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Merge pull request #3 from camsys/main
Sharrow Run Fail: School Escorting
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.github/workflow/ci.yml renamed to .github/workflows/ci.yml

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@@ -50,38 +50,26 @@ jobs:
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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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path: 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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python -m pip install ./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 sandag-abm3-example/test
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cd activitysim-sandag-abm3/test
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python -m pytest test_sandag_abm3.py::test_sandag_abm3_progressive

‎configs/resident/atwork_subtour_destination.csv‎

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@@ -1,8 +1,8 @@
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Label,Description,Expression,atwork
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,,"[email protected](skims['DIST'], 20)",1
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,,_DIST_SQUARED@_DIST**2,1
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,,_DIST_CUBED@_DIST**3,1
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,,[email protected](_DIST + 0.001),1
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ATWORK_DIST_SQUARED,,_DIST_SQUARED@_DIST**2,1
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ATWORK_DIST_CUBED,,_DIST_CUBED@_DIST**3,1
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ATWORK_DIST_LOGGED,,[email protected](_DIST + 0.001),1
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util_Distance,Distance,@_DIST,coef_dist_atwork
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util_Distance_squared,Distance_squared,@_DIST_SQUARED,coef_distsqrd_atwork
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util_Distance_fulltime_worker,Distance_fulltime_worker,@_DIST * (df.ptype==1),coef_dist_ftworker_atwork

‎configs/resident/atwork_subtour_frequency.csv‎

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@@ -4,7 +4,7 @@ util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_ti
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util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business
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util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business
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util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business
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util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business
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util_zero_cars_owned_by_hh_dummy,auto_ownership==0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business
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util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business
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util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business
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util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business

‎configs/resident/cdap.yaml‎

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@@ -45,3 +45,6 @@ annotate_households:
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DF: households
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TABLES:
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- persons
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compute_settings:
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sharrow_skip: true

‎configs/resident/cdap_joint_tour_coefficients.csv‎

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@@ -10,7 +10,7 @@ Label,description,Expression,dependency,coefficient
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,person x is kid and DAP is N,ptype_px > 6,N_px,1.6898
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,Accessibility to retail employment/Non-Mandatory Attractions,shopping_accessibility_p1,,0.055031985
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,Income less than $30k,income_p1 < 30000,,-0.192506367
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,Income between $60k and $100k,(income_p1 >= 60000) & (income_p1 < =100000),,0.104325349
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,Income between $60k and $100k,(income_p1>=60000) & (income_p1<=100000),,0.104325349
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,Income more than $100k,income_p1 > 100000,,0.104325349
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,No Car Households,auto_ownership_p1 == 0,,0
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,Cars Less than Workers,auto_ownership_p1 < num_workers_p1,,0.088402389

‎configs/resident/external_non_mandatory_identification.csv‎

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Label,Description,Expression,external_tour,internal_tour
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util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station,
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util_log_size,Log size of nearest station,"@np.log1p(reindex(land_use.external_nonwork, df.closest_external_zone))",coef_log_size_of_nearest_ext_station,
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util_log_size,Log size of nearest station,@np.log1p(df.reindex_land_use_external_nonwork_zone_to_closest_external_zone),coef_log_size_of_nearest_ext_station,
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util_escort,escort tour ASC,"@np.where(df.tour_type == 'escort', 1, 0)",coef_escort,
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util_shopping,shopping tour ASC,"@np.where(df.tour_type == 'shopping', 1, 0)",coef_shopping,
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util_othmaint,othmaint tour ASC,"@np.where(df.tour_type == 'othmaint', 1, 0)",coef_othmaint,

‎configs/resident/external_non_mandatory_identification.yaml‎

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EXTERNAL_COL_NAME: is_external_tour
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# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True
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INTERNAL_COL_NAME: is_internal_tour
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preprocessor:
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SPEC: external_non_mandatory_identification_preprocessor.csv
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DF: df
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TABLES:
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- land_use

‎configs/resident/external_non_mandatory_identification_preprocessor.csv‎

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Description,Target,Expression
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reindex_land_use_external_nonwork_zone_to_closest_external_zone,reindex_land_use_external_nonwork_zone_to_closest_external_zone,"reindex(land_use.external_nonwork, df.closest_external_zone)"
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‎configs/resident/external_student_identification.yaml‎

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@@ -12,4 +12,4 @@ CHOOSER_FILTER_COLUMN_NAME: is_student
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# Adds this column to the persons table and is set to True if workplace is external
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EXTERNAL_COL_NAME: is_external_student
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# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True
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INTERNAL_COL_NAME: is_internal_student
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INTERNAL_COL_NAME: is_internal_student

‎configs/resident/external_worker_identification.csv‎

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Label,Description,Expression,work_external,work_internal
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util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station,
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util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.external_work,df.closest_external_zone))",coef_size_of_nearest_ext_station,
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util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(df.reindex_land_use_external_to_work_external_zone)",coef_size_of_nearest_ext_station,
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util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5,
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util_part_time,Part time worker,"@np.where(df.is_parttime_worker==""TRUE"",1,0)",coef_part_time,
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util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15,

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