def test_adoption_low_med_high_with_zero():
data_sources = {
'Baseline Cases': {
'zero': str(datadir.joinpath('ad_all_zero.csv')),
'one': str(datadir.joinpath('ad_all_one.csv')),
},
'Conservative Cases': {},
'Ambitious Cases': {},
'100% RES2050 Case': {},
}
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source='ALL SOURCES',
soln_pds_adoption_prognostication_growth='Medium')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=data_sources,
adconfig=g_adconfig)
result = ad.adoption_low_med_high(region='World')
# Zero should be dropped for the mean, to match Excel behavior.
assert all(result.loc[:, 'Medium'] == 1.0)
# With a single source, zero should not be dropped.
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source='zero',
soln_pds_adoption_prognostication_growth='Medium')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=data_sources,
adconfig=g_adconfig)
result = ad.adoption_low_med_high(region='World')
expected = pd.read_csv(str(datadir.joinpath('ad_all_zero.csv')),
index_col=0)
pd.testing.assert_series_equal(result.loc[:, 'Medium'],
expected.loc[:, 'World'],
check_names=False,
check_exact=True)
def test_soln_ref_adoption_args():
ac = advanced_controls.AdvancedControls(soln_ref_adoption_basis="Default")
assert ac.soln_ref_adoption_basis == "Default"
ac = advanced_controls.AdvancedControls(soln_ref_adoption_basis="Custom")
assert ac.soln_ref_adoption_basis == "Custom"
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(soln_ref_adoption_basis="???")
def test_regional_data_sources():
data_sources = {
'Baseline Cases': {
'10': str(datadir.joinpath('ad_region_constant10.csv')),
'20': str(datadir.joinpath('ad_region_constant20.csv')),
},
'Region: OECD90': {
'Baseline Cases': {
'10': str(datadir.joinpath('ad_region_constant10.csv')),
},
},
}
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source='ALL SOURCES',
soln_pds_adoption_prognostication_growth='Medium')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=data_sources,
adconfig=g_adconfig)
result = ad.adoption_trend_per_region()
assert result.loc[2019, 'World'] == pytest.approx(15.0)
assert result.loc[2019, 'OECD90'] == pytest.approx(10.0)
assert result.loc[2019, 'Latin America'] == pytest.approx(15.0)
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source='Baseline Cases',
soln_pds_adoption_prognostication_growth='Medium')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=data_sources,
adconfig=g_adconfig)
result = ad.adoption_trend_per_region()
assert result.loc[2019, 'World'] == pytest.approx(15.0)
assert result.loc[2019, 'OECD90'] == pytest.approx(10.0)
def test_pds_ref_use_years():
ac = advanced_controls.AdvancedControls(ref_adoption_use_pds_years=[2014],
pds_adoption_use_ref_years=[2015])
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(
ref_adoption_use_pds_years=[2014],
pds_adoption_use_ref_years=[2014])
def test_adoption_is_single_source():
s = 'Greenpeace AER'
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=s)
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
assert ad.adoption_is_single_source() == True
s = 'ALL SOURCES'
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=s)
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
assert ad.adoption_is_single_source() == False
s = 'Ambitious Cases'
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=s)
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
assert ad.adoption_is_single_source() == False
s = 'No such name'
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=s)
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
with pytest.raises(ValueError):
_ = ad.adoption_is_single_source()
def test_co2eq_conversion_source():
ac = advanced_controls.AdvancedControls(
co2eq_conversion_source="ar5 with feedback")
assert ac.co2eq_conversion_source == ef.CO2EQ_SOURCE.AR5_WITH_FEEDBACK
ac = advanced_controls.AdvancedControls(
co2eq_conversion_source=ef.CO2EQ_SOURCE.AR4)
assert ac.co2eq_conversion_source == ef.CO2EQ_SOURCE.AR4
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(co2eq_conversion_source="???")
def test_has_var_costs():
ac = advanced_controls.AdvancedControls(soln_var_oper_cost_per_funit=0.0,
soln_fuel_cost_per_funit=0.0,
conv_var_oper_cost_per_funit=0.0,
conv_fuel_cost_per_funit=0.0)
assert ac.has_var_costs
ac = advanced_controls.AdvancedControls(soln_var_oper_cost_per_funit=0.0,
soln_fuel_cost_per_funit=0.0,
conv_var_oper_cost_per_funit=0.0)
assert not ac.has_var_costs
def test_substitute_vma_passthru_value():
ac = advanced_controls.AdvancedControls(seq_rate_global=4.3)
assert ac.seq_rate_global == 4.3
class fakeVMA:
def avg_high_low(self, key):
return (0.0, 0.0, 0.0)
vmas = {'Sequestration Rates': fakeVMA()}
ac = advanced_controls.AdvancedControls(vmas=vmas,
seq_rate_global={'value': 4.3})
assert ac.seq_rate_global == 4.3
def test_emissions_grid():
ac = advanced_controls.AdvancedControls(emissions_grid_source="IPCC Only",
emissions_grid_range="high")
assert ac.emissions_grid_source == ef.GRID_SOURCE.IPCC
assert ac.emissions_grid_range == ef.GRID_RANGE.HIGH
ac = advanced_controls.AdvancedControls(
emissions_grid_source=ef.GRID_SOURCE.META,
emissions_grid_range=ef.GRID_RANGE.MEAN)
assert ac.emissions_grid_source == ef.GRID_SOURCE.META
assert ac.emissions_grid_range == ef.GRID_RANGE.MEAN
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(emissions_grid_source="???")
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(emissions_grid_range="???")
def test_CSP_World():
# ConcentratedSolar World exposed a corner case, test it specifically.
data_sources = {
'Ambitious Cases': {
'source1': str(datadir.joinpath('ad_CSP_World_source1.csv')),
'source2': str(datadir.joinpath('ad_CSP_World_source2.csv')),
'source3': str(datadir.joinpath('ad_CSP_World_source3.csv')),
'source4': str(datadir.joinpath('ad_CSP_World_source4.csv')),
'source5': str(datadir.joinpath('ad_CSP_World_source5.csv')),
'source6': str(datadir.joinpath('ad_CSP_World_source6.csv')),
},
'Conservative Cases': {},
'Baseline Cases': {},
'100% RES2050 Case': {},
}
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source='Ambitious Cases',
soln_pds_adoption_prognostication_growth='Low')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=data_sources,
adconfig=g_adconfig)
result = ad.adoption_trend(region='World')
assert result.loc[2014, 'adoption'] == pytest.approx(34.94818207)
assert result.loc[2015, 'adoption'] == pytest.approx(24.85041545)
assert result.loc[2016, 'adoption'] == pytest.approx(17.78567283)
assert result.loc[2060, 'adoption'] == pytest.approx(4079.461034)
def test_ch4_tons_reduced():
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')
ac = advanced_controls.AdvancedControls(report_start_year=2020,
report_end_year=2050,
ch4_co2_per_funit=0.01,
ch4_is_co2eq=False)
c4 = ch4calcs.CH4Calcs(
ac=ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
result = c4.ch4_tons_reduced()
expected = pd.DataFrame(ch4_tons_reduced_list[1:],
columns=ch4_tons_reduced_list[0]).set_index('Year')
is_data_handler = issubclass(type(c4), DataHandler)
json_data = c4.to_json()
existing_key = 'ch4_tons_reduced' in json_data
pd.testing.assert_frame_equal(result.loc[2015:],
expected,
check_exact=False)
assert is_data_handler == True
assert existing_key == True
pd.testing.assert_frame_equal(json_data['ch4_tons_reduced'].loc[2015:],
expected,
check_exact=False)
def test_substitute_vma_regional_statistics():
vals = {
'World': 0,
'OECD90': 1,
'Eastern Europe': 2,
'Asia (Sans Japan)': 3,
'Middle East and Africa': 4,
'Latin America': 5,
'China': 0,
'India': 0,
'EU': 0,
'USA': 0
}
class fakeVMA:
df = pd.DataFrame(0, index=[0, 1], columns=vma.VMA_columns)
def avg_high_low(self, key, region=None):
if region and key == 'mean': return vals[region]
return (None, None, None)
vmas = {'SOLUTION First Cost per Implementation Unit': fakeVMA()}
ac = advanced_controls.AdvancedControls(vmas=vmas,
pds_2014_cost='mean per region')
expected = pd.Series(data=vals, name='regional values')
pd.testing.assert_series_equal(expected, ac.pds_2014_cost)
def test_ElectricityGenOnGrid_conv_ref_grid_CO2eq_per_KWh_invalid_range():
with pytest.raises(ValueError):
ac = advanced_controls.AdvancedControls(
emissions_grid_source="ipcc_only",
emissions_grid_range="nosuchrange")
eg = ef.ElectricityGenOnGrid(ac=ac)
_ = eg.conv_ref_grid_CO2eq_per_KWh()
def test_substitute_vma_regional_statistics():
vals = {
'World': 0,
'OECD90': 1,
'Eastern Europe': 2,
'Asia (Sans Japan)': 3,
'Middle East and Africa': 4,
'Latin America': 5,
'China': 0,
'India': 0,
'EU': 0,
'USA': 0
}
with mock.patch.object(vma.VMA,
'__init__',
new=lambda *args, **kwargs: None):
with mock.patch.object(
vma.VMA,
'avg_high_low',
new=lambda *args, **kwargs: vals[kwargs['region']]):
ac = advanced_controls.AdvancedControls(
vmas={
'SOLUTION First Cost per Implementation Unit': vma.VMA()
},
pds_2014_cost='mean per region')
expected = pd.Series(data=vals, name='regional values')
pd.testing.assert_series_equal(expected, ac.pds_2014_cost)
def test_adoption_data_per_region_source_None():
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=None)
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
assert all(ad.adoption_data_per_region().isna())
def test_lifetime_replacement():
ac = advanced_controls.AdvancedControls(soln_lifetime_capacity=50000,
soln_avg_annual_use=1000,
conv_lifetime_capacity=10000,
conv_avg_annual_use=3)
assert ac.soln_lifetime_replacement == 50
assert ac.conv_lifetime_replacement == pytest.approx(3333.333333333333)
def test_substitute_vma():
with mock.patch('model.vma.VMA') as MockVMA:
MockVMA.return_value.avg_high_low.return_value = 'expected return'
seq_vma = vma.VMA()
ac = advanced_controls.AdvancedControls(
vmas={'Sequestration Rates': seq_vma}, seq_rate_global='mean')
assert ac.seq_rate_global == 'expected return'
def test_replacement_raises_error():
""" Lifetime replacement values require different inputs for LAND and RRS """
ac = advanced_controls.AdvancedControls(soln_lifetime_capacity=None,
soln_expected_lifetime=None)
with pytest.raises(ValueError):
ac.soln_lifetime_replacement
ac = advanced_controls.AdvancedControls(soln_lifetime_capacity=None,
soln_expected_lifetime=None)
with pytest.raises(ValueError):
ac.soln_lifetime_replacement_rounded
ac = advanced_controls.AdvancedControls(conv_lifetime_capacity=None,
conv_expected_lifetime=None)
with pytest.raises(ValueError):
ac.conv_lifetime_replacement
ac = advanced_controls.AdvancedControls(conv_lifetime_capacity=None,
conv_expected_lifetime=None)
with pytest.raises(ValueError):
ac.conv_lifetime_replacement_rounded
def test_substitute_vma_handles_raw_value_discrepancy():
with mock.patch('model.vma.VMA') as MockVMA:
MockVMA.return_value.avg_high_low.return_value = 1.2
ac = advanced_controls.AdvancedControls(
vmas={'Sequestration Rates': vma.VMA()},
seq_rate_global={
'value': 1.1,
'statistic': 'mean'
})
assert ac.seq_rate_global == 1.1
def test_soln_pds_adoption_args():
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_basis="Existing Adoption Prognostications",
soln_pds_adoption_prognostication_growth="Medium",
soln_pds_adoption_prognostication_source="test1")
assert ac.soln_pds_adoption_basis == "Existing Adoption Prognostications"
assert ac.soln_pds_adoption_prognostication_growth == "Medium"
assert ac.soln_pds_adoption_prognostication_source == "test1"
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_basis="DEFAULT S-Curve",
soln_pds_adoption_prognostication_growth="Low",
soln_pds_adoption_prognostication_source="test2")
assert ac.soln_pds_adoption_basis == "Logistic S-Curve"
assert ac.soln_pds_adoption_prognostication_growth == "Low"
assert ac.soln_pds_adoption_prognostication_source == "test2"
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(soln_pds_adoption_basis="???")
with pytest.raises(ValueError):
_ = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_growth="???")
def test_lookup_vma():
class fakeVMA:
def avg_high_low(self, key):
return (1.2, 1.4, 1.0)
vmas = {'VMA1': fakeVMA(), 'VMA2': fakeVMA()}
ac = advanced_controls.AdvancedControls(vmas=vmas,
vma_values={
'VMA1': 2.0,
'statistic': 'mean'
})
assert ac.lookup_vma('VMA1') == 2.0
def test_substitute_vma_not_has_data():
class fakeVMA:
def __init__(self):
self.v = (np.nan, np.nan, np.nan)
def avg_high_low(self, key):
if key == 'mean': return self.v[0]
if key == 'high': return self.v[1]
if key == 'low': return self.v[2]
return v
v = fakeVMA()
v.df = pd.DataFrame(0, index=[0, 1], columns=vma.VMA_columns)
vmas = {'Sequestration Rates': v}
with pytest.raises(KeyError):
_ = advanced_controls.AdvancedControls(vmas=vmas,
seq_rate_global='mean')
v.v = (1, 2, 3)
ac = advanced_controls.AdvancedControls(vmas=vmas, seq_rate_global='mean')
assert ac.seq_rate_global == 1
def test_ch4_tons_reduced_co2eq():
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')
ac = advanced_controls.AdvancedControls(report_start_year=2020,
report_end_year=2050,
ch4_co2_per_funit=0.01,
ch4_is_co2eq=True)
c4 = ch4calcs.CH4Calcs(
ac=ac, soln_net_annual_funits_adopted=soln_net_annual_funits_adopted)
result = c4.ch4_tons_reduced()
assert result.values.sum() == 0.0
def test_lifetime_replacement_rounded():
ac = advanced_controls.AdvancedControls(soln_lifetime_capacity=63998.595,
soln_avg_annual_use=2844.382,
conv_lifetime_capacity=1.5,
conv_avg_annual_use=1)
assert ac.soln_lifetime_replacement_rounded == 23
assert ac.conv_lifetime_replacement_rounded == 2
ac = advanced_controls.AdvancedControls(conv_lifetime_capacity=63998.595,
conv_avg_annual_use=2844.382,
soln_lifetime_capacity=1.5,
soln_avg_annual_use=1)
assert ac.conv_lifetime_replacement_rounded == 23
assert ac.soln_lifetime_replacement_rounded == 2
# From Water Efficiency
ac = advanced_controls.AdvancedControls(
soln_lifetime_capacity=1086.6259087991305,
soln_avg_annual_use=72.44172725327537,
conv_lifetime_capacity=1629.9388631986958,
conv_avg_annual_use=72.44172725327537)
assert ac.conv_lifetime_replacement_rounded == 23
assert ac.soln_lifetime_replacement_rounded == 15
def test_ElectricityGenOnGrid_conv_ref_grid_CO2eq_per_KWh():
ac = advanced_controls.AdvancedControls(emissions_grid_source="ipcc_only",
emissions_grid_range="mean")
eg = ef.ElectricityGenOnGrid(ac=ac)
table = eg.conv_ref_grid_CO2eq_per_KWh()
assert table.loc[2025, "OECD90"] == pytest.approx(0.454068989)
assert table.loc[2020, 'World'] == pytest.approx(0.483415642)
ac = advanced_controls.AdvancedControls(emissions_grid_source="ipcc_only",
emissions_grid_range="low")
eg = ef.ElectricityGenOnGrid(ac=ac)
table = eg.conv_ref_grid_CO2eq_per_KWh()
assert table.loc[2020, 'World'] == pytest.approx(0.416520905)
ac = advanced_controls.AdvancedControls(emissions_grid_source="ipcc_only",
emissions_grid_range="high")
eg = ef.ElectricityGenOnGrid(ac=ac)
table = eg.conv_ref_grid_CO2eq_per_KWh()
assert table.loc[2020, 'World'] == pytest.approx(0.952177536)
ac = advanced_controls.AdvancedControls(
emissions_grid_source="meta-analysis", emissions_grid_range="mean")
eg = ef.ElectricityGenOnGrid(ac=ac)
table = eg.conv_ref_grid_CO2eq_per_KWh()
assert table.loc[2020, 'World'] == pytest.approx(0.581083120)
ac = advanced_controls.AdvancedControls(
emissions_grid_source="meta-analysis", emissions_grid_range="low")
eg = ef.ElectricityGenOnGrid(ac=ac)
table = eg.conv_ref_grid_CO2eq_per_KWh()
assert table.loc[2020, 'World'] == pytest.approx(0.446005409)
ac = advanced_controls.AdvancedControls(
emissions_grid_source="meta-analysis", emissions_grid_range="high")
eg = ef.ElectricityGenOnGrid(ac=ac)
table = eg.conv_ref_grid_CO2eq_per_KWh()
assert table.loc[2020, 'World'] == pytest.approx(0.726403172)
def test_adoption_trend_global():
s = 'Greenpeace AER'
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=s,
soln_pds_adoption_prognostication_growth='Medium')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
result = ad.adoption_trend(region='World', trend='Linear')
expected = pd.DataFrame(linear_trend_global_list[1:],
columns=linear_trend_global_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
result = ad.adoption_trend(region='World', trend='Degree2')
expected = pd.DataFrame(poly_degree2_trend_global_list[1:],
columns=poly_degree2_trend_global_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
result = ad.adoption_trend(region='World', trend='Degree3')
expected = pd.DataFrame(poly_degree3_trend_global_list[1:],
columns=poly_degree3_trend_global_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
result = ad.adoption_trend(region='World', trend='Exponential')
expected = pd.DataFrame(exponential_trend_global_list[1:],
columns=exponential_trend_global_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
adconfig_mod = g_adconfig.copy()
adconfig_mod.loc['trend', 'World'] = 'Exponential'
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=adconfig_mod)
result = ad.adoption_trend(region='World')
expected = pd.DataFrame(exponential_trend_global_list[1:],
columns=exponential_trend_global_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_adoption_low_med_high_global_all_sources():
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source='ALL SOURCES')
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
result = ad.adoption_low_med_high(region='World')
expected = pd.DataFrame(
adoption_low_med_high_global_all_sources_list[1:],
columns=adoption_low_med_high_global_all_sources_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_adoption_min_max_sd():
s = 'Greenpeace AER'
ac = advanced_controls.AdvancedControls(
soln_pds_adoption_prognostication_source=s)
ad = adoptiondata.AdoptionData(ac=ac,
data_sources=g_data_sources,
adconfig=g_adconfig)
result = ad.adoption_min_max_sd(region='World')
expected = pd.DataFrame(adoption_min_max_sd_global_list[1:],
columns=adoption_min_max_sd_global_list[0],
dtype=np.float64).set_index('Year')
expected.index = expected.index.astype(int)
expected.index.name = 'Year'
pd.testing.assert_frame_equal(result, expected, check_exact=False)
def test_electricity_factors():
conv_annual_energy_used = 2.117
class fakeVMA:
def avg_high_low(self, key):
return (0.0, 0.0, 0.0)
vmas = {'SOLUTION Energy Efficiency Factor': fakeVMA()}
ac = advanced_controls.AdvancedControls(
vmas=vmas, conv_annual_energy_used=conv_annual_energy_used)
assert ac.soln_energy_efficiency_factor == 0
assert ac.conv_annual_energy_used == pytest.approx(conv_annual_energy_used)
assert ac.soln_annual_energy_used == 0
def test_substitute_vma():
class fakeVMA:
df = pd.DataFrame(0, index=[0, 1], columns=vma.VMA_columns)
def avg_high_low(self, key):
if key == 'mean': return 'mean value'
if key == 'high': return 'high value'
if key == 'low': return 'low value'
return ('mean value', 'high value', 'low value')
seq_vma = {'Sequestration Rates': fakeVMA()}
ac = advanced_controls.AdvancedControls(vmas=seq_vma,
seq_rate_global='mean')
assert ac.seq_rate_global == 'mean value'