Make global constants available to interaction-based model expressions

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False
 

activitysim/abm/models/trip_destination.py

@@ -189,7 +189,7 @@ def _destination_sample(
  f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
  )
 
- locals_dict = state.get_global_constants().copy()
+ locals_dict = {}
  locals_dict.update(model_settings.CONSTANTS)
 
  # size_terms of destination zones are purpose-specific, and trips have various purposes

activitysim/core/expressions.py

@@ -321,8 +321,8 @@ def annotate_tables(
  "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.",
  e,
  )
- if locals_dict:
-locals_d.update(locals_dict)
+
+ locals_d.update(locals_dict or {})
 
  results = compute_columns(
  state,

activitysim/core/flow.py

@@ -808,8 +808,10 @@ def apply_flow(
  """
  if sh is None:
  return None, None
- if locals_d is None:
- locals_d = {}
+
+ # Global constants are always available, but can be overridden by locals_d.
+ locals_d = {**state.get_global_constants(), **(locals_d or {})}
+
  with logtime("apply_flow"):
  try:
  flow = get_flow(

activitysim/core/interaction_simulate.py

@@ -100,7 +100,7 @@ def eval_interaction_utilities(
  assert len(spec.columns) == 1
 
  # avoid altering caller's passed-in locals_d parameter (they may be looping)
- locals_d = locals_d.copy() if locals_d is not None else {}
+ locals_d = dict(locals_d or {})
 
  utilities = None
 
@@ -210,6 +210,9 @@ def replace_in_index_level(mi, level, *repls):
  or estimator
  or (sharrow_enabled == "test" and extra_data is None)
  ):
+ # Global constants are always available, but can be overridden by locals_d.
+ # Sharrow calculations receive them in flow.apply_flow instead.
+ locals_d = {**state.get_global_constants(), **locals_d}
 
  def to_series(x):
  if np.isscalar(x):

activitysim/core/simulate.py

@@ -655,7 +655,6 @@ def eval_utilities(
  from .flow import apply_flow # import inside func to prevent circular imports
 
  locals_dict = {}
- locals_dict.update(state.get_global_constants())
  if locals_d is not None:
  locals_dict.update(locals_d)
  sh_util, sh_flow, sh_tree = apply_flow(

activitysim/core/test/configs/preprocessor.csv

@@ -5,4 +5,5 @@ count persons test,num_persons,persons.groupby('household_id').size().reindex(df
 skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')"
 skim wrapper test,od_distance_wrapper,skims2d['DIST']
 sov time,od_sov_time,skims3d['SOV_TIME']
-testing constant from locals_dict,constant_test,test_constant / 2
+testing constant from locals_dict,constant_test,test_constant / 2
+testing global constant,global_constant_test,global_test_constant / 2

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
  return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+ """Global constants are available in both destination-choice substeps."""
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text(
+ "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+ )
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ choosers = pd.DataFrame(
+ {"chooser_attr": [1.0, 2.0]},
+ index=pd.Index([0, 1], name="person_id"),
+ )
+ alternatives = pd.DataFrame(
+ {"alt_attr": [1.0, 2.0]},
+ index=pd.Index([10, 20], name="alt_id"),
+ )
+
+ # Sampling and simulation use separate specifications in location and
+ # destination choice, so exercise each expression-evaluation path.
+ sample_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+ )
+ sample = interaction_sample.interaction_sample(
+ state,
+ choosers,
+ alternatives,
+ sample_spec,
+ sample_size=0,
+ alt_col_name="alt_id",
+ )
+ sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+ simulate_spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+ )
+ results = interaction_sample_simulate.interaction_sample_simulate(
+ state,
+ choosers,
+ sampled_alternatives,
+ simulate_spec,
+ choice_column="alt_id",
+ want_logsums=True,
+ skip_choice=True,
+ )
+
+ expected_logsum = np.logaddexp(2.0, 4.0)
+ np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
  # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,11 +1,13 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_simulate, workflow
+from activitysim.core import flow, interaction_simulate, workflow
 
 
 @pytest.fixture
@@ -15,6 +17,39 @@ def state() -> workflow.State:
  return state
 
 
+def test_apply_flow_global_constants_and_local_override(state, monkeypatch):
+ class FakeFlow:
+ name = "test_flow"
+ compiled_recently = False
+ tree = object()
+
+ def dot(self, coefficients, dtype, compile_watch):
+ return np.array([[1.0]])
+
+ captured_locals = {}
+
+ def fake_get_flow(_state, _spec, locals_d, *_args, **_kwargs):
+ captured_locals.update(locals_d)
+ return FakeFlow()
+
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2, "GLOBAL_ONLY": 4}
+ monkeypatch.setattr(flow, "sh", object())
+ monkeypatch.setattr(flow, "get_flow", fake_get_flow)
+
+ spec = pd.DataFrame(
+ {"alt": [1.0]}, index=pd.Index(["GLOBAL_SCALE"], name="Expression")
+ )
+ result, _, _ = flow.apply_flow(
+ state,
+ spec,
+ pd.DataFrame({"value": [1.0]}),
+ locals_d={"GLOBAL_SCALE": 3},
+ )
+
+ np.testing.assert_allclose(result, [[1.0]])
+ assert captured_locals == {"GLOBAL_SCALE": 3, "GLOBAL_ONLY": 4}
+
+
 def test_interaction_simulate_explicit_error_terms_parity(state):
  # Run interaction_simulate with and without explicit error terms and check that results are similar.
 
@@ -172,3 +207,73 @@ def test_interaction_simulate_eet_large_utilities(state):
  assert not choices_eet.isna().any()
  # With such a large difference, Alt 1 should be the dominant choice
  assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+ # global constants (from constants.yaml) should be available to expressions
+ # evaluated for interaction models (e.g. location choice, destination choice,
+ # tour scheduling), see issue #1015
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d=None,
+ trace_label="test_global_constants",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(
+ utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+ )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+ # values passed in locals_d take precedence over global constants
+
+ configs_dir = tmp_path.joinpath("configs")
+ configs_dir.mkdir()
+ configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+ tmp_path.joinpath("data").mkdir()
+
+ state = workflow.State()
+ state.initialize_filesystem(
+ working_dir=tmp_path, configs_dir=("configs",)
+ ).default_settings()
+ state.settings.check_for_variability = False
+
+ df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+ spec = pd.DataFrame(
+ {"coefficient": [1.0]},
+ index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+ )
+
+ utilities, _ = interaction_simulate.eval_interaction_utilities(
+ state,
+ spec,
+ df,
+ locals_d={"KM_TO_MILE": 1.0},
+ trace_label="test_global_constants_override",
+ trace_rows=None,
+ )
+
+ np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())

activitysim/core/test/test_preprocessing.py

@@ -87,6 +87,7 @@ def check_outputs(tours):
  "od_distance_wrapper",
  "od_sov_time",
  "constant_test",
+ "global_constant_test",
  ]
 
  # check all new columns are added
@@ -109,6 +110,7 @@ def check_outputs(tours):
  "od_distance_wrapper": [0.24, 0.28, 0.57],
  "od_sov_time": [0.78, 0.89, 1.76],
  "constant_test": [21, 21, 21],
+ "global_constant_test": [21, 21, 21],
  }
  ).set_index("tour_id")
  pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False)
@@ -124,7 +126,11 @@ def setup_skims(state: workflow.State):
  return {"skims3d": skims3d, "skims2d": skims2d}
 
 
-def test_preprocessor(state: workflow.State, households, persons, tours):
+def test_preprocessor(state: workflow.State, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in preprocessor
  state.add_table("households", households)
  state.add_table("persons", persons)
@@ -156,7 +162,11 @@ def test_preprocessor(state: workflow.State, households, persons, tours):
  pd.testing.assert_frame_equal(state_tours, original_tours)
 
 
-def test_annotator(state, households, persons, tours):
+def test_annotator(state, households, persons, tours, monkeypatch):
+ monkeypatch.setattr(
+ state, "get_global_constants", lambda: {"global_test_constant": 42}
+ )
+
  # adding dataframes to state so they can be accessed in annotator
  state.add_table("households", households)
  state.add_table("persons", persons)

activitysim/core/test/test_simulate.py

@@ -82,6 +82,33 @@ def test_eval_variables(state, spec, data):
  pdt.assert_frame_equal(result, expected, check_names=False)
 
 
+def test_standard_utilities_global_constants_and_local_override(state):
+ state.get_global_constants = lambda: {"GLOBAL_SCALE": 2}
+ choosers = pd.DataFrame({"value": [1.0, 2.0]})
+ spec = pd.DataFrame(
+ {"alt": [1.0]},
+ index=pd.Index(["@df.value * GLOBAL_SCALE"], name="Expression"),
+ )
+ chunk_sizer = chunk.ChunkSizer(state, "", "", len(choosers))
+
+ utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ chunk_sizer=chunk_sizer,
+ )
+ overridden_utilities = simulate.eval_utilities(
+ state,
+ spec,
+ choosers,
+ locals_d={"GLOBAL_SCALE": 3},
+ chunk_sizer=chunk_sizer,
+ )
+
+ npt.assert_allclose(utilities["alt"], [2.0, 4.0])
+ npt.assert_allclose(overridden_utilities["alt"], [3.0, 6.0])
+
+
 def test_simple_simulate(state, data, spec):
  state.settings.check_for_variability = False