running with full sample

.gitignore

@@ -1,2 +1,4 @@
-output\*
-extensions\__pycache__
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+output/

configs/resident/settings.yaml

@@ -13,7 +13,7 @@ chunk_training_mode: disabled
 # see shadow_pricing.yaml for additional settings
 use_shadow_pricing: True
 
-memory_profile: False
+memory_profile: True
 
 # Sample TAZ before MAZ in destination choice
 want_dest_choice_presampling: True
@@ -24,7 +24,7 @@ want_dest_choice_sample_tables: False
 
 # trace household id; comment out or leave empty for no trace
 # households with all tour types
-trace_hh_id: 791851
+# trace_hh_id: 1051657
 
 chunk_training_mode: disabled
 
@@ -100,7 +100,7 @@ input_table_list:
       - zip09
       - luz_id
 
-cleanup_pipeline_after_run: True
+cleanup_pipeline_after_run: False
 
 output_tables:
   h5_store: False
@@ -127,7 +127,7 @@ distributed_time_factor_nonwork_stddev: 0.6
 distributed_time_factor_min: 0.1
 distributed_time_factor_max: 10
 
-resume_after: workplace_location
+resume_after:
 
 models:
   ### mp_init_proto_pop (single process)

configs/resident/transit_pass_subsidy.yaml

@@ -11,4 +11,4 @@ preprocessor:
     - accessibility
     - land_use
 
-CHOOSER_FILTER_COLUMN_NAME: transit_subsidy_available
+# CHOOSER_FILTER_COLUMN_NAME: transit_subsidy_available

configs/resident/trip_mode_choice.csv

@@ -135,7 +135,7 @@ util_PNR_PRM_Walk_other_time,PNR_PRM - Walk other time,@(odt_skims['PNROUT_PRM_X
 util_PNR_PRM_Fare,PNR_PRM - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent)  * df.outbound",,,,,,,,,,coef_income,,,,,,,,,,,,,
 util_PNR_PRM_ASC,PNR_PRM - Alternative-specific constant,@df.pnr_prm_out_asc * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,,
 util_PNR_PRM_PNR_cost,PNR_PRM - PNR cost,"@(df.auto_op_cost * (odt_skims['PNROUT_PRM_ACC']) *driveSpeed) * df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,,,,,,,,,,
-util_PNRIN_PRM_PNR_cost,PNR_PRM - PNR cost,"@(df.auto_op_cost * (dot_skims['PNRIN_PRM_ACC']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,,,,,,,,,,
+util_PNRIN_PRM_PNR_cost,PNR_PRM - PNR cost,"@(df.auto_op_cost * (odt_skims['PNRIN_PRM_EGR']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,,,,,,,,,,
 util_PNRIN_PRM_In_vehicle_time,PNRIN_PRM - In-vehicle time,@(odt_skims['PNRIN_PRM_TOTALIVTT']) * df.time_factor   * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,,
 util_PNRIN_PRM_In_vehicle_time_on_LRT,PNRIN_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNRIN_PRM_LRTIVTT']) * df.time_factor    * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,,
 util_PNRIN_PRM_In_vehicle_time_on_CMR,PNRIN_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNRIN_PRM_CMRIVTT']) * df.time_factor    * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,,