A Custom Location can grow along a stored country trajectory

SWEET_python/city_params.py

@@ -4938,7 +4938,12 @@ def _calculate_diverted_masses(self, scenario: int = 0) -> None:
         parameters.divs = divs
 
     def dst_baseline_blank(
-        self, country: str, population: int, precipitation: float, temperature: float
+        self,
+        country: str,
+        population: int,
+        precipitation: float,
+        temperature: float,
+        population_series: Optional[pd.Series] = None,
     ) -> None:
         """
         Initializes the baseline scenario with given parameters for a blank/custom city.
@@ -4947,6 +4952,10 @@ def dst_baseline_blank(
             country (str): The country name.
             population (int): Population of the city.
             precipitation (float): Average annual precipitation in mm/year.
+            population_series (pd.Series): the country's population by year, worked
+                out once by country_population_trajectory and stored. The city's waste
+                grows along it instead of along SWEET's own table. Only its shape
+                counts: the city's waste is set by ``population``.
 
         Returns:
             None
@@ -5121,9 +5130,13 @@ def calculate_component_fractions(
         waste_masses_df = waste_fractions_df * waste_mass
         # The country's population, year by year. The constants above are one rate for
         # every country; they stand only for a country SWEET has no series for.
+        stored_series = population_series
         population_series, growth_rate_historic, growth_rate_future = _country_growth(
             iso3, year_of_data_pop, growth_rate_historic, growth_rate_future
         )
+        if stored_series is not None:
+            # The two rates stay the ones a person reads; the stored series is the growth.
+            population_series = stored_series
         waste_generated_df = WasteGeneratedDF.create(
             waste_masses_df,
             MODEL_START_YEAR,

changelog/2026-10.md

@@ -68,6 +68,17 @@ or 9% above the default (model-output change).
   No other number moves. ([#91](https://github.com/RMI/SWEET_python/pull/91),
   [RMI/WasteMAP#888](https://github.com/RMI/WasteMAP/pull/888))
 
+- **A Custom Location can grow along a stored country trajectory.**
+  `City.dst_baseline_blank` takes `population_series`, as `load_csv_new` does: the
+  country's population by year from `country_population_trajectory`, stored with
+  WasteMAP's map build. The Custom Location's waste, and its scenario's, grow along
+  it instead of along SWEET's own table; the city's tonnage still comes from the
+  population it is given, and the two growth rates a person reads are still the
+  country's. Without it nothing changes. Handed the country's own trajectory, a
+  Custom Location grows as before, to within 5.9e-16 in every country, and the 12
+  with fallback rates exactly. ([#94](https://github.com/RMI/SWEET_python/pull/94),
+  [RMI/WasteMAP#890](https://github.com/RMI/WasteMAP/pull/890))
+
 - **Seven more Climate TRACE modelling decisions are marked where SWEET applies them**:
   waste before 1970 counted as zero, a site's last intake in the year before it closes,
   intake following population, the decay rate from climate, each waste type's methane

tests/test_population_trajectory.py

@@ -137,3 +137,64 @@ def test_load_csv_new_grows_a_city_along_the_trajectory_it_is_handed():
         worked_out.growth_rate_historic,
         worked_out.growth_rate_future,
     )
+
+
+def _custom_location(country, population_series=None):
+    """A Custom Location, as WasteMAP's city DST builds one."""
+    city = City("custom")
+    city.dst_baseline_blank(country, 100_000, 800.0, 15.0, population_series=population_series)
+    return city
+
+
+def test_a_custom_location_grows_along_the_country_trajectory_it_is_handed():
+    stored, _ = country_population_trajectory("USA")
+    flat = pd.Series(331_000_000.0, index=YEARS)
+
+    worked_out, along_stored, along_flat = (
+        _custom_location("United States", series).baseline_parameters
+        for series in (None, stored, flat)
+    )
+
+    waste = lambda p: p.waste_generated_df.sum(axis=1)  # noqa: E731
+    np.testing.assert_allclose(waste(along_stored), waste(worked_out), rtol=1e-14)
+    np.testing.assert_allclose(
+        (waste(along_stored) / waste(along_stored).loc[2022]).loc[YEARS],
+        stored / stored.loc[city_params.CUSTOM_LOCATION_POPULATION_YEAR],
+        rtol=1e-14,
+    )
+    # Only the shape counts: the waste is the caller's population's, in every case.
+    assert waste(along_flat).loc[2022] == waste(worked_out).loc[2022]
+    assert waste(along_flat).loc[2050] == pytest.approx(waste(along_flat).loc[2022], rel=1e-12)
+    assert waste(along_flat).loc[2050] != pytest.approx(waste(worked_out).loc[2050], rel=1e-3)
+    # The two rates a person reads are still the country's.
+    assert (along_flat.growth_rate_historic, along_flat.growth_rate_future) == (
+        worked_out.growth_rate_historic,
+        worked_out.growth_rate_future,
+    )
+
+
+def test_a_custom_location_with_its_fallback_trajectory_is_unchanged():
+    stored, source = country_population_trajectory("ATA")
+    assert source == city_params.COUNTRY_FALLBACK_RATES
+
+    worked_out = _custom_location("Antarctica").baseline_parameters
+    along_stored = _custom_location("Antarctica", stored).baseline_parameters
+
+    pd.testing.assert_frame_equal(along_stored.waste_generated_df, worked_out.waste_generated_df)
+    assert (along_stored.growth_rate_historic, along_stored.growth_rate_future) == (
+        city_params.FALLBACK_GROWTH_RATE_HISTORIC,
+        city_params.FALLBACK_GROWTH_RATE_FUTURE,
+    )
+
+
+def test_a_custom_location_scenario_grows_along_the_trajectory_it_is_handed():
+    flat = pd.Series(331_000_000.0, index=YEARS)
+    city = _custom_location("United States", flat)
+    for landfill in city.baseline_parameters.landfills:
+        landfill.estimate_emissions()
+
+    divs = city_params.DiversionFractions(compost=0.1, anaerobic=0.0, combustion=0.0, recycling=0.1)
+    city.implement_dst_changes_simple_v1_5(divs, False, False, 0.0, 0.0, 2030, 1, 0.0)
+    waste = city.scenario_parameters[0].waste_generated_df.df.sum(axis=1)
+
+    assert waste.loc[2050] == pytest.approx(waste.loc[2030], rel=1e-12)