def test_plumeplot_non_unique_lines(plumeplot_scmrun):
quantile_over = "climate_model"
quantile = 0.05
scenario = "a_scenario"
variable = "Surface Air Temperature Change"
summary_stats = ScmRun(
plumeplot_scmrun.quantiles_over(quantile_over, quantiles=(0.05, 0.5, 0.95))
)
error_msg = re.escape(
"More than one timeseries for "
"quantile: {}, "
"scenario: {}, "
"variable: {}.\n"
"Please process your data to create unique quantile timeseries "
"before calling :meth:`plumeplot`.\n"
"Found: {}".format(
quantile,
scenario,
variable,
summary_stats.filter(
quantile=quantile, scenario=scenario, variable=variable
),
)
)
with pytest.raises(ValueError, match=error_msg):
summary_stats.plumeplot(pre_calculated=True)
def test_run(
self,
test_scenarios,
test_data_dir,
update_expected_values,
shuffle_column_order,
):
expected_output_file = os.path.join(
test_data_dir,
"expected-integration-output",
"expected_ciceroscm_test_run_output.json",
)
if shuffle_column_order:
tmp = test_scenarios.data
cols = tmp.columns.tolist()
tmp = tmp[cols[1:] + cols[:1]]
test_scenarios = ScmRun(test_scenarios)
res = run(
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
climate_models_cfgs={
"CICEROSCM": [
{
"model_end": 2100,
"Index": 30040,
"lambda": 0.540,
"akapa": 0.341,
"cpi": 0.556,
"W": 1.897,
"rlamdo": 16.618,
"beto": 3.225,
"mixed": 107.277,
"dirso2_forc": -0.457,
"indso2_forc": -0.514,
"bc_forc": 0.200,
"oc_forc": -0.103,
},
{
"model_end": 2100,
"Index": 1,
"lambda": 0.3925,
"akapa": 0.2421,
"cpi": 0.3745,
"W": 0.8172,
"rlamdo": 16.4599,
"beto": 4.4369,
"mixed": 35.4192,
"dirso2_forc": -0.3428,
"indso2_forc": -0.3856,
"bc_forc": 0.1507,
"oc_forc": -0.0776,
},
]
},
output_variables=(
"Surface Air Temperature Change",
"Surface Air Ocean Blended Temperature Change",
"Heat Content|Ocean",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Anthropogenic",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|Greenhouse Gases",
"Heat Uptake",
"Atmospheric Concentrations|CO2",
"Atmospheric Concentrations|CH4",
"Atmospheric Concentrations|N2O",
"Emissions|CO2",
"Emissions|CH4",
"Emissions|N2O",
),
out_config=None,
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 1
assert res["run_id"].max() == 30040
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "CICERO-SCM"
assert set(res.get_unique_meta("variable")) == set([
"Surface Air Temperature Change",
"Surface Air Ocean Blended Temperature Change",
"Heat Content|Ocean",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Anthropogenic",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|Greenhouse Gases",
"Heat Uptake",
"Atmospheric Concentrations|CO2",
"Atmospheric Concentrations|CH4",
"Atmospheric Concentrations|N2O",
"Emissions|CO2",
"Emissions|N2O",
"Emissions|CH4",
])
# check we can also calcluate quantiles
assert "run_id" in res.meta
quantiles = calculate_quantiles(res,
[0, 0.05, 0.17, 0.5, 0.83, 0.95, 1])
assert "run_id" not in quantiles.meta
assert (res.filter(
variable="Atmospheric Concentrations|CO2").get_unique_meta(
"unit", True) == "ppm")
assert (res.filter(
variable="Atmospheric Concentrations|CH4").get_unique_meta(
"unit", True) == "ppb")
assert (res.filter(
variable="Atmospheric Concentrations|N2O").get_unique_meta(
"unit", True) == "ppb")
assert (res.filter(variable="Emissions|CO2").get_unique_meta(
"unit", True) == "PgC / yr")
assert (res.filter(variable="Emissions|CH4").get_unique_meta(
"unit", True) == "TgCH4 / yr")
assert (res.filter(variable="Emissions|N2O").get_unique_meta(
"unit", True) == "TgN2ON / yr")
# check that emissions were passed through correctly
for (scen, variable, unit, exp_val) in (
("ssp126", "Emissions|CO2", "PgC/yr", -2.3503),
("ssp370", "Emissions|CO2", "PgC/yr", 22.562),
("ssp126", "Emissions|CH4", "TgCH4/yr", 122.195),
("ssp370", "Emissions|CH4", "TgCH4/yr", 777.732),
("ssp126", "Emissions|N2O", "TgN2ON/yr", 5.318),
("ssp370", "Emissions|N2O", "TgN2ON/yr", 13.144),
):
res_scen_2100_emms = res.filter(variable=variable,
year=2100,
scenario=scen).convert_unit(unit)
if res_scen_2100_emms.empty:
raise AssertionError("No {} data for {}".format(
variable, scen))
npt.assert_allclose(
res_scen_2100_emms.values,
exp_val,
rtol=1e-4,
)
for (scen, variable, unit, exp_val14, exp_val16) in (
("ssp126", "Emissions|CH4", "TgCH4/yr", 387.874, 379.956),
("ssp370", "Emissions|CH4", "TgCH4/yr", 387.874, 394.149),
("ssp126", "Emissions|N2O", "TgN2ON/yr", 6.911, 6.858),
("ssp370", "Emissions|N2O", "TgN2ON/yr", 6.911, 7.0477),
):
res_scen_2014_emms = res.filter(variable=variable,
year=2014,
scenario=scen).convert_unit(unit)
if res_scen_2014_emms.empty:
raise AssertionError("No {} data for {}".format(
variable, scen))
res_scen_2016_emms = res.filter(variable=variable,
year=2016,
scenario=scen).convert_unit(unit)
if res_scen_2016_emms.empty:
raise AssertionError("No {} data for {}".format(
variable, scen))
npt.assert_allclose(
res_scen_2014_emms.values,
exp_val14,
rtol=1e-4,
)
npt.assert_allclose(
res_scen_2016_emms.values,
exp_val16,
rtol=1e-4,
)
for (scen, variable) in (
("ssp126", "Effective Radiative Forcing|Aerosols"),
("ssp370", "Effective Radiative Forcing|Aerosols"),
):
res_scen_2015_emms = res.filter(variable=variable,
year=2015,
scenario=scen)
if res_scen_2015_emms.empty:
raise AssertionError(
"No CO2 emissions data for {}".format(scen))
assert not np.equal(res_scen_2015_emms.values, 0).all()
ssp245_ghg_erf_2015 = res.filter(
variable="Effective Radiative Forcing|Greenhouse Gases",
year=2015,
scenario="ssp245",
run_id=1,
)
ssp245_ghg_erf_2014 = res.filter(
variable="Effective Radiative Forcing|Greenhouse Gases",
year=2014,
scenario="ssp245",
run_id=1,
)
# check that jump in GHG ERF isn't there
assert (ssp245_ghg_erf_2015.values.squeeze() -
ssp245_ghg_erf_2014.values.squeeze()) < 0.1
ssp245_ch4_conc_2015 = res.filter(
variable="Atmospheric Concentrations|CH4",
year=2015,
scenario="ssp245",
run_id=1,
)
ssp245_ch4_conc_2014 = res.filter(
variable="Atmospheric Concentrations|CH4",
year=2014,
scenario="ssp245",
run_id=1,
)
# ch
# check that jump in GHG ERF isn't there
assert (ssp245_ch4_conc_2014.values.squeeze() -
ssp245_ch4_conc_2015.values.squeeze()) < 0.1
self._check_output(res, expected_output_file, update_expected_values)
"Cumulative Emissions|CO2",
"Surface Air Temperature Change",
],
"unit": ["GtC / yr", "GtC / yr", "GtC / yr", "GtC / yr", "GtC", "K"],
"region":
["World|NH", "World|NH", "World|SH", "World|SH", "World", "World"],
"model":
"idealised",
"scenario":
"idealised",
},
)
start.head()
fos = start.filter(variable="*Fossil")
fos.head()
afolu = start.filter(variable="*AFOLU")
afolu.head()
total = fos.add(afolu, op_cols={"variable": "Emissions|CO2"})
total.head()
nh = start.filter(region="*NH")
nh.head()
sh = start.filter(region="*SH")
sh.head()
world = nh.add(sh, op_cols={"region": "World"})