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Merge commit 'c34e0cf396e3acd1af27422ebd859c4e4a5bc308' into extended
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‎.github/workflows/ci.yml‎

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name: Implementation Testing
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on:
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push:
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branches:
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- '*'
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pull_request:
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branches:
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- '*'
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env:
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CACHE_NUMBER: 0 # increase to reset cache manually
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jobs:
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foundation:
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strategy:
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matrix:
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python-version: ["3.10"]
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defaults:
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run:
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shell: bash -l {0}
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name: linux-64-py${{ matrix.python-version }}
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runs-on: ubuntu-latest
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steps:
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# checkout the code in this repository
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- uses: actions/checkout@v4
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with:
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path: 'activitysim-prototype-mtc'
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# checkout the main branch of ActivitySim itself
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- uses: actions/checkout@v4
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with:
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repository: 'ActivitySim/activitysim'
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ref: main
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path: 'activitysim'
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fetch-depth: 0 # get all tags, lets setuptools_scm do its thing
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- name: Setup Mambaforge
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uses: conda-incubator/setup-miniconda@v3
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with:
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miniforge-variant: Mambaforge
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miniforge-version: latest
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activate-environment: asim-test
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use-mamba: true
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python-version: ${{ matrix.python-version }}
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- name: Set cache date for year and month
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run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV
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- uses: actions/cache@v4
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with:
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path: /usr/share/miniconda3/envs/asim-test
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key: linux-64-conda-${{ hashFiles('activitysim/conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
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id: cache
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- name: Update environment
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run: |
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mamba env update -n asim-test -f activitysim/conda-environments/github-actions-tests.yml
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mamba install --yes \
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"psutil=5.9.5" \
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"pydantic=2.6.1" \
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"pypyr=5.8.0" \
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"pytables=3.6.1" \
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"pytest-cov" \
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"pytest-regressions=2.5.0" \
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"scikit-learn=1.2.2" \
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"sharrow>=2.6.0" \
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"simwrapper=1.8.5" \
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"xarray=2023.2.0" \
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"zarr=2.14.2" \
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"zstandard=0.21.0"
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if: steps.cache.outputs.cache-hit != 'true'
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- name: Install activitysim
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# installing without dependencies is faster, we trust that all needed dependencies
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# are in the conda environment defined above. Also, this avoids pip getting
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# confused and reinstalling tables (pytables).
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run: |
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python -m pip install -e ./activitysim --no-deps
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- name: Conda checkup
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run: |
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mamba info -a
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mamba list
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- name: Test this implementation
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run: |
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python -m pytest activitysim-prototype-mtc/test

‎ext-configs/annotate_tours_tour_mode_choice.csv‎

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Description,Target,Expression
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non-motorized and walk tran and tnc set to null,selected_vehicle,np.nan
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tours using sov option,selected_vehicle,"np.where(df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY','DRIVE_LOC','DRIVE_LRF','DRIVE_EXP','DRIVE_HVY','DRIVE_COM']), df.vehicle_occup_1, selected_vehicle)"
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tours using sr2 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED2FREE','SHARED2PAY']), df.vehicle_occup_2, selected_vehicle)"
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tours using sr3 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED3FREE','SHARED3PAY']), df['vehicle_occup_3.5'], selected_vehicle)"
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tours using sov option,selected_vehicle,"pd.Series(np.where(df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY','DRIVE_LOC','DRIVE_LRF','DRIVE_EXP','DRIVE_HVY','DRIVE_COM']), df.vehicle_occup_1, selected_vehicle), index=df.index, dtype=df.vehicle_occup_1.dtype)"
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tours using sr2 option,selected_vehicle,"pd.Series(np.where(df.tour_mode.isin(['SHARED2FREE','SHARED2PAY']), df.vehicle_occup_2, selected_vehicle), index=df.index, dtype=df.vehicle_occup_1.dtype)"
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tours using sr3 option,selected_vehicle,"pd.Series(np.where(df.tour_mode.isin(['SHARED3FREE','SHARED3PAY']), df['vehicle_occup_3.5'], selected_vehicle), index=df.index, dtype=df.vehicle_occup_1.dtype)"
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parent tour vehicle,_parent_selected_vehicle,"reindex(df.selected_vehicle, df.parent_tour_id) if 'selected_vehicle' in df.columns else np.nan"
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assign parent tour vehicle to atwork subtours,selected_vehicle,"np.where(df.tour_category == 'atwork', _parent_selected_vehicle, selected_vehicle)"
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assign parent tour vehicle to atwork subtours,selected_vehicle,"pd.Series(np.where(df.tour_category == 'atwork', _parent_selected_vehicle, selected_vehicle), index=df.index, dtype=df.vehicle_occup_1.dtype)"
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is non-driving tour,_is_non_driving_tour,"df.tour_mode.isin(['WALK','BIKE','WALK_LOC','WALK_LRF','WALK_EXP','WALK_HVY','WALK_COM','TAXI','TNC_SINGLE','TNC_SHARED'])"
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atwork subtours whose parent tour did not use a vehicle use non_hh_veh,selected_vehicle,"np.where(~_is_non_driving_tour & (df.tour_category == 'atwork') & pd.isna(selected_vehicle),'non_hh_veh', selected_vehicle)"
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atwork subtours whose parent tour did not use a vehicle use non_hh_veh,selected_vehicle,"pd.Series(np.where(~_is_non_driving_tour & (df.tour_category == 'atwork') & pd.isna(selected_vehicle),'non_hh_veh', selected_vehicle), index=df.index, dtype=df.vehicle_occup_1.dtype)"

‎ext-configs/tour_mode_choice_annotate_choosers_preprocessor.csv‎

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,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False
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#Auto operating costs,,
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,parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan"
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,sov_veh_option,"np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_1', np.nan))"
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,sr2_veh_option,"np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_2', np.nan))"
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,sr3p_veh_option,"np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_3.5', np.nan))"
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,sov_veh_option,"pd.Series(np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_1', np.nan)), index=df.index, dtype=df.vehicle_occup_1.dtype if 'vehicle_occup_1' in df.columns else np.float64)"
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,sr2_veh_option,"pd.Series(np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_2', np.nan)), index=df.index, dtype=df.vehicle_occup_2.dtype if 'vehicle_occup_2' in df.columns else np.float64)"
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,sr3p_veh_option,"pd.Series(np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_3.5', np.nan)), index=df.index, dtype=df['vehicle_occup_3.5'].dtype if 'vehicle_occup_3.5' in df.columns else np.float64)"
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,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan"
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,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)"
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,sr2_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr2_veh_option, df.index)) if 'vehicle_occup_2' in df.columns else np.nan"

‎ext-configs/trip_mode_choice_annotate_trips_preprocessor.csv‎

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,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES)
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,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES)
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#,,
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,selected_tour_vehicle,"reindex(tours.selected_vehicle, df.tour_id)"
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,selected_tour_vehicle,"pd.Series(reindex(tours.selected_vehicle, df.tour_id), index=df.index, dtype=tours.selected_vehicle.dtype)"
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,auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(selected_tour_vehicle, df.index))"
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,auto_op_cost,"np.where(pd.isna(auto_op_cost), costPerMile, auto_op_cost)"
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,inbound,~df.outbound

‎test/configs/settings.yaml‎

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# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings)
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want_dest_choice_sample_tables: False
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cleanup_pipeline_after_run: True
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cleanup_pipeline_after_run: False
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output_tables:
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h5_store: False

‎test/configs_mp/settings.yaml‎

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# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings)
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want_dest_choice_sample_tables: False
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cleanup_pipeline_after_run: True
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cleanup_pipeline_after_run: False
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output_tables:
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h5_store: False

‎test/ext-configs/settings.yaml‎

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- vehicles
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- proto_disaggregate_accessibility
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recode_pipeline_columns: False
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recode_pipeline_columns: True

‎test/ext-configs_chunkless/network_los.yaml‎

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inherit_settings: True
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# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
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read_skim_cache: False
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# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
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write_skim_cache: False

‎test/ext-configs_chunkless/settings.yaml‎

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inherit_settings: True
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# treat warnings as errors
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strict: True
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# number of households to simulate
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households_sample_size: 10
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chunk_size: 0
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chunk_training_mode: disabled
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# - shadow pricing global switches
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use_shadow_pricing: False
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# turn writing of sample_tables on and off for all models
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# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings)
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want_dest_choice_sample_tables: False
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cleanup_pipeline_after_run: True
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output_tables:
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h5_store: False
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action: include
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prefix: final_
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sort: True
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tables:
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- tablename: trips
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decode_columns:
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origin: land_use.zone_id
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destination: land_use.zone_id
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recode_pipeline_columns: True

‎test/ext-configs_mp/settings.yaml‎

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# - shadow pricing global switches
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# turn shadow_pricing on and off for all models (e.g. school and work)
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# see shadow_pricing.yaml for additional settings
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use_shadow_pricing: True
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use_shadow_pricing: False
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# turn writing of sample_tables on and off for all models
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# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings)
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want_dest_choice_sample_tables: False
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cleanup_pipeline_after_run: True
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cleanup_pipeline_after_run: False
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trace_hh_id:
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# global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only)
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want_dest_choice_presampling: True
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recode_pipeline_columns: False
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recode_pipeline_columns: True
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output_tables:
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h5_store: False

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