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change default fastmath to false
1 parent a5e0999 commit 7f8c511

27 files changed

+412
-2
lines changed
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coefficient_name,value,constrain
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coef_one,1,T
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max_walk_distance: 3
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walkSpeed: 3.00
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walkThresh: 1.50
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segment,model_selector,Acres,Population
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work_low,workplace,0,0.602938343
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work_med,workplace,0,0.487310988
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work_high,workplace,0,0.359519129
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work_veryhigh,workplace,0,0.169128839
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university,school,1,0
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preschool,school,0.07552,1
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gradeschool,school,1,0
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highschool,school,1,0
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escort,non_mandatory,0.465,0.144
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shopping,non_mandatory,1,0
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eatout,non_mandatory,0.4,0.6
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othmaint,non_mandatory,0,0.518
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social,non_mandatory,0.2,0.478
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othdiscr,non_mandatory,0.252252252,0.272272272
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atwork,atwork,0,0.258
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work,trip,0.2,0.166666667
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escort,trip,0.655,0.144
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shopping,trip,0.86,0
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eatout,trip,0.2,1
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othmaint,trip,0.001,0.518
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social,trip,0.3,0.478
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othdiscr,trip,0.252252252,0.272272272
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univ,trip,0.999001,0
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read_skim_cache: False
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write_skim_cache: False
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zone_system: 2
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taz_skims:
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omx: min*.omx
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maz: maz.csv
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maz_to_maz:
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tables:
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- maz_to_maz_walk.csv
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skim_time_periods:
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time_window: 1440
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period_minutes: 60
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periods: [0, 5, 9, 15, 18, 20, 24]
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labels: ['EA', 'AM', 'MD', 'PM', 'EV', 'EA']
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sharrow: test
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recode_pipeline_columns: true
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expression_profile: False
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households_sample_size: 0
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sharrow_cache_dir: sharrow_cache
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chunk_training_mode: explicit
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check_for_variability: False
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use_shadow_pricing: False
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want_dest_choice_sample_tables: False
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trace_hh_id:
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cleanup_trace_files_on_resume: True
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input_table_list:
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- tablename: households
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filename: households.csv
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index_col: household_id
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rename_columns:
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MAZ: home_zone_id
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hhno: household_id
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recode_columns:
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home_zone_id: land_use.zone_id
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keep_columns:
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- home_zone_id
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- income
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- tablename: persons
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filename: persons.csv
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index_col: person_id
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rename_columns:
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PERID: person_id
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keep_columns:
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- household_id
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- age
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- PNUM
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- tablename: land_use # MAZ,TAZ,Acres,Population
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filename: land_use.csv
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index_col: zone_id
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rename_columns:
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MAZ: zone_id
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recode_columns:
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zone_id: zero-based
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TAZ: land_use_taz.TAZ
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keep_columns:
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- TAZ
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- Acres
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- Population
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- tablename: land_use_taz
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filename: taz.csv
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index_col: TAZ
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recode_columns:
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TAZ: zero-based
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resume_after:
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models:
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- trip_destination
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Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork
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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
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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
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stop proximity to home (outbound),@df.outbound * od_skims['distwalk'],-0.3800,0,0,0,0,0,0,0,0,0
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stop proximity to home (inbound),@~df.outbound * dp_skims['distwalk'],-0.1500,0,0,0,0,0,0,0,0,0
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stop proximity to main destination (outbound),@df.outbound * dp_skims['distwalk'],-0.26,,,,,,,,,
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"Sample of alternatives correction factor","@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1,1,1
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Mode choice logsum from origin to stop,od_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821
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Mode choice logsum from stop to destination,dp_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821
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meaty,@odt_skims['meat'],-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01
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SAMPLE_SIZE: 30
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DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv
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DESTINATION_SPEC: trip_destination.csv
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COEFFICIENTS: _dummy_coefficients.csv
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LOGSUM_SETTINGS: trip_mode_choice.yaml
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# optional (comment out if not desired)
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DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum
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# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
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DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample
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# model-specific logsum-related settings
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TRIP_ORIGIN: origin
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ALT_DEST_COL_NAME: alt_dest
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PRIMARY_DEST: destination
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REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS:
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- tour_mode
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preprocessor:
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SPEC: trip_destination_annotate_trips_preprocessor
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DF: trips
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TABLES:
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- tours
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- persons
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# drop failed trips and cleanup failed trip leg_mates for consistency
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# (i.e. adjust trip_count, trip_num, first for missing failed trips)
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CLEANUP: False
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# this setting is used by testing code to force failed trip_destination
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# fail_some_trips_for_testing: False
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Description,Target,Expression
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,tour_mode,"reindex(tours.tour_mode, df.tour_id)"
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,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))"
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,trip_period,network_los.skim_time_period_label(_tod)
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,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'"
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,purpose_index_num,"size_terms.get_cols(df.purpose)"
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,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'"
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,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'"
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,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)"
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Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork
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,_od_DIST@od_skims['distwalk'],1,1,1,1,1,1,1,1,1,1
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,_dp_DIST@dp_skims['distwalk'],1,1,1,1,1,1,1,1,1,1
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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
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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
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stop proximity to home (outbound),@df.outbound * _od_DIST,-0.3800,0,0,0,0,0,0,0,0,0
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stop proximity to home (inbound),@~df.outbound * _od_DIST,-0.1500,0,0,0,0,0,0,0,0,0
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stop proximity to main destination (outbound),@df.outbound * _dp_DIST,-0.26,,,,,,,,,
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Label,Description,Expression,DRIVEALONEFREE,SHAREDRIDE,WALK
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#,Drive alone ,,,,
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util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,
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util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,
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util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['meat'],,coef_ivt,
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util_WALK_Time_short,WALK - Time up to 1.5 miles,@od_skims['distwalk'],,,coef_walktime_short

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