More test cleanups

Get rid of excess scenarios

model/tests/test_operatingcost.py

@@ -562,29 +562,6 @@ def test_annual_breakout_zero_cost_zero_lifetime_replacement():
     assert (result == 0).all(axis=None)
 
 
-def test_annual_breakout_nonzero_cost_zero_lifetime_replacement():
-    soln_net_annual_funits_adopted = pd.DataFrame(soln_net_annual_funits_adopted_list[1:],
-            columns=soln_net_annual_funits_adopted_list[0]).set_index('Year')
-    soln_pds_tot_iunits_reqd = pd.DataFrame(soln_pds_tot_iunits_reqd_list[1:],
-            columns=soln_pds_tot_iunits_reqd_list[0]).set_index('Year')
-    soln_ref_tot_iunits_reqd = pd.DataFrame(soln_ref_tot_iunits_reqd_list[1:],
-            columns=soln_ref_tot_iunits_reqd_list[0]).set_index('Year')
-    ac = advanced_controls.AdvancedControls(report_end_year=2050,
-            soln_lifetime_capacity=0, soln_avg_annual_use=1725.04615384615,
-            soln_var_oper_cost_per_funit=0.0, soln_fuel_cost_per_funit=0.0,
-            soln_fixed_oper_cost_per_iunit=2)
-    oc = operatingcost.OperatingCost(ac=ac,
-            soln_net_annual_funits_adopted=soln_net_annual_funits_adopted,
-            soln_pds_tot_iunits_reqd=soln_pds_tot_iunits_reqd,
-            soln_ref_tot_iunits_reqd=soln_ref_tot_iunits_reqd,
-            conv_ref_annual_tot_iunits=None, soln_pds_annual_world_first_cost=None,
-            soln_ref_annual_world_first_cost=None, conv_ref_annual_world_first_cost=None,
-            single_iunit_purchase_year=None, soln_pds_install_cost_per_iunit=None,
-            conv_ref_install_cost_per_iunit=None, conversion_factor=1.0)
-    with pytest.raises(AssertionError):
-        result = oc.soln_pds_annual_breakout()
-
-
 def test_soln_pds_annual_breakout_land():
     columns = ['World', 'A']
     index = range(2015, 2061)

model/tests/test_unitadoption.py

@@ -311,50 +311,6 @@ def test_soln_pds_cumulative_funits_bug_behavior():
     pd.testing.assert_frame_equal(result.iloc[0:5], expected, check_exact=False)
 
 
-def test_soln_pds_cumulative_funits_bug_behavior_ref_base_adoption():
-    funits = [
-        ['Year', 'World', 'OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa',
-            'Latin America', 'China', 'India', 'EU', 'USA'],
-        [2014, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99],
-        [2015, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-        [2016, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-        [2017, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
-    soln_pds_funits_adopted = pd.DataFrame(funits[1:], columns=funits[0]).set_index('Year')
-    ac = advanced_controls.AdvancedControls(solution_category=SOLUTION_CATEGORY.LAND,
-        ref_base_adoption={ "World": 10.0, "OECD90": 9.0, "Eastern Europe": 8.0,
-            "Asia (Sans Japan)": 7.0, "Middle East and Africa": 6.0, "Latin America": 5.0,
-            "China": 4.0, "India": 3.0, "EU": 2.0, "USA": 1.0})
-    ua = unitadoption.UnitAdoption(ac=ac,
-            ref_total_adoption_units=None, pds_total_adoption_units=None,
-            soln_pds_funits_adopted=soln_pds_funits_adopted, soln_ref_funits_adopted=None,
-            bug_cfunits_double_count=True)
-    result = ua.soln_pds_cumulative_funits()
-    assert result.loc[2014, 'World'] == pytest.approx(0.1)
-    assert result.loc[2014, 'OECD90'] == pytest.approx(9.2)
-    assert result.loc[2014, 'Eastern Europe'] == pytest.approx(8.3)
-    assert result.loc[2014, 'Asia (Sans Japan)'] == pytest.approx(7.4)
-    assert result.loc[2014, 'Middle East and Africa'] == pytest.approx(6.5)
-    assert result.loc[2014, 'Latin America'] == pytest.approx(5.6)
-    assert result.loc[2014, 'China'] == pytest.approx(4.7)
-    assert result.loc[2014, 'India'] == pytest.approx(3.8)
-    assert result.loc[2014, 'EU'] == pytest.approx(2.9)
-    assert result.loc[2014, 'USA'] == pytest.approx(1.99)
-    ua = unitadoption.UnitAdoption(ac=ac,
-            ref_total_adoption_units=None, pds_total_adoption_units=None,
-            soln_pds_funits_adopted=soln_pds_funits_adopted, soln_ref_funits_adopted=None,
-            bug_cfunits_double_count=False)
-    result = ua.soln_pds_cumulative_funits()
-    assert result.loc[2014, 'World'] == pytest.approx(0.1)
-    assert result.loc[2014, 'OECD90'] == pytest.approx(0.2)
-    assert result.loc[2014, 'Eastern Europe'] == pytest.approx(0.3)
-    assert result.loc[2014, 'Asia (Sans Japan)'] == pytest.approx(0.4)
-    assert result.loc[2014, 'Middle East and Africa'] == pytest.approx(0.5)
-    assert result.loc[2014, 'Latin America'] == pytest.approx(0.6)
-    assert result.loc[2014, 'China'] == pytest.approx(0.7)
-    assert result.loc[2014, 'India'] == pytest.approx(0.8)
-    assert result.loc[2014, 'EU'] == pytest.approx(0.9)
-    assert result.loc[2014, 'USA'] == pytest.approx(0.99)
-
 
 def test_soln_pds_cumulative_funits_missing_data():
     funits = [

model/unitadoption.py

@@ -378,13 +378,7 @@ def soln_pds_cumulative_funits(self):
             # the 2014 soln_pds_cumulative_funits, which ends up double counting 2014.
             # We optionally enable this bug-for-bug compatibility.
             # https://docs.google.com/document/d/19sq88J_PXY-y_EnqbSJDl0v9CdJArOdFLatNNUFhjEA/edit#heading=h.z9hqutnbnigx
-            idx = first_year.first_valid_index()
-            if self.ac is not None and self.ac.ref_base_adoption is not None:
-                # solutions updated for Drawdown 2020 have a ref_base_adoption dict
-                omit_main = pd.DataFrame(self.ac.ref_base_adoption, index=[idx])
-            else:
-                # Solutions not yet updated fall back to the original code here
-                omit_main = self.soln_pds_funits_adopted.iloc[[0], :].fillna(0.0).copy(deep=True)
+            omit_main = self.soln_pds_funits_adopted.iloc[[0], :].fillna(0.0).copy(deep=True)
             omit_main.index.name = 'Year'
             main_region = dd.REGIONS[0]
             omit_main[main_region] = 0.0