Updates for sharrow compatibility

configs/logging.yaml

@@ -24,6 +24,15 @@ logging:
       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:
@@ -37,7 +46,7 @@ logging:
     console:
       class: logging.StreamHandler
       stream: ext://sys.stdout
-      formatter: simpleFormatter
+      formatter: elapsedFormatter
       level: NOTSET
 
   formatters:
@@ -53,3 +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/settings.yaml

@@ -11,6 +11,8 @@ 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
@@ -56,8 +58,8 @@ input_table_list:
     # 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
@@ -198,11 +200,26 @@ 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]

configs/stop_frequency_annotate_tours_preprocessor.csv

@@ -33,7 +33,7 @@ Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.
 #,,
 Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)"
 Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)"
-tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM')
+tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') | (network_los.skim_time_period_label(df.start) == 1) | (network_los.skim_time_period_label(df.start) == 3)
 AccesibilityAtOrigin fallback,hhacc,0
 AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))"
 AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)"

configs/trip_mode_choice.csv

@@ -196,14 +196,14 @@ PAYMODE - Constants,"@np.where((c_filter_atwrk == 0), (c_ivt)*(pay_md_cons), 0)"
 #MARTA Constants,,,,,,,,,,,,,,,,
 MARTA CONSTANT - Walk All Transit,"@c_ivt*np.minimum(odt_skims['WLK_ALLTRN_WLK_HRT'], 15)*(-1)*(0.1)",,,,,,,,,1,,,,,,
 MARTA CONSTANT - Walk Premium,"@c_ivt*np.minimum(odt_skims['WLK_PRMTRN_WLK_HRT'], 15)*(-1)*(0.1)",,,,,,,,,,1,,,,,
-MARTA CONSTANT - PNR All Transit - Outbound Leg of Tour,"@df.outbound *c_ivt*np.minimum(odt_skims['PNR_ALLTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['EA','AM','MD']))*(c_filter_atwrk == 0)",,,,,,,,,,,1,,,,
-MARTA CONSTANT - PNR All Transit - Inbound Leg of Tour,"@df.inbound *c_ivt*np.minimum(dot_skims['WLK_ALLTRN_PNR_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['PM','EV']))*(c_filter_atwrk == 0)",,,,,,,,,,,1,,,,
-MARTA CONSTANT - PNR Premium - Outbound Leg of Tour,"@df.outbound *c_ivt*np.minimum(odt_skims['PNR_PRMTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['EA','AM','MD']))*(c_filter_atwrk == 0)",,,,,,,,,,,,1,,,
-MARTA CONSTANT - PNR Premium - Inbound Leg of Tour,"@df.inbound * c_ivt*np.minimum(dot_skims['WLK_PRMTRN_PNR_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['PM','EV']))*(c_filter_atwrk == 0)",,,,,,,,,,,,1,,,
-MARTA CONSTANT - KNR All Transit - Outbound Leg of Tour,"@df.outbound *c_ivt*np.minimum(odt_skims['KNR_ALLTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['EA','AM','MD']))*(c_filter_atwrk == 0)",,,,,,,,,,,,,1,,
-MARTA CONSTANT - KNR All Transit - Inbound Leg of Tour,"@df.inbound * c_ivt*np.minimum(dot_skims['WLK_ALLTRN_KNR_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['PM','EV']))*(c_filter_atwrk == 0)",,,,,,,,,,,,,1,,
-MARTA CONSTANT - KNR Premium - Outbound Leg of Tour,"@df.outbound * c_ivt*np.minimum(odt_skims['KNR_PRMTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['EA','AM','MD']))*(c_filter_atwrk == 0)",,,,,,,,,,,,,,1,
-MARTA CONSTANT - KNR Premium - Inbound Leg of Tour,"@df.inbound * c_ivt*np.minimum(dot_skims['WLK_PRMTRN_KNR_HRT'], 15)*(-1)*(0.1)*(df.trip_period.isin(['PM','EV']))*(c_filter_atwrk == 0)",,,,,,,,,,,,,,1,
+MARTA CONSTANT - PNR All Transit - Outbound Leg of Tour,"@df.outbound *c_ivt*np.minimum(odt_skims['PNR_ALLTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.is_out_period)*(c_filter_atwrk == 0)",,,,,,,,,,,1,,,,
+MARTA CONSTANT - PNR All Transit - Inbound Leg of Tour,"@df.inbound *c_ivt*np.minimum(dot_skims['WLK_ALLTRN_PNR_HRT'], 15)*(-1)*(0.1)*(df.is_in_period)*(c_filter_atwrk == 0)",,,,,,,,,,,1,,,,
+MARTA CONSTANT - PNR Premium - Outbound Leg of Tour,"@df.outbound *c_ivt*np.minimum(odt_skims['PNR_PRMTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.is_out_period)*(c_filter_atwrk == 0)",,,,,,,,,,,,1,,,
+MARTA CONSTANT - PNR Premium - Inbound Leg of Tour,"@df.inbound * c_ivt*np.minimum(dot_skims['WLK_PRMTRN_PNR_HRT'], 15)*(-1)*(0.1)*(df.is_in_period)*(c_filter_atwrk == 0)",,,,,,,,,,,,1,,,
+MARTA CONSTANT - KNR All Transit - Outbound Leg of Tour,"@df.outbound *c_ivt*np.minimum(odt_skims['KNR_ALLTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.is_out_period)*(c_filter_atwrk == 0)",,,,,,,,,,,,,1,,
+MARTA CONSTANT - KNR All Transit - Inbound Leg of Tour,"@df.inbound * c_ivt*np.minimum(dot_skims['WLK_ALLTRN_KNR_HRT'], 15)*(-1)*(0.1)*(df.is_in_period)*(c_filter_atwrk == 0)",,,,,,,,,,,,,1,,
+MARTA CONSTANT - KNR Premium - Outbound Leg of Tour,"@df.outbound * c_ivt*np.minimum(odt_skims['KNR_PRMTRN_WLK_HRT'], 15)*(-1)*(0.1)*(df.is_out_period)*(c_filter_atwrk == 0)",,,,,,,,,,,,,,1,
+MARTA CONSTANT - KNR Premium - Inbound Leg of Tour,"@df.inbound * c_ivt*np.minimum(dot_skims['WLK_PRMTRN_KNR_HRT'], 15)*(-1)*(0.1)*(df.is_in_period)*(c_filter_atwrk == 0)",,,,,,,,,,,,,,1,
 #,,,,,,,,,,,,,,,,
 Walk Transit Short Distance Disutility,"@c_ivt*np.minimum(np.maximum((-3*odt_skims['SOV_FREE_DISTANCE']+40),0),30)",,,,,,,,,1,1,,,,,
 #,,,,,,,,,,,,,,,,

configs/trip_mode_choice_annotate_trips_preprocessor.csv

@@ -13,8 +13,10 @@ free parking availability,free_parking_available,(df.tour_type == 'work') & df.f
 #cost coefficients,,
 ,c_cost,(c_cost_low * df.is_low_income)+(c_cost_med * df.is_medium_income)+(c_cost_high * df.is_high_income)+(c_cost_high * df.is_very_high_income)
 #,,
-is_morning_period,_out_period,"(df.trip_period.isin(['EA','AM','MD']))"
-is_afternoon_period,_in_period,"(df.trip_period.isin(['MD','PM']))"
+is_morning_period,_out_period,"(df.trip_period.isin(['EA','AM','MD', 0, 1, 2]))"
+is_afternoon_period,_in_period,"(df.trip_period.isin(['MD','PM', 2, 3]))"
+,is_out_period,_out_period
+,is_in_period,_in_period
 #,,
 WLKALL transit in-vehicle time - outbound direction,walk_allTransit_ivt,odt_skims['WLK_ALLTRN_WLK_LOCAL']+expr*odt_skims['WLK_ALLTRN_WLK_XBUS']+hrt*odt_skims['WLK_ALLTRN_WLK_HRT']+brt*odt_skims['WLK_ALLTRN_WLK_BRT']+lrt*odt_skims['WLK_ALLTRN_WLK_LRT']+crl*odt_skims['WLK_ALLTRN_WLK_COMRAIL']
 WLKPRM transit in-vehicle time - outbound direction,walk_prmTransit_ivt,odt_skims['WLK_PRMTRN_WLK_LOCAL']+expr*odt_skims['WLK_PRMTRN_WLK_XBUS']+hrt*odt_skims['WLK_PRMTRN_WLK_HRT']+brt*odt_skims['WLK_PRMTRN_WLK_BRT']+lrt*odt_skims['WLK_PRMTRN_WLK_LRT']+crl*odt_skims['WLK_PRMTRN_WLK_COMRAIL']

data/.gitignore

@@ -0,0 +1,3 @@
+*.csv
+*.omx
+*.zip