ActivitySim · jpn-- · May 22, 2024 · May 6, 2024 · May 6, 2024 · May 6, 2024
diff --git a/configs/resident/accessibility.csv b/configs/resident/accessibility.csv
@@ -19,11 +19,11 @@ auto off-peak total,auOpTotal,df.emp_total * _decay
 #,, transit peak
 #,, assume peak outbound transit occurs in AM
 o-d peak transit time,_inVehicletime_od,"skim_od[('WALK_MIX_TOTALIVTT', 'AM')]"
-o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WALK_MIX_FIRSTWAIT', 'AM')] + skim_od[('WALK_MIX_XFERWAIT', 'AM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_od[('WALK_MIX_XFERWALK', 'AM')] + reindex(df.walk_dist_local_bus, df.dest)"
+o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WALK_MIX_FIRSTWAIT', 'AM')] + skim_od[('WALK_MIX_XFERWAIT', 'AM')] + df.landuse_orig_walk_dist_local_bus + skim_od[('WALK_MIX_XFERWALK', 'AM')] + df.walk_dist_local_bus"
 total o-d peak transit time,_trPkTime_od,(_inVehicletime_od +  out_of_vehicle_time_weight * _outOfVehicleTime_od)
 #,, assume peak inbound transit occurs in PM
 o-d peak transit time,_inVehicletime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'PM')]"
-o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WALK_MIX_FIRSTWAIT', 'PM')] + skim_do[('WALK_MIX_XFERWAIT', 'PM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_do[('WALK_MIX_XFERWALK', 'PM')] + reindex(df.walk_dist_local_bus, df.dest)"
+o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WALK_MIX_FIRSTWAIT', 'PM')] + skim_do[('WALK_MIX_XFERWAIT', 'PM')] + df.landuse_orig_walk_dist_local_bus + skim_do[('WALK_MIX_XFERWALK', 'PM')] + df.walk_dist_local_bus"
 total o-d peak transit time,_trPkTime_do,(_inVehicletime_do +  out_of_vehicle_time_weight * _outOfVehicleTime_do)
 #,,
 #o-d peak transit time,_trPkTime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'PM')]"

diff --git a/configs/resident/accessibility.yaml b/configs/resident/accessibility.yaml
@@ -1,7 +1,10 @@
 
-# columns from land_use table to add to df
+# columns from land_use table to add to df for destinations
 land_use_columns: ['emp_ret', 'emp_total', 'hh', 'walk_dist_local_bus']
 
+# columns from land_use table to add to df for origins
+land_use_columns_orig: ['walk_dist_local_bus']
+
 CONSTANTS:
   # dispersion parameters
   dispersion_parameter_automobile: -0.05

diff --git a/configs/resident/atwork_subtour_frequency.yaml b/configs/resident/atwork_subtour_frequency.yaml
@@ -9,3 +9,6 @@ preprocessor:
     - land_use
     - tours
     - joint_tour_participants
+
+compute_settings:
+  fastmath: false
diff --git a/configs/resident/cdap.yaml b/configs/resident/cdap.yaml
@@ -47,4 +47,4 @@ annotate_households:
     - persons
 
 compute_settings:
-  sharrow_skip: true
+  fastmath: false
diff --git a/configs/resident/cdap_joint_tour_coefficients.csv b/configs/resident/cdap_joint_tour_coefficients.csv
@@ -15,5 +15,5 @@ Label,description,Expression,dependency,coefficient
 ,No Car Households,auto_ownership_p1 == 0,,0
 ,Cars Less than Workers,auto_ownership_p1 < num_workers_p1,,0.088402389
 ,Cars More than Workers,auto_ownership_p1 > num_workers_p1,,-0.005896499
-,WorkAccessForMandatoryDap,"@df.workplace_modechoice_logsum_px.fillna(0)",M_px,0.17217579
+,WorkAccessForMandatoryDap,"@np.nan_to_num(df.workplace_modechoice_logsum_px)",M_px,0.17217579
 ,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,(ptype_pxprod < 7) | (ptype_pxprod > 6),H_px,-0.988838929
diff --git a/configs/resident/joint_tour_frequency_composition.csv b/configs/resident/joint_tour_frequency_composition.csv
diff --git a/configs/resident/joint_tour_frequency_composition.yaml b/configs/resident/joint_tour_frequency_composition.yaml
@@ -33,6 +33,3 @@ ALTS_TABLE_STRUCTURE:
       1: adults
       2: children
       3: mixed
-
-compute_settings:
-  sharrow_skip: true
diff --git a/configs/resident/joint_tour_scheduling.yaml b/configs/resident/joint_tour_scheduling.yaml
@@ -13,4 +13,5 @@ preprocessor:
     - joint_tour_participants
 
 compute_settings:
-  sharrow_skip: true
+  use_numexpr: true
+  # require numexpr for legacy eval to prevent overflow when squaring start times
diff --git a/configs/resident/logging.yaml b/configs/resident/logging.yaml
@@ -19,8 +19,8 @@ logging:
       handlers: [console, logfile]
       propagate: false
 
-    orca:
-      level: WARN
+    sharrow:
+      level: INFO
       handlers: [console, logfile]
       propagate: false
 

diff --git a/configs/resident/non_mandatory_tour_scheduling.yaml b/configs/resident/non_mandatory_tour_scheduling.yaml
@@ -54,32 +54,38 @@ SPEC_SEGMENTS:
     SPEC: tour_scheduling_nonmandatory_escort.csv
     COEFFICIENTS: tour_scheduling_nonmandatory_escort_coefficients.csv
     compute_settings:
-      sharrow_skip: true
+      use_numexpr: true
+      use_bottleneck: false
   shopping:
     SPEC: tour_scheduling_nonmandatory_shopping.csv
     COEFFICIENTS: tour_scheduling_nonmandatory_shopping_coefficients.csv
     compute_settings:
-      sharrow_skip: true
+      use_numexpr: true
+      use_bottleneck: false
   eatout:
     SPEC: tour_scheduling_nonmandatory_eatout.csv
     COEFFICIENTS: tour_scheduling_nonmandatory_eatout_coefficients.csv
     compute_settings:
-      sharrow_skip: true
+      use_numexpr: true
+      use_bottleneck: false
   othdiscr:
     SPEC: tour_scheduling_nonmandatory_othdiscr.csv
     COEFFICIENTS: tour_scheduling_nonmandatory_othdiscr_coefficients.csv
     compute_settings:
-      sharrow_skip: true
+      use_numexpr: true
+      use_bottleneck: false
   othmaint:
     SPEC: tour_scheduling_nonmandatory_othmaint.csv
     COEFFICIENTS: tour_scheduling_nonmandatory_othmaint_coefficients.csv
     compute_settings:
-      sharrow_skip: true
+      use_numexpr: true
+      use_bottleneck: false
   social:
     SPEC: tour_scheduling_nonmandatory_social.csv
     COEFFICIENTS: tour_scheduling_nonmandatory_social_coefficients.csv
     compute_settings:
-      sharrow_skip: true
+      use_numexpr: true
+      use_bottleneck: false
 
 ## alts preprocessor keyed by <spec_segment>
 #ALTS_PREPROCESSOR:
@@ -105,4 +111,5 @@ SPEC_SEGMENTS:
 DESTINATION_FOR_TOUR_PURPOSE: destination
 
 compute_settings:
-  sharrow_skip: true
+  use_numexpr: true
+  use_bottleneck: false
diff --git a/configs/resident/school_escorting.yaml b/configs/resident/school_escorting.yaml
@@ -47,4 +47,4 @@ CONSTANTS:
   mins_per_time_bin: 30
 
 compute_settings:
-  sharrow_skip: true
+  fastmath: false
diff --git a/configs/resident/settings.yaml b/configs/resident/settings.yaml
@@ -110,15 +110,41 @@ output_tables:
   sort: True
   tables:
     - checkpoints
-    - accessibility
-    - land_use
-    - households
-    - persons
-    - tours
-    - trips
+    - tablename: accessibility
+      decode_columns:
+        zone_id: land_use.zone_id
+    - tablename: land_use
+      decode_columns:
+        zone_id: land_use.zone_id
+        TAZ: land_use_taz.TAZ
+    - tablename: households
+      decode_columns:
+        home_zone_id: land_use.zone_id
+        TAZ: land_use_taz.TAZ
+    - tablename: persons
+      decode_columns:
+        home_zone_id: land_use.zone_id
+        school_zone_id: nonnegative | land_use.zone_id
+        workplace_zone_id: nonnegative | land_use.zone_id
+    - tablename: trips
+      decode_columns:
+        origin: land_use.zone_id
+        destination: land_use.zone_id
+        origin_parking_zone: nonnegative | land_use.zone_id
+        parking_zone: nonnegative | land_use.zone_id
+        trip_period: time_period
+        otaz: land_use_taz.TAZ
+        dtaz: land_use_taz.TAZ
+    - tablename: tours
+      decode_columns:
+        origin: land_use.zone_id
+        destination: land_use.zone_id
     - joint_tour_participants
     - vehicles
-    - proto_disaggregate_accessibility
+    - tablename: proto_disaggregate_accessibility
+      decode_columns:
+        home_zone_id: land_use.zone_id
+        TAZ: land_use_taz.TAZ
     - disaggregate_accessibility
 
 distributed_time_factor_work_mean: 1.0
@@ -186,3 +212,13 @@ models:
   - track_skim_usage
   - write_trip_matrices
   - write_tables
+
+# These skim tables are included in the model as two sets, one as
+#   time-independent  (e.g. BIKE_TIME) and one as time-dependent
+#   (e.g. BIKE_TIME__AM), although the actual data is all time-independent.
+#   This prevents the (pseudo-) time-dependent tables from being loaded,
+#   which would be a waste of memory, and causes problems with the
+#   two-zone data backing these skims.
+omx_ignore_patterns:
+  - "BIKE_TIME__.+"
+  - "BIKE_LOGSUM__.+"
diff --git a/configs/resident/tour_mode_choice.yaml b/configs/resident/tour_mode_choice.yaml
@@ -112,6 +112,3 @@ LOGSUM_CHOOSER_COLUMNS:
   - transit_pass_ownership
 
 MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum
-
-compute_settings:
-  sharrow_skip: true
diff --git a/configs/resident/tour_mode_choice_annotate_choosers_preprocessor.csv b/configs/resident/tour_mode_choice_annotate_choosers_preprocessor.csv
@@ -163,11 +163,11 @@ Destination Terminal Time,dTermTime,"reindex(land_use.terminal_time,destination)
 #,,,
 # Below bike info is not defined and is turned off!!,,,
 Person is female,female,(df.sex == 2),
-bike logsum inbound,bikeLSI,odt_skims['BIKE_LOGSUM'],
-bike logsum outbound (same as inbound),bikeLSO,dot_skims['BIKE_LOGSUM'],
+bike logsum inbound,bikeLSI,od_skims.reverse('BIKE_LOGSUM'),
+bike logsum outbound (same as inbound),bikeLSO,od_skims['BIKE_LOGSUM'],
 bike availability,bikeAvailable,(bikeLSI > -300) & (bikeLSO > -300) & (od_skims['DIST'] < max_bike_distance),
-bike time inbound,bike_time_inb,dot_skims['BIKE_TIME'],
-bike time outbound,bike_time_out,odt_skims['BIKE_TIME'],
+bike time inbound,bike_time_inb,od_skims.reverse('BIKE_TIME'),
+bike time outbound,bike_time_out,od_skims['BIKE_TIME'],
 #,,,
 "Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57,
 "Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37,

diff --git a/configs/resident/tour_scheduling_joint.csv b/configs/resident/tour_scheduling_joint.csv
@@ -21,7 +21,7 @@ util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10
 util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM
 util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM
 util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear
-util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared
+util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)).astype(np.float32) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared
 util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear
 util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM
 util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM
@@ -70,7 +70,7 @@ util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant
 util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM
 util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM
 util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear
-util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared
+util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)).astype(np.float32) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared
 util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear
 util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM
 util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM

diff --git a/configs/resident/tour_scheduling_nonmandatory_othmaint.csv b/configs/resident/tour_scheduling_nonmandatory_othmaint.csv
@@ -32,7 +32,7 @@ util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Co
 util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am
 util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am
 util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where((df.start>17), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear
-util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where((df.start>17), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared
+util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where((df.start>17), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)).astype(np.float32) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared
 util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear
 util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am
 util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am

diff --git a/configs/resident/tour_scheduling_nonmandatory_shopping.csv b/configs/resident/tour_scheduling_nonmandatory_shopping.csv
@@ -29,7 +29,7 @@ util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant
 util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am
 util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am
 util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear
-util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared
+util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), (((df.start-17)*(df.start>17)).astype(np.float32) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared
 util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear
 util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm
 util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm

diff --git a/configs/resident/trip_destination.yaml b/configs/resident/trip_destination.yaml
@@ -56,6 +56,3 @@ CLEANUP: False
 
 # this setting is used by testing code to force failed trip_destination
 # fail_some_trips_for_testing: False
-
-compute_settings:
-  sharrow_skip: true
diff --git a/configs/resident/trip_destination_sample.csv b/configs/resident/trip_destination_sample.csv
@@ -2,8 +2,8 @@ Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,o
 ,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1
 ,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1
 ,_op_DIST@op_skims['DIST'],1,1,1,1,1,1,1,1,1,1
-,_od_bikeL@odt_skims['BIKE_LOGSUM'],1,1,1,1,1,1,1,1,1,1
-,_dp_bikeL@dpt_skims['BIKE_LOGSUM'],1,1,1,1,1,1,1,1,1,1
+,_od_bikeL@od_skims['BIKE_LOGSUM'],1,1,1,1,1,1,1,1,1,1
+,_dp_bikeL@dp_skims['BIKE_LOGSUM'],1,1,1,1,1,1,1,1,1,1
 #,,,,,,,,,,,
 size term,"@np.log1p(size_terms.get(df.alt_dest, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1
 no attractions,"@size_terms.get(df.alt_dest, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999