def test_add_bound_activity_up(test_mp):
scen = Scenario(test_mp, *msg_args)
scen.solve()
# data for act bound
exp = 0.5 * calculate_activity(scen).sum()
data = pd.DataFrame({
'node_loc': 'seattle',
'technology': 'transport_from_seattle',
'year_act': 2010,
'time': 'year',
'unit': 'cases',
'mode': 'to_chicago',
'value': exp,
}, index=[0])
# test limiting one mode
clone = scen.clone('foo', 'bar', keep_solution=False)
clone.check_out()
clone.add_par('bound_activity_up', data)
clone.commit('foo')
clone.solve()
obs = calculate_activity(clone).loc['to_chicago']
assert np.isclose(obs, exp)
orig_obj = scen.var('OBJ')['lvl']
new_obj = clone.var('OBJ')['lvl']
assert new_obj >= orig_obj
def test_price_duality(test_mp):
years = [2020, 2025, 2030, 2040, 2050]
for c in [0.25, 0.5, 0.75]:
# set up a scenario for cumulative constraints
scen = Scenario(test_mp, MODEL, 'cum_many_tecs', version='new')
model_setup(scen, years, simple_tecs=False)
scen.add_cat('year', 'cumulative', years)
scen.add_par('bound_emission', ['World', 'ghg', 'all', 'cumulative'],
0.5, 'tCO2')
scen.commit('initialize test scenario')
scen.solve()
# set up a new scenario with emissions taxes
tax_scen = Scenario(test_mp, MODEL, 'tax_many_tecs', version='new')
model_setup(tax_scen, years, simple_tecs=False)
for y in years:
tax_scen.add_cat('year', y, y)
# use emission prices from cumulative-constraint scenario as taxes
taxes = scen.var('PRICE_EMISSION')\
.rename(columns={'year': 'type_year', 'lvl': 'value'})
taxes['unit'] = 'USD/tCO2'
tax_scen.add_par('tax_emission', taxes)
tax_scen.commit('initialize test scenario for taxes')
tax_scen.solve()
# check that emissions are close between cumulative and tax scenario
filters = {'node': 'World'}
emiss = scen.var('EMISS', filters).set_index('year').lvl
emiss_tax = tax_scen.var('EMISS', filters).set_index('year').lvl
npt.assert_allclose(emiss, emiss_tax, rtol=0.20)
def test_solve_modified(caplog, message_test_mp):
base = Scenario(message_test_mp, **SCENARIO["dantzig"])
# Base objective value
base.solve()
base_obj = base.var("OBJ")["lvl"]
with solve_modified(base, "new scenario name") as scenario:
# Scenario is not yet solved
assert not scenario.has_solution()
# Scenario has the indicated new name
assert "new scenario name" == scenario.scenario
# Change one demand parameter from 325 to 326
scenario.add_par(
"demand",
make_df(
"demand",
node=["new-york"],
commodity=["cases"],
level="consumption",
year=1963,
time="year",
value=326,
unit="case",
),
)
# Scenario is solved at the end of the with: statement
assert scenario.has_solution()
assert scenario.var("OBJ")["lvl"] != base_obj
def test_price_duality(test_mp):
years = [2020, 2025, 2030, 2040, 2050]
for c in [0.25, 0.5, 0.75]:
# set up a scenario for cumulative constraints
scen = Scenario(test_mp, MODEL, "cum_many_tecs", version="new")
model_setup(scen, years, simple_tecs=False)
scen.add_cat("year", "cumulative", years)
scen.add_par("bound_emission", ["World", "ghg", "all", "cumulative"],
0.5, "tCO2")
scen.commit("initialize test scenario")
scen.solve()
# set up a new scenario with emissions taxes
tax_scen = Scenario(test_mp, MODEL, "tax_many_tecs", version="new")
model_setup(tax_scen, years, simple_tecs=False)
for y in years:
tax_scen.add_cat("year", y, y)
# use emission prices from cumulative-constraint scenario as taxes
taxes = scen.var("PRICE_EMISSION").rename(columns={
"year": "type_year",
"lvl": "value"
})
taxes["unit"] = "USD/tCO2"
tax_scen.add_par("tax_emission", taxes)
tax_scen.commit("initialize test scenario for taxes")
tax_scen.solve()
# check that emissions are close between cumulative and tax scenario
filters = {"node": "World"}
emiss = scen.var("EMISS", filters).set_index("year").lvl
emiss_tax = tax_scen.var("EMISS", filters).set_index("year").lvl
npt.assert_allclose(emiss, emiss_tax, rtol=0.20)
def test_add_bound_activity_up_all_modes(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig"]).clone()
scen.solve(quiet=True)
# data for act bound
exp = 0.95 * calculate_activity(scen).sum()
data = pd.DataFrame(
{
"node_loc": "seattle",
"technology": "transport_from_seattle",
"year_act": _year,
"time": "year",
"unit": "cases",
"mode": "all",
"value": exp,
},
index=[0],
)
# test limiting all modes
clone = scen.clone("foo", "baz", keep_solution=False)
clone.check_out()
clone.add_par("bound_activity_up", data)
clone.commit("foo")
clone.solve()
obs = calculate_activity(clone).sum()
assert np.isclose(obs, exp)
orig_obj = scen.var("OBJ")["lvl"]
new_obj = clone.var("OBJ")["lvl"]
assert new_obj >= orig_obj
def test_add_bound_activity_up_all_modes(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO['dantzig']).clone()
scen.solve()
# data for act bound
exp = 0.95 * calculate_activity(scen).sum()
data = pd.DataFrame(
{
'node_loc': 'seattle',
'technology': 'transport_from_seattle',
'year_act': _year,
'time': 'year',
'unit': 'cases',
'mode': 'all',
'value': exp,
},
index=[0])
# test limiting all modes
clone = scen.clone('foo', 'baz', keep_solution=False)
clone.check_out()
clone.add_par('bound_activity_up', data)
clone.commit('foo')
clone.solve()
obs = calculate_activity(clone).sum()
assert np.isclose(obs, exp)
orig_obj = scen.var('OBJ')['lvl']
new_obj = clone.var('OBJ')['lvl']
assert new_obj >= orig_obj
def test_solve_legacy_scenario(test_legacy_mp):
scen = Scenario(test_legacy_mp, *msg_args)
exp = scen.var('OBJ')['lvl']
# solve scenario, assert that the new objective value is close to previous
scen.remove_solution()
scen.solve()
assert np.isclose(exp, scen.var('OBJ')['lvl'])
def test_share_commodity_lo(test_mp):
scen = Scenario(test_mp, *msg_args)
scen.solve()
# data for share bound
def calc_share(s):
a = calculate_activity(
s, tec='transport_from_seattle').loc['to_new-york']
b = calculate_activity(
s, tec='transport_from_san-diego').loc['to_new-york']
return a / (a + b)
exp = 1. * calc_share(scen)
# add share constraints
clone = scen.clone(scenario='share_commodity_lo', keep_solution=False)
clone.check_out()
clone.add_cat('technology', 'share', 'transport_from_seattle')
clone.add_cat('technology', 'total', ['transport_from_seattle',
'transport_from_san-diego'])
clone.add_set('shares', 'test-share')
clone.add_set('map_shares_commodity_share',
pd.DataFrame({
'shares': 'test-share',
'node_share': 'new-york',
'node': 'new-york',
'type_tec': 'share',
'mode': 'all',
'commodity': 'cases',
'level': 'consumption',
}, index=[0]))
clone.add_set('map_shares_commodity_total',
pd.DataFrame({
'shares': 'test-share',
'node_share': 'new-york',
'node': 'new-york',
'type_tec': 'total',
'mode': 'all',
'commodity': 'cases',
'level': 'consumption',
}, index=[0]))
clone.add_par('share_commodity_lo',
pd.DataFrame({
'shares': 'test-share',
'node_share': 'new-york',
'year_act': 2010,
'time': 'year',
'unit': 'cases',
'value': exp,
}, index=[0]))
clone.commit('foo')
clone.solve()
obs = calc_share(clone)
assert np.isclose(obs, exp)
orig_obj = scen.var('OBJ')['lvl']
new_obj = clone.var('OBJ')['lvl']
assert new_obj >= orig_obj
def test_reporter(test_mp):
scen = Scenario(test_mp,
'canning problem (MESSAGE scheme)',
'standard')
# Varies between local & CI contexts
# DEBUG may be due to reuse of test_mp in a non-deterministic order
if not scen.has_solution():
scen.solve()
# IXMPReporter can be initialized on a MESSAGE Scenario
rep_ix = ixmp_Reporter.from_scenario(scen)
# message_ix.Reporter can also be initialized
rep = Reporter.from_scenario(scen)
# Number of quantities available in a rudimentary MESSAGEix Scenario
assert len(rep.graph['all']) == 120
# Quantities have short dimension names
assert 'demand:n-c-l-y-h' in rep.graph
# Aggregates are available
assert 'demand:n-l-h' in rep.graph
# Quantities contain expected data
dims = dict(coords=['chicago new-york topeka'.split()], dims=['n'])
demand = xr.DataArray([300, 325, 275], **dims)
# NB the call to squeeze() drops the length-1 dimensions c-l-y-h
obs = rep.get('demand:n-c-l-y-h').squeeze(drop=True)
# TODO: Squeeze on AttrSeries still returns full index, whereas xarray
# drops everything except node
obs = obs.reset_index(['c', 'l', 'y', 'h'], drop=True)
# check_dtype is false because of casting in pd.Series to float
# check_attrsis false because we don't get the unit addition in bare xarray
assert_qty_equal(obs.sort_index(), demand,
check_attrs=False, check_dtype=False)
# ixmp.Reporter pre-populated with only model quantities and aggregates
assert len(rep_ix.graph) == 5088
# message_ix.Reporter pre-populated with additional, derived quantities
assert len(rep.graph) == 7975
# Derived quantities have expected dimensions
vom_key = rep.full_key('vom')
assert vom_key not in rep_ix
assert vom_key == 'vom:nl-t-yv-ya-m-h'
# …and expected values
vom = (
rep.get(rep.full_key('ACT')) * rep.get(rep.full_key('var_cost'))
).dropna()
# check_attrs false because `vom` multiply above does not add units
assert_qty_equal(vom, rep.get(vom_key), check_attrs=False)
def test_no_constraint(test_mp):
scen = Scenario(test_mp, MODEL, "no_constraint", version="new")
model_setup(scen, [2020, 2030])
scen.commit("initialize test scenario")
scen.solve(quiet=True)
# without emissions constraint, the zero-cost technology satisfies demand
assert scen.var("OBJ")["lvl"] == 0
# without emissions constraint, there are no emission prices
assert scen.var("PRICE_EMISSION").empty
def test_no_constraint(test_mp):
scen = Scenario(test_mp, MODEL, 'no_constraint', version='new')
model_setup(scen, [2020, 2030])
scen.commit('initialize test scenario')
scen.solve()
# without emissions constraint, the zero-cost technology satisfies demand
assert scen.var('OBJ')['lvl'] == 0
# without emissions constraint, there are no emission prices
assert scen.var('PRICE_EMISSION').empty
def test_init(test_mp):
scen = Scenario(test_mp, *msg_args)
scen = scen.clone('foo', 'bar')
scen.check_out()
macro.init(scen)
scen.commit('foo')
scen.solve()
assert np.isclose(scen.var('OBJ')['lvl'], 153.675)
def test_solve_legacy_scenario(tmp_path, test_data_path):
db_path = create_test_platform(tmp_path, test_data_path, "legacy")
mp = Platform(backend="jdbc", driver="hsqldb", path=db_path)
scen = Scenario(mp,
model="canning problem (MESSAGE scheme)",
scenario="standard")
exp = scen.var("OBJ")["lvl"]
# solve scenario, assert that the new objective value is close to previous
scen = scen.clone(keep_solution=False)
scen.solve()
assert np.isclose(exp, scen.var("OBJ")["lvl"])
def test_solve_legacy_scenario(tmp_path, test_data_path):
db_path = create_test_platform(tmp_path, test_data_path, 'legacy')
mp = Platform(backend='jdbc', driver='hsqldb', path=db_path)
scen = Scenario(mp,
model='canning problem (MESSAGE scheme)',
scenario='standard')
exp = scen.var('OBJ')['lvl']
# solve scenario, assert that the new objective value is close to previous
scen.remove_solution()
scen.solve()
assert np.isclose(exp, scen.var('OBJ')['lvl'])
def test_init(test_mp):
scen = Scenario(test_mp, *msg_args)
obs = scen.var('OBJ')['lvl']
scen = scen.clone('foo', 'bar', keep_solution=False)
scen.check_out()
macro.init(scen)
scen.commit('foo')
scen.solve()
exp = scen.var('OBJ')['lvl']
assert np.isclose(obs, exp)
def test_rename_technology_no_rm(test_mp):
scen = Scenario(test_mp, *msg_args)
scen.solve()
assert scen.par('output')['technology'].isin(['canning_plant']).any()
clone = scen.clone('foo', 'bar', keep_solution=False)
# also test if already checked out
clone.check_out()
clone.rename('technology', {'canning_plant': 'foo_bar'}, keep=True)
assert clone.par('output')['technology'].isin(['canning_plant']).any()
assert clone.par('output')['technology'].isin(['foo_bar']).any()
def test_as_pyam(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO['dantzig'])
if not scen.has_solution():
scen.solve()
rep = Reporter.from_scenario(scen)
# Quantities for 'ACT' variable at full resolution
qty = rep.get(rep.full_key('ACT'))
# Call as_pyam() with an empty quantity
p = computations.as_pyam(scen, qty[0:0], year_time_dim='ya')
assert isinstance(p, pyam.IamDataFrame)
def test_as_pyam(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig"])
if not scen.has_solution():
scen.solve()
rep = Reporter.from_scenario(scen)
# Quantities for 'ACT' variable at full resolution
qty = rep.get(rep.full_key("ACT"))
# Call as_pyam() with an empty quantity
as_pyam = rep.get_comp("as_pyam")
p = as_pyam(scen, qty[0:0], rename=dict(nl="region", ya="year"))
assert isinstance(p, pyam.IamDataFrame)
def test_rename_technology(test_mp):
scen = Scenario(test_mp, *msg_args)
scen.solve()
assert scen.par('output')['technology'].isin(['canning_plant']).any()
exp_obj = scen.var('OBJ')['lvl']
clone = scen.clone('foo', 'bar', keep_solution=False)
clone.rename('technology', {'canning_plant': 'foo_bar'})
assert not clone.par('output')['technology'].isin(['canning_plant']).any()
assert clone.par('output')['technology'].isin(['foo_bar']).any()
clone.solve()
obs_obj = clone.var('OBJ')['lvl']
assert obs_obj == exp_obj
def test_reporter_from_scenario(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig"])
# Varies between local & CI contexts
# DEBUG may be due to reuse of test_mp in a non-deterministic order
if not scen.has_solution():
scen.solve(quiet=True)
# IXMPReporter can be initialized on a MESSAGE Scenario
rep_ix = ixmp_Reporter.from_scenario(scen)
# message_ix.Reporter can also be initialized
rep = Reporter.from_scenario(scen)
# Number of quantities available in a rudimentary MESSAGEix Scenario
assert len(rep.graph["all"]) == 123
# Quantities have short dimension names
assert "demand:n-c-l-y-h" in rep
# Aggregates are available
assert "demand:n-l-h" in rep
# Quantities contain expected data
dims = dict(coords=["chicago new-york topeka".split()], dims=["n"])
demand = Quantity(xr.DataArray([300, 325, 275], **dims), name="demand")
# NB the call to squeeze() drops the length-1 dimensions c-l-y-h
obs = rep.get("demand:n-c-l-y-h").squeeze(drop=True)
# check_attrs False because we don't get the unit addition in bare xarray
assert_qty_equal(obs, demand, check_attrs=False)
# ixmp.Reporter pre-populated with only model quantities and aggregates
assert len(rep_ix.graph) == 5225
# message_ix.Reporter pre-populated with additional, derived quantities
# This is the same value as in test_tutorials.py
assert len(rep.graph) == 12690
# Derived quantities have expected dimensions
vom_key = rep.full_key("vom")
assert vom_key not in rep_ix
assert vom_key == "vom:nl-t-yv-ya-m-h"
# …and expected values
var_cost = rep.get(rep.full_key("var_cost"))
ACT = rep.get(rep.full_key("ACT"))
product = rep.get_comp("product")
vom = product(var_cost, ACT)
# check_attrs false because `vom` multiply above does not add units
assert_qty_equal(vom, rep.get(vom_key))
def test_addon_up(test_mp):
scen = Scenario(test_mp, *msg_args).clone(scenario='addon_up',
keep_solution=False)
add_addon(scen, costs=-1, zero_output=True)
scen.check_out()
scen.add_par('addon_up', addon_share)
scen.commit('adding upper bound on addon technology')
scen.solve()
exp = scen.var('ACT', f)['lvl'] * 0.5
obs = scen.var('ACT', g)['lvl']
assert np.isclose(exp, obs)
def test_init(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig"])
scen = scen.clone("foo", "bar")
scen.check_out()
MACRO.initialize(scen)
scen.commit("foo")
scen.solve(quiet=True)
assert np.isclose(scen.var("OBJ")["lvl"], 153.675)
assert "mapping_macro_sector" in scen.set_list()
assert "aeei" in scen.par_list()
assert "DEMAND" in scen.var_list()
assert "COST_ACCOUNTING_NODAL" in scen.equ_list()
def test_init(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO['dantzig'])
scen = scen.clone('foo', 'bar')
scen.check_out()
MACRO.initialize(scen)
scen.commit('foo')
scen.solve()
assert np.isclose(scen.var('OBJ')['lvl'], 153.675)
assert 'mapping_macro_sector' in scen.set_list()
assert 'aeei' in scen.par_list()
assert 'DEMAND' in scen.var_list()
assert 'COST_ACCOUNTING_NODAL' in scen.equ_list()
def test_addon_lo(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO['dantzig']) \
.clone(scenario='addon_lo', keep_solution=False)
add_addon(scen, costs=1, zero_output=True)
scen.check_out()
scen.add_par('addon_lo', addon_share)
scen.commit('adding lower bound on addon technology')
scen.solve()
exp = scen.var('ACT', f)['lvl'] * 0.5
obs = scen.var('ACT', g)['lvl']
assert np.isclose(exp, obs)
def test_addon_up(message_test_mp):
scen = Scenario(message_test_mp,
**SCENARIO["dantzig"]).clone(scenario="addon_up",
keep_solution=False)
add_addon(scen, costs=-1, zero_output=True)
scen.check_out()
scen.add_par("addon_up", addon_share)
scen.commit("adding upper bound on addon technology")
scen.solve()
exp = scen.var("ACT", f)["lvl"] * 0.5
obs = scen.var("ACT", g)["lvl"]
assert np.isclose(exp, obs)
def test_cumulative_equidistant(test_mp):
scen = Scenario(test_mp, MODEL, "cum_equidistant", version="new")
years = [2020, 2030, 2040]
model_setup(scen, years)
scen.add_cat("year", "cumulative", years)
scen.add_par("bound_emission", ["World", "ghg", "all", "cumulative"], 0, "tCO2")
scen.commit("initialize test scenario")
scen.solve(quiet=True)
# with emissions constraint, the technology with costs satisfies demand
assert scen.var("OBJ")["lvl"] > 0
# under a cumulative constraint, the price must increase with the discount
# rate starting from the marginal relaxation in the first year
obs = scen.var("PRICE_EMISSION")["lvl"].values
npt.assert_allclose(obs, [1.05 ** (y - years[0]) for y in years])
def test_per_period_equidistant(test_mp):
scen = Scenario(test_mp, MODEL, 'per_period_equidistant', version='new')
years = [2020, 2030, 2040]
model_setup(scen, years)
for y in years:
scen.add_cat('year', y, y)
scen.add_par('bound_emission', ['World', 'ghg', 'all', y], 0, 'tCO2')
scen.commit('initialize test scenario')
scen.solve()
# with emissions constraint, the technology with costs satisfies demand
assert scen.var('OBJ')['lvl'] > 0
# under per-year emissions constraints, the emission price must be equal to
# the marginal relaxation, ie. the difference in costs between technologies
npt.assert_allclose(scen.var('PRICE_EMISSION')['lvl'], [1] * 3)
def test_per_period_equidistant(test_mp):
scen = Scenario(test_mp, MODEL, "per_period_equidistant", version="new")
years = [2020, 2030, 2040]
model_setup(scen, years)
for y in years:
scen.add_cat("year", y, y)
scen.add_par("bound_emission", ["World", "ghg", "all", y], 0, "tCO2")
scen.commit("initialize test scenario")
scen.solve()
# with emissions constraint, the technology with costs satisfies demand
assert scen.var("OBJ")["lvl"] > 0
# under per-year emissions constraints, the emission price must be equal to
# the marginal relaxation, ie. the difference in costs between technologies
npt.assert_allclose(scen.var("PRICE_EMISSION")["lvl"], [1] * 3)
def test_commodity_price_equality(test_mp):
scen = Scenario(test_mp, "test_commodity_price", "equality", version="new")
model_setup(scen, var_cost=-1)
scen.commit("initialize test model with negative variable costs")
# negative variable costs and supply >= demand causes an unbounded ray
with pytest.raises(ModelError, match="GAMS errored with return code 3"):
scen.solve(quiet=True)
# use the commodity-balance equality feature
scen.check_out()
scen.add_set("balance_equality", ["comm", "level"])
scen.commit("set commodity-balance for `[comm, level]` as equality")
scen.solve(case="price_commodity_equality")
assert scen.var("OBJ")["lvl"] == -1
assert scen.var("PRICE_COMMODITY")["lvl"][0] == -1
def test_addon_tec(test_mp):
scen = Scenario(test_mp, *msg_args).clone(scenario='addon',
keep_solution=False)
add_addon(scen, costs=-1)
scen.check_out()
bda = scen.par('bound_activity_up', f)
bda['value'] = bda['value'] / 2
scen.add_par('bound_activity_up', bda)
scen.commit('changing output and bounds')
scen.solve()
exp = scen.var('ACT', f)['lvl']
obs = scen.var('ACT', g)['lvl']
assert np.isclose(exp, obs)
