Estimation spec changes

.vscode/launch.json

@@ -17,6 +17,20 @@
                 "-o", "outputs/full",
             ]
         },
+        {
+            "name": "Run Estimation Mode",
+            "type": "debugpy",
+            "request": "launch",
+            "cwd": "${workspaceFolder}/resident",
+            "program": "simulation.py",
+            "console": "integratedTerminal",
+            "args": [
+                "-c", "configs_estimation", // check the settings.yaml file for run settings!
+                "-c", "configs",
+                "-d", "model_data/metro/data_full",
+                "-o", "outputs/estimation",
+            ]
+        },
         {
             "name": "Run Cropped Example",
             "type": "debugpy",

resident/configs/accessibility.yaml

@@ -1,2 +1,3 @@
 # columns from land_use table to add to df
 land_use_columns: ['EMP_RET', 'EMP_TOTAL', 'TOTHHS', 'walk_dist_local_bus']
+explicit_chunk: 0.2

resident/configs/annotate_persons.csv

@@ -65,4 +65,4 @@ travel time sensitivity factor for non-work travel,time_factor_nonwork,"np.minim
 ,naics_code,_naics_code.astype('int') if all(_naics_code!=0) else 0
 #,,
 # FIXME does filling with -1 make sense here? depends on what the missing values mean,,
-occupation categories mapped to workplace segments,occupation,persons.OCCP.fillna(-1).map(occupation_xwalk)
+occupation categories mapped to workplace segments,occupation,persons.OCCP.fillna(999).map(occupation_xwalk)

resident/configs/constants.yaml

@@ -99,7 +99,6 @@ occupation_xwalk:
   5: nat_res_cnstr_maint
   6: prod_trans_move
   999: missing # no occupation 
-  -1: missing # no occupation
 
 # RIDEHAIL Settings - all costs in dollars, times in minutes
 Taxi_baseFare: 3.00

resident/configs/destination_choice_size_terms.csv

@@ -11,12 +11,12 @@ school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
 school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0
 school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
 non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
-non_mandatory,shopping,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0
-non_mandatory,othmaint,0,0,0,0,0,0,0,1.60296,0.42255,0.42255,0,0.24001,0,0,0,0,0,0,0,0,0
-non_mandatory,eatout,0.5512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-non_mandatory,social,0.3006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+non_mandatory,shopping,0.01,0,0,0,0,0,0,1,0.1,0,0,0,0,0,0,0,0,0,0,0,0
+non_mandatory,othmaint,0,0,0.1,0.1,0.1,0.1,0.1,1.60296,0.42255,0.42255,0.1,0.24001,0.1,0.1,0.1,0,0,0,0,0,0
+non_mandatory,eatout,0.5512,0,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,1,0,0,0,0,0,0
+non_mandatory,social,0.3006,0,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0,0,0,0,0,0
 non_mandatory,othdiscr,0.04333,0,0,0,0,0.004465,0,0.042025,0.004465,0,1,0.005953,0,0.20337,0.03453,0,0.03167,0.05136,0.02258,3.71685,0
-atwork,atwork,0,0,0,0,0,0,0,0.104,0.0145,0,0,0,0,0,0,0,0,0,0,0,0
+atwork,atwork,0,0,0.1,0.1,0.1,0.1,0.1,0.104,0.0145,0.1,0.1,0.1,0.1,0.1,0.1,0,0,0,0,0,0
 trip,work,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
 trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
 trip,shopping,0.000001,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0.0001,0,0,0,0,0

resident/configs/disaggregate_accessibility.yaml

@@ -94,7 +94,7 @@ CREATE_TABLES:
                 # FIXME this should be updated when occupation_xwalk is updated
                 OCCP: # occupation codes
                     1: 1   # Management Occupations
-                    2: -1    # Non-worker
+                    2: 999    # Non-worker
 
     PROTO_TOURS:
         index_col: proto_tour_id

resident/configs/external_worker_identification.csv

@@ -1,16 +1,16 @@
-Label,Description,Expression,work_external,work_internal
-util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station,
+Label,Description,Expression,work_internal,work_external
+util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,,coef_dist_to_nearest_ext_station
 # FIXME just have a single external indicator not counts at the station here and in destination choice size terms,,,,
-#util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.external_work,df.closest_external_zone))",coef_size_of_nearest_ext_station,
-util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.EXTERNAL,df.closest_external_zone))",coef_size_of_nearest_ext_station,
-util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5,
-util_part_time,Part time worker,"@np.where(df.pemploy == 2,1,0)",coef_part_time,
-util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15,
-util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,coef_inc_15_25,
-util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,coef_inc_25_50,
-util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,coef_inc_150_250,
-util_inc_250plus,Household Income $250k+,@(df.income>=250000),coef_inc_250plus,
-util_asc,Alternative-specific constant for external worker,1,asc_external_worker,
-util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999,
-util_2016,Constant for pre-COVID conditions,@PRE_COVID,asc_external_2016,
-util_calib,Constant for calibration,1,0.35,
+#util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.external_work,df.closest_external_zone))",,coef_size_of_nearest_ext_station
+util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.EXTERNAL,df.closest_external_zone))",,coef_size_of_nearest_ext_station
+util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",,coef_dist_lt_2p5
+util_part_time,Part time worker,"@np.where(df.pemploy == 2,1,0)",,coef_part_time
+util_inc_lt15,Household Income less than $15k,@(df.income<15000),,coef_inc_lt15
+util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,,coef_inc_15_25
+util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,,coef_inc_25_50
+util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,,coef_inc_150_250
+util_inc_250plus,Household Income $250k+,@(df.income>=250000),,coef_inc_250plus
+util_asc,Alternative-specific constant for external worker,1,,asc_external_worker
+util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,,-999
+util_2016,Constant for pre-COVID conditions,@PRE_COVID,,asc_external_2016
+util_calib,Constant for calibration,1,,0.35

resident/configs/external_worker_identification.yaml

@@ -4,6 +4,8 @@ COEFFICIENTS: external_worker_identification_coeffs.csv
 
 LOGIT_TYPE: MNL
 
+EXTERNAL_WORKER_ALT: 1
+
 # boolean column to filter choosers (True means keep)
 # will only expose these people to the model
 CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker

resident/configs/joint_tour_frequency_composition.csv

@@ -21,7 +21,7 @@ util_constant_for_2_visiting_tour,Constant for 2 visiting tour,@df.social==2,coe
 util_constant_for_1_visiting_tour,Constant for 1 visiting tour,@df.social==2,coef_constant_for_1_visiting_tour
 util_1_visiting_and_1_discretionary_tour,1 Visiting and 1 Discretionary Tour,@((df.social==1) & (df.othdiscr==1)),coef_1_visiting_and_1_discretionary_tour
 util_constant_for_2_discretionary_tour,Constant for 2 discretionary tour,othdiscr==2,coef_constant_for_2_discretionary_tour
-util_constant_for_2_discretionary_tour,Constant for 2 discretionary tour,othdiscr==1,coef_constant_for_1_discretionary_tour
+util_constant_for_1_discretionary_tour,Constant for 1 discretionary tour,othdiscr==1,coef_constant_for_1_discretionary_tour
 util_number_of_active_full_time_workers_shopping,Number of Active Full time workers /Shopping,num_travel_active_full_time_workers * shopping,coef_number_of_active_full_time_workers_shopping
 util_number_of_active_nonworkers_shopping,Number of Active Non-workers /Shopping,num_travel_active_non_workers * shopping,coef_number_of_active_nonworkers_shopping
 util_number_of_active_pre_driving_age_school_children_shopping,Number of Active Pre- Driving Age School Children /Shopping,num_travel_active_pre_driving_age_school_kids * shopping,coef_number_of_active_pre_driving_age_school_children_shopping

resident/configs/logging.yaml

@@ -46,12 +46,12 @@ logging:
       class: logging.Formatter
       # format: '%(levelname)s - %(name)s - %(message)s'
       format: '%(levelname)s - %(message)s'
-      datefmt: '%d/%m/%Y %H:%M:%S'
+      datefmt: '%Y-%m-%dT%H:%M:%S%:z'
 
     fileFormatter:
       class: logging.Formatter
       format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
-      datefmt: '%d/%m/%Y %H:%M:%S'
+      datefmt: '%Y-%m-%dT%H:%M:%S%:z'
 
     elapsedFormatter:
       (): activitysim.core.tracing.ElapsedTimeFormatter

resident/configs/non_mandatory_tour_destination.csv

@@ -71,4 +71,4 @@ util_joint_0_2_ASC,joint tours in 0_2bin calibration constant,"@(_DIST.between(0
 util_joint_2_5_ASC,joint tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointdisc_asc
 util_joint_5_10_ASC,joint tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointdisc_asc
 util_joint_10_30_ASC,joint tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointdisc_asc
-util_indiv_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) == 'joint')",,0.10,,0.10,,
+util_joint_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) == 'joint')",,0.10,,0.10,,

resident/configs/non_mandatory_tour_destination.yaml

@@ -3,6 +3,7 @@ SPEC: non_mandatory_tour_destination.csv
 COEFFICIENTS: non_mandatory_tour_destination_coefficients.csv
 
 SAMPLE_SIZE: 30
+ESTIMATION_SAMPLE_SIZE: 30
 
 SIZE_TERM_SELECTOR: non_mandatory
 

resident/configs/school_location.csv

@@ -1,7 +1,7 @@
 Label,Description,Expression,university,highschool,gradeschool,preschool
-local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1
+local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')].fillna(0)",1,1,1,1
 util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mclogsum_univ,coef_mclogsum_hsch,coef_mclogsum_gs,coef_mclogsum_ps
-util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps
+util_Distance,Distance,@_DIST.clip(upper=50).fillna(0),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps
 util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps
 util_Distance_squareroot,Squareroot of distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps
 util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps
@@ -12,10 +12,10 @@ util_Distance_worker_univ,Distance for a worker_university specific,"@np.where(d
 util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(df.COLLEGEENROLL>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero
 util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps
 util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps
-util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize
+util_LoggedSize,Logged Size variable - University specific,@df['size_term'].fillna(0).apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize
 util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999
 util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1
-util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1
+util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'].fillna(0),1,1,1
 #,,,,,,
 util_ABM2calibration_0-1miles,ABM2 calibration_0-1miles,@(_DIST<1),coef_zero,coef_abmcalib_01miles,coef_abmcalib_01miles,coef_abmcalib_01miles
 util_ABM2calibration_1-2miles,ABM2 calibration_1-2miles,@(_DIST<2) * (_DIST>=1),coef_zero,coef_abmcalib_12miles,coef_abmcalib_12miles,coef_abmcalib_12miles

resident/configs/school_location.yaml

@@ -1,5 +1,5 @@
 SAMPLE_SIZE: 30
-ESTIMATION_SAMPLE_SIZE: 10
+ESTIMATION_SAMPLE_SIZE: 30
 
 SIMULATE_CHOOSER_COLUMNS:
   - home_zone_id

resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv

@@ -45,7 +45,7 @@ treat tours as work if tour_type not yet decided,tour_type,"df.get('tour_type',
 popempdenpermi calculated in preprocessor and input via landuse file,_origin_density_measure,"reindex(land_use.popempdenpermi, df[orig_col_name])",
 ,_dest_density_measure,"reindex(land_use.popempdenpermi, df[dest_col_name])",
 ,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)",
-,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)",
+,dest_density,"pd.cut(_dest_density_measure.fillna(0), bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)",
 ,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})",
 ,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})",
 ,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})",

resident/configs/trip_mode_choice_annotate_trips_preprocessor.csv

@@ -46,7 +46,7 @@ Description,Target,Expression
 popempdenpermi calculated in preprocessor and input via landuse file,_origin_density_measure,"reindex(land_use.popempdenpermi, df[orig_col_name])"
 ,_dest_density_measure,"reindex(land_use.popempdenpermi, df[dest_col_name])"
 ,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
-,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
+,dest_density,"pd.cut(_dest_density_measure.fillna(0), bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
 Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,df.origin)"
 Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,df.origin)"
 Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,df.origin)"

resident/configs/workplace_location.yaml

@@ -1,5 +1,5 @@
 SAMPLE_SIZE: 30
-ESTIMATION_SAMPLE_SIZE: 10
+ESTIMATION_SAMPLE_SIZE: 30
 
 SIMULATE_CHOOSER_COLUMNS:
   - income_segment

resident/configs_estimation/estimation.yaml

@@ -0,0 +1,109 @@
+EDB_FILETYPE: parquet   # options: csv, pkl, parquet
+DELETE_MP_SUBDIRS: True
+
+enable: True
+
+bundles:
+  - license_holding_status
+  - bike_comfort
+  - auto_ownership
+  - work_from_home
+  - external_worker_identification
+  - external_workplace_location
+  - school_location
+  - workplace_location
+  - vehicle_type_choice
+  - transit_pass_subsidy
+  - transit_pass_ownership
+  - free_parking
+  - telecommute_frequency
+  - cdap
+  - mandatory_tour_frequency
+  - mandatory_tour_scheduling_work
+  - mandatory_tour_scheduling_school
+  - mandatory_tour_scheduling_univ
+  - school_escorting
+  - joint_tour_frequency_composition
+  - external_joint_tour_identification
+  - joint_tour_participation
+  - joint_tour_destination
+  - joint_tour_scheduling
+  - non_mandatory_tour_frequency
+  - external_non_mandatory_identification
+  - external_non_mandatory_destination
+  - non_mandatory_tour_destination
+  - non_mandatory_tour_scheduling
+  - vehicle_allocation
+  # - park_and_ride_lot_choice
+  - tour_mode_choice
+  - atwork_subtour_frequency
+  - atwork_subtour_destination
+  - atwork_subtour_scheduling
+  - atwork_subtour_mode_choice
+  - stop_frequency
+  - trip_purpose
+  - trip_destination
+  - trip_scheduling
+  - trip_mode_choice
+
+#  - atwork_subtour_mode_choice  subtours.tour_mode
+
+survey_tables:
+  households:
+    file_name: override_households.csv
+    index_col: household_id
+  persons:
+    file_name: override_persons.csv
+    index_col: person_id
+  tours:
+    file_name: override_tours.csv
+    index_col: tour_id
+  joint_tour_participants:
+    file_name: override_joint_tour_participants.csv
+    index_col: participant_id
+  trips:
+    file_name: override_trips.csv
+    index_col: trip_id
+
+estimation_table_types:
+  license_holding_status: simple_simulate
+  bike_comfort: simple_simulate
+  work_from_home: simple_simulate
+  external_worker_identification: simple_simulate
+  external_workplace_location: interaction_sample_simulate
+  school_location: interaction_sample_simulate
+  workplace_location:  interaction_sample_simulate
+  transit_pass_subsidy: simple_simulate
+  transit_pass_ownership: simple_simulate
+  auto_ownership: simple_simulate
+  vehicle_type_choice: interaction_simulate
+  transponder_ownership: simple_simulate
+  free_parking: simple_simulate
+  telecommute_frequency: simple_simulate
+  cdap: cdap_simulate
+  mandatory_tour_frequency: simple_simulate
+  mandatory_tour_scheduling_work: interaction_sample_simulate
+  mandatory_tour_scheduling_school: interaction_sample_simulate
+  mandatory_tour_scheduling_univ: interaction_sample_simulate
+  school_escorting: interaction_simulate
+  joint_tour_frequency_composition: interaction_simulate
+  external_joint_tour_identification: simple_simulate
+  joint_tour_participation: simple_simulate
+  joint_tour_destination: interaction_sample_simulate
+  joint_tour_scheduling: interaction_sample_simulate
+  non_mandatory_tour_frequency: interaction_simulate
+  external_non_mandatory_identification: simple_simulate
+  non_mandatory_tour_destination: interaction_sample_simulate
+  external_non_mandatory_destination: interaction_sample_simulate
+  non_mandatory_tour_scheduling: interaction_sample_simulate
+  vehicle_allocation: simple_simulate
+  tour_mode_choice: simple_simulate
+  atwork_subtour_frequency: simple_simulate
+  atwork_subtour_destination: interaction_sample_simulate
+  atwork_subtour_scheduling: interaction_sample_simulate
+  atwork_subtour_mode_choice: simple_simulate
+  stop_frequency: simple_simulate
+  trip_purpose: simple_probabilistic
+  trip_destination: interaction_sample_simulate
+  trip_scheduling: simple_probabilistic
+  trip_mode_choice: simple_simulate

resident/configs_estimation/logging.yaml

@@ -0,0 +1,59 @@
+# Config for logging
+# ------------------
+# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema
+
+logging:
+  version: 1
+  disable_existing_loggers: true
+
+
+  # Configuring the default (root) logger is highly recommended
+  root:
+    level: NOTSET
+    handlers: [console]
+
+  loggers:
+
+    activitysim:
+      level: DEBUG
+      handlers: [console, logfile]
+      propagate: false
+
+    sharrow:
+      level: INFO
+      handlers: [console, logfile]
+      propagate: false
+
+  handlers:
+
+    logfile:
+      class: logging.FileHandler
+      filename:
+        get_log_file_path: 'activitysim.log'
+      mode: w
+      formatter: fileFormatter
+      level: NOTSET
+
+    console:
+      class: logging.StreamHandler
+      stream: ext://sys.stdout
+      formatter: elapsedFormatter
+      level: INFO
+
+  formatters:
+
+    simpleFormatter:
+      class: logging.Formatter
+      # format: '%(levelname)s - %(name)s - %(message)s'
+      format: '%(levelname)s - %(message)s'
+      datefmt: '%d/%m/%Y %H:%M:%S'
+
+    fileFormatter:
+      class: logging.Formatter
+      format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
+      datefmt: '%d/%m/%Y %H:%M:%S'
+
+    elapsedFormatter:
+      (): activitysim.core.tracing.ElapsedTimeFormatter
+      format: '[{elapsedTime}] {levelname:s}: {message:s}'
+      style: '{'