def test_run_scenarios_multiple(self):
ts1_erf = np.linspace(0, 4, 101)
ts2_erf = np.sin(np.linspace(0, 4, 101))
inp = ScmRun(
data=np.vstack([ts1_erf, ts2_erf]).T,
index=np.linspace(1750, 1850, 101).astype(int),
columns={
"scenario": ["test_scenario_1", "test_scenario_2"],
"model": "unspecified",
"climate_model": "junk input",
"variable": "Effective Radiative Forcing",
"unit": "W/m^2",
"region": "World",
},
)
model = self.tmodel()
res = model.run_scenarios(inp)
for scenario_ts in inp.groupby("scenario"):
scenario = scenario_ts.get_unique_meta("scenario",
no_duplicates=True)
model.set_drivers(
scenario_ts.values.squeeze() *
ur(inp.get_unique_meta("unit", no_duplicates=True)))
model.reset()
model.run()
res_scen = res.filter(scenario=scenario)
npt.assert_allclose(
res_scen.filter(
variable="Surface Temperature|Upper").values.squeeze(),
model._temp_upper_mag,
)
assert (res.filter(
variable="Surface Temperature|Upper").get_unique_meta(
"unit", no_duplicates=True) == "delta_degC")
npt.assert_allclose(
res_scen.filter(
variable="Surface Temperature|Lower").values.squeeze(),
model._temp_lower_mag,
)
assert (res.filter(
variable="Surface Temperature|Lower").get_unique_meta(
"unit", no_duplicates=True) == "delta_degC")
npt.assert_allclose(
res_scen.filter(variable="Heat Uptake").values.squeeze(),
model._rndt_mag,
)
assert (res.filter(variable="Heat Uptake").get_unique_meta(
"unit", no_duplicates=True) == "W/m^2")
def test_groupby_integer_metadata():
def increment_ensemble_member(scmrun):
scmrun["ensemble_member"] += 10
return scmrun
start = ScmRun(
data=[[1, 2], [0, 1]],
index=[2010, 2020],
columns={
"model": "model",
"scenario": "scenario",
"variable": "variable",
"unit": "unit",
"region": "region",
"ensemble_member": [0, 1],
},
)
res = start.groupby(["variable", "region", "scenario",
"ensemble_member"]).map(increment_ensemble_member)
assert (res["ensemble_member"] == start["ensemble_member"] + 10).all()
def scmdf_to_emissions(scmdf, include_cfcs=True, startyear=1765, endyear=2100):
"""
Opens an ScmDataFrame and extracts the data. Interpolates linearly
between non-consecutive years in the SCEN file. Fills in chlorinated gases
from a specified SSP scenario.
Note this is a temporary fix for FaIR 1.6.
Inputs:
scmdf: ScmDataFrame
Keywords:
include_cfcs: bool
MAGICC files do not come loaded with CFCs (indices 24-39).
- if True, use the values from RCMIP for SSPs (all scenarios are
the same).
- Use False to ignore and create a 23-species emission file.
startyear: First year of output file.
endyear: Last year of output file.
Returns:
nt x 40 numpy emissions array (nt x 23 if ``include_cfcs`` is ``False``)
"""
# We expect that aeneris and silicone are going to give us a nicely
# formatted ScmDataFrame with all 23 species present and correct at
# timesteps 2015, 2020 and ten-yearly to 2100.
# We also implicitly assume that data up until 2014 will follow SSP
# historical.
# This adapter will not be tested on anything else!
n_cols = 40
nt = endyear - startyear + 1
data_out = np.ones((nt, n_cols)) * np.nan
data_out[:, 0] = np.arange(startyear, endyear + 1)
if not has_scmdata:
raise ImportError(
"This is not going to work without having scmdata installed")
if not isinstance(scmdf, ScmDataFrame):
raise TypeError("scmdf must be an scmdata.ScmDataFrame instance")
if not include_cfcs:
raise NotImplementedError("include_cfcs equal to False")
if scmdf[["model", "scenario"]].drop_duplicates().shape[0] != 1:
raise AssertionError("Should only have one model-scenario pair")
scen_start_year = 2015
scmdf = ScmRun(scmdf.timeseries()).interpolate(
[dt.datetime(y, 1, 1) for y in range(scen_start_year, endyear + 1)])
years = scmdf["year"].values
first_scenyear = years[0]
first_scen_row = int(first_scenyear - startyear)
# if correct units and interpolation were guaranteed we could do this for scenario too which is quicker
hist_df = ssp245_world_emms_holder.values_fair_units.filter(
year=range(startyear, 2015)).timeseries()
future_ssp245_df = ssp245_world_emms_holder.values_fair_units.filter(
year=range(2015, endyear + 1)).timeseries()
for species in EMISSIONS_SPECIES_UNITS_CONTEXT["species"]:
fair_col, _, _ = _get_fair_col_unit_context(species)
hist_df_row = hist_df.index.get_level_values("variable").str.endswith(
species)
data_out[:first_scen_row,
fair_col] = hist_df[hist_df_row].values.squeeze()
future_ssp245_df_row = future_ssp245_df.index.get_level_values(
"variable").str.endswith(species)
data_out[first_scen_row:, fair_col] = future_ssp245_df[
future_ssp245_df_row].values.squeeze()
for var_df in scmdf.groupby("variable"):
variable = var_df.get_unique_meta("variable", no_duplicates=True)
in_unit = var_df.get_unique_meta("unit", no_duplicates=True)
fair_col, fair_unit, context = _get_fair_col_unit_context(variable)
if in_unit != fair_unit:
var_df_fair_unit = var_df.convert_unit(fair_unit, context=context)
else:
var_df_fair_unit = var_df
data_out[first_scen_row:, fair_col] = var_df_fair_unit.values.squeeze()
return data_out