Merge pull request #1 from wsp-sag/asim-v1.3-integration

ActivitySim v1.3 and Sharrow Compatible

configs/_dummy_coefficients.csv

@@ -0,0 +1,2 @@
+coefficient_name,value,constrain
+coef_one,1,T

configs/atwork_subtour_destination.csv

@@ -12,14 +12,14 @@ Distance_square_oth,"@np.where((df.tour_type == 'maint'), ((np.minimum(skims[('S
 OPOP logsum for work purpose,"@np.where((df.tour_type == 'business'), (df.mode_choice_logsum), 0)",1
 OPOP logsum for eating out purpose,"@np.where((df.tour_type == 'eat'), (df.mode_choice_logsum), 0)",1
 OPOP logsum for other purpose,"@np.where((df.tour_type == 'maint'), (df.mode_choice_logsum), 0)",1
-Size variable applied for work purposes,"@np.where((df.tour_type == 'business'), df['atwork'].apply(np.log1p), 0)",0.461
-Size variable applied for eating out purposes,"@np.where((df.tour_type == 'eat'), df['atwork'].apply(np.log1p), 0)",0.6904
-Size variable applied for other purposes,"@np.where((df.tour_type == 'maint'), df['atwork'].apply(np.log1p), 0)",0.6166
+Size variable applied for work purposes,"@np.where((df.tour_type == 'business'), df['size_term'].apply(np.log1p), 0)",0.461
+Size variable applied for eating out purposes,"@np.where((df.tour_type == 'eat'), df['size_term'].apply(np.log1p), 0)",0.6904
+Size variable applied for other purposes,"@np.where((df.tour_type == 'maint'), df['size_term'].apply(np.log1p), 0)",0.6166
 # shortWalkOrig == 1 for all purposes,,
 Not SOV on mandatory tour & short walk,"@(~df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY']))",2.485
-No Eat subtour Attractions,"@np.where(((df.tour_type == 'eat') & (df['atwork'].apply(np.log1p) == 0)), 1, 0)",-999
-No Business subtour Attractions,"@np.where(((df.tour_type == 'business') & (df['atwork'].apply(np.log1p) == 0)), 1, 0)",-999
-No Maint subtour Attractions,"@np.where(((df.tour_type == 'maint') & (df['atwork'].apply(np.log1p) == 0)), 1, 0)",-999
+No Eat subtour Attractions,"@np.where(((df.tour_type == 'eat') & (df['size_term'].apply(np.log1p) == 0)), 1, 0)",-999
+No Business subtour Attractions,"@np.where(((df.tour_type == 'business') & (df['size_term'].apply(np.log1p) == 0)), 1, 0)",-999
+No Maint subtour Attractions,"@np.where(((df.tour_type == 'maint') & (df['size_term'].apply(np.log1p) == 0)), 1, 0)",-999
 Distance,"@(np.minimum(skims[('SOV_FREE_DISTANCE', 'MD')], 20))",0.135698
 Distance squared,"@((np.minimum(skims[('SOV_FREE_DISTANCE', 'MD')], 20)) ** 2)",-0.0123613
 Distance cubed,"@((np.minimum(skims[('SOV_FREE_DISTANCE', 'MD')], 20)) ** 3)",0.0001337

configs/atwork_subtour_destination_sample.csv

@@ -1,4 +1,4 @@
 Description,Expression,atwork
 offpeak period sov distance,"@skims[('SOV_FREE_DISTANCE', 'MD')]",-0.2
-Size variable ,@df['atwork'].apply(np.log1p),1
-No attractions,@df['atwork']==0,-999
+Size variable ,@df['size_term'].apply(np.log1p),1
+No attractions,@df['size_term']==0,-999

configs/auto_ownership.yaml

@@ -17,8 +17,3 @@ NESTS:
 
 SPEC: auto_ownership.csv
 COEFFICIENTS: auto_ownership_coeffs.csv
-
-LOGSUM_CHOOSER_COLUMNS:
-  - num_drivers
-  - num_workers
-

configs/cdap.yaml

@@ -1,5 +1,7 @@
 
 INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv
+COEFFICIENTS: _dummy_coefficients.csv
+INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv
 
 FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv
 

configs/logging.yaml

@@ -19,24 +19,34 @@ logging:
       handlers: [console, logfile]
       propagate: false
 
-    orca:
+    sharrow:
       level: WARN
       handlers: [console, logfile]
       propagate: false
 
+    black:
+      # sharrow uses black to reformat code, but we don't generally care
+      # about messages emitted by black unless they are serious
+      level: WARN
+
+    blib2to3:
+      # this is a vendored library within black that we also want to squelch
+      level: WARN
+
   handlers:
 
     logfile:
       class: logging.FileHandler
-      filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log']
+      filename: 
+        get_log_file_path: activitysim.log
       mode: w
       formatter: fileFormatter
       level: NOTSET
 
     console:
       class: logging.StreamHandler
       stream: ext://sys.stdout
-      formatter: simpleFormatter
+      formatter: elapsedFormatter
       level: NOTSET
 
   formatters:
@@ -52,3 +62,7 @@ logging:
       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: '{'

configs/mandatory_tour_frequency.csv

@@ -25,10 +25,10 @@ Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),0,0
 Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),0,0,0,0.7114,0
 Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),0,0,0,0.7114,0
 Can walk to school - Pre-driving age child who is in school interaction,((ptype == 7)|(ptype == 8)) & (distance_to_school < 3),0,0,0,0.7114,0
-Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391
-Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391
-Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391
-Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391
+Can walk to work or school - Full-time worker interaction,(ptype == 1) & ((distance_to_work) < 3 | (distance_to_school < 3)),0,0,0,0,0.1391
+Can walk to work or school - Part-time worker interaction,(ptype == 2) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391
+Can walk to work or school - University student interaction,(ptype == 3) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391
+Can walk to work or school - Driving-age child interaction,(ptype == 6) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391
 Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,0,-0.003518,0,0,-0.003124
 Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,0,-0.003518,0,0,-0.003124
 Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,0,-0.003518,0,0,-0.003124

configs/mandatory_tour_scheduling.yaml

@@ -54,3 +54,5 @@ DESTINATION_FOR_TOUR_PURPOSE:
   work: workplace_zone_id
   school: school_zone_id
   univ: school_zone_id
+
+explicit_chunk: 0.2

configs/non_mandatory_tour_destination.csv

@@ -1,14 +1,14 @@
 Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr
 Mode choice logsum,mode_choice_logsum,1,1,1,coef_shopping_mode_logsum,coef_eatout_mode_logsum,coef_othmaint_mode_logsum,1,1
-Intrazonal,@(df['zone_id']==df['home_zone_id']),0,0,0,0,0,0,0,0
+Intrazonal,@(df['alt_dest']==df['home_zone_id']),0,0,0,0,0,0,0,0
 CBD area type,@df['is_CBD'],0,0,0,coef_shopping_cbd,coef_eatout_cbd,coef_othmaint_cbd,coef_social_cbd,0
 Urban high area type,@df['is_urban'],0,0,0,coef_shopping_urban,coef_eatout_urban,coef_othmaint_urban,coef_social_urban,coef_othdiscr_urban
 "# CTRAMP expression has two 'Distance' coefficients. For example, 0.2553 and 0.0100 for Escort, so used their sum here",,,,,,,,,
 Distance,"@skims[('SOV_FREE_DISTANCE', 'MD')]",coef_escort_distance,coef_escortkids_distance,coef_escortnokids_distance,coef_shopping_distance,coef_eatout_distance,coef_othmaint_distance,coef_social_distance,coef_othdiscr_distance
 Distance squared,"@(skims[('SOV_FREE_DISTANCE', 'MD')] ** 2)",coef_escort_distance_squared,coef_escortkids_distance_squared,coef_escortnokids_distance_squared,coef_shopping_distance_squared,coef_eatout_distance_squared,coef_othmaint_distance_squared,coef_social_distance_squared,coef_othdiscr_distance_squared
 Distance cubed,"@(skims[('SOV_FREE_DISTANCE', 'MD')] ** 3)",coef_escort_distance_cubed,coef_escortkids_distance_cubed,coef_escortnokids_distance_cubed,coef_shopping_distance_cubed,coef_eatout_distance_cubed,coef_othmaint_distance_cubed,coef_social_distance_cubed,coef_othdiscr_distance_cubed
 Log of distance,"@np.log(skims[('SOV_FREE_DISTANCE', 'MD')])",coef_escort_log_distance,coef_escortkids_log_distance,coef_escortnokids_log_distance,coef_shopping_log_distance,coef_eatout_log_distance,coef_othmaint_log_distance,coef_social_log_distance,coef_othdiscr_log_distance
-Distance - high income,"@((df['income_in_thousands']>50) & skims[('SOV_FREE_DISTANCE', 'MD')])",0,0,0,0,coef_eatout_distance_high_income,0,0,0
+Distance - high income,"@((df['income_in_thousands']>50) * skims[('SOV_FREE_DISTANCE', 'MD')])",0,0,0,0,coef_eatout_distance_high_income,0,0,0
 Distance - non-driving age student in hh,"@np.where(df['num_nondriving_age_children']>0, skims[('SOV_FREE_DISTANCE', 'MD')], 0)",coef_escort_distance_nondrive,coef_escortkids_distance_nondrive,coef_escortnokids_distance_nondrive,coef_shopping_distance_nondrive,0,0,coef_social_distance_nondrive,coef_othdiscr_distance_nondrive
 Distance bin 0 to 1,"@((skims[('SOV_FREE_DISTANCE', 'MD')]>=0) & (skims[('SOV_FREE_DISTANCE', 'MD')]<1))",0,0,0,coef_shopping_dist_0_1,coef_eatout_dist_0_1,coef_othmaint_dist_0_1,0,coef_othdiscr_dist_0_1
 Distance bin 1 to 2,"@((skims[('SOV_FREE_DISTANCE', 'MD')]>=1) & (skims[('SOV_FREE_DISTANCE', 'MD')]<2))",0,0,0,coef_shopping_dist_1_2,coef_eatout_dist_1_2,coef_othmaint_dist_1_2,0,coef_othdiscr_dist_1_2

configs/non_mandatory_tour_frequency.csv

@@ -139,7 +139,7 @@ util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of U
 util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour
 util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour
 util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour
-util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour
+util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,df.has_school_kid_at_home * df.othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour
 util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour
 util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour
 util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour

configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv

@@ -29,3 +29,5 @@ presence of retiree other than self in household,has_retiree,"other_than(persons
 presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == 6)"
 presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == 7)"
 presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == 8)"
+presence of at home school_kid other than self in household,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == 7)&(~persons.travel_active))"
+presence of at home preschooler other than self in household,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == 8)&(~persons.travel_active))"

configs/non_mandatory_tour_scheduling.yaml

@@ -32,3 +32,4 @@ SIMULATE_CHOOSER_COLUMNS:
   - num_add_shop_maint_tours
   - num_add_soc_discr_tours
 
+explicit_chunk: 0.2

configs/parking_location_choice.yaml

@@ -1,12 +1,12 @@
-METADATA:
-  CHOOSER: trips_merged
-  INPUT:
-    persons:
-    trips:
-    tours:
-  OUTPUT:
-    trips:
-      - parking_zone_id
+# METADATA:
+#   CHOOSER: trips_merged
+#   INPUT:
+#     persons:
+#     trips:
+#     tours:
+#   OUTPUT:
+#     trips:
+#       - parking_zone_id
 
 SPECIFICATION: parking_location_choice.csv
 COEFFICIENTS: parking_location_choice_coeffs.csv
@@ -34,3 +34,13 @@ SEGMENTS:
 ALT_DEST_COL_NAME: parking_zone_id
 TRIP_ORIGIN: origin
 TRIP_DESTINATION: destination
+
+AUTO_MODES:
+  - DRIVEALONEFREE
+  - DRIVEALONEPAY
+  - SHARED2FREE
+  - SHARED2PAY
+  - SHARED3FREE
+  - SHARED3PAY
+
+explicit_chunk: 0.2

configs/school_location.csv

@@ -1,6 +1,6 @@
 Description,Expression,univ,k12_predrive,k12_drive
 Mode choice logsum,mode_choice_logsum,coeff_mode_univ,coeff_mode_k12_predrive,coeff_mode_k12_drive
-Intrazonal (origin == dest),@(df['home_zone_id']==df['zone_id']),-0.1518,-0.5708,-0.5708
+Intrazonal (origin == dest),@(df['home_zone_id']==df['alt_dest']),-0.1518,-0.5708,-0.5708
 Distance,"@skims[('SOV_FREE_DISTANCE', 'MD')]",0.00726,0.3526869,0.3526869
 CBD Area Type,@df['areatype']==1,0.5678,-0.6092,-0.6092
 Urban Area Type,"@(df['areatype'].isin([2,3,4]))",0,-1.759,-1.759

configs/school_location.yaml

@@ -58,3 +58,5 @@ MODELED_SIZE_TABLE: school_modeled_size
 
 # not loaded if commented out
 SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv
+
+explicit_chunk: 0.2

configs/settings.yaml

@@ -1,3 +1,5 @@
+households_sample_size: 0
+
 # input tables
 input_table_list:
   - tablename: households
@@ -9,18 +11,14 @@ input_table_list:
       maz: home_zone_id
       np: hhsize
       nwrkrs_esr: num_workers
+    recode_columns:
+      home_zone_id: land_use.zone_id
     keep_columns:
-      #- household_id
       - home_zone_id
       - hhsize
       - num_workers
       - hincp
-      #- hhincAdj
-      #- adjinc
-      #- veh
       - hht
-      #- bld
-      #- type
   - tablename: persons
     filename: persons.csv
     # The index column is set before keep_columns,
@@ -30,56 +28,36 @@ input_table_list:
       maz: home_zone_id
       sporder: PNUM
     keep_columns:
-      #- person_id
       - household_id
       - home_zone_id
       - PNUM
       - agep
-      #- employed
       - pecasOcc
       - sex
       - esr
       - wkw
       - wkhp
-      #-mil
       - schg
-      #-schl
-      #-indp02
-      #-indp07
-      #-occp02
-      #-occp10
-      #-n
   - tablename: land_use
     filename: land_use.csv
     # The index column is set before keep_columns,
     # so don't put index in keep columns
     index_col: zone_id
-#    rename_columns:
-#
+    recode_columns:
+      zone_id: zero-based
     keep_columns:
-      #- TAZ
-#      - construc
-#      - manufac
-#      - TCU
-#      - wholesl
       - retail
-#      - FIRE
       - service
-#      - private
-#      - govt
       - emp
       - pop
       - hshld
       - univ
       - acres
-#      - otherEmp
-#      - district
       - PARKTOT
       - PARKLNG
       - PROPFREE
       - PARKRATE
       - areatype
-#      - county
       - CBDFlag
       - N11
       - N21
@@ -121,6 +99,8 @@ skims_file: skims.omx
 use_shadow_pricing: True
 
 # chunk_size:
+chunk_method: hybrid_uss
+chunk_training_mode: explicit
 
 models:
   - initialize_landuse
@@ -163,24 +143,21 @@ models:
 
 multiprocess: True
 fail_fast: True
+num_processes: 28
 
 multiprocess_steps:
   - name: mp_initialize_landuse
     begin: initialize_landuse
   - name: mp_accessibility
     begin: compute_accessibility
-    num_processes: 28
     slice:
       tables:
         - accessibility
-      except:
-        - land_use
+      exclude: True
   - name: mp_initialize_households
     begin: initialize_households
   - name: mp_households
     begin: school_location
-    num_processes: 28
-    chunk_size: 300000000
     slice:
       tables:
         - households
@@ -196,11 +173,26 @@ output_tables:
   tables:
     - checkpoints
     - accessibility
-    - land_use
-    - households
-    - persons
-    - tours
-    - trips
+    - tablename: land_use
+      decode_columns:
+        zone_id: land_use.zone_id
+    - tablename: households
+      decode_columns:
+        home_zone_id: land_use.zone_id
+    - 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: tours
+      decode_columns:
+        origin: land_use.zone_id
+        destination: land_use.zone_id
+    - tablename: trips
+      decode_columns:
+        origin: land_use.zone_id
+        destination: land_use.zone_id
+        parking_zone_id: nonnegative | land_use.zone_id
     - joint_tour_participants
 
 # trace_od: [100,10]
@@ -212,3 +204,8 @@ min_value_of_time: 1
 max_value_of_time: 50
 distributed_vot_mu: 0.684
 distributed_vot_sigma: 0.85
+
+# sharrow settings
+# sharrow: require
+# recode_pipeline_columns: True
+# sharrow_cache_dir: # specify only when not using default