308239564,F,,,,,,,,,0.308239564,True
-coef_hhsize4p_sr3p_maint,0.585684607,F,,,,,,,,,0.585684607,True
-coef_oMix_nmot_disc,0.172434,F,,,,,,,,,0.172434,True
-coef_oMix_wTran_disc,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_nmot_disc,0.005711,F,,,,,,,,,0.005711,True
-coef_oIntDen_wTran_disc,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_nmot_disc,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_wTran_disc,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_dTran_disc,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr2_disc,-0.51961,F,,,,,,,,,-0.51961,True
-coef_age1624_sr3p_disc,-1.31632,F,,,,,,,,,-1.31632,True
-coef_age1624_nmot_disc,-0.5557,F,,,,,,,,,-0.5557,True
-coef_age1624_tran_disc,1.063749,F,,,,,,,,,1.063749,True
-coef_age4155_sr2_disc,-1.04157,F,,,,,,,,,-1.04157,True
-coef_age4155_sr3p_disc,-1.21044,F,,,,,,,,,-1.21044,True
-coef_age4155_nmot_disc,-1.14969,F,,,,,,,,,-1.14969,True
-coef_age4155_tran_disc,-0.48434,F,,,,,,,,,-0.48434,True
-coef_age5664_sr2_disc,-0.84295,F,,,,,,,,,-0.84295,True
-coef_age5664_sr3p_disc,-0.96503,F,,,,,,,,,-0.96503,True
-coef_age5664_nmot_disc,-0.97814,F,,,,,,,,,-0.97814,True
-coef_age5664_tran_disc,-1.0845,F,,,,,,,,,-1.0845,True
-coef_age65pl_sr2_disc,-0.89435,F,,,,,,,,,-0.89435,True
-coef_age65pl_sr3p_disc,-1.11463,F,,,,,,,,,-1.11463,True
-coef_age65pl_nmot_disc,-1.69155,F,,,,,,,,,-1.69155,True
-coef_age65pl_tran_disc,-2.49829,F,,,,,,,,,-2.49829,True
-coef_female_sr2_disc,0.261989,F,,,,,,,,,0.261989,True
-coef_female_sr3p_disc,0.273571,F,,,,,,,,,0.273571,True
-coef_female_tran_disc,-0.23309,F,,,,,,,,,-0.23309,True
-coef_female_nmot_disc,0.0,F,,,,,,,,,0.0,True
-coef_beachParkBikeConstant,0.7,F,,,,,,,,,0.7,True
-coef_ageund16_bike_disc,0.0,F,,,,,,,,,0.0,True
-coef_age1624_bike_disc,0.0,F,,,,,,,,,0.0,True
-coef_age4155_bike_disc,-0.77985,F,,,,,,,,,-0.77985,True
-coef_age5664_bike_disc,-0.68642,F,,,,,,,,,-0.68642,True
-coef_age65pl_bike_disc,-1.65192,F,,,,,,,,,-1.65192,True
-coef_female_bike_disc,-1.27322,F,,,,,,,,,-1.27322,True
-coef_inc100plus_bike_disc,0.684139,F,,,,,,,,,0.684139,True
-coef_LUnorm_bike_disc,0.088817,F,,,,,,,,,0.088817,True
-coef_oMlCoast_bike_disc,-1.51486,F,,,,,,,,,-1.51486,True
-coef_oMlCoast2p_bike_disc,1.424081,F,,,,,,,,,1.424081,True
-coef_oMlCoast5p_bike_disc,0.084867,F,,,,,,,,,0.084867,True
-coef_ageund16_walk_disc,2.247434,F,,,,,,,,,2.247434,True
-coef_age1624_walk_disc,1.417757,F,,,,,,,,,1.417757,True
-coef_age4155_walk_disc,-0.50719,F,,,,,,,,,-0.50719,True
-coef_age5664_walk_disc,-0.60023,F,,,,,,,,,-0.60023,True
-coef_age65pl_walk_disc,-0.92046,F,,,,,,,,,-0.92046,True
-coef_female_walk_disc,0.0,F,,,,,,,,,0.0,True
-coef_inc60100_walk_disc,-0.23748,F,,,,,,,,,-0.23748,True
-coef_LUnorm_walk_disc,0.467728,F,,,,,,,,,0.467728,True
-coef_dEmpNorm_walk_disc,0.127776,F,,,,,,,,,0.127776,True
-coef_hhsize2_sr2_disc,0.352972249,F,,,,,,,,,0.352972249,True
-coef_hhsize2_sr3p_disc,-0.936579922,F,,,,,,,,,-0.936579922,True
-coef_hhsize3_sr2_disc,0.412665036,F,,,,,,,,,0.412665036,True
-coef_hhsize3_sr3p_disc,-0.798959661,F,,,,,,,,,-0.798959661,True
-coef_hhsize4p_sr2_disc,0.761157155,F,,,,,,,,,0.761157155,True
-coef_hhsize4p_sr3p_disc,0.578130166,F,,,,,,,,,0.578130166,True
-coef_age1624_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age1624_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age1624_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age1624_tran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age4155_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age4155_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age4155_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age4155_tran_atwork,-1.166,F,,,,,,,,,-1.166,True
-coef_age5664_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age5664_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age5664_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age5664_tran_atwork,-1.263,F,,,,,,,,,-1.263,True
-coef_age65pl_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_age65pl_tran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_sr_da_atwork,-0.824,F,,,,,,,,,-0.824,True
-coef_sr_sr_atwork,2.435,F,,,,,,,,,2.435,True
-coef_female_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_female_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_female_tran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_female_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_oMix_nmot_atwork,0.214,F,,,,,,,,,0.214,True
-coef_oMix_wTran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_oIntDen_wTran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_nmot_atwork,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_wTran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_dEmpDen_dTran_atwork,0.0,F,,,,,,,,,0.0,True
-coef_hhsize2_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_hhsize2_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_hhsize3_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_hhsize3_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_hhsize4p_sr2_atwork,0.0,F,,,,,,,,,0.0,True
-coef_hhsize4p_sr3p_atwork,0.0,F,,,,,,,,,0.0,True
-coef_calib_civtebikeownership_BIKE_school,1.0,F,,,,,,,,,1.0,True
-coef_calib_civtebikeownership_BIKE_atwork,1.0,F,,,,,,,,,1.0,True
-coef_calib_civtebikeownership_BIKE_maint,1.0,F,,,,,,,,,1.0,True
-coef_calib_escorttour_WALK_maint,-1.2579,F,,,,,,,,,-1.2579,True
-coef_calib_escorttour_PNR_TRANSIT_maint,-5.8388,F,,,,,,,,,-5.8388,True
-coef_calib_civtebikeownership_BIKE_disc,1.0,F,,,,,,,,,1.0,True
-coef_calib_probikedistrict_BIKE_maint,1.5524,F,,,,,,,,,1.5524,True
-coef_calib_civtebikeownership_BIKE_univ,1.0,F,,,,,,,,,1.0,True
-coef_calib_escorttour_WALK_TRANSIT_maint,-5.8388,F,,,,,,,,,-5.8388,True
-coef_calib_probikedistrict_BIKE_univ,1.5524,F,,,,,,,,,1.5524,True
-coef_calib_probikedistrict_BIKE_atwork,1.5524,F,,,,,,,,,1.5524,True
-coef_calib_probikedistrict_BIKE_disc,1.5524,F,,,,,,,,,1.5524,True
-coef_calib_escorttour_BIKE_maint,-1.2579,F,,,,,,,,,-1.2579,True
-coef_calib_escorttour_TNC_TRANSIT_maint,-5.8388,F,,,,,,,,,-5.8388,True
-coef_calib_probikedistrict_BIKE_work,1.5524,F,,,,,,,,,1.5524,True
-coef_calib_probikedistrict_BIKE_school,1.5524,F,,,,,,,,,1.5524,True
-coef_calib_escorttour_KNR_TRANSIT_maint,-5.8388,F,,,,,,,,,-5.8388,True
-coef_calib_civtebikeownership_BIKE_work,1.0,F,,,,,,,,,1.0,True
-coef_calib_parkingconst_WLK_TRANSIT_work,0.64,F,,,,,,,,,0.64,True
-coef_calib_parkingconst_WLK_TRANSIT_univ,0.64,F,,,,,,,,,0.64,True
-coef_calib_parkingconst_WLK_TRANSIT_school,0.4,F,,,,,,,,,0.4,True
-coef_calib_parkingconst_WLK_TRANSIT_maint,0.765,F,,,,,,,,,0.765,True
-coef_calib_parkingconst_WLK_TRANSIT_disc,0.9,F,,,,,,,,,0.9,True
-coef_calib_parkingconst_WLK_TRANSIT_atwork,0.96,F,,,,,,,,,0.96,True
-coef_calib_parkingconst_DRV_TRANSIT_work,1.28,F,,,,,,,,,1.28,True
-coef_calib_parkingconst_DRV_TRANSIT_univ,1.28,F,,,,,,,,,1.28,True
-coef_calib_parkingconst_DRV_TRANSIT_school,0.8,F,,,,,,,,,0.8,True
-coef_calib_parkingconst_DRV_TRANSIT_maint,1.36,F,,,,,,,,,1.36,True
-coef_calib_parkingconst_DRV_TRANSIT_disc,1.2,F,,,,,,,,,1.2,True
-coef_calib_parkingconst_DRV_TRANSIT_atwork,1.92,F,,,,,,,,,1.92,True
-coef_calib_distance_WALK_TRANSIT_work,-0.016,F,,,,,,,,,-0.016,True
-coef_calib_distance_WALK_TRANSIT_univ,-0.016,F,,,,,,,,,-0.016,True
-coef_calib_distance_WALK_TRANSIT_school,-0.01,F,,,,,,,,,-0.01,True
-coef_calib_distance_WALK_TRANSIT_maint,-0.017,F,,,,,,,,,-0.017,True
-coef_calib_distance_WALK_TRANSIT_disc,-0.015,F,,,,,,,,,-0.015,True
-coef_calib_distance_WALK_TRANSIT_atwork,-0.032,F,,,,,,,,,-0.032,True
-coef_calib_distance_PNR_TRANSIT_work,-0.016,F,,,,,,,,,-0.016,True
-coef_calib_distance_PNR_TRANSIT_univ,-0.016,F,,,,,,,,,-0.016,True
-coef_calib_distance_PNR_TRANSIT_school,-0.01,F,,,,,,,,,-0.01,True
-coef_calib_distance_PNR_TRANSIT_maint,-0.017,F,,,,,,,,,-0.017,True
-coef_calib_distance_PNR_TRANSIT_disc,-0.015,F,,,,,,,,,-0.015,True
-coef_calib_distance_PNR_TRANSIT_atwork,-0.032,F,,,,,,,,,-0.032,True
-coef_calib_distance_KNR_TRANSIT_work,-0.08,F,,,,,,,,,-0.08,True
-coef_calib_distance_KNR_TRANSIT_univ,-0.08,F,,,,,,,,,-0.08,True
-coef_calib_distance_KNR_TRANSIT_school,-0.05,F,,,,,,,,,-0.05,True
-coef_calib_distance_KNR_TRANSIT_maint,-0.085,F,,,,,,,,,-0.085,True
-coef_calib_distance_KNR_TRANSIT_disc,-0.075,F,,,,,,,,,-0.075,True
-coef_calib_distance_KNR_TRANSIT_atwork,-0.16,F,,,,,,,,,-0.16,True
-coef_calib_distance_TNC_TRANSIT_work,-0.016,F,,,,,,,,,-0.016,True
-coef_calib_distance_TNC_TRANSIT_univ,-0.016,F,,,,,,,,,-0.016,True
-coef_calib_distance_TNC_TRANSIT_school,-0.01,F,,,,,,,,,-0.01,True
-coef_calib_distance_TNC_TRANSIT_maint,-0.017,F,,,,,,,,,-0.017,True
-coef_calib_distance_TNC_TRANSIT_disc,-0.015,F,,,,,,,,,-0.015,True
-coef_calib_distance_TNC_TRANSIT_atwork,-0.032,F,,,,,,,,,-0.032,True
-#coef_calib_zeroautohhindivtou_DRIVEALONE_work,-0.8236,F,,,,,,,,,-0.8236,True
-coef_calib_zeroautohhindivtou_SHARED2_work,-2.505255912,F,work,SHARED2,zeroautohh,175.0,188.0,13.0,6.914893617021277,,-2.505255912,True
-coef_calib_zeroautohhindivtou_SHARED3_work,-4.1746,F,work,SHARED3,zeroautohh,0.0,0.0,0.0,0,,-4.1746,True
-coef_calib_zeroautohhindivtou_WALK_work,-0.553414397,F,work,WALK,zeroautohh,1797.0,1797.0,0.0,0.0,,-0.553414397,True
-coef_calib_zeroautohhindivtou_BIKE_work,-5.6023,F,work,BIKE,zeroautohh,16.0,0.0,-16.0,,,-5.6023,True
-coef_calib_zeroautohhindivtou_WALK_TRANSIT_work,0.364691867,F,work,WALK_TRANSIT,zeroautohh,4239.0,4133.0,-106.0,2.5647229615291556,,0.364691867,True
-coef_calib_zeroautohhindivtou_PNR_TRANSIT_work,-999.0,F,work,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_KNR_TRANSIT_work,0.666496122,F,work,KNR_TRANSIT,zeroautohh,267.0,262.0,-5.0,1.9083969465648856,,0.666496122,True
-coef_calib_zeroautohhindivtou_TNC_TRANSIT_work,-999.0,F,work,TNC_TRANSIT,zeroautohh,0.0,13.0,13.0,,,-999.0,True
-coef_calib_zeroautohhindivtou_TAXI_work,-1.9627,F,work,TAXI,zeroautohh,8.0,0.0,-8.0,,,-1.9627,True
-coef_calib_zeroautohhindivtou_TNC_SINGLE_work,2.112479758,F,work,TNC_SINGLE,zeroautohh,6550.0,6437.0,-113.0,1.7554761534876495,,2.112479758,True
-coef_calib_zeroautohhindivtou_TNC_SHARED_work,-3.9627,F,work,TNC_SHARED,zeroautohh,8.0,0.0,-8.0,,,-3.9627,True
-coef_calib_autodeficienthhind_SHARED2_work,-2.70280219,F,work,SHARED2,autodeficienthh,8829.0,8482.0,-347.0,4.091016269747701,,-2.70280219,True
-coef_calib_autodeficienthhind_SHARED3_work,-2.323413243,F,work,SHARED3,autodeficienthh,8327.0,8125.0,-202.0,2.4861538461538464,,-2.323413243,True
-coef_calib_autodeficienthhind_WALK_work,-1.655602166,F,work,WALK,autodeficienthh,2016.0,2023.0,7.0,0.34602076124567477,,-1.655602166,True
-coef_calib_autodeficienthhind_BIKE_work,-5.788115347,F,work,BIKE,autodeficienthh,474.0,485.0,11.0,2.268041237113402,,-5.788115347,True
-coef_calib_autodeficienthhind_WALK_TRANSIT_work,-2.76181855,F,work,WALK_TRANSIT,autodeficienthh,7781.0,7736.0,-45.0,0.5816959669079628,,-2.76181855,True
-coef_calib_autodeficienthhind_PNR_TRANSIT_work,-5.124417236,F,work,PNR_TRANSIT,autodeficienthh,279.0,257.0,-22.0,8.560311284046692,,-5.124417236,True
-coef_calib_autodeficienthhind_KNR_TRANSIT_work,-3.970001618,F,work,KNR_TRANSIT,autodeficienthh,570.0,543.0,-27.0,4.972375690607735,,-3.970001618,True
-coef_calib_autodeficienthhind_TNC_TRANSIT_work,-9.3988,F,work,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-9.3988,True
-coef_calib_autodeficienthhind_TAXI_work,-9.6667,F,work,TAXI,autodeficienthh,4.0,0.0,-4.0,,,-9.6667,True
-coef_calib_autodeficienthhind_TNC_SINGLE_work,-8.884488626,F,work,TNC_SINGLE,autodeficienthh,100.0,64.0,-36.0,56.25,,-8.884488626,True
-coef_calib_autodeficienthhind_TNC_SHARED_work,-9.6667,F,work,TNC_SHARED,autodeficienthh,32.0,0.0,-32.0,,,-9.6667,True
-coef_calib_autosufficienthhin_SHARED2_work,-2.536054537,F,work,SHARED2,autosufficienthh,25594.0,25665.0,71.0,0.27664134034677573,,-2.536054537,True
-coef_calib_autosufficienthhin_SHARED3_work,-2.061827895,F,work,SHARED3,autosufficienthh,23562.0,23520.0,-42.0,0.17857142857142858,,-2.061827895,True
-coef_calib_autosufficienthhin_WALK_work,-2.692542308,F,work,WALK,autosufficienthh,1538.0,1538.0,0.0,0.0,,-2.692542308,True
-coef_calib_autosufficienthhin_BIKE_work,-3.341858987,F,work,BIKE,autosufficienthh,5940.0,5915.0,-25.0,0.422654268808115,,-3.341858987,True
-coef_calib_autosufficienthhin_WALK_TRANSIT_work,-5.779799017,F,work,WALK_TRANSIT,autosufficienthh,622.0,625.0,3.0,0.48,,-5.779799017,True
-coef_calib_autosufficienthhin_PNR_TRANSIT_work,-5.815524925,F,work,PNR_TRANSIT,autosufficienthh,530.0,539.0,9.0,1.6697588126159555,,-5.815524925,True
-coef_calib_autosufficienthhin_KNR_TRANSIT_work,-4.145282722,F,work,KNR_TRANSIT,autosufficienthh,1761.0,1773.0,12.0,0.676818950930626,,-4.145282722,True
-coef_calib_autosufficienthhin_TNC_TRANSIT_work,-6.465543515,F,work,TNC_TRANSIT,autosufficienthh,1614.0,1589.0,-25.0,1.5733165512901195,,-6.465543515,True
-coef_calib_autosufficienthhin_TAXI_work,-7.9013,F,work,TAXI,autosufficienthh,52.0,0.0,-52.0,,,-7.9013,True
-coef_calib_autosufficienthhin_TNC_SINGLE_work,-7.793168769,F,work,TNC_SINGLE,autosufficienthh,287.0,279.0,-8.0,2.867383512544803,,-7.793168769,True
-coef_calib_autosufficienthhin_TNC_SHARED_work,-9.9013,F,work,TNC_SHARED,autosufficienthh,16.0,0.0,-16.0,,,-9.9013,True
-#coef_calib_zeroautohhindivtou_DRIVEALONE_univ,-19.7064,F,,,,,,,,,-19.7064,True
-coef_calib_zeroautohhindivtou_SHARED2_univ,-17.5741,F,univ,SHARED2,zeroautohh,0.0,0.0,0.0,0,,-17.5741,True
-coef_calib_zeroautohhindivtou_SHARED3_univ,-18.7726,F,univ,SHARED3,zeroautohh,0.0,0.0,0.0,0,,-18.7726,True
-coef_calib_zeroautohhindivtou_WALK_univ,-23.8833,F,univ,WALK,zeroautohh,0.0,0.0,0.0,0,,-23.8833,True
-coef_calib_zeroautohhindivtou_BIKE_univ,-16.0163,F,univ,BIKE,zeroautohh,0.0,0.0,0.0,0,,-16.0163,True
-coef_calib_zeroautohhindivtou_WALK_TRANSIT_univ,6.341576031,F,univ,WALK_TRANSIT,zeroautohh,6016.0,8552.0,2536.0,29.653882132834426,0.3517425870752054,6.693318618075205,False
-coef_calib_zeroautohhindivtou_PNR_TRANSIT_univ,-2.743,F,univ,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-2.743,True
-coef_calib_zeroautohhindivtou_KNR_TRANSIT_univ,-16.4728,F,univ,KNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-16.4728,True
-coef_calib_zeroautohhindivtou_TNC_TRANSIT_univ,-999.0,F,univ,TNC_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TAXI_univ,-999.0,F,univ,TAXI,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TNC_SINGLE_univ,-999.0,F,univ,TNC_SINGLE,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TNC_SHARED_univ,-999.0,F,univ,TNC_SHARED,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_SHARED2_univ,-4.09917176,F,univ,SHARED2,autodeficienthh,323.0,418.0,95.0,22.727272727272727,,-4.09917176,True
-coef_calib_autodeficienthhind_SHARED3_univ,-4.498522953,F,univ,SHARED3,autodeficienthh,155.0,127.0,-28.0,22.04724409448819,,-4.498522953,True
-coef_calib_autodeficienthhind_WALK_univ,1.867582056,F,univ,WALK,autodeficienthh,3028.0,2893.0,-135.0,4.666436225371587,,1.867582056,True
-coef_calib_autodeficienthhind_BIKE_univ,-7.353739618,F,univ,BIKE,autodeficienthh,72.0,64.0,-8.0,12.5,,-7.353739618,True
-coef_calib_autodeficienthhind_WALK_TRANSIT_univ,-0.496556351,F,univ,WALK_TRANSIT,autodeficienthh,5689.0,5641.0,-48.0,0.8509129586952667,,-0.496556351,True
-coef_calib_autodeficienthhind_PNR_TRANSIT_univ,-2.2079,F,univ,PNR_TRANSIT,autodeficienthh,48.0,0.0,-48.0,,,-2.2079,True
-coef_calib_autodeficienthhind_KNR_TRANSIT_univ,-0.936565574,F,univ,KNR_TRANSIT,autodeficienthh,287.0,286.0,-1.0,0.34965034965034963,,-0.936565574,True
-coef_calib_autodeficienthhind_TNC_TRANSIT_univ,-999.0,F,univ,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_TAXI_univ,-11.6732,F,univ,TAXI,autodeficienthh,0.0,0.0,0.0,0,,-11.6732,True
-coef_calib_autodeficienthhind_TNC_SINGLE_univ,-2.580444587,F,univ,TNC_SINGLE,autodeficienthh,566.0,561.0,-5.0,0.8912655971479502,,-2.580444587,True
-coef_calib_autodeficienthhind_TNC_SHARED_univ,-11.6732,F,univ,TNC_SHARED,autodeficienthh,0.0,0.0,0.0,0,,-11.6732,True
-coef_calib_autosufficienthhin_SHARED2_univ,-2.290533723,F,univ,SHARED2,autosufficienthh,3355.0,3249.0,-106.0,3.262542320714066,,-2.290533723,True
-coef_calib_autosufficienthhin_SHARED3_univ,-3.88541567,F,univ,SHARED3,autosufficienthh,171.0,217.0,46.0,21.19815668202765,,-3.88541567,True
-coef_calib_autosufficienthhin_WALK_univ,1.814940512,F,univ,WALK,autosufficienthh,1227.0,1182.0,-45.0,3.807106598984772,,1.814940512,True
-coef_calib_autosufficienthhin_BIKE_univ,-1.234833179,F,univ,BIKE,autosufficienthh,1486.0,1466.0,-20.0,1.364256480218281,,-1.234833179,True
-coef_calib_autosufficienthhin_WALK_TRANSIT_univ,-4.553742843,F,univ,WALK_TRANSIT,autosufficienthh,48.0,38.0,-10.0,26.31578947368421,,-4.553742843,True
-coef_calib_autosufficienthhin_PNR_TRANSIT_univ,-0.363147111,F,univ,PNR_TRANSIT,autosufficienthh,618.0,603.0,-15.0,2.4875621890547266,,-0.363147111,True
-coef_calib_autosufficienthhin_KNR_TRANSIT_univ,-4.0489,F,univ,KNR_TRANSIT,autosufficienthh,4.0,0.0,-4.0,,,-4.0489,True
-coef_calib_autosufficienthhin_TNC_TRANSIT_univ,-8.2328,F,univ,TNC_TRANSIT,autosufficienthh,4.0,0.0,-4.0,,,-8.2328,True
-coef_calib_autosufficienthhin_TAXI_univ,-4.6511,F,univ,TAXI,autosufficienthh,4.0,0.0,-4.0,,,-4.6511,True
-coef_calib_autosufficienthhin_TNC_SINGLE_univ,-4.6511,F,univ,TNC_SINGLE,autosufficienthh,52.0,0.0,-52.0,,,-4.6511,True
-coef_calib_autosufficienthhin_TNC_SHARED_univ,-6.6511,F,univ,TNC_SHARED,autosufficienthh,0.0,0.0,0.0,0,,-6.6511,True
-#coef_calib_zeroautohhindivtou_DRIVEALONE_school,-0.9723,F,,,,,,,,,-0.9723,True
-coef_calib_zeroautohhindivtou_SHARED2_school,-37.0,F,school,SHARED2,zeroautohh,243.0,0.0,-243.0,,-2,-39.0,False
-coef_calib_zeroautohhindivtou_SHARED3_school,20.0,F,school,SHARED3,zeroautohh,2355.0,2092.0,-263.0,12.5717017208413,-0.11842018126570458,19.881579818734295,False
-coef_calib_zeroautohhindivtou_WALK_school,10.0,F,school,WALK,zeroautohh,235.0,352.0,117.0,33.23863636363637,0.4040456614539381,10.404045661453939,False
-coef_calib_zeroautohhindivtou_BIKE_school,12.2159,F,school,BIKE,zeroautohh,4.0,0.0,-4.0,,,12.2159,True
-coef_calib_zeroautohhindivtou_WALK_TRANSIT_school,5.095434049,F,school,WALK_TRANSIT,zeroautohh,44.0,31.0,-13.0,41.935483870967744,,5.095434049,True
-coef_calib_zeroautohhindivtou_PNR_TRANSIT_school,-999.0,F,school,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_KNR_TRANSIT_school,2.9447,F,school,KNR_TRANSIT,zeroautohh,4.0,0.0,-4.0,,,2.9447,True
-coef_calib_zeroautohhindivtou_TNC_TRANSIT_school,-999.0,F,school,TNC_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TAXI_school,-1029.0,F,school,TAXI,zeroautohh,135.0,0.0,-135.0,,-2,-1031.0,False
-coef_calib_zeroautohhindivtou_TNC_SINGLE_school,-1029.0,F,school,TNC_SINGLE,zeroautohh,303.0,0.0,-303.0,,-2,-1031.0,False
-coef_calib_zeroautohhindivtou_TNC_SHARED_school,-1001.0,F,school,TNC_SHARED,zeroautohh,0.0,0.0,0.0,0,,-1001.0,True
-coef_calib_zeroautohhindivtou_SCH_BUS_school,18.11856419,F,school,SCH_BUS,zeroautohh,351.0,1230.0,879.0,71.46341463414633,1.2539832249005973,19.3725474149006,False
-coef_calib_autodeficienthhind_SHARED2_school,-5.295322888,F,school,SHARED2,autodeficienthh,9729.0,10223.0,494.0,4.832241025139392,,-5.295322888,True
-coef_calib_autodeficienthhind_SHARED3_school,1.237719011,F,school,SHARED3,autodeficienthh,19952.0,19813.0,-139.0,0.7015595820925655,,1.237719011,True
-coef_calib_autodeficienthhind_WALK_school,0.363638571,F,school,WALK,autodeficienthh,2231.0,2260.0,29.0,1.2831858407079646,,0.363638571,True
-coef_calib_autodeficienthhind_BIKE_school,2.346790252,F,school,BIKE,autodeficienthh,175.0,175.0,0.0,0.0,,2.346790252,True
-coef_calib_autodeficienthhind_WALK_TRANSIT_school,-39.1291169,F,school,WALK_TRANSIT,autodeficienthh,259.0,22.0,-237.0,1077.2727272727273,-2.4657856083412217,-41.59490250834122,False
-coef_calib_autodeficienthhind_PNR_TRANSIT_school,-5.9201,F,school,PNR_TRANSIT,autodeficienthh,4.0,0.0,-4.0,,,-5.9201,True
-coef_calib_autodeficienthhind_KNR_TRANSIT_school,1.0441,F,school,KNR_TRANSIT,autodeficienthh,56.0,0.0,-56.0,,,1.0441,True
-coef_calib_autodeficienthhind_TNC_TRANSIT_school,-999.0,F,school,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_TAXI_school,-1029.0,F,school,TAXI,autodeficienthh,223.0,0.0,-223.0,,-2,-1031.0,False
-coef_calib_autodeficienthhind_TNC_SINGLE_school,-1027.0,F,school,TNC_SINGLE,autodeficienthh,243.0,0.0,-243.0,,-2,-1029.0,False
-coef_calib_autodeficienthhind_TNC_SHARED_school,-1001.0,F,school,TNC_SHARED,autodeficienthh,8.0,0.0,-8.0,,,-1001.0,True
-coef_calib_autodeficienthhind_SCH_BUS_school,1.185348296,F,school,SCH_BUS,autodeficienthh,2610.0,2591.0,-19.0,0.7333076032419915,,1.185348296,True
-coef_calib_autosufficienthhin_SHARED2_school,-10.21596024,F,school,SHARED2,autosufficienthh,24801.0,22107.0,-2694.0,12.18618537114941,-0.11498967448567633,-10.330949914485675,False
-coef_calib_autosufficienthhin_SHARED3_school,-1.462851151,F,school,SHARED3,autosufficienthh,46191.0,47591.0,1400.0,2.9417326805488435,,-1.462851151,True
-coef_calib_autosufficienthhin_WALK_school,-1.024805777,F,school,WALK,autosufficienthh,3558.0,3734.0,176.0,4.713444027852169,,-1.024805777,True
-coef_calib_autosufficienthhin_BIKE_school,6.19161736,F,school,BIKE,autosufficienthh,11865.0,12345.0,480.0,3.8882138517618468,,6.19161736,True
-coef_calib_autosufficienthhin_WALK_TRANSIT_school,-1.925970648,F,school,WALK_TRANSIT,autosufficienthh,1127.0,1213.0,86.0,7.089859851607584,,-1.925970648,True
-coef_calib_autosufficienthhin_PNR_TRANSIT_school,-10.5186,F,school,PNR_TRANSIT,autosufficienthh,24.0,0.0,-24.0,,,-10.5186,True
-coef_calib_autosufficienthhin_KNR_TRANSIT_school,-4.7303,F,school,KNR_TRANSIT,autosufficienthh,48.0,0.0,-48.0,,,-4.7303,True
-coef_calib_autosufficienthhin_TNC_TRANSIT_school,-1027.0,F,school,TNC_TRANSIT,autosufficienthh,120.0,0.0,-120.0,,-2,-1029.0,False
-coef_calib_autosufficienthhin_TAXI_school,-999.0,F,school,TAXI,autosufficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autosufficienthhin_TNC_SINGLE_school,-1031.0,F,school,TNC_SINGLE,autosufficienthh,155.0,0.0,-155.0,,-2,-1033.0,False
-coef_calib_autosufficienthhin_TNC_SHARED_school,-1001.0,F,school,TNC_SHARED,autosufficienthh,4.0,0.0,-4.0,,,-1001.0,True
-coef_calib_autosufficienthhin_SCH_BUS_school,-5.29090218,F,school,SCH_BUS,autosufficienthh,295.0,298.0,3.0,1.006711409395973,,-5.29090218,True
-#coef_calib_zeroautohhindivtou_DRIVEALONE_atwork,-999.0,F,,,,,,,,,-999.0,True
-coef_calib_zeroautohhindivtou_SHARED2_atwork,-45.0,F,atwork,SHARED2,zeroautohh,60.0,0.0,-60.0,,,-45.0,True
-coef_calib_zeroautohhindivtou_SHARED3_atwork,-45.0,F,atwork,SHARED3,zeroautohh,40.0,0.0,-40.0,,,-45.0,True
-coef_calib_zeroautohhindivtou_WALK_atwork,2.766937465,F,atwork,WALK,zeroautohh,4657.0,5313.0,656.0,12.347073216638433,0.13178518346856608,2.8987226484685658,False
-coef_calib_zeroautohhindivtou_BIKE_atwork,-45.0,F,atwork,BIKE,zeroautohh,36.0,0.0,-36.0,,,-45.0,True
-coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork,-42.15449275,F,atwork,WALK_TRANSIT,zeroautohh,76.0,29.0,-47.0,162.06896551724137,,-42.15449275,True
-coef_calib_zeroautohhindivtou_PNR_TRANSIT_atwork,-999.0,F,atwork,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_KNR_TRANSIT_atwork,-999.0,F,atwork,KNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TNC_TRANSIT_atwork,-999.0,F,atwork,TNC_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TAXI_atwork,-999.0,F,atwork,TAXI,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TNC_SINGLE_atwork,-999.0,F,atwork,TNC_SINGLE,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TNC_SHARED_atwork,-999.0,F,atwork,TNC_SHARED,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_SHARED2_atwork,-2.424228303,F,atwork,SHARED2,autodeficienthh,713.0,4645.0,3932.0,84.65016146393972,1.874065230833643,-0.5501630721663571,False
-coef_calib_autodeficienthhind_SHARED3_atwork,-1.089369135,F,atwork,SHARED3,autodeficienthh,11653.0,1474.0,-10179.0,690.5698778833107,-2.0675838638239816,-3.1569529988239817,False
-coef_calib_autodeficienthhind_WALK_atwork,-4.896696335,F,atwork,WALK,autodeficienthh,315.0,432.0,117.0,27.083333333333332,0.31585294941847725,-4.580843385581522,False
-coef_calib_autodeficienthhind_BIKE_atwork,-14.7861067,F,atwork,BIKE,autodeficienthh,12.0,9.0,-3.0,33.33333333333333,,-14.7861067,True
-coef_calib_autodeficienthhind_WALK_TRANSIT_atwork,-9.536775879,F,atwork,WALK_TRANSIT,autodeficienthh,12.0,0.0,-12.0,,,-9.536775879,True
-coef_calib_autodeficienthhind_PNR_TRANSIT_atwork,-999.0,F,atwork,PNR_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_KNR_TRANSIT_atwork,-999.0,F,atwork,KNR_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_TNC_TRANSIT_atwork,-999.0,F,atwork,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_TAXI_atwork,-10.87852298,F,atwork,TAXI,autodeficienthh,4.0,0.0,-4.0,,,-10.87852298,True
-coef_calib_autodeficienthhind_TNC_SINGLE_atwork,-10.87852298,F,atwork,TNC_SINGLE,autodeficienthh,28.0,0.0,-28.0,,,-10.87852298,True
-coef_calib_autodeficienthhind_TNC_SHARED_atwork,-10.87852298,F,atwork,TNC_SHARED,autodeficienthh,16.0,0.0,-16.0,,,-10.87852298,True
-coef_calib_autosufficienthhin_SHARED2_atwork,-1.70460029,F,atwork,SHARED2,autosufficienthh,6311.0,5337.0,-974.0,18.24995315720442,-0.16763044525029466,-1.8722307352502945,False
-coef_calib_autosufficienthhin_SHARED3_atwork,-1.765085897,F,atwork,SHARED3,autosufficienthh,5737.0,6558.0,821.0,12.519060689234523,0.1337492528027224,-1.6313366441972776,False
-coef_calib_autosufficienthhin_WALK_atwork,0.406698656,F,atwork,WALK,autosufficienthh,34271.0,34247.0,-24.0,0.07007913101877537,,0.406698656,True
-coef_calib_autosufficienthhin_BIKE_atwork,-5.079673239,F,atwork,BIKE,autosufficienthh,7287.0,7306.0,19.0,0.26006022447303584,,-5.079673239,True
-coef_calib_autosufficienthhin_WALK_TRANSIT_atwork,-7.968048166,F,atwork,WALK_TRANSIT,autosufficienthh,32.0,0.0,-32.0,,,-7.968048166,True
-coef_calib_autosufficienthhin_PNR_TRANSIT_atwork,-999.0,F,atwork,PNR_TRANSIT,autosufficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autosufficienthhin_KNR_TRANSIT_atwork,-999.0,F,atwork,KNR_TRANSIT,autosufficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autosufficienthhin_TNC_TRANSIT_atwork,-999.0,F,atwork,TNC_TRANSIT,autosufficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autosufficienthhin_TAXI_atwork,-9.130948946,F,atwork,TAXI,autosufficienthh,28.0,0.0,-28.0,,,-9.130948946,True
-coef_calib_autosufficienthhin_TNC_SINGLE_atwork,-11.13094895,F,atwork,TNC_SINGLE,autosufficienthh,40.0,0.0,-40.0,,,-11.13094895,True
-coef_calib_autosufficienthhin_TNC_SHARED_atwork,-11.13094895,F,atwork,TNC_SHARED,autosufficienthh,52.0,0.0,-52.0,,,-11.13094895,True
-#coef_calib_zeroautohhindivtou_DRIVEALONE_maint,-0.272127,F,,,,,,,,,-0.272127,True
-coef_calib_zeroautohhindivtou_SHARED2_maint,-0.385196604,F,maint,SHARED2,zeroautohh,2390.0,2665.0,275.0,10.318949343339586,0.10891069167438866,-0.27628591232561134,False
-coef_calib_zeroautohhindivtou_SHARED3_maint,-0.796702109,F,maint,SHARED3,zeroautohh,1020.0,705.0,-315.0,44.680851063829785,-0.36936010346604814,-1.1660622124660482,False
-coef_calib_zeroautohhindivtou_WALK_maint,1.166088997,F,maint,WALK,zeroautohh,12884.0,13198.0,314.0,2.3791483558114868,,1.166088997,True
-coef_calib_zeroautohhindivtou_BIKE_maint,-0.340946416,F,maint,BIKE,zeroautohh,845.0,835.0,-10.0,1.1976047904191618,,-0.340946416,True
-coef_calib_zeroautohhindivtou_WALK_TRANSIT_maint,4.167081139,F,maint,WALK_TRANSIT,zeroautohh,9602.0,9907.0,305.0,3.078631270818613,,4.167081139,True
-coef_calib_zeroautohhindivtou_PNR_TRANSIT_maint,-999.0,F,maint,PNR_TRANSIT,zeroautohh,0.0,97.0,97.0,,,-999.0,True
-coef_calib_zeroautohhindivtou_KNR_TRANSIT_maint,6.686633489,F,maint,KNR_TRANSIT,zeroautohh,1781.0,1866.0,85.0,4.555198285101822,,6.686633489,True
-coef_calib_zeroautohhindivtou_TNC_TRANSIT_maint,-999.0,F,maint,TNC_TRANSIT,zeroautohh,0.0,33.0,33.0,,,-999.0,True
-coef_calib_zeroautohhindivtou_TAXI_maint,1.445114228,F,maint,TAXI,zeroautohh,1797.0,1820.0,23.0,1.2637362637362637,,1.445114228,True
-coef_calib_zeroautohhindivtou_TNC_SINGLE_maint,2.099195882,F,maint,TNC_SINGLE,zeroautohh,4339.0,4417.0,78.0,1.7659044600407519,,2.099195882,True
-coef_calib_zeroautohhindivtou_TNC_SHARED_maint,-3.816828,F,maint,TNC_SHARED,zeroautohh,8.0,0.0,-8.0,,,-3.816828,True
-coef_calib_zeroautohhindivtou_EBIKE_maint,-1.33053075,F,maint,EBIKE,zeroautohh,514.0,526.0,12.0,2.2813688212927756,,-1.33053075,True
-coef_calib_zeroautohhindivtou_ESCOOTER_maint,2.424791618,F,maint,ESCOOTER,zeroautohh,303.0,310.0,7.0,2.258064516129032,,2.424791618,True
-coef_calib_autodeficienthhind_SHARED2_maint,-0.311355052,F,maint,SHARED2,autodeficienthh,111857.0,111545.0,-312.0,0.2797077412703393,,-0.311355052,True
-coef_calib_autodeficienthhind_SHARED3_maint,-0.204671675,F,maint,SHARED3,autodeficienthh,103239.0,103014.0,-225.0,0.2184169142058361,,-0.204671675,True
-coef_calib_autodeficienthhind_WALK_maint,-1.518234578,F,maint,WALK,autodeficienthh,39175.0,39010.0,-165.0,0.4229684696231735,,-1.518234578,True
-coef_calib_autodeficienthhind_BIKE_maint,-3.799951508,F,maint,BIKE,autodeficienthh,857.0,871.0,14.0,1.6073478760045925,,-3.799951508,True
-coef_calib_autodeficienthhind_WALK_TRANSIT_maint,-1.918566588,F,maint,WALK_TRANSIT,autodeficienthh,6223.0,6181.0,-42.0,0.6795016987542469,,-1.918566588,True
-coef_calib_autodeficienthhind_PNR_TRANSIT_maint,-1.168882388,F,maint,PNR_TRANSIT,autodeficienthh,1203.0,999.0,-204.0,20.42042042042042,-0.18581893732612534,-1.3547013253261255,False
-coef_calib_autodeficienthhind_KNR_TRANSIT_maint,-0.423810529,F,maint,KNR_TRANSIT,autodeficienthh,3000.0,3202.0,202.0,6.308557151780138,0.06516332590641317,-0.35864720309358683,False
-coef_calib_autodeficienthhind_TNC_TRANSIT_maint,-5.982186239,F,maint,TNC_TRANSIT,autodeficienthh,76.0,76.0,0.0,0.0,,-5.982186239,True
-coef_calib_autodeficienthhind_TAXI_maint,-3.446873878,F,maint,TAXI,autodeficienthh,4789.0,4789.0,0.0,0.0,,-3.446873878,True
-coef_calib_autodeficienthhind_TNC_SINGLE_maint,-4.459512541,F,maint,TNC_SINGLE,autodeficienthh,2016.0,2012.0,-4.0,0.19880715705765406,,-4.459512541,True
-coef_calib_autodeficienthhind_TNC_SHARED_maint,-8.084063,F,maint,TNC_SHARED,autodeficienthh,20.0,0.0,-20.0,,,-8.084063,True
-coef_calib_autodeficienthhind_EBIKE_maint,-7.716614941,F,maint,EBIKE,autodeficienthh,24.0,21.0,-3.0,14.285714285714285,,-7.716614941,True
-coef_calib_autodeficienthhind_ESCOOTER_maint,-4.0,F,maint,ESCOOTER,autodeficienthh,60.0,0.0,-60.0,,,-4.0,True
-coef_calib_autosufficienthhin_SHARED2_maint,-0.01294527,F,maint,SHARED2,autosufficienthh,287932.0,287574.0,-358.0,0.12448969656505804,,-0.01294527,True
-coef_calib_autosufficienthhin_SHARED3_maint,0.474229063,F,maint,SHARED3,autosufficienthh,277347.0,276843.0,-504.0,0.182052643556095,,0.474229063,True
-coef_calib_autosufficienthhin_WALK_maint,-1.48298439,F,maint,WALK,autosufficienthh,66088.0,65958.0,-130.0,0.19709512113769367,,-1.48298439,True
-coef_calib_autosufficienthhin_BIKE_maint,-1.864829368,F,maint,BIKE,autosufficienthh,15159.0,15130.0,-29.0,0.19167217448777266,,-1.864829368,True
-coef_calib_autosufficienthhin_WALK_TRANSIT_maint,-5.844480883,F,maint,WALK_TRANSIT,autosufficienthh,259.0,254.0,-5.0,1.968503937007874,,-5.844480883,True
-coef_calib_autosufficienthhin_PNR_TRANSIT_maint,-4.894496758,F,maint,PNR_TRANSIT,autosufficienthh,163.0,157.0,-6.0,3.821656050955414,,-4.894496758,True
-coef_calib_autosufficienthhin_KNR_TRANSIT_maint,-4.143755229,F,maint,KNR_TRANSIT,autosufficienthh,227.0,233.0,6.0,2.575107296137339,,-4.143755229,True
-coef_calib_autosufficienthhin_TNC_TRANSIT_maint,-8.81036,F,maint,TNC_TRANSIT,autosufficienthh,44.0,0.0,-44.0,,,-8.81036,True
-coef_calib_autosufficienthhin_TAXI_maint,-7.779982,F,maint,TAXI,autosufficienthh,44.0,0.0,-44.0,,,-7.779982,True
-coef_calib_autosufficienthhin_TNC_SINGLE_maint,-6.366232767,F,maint,TNC_SINGLE,autosufficienthh,1482.0,1481.0,-1.0,0.0675219446320054,,-6.366232767,True
-coef_calib_autosufficienthhin_TNC_SHARED_maint,-9.779982,F,maint,TNC_SHARED,autosufficienthh,40.0,0.0,-40.0,,,-9.779982,True
-coef_calib_autosufficienthhin_EBIKE_maint,-4.502828856,F,maint,EBIKE,autosufficienthh,1263.0,1262.0,-1.0,0.07923930269413629,,-4.502828856,True
-coef_calib_autosufficienthhin_ESCOOTER_maint,-4.0,F,maint,ESCOOTER,autosufficienthh,40.0,0.0,-40.0,,,-4.0,True
-#coef_calib_zeroautohhindivtou_DRIVEALONE_disc,-1.878657,F,,,,,,,,,-1.878657,True
-coef_calib_zeroautohhindivtou_SHARED2_disc,0.744825545,F,disc,SHARED2,zeroautohh,1896.0,2400.0,504.0,21.0,0.23572233352106994,0.98054787852107,False
-coef_calib_zeroautohhindivtou_SHARED3_disc,0.831183374,F,disc,SHARED3,zeroautohh,1131.0,288.0,-843.0,292.70833333333337,-1.3678969959801746,-0.5367136219801746,False
-coef_calib_zeroautohhindivtou_WALK_disc,2.148524876,F,disc,WALK,zeroautohh,13478.0,14080.0,602.0,4.275568181818182,,2.148524876,True
-coef_calib_zeroautohhindivtou_BIKE_disc,-0.496234052,F,disc,BIKE,zeroautohh,327.0,336.0,9.0,2.6785714285714284,,-0.496234052,True
-coef_calib_zeroautohhindivtou_WALK_TRANSIT_disc,7.464200805,F,disc,WALK_TRANSIT,zeroautohh,9394.0,9827.0,433.0,4.406227739900275,,7.464200805,True
-coef_calib_zeroautohhindivtou_PNR_TRANSIT_disc,-999.0,F,disc,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_KNR_TRANSIT_disc,6.666434935,F,disc,KNR_TRANSIT,zeroautohh,227.0,268.0,41.0,15.298507462686567,,6.666434935,True
-coef_calib_zeroautohhindivtou_TNC_TRANSIT_disc,-999.0,F,disc,TNC_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhindivtou_TAXI_disc,-2.637451,F,disc,TAXI,zeroautohh,8.0,0.0,-8.0,,,-2.637451,True
-coef_calib_zeroautohhindivtou_TNC_SINGLE_disc,1.061802116,F,disc,TNC_SINGLE,zeroautohh,327.0,353.0,26.0,7.365439093484419,,1.061802116,True
-coef_calib_zeroautohhindivtou_TNC_SHARED_disc,-2.637451,F,disc,TNC_SHARED,zeroautohh,0.0,0.0,0.0,0,,-2.637451,True
-coef_calib_zeroautohhindivtou_EBIKE_disc,-1.127440499,F,disc,EBIKE,zeroautohh,80.0,87.0,7.0,8.045977011494253,,-1.127440499,True
-coef_calib_zeroautohhindivtou_ESCOOTER_disc,1.897551787,F,disc,ESCOOTER,zeroautohh,163.0,166.0,3.0,1.8072289156626504,,1.897551787,True
-coef_calib_autodeficienthhind_SHARED2_disc,-0.167886293,F,disc,SHARED2,autodeficienthh,55406.0,55575.0,169.0,0.30409356725146197,,-0.167886293,True
-coef_calib_autodeficienthhind_SHARED3_disc,0.23303533,F,disc,SHARED3,autodeficienthh,39554.0,39230.0,-324.0,0.8258985470303339,,0.23303533,True
-coef_calib_autodeficienthhind_WALK_disc,-1.577544488,F,disc,WALK,autodeficienthh,27438.0,27316.0,-122.0,0.4466246888270611,,-1.577544488,True
-coef_calib_autodeficienthhind_BIKE_disc,-2.037126579,F,disc,BIKE,autodeficienthh,7410.0,7367.0,-43.0,0.583683996199267,,-2.037126579,True
-coef_calib_autodeficienthhind_WALK_TRANSIT_disc,-2.689047128,F,disc,WALK_TRANSIT,autodeficienthh,1466.0,1503.0,37.0,2.4617431803060548,,-2.689047128,True
-coef_calib_autodeficienthhind_PNR_TRANSIT_disc,-4.013662,F,disc,PNR_TRANSIT,autodeficienthh,28.0,0.0,-28.0,,,-4.013662,True
-coef_calib_autodeficienthhind_KNR_TRANSIT_disc,-3.286034659,F,disc,KNR_TRANSIT,autodeficienthh,40.0,35.0,-5.0,14.285714285714285,,-3.286034659,True
-coef_calib_autodeficienthhind_TNC_TRANSIT_disc,-999.0,F,disc,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhind_TAXI_disc,-6.658369,F,disc,TAXI,autodeficienthh,0.0,0.0,0.0,0,,-6.658369,True
-coef_calib_autodeficienthhind_TNC_SINGLE_disc,-3.328614355,F,disc,TNC_SINGLE,autodeficienthh,1737.0,1744.0,7.0,0.40137614678899086,,-3.328614355,True
-coef_calib_autodeficienthhind_TNC_SHARED_disc,-6.658369,F,disc,TNC_SHARED,autodeficienthh,24.0,0.0,-24.0,,,-6.658369,True
-coef_calib_autodeficienthhind_EBIKE_disc,-2.649758024,F,disc,EBIKE,autodeficienthh,765.0,796.0,31.0,3.8944723618090453,,-2.649758024,True
-coef_calib_autodeficienthhind_ESCOOTER_disc,-1.233972098,F,disc,ESCOOTER,autodeficienthh,398.0,338.0,-60.0,17.75147928994083,,-1.233972098,True
-coef_calib_autosufficienthhin_SHARED2_disc,-0.454190295,F,disc,SHARED2,autosufficienthh,74761.0,75208.0,447.0,0.5943516647165195,,-0.454190295,True
-coef_calib_autosufficienthhin_SHARED3_disc,0.357193014,F,disc,SHARED3,autosufficienthh,93578.0,93224.0,-354.0,0.37973054149146146,,0.357193014,True
-coef_calib_autosufficienthhin_WALK_disc,-0.219796306,F,disc,WALK,autosufficienthh,142534.0,142401.0,-133.0,0.09339822051811435,,-0.219796306,True
-coef_calib_autosufficienthhin_BIKE_disc,-2.923757319,F,disc,BIKE,autosufficienthh,4562.0,4597.0,35.0,0.7613661083315205,,-2.923757319,True
-coef_calib_autosufficienthhin_WALK_TRANSIT_disc,-3.004686125,F,disc,WALK_TRANSIT,autosufficienthh,2649.0,2653.0,4.0,0.15077271013946475,,-3.004686125,True
-coef_calib_autosufficienthhin_PNR_TRANSIT_disc,-3.891756948,F,disc,PNR_TRANSIT,autosufficienthh,147.0,142.0,-5.0,3.5211267605633805,,-3.891756948,True
-coef_calib_autosufficienthhin_KNR_TRANSIT_disc,-3.341971438,F,disc,KNR_TRANSIT,autosufficienthh,92.0,97.0,5.0,5.154639175257731,,-3.341971438,True
-coef_calib_autosufficienthhin_TNC_TRANSIT_disc,-8.897372,F,disc,TNC_TRANSIT,autosufficienthh,24.0,0.0,-24.0,,,-8.897372,True
-coef_calib_autosufficienthhin_TAXI_disc,-7.985417,F,disc,TAXI,autosufficienthh,0.0,0.0,0.0,0,,-7.985417,True
-coef_calib_autosufficienthhin_TNC_SINGLE_disc,-5.408205121,F,disc,TNC_SINGLE,autosufficienthh,741.0,720.0,-21.0,2.9166666666666665,,-5.408205121,True
-coef_calib_autosufficienthhin_TNC_SHARED_disc,-7.985417,F,disc,TNC_SHARED,autosufficienthh,28.0,0.0,-28.0,,,-7.985417,True
-coef_calib_autosufficienthhin_EBIKE_disc,-4.669931526,F,disc,EBIKE,autosufficienthh,315.0,343.0,28.0,8.16326530612245,,-4.669931526,True
-coef_calib_autosufficienthhin_ESCOOTER_disc,-3.065820784,F,disc,ESCOOTER,autosufficienthh,139.0,94.0,-45.0,47.87234042553192,,-3.065820784,True
-#coef_calib_zeroautohhjointtou_DRIVEALONE_maint,-999.0,F,,,,,,,,,-999.0,True
-#coef_calib_zeroautohhjointtou_SHARED2_maint,8.507773009,F,,,,,,,,,8.507773009,True
-coef_calib_zeroautohhjointtou_SHARED3_maint,-33.0,F,maint,SHARED3,zeroautohh,135.0,0.0,-135.0,,-2,-35.0,False
-coef_calib_zeroautohhjointtou_WALK_maint,-21.0,F,maint,WALK,zeroautohh,1307.0,576.0,-731.0,126.90972222222223,-0.8193820529283308,-21.81938205292833,False
-coef_calib_zeroautohhjointtou_BIKE_maint,-999.0,F,maint,BIKE,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_WALK_TRANSIT_maint,-28.0,F,maint,WALK_TRANSIT,zeroautohh,92.0,0.0,-92.0,,,-28.0,True
-coef_calib_zeroautohhjointtou_PNR_TRANSIT_maint,-999.0,F,maint,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_KNR_TRANSIT_maint,-23.0,F,maint,KNR_TRANSIT,zeroautohh,120.0,0.0,-120.0,,-2,-25.0,False
-coef_calib_zeroautohhjointtou_TNC_TRANSIT_maint,-999.0,F,maint,TNC_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_TAXI_maint,-999.0502454,F,maint,TAXI,zeroautohh,0.0,0.0,0.0,0,,-999.0502454,True
-coef_calib_zeroautohhjointtou_TNC_SINGLE_maint,-999.0502454,F,maint,TNC_SINGLE,zeroautohh,0.0,0.0,0.0,0,,-999.0502454,True
-coef_calib_zeroautohhjointtou_TNC_SHARED_maint,-999.0502454,F,maint,TNC_SHARED,zeroautohh,0.0,0.0,0.0,0,,-999.0502454,True
-coef_calib_zeroautohhjointtou_EBIKE_maint,-29.0,F,maint,EBIKE,zeroautohh,231.0,0.0,-231.0,,-2,-31.0,False
-coef_calib_zeroautohhjointtou_ESCOOTER_maint,-27.0,F,maint,ESCOOTER,zeroautohh,167.0,0.0,-167.0,,-2,-29.0,False
-#coef_calib_autodeficienthhjoi_SHARED2_maint,15.5330624,F,,,,,,,,,15.5330624,True
-coef_calib_autodeficienthhjoi_SHARED3_maint,-30.0,F,maint,SHARED3,autodeficienthh,27785.0,43340.0,15555.0,35.89063221042916,0.4445796893763284,-29.55542031062367,False
-coef_calib_autodeficienthhjoi_WALK_maint,-30.0,F,maint,WALK,autodeficienthh,22996.0,10125.0,-12871.0,127.12098765432098,-0.8203126747684416,-30.82031267476844,False
-coef_calib_autodeficienthhjoi_BIKE_maint,-967.0,F,maint,BIKE,autodeficienthh,0.0,777.0,777.0,,2,-965.0,False
-coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint,-31.04873217,F,maint,WALK_TRANSIT,autodeficienthh,5916.0,42.0,-5874.0,13985.714285714286,-4.947746205547322,-35.996478375547326,False
-coef_calib_autodeficienthhjoi_PNR_TRANSIT_maint,-29.90392356,F,maint,PNR_TRANSIT,autodeficienthh,406.0,0.0,-406.0,,-2,-31.90392356,False
-coef_calib_autodeficienthhjoi_KNR_TRANSIT_maint,-28.88159968,F,maint,KNR_TRANSIT,autodeficienthh,5637.0,0.0,-5637.0,,-2,-30.88159968,False
-coef_calib_autodeficienthhjoi_TNC_TRANSIT_maint,-999.0,F,maint,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhjoi_TAXI_maint,-30.97066616,F,maint,TAXI,autodeficienthh,20287.0,0.0,-20287.0,,-2,-32.97066616,False
-coef_calib_autodeficienthhjoi_TNC_SINGLE_maint,-32.97066616,F,maint,TNC_SINGLE,autodeficienthh,725.0,0.0,-725.0,,-2,-34.97066616,False
-coef_calib_autodeficienthhjoi_TNC_SHARED_maint,-32.97066616,F,maint,TNC_SHARED,autodeficienthh,3000.0,0.0,-3000.0,,-2,-34.97066616,False
-coef_calib_autodeficienthhjoi_EBIKE_maint,-28.38857593,F,maint,EBIKE,autodeficienthh,2928.0,45.0,-2883.0,6406.666666666666,-4.175412385310882,-32.56398831531088,False
-coef_calib_autodeficienthhjoi_ESCOOTER_maint,-26.0,F,maint,ESCOOTER,autodeficienthh,3108.0,0.0,-3108.0,,-2,-28.0,False
-#coef_calib_autosufficienthhjo_SHARED2_maint,4.253195896,F,,,,,,,,,4.253195896,True
-coef_calib_autosufficienthhjo_SHARED3_maint,-13.0,F,maint,SHARED3,autosufficienthh,61211.0,48150.0,-13061.0,27.12564901349948,-0.24000577368519732,-13.240005773685198,False
-coef_calib_autosufficienthhjo_WALK_maint,-12.0,F,maint,WALK,autosufficienthh,49665.0,42784.0,-6881.0,16.083115183246075,-0.14913625874778663,-12.149136258747786,False
-coef_calib_autosufficienthhjo_BIKE_maint,-10.78060041,F,maint,BIKE,autosufficienthh,17932.0,1988.0,-15944.0,802.0120724346077,-2.199457718058811,-12.980058128058811,False
-coef_calib_autosufficienthhjo_WALK_TRANSIT_maint,-16.27274185,F,maint,WALK_TRANSIT,autosufficienthh,327.0,0.0,-327.0,,-2,-18.27274185,False
-coef_calib_autosufficienthhjo_PNR_TRANSIT_maint,-14.77467449,F,maint,PNR_TRANSIT,autosufficienthh,665.0,0.0,-665.0,,-2,-16.774674490000002,False
-coef_calib_autosufficienthhjo_KNR_TRANSIT_maint,-14.58383844,F,maint,KNR_TRANSIT,autosufficienthh,147.0,0.0,-147.0,,-2,-16.58383844,False
-coef_calib_autosufficienthhjo_TNC_TRANSIT_maint,-999.0,F,maint,TNC_TRANSIT,autosufficienthh,315.0,0.0,-315.0,,-2,-1001.0,False
-coef_calib_autosufficienthhjo_TAXI_maint,-19.31393156,F,maint,TAXI,autosufficienthh,56.0,0.0,-56.0,,,-19.31393156,True
-coef_calib_autosufficienthhjo_TNC_SINGLE_maint,-19.31393156,F,maint,TNC_SINGLE,autosufficienthh,179.0,0.0,-179.0,,-2,-21.31393156,False
-coef_calib_autosufficienthhjo_TNC_SHARED_maint,-19.31393156,F,maint,TNC_SHARED,autosufficienthh,578.0,0.0,-578.0,,-2,-21.31393156,False
-coef_calib_autosufficienthhjo_EBIKE_maint,-7.518757333,F,maint,EBIKE,autosufficienthh,10402.0,4130.0,-6272.0,151.864406779661,-0.9237206883779393,-8.442478021377939,False
-coef_calib_autosufficienthhjo_ESCOOTER_maint,-8.0,F,maint,ESCOOTER,autosufficienthh,263.0,0.0,-263.0,,-2,-10.0,False
-#coef_calib_zeroautohhjointtou_DRIVEALONE_disc,-999.0,F,,,,,,,,,-999.0,True
-#coef_calib_zeroautohhjointtou_SHARED2_disc,8.507773009,F,,,,,,,,,8.507773009,True
-coef_calib_zeroautohhjointtou_SHARED3_disc,-38.24625539,F,disc,SHARED3,zeroautohh,135.0,0.0,-135.0,,-2,-40.24625539,False
-coef_calib_zeroautohhjointtou_WALK_disc,-35.0,F,disc,WALK,zeroautohh,1307.0,576.0,-731.0,126.90972222222223,-0.8193820529283308,-35.81938205292833,False
-coef_calib_zeroautohhjointtou_BIKE_disc,-999.0,F,disc,BIKE,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc,-40.87568752,F,disc,WALK_TRANSIT,zeroautohh,92.0,0.0,-92.0,,,-40.87568752,True
-coef_calib_zeroautohhjointtou_PNR_TRANSIT_disc,-999.0,F,disc,PNR_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_KNR_TRANSIT_disc,-27.84999945,F,disc,KNR_TRANSIT,zeroautohh,120.0,0.0,-120.0,,-2,-29.84999945,False
-coef_calib_zeroautohhjointtou_TNC_TRANSIT_disc,-999.0,F,disc,TNC_TRANSIT,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_TAXI_disc,-999.0,F,disc,TAXI,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_TNC_SINGLE_disc,-999.0,F,disc,TNC_SINGLE,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_TNC_SHARED_disc,-999.0,F,disc,TNC_SHARED,zeroautohh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_zeroautohhjointtou_EBIKE_disc,-29.0,F,disc,EBIKE,zeroautohh,231.0,0.0,-231.0,,-2,-31.0,False
-coef_calib_zeroautohhjointtou_ESCOOTER_disc,-27.0,F,disc,ESCOOTER,zeroautohh,167.0,0.0,-167.0,,-2,-29.0,False
-#coef_calib_autodeficienthhjoi_SHARED2_disc,10.5330624,F,,,,,,,,,10.5330624,True
-coef_calib_autodeficienthhjoi_SHARED3_disc,-30.0,F,disc,SHARED3,autodeficienthh,27785.0,43340.0,15555.0,35.89063221042916,0.4445796893763284,-29.55542031062367,False
-coef_calib_autodeficienthhjoi_WALK_disc,-30.0,F,disc,WALK,autodeficienthh,22996.0,10125.0,-12871.0,127.12098765432098,-0.8203126747684416,-30.82031267476844,False
-coef_calib_autodeficienthhjoi_BIKE_disc,-967.0,F,disc,BIKE,autodeficienthh,0.0,777.0,777.0,,2,-965.0,False
-coef_calib_autodeficienthhjoi_WALK_TRANSIT_disc,-27.94370067,F,disc,WALK_TRANSIT,autodeficienthh,5916.0,42.0,-5874.0,13985.714285714286,-4.947746205547322,-32.89144687554732,False
-coef_calib_autodeficienthhjoi_PNR_TRANSIT_disc,-27.1684399,F,disc,PNR_TRANSIT,autodeficienthh,406.0,0.0,-406.0,,-2,-29.1684399,False
-coef_calib_autodeficienthhjoi_KNR_TRANSIT_disc,-25.12678499,F,disc,KNR_TRANSIT,autodeficienthh,5637.0,0.0,-5637.0,,-2,-27.12678499,False
-coef_calib_autodeficienthhjoi_TNC_TRANSIT_disc,-999.0,F,disc,TNC_TRANSIT,autodeficienthh,0.0,0.0,0.0,0,,-999.0,True
-coef_calib_autodeficienthhjoi_TAXI_disc,-28.71598351,F,disc,TAXI,autodeficienthh,20287.0,0.0,-20287.0,,-2,-30.71598351,False
-coef_calib_autodeficienthhjoi_TNC_SINGLE_disc,-30.71598351,F,disc,TNC_SINGLE,autodeficienthh,725.0,0.0,-725.0,,-2,-32.71598351,False
-coef_calib_autodeficienthhjoi_TNC_SHARED_disc,-30.71598351,F,disc,TNC_SHARED,autodeficienthh,3000.0,0.0,-3000.0,,-2,-32.71598351,False
-coef_calib_autodeficienthhjoi_EBIKE_disc,-28.38857593,F,disc,EBIKE,autodeficienthh,2928.0,45.0,-2883.0,6406.666666666666,-4.175412385310882,-32.56398831531088,False
-coef_calib_autodeficienthhjoi_ESCOOTER_disc,-26.0,F,disc,ESCOOTER,autodeficienthh,3108.0,0.0,-3108.0,,-2,-28.0,False
-#coef_calib_autosufficienthhjo_SHARED2_disc,4.253195896,F,,,,,,,,,4.253195896,True
-coef_calib_autosufficienthhjo_SHARED3_disc,-11.0,F,disc,SHARED3,autosufficienthh,61211.0,48150.0,-13061.0,27.12564901349948,-0.24000577368519732,-11.240005773685198,False
-coef_calib_autosufficienthhjo_WALK_disc,-10.0,F,disc,WALK,autosufficienthh,49665.0,42784.0,-6881.0,16.083115183246075,-0.14913625874778663,-10.149136258747786,False
-coef_calib_autosufficienthhjo_BIKE_disc,-10.58590174,F,disc,BIKE,autosufficienthh,17932.0,1988.0,-15944.0,802.0120724346077,-2.199457718058811,-12.785359458058812,False
-coef_calib_autosufficienthhjo_WALK_TRANSIT_disc,-14.918341,F,disc,WALK_TRANSIT,autosufficienthh,327.0,0.0,-327.0,,-2,-16.918340999999998,False
-coef_calib_autosufficienthhjo_PNR_TRANSIT_disc,-11.46037795,F,disc,PNR_TRANSIT,autosufficienthh,665.0,0.0,-665.0,,-2,-13.46037795,False
-coef_calib_autosufficienthhjo_KNR_TRANSIT_disc,-20.31188559,F,disc,KNR_TRANSIT,autosufficienthh,147.0,0.0,-147.0,,-2,-22.31188559,False
-coef_calib_autosufficienthhjo_TNC_TRANSIT_disc,-15.31188559,F,disc,TNC_TRANSIT,autosufficienthh,315.0,0.0,-315.0,,-2,-17.31188559,False
-coef_calib_autosufficienthhjo_TAXI_disc,-16.0336306,F,disc,TAXI,autosufficienthh,56.0,0.0,-56.0,,,-16.0336306,True
-coef_calib_autosufficienthhjo_TNC_SINGLE_disc,-16.0336306,F,disc,TNC_SINGLE,autosufficienthh,179.0,0.0,-179.0,,-2,-18.0336306,False
-coef_calib_autosufficienthhjo_TNC_SHARED_disc,-16.0336306,F,disc,TNC_SHARED,autosufficienthh,578.0,0.0,-578.0,,-2,-18.0336306,False
-coef_calib_autosufficienthhjo_EBIKE_disc,-7.518757333,F,disc,EBIKE,autosufficienthh,10402.0,4130.0,-6272.0,151.864406779661,-0.9237206883779393,-8.442478021377939,False
-coef_calib_autosufficienthhjo_ESCOOTER_disc,-8.0,F,disc,ESCOOTER,autosufficienthh,263.0,0.0,-263.0,,-2,-10.0,False
-coef_calib_zeroautohhindivtou_ESCOOTER_work,0.0,F,work,ESCOOTER,zeroautohh,88.0,0.0,-88.0,,,0.0,True
-coef_calib_zeroautohhindivtou_EBIKE_work,-4.0,F,work,EBIKE,zeroautohh,44.0,0.0,-44.0,,,-4.0,True
-coef_calib_zeroautohhindivtou_ESCOOTER_univ,-2.0,F,univ,ESCOOTER,zeroautohh,40.0,0.0,-40.0,,,-2.0,True
-coef_calib_zeroautohhindivtou_EBIKE_univ,-6.0,F,univ,EBIKE,zeroautohh,4.0,0.0,-4.0,,,-6.0,True
-coef_calib_zeroautohhindivtou_ESCOOTER_school,0.0,F,school,ESCOOTER,zeroautohh,16.0,0.0,-16.0,,,0.0,True
-coef_calib_zeroautohhindivtou_EBIKE_school,0.0,F,school,EBIKE,zeroautohh,16.0,0.0,-16.0,,,0.0,True
-coef_calib_zeroautohhindivtou_ESCOOTER_atwork,-25.0,F,atwork,ESCOOTER,zeroautohh,0.0,0.0,0.0,0,,-25.0,True
-coef_calib_zeroautohhindivtou_EBIKE_atwork,-25.0,F,atwork,EBIKE,zeroautohh,56.0,0.0,-56.0,,,-25.0,True
-coef_calib_autodeficienthhind_ESCOOTER_work,-2.0,F,work,ESCOOTER,autodeficienthh,76.0,0.0,-76.0,,,-2.0,True
-coef_calib_autodeficienthhind_EBIKE_work,-6.0,F,work,EBIKE,autodeficienthh,56.0,0.0,-56.0,,,-6.0,True
-coef_calib_autodeficienthhind_ESCOOTER_univ,0.0,F,univ,ESCOOTER,autodeficienthh,8.0,0.0,-8.0,,,0.0,True
-coef_calib_autodeficienthhind_EBIKE_univ,-4.0,F,univ,EBIKE,autodeficienthh,32.0,0.0,-32.0,,,-4.0,True
-coef_calib_autodeficienthhind_ESCOOTER_school,0.0,F,school,ESCOOTER,autodeficienthh,0.0,0.0,0.0,0,,0.0,True
-coef_calib_autodeficienthhind_EBIKE_school,-2.0,F,school,EBIKE,autodeficienthh,12.0,0.0,-12.0,,,-2.0,True
-coef_calib_autodeficienthhind_ESCOOTER_atwork,-4.0,F,atwork,ESCOOTER,autodeficienthh,20.0,0.0,-20.0,,,-4.0,True
-coef_calib_autodeficienthhind_EBIKE_atwork,-8.0,F,atwork,EBIKE,autodeficienthh,36.0,0.0,-36.0,,,-8.0,True
-coef_calib_autosufficienthhin_ESCOOTER_work,-4.0,F,work,ESCOOTER,autosufficienthh,16.0,0.0,-16.0,,,-4.0,True
-coef_calib_autosufficienthhin_EBIKE_work,-3.257409708,F,work,EBIKE,autosufficienthh,1482.0,1487.0,5.0,0.3362474781439139,,-3.257409708,True
-coef_calib_autosufficienthhin_ESCOOTER_univ,0.0,F,univ,ESCOOTER,autosufficienthh,12.0,0.0,-12.0,,,0.0,True
-coef_calib_autosufficienthhin_EBIKE_univ,-4.0,F,univ,EBIKE,autosufficienthh,16.0,0.0,-16.0,,,-4.0,True
-coef_calib_autosufficienthhin_ESCOOTER_school,0.0,F,school,ESCOOTER,autosufficienthh,12.0,0.0,-12.0,,,0.0,True
-coef_calib_autosufficienthhin_EBIKE_school,-32.0,F,school,EBIKE,autosufficienthh,108.0,0.0,-108.0,,-2,-34.0,False
-coef_calib_autosufficienthhin_ESCOOTER_atwork,-2.0,F,atwork,ESCOOTER,autosufficienthh,72.0,0.0,-72.0,,,-2.0,True
-coef_calib_autosufficienthhin_EBIKE_atwork,-4.647000813,F,atwork,EBIKE,autosufficienthh,247.0,246.0,-1.0,0.40650406504065045,,-4.647000813,True
diff --git a/src/asim/calibration/resident/tour_mode_choice/scaled_targets.xlsx b/src/asim/calibration/resident/tour_mode_choice/scaled_targets.xlsx
deleted file mode 100644
index f2cc8c4eb..000000000
Binary files a/src/asim/calibration/resident/tour_mode_choice/scaled_targets.xlsx and /dev/null differ
diff --git a/src/asim/calibration/resident/tour_mode_choice/scripts/asim_calib_util.py b/src/asim/calibration/resident/tour_mode_choice/scripts/asim_calib_util.py
index 4f891b71c..b666c99ab 100644
--- a/src/asim/calibration/resident/tour_mode_choice/scripts/asim_calib_util.py
+++ b/src/asim/calibration/resident/tour_mode_choice/scripts/asim_calib_util.py
@@ -625,7 +625,7 @@ def match_model_and_calib_targets_to_coefficients(original_tour_mc_constants_df,
                 for coef_purpose in output_calibration_purposes_to_coef_names_dict[purpose]:
                     coef_mode = output_calibration_mode_to_coef_name_dict[mode]
                     coef_name = 'coef_calib_' + output_calibration_autosuff_to_coef_names_indiv_dict[auto_suff] + '_' + coef_mode + '_' + coef_purpose
-                    target_name = mode + '_' + auto_suff + '_' + purpose ### DELETE
+                    #target_name = mode + '_' + auto_suff + '_' + purpose ### DELETE
 
                     if purpose == 'joint':
                         coef_name = 'coef_calib_' + output_calibration_autosuff_to_coef_names_joint_dict[auto_suff] + '_' + coef_mode + '_' + coef_purpose
diff --git a/src/asim/calibration/resident/tour_mode_choice/scripts/calibrate_tour_mode_choice.ipynb b/src/asim/calibration/resident/tour_mode_choice/scripts/calibrate_tour_mode_choice.ipynb
index fe9f3cc4c..819d380c2 100644
--- a/src/asim/calibration/resident/tour_mode_choice/scripts/calibrate_tour_mode_choice.ipynb
+++ b/src/asim/calibration/resident/tour_mode_choice/scripts/calibrate_tour_mode_choice.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
    "source": [
     "# csv file containing calibration targets by tour mode choice.\n",
     "# Column names and acceptable values should be set in dict at top of script\n",
-    "tour_mode_choice_calib_targets_file = r\"C:\\abm_runs\\rohans\\calibration\\tour_mc\\target\\tour_mode_choice_calibration_targets.csv\"\n",
+    "tour_mode_choice_calib_targets_file = \"../target/tour_mode_choice_calibration_targets_2024-01-23_updated.csv\"\n",
     "\n",
     "# directory of the simulation.py file\n",
     "simpy_dir = r\"C:\\abm_runs\\rohans\"\n",
@@ -98,5376 +98,66 @@
     "adjust_when_zero_counts = 2 # coefficient change when have target counts but no model counts (or vise-versa)\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "--------------- User should not have to change anything below this line ----------------------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initial Model Run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Change directory to model setup\n",
-    "### i.e. the location of simulation.py script\n",
-    "os.chdir(simpy_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_cold\n",
-      "ActivitySim run started at:  2023-09-12 19:08:58.079735\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_cold_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-12 20:39:44.770760\n",
-      "Run Time:  5446.69 secs =  90.77816666666666  mins\n"
-     ]
-    }
-   ],
-   "source": [
-    "asim_calib_util.run_activitysim(\n",
-    "        data_dir=data_dir,  # data inputs for ActivitySim\n",
-    "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
-    "        configs_common_dir=configs_common_dir,  # these files are copied to the config section of the run directory\n",
-    "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
-    "        output_dir=output_dir,  # location to store run model outputs\n",
-    "        settings_file=cold_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
-    "        tour_mc_coef_file=tour_mc_coef_file  # optional: tour_mode_choice_coefficients.csv to replace the one in configs_dir\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
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SkuXxZcuW1Z9//unGitzPjnO2o9mzZ2vAgAFasGCBEhISNHbs2LQdZ4Zh6JNPPtG0adOUlJSktm3bavjw4SpevLjJVedcdHS0rly5km5ZfHy8hg0bptmzZ2cI4VZgt20sSUePHtWePXvUqVOntGVnzpzR1KlT9cMPPygoKEjNmzfXiy++6JP3W/s7OwY0pJeUlKRff/1VgYGBqlSpktnleFRgYKBeeukl7du3T+Hh4YRwC7DqRb6yEsD/qmLFiurSpYubqoEvIXvfnBVyqB3nbEd2y2Vkb+tvY4nsDfshe5O9rYTsfZ23Zm8a4F4qJSVFs2fP1kcffaTExEQ99thjGjZsmE//uE1OTs7WDzV/f38lJSW5sSL3s+Oc7SggIEDTpk3TsGHDFB4ersTERE2aNEnHjh3T0KFDFRUVpVKlSumdd97RI488Yna5bmPVL3zJftt40qRJmj17tgzDUMeOHeV0OnXp0iV17dpVJ0+eVNGiRVW+fHn9+9//1tq1azVv3rwsnW3jy+wY0GJiYhQVFaXAwEDVr18/y/c58maXLl3SzJkztWfPnnRHaC9dulRjxozRxYsXJV3/4T527FjVqlXLpErN0bBhQ23evNnsMoBcuXr1qlauXKmwsDDt2LFDhmGob9++ZpcFL0b2tkYOteOc7chuuexGyN7W2cZk74zI3mRvOyB7wwp8IXvTAPdCu3bt0qhRo/TLL7/ozjvv1MiRI9W4cWOzywJuaerUqdka/9NPP7mpEs/z8/PThAkTVLhwYc2fP18nTpzQL7/8osTERHXp0kVDhgxRwYIFzS4TuWCXbbxq1SrNmjVLDz30kLp37y6n0ylJ+uijjxQdHa2aNWvq008/VVBQkKKiovT888/r008/Vc+ePU2uPPfsGNCio6M1efJk7dmzR2vXrk1b/sknn+jDDz9USkqKDMNQkSJFNHr0aLVs2dLEanMnPj5eXbt21dGjR1WmTBklJyfL399fUVFRGjJkiAzDUNeuXVWlShVFRETopZdeUkREhO644w6zS/cYPz8/uVwus8vIsYiIiGyN/+2339xTCEyxZ88ehYWF6bvvvlN8fLwMw9Dtt9+url27ml0avBjZG76K7G39XGZndtnGZG+yt0T2Jnv7JrK3vflS9qYB7kXi4uI0YcIERUREKCAgQP3791fPnj0tcXmbVLt27VJKSkqWxv7www9ursYz7DTn7IZwKf099qxg+PDhKlq0qKZOnSqn06mPP/5YzZo1M7ss5CGrb+OFCxeqZs2amjFjRtoywzC0ZMkSORwOvf766woKCpIk1ahRQ0888YS+/fZbnw/hdgxoZ8+eVdeuXRUXF6caNWqkzfn777/X5MmT5e/vrzfeeENVq1bVokWL9MYbb+jLL79U9erVzS49R2bPnq1jx47pX//6l1q3bp22fNq0aTIMQy+88IKGDh0qSercubPat2+vGTNmaNy4cWaV7HH79u1T2bJlzS4jx4YOHZqt3xWGYVjud4jdxMTEaPHixQoLC9Mff/yRdkZcw4YN1bNnTz3wwAMmVwhvRfZOz9dzaCo7zZnsbf1cButvY7I32ZvsTfb2VWRv+/HV7E0D3Et89dVXmjRpki5cuKD7779fI0eO1O233252WXlu0aJFWrRoUZbGWuWD0U5znjt3rtkleIV+/fqpWLFiGjNmjGbNmqW6deta4uhk/I+Vt/H+/fszBOoDBw7o7NmzKliwoBo2bJhu3X333aelS5d6skS3sGNA++STTxQfH6/PP/883RH1n3zyiRwOh/r27Zv2XnjwwQfVqVMnzZw5U//617/MKTiXIiMj9cQTT6TbvvHx8WmXHXv22WfTlufPn1/t27fXV1995fE6zbJixQotXrxYr7zyitml5Jgv/z0i65KSkrR69WqFhYVp69atSklJkZ+fnxo0aKC6detq+vTp6t69u9cGcJiP7J2Rr+fQVHaaM9n7OivnMlxn5W1M9iZ7k73J3r7Kl/8ekXVWyN40wE12+PBhvfPOO9q7d69KliypyZMnq23btmaX5RZ2/GC025wbNGiQ7cfs2rXLDZV41s6dOzMsq1q1qrp06aJFixbphRde0ODBg9MuZ5Wqfv36nioRuWSnbXzlyhUVKVIk3bJt27ZJuj4fPz+/dOuSk5N9eudhKjsGtPXr16tTp07pAvj58+e1e/duSdKTTz6ZttzhcKhNmzb67LPPPF1mnjlx4kS67Shd/9tOTk5WhQoVMjQ/ypYtq7Nnz3qyxDw3bNiwm65PSUlRfHy8fv31Vx07dkx33nmnT4fwjh07ml2CKVatWqU//vgj7b8TExPlcDi0ePHitL/nVD///LOny8tTo0aN0vLly3XhwgXlz59fTZs2VYsWLdS8eXMVK1ZM0dHR+uijj8wuE16K7G1tdpsz2ft/rJrL7MpO25jsTfYme19H9vY9ZO/ryN7en71pgJto/PjxmjdvnlJSUvTwww9r4MCBKliwoE6ePHnTx5UrV85DFeYtO34w2nHOWXHq1CmFh4crIiJCx48f18GDB80uKVeef/75m4aQ/fv368UXX8yw3JfnffTo0XTB9NKlS5Ku71j090//1XLkyBGP1uYOdtrGISEhOn78eLpl69evl8Ph0IMPPphhfFRUlEqXLu2p8tzGjgHtzz//VNWqVdMt2759u1wulypXrpxhu5YoUUIXLlzwZIl5yul0ZrjH1tatWyVJTZo0yTD+3LlzPn92SXh4eJbGVahQQS+88IJee+01n5/zjWT2+7ps2bKW2YkYGRmZYfmN7svmy3NesGCBgoOD1adPH73yyiuWfb8i75G9rc+Oc84Ksvd1vjxvsnd6VtrGZO//IXuTvX39dz3Z+3/I3v/jy3O2SvamAW6iOXPmpP3vdevWad26dVl6nC/+qMuJc+fO6ciRIz55FGdOWXnOV69eVWRkpMLCwrR9+/a0S85l9qPe1/Tt29env9ByYsaMGenuU5Vq/PjxGZb5+uUFJXtt4wcffFDffPONnn32WZUuXVp79uzRrl27lC9fPrVq1Srd2F9++UXffvutnnrqKZOqzTt2DGj58+dXQkJCumVbtmyRw+HQ/fffn2H8n3/+qcKFC3uqvDxXuXJl7dmzR926dZN0/bNp1apVcjgceuSRRzKMX7Nmje666y5Pl5mn1qxZc9P1+fPnV+HChS11z1tJ+uabb/TVV1/po48+UvHixRUXF6dHHnkkw+f4gAED9Oqrr5pUZd6w2yVwO3bsqNWrV+vjjz/WZ599pnr16qUdhV6yZEmzy4MXI3vfnJVz6I1Yec5kb2she1sX2ft/yN7pkb19D9mb7G01VsneNMBN1K9fP7NL8Ki7775bEyZMULt27dKWJSYmatasWerQoYNuu+22dOM3bdqkIUOG+PROBzvO+e/27t2rsLAwfffdd7p8+bIkqXjx4urUqZO6du2q8uXLm1xh7vXv39/sEjzKToE0lZ228WuvvabIyEi1adNGlSpV0s8//yzDMNS3b18VL15c0vXwHRkZqblz5ypfvnzq0aOHyVXnnh0DWmhoqLZu3aoXXnhB0v/u7SNJLVq0SDfWMAytWLFCoaGhHq8zr3To0EHvvfee7rvvPt1///1atGiRTp48qdtvv11NmzZNN3bGjBnau3evhg8fblK1ecMK37HZ9frrr2vlypUqV66cTp48mfa5JUnt27dXhQoVJEmLFy/W9OnT1blzZ5UqVcqscnMtJ5fATU5OdkMlnjFu3DiNGjVK69at09KlS7Vx40Z9//33GjVqlGrXrq06derY7jcKsobsbf0casc5/x3Z23rI3tZG9iZ7S2RvsrfvInvfGtnbfDTATWS3EG4YRoZlCQkJmjZtmurWrZshkFqBHecsSbGxsYqIiFB4eLh+//13GYahoKAgNWnSRFu2bNG7776r5s2bm10mcshOgdSOSpYsqa+//lrTpk3T3r17dc8996hTp07q0qVL2pjw8HDNnj1b5cuX1wcffKCyZcuaWHHesGNAe/rpp/XGG29o7Nixuv/++/XNN9/o7Nmzuueee9KdDZWYmKj3339fv/zyi0/vcHn66ae1e/dujRs3Tg6HQ4ZhqEiRIpo0aVLaPQS//vprffLJJzp+/Ljq1q2rZ555xuSq3SsqKkp79uyR0+lUgwYNVK1aNbNLypUlS5Zo5cqV6t27twYMGJDhvokdOnRQ48aNJUnNmjXTU089pS+//NIyv8nj4+NlGMZNz5DZs2ePRowYoWXLlnmwsrwVEBCgVq1aqVWrVrp06ZJWrFihJUuWaPfu3dq9e7ccDoc+/vhjXb58Wa1atVJgYKDZJcMLWOXvPKvsmEPtOGeJ7G11ZG9rI3uTvcneZG9fRfYme/tK9qYB7kWSkpJ06NAhxcbGyjAMhYSEKDQ0VPnz5ze7NLfKLKhanVXn/N133yksLExbtmxRSkqKChcurHbt2qlly5Zq2rSpTp8+neHIRiuYOnVqth/jcDjUt29fN1Tjft27d9err76a9kPGDuy2jcuUKaPRo0ffcH2nTp30yCOPqE6dOmnhxdfZMaC1bdtWhw8f1qxZszRv3jwZhqHbbrtNU6ZMSRvz3//+Vx999JHi4+PVunVrPfHEEyZWnDsOh0OTJk1St27dtGfPHhUsWFAtWrRId5Tyn3/+KcMw9Oqrr6p3796WeH8fOHBAH3/8sY4cOaLbb79dffr0UY0aNfR///d/CgsLS/tN4nA49Pjjj2vcuHEZ7ifpK8LDw1W7dm0NGjTolmNTd7ht2LDB50P4ypUrNXXqVP3666+Srt9XbsCAAXr88cfTxly5ckWTJ0/WggULMlxy0pcMGzZMTz/9tGrWrClJKlSokLp06aIuXbooJiZGy5Yt07JlyxQVFaUff/xRo0ePVtu2bfXuu++aXDm8DdnbPqw6Z7J31vlyLiN7Z40vb2OyN9k7Fdnb99/fZO/Mkb19k1Wyt2/+hVnMmTNnNHnyZEVGRio+Pj7duqCgILVq1UqDBg1SSEiISRUCWTNo0CAFBwfr2WefVfPmzVW/fv10R4D5wmUxcsJuAW3Hjh3pjki2A7tt41vx9cuPZcauAW3QoEF65plntG/fPhUsWFANGjRQvnz50tbnz59f9913n9q1a6cnn3zSxErzTu3atVW7du1M1/Xr18/nA9lf7dmzR927d5e/v7+qVq2q/fv3q1u3bnr++ef1zTffqH379mrdurWuXLmitWvXatmyZbr77rv18ssvm116jhw4cEB9+vTJ8vgHHnhA06ZNc2NF7rd8+XK98cYbyp8/vx544AEFBQVp165d+uc//5l2/8gff/xRgwYN0okTJ3Tbbbdp1KhRZpedY+Hh4WrSpElaCP+r0qVLq0ePHurRo4eOHDmiJUuWaOnSpfrqq6+8LoTDPGRvWAXZO+t8OZeRvbPGl7fxrZC9yd6+jOxN9k5F9vY9VsneNMBNtnfvXvXu3VsXLlxQzZo11ahRI4WEhMjf31+xsbHauXOnIiIitHbtWk2fPl116tQxu2Tghm677TadOHFCYWFh+u233/Tjjz+qRYsWqlixotmludXcuXPNLgFuZqdtvHPnzhw97q+X7fJldgpoqcqUKaMyZcpkuu65557Tc8895+GKkFemTZumO++8U3PnzlWxYsVkGIbeeustzZkzR+3bt9eECRPSxj7++OO6ePGili1b5rMhPDExUYULF86wvFChQpoxY4buvvvudMsLFCjg0/fkkqT58+erRIkSWrhwYdo91hISEvTqq6/qP//5j0JCQvTyyy/r6tWreumll/T666975WXJ8tpdd92lQYMGadCgQfrhhx/MLgdeguwNKyF7w6rstI3J3mTvvyJ7+zay93Vkb7K3t6EBbqKzZ8+qb9++KlCggD766CPVrVs303EHDhzQwIEDNWDAAC1evFglSpTwcKVA1qxevVr79u3TkiVLtGLFCm3atEmTJ09WpUqV1LJlS1WvXt3sEt2iQYMGtxxz6dIlORyOm94bBN4rK9vYKp5//vlsnzHicDh04MABN1UE5I1hw4Zl+zEOh0Njx451QzWe8eOPP+qVV15RsWLFJF2fT48ePRQeHq6HHnoow/iWLVvq/fff93CVead06dI6efJkhuX+/v6Zzvf333/3+fsoHjlyRM8//3xaAJeun8Xar18/Pffccxo0aJBKlCihSZMmqVatWuYVaiKamJDI3rAesveNkb19G9n75sje8AVkb7L335G97cEbszcNcBPNnz9f8fHx6Y4aycw999yjOXPmqF27dvriiy/Uv39/D1YJZE/NmjVVs2ZNvfXWW9q0aZOWLl2qNWvWaPr06XI4HHI4HFq3bp1CQ0NVvnx5s8vNM4ZhaOPGjfr11191xx136KGHHpK/v7+2bt2qMWPG6OjRo5Kku+++W2+88YYeeOABkyvOnVWrVumPP/7I8ngrX5IslVV2tIwbNy5L41auXKn169dLkmrUqOHGijzDjgGte/fu2X6Mw+HQZ5995oZq3C88PDzLY/+6I8qXt/HFixdVsmTJdMtSLy1YtGjRDOMDAwOVkJDgidLcokaNGlq+fLn69u17y8skJiUlafny5WrWrJmHqnOPS5cu6bbbbsuw/Pbbb5d0/VKKCxcuTNsRYwW7du1SSkpKth7ToUMH9xQDn0H2hhWRvcnemSF7+w6yd9aRvX0L2fs6svd1ZG/fZYXsTQPcRKtXr1b79u1vGsBTlS9fXh07dlRkZKRPh/C//9Gk3ndt8+bNiomJSTfWGy+ZkBN2nLMk+fn5qVmzZmrWrJkSExO1atUqLVu2TJs3b9bXX3+tsLAwNWzYUJ06ddLjjz9udrm5cvHiRfXq1Uv79u2TYRiSpPvuu08jRoxQr169FBQUpBYtWujKlSvat2+fevfurTlz5vj0kc2rVq1SZGRklsdbIYT/dUfL7bffrocfftiSO1o6dux40/XR0dEaPXq01q9fr8KFC+uNN95Q165dPVSd+9gxoJ04cSJL41wul2JiYmQYhk/fT/LQoUO3HPPX93ehQoU0cOBA9xfmRoZhyN8//c/91G3oy9vyRp5++mk999xzevfddzV8+PAMc0/lcrn09ttvKzY2Vk8//bSHq8xbLpcr3T1fU6XeT7BXr16WCuCStGjRIi1atChLY1M/t7wthMPzyN72yKF2nLNE9iZ7p0f29h1k71sje/smsvd1ZG+yt6+zQvamAW6iEydOZOveHqGhodn6keCN/v5HkxpYZs2aleHLwNe/7FPZac69evVSo0aN1KBBA1WvXj1tLoGBgWrXrp3atWunuLg4LV++XEuXLtXWrVu1bds2nw/hH374oQ4dOqS3335bDRs2VHR0tN577z298MILuvPOOzVv3ry0o/3OnTunzp07a/bs2T4dwnv37q0mTZqYXYbH2HFHy98lJyfrv//9r2bMmKGEhAS1b99eQ4cOTTui1dfZMaCtXbv2lmP27dund955R3/++aduu+02jRgxwgOVeV5KSopmz56tjz76SImJiXrsscc0bNiwDEdww7vVq1dPPXr00H//+19t27ZNL7/8sho2bKgyZcrIMAydPn1a27dv1+eff65Dhw5p0KBBCg0NNbtstypXrpzZJeS5p556yraXlEPOkb2tnUNT2WnOZG+yt1WRvcneEtmb7A1vR/bOiOztnWiAmyhfvny6evVqlscnJiYqODjYjRW5V1Yv7WMldpvztm3btHHjxrRLUdWrV08NGzZUo0aN0r7kihUrpm7duqlbt246fvy4vv32W5Orzr21a9fq6aef1jPPPCNJqlSpkt5++229/PLL6tatW7pL3RQvXlxPPfWU5s2bZ1K1eeOuu+6yVMC8FTvuaPmrHTt2aNSoUTpy5IgqVaqkkSNHqmHDhmaX5TF2DGiXLl3SBx98oEWLFsnpdKp379567bXXlD9/frNLy3O7du3SqFGj9Msvv+jOO+/UyJEj1bhxY7PLyjN/v2xmYmKiHA6HFi9erN27d6cb+/PPP3u6vDz3z3/+U+XLl9fkyZP19ttvZ9rwCA4O1jvvvGOJM2jsqF69emrXrp3ZZcDHkL2tz25zJnuTva2K7E32JnuTvX0V2ZvsbTVWyN40wE1UtWpVbdiwIcv3AVm/fr0qV67s5qrc51aX9rEiu835hx9+0IEDB/TDDz9oz5492rt3r9atWyeHw6HChQurfv36atiwoRo2bKiqVauqQoUK6tOnj9ll59rp06d11113pVuW+rea2dFfZcuW1YULFzxSG/KGHXe0SNd3KIwfP15LlixR/vz59frrr+uVV15Ju7yPHVg9oGVm8eLFmjBhgs6ePasGDRpo5MiRGT7jrCAuLk4TJkxQRESEAgIC1L9/f/Xs2VMBAQFml5anIiMjM71sZkRERKbjfflsuFTPPvusOnbsqHXr1mnnzp36888/ZRiGQkJCVKdOHbVo0cLn7xn5V5ndG/RmO1uscGlUILvI3tZntzmTvf+H7G0tZG+yN9mb7O2ryN5kb7K396EBbqInnnhCb7/9tpYvX662bdvedGxERIS2bNmiyZMne6g68504cUJvv/22Zs+ebXYpHuPrc/b391eNGjVUo0YNvfjii5KkU6dOpQvl48ePV0pKiooWLaoGDRqoYcOGevbZZ80tPJeuXbumwMDAdMtSQ0pmYcXhcKS7Nx28nx13tHz55ZeaPHmyLly4oAcffFAjRozI0n0zrcIuAe2vjh49qlGjRmnHjh0qVqyY3n//fa+7d09e+eqrrzRp0iRduHBB999/v0aOHKnbb7/d7LLy3Ny5c80uwTRBQUFq27btLX9jW8GNdrRIme9sIYTDjsjeN+frOTQnfH3OZO//IXtbC9mb7E32thayt/WRva8je3snGuAm6tSpkyIiIjR48GAdPnxY3bp1U0hISLoxsbGxmjNnjubOnatmzZqpTZs2JlWbN/bt26fp06drz549kqR77rlHffv2Vb169dLGGIahTz/9VB9++KESExPNKjXP2HHOf1W2bFk99thjeuyxxyRdv7TPkiVLFBYWppUrVyoyMtLnQ7jd9OvXT9WqVTO7DI+y046WQ4cOaeTIkYqKilLp0qU1evRotWzZ0uyyPMouAS1VUlKSpk2bptmzZys5OVldunTRm2++qcKFC5tdWp47fPiw3nnnHe3du1clS5bU5MmTLR3SrHIpSNyY3Xa0dOzY0dKfx3Afsrc9cqgd5/xXZG/rIXtfR/a2LrI32dsqyN7WR/b2TTTATeR0OjVjxgy9+eab+vjjj/XJJ5+obNmyKlWqlPz8/HT27FkdO3ZMhmGoTZs2eu+998wuOVe2bt2qnj17KiUlRRUrVlRQUJB27typF198UXPmzFH9+vV14sQJ/eMf/1BUVJQKFiyoUaNGmV12rthxzn+XmJionTt3aseOHdq9e7d++uknXbt2TQEBAWmXZLOC8+fP6+TJk2n/nXoE8rlz59Itl64f3erL+vXrl+6/k5KSdOjQIcXGxqZd5iY0NNSS9yuyg06dOsnlckmSSpQoofnz52v+/Pk3fYzD4dBnn33mifLcym4BTZI2bNig0aNHKzo6WtWqVdOoUaNUs2ZNs8tyi/Hjx2vevHlKSUnRww8/rIEDB6pgwYIZPqP/LrMzTaxq4cKF2rRpk6ZOnWp2KTmS1Usb/5Wvf37lZEfLrl273FCJZ9jtHr/IO2Rv6+dQO87578jeZG/4FrI32ZvsnR7Z23eQvbOG7G0+h2EYhtlFQPr++++1ePFiRUVF6fTp02k/ZOvWrasnnnhCjRo1MrvEXHvppZcUFRWlWbNmqXbt2pKkmJgY9enTR/ny5dO4cePUvXt3nT17Vi1bttSIESNUqlQpk6vOHTvOOTk5WXv37tW2bdu0bds27du3T9euXVO+fPlUo0aNtOBdu3Zty1zOKDQ0NNP7thiGcdP7uRw8eNCdZbndmTNnNHnyZEVGRio+Pj7duqCgILVq1UqDBg3KcHaNLwoNDdX//d//qXnz5mnLLly4oI4dO+qDDz5I+/tOtWrVKr3//vs+uY0feeSRHD1u7dq1eVyJZ90ooN2KLwe0AQMGaNWqVZKkhx9+WN27d5efn98tH1e/fn13l+YWoaGhaf87O/fa8sW/45waOXKkFi1a5LNzzurnl8vlUkxMTNr3tK/ONztOnTql8PBwRURE6Pjx47aYM3AjZG9r5lA7zpns/T9kb7K3L25jsjfZ+1bI3tZF9rYusrd3oQEOj2nUqJE6d+6sN998M93yTZs2qWfPnrrrrrt0+vRpvfPOOz5/ublUdptzz549tWvXLiUmJsrpdKp69epq1KiRGjZsqLp162a4jJVVDBs2LEeP8+Ujqfbu3avevXvrwoULqlmzpho1aqSQkBD5+/srNjZWO3fu1M6dO1W4cGFNnz5dderUMbvkXLHrjhY7sWNA++ucpVvP29cDS06PrP77mTdW5ushPCv27dund955RwcPHtRtt92mESNGqFmzZmaX5RZXr15VZGSkwsLCtH379rS/4aZNm+rjjz82uzwAbmS3HCrZb85k7+whe/sOsrf1kb3J3jdC9rYWsjfZ2xtwCXQfsn37dh0+fDhHl5jwBpcuXdJdd92VYXmVKlVkGIbOnz+vr776yhL3Fkhltzl///33ypcvnzp06KDevXvrzjvvNLskj/DlMJ0TZ8+eVd++fVWgQAF99NFHqlu3bqbjDhw4oIEDB2rAgAFavHixSpQo4eFK806HDh2yFczsZNeuXQoPD/f5S4XaKWilsttnV062sdXuDWpnly5d0gcffKBFixbJ6XSqd+/eeu211yx5ydC9e/cqLCxM3333nS5fvixJKl68uDp16qSuXbuqfPnyJlcIeD+yt++x25zJ3vZA9sZfkb19l90+u8je9kb2Jnt7ExrgPmT58uVatGiRz4bwlJQU+ftnfMulXoqrT58+lgmjqew25y5dumj79u1pl/moVKmSGjdurEaNGql+/foqUqSI2SUiD8yfP1/x8fFauHChKlSocMNx99xzj+bMmaN27drpiy++UP/+/T1YZd56//33zS7Bq/z5558KDw9XeHi4jh8/Lkm2DOG+HtA6duyY7cdER0e7oRLvk7pzaeXKlT59zyZct3jxYk2YMEFnz55VgwYNNHLkyEybJL4sNjZWERERCg8P1++//y7DMBQUFKQmTZpoy5Ytevfdd9NdShTAzZG9fY/d5kz2tgeyN8je15G9rYvsbS1kb7K3t6EBDq9htQ/DrLDanEePHi1JOnnypLZs2aJt27ZpxYoVmj9/vpxOp0JDQ9WwYcO0UB4cHGxyxciJ1atXq3379jcN4KnKly+vjh07KjIy0qdDePfu3fXqq6+qcePGacuSk5O1Z88ehYaGqlChQunGL1myREOHDtWBAwc8XarbJCUlpV3OZ9u2bTIMQ4ZhqGHDhnrmmWfMLs+j7BbQrl69qhUrVig8PFw7d+7U/v37zS7JLVJ3LkVEROjYsWMyDENFixY1uyzkwtGjRzVq1Cjt2LFDxYoV0/vvv68OHTqYXVae+u677xQWFqYtW7YoJSVFhQsXVrt27dSyZUs1bdpUp0+fVosWLcwuE4CXsVoOzQqrzZnsbQ9k7+vI3mRvsre1kL2th+xN9vZWNMAB5Lly5cqpc+fO6ty5syTpl19+0bZt27R161Z98803mjNnjvz9/XXvvfeqcePGev31102uGNlx4sQJPffcc1keHxoaqvDwcDdW5H47duxQly5d0i27dOmSunfvrtmzZ6cL56kMw/BUeW7198v5pM7rscceU9++fVWpUiWTK/QMOwa0H374QWFhYVqxYoXi4+NlGIaqVKlidll5KnXnUnh4uLZt2yaXyyXDMFSrVi09/fTTatu2rdkl5kpERES2xv/222/uKcTDkpKSNG3aNM2ePVvJycnq0qWL3nzzTRUuXNjs0vLcoEGDFBwcrGeffVbNmzdX/fr15efnl7aeS4gCgLWRva2N7H0d2ZvsTfb2fWTv9Mjevofs7ZtogMOjdu3apZSUlHTL4uPjJUmbN29WTExMhsf4+tFCdpzz31WpUkVVqlTR888/r6SkJK1YsUJffPGF9u7dq3379hHCfUy+fPl09erVLI9PTEy07BkHVgnaf5fZ5XyKFi2qjh07qlatWho5cqTatm1r+QBu9YCWmZiYmLRt/8cff0iS/P391bZtWz3zzDOqV6+eyRXmjX379umbb75Jt3OpcOHCunTpkkaPHp1hp5uvGjp0aLZCmGEYPh/aNmzYoNGjRys6OlrVqlXTqFGjVLNmTbPLcpvbbrtNJ06cUFhYmH777Tf9+OOPatGihSpWrGh2aQBMZsccasc5/x3Z21rI3v9D9iZ7Ww3Zm+xN9vYtZG/fRAMcHrVo0SItWrQo3bLUH7GzZs1K98Gf+kXg64HUjnP+q2PHjmnfvn3at2+foqKidOjQIV27dk0FChTQgw8+qPr165tdIrKpatWq2rBhQ5bvibh+/XpVrlzZzVUhr/Tq1UubN29WSkqKypYtq2effVYtWrRQw4YN5XQ6FR0dbdmdD6nsEtBSJSUlafXq1frmm2+0bdu2tB3Hd911l44ePaqJEyeqdevWJleZe7GxsVq8eLHCw8P122+/yTAMlStXTh07dlTLli1VunRpPfrooypevLjZpeaZcePGmV2CRw0YMECrVq2SJD388MPq3r27kpKStHPnzps+zpd/i6xevVr79u3TkiVLtGLFCm3atEmTJ09WpUqV1LJlS1WvXt3sEgGYxI451I5z/iuyt/WQva2N7E32JnuTvX0V2Zvs7StogJvo5MmT2RqfeuSyr7LbF4FkvzlfuHBBUVFRaYE7KipKFy5ckGEYKlKkiOrUqaNBgwapfv36uueee+R0Os0uGTnwxBNP6O2339by5ctveRRuRESEtmzZosmTJ3uoOuTWxo0bFRwcrO7du6tbt24qVaqU2SV5hB0DWlRUlMLCwrR8+XJdvHhRTqdTtWvXVsuWLdWyZUulpKSoRYsWypcvn9ml5olHHnlELpdLoaGh6tOnj5o3b6577703bX10dLSJ1blHx44dzS7BoyIjI9P+99q1a7Vu3bqbjk9tfhw8eNDdpblVzZo1VbNmTb311lvatGmTli5dqjVr1mj69OlyOBxyOBxat26dQkNDVb58ebPLBUxB9rY+u82Z7G0PZG9rI3uTvcne1kH2JnuTvb0TDXATPfLII7a6NEZOvgh8/QvRbnNu2LChHA6HDMNQsWLF1KBBA9WvX1/169dXtWrVfPr9i//p1KmTIiIiNHjwYB0+fFjdunVTSEhIujGxsbGaM2eO5s6dq2bNmqlNmzYmVYvs6tevn7799lvNmDFDH3/8sSpWrKgWLVqoRYsWqlGjhtnluY0dA9pTTz2loKAgNW3aVA8++KAeeeSRdDsZrDbn5ORkBQUFqWTJkgoKCspwiVQ7iImJ0e7duxUbGytJCgkJUe3atVW2bFmTK8sbdmt+/J2fn5+aNWumZs2aKTExUatWrdKyZcu0efNmff311woLC1PDhg3VqVMnPf7442aXC3gU2fvWfP17325zJnvbA9nb2sjeZO9UVpsz2ZvsbXVkb99BA9xEHTp0IJRk4urVq1qxYoXCw8O1c+dO7d+/3+yS3M4qc27durUaNGigBg0acNktC3M6nZoxY4befPNNffzxx/rkk09UtmxZlSpVSn5+fjp79qyOHTsmwzDUpk0bvffee2aXjGzo16+f+vXrp59++klLly7V8uXL9cknn2jmzJkqW7as6tata8nvLjsGtKCgICUkJOjIkSMqVqyYgoOD9eCDD6pgwYJml+YW69at09KlS7V06VJNnjxZDodDJUuW1KOPPqpHH33U0kfn/vLLLxozZox27twpwzDSXUrR6XSqbt26Gj58uKpVq2Zilblnt6PubyYwMFDt2rVTu3btFBcXp+XLl2vp0qXaunWrtm3bRgiH7ZC9M2eVHJodVpkz2dseyN7WRvYme5O9rYfsbT9kb+/mMKx+MxH4jB9++EFhYWFasWKF4uPjZRiGqlSpoqVLl5pdmtvYcc6wlu+//16LFy9WVFSUTp8+LcMwFBISorp16+qJJ55Qo0aNzC4xT4SGhur//u//1Lx587RlFy5cUMeOHfXBBx+odu3a6cavWrVK77//vs9f2ke6fgbU1q1btXTpUq1atUqXL1+WJJUvX16dOnVShw4dVK5cOZOrzL1Tp06lBbRffvkl04DWsmVLTZs2Ld37wJclJiZq7dq1WrJkiTZt2qSUlBTly5dPjRs31qOPPqrQ0FB17tzZUnNOdejQobSdTKdOnZLD4UjbKTF06FC98MILZpeYZ9asWaOBAwfK4XCoRYsWatSokUJCQuTv76/Y2Fjt3LlTK1asUHJysqZMmaIWLVqYXTLc6Pjx4/r222/Vp08fs0sBYCI75lA7zhnWQvYme5O9fRfZm+xN9rYfsrd3oAHuI06dOqXw8HBFRESku8eCr4uJiVFERITCw8P1xx9/SJL8/f3VsmVLPfPMM6pXr57JFeY9O84Z8HWhoaGZHnl9q8tjWiGE/1VSUpLWrl2rZcuWacOGDbp27ZqcTqcaN26s//73v2aXl2fsFNBSnT9/Pu0o1b1796Zb16tXL/Xp00dBQUHmFOdmO3bs0NKlSxUZGakLFy7I4XDotttu05NPPqmOHTuqTJkyZpeYYydOnFC7du1UsWJF/fvf/1aFChUyHffnn39qwIAB+vXXX7V48eIbjvN2w4YNy/ZjHA6Hxo4d64ZqAPgqsrd12HHOgK8je19H9iZ7WxHZm+xN9oan0QD3YlevXtXKlSsVHh6u7du3y+Vyyd/fXz/99JPZpeVKUlKSVq9erW+++Ubbtm1Lu9TNXXfdpaNHj2rKlClq3bq1yVXmLTvOGfaRlJSkQ4cOKTY2Nu0o9NDQUOXPn9/s0vJMTn7YSda+J87Fixe1YsUKLV26VLt379aBAwfMLsktrBzQbiQ6OlpLly7VsmXL9Ouvv8rhcCg4OFht2rRRp06dMpx1YRXXrl3Txo0btWTJEq1fv15Xr171+d9d7733nhYvXqwVK1aku8dcZs6fP6+2bduqffv2Gjp0qIcqzFuhoaHZfozD4fDpHaY5OUPE4XBo9erVbqgG8F1kb+uw45xhH2TvGyN7+z6yN9nbl393kb1vjewNM9AA90J79uxReHi4li9fnnZprjJlyqhLly566qmnVKpUKbNLzJGoqCiFhYVp+fLlunjxopxOp2rXrq2WLVuqZcuWSklJUYsWLSx1uRc7zhn2cebMGU2ePFmRkZGKj49Pty4oKEitWrXSoEGDFBISYlKF8JRdu3ZZ/gwaKwa0rDh06JCWLFmi5cuX688///T5wJJVly9fVmRkpJYtW6bZs2ebXU6OtWnTRg8++GCWdyROmDBB69at03fffefmytwjOjo6R4/z5XvQPfLIIxmWGYahU6dOqWTJkgoICMj0cWvXrnV3aYBPIHtbJ4facc6wD7I3UpG9rYvsTfb2JWTv68je3s/f7AJwXUxMjBYvXqywsDD98ccfMgxDTqdTkjRw4ED16tUr7b991VNPPaWgoCA1bdpUDz74oB555JF0R0Tl9IPTm9lxzrCHvXv3qnfv3rpw4YJq1qyZ6X1tIiIitHbtWk2fPl116tQxu2Rk008//aS9e/fKMAzdfffdmYbsy5cva9KkSfrqq6+0f/9+E6r0nHz58ql58+Zq3rx5uoBmdaGhoQoNDdXgwYO1fft2W8xZkgoWLKgWLVrohx9+MLuUXDl16pQqV66c5fGVKlXSggUL3FiRe/lymM6pzML0uXPn1KRJE02cOFGNGzc2oSrAu5G9rZlD7Thn2APZ2/rI3umRvcnevojsbX1kb99EA9xEqZfmCgsL09atW5WSkqL8+fPrkUce0aOPPqpq1aqpY8eOqlKlis8HcElp93A5cuSIihUrpuDgYD344IMqWLCg2aW5jR3nDOs7e/as+vbtqwIFCuijjz5S3bp1Mx134MABDRw4UAMGDNDixYtVokQJD1eKnLhy5YreeOMNbdiwQakXiXE4HGrSpImmT5+edkTj+vXrNXLkSMXExOj22283s2SPs0pAy65y5cr5/M7jEydOaM6cOdqzZ48k6Z577lHPnj11xx13pBsXGRmp0aNH68yZMxozZowZpeaJwMBAXbx4McvjL168qMKFC7uxInMlJSXp119/VWBgoCpVqmR2OW5zs/tjAnZF9rZ+DrXjnGF9ZG9rI3vfGtnbd5G9b47sbQ1kb+9HA9xETZs21cWLF1WkSBE99thjat68uR588EEFBQVJst4Rylu3btXatWu1ZMkSffPNN1q0aJHy5cunxo0b69FHH83RvSO8nR3nDOubP3++4uPjtXDhQlWoUOGG4+655x7NmTNH7dq10xdffKH+/ft7sErk1H/+8x+tX79eTZs2VceOHRUcHKwNGzboyy+/1IQJEzR8+HCNHz9en376qfz8/NSjRw8NGDDA7LLzhN0CmiTt27dP06dPTzfnvn37pjvrwDAMffrpp/rwww+VmJhoVqm5dvDgQT3//PO6fPmyAgMDFRgYqAMHDmj58uVauHChqlatqkuXLmn48OGKjIyUn5+fevXqZXbZuXLvvfcqMjJSPXr0yNL4lStX6u6773ZzVe516dIlzZw5U3v27NG8efPSli9dulRjxoxJ2ylRsWJFjR07VrVq1TKpUgCeRPa2fg6145xhfWRvayN7k73J3mRvX0b2hi+gAW6iCxcuKDg4WK1atVLDhg1Vp06dtABuRYGBgWrbtq3atm2r8+fPa/ny5Vq6dKk2btyojRs3Srp+1ExUVJSaNGliiX8LO84Z1rd69Wq1b9/+pgE8Vfny5dWxY0dFRkYSwn3E2rVr1aBBA82cOTNt2UMPPaQSJUpo3rx5Klq0qObMmaPQ0FCNGzfO53+wp7JjQNu6dat69uyplJQUVaxYUUFBQdq5c6defPFFzZkzR/Xr19eJEyf0j3/8Q1FRUSpYsKBGjRpldtk5lroT4YMPPtBjjz0m6fr9Qt944w2NGTNGkyZNUvfu3fX777/rvvvu05gxY1StWjWTq86dzp07a+DAgZozZ45eeumlm46dMWOGoqKi0v3t+5r4+Hh17dpVR48eVZkyZZScnCx/f39FRUVpyJAhMgxDXbt2VZUqVRQREaGXXnpJERERGXa0AbAesrf1c6gd5wzrI3tbG9mb7E32Jnv7KrI3fIYB0+zcudN4++23jQYNGhihoaHG3XffbTz11FPGrFmzjD/++MM4ceKEUa1aNWP16tVml+pWJ06cMKZPn2489thjRrVq1YzQ0FCjTp06xv/93/8ZP/zwg9nluYUd5wzrqFWrlrFw4cIsj1+0aJFRu3ZtN1aEvFSrVi3j008/zbD8119/NapVq2bcfffdxujRo42kpCQTqnOfPn36GNWrVzeWLVuWtmzfvn1G8+bNjeeff96IiYkxWrVqZVSrVs3o3LmzcejQIROrzRsvvviiUadOnXTfO3/++afRoUMHo0uXLsavv/5qNGnSxKhWrZrRv39/IzY21sRqc69JkybG6NGjMyxfuXKlcc899xjPPvusce+99xozZ840UlJSTKjQPfr372+EhoYa//jHP4w9e/ak+9tNSUkx9uzZkzZmxIgRJlaaex9++KFRvXp147vvvku3vFevXkZoaKgxbty4tGWJiYlGy5YtjaFDh3q6TLc7d+6cUa1aNWPLli1mlwJ4DbL3dXbMoXacM6yD7G1tZG+yN9mb7O2ryN7Xkb29H2eAm6hevXqqV6+eRowYoQ0bNmjp0qVav3699u3bp0mTJqlChQpyOBy6cuWK2aW6Vfny5dWnTx/16dNHhw4d0pIlS7R8+XJ9/fXX+uabb3Tw4EGzS8xzdpwzrCNfvny6evVqlscnJiYqODjYjRUhLyUkJKh48eIZlhcrVkyS9Oijj2r48OGeLsvtoqKi9PTTT6cdnSxJNWrU0ODBgzVo0CANGjRI0dHRevPNN/Xyyy9b4v6gBw8e1DPPPKPatWunLStdurT+8Y9/qGfPnnr99deVnJysKVOmqE2bNiZWmjcuXLiQ6eVP77vvPqWkpOjw4cOaN2+e5S7LNWnSJI0bN04LFy7Ut99+Kz8/PxUtWlR+fn46f/68kpKS5HQ61aNHDw0aNMjscnMlMjJSTzzxhFq3bp22LD4+Xps3b5YkPfvss2nL8+fPr/bt2+urr77yeJ0API/sfZ0dc6gd5wzrIHtbG9mb7E32ruX54tyI7E32hvehAe4F/P391bx5czVv3lzx8fGKjIzU0qVLtX37dhmGoSFDhuibb77Rk08+qVatWil//vxml+w2oaGhCg0N1eDBg7V9+3YtW7bM7JLczo5zhm+rWrWqNmzYoO7du2dp/Pr161W5cmU3VwV3czgckqQOHTqYW4ib2DGgXbp0SXfddVeG5VWqVJFhGDp//ry++uor3X777SZUl/eSk5Mz/Q0VGBgoSerVq5eltm+qgIAAjRw5Ut27d1dERISioqJ0+vRpGYahSpUqqW7dumrXrp0lLkV24sSJdEFbknbu3Knk5GRVqFAhw3u5bNmyOnv2rCdLzHNTp07NsCwxMVEOh0OLFy/W7t27M6x3OBzq27evJ8oDvA7Z+3/smEPtOGf4NrK3PZG9yd6+juxN9iZ7X0f2Nh8NcC9ToEABdezYUR07dtTZs2e1bNkyLV26VNu2bdO2bds0evRo7dy50+wyPaJhw4Zq2LCh2WV4lB3nDN/zxBNP6O2339by5cvVtm3bm46NiIjQli1bNHnyZA9VB3dLDSxWY8eAlpKSIn//jD8FAwICJEl9+vSxTADPinvvvdfsEtyqYsWKPn+U+a04nU65XK50y7Zu3SpJatKkSYbx586dU8GCBT1Sm7tkFsJTRUREZLqcEA5cR/b+HzvmUDvOGb6H7G1vZG/rIHunR/b2fWTv9Mje3osGuBcrUaKEXnjhBb3wwgv6448/tGTJEp8+QjmrR6z+lcPh0GeffeaGajzDjnOG9XXq1EkREREaPHiwDh8+rG7duikkJCTdmNjYWM2ZM0dz585Vs2bNLHEJJztJPeI8u+uszOoBLTOZHaFuZXZ4b8fExGj37t2KjY2VJIWEhKh27doqW7asyZXljcqVK2vPnj3q1q2bJMkwDK1atUoOh0OPPPJIhvFr1qzx+ff53LlzzS4BsASyt+/nUDvOGdZH9rY+sndGZG/rs8N7m+ydHtkbZqEB7iPuuOMO9e/fX/379ze7lBzbsWNHpssdDocMw7jhOl9mxznD+pxOp2bMmKE333xTH3/8sT755BOVLVtWpUqVkp+fn86ePatjx47JMAy1adNG7733ntklI5vGjh2rKVOmpFtmGIYcDofefPPNDEdrOxwOrV692pMlehyfzdZw9OjRDGfzXbp0SZJ0+PDhTI/Kr1+/vkdqc6dffvlFY8aM0c6dO2UYRrrfIE6nU3Xr1tXw4cNVrVo1E6vMvQ4dOui9997Tfffdp/vvv1+LFi3SyZMndfvtt6tp06bpxs6YMUN79+71+fsqNmjQwOwSAMshe/smO84Z1kf2tj6yd0Z8NlsD2ZvsnYrsDTPRADdR9+7d9eqrr6px48Zpy5KTk7Vnzx6FhoaqUKFC6cYvWbJEQ4cO1YEDBzxdap44dOhQhmXnzp1TkyZNNGfOnHT/DlZhxznDHgoVKqSPP/5Y33//vRYvXqyoqCj9/PPPMgxDISEh6tChg5544gk1atTI7FKRTeXKlZOkTHcUph6p+vd1N9qp6GvsGNB27dqllJSUdMvi4+MlSZs3b1ZMTEyGx/jyvehmzJihGTNmZLpu/PjxmS4/ePCgO0tyuzVr1mjgwIFyOBxq3bq1GjVqpJCQEPn7+ys2NlY7d+7UihUr1LlzZ02ZMkUtWrQwu+Qce/rpp7V7926NGzcureFRpEgRTZo0SU6nU5L09ddf65NPPtHx48dVt25dPfPMMyZX7V5JSUn69ddfFRgYqEqVKpldDmAasrf1c6gd5wx7IHtbF9mb7E32To/s7TvI3hmRvb2Tw7DKN6cPCg0N1cSJE9WuXbu0ZXFxcWrSpIlmz56dIaAtWbJEQ4YM8fkvg7+Ki4tT48aNbRVI7ThnAPB2oaGhNzzSPPUI/Mz48nfyjeb815+Gf12f+u/gq3P+z3/+k6OzCfr16+eGajzjxIkTateunSpWrKh///vfqlChQqbj/vzzTw0YMEC//vqrFi9efMNxvmLPnj3as2ePChYsqBYtWqh48eJp66ZOnarF/4+9+46v8fz/OP4+WZIIEntvjb33rFE7tUdtpVRrFi2+FKVUS1tFKa3d2nvHHlV71awRswiRIIis8/vDL6eOJCSR5OScvJ6Ph8eD+77uc3/us5z3dd33da9ZIy8vL/Xs2TPK+w9am8ePH2vWrFk6fvy4FixYYFq+bt06jR07Vo8ePZL04l5048aNs7l7KgIxQfZOnjk0OR4zACR1ZO//kL3Nkb2tD9n7BbJ30sUV4EkQ5yQAsBZBQUFydnaOtPzSpUtKnTp1pPuTIelbvXq1ypYtq+zZs1u6lET16aefJrup1saPH2/pEhKVNU9lG1fz5s2To6Ojfv31V7Mg+qrMmTNr5syZatiwoX7//XcNGTIkEauMf6VKlVKpUqWiXNe7d+9oO1ZCQkJ04sSJKK8GTaqePHmiNm3a6MqVK8qcObNCQ0Pl4OCgU6dO6YsvvpDRaFSbNm1UoEABrV69Wl27dtXq1auVK1cuS5cOJAlkbwDWguxte8jeyQfZ2/aRvSMje5O9kwIGwAEAsRYcHKwJEyZo3bp12rNnT6Qg/v3332vPnj1q0aKFvvjiC7m6ulqoUsTW0KFD9e233ya7EJ4cA1qzZs0sXUKiimr6W1u3b98+NWvW7LUBPIK7u7uaNm2qnTt3Wn0Ij6uHDx+qU6dOUV4NmlTNnj1b169f148//qj69eublk+bNk1Go1GdO3c2vZ4tW7bU+++/rxkzZiS7TjgAAKwV2dt2kb2TD7K37SN7xw7ZG4nFztIFAACsS3BwsLp166bff/9dWbNmlb+/f6Q2NWvWlKenp5YsWaLu3bsrNDTUApUiLpLrlVCdOnXSX3/9ZekyElXt2rW1fft2S5eRaA4dOqT79+9buoxEdfv2beXPnz/G7fPmzas7d+4kYEVJn7V9B3p7e6tJkyZmAfzJkyf6888/JUnt2rUzLU+RIoXef//9ZPddBwCAtSJ72zZr+90ZX8jeto/s/WZkb+v7DiR7WycGwAEAsTJ37lwdPnxYw4cP1+rVq5UlS5ZIbVq1aqUVK1aod+/eOnbsmBYuXGiBSoGYS44B7datW3r69Kmly0ACcnZ2Nt2DKiYePXqk1KlTJ2BFiG83b95U0aJFzZYdPnxYoaGhyp49u3LmzGm2LkuWLPLz80vMEgEAQByRvWGLyN6wRWRv20f2tk5MgY5Es3r16kjLnjx5Ikn6888/dffu3Si3a9q0aQJWlbCS4zHD9q1bt061a9dWhw4d3ti2d+/eOnTokNasWaMuXbokfHGIFwEBAfr3339jtU3WrFkTqBoAcVW0aFF5e3urW7duMWq/ZcsWFSpUKIGrQnyys7NTeHi42bKIs8wrV64cqf2DBw/k5uaWKLUBsJzkmEOT4zHD9pG9bR/ZG7ANZG/bR/a2TgyAW9irP3QePnwo6cUH5NUfQFFNdWRNhgwZIoPBYLYsYqqLX3/9VQaDwfTviL8bDAarDqTJ8Zhh+65du6a2bdvGuP27776ryZMnJ2BFiG/jxo3TuHHjYtzeYDDo7NmzCVgRED+2bt2qa9euxbi9wWDQp59+moAVJayWLVuqf//+mjNnjrp27fratjNmzNCpU6c0a9asRKoO8SF//vw6fvy42rdvL+nF78ytW7fKYDCoVq1akdpv375d+fLlS+wygSSB7G3bOTQ5HjNsH9nb9pG9YavI3tEje1snsrd1YgDcwqL7oTNo0CALVJOwxo8fb+kSEl1yPGbYPldXV4WFhcW4fYoUKeTs7JyAFSG+lSlTRjly5LB0GYkuuQU0SVq6dKn2798f4/YGgyFWHTRJzdatW+Xt7R3j9tb+GtevX19169bVt99+qzNnzqhDhw4qUqSIHB0dJUnh4eE6deqUZs+era1bt6pVq1aqWrWqhatGbDRt2lRff/21ihUrpipVqmjp0qX6999/lTNnTlWrVs2s7YwZM3TixAkNHz7cQtUClkX2tm3J8Zhh+8jeto/sHTPWnssksvebWPtrTPa2fWRv68QAuAU1bdo00hnKtqxZs2ZvbPPw4UM5OzsrRYoUiVBRwkuOxwzblzdvXh07dkydOnWKUfujR48qW7ZsCVwV4lObNm3k5eVl6TISXXILaNKL+xUdPnw4xu2tPYT37NkzyqmpbNnEiRM1fvx4LV68WBs2bJC9vb3c3d1lb2+vgIAABQcHy87OTt26ddOAAQMsXS5iqW3btjp69KjGjx9vuqIxTZo0mjhxouzs7CRJy5cv18yZM3Xjxg2VKVNGH3zwgYWrBhIf2TsyW8uhyfGYYfvIQXOgawABAABJREFU3raP7B0zZG/rQ/Yme9sasrd1YgDcgr755htLl5DoQkJCtHLlSp04ccLsDO1Dhw5p1KhR8vHxkcFgUOXKlTVy5EibOAsyOR4zbFuzZs00cuRIHThwQBUrVnxt24MHD8rb21t9+vRJpOqAuEuOAW3YsGGqXbu2pctINPny5VP58uUtXUaicnJy0siRI9WpUyetXr1ap06d0r1792Q0GpU3b16VKVNGXl5eypUrl6VLRRwYDAZNnDhR7du31/Hjx+Xm5qY6deoobdq0pjZ37tyR0WhUr1691LNnT1M4B5ITsnfyyKHJ8Zhh28jesFVkb9tH9iZ72xqyt3ViANyCunfvrqZNm6pOnTrJYoqikJAQffjhhzp8+LAcHR01ZswYOTg4yMfHR927d1dISIiqVaum/Pnza8uWLWrTpo3Wrl2r9OnTW7r0OEuOxwzb17RpU61YsUIff/yxevbsqVatWkV6z/r6+mrZsmX69ddflT17drVr185C1QIxlxwDmoeHB1eJJBN58uThLHMbVqpUKZUqVSrKdb1791bv3r2jXBcSEqITJ06oYMGCSpUqVUKWCFgU2dv2c2hyPGbYPrI3bBXZG7aM7G3byN7WhVMQLOjQoUMaPHiwKleurCFDhmj//v0yGo2WLivBLFy4UEeOHNHgwYN1+PBhOTi8OP9iypQpCg4OlpeXl2bOnKnPP/9cK1askL29vWbMmGHhqt9Ocjxm2D5HR0dNmzZNJUqU0OTJk1WtWjXVrl1bbdu2VatWrVSrVi3VqFFDU6ZMkaenp+bMmcN/7FYka9ascnV1tXQZAIBE8PDhQ3Xq1EmnT5+2dClAgiJ7234OTY7HDNtH9rZtZG8ASD7I3pbBFeAW9Ndff2nbtm3atGmTNmzYoDVr1ih9+vTy8vLS+++/r4IFC1q6xHi1fv161atXT926dTMtCw4O1o4dO2QwGMyWu7u7q3nz5tqwYYOGDx9uiXLjRXI8ZiQP6dKl07x58+Tt7a0NGzbo7NmzunDhguzs7JQ+fXo1bdpU7733nmrVqmXpUhFLO3bseO368PBw3blzR+nTp5eTk1MiVQW8vd69e8vT09PSZSSqmN4v8mUGg0Hz5s1LgGqShnv37snd3V2Ojo6R1qVJk0bz589XoUKFLFCZ5djyICAQgext+zk0OR4zkgeyt+0ie8NWkb1jhuxN9kbCYwDcglKmTKkmTZqoSZMmevz4sbZs2aKNGzdq3rx5mjNnjvLnz68mTZrIy8tLmTJlsnS5b+3q1atq1qyZ2bKjR48qKChIGTNmjPQfY86cOeXr65uYJca75HjMSF7q1q2runXrWroMJKIHDx6odu3amj17tipVqmTpcuJNcgxo48ePj3baJlsU3TRUEZ4+farZs2eradOmyp49eyJVlbAOHToU5XKDwRBt8DIYDAlZUqKYM2eOli5dqrVr10YK2+PGjdP+/fvVrVs3de/e3eyeXI6OjsluKkYguSB7234OTY7HjOSF7J38kL1tB9nbHNn7v3XWjuyNpI4B8CQiVapUatmypVq2bKkHDx5o8+bN2rRpk77//nt9//33KleunJo2baq6desqZcqUli43TsLDw2Vvb2+27MCBA5KkypUrR2r/+PFjubi4JEptCSU5HjMgvZjWxdnZWSlSpLB0KUgAtnjGYnIMaK92Er8qMDBQX3/9tbp37658+fIlUlWW8/TpU02bNk1lypSxmdf4/PnzkZY9ePBAlStX1pw5c2yqI0168d00aNAgbdiwQenSpdOdO3eUI0cOszb58uXTsWPH9MMPP+j06dP66aefLFQtAEshe9tmDk2OxwxIZG9bR/a2jVxG9jZH9rZ+ZG9YC+4BngSlTZtW7dq104IFC7Rr1y7973//k52dnb788ktVrVrV0uXFWc6cOXXu3DmzZVu3bpXBYNC7774bqf2+ffuUM2fORKouYSTHY0byEBISoiVLlmjo0KFmyw8dOqSGDRuqYsWKKlWqlLp3767r169bqEog/kQEtBs3bli6lEQTFBSk1atXJ6uro2yxg+lVtnCWeXSWLFmiDRs2qEuXLtq1a1ekAC696HDbunWrmjdvrq1bt2rlypUWqBRAUkH2fsEWcmhyPGYkD2RvJDdk7+SB7G3dyN6wFgyAJ3GOjo5ydnaWm5ubHBwcFBwcbOmS4qxRo0Zas2aNtm3bpmfPnmnu3Lm6cuWK0qVLF+leRWvXrtWff/6p2rVrW6ja+JEcjxm2LyQkRB9++KFGjhyp9evXKzQ0VJLk4+Oj7t27y8fHR9WqVVOXLl3k4+Ojtm3b6v79+xauGnh7ySGgvSo5HjOs1/Lly1W+fHkNGTIkyvuMRXByctLYsWNVqFAhLV26NBErBJCUkb2tO4cmx2OG7SN7I7lKjjk0OR4zrBfZG9aCKdCToAcPHmjr1q3avHmzDh8+rNDQUBUuXFj9+vVTo0aNLF1enHXp0kV79+5V7969Tfe/cHR01Ndffy0nJydJL87QXrhwoQ4dOqQ8efKoS5culi36LSXHY4btW7hwoY4cOaLBgwerffv2cnB48V/JlClTFBwcrPfff1/ffvutJKlHjx7y8vLSjBkzNHz4cEuWjXji6OiocuXKKU2aNJYuBQDMXL58Wf369YtRW4PBoPr162v69OkJXBWApIzsbTs5NDkeM2wf2Tt5I3sDSKrI3rAWDIAnEffv35e3t7c2b96so0ePKiwsTNmyZVO3bt30/vvv28T9P5ycnDR37lxt3LhRJ06ckJubm7y8vJQ/f35Tm9OnT+vYsWN6//33NWTIEDk7O1uw4reXHI8Ztm/9+vWqV6+eunXrZloWHBysHTt2yGAwmC13d3dX8+bNtWHDBkK4lXj8+LFSpUoV7fo0adJowYIFZssOHjyoChUqJHRpQIJKlSqVxo8frwIFCli6FMSRg4ODaZAjJlKnTh3pfrEAbB/Z+wVby6HJ8Zhh+8jeto3sjeSK7G39yN6wFgyAW5Cvr6+8vb21ZcsWHTt2TGFhYUqTJo1atmwpLy8vlS1b1tIlxjt7e3t5eXnJy8sryvUff/yx+vXrJzs725mdPzkeM2zb1atX1axZM7NlR48eVVBQkDJmzChPT0+zdTlz5kxW9zGydp06ddKcOXPk7u7+xrbPnz/Xd999pz/++ENnz55N+OIsKDkGtDRp0mj+/PkqVKiQpUtJFClSpFDVqlW5wsKK5cqVS6dPn45x+9OnTytLliwJWBGApILsHZkt5tDkeMywbWRv20b2jhrZ2/aRva0f2RvWggFwC6pRo4akF2cq16lTR15eXqpRo8Zr75tg61xcXCxdQqJLjscM6xYeHh7prL0DBw5IkipXrhyp/ePHj3mfW5Fz586pQ4cOmjt3rtKnTx9tu5MnT+qLL77Q1atXX9vOVqRIkcKs8yksLEwzZ85Ur169LFhVwnJ0dFT58uVN/378+LEmTJigsWPHWrCqtzdnzhwtXbpUa9eujfSba9y4cdq/f7+6deum7t27W30H+erVqyMte/LkiSTpzz//1N27d6PcrmnTpglYVcJp3LixJk2apK5du76xw+zixYtat26dOnXqlEjVAbAksndkyfH3eXI8Zlg3srdtI3tHjexN9rZGZO/okb1hSQaj0Wi0dBHJVceOHdWkSRPVr19fbm5uli4HAGKkSZMmKlGihL766ivTsoYNG8rHx0c//PCD6tevb9a+e/fuevjwoZYtW5bYpSIOfv/9d3399dfKkSOH5s6dG+kMzZCQEP3000+aPXu2wsLC1KRJEw0bNswmztx9/Pixli1bphMnTshoNKpw4cJq3769UqdObdbu77//1vDhw/XPP//o3LlzFqo2fty8eVNz5szR8ePHJUmFCxfWRx99pFy5cpm18/b21pgxY3T//n2rPWaj0ahBgwZpw4YNSpcunRYvXqwcOXKYtZk6daqWLVsmX19fvffee/rpp58sVG38KFiwoAwGg9myl3/6R7XOYDBY7Wv89OlTNW/eXAEBARo2bJgaNWoUqdM4NDRU69ev18SJEyVJq1atUoYMGSxRbqK5d++e3N3doxzoCwkJ0fHjx1WoUKHXTsEJWDuyNwBrRPa2bWRvsjfZm+xtra8x2TtqZO+khwFwAECszJw5U9OmTdOkSZNUpUoVLVmyRN98843Sp0+vHTt2mN0DZu3atfriiy/Ur18/ffzxxxasGrGxfv16DRkyRBkzZtTcuXOVM2dOSS/OUP/iiy/0zz//KGvWrBo9erSqVatm4Wrjx40bN9SpUyfduXPHLKSkT59ey5YtU5YsWRQaGqpJkyZp/vz5CgsLU6NGjTRp0iQLVv12zp07p44dOyowMFDOzs5ydnZWQECAXF1dtXjxYr3zzjt6/Pixhg8fLm9vb9nb26tbt24aMGCApUuPk8WLF2vUqFHq0qWLBg4cGO1Vf8HBwRo9erRWrlypr7/+Ws2bN0/kSuPPqlWr4rTdq1NtWhMfHx99+umn8vHxkaurq4oUKaIMGTIoLCxMfn5+On36tIKCgpQ1a1ZNmzZNBQsWtHTJ8eJ1V1cMGDDApq6uAAAguSB72z6yN9mb7E32tlZkb7K3NWAA3IKimhojJqx1agwAtiE4OFjdunXT4cOHZTAYZDQa5ejoqKlTp5qml9y6dasWLlyoQ4cOKU+ePFq5cqWcnZ0tXDliY/fu3erfv7/c3Nw0a9Ysbd++XTNmzFBoaKg++OADDRw4UClTprR0mfFm4MCB2rhxowYMGKAWLVrIxcVFu3fv1ldffaWSJUvqu+++U48ePXTs2DFlzZpVI0eONL3frVWvXr20d+9eTZgwQY0aNZIknTp1Sp999pmyZs2qiRMnqlOnTrp69aqKFSumsWPHRrrPoDVp2bKlXF1dNX/+/De2NRqNatGihZycnLR48eJEqA7xKTg4WL///rs2bNig8+fPKzQ0VNKLqQVLliypunXrqk2bNmadxtYqOV5dAcQF2RuANSJ7Jw9kb7I32Zvsba3I3mTvpI4BcAuKmBojYsqLN7H2qTEA2I6wsDBt3LhRJ06ckJubm7y8vJQ/f37T+h9++EGzZ89Ww4YNNWTIEHl4eFiwWsTV0aNH1atXLwUGBspoNCpXrlwaO3asypYta+nS4l316tVVpUoVjR8/3mz5qlWrNHLkSFWvXl3btm3TBx98oMGDB8vV1dVClcafKlWqqEGDBho+fLjZcm9vbw0YMEAlS5bUqVOn1K9fP3344YdWf+ZqqVKl1K9fP3Xp0iVG7WfOnKnp06ebpqizdkFBQVF2hl66dEmpU6dWxowZLVBV4njw4IHs7e1tYrrIVyXHqyuAuCB7A7BWZO/kgexN9iZ7k71tAdmb7J3UOFi6gOTs1f/oAcBa2Nvby8vLS15eXlGu//jjj9WvXz+r/9Ge3JUpU0bz589X9+7d9eDBA3311Vc2GcAlyd/fX6VKlYq0vFy5cgoODtbu3bs1efJk1atXzwLVJYyHDx9GOQVVsWLFFBYWpgsXLmjBggUqWbJk4heXABwcHGJ11nHq1Kkj3cPKGgUHB2vChAlat26d9uzZEymIf//999qzZ49atGihL774wiY6mF6VNm1aS5eQYJYvX67y5ctryJAhr23n5OSksWPH6ty5c1q6dCkhHMkO2RuAtSJ7Jw9kb7I32ZvsbQvI3mTvpIYBcAuy5ns8AEi+OnXqpF69eqlSpUqmZaGhoTp+/LgKFiyoVKlSycXFxbQu4l5kXEFjnQoWLKhFixapa9eu6tWrl6ZNm6aKFStauqx4FxISYva+jRAx1VzXrl1tKoBLLz63KVKkiLQ8IqT16NHDZgK4JOXKlUunT5+OcfvTp08rS5YsCVhRwnt52syCBQvK398/0jHVrFlTd+/e1ZIlS3Tx4kXNnz9fDg7WGRGmTp0ap+169+4dz5UknsuXL6tfv34xamswGFS/fn1Nnz49gasCkh6yNwBrRPZOXsjeZG9bQfYme0eH7I3EZp2fsGQqLCxMM2fOVK9evSxdCoBk7NChQ2rVqpXZssePH6tTp06aPXu2WTiH9Rk6dGiUy3PlyqWbN2+qZ8+eatCggdn0oQaDQePGjUusEi2ifPnyli4h0RUtWtTSJcSrxo0ba9KkSeratasKFCjw2rYXL17UunXr1KlTp0SqLmHMnTtXhw8f1vDhw9WhQ4co27Rq1UqtWrXS1KlTNXXqVC1cuDDGU9UlNTEN4a9Of2zNITy5Xl0BJDSyN4CkgOxt28jeUSN7Wz+yN9k7Atmb7G1pDIBb2OPHj7Vs2TKdOHFCRqNRhQsXVvv27ZU6dWqzdn///beGDx+uf/75hxAOIEkyGo2WLgHxYNWqVa9d//z5c61evdpsWXII4cnxR2tM7pFqTVq3bq3FixerY8eOGjZsmBo1ahTpdQ0NDdX69es1ceJEpUqVyupD+Lp161S7du1oA/jLevfurUOHDmnNmjVWG8K3b9/+xjaPHz/Wjz/+qF27dsnBwcHqX+PkeHUFEFdkbwC2guxtG8jeUSN7Wz+y9+uRva0T2ds6MQBuQTdu3FCnTp10584d04/XrVu36vfff9eyZcuUJUsWhYaGatKkSZo/f77CwsLUqFEjC1cNALBlMfkRa6sCAgL077//mi17+PChJOnBgweR1klS1qxZE6W2hHLlyhUdPnzYbNnjx48lSRcuXIhyOq5y5colSm3xzdXVVdOnT9enn36qL774QqNHj1aRIkWUIUMGhYWFyc/PT6dPn1ZQUJCyZs2qadOmKUOGDJYu+61cu3ZNbdu2jXH7d999V5MnT07AihJWtmzZXrt+48aN+uabb+Tr66vSpUtr1KhReueddxKpuoSRHK+uAOKC7A0ASGrI3mRvsjfZ21qRvcne1oIBcAv68ccfdefOHQ0YMEAtWrSQi4uLdu/era+++kpfffWVvvvuO/Xo0UPHjh1T1qxZNXLkSNWoUcPSZQMAbNibfsTasnHjxkV7Nv2gQYMiLTMYDDp79mxCl5WgZsyYoRkzZkS5bsKECVEut+Z7CubJk0erV6/W77//rg0bNujYsWMKDQ2VJDk6OqpkyZKqW7eu2rRpE6uprZIqV1dXhYWFxbh9ihQpTPehsyXXr1/X6NGjtX//fqVJk0Zjx45Vy5YtLV1WvEiOV1cAcUH2BgAkNWRvsncEsjfZ21aQvcneSQ0D4BZ0+PBhNW3aVD169DAta9CggYKCgjRy5EgNGTJEx44d0wcffKDBgwfL1dXVgtUCABDZjBkz5O3trZUrV1q6lLfSrFkzS5eQ6Kz53ktvw8nJSV27dlXXrl0lvbjCwN7eXmnSpLFwZfEvb968OnbsWIxD19GjR22qIy44OFgzZ87UrFmzFBwcrGbNmmnw4MHy8PCwdGnxJjleXQHEBdkbAGDtyN7Wi+xN9n4V2dv6kL2tEwPgFuTv769SpUpFWl6uXDkFBwdr9+7dmjx5surVq2eB6gAAeLPbt29b9VnJEcaPH2/pEhJdcg3hr0qbNq2lS0gwzZo108iRI3XgwAFVrFjxtW0PHjwob29v9enTJ5GqS1j79+/X6NGjde3aNRUoUEAjR45U2bJlLV1WgkhuV1cAcUH2BgBYO7K39SJ7v0D2foHsbb3I3taHAXALCgkJkYuLS6TlKVOmlCR17dqVAA4gSXr1fk2vu1eTv79/otYGAFGZOnVqnLaz5s6Kpk2basWKFfr444/Vs2dPtWrVSunTpzdr4+vrq2XLlunXX39V9uzZ1a5dOwtVGz/u37+v8ePHa+PGjXJ2dtbAgQPVtWvXKO+pZ0uS09UVQFyQvQFYK7I3AGtD9iZ72zKyt3UxGI1Go6WLSK4KFiyo7777Tl5eXmbL/f39ValSJf3666+qWrWqhaoDgKgVLFhQBoMh0nKj0Rjl8gi2cKYyIhs5cqSWLl1q9a9vcgxoQ4cOjfU2BoMh2nu1JXUFCxaMUbtXv8es/b3t5+enzz77TAcPHpTBYFDWrFnNpui6ffu2jEajSpYsqR9++EFZsmSxdMlxtnDhQk2ePFmBgYGqVauWhg8fbtXHAyD+kL0BWCOyN15G9iZ7WwuyN9kbSCps+3QMK2dvb2/pEgAgkuR4vybYvpiG8FcDmjWH8FWrVsW47cvHba0hfPv27W9s8/jxY/3444/atWuXHBwcYnz/rqQsXbp0mjdvnry9vbVhwwadPXtWFy5ckJ2dndKnT6+mTZvqvffeU61atSxd6lsbO3as6e87duzQjh073riNwWDQ2bNnE7KsBJUcOxCBhED2BpAUkb1hi8jer0f2tl5k79cje8MSGAC3sFenMpJeP52RJGXNmjVRagOAqCTH+zXB9iXHgHb+/Pk3trl165bGjBmjXbt2KVWqVOrfv3/CF5ZAsmXL9tr1Gzdu1DfffCNfX1+VLl1ao0aN0jvvvJNI1SW8unXrqm7dupGWP3z4UM7OzhaoKP4lx07i5NiBCMQV2RuAtSF7wxaRvaNG9iZ7WxOyd/TI3kkLU6BbUHRTGUnRT2dk7WfKAACSttie0bhz506dPXvW6qeqehNbD2ivCgsL0+zZs/Xzzz8rKChIDRs21NChQyPdw8oWXL9+XaNHj9b+/fuVJk0aDRo0SC1btrR0WfEmJCREK1eu1IkTJ8w6UQ8dOqRRo0bJx8dHBoNBlStX1pdffqmcOXNasNq3M3ToULVt21YlSpSwdCmJ5tatW29sE1UH4ueff54I1QFJB9kbAJDUkL2jRvYme1srsrdtI3tbJwbALSgu9/+QOAMUAJBwYnqvppcZDAabDeG2HtCicuTIEY0ePVoXL15U7ty5NXLkSFWqVMnSZcW74OBgzZw5U7NmzVJwcLCaNWumwYMHy8PDw9KlxZuQkBB9+OGHOnz4sBwdHXX8+HE5ODjIx8dHTZo0UXBwsKpXr678+fNry5YtevbsmdauXWu1nS3R3eM3OUtuHYhAdMjeAICkhuxtjuxN9rZmZG+QvZMmpkC3IMI0ACCpmT9/vqVLSBKSQ0B7lb+/v7799lutXr1aTk5O6tOnjz766CM5OTlZurR4t3//fo0ePVrXrl1TgQIFNHLkSJUtW9bSZcW7hQsX6siRIxo8eLDat28vB4cXP/2nTJmi4OBgvf/++/r2228lST169JCXl5dmzJih4cOHW7JsxINXOxDHjh1r8x2IwOuQvQEASQ3Z+wWyN9nbFpC9ky+yd9LGAHgS8OzZM61YsUJ79+7V+fPnFRAQIIPBoLRp06pgwYKqXbu2vLy8bPI/QQBA0lK+fPlYb3PkyJEEqMRykktAe9myZcs0ceJEPXz4UFWqVNHIkSOtejqu6Ny/f1/jx4/Xxo0b5ezsrIEDB6pr166mcGpr1q9fr3r16qlbt26mZcHBwdqxY4cMBoPZcnd3dzVv3lwbNmwghFux5NiBCMQG2RsAkFSQvcneZG/bQfZOfsje1sE2v3GsyNGjR9WvXz/dv39fTk5Oypkzp7Jly6bQ0FAFBARo586d2rFjh6ZOnapJkyapdOnSli4ZAADdvn1bq1at0urVq3Xjxg2bmIYtuQU0Sbpw4YJGjRqlEydOKH369Pr+++/VsGFDS5eVIBYuXKjJkycrMDBQtWrV0vDhw5UlSxZLl5Wgrl69qmbNmpktO3r0qIKCgpQxY0Z5enqarcuZM6d8fX0Ts8R4d+TIEYWFhcVqm6ZNmyZMMYksOXYgArFB9gYAWCOyt20ge5O9X0b2tm5kb+thu/+rWIHLly+rW7ducnNz08SJE1W3bt1IZ5oHBgZq8+bN+umnn9S9e3etWrVKuXLlslDFAIDk7Pnz5/L29tbKlSt18OBBGY1GGQwGVa9e3dKlvbXkGNAmTJigBQsWKCwsTDVr1lT//v3l5uamf//997XbZc2aNZEqjF9jx441/X3Hjh3asWPHG7cxGAw6e/ZsQpaVoMLDw2Vvb2+27MCBA5KkypUrR2r/+PFjubi4JEptCWXp0qVaunRpjNpGfIdZewhPjh2IQGyRvQEA1oTsbVvI3mTvV5G9rRPZ2/rwyljQzz//LBcXF61YsUKZMmWKso2bm5tatmyp6tWrq0mTJvr11181ZsyYRK4UAJCcnThxQitXrtSmTZsUGBgoSUqbNq1atGihNm3aKFu2bBau8O0lx4A2Z84c09937typnTt3xmg7a73i4NWzsZODnDlzRnq9tm7dKoPBoHfffTdS+3379ln99HutW7dWyZIlLV1GokmOHYhAXJC9AQDWgOwdNbK3dSF7v0D2ti1kb+vEALgFHT58WM2bN482gL8sY8aMatq0qfbt25cIlQEAkjtfX1+tXr1aq1at0tWrV2U0GuXi4qLKlStr//79+uqrr1S7dm1LlxlvmjZtKoPBYOkyElXv3r1jvY3RaEyAShJP27ZtVaJECUuXkWgaNWqkadOmqXr16qpSpYqWLFmiK1euKH369KpVq5ZZ27Vr1+rPP/9Uv379LFRt/Chbtqy8vLwsXUaiSY4diEBckL0BAEkV2dv2kb1tH9nb9pG9rRMD4Bbk7+8fqynV8ubNq2XLliVgRQCA5G7Tpk1auXKl9u/fr7CwMKVOnVpeXl6qW7euqlWrpnv37qlOnTqWLjPeffPNN5KkkJAQXbp0SaGhocqfP7/VT0n1OlmzZlXz5s1j3P7WrVsaNmxYAlaUsFatWqXKlSsnqxDepUsX7d27V71795bBYJDRaJSjo6O+/vpr09S/W7du1cKFC3Xo0CHlyZNHXbp0sWzRiJXkeHUFEBdkbwBAUkP2JntHh+xtfcjeto/sbZ0YALegkJCQWP3nniJFCj158iQBKwIAJHcDBgyQq6ur2rVrp9q1a6tcuXJm9zGy5TO1f/vtN02fPt30f62Tk5PatWungQMH2uT9fIYPH65nz56pffv2b2y7bNkyTZgwgd8hVsbJyUlz587Vxo0bdeLECbm5ucnLy0v58+c3tTl9+rSOHTum999/X0OGDJGzs7MFK0ZcJLerK4C4IHsDAJIasjfZOypkb+tE9k4eyN7Wx/b+RwEAAHGWPXt23bx5UytXrpSPj4/+/vtv1alTR3ny5LF0aQlqxYoV+u6775QtWzY1bdpUdnZ2OnjwoObOnauwsDCrPvs6Orlz59bYsWP17Nkzde/ePco29+7d0/Dhw7Vnzx45Ojpa/RRdyZG9vb28vLyinZrs448/Vr9+/WRnZ5fIlcW/Zs2aWf191GIrOV5dAQAAYAvI3mTvl5G9rR/Z27aRva0TA+AWFhAQoH///TdGbf39/RO4GgBAcrdt2zadPHlSa9eu1ebNm7Vv3z59//33yps3r+rWrasiRYpYusQEsWTJEpUsWVLz5s1TihQpJL2459aAAQO0ZMkSDRo0yDRtla34448/1L17d02aNEnPnj1Tnz59zNavX79eY8aM0cOHD1WqVCmNHTtW+fLls1C18ePIkSMKCwuL1TZNmzZNmGKSCFuaanD8+PGWLgFAEkb2BgAkJWRvsncEsvcLZG/rQfaGtTAYjUajpYtIrgoWLBin6WzOnTuXANUAAGAuLCxM+/bt07p167R9+3Y9e/bM9P9WixYt1KtXL2XLls3CVcaPMmXK6LPPPos0JdmJEyf0wQcfaNWqVSpYsKCFqks4T58+1SeffKKDBw+qS5cu+uKLL+Tv76+RI0dq69atcnZ21oABA9SxY0ern4Ivtr+7jEajDAYDv7uQpBUsWFDfffddtFcZAHiB7A0ASMrI3mRvsjfZG0kb2ds6cQW4BTVr1szSJQAAEC17e3vVqFFDNWrUUFBQkLZu3ar169frzz//1PLly7Vy5UpVqFBBLVq0UOPGjS1d7lt59uyZUqVKFWl59uzZZTQa9ejRIwtUlfBcXV01c+ZMffbZZ5o7d65u3rypY8eOyc/PT1WqVNFXX31lMx0tktS6dWuVLFnS0mUA8YqrK4A3I3sDAJIysjfZm+wNJH1kb+vDFeAAAMCkR48eqlixosqXL68iRYpEedauv7+/Nm7cqHXr1unEiRM2caZudGdy+vv7q1KlSpozZ44qVapkoeoSXnh4uIYPH66VK1fKzs5OX331lVq2bGnpsuIVZ+vCFnF1BQAAgHUie5O9yd6A9SB7WyeuAAcAACYHDhzQnj17ZDAY5ObmprJly6pChQqqWLGiaRoyDw8PtW/fXu3bt9eNGze0YcMGC1eNt2VnZ6dx48bJ3d1ds2fP1v79+9W0aVM5OPBTEUjquLoCAADA+pC9kyeyN2C9yN7Wh29WAABgcuzYMZ09e1bHjh3T8ePHdeLECe3cuVMGg0GpU6dWuXLlVKFCBVWoUEHvvPOOcuTIoY8//tjSZceLgIAA/fvvv2bLHj58KEl68OBBpHWSlDVr1kSpLSFEdTwdOnTQ06dPtWTJEj158kQjRoyQnZ2dWRtrPmbAFpUtW5arKwAAAKwM2ZvsTfYGrAvZ2/owBToAAHit27dvm4Xy8+fPKywsTO7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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 218 coefficients adjusted\n",
-      "\t 550 coefficients converged\n",
-      "\t 207 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-31.390562</td>\n",
-       "      <td>1060.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-33.390562</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-30.865285</td>\n",
-       "      <td>327.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-32.865285</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-30.496907</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-1.452986</td>\n",
-       "      <td>-31.949893</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>542</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_atwork</td>\n",
-       "      <td>-27.908265</td>\n",
-       "      <td>590.0</td>\n",
-       "      <td>5313.0</td>\n",
-       "      <td>2.197789</td>\n",
-       "      <td>-25.710476</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>468</th>\n",
-       "      <td>#coef_calib_zeroautohhindivtou_DRIVEALONE_univ</td>\n",
-       "      <td>-19.706400</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-19.706400</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>671</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint</td>\n",
-       "      <td>-14.842518</td>\n",
-       "      <td>4490.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-4.671938</td>\n",
-       "      <td>-19.514456</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-16.970666</td>\n",
-       "      <td>5916.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-18.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-16.970666</td>\n",
-       "      <td>1813.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-18.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -35.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -31.390562   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -30.865285   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -30.496907   \n",
-       "542          coef_calib_zeroautohhindivtou_WALK_atwork -27.908265   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "468     #coef_calib_zeroautohhindivtou_DRIVEALONE_univ -19.706400   \n",
-       "671   coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint -14.842518   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -16.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -16.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "541           0.0            0.0        <NA> -35.934870       True  \n",
-       "543        1060.0            0.0          -2 -33.390562      False  \n",
-       "540         327.0            0.0          -2 -32.865285      False  \n",
-       "544         124.0           29.0   -1.452986 -31.949893       True  \n",
-       "542         590.0         5313.0    2.197789 -25.710476      False  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  \n",
-       "468           NaN            NaN        <NA> -19.706400       True  \n",
-       "671        4490.0           42.0   -4.671938 -19.514456      False  \n",
-       "677        5916.0            0.0          -2 -18.970666      False  \n",
-       "676        1813.0            0.0          -2 -18.970666      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = asim_calib_util.perform_tour_mode_choice_model_calibration(\n",
-    "        asim_output_dir=output_dir, # folder containing the activitysim model output\n",
-    "        asim_configs_dir=configs_resident_dir,  # folder containing activitysim tour mode choice config files\n",
-    "        tour_mode_choice_calib_targets_file=tour_mode_choice_calib_targets_file,  # folder containing tour mode choice calibration tables\n",
-    "        max_ASC_adjust=max_ASC_adjust,  \n",
-    "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
-    "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
-    "        output_dir=output_dir,  # location to write model calibration steps\n",
-    "    )\n",
-    "tour_mc_coef_file = os.path.join(output_dir, 'tour_mode_choice_coefficients.csv') "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# if want_to_do_initial_model_run:\n",
-    "#     asim_calib_util.run_activitysim(\n",
-    "#         data_dir=data_dir,  # data inputs for ActivitySim\n",
-    "#         configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
-    "#         configs_common_dir=configs_common_dir,  # just the location of the common config, these files will be used from the original location\n",
-    "#         run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
-    "#         output_dir=output_dir,  # location to store run model outputs\n",
-    "#         settings_file=cold_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
-    "#         tour_mc_coef_file=tour_mc_coef_file  # optional: tour_mode_choice_coefficients.csv to replace the one in configs_dir\n",
-    "#     )\n",
-    "    \n",
-    "#     _ = asim_calib_util.perform_tour_mode_choice_model_calibration(\n",
-    "#         asim_output_dir=output_dir, # folder containing the activitysim model output\n",
-    "#         asim_configs_dir=configs_resident_dir,  # folder containing activitysim tour mode choice config files\n",
-    "#         tour_mode_choice_calib_targets_file=tour_mode_choice_calib_targets_file,  # folder containing tour mode choice calibration tables\n",
-    "#         max_ASC_adjust=max_ASC_adjust,  \n",
-    "#         damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
-    "#         adjust_when_zero_counts=adjust_when_zero_counts,\n",
-    "#         output_dir=output_dir,  # location to write model calibration steps\n",
-    "#     )\n",
-    "#     tour_mc_coef_file = os.path.join(output_dir, 'tour_mode_choice_coefficients.csv') \n",
-    "# else:\n",
-    "#     print(\"No initial model run performed.\")\n",
-    "\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Iterating"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# tour_mc_coef_file = os.path.join(output_dir, 'tour_mode_choice_coefficients.csv')\n",
-    "# tour_mc_coef_file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_1\n",
-      "ActivitySim run started at:  2023-09-12 22:48:37.096980\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-12 23:35:29.558125\n",
-      "Run Time:  2812.46 secs =  46.87433333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 214 coefficients adjusted\n",
-      "\t 567 coefficients converged\n",
-      "\t 190 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-33.390562</td>\n",
-       "      <td>255.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-35.390562</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-32.865285</td>\n",
-       "      <td>175.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-34.865285</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-31.949893</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-1.237874</td>\n",
-       "      <td>-33.187767</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>542</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_atwork</td>\n",
-       "      <td>-25.710476</td>\n",
-       "      <td>1777.0</td>\n",
-       "      <td>5313.0</td>\n",
-       "      <td>1.09523</td>\n",
-       "      <td>-24.615246</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-18.970666</td>\n",
-       "      <td>602.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-18.970666</td>\n",
-       "      <td>1574.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-18.970666</td>\n",
-       "      <td>335.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>671</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint</td>\n",
-       "      <td>-19.514456</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-0.538997</td>\n",
-       "      <td>-20.053453</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -35.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -33.390562   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -32.865285   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -31.949893   \n",
-       "542          coef_calib_zeroautohhindivtou_WALK_atwork -25.710476   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -18.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -18.970666   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -18.970666   \n",
-       "671   coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint -19.514456   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "541           0.0            0.0        <NA> -35.934870       True  \n",
-       "543         255.0            0.0          -2 -35.390562      False  \n",
-       "540         175.0            0.0          -2 -34.865285      False  \n",
-       "544         100.0           29.0   -1.237874 -33.187767       True  \n",
-       "542        1777.0         5313.0     1.09523 -24.615246      False  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  \n",
-       "676         602.0            0.0          -2 -20.970666      False  \n",
-       "677        1574.0            0.0          -2 -20.970666      False  \n",
-       "675         335.0            0.0          -2 -20.970666      False  \n",
-       "671          72.0           42.0   -0.538997 -20.053453       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      " Final coefficient table written to:  C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_1\\tour_mode_choice_coefficients.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "iteration_output_dir = output_dir.strip('_cold') + '_1'\n",
-    "\n",
-    "calibration_iterations_to_run = 1\n",
-    "start_iter_num = 1\n",
-    "\n",
-    "for i in range(start_iter_num, calibration_iterations_to_run+start_iter_num):\n",
-    "    asim_calib_util.run_activitysim(\n",
-    "        data_dir=data_dir,  # data inputs for ActivitySim\n",
-    "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
-    "        configs_common_dir=configs_common_dir,  # just the location of the common config, these files will be used from the original location\n",
-    "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
-    "        output_dir=iteration_output_dir,  # location to store run model outputs\n",
-    "        settings_file=warm_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
-    "        tour_mc_coef_file=tour_mc_coef_file  # optional: tour_mode_choice_coefficients.csv to replace the one in configs_dir\n",
-    "    )\n",
-    "    \n",
-    "    _ = asim_calib_util.perform_tour_mode_choice_model_calibration(\n",
-    "        asim_output_dir=iteration_output_dir, # folder containing the activitysim model output\n",
-    "        asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim tour mode choice config files\n",
-    "        tour_mode_choice_calib_targets_file=tour_mode_choice_calib_targets_file,  # folder containing tour mode choice calibration tables\n",
-    "        max_ASC_adjust=max_ASC_adjust,  # maximum allowed adjustment per iteration\n",
-    "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
-    "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
-    "        output_dir=iteration_output_dir,  # location to write model calibration steps\n",
-    "    )\n",
-    "    tour_mc_coef_file = os.path.join(iteration_output_dir, 'tour_mode_choice_coefficients.csv')\n",
-    "    iteration_output_dir = iteration_output_dir.strip('_'+str(i)) + '_' + str(i+1)\n",
-    "\n",
-    "print(\"\\n\\n\", \"Final coefficient table written to: \", tour_mc_coef_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_1\\tour_mode_choice_coefficients.csv\n",
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_2\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tour_mc_coef_file)\n",
-    "print(iteration_output_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_2\n",
-      "ActivitySim run started at:  2023-09-13 00:32:40.676421\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 01:19:29.017960\n",
-      "Run Time:  2808.34 secs =  46.80566666666667  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 191 coefficients adjusted\n",
-      "\t 609 coefficients converged\n",
-      "\t 148 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-35.390562</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-37.390562</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-34.865285</td>\n",
-       "      <td>143.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-36.865285</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-33.187767</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-1.351203</td>\n",
-       "      <td>-34.538970</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>542</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_atwork</td>\n",
-       "      <td>-24.615246</td>\n",
-       "      <td>1984.0</td>\n",
-       "      <td>5313.0</td>\n",
-       "      <td>0.985042</td>\n",
-       "      <td>-23.630204</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>386.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>203.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>671</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint</td>\n",
-       "      <td>-20.053453</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-0.867501</td>\n",
-       "      <td>-20.920953</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -35.390562   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -34.865285   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -35.934870   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -33.187767   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "542          coef_calib_zeroautohhindivtou_WALK_atwork -24.615246   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -20.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -20.970666   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -20.970666   \n",
-       "671   coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint -20.053453   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "543         127.0            0.0          -2 -37.390562      False  \n",
-       "540         143.0            0.0          -2 -36.865285      False  \n",
-       "541           0.0            0.0        <NA> -35.934870       True  \n",
-       "544         112.0           29.0   -1.351203 -34.538970       True  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  \n",
-       "542        1984.0         5313.0    0.985042 -23.630204      False  \n",
-       "677         386.0            0.0          -2 -22.970666      False  \n",
-       "676         203.0            0.0          -2 -22.970666      False  \n",
-       "675          92.0            0.0        <NA> -20.970666       True  \n",
-       "671         100.0           42.0   -0.867501 -20.920953       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_3\n",
-      "ActivitySim run started at:  2023-09-13 01:20:03.956510\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 02:05:31.639605\n",
-      "Run Time:  2727.68 secs =  45.46133333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 183 coefficients adjusted\n",
-      "\t 622 coefficients converged\n",
-      "\t 135 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-35.934870</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-37.934870</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-37.390562</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-37.390562</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-36.865285</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-36.865285</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-34.538970</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-1.197052</td>\n",
-       "      <td>-35.736022</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-20.970666</td>\n",
-       "      <td>335.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-20.875688</td>\n",
-       "      <td>203.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-22.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>542</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_atwork</td>\n",
-       "      <td>-23.630204</td>\n",
-       "      <td>1992.0</td>\n",
-       "      <td>5313.0</td>\n",
-       "      <td>0.981017</td>\n",
-       "      <td>-22.649186</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -35.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -37.390562   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -36.865285   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -34.538970   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -20.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -22.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -22.970666   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -20.875688   \n",
-       "542          coef_calib_zeroautohhindivtou_WALK_atwork -23.630204   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "541         131.0            0.0          -2 -37.934870      False  \n",
-       "543          68.0            0.0        <NA> -37.390562       True  \n",
-       "540          16.0            0.0        <NA> -36.865285       True  \n",
-       "544          96.0           29.0   -1.197052 -35.736022       True  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  \n",
-       "675         335.0            0.0          -2 -22.970666      False  \n",
-       "676          28.0            0.0        <NA> -22.970666       True  \n",
-       "677          88.0            0.0        <NA> -22.970666       True  \n",
-       "698         203.0            0.0          -2 -22.875688      False  \n",
-       "542        1992.0         5313.0    0.981017 -22.649186      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_4\n",
-      "ActivitySim run started at:  2023-09-13 02:06:05.703048\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 02:51:13.815844\n",
-      "Run Time:  2708.11 secs =  45.13516666666667  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 184 coefficients adjusted\n",
-      "\t 637 coefficients converged\n",
-      "\t 120 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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bLI71ekOGDLlrrdny2ldbLPTWhdlbA8g7LgsGAA7I/nZN+/btrcuqVaumhg0b6rffftNXX30lSTp48KBOnTqlTp06qXz58tZtfX191bVrV6fVU6ZMGfn6+mrdunVauXKlLly4IElq0KCB1q9fn+cbZnbo0MHmcfa3m55++mmb5dWrV5fZbNavv/4q6fdr+0pSu3bt9Ntvv1n/pKenq23btrp48aJ27dplc4yOHTvafPOqfv36kmQ9pqN69+6txYsX5/gzdepU6zb+/v7avXu3pk2bZrNvamqqvL29Jcl6avk333yjrKws9ejRw+ZbV+XLl9eyZcv03nvv3bGWDRs2KDMzU3379pWPj491ucFg0LBhwyQpxzWhGzRooD//+c82yypVqqTvv/9esbGx1tO3K1WqpA0bNmjQoEF5fm8AAABQMEwmk/VyVnl14MABJScn65lnnslxRstf//pX3X///Vq/fr2ysrJ06NAhHT58WK1atZLZbLb5zP3EE0+oVKlSd7y3hj02btwoSXrllVdsPqsHBQXp008/1T/+8Y9c9/vqq6908+ZNPfXUU7p48aK1tosXL1p7puweKdsTTzwhPz8/62MfHx8FBQXd85Jcd9OiRQtVrlxZa9eutVm+atUqeXl5qWPHjpKkkJAQ7dmzR6+++qrNdikpKdaanHGj8v/85z+6ePGi2rVrp7S0NJtxy+6xssetcuXKkqSpU6dq+/btysjIkPT7ZaYSExMVEBBwx+fJzMzUmjVrZDQarWeASH/0q5999pmuX7/u0GtwpNfL7ZJpuclrXy3RWxdFbw3g3rgsGADY6ebNm1qzZo2k3z9g3Xqt0rCwMO3du1fLly9Xhw4ddOLECUm/f2C6Xc2aNZ1Wk6enpyZPnqxRo0ZZG57g4GC1aNFCHTt2vOOp+7e7/Zf6f/rTnyRJFSpUsFnu4fH7/z6y7xly7NgxSVKbNm3ueOzTp0/f9bmyg4tb70PiiJo1a6p58+b33K5UqVL6+uuvtWnTJp04cUKnTp3SL7/8Yv1Qml1H9vg++OCDOY4RHBx81+fIHv9bTxHPVqlSJfn6+ua41u3t74skTZw4UUOHDtW7776rd999V9WrV9ejjz6q9u3bKzQ09J6vFQAAAAUrICBAx44dU0ZGxj0vg5Ttbp8VDQaDatasqeTkZF28eNH6eTs+Pl7x8fG5Hu/2z9uOyP5senvYI0l16tS5437Z9Y0cOfKO29yrH5B+7wluvw+JPYxGoyIjIzVnzhz93//9nxo1aqQLFy5o27Zt6tChg83N2z09PfXll19a7+9x6tQpXbhwIUc/kB/Z78v06dM1ffr0XLfJfl/atWunqKgoJSYmqmfPnvLy8lKTJk3UqlUrPfvsszaXOL7d5s2b9euvv6pevXpKT0+3jqOnp6ceeOABHT9+XF988YX1fjOOvAZ7er3b+8fc2NNXS6K3vkVh9dYA7o1wBQDstHnzZuu3qe50c77du3fryJEj1scWiyXHNvn5oJNbw9GmTRs9+uij2rp1q7777jvt3LlTixYt0uLFizV69GjrDezuJvuD3e3udm3f7HpKly6t2bNn33GbGjVq2Dy+9T4jhe3mzZsaNGiQNm/erLp166pu3bpq166dateurW+//VYffPCBzbbSvd+D3GSP+532NZvNOZrv3MagcePG2rhxo3bs2KGtW7dq586dio+P1/Lly9WrVy+bmy8CAACg8IWGhuro0aP673//q7CwsDtuN2rUKN28efOuIUS27H7B09PT+u9u3brd8Zeud/osb4/sz772yu5PJkyYoMDAwFy3uf0eDwXVDzz33HOaO3euPv/8czVq1Eiff/65srKybIKFtLQ09ejRQ/v371eTJk1Ut25dderUSfXq1VNcXJz1l/72ur1Pyx63IUOGqFGjRrnuU7p0aUm/n/00adIkDRgwQJs2bdJ//vMf7d69W9u2bdMHH3yg+Pj4XIMF6Y8zQPbt26fWrVvnuk18fLxD4YojvZ7JZLrnce3pq28NT+itXau3Btwd4QoA2Cn7g2u/fv2sp9ve6pNPPtHmzZttPrwePXo0x3bJycn3fK7sD6Xp6ek2y7NPTc6WlpamQ4cOKTAwUG3btlXbtm0lST///LN69Oih999/P08fAB0VGBioY8eOKSQkJEfTdPDgQZ0/f956uS1X8OWXX2rz5s3q27ev9fJc2VatWmXzOLs5PHbsWI4PsR9++KGOHDmisWPHysvLK8fzZDc/SUlJ1htnZjtz5oyuXr1qPf3/TtLT03Xo0CHdd999euyxx/TYY49Jkk6ePKmXX35ZcXFxGjRokMqUKZOHVw4AAICC0KFDB8XHx2vZsmV3DFd++eUXrVmzRn5+fipbtqyqVasmSTp8+HCObS0Wi44ePaoyZcrIz8/PJrC4/Sxts9ms9evXW4+XH7d+9q1Vq5bNuunTp+vGjRsaPXr0Hffz8/PLUd/58+f1448/OqW+vKhWrZoeeeQRrVu3TmPHjtXnn3+u+++/Xw8//LB1m48++kg//fSTxo8frxdeeMFm/7xcSslkMunq1as5lt/ep2W/L15eXjnel7S0NH333XfWMxlOnz6tEydOqFmzZurevbu6d++uzMxMxcbGavr06Vq+fLlee+21HM+ZkpKiLVu2qHTp0poyZUqOX96bzWa99tpr2rdvn/bv33/XM5ByU1C9nj199dixY/XAAw9Iord2td4acHdEmwBghwsXLmjr1q0qW7asBg4cqDZt2uT4M2TIEEnS6tWrdf/996tGjRpas2aNzWnON27c0JIlS+75fBUrVpT0+zeQbrV69Wqbx0lJSXrxxRc1Z84cm+U1a9aUr6+vzbdmsr/Vkts3fhzVrl07SdK//vUvm+VpaWkaOnSoBg4cmONDbF5kfwB29unMFy9elKQcDWNycrLWr18v6Y9vMLVu3VoGg0FLly61uY72pUuXtGDBAv3444/WYMVoNNrUGhERIZPJpA8++MDmms0Wi0UzZ86UJD355JP3rLVLly6aMGGCzfJq1aopICBABoOBbyoBAAAUsdDQUEVERGj9+vVavHhxjvVXrlzR3/72N2VmZmrQoEHy9PRU7dq1Va1aNa1ZsybHL4wTEhJ04sQJ6y9269atq6pVq2rVqlXWyyNlW7FihYYOHWr9ZXV+ZJ8VExcXZ7P8xIkT+vDDD3Xy5Mlc92vbtq2MRqPmzZunGzdu2KybPHmyBg4cqJ9++smhmm7/jJ0XUVFR+u2337R69WodOHAgxxkbd+oH/u///s96P4u7XZ6sYsWK+u2332wuz5SRkaF169bZbNeiRQuVLl1aH374ofU5s82bN09/+9vf9O2331of9+zZUz/88IN1Gw8PDzVo0EDSnc8GWb16tTIzM9WpUydFRETk6E/btm2rqKgoSdLy5cvv+JqkP3rFW9/vguj17O2rr1+/rpCQEHprF+ytAXfHmSsAYIfsD67PPfecSpUqles2derUUWhoqHbt2qW1a9fq7bff1ssvv6zOnTurW7duKlOmjBISEnTlypV7Pl9kZKQ++OADvf322zp16pQqVKigTZs2KSkpyeb5GzdurBYtWig+Pl6pqal6+OGHlZWVpfXr1+vkyZM233DKvh7rwoUL1bJly7teyzWvnnvuOf373//WihUrdOLECYWHhyszM1OffPKJjh8/rhEjRtz1Box3kn2jwm+++UZVqlRRRESEypYtm+96W7ZsqX/+85+aOHGiTpw4oQoVKujw4cNKSEiwBiipqamSfr/edN++ffXBBx+oa9euat++vcxms1auXKnLly9bQ5Lsen/++WctW7ZMTZs2VXBwsIYOHap//vOfevbZZ/Xcc8/Jx8dHX3/9tXbs2KEnnnhCzzzzzF1rrVSpkjp37qwVK1aod+/eCg8Pl8Fg0Hfffaddu3YpOjpaPj4++X5PAAAAkD+TJk3S5cuXNXnyZH3++edq27atypUrp+PHj2vVqlX67bff1K1bN3Xr1k3S77/sfPvtt9W3b1917txZXbt2VWBgoH788UetWrVKVatW1fDhw2227devn5577jm98MILql69uvbt26eEhARVr15dAwYMyPdraNmypTp06KCEhASdPXtW4eHhSktL09KlS1WqVCmNGDEi1/0eeOABDR48WDNnzlSnTp0UGRkpPz8/ff311/ruu+/0xBNPWIMie+X2Gfte2rVrpwkTJmjKlCny8PDQs88+a7M+PDxcH3/8sYYPH64XX3xRvr6++umnn7Rq1SqZTCbdvHnT2g/kJioqSrt371bv3r314osvymw2KyEhIUcg4+fnpzfeeEOjRo1Sx44d1aVLF1WsWFE7duzQl19+qfr16+vFF1+UJPXs2VPr1q1T37599cILLygwMFDnzp3T8uXL5evrq+effz7XWhITEyXJepzcvPTSS1q6dKm++OILvf7663c86z27V1yzZo0sFou1h3F2r+dIX925c2d6axfsrQF3R7gCAHZYtWqVDAbDXT+4Sr9/MN61a5fi4+OVkJCgZcuWadasWfrwww8l/f6NsMcff1x/+9vf7nqc6tWra8GCBZo9e7bmz58vb29vtWzZUsuXL1f79u1ttp01a5YWLVpkveSVJIWEhGjatGnq2LGjdbuuXbvq+++/V0JCgnbs2OGUD4Amk0nz5s1TXFycPvvsM02bNk3e3t4KCgrSe++953AjVaNGDfXq1UuffvqpJk6cqMDAQDVr1izf9QYFBWn+/PmaNWuWYmNjJUmVK1dWdHS0nnzyST377LPaunWr9aySV199VQ8++KCWLFmiGTNmyNvbW/Xq1dPUqVNVr14963FHjhypadOmadKkSYqJiVFwcLD69u2rBx98UB9++KH1Xi41atTQG2+8oa5du+bprJM33nhDDz74oFatWqXp06crKytLDz74oP7xj3/ccy4CAACgcPj5+Sk2NlZffvmlEhMTtWzZMv32228qU6aMGjRooG7dulkv8ZotLCxMK1eu1Jw5c5SQkKC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-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-36.865285</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-38.865285</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-37.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-37.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-37.390562</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-37.390562</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-35.736022</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-0.90937</td>\n",
-       "      <td>-36.645393</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>219.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-22.875688</td>\n",
-       "      <td>159.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-24.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-22.246255</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-24.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -36.865285   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -37.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -37.390562   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -35.736022   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -22.970666   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -22.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -22.246255   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -22.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -22.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540         131.0            0.0          -2 -38.865285      False  \n",
-       "541           0.0            0.0        <NA> -37.934870       True  \n",
-       "543         100.0            0.0        <NA> -37.390562       True  \n",
-       "544          72.0           29.0    -0.90937 -36.645393       True  \n",
-       "677         219.0            0.0          -2 -24.970666      False  \n",
-       "698         159.0            0.0          -2 -24.875688      False  \n",
-       "695         124.0            0.0          -2 -24.246255      False  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  \n",
-       "675          32.0            0.0        <NA> -22.970666       True  \n",
-       "676          60.0            0.0        <NA> -22.970666       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_5\n",
-      "ActivitySim run started at:  2023-09-13 02:51:44.198650\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 03:38:29.700390\n",
-      "Run Time:  2805.5 secs =  46.75833333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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cUaO/UbNmzcpVV12VAQMG5LXXXmuA6qDxkL2/JHvL3k2d7F36ZO+aaeq5LJG9v05Tf41l79Iney9eY87eGuA0mJ49e2bAgAH54Q9/mDXXXHOh/eeff34+//zzPPDAAznssMOyzTbbFKHKurXlllvmz3/+czbeeON85zvfSZIsu+yy+eMf/7jQ2JdffjkDBw6cP66p22STTTJgwIAcccQR+fTTT/OrX/1KAC8RU6ZMyTrrrLPQ9vXXXz9z587Nm2++mYEDB2ajjTZq+OKoEz/60Y+y2267FbuMBlduAS1Jxo4dm7Fjx9Z4fFM/a/3ss89u0vd0XVLLLbdcPv300wW2/e1vf0uhUMiWW2650PixY8fOv3KOpmH69Onp1KlTjcd36dKlSd+TbeLEidl7771z4okn5sgjj1zs76QXXnghZ599dt59992suuqqDVwlFJ/sLXvL3qVB9i59snfNyN5Nj+wte5ca2btpZm8NcBrMqaeemgMPPDB77rlnvv3tb+fyyy9f4IeiUCjksssuS4sWLTJo0KC8+OKLRay2bpx22mkZPXp0DjvssGy66aaLvXfCwQcfnBdeeCEdO3Zc4N5kTd0666yT2267LT//+c9z7LHH5tprr80WW2xR7LKopTlz5izyaolWrVolSY466igBnCap3AJaklx++eVl9YVLhw4dstJKKxW7jAbTo0ePPPjggzn88MNTWVmZiRMnZuTIkUmSHXfccYGxU6dOzf3335+ePXsWoVKW1ty5cxd5ZePiVFZWZs6cOfVYUf164IEH0qtXr1x55ZUZOXJkLr300qyyyirz989bJnbevfYOP/zwnHTSSUWsGIpD9pa9Ze/SIHtTqmTv0id7y96lRvZumtlbA5wG07179/zlL3/Jtddem+eff37+B/b/VlFRkT59+mTjjTfO7373u3zyySdFqLTudOrUKffcc09uv/32zJo1a7HjWrVqlb333ju/+MUvluhMosamV69ei9y+6qqr5v3338/RRx+dnXbaaYEzhkrhLE4WtN566xW7BFgq5RbQKH1HHnlk9t9//+y3337ZfPPN88gjj+SLL77ILrvsktVWWy3Jl1+svvTSS7n00kvz6aef5qCDDipy1bUzbNiwvPvuu/P/e+bMmSkUCrn33nvzwgsvLDD2rbfeaujyqKVOnTrlT3/6U2655ZZcfvnl+fGPf5yzzjor++67b5599tn88pe/zLhx47L22mvn4osv9pmEsiV7y96yd3nwd46mSvam1MjesnepKZXsrQFeZO+8884CS75MnTo1SfLmm28udEbJkiyT0lh16NAh55xzzteO22effbLnnnsu8Eu0qWrVqlUOPfTQrxxTKvddGzx48Ffu/+KLLzJkyJAFtpVKCH/++eczd+7c+f89bdq0JMmTTz6ZCRMmLDC2FK6w+CpNfZmmxSmX13izzTZL586di10GUAfWX3/9XHfddbn44ovTv3//VFZWZrfddsuFF144f8xll12WAQMGpKKiIr169UqPHj2KWHHtDR06NEOHDl1o+/9+/pinFP5m/e8XD1+lVL54OOigg7LFFlvk3HPPzfnnn5+BAwdm7NixadasWU466aQcddRRS3R2PuVB9l402btpkr1LP5fVRCl8jlmUcnmNZW8oHbL3f8jeX5K9G4dCdXV1dbGLKFfrrLPOIn/wq6urv3L7G2+80RDl1bvq6uqMGzcukydPTpJ07NgxK6+8cnGLqmelPufx48cv1eOa+lmfi/pZ/u9frYva15R/ltdZZ50ce+yx+e53v7vA9qlTp+a4447LWWedlXXXXXehx2222WYNVWKdK7fXuByts846ZbckWbnNefDgwdl0000XWLKpnEyaNCmtW7de6CrAxx57LK+99lp22WWXRnm/piXx7LPPLtXjNt988zqupOEs6r6gX6eU/j4999xzOe644zJ16tQUCoUcd9xxOfHEE4tdFo2Q7F3aOXRRSn3Osvd/lHIuk72/VMqvcTkqtxyalN+cZW/Ze3Fk76arqWZvDfAiuuaaa5bqcSeccEIdV9KwRo8enX79+uXJJ5/M9OnTF9i3zDLL5Ac/+EF+/vOfL9UvlcaqHOdcTr7u7PvF2XPPPeu4koaxuC8Qk8V/iZikSf/BL6fX+L+vjFoSTflLlqQ8A9qzzz6b1VdfvUkv/wnlrhy/eEiSzz//PJdddlnuvvvutGzZMkceeWQefvjhvPXWW/nOd76T3r17p1u3bsUuk0ZE9i6fHFqOcy4n5ZTLEtl7STTF11j2lr2BpkP2bprZWwOcBtW/f/9cccUVqaioyMYbb5w111wz7dq1y5w5czJ58uS8/vrreeONN1JRUZEzzzwzhxxySLFLrrVynHNN9e3bN0OHDs2gQYOKXUqDmzVrVlq0aFHsMpZKuX6BuKSa6mv8VV+yfJWm/CUL5WFx98r8KqWyVCiLdvvtt+dvf/vbUv9dozhGjhyZ888/PxMmTMgWW2yRiy66KKusskpmz56dq6++Ov3790+rVq1y+umnZ//99y92uVA05ZhDy3HONSV7N71clsjeNdVUX2PZm1Ile/O/ZO+mqRSytwY4Deaxxx7L0Ucfne985zvp06dPVlxxxUWOe++993LhhRfmqaeeSv/+/fOd73yngSutO+U45yVx/vnn584772zyH96vueaaJQqYo0ePTq9evfKXv/ylHquiLpXTa/z73/9+qUJ4U/+SpRwD2tJ86VsoFHLTTTfVQzX1rxyXq/q6L9VatmyZZZddNmuttVZ+9KMfZZ999klFRUUDVlhcpfA55IMPPkjHjh0XWl5vccaNG5cXXnghe+yxR/0WVk9OO+20/OUvf0mbNm1yxhlnZL/99ltozKhRo3LmmWdm3Lhx2XzzzXPxxReX1LLHUBPlmEPLcc5LohT+5iXllcvKVTm9xrJ3zcneTYvsvTDZu+l/DpG9m2b21gAvonL7g//zn/88EydOzKBBg9K8efOvHDtnzpzsscceWXXVVXPttdc2UIV1rxznvCRK4Y9f8uWHnMMPPzynn376V46bM2dOrrnmmvTr1y9z585t8vOuqZkzZ+bTTz9t1MuhfB2vcekr14C2pJrynMvxXpkHH3zwV+6fO3duPvvss7z33nuZPXt2ttxyy/Tt2zfNmjVroAqLqxQ+h3zrW9/KZZddtsD9BKdPn56LLrooRxxxRFZfffUFxt93330588wzm+yc11lnnWy99da56KKLssIKKyx23MyZM/PrX/86t99+e9q0aZMXX3yxAaukMZK9F69Ucmg5znlJlMLfvEQu+zqyN01BueXQpPzmLHsvTPZu+p9DZO9Fa+zZuzx+whqpJbmvzX+fQdRUQ/jrr7+eQw899GvDaJI0a9YsO+20U+67774GqKz+lOOcy9Gmm26a/v37Z8aMGTnvvPMWOebNN9/MmWeemTfffDPLLbdcfvnLXzZwlXVnu+22y9lnn53ttttu/rZZs2blwQcfzFZbbZXOnTsvMH7o0KFN+g9+Un6vcTl65JFHil1CgxszZkyxS2hQTTlML62BAwfWaNzMmTNz22235bLLLsttt932teGdxmNR5zJ/8cUXGTJkSHbfffeFQnhTd8kll9ToHp+tWrXKBRdckB/+8If+HpNE9v4qpZJDy3HO5ajccpnsXfqvcTmSvUuf7L14snfTJXsvWmPP3hrgRVSTP37jx4/PRRddlJEjR2bZZZfNySefXP+F1ZNp06ala9euNR6/4oor5t///nc9VlT/ynHO5ah///456aSTctttt2XGjBnp06fP/C/Oqqur86c//SnXXnttZs2alZ133jnnnHNOOnbsWOSql9748eMzffr0BbZNmzYtvXr1Sv/+/RcK4aWg3F7jJHnnnXcyatSo7LXXXvO3TZw4Mddcc01efPHFtG7dOtttt10OPfTQJnm/tf9VjgGNBc2aNSv/+Mc/0qpVq6y22mrFLqdBtWrVKj//+c/z8ssvZ/DgwUJ4CSjVRb5qEsD/2ze/+c3ss88+9VQNTYns/dVKIYeW45zLUbnlMtm79F/jRPam/Mjesncpkb2/1FiztwZ4IzV37tz0798/f/jDHzJz5szssssu6dWrV5P+cDtnzpwl+qDWrFmzzJo1qx4rqn/lOOdy1KJFi1x77bXp1atXBg8enJkzZ+aKK67Ie++9l7POOiujR49Oly5dcsEFF+QHP/hBscutN6X6Bz8pv9f4iiuuSP/+/VNdXZ0999wzFRUVmTp1avbbb7988MEHad++fVZaaaX87ne/y6OPPpqBAwfW6GqbpqwcA9qECRMyevTotGrVKptttlmN73PUmE2dOjXXX399Ro0atcAZ2vfff3969+6dzz77LMmXH9z79OmTjTbaqEiVFkfPnj3z5JNPFrsMqJUvvvgiDz/8cAYNGpRnn3021dXVOf7444tdFo2Y7F0aObQc51yOyi2XLY7sXTqvsey9MNlb9i4HsjeloClkbw3wRuj555/PhRdemLfffjvdu3fP+eefn+985zvFLgu+1jXXXLNE41999dV6qqThVVZW5rLLLku7du1y88035/3338/bb7+dmTNnZp999smZZ56ZZZZZpthlUgvl8hoPGzYs/fr1y7bbbptDDjkkFRUVSZI//OEPGT9+fDbccMP8+c9/TuvWrTN69OgcfPDB+fOf/5wjjzyyyJXXXjkGtPHjx+fKK6/MqFGj8uijj87f/qc//SlXX3115s6dm+rq6iy33HK56KKL8qMf/aiI1dbOtGnTst9+++Wdd97JCiuskDlz5qRZs2YZPXp0zjzzzFRXV2e//fbLmmuumSFDhuTnP/95hgwZklVXXbXYpTeYysrKVFVVFbuMpTZkyJAlGv/Pf/6zfgqhKEaNGpVBgwbloYceyrRp01JdXZ1vfOMb2W+//YpdGo2Y7E1TJXuXfi4rZ+XyGsvesncie8veTZPsXd6aUvbWAG9EJk2alMsuuyxDhgxJixYtcuKJJ+bII48sieVt5nn++eczd+7cGo198cUX67mahlFOc17SEJ4seI+9UnDOOeekffv2ueaaa1JRUZE//vGP2WabbYpdFnWo1F/j22+/PRtuuGH69u07f1t1dXXuu+++FAqF/OIXv0jr1q2TJBtssEF+/OMf5y9/+UuTD+HlGNA++eST7Lfffpk0aVI22GCD+XN+4okncuWVV6ZZs2Y59dRTs9Zaa+XOO+/MqaeemjvuuCPrrrtusUtfKv379897772X3/72t9lxxx3nb7/22mtTXV2dn/3sZznrrLOSJHvvvXd233339O3bN5dcckmxSm5wL7/8clZcccVil7HUzjrrrCX6XFFdXV1yn0PKzYQJE3Lvvfdm0KBBeffdd+dfEdezZ88ceeSR2WqrrYpcIY2V7L2gpp5D5ymnOcvepZ/LKP3XWPaWvWVv2bupkr3LT1PN3hrgjcRdd92VK664IlOmTMmWW26Z888/P9/4xjeKXVadu/POO3PnnXfWaGyp/GIspzkPGDCg2CU0CieccEI6dOiQ3r17p1+/ftlkk01K4uxk/qOUX+PXXnttoUD9+uuv55NPPskyyyyTnj17LrBv/fXXz/3339+QJdaLcgxof/rTnzJt2rTccsstC5xR/6c//SmFQiHHH3/8/PfC9773vey11165/vrr89vf/rY4BdfS0KFD8+Mf/3iB13fatGnzlx078MAD529v2bJldt9999x1110NXmex/PWvf829996bI444otilLLWm/PNIzc2aNSvDhw/PoEGD8vTTT2fu3LmprKzM5ptvnk022STXXXddDjnkkEYbwCk+2XthTT2HzlNOc5a9v1TKuYwvlfJrLHvL3rK37N1UNeWfR2quFLK3BniRvfnmm7ngggvy0ksvpXPnzrnyyiuz8847F7uselGOvxjLbc6bb775Ej/m+eefr4dKGtZzzz230La11lor++yzT+6888787Gc/yxlnnDF/Oat5Nttss4YqkVoqp9d4+vTpWW655RbY9ve//z3Jl/OprKxcYN+cOXOa9JeH85RjQBs5cmT22muvBQL45MmT88ILLyRJfvKTn8zfXigUstNOO+Wmm25q6DLrzPvvv7/A65h8+bM9Z86crLLKKgs1P1ZcccV88sknDVlinevVq9dX7p87d26mTZuWf/zjH3nvvffSvXv3Jh3C99xzz2KXUBTDhg3Lu+++O/+/Z86cmUKhkHvvvXf+z/M8b731VkOXV6cuvPDCPPjgg5kyZUpatmyZrbfeOttvv3222267dOjQIePHj88f/vCHYpdJIyV7l7Zym7Ps/R+lmsvKVTm9xrK37C17f0n2bnpk7y/J3o0/e2uAF9Gll16agQMHZu7cufn+97+fk08+Ocsss0w++OCDr3xct27dGqjCulWOvxjLcc418eGHH2bw4MEZMmRIxo0blzfeeKPYJdXKwQcf/JUh5LXXXsuhhx660PamPO933nlngWA6derUJF9+sdis2YJ/WsaOHdugtdWHcnqNu3btmnHjxi2wbeTIkSkUCvne97630PjRo0dn+eWXb6jy6k05BrR///vfWWuttRbY9swzz6SqqiprrLHGQq9rp06dMmXKlIYssU5VVFQsdI+tp59+Okny3e9+d6Hxn376aZO/umTw4ME1GrfKKqvkZz/7WY477rgmP+fFWdTn6xVXXLFkvkQcOnToQtsXd1+2pjzn2267LW3atMkxxxyTI444omTfr9Q92bv0leOca0L2/lJTnrfsvaBSeo1l7/+QvWXvpv65Xvb+D9n7P5rynEsle2uAF9GNN944/99HjBiRESNG1OhxTfFD3dL49NNPM3bs2CZ5FufSKuU5f/HFFxk6dGgGDRqUZ555Zv6Sc4v6UN/UHH/88U36D9rS6Nu37wL3qZrn0ksvXWhbU19eMCmv1/h73/te7rnnnhx44IFZfvnlM2rUqDz//PNp3rx5dthhhwXGvv322/nLX/6Sfffdt0jV1p1yDGgtW7bMjBkzFtj21FNPpVAoZMstt1xo/L///e+0a9euocqrc2ussUZGjRqVgw46KMmXv5uGDRuWQqGQH/zgBwuNf+SRR7L66qs3dJl16pFHHvnK/S1btky7du1K6p63SXLPPffkrrvuyh/+8Id07NgxkyZNyg9+8IOFfo+fdNJJOfbYY4tUZd0otyVw99xzzwwfPjx//OMfc9NNN2XTTTedfxZ6586di10ejZjs/dVKOYcuTinPWfYuLbJ36ZK9/0P2XpDs3fTI3rJ3qSmV7K0BXkQnnHBCsUtoUN/61rdy2WWXZbfddpu/bebMmenXr1/22GOPrLzyyguM/9vf/pYzzzyzSX/pUI5z/l8vvfRSBg0alIceeiiff/55kqRjx47Za6+9st9++2WllVYqcoW1d+KJJxa7hAZVToF0nnJ6jY877rgMHTo0O+20U1ZbbbW89dZbqa6uzvHHH5+OHTsm+TJ8Dx06NAMGDEjz5s1z+OGHF7nq2ivHgLbOOuvk6aefzs9+9rMk/7m3T5Jsv/32C4ytrq7OX//616yzzjoNXmdd2WOPPXLxxRdn/fXXz5Zbbpk777wzH3zwQb7xjW9k6623XmBs375989JLL+Wcc84pUrV1oxT+xi6pX/ziF3n44YfTrVu3fPDBB/N/byXJ7rvvnlVWWSVJcu+99+a6667L3nvvnS5duhSr3FpbmiVw58yZUw+VNIxLLrkkF154YUaMGJH7778/jz/+eJ544olceOGF2XjjjdOjR4+y+4xCzcjepZ9Dy3HO/0v2Lj2yd2mTvWXvRPaWvZsu2fvryd7FpwFeROUWwqurqxfaNmPGjFx77bXZZJNNFgqkpaAc55wkH330UYYMGZLBgwfnX//6V6qrq9O6det897vfzVNPPZVf/epX2W677YpdJkupnAJpOercuXPuvvvuXHvttXnppZfy7W9/O3vttVf22Wef+WMGDx6c/v37Z6WVVspvfvObrLjiikWsuG6UY0Dbf//9c+qpp6ZPnz7Zcsstc8899+STTz7Jt7/97QWuhpo5c2Z+/etf5+23327SX7jsv//+eeGFF3LJJZekUCikuro6yy23XK644or59xC8++6786c//Snjxo3LJptskgMOOKDIVdev0aNHZ9SoUamoqMjmm2+etddeu9gl1cp9992Xhx9+OEcffXROOumkhe6buMcee+Q73/lOkmSbbbbJvvvumzvuuKNkPpNPmzYt1dXVX3mFzKhRo3LuuefmgQceaMDK6laLFi2yww47ZIcddsjUqVPz17/+Nffdd19eeOGFvPDCCykUCvnjH/+Yzz//PDvssENatWpV7JJpBErl57ymyjGHluOcE9m71MnepU32lr1lb9m7qZK9Ze+mkr01wBuRWbNmZcyYMfnoo49SXV2drl27Zp111knLli2LXVq9WlRQLXWlOueHHnoogwYNylNPPZW5c+emXbt22W233fKjH/0oW2+9dT7++OOFzmwsBddcc80SP6ZQKOT444+vh2rq3yGHHJJjjz12/geZclBur/EKK6yQiy66aLH799prr/zgBz9Ijx495oeXpq4cA9rOO++cN998M/369cvAgQNTXV2dlVdeOVddddX8MTfccEP+8Ic/ZNq0adlxxx3z4x//uIgV106hUMgVV1yRgw46KKNGjcoyyyyT7bfffoGzlP/973+nuro6xx57bI4++uiSeH+//vrr+eMf/5ixY8fmG9/4Ro455phssMEG+eUvf5lBgwbN/0xSKBSy66675pJLLlnofpJNxeDBg7PxxhvnlFNO+dqx875we+yxx5p8CH/44YdzzTXX5B//+EeSL+8rd9JJJ2XXXXedP2b69Om58sorc9ttty205GRT0qtXr+y///7ZcMMNkyTLLrts9tlnn+yzzz6ZMGFCHnjggTzwwAMZPXp0XnnllVx00UXZeeed86tf/arIldPYyN7lo1TnLHvXXFPOZbJ3zTTl11j2lr3nkb2b/vtb9l402btpKpXs3TR/wkrMxIkTc+WVV2bo0KGZNm3aAvtat26dHXbYIaecckq6du1apAqhZk455ZS0adMmBx54YLbbbrtsttlmC5wB1hSWxVga5RbQnn322QXOSC4H5fYaf52mvvzYopRrQDvllFNywAEH5OWXX84yyyyTzTffPM2bN5+/v2XLlll//fWz22675Sc/+UkRK607G2+8cTbeeONF7jvhhBOafCD7b6NGjcohhxySZs2aZa211sprr72Wgw46KAcffHDuueee7L777tlxxx0zffr0PProo3nggQfyrW99K4cddlixS18qr7/+eo455pgaj99qq61y7bXX1mNF9e/BBx/MqaeempYtW2arrbZK69at8/zzz+f000+ff//IV155Jaecckref//9rLzyyrnwwguLXfZSGzx4cL773e/OD+H/bfnll8/hhx+eww8/PGPHjs19992X+++/P3fddVejC+EUj+xNqZC9a64p5zLZu2aa8mv8dWRv2bspk71l73lk76anVLK3BniRvfTSSzn66KMzZcqUbLjhhtliiy3StWvXNGvWLB999FGee+65DBkyJI8++miuu+669OjRo9glw2KtvPLKef/99zNo0KD885//zCuvvJLtt98+3/zmN4tdWr0aMGBAsUugnpXTa/zcc88t1eP+e9mupqycAto8K6ywQlZYYYVF7vvpT3+an/70pw1cEXXl2muvTffu3TNgwIB06NAh1dXVOfvss3PjjTdm9913z2WXXTZ/7K677prPPvssDzzwQJMN4TNnzky7du0W2r7sssumb9+++da3vrXA9rZt2zbpe3Ilyc0335xOnTrl9ttvn3+PtRkzZuTYY4/N73//+3Tt2jWHHXZYvvjii/z85z/PL37xi0a5LFldW3311XPKKafklFNOyYsvvljscmgkZG9KiexNqSqn11j2lr3/m+zdtMneX5K9Ze/GRgO8iD755JMcf/zxadu2bf7whz9kk002WeS4119/PSeffHJOOumk3HvvvenUqVMDVwo1M3z48Lz88su577778te//jV/+9vfcuWVV2a11VbLj370o6y77rrFLrFebL755l87ZurUqSkUCl95bxAar5q8xqXi4IMPXuIrRgqFQl5//fV6qgjqRq9evZb4MYVCIX369KmHahrGK6+8kiOOOCIdOnRI8uV8Dj/88AwePDjbbrvtQuN/9KMf5de//nUDV1l3ll9++XzwwQcLbW/WrNki5/uvf/2ryd9HcezYsTn44IPnB/Dky6tYTzjhhPz0pz/NKaeckk6dOuWKK67IRhttVLxCi0gTk0T2pvTI3osnezdtsvdXk71pCmRv2ft/yd7loTFmbw3wIrr55pszbdq0Bc4aWZRvf/vbufHGG7Pbbrvl1ltvzYknntiAVcKS2XDDDbPhhhvm7LPPzt/+9rfcf//9eeSRR3LdddelUCikUChkxIgRWWeddbLSSisVu9w6U11dnccffzz/+Mc/suqqq2bbbbdNs2bN8vTTT6d379555513kiTf+ta3cuqpp2arrbYqcsW1M2zYsLz77rs1Hl/KS5LNUypftFxyySU1Gvfwww9n5MiRSZINNtigHitqGOUY0A455JAlfkyhUMhNN91UD9XUv8GDB9d47H9/EdWUX+PPPvssnTt3XmDbvKUF27dvv9D4Vq1aZcaMGQ1RWr3YYIMN8uCDD+b444//2mUSZ82alQcffDDbbLNNA1VXP6ZOnZqVV155oe3f+MY3kny5lOLtt98+/4uYUvD8889n7ty5S/SYPfbYo36KocmQvSlFsrfsvSiyd9Mhe9ec7N20yN5fkr2/JHs3XaWQvTXAi2j48OHZfffdvzKAz7PSSitlzz33zNChQ5t0CP/fH5p591178sknM2HChAXGNsYlE5ZGOc45SSorK7PNNttkm222ycyZMzNs2LA88MADefLJJ3P33Xdn0KBB6dmzZ/baa6/suuuuxS63Vj777LMcddRRefnll1NdXZ0kWX/99XPuuefmqKOOSuvWrbP99ttn+vTpefnll3P00UfnxhtvbNJnNg8bNixDhw6t8fhSCOH//UXLN77xjXz/+98vyS9a9txzz6/cP378+Fx00UUZOXJk2rVrl1NPPTX77bdfA1VXf8oxoL3//vs1GldVVZUJEyakurq6Sd9PcsyYMV875r/f38suu2xOPvnk+i+sHlVXV6dZswU/7s97DZvya7k4+++/f37605/mV7/6Vc4555yF5j5PVVVVzjvvvHz00UfZf//9G7jKulVVVbXAPV/nmXc/waOOOqqkAniS3HnnnbnzzjtrNHbe763GFsJpeLJ3eeTQcpxzInvL3guSvZsO2fvryd5Nk+z9Jdlb9m7qSiF7a4AX0fvvv79E9/ZYZ511luhDQmP0vz808wJLv379Fvpj0NT/2M9TTnM+6qijssUWW2TzzTfPuuuuO38urVq1ym677ZbddtstkyZNyoMPPpj7778/Tz/9dP7+9783+RB+9dVXZ8yYMTnvvPPSs2fPjB8/PhdffHF+9rOfpXv37hk4cOD8s/0+/fTT7L333unfv3+TDuFHH310vvvd7xa7jAZTjl+0/K85c+bkhhtuSN++fTNjxozsvvvuOeuss+af0drUlWNAe/TRR792zMsvv5wLLrgg//73v7Pyyivn3HPPbYDKGt7cuXPTv3///OEPf8jMmTOzyy67pFevXgudwU3jtummm+bwww/PDTfckL///e857LDD0rNnz6ywwgqprq7Oxx9/nGeeeSa33HJLxowZk1NOOSXrrLNOscuuV926dSt2CXVu3333Ldsl5Vh6sndp59B5ymnOsrfsXapkb9k7kb1lbxo72XthsnfjpAFeRM2bN88XX3xR4/EzZ85MmzZt6rGi+lXTpX1KSbnN+e9//3sef/zx+UtRbbrppunZs2e22GKL+X/kOnTokIMOOigHHXRQxo0bl7/85S9Frrr2Hn300ey///454IADkiSrrbZazjvvvBx22GE56KCDFljqpmPHjtl3330zcODAIlVbN1ZfffWSCphfpxy/aPlvzz77bC688MKMHTs2q622Ws4///z07Nmz2GU1mHIMaFOnTs1vfvOb3HnnnamoqMjRRx+d4447Li1btix2aXXu+eefz4UXXpi333473bt3z/nnn5/vfOc7xS6rzvzvspkzZ85MoVDIvffemxdeeGGBsW+99VZDl1fnTj/99Ky00kq58sorc9555y2y4dGmTZtccMEFJXEFTTnadNNNs9tuuxW7DJoY2bv0lducZW/Zu1TJ3rK37C17N1Wyt+xdakohe2uAF9Faa62Vxx57rMb3ARk5cmTWWGONeq6q/nzd0j6lqNzm/OKLL+b111/Piy++mFGjRuWll17KiBEjUigU0q5du2y22Wbp2bNnevbsmbXWWiurrLJKjjnmmGKXXWsff/xxVl999QW2zftZXdTZXyuuuGKmTJnSILVRN8rxi5bkyy8ULr300tx3331p2bJlfvGLX+SII46Yv7xPOSj1gLYo9957by677LJ88skn2XzzzXP++ecv9DuuFEyaNCmXXXZZhgwZkhYtWuTEE0/MkUcemRYtWhS7tDo1dOjQRS6bOWTIkEWOb8pXw81z4IEHZs8998yIESPy3HPP5d///neqq6vTtWvX9OjRI9tvv32Tv2fkf1vUvUG/6suWUlgaFZaU7F36ym3Osvd/yN6lRfaWvWVv2bupkr1lb9m78dEAL6If//jHOe+88/Lggw9m5513/sqxQ4YMyVNPPZUrr7yygaorvvfffz/nnXde+vfvX+xSGkxTn3OzZs2ywQYbZIMNNsihhx6aJPnwww8XCOWXXnpp5s6dm/bt22fzzTdPz549c+CBBxa38FqaPXt2WrVqtcC2eSFlUWGlUCgscG86Gr9y/KLljjvuyJVXXpkpU6bke9/7Xs4999wa3TezVJRLQPtv77zzTi688MI8++yz6dChQ3796183unv31JW77rorV1xxRaZMmZItt9wy559/fr7xjW8Uu6w6N2DAgGKXUDStW7fOzjvv/LWfsUvB4r5oSRb9ZYsQTjmSvb9aU8+hS6Opz1n2/g/Zu7TI3rK37F1aZO/SJ3t/SfZunDTAi2ivvfbKkCFDcsYZZ+TNN9/MQQcdlK5duy4w5qOPPsqNN96YAQMGZJtttslOO+1UpGrrxssvv5zrrrsuo0aNSpJ8+9vfzvHHH59NN910/pjq6ur8+c9/ztVXX52ZM2cWq9Q6U45z/m8rrrhidtlll+yyyy5Jvlza57777sugQYPy8MMPZ+jQoU0+hJebE044IWuvvXaxy2hQ5fRFy5gxY3L++edn9OjRWX755XPRRRflRz/6UbHLalDlEtDmmTVrVq699tr0798/c+bMyT777JPTTjst7dq1K3Zpde7NN9/MBRdckJdeeimdO3fOlVdeWdIhrVSWgmTxyu2Llj333LOkfx9Tf2Tv8sih5Tjn/yZ7lx7Z+0uyd+mSvWXvUiF7lz7Zu2nSAC+iioqK9O3bN6eddlr++Mc/5k9/+lNWXHHFdOnSJZWVlfnkk0/y3nvvpbq6OjvttFMuvvji/9fevcfVfD9+AH+dbioht0Zuc1vHbSRdtC82JbdFucxtGnP/jmEYtraGiLnNxhi+ctu+LlMpkiKXkS5UmoUvY+7kVtLk1Onz+8Oj89tRqHTOp8/n83o+Hns8+Jz3Z15vpXNen8v7I3bk13LixAmMGTMGWq0WjRs3hpWVFZKSkjBixAgEBwfD2dkZ169fx7Rp05CWlgYbGxvMmTNH7NivRYlzfl5ubi6SkpKQmJiIU6dO4cyZM8jLy4OFhYVuSTY5yMzMxM2bN3W/L7wC+cGDB3rbgWdXt0rZxIkT9X6v0Whw7tw5ZGRk6Ja5UavVsnxekRL0798fBQUFAICaNWti69at2Lp160v3UalU2LRpkzHiGZTSChoAHDlyBPPmzcONGzfg4OCAOXPmoG3btmLHMohFixZhy5Yt0Gq1eO+99zBlyhTY2NgU+Rn9vOLuNJGrbdu24dixY1i5cqXYUcqkpEsb/5PUf36V5UDLyZMnDZDEOJT2jF8qP+ze8u+hSpzz89i92b1JWti92b3ZvfWxe0sHu3fJsHuLTyUIgiB2CAJ+++037N69G2lpabh7967ug6yTkxP69u0LNzc3sSO+tpEjRyItLQ3r16+Ho6MjAODOnTsYP348zM3NERQUBD8/P9y/fx9eXl746quvULt2bZFTvx4lzjk/Px+pqamIj49HfHw8Tp8+jby8PJibm+Ptt9/WFW9HR0fZLGekVquLfW6LIAgvfZ7L2bNnDRnL4O7du4dly5YhOjoaOTk5eq9ZWVmhe/fumDp1apG7a6RIrVbjyy+/hIeHh25bVlYWfH19sXTpUt2/70IxMTFYuHChJL/GXbt2LdN+sbGx5ZzEuF5U0F5FygXt008/RUxMDADgvffeg5+fH0xNTV+5n7Ozs6GjGYRardb9ujTP2pLiv+OyCggIwI4dOyQ755L+/CooKMCdO3d079NSnW9p3Lp1C6GhoQgLC8O1a9cUMWeiF2H3lmcPVeKc2b3/H7s3u7cUv8bs3uzer8LuLV/s3vLF7l2x8AQ4GY2bmxsGDBiA6dOn620/duwYxowZg6ZNm+Lu3bv45ptvJL/cXCGlzXnMmDE4efIkcnNzYWJiglatWsHNzQ2urq5wcnIqsoyVXMyePbtM+0n5SqrU1FSMGzcOWVlZaNu2Ldzc3GBnZwczMzNkZGQgKSkJSUlJqFq1KlavXo327duLHfm1KPVAi5IosaD9c87Aq+ct9cJS1iurn7/zRs6kXsJL4vTp0/jmm29w9uxZ1K9fH1999RW6dOkidiyDePr0KaKjoxESEoKEhATdv+FOnTrhp59+EjseERmQ0noooLw5s3uXDru3dLB7yx+7N7v3i7B7ywu7N7t3RcAl0CUkISEB58+fL9MSExVBdnY2mjZtWmR78+bNIQgCMjMzsXPnTlk8W6CQ0ub822+/wdzcHD4+Phg3bhzefPNNsSMZhZTLdFncv38fn3zyCSpXrowff/wRTk5OxY5LT0/HlClT8Omnn2L37t2oWbOmkZOWHx8fn1IVMyU5efIkQkNDJb9UqJKKViGl/ewqy9dYbs8GVbLs7GwsXboUO3bsgImJCcaNG4d///vfslwyNDU1FSEhIdi3bx8eP34MAKhRowb69++PQYMGoV69eiInJKr42L2lR2lzZvdWBnZv+id2b+lS2s8udm9lY/dm965IeAJcQiIjI7Fjxw7JlnCtVgszs6LfcoVLcY0fP142ZbSQ0uY8cOBAJCQk6Jb5aNKkCTp27Ag3Nzc4OzujWrVqYkekcrB161bk5ORg27ZtaNCgwQvHtWzZEsHBwfD29sYvv/yCSZMmGTFl+Vq4cKHYESqU27dvIzQ0FKGhobh27RoAKLKES72g+fr6lnqfGzduGCBJxVN4cGn//v2SfmYTPbN79258++23uH//PlxcXBAQEFDsSRIpy8jIQFhYGEJDQ/HXX39BEARYWVnB3d0dcXFxmDt3rt5SokT0cuze0qO0ObN7KwO7N7F7P8PuLV/s3vLC7s3uXdHwBDhVGHL7YVgScpvzvHnzAAA3b95EXFwc4uPjERUVha1bt8LExARqtRqurq66Um5tbS1yYiqLAwcOoE+fPi8t4IXq1asHX19fREdHS7qE+/n5YcKECejYsaNuW35+PlJSUqBWq1GlShW98eHh4Zg1axbS09ONHdVgNBqNbjmf+Ph4CIIAQRDg6uqKIUOGiB3PqJRW0J4+fYqoqCiEhoYiKSkJf/zxh9iRDKLw4FJYWBiuXr0KQRBga2srdix6DZcuXcKcOXOQmJiI6tWrY+HChfDx8RE7Vrnat28fQkJCEBcXB61Wi6pVq8Lb2xteXl7o1KkT7t69C09PT7FjElEFI7ceWhJymzO7tzKwez/D7s3uze4tL+ze8sPuze5dUfEEOBGVO3t7ewwYMAADBgwAAFy4cAHx8fE4ceIEdu3aheDgYJiZmaF169bo2LEjJk+eLHJiKo3r16/jww8/LPF4tVqN0NBQAyYyvMTERAwcOFBvW3Z2Nvz8/LBhwwa9cl5IEARjxTOo55fzKZxX79698cknn6BJkyYiJzQOJRa05ORkhISEICoqCjk5ORAEAc2bNxc7VrkqPLgUGhqK+Ph4FBQUQBAEtGvXDoMHD0avXr3EjvhawsLCSjX+8uXLhgliZBqNBqtWrcKGDRuQn5+PgQMHYvr06ahatarY0crd1KlTYW1tjaFDh8LDwwPOzs4wNTXVvc4lRImI5I3dW97YvZ9h92b3ZveWPnZvfeze0sPuLU08AU5GdfLkSWi1Wr1tOTk5AIDjx4/jzp07RfaR+tVCSpzz85o3b47mzZtj+PDh0Gg0iIqKwi+//ILU1FScPn2aJVxizM3N8fTp0xKPz83Nle0dB3Ip2s8rbjkfW1tb+Pr6ol27dggICECvXr1kX8DlXtCKc+fOHd3X/sqVKwAAMzMz9OrVC0OGDEGHDh1ETlg+Tp8+jV27dukdXKpatSqys7Mxb968IgfdpGrWrFmlKmGCIEi+tB05cgTz5s3DjRs34ODggDlz5qBt27ZixzKY+vXr4/r16wgJCcHly5fx+++/w9PTE40bNxY7GhGJTIk9VIlzfh67t7ywe/8/dm92b7lh92b3ZveWFnZvaeIJcDKqHTt2YMeOHXrbCj/Erl+/Xu8Hf+EbgdQLqRLn/E9Xr17F6dOncfr0aaSlpeHcuXPIy8tD5cqV0blzZzg7O4sdkUrprbfewpEjR0r8TMTDhw+jWbNmBk5F5WXs2LE4fvw4tFot6tati6FDh8LT0xOurq4wMTHBjRs3ZHvwoZBSClohjUaDAwcOYNeuXYiPj9cdOG7atCkuXbqExYsXo0ePHiKnfH0ZGRnYvXs3QkNDcfnyZQiCAHt7e/j6+sLLywtvvPEGunXrhho1aogdtdwEBQWJHcGoPv30U8TExAAA3nvvPfj5+UGj0SApKeml+0n5s8iBAwdw+vRphIeHIyoqCseOHcOyZcvQpEkTeHl5oVWrVmJHJCKRKLGHKnHO/8TuLT/s3vLG7s3uze7N7i1V7N7s3lLBE+AiunnzZqnGF165LFVKeyMAlDfnrKwspKWl6Qp3WloasrKyIAgCqlWrhvbt22Pq1KlwdnZGy5YtYWJiInZkKoO+ffvi66+/RmRk5Cuvwg0LC0NcXByWLVtmpHT0uo4ePQpra2v4+flh2LBhqF27ttiRjEKJBS0tLQ0hISGIjIzEo0ePYGJiAkdHR3h5ecHLywtarRaenp4wNzcXO2q56Nq1KwoKCqBWqzF+/Hh4eHigdevWutdv3LghYjrD8PX1FTuCUUVHR+t+HRsbi0OHDr10fOHJj7Nnzxo6mkG1bdsWbdu2xRdffIFjx44hIiICBw8exOrVq6FSqaBSqXDo0CGo1WrUq1dP7LhEomD3lj+lzZndWxnYveWN3Zvdm91bPti92b3ZvSsmngAXUdeuXRW1NEZZ3gik/oaotDm7urpCpVJBEARUr14dLi4ucHZ2hrOzMxwcHCT9/Uv/r3///ggLC8Pnn3+O8+fPY9iwYbCzs9Mbk5GRgeDgYGzevBldunRBz549RUpLpTVx4kTs3bsXa9aswU8//YTGjRvD09MTnp6eePvtt8WOZzBKLGgffPABrKys0KlTJ3Tu3Bldu3bVO8ggtznn5+fDysoKtWrVgpWVVZElUpXgzp07OHXqFDIyMgAAdnZ2cHR0RN26dUVOVj6UdvLjeaampujSpQu6dOmC3NxcxMTEYM+ePTh+/Dh+/fVXhISEwNXVFf3798f7778vdlwio2L3fjWpv+8rbc7s3srA7i1v7N7s3oXkNmd2b3ZvuWP3lg6eABeRj48PS0kxnj59iqioKISGhiIpKQl//PGH2JEMTi5z7tGjB1xcXODi4sJlt2TMxMQEa9aswfTp0/HTTz9h7dq1qFu3LmrXrg1TU1Pcv38fV69ehSAI6NmzJ+bPny92ZCqFiRMnYuLEiThz5gwiIiIQGRmJtWvXYt26dahbty6cnJxk+d6lxIJmZWWFJ0+e4M8//0T16tVhbW2Nzp07w8bGRuxoBnHo0CFEREQgIiICy5Ytg0qlQq1atdCtWzd069ZN1lfnXrhwAYGBgUhKSoIgCHpLKZqYmMDJyQn+/v5wcHAQMeXrU9pV9y9jaWkJb29veHt74+HDh4iMjERERAROnDiB+Ph4lnBSHHbv4smlh5aGXObM7q0M7N7yxu7N7s3uLT/s3srD7l2xqQS5P0yEJCM5ORkhISGIiopCTk4OBEFA8+bNERERIXY0g1HinElefvvtN+zevRtpaWm4e/cuBEGAnZ0dnJyc0LdvX7i5uYkdsVyo1Wp8+eWX8PDw0G3LysqCr68vli5dCkdHR73xMTExWLhwoeSX9gGe3QF14sQJREREICYmBo8fPwYA1KtXD/3794ePjw/s7e1FTvn6bt26pStoFy5cKLageXl5YdWqVXrfB1KWm5uL2NhYhIeH49ixY9BqtTA3N0fHjh3RrVs3qNVqDBgwQFZzLnTu3DndQaZbt25BpVLpDkrMmjULH330kdgRy83BgwcxZcoUqFQqeHp6ws3NDXZ2djAzM0NGRgaSkpIQFRWF/Px8LF++HJ6enmJHJgO6du0a9u7di/Hjx4sdhYhEpMQeqsQ5k7ywe7N7s3tLF7s3uze7t/Kwe1cMPAEuEbdu3UJoaCjCwsL0nrEgdXfu3EFYWBhCQ0Nx5coVAICZmRm8vLwwZMgQdOjQQeSE5U+JcyaSOrVaXeyV169aHlMOJfyfNBoNYmNjsWfPHhw5cgR5eXkwMTFBx44d8Z///EfseOVGSQWtUGZmpu4q1dTUVL3Xxo4di/Hjx8PKykqccAaWmJiIiIgIREdHIysrCyqVCvXr10e/fv3g6+uLOnXqiB2xzK5fvw5vb280btwYK1asQIMGDYodd/v2bXz66ae4ePEidu/e/cJxFd3s2bNLvY9KpcKCBQsMkIaIpIrdWz6UOGciqWP3fobdm91bjti92b3ZvcnYeAK8Anv69Cn279+P0NBQJCQkoKCgAGZmZjhz5ozY0V6LRqPBgQMHsGvXLsTHx+uWumnatCkuXbqE5cuXo0ePHiKnLF9KnDMph0ajwblz55CRkaG7Cl2tVqNSpUpiRys3ZflgB8j7mTiPHj1CVFQUIiIicOrUKaSnp4sdySDkXNBe5MaNG4iIiMCePXtw8eJFqFQqWFtbo2fPnujfv3+Ruy7kIi8vD0ePHkV4eDgOHz6Mp0+fSv5z1/z587F7925ERUXpPWOuOJmZmejVqxf69OmDWbNmGSlh+VKr1aXeR6VSSfqAaVnuEFGpVDhw4IAB0hBJF7u3fChxzqQc7N4vxu4tfeze7N5S/tzF7v1q7N4kBp4Ar4BSUlIQGhqKyMhI3dJcderUwcCBA/HBBx+gdu3aYkcsk7S0NISEhCAyMhKPHj2CiYkJHB0d4eXlBS8vL2i1Wnh6espquRclzpmU4969e1i2bBmio6ORk5Oj95qVlRW6d++OqVOnws7OTqSEZCwnT56U/R00cixoJXHu3DmEh4cjMjISt2/flnxhKanHjx8jOjoae/bswYYNG8SOU2Y9e/ZE586dS3wg8dtvv8WhQ4ewb98+AyczjBs3bpRpPyk/g65r165FtgmCgFu3bqFWrVqwsLAodr/Y2FhDRyOSBHZv+fRQJc6ZlIPdmwqxe8sXuze7t5Swez/D7l3xmYkdgJ65c+cOdu/ejZCQEFy5cgWCIMDExAQAMGXKFIwdO1b3e6n64IMPYGVlhU6dOqFz587o2rWr3hVRZf3BWZEpcc6kDKmpqRg3bhyysrLQtm3bYp9rExYWhtjYWKxevRrt27cXOzKV0pkzZ5CamgpBENCiRYtiS/bjx38xGloAAEV+SURBVI+xZMkS7Ny5E3/88YcIKY3H3NwcHh4e8PDw0CtocqdWq6FWq/H5558jISFBEXMGABsbG3h6eiI5OVnsKK/l1q1baNasWYnHN2nSBP/9738NmMiwpFymy6q4Mv3gwQO4u7tj8eLF6NixowipiCo2dm959lAlzpmUgd1b/ti99bF7s3tLEbu3/LF7SxNPgIuocGmukJAQnDhxAlqtFpUqVULXrl3RrVs3ODg4wNfXF82bN5d8AQege4bLn3/+ierVq8Pa2hqdO3eGjY2N2NEMRolzJvm7f/8+PvnkE1SuXBk//vgjnJycih2Xnp6OKVOm4NNPP8Xu3btRs2ZNIyelsvj777/x2Wef4ciRIyhcJEalUsHd3R2rV6/WXdF4+PBhBAQE4M6dO2jYsKGYkY1OLgWttOzt7SV/8Pj69esIDg5GSkoKAKBly5YYM2YMGjVqpDcuOjoa8+bNw7179xAYGChG1HJhaWmJR48elXj8o0ePULVqVQMmEpdGo8HFixdhaWmJJk2aiB3HYF72fEwipWL3ln8PVeKcSf7YveWN3fvV2L2li9375di95YHdu+LjCXARderUCY8ePUK1atXQu3dveHh4oHPnzrCysgIgvyuUT5w4gdjYWISHh2PXrl3YsWMHzM3N0bFjR3Tr1q1Mz46o6JQ4Z5K/rVu3IicnB9u2bUODBg1eOK5ly5YIDg6Gt7c3fvnlF0yaNMmIKamsfvjhBxw+fBidOnWCr68vrK2tceTIEWzfvh3ffvst/P39sWjRImzcuBGmpqYYNWoUPv30U7FjlwulFTQAOH36NFavXq03508++UTvrgNBELBx40Z8//33yM3NFSvqazt79iyGDx+Ox48fw9LSEpaWlkhPT0dkZCS2bduGt956C9nZ2fD390d0dDRMTU0xduxYsWO/ltatWyM6OhqjRo0q0fj9+/ejRYsWBk5lWNnZ2Vi3bh1SUlKwZcsW3faIiAgEBgbqDko0btwYCxYsQLt27URKSkTGxO4t/x6qxDmT/LF7yxu7N7s3uze7t5Sxe5MU8AS4iLKysmBtbY3u3bvD1dUV7du31xVwObK0tESvXr3Qq1cvZGZmIjIyEhERETh69CiOHj0K4NlVM2lpaXB3d5fF34US50zyd+DAAfTp0+elBbxQvXr14Ovri+joaJZwiYiNjYWLiwvWrVun2/buu++iZs2a2LJlC2xtbREcHAy1Wo2goCDJf2AvpMSCduLECYwZMwZarRaNGzeGlZUVkpKSMGLECAQHB8PZ2RnXr1/HtGnTkJaWBhsbG8yZM0fs2GVWeBBh6dKl6N27N4Bnzwv97LPPEBgYiCVLlsDPzw9//fUX2rRpg8DAQDg4OIic+vUMGDAAU6ZMQXBwMEaOHPnSsWvWrEFaWprev32pycnJwaBBg3Dp0iXUqVMH+fn5MDMzQ1paGmbOnAlBEDBo0CA0b94cYWFhGDlyJMLCwoocaCMi+WH3ln8PVeKcSf7YveWN3Zvdm92b3Vuq2L1JMgQSTVJSkvD1118LLi4uglqtFlq0aCF88MEHwvr164UrV64I169fFxwcHIQDBw6IHdWgrl+/LqxevVro3bu34ODgIKjVaqF9+/bCl19+KSQnJ4sdzyCUOGeSj3bt2gnbtm0r8fgdO3YIjo6OBkxE5aldu3bCxo0bi2y/ePGi4ODgILRo0UKYN2+eoNFoREhnOOPHjxdatWol7NmzR7ft9OnTgoeHhzB8+HDhzp07Qvfu3QUHBwdhwIABwrlz50RMWz5GjBghtG/fXu995/bt24KPj48wcOBA4eLFi4K7u7vg4OAgTJo0ScjIyBAx7etzd3cX5s2bV2T7/v37hZYtWwpDhw4VWrduLaxbt07QarUiJDSMSZMmCWq1Wpg2bZqQkpKi929Xq9UKKSkpujFfffWViElf3/fffy+0atVK2Ldvn972sWPHCmq1WggKCtJty83NFby8vIRZs2YZO6bBPXjwQHBwcBDi4uLEjkJUYbB7P6PEHqrEOZN8sHvLG7s3uze7N7u3VLF7P8PuXfHxDnARdejQAR06dMBXX32FI0eOICIiAocPH8bp06exZMkSNGjQACqVCn///bfYUQ2qXr16GD9+PMaPH49z584hPDwckZGR+PXXX7Fr1y6cPXtW7IjlTolzJvkwNzfH06dPSzw+NzcX1tbWBkxE5enJkyeoUaNGke3Vq1cHAHTr1g3+/v7GjmVwaWlpGDx4sO7qZAB4++238fnnn2Pq1KmYOnUqbty4genTp+Pjjz+WxfNBz549iyFDhsDR0VG37Y033sC0adMwZswYTJ48Gfn5+Vi+fDl69uwpYtLykZWVVezyp23atIFWq8X58+exZcsW2S3LtWTJEgQFBWHbtm3Yu3cvTE1NYWtrC1NTU2RmZkKj0cDExASjRo3C1KlTxY77WqKjo9G3b1/06NFDty0nJwfHjx8HAAwdOlS3vVKlSujTpw927txp9JxEZHzs3s8osYcqcc4kH+ze8sbuze7N7t3O+OEMiN2b3ZsqHp4ArwDMzMzg4eEBDw8P5OTkIDo6GhEREUhISIAgCJg5cyZ27dqFfv36oXv37qhUqZLYkQ1GrVZDrVbj888/R0JCAvbs2SN2JINT4pxJ2t566y0cOXIEfn5+JRp/+PBhNGvWzMCpyNBUKhUAwMfHR9wgBqLEgpadnY2mTZsW2d68eXMIgoDMzEzs3LkTDRs2FCFd+cvPzy/2M5SlpSUAYOzYsbL6+haysLBAQEAA/Pz8EBYWhrS0NNy9exeCIKBJkyZwcnKCt7e3LJYiu379ul7RBoCkpCTk5+ejQYMGRb6X69ati/v37xszYrlbuXJlkW25ublQqVTYvXs3Tp06VeR1lUqFTz75xBjxiCocdu//p8QeqsQ5k7SxeysTuze7t9Sxe7N7s3s/w+4tPp4Ar2AqV64MX19f+Pr64v79+9izZw8iIiIQHx+P+Ph4zJs3D0lJSWLHNApXV1e4urqKHcOolDhnkp6+ffvi66+/RmRkJHr16vXSsWFhYYiLi8OyZcuMlI4MrbCwyI0SC5pWq4WZWdGPghYWFgCA8ePHy6aAl0Tr1q3FjmBQjRs3lvxV5q9iYmKCgoICvW0nTpwAALi7uxcZ/+DBA9jY2Bglm6EUV8ILhYWFFbudJZzoGXbv/6fEHqrEOZP0sHsrG7u3fLB762P3lj52b33s3hUXT4BXYDVr1sRHH32Ejz76CFeuXEF4eLikr1Au6RWr/6RSqbBp0yYDpDEOJc6Z5K9///4ICwvD559/jvPnz2PYsGGws7PTG5ORkYHg4GBs3rwZXbp0kcUSTkpSeMV5aV+TM7kXtOIUd4W6nCnhe/vOnTs4deoUMjIyAAB2dnZwdHRE3bp1RU5WPpo1a4aUlBQMGzYMACAIAmJiYqBSqdC1a9ci4w8ePCj57/PNmzeLHYFIFti9pd9DlThnkj92b/lj9y6K3Vv+lPC9ze6tj92bxMIT4BLRqFEjTJo0CZMmTRI7SpklJiYWu12lUkEQhBe+JmVKnDPJn4mJCdasWYPp06fjp59+wtq1a1G3bl3Url0bpqamuH//Pq5evQpBENCzZ0/Mnz9f7MhUSgsWLMDy5cv1tgmCAJVKhenTpxe5WlulUuHAgQPGjGh0/NksD5cuXSpyN192djYA4Pz588Vele/s7GyUbIZ04cIFBAYGIikpCYIg6H0GMTExgZOTE/z9/eHg4CBiytfn4+OD+fPno02bNnjnnXewY8cO3Lx5Ew0bNkSnTp30xq5ZswapqamSf66ii4uL2BGIZIfdW5qUOGeSP3Zv+WP3Loo/m+WB3ZvduxC7N4mJJ8BF5OfnhwkTJqBjx466bfn5+UhJSYFarUaVKlX0xoeHh2PWrFlIT083dtRyce7cuSLbHjx4AHd3dwQHB+v9PciFEudMylClShX89NNP+O2337B7926kpaXhf//7HwRBgJ2dHXx8fNC3b1+4ubmJHZVKyd7eHgCKPVBYeKXq86+96KCi1CixoJ08eRJarVZvW05ODgDg+PHjuHPnTpF9pPwsujVr1mDNmjXFvrZo0aJit589e9aQkQzu4MGDmDJlClQqFXr06AE3NzfY2dnBzMwMGRkZSEpKQlRUFAYMGIDly5fD09NT7MhlNnjwYJw6dQpBQUG6Ex7VqlXDkiVLYGJiAgD49ddfsXbtWly7dg1OTk4YMmSIyKkNS6PR4OLFi7C0tESTJk3EjkMkGnZv+fdQJc6ZlIHdW77Yvdm92b31sXtLB7t3UezeFZNKkMs7pwSp1WosXrwY3t7eum0PHz6Eu7s7NmzYUKSghYeHY+bMmZJ/M/inhw8fomPHjooqpEqcMxFRRadWq194pXnhFfjFkfJ78ovm/M+Phv98vfDvQapz/uGHH8p0N8HEiRMNkMY4rl+/Dm9vbzRu3BgrVqxAgwYNih13+/ZtfPrpp7h48SJ27979wnFSkZKSgpSUFNjY2MDT0xM1atTQvbZy5Urs3r0b3t7eGDduXLHPH5Sa7OxsrFu3DikpKdiyZYtue0REBAIDA/Ho0SMAz55Ft2DBAtk9U5GoJNi9ldlDlThnIqKKjt37/7F762P3lh5272fYvSsu3gFeAfGaBCKSitzcXFhaWhbZfvHiRVStWrXI88mo4gsLC0OHDh1Qv359saMY1SeffKK4pdaCgoLEjmBUUl7Ktqw2bdoEc3NzrF+/Xq+IPq9OnTpYu3YtevXqhZ9//hmzZs0yYsry5+joCEdHx2Jfmzhx4gsPrOTl5SE1NbXYu0ErqpycHAwaNAiXLl1CnTp1kJ+fDzMzM6SlpWHmzJkQBAGDBg1C8+bNERYWhpEjRyIsLAyNGjUSOzpRhcDuTURSwe4tP+zeysHuLX/s3kWxe7N7VwQ8AU5ERKWm0WiwaNEiRERE4OjRo0WK+LJly3D06FH0798fM2fOhLW1tUhJqbRmz56Nb7/9VnElXIkFzdfXV+wIRlXc8rdyd+zYMfj6+r60gBeytbWFj48PDh06JPkSXlZZWVnw8/Mr9m7QimrDhg24evUqvvvuO/To0UO3fdWqVRAEAR999JHu6zlgwAD06dMHa9asUdxBOCIiIqli95Yvdm/lYPeWP3bv0mH3JmMxETsAERFJi0ajwahRo/Dzzz/D3t4eDx8+LDLmvffeg4ODA7Zv347Ro0cjPz9fhKRUFkq9E8rPzw8nTpwQO4ZReXh44ODBg2LHMJrExETcu3dP7BhGdevWLTRr1qzE45s0aYLbt28bMFHFJ7WfgdHR0ejbt69eAc/JycHx48cBAEOHDtVtr1SpEvr06aO4n3VERERSxe4tb1L73Fle2L3lj9371di9pfczkN1bmngCnIiISmXjxo1ISkqCv78/wsLCULdu3SJjBg4ciF27dmHixIlITk7G1q1bRUhKVHJKLGg3btzA33//LXYMMiBLS0vdM6hK4tGjR6hataoBE1F5u379Olq3bq23LSkpCfn5+ahfvz4aNmyo91rdunVx//59Y0YkIiKiMmL3Jjli9yY5YveWP3ZvaeIS6GQ0YWFhRbbl5OQAAI4fP447d+4Uu5+Pj48BUxmWEudM8hcREQEPDw98+OGHrxw7ceJEJCYmYvfu3RgxYoThw1G5yMzMxM2bN0u1j729vYHSEFFZtW7dGtHR0Rg1alSJxu/fvx8tWrQwcCoqTyYmJigoKNDbVniVubu7e5HxDx48gI2NjVGyEZF4lNhDlThnkj92b/lj9yaSB3Zv+WP3liaeABfZ8x90srKyADz7B/L8B6DiljqSklmzZkGlUultK1zqYv369VCpVLrfF/5apVJJupAqcc4kf1euXMHgwYNLPP7dd9/FihUrDJiIytuCBQuwYMGCEo9XqVRIT083YCKi8hETE4MrV66UeLxKpcInn3xiwESGNWDAAEyZMgXBwcEYOXLkS8euWbMGaWlpWLdunZHSUXlo1qwZUlJSMGzYMADPPmfGxMRApVKha9euRcYfPHgQTZs2NXZMogqB3VvePVSJcyb5Y/eWP3Zvkit27xdj95Ymdm9p4glwkb3og8706dNFSGNYQUFBYkcwOiXOmeTP2toaWq22xOMrVaoES0tLAyai8ubk5IQGDRqIHcPolFbQAGDHjh2Ii4sr8XiVSlWqAzQVTUxMDKKjo0s8Xupf4x49esDLywvffvst/vjjD3z44Ydo1aoVzM3NAQAFBQVIS0vDhg0bEBMTg4EDB+Jf//qXyKmpNHx8fDB//ny0adMG77zzDnbs2IGbN2+iYcOG6NSpk97YNWvWIDU1Ff7+/iKlJRIXu7e8KXHOJH/s3vLH7l0yUu9lALv3q0j9a8zuLX/s3tLEE+Ai8vHxKXKFspz5+vq+ckxWVhYsLS1RqVIlIyQyPCXOmeSvSZMmSE5Ohp+fX4nGnzp1CvXq1TNwKipPgwYNgre3t9gxjE5pBQ149ryipKSkEo+XegkfN25csUtTydmSJUsQFBSEbdu2Ye/evTA1NYWtrS1MTU2RmZkJjUYDExMTjBo1ClOnThU7LpXS4MGDcerUKQQFBenuaKxWrRqWLFkCExMTAMCvv/6KtWvX4tq1a3BycsKQIUNETk1kfOzeRcmthypxziR/7N7yx+5dMuze0sPuze4tN+ze0sQT4CJauHCh2BGMLi8vDyEhIUhNTdW7QjsxMRHffPMNLl++DJVKBXd3dwQEBMjiKkglzpnkzdfXFwEBAYiPj4ebm9tLxyYkJCA6OhqTJk0yUjqislNiQfviiy/g4eEhdgyjadq0KVxcXMSOYVQWFhYICAiAn58fwsLCkJaWhrt370IQBDRp0gROTk7w9vZGo0aNxI5KZaBSqbBkyRIMGzYMKSkpsLGxgaenJ2rUqKEbc/v2bQiCgAkTJmDcuHG6ck6kJOzeyuihSpwzyRu7N8kVu7f8sXuze8sNu7c08QS4iEaPHg0fHx94enoqYomivLw8fPzxx0hKSoK5uTnmzZsHMzMzXL58GaNHj0ZeXh46deqEZs2aYf/+/Rg0aBDCw8NRq1YtsaOXmRLnTPLn4+ODXbt2Yfz48Rg3bhwGDhxY5Hs2IyMDO3fuxPr161G/fn0MHTpUpLREJafEgla9enXeJaIQjRs35lXmMubo6AhHR8diX5s4cSImTpxY7Gt5eXlITU2FWq1GlSpVDBmRSFTs3vLvoUqcM8kfuzfJFbs3yRm7t7yxe0sLL0EQUWJiImbMmAF3d3fMmjULcXFxEARB7FgGs3XrVpw8eRIzZsxAUlISzMyeXX/xww8/QKPRwNvbG2vXrsXnn3+OXbt2wdTUFGvWrBE59etR4pxJ/szNzbFq1Sq0bdsWK1asQKdOneDh4YHBgwdj4MCB6Nq1K7p06YIffvgBDg4OCA4O5hu7hNjb28Pa2lrsGEREZARZWVnw8/PDmTNnxI5CZFDs3vLvoUqcM8kfu7e8sXsTESkHu7c4eAe4iE6cOIEDBw5g37592Lt3L3bv3o1atWrB29sbffr0gVqtFjtiudqzZw+6d++OUaNG6bZpNBrExsZCpVLpbbe1tUW/fv2wd+9e+Pv7ixG3XChxzqQMNWvWxKZNmxAdHY29e/ciPT0d58+fh4mJCWrVqgUfHx9069YNXbt2FTsqlVJsbOxLXy8oKMDt27dRq1YtWFhYGCkV0eubOHEiHBwcxI5hVCV9XuQ/qVQqbNq0yQBpKoa7d+/C1tYW5ubmRV6rVq0aNm/ejBYtWoiQTDxyPglIVIjdW/49VIlzJmVg95Yvdm+SK3bvkmH3Zvcmw+MJcBFVrlwZffv2Rd++fZGdnY39+/cjMjISmzZtQnBwMJo1a4a+ffvC29sbb7zxhthxX9tff/0FX19fvW2nTp1Cbm4u7OzsirwxNmzYEBkZGcaMWO6UOGdSFi8vL3h5eYkdg4zowYMH8PDwwIYNG9CxY0ex45QbJRa0oKCgFy7bJEcvWoaq0N9//40NGzbAx8cH9evXN1Iqw0pMTCx2u0qlemHxUqlUhoxkFMHBwdixYwfCw8OLlO0FCxYgLi4Oo0aNwujRo/WeyWVubq64pRiJlILdW/49VIlzJmVh91Yedm/5YPfWx+79/69JHbs3VXQ8AV5BVKlSBQMGDMCAAQPw4MEDREVFYd++fVi2bBmWLVsGZ2dn+Pj4wMvLC5UrVxY7bpkUFBTA1NRUb1t8fDwAwN3dvcj47OxsWFlZGSWboShxzkTAs2VdLC0tUalSJbGjkAHI8YpFJRa05w8SP+/x48eYP38+Ro8ejaZNmxoplXj+/vtvrFq1Ck5OTrL5Gp87d67ItgcPHsDd3R3BwcGyOpAGPPvZNH36dOzduxc1a9bE7du30aBBA70xTZs2RXJyMpYvX44zZ87g+++/FyktEYmF3VuePVSJcyYC2L3ljt1bHr2M3Vsfu7f0sXuTVPAZ4BVQjRo1MHToUGzZsgWHDx/Gl19+CRMTE3z99df417/+JXa8MmvYsCHOnj2rty0mJgYqlQrvvvtukfHHjh1Dw4YNjZTOMJQ4Z1KGvLw8bN++HbNnz9bbnpiYiF69esHNzQ2Ojo4YPXo0rl69KlJKovJTWNCuXbsmdhSjyc3NRVhYmKLujpLjAabnyeEq8xfZvn079u7dixEjRuDw4cNFCjjw7IBbTEwM+vXrh5iYGISEhIiQlIgqCnbvZ+TQQ5U4Z1IGdm9SGnZvZWD3ljZ2b5IKngCv4MzNzWFpaQkbGxuYmZlBo9GIHanMevfujd27d+PAgQN48uQJNm7ciEuXLqFmzZpFnlUUHh6O48ePw8PDQ6S05UOJcyb5y8vLw8cff4yAgADs2bMH+fn5AIDLly9j9OjRuHz5Mjp16oQRI0bg8uXLGDx4MO7duydyaqLXp4SC9jwlzpmk69dff4WLiwtmzZpV7HPGCllYWCAwMBAtWrTAjh07jJiQiCoydm9p91Alzpnkj92blEqJPVSJcybpYvcmqeAS6BXQgwcPEBMTg6ioKCQlJSE/Px8tW7bE5MmT0bt3b7HjldmIESPw22+/YeLEibrnX5ibm2P+/PmwsLAA8OwK7a1btyIxMRGNGzfGiBEjxA39mpQ4Z5K/rVu34uTJk5gxYwaGDRsGM7NnbyU//PADNBoN+vTpg2+//RYAMHbsWHh7e2PNmjXw9/cXMzaVE3Nzczg7O6NatWpiRyEi0vPnn39i8uTJJRqrUqnQo0cPrF692sCpiKgiY/eWTw9V4pxJ/ti9lY3dm4gqKnZvkgqeAK8g7t27h+joaERFReHUqVPQarWoV68eRo0ahT59+sji+R8WFhbYuHEjIiMjkZqaChsbG3h7e6NZs2a6MWfOnEFycjL69OmDWbNmwdLSUsTEr0+Jcyb527NnD7p3745Ro0bptmk0GsTGxkKlUultt7W1Rb9+/bB3716WcInIzs5GlSpVXvh6tWrVsGXLFr1tCQkJcHV1NXQ0IoOqUqUKgoKC0Lx5c7GjUBmZmZnpTnKURNWqVYs8L5aI5I/d+xm59VAlzpnkj91b3ti9SanYvaWP3ZukgifARZSRkYHo6Gjs378fycnJ0Gq1qFatGgYMGABvb2906NBB7IjlztTUFN7e3vD29i729fHjx2Py5MkwMZHP6vxKnDPJ219//QVfX1+9badOnUJubi7s7Ozg4OCg91rDhg0V9RwjqfPz80NwcDBsbW1fOfbp06dYvHgxfvnlF6Snpxs+nIiUWNCqVauGzZs3o0WLFmJHMYpKlSrhX//6F++wkLBGjRrhzJkzJR5/5swZ1K1b14CJiKiiYPcuSo49VIlzJnlj95Y3du/isXvLH7u39LF7k1TwBLiIunTpAuDZlcqenp7w9vZGly5dXvrcBLmzsrISO4LRKXHOJG0FBQVFrtqLj48HALi7uxcZn52dze9zCTl79iw+/PBDbNy4EbVq1XrhuNOnT2PmzJn466+/XjpOLipVqqR38Emr1WLt2rWYMGGCiKkMy9zcHC4uLrrfZ2dnY9GiRQgMDBQx1esLDg7Gjh07EB4eXuQz14IFCxAXF4dRo0Zh9OjRkj9AHhYWVmRbTk4OAOD48eO4c+dOsfv5+PgYMJXhvP/++1i6dClGjhz5ygNmFy5cQEREBPz8/IyUjojExO5dlBI/nytxziRt7N7yxu5dPHZvdm8pYvd+MXZvEpNKEARB7BBKNXz4cPTt2xc9evSAjY2N2HGIiEqkb9++aNu2LebOnavb1qtXL1y+fBnLly9Hjx499MaPHj0aWVlZ2Llzp7GjUhn8/PPPmD9/Pho0aICNGzcWuUIzLy8P33//PTZs2ACtVou+ffviiy++kMWVu9nZ2di5cydSU1MhCAJatmyJYcOGoWrVqnrjfv/9d/j7++N///sfzp49K1La8nH9+nUEBwcjJSUFANCyZUuMGTMGjRo10hsXHR2NefPm4d69e5KdsyAImD59Ovbu3YuaNWti27ZtaNCggd6YlStXYufOncjIyEC3bt3w/fffi5S2fKjVaqhUKr1t//zoX9xrKpVKsl/jv//+G/369UNmZia++OIL9O7du8hB4/z8fOzZswdLliwBAISGhqJ27dpixDWau3fvwtbWttgTfXl5eUhJSUGLFi1eugQnkdSxexORFLF7yxu7N7s3uze7t1S/xuzexWP3rnh4ApyIiEpl7dq1WLVqFZYuXYp33nkH27dvx8KFC1GrVi3ExsbqPQMmPDwcM2fOxOTJkzF+/HgRU1Np7NmzB7NmzYKdnR02btyIhg0bAnh2hfrMmTPxv//9D/b29pgzZw46deokctryce3aNfj5+eH27dt6JaVWrVrYuXMn6tati/z8fCxduhSbN2+GVqtF7969sXTpUhFTv56zZ89i+PDhePz4MSwtLWFpaYnMzExYW1tj27ZteOutt5CdnQ1/f39ER0fD1NQUo0aNwtSpU8WOXibbtm3DN998gxEjRmDatGkvvOtPo9Fgzpw5CAkJwfz589GvXz8jJy0/oaGhZdrv+aU2peTy5cv45JNPcPnyZVhbW6NVq1aoXbs2tFot7t+/jzNnziA3Nxf29vZYtWoV1Gq12JHLxcvurpg6daqs7q4gIiJSCnZv+WP3Zvdm92b3lip2b3ZvKeAJcBEVtzRGSUh1aQwikgeNRoNRo0YhKSkJKpUKgiDA3NwcK1eu1C0vGRMTg61btyIxMRGNGzdGSEgILC0tRU5OpXHkyBFMmTIFNjY2WLduHQ4ePIg1a9YgPz8fQ4YMwbRp01C5cmWxY5abadOmITIyElOnTkX//v1hZWWFI0eOYO7cuWjXrh0WL16MsWPHIjk5Gfb29ggICNB9v0vVhAkT8Ntvv2HRokXo3bs3ACAtLQ2fffYZ7O3tsWTJEvj5+eGvv/5CmzZtEBgYWOQ5g1IyYMAAWFtbY/Pmza8cKwgC+vfvDwsLC2zbts0I6ag8aTQa/Pzzz9i7dy/OnTuH/Px8AM+WFmzXrh28vLwwaNAgvYPGUqXEuyuIyoLdm4ikiN1bGdi92b3Zvdm9pYrdm927ouMJcBEVLo1RuOTFq0h9aQwikg+tVovIyEikpqbCxsYG3t7eaNasme715cuXY8OGDejVqxdmzZqF6tWri5iWyurUqVOYMGECHj9+DEEQ0KhRIwQGBqJDhw5iRyt3nTt3xjvvvIOgoCC97aGhoQgICEDnzp1x4MABDBkyBDNmzIC1tbVIScvPO++8g549e8Lf319ve3R0NKZOnYp27dohLS0NkydPxscffyz5K1cdHR0xefJkjBgxokTj165di9WrV+uWqJO63NzcYg+GXrx4EVWrVoWdnZ0IqYzjwYMHMDU1lcVykc9T4t0VRGXB7k1EUsXurQzs3uze7N7s3nLA7s3uXdGYiR1AyZ5/oycikgpTU1N4e3vD29u72NfHjx+PyZMnS/5Du9I5OTlh8+bNGD16NB48eIC5c+fKsoADwMOHD+Ho6Fhku7OzMzQaDY4cOYIVK1age/fuIqQzjKysrGKXoGrTpg20Wi3Onz+PLVu2oF27dsYPZwBmZmaluuq4atWqRZ5hJUUajQaLFi1CREQEjh49WqSIL1u2DEePHkX//v0xc+ZMWRxgel6NGjXEjmAwv/76K1xcXDBr1qyXjrOwsEBgYCDOnj2LHTt2sIST4rB7E5FUsXsrA7s3uze7N7u3HLB7s3tXNDwBLiIpP+OBiJTLz88PEyZMQMeOHXXb8vPzkZKSArVajSpVqsDKykr3WuGzyHgHjTSp1Wr897//xciRIzFhwgSsWrUKbm5uYscqd3l5eXrft4UKl5obOXKkrAo48OzfbaVKlYpsLyxpY8eOlU0BB4BGjRrhzJkzJR5/5swZ1K1b14CJDO+fy2aq1Wo8fPiwyJzee+893LlzB9u3b8eFCxewefNmmJlJsyKsXLmyTPtNnDixnJMYz59//onJkyeXaKxKpUKPHj2wevVqA6ciqnjYvYlIiti9lYXdm91bLti92b1fhN2bjE2a/8IUSqvVYu3atZgwYYLYUYhIwRITEzFw4EC9bdnZ2fDz88OGDRv0yjlJz+zZs4vd3qhRI1y/fh3jxo1Dz5499ZYPValUWLBggbEiisLFxUXsCEbXunVrsSOUq/fffx9Lly7FyJEj0bx585eOvXDhAiIiIuDn52ekdIaxceNGJCUlwd/fHx9++GGxYwYOHIiBAwdi5cqVWLlyJbZu3VripeoqmpKW8OeXP5ZyCVfq3RVEhsbuTUQVAbu3vLF7F4/dW/rYvdm9C7F7s3uLjSfARZadnY2dO3ciNTUVgiCgZcuWGDZsGKpWrao37vfff4e/vz/+97//sYQTUYUkCILYEagchIaGvvT1p0+fIiwsTG+bEkq4Ej+0luQZqVLywQcfYNu2bRg+fDi++OIL9O7du8jXNT8/H3v27MGSJUtQpUoVyZfwiIgIeHh4vLCA/9PEiRORmJiI3bt3S7aEHzx48JVjsrOz8d133+Hw4cMwMzOT/NdYiXdXEJUVuzcRyQW7tzywexeP3Vv62L1fjt1bmti9pYknwEV07do1+Pn54fbt27oPrzExMfj555+xc+dO1K1bF/n5+Vi6dCk2b94MrVaL3r17i5yaiIjkrCQfYuUqMzMTN2/e1NuWlZUFAHjw4EGR1wDA3t7eKNkM5dKlS0hKStLblp2dDQA4f/58sctxOTs7GyVbebO2tsbq1avxySefYObMmZgzZw5atWqF2rVrQ6vV4v79+zhz5gxyc3Nhb2+PVatWoXbt2mLHfi1XrlzB4MGDSzz+3XffxYoVKwyYyLDq1av30tcjIyOxcOFCZGRkoH379vjmm2/w1ltvGSmdYSjx7gqismD3JiKiiobdm92b3ZvdW6rYvdm9pYInwEX03Xff4fbt25g6dSr69+8PKysrHDlyBHPnzsXcuXOxePFijB07FsnJybC3t0dAQAC6dOkidmwiIpKxV32IlbMFCxa88Gr66dOnF9mmUqmQnp5u6FgGtWbNGqxZs6bY1xYtWlTsdik/U7Bx48YICwvDzz//jL179yI5ORn5+fkAAHNzc7Rr1w5eXl4YNGhQqZa2qqisra2h1WpLPL5SpUq659DJydWrVzFnzhzExcWhWrVqCAwMxIABA8SOVS6UeHcFUVmwexMRUUXD7s3uXYjdm91bLti92b0rGp4AF1FSUhJ8fHwwduxY3baePXsiNzcXAQEBmDVrFpKTkzFkyBDMmDED1tbWIqYlIiIqas2aNYiOjkZISIjYUV6Lr6+v2BGMTsrPXnodFhYWGDlyJEaOHAng2R0GpqamqFatmsjJyl+TJk2QnJxc4tJ16tQpWR2I02g0WLt2LdatWweNRgNfX1/MmDED1atXFztauVHi3RVEZcHuTUREUsfuLV3s3uzez2P3lh52b2niCXARPXz4EI6OjkW2Ozs7Q6PR4MiRI1ixYgW6d+8uQjoiIqJXu3XrlqSvSi4UFBQkdgSjU2oJf16NGjXEjmAwvr6+CAgIQHx8PNzc3F46NiEhAdHR0Zg0aZKR0hlWXFwc5syZgytXrqB58+YICAhAhw4dxI5lEEq7u4KoLNi9iYhI6ti9pYvd+xl272fYvaWL3Vt6eAJcRHl5ebCysiqyvXLlygCAkSNHsoATUYX0/POaXvaspocPHxo1GxFRcVauXFmm/aR8sMLHxwe7du3C+PHjMW7cOAwcOBC1atXSG5ORkYGdO3di/fr1qF+/PoYOHSpS2vJx7949BAUFITIyEpaWlpg2bRpGjhxZ7DP15ERJd1cQlQW7NxFJFbs3EUkNuze7t5yxe0uLShAEQewQSqVWq7F48WJ4e3vrbX/48CE6duyI9evX41//+pdI6YiIiqdWq6FSqYpsFwSh2O2F5HClMhUVEBCAHTt2SP7rq8SCNnv27FLvo1KpXvistopOrVaXaNzzP8ek/r19//59fPbZZ0hISIBKpYK9vb3eEl23bt2CIAho164dli9fjrp164oducy2bt2KFStW4PHjx+jatSv8/f0lPR8iKj/s3kQkReze9E/s3uzeUsHuze5NVFHI+3IMiTM1NRU7AhFREUp8XhPJX0lL+PMFTcolPDQ0tMRj/zlvqZbwgwcPvnJMdnY2vvvuOxw+fBhmZmYlfn5XRVazZk1s2rQJ0dHR2Lt3L9LT03H+/HmYmJigVq1a8PHxQbdu3dC1a1exo762wMBA3a9jY2MRGxv7yn1UKhXS09MNGcuglHgAkcgQ2L2JqCJi9yY5Yvd+OXZv6WL3fjl2bxIDT4CL7PmljICXL2cEAPb29kbJRkRUHCU+r4nkT4kF7dy5c68cc+PGDcybNw+HDx9GlSpVMGXKFMMHM5B69eq99PXIyEgsXLgQGRkZaN++Pb755hu89dZbRkpneF5eXvDy8iqyPSsrC5aWliIkKn9KPEisxAOIRGXF7k1EUsPuTXLE7l08dm92bylh934xdu+KhUugi+hFSxkBL17OSOpXyhARUcVW2isaDx06hPT0dMkvVfUqci9oz9NqtdiwYQN+/PFH5ObmolevXpg9e3aRZ1jJwdWrVzFnzhzExcWhWrVqmD59OgYMGCB2rHKTl5eHkJAQpKam6h1ETUxMxDfffIPLly9DpVLB3d0dX3/9NRo2bChi2tcze/ZsDB48GG3bthU7itHcuHHjlWOKO4D4+eefGyEdUcXB7k1ERBUNu3fx2L3ZvaWK3Vve2L2liSfARVSW538AvAKUiIgMp6TPavonlUol2xIu94JWnJMnT2LOnDm4cOEC3nzzTQQEBKBjx45ixyp3Go0Ga9euxbp166DRaODr64sZM2agevXqYkcrN3l5efj444+RlJQEc3NzpKSkwMzMDJcvX0bfvn2h0WjQuXNnNGvWDPv378eTJ08QHh4u2YMtL3rGr5Ip7QAi0YuwexMRUUXD7q2P3ZvdW8rYvYndu2LiEugiYpkmIqKKZvPmzWJHqBCUUNCe9/DhQ3z77bcICwuDhYUFJk2ahDFjxsDCwkLsaOUuLi4Oc+bMwZUrV9C8eXMEBASgQ4cOYscqd1u3bsXJkycxY8YMDBs2DGZmzz76//DDD9BoNOjTpw++/fZbAMDYsWPh7e2NNWvWwN/fX8zYVA6eP4AYGBgo+wOIRC/D7k1ERBUNu/cz7N7s3nLA7q1c7N4VG0+AVwBPnjzBrl278Ntvv+HcuXPIzMyESqVCjRo1oFar4eHhAW9vb1m+CRIRUcXi4uJS6n1OnjxpgCTiUUpB+6edO3diyZIlyMrKwjvvvIOAgABJL8f1Ivfu3UNQUBAiIyNhaWmJadOmYeTIkbpyKjd79uxB9+7dMWrUKN02jUaD2NhYqFQqve22trbo168f9u7dyxIuYUo8gEhUGuzeRERUUbB7s3uze8sHu7fysHtLgzx/4kjIqVOnMHnyZNy7dw8WFhZo2LAh6tWrh/z8fGRmZuLQoUOIjY3FypUrsXTpUrRv317syERERLh16xZCQ0MRFhaGa9euyWIZNqUVNAA4f/48vvnmG6SmpqJWrVpYtmwZevXqJXYsg9i6dStWrFiBx48fo2vXrvD390fdunXFjmVQf/31F3x9ffW2nTp1Crm5ubCzs4ODg4Peaw0bNkRGRoYxI5a7kydPQqvVlmofHx8fw4QxMiUeQCQqDXZvIiKSInZveWD3Zvf+J3ZvaWP3lg75vqtIwJ9//olRo0bBxsYGS5YsgZeXV5ErzR8/foyoqCh8//33GD16NEJDQ9GoUSOREhMRkZI9ffoU0dHRCAkJQUJCAgRBgEqlQufOncWO9tqUWNAWLVqELVu2QKvV4r333sOUKVNgY2ODmzdvvnQ/e3t7IyUsX4GBgbpfx8bGIjY29pX7qFQqpKenGzKWQRUUFMDU1FRvW3x8PADA3d29yPjs7GxYWVkZJZuh7NixAzt27CjR2MKfYVIv4Uo8gEhUWuzeREQkJeze8sLuze79PHZvaWL3lh5+ZUT0448/wsrKCrt27cIbb7xR7BgbGxsMGDAAnTt3Rt++fbF+/XrMmzfPyEmJiEjJUlNTERISgn379uHx48cAgBo1aqB///4YNGgQ6tWrJ3LC16fEghYcHKz79aFDh3Do0KES7SfVOw6evxpbCRo2bFjk6xUTEwOVSoV33323yPhjx45Jfvm9Dz74AO3atRM7htEo8QAiUVmwexMRkRSwexeP3Vta2L2fYfeWF3ZvaeIJcBElJSWhX79+Lyzg/2RnZwcfHx8cO3bMCMmIiEjpMjIyEBYWhtDQUPz1118QBAFWVlZwd3dHXFwc5s6dCw8PD7FjlhsfHx+oVCqxYxjVxIkTS72PIAgGSGI8gwcPRtu2bcWOYTS9e/fGqlWr0LlzZ7zzzjvYvn07Ll26hFq1aqFr1656Y8PDw3H8+HFMnjxZpLTlo0OHDvD29hY7htEo8QAiUVmwexMRUUXF7i1/7N7yx+4tf+ze0sQT4CJ6+PBhqZZUa9KkCXbu3GnAREREpHT79u1DSEgI4uLioNVqUbVqVXh7e8PLywudOnXC3bt34enpKXbMcrdw4UIAQF5eHi5evIj8/Hw0a9ZM8ktSvYy9vT369etX4vE3btzAF198YcBEhhUaGgp3d3dFlfARI0bgt99+w8SJE6FSqSAIAszNzTF//nzd0r8xMTHYunUrEhMT0bhxY4wYMULc0FQqSry7gqgs2L2JiKiiYfdm934Rdm/pYfeWP3ZvaeIJcBHl5eWV6s29UqVKyMnJMWAiIiJSuqlTp8La2hpDhw6Fh4cHnJ2d9Z5jJOcrtf/zn/9g9erVuvdaCwsLDB06FNOmTZPl83z8/f3x5MkTDBs27JVjd+7ciUWLFvFziMRYWFhg48aNiIyMRGpqKmxsbODt7Y1mzZrpxpw5cwbJycno06cPZs2aBUtLSxETU1ko7e4KorJg9yYiooqG3Zvduzjs3tLE7q0M7N7SI793FCIiIiqz+vXr4/r16wgJCcHly5fx+++/w9PTE40bNxY7mkHt2rULixcvRr169eDj4wMTExMkJCRg48aN0Gq1kr76+kXefPNNBAYG4smTJxg9enSxY+7evQt/f38cPXoU5ubmkl+iS4lMTU3h7e39wqXJxo8fj8mTJ8PExMTIycqfr6+v5J+jVlpKvLuCiIiISA7Yvdm9/4ndW/rYveWN3VuaeAJcZJmZmbh582aJxj58+NDAaYiISOkOHDiA06dPIzw8HFFRUTh27BiWLVuGJk2awMvLC61atRI7okFs374d7dq1w6ZNm1CpUiUAz565NXXqVGzfvh3Tp0/XLVslF7/88gtGjx6NpUuX4smTJ5g0aZLe63v27MG8efOQlZUFR0dHBAYGomnTpiKlLR8nT56EVqst1T4+Pj6GCVNByGmpwaCgILEjEFEFxu5NREQVCbs3u3chdu9n2L2lg92bpEIlCIIgdgilUqvVZVrO5uzZswZIQ0REpE+r1eLYsWOIiIjAwYMH8eTJE937Vv/+/TFhwgTUq1dP5JTlw8nJCZ999lmRJclSU1MxZMgQhIaGQq1Wi5TOcP7++2/8+9//RkJCAkaMGIGZM2fi4cOHCAgIQExMDCwtLTF16lQMHz5c8kvwlfZzlyAIUKlU/NxFFZparcbixYtfeJcBET3D7k1ERBUZuze7N7s3uzdVbOze0sQ7wEXk6+srdgQiIqIXMjU1RZcuXdClSxfk5uYiJiYGe/bswfHjx/Hrr78iJCQErq6u6N+/P95//32x476WJ0+eoEqVKkW2169fH4Ig4NGjRyKkMjxra2usXbsWn332GTZu3Ijr168jOTkZ9+/fxzvvvIO5c+fK5kALAHzwwQdo166d2DGIyhXvriB6NXZvIiKqyNi92b3ZvYkqPnZv6eEd4ERERKQzduxYuLm5wcXFBa1atSr2qt2HDx8iMjISERERSE1NlcWVui+6kvPhw4fo2LEjgoOD0bFjR5HSGV5BQQH8/f0REhICExMTzJ07FwMGDBA7Vrni1bokR7y7goiIiEia2L3Zvdm9iaSD3VuaeAc4ERER6cTHx+Po0aNQqVSwsbFBhw4d4OrqCjc3N90yZNWrV8ewYcMwbNgwXLt2DXv37hU5Nb0uExMTLFiwALa2ttiwYQPi4uLg4+MDMzN+VCSq6Hh3BREREZH0sHsrE7s3kXSxe0sPf7ISERGRTnJyMtLT05GcnIyUlBSkpqbi0KFDUKlUqFq1KpydneHq6gpXV1e89dZbaNCgAcaPHy927HKRmZmJmzdv6m3LysoCADx48KDIawBgb29vlGyGUNx8PvzwQ/z999/Yvn07cnJy8NVXX8HExERvjJTnTCRHHTp04N0VRERERBLD7s3uze5NJC3s3tLDJdCJiIjopW7duqVXys+dOwetVgtbW1u4uLjA1dUVQ4cOFTvma3nZUkaFyxY9T6VSIT093dDRDOZVcwZQ5HUpz3n27NkYPHgw2rZtK3YUonLD5QWJiIiI5IPdm927kJTnzO5NcsTuLU28A5yIiIheqm7duujduzd69+4NAMjOzkZ4eDhCQkKwf/9+REdHS76E+/r6ih3B6Hx8fEr1/CKpCwoKEjsCERERERHRC7F7yxO7NxGROHgCnIiIiF4qNzcXSUlJSExMxKlTp3DmzBnk5eXBwsJCtySb1CmxoC1cuFDsCET0mnx9fdGwYUOxYxARERFROWD3lid2byLpY/eWJi6BTkRERHry8/ORmpqK+Ph4xMfH4/Tp08jLy4O5uTnefvttXfF2dHSEhYWF2HGJiIiIiIiIJIfdm4iIyHB4ApyIiIh0xowZg5MnTyI3NxcmJiZo1aoV3Nzc4OrqCicnJ1haWoodkYiIiIiIiEjS2L2JiIgMiyfAiYiISEetVsPc3Bzvv/8+xo0bhzfffFPsSERERERERESywu5NRERkWDwBTkRERDpfffUVEhIScPXqVahUKjRp0gQdO3aEm5sbnJ2dUa1aNbEjEhEREREREUkauzcREZFh8QQ4ERERFXHz5k3ExcXpnkV27949mJiYQK1Ww9XVVVfKra2txY5KREREREREJEns3kRERIbBE+BERET0ShcuXEB8fDxOnDiBkydP4tGjRzAzM0Pr1q3RsWNHTJ48WeyIRERERERERJLG7k1ERFQ+eAKciIiISkWj0SAqKgq//PILUlNToVKpcPbsWbFjEREREREREckGuzcREVHZmYkdgIiIiCq2q1ev4vTp0zh9+jTS0tJw7tw55OXloXLlyujcuTOcnZ3FjkhEREREREQkaezeRERE5Yd3gBMREZFOVlYW0tLSdIU7LS0NWVlZEAQB1apVQ/v27eHi4gJnZ2e0bNkSJiYmYkcmIiIiIiIikhR2byIiIsPiCXAiIiLSUavVUKlUEAQB1atXh7Ozs+4/BwcHqFQqsSMSERERERERSRq7NxERkWFxCXQiIiLS6dGjB1xcXODi4oJmzZqJHYeIiIiIiIhIdti9iYiIDIt3gBMRERERERERERERERERkSzw4SFERERERERERERERERERCQLPAFORERERERERERERERERESywBPgREREREREREREREREREQkCzwBTkREREREREREREREREREsmAmdgAiIiIl+OGHH7By5cpS7XPw4EHUr1/fQInKrmvXrrhx4wYA4Msvv4Sfn99Lx48aNQrHjh0DAAQHB8Pd3d2guaKjo9GoUSOD/BlERERERERUcbF7s3sTEREBPAFORERkFA4ODvD29tbbdv/+fcTFxcHa2hoeHh5F9rG2tjZWvDKLiop6aQl/8OAB4uPjjZiIiIiIiIiIlIrdm4iIiACeACciIjIKLy8veHl56W1LSEhAXFwcqlevjiVLloiUrOyqVq2K5ORk3LlzB2+88UaxY6Kjo5Gfnw9zc3Pk5eUZOSEREREREREpCbs3uzcRERHAZ4ATERFRGXl6ekIQBERHR79wzN69e2Fra4s2bdoYMRkRERERERGRPLB7ExERlR5PgBMREVVghw8fxqhRo+Di4oI2bdqge/fuWLx4MTIzM/XGXb9+HQ4ODujcuXOx/5/hw4fDwcEBCQkJum2zZs2Cg4MDEhMTMWnSJLz99ttwc3PDli1bSpStR48eAJ4txVacjIwMnDx5Et27d4eZ2YsXndm9ezeGDh2K9u3b4+2334a3tzdWr16NJ0+eFDv+xIkTGDlyJJydndGhQwdMmTJF91y04mi1Wvz3v//FwIED4ejoCEdHRwwaNAihoaEQBKFEcyUiIiIiIiL5Yvcuit2biIikjEugExERVVBLlizBunXrYGpqCicnJ1SvXh2pqalYv3499u3bh02bNqFBgwav/ed89dVXuH//Pjp16oQLFy5ArVaXaL8333wTLVq0QHJyMjIyMmBnZ6f3elRUFAoKCtC7d2+sXLmyyP4FBQWYMWMG9uzZAwsLC7i4uMDKygpJSUn47rvvsH//fgQHB6N69eq6fXbu3Imvv/4aANChQwdUrVoVx44dw8mTJ6HRaIr8GXl5efj3v/+No0ePwsbGBo6OjjA3N0diYiJmzZqFhIQELFy4sDR/XURERERERCQj7N7s3kREJD88AU5ERFQBxcbGYt26dbC1tcX69et1y5hpNBrMnTsXO3fuxOTJk7Fr1y6oVKrX+rMyMjIQHh6OBg0aoKCgACYmJV8gplevXjh79iyio6Px4Ycf6r0WGRkJOzs7ODs7F7vv1q1bsWfPHjRo0AAbNmxAw4YNAQCPHz/GtGnTcPjwYXz99df44YcfAAC3bt1CYGAgzMzMsHbtWnTs2BEA8ODBA4waNQrp6elF/owff/wRR48ehYuLC1asWIEaNWoAAO7du4cxY8YgNDQUTk5OGDhwYInnTERERERERPLA7s3uTURE8sQl0ImIiCqgjRs3AgA+//xzvWd4WVhY4JtvvkGjRo3wxx9/ID4+/rX/LA8PD93V7KUp4ADQs2dPAEWXYrt58yZSU1PRs2fPF/4/N23aBAAIDAzUFXAAsLGxwZIlS1ClShVER0fjypUrAIDQ0FDk5uZi8ODBugIOADVq1MCCBQuK/P81Gg22bNkCc3NzLFmyRFfAAaBWrVqYO3cuAOA///lPqeZMRERERERE8sDuze5NRETyxBPgREREFUx+fj6Sk5OhUqnQvXv3Iq+bmZnBy8sLAPSeK1ZWb731Vpn3bdCgAVq1aoVTp04hIyNDtz0yMhKCIOD9998vdr9bt27h+vXrqF69Otzc3Iq8XqVKFXTq1AkAkJiYCABISkoCAHTp0qXI+BYtWqB+/fp62/744w9kZ2ejSZMmeOONN4rs06ZNG9SsWROXL1/G3bt3SzhjIiIiIiIikgN2b3ZvIiKSLy6BTkREVMFkZmYiLy8P1atXh42NTbFjCgtneZTHatWqvdb+vXr1wh9//IGYmBgMGzYMwLMS3qBBA7z99tvF7lNY2OvVq/fC/+/zcyzcp06dOi8cf/36dd3vb926BQA4f/48HBwcXjqHW7duoXbt2i8dQ0RERERERPLB7v0MuzcREckRT4ATERFVMIIgAMBLny9WOMbCwqJE/0+tVvvC10q79NrzevbsicWLF2Pfvn0YNmwYrly5gj/++APjxo174T5lmeOrnrdmZqb/saagoAAAYG9vDycnp5fuW7ly5Ze+TkRERERERPLC7q0/ht2biIjkhCfAiYiIKhhbW1uYm5sjMzMTjx8/LvZK9GvXrgEAatasCeD/i3Rh8XxeVlaWgdI+u5L87bffxqlTp3D37l1ERkYCAHr37v3Cfezs7ABA76rx5xXOsVatWgCAN954AxcuXMCNGzfQrFmzIuP/uQwcAN1V5XXq1MGSJUtKMSMiIiIiIiKSO3bvZ9i9iYhIjvgMcCIiogrG3Nwcjo6OKCgoQExMTJHX8/PzddtdXV0BANbW1gCele28vDy98Q8fPsTly5cNmrlXr14oKChAdHQ09u3bh2bNmr106TN7e3vUq1cPDx8+1D1n7J+ys7Nx7NgxAICzszMAwN3dHQCK/Tu5du0aLl68qLetTZs2sLS0xLlz54oUdAC4c+cOevbsiZEjRyInJ6fkkyUiIiIiIiLJY/dm9yYiIvniCXAiIqIK6KOPPgIAfPvtt0hPT9dtz8vLw5w5c3D16lW0aNFCt7yYra0t6tSpA41Gg23btunGP336FF9//fVLl2ErDz169IBKpcLWrVtx/vz5l16BXqhwjv7+/rorzgEgJycHM2bMwOPHj/Hee+/pnlXm6+sLW1tb7Nq1C/v379eNf/z4Mb744osiV+BbW1vjgw8+wN9//40ZM2bg/v37en/G7NmzcenSJVhbW3MZNiIiIiIiIgVi92b3JiIieeIS6ERERBWQp6cnPv74Y2zYsAEDBgyAk5MTqlevjtOnT+P27duoV68eli9frvcMsdGjRyMwMBCBgYHYu3cvatWqheTkZGi1Wrz33ns4dOiQwfLWrVsX7dq1Q0pKCoCXL8FWaPjw4UhJScG+ffvQu3dvODs7w8rKCidPnsTDhw+hVquxYMEC3fgaNWpgwYIFmDJlCj799FM4OjrCzs4OSUlJ0Gq1aNy4cZGr7adNm4azZ88iPj4e3bp1Q5s2bWBlZYWUlBRkZmbizTffxJw5c8r3L4OIiIiIiIgkgd2b3ZuIiOSJd4ATERFVUDNnzsSPP/4IV1dXnDt3DocPH0blypUxYcIEhIaGonHjxnrjhw8fjkWLFqF169ZIT09HUlISXF1d8euvvxYZawg9e/YEALRq1QqNGjV65XgTExMsX74cQUFBaNWqFZKTk3H8+HHUqVMHM2bMwI4dO1CjRg29fTw8PPDLL7/Aw8MDly9fxm+//YaWLVvi559/Rp06dYr8GZaWltiwYQO+/PJLNGnSBGlpaUhISICdnR0mTZqEnTt36p5zRkRERERERMrD7s3uTURE8qMSBEEQOwQREREREREREREREREREdHr4h3gREREREREREREREREREQkCzwBTkREREREREREREREREREssAT4EREREREREREREREREREJAs8AU5ERERERERERERERERERLLAE+BERERERERERERERERERCQLPAFORERERERERERERERERESywBPgREREREREREREREREREQkCzwBTkREREREREREREREREREssAT4EREREREREREREREREREJAs8AU5ERERERERERERERERERLLAE+BERERERERERERERERERCQLPAFORERERERERERERERERESywBPgREREREREREREREREREQkC/8Hvr6aqZmEdGEAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 179 coefficients adjusted\n",
-      "\t 658 coefficients converged\n",
-      "\t 99 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-37.934870</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-39.934870</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-37.390562</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-39.390562</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-38.865285</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-38.865285</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-36.645393</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-0.727049</td>\n",
-       "      <td>-37.372441</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-24.875688</td>\n",
-       "      <td>139.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-26.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-24.246255</td>\n",
-       "      <td>116.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-26.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>219.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -37.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -37.390562   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -38.865285   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -36.645393   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -24.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -24.246255   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -22.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -24.970666   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -22.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "541         108.0            0.0          -2 -39.934870      False  \n",
-       "543         108.0            0.0          -2 -39.390562      False  \n",
-       "540           8.0            0.0        <NA> -38.865285       True  \n",
-       "544          60.0           29.0   -0.727049 -37.372441       True  \n",
-       "698         139.0            0.0          -2 -26.875688      False  \n",
-       "695         116.0            0.0          -2 -26.246255      False  \n",
-       "676         219.0            0.0          -2 -24.970666      False  \n",
-       "677          20.0            0.0        <NA> -24.970666       True  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  \n",
-       "675          68.0            0.0        <NA> -22.970666       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_6\n",
-      "ActivitySim run started at:  2023-09-13 03:38:57.140971\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 04:26:17.760735\n",
-      "Run Time:  2840.62 secs =  47.343666666666664  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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cuXKqv6tdu7a7Ssx2dtvGdhQUFGS7S5LZbc6hoaGqVatWiks22cnFixfl7++f6izAjRs3av/+/XrmmWc88n5NGbFjx45M/V2dOnWyuRL3Seu+oPdipe+niIgI9e7dW1evXpXD4VDv3r3Vr18/s8uCByJ7WzuHpsXqcyZ7/4+VcxnZ+zYrb2M7slsOlew3Z7I32ftOyN7ey1uzNw1wE02aNClTf9e3b99srsS9IiMjNX36dG3evFnXr19PsS5Pnjxq3LixXn311Ux9qHgqO87ZTu519P2dBAcHZ3Ml7nGnHYjSnXciSvLqL3w7beO/nhmVEd68k0WyZ0DbsWOHypcv79WX/wTszo47HiTp2rVrGjdunL777jvlypVL3bt316pVq/TLL7+ofv36GjVqlEqVKmV2mfAgZG/75FA7ztlO7JTLJLJ3RnjjNiZ7k70BeA+yt3dmbxrgcKsZM2Zo/Pjxcjqdql69uipWrKh8+fIpPj5ely5d0oEDB3Tw4EE5nU4NGjRIISEhZpecZXacc3pNnTpV4eHhWrRokdmluF1cXJxy5sxpdhmZYtcdiBnlrdv4bjtZ7sabd7LAHu50r8y7scqlQpG2+fPna9OmTZn+XoM5NmzYoOHDhys6Olr16tXTyJEjVbZsWd26dUuffvqpZsyYIT8/P/3zn/9U586dzS4XMI0dc6gd55xeZG/vy2US2Tu9vHUbk71hVWRv/B3Z2ztZIXvTAIfbbNy4UT179lT9+vU1evRolSxZMs1xx48f14gRI7RlyxbNmDFD9evXd3Ol2ceOc86I4cOHa8GCBV7/433SpEkZCpiRkZEaMmSIli9f7sKqkJ3stI0/++yzTIVwb9/JYseAlpmdvg6HQ7NmzXJBNa5nx8tV3WunWq5cuZQ3b15VqlRJzZo1U8eOHeV0Ot1Yobms8Dvk1KlTKlSoUKrL693JiRMntGvXLrVr1861hbnI22+/reXLlysgIEDvvPOOOnXqlGrMnj17NGjQIJ04cUJ16tTRBx98YKnLHgPpYcccasc5Z4QVvvMke+Uyu7LTNiZ7px/Z27uQvVMje3v/7xCyt3dmbxrgJrLbF/6rr76qc+fOadGiRcqRI8ddx8bHx6tdu3a6//77NXnyZDdVmP3sOOeMsMKXn3T7R063bt30z3/+867j4uPjNWnSJE2fPl0JCQleP+/0io2N1YULFzz6cij3wja2PrsGtIzy5jnb8V6ZL7300l3XJyQk6MqVKzp+/Lhu3bqlxx57TFOnTpWvr6+bKjSXFX6HPPTQQxo3blyK+wlev35dI0eO1Ouvv67y5cunGL9kyRINGjTIa+ccFBSkhg0bauTIkSpRosQdx8XGxurDDz/U/PnzFRAQoN27d7uxSngisvedWSWH2nHOGWGF7zyJXHYvZG94A7vlUMl+cyZ7p0b29v7fIWTvtHl69rbHO8xDZeS+Nn89gshbQ/iBAwf0yiuv3DOMSpKvr69atmypJUuWuKEy17HjnO2oVq1amjFjhm7cuKH33nsvzTGHDx/WoEGDdPjwYeXPn1//+te/3Fxl9mnSpIneffddNWnSJHlZXFycVqxYoccff1xFihRJMT48PNyrv/Al+21jO1q7dq3ZJbjdoUOHzC7Brbw5TGfWnDlz0jUuNjZW8+bN07hx4zRv3rx7hnd4jrSOZb5586bCwsLUpk2bVCHc240ZMyZd9/j08/PT+++/r6effprvY0gie9+NVXKoHedsR3bLZWRv629jOyJ7Wx/Z+87I3t6L7J02T8/eNMBNlJ4vv6ioKI0cOVIbNmxQ3rx5NWDAANcX5iIxMTEqVqxYuseXLFlSf/75pwsrcj07ztmOZsyYof79+2vevHm6ceOGRo8enbzjzDAMTZs2TZMnT1ZcXJxatWqloUOHqlChQiZXnXlRUVG6fv16imUxMTEaMmSIZsyYkSqEW4HdtrEkHT16VHv27FH79u2Tl507d06TJk3S7t275e/vryZNmuiVV17xyvut/Z0dAxpSiouL02+//SY/Pz+VK1fO7HLcys/PT6+++qr27dun0NBQQrgFWPUiX+kJ4H/14IMPqmPHji6qBt6E7H13VsihdpyzHdktl5G9rb+NJbI37IfsTfa2ErL3bZ6avWmAe6iEhATNmDFDn3/+uWJjY/XMM89oyJAhXv3jNj4+PkM/1Hx9fRUXF+fCilzPjnO2o5w5c2ry5MkaMmSIQkNDFRsbq/Hjx+v48eMaPHiwIiMjVbRoUb3//vtq3Lix2eW6jFW/8CX7bePx48drxowZMgxDwcHBcjqdunr1qjp16qRTp06pQIECKl26tD755BOtW7dOc+bMSdfZNt7MjgEtOjpakZGR8vPzU+3atdN9nyNPdvXqVX355Zfas2dPiiO0ly5dqlGjRunKlSuSbv9wHz16tKpVq2ZSpeaoW7euNm/ebHYZQJbcvHlTq1at0qJFi7Rjxw4ZhqE+ffqYXRY8GNnbGjnUjnO2I7vlsjshe1tnG5O9UyN7k73tgOwNK/CG7E0D3APt3LlTI0aM0K+//qoHHnhAw4cPV/369c0uC7inSZMmZWj8zz//7KJK3M/Hx0fjxo1Tvnz5NHfuXJ08eVK//vqrYmNj1bFjRw0aNEh58uQxu0xkgV228erVqzV9+nQ9+eSTCgkJkdPplCR9/vnnioqKUtWqVfXVV1/J399fkZGReumll/TVV1+pe/fuJleedXYMaFFRUZowYYL27NmjdevWJS+fNm2aPv30UyUkJMgwDOXPn18jR45Us2bNTKw2a2JiYtSpUycdPXpUJUqUUHx8vHx9fRUZGalBgwbJMAx16tRJFStWVFhYmF599VWFhYXp/vvvN7t0t/Hx8VFiYqLZZWRaWFhYhsb//vvvrikEptizZ48WLVqk77//XjExMTIMQ/fdd586depkdmnwYGRveCuyt/VzmZ3ZZRuTvcneEtmb7O2dyN725k3Zmwa4B7l48aLGjRunsLAw5cyZU/369VP37t0tcXmbJDt37lRCQkK6xu7evdvF1biHneac0RAupbzHnhUMHTpUBQoU0KRJk+R0OvXFF1+oUaNGZpeFbGT1bTx//nxVrVpVU6dOTV5mGIaWLFkih8OhN998U/7+/pKkKlWqqG3btlq+fLnXh3A7BrTz58+rU6dOunjxoqpUqZI85x9//FETJkyQr6+v3nrrLVWqVEkLFizQW2+9pW+++UaVK1c2u/RMmTFjho4fP67//Oc/atGiRfLyyZMnyzAMvfzyyxo8eLAkqUOHDmrTpo2mTp2qMWPGmFWy2+3bt08lS5Y0u4xMGzx4cIZ+VxiGYbnfIXYTHR2txYsXa9GiRTp27FjyGXF169ZV9+7d9fjjj5tcITwV2Tslb8+hSew0Z7K39XMZrL+Nyd5kb7I32dtbkb3tx1uzNw1wD/Htt99q/Pjxunz5sh577DENHz5c9913n9llZbsFCxZowYIF6RprlQ9GO8159uzZZpfgEfr27auCBQtq1KhRmj59umrWrGmJo5PxP1bexvv3708VqA8cOKDz588rT548qlu3bop1jz76qJYuXerOEl3CjgFt2rRpiomJ0ddff53iiPpp06bJ4XCoT58+ya+FJ554Qu3bt9eXX36p//znP+YUnEXh4eFq27Ztiu0bExOTfNmxLl26JC/PlSuX2rRpo2+//dbtdZpl5cqVWrx4sV5//XWzS8k0b34/Iv3i4uK0Zs0aLVq0SFu3blVCQoJ8fHxUp04d1axZU1OmTFFISIjHBnCYj+ydmrfn0CR2mjPZ+zYr5zLcZuVtTPYme5O9yd7eypvfj0g/K2RvGuAmO3z4sN5//33t3btXRYoU0YQJE9SqVSuzy3IJO34w2m3OderUyfDf7Ny50wWVuFdERESqZZUqVVLHjh21YMECvfzyy3rnnXeSL2eVpHbt2u4qEVlkp218/fp15c+fP8Wybdu2Sbo9Hx8fnxTr4uPjvXrnYRI7BrQNGzaoffv2KQL4pUuXtGvXLknSc889l7zc4XCoZcuWmjVrlrvLzDYnT55MsR2l2+/t+Ph4lS1bNlXzo2TJkjp//rw7S8x2Q4YMuev6hIQExcTE6LffftPx48f1wAMPeHUIDw4ONrsEU6xevVrHjh1L/u/Y2Fg5HA4tXrw4+f2c5JdffnF3edlqxIgRWrFihS5fvqxcuXKpYcOGatq0qZo0aaKCBQsqKipKn3/+udllwkORva3NbnMme/+PVXOZXdlpG5O9yd5k79vI3t6H7H0b2dvzszcNcBONHTtWc+bMUUJCgp566ikNGDBAefLk0alTp+76d6VKlXJThdnLjh+Mdpxzepw+fVqhoaEKCwvTiRMndPDgQbNLypKXXnrpriFk//79euWVV1It9+Z5Hz16NEUwvXr1qqTbOxZ9fVN+tRw5csSttbmCnbZxsWLFdOLEiRTLNmzYIIfDoSeeeCLV+MjISBUvXtxd5bmMHQPan3/+qUqVKqVYtn37diUmJqpChQqptmvhwoV1+fJld5aYrZxOZ6p7bG3dulWS1KBBg1TjL1y44PVnl4SGhqZrXNmyZfXyyy+rd+/eXj/nO0nr93XJkiUtsxMxPDw81fI73ZfNm+c8b948BQQEqFevXnr99dct+3pF9iN7W58d55weZO/bvHneZO+UrLSNyd7/Q/Yme3v773qy9/+Qvf/Hm+dslexNA9xEM2fOTP739evXa/369en6O2/8UZcZFy5c0JEjR7zyKM7MsvKcb968qfDwcC1atEjbt29PvuRcWj/qvU2fPn28+gstM6ZOnZriPlVJxo4dm2qZt19eULLXNn7iiSe0cOFCdenSRcWLF9eePXu0c+dO5ciRQ82bN08x9tdff9Xy5cv1/PPPm1Rt9rFjQMuVK5du3LiRYtmWLVvkcDj02GOPpRr/559/Kl++fO4qL9tVqFBBe/bsUdeuXSXd/mxavXq1HA6HGjdunGr82rVrVb58eXeXma3Wrl171/W5cuVSvnz5LHXPW0lauHChvv32W33++ecqVKiQLl68qMaNG6f6HO/fv7/eeOMNk6rMHna7BG5wcLDWrFmjL774QrNmzVKtWrWSj0IvUqSI2eXBg5G9787KOfROrDxnsre1kL2ti+z9P2TvlMje3ofsTfa2GqtkbxrgJurbt6/ZJbjVQw89pHHjxql169bJy2JjYzV9+nS1a9dOZcqUSTF+06ZNGjRokFfvdLDjnP9u7969WrRokb7//ntdu3ZNklSoUCG1b99enTp1UunSpU2uMOv69etndgluZadAmsRO27h3794KDw9Xy5YtVa5cOf3yyy8yDEN9+vRRoUKFJN0O3+Hh4Zo9e7Zy5Mihbt26mVx11tkxoAUFBWnr1q16+eWXJf3v3j6S1LRp0xRjDcPQypUrFRQU5PY6s0u7du30wQcf6NFHH9Vjjz2mBQsW6NSpU7rvvvvUsGHDFGOnTp2qvXv3aujQoSZVmz2s8B2bUW+++aZWrVqlUqVK6dSpU8mfW5LUpk0blS1bVpK0ePFiTZkyRR06dFDRokXNKjfLMnMJ3Pj4eBdU4h5jxozRiBEjtH79ei1dulQ//PCDfvzxR40YMULVq1dXjRo1bPcbBelD9rZ+DrXjnP+O7G09ZG9rI3uTvSWyN9nbe5G9743sbT4a4CayWwg3DCPVshs3bmjy5MmqWbNmqkBqBXacsySdOXNGYWFhCg0N1R9//CHDMOTv768GDRpoy5Yt+ve//60mTZqYXSYyyU6B1I6KFCmi7777TpMnT9bevXv18MMPq3379urYsWPymNDQUM2YMUOlS5fWxx9/rJIlS5pYcfawY0Dr3Lmz3nrrLY0ePVqPPfaYFi5cqPPnz+vhhx9OcTZUbGysPvzwQ/36669evcOlc+fO2rVrl8aMGSOHwyHDMJQ/f36NHz8++R6C3333naZNm6YTJ06oZs2aeuGFF0yu2rUiIyO1Z88eOZ1O1alTR4GBgWaXlCVLlizRqlWr1LNnT/Xv3z/VfRPbtWun+vXrS5IaNWqk559/Xt98841lfpPHxMTIMIy7niGzZ88eDRs2TMuWLXNjZdkrZ86cat68uZo3b66rV69q5cqVWrJkiXbt2qVdu3bJ4XDoiy++0LVr19S8eXP5+fmZXTI8gFXe5+llxxxqxzlLZG+rI3tbG9mb7E32Jnt7K7I32dtbsjcNcA8SFxenQ4cO6cyZMzIMQ8WKFVNQUJBy5cpldmkulVZQtTqrzvn777/XokWLtGXLFiUkJChfvnxq3bq1mjVrpoYNG+rs2bOpjmy0gkmTJmX4bxwOh/r06eOCalwvJCREb7zxRvIPGTuw2zYuUaKERo4cecf17du3V+PGjVWjRo3k8OLt7BjQWrVqpcOHD2v69OmaM2eODMNQmTJlNHHixOQx//d//6fPP/9cMTExatGihdq2bWtixVnjcDg0fvx4de3aVXv27FGePHnUtGnTFEcp//nnnzIMQ2+88YZ69uxpidf3gQMH9MUXX+jIkSO677771KtXL1WpUkX/+te/tGjRouTfJA6HQ88++6zGjBmT6n6S3iI0NFTVq1fXwIED7zk2aYfbxo0bvT6Er1q1SpMmTdJvv/0m6fZ95fr3769nn302ecz169c1YcIEzZs3L9UlJ73JkCFD1LlzZ1WtWlWSlDdvXnXs2FEdO3ZUdHS0li1bpmXLlikyMlI//fSTRo4cqVatWunf//63yZXD05C97cOqcyZ7p5835zKyd/p48zYme5O9k5C9vf/1TfZOG9nbO1kle3vnO8xizp07pwkTJig8PFwxMTEp1vn7+6t58+YaOHCgihUrZlKFQPoMHDhQAQEB6tKli5o0aaLatWunOALMGy6LkRl2C2g7duxIcUSyHdhtG9+Lt19+LC12DWgDBw7UCy+8oH379ilPnjyqU6eOcuTIkbw+V65cevTRR9W6dWs999xzJlaafapXr67q1aunua5v375eH8j+as+ePQoJCZGvr68qVaqk/fv3q2vXrnrppZe0cOFCtWnTRi1atND169e1bt06LVu2TA899JBee+01s0vPlAMHDqhXr17pHv/4449r8uTJLqzI9VasWKG33npLuXLl0uOPPy5/f3/t3LlT//znP5PvH/nTTz9p4MCBOnnypMqUKaMRI0aYXXamhYaGqkGDBskh/K+KFy+ubt26qVu3bjpy5IiWLFmipUuX6ttvv/W4EA7zkL1hFWTv9PPmXEb2Th9v3sb3QvYme3szsjfZOwnZ2/tYJXvTADfZ3r171bNnT12+fFlVq1ZVvXr1VKxYMfn6+urMmTOKiIhQWFiY1q1bpylTpqhGjRpmlwzcUZkyZXTy5EktWrRIv//+u3766Sc1bdpUDz74oNmludTs2bPNLgEuZqdtHBERkam/++tlu7yZnQJakhIlSqhEiRJprnvxxRf14osvurkiZJfJkyfrgQce0OzZs1WwYEEZhqF3331XM2fOVJs2bTRu3Ljksc8++6yuXLmiZcuWeW0Ij42NVb58+VItz5s3r6ZOnaqHHnooxfLcuXN79T25JGnu3LkqXLiw5s+fn3yPtRs3buiNN97QZ599pmLFium1117TzZs39eqrr+rNN9/0yMuSZbfy5ctr4MCBGjhwoHbv3m12OfAQZG9YCdkbVmWnbUz2Jnv/Fdnbu5G9byN7k709DQ1wE50/f159+vRR7ty59fnnn6tmzZppjjtw4IAGDBig/v37a/HixSpcuLCbKwXSZ82aNdq3b5+WLFmilStXatOmTZowYYLKlSunZs2aqXLlymaX6BJ16tS555irV6/K4XDc9d4g8Fzp2cZW8dJLL2X4jBGHw6EDBw64qCIgewwZMiTDf+NwODR69GgXVOMeP/30k15//XUVLFhQ0u35dOvWTaGhoXryySdTjW/WrJk+/PBDN1eZfYoXL65Tp06lWu7r65vmfP/44w+vv4/ikSNH9NJLLyUHcOn2Wax9+/bViy++qIEDB6pw4cIaP368qlWrZl6hJqKJCYnsDeshe98Z2du7kb3vjuwNb0D2Jnv/HdnbHjwxe9MAN9HcuXMVExOT4qiRtDz88MOaOXOmWrdurf/+97/q16+fG6sEMqZq1aqqWrWq3n33XW3atElLly7V2rVrNWXKFDkcDjkcDq1fv15BQUEqXbq02eVmG8Mw9MMPP+i3337T/fffryeffFK+vr7aunWrRo0apaNHj0qSHnroIb311lt6/PHHTa44a1avXq1jx46le7yVL0mWxCo7WsaMGZOucatWrdKGDRskSVWqVHFhRe5hx4AWEhKS4b9xOByaNWuWC6pxvdDQ0HSP/euOKG/exleuXFGRIkVSLEu6tGCBAgVSjffz89ONGzfcUZpLVKlSRStWrFCfPn3ueZnEuLg4rVixQo0aNXJTda5x9epVlSlTJtXy++67T9LtSynOnz8/eUeMFezcuVMJCQkZ+pt27dq5phh4DbI3rIjsTfZOC9nbe5C904/s7V3I3reRvW8je3svK2RvGuAmWrNmjdq0aXPXAJ6kdOnSCg4OVnh4uFeH8L+/aZLuu7Z582ZFR0enGOuJl0zIDDvOWZJ8fHzUqFEjNWrUSLGxsVq9erWWLVumzZs367vvvtOiRYtUt25dtW/fXs8++6zZ5WbJlStX1KNHD+3bt0+GYUiSHn30UQ0bNkw9evSQv7+/mjZtquvXr2vfvn3q2bOnZs6c6dVHNq9evVrh4eHpHm+FEP7XHS333XefnnrqKUvuaAkODr7r+qioKI0cOVIbNmxQvnz59NZbb6lTp05uqs517BjQTp48ma5xiYmJio6OlmEYXn0/yUOHDt1zzF9f33nz5tWAAQNcX5gLGYYhX9+UP/eTtqE3b8s76dy5s1588UX9+9//1tChQ1PNPUliYqLee+89nTlzRp07d3ZzldkrMTExxT1fkyTdT7BHjx6WCuCStGDBAi1YsCBdY5M+tzwthMP9yN72yKF2nLNE9iZ7p0T29h5k73sje3snsvdtZG+yt7ezQvamAW6ikydPZujeHkFBQRn6keCJ/v6mSQos06dPT/Vl4O1f9knsNOcePXqoXr16qlOnjipXrpw8Fz8/P7Vu3VqtW7fWxYsXtWLFCi1dulRbt27Vtm3bvD6Ef/rppzp06JDee+891a1bV1FRUfrggw/08ssv64EHHtCcOXOSj/a7cOGCOnTooBkzZnh1CO/Zs6caNGhgdhluY8cdLX8XHx+v//u//9PUqVN148YNtWnTRoMHD04+otXb2TGgrVu37p5j9u3bp/fff19//vmnypQpo2HDhrmhMvdLSEjQjBkz9Pnnnys2NlbPPPOMhgwZkuoIbni2WrVqqVu3bvq///s/bdu2Ta+99prq1q2rEiVKyDAMnT17Vtu3b9fXX3+tQ4cOaeDAgQoKCjK7bJcqVaqU2SVku+eff962l5RD5pG9rZ1Dk9hpzmRvsrdVkb3J3hLZm+wNT0f2To3s7ZlogJsoR44cunnzZrrHx8bGKiAgwIUVuVZ6L+1jJXab87Zt2/TDDz8kX4qqVq1aqlu3rurVq5f8JVewYEF17dpVXbt21YkTJ7R8+XKTq866devWqXPnznrhhRckSeXKldN7772n1157TV27dk1xqZtChQrp+eef15w5c0yqNnuUL1/eUgHzXuy4o+WvduzYoREjRujIkSMqV66chg8frrp165pdltvYMaBdvXpVH3/8sRYsWCCn06mePXuqd+/eypUrl9mlZbudO3dqxIgR+vXXX/XAAw9o+PDhql+/vtllZZu/XzYzNjZWDodDixcv1q5du1KM/eWXX9xdXrb75z//qdKlS2vChAl677330mx4BAQE6P3337fEGTR2VKtWLbVu3drsMuBlyN7WZ7c5k73J3lZF9iZ7k73J3t6K7E32thorZG8a4CaqVKmSNm7cmO77gGzYsEEVKlRwcVWuc69L+1iR3ea8e/duHThwQLt379aePXu0d+9erV+/Xg6HQ/ny5VPt2rVVt25d1a1bV5UqVVLZsmXVq1cvs8vOsrNnz6p8+fIpliW9V9M6+qtkyZK6fPmyW2pD9rDjjhbp9g6FsWPHasmSJcqVK5fefPNNvf7668mX97EDqwe0tCxevFjjxo3T+fPnVadOHQ0fPjzVZ5wVXLx4UePGjVNYWJhy5sypfv36qXv37sqZM6fZpWWr8PDwNC+bGRYWluZ4bz4bLkmXLl0UHBys9evXKyIiQn/++acMw1CxYsVUo0YNNW3a1OvvGflXad0b9G47W6xwaVQgo8je1me3OZO9/4fsbS1kb7I32Zvs7a3I3mRvsrfnoQFuorZt2+q9997TihUr1KpVq7uODQsL05YtWzRhwgQ3VWe+kydP6r333tOMGTPMLsVtvH3Ovr6+qlKliqpUqaJXXnlFknT69OkUoXzs2LFKSEhQgQIFVKdOHdWtW1ddunQxt/AsunXrlvz8/FIsSwopaYUVh8OR4t508Hx23NHyzTffaMKECbp8+bKeeOIJDRs2LF33zbQKuwS0vzp69KhGjBihHTt2qGDBgvrwww897t492eXbb7/V+PHjdfnyZT322GMaPny47rvvPrPLynazZ882uwTT+Pv7q1WrVvf8jW0Fd9rRIqW9s4UQDjsie9+dt+fQzPD2OZO9/4fsbS1kb7I32dtayN7WR/a+jeztmWiAm6h9+/YKCwvTO++8o8OHD6tr164qVqxYijFnzpzRzJkzNXv2bDVq1EgtW7Y0qdrssW/fPk2ZMkV79uyRJD388MPq06ePatWqlTzGMAx99dVX+vTTTxUbG2tWqdnGjnP+q5IlS+qZZ57RM888I+n2pX2WLFmiRYsWadWqVQoPD/f6EG43ffv2VWBgoNlluJWddrQcOnRIw4cPV2RkpIoXL66RI0eqWbNmZpflVnYJaEni4uI0efJkzZgxQ/Hx8erYsaPefvtt5cuXz+zSst3hw4f1/vvva+/evSpSpIgmTJhg6ZBmlUtB4s7stqMlODjY0p/HcB2ytz1yqB3n/Fdkb+she99G9rYusjfZ2yrI3tZH9vZONMBN5HQ6NXXqVL399tv64osvNG3aNJUsWVJFixaVj4+Pzp8/r+PHj8swDLVs2VIffPCB2SVnydatW9W9e3clJCTowQcflL+/vyIiIvTKK69o5syZql27tk6ePKl//OMfioyMVJ48eTRixAizy84SO87572JjYxUREaEdO3Zo165d+vnnn3Xr1i3lzJkz+ZJsVnDp0iWdOnUq+b+TjkC+cOFCiuXS7aNbvVnfvn1T/HdcXJwOHTqkM2fOJF/mJigoyJL3K7KD9u3bKzExUZJUuHBhzZ07V3Pnzr3r3zgcDs2aNcsd5bmU3QKaJG3cuFEjR45UVFSUAgMDNWLECFWtWtXsslxi7NixmjNnjhISEvTUU09pwIABypMnT6rP6L9L60wTq5o/f742bdqkSZMmmV1KpqT30sZ/5e2fX5nZ0bJz504XVOIedrvHL7IP2dv6OdSOc/47sjfZG96F7E32JnunRPb2HmTv9CF7m89hGIZhdhGQfvzxRy1evFiRkZE6e/Zs8g/ZmjVrqm3btqpXr57ZJWbZq6++qsjISE2fPl3Vq1eXJEVHR6tXr17KkSOHxowZo5CQEJ0/f17NmjXTsGHDVLRoUZOrzho7zjk+Pl579+7Vtm3btG3bNu3bt0+3bt1Sjhw5VKVKleTgXb16dctczigoKCjN+7YYhnHX+7kcPHjQlWW53Llz5zRhwgSFh4crJiYmxTp/f381b95cAwcOTHV2jTcKCgrSv/71LzVp0iR52eXLlxUcHKyPP/44+f2dZPXq1frwww+9chs3btw4U3+3bt26bK7Eve4U0O7FmwNa//79tXr1aknSU089pZCQEPn4+Nzz72rXru3q0lwiKCgo+d8zcq8tb3wfZ9bw4cO1YMECr51zej+/EhMTFR0dnfw97a3zzYjTp08rNDRUYWFhOnHihC3mDNwJ2duaOdSOcyZ7/w/Zm+ztjduY7E32vheyt3WRva2L7O1ZaIDDberVq6cOHTro7bffTrF806ZN6t69u8qXL6+zZ8/q/fff9/rLzSWx25y7d++unTt3KjY2Vk6nU5UrV1a9evVUt25d1axZM9VlrKxiyJAhmfo7bz6Sau/everZs6cuX76sqlWrql69eipWrJh8fX115swZRUREKCIiQvny5dOUKVNUo0YNs0vOErvuaLETOwa0v85Zuve8vT2wZPbI6r+feWNl3h7C02Pfvn16//33dfDgQZUpU0bDhg1To0aNzC7LJW7evKnw8HAtWrRI27dvT34PN2zYUF988YXZ5QFwIbvlUMl+cyZ7ZwzZ23uQva2P7E32vhOyt7WQvcnenoBLoHuR7du36/Dhw5m6xIQnuHr1qsqXL59qecWKFWUYhi5duqRvv/3WEvcWSGK3Of/444/KkSOH2rVrp549e+qBBx4wuyS38OYwnRnnz59Xnz59lDt3bn3++eeqWbNmmuMOHDigAQMGqH///lq8eLEKFy7s5kqzT7t27TIUzOxk586dCg0N9fpLhdopaCWx22dXZrax1e4NamdXr17Vxx9/rAULFsjpdKpnz57q3bu3JS8ZunfvXi1atEjff/+9rl27JkkqVKiQ2rdvr06dOql06dImVwh4PrK397HbnMne9kD2xl+Rvb2X3T67yN72RvYme3sSGuBeZMWKFVqwYIHXhvCEhAT5+qZ+ySVdiqtXr16WCaNJ7Dbnjh07avv27cmX+ShXrpzq16+vevXqqXbt2sqfP7/ZJSIbzJ07VzExMZo/f77Kli17x3EPP/ywZs6cqdatW+u///2v+vXr58Yqs9eHH35odgke5c8//1RoaKhCQ0N14sQJSbJlCPf2gBYcHJzhv4mKinJBJZ4naefSqlWrvPqeTbht8eLFGjdunM6fP686depo+PDhaTZJvNmZM2cUFham0NBQ/fHHHzIMQ/7+/mrQoIG2bNmif//73ykuJQrg7sje3sducyZ72wPZG2Tv28je1kX2thayN9nb09AAh8ew2odhelhtziNHjpQknTp1Slu2bNG2bdu0cuVKzZ07V06nU0FBQapbt25yKA8ICDC5YmTGmjVr1KZNm7sG8CSlS5dWcHCwwsPDvTqEh4SE6I033lD9+vWTl8XHx2vPnj0KCgpS3rx5U4xfsmSJBg8erAMHDri7VJeJi4tLvpzPtm3bZBiGDMNQ3bp19cILL5hdnlvZLaDdvHlTK1euVGhoqCIiIrR//36zS3KJpJ1LYWFhOn78uAzDUIECBcwuC1lw9OhRjRgxQjt27FDBggX14Ycfql27dmaXla2+//57LVq0SFu2bFFCQoLy5cun1q1bq1mzZmrYsKHOnj2rpk2bml0mAA9jtRyaHlabM9nbHsjet5G9yd5kb2she1sP2Zvs7alogAPIdqVKlVKHDh3UoUMHSdKvv/6qbdu2aevWrVq4cKFmzpwpX19fPfLII6pfv77efPNNkytGRpw8eVIvvvhiuscHBQUpNDTUhRW53o4dO9SxY8cUy65evaqQkBDNmDEjRThPYhiGu8pzqb9fzidpXs8884z69OmjcuXKmVyhe9gxoO3evVuLFi3SypUrFRMTI8MwVLFiRbPLylZJO5dCQ0O1bds2JSYmyjAMVatWTZ07d1arVq3MLjFLwsLCMjT+999/d00hbhYXF6fJkydrxowZio+PV8eOHfX2228rX758ZpeW7QYOHKiAgAB16dJFTZo0Ue3ateXj45O8nkuIAoC1kb2tjex9G9mb7E329n5k75TI3t6H7O2daIDDrXbu3KmEhIQUy2JiYiRJmzdvVnR0dKq/8fajhew457+rWLGiKlasqJdeeklxcXFauXKl/vvf/2rv3r3at28fIdzL5MiRQzdv3kz3+NjYWMuecWCVoP13aV3Op0CBAgoODla1atU0fPhwtWrVyvIB3OoBLS3R0dHJ2/7YsWOSJF9fX7Vq1UovvPCCatWqZXKF2WPfvn1auHBhip1L+fLl09WrVzVy5MhUO9281eDBgzMUwgzD8PrQtnHjRo0cOVJRUVEKDAzUiBEjVLVqVbPLcpkyZcro5MmTWrRokX7//Xf99NNPatq0qR588EGzSwNgMjvmUDvO+e/I3tZC9v4fsjfZ22rI3mRvsrd3IXt7JxrgcKsFCxZowYIFKZYl/YidPn16ig/+pC8Cbw+kdpzzXx0/flz79u3Tvn37FBkZqUOHDunWrVvKnTu3nnjiCdWuXdvsEpFBlSpV0saNG9N9T8QNGzaoQoUKLq4K2aVHjx7avHmzEhISVLJkSXXp0kVNmzZV3bp15XQ6FRUVZdmdD0nsEtCSxMXFac2aNVq4cKG2bduWvOO4fPnyOnr0qD766CO1aNHC5Cqz7syZM1q8eLFCQ0P1+++/yzAMlSpVSsHBwWrWrJmKFy+up59+WoUKFTK71GwzZswYs0twq/79+2v16tWSpKeeekohISGKi4tTRETEXf/Om3+LrFmzRvv27dOSJUu0cuVKbdq0SRMmTFC5cuXUrFkzVa5c2ewSAZjEjjnUjnP+K7K39ZC9rY3sTfYme5O9vRXZm+ztLWiAm+jUqVMZGp905LK3stsXgWS/OV++fFmRkZHJgTsyMlKXL1+WYRjKnz+/atSooYEDB6p27dp6+OGH5XQ6zS4ZmdC2bVu99957WrFixT2Pwg0LC9OWLVs0YcIEN1WHrPrhhx8UEBCgkJAQde3aVUWLFjW7JLewY0CLjIzUokWLtGLFCl25ckVOp1PVq1dXs2bN1KxZMyUkJKhp06bKkSOH2aVmi8aNGysxMVFBQUHq1auXmjRpokceeSR5fVRUlInVuUZwcLDZJbhVeHh48r+vW7dO69evv+v4pObHwYMHXV2aS1WtWlVVq1bVu+++q02bNmnp0qVau3atpkyZIofDIYfDofXr1ysoKEilS5c2u1zAFGRv67PbnMne9kD2tjayN9mb7G0dZG+yN9nbM9EAN1Hjxo1tdWmMzHwRePsXot3mXLduXTkcDhmGoYIFC6pOnTqqXbu2ateurcDAQK9+/eJ/2rdvr7CwML3zzjs6fPiwunbtqmLFiqUYc+bMGc2cOVOzZ89Wo0aN1LJlS5OqRUb17dtXy5cv19SpU/XFF1/owQcfVNOmTdW0aVNVqVLF7PJcxo4B7fnnn5e/v78aNmyoJ554Qo0bN06xk8Fqc46Pj5e/v7+KFCkif3//VJdItYPo6Gjt2rVLZ86ckSQVK1ZM1atXV8mSJU2uLHvYrfnxdz4+PmrUqJEaNWqk2NhYrV69WsuWLdPmzZv13XffadGiRapbt67at2+vZ5991uxyAbcie9+bt3/v223OZG97IHtbG9mb7J3EanMme5O9rY7s7T1ogJuoXbt2hJI03Lx5UytXrlRoaKgiIiK0f/9+s0tyOavMuUWLFqpTp47q1KnDZbcszOl0aurUqXr77bf1xRdfaNq0aSpZsqSKFi0qHx8fnT9/XsePH5dhGGrZsqU++OADs0tGBvTt21d9+/bVzz//rKVLl2rFihWaNm2avvzyS5UsWVI1a9a05HeXHQOav7+/bty4oSNHjqhgwYIKCAjQE088oTx58phdmkusX79eS5cu1dKlSzVhwgQ5HA4VKVJETz/9tJ5++mlLH53766+/atSoUYqIiJBhGCkupeh0OlWzZk0NHTpUgYGBJlaZdXY76v5u/Pz81Lp1a7Vu3VoXL17UihUrtHTpUm3dulXbtm0jhMN2yN5ps0oOzQirzJnsbQ9kb2sje5O9yd7WQ/a2H7K3Z3MYVr+ZCLzG7t27tWjRIq1cuVIxMTEyDEMVK1bU0qVLzS7NZew4Z1jLjz/+qMWLFysyMlJnz56VYRgqVqyYatasqbZt26pevXpml5gtgoKC9K9//UtNmjRJXnb58mUFBwfr448/VvXq1VOMX716tT788EOvv7SPdPsMqK1bt2rp0qVavXq1rl27JkkqXbq02rdvr3bt2qlUqVImV5l1p0+fTg5ov/76a5oBrVmzZpo8eXKK14E3i42N1bp167RkyRJt2rRJCQkJypEjh+rXr6+nn35aQUFB6tChg6XmnOTQoUPJO5lOnz4th8ORvFNi8ODBevnll80uMdusXbtWAwYMkMPhUNOmTVWvXj0VK1ZMvr6+OnPmjCIiIrRy5UrFx8dr4sSJatq0qdklw4VOnDih5cuXq1evXmaXAsBEdsyhdpwzrIXsTfYme3svsjfZm+xtP2Rvz0AD3EucPn1aoaGhCgsLS3GPBW8XHR2tsLAwhYaG6tixY5IkX19fNWvWTC+88IJq1aplcoXZz45zBrxdUFBQmkde3+vymFYI4X8VFxendevWadmyZdq4caNu3bolp9Op+vXr6//+7//MLi/b2CmgJbl06VLyUap79+5Nsa5Hjx7q1auX/P39zSnOxXbs2KGlS5cqPDxcly9flsPhUJkyZfTcc88pODhYJUqUMLvETDt58qRat26tBx98UJ988onKli2b5rg///xT/fv312+//abFixffcZynGzJkSIb/xuFwaPTo0S6oBoC3Intbhx3nDHg7svdtZG+ytxWRvcneZG+4Gw1wD3bz5k2tWrVKoaGh2r59uxITE+Xr66uff/7Z7NKyJC4uTmvWrNHChQu1bdu25EvdlC9fXkePHtXEiRPVokULk6vMXnacM+wjLi5Ohw4d0pkzZ5KPQg8KClKuXLnMLi3bZOaHnWTte+JcuXJFK1eu1NKlS7Vr1y4dOHDA7JJcwsoB7U6ioqK0dOlSLVu2TL/99pscDocCAgLUsmVLtW/fPtVZF1Zx69Yt/fDDD1qyZIk2bNigmzdvev3vrg8++ECLFy/WypUrU9xjLi2XLl1Sq1at1KZNGw0ePNhNFWavoKCgDP+Nw+Hw6h2mmTlDxOFwaM2aNS6oBvBeZG/rsOOcYR9k7zsje3s/sjfZ25t/d5G9743sDTPQAPdAe/bsUWhoqFasWJF8aa4SJUqoY8eOev7551W0aFGzS8yUyMhILVq0SCtWrNCVK1fkdDpVvXp1NWvWTM2aNVNCQoKaNm1qqcu92HHOsI9z585pwoQJCg8PV0xMTIp1/v7+at68uQYOHKhixYqZVCHcZefOnZY/g8aKAS09Dh06pCVLlmjFihX6888/vT6wpNe1a9cUHh6uZcuWacaMGWaXk2ktW7bUE088ke4diePGjdP69ev1/fffu7gy14iKisrU33nzPegaN26caplhGDp9+rSKFCminDlzpvl369atc3VpgFcge1snh9pxzrAPsjeSkL2ti+xN9vYmZO/byN6ez9fsAnBbdHS0Fi9erEWLFunYsWMyDENOp1OSNGDAAPXo0SP5v73V888/L39/fzVs2FBPPPGEGjdunOKIqMx+cHoyO84Z9rB371717NlTly9fVtWqVdO8r01YWJjWrVunKVOmqEaNGmaXjAz6+eeftXfvXhmGoYceeijNkH3t2jWNHz9e3377rfbv329Cle6TI0cONWnSRE2aNEkR0KwuKChIQUFBeuedd7R9+3ZbzFmS8uTJo6ZNm2r37t1ml5Ilp0+fVoUKFdI9vly5cpo3b54LK3Itbw7TmZVWmL5w4YIaNGigjz76SPXr1zehKsCzkb2tmUPtOGfYA9nb+sjeKZG9yd7eiOxtfWRv70QD3ERJl+ZatGiRtm7dqoSEBOXKlUuNGzfW008/rcDAQAUHB6tixYpeH8AlJd/D5ciRIypYsKACAgL0xBNPKE+ePGaX5jJ2nDOs7/z58+rTp49y586tzz//XDVr1kxz3IEDBzRgwAD1799fixcvVuHChd1cKTLj+vXreuutt7Rx40YlXSTG4XCoQYMGmjJlSvIRjRs2bNDw4cMVHR2t++67z8yS3c4qAS2jSpUq5fU7j0+ePKmZM2dqz549kqSHH35Y3bt31/33359iXHh4uEaOHKlz585p1KhRZpSaLfz8/HTlypV0j79y5Yry5cvnworMFRcXp99++01+fn4qV66c2eW4zN3ujwnYFdnb+jnUjnOG9ZG9rY3sfW9kb+9F9r47src1kL09Hw1wEzVs2FBXrlxR/vz59cwzz6hJkyZ64okn5O/vL8l6Ryhv3bpV69at05IlS7Rw4UItWLBAOXLkUP369fX0009n6t4Rns6Oc4b1zZ07VzExMZo/f77Kli17x3EPP/ywZs6cqdatW+u///2v+vXr58YqkVmfffaZNmzYoIYNGyo4OFgBAQHauHGjvvnmG40bN05Dhw7V2LFj9dVXX8nHx0fdunVT//79zS47W9gtoEnSvn37NGXKlBRz7tOnT4qzDgzD0FdffaVPP/1UsbGxZpWaZQcPHtRLL72ka9euyc/PT35+fjpw4IBWrFih+fPnq1KlSrp69aqGDh2q8PBw+fj4qEePHmaXnSWPPPKIwsPD1a1bt3SNX7VqlR566CEXV+VaV69e1Zdffqk9e/Zozpw5ycuXLl2qUaNGJe+UePDBBzV69GhVq1bNpEoBuBPZ2/o51I5zhvWRva2N7E32JnuTvb0Z2RvegAa4iS5fvqyAgAA1b95cdevWVY0aNZIDuBX5+fmpVatWatWqlS5duqQVK1Zo6dKl+uGHH/TDDz9Iun3UTGRkpBo0aGCJ/y/sOGdY35o1a9SmTZu7BvAkpUuXVnBwsMLDwwnhXmLdunWqU6eOvvzyy+RlTz75pAoXLqw5c+aoQIECmjlzpoKCgjRmzBiv/8GexI4BbevWrerevbsSEhL04IMPyt/fXxEREXrllVc0c+ZM1a5dWydPntQ//vEPRUZGKk+ePBoxYoTZZWda0k6Ejz/+WM8884yk2/cLfeuttzRq1CiNHz9eISEh+uOPP/Too49q1KhRCgwMNLnqrOnQoYMGDBigmTNn6tVXX73r2KlTpyoyMjLFe9/bxMTEqFOnTjp69KhKlCih+Ph4+fr6KjIyUoMGDZJhGOrUqZMqVqyosLAwvfrqqwoLC0u1ow2A9ZC9rZ9D7ThnWB/Z29rI3mRvsjfZ21uRveE1DJgmIiLCeO+994w6deoYQUFBxkMPPWQ8//zzxvTp041jx44ZJ0+eNAIDA401a9aYXapLnTx50pgyZYrxzDPPGIGBgUZQUJBRo0YN41//+pexe/dus8tzCTvOGdZRrVo1Y/78+ekev2DBAqN69eourAjZqVq1asZXX32Vavlvv/1mBAYGGg899JAxcuRIIy4uzoTqXKdXr15G5cqVjWXLliUv27dvn9GkSRPjpZdeMqKjo43mzZsbgYGBRocOHYxDhw6ZWG32eOWVV4waNWqk+N75888/jXbt2hkdO3Y0fvvtN6NBgwZGYGCg0a9fP+PMmTMmVpt1DRo0MEaOHJlq+apVq4yHH37Y6NKli/HII48YX375pZGQkGBCha7Rr18/IygoyPjHP/5h7NmzJ8V7NyEhwdizZ0/ymGHDhplYadZ9+umnRuXKlY3vv/8+xfIePXoYQUFBxpgxY5KXxcbGGs2aNTMGDx7s7jJd7sKFC0ZgYKCxZcsWs0sBPAbZ+zY75lA7zhnWQfa2NrI32ZvsTfb2VmTv28jeno8zwE1Uq1Yt1apVS8OGDdPGjRu1dOlSbdiwQfv27dP48eNVtmxZORwOXb9+3exSXap06dLq1auXevXqpUOHDmnJkiVasWKFvvvuOy1cuFAHDx40u8RsZ8c5wzpy5Mihmzdvpnt8bGysAgICXFgRstONGzdUqFChVMsLFiwoSXr66ac1dOhQd5flcpGRkercuXPy0cmSVKVKFb3zzjsaOHCgBg4cqKioKL399tt67bXXLHF/0IMHD+qFF15Q9erVk5cVL15c//jHP9S9e3e9+eabio+P18SJE9WyZUsTK80ely9fTvPyp48++qgSEhJ0+PBhzZkzx3KX5Ro/frzGjBmj+fPna/ny5fLx8VGBAgXk4+OjS5cuKS4uTk6nU926ddPAgQPNLjdLwsPD1bZtW7Vo0SJ5WUxMjDZv3ixJ6tKlS/LyXLlyqU2bNvr222/dXicA9yN732bHHGrHOcM6yN7WRvYme5O9q7m/OBcie5O94XlogHsAX19fNWnSRE2aNFFMTIzCw8O1dOlSbd++XYZhaNCgQVq4cKGee+45NW/eXLly5TK7ZJcJCgpSUFCQ3nnnHW3fvl3Lli0zuySXs+Oc4d0qVaqkjRs3KiQkJF3jN2zYoAoVKri4Kriaw+GQJLVr187cQlzEjgHt6tWrKl++fKrlFStWlGEYunTpkr799lvdd999JlSX/eLj49P8DeXn5ydJ6tGjh6W2b5KcOXNq+PDhCgkJUVhYmCIjI3X27FkZhqFy5cqpZs2aat26tSUuRXby5MkUQVuSIiIiFB8fr7Jly6Z6LZcsWVLnz593Z4nZbtKkSamWxcbGyuFwaPHixdq1a1eq9Q6HQ3369HFHeYDHIXv/jx1zqB3nDO9G9rYnsjfZ29uRvcneZO/byN7mowHuYXLnzq3g4GAFBwfr/PnzWrZsmZYuXapt27Zp27ZtGjlypCIiIswu0y3q1q2runXrml2GW9lxzvA+bdu21XvvvacVK1aoVatWdx0bFhamLVu2aMKECW6qDq6WFFisxo4BLSEhQb6+qX8K5syZU5LUq1cvywTw9HjkkUfMLsGlHnzwQa8/yvxenE6nEhMTUyzbunWrJKlBgwapxl+4cEF58uRxS22uklYITxIWFpbmckI4cBvZ+3/smEPtOGd4H7K3vZG9rYPsnRLZ2/uRvVMie3suGuAerHDhwnr55Zf18ssv69ixY1qyZIlXH6Gc3iNW/8rhcGjWrFkuqMY97DhnWF/79u0VFhamd955R4cPH1bXrl1VrFixFGPOnDmjmTNnavbs2WrUqJElLuFkJ0lHnGd0nZVZPaClJa0j1K3MDq/t6Oho7dq1S2fOnJEkFStWTNWrV1fJkiVNrix7VKhQQXv27FHXrl0lSYZhaPXq1XI4HGrcuHGq8WvXrvX61/ns2bPNLgGwBLK39+dQO84Z1kf2tj6yd2pkb+uzw2ub7J0S2RtmoQHuJe6//37169dP/fr1M7uUTNuxY0eayx0OhwzDuOM6b2bHOcP6nE6npk6dqrfffltffPGFpk2bppIlS6po0aLy8fHR+fPndfz4cRmGoZYtW+qDDz4wu2Rk0OjRozVx4sQUywzDkMPh0Ntvv53qaG2Hw6E1a9a4s0S347PZGo4ePZrqbL6rV69Kkg4fPpzmUfm1a9d2S22u9Ouvv2rUqFGKiIiQYRgpfoM4nU7VrFlTQ4cOVWBgoIlVZl27du30wQcf6NFHH9Vjjz2mBQsW6NSpU7rvvvvUsGHDFGOnTp2qvXv3ev19FevUqWN2CYDlkL29kx3nDOsje1sf2Ts1PputgexN9k5C9oaZaICbKCQkRG+88Ybq16+fvCw+Pl579uxRUFCQ8ubNm2L8kiVLNHjwYB04cMDdpWaLQ4cOpVp24cIFNWjQQDNnzkzx/4NV2HHOsIe8efPqiy++0I8//qjFixcrMjJSv/zyiwzDULFixdSuXTu1bdtW9erVM7tUZFCpUqUkKc0dhUlHqv593Z12KnobOwa0nTt3KiEhIcWymJgYSdLmzZsVHR2d6m+8+V50U6dO1dSpU9NcN3bs2DSXHzx40JUludzatWs1YMAAORwOtWjRQvXq1VOxYsXk6+urM2fOKCIiQitXrlSHDh00ceJENW3a1OySM61z587atWuXxowZk9zwyJ8/v8aPHy+n0ylJ+u677zRt2jSdOHFCNWvW1AsvvGBy1a4VFxen3377TX5+fipXrpzZ5QCmIXtbP4facc6wB7K3dZG9yd5k75TI3t6D7J0a2dszOQyrfHN6oaCgIH300Udq3bp18rKLFy+qQYMGmjFjRqqAtmTJEg0aNMjrvwz+6uLFi6pfv76tAqkd5wwAni4oKOiOR5onHYGfFm/+Tr7TnP/60/Cv65P+f/DWOX/22WeZOpugb9++LqjGPU6ePKnWrVvrwQcf1CeffKKyZcumOe7PP/9U//799dtvv2nx4sV3HOct9uzZoz179ihPnjxq2rSpChUqlLxu0qRJWrx4sVq3bq2ePXumef9Bb3P16lV9+eWX2rNnj+bMmZO8fOnSpRo1apSuXLki6fa96EaPHm25eyoC6UH2tmcOteOcAcDTkb3/h+ydEtnb+5C9byN7ey7OAPdAHJMAwFvExsbKz88v1fLffvtN+fLlS3V/Mni+sLAw1apVS2XKlDG7FLfq06eP7S61NmbMGLNLcCtvvpRtZs2aNUs5cuTQ9OnTUwTRvytRooSmTZumVq1a6euvv9bgwYPdWGX2q169uqpXr57mur59+95xx8qtW7e0d+/eNM8G9VQxMTHq1KmTjh49qhIlSig+Pl6+vr6KjIzUoEGDZBiGOnXqpIoVKyosLEyvvvqqwsLCdP/995tdOuARyN4AvAXZ23rI3vZB9rY+sndqZG+ytyegAQ4AyLC4uDiNHTtWS5cu1Q8//JAqiE+YMEE//PCD2rdvr0GDBikgIMCkSpFRQ4YM0bhx42wXwu0Y0IKDg80uwa3Suvyt1W3atEnBwcF3DeBJChQooHbt2mn9+vVeH8Iz6/LlywoJCUnzbFBPNWPGDB0/flz/+c9/1KJFi+TlkydPlmEYevnll5O3Z4cOHdSmTRtNnTrVdjvhAADwVmRv6yJ72wfZ2/rI3hlD9oa7OM0uAADgXeLi4tStWzd9/fXXKlWqlC5evJhqzFNPPaXAwEB98803ev311xUfH29CpcgMu54JFRISoq1bt5pdhls1adJEa9euNbsMt9mxY4fOnTtndhludfr0aVWoUCHd48uVK6c///zThRV5Pm/7DAwPD1fbtm1TBPCYmBht3rxZktSlS5fk5bly5VKbNm1s91kHAIC3Intbm7f97swuZG/rI3vfG9nb+z4Dyd7eiQY4ACBDvvrqK0VERGjo0KEKCwtTyZIlU43p2LGjFi5cqL59+2r37t2aO3euCZUC6WfHgBYVFaXr16+bXQZcyM/PL/keVOlx5coV5cuXz4UVIbudPHlSjzzySIplERERio+PV5kyZXTfffelWFeyZEmdP3/enSUCAIBMInvDisjesCKyt/WRvb0Tl0CH24SFhaVaFhMTI0navHmzoqOj0/y7du3aubAq17LjnGF9S5cuVZMmTfTiiy/ec2zfvn21Y8cOLV68WK+88orri0O2uHTpkk6dOpWhvylVqpSLqgGQWY888ojCw8PVrVu3dI1ftWqVHnroIRdXhezkdDqVmJiYYlnSUeYNGjRINf7ChQvKkyePW2oDYB475lA7zhnWR/a2PrI3YA1kb+sje3snGuAm+/sPncuXL0u6/Qb5+w+gtC515E0GDx4sh8ORYlnSpS6mT58uh8OR/N9J/+5wOLw6kNpxzrC+Y8eOqXPnzuke/+STT+qTTz5xYUXIbqNHj9bo0aPTPd7hcOjAgQMurAjIHqtXr9axY8fSPd7hcKhPnz4urMi1OnTooAEDBmjmzJl69dVX7zp26tSpioyM1Jdffumm6pAdKlSooD179qhr166Sbv/OXL16tRwOhxo3bpxq/Nq1a1W+fHl3lwl4BLK3tXOoHecM6yN7Wx/ZG1ZF9r4zsrd3Int7JxrgJrvTD523337bhGpca8yYMWaX4HZ2nDOsLyAgQAkJCekenytXLvn5+bmwImS3mjVrqmzZsmaX4XZ2C2iStGDBAm3ZsiXd4x0OR4Z20Hia1atXKzw8PN3jvX0bt2jRQs2aNdO4ceO0f/9+vfjii6pcubJy5MghSUpMTFRkZKRmzJih1atXq2PHjnr88cdNrhoZ0a5dO33wwQd69NFH9dhjj2nBggU6deqU7rvvPjVs2DDF2KlTp2rv3r0aOnSoSdUC5iJ7W5sd5wzrI3tbH9k7fbw9l0lk73vx9m1M9rY+srd3ogFuonbt2qU6QtnKgoOD7znm8uXL8vPzU65cudxQkevZcc6wvnLlymn37t0KCQlJ1/hdu3apdOnSLq4K2alTp05q3bq12WW4nd0CmnT7fkURERHpHu/tIbxnz55pXprKysaPH68xY8Zo/vz5Wr58uXx8fFSgQAH5+Pjo0qVLiouLk9PpVLdu3TRw4ECzy0UGde7cWbt27dKYMWOSz2jMnz+/xo8fL6fTKUn67rvvNG3aNJ04cUI1a9bUCy+8YHLVgPuRvVOzWg6145xhfWRv6yN7pw/Z2/uQvcneVkP29k40wE304Ycfml2C2926dUuLFi3S3r17UxyhvWPHDr3//vv6/fff5XA41KBBAw0fPtwSR0Hacc6wtuDgYA0fPlzbtm1TvXr17jp2+/btCg8PV79+/dxUHZB5dgxo7777rpo0aWJ2GW5Tvnx51alTx+wy3CpnzpwaPny4QkJCFBYWpsjISJ09e1aGYahcuXKqWbOmWrdurfvvv9/sUpEJDodD48ePV9euXbVnzx7lyZNHTZs2VaFChZLH/PnnnzIMQ2+88YZ69uyZHM4BOyF72yOH2nHOsDayN6yK7G19ZG+yt9WQvb0TDXATvf7662rXrp2aNm1qi0sU3bp1S6+99poiIiKUI0cOjRw5Ur6+vvr999/1+uuv69atW2rYsKEqVKigVatWqVOnTlqyZImKFClidumZZsc5w/ratWunhQsXqlevXurZs6c6duyY6jV75swZffvtt5o+fbrKlCmjLl26mFQtkH52DGgFCxbkLBGbePDBBznK3MKqV6+u6tWrp7mub9++6tu3b5rrbt26pb179yooKEh58+Z1ZYmAqcje1s+hdpwzrI/sDasie8PKyN7WRvb2LhyCYKIdO3bon//8pxo0aKDBgwdry5YtMgzD7LJcZu7cudq5c6f++c9/KiIiQr6+t4+/+OyzzxQXF6fWrVtr2rRpeuedd7Rw4UL5+Pho6tSpJledNXacM6wvR44cmjx5sqpWrapPPvlEDRs2VJMmTdS5c2d17NhRjRs3VqNGjfTZZ58pMDBQM2fO5Ivdi5QqVUoBAQFmlwEAcIPLly8rJCREP//8s9mlAC5F9rZ+DrXjnGF9ZG9rI3sDgH2Qvc3BGeAm2rp1q9asWaPvv/9ey5cv1+LFi1WkSBG1bt1abdq0UVBQkNklZqtly5apefPm6tatW/KyuLg4rVu3Tg6HI8XyAgUK6LnnntPy5cs1dOhQM8rNFnacM+yhcOHCmjVrlsLDw7V8+XIdOHBAhw8fltPpVJEiRdSuXTs9/fTTaty4sdmlIoPWrVt31/WJiYn6888/VaRIEeXMmdNNVQFZ17dvXwUGBppdhlul936Rf+VwODRr1iwXVOMZzp49qwIFCihHjhyp1uXPn1+zZ8/WQw89ZEJl5rFyExBIQva2fg6145xhD2Rv6yJ7w6rI3ulD9iZ7w/VogJsod+7catu2rdq2baurV69q1apVWrFihWbNmqX/x959x9d4/n8cf58sWUjsPWok9t6rRu3U1tqUammt0hZfitKqoq2ilNZuUSM2DbWr9qpZI2YRIkZIZJ3fH345dSQhiSQn5+T1fDz6KPc49+c+y3lf131f19y5c1W4cGG1aNFCPj4+yp49u6XLfWWXLl1Sq1atzJYdOnRIoaGhypYtW4x/GPPly6eAgICULDHJpcVzRtrSsGFDNWzY0NJlIAXdvXtX9evX15w5c1StWjVLl5Nk0mJAGz9+fJzDNtmiuIahivb48WPNmTNHLVu2VJ48eVKoquS1f//+WJcbDIY4g5fBYEjOklLE3Llz9dtvv2nNmjUxwvaXX36pPXv2qGfPnurVq5fZnFyOjo5pbihGIK0ge9t+Dk2L54y0heyd9pC9bQfZ2xzZ+7911o7sjdSODvBUIn369Grbtq3atm2ru3fvatOmTdq4caO++eYbffPNN6pUqZJatmyphg0bys3NzdLlJkpUVJTs7e3Nlu3du1eSVL169RjbP3z4UC4uLilSW3JJi+cMSE+HdXF2dla6dOksXQqSgS1esZgWA9rzjcTPCw4O1hdffKFevXqpUKFCKVSV5Tx+/FjTp09XhQoVbOY1PnPmTIxld+/eVfXq1TV37lybakiTnn43DRkyROvXr1fmzJl18+ZN5c2b12ybQoUK6fDhw/r222914sQJff/99xaqFoClkL1tM4emxXMGJLK3rSN720YuI3ubI3tbP7I3rAVzgKdCmTJlUseOHbVw4UJt375d//vf/2RnZ6fPPvtMNWvWtHR5iZYvXz6dPn3abNnmzZtlMBj0+uuvx9h+9+7dypcvXwpVlzzS4jkjbQgPD9fSpUs1bNgws+X79+9X06ZNVbVqVZUrV069evXSlStXLFQlkHSiA9rVq1ctXUqKCQ0N1apVq9LU3VG22MD0PFu4yjwuS5cu1fr169W9e3dt3749RgCXnja4bd68Wa1bt9bmzZu1cuVKC1QKILUgez9lCzk0LZ4z0gayN9IasnfaQPa2bmRvWAs6wFM5R0dHOTs7y93dXQ4ODgoLC7N0SYnWrFkzrV69Wlu2bFFISIjmzZunixcvKnPmzDHmKlqzZo3+/PNP1a9f30LVJo20eM6wfeHh4XrnnXc0atQorVu3ThEREZIkf39/9erVS/7+/qpVq5a6d+8uf39/vf3227pz546FqwZeXVoIaM9Li+cM67V8+XJVrlxZQ4cOjXWesWhOTk4aN26cihUrpt9++y0FKwSQmpG9rTuHpsVzhu0jeyOtSos5NC2eM6wX2RvWgiHQU6G7d+9q8+bN2rRpkw4cOKCIiAgVL15cAwYMULNmzSxdXqJ1795du3bt0ocffmia/8LR0VFffPGFnJycJD29QnvRokXav3+/ChYsqO7du1u26FeUFs8Ztm/RokU6ePCgPv74Y3Xq1EkODk//KZk6darCwsL05ptv6uuvv5Yk9e7dWz4+Ppo5c6ZGjBhhybKRRBwdHVWpUiVlzJjR0qUAgJkLFy5owIAB8drWYDCocePGmjFjRjJXBSA1I3vbTg5Ni+cM20f2TtvI3gBSK7I3rAUd4KnEnTt35Ofnp02bNunQoUOKjIxU7ty51bNnT7355ps2Mf+Hk5OT5s2bpw0bNujo0aNyd3eXj4+PChcubNrmxIkTOnz4sN58800NHTpUzs7OFqz41aXFc4btW7dunRo1aqSePXualoWFhWnr1q0yGAxmyz08PNS6dWutX7+eEG4lHj58qPTp08e5PmPGjFq4cKHZsn379qlKlSrJXRqQrNKnT6/x48erSJEili4FieTg4GDq5IiPDBkyxJgvFoDtI3s/ZWs5NC2eM2wf2du2kb2RVpG9rR/ZG9aCDnALCggIkJ+fn37//XcdPnxYkZGRypgxo9q2bSsfHx9VrFjR0iUmOXt7e/n4+MjHxyfW9e+//74GDBggOzvbGZ0/LZ4zbNulS5fUqlUrs2WHDh1SaGiosmXLJi8vL7N1+fLlS1PzGFm7rl27au7cufLw8Hjptk+ePNHEiRP166+/6tSpU8lfnAWlxYCWMWNGLViwQMWKFbN0KSkiXbp0qlmzJndYWLH8+fPrxIkT8d7+xIkTypkzZzJWuVmmbAABAABJREFUBCC1IHvHZIs5NC2eM2wb2du2kb1jR/a2fWRv60f2hrWgA9yC6tSpI+nplcoNGjSQj4+P6tSp88J5E2ydi4uLpUtIcWnxnGHdoqKiYly1t3fvXklS9erVY2z/8OFD3udW5PTp0+rcubPmzZunLFmyxLndsWPH9Omnn+rSpUsv3M5WpEuXzqzxKTIyUrNmzVKfPn0sWFXycnR0VOXKlU1/f/jwoSZMmKBx48ZZsKpXN3fuXP32229as2ZNjN9cX375pfbs2aOePXuqV69eVt9AvmrVqhjLHj16JEn6888/devWrVj3a9myZTJWlXyaN2+uyZMnq0ePHi9tMDt37pzWrl2rrl27plB1ACyJ7B1TWvx9nhbPGdaN7G3byN6xI3uTva0R2TtuZG9YksFoNBotXURa1aVLF7Vo0UKNGzeWu7u7pcsBgHhp0aKFypQpo88//9y0rGnTpvL399e3336rxo0bm23fq1cv3b9/X8uWLUvpUpEIv/zyi7744gvlzZtX8+bNi3GFZnh4uL7//nvNmTNHkZGRatGihYYPH24TV+4+fPhQy5Yt09GjR2U0GlW8eHF16tRJGTJkMNvu77//1ogRI/TPP//o9OnTFqo2aVy7dk1z587VkSNHJEnFixfXu+++q/z585tt5+fnp7Fjx+rOnTtWe85Go1FDhgzR+vXrlTlzZi1ZskR58+Y122batGlatmyZAgIC9MYbb+j777+3ULVJw9vbWwaDwWzZsz/9Y1tnMBis9jV+/PixWrdurXv37mn48OFq1qxZjEbjiIgIrVu3TpMmTZIk+fr6KmvWrJYoN8Xcvn1bHh4esXb0hYeH68iRIypWrNgLh+AErB3ZG4A1InvbNrI32ZvsTfa21teY7B07snfqQwc4ACBBZs2apenTp2vy5MmqUaOGli5dqq+++kpZsmTR1q1bzeaAWbNmjT799FMNGDBA77//vgWrRkKsW7dOQ4cOVbZs2TRv3jzly5dP0tMr1D/99FP9888/ypUrl8aMGaNatWpZuNqkcfXqVXXt2lU3b940CylZsmTRsmXLlDNnTkVERGjy5MlasGCBIiMj1axZM02ePNmCVb+a06dPq0uXLgoODpazs7OcnZ117949ubq6asmSJSpatKgePnyoESNGyM/PT/b29urZs6cGDRpk6dITZcmSJRo9erS6d++uwYMHx3nXX1hYmMaMGaOVK1fqiy++UOvWrVO40qTj6+ubqP2eH2rTmvj7++uDDz6Qv7+/XF1dVaJECWXNmlWRkZEKDAzUiRMnFBoaqly5cmn69Ony9va2dMlJ4kV3VwwaNMim7q4AACCtIHvbPrI32ZvsTfa2VmRvsrc1oAPcgmIbGiM+rHVoDAC2ISwsTD179tSBAwdkMBhkNBrl6OioadOmmYaX3Lx5sxYtWqT9+/erYMGCWrlypZydnS1cORJix44dGjhwoNzd3TV79mz98ccfmjlzpiIiItShQwcNHjxYbm5uli4zyQwePFgbNmzQoEGD1KZNG7m4uGjHjh36/PPPVbZsWU2cOFG9e/fW4cOHlStXLo0aNcr0frdWffr00a5duzRhwgQ1a9ZMknT8+HF99NFHypUrlyZNmqSuXbvq0qVLKlWqlMaNGxdjnkFr0rZtW7m6umrBggUv3dZoNKpNmzZycnLSkiVLUqA6JKWwsDD98ssvWr9+vc6cOaOIiAhJT4cWLFu2rBo2bKi33nrLrNHYWqXFuyuAxCB7A7BGZO+0gexN9iZ7k72tFdmb7J3a0QFuQdFDY0QPefEy1j40BgDbERkZqQ0bNujo0aNyd3eXj4+PChcubFr/7bffas6cOWratKmGDh0qT09PC1aLxDp06JD69Omj4OBgGY1G5c+fX+PGjVPFihUtXVqSq127tmrUqKHx48ebLff19dWoUaNUu3ZtbdmyRR06dNDHH38sV1dXC1WadGrUqKEmTZpoxIgRZsv9/Pw0aNAglS1bVsePH9eAAQP0zjvvWP2Vq+XKldOAAQPUvXv3eG0/a9YszZgxwzREnbULDQ2NtTH0/PnzypAhg7Jly2aBqlLG3bt3ZW9vbxPDRT4vLd5dASQG2RuAtSJ7pw1kb7I32ZvsbQvI3mTv1MbB0gWkZc//Qw8A1sLe3l4+Pj7y8fGJdf3777+vAQMGWP2P9rSuQoUKWrBggXr16qW7d+/q888/t8kALklBQUEqV65cjOWVKlVSWFiYduzYoSlTpqhRo0YWqC553L9/P9YhqEqVKqXIyEidPXtWCxcuVNmyZVO+uGTg4OCQoKuOM2TIEGMOK2sUFhamCRMmaO3atdq5c2eMIP7NN99o586datOmjT799FObaGB6XqZMmSxdQrJZvny5KleurKFDh75wOycnJ40bN06nT5/Wb7/9RghHmkP2BmCtyN5pA9mb7E32JnvbArI32Tu1oQPcgqx5jgcAaVfXrl3Vp08fVatWzbQsIiJCR44ckbe3t9KnTy8XFxfTuui5yLiDxjp5e3tr8eLF6tGjh/r06aPp06eratWqli4ryYWHh5u9b6NFDzXXo0cPmwrg0tPPbbp06WIsjw5pvXv3tpkALkn58+fXiRMn4r39iRMnlDNnzmSsKPk9O2ymt7e3goKCYpxT3bp1devWLS1dulTnzp3TggUL5OBgnRFh2rRpidrvww8/TOJKUs6FCxc0YMCAeG1rMBjUuHFjzZgxI5mrAlIfsjcAa0T2TlvI3mRvW0H2JnvHheyNlGadn7A0KjIyUrNmzVKfPn0sXQqANGz//v1q166d2bKHDx+qa9eumjNnjlk4h/UZNmxYrMvz58+va9eu6b333lOTJk3Mhg81GAz68ssvU6pEi6hcubKlS0hxJUuWtHQJSap58+aaPHmyevTooSJFirxw23Pnzmnt2rXq2rVrClWXPObNm6cDBw5oxIgR6ty5c6zbtGvXTu3atdO0adM0bdo0LVq0KN5D1aU28Q3hzw9/bM0hPK3eXQEkN7I3gNSA7G3byN6xI3tbP7I32Tsa2ZvsbWl0gFvYw4cPtWzZMh09elRGo1HFixdXp06dlCFDBrPt/v77b40YMUL//PMPIRxAqmQ0Gi1dApKAr6/vC9c/efJEq1atMluWFkJ4WvzRGp85Uq1J+/bttWTJEnXp0kXDhw9Xs2bNYryuERERWrdunSZNmqT06dNbfQhfu3at6tevH2cAf9aHH36o/fv3a/Xq1VYbwv/444+XbvPw4UN999132r59uxwcHKz+NU6Ld1cAiUX2BmAryN62gewdO7K39SN7vxjZ2zqRva0THeAWdPXqVXXt2lU3b940/XjdvHmzfvnlFy1btkw5c+ZURESEJk+erAULFigyMlLNmjWzcNUAAFsWnx+xturevXv6999/zZbdv39fknT37t0Y6yQpV65cKVJbcrl48aIOHDhgtuzhw4eSpLNnz8Y6HFelSpVSpLak5urqqhkzZuiDDz7Qp59+qjFjxqhEiRLKmjWrIiMjFRgYqBMnTig0NFS5cuXS9OnTlTVrVkuX/UouX76st99+O97bv/7665oyZUoyVpS8cufO/cL1GzZs0FdffaWAgACVL19eo0ePVtGiRVOouuSRFu+uABKD7A0ASG3I3mRvsjfZ21qRvcne1oIOcAv67rvvdPPmTQ0aNEht2rSRi4uLduzYoc8//1yff/65Jk6cqN69e+vw4cPKlSuXRo0apTp16li6bACADXvZj1hb9uWXX8Z5Nf2QIUNiLDMYDDp16lRyl5WsZs6cqZkzZ8a6bsKECbEut+Y5BQsWLKhVq1bpl19+0fr163X48GFFRERIkhwdHVW2bFk1bNhQb731VoKGtkqtXF1dFRkZGe/t06VLZ5qHzpZcuXJFY8aM0Z49e5QxY0aNGzdObdu2tXRZSSIt3l0BJAbZGwCQ2pC9yd7RyN5kb1tB9iZ7pzZ0gFvQgQMH1LJlS/Xu3du0rEmTJgoNDdWoUaM0dOhQHT58WB06dNDHH38sV1dXC1YLAEBMM2fOlJ+fn1auXGnpUl5Jq1atLF1CirPmuZdehZOTk3r06KEePXpIenqHgb29vTJmzGjhypLea6+9psOHD8c7dB06dMimGuLCwsI0a9YszZ49W2FhYWrVqpU+/vhjeXp6Wrq0JJMW764AEoPsDQCwdmRv60X2Jns/j+xtfcje1okOcAsKCgpSuXLlYiyvVKmSwsLCtGPHDk2ZMkWNGjWyQHUAALzcjRs3rPqq5Gjjx4+3dAkpLq2G8OdlypTJ0iUkm1atWmnUqFHau3evqlat+sJt9+3bJz8/P/Xr1y+Fqktee/bs0ZgxY3T58mUVKVJEo0aNUsWKFS1dVrJIa3dXAIlB9gYAWDuyt/Uiez9F9n6K7G29yN7Whw5wCwoPD5eLi0uM5W5ubpKkHj16EMABpErPz9f0ormagoKCUrQ2AIjNtGnTErWfNTdWtGzZUitWrND777+v9957T+3atVOWLFnMtgkICNCyZcv0008/KU+ePOrYsaOFqk0ad+7c0fjx47VhwwY5Oztr8ODB6tGjR6xz6tmStHR3BZAYZG8A1orsDcDakL3J3raM7G1dDEaj0WjpItIqb29vTZw4UT4+PmbLg4KCVK1aNf3000+qWbOmhaoDgNh5e3vLYDDEWG40GmNdHs0WrlRGTKNGjdJvv/1m9a9vWgxow4YNS/A+BoMhzrnaUjtvb+94bff895i1v7cDAwP10Ucfad++fTIYDMqVK5fZEF03btyQ0WhU2bJl9e233ypnzpyWLjnRFi1apClTpig4OFj16tXTiBEjrPp8ACQdsjcAa0T2xrPI3mRva0H2JnsDqYVtX45h5ezt7S1dAgDEkBbna4Lti28Ifz6gWXMI9/X1jfe2z563tYbwP/7446XbPHz4UN999522b98uBweHeM/flZplzpxZ8+fPl5+fn9avX69Tp07p7NmzsrOzU5YsWdSyZUu98cYbqlevnqVLfWXjxo0z/Xnr1q3aunXrS/cxGAw6depUcpaVrNJiAyKQHMjeAFIjsjdsEdn7xcje1ovs/WJkb1gCHeAW9vxQRtKLhzOSpFy5cqVIbQAQm7Q4XxNsX1oMaGfOnHnpNtevX9fYsWO1fft2pU+fXgMHDkz+wpJJ7ty5X7h+w4YN+uqrrxQQEKDy5ctr9OjRKlq0aApVl/waNmyohg0bxlh+//59OTs7W6CipJcWG4nTYgMikFhkbwDWhuwNW0T2jh3Zm+xtTcjecSN7py4MgW5BcQ1lJMU9nJG1XykDAEjdEnpF47Zt23Tq1CmrH6rqZWw9oD0vMjJSc+bM0Q8//KDQ0FA1bdpUw4YNizGHlS24cuWKxowZoz179ihjxowaMmSI2rZta+mykkx4eLhWrlypo0ePmjWi7t+/X6NHj5a/v78MBoOqV6+uzz77TPny5bNgta9m2LBhevvtt1WmTBlLl5Jirl+//tJtYmtA/OSTT1KgOiD1IHsDAFIbsnfsyN5kb2tF9rZtZG/rRAe4BSVm/g+JK0ABAMknvnM1PctgMNhsCLf1gBabgwcPasyYMTp37pwKFCigUaNGqVq1apYuK8mFhYVp1qxZmj17tsLCwtSqVSt9/PHH8vT0tHRpSSY8PFzvvPOODhw4IEdHRx05ckQODg7y9/dXixYtFBYWptq1a6tw4cL6/fffFRISojVr1lhtY0tcc/ymZWmtARGIC9kbAJDakL3Nkb3J3taM7A2yd+rEEOgWRJgGAKQ2CxYssHQJqUJaCGjPCwoK0tdff61Vq1bJyclJ/fr107vvvisnJydLl5bk9uzZozFjxujy5csqUqSIRo0apYoVK1q6rCS3aNEiHTx4UB9//LE6deokB4enP/2nTp2qsLAwvfnmm/r6668lSb1795aPj49mzpypESNGWLJsJIHnGxDHjRtn8w2IwIuQvQEAqQ3Z+ymyN9nbFpC90y6yd+pGB3gqEBISohUrVmjXrl06c+aM7t27J4PBoEyZMsnb21v169eXj4+PTf4jCABIXSpXrpzgfQ4ePJgMlVhOWgloz1q2bJkmTZqk+/fvq0aNGho1apRVD8cVlzt37mj8+PHasGGDnJ2dNXjwYPXo0cMUTm3NunXr1KhRI/Xs2dO0LCwsTFu3bpXBYDBb7uHhodatW2v9+vWEcCuWFhsQgYQgewMAUguyN9mb7G07yN5pD9nbOtjmN44VOXTokAYMGKA7d+7IyclJ+fLlU+7cuRUREaF79+5p27Zt2rp1q6ZNm6bJkyerfPnyli4ZAADduHFDvr6+WrVqla5evWoTw7CltYAmSWfPntXo0aN19OhRZcmSRd98842aNm1q6bKSxaJFizRlyhQFBwerXr16GjFihHLmzGnpspLVpUuX1KpVK7Nlhw4dUmhoqLJlyyYvLy+zdfny5VNAQEBKlpjkDh48qMjIyATt07Jly+QpJoWlxQZEICHI3gAAa0T2tg1kb7L3s8je1o3sbT1s918VK3DhwgX17NlT7u7umjRpkho2bBjjSvPg4GBt2rRJ33//vXr16iVfX1/lz5/fQhUDANKyJ0+eyM/PTytXrtS+fftkNBplMBhUu3ZtS5f2ytJiQJswYYIWLlyoyMhI1a1bVwMHDpS7u7v+/fffF+6XK1euFKowaY0bN870561bt2rr1q0v3cdgMOjUqVPJWVayioqKkr29vdmyvXv3SpKqV68eY/uHDx/KxcUlRWpLLr/99pt+++23eG0b/R1m7SE8LTYgAglF9gYAWBOyt20he5O9n0f2tk5kb+vDK2NBP/zwg1xcXLRixQplz5491m3c3d3Vtm1b1a5dWy1atNBPP/2ksWPHpnClAIC07OjRo1q5cqU2btyo4OBgSVKmTJnUpk0bvfXWW8qdO7eFK3x1aTGgzZ071/Tnbdu2adu2bfHaz1rvOHj+auy0IF++fDFer82bN8tgMOj111+Psf3u3butfvi99u3bq2zZspYuI8WkxQZEIDHI3gAAa0D2jh3Z27qQvZ8ie9sWsrd1ogPcgg4cOKDWrVvHGcCflS1bNrVs2VK7d+9OgcoAAGldQECAVq1aJV9fX126dElGo1EuLi6qXr269uzZo88//1z169e3dJlJpmXLljIYDJYuI0V9+OGHCd7HaDQmQyUp5+2331aZMmUsXUaKadasmaZPn67atWurRo0aWrp0qS5evKgsWbKoXr16ZtuuWbNGf/75pwYMGGChapNGxYoV5ePjY+kyUkxabEAEEoPsDQBIrcjeto/sbfvI3raP7G2d6AC3oKCgoAQNqfbaa69p2bJlyVgRACCt27hxo1auXKk9e/YoMjJSGTJkkI+Pjxo2bKhatWrp9u3batCggaXLTHJfffWVJCk8PFznz59XRESEChcubPVDUr1Irly51Lp163hvf/36dQ0fPjwZK0pevr6+ql69epoK4d27d9e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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 177 coefficients adjusted\n",
-      "\t 670 coefficients converged\n",
-      "\t 87 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-38.865285</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-40.865285</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-39.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-39.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-39.390562</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-39.390562</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-37.372441</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-1.063521</td>\n",
-       "      <td>-38.435962</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-26.875688</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-28.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-26.246255</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-26.246255</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-22.970666</td>\n",
-       "      <td>207.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_univ</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-23.883300</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -38.865285   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -39.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -39.390562   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -37.372441   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -26.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -26.246255   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -22.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -24.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -24.970666   \n",
-       "471            coef_calib_zeroautohhindivtou_WALK_univ -23.883300   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540         124.0            0.0          -2 -40.865285      False  \n",
-       "541           0.0            0.0        <NA> -39.934870       True  \n",
-       "543          64.0            0.0        <NA> -39.390562       True  \n",
-       "544          84.0           29.0   -1.063521 -38.435962       True  \n",
-       "698         112.0            0.0          -2 -28.875688      False  \n",
-       "695          96.0            0.0        <NA> -26.246255       True  \n",
-       "675         207.0            0.0          -2 -24.970666      False  \n",
-       "676          24.0            0.0        <NA> -24.970666       True  \n",
-       "677          56.0            0.0        <NA> -24.970666       True  \n",
-       "471           0.0            0.0        <NA> -23.883300       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_7\n",
-      "ActivitySim run started at:  2023-09-13 04:26:45.337503\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 05:12:15.645622\n",
-      "Run Time:  2730.31 secs =  45.50516666666667  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 175 coefficients adjusted\n",
-      "\t 679 coefficients converged\n",
-      "\t 78 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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AABNIrgAAAAAAAAAAAJjwf4I9ergHlwqHAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-39.934870</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-41.934870</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-40.865285</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-40.865285</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-39.390562</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-39.390562</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-38.435962</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-0.852212</td>\n",
-       "      <td>-39.288174</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-28.875688</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-28.875688</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-26.246255</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-28.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>143.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>717</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_disc</td>\n",
-       "      <td>-22.715984</td>\n",
-       "      <td>143.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-24.715984</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -39.934870   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -40.865285   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -39.390562   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -38.435962   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -28.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -26.246255   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -24.970666   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -24.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -24.970666   \n",
-       "717      coef_calib_autodeficienthhjoi_TNC_SHARED_disc -22.715984   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "541         104.0            0.0          -2 -41.934870      False  \n",
-       "540           8.0            0.0        <NA> -40.865285       True  \n",
-       "543          84.0            0.0        <NA> -39.390562       True  \n",
-       "544          68.0           29.0   -0.852212 -39.288174       True  \n",
-       "698          72.0            0.0        <NA> -28.875688       True  \n",
-       "695         131.0            0.0          -2 -28.246255      False  \n",
-       "677         143.0            0.0          -2 -26.970666      False  \n",
-       "675          24.0            0.0        <NA> -24.970666       True  \n",
-       "676          68.0            0.0        <NA> -24.970666       True  \n",
-       "717         143.0            0.0          -2 -24.715984      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_8\n",
-      "ActivitySim run started at:  2023-09-13 05:12:45.784381\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 05:58:00.925618\n",
-      "Run Time:  2715.14 secs =  45.25233333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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ZN25cTjvttBx22GEL3GOQpm+HHXYo2S9Ovkq5BbQkue6663LXXXfVenyhUMjNN9/cgBU1rOeff36xrrBo7q/xpEmTsvzyy8+37e9//3uSZMMNN1zgC/E2bdrkiy++aLT6qLt//OMfi3UP2+233z533nlnA1bUsH7yk5/kiiuuqNXfqJkzZ+aqq65K//798/rrrzdCddB0yN5fkr1l7+ZO9i59snftNPdclsjeX6e5v8ayd+mTvRetKWdvDXAaTc+ePdO/f//84Ac/yJprrrnA/vPPPz+ff/55HnzwwRx22GHZZpttilBl/dpyyy3z5z//ORtvvHG+853vJEmWXnrp/PGPf1xg7CuvvJIBAwbMG9fcbbLJJunfv3+OOOKIfPrpp/nVr34lgJeIKVOmZJ111llg+/rrr585c+bkrbfeyoABA7LRRhs1fnHUix/+8IfZbbfdil1Goyu3gJYkY8aMyZgxY2o9vrmftX722Wc363u6Lq5lllkmn3766Xzb/va3v6VQKGTLLbdcYPyYMWPmXTlH8zB9+vR07ty51uO7du3arO/JNnHixOy999458cQTc+SRRy7yd9KLL76Ys88+O++9915WWWWVRq4Sik/2lr1l79Ige5c+2bt2ZO/mR/aWvUuN7N08s7cGOI3m1FNPzYEHHpg999wz3/72t3P55ZfP90NRKBRy2WWXpVWrVhk4cGBeeumlIlZbP0477bSMGjUqhx12WDbddNNF3jvh4IMPzosvvphll112vnuTNXfrrLNObr/99vz85z/Psccem2uvvTZbbLFFscuijmbPnr3QqyXatGmTJDnqqKMEcJqlcgtoSXL55ZeX1RcunTp1yoorrljsMhpNjx498tBDD+Xwww9PZWVlJk6cmBEjRiRJdtppp/nGTp06NQ888EB69uxZhEpZUnPmzFnolY2LUllZmdmzZzdgRQ3rwQcfTK9evXLllVdmxIgRufTSS7PyyivP2z93mdi599o7/PDDc9JJJxWxYigO2Vv2lr1Lg+xNqZK9S5/sLXuXGtm7eWZvDXAazaqrrpq//OUvufbaa/PCCy/M+8D+3yoqKtKnT59svPHG+d3vfpdPPvmkCJXWn86dO+fee+/NHXfckZkzZy5yXJs2bbL33nvnF7/4xWKdSdTU9OrVa6HbV1lllXzwwQc5+uij86Mf/Wi+M4ZK4SxO5rfeeusVuwRYIuUW0Ch9Rx55ZPbff//st99+2XzzzfPoo4/miy++yC677JLVVlstyZdfrL788su59NJL8+mnn+aggw4qctV1M3To0Lz33nvz/nvGjBkpFAq577778uKLL8439u23327s8qijzp07509/+lNuvfXWXH755fnxj3+cs846K/vuu2+ee+65/PKXv8zYsWOz9tpr5+KLL/aZhLIle8vesnd58HeO5kr2ptTI3rJ3qSmV7K0BXmTvvvvufEu+TJ06NUny1ltvLXBGyeIsk9JUderUKeecc87Xjttnn32y5557zvdLtLlq06ZNDj300K8cUyr3XRs0aNBX7v/iiy8yePDg+baVSgh/4YUXMmfOnHn/PW3atCTJU089lfHjx883thSusPgqzX2ZpkUpl9d4s802S5cuXYpdBlAP1l9//Vx33XW5+OKL069fv1RWVma33XbLhRdeOG/MZZddlv79+6eioiK9evVKjx49ilhx3Q0ZMiRDhgxZYPv/fv6YqxT+Zv3vFw9fpVS+eDjooIOyxRZb5Nxzz83555+fAQMGZMyYMWnRokVOOumkHHXUUYt1dj7lQfZeONm7eZK9Sz+X1UYpfI5ZmHJ5jWVvKB2y93/I3l+SvZuGQk1NTU2xiyhX66yzzkJ/8Gtqar5y+5tvvtkY5TW4mpqajB07NpMnT06SLLvssllppZWKW1QDK/U5jxs3boke19zP+lzYz/J//2pd2L7m/LO8zjrr5Nhjj813v/vd+bZPnTo1xx13XM4666ysu+66Czxus802a6wS6125vcblaJ111im7JcnKbc6DBg3KpptuOt+STeVk0qRJadu27QJXAT7++ON5/fXXs8suuzTJ+zUtjueee26JHrf55pvXcyWNZ2H3Bf06pfT36fnnn89xxx2XqVOnplAo5LjjjsuJJ55Y7LJogmTv0s6hC1Pqc5a9/6OUc5ns/aVSfo3LUbnl0KT85ix7y96LIns3X801e2uAF9E111yzRI874YQT6rmSxjVq1Kj07ds3Tz31VKZPnz7fvqWWWirf//738/Of/3yJfqk0VeU453LydWffL8qee+5Zz5U0jkV9gZgs+kvEJM36D345vcb/fWXU4mjOX7Ik5RnQnnvuuay++urNevlPKHfl+MVDknz++ee57LLLcs8996R169Y58sgj88gjj+Ttt9/Od77znfTu3Tvdu3cvdpk0IbJ3+eTQcpxzOSmnXJbI3oujOb7GsrfsDTQfsnfzzN4a4DSqfv365YorrkhFRUU23njjrLnmmunQoUNmz56dyZMn54033sibb76ZioqKnHnmmTnkkEOKXXKdleOca+v666/PkCFDMnDgwGKX0uhmzpyZVq1aFbuMJVKuXyAurub6Gn/VlyxfpTl/yUJ5WNS9Mr9KqSwVysLdcccd+dvf/rbEf9cojhEjRuT888/P+PHjs8UWW+Siiy7KyiuvnFmzZuXqq69Ov3790qZNm5x++unZf//9i10uFE055tBynHNtyd7NL5clsndtNdfXWPamVMne/C/Zu3kqheytAU6jefzxx3P00UfnO9/5Tvr06ZMVVlhhoePef//9XHjhhXn66afTr1+/fOc732nkSutPOc55cZx//vm56667mv2H92uuuWaxAuaoUaPSq1ev/OUvf2nAqqhP5fQa//73v1+iEN7cv2Qpx4C2JF/6FgqF3HzzzQ1QTcMrx+Wqvu5LtdatW2fppZfOWmutlR/+8IfZZ599UlFR0YgVFlcpfA758MMPs+yyyy6wvN6ijB07Ni+++GL22GOPhi2sgZx22mn5y1/+knbt2uWMM87Ifvvtt8CYkSNH5swzz8zYsWOz+eab5+KLLy6pZY+hNsoxh5bjnBdHKfzNS8orl5WrcnqNZe/ak72bF9l7QbJ38/8cIns3z+ytAV5E5fYH/+c//3kmTpyYgQMHpmXLll85dvbs2dljjz2yyiqr5Nprr22kCutfOc55cZTCH7/kyw85hx9+eE4//fSvHDd79uxcc8016du3b+bMmdPs511bM2bMyKefftqkl0P5Ol7j0leuAW1xNec5l+O9Mg8++OCv3D9nzpx89tlnef/99zNr1qxsueWWuf7669OiRYtGqrC4SuFzyLe+9a1cdtll891PcPr06bnoootyxBFHZPXVV59v/P33358zzzyz2c55nXXWydZbb52LLrooyy+//CLHzZgxI7/+9a9zxx13pF27dnnppZcasUqaItl70Uolh5bjnBdHKfzNS+SyryN70xyUWw5Nym/OsveCZO/m/zlE9l64pp69y+MnrIlanPva/PcZRM01hL/xxhs59NBDvzaMJkmLFi3yox/9KPfff38jVNZwynHO5WjTTTdNv379UlVVlfPOO2+hY956662ceeaZeeutt7LMMsvkl7/8ZSNXWX+23377nH322dl+++3nbZs5c2YeeuihbLXVVunSpct844cMGdKs/+An5fcal6NHH3202CU0utGjRxe7hEbVnMP0khowYECtxs2YMSO33357Lrvsstx+++1fG95pOhZ2LvMXX3yRwYMHZ/fdd18ghDd3l1xySa3u8dmmTZtccMEF+cEPfuDvMUlk769SKjm0HOdcjsotl8nepf8alyPZu/TJ3osmezdfsvfCNfXsrQFeRLX54zdu3LhcdNFFGTFiRJZeeumcfPLJDV9YA5k2bVq6detW6/ErrLBC/v3vfzdgRQ2vHOdcjvr165eTTjopt99+e6qqqtKnT595X5zV1NTkT3/6U6699trMnDkzO++8c84555wsu+yyRa56yY0bNy7Tp0+fb9u0adPSq1ev9OvXb4EQXgrK7TVOknfffTcjR47MXnvtNW/bxIkTc8011+Sll15K27Zts/322+fQQw9tlvdb+1/lGNCY38yZM/OPf/wjbdq0yWqrrVbschpVmzZt8vOf/zyvvPJKBg0aJISXgFJd5Ks2Afy/ffOb38w+++zTQNXQnMjeX60Ucmg5zrkclVsuk71L/zVOZG/Kj+wte5cS2ftLTTV7a4A3UXPmzEm/fv3yhz/8ITNmzMguu+ySXr16NesPt7Nnz16sD2otWrTIzJkzG7CihleOcy5HrVq1yrXXXptevXpl0KBBmTFjRq644oq8//77OeusszJq1Kh07do1F1xwQb7//e8Xu9wGU6p/8JPye42vuOKK9OvXLzU1Ndlzzz1TUVGRqVOnZr/99suHH36Yjh07ZsUVV8zvfve7PPbYYxkwYECtrrZpzsoxoI0fPz6jRo1KmzZtstlmm9X6PkdN2dSpU3PDDTdk5MiR852h/cADD6R379757LPPknz5wb1Pnz7ZaKONilRpcfTs2TNPPfVUscuAOvniiy/yyCOPZODAgXnuuedSU1OT448/vthl0YTJ3qWRQ8txzuWo3HLZosjepfMay94Lkr1l73Ige1MKmkP21gBvgl544YVceOGFeeedd7Lqqqvm/PPPz3e+851ilwVf65prrlms8a+99loDVdL4Kisrc9lll6VDhw655ZZb8sEHH+Sdd97JjBkzss8+++TMM8/MUkstVewyqYNyeY2HDh2avn37Ztttt80hhxySioqKJMkf/vCHjBs3LhtuuGH+/Oc/p23bthk1alQOPvjg/PnPf86RRx5Z5MrrrhwD2rhx43LllVdm5MiReeyxx+Zt/9Of/pSrr746c+bMSU1NTZZZZplcdNFF+eEPf1jEautm2rRp2W+//fLuu+9m+eWXz+zZs9OiRYuMGjUqZ555ZmpqarLffvtlzTXXzODBg/Pzn/88gwcPziqrrFLs0htNZWVlqquri13GEhs8ePBijf/nP//ZMIVQFCNHjszAgQPz8MMPZ9q0aampqck3vvGN7LfffsUujSZM9qa5kr1LP5eVs3J5jWVv2TuRvWXv5kn2Lm/NKXtrgDchkyZNymWXXZbBgwenVatWOfHEE3PkkUeWxPI2c73wwguZM2dOrca+9NJLDVxN4yinOS9uCE/mv8deKTjnnHPSsWPHXHPNNamoqMgf//jHbLPNNsUui3pU6q/xHXfckQ033DDXX3/9vG01NTW5//77UygU8otf/CJt27ZNkmywwQb58Y9/nL/85S/NPoSXY0D75JNPst9++2XSpEnZYIMN5s35ySefzJVXXpkWLVrk1FNPzVprrZW77rorp556au68886su+66xS59ifTr1y/vv/9+fvvb32annXaat/3aa69NTU1Nfvazn+Wss85Kkuy9997Zfffdc/311+eSSy4pVsmN7pVXXskKK6xQ7DKW2FlnnbVYnytqampK7nNIuRk/fnzuu+++DBw4MO+99968K+J69uyZI488MltttVWRK6Spkr3n19xz6FzlNGfZu/RzGaX/GsvesrfsLXs3V7J3+Wmu2VsDvIm4++67c8UVV2TKlCnZcsstc/755+cb3/hGscuqd3fddVfuuuuuWo0tlV+M5TTn/v37F7uEJuGEE05Ip06d0rt37/Tt2zebbLJJSZydzH+U8mv8+uuvLxCo33jjjXzyySdZaqml0rNnz/n2rb/++nnggQcas8QGUY4B7U9/+lOmTZuWW2+9db4z6v/0pz+lUCjk+OOPn/de+N73vpe99torN9xwQ377298Wp+A6GjJkSH784x/P9/pOmzZt3rJjBx544LztrVu3zu67756777670esslr/+9a+57777csQRRxS7lCXWnH8eqb2ZM2dm2LBhGThwYJ555pnMmTMnlZWV2XzzzbPJJpvkuuuuyyGHHNJkAzjFJ3svqLnn0LnKac6y95dKOZfxpVJ+jWVv2Vv2lr2bq+b880jtlUL21gAvsrfeeisXXHBBXn755XTp0iVXXnlldt5552KX1SDK8Rdjuc158803X+zHvPDCCw1QSeN6/vnnF9i21lprZZ999sldd92Vn/3sZznjjDPmLWc112abbdZYJVJH5fQaT58+Pcsss8x82/7+978n+XI+lZWV8+2bPXt2s/7ycK5yDGgjRozIXnvtNV8Anzx5cl588cUkyU9+8pN52wuFQn70ox/l5ptvbuwy680HH3ww3+uYfPmzPXv27Ky88soLND9WWGGFfPLJJ41ZYr3r1avXV+6fM2dOpk2bln/84x95//33s+qqqzbrEL7nnnsWu4SiGDp0aN577715/z1jxowUCoXcd999836e53r77bcbu7x6deGFF+ahhx7KlClT0rp162y99dbZYYcdsv3226dTp04ZN25c/vCHPxS7TJoo2bu0lducZe//KNVcVq7K6TWWvWVv2ftLsnfzI3t/SfZu+tlbA7yILr300gwYMCBz5szJdtttl5NPPjlLLbVUPvzww698XPfu3RupwvpVjr8Yy3HOtfHRRx9l0KBBGTx4cMaOHZs333yz2CXVycEHH/yVIeT111/PoYceusD25jzvd999d75gOnXq1CRffrHYosX8f1rGjBnTqLU1hHJ6jbt165axY8fOt23EiBEpFAr53ve+t8D4UaNGZbnllmus8hpMOQa0f//731lrrbXm2/bss8+muro6a6yxxgKva+fOnTNlypTGLLFeVVRULHCPrWeeeSZJ8t3vfneB8Z9++mmzv7pk0KBBtRq38sor52c/+1mOO+64Zj/nRVnY5+sVVlihZL5EHDJkyALbF3VftuY859tvvz3t2rXLMccckyOOOKJk36/UP9m79JXjnGtD9v5Sc5637D2/UnqNZe//kL1l7+b+uV72/g/Z+z+a85xLJXtrgBfRTTfdNO/fhw8fnuHDh9fqcc3xQ92S+PTTTzNmzJhmeRbnkirlOX/xxRcZMmRIBg4cmGeffXbeknML+1Df3Bx//PHN+g/akrj++uvnu0/VXJdeeukC25r78oJJeb3G3/ve93LvvffmwAMPzHLLLZeRI0fmhRdeSMuWLbPjjjvON/add97JX/7yl+y7775Fqrb+lGNAa926daqqqubb9vTTT6dQKGTLLbdcYPy///3vdOjQobHKq3drrLFGRo4cmYMOOijJl7+bhg4dmkKhkO9///sLjH/00Uez+uqrN3aZ9erRRx/9yv2tW7dOhw4dSuqet0ly77335u67784f/vCHLLvsspk0aVK+//3vL/B7/KSTTsqxxx5bpCrrR7ktgbvnnntm2LBh+eMf/5ibb745m2666byz0Lt06VLs8mjCZO+vVso5dFFKec6yd2mRvUuX7P0fsvf8ZO/mR/aWvUtNqWRvDfAiOuGEE4pdQqP61re+lcsuuyy77bbbvG0zZsxI3759s8cee2SllVaab/zf/va3nHnmmc36S4dynPP/evnllzNw4MA8/PDD+fzzz5Mkyy67bPbaa6/st99+WXHFFYtcYd2deOKJxS6hUZVTIJ2rnF7j4447LkOGDMmPfvSjrLbaann77bdTU1OT448/Pssuu2ySL8P3kCFD0r9//7Rs2TKHH354kauuu3IMaOuss06eeeaZ/OxnP0vyn3v7JMkOO+ww39iampr89a9/zTrrrNPoddaXPfbYIxdffHHWX3/9bLnllrnrrrvy4Ycf5hvf+Ea23nrr+cZef/31efnll3POOecUqdr6UQp/YxfXL37xizzyyCPp3r17Pvzww3m/t5Jk9913z8orr5wkue+++3Lddddl7733TteuXYtVbp0tyRK4s2fPboBKGscll1ySCy+8MMOHD88DDzyQJ554Ik8++WQuvPDCbLzxxunRo0fZfUahdmTv0s+h5Tjn/yV7lx7Zu7TJ3rJ3InvL3s2X7P31ZO/i0wAvonIL4TU1NQtsq6qqyrXXXptNNtlkgUBaCspxzkkyYcKEDB48OIMGDcq//vWv1NTUpG3btvnud7+bp59+Or/61a+y/fbbF7tMllA5BdJy1KVLl9xzzz259tpr8/LLL+fb3/529tprr+yzzz7zxgwaNCj9+vXLiiuumN/85jdZYYUVilhx/SjHgLb//vvn1FNPTZ8+fbLlllvm3nvvzSeffJJvf/vb810NNWPGjPz617/OO++806y/cNl///3z4osv5pJLLkmhUEhNTU2WWWaZXHHFFfPuIXjPPffkT3/6U8aOHZtNNtkkBxxwQJGrblijRo3KyJEjU1FRkc033zxrr712sUuqk/vvvz+PPPJIjj766Jx00kkL3Ddxjz32yHe+850kyTbbbJN99903d955Z8l8Jp82bVpqamq+8gqZkSNH5txzz82DDz7YiJXVr1atWmXHHXfMjjvumKlTp+avf/1r7r///rz44ot58cUXUygU8sc//jGff/55dtxxx7Rp06bYJdMElMrPeW2VYw4txzknsnepk71Lm+wte8vesndzJXvL3s0le2uANyEzZ87M6NGjM2HChNTU1KRbt25ZZ5110rp162KX1qAWFlRLXanO+eGHH87AgQPz9NNPZ86cOenQoUN22223/PCHP8zWW2+djz/+eIEzG0vBNddcs9iPKRQKOf744xugmoZ3yCGH5Nhjj533QaYclNtrvPzyy+eiiy5a5P699tor3//+99OjR4954aW5K8eAtvPOO+ett95K3759M2DAgNTU1GSllVbKVVddNW/MjTfemD/84Q+ZNm1adtppp/z4xz8uYsV1UygUcsUVV+Sggw7KyJEjs9RSS2WHHXaY7yzlf//736mpqcmxxx6bo48+uiTe32+88Ub++Mc/ZsyYMfnGN76RY445JhtssEF++ctfZuDAgfM+kxQKhey666655JJLFrifZHMxaNCgbLzxxjnllFO+duzcL9wef/zxZh/CH3nkkVxzzTX5xz/+keTL+8qddNJJ2XXXXeeNmT59eq688srcfvvtCyw52Zz06tUr+++/fzbccMMkydJLL5199tkn++yzT8aPH58HH3wwDz74YEaNGpVXX301F110UXbeeef86le/KnLlNDWyd/ko1TnL3rXXnHOZ7F07zfk1lr1l77lk7+b//pa9F072bp5KJXs3z5+wEjNx4sRceeWVGTJkSKZNmzbfvrZt22bHHXfMKaeckm7duhWpQqidU045Je3atcuBBx6Y7bffPpttttl8Z4A1h2UxlkS5BbTnnntuvjOSy0G5vcZfp7kvP7Yw5RrQTjnllBxwwAF55ZVXstRSS2XzzTdPy5Yt5+1v3bp11l9//ey22275yU9+UsRK68/GG2+cjTfeeKH7TjjhhGYfyP7byJEjc8ghh6RFixZZa6218vrrr+eggw7KwQcfnHvvvTe77757dtppp0yfPj2PPfZYHnzwwXzrW9/KYYcdVuzSl8gbb7yRY445ptbjt9pqq1x77bUNWFHDe+ihh3LqqaemdevW2WqrrdK2bdu88MILOf300+fdP/LVV1/NKaeckg8++CArrbRSLrzwwmKXvcQGDRqU7373u/NC+H9bbrnlcvjhh+fwww/PmDFjcv/99+eBBx7I3Xff3eRCOMUje1MqZO/aa865TPauneb8Gn8d2Vv2bs5kb9l7Ltm7+SmV7K0BXmQvv/xyjj766EyZMiUbbrhhtthii3Tr1i0tWrTIhAkT8vzzz2fw4MF57LHHct1116VHjx7FLhkWaaWVVsoHH3yQgQMH5p///GdeffXV7LDDDvnmN79Z7NIaVP/+/YtdAg2snF7j559/foke99/LdjVn5RTQ5lp++eWz/PLLL3TfT3/60/z0pz9t5IqoL9dee21WXXXV9O/fP506dUpNTU3OPvvs3HTTTdl9991z2WWXzRu766675rPPPsuDDz7YbEP4jBkz0qFDhwW2L7300rn++uvzrW99a77t7du3b9b35EqSW265JZ07d84dd9wx7x5rVVVVOfbYY/P73/8+3bp1y2GHHZYvvvgiP//5z/OLX/yiSS5LVt9WX331nHLKKTnllFPy0ksvFbscmgjZm1Iie1Oqyuk1lr1l7/8mezdvsveXZG/Zu6nRAC+iTz75JMcff3zat2+fP/zhD9lkk00WOu6NN97IySefnJNOOin33XdfOnfu3MiVQu0MGzYsr7zySu6///789a9/zd/+9rdceeWVWW211fLDH/4w6667brFLbBCbb775146ZOnVqCoXCV94bhKarNq9xqTj44IMX+4qRQqGQN954o4EqgvrRq1evxX5MoVBInz59GqCaxvHqq6/miCOOSKdOnZJ8OZ/DDz88gwYNyrbbbrvA+B/+8If59a9/3chV1p/lllsuH3744QLbW7RosdD5/utf/2r291EcM2ZMDj744HkBPPnyKtYTTjghP/3pT3PKKaekc+fOueKKK7LRRhsVr9Ai0sQkkb0pPbL3osnezZvs/dVkb5oD2Vv2/l+yd3loitlbA7yIbrnllkybNm2+s0YW5tvf/nZuuumm7Lbbbrntttty4oknNmKVsHg23HDDbLjhhjn77LPzt7/9LQ888EAeffTRXHfddSkUCikUChk+fHjWWWedrLjiisUut97U1NTkiSeeyD/+8Y+sssoq2XbbbdOiRYs888wz6d27d959990kybe+9a2ceuqp2WqrrYpccd0MHTo07733Xq3Hl/KSZHOVyhctl1xySa3GPfLIIxkxYkSSZIMNNmjAihpHOQa0Qw45ZLEfUygUcvPNNzdANQ1v0KBBtR77319ENefX+LPPPkuXLl3m2zZ3acGOHTsuML5NmzapqqpqjNIaxAYbbJCHHnooxx9//Ncukzhz5sw89NBD2WabbRqpuoYxderUrLTSSgts/8Y3vpHky6UU77jjjnlfxJSCF154IXPmzFmsx+yxxx4NUwzNhuxNKZK9Ze+Fkb2bD9m79mTv5kX2/pLs/SXZu/kqheytAV5Ew4YNy+677/6VAXyuFVdcMXvuuWeGDBnSrEP4//7QzL3v2lNPPZXx48fPN7YpLpmwJMpxzklSWVmZbbbZJttss01mzJiRoUOH5sEHH8xTTz2Ve+65JwMHDkzPnj2z1157Zddddy12uXXy2Wef5aijjsorr7ySmpqaJMn666+fc889N0cddVTatm2bHXbYIdOnT88rr7ySo48+OjfddFOzPrN56NChGTJkSK3Hl0II/+8vWr7xjW9ku+22K8kvWvbcc8+v3D9u3LhcdNFFGTFiRDp06JBTTz01++23XyNV13DKMaB98MEHtRpXXV2d8ePHp6amplnfT3L06NFfO+a/399LL710Tj755IYvrAHV1NSkRYv5P+7PfQ2b82u5KPvvv39++tOf5le/+lXOOeecBeY+V3V1dc4777xMmDAh+++/fyNXWb+qq6vnu+frXHPvJ3jUUUeVVABPkrvuuit33XVXrcbO/b3V1EI4jU/2Lo8cWo5zTmRv2Xt+snfzIXt/Pdm7eZK9vyR7y97NXSlkbw3wIvrggw8W694e66yzzmJ9SGiK/veHZm5g6du37wJ/DJr7H/u5ymnORx11VLbYYotsvvnmWXfddefNpU2bNtltt92y2267ZdKkSXnooYfywAMP5Jlnnsnf//73Zh/Cr7766owePTrnnXdeevbsmXHjxuXiiy/Oz372s6y66qoZMGDAvLP9Pv300+y9997p169fsw7hRx99dL773e8Wu4xGU45ftPyv2bNn58Ybb8z111+fqqqq7L777jnrrLPmndHa3JVjQHvssce+dswrr7ySCy64IP/+97+z0kor5dxzz22EyhrfnDlz0q9fv/zhD3/IjBkzsssuu6RXr14LnMFN07bpppvm8MMPz4033pi///3vOeyww9KzZ88sv/zyqampyccff5xnn302t956a0aPHp1TTjkl66yzTrHLblDdu3cvdgn1bt999y3bJeVYcrJ3aefQucppzrK37F2qZG/ZO5G9ZW+aOtl7QbJ306QBXkQtW7bMF198UevxM2bMSLt27RqwooZV26V9Skm5zfnvf/97nnjiiXlLUW266abp2bNntthii3l/5Dp16pSDDjooBx10UMaOHZu//OUvRa667h577LHsv//+OeCAA5Ikq622Ws4777wcdthhOeigg+Zb6mbZZZfNvvvumwEDBhSp2vqx+uqrl1TA/Drl+EXLf3vuuedy4YUXZsyYMVlttdVy/vnnp2fPnsUuq9GUY0CbOnVqfvOb3+Suu+5KRUVFjj766Bx33HFp3bp1sUurdy+88EIuvPDCvPPOO1l11VVz/vnn5zvf+U6xy6o3/7ts5owZM1IoFHLfffflxRdfnG/s22+/3djl1bvTTz89K664Yq688sqcd955C214tGvXLhdccEFJXEFTjjbddNPstttuxS6DZkb2Ln3lNmfZW/YuVbK37C17y97Nlewte5eaUsjeGuBFtNZaa+Xxxx+v9X1ARowYkTXWWKOBq2o4X7e0Tykqtzm/9NJLeeONN/LSSy9l5MiRefnllzN8+PAUCoV06NAhm222WXr27JmePXtmrbXWysorr5xjjjmm2GXX2ccff5zVV199vm1zf1YXdvbXCiuskClTpjRKbdSPcvyiJfnyC4VLL700999/f1q3bp1f/OIXOeKII+Yt71MOSj2gLcx9992Xyy67LJ988kk233zznH/++Qv8jisFkyZNymWXXZbBgwenVatWOfHEE3PkkUemVatWxS6tXg0ZMmShy2YOHjx4oeOb89Vwcx144IHZc889M3z48Dz//PP597//nZqamnTr1i09evTIDjvs0OzvGfnfFnZv0K/6sqUUlkaFxSV7l75ym7Ps/R+yd2mRvWVv2Vv2bq5kb9lb9m56NMCL6Mc//nHOO++8PPTQQ9l5552/cuzgwYPz9NNP58orr2yk6orvgw8+yHnnnZd+/foVu5RG09zn3KJFi2ywwQbZYIMNcuihhyZJPvroo/lC+aWXXpo5c+akY8eO2XzzzdOzZ88ceOCBxS28jmbNmpU2bdrMt21uSFlYWCkUCvPdm46mrxy/aLnzzjtz5ZVXZsqUKfne976Xc889t1b3zSwV5RLQ/tu7776bCy+8MM8991w6deqUX//6103u3j315e67784VV1yRKVOmZMstt8z555+fb3zjG8Uuq97179+/2CUUTdu2bbPzzjt/7WfsUrCoL1qShX/ZIoRTjmTvr9bcc+iSaO5zlr3/Q/YuLbK37C17lxbZu/TJ3l+SvZsmDfAi2muvvTJ48OCcccYZeeutt3LQQQelW7du842ZMGFCbrrppvTv3z/bbLNNfvSjHxWp2vrxyiuv5LrrrsvIkSOTJN/+9rdz/PHHZ9NNN503pqamJn/+859z9dVXZ8aMGcUqtd6U45z/2worrJBddtklu+yyS5Ivl/a5//77/197dx4WVbn4Afw7bAKi4kaKW24xbikii3TVEsQtFFxyS9Lcb5qamloULiiaW5amqVfc6rokICgiKC4psigg11CvXs1dcQORxIHh/P7wYX6NoAIyczjnfD/P0/PkmXfy+4bCfM97znsQEhKCAwcOIDo6WvIlXGkmTpwIBwcHsWMYlZJOtJw/fx4BAQFIS0vDW2+9hfnz58PLy0vsWEallIJWSKPRYPXq1di4cSPy8/MxcOBATJ8+HVWrVhU7Wrm7cOEC5syZg9TUVNSqVQvLly+XdUmTy1aQ9HJKO9Hi6+sr6+/HZDjs3srooUqc89+xe8sPu/dz7N7yxe7N7i0X7N7yx+4tTVwAF5GJiQnWrl2L6dOn4+eff8a6detQt25d1K5dG6ampnjw4AGuXbsGQRDQs2dPLFiwQOzIb+TkyZMYM2YMtFotGjduDCsrKyQlJWHEiBEIDg6Gs7Mzbty4gWnTpiEtLQ02NjaYO3eu2LHfiBLn/KLc3FwkJSUhMTERp0+fxtmzZ5GXlwcLCwvdlmxykJmZiVu3bul+XXgF8sOHD/WOA8+vbpWyiRMn6v1ao9Hg/PnzyMjI0G1zo1arZfm8IiXo378/CgoKAAA1a9bEtm3bsG3btle+R6VSYfPmzcaIZ1BKK2gAcPToUcyfPx83b96Eg4MD5s6di7Zt24odyyAWL16MrVu3QqvV4oMPPsCUKVNgY2NT5Hv0i4q700Sutm/fjuPHj2PVqlViRymTkm5t/HdS//5VlhMtp06dMkAS41DaM36p/LB7y7+HKnHOL2L3ZvcmaWH3Zvdm99bH7i0d7N4lw+4tPpUgCILYIQj4/fffsWfPHqSlpeHevXu6D7JOTk7o27cv3NzcxI74xkaOHIm0tDRs2LABjo6OAIC7d+9i/PjxMDc3R1BQEPz8/PDgwQN4eXnhm2++Qe3atUVO/WaUOOf8/HykpqYiPj4e8fHxOHPmDPLy8mBubo53331XV7wdHR1ls52RWq0u9rktgiC88nku586dM2Qsg7t//z6WL1+O6Oho5OTk6L1mZWWF7t27Y+rUqUXurpEitVqNr7/+Gh4eHrpjWVlZ8PX1xbJly3R/vwvFxMRg0aJFkvwad+3atUzvi42NLeckxvWygvY6Ui5on3/+OWJiYgAAH3zwAfz8/GBqavra9zk7Oxs6mkGo1Wrdv5fmWVtS/HtcVgEBAdi5c6dk51zS718FBQW4e/eu7ue0VOdbGrdv30ZoaCjCwsJw/fp1RcyZ6GXYveXZQ5U4Z3bv/8fuze4txa8xuze79+uwe8sXu7d8sXtXLFwAJ6Nxc3PDgAEDMH36dL3jx48fx5gxY9C0aVPcu3cPc+bMkfx2c4WUNucxY8bg1KlTyM3NhYmJCVq1agU3Nze4urrCycmpyDZWcjF79uwyvU/KV1KlpqZi3LhxyMrKQtu2beHm5gY7OzuYmZkhIyMDSUlJSEpKQtWqVbFmzRq0b99e7MhvRKknWpREiQXt73MGXj9vqReWsl5Z/eKdN3Im9RJeEmfOnMGcOXNw7tw51K9fH9988w26dOkidiyDePbsGaKjoxESEoKEhATd3+FOnTrh559/FjseERmQ0noooLw5s3uXDru3dLB7yx+7N7v3y7B7ywu7N7t3RcAt0CUkISEBFy5cKNMWExVBdnY2mjZtWuR48+bNIQgCMjMzsWvXLlk8W6CQ0ub8+++/w9zcHD4+Phg3bhzefvttsSMZhZTLdFk8ePAAn332GSpXroyffvoJTk5OxY5LT0/HlClT8Pnnn2PPnj2oWbOmkZOWHx8fn1IVMyU5deoUQkNDJb9VqJKKViGlfe8qy9dYbs8GVbLs7GwsW7YMO3fuhImJCcaNG4d//vOfstwyNDU1FSEhIdi/fz+ePHkCAKhRowb69++PQYMGoV69eiInJKr42L2lR2lzZvdWBnZv+jt2b+lS2vcudm9lY/dm965IuAAuIZGRkdi5c6dkS7hWq4WZWdE/coVbcY0fP142ZbSQ0uY8cOBAJCQk6Lb5aNKkCTp27Ag3Nzc4OzujWrVqYkekcrBt2zbk5ORg+/btaNCgwUvHtWzZEsHBwfD29savv/6KSZMmGTFl+Vq0aJHYESqUO3fuIDQ0FKGhobh+/ToAKLKES72g+fr6lvo9N2/eNECSiqfw5NKBAwck/cwmem7Pnj347rvv8ODBA7i4uCAgIKDYRRIpy8jIQFhYGEJDQ/Hnn39CEARYWVnB3d0dcXFxmDdvnt5WokT0auze0qO0ObN7KwO7N7F7P8fuLV/s3vLC7s3uXdFwAZwqDLl9MywJuc15/vz5AIBbt24hLi4O8fHxiIqKwrZt22BiYgK1Wg1XV1ddKbe2thY5MZXFwYMH0adPn1cW8EL16tWDr68voqOjJV3C/fz8MGHCBHTs2FF3LD8/HykpKVCr1ahSpYre+PDwcMyaNQvp6enGjmowGo1Gt51PfHw8BEGAIAhwdXXFkCFDxI5nVEoraM+ePUNUVBRCQ0ORlJSEP/74Q+xIBlF4ciksLAzXrl2DIAiwtbUVOxa9gcuXL2Pu3LlITExE9erVsWjRIvj4+Igdq1zt378fISEhiIuLg1arRdWqVeHt7Q0vLy906tQJ9+7dg6enp9gxiaiCkVsPLQm5zZndWxnYvZ9j92b3ZveWF3Zv+WH3ZveuqLgATkTlzt7eHgMGDMCAAQMAABcvXkR8fDxOnjyJ3bt3Izg4GGZmZmjdujU6duyIyZMni5yYSuPGjRv4+OOPSzxerVYjNDTUgIkMLzExEQMHDtQ7lp2dDT8/P2zcuFGvnBcSBMFY8Qzqxe18CufVu3dvfPbZZ2jSpInICY1DiQUtOTkZISEhiIqKQk5ODgRBQPPmzcWOVa4KTy6FhoYiPj4eBQUFEAQB7dq1w+DBg9GrVy+xI76RsLCwUo2/cuWKYYIYmUajwerVq7Fx40bk5+dj4MCBmD59OqpWrSp2tHI3depUWFtbY+jQofDw8ICzszNMTU11r3MLUSIieWP3ljd27+fYvdm92b2lj91bH7u39LB7SxMXwMmoTp06Ba1Wq3csJycHAHDixAncvXu3yHukfrWQEuf8oubNm6N58+YYPnw4NBoNoqKi8OuvvyI1NRVnzpxhCZcYc3NzPHv2rMTjc3NzZXvHgVyK9ouK287H1tYWvr6+aNeuHQICAtCrVy/ZF3C5F7Ti3L17V/e1v3r1KgDAzMwMvXr1wpAhQ9ChQweRE5aPM2fOYPfu3Xonl6pWrYrs7GzMnz+/yEk3qZo1a1apSpggCJIvbUePHsX8+fNx8+ZNODg4YO7cuWjbtq3YsQymfv36uHHjBkJCQnDlyhX85z//gaenJxo3bix2NCISmRJ7qBLn/CJ2b3lh9/5/7N7s3nLD7s3uze4tLeze0sQFcDKqnTt3YufOnXrHCj/EbtiwQe8bf+EPAqkXUiXO+e+uXbuGM2fO4MyZM0hLS8P58+eRl5eHypUro3PnznB2dhY7IpXSO++8g6NHj5b4mYhHjhxBs2bNDJyKysvYsWNx4sQJaLVa1K1bF0OHDoWnpydcXV1hYmKCmzdvyvbkQyGlFLRCGo0GBw8exO7duxEfH687cdy0aVNcvnwZS5YsQY8ePURO+eYyMjKwZ88ehIaG4sqVKxAEAfb29vD19YWXlxfeeustdOvWDTVq1BA7arkJCgoSO4JRff7554iJiQEAfPDBB/Dz84NGo0FSUtIr3yflzyIHDx7EmTNnEB4ejqioKBw/fhzLly9HkyZN4OXlhVatWokdkYhEosQeqsQ5/x27t/ywe8sbuze7N7s3u7dUsXuze0sFF8BFdOvWrVKNL7xyWaqU9oMAUN6cs7KykJaWpivcaWlpyMrKgiAIqFatGtq3b4+pU6fC2dkZLVu2hImJidiRqQz69u2Lb7/9FpGRka+9CjcsLAxxcXFYvny5kdLRmzp27Bisra3h5+eHYcOGoXbt2mJHMgolFrS0tDSEhIQgMjISjx8/homJCRwdHeHl5QUvLy9otVp4enrC3Nxc7KjlomvXrigoKIBarcb48ePh4eGB1q1b616/efOmiOkMw9fXV+wIRhUdHa3799jYWBw+fPiV4wsXP86dO2foaAbVtm1btG3bFl999RWOHz+OiIgIHDp0CGvWrIFKpYJKpcLhw4ehVqtRr149seMSiYLdW/6UNmd2b2Vg95Y3dm92b3Zv+WD3Zvdm966YuAAuoq5duypqa4yy/CCQ+g9Epc3Z1dUVKpUKgiCgevXqcHFxgbOzM5ydneHg4CDpP7/0//r374+wsDB8+eWXuHDhAoYNGwY7Ozu9MRkZGQgODsaWLVvQpUsX9OzZU6S0VFoTJ07Evn37sHbtWvz8889o3LgxPD094enpiXfffVfseAajxIL20UcfwcrKCp06dULnzp3RtWtXvZMMcptzfn4+rKysUKtWLVhZWRXZIlUJ7t69i9OnTyMjIwMAYGdnB0dHR9StW1fkZOVDaYsfLzI1NUWXLl3QpUsX5ObmIiYmBnv37sWJEyfw22+/ISQkBK6urujfvz8+/PBDseMSGRW79+tJ/ee+0ubM7q0M7N7yxu7N7l1IbnNm92b3ljt2b+ngAriIfHx8WEqK8ezZM0RFRSE0NBRJSUn4448/xI5kcHKZc48ePeDi4gIXFxduuyVjJiYmWLt2LaZPn46ff/4Z69atQ926dVG7dm2YmpriwYMHuHbtGgRBQM+ePbFgwQKxI1MpTJw4ERMnTsTZs2cRERGByMhIrFu3DuvXr0fdunXh5OQky59dSixoVlZWePr0Kf73v/+hevXqsLa2RufOnWFjYyN2NIM4fPgwIiIiEBERgeXLl0OlUqFWrVro1q0bunXrJuurcy9evIjAwEAkJSVBEAS9rRRNTEzg5OQEf39/ODg4iJjyzSntqvtXsbS0hLe3N7y9vfHo0SNERkYiIiICJ0+eRHx8PEs4KQ67d/Hk0kNLQy5zZvdWBnZveWP3Zvdm95Yfdm/lYfeu2FSC3B8mQpKRnJyMkJAQREVFIScnB4IgoHnz5oiIiBA7msEocc4kL7///jv27NmDtLQ03Lt3D4IgwM7ODk5OTujbty/c3NzEjlgu1Go1vv76a3h4eOiOZWVlwdfXF8uWLYOjo6Pe+JiYGCxatEjyW/sAz++AOnnyJCIiIhATE4MnT54AAOrVq4f+/fvDx8cH9vb2Iqd8c7dv39YVtIsXLxZb0Ly8vLB69Wq9PwdSlpubi9jYWISHh+P48ePQarUwNzdHx44d0a1bN6jVagwYMEBWcy50/vx53Umm27dvQ6VS6U5KzJo1C5988onYEcvNoUOHMGXKFKhUKnh6esLNzQ12dnYwMzNDRkYGkpKSEBUVhfz8fKxYsQKenp5iRyYDun79Ovbt24fx48eLHYWIRKTEHqrEOZO8sHuze7N7Sxe7N7s3u7fysHtXDFwAl4jbt28jNDQUYWFhes9YkLq7d+8iLCwMoaGhuHr1KgDAzMwMXl5eGDJkCDp06CBywvKnxDkTSZ1arS72yuvXbY8phxL+dxqNBrGxsdi7dy+OHj2KvLw8mJiYoGPHjvjXv/4ldrxyo6SCVigzM1N3lWpqaqrea2PHjsX48eNhZWUlTjgDS0xMREREBKKjo5GVlQWVSoX69eujX79+8PX1RZ06dcSOWGY3btyAt7c3GjdujJUrV6JBgwbFjrtz5w4+//xzXLp0CXv27HnpuIpu9uzZpX6PSqXCwoULDZCGiKSK3Vs+lDhnIqlj936O3ZvdW47Yvdm92b3J2LgAXoE9e/YMBw4cQGhoKBISElBQUAAzMzOcPXtW7GhvRKPR4ODBg9i9ezfi4+N1W900bdoUly9fxooVK9CjRw+RU5YvJc6ZlEOj0eD8+fPIyMjQXYWuVqtRqVIlsaOVm7J8sAPk/Uycx48fIyoqChERETh9+jTS09PFjmQQci5oL3Pz5k1ERERg7969uHTpElQqFaytrdGzZ0/079+/yF0XcpGXl4djx44hPDwcR44cwbNnzyT/uWvBggXYs2cPoqKi9J4xV5zMzEz06tULffr0waxZs4yUsHyp1epSv0elUkn6hGlZ7hBRqVQ4ePCgAdIQSRe7t3wocc6kHOzeL8fuLX3s3uzeUv7cxe79euzeJAYugFdAKSkpCA0NRWRkpG5rrjp16mDgwIH46KOPULt2bbEjlklaWhpCQkIQGRmJx48fw8TEBI6OjvDy8oKXlxe0Wi08PT1ltd2LEudMynH//n0sX74c0dHRyMnJ0XvNysoK3bt3x9SpU2FnZydSQjKWU6dOyf4OGjkWtJI4f/48wsPDERkZiTt37ki+sJTUkydPEB0djb1792Ljxo1ixymznj17onPnziU+kfjdd9/h8OHD2L9/v4GTGcbNmzfL9D4pP4Oua9euRY4JgoDbt2+jVq1asLCwKPZ9sbGxho5GJAns3vLpoUqcMykHuzcVYveWL3Zvdm8pYfd+jt274jMTOwA9d8y0lWwAAEXzSURBVPfuXezZswchISG4evUqBEGAiYkJAGDKlCkYO3as7tdS9dFHH8HKygqdOnVC586d0bVrV70rosr6jbMiU+KcSRlSU1Mxbtw4ZGVloW3btsU+1yYsLAyxsbFYs2YN2rdvL3ZkKqWzZ88iNTUVgiCgRYsWxZbsJ0+eYOnSpdi1axf++OMPEVIaj7m5OTw8PODh4aFX0OROrVZDrVbjyy+/REJCgiLmDAA2Njbw9PREcnKy2FHeyO3bt9GsWbMSj2/SpAn+/e9/GzCRYUm5TJdVcWX64cOHcHd3x5IlS9CxY0cRUhFVbOze8uyhSpwzKQO7t/yxe+tj92b3liJ2b/lj95YmLoCLqHBrrpCQEJw8eRJarRaVKlVC165d0a1bNzg4OMDX1xfNmzeXfAEHoHuGy//+9z9Ur14d1tbW6Ny5M2xsbMSOZjBKnDPJ34MHD/DZZ5+hcuXK+Omnn+Dk5FTsuPT0dEyZMgWff/459uzZg5o1axo5KZXFX3/9hS+++AJHjx5F4SYxKpUK7u7uWLNmje6KxiNHjiAgIAB3795Fw4YNxYxsdHIpaKVlb28v+ZPHN27cQHBwMFJSUgAALVu2xJgxY9CoUSO9cdHR0Zg/fz7u37+PwMBAMaKWC0tLSzx+/LjE4x8/foyqVasaMJG4NBoNLl26BEtLSzRp0kTsOAbzqudjEikVu7f8e6gS50zyx+4tb+zer8fuLV3s3q/G7i0P7N4VHxfARdSpUyc8fvwY1apVQ+/eveHh4YHOnTvDysoKgPyuUD558iRiY2MRHh6O3bt3Y+fOnTA3N0fHjh3RrVu3Mj07oqJT4pxJ/rZt24acnBxs374dDRo0eOm4li1bIjg4GN7e3vj1118xadIkI6aksvrxxx9x5MgRdOrUCb6+vrC2tsbRo0exY8cOfPfdd/D398fixYuxadMmmJqaYtSoUfj888/Fjl0ulFbQAODMmTNYs2aN3pw/++wzvbsOBEHApk2b8MMPPyA3N1esqG/s3LlzGD58OJ48eQJLS0tYWloiPT0dkZGR2L59O9555x1kZ2fD398f0dHRMDU1xdixY8WO/UZat26N6OhojBo1qkTjDxw4gBYtWhg4lWFlZ2dj/fr1SElJwdatW3XHIyIiEBgYqDsp0bhxYyxcuBDt2rUTKSkRGRO7t/x7qBLnTPLH7i1v7N7s3uze7N5Sxu5NUsAFcBFlZWXB2toa3bt3h6urK9q3b68r4HJkaWmJXr16oVevXsjMzERkZCQiIiJw7NgxHDt2DMDzq2bS0tLg7u4ui/8XSpwzyd/BgwfRp0+fVxbwQvXq1YOvry+io6NZwiUiNjYWLi4uWL9+ve7Y+++/j5o1a2Lr1q2wtbVFcHAw1Go1goKCJP+BvZASC9rJkycxZswYaLVaNG7cGFZWVkhKSsKIESMQHBwMZ2dn3LhxA9OmTUNaWhpsbGwwd+5csWOXWeFJhGXLlqF3794Anj8v9IsvvkBgYCCWLl0KPz8//Pnnn2jTpg0CAwPh4OAgcuo3M2DAAEyZMgXBwcEYOXLkK8euXbsWaWlpen/3pSYnJweDBg3C5cuXUadOHeTn58PMzAxpaWmYOXMmBEHAoEGD0Lx5c4SFhWHkyJEICwsrcqKNiOSH3Vv+PVSJcyb5Y/eWN3Zvdm92b3ZvqWL3JskQSDRJSUnCt99+K7i4uAhqtVpo0aKF8NFHHwkbNmwQrl69Kty4cUNwcHAQDh48KHZUg7px44awZs0aoXfv3oKDg4OgVquF9u3bC19//bWQnJwsdjyDUOKcST7atWsnbN++vcTjd+7cKTg6OhowEZWndu3aCZs2bSpy/NKlS4KDg4PQokULYf78+YJGoxEhneGMHz9eaNWqlbB3717dsTNnzggeHh7C8OHDhbt37wrdu3cXHBwchAEDBgjnz58XMW35GDFihNC+fXu9nzt37twRfHx8hIEDBwqXLl0S3N3dBQcHB2HSpElCRkaGiGnfnLu7uzB//vwixw8cOCC0bNlSGDp0qNC6dWth/fr1glarFSGhYUyaNElQq9XCtGnThJSUFL2/u1qtVkhJSdGN+eabb0RM+uZ++OEHoVWrVsL+/fv1jo8dO1ZQq9VCUFCQ7lhubq7g5eUlzJo1y9gxDe7hw4eCg4ODEBcXJ3YUogqD3fs5JfZQJc6Z5IPdW97Yvdm92b3ZvaWK3fs5du+Kj3eAi6hDhw7o0KEDvvnmGxw9ehQRERE4cuQIzpw5g6VLl6JBgwZQqVT466+/xI5qUPXq1cP48eMxfvx4nD9/HuHh4YiMjMRvv/2G3bt349y5c2JHLHdKnDPJh7m5OZ49e1bi8bm5ubC2tjZgIipPT58+RY0aNYocr169OgCgW7du8Pf3N3Ysg0tLS8PgwYN1VycDwLvvvosvv/wSU6dOxdSpU3Hz5k1Mnz4dn376qSyeD3ru3DkMGTIEjo6OumNvvfUWpk2bhjFjxmDy5MnIz8/HihUr0LNnTxGTlo+srKxitz9t06YNtFotLly4gK1bt8puW66lS5ciKCgI27dvx759+2BqagpbW1uYmpoiMzMTGo0GJiYmGDVqFKZOnSp23DcSHR2Nvn37okePHrpjOTk5OHHiBABg6NChuuOVKlVCnz59sGvXLqPnJCLjY/d+Tok9VIlzJvlg95Y3dm92b3bvdsYPZ0Ds3uzeVPFwAbwCMDMzg4eHBzw8PJCTk4Po6GhEREQgISEBgiBg5syZ2L17N/r164fu3bujUqVKYkc2GLVaDbVajS+//BIJCQnYu3ev2JEMTolzJml75513cPToUfj5+ZVo/JEjR9CsWTMDpyJDU6lUAAAfHx9xgxiIEgtadnY2mjZtWuR48+bNIQgCMjMzsWvXLjRs2FCEdOUvPz+/2M9QlpaWAICxY8fK6utbyMLCAgEBAfDz80NYWBjS0tJw7949CIKAJk2awMnJCd7e3rLYiuzGjRt6RRsAkpKSkJ+fjwYNGhT5s1y3bl08ePDAmBHL3apVq4ocy83NhUqlwp49e3D69Okir6tUKnz22WfGiEdU4bB7/z8l9lAlzpmkjd1bmdi92b2ljt2b3Zvd+zl2b/FxAbyCqVy5Mnx9feHr64sHDx5g7969iIiIQHx8POLj4zF//nwkJSWJHdMoXF1d4erqKnYMo1LinEl6+vbti2+//RaRkZHo1avXK8eGhYUhLi4Oy5cvN1I6MrTCwiI3SixoWq0WZmZFPwpaWFgAAMaPHy+bAl4SrVu3FjuCQTVu3FjyV5m/jomJCQoKCvSOnTx5EgDg7u5eZPzDhw9hY2NjlGyGUlwJLxQWFlbscZZwoufYvf+fEnuoEudM0sPurWzs3vLB7q2P3Vv62L31sXtXXFwAr8Bq1qyJTz75BJ988gmuXr2K8PBwSV+hXNIrVv9OpVJh8+bNBkhjHEqcM8lf//79ERYWhi+//BIXLlzAsGHDYGdnpzcmIyMDwcHB2LJlC7p06SKLLZyUpPCK89K+JmdyL2jFKe4KdTlTwp/tu3fv4vTp08jIyAAA2NnZwdHREXXr1hU5Wflo1qwZUlJSMGzYMACAIAiIiYmBSqVC165di4w/dOiQ5P+cb9myRewIRLLA7i39HqrEOZP8sXvLH7t3Ueze8qeEP9vs3vrYvUksXACXiEaNGmHSpEmYNGmS2FHKLDExsdjjKpUKgiC89DUpU+KcSf5MTEywdu1aTJ8+HT///DPWrVuHunXronbt2jA1NcWDBw9w7do1CIKAnj17YsGCBWJHplJauHAhVqxYoXdMEASoVCpMnz69yNXaKpUKBw8eNGZEo+P3Znm4fPlykbv5srOzAQAXLlwo9qp8Z2dno2QzpIsXLyIwMBBJSUkQBEHvM4iJiQmcnJzg7+8PBwcHEVO+OR8fHyxYsABt2rTBe++9h507d+LWrVto2LAhOnXqpDd27dq1SE1NlfxzFV1cXMSOQCQ77N7SpMQ5k/yxe8sfu3dR/N4sD+ze7N6F2L1JTFwAF5Gfnx8mTJiAjh076o7l5+cjJSUFarUaVapU0RsfHh6OWbNmIT093dhRy8X58+eLHHv48CHc3d0RHBys9/9BLpQ4Z1KGKlWq4Oeff8bvv/+OPXv2IC0tDf/9738hCALs7Ozg4+ODvn37ws3NTeyoVEr29vYAUOyJwsIrVV987WUnFaVGiQXt1KlT0Gq1esdycnIAACdOnMDdu3eLvEfKz6Jbu3Yt1q5dW+xrixcvLvb4uXPnDBnJ4A4dOoQpU6ZApVKhR48ecHNzg52dHczMzJCRkYGkpCRERUVhwIABWLFiBTw9PcWOXGaDBw/G6dOnERQUpFvwqFatGpYuXQoTExMAwG+//YZ169bh+vXrcHJywpAhQ0RObVgajQaXLl2CpaUlmjRpInYcItGwe8u/hypxzqQM7N7yxe7N7s3urY/dWzrYvYti966YVIJcfnJKkFqtxpIlS+Dt7a079ujRI7i7u2Pjxo1FClp4eDhmzpwp+R8Gf/fo0SN07NhRUYVUiXMmIqro1Gr1S680L7wCvzhS/pn8sjn//aPh318v/P8g1Tn/+OOPZbqbYOLEiQZIYxw3btyAt7c3GjdujJUrV6JBgwbFjrtz5w4+//xzXLp0CXv27HnpOKlISUlBSkoKbGxs4OnpiRo1auheW7VqFfbs2QNvb2+MGzeu2OcPSk12djbWr1+PlJQUbN26VXc8IiICgYGBePz4MYDnz6JbuHCh7J6pSFQS7N7K7KFKnDMRUUXH7v3/2L31sXtLD7v3c+zeFRfvAK+AeE0CEUlFbm4uLC0tixy/dOkSqlatWuT5ZFTxhYWFoUOHDqhfv77YUYzqs88+U9xWa0FBQWJHMCopb2VbVps3b4a5uTk2bNigV0RfVKdOHaxbtw69evXCL7/8glmzZhkxZflzdHSEo6Njsa9NnDjxpSdW8vLykJqaWuzdoBVVTk4OBg0ahMuXL6NOnTrIz8+HmZkZ0tLSMHPmTAiCgEGDBqF58+YICwvDyJEjERYWhkaNGokdnahCYPcmIqlg95Yfdm/lYPeWP3bvoti92b0rAi6AExFRqWk0GixevBgRERE4duxYkSK+fPlyHDt2DP3798fMmTNhbW0tUlIqrdmzZ+O7775TXAlXYkHz9fUVO4JRFbf9rdwdP34cvr6+ryzghWxtbeHj44PDhw9LvoSXVVZWFvz8/Iq9G7Si2rhxI65du4bvv/8ePXr00B1fvXo1BEHAJ598ovt6DhgwAH369MHatWsVdxKOiIhIqti95YvdWznYveWP3bt02L3JWEzEDkBERNKi0WgwatQo/PLLL7C3t8ejR4+KjPnggw/g4OCAHTt2YPTo0cjPzxchKZWFUu+E8vPzw8mTJ8WOYVQeHh44dOiQ2DGMJjExEffv3xc7hlHdvn0bzZo1K/H4Jk2a4M6dOwZMVPFJ7XtgdHQ0+vbtq1fAc3JycOLECQDA0KFDdccrVaqEPn36KO57HRERkVSxe8ub1D53lhd2b/lj9349dm/pfQ9k95YmLoATEVGpbNq0CUlJSfD390dYWBjq1q1bZMzAgQOxe/duTJw4EcnJydi2bZsISYlKTokF7ebNm/jrr7/EjkEGZGlpqXsGVUk8fvwYVatWNWAiKm83btxA69at9Y4lJSUhPz8f9evXR8OGDfVeq1u3Lh48eGDMiERERFRG7N4kR+zeJEfs3vLH7i1N3AKdjCYsLKzIsZycHADAiRMncPfu3WLf5+PjY8BUhqXEOZP8RUREwMPDAx9//PFrx06cOBGJiYnYs2cPRowYYfhwVC4yMzNx69atUr3H3t7eQGmIqKxat26N6OhojBo1qkTjDxw4gBYtWhg4FZUnExMTFBQU6B0rvMrc3d29yPiHDx/CxsbGKNmISDxK7KFKnDPJH7u3/LF7E8kDu7f8sXtLExfARfbiB52srCwAz/+CvPgBqLitjqRk1qxZUKlUescKt7rYsGEDVCqV7teF/65SqSRdSJU4Z5K/q1evYvDgwSUe//7772PlypUGTETlbeHChVi4cGGJx6tUKqSnpxswEVH5iImJwdWrV0s8XqVS4bPPPjNgIsMaMGAApkyZguDgYIwcOfKVY9euXYu0tDSsX7/eSOmoPDRr1gwpKSkYNmwYgOefM2NiYqBSqdC1a9ci4w8dOoSmTZsaOyZRhcDuLe8eqsQ5k/yxe8sfuzfJFbv3y7F7SxO7tzRxAVxkL/ugM336dBHSGFZQUJDYEYxOiXMm+bO2toZWqy3x+EqVKsHS0tKAiai8OTk5oUGDBmLHMDqlFTQA2LlzJ+Li4ko8XqVSleoETUUTExOD6OjoEo+X+te4R48e8PLywnfffYc//vgDH3/8MVq1agVzc3MAQEFBAdLS0rBx40bExMRg4MCB+Mc//iFyaioNHx8fLFiwAG3atMF7772HnTt34tatW2jYsCE6deqkN3bt2rVITU2Fv7+/SGmJxMXuLW9KnDPJH7u3/LF7l4zUexnA7v06Uv8as3vLH7u3NHEBXEQ+Pj5FrlCWM19f39eOycrKgqWlJSpVqmSERIanxDmT/DVp0gTJycnw8/Mr0fjTp0+jXr16Bk5F5WnQoEHw9vYWO4bRKa2gAc+fV5SUlFTi8VIv4ePGjSt2ayo5W7p0KYKCgrB9+3bs27cPpqamsLW1hampKTIzM6HRaGBiYoJRo0Zh6tSpYselUho8eDBOnz6NoKAg3R2N1apVw9KlS2FiYgIA+O2337Bu3Tpcv34dTk5OGDJkiMipiYyP3bsoufVQJc6Z5I/dW/7YvUuG3Vt62L3ZveWG3VuauAAuokWLFokdwejy8vIQEhKC1NRUvSu0ExMTMWfOHFy5cgUqlQru7u4ICAiQxVWQSpwzyZuvry8CAgIQHx8PNze3V45NSEhAdHQ0Jk2aZKR0RGWnxIL21VdfwcPDQ+wYRtO0aVO4uLiIHcOoLCwsEBAQAD8/P4SFhSEtLQ337t2DIAho0qQJnJyc4O3tjUaNGokdlcpApVJh6dKlGDZsGFJSUmBjYwNPT0/UqFFDN+bOnTsQBAETJkzAuHHjdOWcSEnYvZXRQ5U4Z5I3dm+SK3Zv+WP3ZveWG3ZvaeICuIhGjx4NHx8feHp6KmKLory8PHz66adISkqCubk55s+fDzMzM1y5cgWjR49GXl4eOnXqhGbNmuHAgQMYNGgQwsPDUatWLbGjl5kS50zy5+Pjg927d2P8+PEYN24cBg4cWOTPbEZGBnbt2oUNGzagfv36GDp0qEhpiUpOiQWtevXqvEtEIRo3bsyrzGXM0dERjo6Oxb42ceJETJw4sdjX8vLykJqaCrVajSpVqhgyIpGo2L3l30OVOGeSP3Zvkit2b5Izdm95Y/eWFl6CIKLExETMmDED7u7umDVrFuLi4iAIgtixDGbbtm04deoUZsyYgaSkJJiZPb/+4scff4RGo4G3tzfWrVuHL7/8Ert374apqSnWrl0rcuo3o8Q5k/yZm5tj9erVaNu2LVauXIlOnTrBw8MDgwcPxsCBA9G1a1d06dIFP/74IxwcHBAcHMwf7BJib28Pa2trsWMQEZERZGVlwc/PD2fPnhU7CpFBsXvLv4cqcc4kf+ze8sbuTUSkHOze4uAd4CI6efIkDh48iP3792Pfvn3Ys2cPatWqBW9vb/Tp0wdqtVrsiOVq79696N69O0aNGqU7ptFoEBsbC5VKpXfc1tYW/fr1w759++Dv7y9G3HKhxDmTMtSsWRObN29GdHQ09u3bh/T0dFy4cAEmJiaoVasWfHx80K1bN3Tt2lXsqFRKsbGxr3y9oKAAd+7cQa1atWBhYWGkVERvbuLEiXBwcBA7hlGV9HmRf6dSqbB582YDpKkY7t27B1tbW5ibmxd5rVq1atiyZQtatGghQjLxyHkRkKgQu7f8e6gS50zKwO4tX+zeJFfs3iXD7s3uTYbHBXARVa5cGX379kXfvn2RnZ2NAwcOIDIyEps3b0ZwcDCaNWuGvn37wtvbG2+99ZbYcd/Yn3/+CV9fX71jp0+fRm5uLuzs7Ir8YGzYsCEyMjKMGbHcKXHOpCxeXl7w8vISOwYZ0cOHD+Hh4YGNGzeiY8eOYscpN0osaEFBQS/dtkmOXrYNVaG//voLGzduhI+PD+rXr2+kVIaVmJhY7HGVSvXS4qVSqQwZySiCg4Oxc+dOhIeHFynbCxcuRFxcHEaNGoXRo0frPZPL3NxccVsxEikFu7f8e6gS50zKwu6tPOze8sHurY/d+/9fkzp2b6rouABeQVSpUgUDBgzAgAED8PDhQ0RFRWH//v1Yvnw5li9fDmdnZ/j4+MDLywuVK1cWO26ZFBQUwNTUVO9YfHw8AMDd3b3I+OzsbFhZWRklm6Eocc5EwPNtXSwtLVGpUiWxo5AByPGKRSUWtBdPEr/oyZMnWLBgAUaPHo2mTZsaKZV4/vrrL6xevRpOTk6y+RqfP3++yLGHDx/C3d0dwcHBsjqRBjz/3jR9+nTs27cPNWvWxJ07d9CgQQO9MU2bNkVycjJWrFiBs2fP4ocffhApLRGJhd1bnj1UiXMmAti95Y7dWx69jN1bH7u39LF7k1TwGeAVUI0aNTB06FBs3boVR44cwddffw0TExN8++23+Mc//iF2vDJr2LAhzp07p3csJiYGKpUK77//fpHxx48fR8OGDY2UzjCUOGdShry8POzYsQOzZ8/WO56YmIhevXrBzc0Njo6OGD16NK5duyZSSqLyU1jQrl+/LnYUo8nNzUVYWJii7o6S4wmmF8nhKvOX2bFjB/bt24cRI0bgyJEjRQo48PyEW0xMDPr164eYmBiEhISIkJSIKgp27+fk0EOVOGdSBnZvUhp2b2Vg95Y2dm+SCi6AV3Dm5uawtLSEjY0NzMzMoNFoxI5UZr1798aePXtw8OBBPH36FJs2bcLly5dRs2bNIs8qCg8Px4kTJ+Dh4SFS2vKhxDmT/OXl5eHTTz9FQEAA9u7di/z8fADAlStXMHr0aFy5cgWdOnXCiBEjcOXKFQwePBj3798XOTXRm1NCQXuREudM0vXbb7/BxcUFs2bNKvY5Y4UsLCwQGBiIFi1aYOfOnUZMSEQVGbu3tHuoEudM8sfuTUqlxB6qxDmTdLF7k1RwC/QK6OHDh4iJiUFUVBSSkpKQn5+Pli1bYvLkyejdu7fY8cpsxIgR+P333zFx4kTd8y/Mzc2xYMECWFhYAHh+hfa2bduQmJiIxo0bY8SIEeKGfkNKnDPJ37Zt23Dq1CnMmDEDw4YNg5nZ8x8lP/74IzQaDfr06YPvvvsOADB27Fh4e3tj7dq18Pf3FzM2lRNzc3M4OzujWrVqYkchItLzv//9D5MnTy7RWJVKhR49emDNmjUGTkVEFRm7t3x6qBLnTPLH7q1s7N5EVFGxe5NUcAG8grh//z6io6MRFRWF06dPQ6vVol69ehg1ahT69Okji+d/WFhYYNOmTYiMjERqaipsbGzg7e2NZs2a6cacPXsWycnJ6NOnD2bNmgVLS0sRE785Jc6Z5G/v3r3o3r07Ro0apTum0WgQGxsLlUqld9zW1hb9+vXDvn37WMIlIjs7G1WqVHnp69WqVcPWrVv1jiUkJMDV1dXQ0YgMqkqVKggKCkLz5s3FjkJlZGZmplvkKImqVasWeV4sEckfu/dzcuuhSpwzyR+7t7yxe5NSsXtLH7s3SQUXwEWUkZGB6OhoHDhwAMnJydBqtahWrRoGDBgAb29vdOjQQeyI5c7U1BTe3t7w9vYu9vXx48dj8uTJMDGRz+78Spwzyduff/4JX19fvWOnT59Gbm4u7Ozs4ODgoPdaw4YNFfUcI6nz8/NDcHAwbG1tXzv22bNnWLJkCX799Vekp6cbPpyIlFjQqlWrhi1btqBFixZiRzGKSpUq4R//+AfvsJCwRo0a4ezZsyUef/bsWdStW9eAiYioomD3LkqOPVSJcyZ5Y/eWN3bv4rF7yx+7t/Sxe5NUcAFcRF26dAHw/EplT09PeHt7o0uXLq98boLcWVlZiR3B6JQ4Z5K2goKCIlftxcfHAwDc3d2LjM/Ozuafcwk5d+4cPv74Y2zatAm1atV66bgzZ85g5syZ+PPPP185Ti4qVaqkd/JJq9Vi3bp1mDBhgoipDMvc3BwuLi66X2dnZ2Px4sUIDAwUMdWbCw4Oxs6dOxEeHl7kM9fChQsRFxeHUaNGYfTo0ZI/QR4WFlbkWE5ODgDgxIkTuHv3brHv8/HxMWAqw/nwww+xbNkyjBw58rUnzC5evIiIiAj4+fkZKR0RiYnduyglfj5X4pxJ2ti95Y3du3js3uzeUsTu/XLs3iQmlSAIgtghlGr48OHo27cvevToARsbG7HjEBGVSN++fdG2bVvMmzdPd6xXr164cuUKVqxYgR49euiNHz16NLKysrBr1y5jR6Uy+OWXX7BgwQI0aNAAmzZtKnKFZl5eHn744Qds3LgRWq0Wffv2xVdffSWLK3ezs7Oxa9cupKamQhAEtGzZEsOGDUPVqlX1xv3nP/+Bv78//vvf/+LcuXMipS0fN27cQHBwMFJSUgAALVu2xJgxY9CoUSO9cdHR0Zg/fz7u378v2TkLgoDp06dj3759qFmzJrZv344GDRrojVm1ahV27dqFjIwMdOvWDT/88INIacuHWq2GSqXSO/b3j/7FvaZSqST7Nf7rr7/Qr18/ZGZm4quvvkLv3r2LnDTOz8/H3r17sXTpUgBAaGgoateuLUZco7l37x5sbW2LXejLy8tDSkoKWrRo8cotOImkjt2biKSI3Vve2L3Zvdm92b2l+jVm9y4eu3fFwwVwIiIqlXXr1mH16tVYtmwZ3nvvPezYsQOLFi1CrVq1EBsbq/cMmPDwcMycOROTJ0/G+PHjRUxNpbF3717MmjULdnZ22LRpExo2bAjg+RXqM2fOxH//+1/Y29tj7ty56NSpk8hpy8f169fh5+eHO3fu6JWUWrVqYdeuXahbty7y8/OxbNkybNmyBVqtFr1798ayZctETP1mzp07h+HDh+PJkyewtLSEpaUlMjMzYW1tje3bt+Odd95BdnY2/P39ER0dDVNTU4waNQpTp04VO3qZbN++HXPmzMGIESMwbdq0l971p9FoMHfuXISEhGDBggXo16+fkZOWn9DQ0DK978WtNqXkypUr+Oyzz3DlyhVYW1ujVatWqF27NrRaLR48eICzZ88iNzcX9vb2WL16NdRqtdiRy8Wr7q6YOnWqrO6uICIiUgp2b/lj92b3Zvdm95Yqdm92byngAriIitsaoySkujUGEcmDRqPBqFGjkJSUBJVKBUEQYG5ujlWrVum2l4yJicG2bduQmJiIxo0bIyQkBJaWliInp9I4evQopkyZAhsbG6xfvx6HDh3C2rVrkZ+fjyFDhmDatGmoXLmy2DHLzbRp0xAZGYmpU6eif//+sLKywtGjRzFv3jy0a9cOS5YswdixY5GcnAx7e3sEBATo/rxL1YQJE/D7779j8eLF6N27NwAgLS0NX3zxBezt7bF06VL4+fnhzz//RJs2bRAYGFjkOYNSMmDAAFhbW2PLli2vHSsIAvr37w8LCwts377dCOmoPGk0Gvzyyy/Yt28fzp8/j/z8fADPtxZs164dvLy8MGjQIL2TxlKlxLsriMqC3ZuIpIjdWxnYvdm92b3ZvaWK3Zvdu6LjAriICrfGKNzy4nWkvjUGEcmHVqtFZGQkUlNTYWNjA29vbzRr1kz3+ooVK7Bx40b06tULs2bNQvXq1UVMS2V1+vRpTJgwAU+ePIEgCGjUqBECAwPRoUMHsaOVu86dO+O9995DUFCQ3vHQ0FAEBASgc+fOOHjwIIYMGYIZM2bA2tpapKTl57333kPPnj3h7++vdzw6OhpTp05Fu3btkJaWhsmTJ+PTTz+V/JWrjo6OmDx5MkaMGFGi8evWrcOaNWt0W9RJXW5ubrEnQy9duoSqVavCzs5OhFTG8fDhQ5iamspiu8gXKfHuCqKyYPcmIqli91YGdm92b3Zvdm85YPdm965ozMQOoGQv/qAnIpIKU1NTeHt7w9vbu9jXx48fj8mTJ0v+Q7vSOTk5YcuWLRg9ejQePnyIefPmybKAA8CjR4/g6OhY5LizszM0Gg2OHj2KlStXonv37iKkM4ysrKxit6Bq06YNtFotLly4gK1bt6Jdu3bGD2cAZmZmpbrquGrVqkWeYSVFGo0GixcvRkREBI4dO1akiC9fvhzHjh1D//79MXPmTFmcYHpRjRo1xI5gML/99htcXFwwa9asV46zsLBAYGAgzp07h507d7KEk+KwexORVLF7KwO7N7s3uze7txywe7N7VzRcABeRlJ/xQETK5efnhwkTJqBjx466Y/n5+UhJSYFarUaVKlVgZWWle63wWWS8g0aa1Go1/v3vf2PkyJGYMGECVq9eDTc3N7Fjlbu8vDy9P7eFCreaGzlypKwKOPD8722lSpWKHC8saWPHjpVNAQeARo0a4ezZsyUef/bsWdStW9eAiQzv79tmqtVqPHr0qMicPvjgA9y9exc7duzAxYsXsWXLFpiZSbMirFq1qkzvmzhxYjknMZ7//e9/mDx5conGqlQq9OjRA2vWrDFwKqKKh92biKSI3VtZ2L3ZveWC3Zvd+2XYvcnYpPk3TKG0Wi3WrVuHCRMmiB2FiBQsMTERAwcO1DuWnZ0NPz8/bNy4Ua+ck/TMnj272OONGjXCjRs3MG7cOPTs2VNv+1CVSoWFCxcaK6IoXFxcxI5gdK1btxY7Qrn68MMPsWzZMowcORLNmzd/5diLFy8iIiICfn5+RkpnGJs2bUJSUhL8/f3x8ccfFztm4MCBGDhwIFatWoVVq1Zh27ZtJd6qrqIpaQl/cftjKZdwpd5dQWRo7N5EVBGwe8sbu3fx2L2lj92b3bsQuze7t9i4AC6y7Oxs7Nq1C6mpqRAEAS1btsSwYcNQtWpVvXH/+c9/4O/vj//+978s4URUIQmCIHYEKgehoaGvfP3Zs2cICwvTO6aEEq7ED60leUaqlHz00UfYvn07hg8fjq+++gq9e/cu8nXNz8/H3r17sXTpUlSpUkXyJTwiIgIeHh4vLeB/N3HiRCQmJmLPnj2SLeGHDh167Zjs7Gx8//33OHLkCMzMzCT/NVbi3RVEZcXuTURywe4tD+zexWP3lj5271dj95Ymdm9p4gK4iK5fvw4/Pz/cuXNH9+E1JiYGv/zyC3bt2oW6desiPz8fy5Ytw5YtW6DVatG7d2+RUxMRkZyV5EOsXGVmZuLWrVt6x7KysgAADx8+LPIaANjb2xslm6FcvnwZSUlJeseys7MBABcuXCh2Oy5nZ2ejZCtv1tbWWLNmDT777DPMnDkTc+fORatWrVC7dm1otVo8ePAAZ8+eRW5uLuzt7bF69WrUrl1b7Nhv5OrVqxg8eHCJx7///vtYuXKlARMZVr169V75emRkJBYtWoSMjAy0b98ec+bMwTvvvGOkdIahxLsriMqC3ZuIiCoadm92b3Zvdm+pYvdm95YKLoCL6Pvvv8edO3cwdepU9O/fH1ZWVjh69CjmzZuHefPmYcmSJRg7diySk5Nhb2+PgIAAdOnSRezYREQkY6/7ECtnCxcufOnV9NOnTy9yTKVSIT093dCxDGrt2rVYu3Ztsa8tXry42ONSfqZg48aNERYWhl9++QX79u1DcnIy8vPzAQDm5uZo164dvLy8MGjQoFJtbVVRWVtbQ6vVlnh8pUqVdM+hk5Nr165h7ty5iIuLQ7Vq1RAYGIgBAwaIHatcKPHuCqKyYPcmIqKKht2b3bsQuze7t1ywe7N7VzRcABdRUlISfHx8MHbsWN2xnj17Ijc3FwEBAZg1axaSk5MxZMgQzJgxA9bW1iKmJSIiKmrt2rWIjo5GSEiI2FHeiK+vr9gRjE7Kz156ExYWFhg5ciRGjhwJ4PkdBqampqhWrZrIycpfkyZNkJycXOLSdfr0aVmdiNNoNFi3bh3Wr18PjUYDX19fzJgxA9WrVxc7WrlR4t0VRGXB7k1ERFLH7i1d7N7s3i9i95Yedm9p4gK4iB49egRHR8cix52dnaHRaHD06FGsXLkS3bt3FyEdERHR692+fVvSVyUXCgoKEjuC0Sm1hL+oRo0aYkcwGF9fXwQEBCA+Ph5ubm6vHJuQkIDo6GhMmjTJSOkMKy4uDnPnzsXVq1fRvHlzBAQEoEOHDmLHMgil3V1BVBbs3kREJHXs3tLF7v0cu/dz7N7Sxe4tPVwAF1FeXh6srKyKHK9cuTIAYOTIkSzgRFQhvfi8plc9q+nRo0dGzUZEVJxVq1aV6X1SPlnh4+OD3bt3Y/z48Rg3bhwGDhyIWrVq6Y3JyMjArl27sGHDBtSvXx9Dhw4VKW35uH//PoKCghAZGQlLS0tMmzYNI0eOLPaZenKipLsriMqC3ZuIpIrdm4ikht2b3VvO2L2lRSUIgiB2CKVSq9VYsmQJvL299Y4/evQIHTt2xIYNG/CPf/xDpHRERMVTq9VQqVRFjguCUOzxQnK4UpmKCggIwM6dOyX/9VViQZs9e3ap36NSqV76rLaKTq1Wl2jci9/HpP5n+8GDB/jiiy+QkJAAlUoFe3t7vS26bt++DUEQ0K5dO6xYsQJ169YVO3KZbdu2DStXrsSTJ0/QtWtX+Pv7S3o+RFR+2L2JSIrYvenv2L3ZvaWC3Zvdm6iikPflGBJnamoqdgQioiKU+Lwmkr+SlvAXC5qUS3hoaGiJx/593lIt4YcOHXrtmOzsbHz//fc4cuQIzMzMSvz8roqsZs2a2Lx5M6Kjo7Fv3z6kp6fjwoULMDExQa1ateDj44Nu3bqha9euYkd9Y4GBgbp/j42NRWxs7Gvfo1KpkJ6ebshYBqXEE4hEhsDuTUQVEbs3yRG796uxe0sXu/ersXuTGLgALrIXtzICXr2dEQDY29sbJRsRUXGU+Lwmkj8lFrTz58+/dszNmzcxf/58HDlyBFWqVMGUKVMMH8xA6tWr98rXIyMjsWjRImRkZKB9+/aYM2cO3nnnHSOlMzwvLy94eXkVOZ6VlQVLS0sREpU/JZ4kVuIJRKKyYvcmIqlh9yY5YvcuHrs3u7eUsHu/HLt3xcIt0EX0sq2MgJdvZyT1K2WIiKhiK+0VjYcPH0Z6errkt6p6HbkXtBdptVps3LgRP/30E3Jzc9GrVy/Mnj27yDOs5ODatWuYO3cu4uLiUK1aNUyfPh0DBgwQO1a5ycvLQ0hICFJTU/VOoiYmJmLOnDm4cuUKVCoV3N3d8e2336Jhw4Yipn0zs2fPxuDBg9G2bVuxoxjNzZs3XzumuBOIX375pRHSEVUc7N5ERFTRsHsXj92b3Vuq2L3ljd1bmrgALqKyPP8D4BWgRERkOCV9VtPfqVQq2ZZwuRe04pw6dQpz587FxYsX8fbbbyMgIAAdO3YUO1a502g0WLduHdavXw+NRgNfX1/MmDED1atXFztaucnLy8Onn36KpKQkmJubIyUlBWZmZrhy5Qr69u0LjUaDzp07o1mzZjhw4ACePn2K8PBwyZ5sedkzfpVMaScQiV6G3ZuIiCoadm997N7s3lLG7k3s3hUTt0AXEcs0ERFVNFu2bBE7QoWghIL2okePHuG7775DWFgYLCwsMGnSJIwZMwYWFhZiRyt3cXFxmDt3Lq5evYrmzZsjICAAHTp0EDtWudu2bRtOnTqFGTNmYNiwYTAze/7R/8cff4RGo0GfPn3w3XffAQDGjh0Lb29vrF27Fv7+/mLGpnLw4gnEwMBA2Z9AJHoVdm8iIqpo2L2fY/dm95YDdm/lYveu2LgAXgE8ffoUu3fvxu+//47z588jMzMTKpUKNWrUgFqthoeHB7y9vWX5Q5CIiCoWFxeXUr/n1KlTBkgiHqUUtL/btWsXli5diqysLLz33nsICAiQ9HZcL3P//n0EBQUhMjISlpaWmDZtGkaOHKkrp3Kzd+9edO/eHaNGjdId02g0iI2NhUql0jtua2uLfv36Yd++fSzhEqbEE4hEpcHuTUREFQW7N7s3u7d8sHsrD7u3NMjzO46EnD59GpMnT8b9+/dhYWGBhg0bol69esjPz0dmZiYOHz6M2NhYrFq1CsuWLUP79u3FjkxERITbt28jNDQUYWFhuH79uiy2YVNaQQOACxcuYM6cOUhNTUWtWrWwfPly9OrVS+xYBrFt2zasXLkST548QdeuXeHv74+6deuKHcug/vzzT/j6+uodO336NHJzc2FnZwcHBwe91xo2bIiMjAxjRix3p06dglarLdV7fHx8DBPGyJR4ApGoNNi9iYhIiti95YHdm93779i9pY3dWzrk+1NFAv73v/9h1KhRsLGxwdKlS+Hl5VXkSvMnT54gKioKP/zwA0aPHo3Q0FA0atRIpMRERKRkz549Q3R0NEJCQpCQkABBEKBSqdC5c2exo70xJRa0xYsXY+vWrdBqtfjggw8wZcoU2NjY4NatW698n729vZESlq/AwEDdv8fGxiI2Nva171GpVEhPTzdkLIMqKCiAqamp3rH4+HgAgLu7e5Hx2dnZsLKyMko2Q9m5cyd27txZorGF38OkXsKVeAKRqLTYvYmISErYveWF3Zvd+0Xs3tLE7i09/MqI6KeffoKVlRV2796Nt956q9gxNjY2GDBgADp37oy+fftiw4YNmD9/vpGTEhGRkqWmpiIkJAT79+/HkydPAAA1atRA//79MWjQINSrV0/khG9OiQUtODhY9++HDx/G4cOHS/Q+qd5x8OLV2ErQsGHDIl+vmJgYqFQqvP/++0XGHz9+XPLb73300Udo166d2DGMRoknEInKgt2biIikgN27eOze0sLu/Ry7t7ywe0sTF8BFlJSUhH79+r20gP+dnZ0dfHx8cPz4cSMkIyIipcvIyEBYWBhCQ0Px559/QhAEWFlZwd3dHXFxcZg3bx48PDzEjllufHx8oFKpxI5hVBMnTiz1ewRBMEAS4xk8eDDatm0rdgyj6d27N1avXo3OnTvjvffew44dO3D58mXUqlULXbt21RsbHh6OEydOYPLkySKlLR8dOnSAt7e32DGMRoknEInKgt2biIgqKnZv+WP3lj92b/lj95YmLoCL6NGjR6XaUq1JkybYtWuXARMREZHS7d+/HyEhIYiLi4NWq0XVqlXh7e0NLy8vdOrUCffu3YOnp6fYMcvdokWLAAB5eXm4dOkS8vPz0axZM8lvSfUq9vb26NevX4nH37x5E1999ZUBExlWaGgo3N3dFVXCR4wYgd9//x0TJ06ESqWCIAgwNzfHggULdFv/xsTEYNu2bUhMTETjxo0xYsQIcUNTqSjx7gqismD3JiKiiobdm937Zdi9pYfdW/7YvaWJC+AiysvLK9UP90qVKiEnJ8eAiYiISOmmTp0Ka2trDB06FB4eHnB2dtZ7jpGcr9T+17/+hTVr1uh+1lpYWGDo0KGYNm2aLJ/n4+/vj6dPn2LYsGGvHbtr1y4sXryYn0MkxsLCAps2bUJkZCRSU1NhY2MDb29vNGvWTDfm7NmzSE5ORp8+fTBr1ixYWlqKmJjKQml3VxCVBbs3ERFVNOze7N7FYfeWJnZvZWD3lh75/UQhIiKiMqtfvz5u3LiBkJAQXLlyBf/5z3/g6emJxo0bix3NoHbv3o0lS5agXr168PHxgYmJCRISErBp0yZotVpJX339Mm+//TYCAwPx9OlTjB49utgx9+7dg7+/P44dOwZzc3PJb9GlRKampvD29n7p1mTjx4/H5MmTYWJiYuRk5c/X11fyz1ErLSXeXUFEREQkB+ze7N5/x+4tfeze8sbuLU1cABdZZmYmbt26VaKxjx49MnAaIiJSuoMHD+LMmTMIDw9HVFQUjh8/juXLl6NJkybw8vJCq1atxI5oEDt27EC7du2wefNmVKpUCcDzZ25NnToVO3bswPTp03XbVsnFr7/+itGjR2PZsmV4+vQpJk2apPf63r17MX/+fGRlZcHR0RGBgYFo2rSpSGnLx6lTp6DVakv1Hh8fH8OEqSDktNVgUFCQ2BGIqAJj9yYiooqE3ZvduxC793Ps3tLB7k1SoRIEQRA7hFKp1eoybWdz7tw5A6QhIiLSp9Vqcfz4cURERODQoUN4+vSp7udW//79MWHCBNSrV0/klOXDyckJX3zxRZEtyVJTUzFkyBCEhoZCrVaLlM5w/vrrL/zzn/9EQkICRowYgZkzZ+LRo0cICAhATEwMLC0tMXXqVAwfPlzyW/CV9nOXIAhQqVT83EUVmlqtxpIlS156lwERPcfuTUREFRm7N7s3uze7N1Vs7N7SxDvAReTr6yt2BCIiopcyNTVFly5d0KVLF+Tm5iImJgZ79+7FiRMn8NtvvyEkJASurq7o378/PvzwQ7HjvpGnT5+iSpUqRY7Xr18fgiDg8ePHIqQyPGtra6xbtw5ffPEFNm3ahBs3biA5ORkPHjzAe++9h3nz5snmRAsAfPTRR2jXrp3YMYjKFe+uIHo9dm8iIqrI2L3Zvdm9iSo+dm/p4R3gREREpDN27Fi4ubnBxcUFrVq1Kvaq3UePHiEyMhIRERFITU2VxZW6L7uS89GjR+jYsSOCg4PRsWNHkdIZXkFBAfz9/RESEgITExPMmzcPAwYMEDtWueLVuiRHvLuCiIiISJrYvdm92b2JpIPdW5p4BzgRERHpxMfH49ixY1CpVLCxsUGHDh3g6uoKNzc33TZk1atXx7BhwzBs2DBcv34d+/btEzk1vSkTExMsXLgQtra22LhxI+Li4uDj4wMzM35UJKroeHcFERERkfSweysTuzeRdLF7Sw+/sxIREZFOcnIy0tPTkZycjJSUFKSmpuLw4cNQqVSoWrUqnJ2d4erqCldXV7zzzjto0KABxo8fL3bscpGZmYlbt27pHcvKygIAPHz4sMhrAGBvb2+UbIZQ3Hw+/vhj/PXXX9ixYwdycnLwzTffwMTERG+MlOdMJEcdOnTg3RVEREREEsPuze7N7k0kLeze0sMt0ImIiOiVbt++rVfKz58/D61WC1tbW7i4uMDV1RVDhw4VO+YbedVWRoXbFr1IpVIhPT3d0NEM5nVzBlDkdSnPefbs2Rg8eDDatm0rdhSicsPtBYmIiIjkg92b3buQlOfM7k1yxO4tTbwDnIiIiF6pbt266N27N3r37g0AyM7ORnh4OEJCQnDgwAFER0dLvoT7+vqKHcHofHx8SvX8IqkLCgoSOwIREREREdFLsXvLE7s3EZE4uABOREREr5Sbm4ukpCQkJibi9OnTOHv2LPLy8mBhYaHbkk3qlFjQFi1aJHYEInpDvr6+aNiwodgxiIiIiKgcsHvLE7s3kfSxe0sTt0AnIiIiPfn5+UhNTUV8fDzi4+Nx5swZ5OXlwdzcHO+++66ueDs6OsLCwkLsuERERERERESSw+5NRERkOFwAJyIiIp0xY8bg1KlTyM3NhYmJCVq1agU3Nze4urrCyckJlpaWYkckIiIiIiIikjR2byIiIsPiAjgRERHpqNVqmJub48MPP8S4cePw9ttvix2JiIiIiIiISFbYvYmIiAyLC+BERESk88033yAhIQHXrl2DSqVCkyZN0LFjR7i5ucHZ2RnVqlUTOyIRERERERGRpLF7ExERGRYXwImIiKiIW7duIS4uTvcssvv378PExARqtRqurq66Um5tbS12VCIiIiIiIiJJYvcmIiIyDC6AExER0WtdvHgR8fHxOHnyJE6dOoXHjx/DzMwMrVu3RseOHTF58mSxIxIRERERERFJGrs3ERFR+eACOBEREZWKRqNBVFQUfv31V6SmpkKlUuHcuXNixyIiIiIiIiKSDXZvIiKisjMTOwARERFVbNeuXcOZM2dw5swZpKWl4fz588jLy0PlypXRuXNnODs7ix2RiIiIiIiISNLYvYmIiMoP7wAnIiIinaysLKSlpekKd1paGrKysiAIAqpVq4b27dvDxcUFzs7OaNmyJUxMTMSOTERERERERCQp7N5ERESGxQVwIiIi0lGr1VCpVBAEAdWrV4ezs7PuHwcHB6hUKrEjEhEREREREUkauzcREZFhcQt0IiIi0unRowdcXFzg4uKCZs2aiR2HiIiIiIiISHbYvYmIiAyLd4ATEREREREREREREREREZEs8OEhREREREREREREREREREQkC1wAJyIiIiIiIiIiIiIiIiIiWeACOBERERERERERERERERERyQIXwImIiIiIiIiIiIiIiIiISBbMxA5ARESkBD/++CNWrVpVqvccOnQI9evXN1CisuvatStu3rwJAPj666/h5+f3yvGjRo3C8ePHAQDBwcFwd3c3aK7o6Gg0atTIIL8HERERERERVVzs3uzeREREABfAiYiIjMLBwQHe3t56xx48eIC4uDhYW1vDw8OjyHusra2NFa/MoqKiXlnCHz58iPj4eCMmIiIiIiIiIqVi9yYiIiKAC+BERERG4eXlBS8vL71jCQkJiIuLQ/Xq1bF06VKRkpVd1apVkZycjLt37+Ktt94qdkx0dDTy8/Nhbm6OvLw8IyckIiIiIiIiJWH3ZvcmIiIC+AxwIiIiKiNPT08IgoDo6OiXjtm3bx9sbW3Rpk0bIyYjIiIiIiIikgd2byIiotLjAjgREVEFduTIEYwaNQouLi5o06YNunfvjiVLliAzM1Nv3I0bN+Dg4IDOnTsX+98ZPnw4HBwckJCQoDs2a9YsODg4IDExEZMmTcK7774LNzc3bN26tUTZevToAeD5VmzFycjIwKlTp9C9e3eYmb1805k9e/Zg6NChaN++Pd599114e3tjzZo1ePr0abHjT548iZEjR8LZ2RkdOnTAlClTdM9FK45Wq8W///1vDBw4EI6OjnB0dMSgQYMQGhoKQRBKNFciIiIiIiKSL3bvoti9iYhIyrgFOhERUQW1dOlSrF+/HqampnByckL16tWRmpqKDRs2YP/+/di8eTMaNGjwxr/PN998gwcPHqBTp064ePEi1Gp1id739ttvo0WLFkhOTkZGRgbs7Oz0Xo+KikJBQQF69+6NVatWFXl/QUEBZsyYgb1798LCwgIuLi6wsrJCUlISvv/+exw4cADBwcGoXr267j27du3Ct99+CwDo0KEDqlatiuPHj+PUqVPQaDRFfo+8vDz885//xLFjx2BjYwNHR0eYm5sjMTERs2bNQkJCAhYtWlSa/11EREREREQkI+ze7N5ERCQ/XAAnIiKqgGJjY7F+/XrY2tpiw4YNum3MNBoN5s2bh127dmHy5MnYvXs3VCrVG/1eGRkZCA8PR4MGDVBQUAATk5JvENOrVy+cO3cO0dHR+Pjjj/Vei4yMhJ2dHZydnYt977Zt27B37140aNAAGzduRMOGDQEAT548wbRp03DkyBF8++23+PHHHwEAt2/fRmBgIMzMzLBu3Tp07NgRAPDw4UOMGjUK6enpRX6Pn376CceOHYOLiwtWrlyJGjVqAADu37+PMWPGIDQ0FE5OThg4cGCJ50xERERERETywO7N7k1ERPLELdCJiIgqoE2bNgEAvvzyS71neFlYWGDOnDlo1KgR/vjjD8THx7/x7+Xh4aG7mr00BRwAevbsCaDoVmy3bt1Camoqevbs+dL/5ubNmwEAgYGBugIOADY2Nli6dCmqVKmC6OhoXL16FQAQGhqK3NxcDB48WFfAAaBGjRpYuHBhkf++RqPB1q1bYW5ujqVLl+oKOADUqlUL8+bNAwD861//KtWciYiIiIiISB7Yvdm9iYhInrgATkREVMHk5+cjOTkZKpUK3bt3L/K6mZkZvLy8AEDvuWJl9c4775T5vQ0aNECrVq1w+vRpZGRk6I5HRkZCEAR8+OGHxb7v9u3buHHjBqpXrw43N7cir1epUgWdOnUCACQmJgIAkpKSAABdunQpMr5FixaoX7++3rE//vgD2dnZaNKkCd56660i72nTpg1q1qyJK1eu4N69eyWcMREREREREckBuze7NxERyRe3QCciIqpgMjMzkZeXh+rVq8PGxqbYMYWFszzKY7Vq1d7o/b169cIff/yBmJgYDBs2DMDzEt6gQQO8++67xb6nsLDXq1fvpf/dF+dY+J46deq8dPyNGzd0v759+zYA4MKFC3BwcHjlHG7fvo3atWu/cgwRERERERHJB7v3c+zeREQkR1wAJyIiqmAEQQCAVz5frHCMhYVFif6bWq32pa+Vduu1F/Xs2RNLlizB/v37MWzYMFy9ehV//PEHxo0b99L3lGWOr3vempmZ/seagoICAIC9vT2cnJxe+d7KlSu/8nUiIiIiIiKSF3Zv/THs3kREJCdcACciIqpgbG1tYW5ujszMTDx58qTYK9GvX78OAKhZsyaA/y/ShcXzRVlZWQZK+/xK8nfffRenT5/GvXv3EBkZCQDo3bv3S99jZ2cHAHpXjb+ocI61atUCALz11lu4ePEibt68iWbNmhUZ//dt4ADoriqvU6cOli5dWooZERERERERkdyxez/H7k1ERHLEZ4ATERFVMObm5nB0dERBQQFiYmKKvJ6fn6877urqCgCwtrYG8Lxs5+Xl6Y1/9OgRrly5YtDMvXr1QkFBAaKjo7F//340a9bslVuf2dvbo169enj06JHuOWN/l52djePHjwMAnJ2dAQDu7u4AUOz/k+vXr+PSpUt6x9q0aQNLS0ucP3++SEEHgLt376Jnz54YOXIkcnJySj5ZIiIiIiIikjx2b3ZvIiKSLy6AExERVUCffPIJAOC7775Denq67nheXh7mzp2La9euoUWLFrrtxWxtbVGnTh1oNBps375dN/7Zs2f49ttvX7kNW3no0aMHVCoVtm3bhgsXLrzyCvRChXP09/fXXXEOADk5OZgxYwaePHmCDz74QPesMl9fX9ja2mL37t04cOCAbvyTJ0/w1VdfFbkC39raGh999BH++usvzJgxAw8ePND7PWbPno3Lly/D2tqa27AREREREREpELs3uzcREckTt0AnIiKqgDw9PfHpp59i48aNGDBgAJycnFC9enWcOXMGd+7cQb169bBixQq9Z4iNHj0agYGBCAwMxL59+1CrVi0kJydDq9Xigw8+wOHDhw2Wt27dumjXrh1SUlIAvHoLtkLDhw9HSkoK9u/fj969e8PZ2RlWVlY4deoUHj16BLVajYULF+rG16hRAwsXLsSUKVPw+eefw9HREXZ2dkhKSoJWq0Xjxo2LXG0/bdo0nDt3DvHx8ejWrRvatGkDKysrpKSkIDMzE2+//Tbmzp1bvv8ziIiIiIiISBLYvdm9iYhInngHOBERUQU1c+ZM/PTTT3B1dcX58+dx5MgRVK5cGRMmTEBoaCgaN26sN3748OFYvHgxWrdujfT0dCQlJcHV1RW//fZbkbGG0LNnTwBAq1at0KhRo9eONzExwYoVKxAUFIRWrVohOTkZJ06cQJ06dTBjxgzs3LkTNWrU0HuPh4cHfv31V3h4eODKlSv4/fff0bJlS/zyyy+oU6dOkd/D0tISGzduxNdff40mTZogLS0NCQkJsLOzw6RJk7Br1y7dc86IiIiIiIhIedi92b2JiEh+VIIgCGKHICIiIiIiIiIiIiIiIiIielO8A5yIiIiIiIiIiIiIiIiIiGSBC+BERERERERERERERERERCQLXAAnIiIiIiIiIiIiIiIiIiJZ4AI4ERERERERERERERERERHJAhfAiYiIiIiIiIiIiIiIiIhIFrgATkREREREREREREREREREssAFcCIiIiIiIiIiIiIiIiIikgUugBMRERERERERERERERERkSxwAZyIiIiIiIiIiIiIiIiIiGSBC+BERERERERERERERERERCQLXAAnIiIiIiIiIiIiIiIiIiJZ4AI4ERERERERERERERERERHJAhfAiYiIiIiIiIiIiIiIiIhIFv4PBil5lObl8QwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 173 coefficients adjusted\n",
-      "\t 684 coefficients converged\n",
-      "\t 73 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-41.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-41.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-39.390562</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-41.390562</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-40.865285</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-40.865285</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-39.288174</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-0.503905</td>\n",
-       "      <td>-39.792080</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-28.875688</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-30.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-28.246255</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-28.246255</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>717</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_disc</td>\n",
-       "      <td>-24.715984</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-24.715984</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -41.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -39.390562   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -40.865285   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -39.288174   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -28.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -28.246255   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -26.970666   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -24.970666   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -24.970666   \n",
-       "717      coef_calib_autodeficienthhjoi_TNC_SHARED_disc -24.715984   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "541           0.0            0.0        <NA> -41.934870       True  \n",
-       "543         112.0            0.0          -2 -41.390562      False  \n",
-       "540         100.0            0.0        <NA> -40.865285       True  \n",
-       "544          48.0           29.0   -0.503905 -39.792080       True  \n",
-       "698         112.0            0.0          -2 -30.875688      False  \n",
-       "695          92.0            0.0        <NA> -28.246255       True  \n",
-       "677          20.0            0.0        <NA> -26.970666       True  \n",
-       "676         155.0            0.0          -2 -26.970666      False  \n",
-       "675          64.0            0.0        <NA> -24.970666       True  \n",
-       "717          20.0            0.0        <NA> -24.715984       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_9\n",
-      "ActivitySim run started at:  2023-09-13 05:58:30.385875\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 06:44:00.485939\n",
-      "Run Time:  2730.1 secs =  45.501666666666665  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 171 coefficients adjusted\n",
-      "\t 687 coefficients converged\n",
-      "\t 70 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-40.865285</td>\n",
-       "      <td>116.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-42.865285</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-41.934870</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-41.934870</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-41.390562</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-41.390562</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-39.792080</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-0.852212</td>\n",
-       "      <td>-40.644291</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-30.875688</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-30.875688</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-28.246255</td>\n",
-       "      <td>139.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-30.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-24.970666</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>671</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint</td>\n",
-       "      <td>-24.691160</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-0.784119</td>\n",
-       "      <td>-25.475279</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -40.865285   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -41.934870   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -41.390562   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -39.792080   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -30.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -28.246255   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -26.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -26.970666   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -24.970666   \n",
-       "671   coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint -24.691160   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540         116.0            0.0          -2 -42.865285      False  \n",
-       "541           0.0            0.0        <NA> -41.934870       True  \n",
-       "543          68.0            0.0        <NA> -41.390562       True  \n",
-       "544          68.0           29.0   -0.852212 -40.644291       True  \n",
-       "698          88.0            0.0        <NA> -30.875688       True  \n",
-       "695         139.0            0.0          -2 -30.246255      False  \n",
-       "676          12.0            0.0        <NA> -26.970666       True  \n",
-       "677          40.0            0.0        <NA> -26.970666       True  \n",
-       "675         155.0            0.0          -2 -26.970666      False  \n",
-       "671          92.0           42.0   -0.784119 -25.475279       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      " Final coefficient table written to:  C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_9\\tour_mode_choice_coefficients.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "calibration_iterations_to_run = 8\n",
-    "start_iter_num = 2\n",
-    "\n",
-    "for i in range(start_iter_num, calibration_iterations_to_run+start_iter_num):\n",
-    "    asim_calib_util.run_activitysim(\n",
-    "        data_dir=data_dir,  # data inputs for ActivitySim\n",
-    "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
-    "        configs_common_dir=configs_common_dir,  # just the location of the common config, these files will be used from the original location\n",
-    "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
-    "        output_dir=iteration_output_dir,  # location to store run model outputs\n",
-    "        settings_file=warm_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
-    "        tour_mc_coef_file=tour_mc_coef_file  # optional: tour_mode_choice_coefficients.csv to replace the one in configs_dir\n",
-    "    )\n",
-    "    \n",
-    "    _ = asim_calib_util.perform_tour_mode_choice_model_calibration(\n",
-    "        asim_output_dir=iteration_output_dir, # folder containing the activitysim model output\n",
-    "        asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim tour mode choice config files\n",
-    "        tour_mode_choice_calib_targets_file=tour_mode_choice_calib_targets_file,  # folder containing tour mode choice calibration tables\n",
-    "        max_ASC_adjust=max_ASC_adjust,  # maximum allowed adjustment per iteration\n",
-    "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
-    "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
-    "        output_dir=iteration_output_dir,  # location to write model calibration steps\n",
-    "    )\n",
-    "    tour_mc_coef_file = os.path.join(iteration_output_dir, 'tour_mode_choice_coefficients.csv')\n",
-    "    iteration_output_dir = iteration_output_dir.strip('_'+str(i)) + '_' + str(i+1)\n",
-    "\n",
-    "print(\"\\n\\n\", \"Final coefficient table written to: \", tour_mc_coef_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_9\\tour_mode_choice_coefficients_UPDATED.csv\n",
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_10\n"
-     ]
-    }
-   ],
-   "source": [
-    "tour_mc_coef_file = r'C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_9\\tour_mode_choice_coefficients_UPDATED.csv'\n",
-    "iteration_output_dir = r'C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_10'\n",
-    "\n",
-    "print(tour_mc_coef_file)\n",
-    "print(iteration_output_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_10\n",
-      "ActivitySim run started at:  2023-09-13 14:37:43.903139\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-13 15:25:25.378209\n",
-      "Run Time:  2861.48 secs =  47.69133333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:732: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:750: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  total_tour_per_source_df = data.groupby('source').sum()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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eJNl4440X+kK8VatW+eKLLxqsPmrvH//4xxLdw3bHHXfMXXfdVY8V1a8f//jHufLKK2v0N2rWrFm5+uqr069fv7z++usNUB00HrL3l2Rv2bupk71Ln+xdM009lyWy99dp6q+x7F36ZO/Fa8zZWwOcBtOjR4/069cv3//+97P22msvtP+CCy7I559/noceeihHHHFEtttuuyJUWbe23nrr/PnPf86mm26ab3/720mSZZddNn/84x8XGjty5Mj0799//rimbrPNNku/fv1y1FFH5dNPP82vfvUrAbxETJkyJeutt95C2zfccMPMnTs3b731Vvr3759NNtmk4YujTvzgBz/IHnvsUewyGly5BbQkGTNmTMaMGVPj8U39rPVzzjmnSd/TdUktt9xy+fTTTxfY9re//S2FQiFbb731QuPHjBkz/8o5mobp06enY8eONR7fuXPnJn1PtokTJ2bffffNSSedlKOPPnqxv5NeeumlnHPOOXnvvfey+uqrN3CVUHyyt+wte5cG2bv0yd41I3s3PbK37F1qZO+mmb01wGkwp512Wg4++ODsvffe+da3vpUrrrhigR+KQqGQyy+/PC1atMiAAQPy8ssvF7HaunH66adn1KhROeKII7L55psv9t4Jhx56aF566aUsv/zyC9ybrKlbb731cscdd+RnP/tZjj/++Fx33XXZaqutil0WtTRnzpxFXi3RqlWrJMkxxxwjgNMklVtAS5IrrriirL5w6dChQ1ZeeeVil9FgunfvnocffjhHHnlkKisrM3HixAwfPjxJsssuuywwdurUqXnwwQfTo0ePIlTK0po7d+4ir2xcnMrKysyZM6ceK6pfDz30UHr27Jmrrroqw4cPz2WXXZZVV111/v55y8TOu9fekUcemZNPPrmIFUNxyN6yt+xdGmRvSpXsXfpkb9m71MjeTTN7a4DTYLp165a//OUvue666/Liiy/O/8D+3yoqKtK7d+9suumm+d3vfpdPPvmkCJXWnY4dO+a+++7LnXfemVmzZi12XKtWrbLvvvvmF7/4xRKdSdTY9OzZc5HbV1999XzwwQc59thj88Mf/nCBM4ZK4SxOFrTBBhsUuwRYKuUW0Ch9Rx99dA488MAccMAB2XLLLfPYY4/liy++yG677ZY11lgjyZdfrL7yyiu57LLL8umnn+aQQw4pctW1M2TIkLz33nvz/3vmzJkpFAq5//7789JLLy0w9u23327o8qiljh075k9/+lNuu+22XHHFFfnRj36Us88+O/vvv3+ef/75/PKXv8zYsWOz7rrr5pJLLvGZhLIle8vesnd58HeOpkr2ptTI3rJ3qSmV7K0BXmTvvvvuAku+TJ06NUny1ltvLXRGyZIsk9JYdejQIeeee+7Xjttvv/2y9957L/BLtKlq1apVDj/88K8cUyr3XRs4cOBX7v/iiy8yaNCgBbaVSgh/8cUXM3fu3Pn/PW3atCTJ008/nfHjxy8wthSusPgqTX2ZpsUpl9d4iy22SKdOnYpdBlAHNtxww1x//fW55JJL0rdv31RWVmaPPfbIRRddNH/M5Zdfnn79+qWioiI9e/ZM9+7di1hx7Q0ePDiDBw9eaPv/fv6YpxT+Zv3vFw9fpVS+eDjkkEOy1VZb5bzzzssFF1yQ/v37Z8yYMWnWrFlOPvnkHHPMMUt0dj7lQfZeNNm7aZK9Sz+X1UQpfI5ZlHJ5jWVvKB2y93/I3l+SvRuHQnV1dXWxiyhX66233iJ/8Kurq79y+5tvvtkQ5dW76urqjB07NpMnT06SLL/88llllVWKW1Q9K/U5jxs3bqke19TP+lzUz/J//2pd1L6m/LO83nrr5fjjj893vvOdBbZPnTo1J5xwQs4+++ysv/76Cz1uiy22aKgS61y5vcblaL311iu7JcnKbc4DBw7M5ptvvsCSTeVk0qRJad269UJXAT7xxBN5/fXXs9tuuzXK+zUtieeff36pHrflllvWcSUNZ1H3Bf06pfT36YUXXsgJJ5yQqVOnplAo5IQTTshJJ51U7LJohGTv0s6hi1Lqc5a9/6OUc5ns/aVSfo3LUbnl0KT85ix7y96LI3s3XU01e2uAF9G11167VI878cQT67iShjVq1Kj06dMnTz/9dKZPn77AvmWWWSbf+9738rOf/Wypfqk0VuU453LydWffL87ee+9dx5U0jMV9gZgs/kvEJE36D345vcb/fWXUkmjKX7Ik5RnQnn/++ay55ppNevlPKHfl+MVDknz++ee5/PLLc++996Zly5Y5+uij8+ijj+btt9/Ot7/97fTq1Stdu3Ytdpk0IrJ3+eTQcpxzOSmnXJbI3kuiKb7GsrfsDTQdsnfTzN4a4DSovn375sorr0xFRUU23XTTrL322mnXrl3mzJmTyZMn54033sibb76ZioqKnHXWWTnssMOKXXKtleOca+qGG27I4MGDM2DAgGKX0uBmzZqVFi1aFLuMpVKuXyAuqab6Gn/VlyxfpSl/yUJ5WNy9Mr9KqSwVyqLdeeed+dvf/rbUf9cojuHDh+eCCy7I+PHjs9VWW+Xiiy/OqquumtmzZ+eaa65J375906pVq5xxxhk58MADi10uFE055tBynHNNyd5NL5clsndNNdXXWPamVMne/C/Zu2kqheytAU6DeeKJJ3Lsscfm29/+dnr37p2VVlppkePef//9XHTRRXnmmWfSt2/ffPvb327gSutOOc55SVxwwQW5++67m/yH92uvvXaJAuaoUaPSs2fP/OUvf6nHqqhL5fQa//73v1+qEN7Uv2Qpx4C2NF/6FgqF3HLLLfVQTf0rx+Wqvu5LtZYtW2bZZZfNOuuskx/84AfZb7/9UlFR0YAVFlcpfA758MMPs/zyyy+0vN7ijB07Ni+99FL22muv+i2snpx++un5y1/+kjZt2uTMM8/MAQccsNCYESNG5KyzzsrYsWOz5ZZb5pJLLimpZY+hJsoxh5bjnJdEKfzNS8orl5WrcnqNZe+ak72bFtl7YbJ30/8cIns3zeytAV5E5fYH/2c/+1kmTpyYAQMGpHnz5l85ds6cOdlrr72y+uqr57rrrmugCuteOc55SZTCH7/kyw85Rx55ZM4444yvHDdnzpxce+216dOnT+bOndvk511TM2fOzKefftqol0P5Ol7j0leuAW1JNeU5l+O9Mg899NCv3D937tx89tlnef/99zN79uxsvfXWueGGG9KsWbMGqrC4SuFzyDe/+c1cfvnlC9xPcPr06bn44otz1FFHZc0111xg/AMPPJCzzjqryc55vfXWy7bbbpuLL744K6644mLHzZw5M7/+9a9z5513pk2bNnn55ZcbsEoaI9l78Uolh5bjnJdEKfzNS+SyryN70xSUWw5Nym/OsvfCZO+m/zlE9l60xp69y+MnrJFakvva/PcZRE01hL/xxhs5/PDDvzaMJkmzZs3ywx/+MA888EADVFZ/ynHO5WjzzTdP3759M2PGjJx//vmLHPPWW2/lrLPOyltvvZXlllsuv/zlLxu4yrqz44475pxzzsmOO+44f9usWbPy8MMPZ5tttkmnTp0WGD948OAm/Qc/Kb/XuBw99thjxS6hwY0ePbrYJTSophyml1b//v1rNG7mzJm54447cvnll+eOO+742vBO47Goc5m/+OKLDBo0KHvuuedCIbypu/TSS2t0j89WrVrlwgsvzPe//31/j0kie3+VUsmh5TjnclRuuUz2Lv3XuBzJ3qVP9l482bvpkr0XrbFnbw3wIqrJH79x48bl4osvzvDhw7PsssvmlFNOqf/C6sm0adPSpUuXGo9faaWV8u9//7seK6p/5TjnctS3b9+cfPLJueOOOzJjxoz07t17/hdn1dXV+dOf/pTrrrsus2bNyq677ppzzz03yy+/fJGrXnrjxo3L9OnTF9g2bdq09OzZM3379l0ohJeCcnuNk+Tdd9/NiBEjss8++8zfNnHixFx77bV5+eWX07p16+y44445/PDDm+T91v5XOQY0FjRr1qz84x//SKtWrbLGGmsUu5wG1apVq/zsZz/LyJEjM3DgQCG8BJTqIl81CeD/7Rvf+Eb222+/eqqGpkT2/mqlkEPLcc7lqNxymexd+q9xIntTfmRv2buUyN5faqzZWwO8kZo7d2769u2bP/zhD5k5c2Z222239OzZs0l/uJ0zZ84SfVBr1qxZZs2aVY8V1b9ynHM5atGiRa677rr07NkzAwcOzMyZM3PllVfm/fffz9lnn51Ro0alc+fOufDCC/O9732v2OXWm1L9g5+U32t85ZVXpm/fvqmurs7ee++dioqKTJ06NQcccEA+/PDDtG/fPiuvvHJ+97vf5fHHH0///v1rdLVNU1aOAW38+PEZNWpUWrVqlS222KLG9zlqzKZOnZobb7wxI0aMWOAM7QcffDC9evXKZ599luTLD+69e/fOJptsUqRKi6NHjx55+umni10G1MoXX3yRRx99NAMGDMjzzz+f6urq/PznPy92WTRisndp5NBynHM5Krdctjiyd+m8xrL3wmRv2bscyN6UgqaQvTXAG6EXX3wxF110Ud55551069YtF1xwQb797W8Xuyz4Wtdee+0SjX/ttdfqqZKGV1lZmcsvvzzt2rXLrbfemg8++CDvvPNOZs6cmf322y9nnXVWlllmmWKXSS2Uy2s8ZMiQ9OnTJ9tvv30OO+ywVFRUJEn+8Ic/ZNy4cdl4443z5z//Oa1bt86oUaNy6KGH5s9//nOOPvroIldee+UY0MaNG5errroqI0aMyOOPPz5/+5/+9Kdcc801mTt3bqqrq7Pccsvl4osvzg9+8IMiVls706ZNywEHHJB33303K664YubMmZNmzZpl1KhROeuss1JdXZ0DDjgga6+9dgYNGpSf/exnGTRoUFZfffVil95gKisrU1VVVewyltqgQYOWaPw///nP+imEohgxYkQGDBiQRx55JNOmTUt1dXVWW221HHDAAcUujUZM9qapkr1LP5eVs3J5jWVv2TuRvWXvpkn2Lm9NKXtrgDcikyZNyuWXX55BgwalRYsWOemkk3L00UeXxPI287z44ouZO3dujca+/PLL9VxNwyinOS9pCE8WvMdeKTj33HPTvn37XHvttamoqMgf//jHbLfddsUuizpU6q/xnXfemY033jg33HDD/G3V1dV54IEHUigU8otf/CKtW7dOkmy00Ub50Y9+lL/85S9NPoSXY0D75JNPcsABB2TSpEnZaKON5s/5qaeeylVXXZVmzZrltNNOyzrrrJO77747p512Wu66666sv/76xS59qfTt2zfvv/9+fvvb32aXXXaZv/26665LdXV1fvrTn+bss89Okuy7777Zc889c8MNN+TSSy8tVskNbuTIkVlppZWKXcZSO/vss5foc0V1dXXJfQ4pN+PHj8/999+fAQMG5L333pt/RVyPHj1y9NFHZ5tttilyhTRWsveCmnoOnaec5ix7l34uo/RfY9lb9pa9Ze+mSvYuP001e2uANxL33HNPrrzyykyZMiVbb711Lrjggqy22mrFLqvO3X333bn77rtrNLZUfjGW05z79etX7BIahRNPPDEdOnRIr1690qdPn2y22WYlcXYy/1HKr/Hrr7++UKB+44038sknn2SZZZZJjx49Fti34YYb5sEHH2zIEutFOQa0P/3pT5k2bVpuu+22Bc6o/9Of/pRCoZCf//zn898L3/3ud7PPPvvkxhtvzG9/+9viFFxLgwcPzo9+9KMFXt9p06bNX3bs4IMPnr+9ZcuW2XPPPXPPPfc0eJ3F8te//jX3339/jjrqqGKXstSa8s8jNTdr1qwMHTo0AwYMyLPPPpu5c+emsrIyW265ZTbbbLNcf/31OeywwxptAKf4ZO+FNfUcOk85zVn2/lIp5zK+VMqvsewte8vesndT1ZR/Hqm5UsjeGuBF9tZbb+XCCy/MK6+8kk6dOuWqq67KrrvuWuyy6kU5/mIstzlvueWWS/yYF198sR4qaVgvvPDCQtvWWWed7Lfffrn77rvz05/+NGeeeeb85azm2WKLLRqqRGqpnF7j6dOnZ7nllltg29///vckX86nsrJygX1z5sxp0l8ezlOOAW348OHZZ599FgjgkydPzksvvZQk+fGPfzx/e6FQyA9/+MPccsstDV1mnfnggw8WeB2TL3+258yZk1VXXXWh5sdKK62UTz75pCFLrHM9e/b8yv1z587NtGnT8o9//CPvv/9+unXr1qRD+N57713sEopiyJAhee+99+b/98yZM1MoFHL//ffP/3me5+23327o8urURRddlIcffjhTpkxJy5Yts+2222annXbKjjvumA4dOmTcuHH5wx/+UOwyaaRk79JWbnOWvf+jVHNZuSqn11j2lr1l7y/J3k2P7P0l2bvxZ28N8CK67LLL0r9//8ydOzc77LBDTjnllCyzzDL58MMPv/JxXbt2baAK61Y5/mIsxznXxEcffZSBAwdm0KBBGTt2bN58881il1Qrhx566FeGkNdffz2HH374Qtub8rzffffdBYLp1KlTk3z5xWKzZgv+aRkzZkyD1lYfyuk17tKlS8aOHbvAtuHDh6dQKOS73/3uQuNHjRqVFVZYoaHKqzflGND+/e9/Z5111llg23PPPZeqqqqstdZaC72uHTt2zJQpUxqyxDpVUVGx0D22nn322STJd77znYXGf/rpp03+6pKBAwfWaNyqq66an/70pznhhBOa/JwXZ1Gfr1daaaWS+RJx8ODBC21f3H3ZmvKc77jjjrRp0ybHHXdcjjrqqJJ9v1L3ZO/SV45zrgnZ+0tNed6y94JK6TWWvf9D9pa9m/rnetn7P2Tv/2jKcy6V7K0BXkQ333zz/H8fNmxYhg0bVqPHNcUPdUvj008/zZgxY5rkWZxLq5Tn/MUXX2Tw4MEZMGBAnnvuuflLzi3qQ31T8/Of/7xJ/0FbGjfccMMC96ma57LLLltoW1NfXjApr9f4u9/9bu67774cfPDBWWGFFTJixIi8+OKLad68eXbeeecFxr7zzjv5y1/+kv33379I1dadcgxoLVu2zIwZMxbY9swzz6RQKGTrrbdeaPy///3vtGvXrqHKq3NrrbVWRowYkUMOOSTJl7+bhgwZkkKhkO9973sLjX/sscey5pprNnSZdeqxxx77yv0tW7ZMu3btSuqet0ly33335Z577skf/vCHLL/88pk0aVK+973vLfR7/OSTT87xxx9fpCrrRrktgbv33ntn6NCh+eMf/5hbbrklm2+++fyz0Dt16lTs8mjEZO+vVso5dHFKec6yd2mRvUuX7P0fsveCZO+mR/aWvUtNqWRvDfAiOvHEE4tdQoP65je/mcsvvzx77LHH/G0zZ85Mnz59stdee2WVVVZZYPzf/va3nHXWWU36S4dynPP/euWVVzJgwIA88sgj+fzzz5Mkyy+/fPbZZ58ccMABWXnllYtcYe2ddNJJxS6hQZVTIJ2nnF7jE044IYMHD84Pf/jDrLHGGnn77bdTXV2dn//851l++eWTfBm+Bw8enH79+qV58+Y58sgji1x17ZVjQFtvvfXy7LPP5qc//WmS/9zbJ0l22mmnBcZWV1fnr3/9a9Zbb70Gr7Ou7LXXXrnkkkuy4YYbZuutt87dd9+dDz/8MKuttlq23XbbBcbecMMNeeWVV3LuuecWqdq6UQp/Y5fUL37xizz66KPp2rVrPvzww/m/t5Jkzz33zKqrrpokuf/++3P99ddn3333TefOnYtVbq0tzRK4c+bMqYdKGsall16aiy66KMOGDcuDDz6YJ598Mk899VQuuuiibLrppunevXvZfUahZmTv0s+h5Tjn/yV7lx7Zu7TJ3rJ3InvL3k2X7P31ZO/i0wAvonIL4dXV1QttmzFjRq677rpsttlmCwXSUlCOc06SCRMmZNCgQRk4cGD+9a9/pbq6Oq1bt853vvOdPPPMM/nVr36VHXfcsdhlspTKKZCWo06dOuXee+/Nddddl1deeSXf+ta3ss8++2S//fabP2bgwIHp27dvVl555fzmN7/JSiutVMSK60Y5BrQDDzwwp512Wnr37p2tt9469913Xz755JN861vfWuBqqJkzZ+bXv/513nnnnSb9hcuBBx6Yl156KZdeemkKhUKqq6uz3HLL5corr5x/D8F77703f/rTnzJ27NhsttlmOeigg4pcdf0aNWpURowYkYqKimy55ZZZd911i11SrTzwwAN59NFHc+yxx+bkk09e6L6Je+21V7797W8nSbbbbrvsv//+ueuuu0rmM/m0adNSXV39lVfIjBgxIuedd14eeuihBqysbrVo0SI777xzdt5550ydOjV//etf88ADD+Sll17KSy+9lEKhkD/+8Y/5/PPPs/POO6dVq1bFLplGoFR+zmuqHHNoOc45kb1Lnexd2mRv2Vv2lr2bKtlb9m4q2VsDvBGZNWtWRo8enQkTJqS6ujpdunTJeuutl5YtWxa7tHq1qKBa6kp1zo888kgGDBiQZ555JnPnzk27du2yxx575Ac/+EG23XbbfPzxxwud2VgKrr322iV+TKFQyM9//vN6qKb+HXbYYTn++OPnf5ApB+X2Gq+44oq5+OKLF7t/n332yfe+97107959fnhp6soxoO26665566230qdPn/Tv3z/V1dVZZZVVcvXVV88fc9NNN+UPf/hDpk2bll122SU/+tGPilhx7RQKhVx55ZU55JBDMmLEiCyzzDLZaaedFjhL+d///neqq6tz/PHH59hjjy2J9/cbb7yRP/7xjxkzZkxWW221HHfccdloo43yy1/+MgMGDJj/maRQKGT33XfPpZdeutD9JJuKgQMHZtNNN82pp576tWPnfeH2xBNPNPkQ/uijj+baa6/NP/7xjyRf3lfu5JNPzu677z5/zPTp03PVVVfljjvuWGjJyaakZ8+eOfDAA7PxxhsnSZZddtnst99+2W+//TJ+/Pg89NBDeeihhzJq1Ki8+uqrufjii7PrrrvmV7/6VZErp7GRvctHqc5Z9q65ppzLZO+aacqvsewte88jezf997fsvWiyd9NUKtm7af6ElZiJEyfmqquuyuDBgzNt2rQF9rVu3To777xzTj311HTp0qVIFULNnHrqqWnTpk0OPvjg7Ljjjtliiy0WOAOsKSyLsTTKLaA9//zzC5yRXA7K7TX+Ok19+bFFKdeAduqpp+aggw7KyJEjs8wyy2TLLbdM8+bN5+9v2bJlNtxww+yxxx758Y9/XMRK686mm26aTTfddJH7TjzxxCYfyP7biBEjcthhh6VZs2ZZZ5118vrrr+eQQw7JoYcemvvuuy977rlndtlll0yfPj2PP/54HnrooXzzm9/MEUccUezSl8obb7yR4447rsbjt9lmm1x33XX1WFH9e/jhh3PaaaelZcuW2WabbdK6deu8+OKLOeOMM+bfP/LVV1/Nqaeemg8++CCrrLJKLrroomKXvdQGDhyY73znO/ND+H9bYYUVcuSRR+bII4/MmDFj8sADD+TBBx/MPffc0+hCOMUje1MqZO+aa8q5TPaumab8Gn8d2Vv2bspkb9l7Htm76SmV7K0BXmSvvPJKjj322EyZMiUbb7xxttpqq3Tp0iXNmjXLhAkT8sILL2TQoEF5/PHHc/3116d79+7FLhkWa5VVVskHH3yQAQMG5J///GdeffXV7LTTTvnGN75R7NLqVb9+/YpdAvWsnF7jF154Yake99/LdjVl5RTQ5llxxRWz4oorLnLfT37yk/zkJz9p4IqoK9ddd126deuWfv36pUOHDqmurs4555yTm2++OXvuuWcuv/zy+WN33333fPbZZ3nooYeabAifOXNm2rVrt9D2ZZddNjfccEO++c1vLrC9bdu2TfqeXEly6623pmPHjrnzzjvn32NtxowZOf744/P73/8+Xbp0yRFHHJEvvvgiP/vZz/KLX/yiUS5LVtfWXHPNnHrqqTn11FPz8ssvF7scGgnZm1Iie1Oqyuk1lr1l7/8mezdtsveXZG/Zu7HRAC+iTz75JD//+c/Ttm3b/OEPf8hmm222yHFvvPFGTjnllJx88sm5//7707FjxwauFGpm6NChGTlyZB544IH89a9/zd/+9rdcddVVWWONNfKDH/wg66+/frFLrBdbbrnl146ZOnVqCoXCV94bhMarJq9xqTj00EOX+IqRQqGQN954o54qgrrRs2fPJX5MoVBI796966GahvHqq6/mqKOOSocOHZJ8OZ8jjzwyAwcOzPbbb7/Q+B/84Af59a9/3cBV1p0VVlghH3744ULbmzVrtsj5/utf/2ry91EcM2ZMDj300PkBPPnyKtYTTzwxP/nJT3LqqaemY8eOufLKK7PJJpsUr9Ai0sQkkb0pPbL34sneTZvs/dVkb5oC2Vv2/l+yd3lojNlbA7yIbr311kybNm2Bs0YW5Vvf+lZuvvnm7LHHHrn99ttz0kknNWCVsGQ23njjbLzxxjnnnHPyt7/9LQ8++GAee+yxXH/99SkUCikUChk2bFjWW2+9rLzyysUut85UV1fnySefzD/+8Y+svvrq2X777dOsWbM8++yz6dWrV959990kyTe/+c2cdtpp2WabbYpcce0MGTIk7733Xo3Hl/KSZPOUyhctl156aY3GPfrooxk+fHiSZKONNqrHihpGOQa0ww47bIkfUygUcsstt9RDNfVv4MCBNR77319ENeXX+LPPPkunTp0W2DZvacH27dsvNL5Vq1aZMWNGQ5RWLzbaaKM8/PDD+fnPf/61yyTOmjUrDz/8cLbbbrsGqq5+TJ06NaussspC21dbbbUkXy6leOedd87/IqYUvPjii5k7d+4SPWavvfaqn2JoMmRvSpHsLXsviuzddMjeNSd7Ny2y95dk7y/J3k1XKWRvDfAiGjp0aPbcc8+vDODzrLzyytl7770zePDgJh3C//eHZt59155++umMHz9+gbGNccmEpVGOc06SysrKbLfddtluu+0yc+bMDBkyJA899FCefvrp3HvvvRkwYEB69OiRffbZJ7vvvnuxy62Vzz77LMccc0xGjhyZ6urqJMmGG26Y8847L8ccc0xat26dnXbaKdOnT8/IkSNz7LHH5uabb27SZzYPGTIkgwcPrvH4Ugjh//1Fy2qrrZYddtihJL9o2Xvvvb9y/7hx43LxxRdn+PDhadeuXU477bQccMABDVRd/SnHgPbBBx/UaFxVVVXGjx+f6urqJn0/ydGjR3/tmP9+fy+77LI55ZRT6r+welRdXZ1mzRb8uD/vNWzKr+XiHHjggfnJT36SX/3qVzn33HMXmvs8VVVVOf/88zNhwoQceOCBDVxl3aqqqlrgnq/zzLuf4DHHHFNSATxJ7r777tx99901Gjvv91ZjC+E0PNm7PHJoOc45kb1l7wXJ3k2H7P31ZO+mSfb+kuwtezd1pZC9NcCL6IMPPliie3ust956S/QhoTH63x+aeYGlT58+C/0xaOp/7Ocppzkfc8wx2WqrrbLllltm/fXXnz+XVq1aZY899sgee+yRSZMm5eGHH86DDz6YZ599Nn//+9+bfAi/5pprMnr06Jx//vnp0aNHxo0bl0suuSQ//elP061bt/Tv33/+2X6ffvpp9t133/Tt27dJh/Bjjz023/nOd4pdRoMpxy9a/tecOXNy00035YYbbsiMGTOy55575uyzz55/RmtTV44B7fHHH//aMSNHjsyFF16Yf//731lllVVy3nnnNUBlDW/u3Lnp27dv/vCHP2TmzJnZbbfd0rNnz4XO4KZx23zzzXPkkUfmpptuyt///vccccQR6dGjR1ZcccVUV1fn448/znPPPZfbbrsto0ePzqmnnpr11luv2GXXq65duxa7hDq3//77l+2Sciw92bu0c+g85TRn2Vv2LlWyt+ydyN6yN42d7L0w2btx0gAvoubNm+eLL76o8fiZM2emTZs29VhR/arp0j6lpNzm/Pe//z1PPvnk/KWoNt988/To0SNbbbXV/D9yHTp0yCGHHJJDDjkkY8eOzV/+8pciV117jz/+eA488MAcdNBBSZI11lgj559/fo444ogccsghCyx1s/zyy2f//fdP//79i1Rt3VhzzTVLKmB+nXL8ouW/Pf/887nooosyZsyYrLHGGrngggvSo0ePYpfVYMoxoE2dOjW/+c1vcvfdd6eioiLHHntsTjjhhLRs2bLYpdW5F198MRdddFHeeeeddOvWLRdccEG+/e1vF7usOvO/y2bOnDkzhUIh999/f1566aUFxr799tsNXV6dO+OMM7Lyyivnqquuyvnnn7/IhkebNm1y4YUXlsQVNOVo8803zx577FHsMmhiZO/SV25zlr1l71Ile8vesrfs3VTJ3rJ3qSmF7K0BXkTrrLNOnnjiiRrfB2T48OFZa6216rmq+vN1S/uUonKb88svv5w33ngjL7/8ckaMGJFXXnklw4YNS6FQSLt27bLFFlukR48e6dGjR9ZZZ52suuqqOe6444pddq19/PHHWXPNNRfYNu9ndVFnf6200kqZMmVKg9RG3SjHL1qSL79QuOyyy/LAAw+kZcuW+cUvfpGjjjpq/vI+5aDUA9qi3H///bn88svzySefZMstt8wFF1yw0O+4UjBp0qRcfvnlGTRoUFq0aJGTTjopRx99dFq0aFHs0urU4MGDF7ls5qBBgxY5vilfDTfPwQcfnL333jvDhg3LCy+8kH//+9+prq5Oly5d0r179+y0005N/p6R/21R9wb9qi9bSmFpVFhSsnfpK7c5y97/IXuXFtlb9pa9Ze+mSvaWvWXvxkcDvIh+9KMf5fzzz8/DDz+cXXfd9SvHDho0KM8880yuuuqqBqqu+D744IOcf/756du3b7FLaTBNfc7NmjXLRhttlI022iiHH354kuSjjz5aIJRfdtllmTt3btq3b58tt9wyPXr0yMEHH1zcwmtp9uzZadWq1QLb5oWURYWVQqGwwL3paPzK8YuWu+66K1dddVWmTJmS7373uznvvPNqdN/MUlEuAe2/vfvuu7nooovy/PPPp0OHDvn1r3/d6O7dU1fuueeeXHnllZkyZUq23nrrXHDBBVlttdWKXVad69evX7FLKJrWrVtn1113/drP2KVgcV+0JIv+skUIpxzJ3l+tqefQpdHU5yx7/4fsXVpkb9lb9i4tsnfpk72/JHs3ThrgRbTPPvtk0KBBOfPMM/PWW2/lkEMOSZcuXRYYM2HChNx8883p169ftttuu/zwhz8sUrV1Y+TIkbn++uszYsSIJMm3vvWt/PznP8/mm28+f0x1dXX+/Oc/55prrsnMmTOLVWqdKcc5/7eVVlopu+22W3bbbbckXy7t88ADD2TAgAF59NFHM3jw4CYfwsvNiSeemHXXXbfYZTSocvqiZfTo0bngggsyatSorLDCCrn44ovzgx/8oNhlNahyCWjzzJo1K9ddd1369u2bOXPmZL/99sv/a+/Ow6IqFz+Af4dNQFTcSHHLLcYtRWSRrlqCuIWCS25JmvtNU1NTi8IFRXPL0jT1iltdlwQERQTFJUUWBeQa6tWruStuIJI4MJzfHz7MrxFUQGYO55zv53l6njzzTn7fUJjvec95z/Tp01G1alWxo5W7CxcuYM6cOUhNTUWtWrWwfPlyWZc0uWwFSS+ntBMtvr6+sv5+TIbD7q2MHqrEOf8du7f8sHs/x+4tX+ze7N5ywe4tf+ze0sQFcBGZmJhg7dq1mD59On7++WesW7cOdevWRe3atWFqaooHDx7g2rVrEAQBPXv2xIIFC8SO/EZOnjyJMWPGQKvVonHjxrCyskJSUhJGjBiB4OBgODs748aNG5g2bRrS0tJgY2ODuXPnih37jShxzi/Kzc1FUlISEhMTcfr0aZw9exZ5eXmwsLDQbckmB5mZmbh165bu14VXID98+FDvOPD86lYpmzhxot6vNRoNzp8/j4yMDN02N2q1WpbPK1KC/v37o6CgAABQs2ZNbNu2Ddu2bXvle1QqFTZv3myMeAaltIIGAEePHsX8+fNx8+ZNODg4YO7cuWjbtq3YsQxi8eLF2Lp1K7RaLT744ANMmTIFNjY2Rb5Hv6i4O03kavv27Th+/DhWrVoldpQyKenWxn8n9e9fZTnRcurUKQMkMQ6lPeOXyg+7t/x7qBLn/CJ2b3ZvkhZ2b3Zvdm997N7Swe5dMuze4lMJgiCIHYKA33//HXv27EFaWhru3bun+yDr5OSEvn37ws3NTeyIb2zkyJFIS0vDhg0b4OjoCAC4e/cuxo8fD3NzcwQFBcHPzw8PHjyAl5cXvvnmG9SuXVvk1G9GiXPOz89Hamoq4uPjER8fjzNnziAvLw/m5uZ49913dcXb0dFRNtsZqdXqYp/bIgjCK5/ncu7cOUPGMrj79+9j+fLliI6ORk5Ojt5rVlZW6N69O6ZOnVrk7hopUqvV+Prrr+Hh4aE7lpWVBV9fXyxbtkz397tQTEwMFi1aJMmvcdeuXcv0vtjY2HJOYlwvK2ivI+WC9vnnnyMmJgYA8MEHH8DPzw+mpqavfZ+zs7OhoxmEWq3W/XtpnrUlxb/HZRUQEICdO3dKds4l/f5VUFCAu3fv6n5OS3W+pXH79m2EhoYiLCwM169fV8SciV6G3VuePVSJc2b3/n/s3uzeUvwas3uze78Ou7d8sXvLF7t3xcIFcDIaNzc3DBgwANOnT9c7fvz4cYwZMwZNmzbFvXv3MGfOHMlvN1dIaXMeM2YMTp06hdzcXJiYmKBVq1Zwc3ODq6srnJycimxjJRezZ88u0/ukfCVVamoqxo0bh6ysLLRt2xZubm6ws7ODmZkZMjIykJSUhKSkJFStWhVr1qxB+/btxY78RpR6okVJlFjQ/j5n4PXzlnphKeuV1S/eeSNnUi/hJXHmzBnMmTMH586dQ/369fHNN9+gS5cuYscyiGfPniE6OhohISFISEjQ/R3u1KkTfv75Z7HjEZEBKa2HAsqbM7t36bB7Swe7t/yxe7N7vwy7t7ywe7N7VwTcAl1CEhIScOHChTJtMVERZGdno2nTpkWON2/eHIIgIDMzE7t27ZLFswUKKW3Ov//+O8zNzeHj44Nx48bh7bffFjuSUUi5TJfFgwcP8Nlnn6Fy5cr46aef4OTkVOy49PR0TJkyBZ9//jn27NmDmjVrGjlp+fHx8SlVMVOSU6dOITQ0VPJbhSqpaBVS2veusnyN5fZsUCXLzs7GsmXLsHPnTpiYmGDcuHH45z//KcstQ1NTUxESEoL9+/fjyZMnAIAaNWqgf//+GDRoEOrVqydyQqKKj91bepQ2Z3ZvZWD3pr9j95YupX3vYvdWNnZvdu+KhAvgEhIZGYmdO3dKtoRrtVqYmRX9I1e4Fdf48eNlU0YLKW3OAwcOREJCgm6bjyZNmqBjx45wc3ODs7MzqlWrJnZEKgfbtm1DTk4Otm/fjgYNGrx0XMuWLREcHAxvb2/8+uuvmDRpkhFTlq9FixaJHaFCuXPnDkJDQxEaGorr168DgCJLuNQLmq+vb6nfc/PmTQMkqXgKTy4dOHBA0s9souf27NmD7777Dg8ePICLiwsCAgKKXSSRsoyMDISFhSE0NBR//vknBEGAlZUV3N3dERcXh3nz5ultJUpEr8buLT1KmzO7tzKwexO793Ps3vLF7i0v7N7s3hUNF8CpwpDbN8OSkNuc58+fDwC4desW4uLiEB8fj6ioKGzbtg0mJiZQq9VwdXXVlXJra2uRE1NZHDx4EH369HllAS9Ur149+Pr6Ijo6WtIl3M/PDxMmTEDHjh11x/Lz85GSkgK1Wo0qVarojQ8PD8esWbOQnp5u7KgGo9FodNv5xMfHQxAECIIAV1dXDBkyROx4RqW0gvbs2TNERUUhNDQUSUlJ+OOPP8SOZBCFJ5fCwsJw7do1CIIAW1tbsWPRG7h8+TLmzp2LxMREVK9eHYsWLYKPj4/YscrV/v37ERISgri4OGi1WlStWhXe3t7w8vJCp06dcO/ePXh6eoodk4gqGLn10JKQ25zZvZWB3fs5dm92b3ZveWH3lh92b3bviooL4ERU7uzt7TFgwAAMGDAAAHDx4kXEx8fj5MmT2L17N4KDg2FmZobWrVujY8eOmDx5ssiJqTRu3LiBjz/+uMTj1Wo1QkNDDZjI8BITEzFw4EC9Y9nZ2fDz88PGjRv1ynkhQRCMFc+gXtzOp3BevXv3xmeffYYmTZqInNA4lFjQkpOTERISgqioKOTk5EAQBDRv3lzsWOWq8ORSaGgo4uPjUVBQAEEQ0K5dOwwePBi9evUSO+IbCQsLK9X4K1euGCaIkWk0GqxevRobN25Efn4+Bg4ciOnTp6Nq1apiRyt3U6dOhbW1NYYOHQoPDw84OzvD1NRU9zq3ECUikjd2b3lj936O3Zvdm91b+ti99bF7Sw+7tzRxAZyM6tSpU9BqtXrHcnJyAAAnTpzA3bt3i7xH6lcLKXHOL2revDmaN2+O4cOHQ6PRICoqCr/++itSU1Nx5swZlnCJMTc3x7Nnz0o8Pjc3V7Z3HMilaL+ouO18bG1t4evri3bt2iEgIAC9evWSfQGXe0Erzt27d3Vf+6tXrwIAzMzM0KtXLwwZMgQdOnQQOWH5OHPmDHbv3q13cqlq1arIzs7G/Pnzi5x0k6pZs2aVqoQJgiD50nb06FHMnz8fN2/ehIODA+bOnYu2bduKHctg6tevjxs3biAkJARXrlzBf/7zH3h6eqJx48ZiRyMikSmxhypxzi9i95YXdu//x+7N7i037N7s3uze0sLuLU1cACej2rlzJ3bu3Kl3rPBD7IYNG/S+8Rf+IJB6IVXinP/u2rVrOHPmDM6cOYO0tDScP38eeXl5qFy5Mjp37gxnZ2exI1IpvfPOOzh69GiJn4l45MgRNGvWzMCpqLyMHTsWJ06cgFarRd26dTF06FB4enrC1dUVJiYmuHnzpmxPPhRSSkErpNFocPDgQezevRvx8fG6E8dNmzbF5cuXsWTJEvTo0UPklG8uIyMDe/bsQWhoKK5cuQJBEGBvbw9fX194eXnhrbfeQrdu3VCjRg2xo5aboKAgsSMY1eeff46YmBgAwAcffAA/Pz9oNBokJSW98n1S/ixy8OBBnDlzBuHh4YiKisLx48exfPlyNGnSBF5eXmjVqpXYEYlIJErsoUqc89+xe8sPu7e8sXuze7N7s3tLFbs3u7dUcAFcRLdu3SrV+MIrl6VKaT8IAOXNOSsrC2lpabrCnZaWhqysLAiCgGrVqqF9+/aYOnUqnJ2d0bJlS5iYmIgdmcqgb9+++PbbbxEZGfnaq3DDwsIQFxeH5cuXGykdvaljx47B2toafn5+GDZsGGrXri12JKNQYkFLS0tDSEgIIiMj8fjxY5iYmMDR0RFeXl7w8vKCVquFp6cnzM3NxY5aLrp27YqCggKo1WqMHz8eHh4eaN26te71mzdvipjOMHx9fcWOYFTR0dG6f4+NjcXhw4dfOb5w8ePcuXOGjmZQbdu2Rdu2bfHVV1/h+PHjiIiIwKFDh7BmzRqoVCqoVCocPnwYarUa9erVEzsukSjYveVPaXNm91YGdm95Y/dm92b3lg92b3Zvdu+KiQvgIuratauitsYoyw8Cqf9AVNqcXV1doVKpIAgCqlevDhcXFzg7O8PZ2RkODg6S/vNL/69///4ICwvDl19+iQsXLmDYsGGws7PTG5ORkYHg4GBs2bIFXbp0Qc+ePUVKS6U1ceJE7Nu3D2vXrsXPP/+Mxo0bw9PTE56ennj33XfFjmcwSixoH330EaysrNCpUyd07twZXbt21TvJILc55+fnw8rKCrVq1YKVlVWRLVKV4O7duzh9+jQyMjIAAHZ2dnB0dETdunVFTlY+lLb48SJTU1N06dIFXbp0QW5uLmJiYrB3716cOHECv/32G0JCQuDq6or+/fvjww8/FDsukVGxe7+e1H/uK23O7N7KwO4tb+ze7N6F5DZndm92b7lj95YOLoCLyMfHh6WkGM+ePUNUVBRCQ0ORlJSEP/74Q+xIBieXOffo0QMuLi5wcXHhtlsyZmJigrVr12L69On4+eefsW7dOtStWxe1a9eGqakpHjx4gGvXrkEQBPTs2RMLFiwQOzKVwsSJEzFx4kScPXsWERERiIyMxLp167B+/XrUrVsXTk5OsvzZpcSCZmVlhadPn+J///sfqlevDmtra3Tu3Bk2NjZiRzOIw4cPIyIiAhEREVi+fDlUKhVq1aqFbt26oVu3brK+OvfixYsIDAxEUlISBEHQ20rRxMQETk5O8Pf3h4ODg4gp35zSrrp/FUtLS3h7e8Pb2xuPHj1CZGQkIiIicPLkScTHx7OEk+KwexdPLj20NOQyZ3ZvZWD3ljd2b3Zvdm/5YfdWHnbvik0lyP1hIiQZycnJCAkJQVRUFHJyciAIApo3b46IiAixoxmMEudM8vL7779jz549SEtLw7179yAIAuzs7ODk5IS+ffvCzc1N7IjlQq1W4+uvv4aHh4fuWFZWFnx9fbFs2TI4OjrqjY+JicGiRYskv7UP8PwOqJMnTyIiIgIxMTF48uQJAKBevXro378/fHx8YG9vL3LKN3f79m1dQbt48WKxBc3LywurV6/W+3MgZbm5uYiNjUV4eDiOHz8OrVYLc3NzdOzYEd26dYNarcaAAQNkNedC58+f151kun37NlQqle6kxKxZs/DJJ5+IHbHcHDp0CFOmTIFKpYKnpyfc3NxgZ2cHMzMzZGRkICkpCVFRUcjPz8eKFSvg6ekpdmQyoOvXr2Pfvn0YP3682FGISERK7KFKnDPJC7s3uze7t3Sxe7N7s3srD7t3xcAFcIm4ffs2QkNDERYWpveMBam7e/cuwsLCEBoaiqtXrwIAzMzM4OXlhSFDhqBDhw4iJyx/SpwzkdSp1epir7x+3faYcijhf6fRaBAbG4u9e/fi6NGjyMvLg4mJCTp27Ih//etfYscrN0oqaIUyMzN1V6mmpqbqvTZ27FiMHz8eVlZW4oQzsMTERERERCA6OhpZWVlQqVSoX78++vXrB19fX9SpU0fsiGV248YNeHt7o3Hjxli5ciUaNGhQ7Lg7d+7g888/x6VLl7Bnz56XjqvoZs+eXer3qFQqLFy40ABpiEiq2L3lQ4lzJpI6du/n2L3ZveWI3Zvdm92bjI0L4BXYs2fPcODAAYSGhiIhIQEFBQUwMzPD2bNnxY72RjQaDQ4ePIjdu3cjPj5et9VN06ZNcfnyZaxYsQI9evQQOWX5UuKcSTk0Gg3Onz+PjIwM3VXoarUalSpVEjtauSnLBztA3s/Eefz4MaKiohAREYHTp08jPT1d7EgGIeeC9jI3b95EREQE9u7di0uXLkGlUsHa2ho9e/ZE//79i9x1IRd5eXk4duwYwsPDceTIETx79kzyn7sWLFiAPXv2ICoqSu8Zc8XJzMxEr1690KdPH8yaNctICcuXWq0u9XtUKpWkT5iW5Q4RlUqFgwcPGiANkXSxe8uHEudMysHu/XLs3tLH7s3uLeXPXezer8fuTWLgAngFlJKSgtDQUERGRuq25qpTpw4GDhyIjz76CLVr1xY7YpmkpaUhJCQEkZGRePz4MUxMTODo6AgvLy94eXlBq9XC09NTVtu9KHHOpBz379/H8uXLER0djZycHL3XrKys0L17d0ydOhV2dnYiJSRjOXXqlOzvoJFjQSuJ8+fPIzw8HJGRkbhz547kC0tJPXnyBNHR0di7dy82btwodpwy69mzJzp37lziE4nfffcdDh8+jP379xs4mWHcvHmzTO+T8jPounbtWuSYIAi4ffs2atWqBQsLi2LfFxsba+hoRJLA7i2fHqrEOZNysHtTIXZv+WL3ZveWEnbv59i9Kz4zsQPQc3fv3sWePXsQEhKCq1evQhAEmJiYAACmTJmCsWPH6n4tVR999BGsrKzQqVMndO7cGV27dtW7Iqqs3zgrMiXOmZQhNTUV48aNQ9eQgZwAAEWsSURBVFZWFtq2bVvsc23CwsIQGxuLNWvWoH379mJHplI6e/YsUlNTIQgCWrRoUWzJfvLkCZYuXYpdu3bhjz/+ECGl8Zibm8PDwwMeHh56BU3u1Go11Go1vvzySyQkJChizgBgY2MDT09PJCcnix3ljdy+fRvNmjUr8fgmTZrg3//+twETGZaUy3RZFVemHz58CHd3dyxZsgQdO3YUIRVRxcbuLc8eqsQ5kzKwe8sfu7c+dm92byli95Y/dm9p4gK4iAq35goJCcHJkyeh1WpRqVIldO3aFd26dYODgwN8fX3RvHlzyRdwALpnuPzvf/9D9erVYW1tjc6dO8PGxkbsaAajxDmT/D148ACfffYZKleujJ9++glOTk7FjktPT8eUKVPw+eefY8+ePahZs6aRk1JZ/PXXX/jiiy9w9OhRFG4So1Kp4O7ujjVr1uiuaDxy5AgCAgJw9+5dNGzYUMzIRieXglZa9vb2kj95fOPGDQQHByMlJQUA0LJlS4wZMwaNGjXSGxcdHY358+fj/v37CAwMFCNqubC0tMTjx49LPP7x48eoWrWqAROJS6PR4NKlS7C0tESTJk3EjmMwr3o+JpFSsXvLv4cqcc4kf+ze8sbu/Xrs3tLF7v1q7N7ywO5d8XEBXESdOnXC48ePUa1aNfTu3RseHh7o3LkzrKysAMjvCuWTJ08iNjYW4eHh2L17N3bu3Alzc3N07NgR3bp1K9OzIyo6Jc6Z5G/btm3IycnB9u3b0aBBg5eOa9myJYKDg+Ht7Y1ff/0VkyZNMmJKKqsff/wRR44cQadOneDr6wtra2scPXoUO3bswHfffQd/f38sXrwYmzZtgqmpKUaNGoXPP/9c7NjlQmkFDQDOnDmDNWvW6M35s88+07vrQBAEbNq0CT/88ANyc3PFivrGzp07h+HDh+PJkyewtLSEpaUl0tPTERkZie3bt+Odd95BdnY2/P39ER0dDVNTU4wdO1bs2G+kdevWiI6OxqhRo0o0/sCBA2jRooWBUxlWdnY21q9fj5SUFGzdulV3PCIiAoGBgbqTEo0bN8bChQvRrl07kZISkTGxe8u/hypxziR/7N7yxu7N7s3uze4tZezeJAVcABdRVlYWrK2t0b17d7i6uqJ9+/a6Ai5HlpaW6NWrF3r16oXMzExERkYiIiICx44dw7FjxwA8v2omLS0N7u7usvh/ocQ5k/wdPHgQffr0eWUBL1SvXj34+voiOjqaJVwiYmNj4eLigvXr1+uOvf/++6hZsya2bt0KW1tbBAcHQ61WIygoSPIf2AspsaCdPHkSY8aMgVarRePGjWFlZYWkpCSMGDECwcHBcHZ2xo0bNzBt2jSkpaXBxsYGc+fOFTt2mRWeRFi2bBl69+4N4PnzQr/44gsEBgZi6dKl8PPzw59//ok2bdogMDAQDg4OIqd+MwMGDMCUKVMQHByMkSNHvnLs2rVrkZaWpvd3X2pycnIwaNAgXL58GXXq1EF+fj7MzMyQlpaGmTNnQhAEDBo0CM2bN0dYWBhGjhyJsLCwIifaiEh+2L3l30OVOGeSP3ZveWP3Zvdm92b3lip2b5IMgUSTlJQkfPvtt4KLi4ugVquFFi1aCB999JGwYcMG4erVq8KNGzcEBwcH4eDBg2JHNagbN24Ia9asEXr37i04ODgIarVaaN++vfD1118LycnJYsczCCXOmeSjXbt2wvbt20s8fufOnYKjo6MBE1F5ateunbBp06Yixy9duiQ4ODgILVq0EObPny9oNBoR0hnO+PHjhVatWgl79+7VHTtz5ozg4eEhDB8+XLh7967QvXt3wcHBQRgwYIBw/vx5EdOWjxEjRgjt27fX+7lz584dwcfHRxg4cKBw6dIlwd3dXXBwcBAmTZokZGRkiJj2zbm7uwvz588vcvzAgQNCy5YthaFDhwqtW7cW1q9fL2i1WhESGsakSZMEtVotTJs2TUhJSdH7u6vVaoWUlBTdmG+++UbEpG/uhx9+EFq1aiXs379f7/jYsWMFtVotBAUF6Y7l5uYKXl5ewqxZs4wd0+AePnwoODg4CHFxcWJHIaow2L2fU2IPVeKcST7YveWN3Zvdm92b3Vuq2L2fY/eu+HgHuIg6dOiADh064JtvvsHRo0cRERGBI0eO4MyZM1i6dCkaNGgAlUqFv/76S+yoBlWvXj2MHz8e48ePx/nz5xEeHo7IyEj89ttv2L17N86dOyd2xHKnxDmTfJibm+PZs2clHp+bmwtra2sDJqLy9PTpU9SoUaPI8erVqwMAunXrBn9/f2PHMri0tDQMHjxYd3UyALz77rv48ssvMXXqVEydOhU3b97E9OnT8emnn8ri+aDnzp3DkCFD4OjoqDv21ltvYdq0aRgzZgwmT56M/Px8rFixAj179hQxafnIysoqdvvTNm3aQKvV4sKFC9i6davstuVaunQpgoKCsH37duzbtw+mpqawtbWFqakpMjMzodFoYGJiglGjRmHq1Klix30j0dHR6Nu3L3r06KE7lpOTgxMnTgAAhg4dqjteqVIl9OnTB7t27TJ6TiIyPnbv55TYQ5U4Z5IPdm95Y/dm92b3bmf8cAbE7s3uTRUPF8ArADMzM3h4eMDDwwM5OTmIjo5GREQEEhISIAgCZs6cid27d6Nfv37o3r07KlWqJHZkg1Gr1VCr1fjyyy+RkJCAvXv3ih3J4JQ4Z5K2d955B0ePHoWfn1+Jxh85cgTNmjUzcCoyNJVKBQDw8fERN4iBKLGgZWdno2nTpkWON2/eHIIgIDMzE7t27ULDhg1FSFf+8vPzi/0MZWlpCQAYO3asrL6+hSwsLBAQEAA/Pz+EhYUhLS0N9+7dgyAIaNKkCZycnODt7S2Lrchu3LihV7QBICkpCfn5+WjQoEGRP8t169bFgwcPjBmx3K1atarIsdzcXKhUKuzZswenT58u8rpKpcJnn31mjHhEFQ679/9TYg9V4pxJ2ti9lYndm91b6ti92b3ZvZ9j9xYfF8ArmMqVK8PX1xe+vr548OAB9u7di4iICMTHxyM+Ph7z589HUlKS2DGNwtXVFa6urmLHMColzpmkp2/fvvj2228RGRmJXr16vXJsWFgY4uLisHz5ciOlI0MrLCxyo8SCptVqYWZW9KOghYUFAGD8+PGyKeAl0bp1a7EjGFTjxo0lf5X565iYmKCgoEDv2MmTJwEA7u7uRcY/fPgQNjY2RslmKMWV8EJhYWHFHmcJJ3qO3fv/KbGHKnHOJD3s3srG7i0f7N762L2lj91bH7t3xcUF8AqsZs2a+OSTT/DJJ5/g6tWrCA8Pl/QVyiW9YvXvVCoVNm/ebIA0xqHEOZP89e/fH2FhYfjyyy9x4cIFDBs2DHZ2dnpjMjIyEBwcjC1btqBLly6y2MJJSQqvOC/ta3Im94JWnOKuUJczJfzZvnv3Lk6fPo2MjAwAgJ2dHRwdHVG3bl2Rk5WPZs2aISUlBcOGDQMACIKAmJgYqFQqdO3atcj4Q4cOSf7P+ZYtW8SOQCQL7N7S76FKnDPJH7u3/LF7F8XuLX9K+LPN7q2P3ZvEwgVwiWjUqBEmTZqESZMmiR2lzBITE4s9rlKpIAjCS1+TMiXOmeTPxMQEa9euxfTp0/Hzzz9j3bp1qFu3LmrXrg1TU1M8ePAA165dgyAI6NmzJxYsWCB2ZCqlhQsXYsWKFXrHBEGASqXC9OnTi1ytrVKpcPDgQWNGNDp+b5aHy5cvF7mbLzs7GwBw4cKFYq/Kd3Z2Nko2Q7p48SICAwORlJQEQRD0PoOYmJjAyckJ/v7+cHBwEDHlm/Px8cGCBQvQpk0bvPfee9i5cydu3bqFhg0bolOnTnpj165di9TUVMk/V9HFxUXsCESyw+4tTUqcM8kfu7f8sXsXxe/N8sDuze5diN2bxMQFcBH5+flhwoQJ6Nixo+5Yfn4+UlJSoFarUaVKFb3x4eHhmDVrFtLT040dtVycP3++yLGHDx/C3d0dwcHBev8f5EKJcyZlqFKlCn7++Wf8/vvv2LNnD9LS0vDf//4XgiDAzs4OPj4+6Nu3L9zc3MSOSqVkb28PAMWeKCy8UvXF1152UlFqlFjQTp06Ba1Wq3csJycHAHDixAncvXu3yHuk/Cy6tWvXYu3atcW+tnjx4mKPnzt3zpCRDO7QoUOYMmUKVCoVevToATc3N9jZ2cHMzAwZGRlISkpCVFQUBgwYgBUrVsDT01PsyGU2ePBgnD59GkFBQboFj2rVqmHp0qUwMTEBAPz2229Yt24drl+/DicnJwwZMkTk1Ial0Whw6dIlWFpaokmTJmLHIRINu7f8e6gS50zKwO4tX+ze7N7s3vrYvaWD3bsodu+KSSXI5SenBKnVaixZsgTe3t66Y48ePYK7uzs2btxYpKCFh4dj5syZkv9h8HePHj1Cx44dFVVIlThnIqKKTq1Wv/RK88Ir8Isj5Z/JL5vz3z8a/v31wv8PUp3zjz/+WKa7CSZOnGiANMZx48YNeHt7o3Hjxli5ciUaNGhQ7Lg7d+7g888/x6VLl7Bnz56XjpOKlJQUpKSkwMbGBp6enqhRo4butVWrVmHPnj3w9vbGuHHjin3+oNRkZ2dj/fr1SElJwdatW3XHIyIiEBgYiMePHwN4/iy6hQsXyu6ZikQlwe6tzB6qxDkTEVV07N7/j91bH7u39LB7P8fuXXHxDvAKiNckEJFU5ObmwtLSssjxS5cuoWrVqkWeT0YVX1hYGDp06ID69euLHcWoPvvsM8VttRYUFCR2BKOS8la2ZbV582aYm5tjw4YNekX0RXXq1MG6devQq1cv/PLLL5g1a5YRU5Y/R0dHODo6FvvaxIkTX3piJS8vD6mpqcXeDVpR5eTkYNCgQbh8+TLq1KmD/Px8mJmZIS0tDTNnzoQgCBg0aBCaN2+OsLAwjBw5EmFhYWjUqJHY0YkqBHZvIpIKdm/5YfdWDnZv+WP3Lordm927IuACOBERlZpGo8HixYsRERGBY8eOFSniy5cvx7Fjx9C/f3/MnDkT1tbWIiWl0po9eza+++47xZVwJRY0X19fsSMYVXHb38rd8ePH4evr+8oCXsjW1hY+Pj44fPiw5Et4WWVlZcHPz6/Yu0Erqo0bN+LatWv4/vvv0aNHD93x1atXQxAEfPLJJ7qv54ABA9CnTx+sXbtWcSfhiIiIpIrdW77YvZWD3Vv+2L1Lh92bjMVE7ABERCQtGo0Go0aNwi+//AJ7e3s8evSoyJgPPvgADg4O2LFjB0aPHo38/HwRklJZKPVOKD8/P5w8eVLsGEbl4eGBQ4cOiR3DaBITE3H//n2xYxjV7du30axZsxKPb9KkCe7cuWPARBWf1L4HRkdHo2/fvnoFPCcnBydOnAAADB06VHe8UqVK6NOnj+K+1xEREUkVu7e8Se1zZ3lh95Y/du/XY/eW3vdAdm9p4gI4ERGVyqZNm5CUlAR/f3+EhYWhbt26RcYMHDgQu3fvxsSJE5GcnIxt27aJkJSo5JRY0G7evIm//vpL7BhkQJaWlrpnUJXE48ePUbVqVQMmovJ248YNtG7dWu9YUlIS8vPzUb9+fTRs2FDvtbp16+LBgwfGjEhERERlxO5NcsTuTXLE7i1/7N7SxC3QyWjCwsKKHMvJyQEAnDhxAnfv3i32fT4+PgZMZVhKnDPJX0REBDw8PPDxxx+/duzEiRORmJiIPXv2YMSIEYYPR+UiMzMTt27dKtV77O3tDZSGiMqqdevWiI6OxqhRo0o0/sCBA2jRooWBU1F5MjExQUFBgd6xwqvM3d3di4x/+PAhbGxsjJKNiMSjxB6qxDmT/LF7yx+7N5E8sHvLH7u3NHEBXGQvftDJysoC8PwvyIsfgIrb6khKZs2aBZVKpXescKuLDRs2QKVS6X5d+O8qlUrShVSJcyb5u3r1KgYPHlzi8e+//z5WrlxpwERU3hYuXIiFCxeWeLxKpUJ6eroBExGVj5iYGFy9erXE41UqFT777DMDJjKsAQMGYMqUKQgODsbIkSNfOXbt2rVIS0vD+vXrjZSOykOzZs2QkpKCYcOGAXj+OTMmJgYqlQpdu3YtMv7QoUNo2rSpsWMSVQjs3vLuoUqcM8kfu7f8sXuTXLF7vxy7tzSxe0sTF8BF9rIPOtOnTxchjWEFBQWJHcHolDhnkj9ra2totdoSj69UqRIsLS0NmIjKm5OTExo0aCB2DKNTWkEDgJ07dyIuLq7E41UqValO0FQ0MTExiI6OLvF4qX+Ne/ToAS8vL3z33Xf4448/8PHHH6NVq1YwNzcHABQUFCAtLQ0bN25ETEwMBg4ciH/84x8ip6bS8PHxwYIFC9CmTRu899572LlzJ27duoWGDRuiU6dOemPXrl2L1NRU+Pv7i5SWSFzs3vKmxDmT/LF7yx+7d8lIvZcB7N6vI/WvMbu3/LF7SxMXwEXk4+NT5AplOfP19X3tmKysLFhaWqJSpUpGSGR4SpwzyV+TJk2QnJwMPz+/Eo0/ffo06tWrZ+BUVJ4GDRoEb29vsWMYndIKGvD8eUVJSUklHi/1Ej5u3Lhit6aSs6VLlyIoKAjbt2/Hvn37YGpqCltbW5iamiIzMxMajQYmJiYYNWoUpk6dKnZcKqXBgwfj9OnTCAoK0t3RWK1aNSxduhQmJiYAgN9++w3r1q3D9evX4eTkhCFDhoicmsj42L2LklsPVeKcSf7YveWP3btk2L2lh92b3Vtu2L2liQvgIlq0aJHYEYwuLy8PISEhSE1N1btCOzExEXPmzMGVK1egUqng7u6OgIAAWVwFqcQ5k7z5+voiICAA8fHxcHNze+XYhIQEREdHY9KkSUZKR1R2SixoX331FTw8PMSOYTRNmzaFi4uL2DGMysLCAgEBAfDz80NYWBjS0tJw7949CIKAJk2awMnJCd7e3mjUqJHYUakMVCoVli5dimHDhiElJQU2Njbw9PREjRo1dGPu3LkDQRAwYcIEjBs3TlfOiZSE3VsZPVSJcyZ5Y/cmuWL3lj92b3ZvuWH3liYugIto9OjR8PHxgaenpyK2KMrLy8Onn36KpKQkmJubY/78+TAzM8OVK1cwevRo5OXloVOnTmjWrBkOHDiAQYMGITw8HLVq1RI7epkpcc4kfz4+Pti9ezfGjx+PcePGYeDAgUX+zGZkZGDXrl3YsGED6tevj6FDh4qUlqjklFjQqlevzrtEFKJx48a8ylzGHB0d4ejoWOxrEydOxMSJE4t9LS8vD6mpqVCr1ahSpYohIxKJit1b/j1UiXMm+WP3Jrli9yY5Y/eWN3ZvaeElCCJKTEzEjBkz4O7ujlmzZiEuLg6CIIgdy2C2bduGU6dOYcaMGUhKSoKZ2fPrL3788UdoNBp4e3tj3bp1+PLLL7F7926Ymppi7dq1Iqd+M0qcM8mfubk5Vq9ejbZt22LlypXo1KkTPDw8MHjwYAwcOBBdu3ZFly5d8OOPP8LBwQHBwcH8wS4h9vb2sLa2FjsGEREZQVZWFvz8/HD27FmxoxAZFLu3/HuoEudM8sfuLW/s3kREysHuLQ7eAS6ikydP4uDBg9i/fz/27duHPXv2oFatWvD29kafPn2gVqvFjliu9u7di+7du2PUqFG6YxqNBrGxsVCpVHrHbW1t0a9fP+zbtw/+/v5ixC0XSpwzKUPNmjWxefNmREdHY9++fUhPT8eFCxdgYmKCWrVqwcfHB926dUPXrl3FjkqlFBsb+8rXCwoKcOfOHdSqVQsWFhZGSkX05iZOnAgHBwexYxhVSZ8X+XcqlQqbN282QJqK4d69e7C1tYW5uXmR16pVq4YtW7agRYsWIiQTj5wXAYkKsXvLv4cqcc6kDOze8sXuTXLF7l0y7N7s3mR4XAAXUeXKldG3b1/07dsX2dnZOHDgACIjI7F582YEBwejWbNm6Nu3L7y9vfHWW2+JHfeN/fnnn/D19dU7dvr0aeTm5sLOzq7ID8aGDRsiIyPDmBHLnRLnTMri5eUFLy8vsWOQET18+BAeHh7YuHEjOnbsKHaccqPEghYUFPTSbZvk6GXbUBX666+/sHHjRvj4+KB+/fpGSmVYiYmJxR5XqVQvLV4qlcqQkYwiODgYO3fuRHh4eJGyvXDhQsTFxWHUqFEYPXq03jO5zM3NFbcVI5FSsHvLv4cqcc6kLOzeysPuLR/s3vrYvf//Nalj96aKjgvgFUSVKlUwYMAADBgwAA8fPkRUVBT279+P5cuXY/ny5XB2doaPjw+8vLxQuXJlseOWSUFBAUxNTfWOxcfHAwDc3d2LjM/OzoaVlZVRshmKEudMBDzf1sXS0hKVKlUSOwoZgByvWFRiQXvxJPGLnjx5ggULFmD06NFo2rSpkVKJ56+//sLq1avh5OQkm6/x+fPnixx7+PAh3N3dERwcLKsTacDz703Tp0/Hvn37ULNmTdy5cwcNGjTQG9O0aVMkJydjxYoVOHv2LH744QeR0hKRWNi95dlDlThnIoDdW+7YveXRy9i99bF7Sx+7N0kFnwFeAdWoUQNDhw7F1q1bceTIEXz99dcwMTHBt99+i3/84x9ixyuzhg0b4ty5c3rHYmJioFKp8P777xcZf/z4cTRs2NBI6QxDiXMmZcjLy8OOHTswe/ZsveOJiYno1asX3Nzc4OjoiNGjR+PatWsipSQqP4UF7fr162JHMZrc3FyEhYUp6u4oOZ5gepEcrjJ/mR07dmDfvn0YMWIEjhw5UqSAA89PuMXExKBfv36IiYlBSEiICEmJqKJg935ODj1UiXMmZWD3JqVh91YGdm9pY/cmqeACeAVnbm4OS0tL2NjYwMzMDBqNRuxIZda7d2/s2bMHBw8exNOnT7Fp0yZcvnwZNWvWLPKsovDwcJw4cQIeHh4ipS0fSpwzyV9eXh4+/fRTBAQEYO/evcjPzwcAXLlyBaNHj8aVK1fQqVMnjBgxAleuXMHgwYNx//59kVMTvTklFLQXKXHOJF2//fYbXFxcMGvWrGKfM1bIwsICgYGBaNGiBXbu3GnEhERUkbF7S7uHKnHOJH/s3qRUSuyhSpwzSRe7N0kFt0CvgB4+fIiYmBhERUUhKSkJ+fn5aNmyJSZPnozevXuLHa/MRowYgd9//x0TJ07UPf/C3NwcCxYsgIWFBYDnV2hv27YNiYmJaNy4MUaMGCFu6DekxDmT/G3btg2nTp3CjBkzMGzYMJiZPf9R8uOPP0Kj0aBPnz747rvvAABjx46Ft7c31q5dC39/fzFjUzkxNzeHs7MzqlWrJnYUIiI9//vf/zB58uQSjVWpVOjRowfWrFlj4FREVJGxe8unhypxziR/7N7Kxu5NRBUVuzdJBRfAK4j79+8jOjoaUVFROH36NLRaLerVq4dRo0ahT58+snj+h4WFBTZt2oTIyEikpqbCxsYG3t7eaNasmW7M2bNnkZycjD59+mDWrFmwtLQUMfGbU+KcSf727t2L7t27Y9SoUbpjGo0GsbGxUKlUesdtbW3Rr18/7Nu3jyVcIrKzs1GlSpWXvl6tWjVs3bpV71hCQgJcXV0NHY3IoKpUqYKgoCA0b95c7ChURmZmZrpFjpKoWrVqkefFEpH8sXs/J7ceqsQ5k/yxe8sbuzcpFbu39LF7k1RwAVxEGRkZiI6OxoEDB5CcnAytVotq1aphwIAB8Pb2RocOHcSOWO5MTU3h7e0Nb2/vYl8fP348Jk+eDBMT+ezOr8Q5k7z9+eef8PX11Tt2+vRp5Obmws7ODg4ODnqvNWzYUFHPMZI6Pz8/BAcHw9bW9rVjnz17hiVLluDXX39Fenq64cOJSIkFrVq1atiyZQtatGghdhSjqFSpEv7xj3/wDgsJa9SoEc6ePVvi8WfPnkXdunUNmIiIKgp276Lk2EOVOGeSN3ZveWP3Lh67t/yxe0sfuzdJBRfARdSlSxcAz69U9vT0hLe3N7p06fLK5ybInZWVldgRjE6JcyZpKygoKHLVXnx8PADA3d29yPjs7Gz+OZeQc+fO4eOPP8amTZtQq1atl447c+YMZs6ciT///POV4+SiUqVKeieftFot1q1bhwkTJoiYyrDMzc3h4uKi+3V2djYWL16MwMBAEVO9ueDgYOzcuRPh4eFFPnMtXLgQcXFxGDVqFEaPHi35E+RhYWFFjuXk5AAATpw4gbt37xb7Ph8fHwOmMpwPP/wQy5Ytw8iRI197wuzixYuIiIiAn5+fkdIRkZjYvYtS4udzJc6ZpI3dW97YvYvH7s3uLUXs3i/H7k1iUgmCIIgdQqmGDx+Ovn37okePHrCxsRE7DhFRifTt2xdt27bFvHnzdMd69eqFK1euYMWKFejRo4fe+NGjRyMrKwu7du0ydlQqg19++QULFixAgwYNsGnTpiJXaObl5eGHH37Axo0bodVq0bdvX3z11VeyuHI3Ozsbu3btQmpqKgRBQMuWLTFs2DBUrVpVb9x//vMf+Pv747///S/OnTsnUtrycePGDQQHByMlJQUA0LJlS4wZMwaNGjXSGxcdHY358+fj/v37kp2zIAiYPn069u3bh5o1a2L79u1o0KCB3phVq1Zh165dyMjIQLdu3fDDDz+IlLZ8qNVqqFQqvWN//+hf3GsqlUqyX+O//voL/fr1Q2ZmJr766iv07t27yEnj/Px87N27F0uXLgUAhIaGonbt2mLENZp79+7B1ta22IW+vLw8pKSkoEWLFq/cgpNI6ti9iUiK2L3ljd2b3Zvdm91bql9jdu/isXtXPFwAJyKiUlm3bh1Wr16NZcuW4b333sOOHTuwaNEi1KpVC7GxsXrPgAkPD8fMmTMxefJkjB8/XsTUVBp79+7FrFmzYGdnh02bNqFhw4YAnl+hPnPmTPz3v/+Fvb095s6di06dOomctnxcv34dfn5+uHPnjl5JqVWrFnbt2oW6desiPz8fy5Ytw5YtW6DVatG7d28sW7ZMxNRv5ty5cxg+fDiePHkCS0tLWFpaIjMzE9bW1ti+fTveeecdZGdnw9/fH9HR0TA1NcWoUaMwdepUsaOXyfbt2zFnzhyMGDEC06ZNe+ldfxqNBnPnzkVISAgWLFiAfv36GTlp+QkNDS3T+17calNKrly5gs8++wxXrlyBtbU1WrVqhdq1a0Or1eLBgwc4e/YscnNzYW9vj9WrV0OtVosduVy86u6KqVOnyuruCiIiIqVg95Y/dm92b3Zvdm+pYvdm95YCLoCLqLitMUpCqltjEJE8aDQajBo1CklJSVCpVBAEAebm5li1apVue8mYmBhs27YNiYmJaNy4MUJCQmBpaSlyciqNo0ePYsqUKbCxscH69etx6NAhrF27Fvn5+RgyZAimTZuGypUrix2z3EybNg2RkZGYOnUq+vfvDysrKxw9ehTz5s1Du3btsGTJEowdOxbJycmwt7dHQECA7s+7VE2YMAG///47Fi9ejN69ewMA0tLS8MUXX8De3h5Lly6Fn58f/vzzT7Rp0waBgYFFnjMoJQMGDIC1tTW2bNny2rGCIKB///6wsLDA9u3bjZCOypNGo8Evv/yCffv24fz588jPzwfwfGvBdu3awcvLC4MGDdI7aSxVSry7gqgs2L2JSIrYvZWB3Zvdm92b3Vuq2L3ZvSs6LoCLqHBrjMItL15H6ltjEJF8aLVaREZGIjU1FTY2NvD29kazZs10r69YsQIbN25Er169MGvWLFSvXl3EtFRWp0+fxoQJE/DkyRMIgoBGjRohMDAQHTp0EDtauevcuTPee+89BAUF6R0PDQ1FQEAAOnfujIMHD2LIkCGYMWMGrK2tRUpaft577z307NkT/v7+esejo6MxdepUtGvXDmlpaZg8eTI+/fRTyV+56ujoiMmTJ2PEiBElGr9u3TqsWbNGt0Wd1OXm5hZ7MvTSpUuoWrUq7OzsREhlHA8fPoSpqakstot8kRLvriAqC3ZvIpIqdm9lYPdm92b3ZveWA3Zvdu+KxkzsAEr24g96IiKpMDU1hbe3N7y9vYt9ffz48Zg8ebLkP7QrnZOTE7Zs2YLRo0fj4cOHmDdvniwLOAA8evQIjo6ORY47OztDo9Hg6NGjWLlyJbp37y5COsPIysoqdguqNm3aQKvV4sKFC9i6dSvatWtn/HAGYGZmVqqrjqtWrVrkGVZSpNFosHjxYkRERODYsWNFivjy5ctx7Ngx9O/fHzNnzpTFCaYX1ahRQ+wIBvPbb7/BxcUFs2bNeuU4CwsLBAYG4ty5c9i5cydLOCkOuzcRSRW7tzKwe7N7s3uze8sBuze7d0XDBXARSfkZD0SkXH5+fpgwYQI6duyoO5afn4+UlBSo1WpUqVIFVlZWutcKn0XGO2ikSa1W49///jdGjhyJCRMmYPXq1XBzcxM7VrnLy8vT+3NbqHCruZEjR8qqgAPP/95WqlSpyPHCkjZ27FjZFHAAaNSoEc6ePVvi8WfPnkXdunUNmMjw/r5tplqtxqNHj4rM6YMPPsDdu3exY8cOXLx4EVu2bIGZmTQrwqpVq8r0vokTJ5ZzEuP53//+h8mTJ5dorEqlQo8ePbBmzRoDpyKqeNi9iUiK2L2Vhd2b3Vsu2L3ZvV+G3ZuMTZp/wxRKq9Vi3bp1mDBhgthRiEjBEhMTMXDgQL1j2dnZ8PPzw8aNG/XKOUnP7Nmziz3eqFEj3LhxA+PGjUPPnj31tg9VqVRYuHChsSKKwsXFRewIRte6dWuxI5SrDz/8EMuWLcPIkSPRvHnzV469ePEiIiIi4OfnZ6R0hrFp0yYkJSXB398fH3/8cbFjBg4ciIEDB2LVqlVYtWoVtm3bVuKt6iqakpbwF7c/lnIJV+rdFUSGxu5NRBUBu7e8sXsXj91b+ti92b0LsXuze4uNC+Aiy87Oxq5du5CamgpBENCyZUsMGzYMVatW1Rv3n//8B/7+/vjvf//LEk5EFZIgCGJHoHIQGhr6ytefPXuGsLAwvWNKKOFK/NBakmekSslHH32E7du3Y/jw4fjqq6/Qu3fvIl/X/Px87N27F0uXLkWVKlUkX8IjIiLg4eHx0gL+dxMnTkRiYiL27Nkj2RJ+6NCh147Jzs7G999/jyNHjsDMzEzyX2Ml3l1BVFbs3kQkF+ze8sDuXTx2b+lj9341dm9pYveWJi6Ai+j69evw8/PDnTt3dB9eY2Ji8Msvv2DXrl2oW7cu8vPzsWzZMmzZsgVarRa9e/cWOTUREclZST7EylVmZiZu3bqldywrKwsA8PDhwyKvAYC9vb1RshnK5cuXkZSUpHcsOzsbAHDhwoVit+NydnY2SrbyZm1tjTVr1uCzzz7DzJkzMXfuXLRq1Qq1a9eGVqvFgwcPcPbsWeTm5sLe3h6rV69G7dq1xY79Rq5evYrBgweXePz777+PlStXGjCRYdWrV++Vr0dGRmLRokXIyMhA+/btMWfOHLzzzjtGSmcYSry7gqgs2L2JiKiiYfdm92b3ZveWKnZvdm+p4AK4iL7//nvcuXMHU6dORf/+/WFlZYWjR49i3rx5mDdvHpYsWYKxY8ciOTkZ9vb2CAgIQJcuXcSOTUREMva6D7FytnDhwpdeTT99+vQix1QqFdLT0w0dy6DWrl2LtWvXFvva4sWLiz0u5WcKNm7cGGFhYfjll1+wb98+JCcnIz8/HwBgbm6Odu3awcvLC4MGDSrV1lYVlbW1NbRabYnHV6pUSfccOjm5du0a5s6di7i4OFSrVg2BgYEYMGCA2LHKhRLvriAqC3ZvIiKqaNi92b0LsXuze8sFuze7d0XDBXARJSUlwcfHB2PHjtUd69mzJ3JzcxEQEIBZs2YhOTkZQ4YMwYwZM2BtbS1iWiIioqLWrl2L6OhohISEiB3ljfj6+oodweik/OylN2FhYYGRI0di5MiRAJ7fYWBqaopq1aqJnKz8NWnSBMnJySUuXadPn5bViTiNRoN169Zh/fr10Gg08PX1xYwZM1C9enWxo5UbJd5dQVQW7N5ERCR17N7Sxe7N7v0idm/pYfeWJi6Ai+jRo0dwdHQsctzZ2RkajQZHjx7FypUr0b17dxHSERERvd7t27clfVVyoaCgILEjGJ1SS/iLatSoIXYEg/H19UVAQADi4+Ph5ub2yrEJCQmIjo7GpEmTjJTOsOLi4jB37lxcvXoVzZs3R0BAADp06CB2LINQ2t0VRGXB7k1ERFLH7i1d7N7PsXs/x+4tXeze0sMFcBHl5eXBysqqyPHKlSsDAEaOHMkCTkQV0ovPa3rVs5oePXpk1GxERMVZtWpVmd4n5ZMVPj4+2L17N8aPH49x48Zh4MCBqFWrlt6YjIwM7Nq1Cxs2bED9+vUxdOhQkdKWj/v37yMoKAiRkZGwtLTEtGnTMHLkyGKfqScnSrq7gqgs2L2JSKrYvYlIati92b3ljN1bWlSCIAhih1AqtVqNJUuWwNvbW+/4o0eP0LFjR2zYsAH/+Mc/REpHRFQ8tVoNlUpV5LggCMUeLySHK5WpqICAAOzcuVPyX18lFrTZs2eX+j0qleqlz2qr6NRqdYnGvfh9TOp/th88eIAvvvgCCQkJUKlUsLe319ui6/bt2xAEAe3atcOKFStQt25dsSOX2bZt27By5Uo8efIEXbt2hb+/v6TnQ0Tlh92biKSI3Zv+jt2b3Vsq2L3ZvYkqCnlfjiFxpqamYkcgIipCic9rIvkraQl/saBJuYSHhoaWeOzf5y3VEn7o0KHXjsnOzsb333+PI0eOwMzMrMTP76rIatasic2bNyM6Ohr79u1Deno6Lly4ABMTE9SqVQs+Pj7o1q0bunbtKnbUNxYYGKj799jYWMTGxr72PSqVCunp6YaMZVBKPIFIZAjs3kRUEbF7kxyxe78au7d0sXu/Grs3iYEL4CJ7cSsj4NXbGQGAvb29UbIRERVHic9rIvlTYkE7f/78a8fcvHkT8+fPx5EjR1ClShVMmTLF8MEMpF69eq98PTIyEosWLUJGRgbat2+POXPm4J133jFSOsPz8vKCl5dXkeNZWVmwtLQUIVH5U+JJYiWeQCQqK3ZvIpIadm+SI3bv4rF7s3tLCbv3y7F7VyzcAl1EL9vKCHj5dkZSv1KGiIgqttJe0Xj48GGkp6dLfquq15F7QXuRVqvFxo0b8dNPPyE3Nxe9evXC7NmzizzDSg6uXbuGuXPnIi4uDtWqVcP06dMxYMAAsWOVm7y8PISEhCA1NVXvJGpiYiLmzJmDK1euQKVSwd3dHd9++y0aNmwoYto3M3v2bAwePBht27YVO4rR3Lx587VjijuB+OWXXxohHVHFwe5NREQVDbt38di92b2lit1b3ti9pYkL4CIqy/M/AF4BSkREhlPSZzX9nUqlkm0Jl3tBK86pU6cwd+5cXLx4EW+//TYCAgLQsWNHsWOVO41Gg3Xr1mH9+vXQaDTw9fXFjBkzUL16dbGjlZu8vDx8+umnSEpKgrm5OVJSUmBmZoYrV66gb9++0Gg06Ny5M5o1a4YDBw7g6dOnCA8Pl+zJlpc941fJlHYCkehl2L2JiKiiYffWx+7N7i1l7N7E7l0xcQt0EbFMExFRRbNlyxaxI1QISihoL3r06BG+++47hIWFwcLCApMmTcKYMWNgYWEhdrRyFxcXh7lz5+Lq1ato3rw5AgIC0KFDB7Fjlbtt27bh1KlTmDFjBoYNGwYzs+cf/X/88UdoNBr06dMH3333HQBg7Nix8Pb2xtq1a+Hv7y9mbCoHL55ADAwMlP0JRKJXYfcmIqKKht37OXZvdm85YPdWLnbvio0L4BXA06dPsXv3bvz+++84f/48MjMzoVKpUKNGDajVanh4eMDb21uWPwSJiKhicXFxKfV7Tp06ZYAk4lFKQfu7Xbt2YenSpcjKysJ7772HgIAASW/H9TL3799HUFAQIiMjYWlpiWnTpmHkyJG6cio3e/fuRffu3TFq1CjdMY1Gg9jYWKhUKr3jtra26NevH/bt28cSLmFKPIFIVBrs3kREVFGwe7N7s3vLB7u38rB7S4M8v+NIyOnTpzF58mTcv38fFhYWaNiwIerVq4f8/HxkZmbi8OHDiI2NxapVq7Bs2TK0b99e7MhERES4ffs2QkNDERYWhuvXr8tiGzalFTQAuHDhAubMmYPU1FTUqlULy5cvR69evcSOZRDbtm3DypUr8eTJE3Tt2hX+/v6oW7eu2LEM6s8//4Svr6/esdOnTyM3Nxd2dnZwcHDQe61hw4bIyMgwZsRyd+rUKWi12lK9x8fHxzBhjEyJJxCJSoPdm4iIpIjdWx7Yvdm9/47dW9rYvaVDvj9VJOB///sfRo0aBRsbGyxduhReXl5FrjR/8uQJoqKi8MMPP2D06NEIDQ1Fo0aNREpMRERK9uzZM0RHRyMkJAQJCQkQBAEqlQqdO3cWO9obU2JBW7x4MbZu3QqtVosPPvgAU6ZMgY2NDW7duvXK99nb2xspYfkKDAzU/XtsbCxiY2Nf+x6VSoX09HRDxjKogoICmJqa6h2Lj48HALi7uxcZn52dDSsrK6NkM5SdO3di586dJRpb+D1M6iVciScQiUqL3ZuIiKSE3Vte2L3ZvV/E7i1N7N7Sw6+MiH766SdYWVlh9+7deOutt4odY2NjgwEDBqBz587o27cvNmzYgPnz5xs5KRERKVlqaipCQkKwf/9+PHnyBABQo0YN9O/fH4MGDUK9evVETvjmlFjQgoODdf9++PBhHD58uETvk+odBy9eja0EDRs2LPL1iomJgUqlwvvvv19k/PHjxyW//d5HH32Edu3aiR3DaJR4ApGoLNi9iYhICti9i8fuLS3s3s+xe8sLu7c0cQFcRElJSejXr99LC/jf2dnZwcfHB8ePHzdCMiIiUrqMjAyEhYUhNDQUf/75JwRBgJWVFdzd3REXF4d58+bBw8ND7JjlxsfHByqVSuwYRjVx4sRSv0cQBAMkMZ7Bgwejbdu2Yscwmt69e2P16tXo3Lkz3nvvPezYsQOXL19GrVq10LVrV72x4eHhOHHiBCZPnixS2vLRoUMHeHt7ix3DaJR4ApGoLNi9iYioomL3lj92b/lj95Y/dm9p4gK4iB49elSqLdWaNGmCXbt2GTAREREp3f79+xESEoK4uDhotVpUrVoV3t7e8PLyQqdOnXDv3j14enqKHbPcLVq0CACQl5eHS5cuIT8/H82aNZP8llSvYm9vj379+pV4/M2bN/HVV18ZMJFhhYaGwt3dXVElfMSIEfj9998xceJEqFQqCIIAc3NzLFiwQLf1b0xMDLZt24bExEQ0btwYI0aMEDc0lYoS764gKgt2byIiqmjYvdm9X4bdW3rYveWP3VuauAAuory8vFL9cK9UqRJycnIMmIiIiJRu6tSpsLa2xtChQ+Hh4QFnZ2e95xjJ+Urtf/3rX1izZo3uZ62FhQWGDh2KadOmyfJ5Pv7+/nj69CmGDRv22rG7du3C4sWL+TlEYiwsLLBp0yZERkYiNTUVNjY28Pb2RrNmzXRjzp49i+TkZPTp0wezZs2CpaWliImpLJR2dwVRWbB7ExFRRcPuze5dHHZvaWL3VgZ2b+mR308UIiIiKrP69evjxo0bCAkJwZUrV/Cf//wHnp6eaNy4sdjRDGr37t1YsmQJ6tWrBx8fH5iYmCAhIQGbNm2CVquV9NXXL/P2228jMDAQT58+xejRo4sdc+/ePfj7++PYsWMwNzeX/BZdSmRqagpvb++Xbk02fvx4TJ48GSYmJkZOVv58fX0l/xy10lLi3RVEREREcsDuze79d+ze0sfuLW/s3tLEBXCRZWZm4tatWyUa++jRIwOnISIipTt48CDOnDmD8PBwREVF4fjx41i+fDmaNGkCLy8vtGrVSuyIBrFjxw60a9cOmzdvRqVKlQA8f+bW1KlTsWPHDkyfPl23bZVc/Prrrxg9ejSWLVuGp0+fYtKkSXqv7927F/Pnz0dWVhYcHR0RGBiIpk2bipS2fJw6dQparbZU7/Hx8TFMmApCTlsNBgUFiR2BiCowdm8iIqpI2L3ZvQuxez/H7i0d7N4kFSpBEASxQyiVWq0u03Y2586dM0AaIiIifVqtFsePH0dERAQOHTqEp0+f6n5u9e/fHxMmTEC9evVETlk+nJyc8MUXXxTZkiw1NRVDhgxBaGgo1Gq1SOkM56+//sI///lPJCQkYMSIEZg5cyYePXqEgIAAxMTEwNLSElOnTsXw4cMlvwVfaT93CYIAlUrFz11UoanVaixZsuSldxkQ0XPs3kREVJGxe7N7s3uze1PFxu4tTbwDXES+vr5iRyAiInopU1NTdOnSBV26dEFubi5iYmKwd+9enDhxAr/99htCQkLg6uqK/v3748MPPxQ77ht5+vQpqlSpUuR4/fr1IQgCHj9+LEIqw7O2tsa6devwxRdfYNOmTbhx4waSk5Px4MEDvPfee5g3b55sTrQAwEcffYR27dqJHYOoXPHuCqLXY/cmIqKKjN2b3Zvdm6jiY/eWHt4BTkRERDpjx46Fm5sbXFxc0KpVq2Kv2n306BEiIyMRERGB1NRUWVyp+7IrOR89eoSOHTsiODgYHTt2FCmd4RUUFMDf3x8hISEwMTHBvHnzMGDAALFjlSterUtyxLsriIiIiKSJ3Zvdm92bSDrYvaWJd4ATERGRTnx8PI4dOwaVSgUbGxt06NABrq6ucHNz021DVr16dQwbNgzDhg3D9evXsW/fPpFT05syMTHBwoULYWtri40bNyIuLg4+Pj4wM+NHRaKKjndXEBEREUkPu7cysXsTSRe7t/TwOysRERHpJCcnIz09HcnJyUhJSUFqaioOHz4MlUqFqlWrwtnZGa6urnB1dcU777yDBg0aYPz48WLHLheZmZm4deuW3rGsrCwAwMOHD4u8BgD29vZGyWYIxc3n448/xl9//YUdO3YgJycH33zzDUxMTPTGSHnORHLUoUMH3l1BREREJDHs3uze7N5E0sLuLT3cAp2IiIhe6fbt23ql/Pz589BqtbC1tYWLiwtcXV0xdOhQsWO+kVdtZVS4bdGLVCoV0tPTDR3NYF43ZwBFXpfynGfPno3Bgwejbdu2YkchKjfcXpCIiIhIPti92b0LSXnO7N4kR+ze0sQ7wImIiOiV6tati969e6N3794AgOzsbISHhyMkJAQHDhxAdHS05Eu4r6+v2BGMzsfHp1TPL5K6oKAgsSMQERERERG9FLu3PLF7ExGJgwvgRERE9Eq5ublISkpCYmIiTp8+jbNnzyIvLw8WFha6LdmkTokFbdGiRWJHIKI35Ovri4YNG4odg4iIiIjKAbu3PLF7E0kfu7c0cQt0IiIi0pOfn4/U1FTEx8cjPj4eZ86cQV5eHszNzfHuu+/qirejoyMsLCzEjktEREREREQkOezeREREhsMFcCIiItIZM2YMTp06hdzcXJiYmKBVq1Zwc3ODq6srnJycYGlpKXZEIiIiIiIiIklj9yYiIjIsLoATERGRjlqthrm5OT788EOMGzcOb7/9ttiRiIiIiIiIiGSF3ZuIiMiwuABOREREOt988w0SEhJw7do1qFQqNGnSBB07doSbmxucnZ1RrVo1sSMSERERERERSRq7NxERkWFxAZyIiIiKuHXrFuLi4nTPIrt//z5MTEygVqvh6uqqK+XW1tZiRyUiIiIiIiKSJHZvIiIiw+ACOBEREb3WxYsXER8fj5MnT+LUqVN4/PgxzMzM0Lp1a3Ts2BGTJ08WOyIRERERERGRpLF7ExERlQ8ugBMREVGpaDQaREVF4ddff0VqaipUKhXOnTsndiwiIiIiIiIi2WD3JiIiKjszsQMQERFRxXbt2jWcOXMGZ86cQVpaGs6fP4+8vDxUrlwZnTt3hrOzs9gRiYiIiIiIiCSN3ZuIiKj88A5wIiIi0snKykJaWpqucKelpSErKwuCIKBatWpo3749XFxc4OzsjJYtW8LExETsyERERERERESSwu5NRERkWFwAJyIiIh21Wg2VSgVBEFC9enU4Ozvr/nFwcIBKpRI7IhEREREREZGksXsTEREZFrdAJyIiIp0ePXrAxcUFLi4uaNasmdhxiIiIiIiIiGSH3ZuIiMiweAc4ERERERERERERERERERHJAh8eQkREREREREREREREREREssAFcCIiIiIiIiIiIiIiIiIikgUugBMRERERERERERERERERkSxwAZyIiIiIiIiIiIiIiIiIiGTBTOwARERESvDjjz9i1apVpXrPoUOHUL9+fQMlKruuXbvi5s2bAICvv/4afn5+rxw/atQoHD9+HAAQHBwMd3d3g+aKjo5Go0aNDPJ7EBERERERUcXF7s3uTUREBHABnIiIyCgcHBzg7e2td+zBgweIi4uDtbU1PDw8irzH2traWPHKLCoq6pUl/OHDh4iPjzdiIiIiIiIiIlIqdm8iIiICuABORERkFF5eXvDy8tI7lpCQgLi4OFSvXh1Lly4VKVnZVa1aFcnJybh79y7eeuutYsdER0cjPz8f5ubmyMvLM3JCIiIiIiIiUhJ2b3ZvIiIigM8AJyIiojLy9PSEIAiIjo5+6Zh9+/bB1tYWbdq0MWIyIiIiIiIiInlg9yYiIio9LoATERFVYEeOHMGoUaPg4uKCNm3aoHv37liyZAkyMzP1xt24cQMODg7o3Llzsf+d4cOHw8HBAQkJCbpjs2bNgoODAxITEzFp0iS8++67cHNzw9atW0uUrUePHgCeb8VWnIyMDJw6dQrdu3eHmdnLN53Zs2cPhg4divbt2+Pdd9+Ft7c31qxZg6dPnxY7/uTJkxg5ciScnZ3RoUMHTJkyRfdctOJotVr8+9//xsCBA+Ho6AhHR0cMGjQIoaGhEAShRHMlIiIiIiIi+WL3Lordm4iIpIxboBMREVVQS5cuxfr162FqagonJydUr14dqamp2LBhA/bv34/NmzejQYMGb/z7fPPNN3jw4AE6deqEixcvQq1Wl+h9b7/9Nlq0aIHk5GRkZGTAzs5O7/WoqCgUFBSgd+/eWLVqVZH3FxQUYMaMGdi7dy8sLCzg4uICKysrJCUl4fvvv8eBAwcQHByM6tWr696za9cufPvttwCADh06oGrVqjh+/DhOnToFjUZT5PfIy8vDP//5Txw7dgw2NjZwdHSEubk5EhMTMWvWLCQkJGDRokWl+d9FREREREREMsLuze5NRETywwVwIiKiCig2Nhbr16+Hra0tNmzYoNvGTKPRYN68edi1axcmT56M3bt3Q6VSvdHvlZGRgfDwcDRo0AAFBQUwMSn5BjG9evXCuXPnEB0djY8//ljvtcjISNjZ2cHZ2bnY927btg179+5FgwYNsHHjRjRs2BAA8OTJE0ybNg1HjhzBt99+ix9//BEAcPv2bQQGBsLMzAzr1q1Dx44dAQAPHz7EqFGjkJ6eXuT3+Omnn3Ds2DG4uLhg5cqVqFGjBgDg/v37GDNmDEJDQ+Hk5ISBAweWeM5EREREREQkD+ze7N5ERCRP3AKdiIioAtq0aRMA4Msvv9R7hpeFhQXmzJmDRo0a4Y8//kB8fPwb/14eHh66q9lLU8ABoGfPngCKbsV269YtpKamomfPni/9b27evBkAEBgYqCvgAGBjY4OlS5eiSpUqiI6OxtWrVwEAoaGhyM3NxeDBg3UFHABq1KiBhQsXFvnvazQabN26Febm5li6dKmugANArVq1MG/ePADAv/71r1LNmYiIiIiIiOSB3Zvdm4iI5IkL4ERERBVMfn4+kpOToVKp0L179yKvm5mZwcvLCwD0nitWVu+8806Z39ugQQO0atUKp0+fRkZGhu54ZGQkBEHAhx9+WOz7bt++jRs3bqB69epwc3Mr8nqVKlXQqVMnAEBiYiIAICkpCQDQpUuXIuNbtGiB+vXr6x37448/kJ2djSZNmuCtt94q8p42bdqgZs2auHLlCu7du1fCGRMREREREZEcsHuzexMRkXxxC3QiIqIKJjMzE3l5eahevTpsbGyKHVNYOMujPFarVu2N3t+rVy/88ccfiImJwbBhwwA8L+ENGjTAu+++W+x7Cgt7vXr1XvrffXGOhe+pU6fOS8ffuHFD9+vbt28DAC5cuAAHB4dXzuH27duoXbv2K8cQERERERGRfLB7P8fuTUREcsQFcCIiogpGEAQAeOXzxQrHWFhYlOi/qdVqX/paabdee1HPnj2xZMkS7N+/H8OGDcPVq1fxxx9/YNy4cS99T1nm+LrnrZmZ6X+sKSgoAADY29vDycnple+tXLnyK18nIiIiIiIieWH31h/D7k1ERHLCBXAiIqIKxtbWFubm5sjMzMSTJ0+KvRL9+vXrAICaNWsC+P8iXVg8X5SVlWWgtM+vJH/33Xdx+vRp3Lt3D5GRkQCA3r17v/Q9dnZ2AKB31fiLCudYq1YtAMBbb72Fixcv4ubNm2jWrFmR8X/fBg6A7qryOnXqYOnSpaWYEREREREREckdu/dz7N5ERCRHfAY4ERFRBWNubg5HR0cUFBQgJiamyOv5+fm6466urgAAa2trAM/Ldl5ent74R48e4cqVKwbN3KtXLxQUFCA6Ohr79+9Hs2bNXrn1mb29PerVq4dHjx7pnjP2d9nZ2Th+/DgAwNnZGQDg7u4OAMX+P7l+/TouXbqkd6xNmzawtLTE+fPnixR0ALh79y569uyJkSNHIicnp+STJSIiIiIiIslj92b3JiIi+eICOBERUQX0ySefAAC+++47pKen647n5eVh7ty5uHbtGlq0aKHbXszW1hZ16tSBRqPB9u3bdeOfPXuGb7/99pXbsJWHHj16QKVSYdu2bbhw4cIrr0AvVDhHf39/3RXnAJCTk4MZM2bgyZMn+OCDD3TPKvP19YWtrS12796NAwcO6MY/efIEX331VZEr8K2trfHRRx/hr7/+wowZM/DgwQO932P27Nm4fPkyrK2tuQ0bERERERGRArF7s3sTEZE8cQt0IiKiCsjT0xOffvopNm7ciAEDBsDJyQnVq1fHmTNncOfOHdSrVw8rVqzQe4bY6NGjERgYiMDAQOzbtw+1atVCcnIytFotPvjgAxw+fNhgeevWrYt27dohJSUFwKu3YCs0fPhwpKSkYP/+/ejduzecnZ1hZWWFU6dO4dGjR1Cr1Vi4cKFufI0aNbBw4UJMmTIFn3/+ORwdHWFnZ4ekpCRotVo0bty4yNX206ZNw7lz5xAfH49u3bqhTZs2sLKyQkpKCjIzM/H2229j7ty55fs/g4iIiIiIiCSB3Zvdm4iI5Il3gBMREVVQM2fOxE8//QRXV1ecP38eR44cQeXKlTFhwgSEhoaicePGeuOHDx+OxYsXo3Xr1khPT0dSUhJcXV3x22+/FRlrCD179gQAtGrVCo0aNXrteBMTE6xYsQJBQUFo1aoVkpOTceLECdSpUwczZszAzp07UaNGDb33eHh44Ndff4WHhweuXLmC33//HS1btsQvv/yCOnXqFPk9LC0tsXHjRnz99ddo0qQJ0tLSkJCQADs7O0yaNAm7du3SPeeMiIiIiIiIlIfdm92biIjkRyUIgiB2CCIiIiIiIiIiIiIiIiIiojfFO8CJiIiIiIiIiIiIiIiIiEgWuABORERERERERERERERERESywAVwIiIiIiIiIiIiIiIiIiKSBS6AExERERERERERERERERGRLHABnIiIiIiIiIiIiIiIiIiIZIEL4EREREREREREREREREREJAtcACciIiIiIiIiIiIiIiIiIlngAjgREREREREREREREREREckCF8CJiIiIiIiIiIiIiIiIiEgWuABORERERERERERERERERESywAVwIiIiIiIiIiIiIiIiIiKSBS6AExERERERERERERERERGRLHABnIiIiIiIiIiIiIiIiIiIZOH/AIpsfuSIxuhWAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 178 coefficients adjusted\n",
-      "\t 677 coefficients converged\n",
-      "\t 80 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-41.000000</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>-1.154493</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-30.875688</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-32.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-30.246255</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-32.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>566.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-26.970666</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>671</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint</td>\n",
-       "      <td>-25.475279</td>\n",
-       "      <td>518.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-2.512306</td>\n",
-       "      <td>-27.987585</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -41.000000   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -30.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -30.246255   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -26.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -26.970666   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -26.970666   \n",
-       "671   coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint -25.475279   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "543          52.0            0.0        <NA> -45.000000       True  \n",
-       "541          52.0            0.0        <NA> -45.000000       True  \n",
-       "540          56.0            0.0        <NA> -45.000000       True  \n",
-       "544          92.0           29.0   -1.154493 -42.154493       True  \n",
-       "698         155.0            0.0          -2 -32.875688      False  \n",
-       "695         108.0            0.0          -2 -32.246255      False  \n",
-       "676         151.0            0.0          -2 -28.970666      False  \n",
-       "677         566.0            0.0          -2 -28.970666      False  \n",
-       "675         127.0            0.0          -2 -28.970666      False  \n",
-       "671         518.0           42.0   -2.512306 -27.987585      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      " Final coefficient table written to:  C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_10\\tour_mode_choice_coefficients.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "calibration_iterations_to_run = 1\n",
-    "start_iter_num = 10\n",
-    "\n",
-    "for i in range(start_iter_num, calibration_iterations_to_run+start_iter_num):\n",
-    "    asim_calib_util.run_activitysim(\n",
-    "        data_dir=data_dir,  # data inputs for ActivitySim\n",
-    "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
-    "        configs_common_dir=configs_common_dir,  # just the location of the common config, these files will be used from the original location\n",
-    "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
-    "        output_dir=iteration_output_dir,  # location to store run model outputs\n",
-    "        settings_file=warm_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
-    "        tour_mc_coef_file=tour_mc_coef_file  # optional: tour_mode_choice_coefficients.csv to replace the one in configs_dir\n",
-    "    )\n",
-    "    \n",
-    "    _ = asim_calib_util.perform_tour_mode_choice_model_calibration(\n",
-    "        asim_output_dir=iteration_output_dir, # folder containing the activitysim model output\n",
-    "        asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim tour mode choice config files\n",
-    "        tour_mode_choice_calib_targets_file=tour_mode_choice_calib_targets_file,  # folder containing tour mode choice calibration tables\n",
-    "        max_ASC_adjust=max_ASC_adjust,  # maximum allowed adjustment per iteration\n",
-    "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
-    "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
-    "        output_dir=iteration_output_dir,  # location to write model calibration steps\n",
-    "    )\n",
-    "    tour_mc_coef_file = os.path.join(iteration_output_dir, 'tour_mode_choice_coefficients.csv')\n",
-    "    iteration_output_dir = iteration_output_dir.strip('_'+str(i)) + '_' + str(i+1)\n",
-    "\n",
-    "print(\"\\n\\n\", \"Final coefficient table written to: \", tour_mc_coef_file)"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_10\\tour_mode_choice_coefficients_UPDATED_2.csv\n",
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_11\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "tour_mc_coef_file = r'C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_10\\tour_mode_choice_coefficients_UPDATED_2.csv'\n",
-    "iteration_output_dir = r'C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_11'\n",
-    "\n",
-    "print(tour_mc_coef_file)\n",
-    "print(iteration_output_dir)"
+    "### Change directory to model setup\n",
+    "### i.e. the location of simulation.py script\n",
+    "os.chdir(simpy_dir)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_11\n",
-      "ActivitySim run started at:  2023-09-14 13:45:16.633700\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-14 14:34:02.587656\n",
-      "Run Time:  2925.95 secs =  48.76583333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 61 coefficients adjusted\n",
-      "\t 696 coefficients converged\n",
-      "\t 61 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-32.875688</td>\n",
-       "      <td>147.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-34.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-32.246255</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-32.246255</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>coef_calib_autodeficienthhind_WALK_TRANSIT_school</td>\n",
-       "      <td>-26.769537</td>\n",
-       "      <td>259.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>-2.465786</td>\n",
-       "      <td>-29.235323</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_school</td>\n",
-       "      <td>-27.000000</td>\n",
-       "      <td>311.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-29.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TAXI_maint</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -42.154493   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -32.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -32.246255   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -28.970666   \n",
-       "519  coef_calib_autodeficienthhind_WALK_TRANSIT_school -26.769537   \n",
-       "503       coef_calib_zeroautohhindivtou_SHARED2_school -27.000000   \n",
-       "675           coef_calib_autodeficienthhjoi_TAXI_maint -28.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "543          32.0            0.0        <NA> -45.000000       True  \n",
-       "540          56.0            0.0        <NA> -45.000000       True  \n",
-       "541          40.0            0.0        <NA> -45.000000       True  \n",
-       "544          72.0           29.0        <NA> -42.154493       True  \n",
-       "698         147.0            0.0          -2 -34.875688      False  \n",
-       "695          96.0            0.0        <NA> -32.246255       True  \n",
-       "677         155.0            0.0          -2 -30.970666      False  \n",
-       "519         259.0           22.0   -2.465786 -29.235323      False  \n",
-       "503         311.0            0.0          -2 -29.000000      False  \n",
-       "675          36.0            0.0        <NA> -28.970666       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_12\n",
-      "ActivitySim run started at:  2023-09-14 14:34:30.084773\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-14 15:23:19.689578\n",
-      "Run Time:  2929.61 secs =  48.82683333333333  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 69 coefficients adjusted\n",
-      "\t 688 coefficients converged\n",
-      "\t 69 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-34.875688</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-36.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-32.246255</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-34.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>coef_calib_autodeficienthhind_WALK_TRANSIT_school</td>\n",
-       "      <td>-29.235323</td>\n",
-       "      <td>263.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>-2.481112</td>\n",
-       "      <td>-31.716434</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_school</td>\n",
-       "      <td>-29.000000</td>\n",
-       "      <td>247.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-31.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-28.970666</td>\n",
-       "      <td>159.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -42.154493   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -34.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -32.246255   \n",
-       "519  coef_calib_autodeficienthhind_WALK_TRANSIT_school -29.235323   \n",
-       "503       coef_calib_zeroautohhindivtou_SHARED2_school -29.000000   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -28.970666   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -30.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540          56.0            0.0        <NA> -45.000000       True  \n",
-       "541          36.0            0.0        <NA> -45.000000       True  \n",
-       "543          40.0            0.0        <NA> -45.000000       True  \n",
-       "544          80.0           29.0        <NA> -42.154493       True  \n",
-       "698         104.0            0.0          -2 -36.875688      False  \n",
-       "695         131.0            0.0          -2 -34.246255      False  \n",
-       "519         263.0           22.0   -2.481112 -31.716434      False  \n",
-       "503         247.0            0.0          -2 -31.000000      False  \n",
-       "676         159.0            0.0          -2 -30.970666      False  \n",
-       "677          32.0            0.0        <NA> -30.970666       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_13\n",
-      "ActivitySim run started at:  2023-09-14 15:23:50.644523\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-14 16:11:25.866134\n",
-      "Run Time:  2855.22 secs =  47.586999999999996  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 58 coefficients adjusted\n",
-      "\t 699 coefficients converged\n",
-      "\t 58 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-36.875688</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-38.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-34.246255</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-36.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>coef_calib_autodeficienthhind_WALK_TRANSIT_school</td>\n",
-       "      <td>-31.716434</td>\n",
-       "      <td>263.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>-2.481112</td>\n",
-       "      <td>-34.197546</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_school</td>\n",
-       "      <td>-31.000000</td>\n",
-       "      <td>311.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-33.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>655</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_maint</td>\n",
-       "      <td>-29.000000</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-31.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -42.154493   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -36.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -34.246255   \n",
-       "519  coef_calib_autodeficienthhind_WALK_TRANSIT_school -31.716434   \n",
-       "503       coef_calib_zeroautohhindivtou_SHARED2_school -31.000000   \n",
-       "655        coef_calib_zeroautohhjointtou_SHARED3_maint -29.000000   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -30.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540          64.0            0.0        <NA> -45.000000       True  \n",
-       "541          40.0            0.0        <NA> -45.000000       True  \n",
-       "543          40.0            0.0        <NA> -45.000000       True  \n",
-       "544          72.0           29.0        <NA> -42.154493       True  \n",
-       "698         104.0            0.0          -2 -38.875688      False  \n",
-       "695         124.0            0.0          -2 -36.246255      False  \n",
-       "519         263.0           22.0   -2.481112 -34.197546      False  \n",
-       "503         311.0            0.0          -2 -33.000000      False  \n",
-       "655         124.0            0.0          -2 -31.000000      False  \n",
-       "676          12.0            0.0        <NA> -30.970666       True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_14\n",
-      "ActivitySim run started at:  2023-09-14 16:11:56.297978\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-14 16:59:30.777037\n",
-      "Run Time:  2854.48 secs =  47.574666666666666  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 54 coefficients adjusted\n",
-      "\t 703 coefficients converged\n",
-      "\t 54 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-38.875688</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-38.875688</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-36.246255</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-38.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>coef_calib_autodeficienthhind_WALK_TRANSIT_school</td>\n",
-       "      <td>-34.197546</td>\n",
-       "      <td>259.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>-2.465786</td>\n",
-       "      <td>-36.663331</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_school</td>\n",
-       "      <td>-33.000000</td>\n",
-       "      <td>243.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-35.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>655</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_maint</td>\n",
-       "      <td>-31.000000</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-33.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SHARED_maint</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-32.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -42.154493   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -38.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -36.246255   \n",
-       "519  coef_calib_autodeficienthhind_WALK_TRANSIT_school -34.197546   \n",
-       "503       coef_calib_zeroautohhindivtou_SHARED2_school -33.000000   \n",
-       "655        coef_calib_zeroautohhjointtou_SHARED3_maint -31.000000   \n",
-       "677     coef_calib_autodeficienthhjoi_TNC_SHARED_maint -30.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540          68.0            0.0        <NA> -45.000000       True  \n",
-       "541          40.0            0.0        <NA> -45.000000       True  \n",
-       "543          36.0            0.0        <NA> -45.000000       True  \n",
-       "544          76.0           29.0        <NA> -42.154493       True  \n",
-       "698          76.0            0.0        <NA> -38.875688       True  \n",
-       "695         112.0            0.0          -2 -38.246255      False  \n",
-       "519         259.0           22.0   -2.465786 -36.663331      False  \n",
-       "503         243.0            0.0          -2 -35.000000      False  \n",
-       "655         112.0            0.0          -2 -33.000000      False  \n",
-       "677         112.0            0.0          -2 -32.970666      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_15\n",
-      "ActivitySim run started at:  2023-09-14 17:00:05.523747\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-14 17:49:21.887244\n",
-      "Run Time:  2956.36 secs =  49.272666666666666  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 56 coefficients adjusted\n",
-      "\t 701 coefficients converged\n",
-      "\t 56 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-38.875688</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-40.875688</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>coef_calib_autodeficienthhind_WALK_TRANSIT_school</td>\n",
-       "      <td>-36.663331</td>\n",
-       "      <td>259.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>-2.465786</td>\n",
-       "      <td>-39.129117</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-38.246255</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-38.246255</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_school</td>\n",
-       "      <td>-35.000000</td>\n",
-       "      <td>311.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-37.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>655</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_maint</td>\n",
-       "      <td>-33.000000</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-33.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_TNC_SINGLE_maint</td>\n",
-       "      <td>-30.970666</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-32.970666</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -42.154493   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -38.875688   \n",
-       "519  coef_calib_autodeficienthhind_WALK_TRANSIT_school -36.663331   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -38.246255   \n",
-       "503       coef_calib_zeroautohhindivtou_SHARED2_school -35.000000   \n",
-       "655        coef_calib_zeroautohhjointtou_SHARED3_maint -33.000000   \n",
-       "676     coef_calib_autodeficienthhjoi_TNC_SINGLE_maint -30.970666   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540          64.0            0.0        <NA> -45.000000       True  \n",
-       "541          40.0            0.0        <NA> -45.000000       True  \n",
-       "543          36.0            0.0        <NA> -45.000000       True  \n",
-       "544          76.0           29.0        <NA> -42.154493       True  \n",
-       "698         127.0            0.0          -2 -40.875688      False  \n",
-       "519         259.0           22.0   -2.465786 -39.129117      False  \n",
-       "695          84.0            0.0        <NA> -38.246255       True  \n",
-       "503         311.0            0.0          -2 -37.000000      False  \n",
-       "655          84.0            0.0        <NA> -33.000000       True  \n",
-       "676         112.0            0.0          -2 -32.970666      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      " Final coefficient table written to:  C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_15\\tour_mode_choice_coefficients.csv\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
-    "calibration_iterations_to_run = 5\n",
-    "start_iter_num = 11\n",
-    "\n",
-    "for i in range(start_iter_num, start_iter_num + calibration_iterations_to_run):\n",
+    "if want_to_do_initial_model_run:\n",
     "    asim_calib_util.run_activitysim(\n",
     "        data_dir=data_dir,  # data inputs for ActivitySim\n",
     "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
     "        configs_common_dir=configs_common_dir,  # just the location of the common config, these files will be used from the original location\n",
     "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
-    "        output_dir=iteration_output_dir,  # location to store run model outputs\n",
-    "        settings_file=warm_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
+    "        output_dir=output_dir,  # location to store run model outputs\n",
+    "        settings_file=cold_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
     "        tour_mc_coef_file=tour_mc_coef_file  # optional: tour_mode_choice_coefficients.csv to replace the one in configs_dir\n",
     "    )\n",
     "    \n",
     "    _ = asim_calib_util.perform_tour_mode_choice_model_calibration(\n",
-    "        asim_output_dir=iteration_output_dir, # folder containing the activitysim model output\n",
-    "        asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim tour mode choice config files\n",
+    "        asim_output_dir=output_dir, # folder containing the activitysim model output\n",
+    "        asim_configs_dir=configs_resident_dir,  # folder containing activitysim tour mode choice config files\n",
     "        tour_mode_choice_calib_targets_file=tour_mode_choice_calib_targets_file,  # folder containing tour mode choice calibration tables\n",
-    "        max_ASC_adjust=max_ASC_adjust,  # maximum allowed adjustment per iteration\n",
+    "        max_ASC_adjust=max_ASC_adjust,  \n",
     "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
     "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
-    "        output_dir=iteration_output_dir,  # location to write model calibration steps\n",
+    "        output_dir=output_dir,  # location to write model calibration steps\n",
     "    )\n",
-    "    tour_mc_coef_file = os.path.join(iteration_output_dir, 'tour_mode_choice_coefficients.csv')\n",
-    "    iteration_output_dir = iteration_output_dir.strip('_'+str(i)) + '_' + str(i+1)\n",
+    "    tour_mc_coef_file = os.path.join(output_dir, 'tour_mode_choice_coefficients.csv') \n",
+    "else:\n",
+    "    print(\"No initial model run performed.\")\n",
     "\n",
-    "print(\"\\n\\n\", \"Final coefficient table written to: \", tour_mc_coef_file)"
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_15\\tour_mode_choice_coefficients_UPDATED.csv\n",
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_16\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
-    "tour_mc_coef_file = r'C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_15\\tour_mode_choice_coefficients_UPDATED.csv'\n",
-    "iteration_output_dir = r'C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_16'\n",
+    "iteration_output_dir = output_dir.strip('_cold') + '_1'\n",
     "\n",
-    "print(tour_mc_coef_file)\n",
-    "print(iteration_output_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating output_dir at C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_16\n",
-      "ActivitySim run started at:  2023-09-14 23:16:38.259320\n",
-      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\activitysim_run_dir\n",
-      "ActivitySim ended at 2023-09-15 00:02:47.553829\n",
-      "Run Time:  2769.29 secs =  46.154833333333336  mins\n",
-      "Sample rate of  0.251 results in  857925 out of 3418027 tours\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:516: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  scaling_factor = ((model_tours - transit_calib_tours)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:481: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
-      "  excel_writer.save()\n",
-      "c:\\Users\\rsirupa\\.conda\\envs\\asim_baydag\\lib\\site-packages\\xlsxwriter\\workbook.py:339: UserWarning: Calling close() on already closed file.\n",
-      "  warn(\"Calling close() on already closed file.\")\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:742: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n",
-      "c:\\abm_runs\\rohans\\calibration\\tour_mc\\scripts\\asim_calib_util.py:760: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
-      "  plt.yticks(fontsize=16)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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79qhQoUIyGAzKnTt3ku4j+kels7OzPD09k+Qx7969q65du+r06dMqXLiwnjx5ogIFCpi1Wb58ud5//32dPHlSBQsWVLp06bRv3z59+OGHGjt2bIzHDA4OVseOHTV8+HDt379f6dOnl5eXlx4+fKjVq1erbdu2mjNnjtk2YWFh6tatmyZNmqTjx4/Lw8NDRYoUUXBwsNauXauOHTtq6dKlMfb166+/qnXr1lq5cqXu37+vIkWKyNXVVbt371a/fv00aNCgWH9cJ9by5ctlNBqVL18+09m9zZs3lyQdOHBAFy5cSLJ9JUZwcLD8/Pwk/VdXoUKFVKxYMUnS4sWLk2xfmTNnVvny5U2dHjlz5lT58uXN3j9nz55V8+bNNWXKFJ09e1a5c+dWwYIFdeXKFc2ePVvNmzfXvn374r1PV1dXlS9fXtLTwb3PPvtMBw4ciPU1zpo1qwoXLvzKn5V06dKpfPnyKlq0qGlZ+fLlTcca23MQXeOL/Prrr2rbtq1+//133b171/Te3bt3r/r37x/noObzNm/erObNm2vRokUKCAjQa6+9pkyZMunQoUMaPny4evToYeqMet6pU6fUokULrVy5Ui4uLsqfP79u3bqlZcuWqV27drpx40aMbb799lt16tRJa9eu1ePHj+Xl5SU7Oztt27ZN3bp1M53FX6NGDeXMmVMRERFau3ZtnLUHBwerUKFCLzxbPj4uXbqkN998U5MmTdLRo0eVKVMmeXl5yd7eXidPntTUqVP11ltvxflcxFf69OlVvnx5Zc6cWdLTM/mff3/cvHlTrVq10rhx43Ts2DFlzZpVRYoU0e3bt7VkyRK9+eabpvulRbt3757atWunGTNm6Ny5c8qaNasKFiyoO3fuaNmyZWrTpo127tyZJLVHf0adnJxM71cHBweVL1/edIVA9Hu7ZMmSL33c+L4nXuZVvs937typtm3basuWLfL09FTOnDl17do1zZs3T2+//bbZwPOzz4Gjo2Ocn1lfX1917NhRe/bsUc6cOZUpUyZdunTJ1AkaGhoaay2J3Q4AANgu+ijijz6KhKGP4j/0UdBHkZr6KKTU//kEYH0YAAegr7/+Wps3b5arq6smT56s3bt3a9myZfrzzz/1+eefy8HBQfPmzdPcuXOTbJ8PHjzQ7du3tXLlSvn6+mrXrl3q1atXkj3+3bt3tWrVKklPf7Q9e5bpq7h69apcXFy0adMmU93R96SJtmDBAlWvXl3bt2/XihUrtGPHDo0ZM0YODg5asGBBjB+OgwcP1pEjR5Q1a1bNnz9fW7du1fLly/XXX3+pT58+ioqK0tdff20KQdLTH4VHjhxRgQIFtGXLFm3YsEErVqzQ7t271bFjRxmNRo0fP15PnjwxbbNz506NGTNGdnZ2+t///qeDBw/K19dX27Zt09y5c5U5c2atXbvW7Gx4ScqePbvWr1+v9evX64033oj3cxUVFWV6DaLPWJakOnXqyMPDQ5Llz+Bct26dQkJC5OLiYnZsb775piRpy5YtunPnTpLsq06dOlq0aJGKFy8uSWrRooUWLVqkDz74QNLToNuzZ08FBASoXLly8vPz09q1a7Vy5Urt2LFDdevW1f379/Xhhx/q6tWr8d7viBEj5OrqKqPRqJUrV6pTp06qXLmyevXqpZkzZ+rYsWOm6b6SQtasWbVo0SINHz7ctGzRokWmY332OWjTpo1p3YscPnxYY8eOVXh4uHr16qW//vrL9NkaN26c7OzsNGPGDO3evfuFj3PmzBl9/PHHevLkiXr37q39+/dr1apV8vPzk6+vrwoUKKC//vpLo0aNinX733//XYULF9b69eu1adMmrVu3TosXL5abm5vu3r2r2bNnm7Vft26dfvrpJ9nZ2WnYsGHas2ePli9frl27dmnAgAGSpNGjR+vChQuys7MzfZdEf26et3LlSklS69atTcsS+/kcMWKEbty4obJly2rbtm2m75C9e/dq/PjxsrOzk7+/v2mfiVW8eHEtWrTIdCV0jRo1tGjRIo0YMULS0ytLPvjgA128eFEFCxbUqlWrtGnTJq1YsUJ//vmn2rVrpydPnujTTz81nVEuST///LMuX76s8uXLa8eOHVq3bp1WrlypnTt3qmHDhgoPD3/lTtHo2t9//31J/723Fy1aJD8/Py1atEhZs2aVJL3//vtatGiRfvjhhxc+ZkLeEy+S2O/zaAsWLFCNGjW0bds2rVu3Tps3b9aPP/4oe3t7Xb58WcuWLTO1ffY5yJQpU5yf2UOHDql58+bauXOnVq5cqe3bt2vChAmSpHPnzsV59n9itwMAALaLPor4o4+CPorEoo+CPorU1EdhDZ9PANaHAXAgjbt586bpDNIxY8aocePGpnX29vbq2LGj+vfvL+npVECvejXgszp06KDChQtLenpl3bPTgSWG0WhUUFCQtm7dqnfffVf37t2To6Oj+vXrlxTlmrz//vvKnj27JMnNzU3p0qUzW58rVy5NmzbNdMWjwWBQ+/bt1aNHD0lPn8doR48eNU379MMPP6hKlSqmdU5OTurfv7/eeustSdLEiRNN686cOSPp6T1xcuXKZVqeLl06DRkyRDVr1tQbb7yhe/fumdZ9++23MhqNGjx4sLp06SJ7e3vTumrVqmncuHGSpDlz5igoKMi0ztHRUYUKFVKhQoWUPn36eD9Pu3fvNp1tGh3Woh8v+n22atUqi17dFz21WP369eXm5mZa3rx5c9nZ2Sk8PNzUJrn99ttvCggIUJYsWfTTTz8pb968pnVZsmTRDz/8oKJFi+rhw4eaMWNGvB+3ePHiWrp0qSpUqGBaFhwcrB07dmjSpElq3769atasqe+++04hISFJekxJ5ccff1RUVJSaNGmiQYMGmX3mWrdurbZt20rSSwfKpkyZorCwMHXq1EkDBgwwe5zixYvrhx9+kL29vdasWaPz58/H2N7R0VFTp05VwYIFTcvKlStnCnuHDx82ax/9We/evbu6du1q+szZ29urd+/eqlGjhiIjI02hsU2bNjIYDDp58mSMAdBbt25pz549sre3V4sWLcxqSujnMzAwUOfOnZP09Hs/W7ZspnUGg0EtW7ZU5cqVJSnWqcST0saNG3X69GmlS5dOs2bNkre3t2mdu7u7xo4dq1q1aik8PNzsDPro78BGjRopU6ZMpuXp06fX8OHDVb16dVWqVCnVXT2c0PdEXBL7fR4tc+bM+uGHH8xe+/r165tOVHj+vRwfhQoV0vjx402dE9LT7/4aNWpIUpzTXiZ2OwAAYJvoo0g4+ijihz6KhKGPIm70USRtH4VkHZ9PANaHAXAgjdu5c6ciIiKUNWtWNW3aNNY2nTp1kqOjox4+fGi6L05SePbHbkI9ey/W6P+8vb1VtWpV9e7dW6dPn1b69On1/fffmw2oJIWX1d2+fXu5urrGWP72229Lkvz9/XXx4kVJ/93zqHTp0nFOsfTuu+9Kejpd2j///CNJpimpli1bpt9++83snq1OTk765ZdfNG7cOFMIvnbtmk6fPi3J/Ifks+rUqSNPT0+FhobGeU+1hIj+oV+6dOkYU7BF1/DgwQOtW7fulfeVGBcuXDBdUfr8c5ItWzZVrVpVkrRkyZIkPfs4Llu3bpUktWzZUhkzZoyx3snJyXRv4q1bt8poNMb7sQsXLqzffvtNK1eu1EcffaRy5crJ0dHRtD4wMFAzZszQm2++qZs3b77ikSStkJAQ033OojtanjdgwABt3LjRdK+72ISFhZmmxI7rMxD9PWI0GmO9H1nJkiVNV/w+67XXXpMkPXz40LTs8uXLps959Gf/eV9++aW2bNmigQMHSpLy5s2rSpUqSVKMAdBVq1YpKipKtWvXjrWGhMicObP27t2rY8eOmU0DFy0yMtLU2Zfc4TL6fV+vXj2zDpVnde/eXZK0f/9+03Mc/Z3y888/a/Xq1WbPffbs2TVnzhyNGTNGzs7OyVh9wiTmPRGbpPg+r1atWoyOUenpYLRk/l6Or/r165t1WkaLvn/e8/cWf9XtAACAbaKPIuHoo4gf+igShj6K2NFH8VRS9lFIqf/zCcA6Jc18OwCsVvSPn2LFisnOLvZzYlxdXVWwYEH9888/8vf3V926dZNk36/yA8nJySnGvVbt7Ozk5uam7Nmzq0yZMmrcuPErn7Edm5fVXbp06ViX58qVS+nTp9fDhw916dIlvfbaa6bnv0SJEnE+XoECBeTu7q7g4GD5+/uraNGiateunZYtW6bz589r9OjR+uKLL1SsWDFVq1ZNtWrVUqVKlcymVIu+2lOSPvzwwzj3FT0dWXRdiXXv3j398ccfkmL/IV+hQgXlzZtXV69e1aJFi9SmTZtX2l9iRE/xmzlzZtOVhs9q0aKF9uzZo+vXr2vXrl2qU6dOstbj7+8v6cXvheh1d+/e1b179xJ8L6xixYqpWLFi6tu3r0JCQnT48GHt3r1bq1atUmBgoK5cuaL+/fvH+x7EKeHff/9VeHi4JMXZUZQ5c2bT1QxxuXTpksLCwiQ9ndIr+t7Nse1Piv0zEN1Z87zoQdaIiAjTsuj7+7m6usY5sJszZ84Yy9q0aaP9+/drzZo1+vjjj2UwGCT9N+XYs1OLvSpnZ2fduHFDx44d05UrV3T16lVduHBBp0+f1uPHjyUp2TtWEvK+j4yM1OXLl1WyZEn16NFDGzdu1O3bt/XJJ5/IwcFBpUqVUvXq1VW7dm2VKVPG9NylFol9TzwvKb7PE/Jejq+4HjO6szWukykSux0AALBN9FEkHH0UL0cfRcLRRxE7+iiSvo/CGj6fAKwTA+BAGhccHCxJL52WJjqkJeX0Yq9yZV70PXws4WV1x3ZmbDRXV1c9fPhQDx48kBT/59/NzU3BwcGm59/d3V1LlizR7NmztXbtWl2+fFmnTp3SqVOn9Msvvyhz5swaMGCA2rdvL8n8rM/4TG2bmKv/nrVmzRrTD/mxY8dq7Nixcbb9+++/dfLkyReGqqQWERGh1atXS3p6ZvHL9r1o0aJkD5fxeS8821ny6NGjBIfLZ7m4uKhGjRqqUaOG+vfvr2HDhmndunU6evRoir8eL/LsFHnPTgGXUM++p0+cOJGg9tGePSP9ZaLrTmjNjRs31pgxY3Tjxg3t27dPVatW1d9//63z58/L09MzyTr3Ll68qG+++UY7duwwG+R2d3dXxYoVFRAQYJrGMDkl5n0vPQ3mq1at0k8//aSNGzfq1q1bOnLkiI4cOaJp06Ypd+7cGjZsmBo0aJC8B5AAiX1PPC8pvs8T8l6Or9iuKE/O7QAAgG2ijyLh6KN4OfooEo4+itjRR5H0fRSp/fMJwHoxAA6kcdE/fF4WJqLDUGw/lOKa5ij6CsLUKrnqftE9iqIDRPSZoPF9/qPXP/v8u7u7q1+/furXr58uX76sffv2ad++fdqxY4cCAwM1YsQIeXh4qGHDhqYr6Tw8PLRv377EH1w8Rd+TytXV9YVhKSAgQEajUYsXL9aYMWOSva5o27dv1507dyQ9nUosritFHz9+rIcPH2rnzp26ceNGrGfDJtX7yM3NTffv33/he+H+/ftm7V/m888/1969e9WqVSv17t07znbOzs764osv5Ofnp/DwcPn7+8cIE3EdZ3Lfk+vZqfqCg4PN7vec2Mc5fPjwKw9Cxnd/Ce2Qc3Z2VrNmzbRkyRKtWbNGVatWNZ1Z7ePjkyQDl4GBgerUqZMCAwOVK1cutW/fXsWLF9drr72mPHnyyGAwaNCgQS8cAE/K97304u/A6H9/nm0vPf0eHTZsmIYNG6azZ89q//792rt3r3bv3q3r16+rX79+Wrx4cZxXvLxq7QmV2PdEXI+TUt/nAAAAKYk+ipjoo3h19FHQR5FU6KNI+j6K1P75BGC9uAc4kMZF3xfm9OnTcU51GxwcrEuXLkmS8ufPb1oefc/O6LP0nhcQEJCElSaNZ6fciq3u0NDQVz6zOPoeWM+7cuWK6Ydm4cKFJf33/J88eTLOx7tw4YIpqEQ//4GBgTp48KDpvlr58+dX+/btNWnSJO3YscM09Vr0j9KCBQtKenrG5+3bt+Pc18GDB3XhwoVXmnL2zJkzpnt5jRs3Tjt37ozzv+gzlteuXWsK3ikh+t5CRYoU0a5du+Ksb86cOZKeTr38+++/m7ZPjvdRfN4L0WcFZ8yYMV5nVj958kSXL1/Wli1bXtrW3d3dFLieDXDRn/PoKb6el9yf87x585pqeHaavGf9/fffeueddzR06NA4Q/Czj3P+/Pk493f8+HGdPXv2lQcpo+9Z9fjxY127di3WNn/88Yc6d+6sb775xmx59HRef/zxhyIjI01TgSXVNF/Lly9XYGCgPDw8tHz5cvXu3Vt16tRR3rx5TR0tt27dinXbpP7ej8/7/u+//5YkGQwG5cuXz1Tf3r17Td9VXl5e6ty5s6ZNm6Y//vhDuXPnVmRkpNauXZtstSfUq7wnnpWS3+cAAAApjT4Kc/RR0EdBHwV9FLbcR2ENn08A1osBcCCNq127thwcHHT79m2tX78+1jYLFy5URESEXFxcVLlyZdPy6B+3sd2H5vjx46kyXHp4eJgGeGKre+vWrYm69+mzVqxYEWtQj54OrWzZsqazdKOnCjp+/Hic037NnTtXkpQjRw55eXlJknr06KGOHTvK19c3Rns3NzeVLVtW0tNQJEmFChUyBdOFCxfGup9Dhw6pY8eOatq0qY4ePRqPI41d9H2rPD09Va9evRe2feeddyQ9/QEeHYSTW2BgoHbs2CHp5T/WS5UqZQrqS5cuNb03XuV9FNeZ3NHvhZUrV5qdRR0tLCzM9B6qVavWC+uOFn3vpBMnTpgCdVx2796te/fuycPDQ2XKlDEtf9HnPDIyUlu3bo1XLYnl7u6uChUqSPrvrODnrV27VocPH9a1a9fifH7d3d1N31/z58+Ptc3Vq1fVoUMHvfnmm9q4ceMr1V2oUCHlzp37hXX7+vpq//79pk6iaGXKlFGRIkUUFBSkBQsW6N9//1Xx4sXjvL9YQkWH3Vy5csV6tvr58+dN3wHR3yHREvu9H/26PB/+o9/3W7du1dWrV2PdNvr1Klu2rDJkyKCIiAi1bNlSXbt21fbt22O0z5Ili4oWLSrJ/B7mL6r90aNH+uuvv2Ldf1J5lffE84+TUt/n0aLvvxlX5w0AAEBSoY/CHH0U9FE8iz4K+igSK7X2UaT2zycA68YAOJDG5cyZ03QPphEjRpj9oIqKitJvv/2mKVOmSJL69OljNhVN9A++OXPm6MKFC6blf//9tz7++OOUKD/BnJ2dVbx4cUnSlClTzK5y3L17t7744otX3seJEyc0YsQI07RLUVFRWrhwoSkkDhw40NS2XLlypjMY+/XrZzb1V1hYmH744QfTWb2ffvqp6YdzixYtJElTp07Vzp07zfZ/8OBB0w/BZ+8J1b9/f0nSzJkzNWvWLLOzgg8ePGhaX7ZsWVWtWtW0Ljw8XBcuXNCFCxdeesZwWFiY1qxZI+npVEhOTk4vbF+7dm3TD/DFixe/sO2L/Pvvv7pw4YL+/fffl7ZduXKlIiIi5OjoaHoeXyT6B/bt27dNZ7m+yvsoetqp69evx9hP9uzZdefOHb3//vtmg4GBgYHq37+//vnnH7m5ualv374vrVuSatSooUaNGkmShg8fri+//DLGmb5PnjzR8uXLNWDAAElP3yfPTr0V/Tk/d+6c5s+fbxoAu3//voYNGxbn1QRJqU+fPjIYDFq1apVmzJhhFtxXrlypBQsWSJJ69uz5wsfp27ev7O3ttXbtWo0bN87sDOp//vlHvXr1Unh4uHLnzi0fH59XqtlgMKhPnz6SpFmzZmnp0qWm5y4yMlIzZ87U5s2b5eDgoG7dusXYPrrjY/LkyZKk1q1bx7qfhHw+o0WfyX/mzBlt2rTJtNxoNGrnzp3q2bOn6Wz656ePS+z3fvR76vnPaOPGjeXl5aUnT57ovffeM5t2PTg4WCNGjNDu3bvl4OCgwYMHS3p6dUOzZs0kSV9++aWOHz9u9ph+fn7avXu3pKffMc/XvmvXLvn5+ZmWBwQEqF+/fgoMDIyz/qTwqu+JZyX2+zyxor+3Hjx4wJn2AAAgWdFHQR9F9Db0UcSOPgr6KBIjNfZRWOrzCSDt4B7gADR06FDdunVLf/zxh/r3769s2bIpR44cunr1qoKCgiRJnTp10nvvvWe2Xe/evbVr1y7dvn1bPj4+Kly4sJ48eaJLly4pb968atOmTZxnFVrSgAED1Lt3b50/f14NGjRQ4cKFdf/+fV2/fl2lSpVS+fLlTSEiMYoWLaply5Zpw4YNeu2113Tz5k3dvn1bdnZ2Gjp0aIzBiG+++UYffPCBjhw5oi5duih37tzKlCmT/P39FRwcLHt7ew0YMMA04CNJXbp00Z49e7Rz50699957ypYtm7Jly6agoCBTaKlXr57atWtn2qZZs2a6dOmSpkyZookTJ+qnn35SgQIFdPfuXdM2BQsW1I8//mhW361bt9S0aVNJT6cjiutHriRt2bJF9+7dkxS/qZDs7Oz01ltv6dtvv9U///yjQ4cOmcJMQnz22Wfav3+/KleubAoacYk+y/j111+P172amjVrpvHjx+vBgwdavHixKawl9n1UvHhxbdu2TWvWrNHZs2dVsWJFjRw5UhkyZNCMGTPUq1cvHTlyRA0bNlThwoXl4OCgc+fOKTw8XB4eHpo4caJp6qr4mDhxolxdXbVy5UrNnz9f8+fPV65cuZQ5c2bT5zUsLEyOjo4aNGiQOnToYLZ9nTp1VLFiRR08eFBffvmlZs+eLU9PT128eFHh4eHq27evqQMquVSrVk1Dhw7V119/re+++06zZ89W3rx5dfPmTdN90j788EOzzpTYVKhQQWPGjNHIkSM1d+5cLV68WIUKFdKjR490+fJlGY1GZcmSRb/88stLg1d8tG3bVufPn9ecOXM0fPhwff/998qRI4euXbume/fuyd7eXqNGjYr1rOkWLVpo0qRJevz4sRwdHdW8efNY95GQz+ezdf3222+6fPmy+vXrp9y5c8vT01M3btxQYGCgHB0dVblyZe3fvz/GVOiJ/d4vVqyYpKf3NmvcuLEKFy6sqVOnysHBQT/++KPee+89Xbx4US1atFCBAgXk5uZmmurQ2dlZo0ePVsWKFU2PN3DgQB06dEinTp1Su3btTMcQEBBgurLnnXfeMRsAb9WqlRYuXCh/f3/17dtX+fLlk6urqy5cuCB7e3t98MEHmjFjxkufv1fxKu+JZyX2+zyxvLy8ZGdnp9DQUDVu3FjZsmXTL7/8Eq9pDgEAABKKPgr6KOijiBt9FPRRJFZq66Ow1OcTQNrBFeAA5OTkpGnTpum7775TzZo1FRYWptOnT8vFxUXNmjXT/PnzNWLEiBjT9hQrVkzLli2Tj4+PMmXKpIsXLyoyMlLvvvuufH19lTVrVgsd0YvVrl1bv/32mxo0aCBXV1edP39e6dKlU79+/fTbb7+Zzn5NrC5duujbb79VwYIFTWeeNmrUSIsXL471LEoPDw8tWLBAY8aMUaVKlfTw4UOdPXtWnp6eatu2rZYtW6ZevXqZbWNvb69p06Zp2LBhKleunEJDQ3XmzBmFhISoZs2a+uabb/Tjjz+a3QdKevoDfMmSJfLx8ZG7u7vOnDmjoKAgFS9eXP3799fy5cuVOXPmRB97dHArUaJEvKdCatu2rRwdHSUl/xmcx44dM91bKb73KnJxcVHLli0lSX/99ZfpXnOJfR+99957ateunTw8PHTp0iWdPXvWtK548eJau3at+vTpoyJFiujq1au6dOmSChYsqA8++ECrV6+O99Ri0ZycnPT1119r6dKlevfdd1WiRAmFhYXpzJkzunnzpgoWLKgePXpo9erVMd5n0tOA8csvv2jAgAEqUqSIAgMD9e+//6patWpatGjRK5+FHF9du3bVkiVL1Lx5c6VLl05nz55VRESE6tSpozlz5qhfv37xepw2bdpo1apVeuutt5Q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",
-      "text/plain": [
-       "<Figure size 2000x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficient Statistics: \n",
-      "\t 757 total coefficients\n",
-      "\t 13 constrained coefficients\n",
-      "\t 80 coefficients adjusted\n",
-      "\t 677 coefficients converged\n",
-      "\t 80 coefficients not converged\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Wb7U61+sNGTLktrXmyG9fbbXSW7uztwaQf9wWDACckPPtmg4dOtiWVa9eXY0aNdJvv/2mL7/8UpK0f/9+HT9+XJ07d1b58uVt2/r7+6tbt24uq6d06dLy9/fX2rVrtWLFCp09e1aS1LBhQ61bty7fD8zs2LGj3eucbzf99a9/tVteo0YNWSwW/frrr5Ju3NtXktq3b6/ffvvN9pORkaF27drp3Llz2rlzp917dOrUye6bVw0aNJAk23s6q0+fPlqwYEGun8mTJ9u2KVu2rJKSkjRlyhS7fdPT0+Xn5ydJtkvLv/rqK2VnZ6tnz55237oqX768li5dqvfff/+Wtaxfv15ZWVnq27evSpYsaVtuMpn00ksvSVKue0I3bNhQf/7zn+2WVapUSd99953i4uJsl29XqlRJ69ev16BBg/I9NgAAACgcXl5etttZ5de+ffuUmpqqJ598MtcVLX/729907733at26dcrOztaBAwd08OBBtW7dWhaLxe6c+7HHHlOJEiVu+WwNR2zYsEGS9MILL9idqwcFBenjjz/Wa6+9lud+X375pa5fv64nnnhC586ds9V27tw5W8+U0yPleOyxxxQQEGB7XbJkSQUFBd3xlly306JFC1WuXFmfffaZ3fJVq1bJ19dXnTp1kiSFhIRo165dGjp0qN12aWlptppc8aDy//73vzp37pzat2+vS5cu2c1bTo+VM2+VK1eWJE2ePFnbtm1TZmampBu3mVq5cqUCAwNveZysrCytWbNGZrPZdgWI9Hu/+sknn+jq1atOfQZner28bpmWl/z21RK9dVH01gDujNuCAYCDrl+/rjVr1ki6cYJ1871Kw8LCtHv3bi1btkwdO3bU0aNHJd04YfqjWrVquawmHx8fTZw4USNHjrQ1PMHBwWrRooU6dep0y0v3/+iP/6j/pz/9SZJUoUIFu+Xe3jf+7yPnmSEpKSmSpLZt297yvU+cOHHbY+UEFzc/h8QZtWrV0sMPP3zH7UqUKKGNGzdq06ZNOnr0qI4fP65ffvnFdlKaU0fO/N5///253iM4OPi2x8iZ/5svEc9RqVIl+fv757rX7R/HRZLGjx+v2NhYvfPOO3rnnXdUo0YNPfLII+rQoYNCQ0Pv+FkBAABQuAIDA5WSkqLMzMw73gYpx+3OFU0mk2rVqqXU1FSdO3fOdr6dkJCghISEPN/vj+fbzsg5N/1j2CNJdevWveV+OfWNGDHiltvcqR+QbvQEf3wOiSPMZrMiIyM1c+ZM/d///Z8aN26ss2fPauvWrerYsaPdw9t9fHz0xRdf2J7vcfz4cZ09ezZXP1AQOeMydepUTZ06Nc9tcsalffv2ioqK0sqVK9WrVy/5+vqqadOmat26tZ566im7Wxz/0ebNm/Xrr7+qfv36ysjIsM2jj4+P7rvvPh05ckSff/657XkzznwGR3q9P/aPeXGkr5ZEb30Td/XWAO6McAUAHLR582bbt6lu9XC+pKQkHTp0yPbaarXm2qYgJzp5NRxt27bVI488om+++UbffvutduzYofnz52vBggV69dVXbQ+wu52cE7s/ut29fXPqKVWqlGbMmHHLbWrWrGn3+ubnjLjb9evXNWjQIG3evFn16tVTvXr11L59e9WpU0dff/21PvzwQ7ttpTuPQV5y5v1W+1osllzNd15z0KRJE23YsEHbt2/XN998ox07dighIUHLli1T79697R6+CAAAAPcLDQ3V4cOH9b///U9hYWG33G7kyJG6fv36bUOIHDn9go+Pj+2/u3fvfst/dL3Vubwjcs59HZXTn4wbN07VqlXLc5s/PuOhsPqBp59+WrNmzdKnn36qxo0b69NPP1V2drZdsHDp0iX17NlTe/fuVdOmTVWvXj117txZ9evXV3x8vO0f/R31xz4tZ96GDBmixo0b57lPqVKlJN24+mnChAkaMGCANm3apP/+979KSkrS1q1b9eGHHyohISHPYEH6/QqQPXv2qE2bNnluk5CQ4FS44kyv5+Xldcf3daSvvjk8obc2Vm8NeDrCFQBwUM6Ja79+/WyX297s3//+tzZv3mx38nr48OFc26Wmpt7xWDknpRkZGXbLcy5NznHp0iUdOHBA1apVU7t27dSuXTtJ0k8//aSePXvqgw8+yNcJoLOqVaumlJQUhYSE5Gqa9u/frzNnzthut2UEX3zxhTZv3qy+ffvabs+VY9WqVXavc5rDlJSUXCexCxcu1KFDhzR69Gj5+vrmOk5O85OcnGx7cGaOkydP6vLly7bL/28lIyNDBw4c0D333KNWrVqpVatWkqRjx47p+eefV3x8vAYNGqTSpUvn45MDAACgMHTs2FEJCQlaunTpLcOVX375RWvWrFFAQIDKlCmj6tWrS5IOHjyYa1ur1arDhw+rdOnSCggIsAss/niVtsVi0bp162zvVxA3n/vWrl3bbt3UqVN17do1vfrqq7fcLyAgIFd9Z86c0Q8//OCS+vKjevXqeuihh7R27VqNHj1an376qe699149+OCDtm0WLVqkH3/8UWPHjtWzzz5rt39+bqXk5eWly5cv51r+xz4tZ1x8fX1zjculS5f07bff2q5kOHHihI4eParmzZurR48e6tGjh7KyshQXF6epU6dq2bJlevnll3MdMy0tTVu2bFGpUqU0adKkXP94b7FY9PLLL2vPnj3au3fvba9Aykth9XqO9NWjR4/WfffdJ4ne2mi9NeDpiDYBwAFnz57VN998ozJlymjgwIFq27Ztrp8hQ4ZIklavXq17771XNWvW1Jo1a+wuc7527ZoWL158x+NVrFhR0o1vIN1s9erVdq+Tk5P13HPPaebMmXbLa9WqJX9/f7tvzeR8qyWvb/w4q3379pKk9957z275pUuXFBsbq4EDB+Y6ic2PnBNgV1/OfO7cOUnK1TCmpqZq3bp1kn7/BlObNm1kMpm0ZMkSu/tonz9/XnPnztUPP/xgC1bMZrNdrREREfLy8tKHH35od89mq9WqadOmSZIef/zxO9batWtXjRs3zm559erVFRgYKJPJxDeVAAAAilhoaKgiIiK0bt06LViwINf6ixcv6h//+IeysrI0aNAg+fj4qE6dOqpevbrWrFmT6x+MExMTdfToUds/7NarV09Vq1bVqlWrbLdHyrF8+XLFxsba/rG6IHKuiomPj7dbfvToUS1cuFDHjh3Lc7927drJbDZr9uzZunbtmt26iRMnauDAgfrxxx+dqumP59j5ERUVpd9++02rV6/Wvn37cl2xcat+4P/+7/9sz7O43e3JKlasqN9++83u9kyZmZlau3at3XYtWrRQqVKltHDhQtsxc8yePVv/+Mc/9PXXX9te9+rVS99//71tG29vbzVs2FDSra8GWb16tbKystS5c2dFRETk6k/btWunqKgoSdKyZctu+Zmk33vFm8e7MHo9R/vqq1evKiQkhN7agL014Om4cgUAHJBz4vr000+rRIkSeW5Tt25dhYaGaufOnfrss8/01ltv6fnnn1eXLl3UvXt3lS5dWomJibp48eIdjxcZGakPP/xQb731lo4fP64KFSpo06ZNSk5Otjt+kyZN1KJFCyUkJCg9PV0PPvigsrOztW7dOh07dszuG04592OdN2+eWrZsedt7uebX008/rf/85z9avny5jh49qvDwcGVlZenf//63jhw5ouHDh9/2AYy3kvOgwq+++kpVqlRRRESEypQpU+B6W7ZsqX/9618aP368jh49qgoVKujgwYNKTEy0BSjp6emSbtxvum/fvvrwww/VrVs3dejQQRaLRStWrNCFCxdsIUlOvT/99JOWLl2qZs2aKTg4WLGxsfrXv/6lp556Sk8//bRKliypjRs3avv27Xrsscf05JNP3rbWSpUqqUuXLlq+fLn69Omj8PBwmUwmffvtt9q5c6eio6NVsmTJAo8JAAAACmbChAm6cOGCJk6cqE8//VTt2rVTuXLldOTIEa1atUq//fabunfvru7du0u68Y+db731lvr27asuXbqoW7duqlatmn744QetWrVKVatW1bBhw+y27devn55++mk9++yzqlGjhvbs2aPExETVqFFDAwYMKPBnaNmypTp27KjExESdOnVK4eHhunTpkpYsWaISJUpo+PDhee533333afDgwZo2bZo6d+6syMhIBQQEaOPGjfr222/12GOP2YIiR+V1jn0n7du317hx4zRp0iR5e3vrqaeeslsfHh6ujz76SMOGDdNzzz0nf39//fjjj1q1apW8vLx0/fp1Wz+Ql6ioKCUlJalPnz567rnnZLFYlJiYmCuQCQgI0Ouvv66RI0eqU6dO6tq1qypWrKjt27friy++UIMGDfTcc89Jknr16qW1a9eqb9++evbZZ1WtWjWdPn1ay5Ytk7+/v5555pk8a1m5cqUk2d4nL3//+9+1ZMkSff7553rllVduedV7Tq+4Zs0aWa1WWw/j6l7Pmb66S5cu9NYG7K0BT0e4AgAOWLVqlUwm021PXKUbJ8Y7d+5UQkKCEhMTtXTpUk2fPl0LFy6UdOMbYY8++qj+8Y9/3PZ9atSooblz52rGjBmaM2eO/Pz81LJlSy1btkwdOnSw23b69OmaP3++7ZZXkhQSEqIpU6aoU6dOtu26deum7777TomJidq+fbtLTgC9vLw0e/ZsxcfH65NPPtGUKVPk5+enoKAgvf/++043UjVr1lTv3r318ccfa/z48apWrZqaN29e4HqDgoI0Z84cTZ8+XXFxcZKkypUrKzo6Wo8//rieeuopffPNN7arSoYOHar7779fixcv1rvvvis/Pz/Vr19fkydPVv369W3vO2LECE2ZMkUTJkxQTEyMgoOD1bdvX91///1auHCh7VkuNWvW1Ouvv65u3brl66qT119/Xffff79WrVqlqVOnKjs7W/fff79ee+21O/4uAgAAwD0CAgIUFxenL774QitXrtTSpUv122+/qXTp0mrYsKG6d+9uu8VrjrCwMK1YsUIzZ85UYmK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-      "text/plain": [
-       "<Figure size 2000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Top 10 largest coefficients:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coefficient_name</th>\n",
-       "      <th>value</th>\n",
-       "      <th>model_counts</th>\n",
-       "      <th>target_counts</th>\n",
-       "      <th>coef_change</th>\n",
-       "      <th>new_value</th>\n",
-       "      <th>converged</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED3_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_BIKE_atwork</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-45.000000</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-42.154493</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>coef_calib_autodeficienthhind_WALK_TRANSIT_school</td>\n",
-       "      <td>-39.129117</td>\n",
-       "      <td>259.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>-2.465786</td>\n",
-       "      <td>-41.594903</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>698</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc</td>\n",
-       "      <td>-40.875688</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>-40.875688</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>695</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_SHARED3_disc</td>\n",
-       "      <td>-38.246255</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-40.246255</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>coef_calib_zeroautohhindivtou_SHARED2_school</td>\n",
-       "      <td>-37.000000</td>\n",
-       "      <td>243.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>-39.000000</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>671</th>\n",
-       "      <td>coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint</td>\n",
-       "      <td>-31.048732</td>\n",
-       "      <td>5916.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>-4.947746</td>\n",
-       "      <td>-35.996478</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>696</th>\n",
-       "      <td>coef_calib_zeroautohhjointtou_WALK_disc</td>\n",
-       "      <td>-35.000000</td>\n",
-       "      <td>1307.0</td>\n",
-       "      <td>576.0</td>\n",
-       "      <td>-0.819382</td>\n",
-       "      <td>-35.819382</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      coefficient_name      value  \\\n",
-       "540       coef_calib_zeroautohhindivtou_SHARED2_atwork -45.000000   \n",
-       "541       coef_calib_zeroautohhindivtou_SHARED3_atwork -45.000000   \n",
-       "543          coef_calib_zeroautohhindivtou_BIKE_atwork -45.000000   \n",
-       "544  coef_calib_zeroautohhindivtou_WALK_TRANSIT_atwork -42.154493   \n",
-       "519  coef_calib_autodeficienthhind_WALK_TRANSIT_school -39.129117   \n",
-       "698    coef_calib_zeroautohhjointtou_WALK_TRANSIT_disc -40.875688   \n",
-       "695         coef_calib_zeroautohhjointtou_SHARED3_disc -38.246255   \n",
-       "503       coef_calib_zeroautohhindivtou_SHARED2_school -37.000000   \n",
-       "671   coef_calib_autodeficienthhjoi_WALK_TRANSIT_maint -31.048732   \n",
-       "696            coef_calib_zeroautohhjointtou_WALK_disc -35.000000   \n",
-       "\n",
-       "     model_counts  target_counts coef_change  new_value  converged  \n",
-       "540          60.0            0.0        <NA> -45.000000       True  \n",
-       "541          40.0            0.0        <NA> -45.000000       True  \n",
-       "543          36.0            0.0        <NA> -45.000000       True  \n",
-       "544          76.0           29.0        <NA> -42.154493       True  \n",
-       "519         259.0           22.0   -2.465786 -41.594903      False  \n",
-       "698          92.0            0.0        <NA> -40.875688       True  \n",
-       "695         135.0            0.0          -2 -40.246255      False  \n",
-       "503         243.0            0.0          -2 -39.000000      False  \n",
-       "671        5916.0           42.0   -4.947746 -35.996478      False  \n",
-       "696        1307.0          576.0   -0.819382 -35.819382      False  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      " Final coefficient table written to:  C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_16\\tour_mode_choice_coefficients.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "calibration_iterations_to_run = 1\n",
-    "start_iter_num = 16\n",
+    "calibration_iterations_to_run = 2\n",
+    "start_iter_num = 1\n",
     "\n",
-    "for i in range(start_iter_num, start_iter_num + calibration_iterations_to_run):\n",
+    "for i in range(start_iter_num, calibration_iterations_to_run+start_iter_num):\n",
     "    asim_calib_util.run_activitysim(\n",
     "        data_dir=data_dir,  # data inputs for ActivitySim\n",
     "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
@@ -5493,39 +183,6 @@
     "print(\"\\n\\n\", \"Final coefficient table written to: \", tour_mc_coef_file)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_16\\tour_mode_choice_coefficients.csv\n",
-      "C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_17\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tour_mc_coef_file)\n",
-    "print(iteration_output_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -5542,20 +199,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\rsirupa\\AppData\\Local\\Temp\\9\\ipykernel_193712\\4111903590.py:4: DtypeWarning: Columns (15,16,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  tours = pd.read_csv(os.path.join(iteration_output_dir, 'final_tours.csv'))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# iteration_output_dir = r\"C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_9\"\n",
+    "# iteration_output_dir = r\"C:\\abm_runs\\rohans\\calibration\\tour_mc\\output\\calibration_output_an_iter_5\"\n",
     "\n",
     "# ### read data\n",
     "# tours = pd.read_csv(os.path.join(iteration_output_dir, 'final_tours.csv'))\n",
@@ -5581,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5662,7 +310,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
diff --git a/src/asim/calibration/resident/tour_mode_choice/scripts/settings_mp_warm_start.yaml b/src/asim/calibration/resident/tour_mode_choice/scripts/settings_mp_warm_start.yaml
index 8f8843779..4c58d9db4 100644
--- a/src/asim/calibration/resident/tour_mode_choice/scripts/settings_mp_warm_start.yaml
+++ b/src/asim/calibration/resident/tour_mode_choice/scripts/settings_mp_warm_start.yaml
@@ -3,7 +3,7 @@ inherit_settings: settings.yaml
 multiprocess: True
 households_sample_size: 320000
 num_processes: 40
-trace_hh_id: 9632
+trace_hh_id: 
 chunk_training_mode: disabled
 # chunk_size: 240_000_000_000
 
diff --git a/src/asim/calibration/resident/tour_mode_choice/targets/tour_mode_choice_calibration_targets_2024-01-23_updated.csv b/src/asim/calibration/resident/tour_mode_choice/targets/tour_mode_choice_calibration_targets_2024-01-23_updated.csv
new file mode 100644
index 000000000..3f333d45d
--- /dev/null
+++ b/src/asim/calibration/resident/tour_mode_choice/targets/tour_mode_choice_calibration_targets_2024-01-23_updated.csv
@@ -0,0 +1,577 @@
+grouped_tour_mode,auto_suff,tours,purpose
+All,All,4330498.314823359,Total
+DRIVEALONE,All,1742024.788590247,Total
+SHARED2,All,938842.7632010597,Total
+SHARED3,All,927861.4588698392,Total
+WALK,All,495947.5026527777,Total
+BIKE,All,65282.789565030565,Total
+WALK-TRANSIT,All,58620.44345858439,Total
+PNR-TRANSIT,All,4557.200397090634,Total
+KNR-TRANSIT,All,12555.36323152418,Total
+TNC-TRANSIT,All,179.1126537142125,Total
+TAXI,All,3064.34343410283,Total
+TNC-REG,All,12186.249846154564,Total
+TNC-SHARED,All,0.0,Total
+SCHOOLBUS,All,4602.63342841512,Total
+ESCOOTER,All,1788.1635876779364,Total
+EBIKE,All,4511.826891836025,Total
+All,0,112237.34200784458,Total
+DRIVEALONE,0,13807.93887735332,Total
+SHARED2,0,6404.5521627789785,Total
+SHARED3,0,1247.686051538898,Total
+WALK,0,32648.184816327062,Total
+BIKE,0,1317.3320154626326,Total
+WALK-TRANSIT,0,25681.200043903496,Total
+PNR-TRANSIT,0,173.69094305011743,Total
+KNR-TRANSIT,0,3746.300988386011,Total
+TNC-TRANSIT,0,66.2159368921275,Total
+TAXI,0,324.276190548646,Total
+TNC-REG,0,3615.8763453015295,Total
+TNC-SHARED,0,0.0,Total
+SCHOOLBUS,0,169.895019270037,Total
+ESCOOTER,0,381.6356152965594,Total
+EBIKE,0,422.17189411235057,Total
+All,1,1320260.9490520607,Total
+DRIVEALONE,1,496340.9172173532,Total
+SHARED2,1,325189.18490801816,Total
+SHARED3,1,304377.16504615155,Total
+WALK,1,107970.35871221956,Total
+BIKE,1,8513.90859292899,Total
+WALK-TRANSIT,1,24065.86919517013,Total
+PNR-TRANSIT,1,2247.897357003518,Total
+KNR-TRANSIT,1,7028.204889346652,Total
+TNC-TRANSIT,1,112.896716822085,Total
+TAXI,1,2740.067243554184,Total
+TNC-REG,1,5861.832298605265,Total
+TNC-SHARED,1,0.0,Total
+SCHOOLBUS,1,3914.69624565127,Total
+ESCOOTER,1,1268.42376401554,Total
+EBIKE,1,803.0614210727493,Total
+All,2,2898000.0237634545,Total
+DRIVEALONE,2,1231875.9324955405,Total
+SHARED2,2,607249.0261302624,Total
+SHARED3,2,622236.6077721489,Total
+WALK,2,355328.95912423107,Total
+BIKE,2,55451.548956638944,Total
+WALK-TRANSIT,2,8873.374219510782,Total
+PNR-TRANSIT,2,2135.6120970369984,Total
+KNR-TRANSIT,2,1780.8573537915172,Total
+TNC-TRANSIT,2,0.0,Total
+TAXI,2,0.0,Total
+TNC-REG,2,2708.541202247769,Total
+TNC-SHARED,2,0.0,Total
+SCHOOLBUS,2,518.042163493812,Total
+ESCOOTER,2,138.104208365837,Total
+EBIKE,2,3286.5935766509256,Total
+All,All,795338.6530190805,Work
+DRIVEALONE,All,595016.7211338528,Work
+SHARED2,All,84603.09436188595,Work
+SHARED3,All,77417.0422954419,Work
+WALK,All,10604.674393791389,Work
+BIKE,All,9514.20063861994,Work
+WALK-TRANSIT,All,7123.176176904248,Work
+PNR-TRANSIT,All,1747.0007252606824,Work
+KNR-TRANSIT,All,3095.3114680811277,Work
+TNC-TRANSIT,All,9.8773380966108,Work
+TAXI,All,0.0,Work
+TNC-REG,All,781.5152766961806,Work
+TNC-SHARED,All,0.0,Work
+SCHOOLBUS,All,0.0,Work
+ESCOOTER,All,0.0,Work
+EBIKE,All,249.712356526178,Work
+All,0,9985.523811054658,Work
+DRIVEALONE,0,399.025870407897,Work
+SHARED2,0,151.87421757494,Work
+SHARED3,0,0.0,Work
+WALK,0,790.221420803447,Work
+BIKE,0,0.0,Work
+WALK-TRANSIT,0,4323.969105522798,Work
+PNR-TRANSIT,0,0.0,Work
+KNR-TRANSIT,0,286.1612591479606,Work
+TNC-TRANSIT,0,9.8773380966108,Work
+TAXI,0,0.0,Work
+TNC-REG,0,496.078623958397,Work
+TNC-SHARED,0,0.0,Work
+SCHOOLBUS,0,0.0,Work
+ESCOOTER,0,0.0,Work
+EBIKE,0,0.0,Work
+All,1,258890.9152881544,Work
+DRIVEALONE,1,194824.447561643,Work
+SHARED2,1,26042.7995240626,Work
+SHARED3,1,22306.8953970716,Work
+WALK,1,8920.80633456379,Work
+BIKE,1,529.2243619855,Work
+WALK-TRANSIT,1,2403.6980836745797,Work
+PNR-TRANSIT,1,734.0107064431742,Work
+KNR-TRANSIT,1,1191.1691069594553,Work
+TNC-TRANSIT,1,0.0,Work
+TAXI,1,0.0,Work
+TNC-REG,1,245.348853515889,Work
+TNC-SHARED,1,0.0,Work
+SCHOOLBUS,1,0.0,Work
+ESCOOTER,1,0.0,Work
+EBIKE,1,0.0,Work
+All,2,526462.2139198715,Work
+DRIVEALONE,2,399793.247701802,Work
+SHARED2,2,58408.4206202484,Work
+SHARED3,2,55110.1468983703,Work
+WALK,2,893.646638424153,Work
+BIKE,2,8984.97627663444,Work
+WALK-TRANSIT,2,395.50898770687047,Work
+PNR-TRANSIT,2,1012.9900188175084,Work
+KNR-TRANSIT,2,1617.9811019737115,Work
+TNC-TRANSIT,2,0.0,Work
+TAXI,2,0.0,Work
+TNC-REG,2,40.0877992218947,Work
+TNC-SHARED,2,0.0,Work
+SCHOOLBUS,2,0.0,Work
+ESCOOTER,2,0.0,Work
+EBIKE,2,249.712356526178,Work
+All,All,72076.54660354409,University
+DRIVEALONE,All,21481.26523689866,University
+SHARED2,All,9648.62728130121,University
+SHARED3,All,12719.50056023019,University
+WALK,All,6848.518498043601,University
+BIKE,All,1569.7864994990675,University
+WALK-TRANSIT,All,10181.487674631964,University
+PNR-TRANSIT,All,319.83610074895876,University
+KNR-TRANSIT,All,140.31612912784897,University
+TNC-TRANSIT,All,0.0,University
+TAXI,All,4.0,University
+TNC-REG,All,958.904704155316,University
+TNC-SHARED,All,0.0,University
+SCHOOLBUS,All,20.0,University
+ESCOOTER,All,0.0,University
+EBIKE,All,0.0,University
+All,0,12791.0,University
+DRIVEALONE,0,49.0,University
+SHARED2,0,1538.0,University
+SHARED3,0,214.0,University
+WALK,0,99.0,University
+BIKE,0,1233.0,University
+WALK-TRANSIT,0,5270.949936000001,University
+PNR-TRANSIT,0,89.7804,University
+KNR-TRANSIT,0,0.0,University
+TNC-TRANSIT,0,0.0,University
+TAXI,0,4.0,University
+TNC-REG,0,0.0,University
+TNC-SHARED,0,0.0,University
+SCHOOLBUS,0,20.0,University
+ESCOOTER,0,0.0,University
+EBIKE,0,0.0,University
+All,1,34251.581673910594,University
+DRIVEALONE,1,9252.43914484136,University
+SHARED2,1,4261.36050935122,University
+SHARED3,1,7029.78579044377,University
+WALK,1,3470.94867299096,University
+BIKE,1,260.15240256449,University
+WALK-TRANSIT,1,4890.927463666941,University
+PNR-TRANSIT,1,0.0,University
+KNR-TRANSIT,1,140.31612912784897,University
+TNC-TRANSIT,1,0.0,University
+TAXI,1,0.0,University
+TNC-REG,1,958.904704155316,University
+TNC-SHARED,1,0.0,University
+SCHOOLBUS,1,0.0,University
+ESCOOTER,1,0.0,University
+EBIKE,1,0.0,University
+All,2,25033.964929633494,University
+DRIVEALONE,2,12179.8260920573,University
+SHARED2,2,3849.26677194999,University
+SHARED3,2,5475.71476978642,University
+WALK,2,3278.56982505264,University
+BIKE,2,76.6340969345775,University
+WALK-TRANSIT,2,19.6102749650194,University
+PNR-TRANSIT,2,230.05570074895874,University
+KNR-TRANSIT,2,0.0,University
+TNC-TRANSIT,2,0.0,University
+TAXI,2,0.0,University
+TNC-REG,2,0.0,University
+TNC-SHARED,2,0.0,University
+SCHOOLBUS,2,0.0,University
+ESCOOTER,2,0.0,University
+EBIKE,2,0.0,University
+All,All,370551.5369245041,School
+DRIVEALONE,All,50096.37525185679,School
+SHARED2,All,84971.08824780851,School
+SHARED3,All,157970.27055362595,School
+WALK,All,50215.19837603348,School
+BIKE,All,18582.42717827229,School
+WALK-TRANSIT,All,2125.28900825939,School
+PNR-TRANSIT,All,0.0,School
+KNR-TRANSIT,All,0.0,School
+TNC-TRANSIT,All,0.0,School
+TAXI,All,0.0,School
+TNC-REG,All,0.0,School
+TNC-SHARED,All,0.0,School
+SCHOOLBUS,All,4582.63342841512,School
+ESCOOTER,All,0.0,School
+EBIKE,All,277.262891367173,School
+All,0,535.7411600331474,School
+DRIVEALONE,0,0.0,School
+SHARED2,0,0.0,School
+SHARED3,0,283.638030645744,School
+WALK,0,40.0723114816791,School
+BIKE,0,0.0,School
+WALK-TRANSIT,0,34.24452988729453,School
+PNR-TRANSIT,0,0.0,School
+KNR-TRANSIT,0,0.0,School
+TNC-TRANSIT,0,0.0,School
+TAXI,0,0.0,School
+TNC-REG,0,0.0,School
+TNC-SHARED,0,0.0,School
+SCHOOLBUS,0,149.895019270037,School
+ESCOOTER,0,0.0,School
+EBIKE,0,0.0,School
+All,1,93492.8280014621,School
+DRIVEALONE,1,7559.11702709989,School
+SHARED2,1,23075.8097766524,School
+SHARED3,1,47209.0185256672,School
+WALK,1,11641.4345244784,School
+BIKE,1,46.9319312323893,School
+WALK-TRANSIT,1,25.25248552134667,School
+PNR-TRANSIT,1,0.0,School
+KNR-TRANSIT,1,0.0,School
+TNC-TRANSIT,1,0.0,School
+TAXI,1,0.0,School
+TNC-REG,1,0.0,School
+TNC-SHARED,1,0.0,School
+SCHOOLBUS,1,3914.69624565127,School
+ESCOOTER,1,0.0,School
+EBIKE,1,0.0,School
+All,2,276522.96776300884,School
+DRIVEALONE,2,42537.2582247569,School
+SHARED2,2,61895.2784711561,School
+SHARED3,2,110477.613997313,School
+WALK,2,38533.6915400734,School
+BIKE,2,18535.4952470399,School
+WALK-TRANSIT,2,2065.7919928507486,School
+PNR-TRANSIT,2,0.0,School
+KNR-TRANSIT,2,0.0,School
+TNC-TRANSIT,2,0.0,School
+TAXI,2,0.0,School
+TNC-REG,2,0.0,School
+TNC-SHARED,2,0.0,School
+SCHOOLBUS,2,518.042163493812,School
+ESCOOTER,2,0.0,School
+EBIKE,2,277.262891367173,School
+All,All,1619658.1682006032,Ind-Maintenance
+DRIVEALONE,All,602926.3844597035,Ind-Maintenance
+SHARED2,All,392555.5930384987,Ind-Maintenance
+SHARED3,All,410840.3465459006,Ind-Maintenance
+WALK,All,140166.4102470719,Ind-Maintenance
+BIKE,All,18776.318625145806,Ind-Maintenance
+WALK-TRANSIT,All,12707.897320229486,Ind-Maintenance
+PNR-TRANSIT,All,1914.342883392162,Ind-Maintenance
+KNR-TRANSIT,All,8936.193905360975,Ind-Maintenance
+TNC-TRANSIT,All,169.2353156176017,Ind-Maintenance
+TAXI,All,1947.2792086913,Ind-Maintenance
+TNC-REG,All,8744.755247976791,Ind-Maintenance
+TNC-SHARED,All,0.0,Ind-Maintenance
+SCHOOLBUS,All,0.0,Ind-Maintenance
+ESCOOTER,All,288.741959382586,Ind-Maintenance
+EBIKE,All,2103.474931681188,Ind-Maintenance
+All,0,37847.372398730695,Ind-Maintenance
+DRIVEALONE,0,2730.47102330347,Ind-Maintenance
+SHARED2,0,1787.56186559976,Ind-Maintenance
+SHARED3,0,599.391278527622,Ind-Maintenance
+WALK,0,10136.1543774924,Ind-Maintenance
+BIKE,0,0.0,Ind-Maintenance
+WALK-TRANSIT,0,7796.769471218247,Ind-Maintenance
+PNR-TRANSIT,0,83.91054305011743,Ind-Maintenance
+KNR-TRANSIT,0,3250.6009941330963,Ind-Maintenance
+TNC-TRANSIT,0,56.3385987955167,Ind-Maintenance
+TAXI,0,320.276190548646,Ind-Maintenance
+TNC-REG,0,2764.94926723099,Ind-Maintenance
+TNC-SHARED,0,0.0,Ind-Maintenance
+SCHOOLBUS,0,0.0,Ind-Maintenance
+ESCOOTER,0,288.741959382586,Ind-Maintenance
+EBIKE,0,359.797652502899,Ind-Maintenance
+All,1,470929.0903270772,Ind-Maintenance
+DRIVEALONE,1,144894.419918633,Ind-Maintenance
+SHARED2,1,115779.244494685,Ind-Maintenance
+SHARED3,1,141222.355572469,Ind-Maintenance
+WALK,1,42409.8929031677,Ind-Maintenance
+BIKE,1,483.170513427805,Ind-Maintenance
+WALK-TRANSIT,1,4181.6411678993245,Ind-Maintenance
+PNR-TRANSIT,1,1513.8866505603437,Ind-Maintenance
+KNR-TRANSIT,1,5660.150693979755,Ind-Maintenance
+TNC-TRANSIT,1,112.896716822085,Ind-Maintenance
+TAXI,1,1627.003018142654,Ind-Maintenance
+TNC-REG,1,3561.47541340738,Ind-Maintenance
+TNC-SHARED,1,0.0,Ind-Maintenance
+SCHOOLBUS,1,0.0,Ind-Maintenance
+ESCOOTER,1,0.0,Ind-Maintenance
+EBIKE,1,42.7448851482284,Ind-Maintenance
+All,2,1110881.7054747953,Ind-Maintenance
+DRIVEALONE,2,455301.493517767,Ind-Maintenance
+SHARED2,2,274988.786678214,Ind-Maintenance
+SHARED3,2,269018.599694904,Ind-Maintenance
+WALK,2,87620.3629664118,Ind-Maintenance
+BIKE,2,18293.148111718,Ind-Maintenance
+WALK-TRANSIT,2,729.4866811119136,Ind-Maintenance
+PNR-TRANSIT,2,316.54568978170056,Ind-Maintenance
+KNR-TRANSIT,2,25.44221724812555,Ind-Maintenance
+TNC-TRANSIT,2,0.0,Ind-Maintenance
+TAXI,2,0.0,Ind-Maintenance
+TNC-REG,2,2418.33056733842,Ind-Maintenance
+TNC-SHARED,2,0.0,Ind-Maintenance
+SCHOOLBUS,2,0.0,Ind-Maintenance
+ESCOOTER,2,0.0,Ind-Maintenance
+EBIKE,2,1700.93239403006,Ind-Maintenance
+All,All,1012237.3506941432,Ind-Discretionary
+DRIVEALONE,All,408676.4993708737,Ind-Discretionary
+SHARED2,All,148304.09424719686,Ind-Discretionary
+SHARED3,All,172053.13483339292,Ind-Discretionary
+WALK,All,209989.80779105332,Ind-Discretionary
+BIKE,All,14354.853949815271,Ind-Discretionary
+WALK-TRANSIT,All,26436.045315868825,Ind-Discretionary
+PNR-TRANSIT,All,576.020687688831,Ind-Discretionary
+KNR-TRANSIT,All,383.5417289542265,Ind-Discretionary
+TNC-TRANSIT,All,0.0,Ind-Discretionary
+TAXI,All,1113.06422541153,Ind-Discretionary
+TNC-REG,All,1701.074617326277,Ind-Discretionary
+TNC-SHARED,All,0.0,Ind-Discretionary
+SCHOOLBUS,All,0.0,Ind-Discretionary
+ESCOOTER,All,1499.4216282953505,Ind-Discretionary
+EBIKE,All,1386.8466477160664,Ind-Discretionary
+All,0,50060.78320702102,Ind-Discretionary
+DRIVEALONE,0,10584.5536337407,Ind-Discretionary
+SHARED2,0,2432.63826989888,Ind-Discretionary
+SHARED3,0,150.656742365532,Ind-Discretionary
+WALK,0,21176.9865947851,Ind-Discretionary
+BIKE,0,84.3320154626326,Ind-Discretionary
+WALK-TRANSIT,0,8215.693454477043,Ind-Discretionary
+PNR-TRANSIT,0,0.0,Ind-Discretionary
+KNR-TRANSIT,0,209.53873510495376,Ind-Discretionary
+TNC-TRANSIT,0,0.0,Ind-Discretionary
+TAXI,0,0.0,Ind-Discretionary
+TNC-REG,0,354.848454112143,Ind-Discretionary
+TNC-SHARED,0,0.0,Ind-Discretionary
+SCHOOLBUS,0,0.0,Ind-Discretionary
+ESCOOTER,0,92.8936559139734,Ind-Discretionary
+EBIKE,0,62.3742416094516,Ind-Discretionary
+All,1,312637.6802063424,Ind-Discretionary
+DRIVEALONE,1,113420.316917965,Ind-Discretionary
+SHARED2,1,72485.063962059,Ind-Discretionary
+SHARED3,1,57075.9170093754,Ind-Discretionary
+WALK,1,31915.3426628573,Ind-Discretionary
+BIKE,1,6228.44037643549,Ind-Discretionary
+WALK-TRANSIT,1,12557.375578515552,Ind-Discretionary
+PNR-TRANSIT,1,0.0,Ind-Discretionary
+KNR-TRANSIT,1,36.56895927959254,Ind-Discretionary
+TNC-TRANSIT,1,0.0,Ind-Discretionary
+TAXI,1,1113.06422541153,Ind-Discretionary
+TNC-REG,1,1096.10332752668,Ind-Discretionary
+TNC-SHARED,1,0.0,Ind-Discretionary
+SCHOOLBUS,1,0.0,Ind-Discretionary
+ESCOOTER,1,1268.42376401554,Ind-Discretionary
+EBIKE,1,760.316535924521,Ind-Discretionary
+All,2,649538.8872807798,Ind-Discretionary
+DRIVEALONE,2,284671.628819168,Ind-Discretionary
+SHARED2,2,73386.392015239,Ind-Discretionary
+SHARED3,2,114826.561081652,Ind-Discretionary
+WALK,2,156897.478533411,Ind-Discretionary
+BIKE,2,8042.08155791715,Ind-Discretionary
+WALK-TRANSIT,2,5662.976282876228,Ind-Discretionary
+PNR-TRANSIT,2,576.020687688831,Ind-Discretionary
+KNR-TRANSIT,2,137.43403456968022,Ind-Discretionary
+TNC-TRANSIT,2,0.0,Ind-Discretionary
+TAXI,2,0.0,Ind-Discretionary
+TNC-REG,2,250.122835687454,Ind-Discretionary
+TNC-SHARED,2,0.0,Ind-Discretionary
+SCHOOLBUS,2,0.0,Ind-Discretionary
+ESCOOTER,2,138.104208365837,Ind-Discretionary
+EBIKE,2,564.155870182094,Ind-Discretionary
+All,All,141862.9803750819,Joint-Maintenance
+DRIVEALONE,All,0.0,Joint-Maintenance
+SHARED2,All,92437.460474125,Joint-Maintenance
+SHARED3,All,47304.1684960004,Joint-Maintenance
+WALK,All,1201.476447211716,Joint-Maintenance
+BIKE,All,857.286224187774,Joint-Maintenance
+WALK-TRANSIT,All,6.974415892381714,Joint-Maintenance
+PNR-TRANSIT,All,0.0,Joint-Maintenance
+KNR-TRANSIT,All,0.0,Joint-Maintenance
+TNC-TRANSIT,All,0.0,Joint-Maintenance
+TAXI,All,0.0,Joint-Maintenance
+TNC-REG,All,0.0,Joint-Maintenance
+TNC-SHARED,All,0.0,Joint-Maintenance
+SCHOOLBUS,All,0.0,Joint-Maintenance
+ESCOOTER,All,0.0,Joint-Maintenance
+EBIKE,All,49.9338393909406,Joint-Maintenance
+All,0,0.0,Joint-Maintenance
+DRIVEALONE,0,0.0,Joint-Maintenance
+SHARED2,0,0.0,Joint-Maintenance
+SHARED3,0,0.0,Joint-Maintenance
+WALK,0,0.0,Joint-Maintenance
+BIKE,0,0.0,Joint-Maintenance
+WALK-TRANSIT,0,0.0,Joint-Maintenance
+PNR-TRANSIT,0,0.0,Joint-Maintenance
+KNR-TRANSIT,0,0.0,Joint-Maintenance
+TNC-TRANSIT,0,0.0,Joint-Maintenance
+TAXI,0,0.0,Joint-Maintenance
+TNC-REG,0,0.0,Joint-Maintenance
+TNC-SHARED,0,0.0,Joint-Maintenance
+SCHOOLBUS,0,0.0,Joint-Maintenance
+ESCOOTER,0,0.0,Joint-Maintenance
+EBIKE,0,0.0,Joint-Maintenance
+All,1,48413.0711207117,Joint-Maintenance
+DRIVEALONE,1,0.0,Joint-Maintenance
+SHARED2,1,31160.76046473,Joint-Maintenance
+SHARED3,1,15778.2085318302,Joint-Maintenance
+WALK,1,604.161005797653,Joint-Maintenance
+BIKE,1,857.286224187774,Joint-Maintenance
+WALK-TRANSIT,1,6.974415892381714,Joint-Maintenance
+PNR-TRANSIT,1,0.0,Joint-Maintenance
+KNR-TRANSIT,1,0.0,Joint-Maintenance
+TNC-TRANSIT,1,0.0,Joint-Maintenance
+TAXI,1,0.0,Joint-Maintenance
+TNC-REG,1,0.0,Joint-Maintenance
+TNC-SHARED,1,0.0,Joint-Maintenance
+SCHOOLBUS,1,0.0,Joint-Maintenance
+ESCOOTER,1,0.0,Joint-Maintenance
+EBIKE,1,0.0,Joint-Maintenance
+All,2,93449.9092543702,Joint-Maintenance
+DRIVEALONE,2,0.0,Joint-Maintenance
+SHARED2,2,61276.700009395,Joint-Maintenance
+SHARED3,2,31525.9599641702,Joint-Maintenance
+WALK,2,597.315441414063,Joint-Maintenance
+BIKE,2,0.0,Joint-Maintenance
+WALK-TRANSIT,2,0.0,Joint-Maintenance
+PNR-TRANSIT,2,0.0,Joint-Maintenance
+KNR-TRANSIT,2,0.0,Joint-Maintenance
+TNC-TRANSIT,2,0.0,Joint-Maintenance
+TAXI,2,0.0,Joint-Maintenance
+TNC-REG,2,0.0,Joint-Maintenance
+TNC-SHARED,2,0.0,Joint-Maintenance
+SCHOOLBUS,2,0.0,Joint-Maintenance
+ESCOOTER,2,0.0,Joint-Maintenance
+EBIKE,2,49.9338393909406,Joint-Maintenance
+All,All,203982.6431771227,Joint-Discretionary
+DRIVEALONE,All,0.0,Joint-Discretionary
+SHARED2,All,114473.0686075304,Joint-Discretionary
+SHARED3,All,43811.2101885028,Joint-Discretionary
+WALK,All,45235.84961475972,Joint-Discretionary
+BIKE,All,239.3403289589178,Joint-Discretionary
+WALK-TRANSIT,All,0.0,Joint-Discretionary
+PNR-TRANSIT,All,0.0,Joint-Discretionary
+KNR-TRANSIT,All,0.0,Joint-Discretionary
+TNC-TRANSIT,All,0.0,Joint-Discretionary
+TAXI,All,0.0,Joint-Discretionary
+TNC-REG,All,0.0,Joint-Discretionary
+TNC-SHARED,All,0.0,Joint-Discretionary
+SCHOOLBUS,All,0.0,Joint-Discretionary
+ESCOOTER,All,0.0,Joint-Discretionary
+EBIKE,All,223.17443737088,Joint-Discretionary
+All,0,556.5208837134279,Joint-Discretionary
+DRIVEALONE,0,0.0,Joint-Discretionary
+SHARED2,0,494.477809705399,Joint-Discretionary
+SHARED3,0,0.0,Joint-Discretionary
+WALK,0,62.0430740080289,Joint-Discretionary
+BIKE,0,0.0,Joint-Discretionary
+WALK-TRANSIT,0,0.0,Joint-Discretionary
+PNR-TRANSIT,0,0.0,Joint-Discretionary
+KNR-TRANSIT,0,0.0,Joint-Discretionary
+TNC-TRANSIT,0,0.0,Joint-Discretionary
+TAXI,0,0.0,Joint-Discretionary
+TNC-REG,0,0.0,Joint-Discretionary
+TNC-SHARED,0,0.0,Joint-Discretionary
+SCHOOLBUS,0,0.0,Joint-Discretionary
+ESCOOTER,0,0.0,Joint-Discretionary
+EBIKE,0,0.0,Joint-Discretionary
+All,1,66248.99621834104,Joint-Discretionary
+DRIVEALONE,1,0.0,Joint-Discretionary
+SHARED2,1,45400.6397549459,Joint-Discretionary
+SHARED3,1,12214.0317464235,Joint-Discretionary
+WALK,1,8535.26485878479,Joint-Discretionary
+BIKE,1,99.0598581868538,Joint-Discretionary
+WALK-TRANSIT,1,0.0,Joint-Discretionary
+PNR-TRANSIT,1,0.0,Joint-Discretionary
+KNR-TRANSIT,1,0.0,Joint-Discretionary
+TNC-TRANSIT,1,0.0,Joint-Discretionary
+TAXI,1,0.0,Joint-Discretionary
+TNC-REG,1,0.0,Joint-Discretionary
+TNC-SHARED,1,0.0,Joint-Discretionary
+SCHOOLBUS,1,0.0,Joint-Discretionary
+ESCOOTER,1,0.0,Joint-Discretionary
+EBIKE,1,0.0,Joint-Discretionary
+All,2,137177.12607506823,Joint-Discretionary
+DRIVEALONE,2,0.0,Joint-Discretionary
+SHARED2,2,68577.9510428791,Joint-Discretionary
+SHARED3,2,31597.1784420793,Joint-Discretionary
+WALK,2,36638.5416819669,Joint-Discretionary
+BIKE,2,140.280470772064,Joint-Discretionary
+WALK-TRANSIT,2,0.0,Joint-Discretionary
+PNR-TRANSIT,2,0.0,Joint-Discretionary
+KNR-TRANSIT,2,0.0,Joint-Discretionary
+TNC-TRANSIT,2,0.0,Joint-Discretionary
+TAXI,2,0.0,Joint-Discretionary
+TNC-REG,2,0.0,Joint-Discretionary
+TNC-SHARED,2,0.0,Joint-Discretionary
+SCHOOLBUS,2,0.0,Joint-Discretionary
+ESCOOTER,2,0.0,Joint-Discretionary
+EBIKE,2,223.17443737088,Joint-Discretionary
+All,All,114790.43582928032,Work sub-tour
+DRIVEALONE,All,63827.54313706136,Work sub-tour
+SHARED2,All,11849.736942712909,Work sub-tour
+SHARED3,All,5745.7853967445,Work sub-tour
+WALK,All,31685.56728481248,Work sub-tour
+BIKE,All,1388.5761205314982,Work sub-tour
+WALK-TRANSIT,All,39.573546798112915,Work sub-tour
+PNR-TRANSIT,All,0.0,Work sub-tour
+KNR-TRANSIT,All,0.0,Work sub-tour
+TNC-TRANSIT,All,0.0,Work sub-tour
+TAXI,All,0.0,Work sub-tour
+TNC-REG,All,0.0,Work sub-tour
+TNC-SHARED,All,0.0,Work sub-tour
+SCHOOLBUS,All,0.0,Work sub-tour
+ESCOOTER,All,0.0,Work sub-tour
+EBIKE,All,221.4217877836,Work sub-tour
+All,0,460.40054729163694,Work sub-tour
+DRIVEALONE,0,44.8883499012554,Work sub-tour
+SHARED2,0,0.0,Work sub-tour
+SHARED3,0,0.0,Work sub-tour
+WALK,0,343.70703775641,Work sub-tour
+BIKE,0,0.0,Work sub-tour
+WALK-TRANSIT,0,39.573546798112915,Work sub-tour
+PNR-TRANSIT,0,0.0,Work sub-tour
+KNR-TRANSIT,0,0.0,Work sub-tour
+TNC-TRANSIT,0,0.0,Work sub-tour
+TAXI,0,0.0,Work sub-tour
+TNC-REG,0,0.0,Work sub-tour
+TNC-SHARED,0,0.0,Work sub-tour
+SCHOOLBUS,0,0.0,Work sub-tour
+ESCOOTER,0,0.0,Work sub-tour
+EBIKE,0,0.0,Work sub-tour
+All,1,35396.78621606146,Work sub-tour
+DRIVEALONE,1,26390.1766471709,Work sub-tour
+SHARED2,1,6983.50642153205,Work sub-tour
+SHARED3,1,1540.95247287085,Work sub-tour
+WALK,1,472.507749578968,Work sub-tour
+BIKE,1,9.64292490868824,Work sub-tour
+WALK-TRANSIT,1,0.0,Work sub-tour
+PNR-TRANSIT,1,0.0,Work sub-tour
+KNR-TRANSIT,1,0.0,Work sub-tour
+TNC-TRANSIT,1,0.0,Work sub-tour
+TAXI,1,0.0,Work sub-tour
+TNC-REG,1,0.0,Work sub-tour
+TNC-SHARED,1,0.0,Work sub-tour
+SCHOOLBUS,1,0.0,Work sub-tour
+ESCOOTER,1,0.0,Work sub-tour
+EBIKE,1,0.0,Work sub-tour
+All,2,78933.24906592722,Work sub-tour
+DRIVEALONE,2,37392.4781399892,Work sub-tour
+SHARED2,2,4866.23052118086,Work sub-tour
+SHARED3,2,4204.83292387365,Work sub-tour
+WALK,2,30869.3524974771,Work sub-tour
+BIKE,2,1378.93319562281,Work sub-tour
+WALK-TRANSIT,2,0.0,Work sub-tour
+PNR-TRANSIT,2,0.0,Work sub-tour
+KNR-TRANSIT,2,0.0,Work sub-tour
+TNC-TRANSIT,2,0.0,Work sub-tour
+TAXI,2,0.0,Work sub-tour
+TNC-REG,2,0.0,Work sub-tour
+TNC-SHARED,2,0.0,Work sub-tour
+SCHOOLBUS,2,0.0,Work sub-tour
+ESCOOTER,2,0.0,Work sub-tour
+EBIKE,2,221.4217877836,Work sub-tour
diff --git a/src/asim/calibration/resident/trip_mode_choice/scripts/asim_trip_calib_util.py b/src/asim/calibration/resident/trip_mode_choice/scripts/asim_trip_calib_util.py
new file mode 100644
index 000000000..969c5d7db
--- /dev/null
+++ b/src/asim/calibration/resident/trip_mode_choice/scripts/asim_trip_calib_util.py
@@ -0,0 +1,1339 @@
+import pandas as pd
+import numpy as np
+import os
+import shutil
+import time
+import datetime
+from IPython.display import display
+import matplotlib
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set()
+
+# --------------------------------------------------------------------------------------------------
+# dictionary mappings
+total_keyword = 'All'
+output_calibration_trip_purposes = [
+    'work',
+    'univ',
+    'school',
+    'atwork',
+    'ind_maint',
+    'ind_discr',
+    'joint',
+    total_keyword  # last entry should be for all purposes
+]
+
+output_calibration_tour_modes = [
+    'DRIVEALONE',
+    'SHARED2',
+    'SHARED3',
+    'WALK',
+    'BIKE',
+    'ESCOOTER',
+    'EBIKE',
+    'WALK-TRANSIT',
+    'PNR-TRANSIT',
+    'KNR-TRANSIT',
+    'TNC-TRANSIT',
+    'SCHOOLBUS',
+    'RIDE-HAIL',    
+    total_keyword]
+
+output_calibration_trip_modes = [
+    'DRIVEALONE',
+    'SHARED2',
+    'SHARED3',
+    'WALK',
+    'BIKE',
+    'ESCOOTER',
+    'EBIKE',
+    'WALK-TRANSIT',
+    'PNR-TRANSIT',
+    'KNR-TRANSIT',
+    'TNC-TRANSIT',
+    'SCHOOLBUS',
+    'TNC_SINGLE',
+    'TNC_SHARED',
+    'TAXI',    
+    total_keyword]
+
+# transit calibraiton modes are not scaled when comparing to model
+output_calibration_transit_modes = [
+    'WALK-TRANSIT',
+    'PNR-TRANSIT',
+    'KNR-TRANSIT',
+    'TNC-TRANSIT' ### ASK DAVID ALI
+]
+
+# # used to switch drivealone mode to walk transit for people parked at major university
+# output_calibration_auto_modes = [
+#     'DRIVEALONE',
+#     'SHARED2',
+#     'SHARED3',
+# ]
+# parked_at_univ_calib_mode = 'WALK-TRANSIT'
+
+# should match the mode choice coefficient names
+output_auto_suff = [
+    '0',
+    '1',
+    '2'
+] 
+
+# Mapping calibration mode to trip mode choice coefficient names
+output_calibration_tour_mode_to_coef_name_dict = {
+    'DRIVEALONE': 'da',
+    'SHARED2': 's2',
+    'SHARED3': 's3',
+    'WALK': 'walk',
+    'BIKE': 'bike',
+    'ESCOOTER': 'escooter',
+    'EBIKE': 'ebike',
+    'WALK-TRANSIT': 'wtran',
+    'PNR-TRANSIT': 'pnr',
+    'KNR-TRANSIT': 'knr',
+    'TNC-TRANSIT': 'tnr',
+    'SCHOOLBUS': 'schbus',
+    'RIDE-HAIL': 'maas',
+    total_keyword: 'all',
+}
+
+output_calibration_trip_mode_to_coef_name_dict = {
+    'DRIVEALONE': 'DRIVEALONE',
+    'SHARED2': 'SHARED2',
+    'SHARED3': 'SHARED3',
+    'WALK': 'WALK',
+    'BIKE': 'BIKE',
+    'ESCOOTER': 'ESCOOTER',
+    'EBIKE': 'EBIKE',
+    'WALK-TRANSIT': 'WALK_TRANSIT',
+    'PNR-TRANSIT': 'PNR_TRANSIT',
+    'KNR-TRANSIT': 'KNR_TRANSIT',
+    'TNC-TRANSIT': 'TNC_TRANSIT',
+    'SCHOOLBUS': 'SCH_BUS',
+    'TNC_SINGLE': 'TNC_SINGLE',
+    'TNC_SHARED': 'TNC_SHARED',
+    'TAXI': 'TAXI',
+    total_keyword: 'all',
+}
+
+output_calibration_purposes_to_coef_names_dict = {
+    'work': ['work'],
+    'univ': ['univ'],
+    'school': ['school'],
+    'atwork': ['atwork'],
+    # 'ind_maint': ['shopping', 'escort', 'othmaint'], ### DELETE
+    # 'ind_discr': ['social', 'eatout', 'othdiscr'], ### DELETE
+    'ind_maint': ['maint'],
+    'ind_discr': ['disc'],
+    'joint': ['maint', 'disc'], 
+    # 'joint': ['joint'],  # placeholder ### DELETE
+    # 'joint': ['joint'],  # placeholder ### DELETE
+    total_keyword: ['all']
+}
+
+coef_name_to_output_calibration_purpose_dict = {
+    'work': 'work',
+    'univ': 'univ',
+    'school': 'school',
+    'atwork': 'atwork',
+    'shopping': 'ind_maint',  # individual split to joint in code
+    'escort': 'ind_maint',
+    'othmaint': 'ind_maint',
+    'social': 'ind_discr',
+    'eatout': 'ind_discr',
+    'othdiscr': 'ind_discr',
+    'joint': 'joint',
+    'all': total_keyword  # last entry should be for all purposes
+}
+
+# ------------------------------------------
+# Mapping activitysim to calibration definitions
+asim_to_calib_purpose_dict = {
+    'work': 'work',
+    'univ': 'univ',
+    'school': 'school',
+    'shopping': 'ind_maint',  # individual split to joint in code
+    'escort': 'ind_maint',
+    'othmaint': 'ind_maint',
+    'social': 'ind_discr',
+    'eatout': 'ind_discr',
+    'othdiscr': 'ind_discr',
+    'atwork': 'atwork',
+    'eat': 'atwork',
+    'maint': 'atwork',
+    'business': 'atwork',
+    'all': total_keyword
+}
+
+asim_to_calib_trip_mode_dict = {
+    'DRIVEALONE': 'DRIVEALONE',
+    'SHARED2': 'SHARED2',
+    'SHARED3': 'SHARED3',
+    'WALK': 'WALK',
+    'BIKE': 'BIKE',
+    'ESCOOTER': 'ESCOOTER',
+    'EBIKE': 'EBIKE',
+    'WALK_LOC': 'WALK-TRANSIT',
+    'WALK_PRM': 'WALK-TRANSIT',
+    'WALK_MIX': 'WALK-TRANSIT',
+    'PNR_LOC': 'PNR-TRANSIT',
+    'PNR_PRM': 'PNR-TRANSIT',
+    'PNR_MIX': 'PNR-TRANSIT',
+    'KNR_LOC': 'KNR-TRANSIT',
+    'KNR_PRM': 'KNR-TRANSIT',
+    'KNR_MIX': 'KNR-TRANSIT',
+    'TNC_LOC': 'TNC-TRANSIT',
+    'TNC_PRM': 'TNC-TRANSIT',
+    'TNC_MIX': 'TNC-TRANSIT',
+    'TAXI': 'TAXI',
+    'TNC_SINGLE': 'TNC_SINGLE',
+    'TNC_SHARED': 'TNC_SHARED',
+    'SCH_BUS': 'SCHOOLBUS'
+}
+
+# added for trip tables
+asim_to_calib_tour_mode_dict = {
+    'DRIVEALONE': 'DRIVEALONE',
+    'SHARED2': 'SHARED2',
+    'SHARED3': 'SHARED3',
+    'WALK': 'WALK',
+    'BIKE': 'BIKE',
+    'ESCOOTER': 'ESCOOTER',
+    'EBIKE': 'EBIKE',
+    'WALK_LOC': 'WALK-TRANSIT',
+    'WALK_PRM': 'WALK-TRANSIT',
+    'WALK_MIX': 'WALK-TRANSIT',
+    'PNR_LOC': 'PNR-TRANSIT',
+    'PNR_PRM': 'PNR-TRANSIT',
+    'PNR_MIX': 'PNR-TRANSIT',
+    'KNR_LOC': 'KNR-TRANSIT',
+    'KNR_PRM': 'KNR-TRANSIT',
+    'KNR_MIX': 'KNR-TRANSIT',
+    'TNC_LOC': 'TNC-TRANSIT',
+    'TNC_PRM': 'TNC-TRANSIT',
+    'TNC_MIX': 'TNC-TRANSIT',
+    'TAXI': 'RIDE-HAIL',
+    'TNC_SINGLE': 'RIDE-HAIL',
+    'TNC_SHARED': 'RIDE-HAIL',
+    'SCH_BUS': 'SCHOOLBUS'
+}
+
+asim_auto_suff_dict = {
+    0: output_auto_suff[0],
+    1: output_auto_suff[1],
+    2: output_auto_suff[2],
+}
+
+# ------------------------------------------
+# Mapping Survey targets to calibration definitions
+survey_to_calib_purposes_dict = {
+    'Work': 'work',
+    'University': 'univ',
+    'School': 'school',
+    'Work sub-tour': 'atwork',
+    'Ind-Maintenance': 'ind_maint',
+    'Ind-Discretionary': 'ind_discr',
+    'Joint-Maintenance': 'joint',
+    'Joint-Discretionary': 'joint',
+    'Total': total_keyword
+}
+
+survey_to_calib_trip_mode_dict = {
+    'DRIVEALONE': 'DRIVEALONE', 
+    'SHARED2': 'SHARED2', 
+    'SHARED3': 'SHARED3', 
+    'WALK': 'WALK', 
+    'BIKE': 'BIKE',
+    'ESCOOTER': 'ESCOOTER',
+    'EBIKE': 'EBIKE',
+    'WALK-TRANSIT': 'WALK-TRANSIT', 
+    'PNR-TRANSIT': 'PNR-TRANSIT', 
+    'KNR-TRANSIT': 'KNR-TRANSIT', 
+    'TNC-TRANSIT': 'TNC-TRANSIT',
+    'TAXI': 'TAXI', 
+    'TNC-REG': 'TNC_SINGLE', 
+    'TNC-SHARED': 'TNC_SHARED', 
+    'SCHOOLBUS': 'SCHOOLBUS',
+    'All': total_keyword
+}
+
+survey_to_calib_tour_mode_dict = {
+    'DRIVEALONE': 'DRIVEALONE', 
+    'SHARED2': 'SHARED2', 
+    'SHARED3': 'SHARED3', 
+    'WALK': 'WALK', 
+    'BIKE': 'BIKE',
+    'ESCOOTER': 'ESCOOTER',
+    'EBIKE': 'EBIKE',
+    'WALK-TRANSIT': 'WALK-TRANSIT', 
+    'PNR-TRANSIT': 'PNR-TRANSIT', 
+    'KNR-TRANSIT': 'KNR-TRANSIT', 
+    'TNC-TRANSIT': 'TNC-TRANSIT',
+    'TAXI': 'RIDE-HAIL', 
+    'TNC-REG': 'RIDE-HAIL', 
+    'TNC-SHARED': 'RIDE-HAIL', 
+    'SCHOOLBUS': 'SCHOOLBUS',
+    'All': total_keyword
+}
+
+survey_calib_target_auto_suff_dict = {
+    '0': output_auto_suff[0],
+    '1': output_auto_suff[1],
+    '2': output_auto_suff[2],
+}
+
+survey_table_purpose_col = 'purpose'
+survey_table_trip_mode_col = 'grouped_linked_trip_mode'
+# added for trip mc calibration
+survey_table_tour_mode_col = 'grouped_tour_mode'
+# not needed for trip mc calibration
+#survey_table_auto_suff_col = 'auto_suff'
+survey_table_trips_col = 'trips'
+
+# HTML visualizer dictionaries.  needs to match HTS summary script.
+#  need to be checked only if the scaled calibration targets want to be included in the visualizer
+calib_trip_mode_to_vis_dict = { #tripmode vis
+    # calibration_trip_mode: visualizer_trip_mode
+    'DRIVEALONE': 1,
+    'SHARED2': 2,
+    'SHARED3': 3,
+    'WALK': 4,
+    'BIKE': 5,
+    'WALK-TRANSIT': 6,
+    'PNR-TRANSIT': 7,
+    'KNR-TRANSIT': 8,
+    'TNC-TRANSIT': 9,
+    'TAXI': 10,
+    'TNC_SINGLE': 11,
+    'TNC_SHARED':12,
+    'SCHOOLBUS': 13,
+    'ESCOOTER': 14, ### FIX_IT: The number for ESCOOTER and EBIKE are temporary, need to decide and finalize them later
+    'EBIKE': 15,
+    total_keyword: 'Total'
+}
+calib_tourmode_num_to_vis_dict = { #tourmode vis
+    # calibration_tour_mode: visualizer_tour_mode
+    'DRIVEALONE': 1,
+    'SHARED2': 2,
+    'SHARED3': 3,
+    'WALK': 4,
+    'BIKE': 5,
+    'WALK-TRANSIT': 6,
+    'PNR-TRANSIT': 7,
+    'KNR-TRANSIT': 8,
+    'TNC-TRANSIT': 9,
+    # 'TAXI': 10,
+    # 'TNC_SINGLE': 11,
+    # 'TNC_SHARED':12,
+    'RIDE-HAIL': 10,
+    'SCHOOLBUS': 11,
+    'ESCOOTER': 12, ### FIX_IT: The number for ESCOOTER and EBIKE are temporary, need to decide and finalize them later
+    'EBIKE': 13,
+    total_keyword: 'Total'
+}
+calib_tour_mode_to_vis_dict = {  #tourmode vis
+    'DRIVEALONE': 'Drivealone',
+    'SHARED2': 'Shared2',
+    'SHARED3': 'Shared3',
+    'WALK': 'Walk',
+    'BIKE': 'Bike',
+    'ESCOOTER': 'Escooter',
+    'EBIKE': 'Ebike',
+    'WALK-TRANSIT': 'Walk-Transit',
+    'PNR-TRANSIT': 'PNR-Transit',
+    'KNR-TRANSIT': 'KNR-Transit',
+    'TNC-TRANSIT': 'TNC-Transit',
+    'SCHOOLBUS': 'SchoolBus',
+    'RIDE-HAIL': 'Ride Hail',
+    'TAXI': 'Ridehail',
+    'TNC-SINGLE': 'Ride Hail',
+    'TNC-SHARED': 'Ride Hail',
+    total_keyword: 'Total'
+}
+purpose_vis_dict = {  # same purposes used for both trip and tour
+    # calibration purpose: visualizer purpose
+    'univ': 'univ',
+    'school': 'sch',
+    'work': 'work',
+    'atwork': 'atwork',
+    'ind_discr': 'idisc',
+    'ind_maint': 'imain',
+    'joint': 'jmain',
+    'jdisc': 'jdisc',  # joint is copied to produce jdisc purpose in the code
+    total_keyword: 'total'
+}
+
+
+# --------------------------------------------------------------------------------------------------
+# Helper functions
+def check_input_dictionaries_for_consistency():
+    # checking trip purposes
+    for purpose in list(asim_to_calib_purpose_dict.values()):
+        assert purpose in output_calibration_trip_purposes, "ActivitySim purpose not in calibration"
+    for purpose in list(survey_to_calib_purposes_dict.values()):
+        assert purpose in output_calibration_trip_purposes, "Survey purpose not in calibration"
+
+    # checking trip mode
+    for mode in list(asim_to_calib_trip_mode_dict.values()):
+        assert mode in output_calibration_trip_modes, f"ActivitySim trip mode {mode} not in calibration"
+    for mode in list(asim_to_calib_tour_mode_dict.values()):
+        assert mode in output_calibration_tour_modes, f"ActivitySim trip mode {mode} not in calibration"
+    for mode in list(survey_to_calib_trip_mode_dict.values()):
+        assert mode in output_calibration_trip_modes, f"Survey trip mode {mode} not in calibration"
+    for mode in list(survey_to_calib_tour_mode_dict.values()):
+        assert mode in output_calibration_tour_modes, f"Survey tour mode {mode} not in calibration"
+
+    print("No problems found in input dictionaries")
+
+
+def write_tables_to_excel(dfs,
+                          excel_writer,
+                          excel_sheet_name,
+                          start_row,
+                          start_col,
+                          title,
+                          sep_for_col_title,
+                          col_title):
+
+    # have to write first table to initialize sheet before writing title
+    dfs[0].to_excel(excel_writer, excel_sheet_name, startrow=start_row+2, startcol=start_col)
+    worksheet = excel_writer.sheets[excel_sheet_name]
+
+    # writing title at and first table name
+    worksheet.write(start_row, start_col, title)
+    worksheet.write(start_row+1, start_col, dfs[0].name)
+    worksheet.write(start_row+1, start_col + sep_for_col_title, col_title)
+    start_row += len(dfs[0]) + 6
+
+    for df in dfs[1:]:
+        df.to_excel(excel_writer, excel_sheet_name, startrow=start_row, startcol=start_col)
+        worksheet.write(start_row-1, start_col, df.name)
+        worksheet.write(start_row-1, start_col + sep_for_col_title, col_title)
+        start_row += len(df) + 4
+    return
+
+
+def map_asim_trip_table_to_calib(trips_df):
+    trips_df['calib_tour_purpose'] = trips_df['primary_purpose'].apply(
+        lambda x: asim_to_calib_purpose_dict[x])
+
+    # added modification for trips mc calibration
+    trips_df.loc[(trips_df['calib_tour_purpose'] == 'ind_maint')
+                 & (trips_df['tour_category'] == 'joint'), 'calib_tour_purpose'] = 'joint'
+    trips_df.loc[(trips_df['calib_tour_purpose'] == 'ind_discr')
+                 & (trips_df['tour_category'] == 'joint'), 'calib_tour_purpose'] = 'joint'
+
+    trips_df['calib_trip_mode'] = trips_df['trip_mode'].apply(
+        lambda x: asim_to_calib_trip_mode_dict[x])
+
+    trips_df['calib_tour_mode'] = trips_df['tour_mode'].apply(
+        lambda x: asim_to_calib_tour_mode_dict[x])
+
+    ## if parked at university, act as if tour mode is walk-transit
+    #trips_df.loc[(trips_df['parked_at_university'] == True)
+    #            & trips_df['tour_mode'].isin(output_calibration_auto_modes),
+    #            'calib_tour_mode'] = parked_at_univ_calib_mode
+
+    return trips_df
+
+
+def create_asim_trip_mode_choice_tables(trips_df):
+    asim_trip_mode_purpose_cts = []
+
+    columns = list(output_calibration_tour_modes)
+
+    for trip_purpose in output_calibration_trip_purposes[:-1]:
+        asim_trip_mode_purpose_ct = pd.crosstab(
+            trips_df['calib_trip_mode'],
+            trips_df[trips_df['calib_tour_purpose'] == trip_purpose]['calib_tour_mode'],
+            margins=True,
+            margins_name=total_keyword,
+            dropna=False
+        )
+        asim_trip_mode_purpose_ct = asim_trip_mode_purpose_ct.reindex(
+            index=output_calibration_trip_modes, columns=columns, fill_value=0)
+        asim_trip_mode_purpose_ct.index.name = 'trip_mode'
+        asim_trip_mode_purpose_ct.columns.name = 'tour_mode'
+        # not needed for trip mc calibration? will not rename columns?
+        #asim_trip_mode_purpose_ct.rename(columns=columns, inplace=True)
+        asim_trip_mode_purpose_ct.name = trip_purpose
+        asim_trip_mode_purpose_cts.append(asim_trip_mode_purpose_ct)
+
+    asim_trip_mode_all_ct = pd.crosstab(
+        trips_df['calib_trip_mode'],
+        trips_df['calib_tour_mode'],
+        margins=True,
+        margins_name=total_keyword,
+        dropna=False
+    )
+    asim_trip_mode_all_ct = asim_trip_mode_all_ct.reindex(
+        index=output_calibration_trip_modes, columns=columns, fill_value=0)
+    asim_trip_mode_all_ct.index.name = 'trip_mode'
+    asim_trip_mode_all_ct.columns.name = 'tour_mode'
+    # not needed for trip mc calibration? will not rename columns?
+    #asim_trip_mode_all_ct.rename(columns=asim_auto_suff_dict, inplace=True)
+    asim_trip_mode_all_ct.name = output_calibration_trip_purposes[-1]
+    asim_trip_mode_purpose_cts.append(asim_trip_mode_all_ct)
+    return asim_trip_mode_purpose_cts
+
+
+def process_asim_tables_for_trip_mode_choice(asim_tables_dir):
+    households_df = pd.read_csv(
+        os.path.join(asim_tables_dir, 'final_households.csv'), low_memory=False)
+    sample_rate = households_df['sample_rate'].max()
+    trips_df = pd.read_csv(os.path.join(asim_tables_dir, 'final_trips.csv'), low_memory=False)
+    tours_df = pd.read_csv(os.path.join(asim_tables_dir, 'final_tours.csv'), low_memory=False)
+
+    trips_df = pd.merge(
+        trips_df,
+        tours_df[['tour_id', 'tour_mode', 'tour_category', 'number_of_participants']],
+        how='left',
+        on='tour_id'
+    )
+
+    trips_df = map_asim_trip_table_to_calib(trips_df)
+    asim_trip_mode_tables = create_asim_trip_mode_choice_tables(trips_df)
+
+    total_model_trips = len(trips_df)
+    num_trips_full_model = int(total_model_trips / sample_rate)
+    print("Sample rate of ", sample_rate, "results in ", total_model_trips, "out of",
+          num_trips_full_model, "tours")
+
+    return asim_trip_mode_tables, num_trips_full_model, trips_df
+
+
+def read_trip_mode_choice_calibration_file(trip_mode_choice_calib_targets_file):
+    trip_mc_calib_df = pd.read_csv(trip_mode_choice_calib_targets_file)
+    trip_mc_calib_df['calib_trip_mode'] = trip_mc_calib_df[survey_table_trip_mode_col].apply(
+        lambda x: survey_to_calib_trip_mode_dict[x])
+    trip_mc_calib_df['calib_tour_mode'] = trip_mc_calib_df[survey_table_tour_mode_col].apply(
+        lambda x: survey_to_calib_tour_mode_dict[x])
+
+    columns = list(output_calibration_tour_modes)
+
+    trip_mc_calib_target_tables = []
+
+    for purpose in list(survey_to_calib_purposes_dict.keys()):
+        df = trip_mc_calib_df[(trip_mc_calib_df[survey_table_purpose_col] == purpose)
+                              & (trip_mc_calib_df[survey_table_trip_mode_col] != total_keyword)
+                              & (trip_mc_calib_df[survey_table_tour_mode_col] != total_keyword)]
+
+        df_ct = pd.crosstab(
+            df['calib_trip_mode'],
+            df['calib_tour_mode'],
+            values=df[survey_table_trips_col],
+            aggfunc='sum',
+            margins=True,
+            margins_name=total_keyword,
+            dropna=False,
+        )
+        df_ct = df_ct.reindex(index=output_calibration_trip_modes, columns=columns, fill_value=0)
+        df_ct.index.name = 'trip_mode'
+        df_ct.columns.name = 'tour_mode'
+        df_ct.name = survey_to_calib_purposes_dict[purpose]
+
+        # looking to see if calibration purposes need to be combined
+        # e.g. joint = joint_ind + joint_maint
+        for i in range(len(trip_mc_calib_target_tables)):
+            prev_df_ct = trip_mc_calib_target_tables[i]
+            if prev_df_ct.name == df_ct.name:
+                prev_df_ct = prev_df_ct + df_ct
+                prev_df_ct.name = df_ct.name  # reassignment does not preserve name
+                trip_mc_calib_target_tables[i] = prev_df_ct
+                break
+        else:  # only enters if above for loop breaks
+            trip_mc_calib_target_tables.append(df_ct)
+
+    return trip_mc_calib_target_tables
+
+
+def read_trip_mode_choice_constants(configs_dir, coef_file="trip_mode_choice_coefficients.csv"):
+    constants_config_path = os.path.join(configs_dir, coef_file)
+    trip_mc_constants_df = pd.read_csv(constants_config_path)
+    return trip_mc_constants_df
+
+
+def write_scaled_targets_to_excel(unscaled_model_tables,
+                                  unscaled_calib_tables,
+                                  full_model_tables,
+                                  full_calib_tables,
+                                  scaled_model_tables,
+                                  scaled_calib_tables,
+                                  output_dir):
+    excel_writer = pd.ExcelWriter(os.path.join(output_dir, 'scaled_targets.xlsx'))
+
+    start_col = 0
+    # unscaled model
+    write_tables_to_excel(
+        dfs=unscaled_model_tables,
+        excel_writer=excel_writer,
+        excel_sheet_name='trip_mode_choice',
+        start_row=0,
+        start_col=start_col,
+        title='Unscaled Model Trips',
+        sep_for_col_title=3,
+        col_title='Tour Mode'
+    )
+    start_col += 19
+    # unscaled calibration targets
+    write_tables_to_excel(
+        dfs=unscaled_calib_tables,
+        excel_writer=excel_writer,
+        excel_sheet_name='trip_mode_choice',
+        start_row=0,
+        start_col=start_col,
+        title='Unscaled Calibration Trips',
+        sep_for_col_title=3,
+        col_title='Tour Mode'
+    )
+    start_col += 19
+    # full model
+    write_tables_to_excel(
+        dfs=full_model_tables,
+        excel_writer=excel_writer,
+        excel_sheet_name='trip_mode_choice',
+        start_row=0,
+        start_col=start_col,
+        title='Full Model Trips',
+        sep_for_col_title=3,
+        col_title='Tour Mode'
+    )
+    start_col += 19
+    # full calibration targets
+    write_tables_to_excel(
+        dfs=full_calib_tables,
+        excel_writer=excel_writer,
+        excel_sheet_name='trip_mode_choice',
+        start_row=0,
+        start_col=start_col,
+        title='Calibration Trips Matching Model',
+        sep_for_col_title=3,
+        col_title='Tour Mode'
+    )
+    start_col += 19
+    # scaled model
+    write_tables_to_excel(
+        dfs=scaled_model_tables,
+        excel_writer=excel_writer,
+        excel_sheet_name='trip_mode_choice',
+        start_row=0,
+        start_col=start_col,
+        title='Scaled Model Trips',
+        sep_for_col_title=3,
+        col_title='Tour Mode'
+    )
+    start_col += 19
+    # scaled calibration targets
+    write_tables_to_excel(
+        dfs=scaled_calib_tables,
+        excel_writer=excel_writer,
+        excel_sheet_name='trip_mode_choice',
+        start_row=0,
+        start_col=start_col,
+        title='Scaled Calibration Trips',
+        sep_for_col_title=3,
+        col_title='Tour Mode'
+    )
+    start_col += 19
+
+    excel_writer.save()
+    # excel_writer.close()
+
+
+def calculate_share_by_tour_mode(trip_mc_calib_target_tables, asim_trip_mc_tables):
+    '''
+    Calibration target and model counts tables are scaled to represent
+        the distribution of trip modes for each tour mode.
+    This is done by dividing each column by the total number of trips in that column
+        and converting to a percentage.
+    '''
+    assert asim_trip_mc_tables[-1].name == total_keyword, "Last table is not total!"
+
+    scaled_trip_mc_calib_target_tables = []
+    scaled_asim_trip_mc_tables = []
+    total_model_df = None
+    total_calib_df = None
+
+    # making total tables sum of all purposes
+    for i in range(len(trip_mc_calib_target_tables) - 1):
+        model_df = asim_trip_mc_tables[i]
+        calib_target_df = trip_mc_calib_target_tables[i]
+        # total purpose should be the sum of all prev purposes
+        if total_model_df is not None:
+            total_model_df = total_model_df + model_df
+            total_calib_df = total_calib_df + calib_target_df
+        else:
+            total_model_df = model_df
+            total_calib_df = calib_target_df
+
+    # replacing total table as sum of all purposes
+    total_model_df.name = total_keyword
+    total_calib_df.name = total_keyword
+    asim_trip_mc_tables[len(asim_trip_mc_tables) - 1] = total_model_df
+    trip_mc_calib_target_tables[len(trip_mc_calib_target_tables) - 1] = total_calib_df
+
+    # iterating over all purposes
+    for i in range(len(trip_mc_calib_target_tables)):
+        model_df = asim_trip_mc_tables[i]
+        calib_target_df = trip_mc_calib_target_tables[i]
+        assert model_df.name == calib_target_df.name, "Tables do not match!"
+
+        scaled_calib_target_df = calib_target_df.copy()
+        scaled_model_df = model_df.copy()
+
+        # for tour_mode in output_calibration_tour_modes:
+        #     scaled_calib_target_df[tour_mode] = scaled_calib_target_df[tour_mode] / scaled_calib_target_df.loc[total_keyword, tour_mode] * 100
+        #     scaled_model_df[tour_mode] = scaled_model_df[tour_mode] / scaled_model_df.loc[total_keyword, tour_mode] * 100
+        #     scaled_calib_target_df = scaled_calib_target_df.fillna(0)
+        #     scaled_model_df = scaled_model_df.fillna(0)
+
+        scaled_calib_target_df.name = calib_target_df.name
+        scaled_model_df.name = model_df.name
+
+        scaled_trip_mc_calib_target_tables.append(scaled_calib_target_df)
+        scaled_asim_trip_mc_tables.append(scaled_model_df)
+
+    return scaled_trip_mc_calib_target_tables, scaled_asim_trip_mc_tables
+
+
+def scale_targets_to_match_model_by_tour_mode(trip_mc_calib_target_tables,
+                                 asim_trip_mc_tables,
+                                 full_model_trips):
+
+    assert asim_trip_mc_tables[-1].name == total_keyword, "Last table is not total!"
+    total_model_trips = asim_trip_mc_tables[-1].loc[total_keyword, total_keyword]
+    model_scale_factor = full_model_trips / total_model_trips
+
+    scaled_trip_mc_calib_target_tables = []
+    scaled_asim_trip_mc_tables = []
+    total_model_df = None
+    total_calib_df = None
+
+    # iterating over all non-total tables
+    for i in range(len(trip_mc_calib_target_tables) - 1):
+        model_df = asim_trip_mc_tables[i]
+        calib_target_df = trip_mc_calib_target_tables[i]
+        assert model_df.name == calib_target_df.name, "Tables do not match!"
+
+        # counts from model run are scaled to match full model run counts
+        scaled_model_df = model_df * model_scale_factor
+
+        # calibration counts need to match full model run counts, but leave transit trips unscaled
+        scaled_calib_target_df = calib_target_df.copy()
+        no_transit_idxs = ~(scaled_calib_target_df.index.isin(output_calibration_transit_modes))
+
+        # iterating over all non-total tour modes (columns)
+        for tour_mode in output_calibration_tour_modes[:-1]:
+            tot_calib_trips_for_tour_mode = calib_target_df.loc[total_keyword, tour_mode]
+            tot_model_trips_for_tour_mode = scaled_model_df.loc[total_keyword, tour_mode]
+
+            # number of trips for each tour mode in calibration targets should match model counts
+            if tour_mode not in output_calibration_transit_modes:
+                if tot_calib_trips_for_tour_mode == 0:
+                    # no scaling if no calib trip targets
+                    tour_mode_scaling_factor = 1
+                else:
+                    tour_mode_scaling_factor = tot_model_trips_for_tour_mode / tot_calib_trips_for_tour_mode
+                scaled_calib_target_df[tour_mode] = scaled_calib_target_df[tour_mode] * tour_mode_scaling_factor
+            else:
+                # leave the transit trip counts the same
+                calib_transit_trips_in_tour_mode = calib_target_df.loc[~no_transit_idxs, tour_mode].sum()
+                numerator = tot_model_trips_for_tour_mode - calib_transit_trips_in_tour_mode
+                denom = tot_calib_trips_for_tour_mode - calib_transit_trips_in_tour_mode
+                if denom == 0 or numerator < 0:
+                    # don't scale if no non-transit trips in calibration targets
+                    #  or number of calib transit trips is larger than total model trips
+                    tour_mode_scaling_factor = 1
+                else:
+                    tour_mode_scaling_factor = numerator / denom
+                scaled_calib_target_df.loc[no_transit_idxs, tour_mode] = \
+                    scaled_calib_target_df.loc[no_transit_idxs, tour_mode] * tour_mode_scaling_factor
+
+        # recomputing margins
+        scaled_calib_target_df = scaled_calib_target_df.applymap(lambda x: 0 if x < 0 else x)
+        scaled_calib_target_df.fillna(0, inplace=True)
+        scaled_calib_target_df.loc[total_keyword] = scaled_calib_target_df.drop(
+            labels=total_keyword, axis=0, inplace=False).sum()
+        scaled_calib_target_df.loc[:, total_keyword] = scaled_calib_target_df.drop(
+            labels=total_keyword, axis=1, inplace=False).sum(axis=1)
+
+        scaled_calib_target_df = round(scaled_calib_target_df)
+        scaled_model_df = round(scaled_model_df)
+        scaled_calib_target_df.name = calib_target_df.name
+        scaled_model_df.name = model_df.name
+
+        scaled_trip_mc_calib_target_tables.append(scaled_calib_target_df)
+        scaled_asim_trip_mc_tables.append(scaled_model_df)
+
+        # total purpose should be the sum of all prev purposes
+        if total_model_df is not None:
+            total_model_df = total_model_df + scaled_model_df
+            total_calib_df = total_calib_df + scaled_calib_target_df
+        else:
+            total_model_df = scaled_model_df
+            total_calib_df = scaled_calib_target_df
+
+    # adding total tables
+    total_model_df.name = total_keyword
+    total_calib_df.name = total_keyword
+    scaled_asim_trip_mc_tables.append(total_model_df)
+    scaled_trip_mc_calib_target_tables.append(total_calib_df)
+
+    return scaled_trip_mc_calib_target_tables, scaled_asim_trip_mc_tables
+
+
+def melt_df(df, melt_id_var, value_name):
+    melted_df = df.reset_index().melt(id_vars=[melt_id_var])
+    melted_df.rename(columns={'value': value_name}, inplace=True)
+    melted_df['purpose'] = df.name
+    return melted_df
+
+
+def write_visualizer_calibration_target_table(full_trip_mc_calib_target_tables, asim_trips_df, output_dir):
+    # multiplying output visualizer tables to transform from trips to person trips
+    avg_tour_participants_df = asim_trips_df.groupby(
+        ['calib_tour_purpose', 'calib_tour_mode', 'calib_trip_mode'])['number_of_participants'].agg('mean').to_frame()
+    total_df = None
+    for df in full_trip_mc_calib_target_tables:
+        trip_purpose = df.name
+        if trip_purpose == total_keyword:
+            df = total_df
+            continue
+        for tour_mode in output_calibration_tour_modes[:-1]:
+            for trip_mode in output_calibration_trip_modes[:-1]:
+                try:
+                    avg_tour_participants = avg_tour_participants_df.loc[
+                        (trip_purpose, tour_mode, trip_mode), 'number_of_participants']
+                except KeyError:
+                    avg_tour_participants = 1
+                # print("Average number of particapants for",
+                #       trip_purpose, tour_mode, trip_mode, " = ", avg_tour_participants)
+                df.loc[trip_mode, tour_mode] = df.loc[trip_mode, tour_mode] * avg_tour_participants
+
+        # recompute marginals
+        df.loc[total_keyword] = df.drop(
+            labels=total_keyword, axis=0, inplace=False).sum()
+        df.loc[:, total_keyword] = df.drop(
+            labels=total_keyword, axis=1, inplace=False).sum(axis=1)
+
+        # total purpose should be the sum of all prev purposes
+        if total_df is not None:
+            total_df = total_df + df
+        else:
+            total_df = df
+
+    total_df.name = total_keyword
+    full_trip_mc_calib_target_tables[-1] = total_df
+
+    melted_dfs = []
+    for df in full_trip_mc_calib_target_tables:
+        if df.name == 'joint':
+            # create an extra joint Discretionary table for visualizer
+            df_copy = df.copy()
+            df_copy.name = 'jdisc'
+            melted_df_copy = melt_df(df_copy, melt_id_var='trip_mode', value_name='trips')
+            melted_dfs.append(melted_df_copy)
+        melted_df = melt_df(df, melt_id_var='trip_mode', value_name='trips')
+        melted_dfs.append(melted_df)
+    trip_mode_choice_calibration_table = pd.concat(melted_dfs)
+
+    trip_mc_vis = trip_mode_choice_calibration_table.copy()
+    trip_mc_vis = trip_mc_vis[trip_mc_vis['trip_mode'] != total_keyword]
+    trip_mc_vis['tripmode'] = trip_mc_vis['trip_mode'].apply(lambda x: calib_trip_mode_to_vis_dict[x])
+    trip_mc_vis['tourmode'] = trip_mc_vis['tour_mode'].apply(lambda x: calib_tour_mode_to_vis_dict[x])
+    trip_mc_vis['value'] = trip_mc_vis['trips']
+    trip_mc_vis['tourmode_num'] = trip_mc_vis['tour_mode'].apply(lambda x: calib_tourmode_num_to_vis_dict[x])
+    trip_mc_vis['purpose'] = trip_mc_vis['purpose'].apply(lambda x: purpose_vis_dict[x])
+    trip_mc_vis['grp_var'] = trip_mc_vis.apply(lambda row: \
+                                               row['purpose'] + 'tourmode' + str(row['tourmode_num'])
+                                               if row['tourmode'] != 'Total' \
+                                               else  row['purpose'] + str(row['tourmode_num']), axis=1)
+    trip_mc_vis_cols = ['tripmode', 'tourmode', 'purpose', 'value', 'grp_var']
+    trip_mc_vis[trip_mc_vis_cols].to_csv(
+        os.path.join(output_dir, 'tripModeProfile_vis_calib.csv'), index=False)
+
+
+def match_model_and_calib_targets_to_coefficients(original_trip_mc_constants_df,
+                                                  scaled_asim_trip_mc_tables,
+                                                  scaled_trip_mc_calib_target_tables):
+
+    columns = ['coefficient_name', 'purpose', 'tour_mode', 'trip_mode', 'scaled_model_percent', 'scaled_target_percent']
+    model_calib_target_match_df = pd.DataFrame(columns=columns)
+
+    for i, purpose in enumerate(output_calibration_trip_purposes):
+        asim_trip_mc_table = scaled_asim_trip_mc_tables[i]
+        calib_target_mc_table = scaled_trip_mc_calib_target_tables[i]
+        assert asim_trip_mc_table.name == purpose, "Purpose doesn't match!"
+        assert calib_target_mc_table.name == purpose, "Purpose doesn't match!"
+
+        for mode in output_calibration_trip_modes:
+            for tour_mode in output_calibration_tour_modes[:-1]:
+
+                asim_mc_count = round(asim_trip_mc_table.loc[mode, tour_mode], 3)
+                calib_target_count = round(calib_target_mc_table.loc[mode, tour_mode], 3)
+
+                # multiple coefficients match a single calibration purpose.
+                # e.g. ind_maint = shopping and escort, both are adjusted the same amount
+                for coef_purpose in output_calibration_purposes_to_coef_names_dict[purpose]:
+                    coef_trip_mode = output_calibration_trip_mode_to_coef_name_dict[mode]
+                    coef_tour_mode = output_calibration_tour_mode_to_coef_name_dict[tour_mode]
+                    coef_name = 'coef_calib_tour' + coef_tour_mode + '_' + coef_trip_mode + '_' + coef_purpose
+                    #target_name = mode + '_' + coef_tour_mode
+
+                    if coef_purpose in ['maint', 'disc']:
+                        coef_name = 'coef_calib_tour' + coef_tour_mode + 'jointtour0_' + coef_trip_mode + '_' + coef_purpose
+                        if purpose == 'joint':
+                            coef_name = 'coef_calib_tour' + coef_tour_mode + 'jointtour1_' + coef_trip_mode + '_' + coef_purpose
+
+                    row = [coef_name, coef_purpose, coef_tour_mode, coef_trip_mode,
+                           asim_mc_count, calib_target_count]
+                    model_calib_target_match_df.loc[len(model_calib_target_match_df)] = row
+
+    return model_calib_target_match_df
+
+
+def calculate_new_coefficient(row, damping_factor, max_ASC_adjust, adjust_when_zero_counts):
+    row['difference'] = row.scaled_target_percent - row.scaled_model_percent
+    row['percent_diff'] = pd.NA
+    row['coef_change'] = pd.NA
+    row['new_value'] = row.value
+    row['converged'] = True
+
+    # added for trip mc calibration. we kept those coefficients (from melted table) that
+    # had formulas as values. those are non ASC coefficients though
+    # omit rows that have no value or values as a string formula
+    if pd.isna(row.value) or isinstance(row.value, str):
+        return row
+
+    # do not adjust parameters that should not be adjusted
+    # if row.value > 900 or (pd.isna(row.scaled_target_percent) and pd.isna(row.scaled_model_percent)):
+    if row.constrain == 'T' or abs(row.value) > 900 or (pd.isna(row.scaled_target_percent) and pd.isna(row.scaled_model_percent)):
+        return row
+
+    # have neither target counts or model counts
+    if row.scaled_target_percent == 0 and row.scaled_model_percent == 0:
+        row.percent_diff = 0
+        row.difference = 0
+        return row
+
+    # have model counts but not target counts
+    if row.scaled_target_percent == 0 and row.scaled_model_percent > 0:
+        row.converged = False
+        row.coef_change = -adjust_when_zero_counts
+        row.new_value = row.value + row.coef_change
+        # if row.value + row.coef_change > -10: 
+        #     row.new_value = row.value + row.coef_change
+        # else:
+        #     row.new_value = -10
+        return row
+
+    # have target counts but not model counts
+    if row.scaled_target_percent > 0 and row.scaled_model_percent == 0:
+        row.converged = False
+        row.coef_change = adjust_when_zero_counts
+        row.new_value = row.value + row.coef_change
+        return row
+
+    # normal calculations for counts in both model and target
+    row.percent_diff = (abs(row.scaled_target_percent - row.scaled_model_percent)
+                        / row.scaled_target_percent) * 100
+    row.coef_change = np.log(row.scaled_target_percent / row.scaled_model_percent) * damping_factor
+    if ('WALK' in row.coefficient_name) and ('school' not in row.coefficient_name) and ('univ' not in row.coefficient_name): 
+        # walk unavailable > 3 mi, increase coef_change for faster convergence
+        row.coef_change = np.log(row.scaled_target_percent / row.scaled_model_percent) * damping_factor * 2
+
+    if abs(row.coef_change) > max_ASC_adjust:
+        row.coef_change = max_ASC_adjust if row.coef_change > 0 else -max_ASC_adjust
+    row.new_value = row.value + row.coef_change
+
+    if (row.percent_diff > 5):
+        row.converged = False
+    return row
+
+
+def calculate_coefficient_change(original_trip_mc_constants_df,
+                                 model_calib_target_match_df,
+                                 damping_factor,
+                                 max_ASC_adjust,
+                                 adjust_when_zero_counts,
+                                 output_dir):
+    # coef_update_df = pd.merge(
+    #     original_trip_mc_constants_df,
+    #     model_calib_target_match_df,
+    #     how='left',
+    #     on=['coefficient_name', 'purpose']
+    # )
+    original_trip_mc_constants_df.to_csv(os.path.join(output_dir, 'original_trip_mc_constants_df.csv'), index=False)
+    model_calib_target_match_df.to_csv(os.path.join(output_dir, 'model_calib_target_match_df.csv'), index=False)
+    coef_update_df = pd.merge(
+        original_trip_mc_constants_df,
+        model_calib_target_match_df,
+        how='left',
+        on='coefficient_name',
+    )
+    assert str(max_ASC_adjust).isdigit(), "max_ASC_adjust is not numeric"
+    # assert str(damping_factor).isdigit(), "damping_factor is not numeric"
+    assert str(adjust_when_zero_counts).isdigit(), "adjust_when_zero_counts is not numeric"
+
+    coef_update_df = coef_update_df.apply(
+        lambda row: calculate_new_coefficient(
+            row, damping_factor, max_ASC_adjust, adjust_when_zero_counts), axis=1)
+
+    coef_update_df.to_csv(os.path.join(output_dir, 'coefficient_updates.csv'), index=False)
+
+    new_config_df = coef_update_df[['coefficient_name', 'new_value', 'constrain']].copy()
+    new_config_df.rename(columns={'new_value': 'value'}, inplace=True)
+    new_config_df.to_csv(os.path.join(output_dir, 'trip_mode_choice_coefficients.csv'), index=False)
+
+    assert (new_config_df['value'].notna()).all(), f"Missing coefficient values:\n {new_config_df[new_config_df['value'].isna()]}"
+
+    return coef_update_df
+
+
+def make_trip_mode_choice_comparison_plots(viz_df, purpose):
+    fig = plt.figure(figsize=(30, 28))
+
+    # not needed for trip mc plots given we need 3x3 plot grid size
+    #plot_idx = 221
+    # added for trip mc plots to keep track of grid number
+    plot_no = 1
+    for tour_mode in output_calibration_tour_modes[:-1]:
+        plt.subplot(4, 4, plot_no)
+        data = viz_df[(viz_df['tour_mode'] == tour_mode)
+                      & (viz_df['trip_mode'] != total_keyword)].copy()
+        total_trip_per_source_df = data.groupby('source').sum()
+        for i in range(len(total_trip_per_source_df)):
+            source = total_trip_per_source_df.index[i]
+            total_trips = total_trip_per_source_df.trips[i]
+            data.loc[data['source'] == source, 'percent'] = \
+                data.loc[data['source'] == source, 'trips'] / total_trips * 100
+
+        sns.barplot(data=data, x='trip_mode', y='percent', hue='source')
+        plt.title('Trip Purpose: ' + purpose + ', Tour Mode: ' + tour_mode, fontsize=18)
+        plt.xticks(rotation=90, fontsize=13)
+        plt.yticks(fontsize=16)
+        plt.ylabel('Percent', fontsize=16)
+        plt.xlabel('Trip Mode', fontsize=16)
+        plot_no += 1
+
+    plt.subplot(4, 4, plot_no)
+    data = viz_df[(viz_df['tour_mode'] == total_keyword)
+                  & (viz_df['trip_mode'] != total_keyword)].copy()
+    total_trip_per_source_df = data.groupby('source').sum()
+    for i in range(len(total_trip_per_source_df)):
+        source = total_trip_per_source_df.index[i]
+        total_trips = total_trip_per_source_df.trips[i]
+        data.loc[data['source'] == source, 'percent'] = \
+            data.loc[data['source'] == source, 'trips'] / total_trips * 100
+
+    sns.barplot(data=data, x='trip_mode', y='percent', hue='source')
+    plt.title('Tour Purpose: ' + purpose + ', Tour Mode: All', fontsize=18)
+    plt.xticks(rotation=90, fontsize=13)
+    plt.yticks(fontsize=16)
+    plt.ylabel('Percent', fontsize=16)
+    plt.xlabel('Trip Mode', fontsize=16)
+
+    plt.tight_layout()
+    plt.close()
+    return fig
+
+
+def visualize_trip_mode_choice(scaled_tables_1, scaled_tables_2, source_1, source_2, output_dir):
+    for i in range(len(scaled_tables_1)):
+        df_1 = scaled_tables_1[i]
+        df_2 = scaled_tables_2[i]
+        assert df_1.name == df_2.name, "Table purposes do not match!"
+
+        viz_df_1 = df_1.reset_index().melt(id_vars=['trip_mode']).rename(
+            columns={'variable': 'tour_mode', 'value': 'trips'})
+        viz_df_1['source'] = source_1
+
+        viz_df_2 = df_2.reset_index().melt(id_vars=['trip_mode']).rename(
+            columns={'variable': 'tour_mode', 'value': 'trips'})
+        viz_df_2['source'] = 'Model'
+
+        viz_df = pd.concat([viz_df_1, viz_df_2])
+
+        plot_name = df_1.name + '_' + source_1 + '_' + source_2 + '.png'
+        fig = make_trip_mode_choice_comparison_plots(viz_df, df_1.name)
+
+        fig.savefig(os.path.join(output_dir, plot_name))
+        # fig.show()
+    return fig
+
+
+def evaluate_coefficient_updates(coef_update_df, output_dir):
+    print('Coefficient Statistics: ')
+    tot_coef = len(coef_update_df)
+    print('\t', tot_coef, 'total coefficients')
+
+    # not needed for trip mc calibration given trip mc coefficients do not have a 'constrain' field
+    #num_constrained_coef = len(coef_update_df[
+    #    (coef_update_df['constrain'] == 'T')
+    #    | (coef_update_df['value'] > 900)])
+    #print('\t', num_constrained_coef, 'constrained coefficients')
+
+    num_changed_coef = len(coef_update_df[pd.notna(coef_update_df['coef_change'])])
+    print('\t', num_changed_coef, 'coefficients adjusted')
+    num_converged_coef = len(coef_update_df[coef_update_df['converged'] == True])
+    num_unconverged_coef = len(coef_update_df[coef_update_df['converged'] == False])
+    print('\t', num_converged_coef, 'coefficients converged')
+    print('\t', num_unconverged_coef, 'coefficients not converged')
+
+    fig = plt.figure(figsize=(20, 7))
+    plt.subplot(121)
+    coef_update_df[pd.notna(coef_update_df['coef_change'])]['coef_change'].plot(
+        kind='hist', bins=50)
+    plt.xlabel('Coefficient Change [Utiles]', fontsize=14)
+    plt.ylabel('Number of Coefficients', fontsize=14)
+    plt.title('Adjustment Factors', fontsize=14)
+
+    plt.subplot(122)
+    coef_update_df[coef_update_df['new_value'] > -900]['new_value'].plot(kind='hist', bins=50)
+    plt.xlabel('Coefficient Value [Utiles]', fontsize=14)
+    plt.ylabel('Number of Coefficients', fontsize=14)
+    plt.title('Coefficient Values After Adjustment', fontsize=14)
+    plt.savefig(os.path.join(output_dir, 'coef_change.png'))
+    plt.close()
+    return fig
+
+
+def display_largest_coefficients(coef_update_df, num_to_display=10):
+    coef_update_df['value_size'] = abs(coef_update_df['new_value'])
+    top_coef_df = coef_update_df[coef_update_df['value_size'] < 900].sort_values(
+        'value_size', ascending=False).head(10)
+    # added 'purpose' (i.e. tour purpose) given trip mc coefficients includes it as separate column
+    cols_to_display = ['coefficient_name', 'purpose', 'value', 'scaled_model_percent',
+                       'scaled_target_percent', 'coef_change', 'new_value', 'converged']
+    top_coef_df = top_coef_df[cols_to_display]
+    print("Top", num_to_display, "largest coefficients:")
+    display(top_coef_df)
+
+
+def copy_directory(dir_to_copy, copy_location):
+    if os.path.exists(copy_location):
+        shutil.rmtree(copy_location)
+    # copy config files from configs_dir to run dir
+    shutil.copytree(dir_to_copy, copy_location)
+
+
+def launch_activitysim(activitysim_run_command):
+    start_time = time.time()
+    print("ActivitySim run started at: ", datetime.datetime.now())
+    print(activitysim_run_command)
+    ret_value = os.system(activitysim_run_command)
+    end_time = time.time()
+    print("ActivitySim ended at", datetime.datetime.now())
+    run_time = round(time.time() - start_time, 2)
+    print("Run Time: ", run_time, "secs = ", run_time/60, " mins")
+    assert ret_value == 0, "ActivitySim run not completed! See ActivitySim log file for details."
+
+
+def melt_trip_mc_coef_file(trip_mc_coef_file, output_dir):
+    # obtain original original column and row orders
+    trip_purpose = trip_mc_coef_file.columns[1:]
+    trip_mc_coef_col_order = list(trip_mc_coef_file.columns)
+    trip_mc_coef_col_order[0] = 'coefficient_name'
+    trip_purpose_check = all(col in trip_mc_coef_col_order for col in trip_purpose)
+    assert trip_purpose_check, 'Missing trip purpose!'
+    trip_mc_coef_row_order = trip_mc_coef_file['Expression'].copy().rename('coefficient_name')
+
+    # melt trip mc coefficient file
+    trip_mc_coef_melted_full = pd.melt(trip_mc_coef_file, id_vars = ['Expression'], value_vars = trip_purpose,
+                                  var_name = 'purpose')
+    num_rows = len(trip_mc_coef_melted_full)
+    trip_mc_coef_melted_full.rename(columns = {'Expression':'coefficient_name'}, inplace = True)
+
+    # include only ASC coefficients and drop commented out coefficients
+    trip_mc_coef_melted = trip_mc_coef_melted_full[(trip_mc_coef_melted_full.loc[:,'coefficient_name'].str.contains('_ASC_')) &
+                                              (~trip_mc_coef_melted_full.loc[:,'coefficient_name'].str.contains('#'))].copy()
+    trip_mc_coef_melted.loc[:,'value'] = pd.to_numeric(trip_mc_coef_melted.loc[:,'value'])
+
+    # creates a dataframe (omit) filled with values that do not contain ASC coefficients
+    # will add it back when un-melting the dataframe
+    trip_mc_coef_melted_omit = trip_mc_coef_melted_full[~trip_mc_coef_melted_full.index.isin(trip_mc_coef_melted.index)]
+
+    assert len(trip_mc_coef_melted) + len(trip_mc_coef_melted_omit) == num_rows, \
+        'Missing trip mode choice coefficients!'
+
+    # added purpose_original to remember which coefficient corresponds to which mode
+    # added 'joint' as purpose for non-work, -univ, -school, and -atwork purpose records
+    trip_mc_coef_melted.loc[:,'purpose_original'] = trip_mc_coef_melted.loc[:,'purpose']
+    joint_purposes = ['escort', 'shopping', 'eatout', 'othmaint', 'social', 'othdiscr']
+    trip_mc_coef_melted.loc[trip_mc_coef_melted.loc[:,'coefficient_name'].str.contains('joint_') &
+                            trip_mc_coef_melted.loc[:,'purpose'].isin(joint_purposes), 'purpose'] = 'joint'
+
+    #trip_mc_coef_melted.to_csv(os.path.join(output_dir, 'trip_mode_choice_coefficients.csv'), index = False)
+
+    # reset indices
+    trip_mc_coef_melted.reset_index(inplace = True, drop = True)
+    trip_mc_coef_melted_omit.reset_index(inplace = True, drop = True)
+
+    return trip_mc_coef_melted, trip_mc_coef_melted_omit, trip_mc_coef_col_order, trip_mc_coef_row_order
+
+
+def unmelt_trip_mc_coef(trip_mc_coef_update, trip_mc_coef_omit,
+                        trip_mc_coef_col_order, trip_mc_coef_row_order, output_dir):
+
+    num_rows = len(trip_mc_coef_row_order)
+
+    # obtain new trip mc coefficient values
+    trip_mc_coef = trip_mc_coef_update[['coefficient_name', 'purpose_original', 'new_value']].copy()
+    trip_mc_coef.rename(columns={'new_value': 'value', 'purpose_original': 'purpose'}, inplace = True)
+
+    # re-add previously removed trip mc coefficient records
+    trip_mc_coef_new = trip_mc_coef.append(trip_mc_coef_omit)
+    trip_mc_coef_new.reset_index(inplace = True, drop = True)
+
+    # unmelt trip mc coefficient dataframe
+    trip_mc_coef_new_unmelted = trip_mc_coef_new.pivot_table(index = 'coefficient_name',
+                                                             columns = 'purpose',
+                                                             dropna = False,
+                                                             aggfunc = lambda x: x)
+    trip_mc_coef_new_unmelted.columns = trip_mc_coef_new_unmelted.columns.droplevel()
+    trip_mc_coef_new_unmelted.reset_index(inplace = True)
+
+    # rearrange columns and rows per original trip mc coefficient file
+    trip_mc_coef_new_unmelted = pd.merge(trip_mc_coef_row_order.to_frame(), trip_mc_coef_new_unmelted,
+                                how = 'left', on = 'coefficient_name')
+    trip_mc_coef_new_unmelted = trip_mc_coef_new_unmelted[trip_mc_coef_col_order]
+    trip_mc_coef_new_unmelted.rename(columns={'coefficient_name':'Expression'}, inplace = True)
+    assert len(trip_mc_coef_new_unmelted) == num_rows, 'Missing trip mode choice coefficients!'
+
+    # print
+    trip_mc_coef_new_unmelted.to_csv(os.path.join(output_dir, 'trip_mode_choice_coefficients.csv'), index=False)
+
+    return trip_mc_coef_new_unmelted
+
+# -------------------------------------------------------------------------------------------------
+# Entry Points
+def perform_trip_mode_choice_model_calibration(asim_output_dir,
+                                               asim_configs_dir,
+                                               trip_mode_choice_calib_targets_file,
+                                               max_ASC_adjust,
+                                               damping_factor,
+                                               adjust_when_zero_counts,
+                                               output_dir):
+
+    # ActivitySim model output transformed into trip mode choice calibration format
+    asim_trip_mc_tables, num_trips_full_model, asim_trips_df = \
+        process_asim_tables_for_trip_mode_choice(asim_output_dir)
+
+    # trip mode choice constants read from config file
+    original_trip_mc_constants_df = read_trip_mode_choice_constants(asim_configs_dir)
+
+    # melt trip mode choice coefficient file
+    # original_trip_mc_constants_df, original_trip_mc_constants_df_omit, \
+    #     trip_mc_coef_col_order, trip_mc_coef_row_order = \
+    #     melt_trip_mc_coef_file(original_trip_mc_constants_df, output_dir)
+
+    # Calibration targets from survey data
+    trip_mc_calib_target_tables = read_trip_mode_choice_calibration_file(
+        trip_mode_choice_calib_targets_file)
+
+    full_trip_mc_calib_target_tables, full_asim_trip_mc_tables = scale_targets_to_match_model_by_tour_mode(
+        trip_mc_calib_target_tables,
+        asim_trip_mc_tables,
+        full_model_trips=num_trips_full_model,
+    )
+
+    scaled_trip_mc_calib_target_tables, scaled_asim_trip_mc_tables = calculate_share_by_tour_mode(
+        full_trip_mc_calib_target_tables,
+        full_asim_trip_mc_tables,
+    )
+
+    write_scaled_targets_to_excel(
+        unscaled_model_tables=asim_trip_mc_tables,
+        unscaled_calib_tables=trip_mc_calib_target_tables,
+        full_model_tables=full_asim_trip_mc_tables,
+        full_calib_tables=full_trip_mc_calib_target_tables,
+        scaled_model_tables=scaled_asim_trip_mc_tables,
+        scaled_calib_tables=scaled_trip_mc_calib_target_tables,
+        output_dir=output_dir)
+
+    # Compare calibration targets to model outputs
+    all_purposes_fig = visualize_trip_mode_choice(
+        scaled_tables_1=scaled_trip_mc_calib_target_tables,
+        scaled_tables_2=scaled_asim_trip_mc_tables,
+        source_1='Survey',
+        source_2='Model',
+        output_dir=output_dir)
+    display(all_purposes_fig)
+
+    # Match model counts and calibration targets to model coefficients
+    model_calib_target_match_df = match_model_and_calib_targets_to_coefficients(
+        original_trip_mc_constants_df,
+        scaled_asim_trip_mc_tables,
+        scaled_trip_mc_calib_target_tables)
+
+    # Update model coefficients
+    coef_update_df = calculate_coefficient_change(
+        original_trip_mc_constants_df,
+        model_calib_target_match_df,
+        damping_factor=damping_factor,
+        max_ASC_adjust=max_ASC_adjust,
+        adjust_when_zero_counts=adjust_when_zero_counts,
+        output_dir=output_dir
+    )
+
+    # unmelt new trip mode choice coefficient file
+    # unmelt_trip_mc_coef(coef_update_df, original_trip_mc_constants_df_omit,
+    #                     trip_mc_coef_col_order, trip_mc_coef_row_order, output_dir)
+
+    coef_hists = evaluate_coefficient_updates(coef_update_df, output_dir)
+    display(coef_hists)
+
+    display_largest_coefficients(coef_update_df)
+
+    # write scaled calibration targets for HTML visualizer
+    write_visualizer_calibration_target_table(full_trip_mc_calib_target_tables, asim_trips_df, output_dir)
+
+    return coef_update_df
+
+
+def run_activitysim(data_dir,
+                    configs_resident_dir,
+                    configs_common_dir,
+                    run_dir,
+                    output_dir,
+                    settings_file=None,
+                    trip_mc_coef_file=None):
+    assert os.path.exists(configs_resident_dir), "configs_resident not found!"
+    assert os.path.exists(configs_common_dir), "configs_common not found!"
+    assert os.path.exists(data_dir), "data_dir not found!"
+
+    if not os.path.exists(output_dir):
+        print("creating output_dir at", output_dir)
+        os.mkdir(output_dir)
+
+    # creating new config folder(s) in run directory
+    run_config_resident_dir = os.path.join(run_dir, 'configs')
+    copy_directory(dir_to_copy=configs_resident_dir, copy_location=run_config_resident_dir)
+    
+    # optional copy of settings and coefficient file
+    if settings_file is not None:
+        shutil.copyfile(settings_file, os.path.join(run_config_resident_dir, 'settings_mp.yaml'))
+    if trip_mc_coef_file is not None:
+        shutil.copyfile(trip_mc_coef_file, os.path.join(run_config_resident_dir, 'trip_mode_choice_coefficients.csv'))
+
+    activitysim_run_command = 'python simulation.py -s ' + settings_file + ' -c ' + run_config_resident_dir \
+        + ' -c ' + configs_common_dir + ' -d ' + data_dir + ' -o ' + run_dir
+
+    launch_activitysim(activitysim_run_command)
+
+    activitysim_output_tables = [
+        'final_households.csv',
+        'final_persons.csv',
+        'final_tours.csv',
+        'final_trips.csv',
+        'final_joint_tour_participants.csv',
+        'activitysim.log',
+    ]
+
+    for asim_table in activitysim_output_tables:
+        if os.path.exists(os.path.join(run_dir, asim_table)):
+            shutil.copyfile(os.path.join(run_dir, asim_table), os.path.join(output_dir, asim_table))
+
+    return
diff --git a/src/asim/calibration/resident/trip_mode_choice/scripts/calibrate_trip_mode_choice.ipynb b/src/asim/calibration/resident/trip_mode_choice/scripts/calibrate_trip_mode_choice.ipynb
new file mode 100644
index 000000000..402c0810e
--- /dev/null
+++ b/src/asim/calibration/resident/trip_mode_choice/scripts/calibrate_trip_mode_choice.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calibrating Trip Mode Choice\n",
+    "This script will iteratively perform updates to the trip mode choice coefficients config file in order to match model outputs to calibration targets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No problems found in input dictionaries\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "# import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from IPython.display import display\n",
+    "import importlib\n",
+    "import asim_trip_calib_util\n",
+    "importlib.reload(asim_trip_calib_util)\n",
+    "# from asim_trip_calib_util import *\n",
+    "# check to make sure the dictionaries specifying names for calibration targets and activitysim outputs are consistent\n",
+    "asim_trip_calib_util.check_input_dictionaries_for_consistency()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Required Inputs\n",
+    "This script requires a working version of ActivitySim installed in the conda environment.  Input data sources are:\n",
+    "* Initial model output directory that includes household, tour and trip files\n",
+    "* trip mode choice calibration target tables. Tables should be indexed by trip mode and columns should be tour mode.  Tables should be broken down by tour purpose\n",
+    "* Model config directory containing the trip mode choice coefficients\n",
+    "\n",
+    "Changes in tour, trip modes and purposes can be implemented by changing the dictionaries at the top of asim_trip_calib_util_CMAP.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# csv file containing calibration targets by tour mode choice.\n",
+    "# Column names and acceptable values should be set in dict at top of script\n",
+    "trip_mode_choice_calib_targets_file = r\"C:\\abm_runs\\rohans\\calibration\\trip_mc\\targets\\trip_mode_choice_calibration_targets_2024-01-23_updated.csv\"\n",
+    "\n",
+    "# directory of the simulation.py file\n",
+    "simpy_dir = r\"C:\\abm_runs\\rohans\"\n",
+    "\n",
+    "# location of configuration files\n",
+    "settings_dir = r\"C:\\abm_runs\\rohans\\configs\\resident\\settings_mp.yaml\"\n",
+    "configs_resident_dir = r\"C:\\abm_runs\\rohans\\configs\\resident\"\n",
+    "configs_common_dir = r\"C:\\abm_runs\\rohans\\configs\\common\"\n",
+    "\n",
+    "warm_start_settings_mp_file = r\"C:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\settings_mp_warm_start.yaml\"\n",
+    "cold_start_settings_mp_file = r\"C:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\settings_mp_cold_start.yaml\"\n",
+    "trip_mc_coef_file = None \n",
+    "\n",
+    "# input data location\n",
+    "data_dir = r\"C:\\abm_runs\\rohans\\input_2022\"\n",
+    "\n",
+    "# output location\n",
+    "output_dir = r\"C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\calibration_output_an_iter_cold\"\n",
+    "activitysim_run_dir = r\"C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\activitysim_run_dir\"\n",
+    "\n",
+    "# calibration iterations\n",
+    "calibration_iterations_to_run = 5\n",
+    "\n",
+    "# want to do intial model run first?\n",
+    "want_to_do_initial_model_run = True  # True or False\n",
+    "\n",
+    "# calibration settings\n",
+    "max_ASC_adjust = 5 # maximum allowed adjustment per iteration\n",
+    "damping_factor = 1 # constant multiplied to all adjustments\n",
+    "adjust_when_zero_counts = 2 # coefficient change when have target counts but no model counts (or vise-versa)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "--------------- User should not have to change anything below this line ----------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial Model Run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Change directory to model setup\n",
+    "### i.e. the location of simulation.py script\n",
+    "os.chdir(simpy_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ActivitySim run started at:  2024-05-16 14:45:16.374744\n",
+      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\settings_mp_cold_start.yaml -c C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\activitysim_run_dir\n",
+      "ActivitySim ended at 2024-05-16 19:11:18.495818\n",
+      "Run Time:  15962.12 secs =  266.03533333333337  mins\n"
+     ]
+    }
+   ],
+   "source": [
+    "if want_to_do_initial_model_run:\n",
+    "    asim_trip_calib_util.run_activitysim(\n",
+    "        data_dir=data_dir,  # data inputs for ActivitySim\n",
+    "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
+    "        configs_common_dir=configs_common_dir,  # these files are copied to the config section of the run directory\n",
+    "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
+    "        output_dir=output_dir,  # location to store run model outputs\n",
+    "        settings_file=cold_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
+    "        trip_mc_coef_file=trip_mc_coef_file  # optional: trip_mode_choice_coefficients.csv to replace the one in configs_dir\n",
+    "    )\n",
+    "    \n",
+    "    # _ = asim_trip_calib_util.perform_trip_mode_choice_model_calibration(\n",
+    "    #     asim_output_dir=output_dir, # folder containing the activitysim model output\n",
+    "    #     asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim trip mode choice config files\n",
+    "    #     trip_mode_choice_calib_targets_file=trip_mode_choice_calib_targets_file,  # folder containing trip mode choice calibration tables\n",
+    "    #     max_ASC_adjust=max_ASC_adjust,  \n",
+    "    #     damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
+    "    #     adjust_when_zero_counts=adjust_when_zero_counts,\n",
+    "    #     output_dir=output_dir,  # location to write model calibration steps\n",
+    "    # )\n",
+    "    # trip_mc_coef_file = os.path.join(output_dir, 'trip_mode_choice_coefficients.csv') \n",
+    "else:\n",
+    "    print(\"No initial model run performed.\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample rate of  1 results in  12523739 out of 12523739 tours\n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'C:\\\\abm_runs\\\\rohans\\\\calibration\\\\trip_mc\\\\output\\\\activitysim_run_dir\\\\configs\\\\trip_mode_choice_coefficients.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43masim_trip_calib_util\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_trip_mode_choice_model_calibration\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[43m        \u001b[49m\u001b[43masim_output_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# folder containing the activitysim model output\u001b[39;49;00m\n\u001b[0;32m      3\u001b[0m \u001b[43m        \u001b[49m\u001b[43masim_configs_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactivitysim_run_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconfigs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# folder containing activitysim trip mode choice config files\u001b[39;49;00m\n\u001b[0;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrip_mode_choice_calib_targets_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrip_mode_choice_calib_targets_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# folder containing trip mode choice calibration tables\u001b[39;49;00m\n\u001b[0;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_ASC_adjust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_ASC_adjust\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdamping_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdamping_factor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# constant multiplied to all adjustments\u001b[39;49;00m\n\u001b[0;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[43madjust_when_zero_counts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madjust_when_zero_counts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# location to write model calibration steps\u001b[39;49;00m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\asim_trip_calib_util.py:1225\u001b[0m, in \u001b[0;36mperform_trip_mode_choice_model_calibration\u001b[1;34m(asim_output_dir, asim_configs_dir, trip_mode_choice_calib_targets_file, max_ASC_adjust, damping_factor, adjust_when_zero_counts, output_dir)\u001b[0m\n\u001b[0;32m   1221\u001b[0m asim_trip_mc_tables, num_trips_full_model, asim_trips_df \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m   1222\u001b[0m     process_asim_tables_for_trip_mode_choice(asim_output_dir)\n\u001b[0;32m   1224\u001b[0m \u001b[38;5;66;03m# trip mode choice constants read from config file\u001b[39;00m\n\u001b[1;32m-> 1225\u001b[0m original_trip_mc_constants_df \u001b[38;5;241m=\u001b[39m \u001b[43mread_trip_mode_choice_constants\u001b[49m\u001b[43m(\u001b[49m\u001b[43masim_configs_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1227\u001b[0m \u001b[38;5;66;03m# melt trip mode choice coefficient file\u001b[39;00m\n\u001b[0;32m   1228\u001b[0m \u001b[38;5;66;03m# original_trip_mc_constants_df, original_trip_mc_constants_df_omit, \\\u001b[39;00m\n\u001b[0;32m   1229\u001b[0m \u001b[38;5;66;03m#     trip_mc_coef_col_order, trip_mc_coef_row_order = \\\u001b[39;00m\n\u001b[0;32m   1230\u001b[0m \u001b[38;5;66;03m#     melt_trip_mc_coef_file(original_trip_mc_constants_df, output_dir)\u001b[39;00m\n\u001b[0;32m   1231\u001b[0m \n\u001b[0;32m   1232\u001b[0m \u001b[38;5;66;03m# Calibration targets from survey data\u001b[39;00m\n\u001b[0;32m   1233\u001b[0m trip_mc_calib_target_tables \u001b[38;5;241m=\u001b[39m read_trip_mode_choice_calibration_file(\n\u001b[0;32m   1234\u001b[0m     trip_mode_choice_calib_targets_file)\n",
+      "File \u001b[1;32mc:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\asim_trip_calib_util.py:554\u001b[0m, in \u001b[0;36mread_trip_mode_choice_constants\u001b[1;34m(configs_dir, coef_file)\u001b[0m\n\u001b[0;32m    552\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_trip_mode_choice_constants\u001b[39m(configs_dir, coef_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrip_mode_choice_coefficients.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m    553\u001b[0m     constants_config_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(configs_dir, coef_file)\n\u001b[1;32m--> 554\u001b[0m     trip_mc_constants_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconstants_config_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    555\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m trip_mc_constants_df\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mc:\\Users\\alie\\.conda\\envs\\asim_baydag\\lib\\site-packages\\pandas\\io\\common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    857\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    858\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    859\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    862\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\abm_runs\\\\rohans\\\\calibration\\\\trip_mc\\\\output\\\\activitysim_run_dir\\\\configs\\\\trip_mode_choice_coefficients.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "_ = asim_trip_calib_util.perform_trip_mode_choice_model_calibration(\n",
+    "        asim_output_dir=output_dir, # folder containing the activitysim model output\n",
+    "        asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim trip mode choice config files\n",
+    "        trip_mode_choice_calib_targets_file=trip_mode_choice_calib_targets_file,  # folder containing trip mode choice calibration tables\n",
+    "        max_ASC_adjust=max_ASC_adjust,  \n",
+    "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
+    "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
+    "        output_dir=output_dir,  # location to write model calibration steps\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Iterating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ActivitySim run started at:  2024-05-14 12:13:38.539136\n",
+      "python simulation.py -s C:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\settings_mp_warm_start.yaml -c C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\activitysim_run_dir\\configs -c C:\\abm_runs\\rohans\\configs\\common -d C:\\abm_runs\\rohans\\input_2022 -o C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\activitysim_run_dir\n",
+      "ActivitySim ended at 2024-05-14 16:14:59.984177\n",
+      "Run Time:  14481.45 secs =  241.35750000000002  mins\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "ActivitySim run not completed! See ActivitySim log file for details.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m start_iter_num \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(start_iter_num, calibration_iterations_to_run\u001b[38;5;241m+\u001b[39mstart_iter_num):\n\u001b[1;32m----> 7\u001b[0m     \u001b[43masim_trip_calib_util\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_activitysim\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# data inputs for ActivitySim\u001b[39;49;00m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfigs_resident_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfigs_resident_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# these files are copied to the config section of the run directory\u001b[39;49;00m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfigs_common_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfigs_common_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# these files are copied to the config section of the run directory\u001b[39;49;00m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mactivitysim_run_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# ActivitySim run directory\u001b[39;49;00m\n\u001b[0;32m     12\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miteration_output_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# location to store run model outputs\u001b[39;49;00m\n\u001b[0;32m     13\u001b[0m \u001b[43m        \u001b[49m\u001b[43msettings_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwarm_start_settings_mp_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# optional: ActivitySim settings.yaml to replace the one in configs_dir\u001b[39;49;00m\n\u001b[0;32m     14\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrip_mc_coef_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrip_mc_coef_file\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# optional: trip_mode_choice_coefficients.csv to replace the one in configs_dir\u001b[39;49;00m\n\u001b[0;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m     _ \u001b[38;5;241m=\u001b[39m asim_trip_calib_util\u001b[38;5;241m.\u001b[39mperform_trip_mode_choice_model_calibration(\n\u001b[0;32m     18\u001b[0m         asim_output_dir\u001b[38;5;241m=\u001b[39miteration_output_dir, \u001b[38;5;66;03m# folder containing the activitysim model output\u001b[39;00m\n\u001b[0;32m     19\u001b[0m         asim_configs_dir\u001b[38;5;241m=\u001b[39mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(activitysim_run_dir, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconfigs\u001b[39m\u001b[38;5;124m'\u001b[39m),  \u001b[38;5;66;03m# folder containing activitysim trip mode choice config files\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     24\u001b[0m         output_dir\u001b[38;5;241m=\u001b[39miteration_output_dir,  \u001b[38;5;66;03m# location to write model calibration steps\u001b[39;00m\n\u001b[0;32m     25\u001b[0m     )\n\u001b[0;32m     26\u001b[0m     trip_mc_coef_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(iteration_output_dir, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrip_mode_choice_coefficients.csv\u001b[39m\u001b[38;5;124m'\u001b[39m) \n",
+      "File \u001b[1;32mc:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\asim_trip_calib_util.py:1324\u001b[0m, in \u001b[0;36mrun_activitysim\u001b[1;34m(data_dir, configs_resident_dir, configs_common_dir, run_dir, output_dir, settings_file, trip_mc_coef_file)\u001b[0m\n\u001b[0;32m   1319\u001b[0m     shutil\u001b[38;5;241m.\u001b[39mcopyfile(trip_mc_coef_file, os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(run_config_resident_dir, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrip_mode_choice_coefficients.csv\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m   1321\u001b[0m activitysim_run_command \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpython simulation.py -s \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m settings_file \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -c \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m run_config_resident_dir \\\n\u001b[0;32m   1322\u001b[0m     \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -c \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m configs_common_dir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -d \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data_dir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -o \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m run_dir\n\u001b[1;32m-> 1324\u001b[0m \u001b[43mlaunch_activitysim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactivitysim_run_command\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1326\u001b[0m activitysim_output_tables \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   1327\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfinal_households.csv\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1328\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfinal_persons.csv\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1332\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactivitysim.log\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1333\u001b[0m ]\n\u001b[0;32m   1335\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m asim_table \u001b[38;5;129;01min\u001b[39;00m activitysim_output_tables:\n",
+      "File \u001b[1;32mc:\\abm_runs\\rohans\\calibration\\trip_mc\\scripts\\asim_trip_calib_util.py:1131\u001b[0m, in \u001b[0;36mlaunch_activitysim\u001b[1;34m(activitysim_run_command)\u001b[0m\n\u001b[0;32m   1129\u001b[0m run_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time, \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m   1130\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRun Time: \u001b[39m\u001b[38;5;124m\"\u001b[39m, run_time, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msecs = \u001b[39m\u001b[38;5;124m\"\u001b[39m, run_time\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m60\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m mins\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1131\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m ret_value \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mActivitySim run not completed! See ActivitySim log file for details.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: ActivitySim run not completed! See ActivitySim log file for details."
+     ]
+    }
+   ],
+   "source": [
+    "iteration_output_dir = output_dir.strip('_cold') + '_10'\n",
+    "\n",
+    "calibration_iterations_to_run = 3\n",
+    "start_iter_num = 1\n",
+    "\n",
+    "for i in range(start_iter_num, calibration_iterations_to_run+start_iter_num):\n",
+    "    asim_trip_calib_util.run_activitysim(\n",
+    "        data_dir=data_dir,  # data inputs for ActivitySim\n",
+    "        configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
+    "        configs_common_dir=configs_common_dir,  # these files are copied to the config section of the run directory\n",
+    "        run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
+    "        output_dir=iteration_output_dir,  # location to store run model outputs\n",
+    "        settings_file=warm_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
+    "        trip_mc_coef_file=trip_mc_coef_file  # optional: trip_mode_choice_coefficients.csv to replace the one in configs_dir\n",
+    "    )\n",
+    "    \n",
+    "    _ = asim_trip_calib_util.perform_trip_mode_choice_model_calibration(\n",
+    "        asim_output_dir=iteration_output_dir, # folder containing the activitysim model output\n",
+    "        asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim trip mode choice config files\n",
+    "        trip_mode_choice_calib_targets_file=trip_mode_choice_calib_targets_file,  # folder containing trip mode choice calibration tables\n",
+    "        max_ASC_adjust=max_ASC_adjust,  \n",
+    "        damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
+    "        adjust_when_zero_counts=adjust_when_zero_counts,\n",
+    "        output_dir=iteration_output_dir,  # location to write model calibration steps\n",
+    "    )\n",
+    "    trip_mc_coef_file = os.path.join(iteration_output_dir, 'trip_mode_choice_coefficients.csv') \n",
+    "    iteration_output_dir = iteration_output_dir.strip('_'+str(i)) + '_' + str(i+1)\n",
+    "\n",
+    "print(\"\\n\\n\", \"Final coefficient table written to: \", trip_mc_coef_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# trip_mc_coef_file = r'C:\\abm_runs\\rohans\\calibration\\trip_mc\\output\\calibration_output_an_iter_3\\trip_mode_choice_coefficients.csv'\n",
+    "# iteration_output_dir = output_dir.strip('_cold') + '_4'\n",
+    "\n",
+    "# print(trip_mc_coef_file)\n",
+    "# print(iteration_output_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calibration_iterations_to_run = 3\n",
+    "# start_iter_num = 4\n",
+    "\n",
+    "# for i in range(start_iter_num, calibration_iterations_to_run+start_iter_num):\n",
+    "#     asim_trip_calib_util.run_activitysim(\n",
+    "#         data_dir=data_dir,  # data inputs for ActivitySim\n",
+    "#         configs_resident_dir=configs_resident_dir,  # these files are copied to the config section of the run directory\n",
+    "#         configs_common_dir=configs_common_dir,  # these files are copied to the config section of the run directory\n",
+    "#         run_dir=activitysim_run_dir,  # ActivitySim run directory\n",
+    "#         output_dir=iteration_output_dir,  # location to store run model outputs\n",
+    "#         settings_file=warm_start_settings_mp_file,  # optional: ActivitySim settings.yaml to replace the one in configs_dir\n",
+    "#         trip_mc_coef_file=trip_mc_coef_file  # optional: trip_mode_choice_coefficients.csv to replace the one in configs_dir\n",
+    "#     )\n",
+    "    \n",
+    "#     _ = asim_trip_calib_util.perform_trip_mode_choice_model_calibration(\n",
+    "#         asim_output_dir=iteration_output_dir, # folder containing the activitysim model output\n",
+    "#         asim_configs_dir=os.path.join(activitysim_run_dir, 'configs'),  # folder containing activitysim trip mode choice config files\n",
+    "#         trip_mode_choice_calib_targets_file=trip_mode_choice_calib_targets_file,  # folder containing trip mode choice calibration tables\n",
+    "#         max_ASC_adjust=max_ASC_adjust,  \n",
+    "#         damping_factor=damping_factor,  # constant multiplied to all adjustments\n",
+    "#         adjust_when_zero_counts=adjust_when_zero_counts,\n",
+    "#         output_dir=iteration_output_dir,  # location to write model calibration steps\n",
+    "#     )\n",
+    "#     trip_mc_coef_file = os.path.join(iteration_output_dir, 'trip_mode_choice_coefficients.csv') \n",
+    "#     iteration_output_dir = iteration_output_dir.strip('_'+str(i)) + '_' + str(i+1)\n",
+    "\n",
+    "# print(\"\\n\\n\", \"Final coefficient table written to: \", trip_mc_coef_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "2eb7c6f9f406f6f9f49420dd65650c4cb08a10ab5f4a505e57e75dbf5eb536c1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/asim/calibration/resident/trip_mode_choice/scripts/settings_mp_cold_start.yaml b/src/asim/calibration/resident/trip_mode_choice/scripts/settings_mp_cold_start.yaml
new file mode 100644
index 000000000..70fa6e1d7
--- /dev/null
+++ b/src/asim/calibration/resident/trip_mode_choice/scripts/settings_mp_cold_start.yaml
@@ -0,0 +1,106 @@
+inherit_settings: settings.yaml
+
+multiprocess: True
+households_sample_size: 320000
+num_processes: 40
+trace_hh_id: #152855
+chunk_training_mode: disabled
+# chunk_size: 240_000_000_000
+
+use_shadow_pricing: True
+
+memory_profile: False
+
+# raise error if any sub-process fails without waiting for others to complete
+# (Shadow pricing requires fail_fast setting in multiprocessing mode)
+fail_fast: True
+
+resume_after:
+
+models:
+  ### mp_init_proto_pop (single process)
+  - initialize_proto_population # Separate step so proto tables can be split for multiprocess.
+  ### mp_disaggregate_accessibility
+  - compute_disaggregate_accessibility
+  ### mp_initialize_hhs (single process)
+  - initialize_landuse
+  - initialize_households
+  ### mp_accessibility
+  - compute_accessibility
+  ### mp_households
+  - av_ownership
+  - auto_ownership_simulate
+  - work_from_home
+  - external_worker_identification
+  - external_workplace_location
+  - school_location
+  - workplace_location
+  - transit_pass_subsidy
+  - transit_pass_ownership
+  - vehicle_type_choice
+  - transponder_ownership
+  - free_parking
+  - telecommute_frequency
+  - cdap_simulate
+  - mandatory_tour_frequency
+  - mandatory_tour_scheduling
+  - school_escorting
+  - joint_tour_frequency_composition
+  - external_joint_tour_identification
+  - joint_tour_participation
+  - joint_tour_destination
+  - external_joint_tour_destination
+  - joint_tour_scheduling
+  - non_mandatory_tour_frequency
+  - external_non_mandatory_identification
+  - non_mandatory_tour_destination
+  - external_non_mandatory_destination
+  - non_mandatory_tour_scheduling
+  - vehicle_allocation
+  - tour_mode_choice_simulate
+  - atwork_subtour_frequency
+  - atwork_subtour_destination
+  - atwork_subtour_scheduling
+  - atwork_subtour_mode_choice
+  - stop_frequency
+  - trip_purpose
+  - trip_destination
+  - trip_purpose_and_destination
+  - trip_scheduling
+  - trip_mode_choice
+  - parking_location
+  ### mp_summarize (single process)
+  - write_data_dictionary
+  # - track_skim_usage
+  - write_trip_matrices
+  - write_tables
+
+
+multiprocess_steps:
+  - name: mp_init_proto_pop
+    begin: initialize_proto_population
+  - name: mp_disaggregate_accessibility
+    num_processes: 20
+    begin: compute_disaggregate_accessibility
+    slice:
+      tables:
+        - proto_households
+        - proto_persons
+        - proto_tours
+  - name: mp_initialize_hhs
+    begin: initialize_landuse
+  - name: mp_accessibility
+    begin: compute_accessibility
+    num_processes: 10
+    slice:
+      tables:
+        - accessibility
+      except: True  # this is needed so landuse (i.e. destinations) doesn't get split
+  - name: mp_households
+    begin: av_ownership
+    slice:
+      tables:
+        - households
+        - persons
+  - name: mp_summarize
+    begin: write_data_dictionary
diff --git a/src/asim/calibration/resident/trip_mode_choice/scripts/settings_mp_warm_start.yaml b/src/asim/calibration/resident/trip_mode_choice/scripts/settings_mp_warm_start.yaml
new file mode 100644
index 000000000..18704d2a4
--- /dev/null
+++ b/src/asim/calibration/resident/trip_mode_choice/scripts/settings_mp_warm_start.yaml
@@ -0,0 +1,109 @@
+inherit_settings: settings.yaml
+
+multiprocess: True
+households_sample_size: 320000
+num_processes: 40
+trace_hh_id:
+chunk_training_mode: disabled
+# chunk_size: 240_000_000_000
+
+use_shadow_pricing: True
+
+memory_profile: False
+
+# raise error if any sub-process fails without waiting for others to complete
+# (Shadow pricing requires fail_fast setting in multiprocessing mode)
+fail_fast: True
+
+resume_after: trip_scheduling
+
+models:
+  ### mp_init_proto_pop (single process)
+  - initialize_proto_population # Separate step so proto tables can be split for multiprocess.
+  ### mp_disaggregate_accessibility
+  - compute_disaggregate_accessibility
+  ### mp_initialize_hhs (single process)
+  - initialize_landuse
+  - initialize_households
+  ### mp_accessibility
+  - compute_accessibility
+  ### mp_households
+  - av_ownership
+  - auto_ownership_simulate
+  - work_from_home
+  - external_worker_identification
+  - external_workplace_location
+  - school_location
+  - workplace_location
+  - transit_pass_subsidy
+  - transit_pass_ownership
+  - vehicle_type_choice
+  - adjust_auto_operating_cost
+  - transponder_ownership
+  - free_parking
+  - telecommute_frequency
+  - cdap_simulate
+  - mandatory_tour_frequency
+  - mandatory_tour_scheduling
+  - school_escorting
+  - joint_tour_frequency_composition
+  - external_joint_tour_identification
+  - joint_tour_participation
+  - joint_tour_destination
+  - external_joint_tour_destination
+  - joint_tour_scheduling
+  - non_mandatory_tour_frequency
+  - external_non_mandatory_identification
+  - non_mandatory_tour_destination
+  - external_non_mandatory_destination
+  - non_mandatory_tour_scheduling
+  - vehicle_allocation
+  - tour_mode_choice_simulate
+  - atwork_subtour_frequency
+  - atwork_subtour_destination
+  - atwork_subtour_scheduling
+  - atwork_subtour_mode_choice
+  - stop_frequency
+  - trip_purpose
+  - trip_destination
+  - trip_purpose_and_destination
+  - trip_scheduling
+  - trip_mode_choice
+  - parking_location
+  ### mp_summarize (single process)
+  - write_data_dictionary
+  - track_skim_usage
+  - write_trip_matrices
+  # - write_to_datalake
+  - update_tables
+  - write_tables
+
+
+multiprocess_steps:
+  - name: mp_init_proto_pop
+    begin: initialize_proto_population
+  - name: mp_disaggregate_accessibility
+    num_processes: 20
+    begin: compute_disaggregate_accessibility
+    slice:
+      tables:
+        - proto_households
+        - proto_persons
+        - proto_tours
+  - name: mp_initialize_hhs
+    begin: initialize_landuse
+  - name: mp_accessibility
+    begin: compute_accessibility
+    num_processes: 10
+    slice:
+      tables:
+        - accessibility
+      except: True  # this is needed so landuse (i.e. destinations) doesn't get split
+  - name: mp_households
+    begin: av_ownership
+    slice:
+      tables:
+        - households
+        - persons
+  - name: mp_summarize
+    begin: write_data_dictionary
diff --git a/src/asim/calibration/resident/trip_mode_choice/targets/trip_mode_choice_calibration_targets_2024-01-23_updated.csv b/src/asim/calibration/resident/trip_mode_choice/targets/trip_mode_choice_calibration_targets_2024-01-23_updated.csv
new file mode 100644
index 000000000..1a115a272
--- /dev/null
+++ b/src/asim/calibration/resident/trip_mode_choice/targets/trip_mode_choice_calibration_targets_2024-01-23_updated.csv
@@ -0,0 +1,2305 @@
+grouped_linked_trip_mode,grouped_tour_mode,trips,purpose
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+SHARED2,All,2711619.7715306785,Total
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+PNR-TRANSIT,All,9159.27528574814,Total
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+TNC-SHARED,All,0.0,Total
+SCHOOLBUS,All,13019.9328298986,Total
+ESCOOTER,All,8955.813478970831,Total
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+SCHOOLBUS,DRIVEALONE,0.0,Joint-Maintenance
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+EBIKE,DRIVEALONE,0.0,Joint-Maintenance
+All,SHARED2,548599.0463902731,Joint-Maintenance
+DRIVEALONE,SHARED2,0.0,Joint-Maintenance
+SHARED2,SHARED2,530295.591315327,Joint-Maintenance
+SHARED3,SHARED2,0.0,Joint-Maintenance
+WALK,SHARED2,18303.4550749462,Joint-Maintenance
+BIKE,SHARED2,0.0,Joint-Maintenance
+WALK-TRANSIT,SHARED2,0.0,Joint-Maintenance
+PNR-TRANSIT,SHARED2,0.0,Joint-Maintenance
+KNR-TRANSIT,SHARED2,0.0,Joint-Maintenance
+TNC-TRANSIT,SHARED2,0.0,Joint-Maintenance
+TAXI,SHARED2,0.0,Joint-Maintenance
+TNC-REG,SHARED2,0.0,Joint-Maintenance
+TNC-SHARED,SHARED2,0.0,Joint-Maintenance
+SCHOOLBUS,SHARED2,0.0,Joint-Maintenance
+ESCOOTER,SHARED2,0.0,Joint-Maintenance
+EBIKE,SHARED2,0.0,Joint-Maintenance
+All,SHARED3,313182.9700433132,Joint-Maintenance
+DRIVEALONE,SHARED3,0.0,Joint-Maintenance
+SHARED2,SHARED3,4800.26349208372,Joint-Maintenance
+SHARED3,SHARED3,306519.590934374,Joint-Maintenance
+WALK,SHARED3,1863.11561685545,Joint-Maintenance
+BIKE,SHARED3,0.0,Joint-Maintenance
+WALK-TRANSIT,SHARED3,0.0,Joint-Maintenance
+PNR-TRANSIT,SHARED3,0.0,Joint-Maintenance
+KNR-TRANSIT,SHARED3,0.0,Joint-Maintenance
+TNC-TRANSIT,SHARED3,0.0,Joint-Maintenance
+TAXI,SHARED3,0.0,Joint-Maintenance
+TNC-REG,SHARED3,0.0,Joint-Maintenance
+TNC-SHARED,SHARED3,0.0,Joint-Maintenance
+SCHOOLBUS,SHARED3,0.0,Joint-Maintenance
+ESCOOTER,SHARED3,0.0,Joint-Maintenance
+EBIKE,SHARED3,0.0,Joint-Maintenance
+All,WALK,6097.79292568895,Joint-Maintenance
+DRIVEALONE,WALK,0.0,Joint-Maintenance
+SHARED2,WALK,0.0,Joint-Maintenance
+SHARED3,WALK,0.0,Joint-Maintenance
+WALK,WALK,6097.79292568895,Joint-Maintenance
+BIKE,WALK,0.0,Joint-Maintenance
+WALK-TRANSIT,WALK,0.0,Joint-Maintenance
+PNR-TRANSIT,WALK,0.0,Joint-Maintenance
+KNR-TRANSIT,WALK,0.0,Joint-Maintenance
+TNC-TRANSIT,WALK,0.0,Joint-Maintenance
+TAXI,WALK,0.0,Joint-Maintenance
+TNC-REG,WALK,0.0,Joint-Maintenance
+TNC-SHARED,WALK,0.0,Joint-Maintenance
+SCHOOLBUS,WALK,0.0,Joint-Maintenance
+ESCOOTER,WALK,0.0,Joint-Maintenance
+EBIKE,WALK,0.0,Joint-Maintenance
+All,BIKE,5143.71734512664,Joint-Maintenance
+DRIVEALONE,BIKE,0.0,Joint-Maintenance
+SHARED2,BIKE,0.0,Joint-Maintenance
+SHARED3,BIKE,0.0,Joint-Maintenance
+WALK,BIKE,0.0,Joint-Maintenance
+BIKE,BIKE,5143.71734512664,Joint-Maintenance
+WALK-TRANSIT,BIKE,0.0,Joint-Maintenance
+PNR-TRANSIT,BIKE,0.0,Joint-Maintenance
+KNR-TRANSIT,BIKE,0.0,Joint-Maintenance
+TNC-TRANSIT,BIKE,0.0,Joint-Maintenance
+TAXI,BIKE,0.0,Joint-Maintenance
+TNC-REG,BIKE,0.0,Joint-Maintenance
+TNC-SHARED,BIKE,0.0,Joint-Maintenance
+SCHOOLBUS,BIKE,0.0,Joint-Maintenance
+ESCOOTER,BIKE,0.0,Joint-Maintenance
+EBIKE,BIKE,0.0,Joint-Maintenance
+All,WALK-TRANSIT,0.0,Joint-Maintenance
+DRIVEALONE,WALK-TRANSIT,0.0,Joint-Maintenance
+SHARED2,WALK-TRANSIT,0.0,Joint-Maintenance
+SHARED3,WALK-TRANSIT,0.0,Joint-Maintenance
+WALK,WALK-TRANSIT,0.0,Joint-Maintenance
+BIKE,WALK-TRANSIT,0.0,Joint-Maintenance
+WALK-TRANSIT,WALK-TRANSIT,0.0,Joint-Maintenance
+PNR-TRANSIT,WALK-TRANSIT,0.0,Joint-Maintenance
+KNR-TRANSIT,WALK-TRANSIT,0.0,Joint-Maintenance
+TNC-TRANSIT,WALK-TRANSIT,0.0,Joint-Maintenance
+TAXI,WALK-TRANSIT,0.0,Joint-Maintenance
+TNC-REG,WALK-TRANSIT,0.0,Joint-Maintenance
+TNC-SHARED,WALK-TRANSIT,0.0,Joint-Maintenance
+SCHOOLBUS,WALK-TRANSIT,0.0,Joint-Maintenance
+ESCOOTER,WALK-TRANSIT,0.0,Joint-Maintenance
+EBIKE,WALK-TRANSIT,0.0,Joint-Maintenance
+All,PNR-TRANSIT,0.0,Joint-Maintenance
+DRIVEALONE,PNR-TRANSIT,0.0,Joint-Maintenance
+SHARED2,PNR-TRANSIT,0.0,Joint-Maintenance
+SHARED3,PNR-TRANSIT,0.0,Joint-Maintenance
+WALK,PNR-TRANSIT,0.0,Joint-Maintenance
+BIKE,PNR-TRANSIT,0.0,Joint-Maintenance
+WALK-TRANSIT,PNR-TRANSIT,0.0,Joint-Maintenance
+PNR-TRANSIT,PNR-TRANSIT,0.0,Joint-Maintenance
+KNR-TRANSIT,PNR-TRANSIT,0.0,Joint-Maintenance
+TNC-TRANSIT,PNR-TRANSIT,0.0,Joint-Maintenance
+TAXI,PNR-TRANSIT,0.0,Joint-Maintenance
+TNC-REG,PNR-TRANSIT,0.0,Joint-Maintenance
+TNC-SHARED,PNR-TRANSIT,0.0,Joint-Maintenance
+SCHOOLBUS,PNR-TRANSIT,0.0,Joint-Maintenance
+ESCOOTER,PNR-TRANSIT,0.0,Joint-Maintenance
+EBIKE,PNR-TRANSIT,0.0,Joint-Maintenance
+All,KNR-TRANSIT,0.0,Joint-Maintenance
+DRIVEALONE,KNR-TRANSIT,0.0,Joint-Maintenance
+SHARED2,KNR-TRANSIT,0.0,Joint-Maintenance
+SHARED3,KNR-TRANSIT,0.0,Joint-Maintenance
+WALK,KNR-TRANSIT,0.0,Joint-Maintenance
+BIKE,KNR-TRANSIT,0.0,Joint-Maintenance
+WALK-TRANSIT,KNR-TRANSIT,0.0,Joint-Maintenance
+PNR-TRANSIT,KNR-TRANSIT,0.0,Joint-Maintenance
+KNR-TRANSIT,KNR-TRANSIT,0.0,Joint-Maintenance
+TNC-TRANSIT,KNR-TRANSIT,0.0,Joint-Maintenance
+TAXI,KNR-TRANSIT,0.0,Joint-Maintenance
+TNC-REG,KNR-TRANSIT,0.0,Joint-Maintenance
+TNC-SHARED,KNR-TRANSIT,0.0,Joint-Maintenance
+SCHOOLBUS,KNR-TRANSIT,0.0,Joint-Maintenance
+ESCOOTER,KNR-TRANSIT,0.0,Joint-Maintenance
+EBIKE,KNR-TRANSIT,0.0,Joint-Maintenance
+All,TNC-TRANSIT,0.0,Joint-Maintenance
+DRIVEALONE,TNC-TRANSIT,0.0,Joint-Maintenance
+SHARED2,TNC-TRANSIT,0.0,Joint-Maintenance
+SHARED3,TNC-TRANSIT,0.0,Joint-Maintenance
+WALK,TNC-TRANSIT,0.0,Joint-Maintenance
+BIKE,TNC-TRANSIT,0.0,Joint-Maintenance
+WALK-TRANSIT,TNC-TRANSIT,0.0,Joint-Maintenance
+PNR-TRANSIT,TNC-TRANSIT,0.0,Joint-Maintenance
+KNR-TRANSIT,TNC-TRANSIT,0.0,Joint-Maintenance
+TNC-TRANSIT,TNC-TRANSIT,0.0,Joint-Maintenance
+TAXI,TNC-TRANSIT,0.0,Joint-Maintenance
+TNC-REG,TNC-TRANSIT,0.0,Joint-Maintenance
+TNC-SHARED,TNC-TRANSIT,0.0,Joint-Maintenance
+SCHOOLBUS,TNC-TRANSIT,0.0,Joint-Maintenance
+ESCOOTER,TNC-TRANSIT,0.0,Joint-Maintenance
+EBIKE,TNC-TRANSIT,0.0,Joint-Maintenance
+All,TAXI,0.0,Joint-Maintenance
+DRIVEALONE,TAXI,0.0,Joint-Maintenance
+SHARED2,TAXI,0.0,Joint-Maintenance
+SHARED3,TAXI,0.0,Joint-Maintenance
+WALK,TAXI,0.0,Joint-Maintenance
+BIKE,TAXI,0.0,Joint-Maintenance
+WALK-TRANSIT,TAXI,0.0,Joint-Maintenance
+PNR-TRANSIT,TAXI,0.0,Joint-Maintenance
+KNR-TRANSIT,TAXI,0.0,Joint-Maintenance
+TNC-TRANSIT,TAXI,0.0,Joint-Maintenance
+TAXI,TAXI,0.0,Joint-Maintenance
+TNC-REG,TAXI,0.0,Joint-Maintenance
+TNC-SHARED,TAXI,0.0,Joint-Maintenance
+SCHOOLBUS,TAXI,0.0,Joint-Maintenance
+ESCOOTER,TAXI,0.0,Joint-Maintenance
+EBIKE,TAXI,0.0,Joint-Maintenance
+All,TNC-REG,0.0,Joint-Maintenance
+DRIVEALONE,TNC-REG,0.0,Joint-Maintenance
+SHARED2,TNC-REG,0.0,Joint-Maintenance
+SHARED3,TNC-REG,0.0,Joint-Maintenance
+WALK,TNC-REG,0.0,Joint-Maintenance
+BIKE,TNC-REG,0.0,Joint-Maintenance
+WALK-TRANSIT,TNC-REG,0.0,Joint-Maintenance
+PNR-TRANSIT,TNC-REG,0.0,Joint-Maintenance
+KNR-TRANSIT,TNC-REG,0.0,Joint-Maintenance
+TNC-TRANSIT,TNC-REG,0.0,Joint-Maintenance
+TAXI,TNC-REG,0.0,Joint-Maintenance
+TNC-REG,TNC-REG,0.0,Joint-Maintenance
+TNC-SHARED,TNC-REG,0.0,Joint-Maintenance
+SCHOOLBUS,TNC-REG,0.0,Joint-Maintenance
+ESCOOTER,TNC-REG,0.0,Joint-Maintenance
+EBIKE,TNC-REG,0.0,Joint-Maintenance
+All,TNC-SHARED,0.0,Joint-Maintenance
+DRIVEALONE,TNC-SHARED,0.0,Joint-Maintenance
+SHARED2,TNC-SHARED,0.0,Joint-Maintenance
+SHARED3,TNC-SHARED,0.0,Joint-Maintenance
+WALK,TNC-SHARED,0.0,Joint-Maintenance
+BIKE,TNC-SHARED,0.0,Joint-Maintenance
+WALK-TRANSIT,TNC-SHARED,0.0,Joint-Maintenance
+PNR-TRANSIT,TNC-SHARED,0.0,Joint-Maintenance
+KNR-TRANSIT,TNC-SHARED,0.0,Joint-Maintenance
+TNC-TRANSIT,TNC-SHARED,0.0,Joint-Maintenance
+TAXI,TNC-SHARED,0.0,Joint-Maintenance
+TNC-REG,TNC-SHARED,0.0,Joint-Maintenance
+TNC-SHARED,TNC-SHARED,0.0,Joint-Maintenance
+SCHOOLBUS,TNC-SHARED,0.0,Joint-Maintenance
+ESCOOTER,TNC-SHARED,0.0,Joint-Maintenance
+EBIKE,TNC-SHARED,0.0,Joint-Maintenance
+All,SCHOOLBUS,0.0,Joint-Maintenance
+DRIVEALONE,SCHOOLBUS,0.0,Joint-Maintenance
+SHARED2,SCHOOLBUS,0.0,Joint-Maintenance
+SHARED3,SCHOOLBUS,0.0,Joint-Maintenance
+WALK,SCHOOLBUS,0.0,Joint-Maintenance
+BIKE,SCHOOLBUS,0.0,Joint-Maintenance
+WALK-TRANSIT,SCHOOLBUS,0.0,Joint-Maintenance
+PNR-TRANSIT,SCHOOLBUS,0.0,Joint-Maintenance
+KNR-TRANSIT,SCHOOLBUS,0.0,Joint-Maintenance
+TNC-TRANSIT,SCHOOLBUS,0.0,Joint-Maintenance
+TAXI,SCHOOLBUS,0.0,Joint-Maintenance
+TNC-REG,SCHOOLBUS,0.0,Joint-Maintenance
+TNC-SHARED,SCHOOLBUS,0.0,Joint-Maintenance
+SCHOOLBUS,SCHOOLBUS,0.0,Joint-Maintenance
+ESCOOTER,SCHOOLBUS,0.0,Joint-Maintenance
+EBIKE,SCHOOLBUS,0.0,Joint-Maintenance
+All,ESCOOTER,0.0,Joint-Maintenance
+DRIVEALONE,ESCOOTER,0.0,Joint-Maintenance
+SHARED2,ESCOOTER,0.0,Joint-Maintenance
+SHARED3,ESCOOTER,0.0,Joint-Maintenance
+WALK,ESCOOTER,0.0,Joint-Maintenance
+BIKE,ESCOOTER,0.0,Joint-Maintenance
+WALK-TRANSIT,ESCOOTER,0.0,Joint-Maintenance
+PNR-TRANSIT,ESCOOTER,0.0,Joint-Maintenance
+KNR-TRANSIT,ESCOOTER,0.0,Joint-Maintenance
+TNC-TRANSIT,ESCOOTER,0.0,Joint-Maintenance
+TAXI,ESCOOTER,0.0,Joint-Maintenance
+TNC-REG,ESCOOTER,0.0,Joint-Maintenance
+TNC-SHARED,ESCOOTER,0.0,Joint-Maintenance
+SCHOOLBUS,ESCOOTER,0.0,Joint-Maintenance
+ESCOOTER,ESCOOTER,0.0,Joint-Maintenance
+EBIKE,ESCOOTER,0.0,Joint-Maintenance
+All,EBIKE,299.603036345644,Joint-Maintenance
+DRIVEALONE,EBIKE,0.0,Joint-Maintenance
+SHARED2,EBIKE,0.0,Joint-Maintenance
+SHARED3,EBIKE,0.0,Joint-Maintenance
+WALK,EBIKE,0.0,Joint-Maintenance
+BIKE,EBIKE,0.0,Joint-Maintenance
+WALK-TRANSIT,EBIKE,0.0,Joint-Maintenance
+PNR-TRANSIT,EBIKE,0.0,Joint-Maintenance
+KNR-TRANSIT,EBIKE,0.0,Joint-Maintenance
+TNC-TRANSIT,EBIKE,0.0,Joint-Maintenance
+TAXI,EBIKE,0.0,Joint-Maintenance
+TNC-REG,EBIKE,0.0,Joint-Maintenance
+TNC-SHARED,EBIKE,0.0,Joint-Maintenance
+SCHOOLBUS,EBIKE,0.0,Joint-Maintenance
+ESCOOTER,EBIKE,0.0,Joint-Maintenance
+EBIKE,EBIKE,299.603036345644,Joint-Maintenance
+All,All,1113973.2569043974,Joint-Discretionary
+DRIVEALONE,All,0.0,Joint-Discretionary
+SHARED2,All,554571.6827167514,Joint-Discretionary
+SHARED3,All,307487.732136806,Joint-Discretionary
+WALK,All,249778.2876354955,Joint-Discretionary
+BIKE,All,1242.85666586099,Joint-Discretionary
+WALK-TRANSIT,All,0.0,Joint-Discretionary
+PNR-TRANSIT,All,0.0,Joint-Discretionary
+KNR-TRANSIT,All,0.0,Joint-Discretionary
+TNC-TRANSIT,All,0.0,Joint-Discretionary
+TAXI,All,0.0,Joint-Discretionary
+TNC-REG,All,0.0,Joint-Discretionary
+TNC-SHARED,All,0.0,Joint-Discretionary
+SCHOOLBUS,All,0.0,Joint-Discretionary
+ESCOOTER,All,0.0,Joint-Discretionary
+EBIKE,All,892.69774948352,Joint-Discretionary
+All,DRIVEALONE,0.0,Joint-Discretionary
+DRIVEALONE,DRIVEALONE,0.0,Joint-Discretionary
+SHARED2,DRIVEALONE,0.0,Joint-Discretionary
+SHARED3,DRIVEALONE,0.0,Joint-Discretionary
+WALK,DRIVEALONE,0.0,Joint-Discretionary
+BIKE,DRIVEALONE,0.0,Joint-Discretionary
+WALK-TRANSIT,DRIVEALONE,0.0,Joint-Discretionary
+PNR-TRANSIT,DRIVEALONE,0.0,Joint-Discretionary
+KNR-TRANSIT,DRIVEALONE,0.0,Joint-Discretionary
+TNC-TRANSIT,DRIVEALONE,0.0,Joint-Discretionary
+TAXI,DRIVEALONE,0.0,Joint-Discretionary
+TNC-REG,DRIVEALONE,0.0,Joint-Discretionary
+TNC-SHARED,DRIVEALONE,0.0,Joint-Discretionary
+SCHOOLBUS,DRIVEALONE,0.0,Joint-Discretionary
+ESCOOTER,DRIVEALONE,0.0,Joint-Discretionary
+EBIKE,DRIVEALONE,0.0,Joint-Discretionary
+All,SHARED2,566512.4177509932,Joint-Discretionary
+DRIVEALONE,SHARED2,0.0,Joint-Discretionary
+SHARED2,SHARED2,546580.82017769,Joint-Discretionary
+SHARED3,SHARED2,0.0,Joint-Discretionary
+WALK,SHARED2,19931.5975733031,Joint-Discretionary
+BIKE,SHARED2,0.0,Joint-Discretionary
+WALK-TRANSIT,SHARED2,0.0,Joint-Discretionary
+PNR-TRANSIT,SHARED2,0.0,Joint-Discretionary
+KNR-TRANSIT,SHARED2,0.0,Joint-Discretionary
+TNC-TRANSIT,SHARED2,0.0,Joint-Discretionary
+TAXI,SHARED2,0.0,Joint-Discretionary
+TNC-REG,SHARED2,0.0,Joint-Discretionary
+TNC-SHARED,SHARED2,0.0,Joint-Discretionary
+SCHOOLBUS,SHARED2,0.0,Joint-Discretionary
+ESCOOTER,SHARED2,0.0,Joint-Discretionary
+EBIKE,SHARED2,0.0,Joint-Discretionary
+All,SHARED3,331477.6360925957,Joint-Discretionary
+DRIVEALONE,SHARED3,0.0,Joint-Discretionary
+SHARED2,SHARED3,7990.86253906131,Joint-Discretionary
+SHARED3,SHARED3,307487.732136806,Joint-Discretionary
+WALK,SHARED3,15999.0414167284,Joint-Discretionary
+BIKE,SHARED3,0.0,Joint-Discretionary
+WALK-TRANSIT,SHARED3,0.0,Joint-Discretionary
+PNR-TRANSIT,SHARED3,0.0,Joint-Discretionary
+KNR-TRANSIT,SHARED3,0.0,Joint-Discretionary
+TNC-TRANSIT,SHARED3,0.0,Joint-Discretionary
+TAXI,SHARED3,0.0,Joint-Discretionary
+TNC-REG,SHARED3,0.0,Joint-Discretionary
+TNC-SHARED,SHARED3,0.0,Joint-Discretionary
+SCHOOLBUS,SHARED3,0.0,Joint-Discretionary
+ESCOOTER,SHARED3,0.0,Joint-Discretionary
+EBIKE,SHARED3,0.0,Joint-Discretionary
+All,WALK,213847.648645464,Joint-Discretionary
+DRIVEALONE,WALK,0.0,Joint-Discretionary
+SHARED2,WALK,0.0,Joint-Discretionary
+SHARED3,WALK,0.0,Joint-Discretionary
+WALK,WALK,213847.648645464,Joint-Discretionary
+BIKE,WALK,0.0,Joint-Discretionary
+WALK-TRANSIT,WALK,0.0,Joint-Discretionary
+PNR-TRANSIT,WALK,0.0,Joint-Discretionary
+KNR-TRANSIT,WALK,0.0,Joint-Discretionary
+TNC-TRANSIT,WALK,0.0,Joint-Discretionary
+TAXI,WALK,0.0,Joint-Discretionary
+TNC-REG,WALK,0.0,Joint-Discretionary
+TNC-SHARED,WALK,0.0,Joint-Discretionary
+SCHOOLBUS,WALK,0.0,Joint-Discretionary
+ESCOOTER,WALK,0.0,Joint-Discretionary
+EBIKE,WALK,0.0,Joint-Discretionary
+All,BIKE,1242.85666586099,Joint-Discretionary
+DRIVEALONE,BIKE,0.0,Joint-Discretionary
+SHARED2,BIKE,0.0,Joint-Discretionary
+SHARED3,BIKE,0.0,Joint-Discretionary
+WALK,BIKE,0.0,Joint-Discretionary
+BIKE,BIKE,1242.85666586099,Joint-Discretionary
+WALK-TRANSIT,BIKE,0.0,Joint-Discretionary
+PNR-TRANSIT,BIKE,0.0,Joint-Discretionary
+KNR-TRANSIT,BIKE,0.0,Joint-Discretionary
+TNC-TRANSIT,BIKE,0.0,Joint-Discretionary
+TAXI,BIKE,0.0,Joint-Discretionary
+TNC-REG,BIKE,0.0,Joint-Discretionary
+TNC-SHARED,BIKE,0.0,Joint-Discretionary
+SCHOOLBUS,BIKE,0.0,Joint-Discretionary
+ESCOOTER,BIKE,0.0,Joint-Discretionary
+EBIKE,BIKE,0.0,Joint-Discretionary
+All,WALK-TRANSIT,0.0,Joint-Discretionary
+DRIVEALONE,WALK-TRANSIT,0.0,Joint-Discretionary
+SHARED2,WALK-TRANSIT,0.0,Joint-Discretionary
+SHARED3,WALK-TRANSIT,0.0,Joint-Discretionary
+WALK,WALK-TRANSIT,0.0,Joint-Discretionary
+BIKE,WALK-TRANSIT,0.0,Joint-Discretionary
+WALK-TRANSIT,WALK-TRANSIT,0.0,Joint-Discretionary
+PNR-TRANSIT,WALK-TRANSIT,0.0,Joint-Discretionary
+KNR-TRANSIT,WALK-TRANSIT,0.0,Joint-Discretionary
+TNC-TRANSIT,WALK-TRANSIT,0.0,Joint-Discretionary
+TAXI,WALK-TRANSIT,0.0,Joint-Discretionary
+TNC-REG,WALK-TRANSIT,0.0,Joint-Discretionary
+TNC-SHARED,WALK-TRANSIT,0.0,Joint-Discretionary
+SCHOOLBUS,WALK-TRANSIT,0.0,Joint-Discretionary
+ESCOOTER,WALK-TRANSIT,0.0,Joint-Discretionary
+EBIKE,WALK-TRANSIT,0.0,Joint-Discretionary
+All,PNR-TRANSIT,0.0,Joint-Discretionary
+DRIVEALONE,PNR-TRANSIT,0.0,Joint-Discretionary
+SHARED2,PNR-TRANSIT,0.0,Joint-Discretionary
+SHARED3,PNR-TRANSIT,0.0,Joint-Discretionary
+WALK,PNR-TRANSIT,0.0,Joint-Discretionary
+BIKE,PNR-TRANSIT,0.0,Joint-Discretionary
+WALK-TRANSIT,PNR-TRANSIT,0.0,Joint-Discretionary
+PNR-TRANSIT,PNR-TRANSIT,0.0,Joint-Discretionary
+KNR-TRANSIT,PNR-TRANSIT,0.0,Joint-Discretionary
+TNC-TRANSIT,PNR-TRANSIT,0.0,Joint-Discretionary
+TAXI,PNR-TRANSIT,0.0,Joint-Discretionary
+TNC-REG,PNR-TRANSIT,0.0,Joint-Discretionary
+TNC-SHARED,PNR-TRANSIT,0.0,Joint-Discretionary
+SCHOOLBUS,PNR-TRANSIT,0.0,Joint-Discretionary
+ESCOOTER,PNR-TRANSIT,0.0,Joint-Discretionary
+EBIKE,PNR-TRANSIT,0.0,Joint-Discretionary
+All,KNR-TRANSIT,0.0,Joint-Discretionary
+DRIVEALONE,KNR-TRANSIT,0.0,Joint-Discretionary
+SHARED2,KNR-TRANSIT,0.0,Joint-Discretionary
+SHARED3,KNR-TRANSIT,0.0,Joint-Discretionary
+WALK,KNR-TRANSIT,0.0,Joint-Discretionary
+BIKE,KNR-TRANSIT,0.0,Joint-Discretionary
+WALK-TRANSIT,KNR-TRANSIT,0.0,Joint-Discretionary
+PNR-TRANSIT,KNR-TRANSIT,0.0,Joint-Discretionary
+KNR-TRANSIT,KNR-TRANSIT,0.0,Joint-Discretionary
+TNC-TRANSIT,KNR-TRANSIT,0.0,Joint-Discretionary
+TAXI,KNR-TRANSIT,0.0,Joint-Discretionary
+TNC-REG,KNR-TRANSIT,0.0,Joint-Discretionary
+TNC-SHARED,KNR-TRANSIT,0.0,Joint-Discretionary
+SCHOOLBUS,KNR-TRANSIT,0.0,Joint-Discretionary
+ESCOOTER,KNR-TRANSIT,0.0,Joint-Discretionary
+EBIKE,KNR-TRANSIT,0.0,Joint-Discretionary
+All,TNC-TRANSIT,0.0,Joint-Discretionary
+DRIVEALONE,TNC-TRANSIT,0.0,Joint-Discretionary
+SHARED2,TNC-TRANSIT,0.0,Joint-Discretionary
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+EBIKE,EBIKE,892.69774948352,Joint-Discretionary
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+SHARED2,SHARED2,17555.8340173163,Work sub-tour
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+WALK,SHARED2,312.172259764168,Work sub-tour
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+SHARED2,SHARED3,10.4988032707324,Work sub-tour
+SHARED3,SHARED3,11376.833426079,Work sub-tour
+WALK,SHARED3,10.4988032707324,Work sub-tour
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+All,WALK,67424.2196809755,Work sub-tour
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+WALK,WALK,67424.2196809755,Work sub-tour
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+All,BIKE,2794.273584432741,Work sub-tour
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+SHARED3,BIKE,0.0,Work sub-tour
+WALK,BIKE,17.2840002974213,Work sub-tour
+BIKE,BIKE,2776.98958413532,Work sub-tour
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+EBIKE,ESCOOTER,0.0,Work sub-tour
+All,EBIKE,442.843575567199,Work sub-tour
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+SCHOOLBUS,EBIKE,0.0,Work sub-tour
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+EBIKE,EBIKE,442.843575567199,Work sub-tour