def make_dantzig(mp: Platform,
solve: bool = False,
quiet: bool = False) -> Scenario:
"""Return :class:`ixmp.Scenario` of Dantzig's canning/transport problem.
Parameters
----------
mp : .Platform
Platform on which to create the scenario.
solve : bool, optional
If :obj:`True`. then solve the scenario before returning. Default :obj:`False`.
quiet : bool, optional
If :obj:`True`, suppress console output when solving.
Returns
-------
.Scenario
See also
--------
.DantzigModel
"""
# add custom units and region for timeseries data
try:
mp.add_unit("USD/km")
except Exception:
# Unit already exists. Pending bugfix from zikolach
pass
mp.add_region("DantzigLand", "country")
# Initialize a new Scenario, and use the DantzigModel class' initialize()
# method to populate it
annot = "Dantzig's transportation problem for illustration and testing"
scen = Scenario(
mp,
**models["dantzig"], # type: ignore [arg-type]
version="new",
annotation=annot,
scheme="dantzig",
with_data=True,
)
# commit the scenario
scen.commit("Import Dantzig's transport problem for testing.")
# set this new scenario as the default version for the model/scenario name
scen.set_as_default()
if solve:
# Solve the model using the GAMS code provided in the `tests` folder
scen.solve(model="dantzig", case="transport_standard", quiet=quiet)
# add timeseries data for testing `clone(keep_solution=False)`
# and `remove_solution()`
scen.check_out(timeseries_only=True)
scen.add_timeseries(HIST_DF, meta=True)
scen.add_timeseries(INP_DF)
scen.commit("Import Dantzig's transport problem for testing.")
return scen
def test_multi_db_run(tmpdir):
# create a new instance of the transport problem and solve it
mp1 = Platform(driver="hsqldb", path=tmpdir / "mp1")
scen1 = make_dantzig(mp1, solve=True)
mp2 = Platform(driver="hsqldb", path=tmpdir / "mp2")
# add other unit to make sure that the mapping is correct during clone
mp2.add_unit("wrong_unit")
mp2.add_region("wrong_region", "country")
# check that cloning across platforms must copy the full solution
dest = dict(platform=mp2)
pytest.raises(NotImplementedError,
scen1.clone,
keep_solution=False,
**dest)
pytest.raises(NotImplementedError,
scen1.clone,
shift_first_model_year=1964,
**dest)
# clone solved model across platforms (with default settings)
scen1.clone(platform=mp2, keep_solution=True)
# close the db to ensure that data and solution of the clone are saved
mp2.close_db()
del mp2
# reopen the connection to the second platform and reload scenario
_mp2 = Platform(driver="hsqldb", path=tmpdir / "mp2")
scen2 = Scenario(_mp2, **SCENARIO["dantzig"])
assert_multi_db(mp1, _mp2)
# check that sets, variables and parameter were copied correctly
npt.assert_array_equal(scen1.set("node"), scen2.set("node"))
scen2.firstmodelyear == 1963
assert_frame_equal(scen1.par("var_cost"), scen2.par("var_cost"))
assert np.isclose(scen2.var("OBJ")["lvl"], 153.675)
assert_frame_equal(scen1.var("ACT"), scen2.var("ACT"))
# check that custom unit, region and timeseries are migrated correctly
assert_frame_equal(scen2.timeseries(iamc=True), TS_DF)
def test_multi_db_run(tmpdir):
# create a new instance of the transport problem and solve it
mp1 = Platform(tmpdir / 'mp1', dbtype='HSQLDB')
scen1 = make_dantzig(mp1, solve=True)
mp2 = Platform(tmpdir / 'mp2', dbtype='HSQLDB')
# add other unit to make sure that the mapping is correct during clone
mp2.add_unit('wrong_unit')
mp2.add_region('wrong_region', 'country')
# check that cloning across platforms must copy the full solution
dest = dict(platform=mp2)
pytest.raises(ValueError, scen1.clone, keep_solution=False, **dest)
pytest.raises(ValueError, scen1.clone, shift_first_model_year=1964, **dest)
# clone solved model across platforms (with default settings)
scen1.clone(platform=mp2, keep_solution=True)
# close the db to ensure that data and solution of the clone are saved
mp2.close_db()
del mp2
# reopen the connection to the second platform and reload scenario
_mp2 = Platform(tmpdir / 'mp2', dbtype='HSQLDB')
scen2 = Scenario(_mp2, **models['dantzig'])
assert_multi_db(mp1, _mp2)
# check that sets, variables and parameter were copied correctly
npt.assert_array_equal(scen1.set('node'), scen2.set('node'))
scen2.firstmodelyear == 1963
pdt.assert_frame_equal(scen1.par('var_cost'), scen2.par('var_cost'))
assert np.isclose(scen2.var('OBJ')['lvl'], 153.675)
pdt.assert_frame_equal(scen1.var('ACT'), scen2.var('ACT'))
# check that custom unit, region and timeseries are migrated correctly
pdt.assert_frame_equal(scen2.timeseries(iamc=True), TS_DF)
tmp[year] = tmp[year] * ghg_conversion[s]
ghg = tmp.combine_first(ghg)
ghg = ghg.groupby(['Technology', 'Parameter', 'Region', 'Mode',
'Units']).sum()
ghg['Species'] = 'GHG'
ghg = ghg.reset_index()
ap.add_tec_emi_fac(ghg)
# ## Add Carbon price (in INR per MtCO2e/yr)
if price_carbon:
if type(price) != float:
print('Please ensure that the price is specified as a float')
else:
unit = 'USD/MtCO2e'
if unit not in mp.units():
mp.add_unit(unit,
comment="Adding new unit required for emission tax")
years = [
y for y in ds.set("year")
if int(y) >= ds.set("cat_year",
filters={"type_year": ['firstmodelyear']})
['year'][0]
]
vals = []
for y in years:
if y not in scenario.set('type_year'):
scenario.add_set('type_year', y)
if y == '2015':
val = price
else:
val = val * pow(
scenario.par("interestrate", filters={'year': ['2015']