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(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_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 solve_modified(base: Scenario, new_name: str):
"""Context manager for a cloned scenario.
At the end of the block, the modified Scenario yielded by :func:`solve_modified` is
committed, set as default, and solved. Use in a ``with:`` statement to make small
modifications and leave a variable in the current scope with the solved scenario.
Examples
--------
>>> with solve_modified(base_scen, "new name") as s:
... s.add_par( ... ) # Modify the scenario
... # `s` is solved at the end of the block
Yields
------
.Scenario
Cloned from `base`, with the scenario name `new_name` and no solution.
"""
s = base.clone(
scenario=new_name,
annotation=f"Cloned by solve_modified() from {repr(base.scenario)}",
keep_solution=False,
)
s.check_out()
yield s
s.commit("Commit by solve_modified() at end of 'with:' statement")
s.set_as_default()
s.solve()
def test_new_timeseries_long_name64plus(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig multi-year"])
scen = scen.clone(keep_solution=False)
scen.check_out(timeseries_only=True)
df = pd.DataFrame(
{
"region": [
"India",
],
"variable": [
(
"Emissions|CO2|Energy|Demand|Transportation|Aviation|"
"Domestic|Freight|Oil"
),
],
"unit": [
"Mt CO2/yr",
],
"2012": [
0.257009,
],
}
)
scen.add_timeseries(df)
scen.commit("importing a testing timeseries")
def test_add_cat_unique(test_mp):
scen = Scenario(test_mp, *msg_multiyear_args)
scen2 = scen.clone(keep_solution=False)
scen2.check_out()
scen2.add_cat('year', 'firstmodelyear', 2020, True)
df = scen2.cat('year', 'firstmodelyear')
npt.assert_array_equal(df, ['2020'])
scen2.discard_changes()
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_add_bound_activity_up_modes(test_mp):
def calculate(scen):
return (
scen
.var('ACT')
.groupby(['technology', 'mode'])['lvl']
.sum()
.loc['transport_from_seattle']
)
scen = Scenario(test_mp, *msg_args)
scen.solve()
# data for act bound
data = pd.DataFrame({
'node_loc': 'seattle',
'technology': 'transport_from_seattle',
'year_act': 2010,
'time': 'year',
'unit': 'cases',
}, index=[0])
# test limiting one mode
clone = scen.clone('foo', 'bar', keep_solution=False)
clone.check_out()
exp = 0.5 * calculate(scen).sum()
data['mode'] = 'to_chicago'
data['value'] = exp
clone.add_par('bound_activity_up', data)
clone.commit('foo')
clone.solve()
obs = calculate(clone).loc['to_chicago']
assert np.isclose(obs, exp)
# test limiting all modes
clone2 = scen.clone('foo', 'baz', keep_solution=False)
clone2.check_out()
exp = 0.95 * calculate(scen).sum()
data['mode'] = 'all'
data['value'] = exp
clone2.add_par('bound_activity_up', data)
clone2.commit('foo')
clone2.solve()
obs = calculate(clone2).sum()
assert np.isclose(obs, exp)
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_years_active_extend(test_mp):
scen = Scenario(test_mp, *msg_multiyear_args)
scen = scen.clone(keep_solution=False)
scen.check_out()
scen.add_set('year', ['2040', '2050'])
scen.add_par('duration_period', '2040', 10, 'y')
scen.add_par('duration_period', '2050', 10, 'y')
df = scen.years_active('seattle', 'canning_plant', '2020')
npt.assert_array_equal(df, [2020, 2030, 2040])
scen.discard_changes()
def test_rename_technology(test_mp):
scen = Scenario(test_mp, *msg_args)
assert scen.par('output')['technology'].isin(['canning_plant']).any()
clone = scen.clone('foo', 'bar')
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()
assert np.isclose(clone.var('OBJ')['lvl'], 153.675)
def test_add_cat(test_mp):
scen = Scenario(test_mp, *msg_args)
scen2 = scen.clone(keep_solution=False)
scen2.check_out()
scen2.add_cat('technology', 'trade',
['transport_from_san-diego', 'transport_from_seattle'])
df = scen2.cat('technology', 'trade')
npt.assert_array_equal(
df, ['transport_from_san-diego', 'transport_from_seattle'])
scen2.discard_changes()
def test_rename_technology_no_rm(test_mp):
scen = Scenario(test_mp, *msg_args)
assert scen.par('output')['technology'].isin(['canning_plant']).any()
clone = scen.clone('foo', 'bar')
# 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_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_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_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_new_timeseries_long_name64plus(test_mp):
scen = Scenario(test_mp, *msg_multiyear_args)
scen = scen.clone(keep_solution=False)
scen.check_out(timeseries_only=True)
df = pd.DataFrame({
'region': ['India', ],
'variable': [('Emissions|CO2|Energy|Demand|Transportation|Aviation|'
'Domestic|Freight|Oil'), ],
'unit': ['Mt CO2/yr', ],
'2012': [0.257009, ]
})
scen.add_timeseries(df)
scen.commit('importing a testing timeseries')
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_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_excel_read_write(message_test_mp, tmp_path):
# Path to temporary file
tmp_path /= 'excel_read_write.xlsx'
# Convert to string to ensure this can be handled
fname = str(tmp_path)
scen1 = Scenario(message_test_mp, **SCENARIO['dantzig'])
scen1 = scen1.clone(keep_solution=False)
scen1.check_out()
scen1.init_set('new_set')
scen1.add_set('new_set', 'member')
scen1.init_par('new_par', idx_sets=['new_set'])
scen1.add_par('new_par', 'member', 2, '-')
scen1.commit('new set and parameter added.')
# Writing to Excel without solving
scen1.to_excel(fname)
# Writing to Excel when scenario has a solution
scen1.solve()
scen1.to_excel(fname)
scen2 = Scenario(message_test_mp,
model='foo',
scenario='bar',
version='new')
# Fails without init_items=True
with pytest.raises(ValueError, match="no set 'new_set'"):
scen2.read_excel(fname)
# Succeeds with init_items=True
scen2.read_excel(fname, init_items=True, commit_steps=True)
exp = scen1.par('input')
obs = scen2.par('input')
pdt.assert_frame_equal(exp, obs)
assert scen2.has_par('new_par')
assert float(scen2.par('new_par')['value']) == 2
scen2.commit('foo') # must be checked in
scen2.solve()
assert np.isclose(scen2.var('OBJ')['lvl'], scen1.var('OBJ')['lvl'])
def test_excel_read_write(message_test_mp, tmp_path):
# Path to temporary file
tmp_path /= "excel_read_write.xlsx"
# Convert to string to ensure this can be handled
fname = str(tmp_path)
scen1 = Scenario(message_test_mp, **SCENARIO["dantzig"])
scen1 = scen1.clone(keep_solution=False)
scen1.check_out()
scen1.init_set("new_set")
scen1.add_set("new_set", "member")
scen1.init_par("new_par", idx_sets=["new_set"])
scen1.add_par("new_par", "member", 2, "-")
scen1.commit("new set and parameter added.")
# Writing to Excel without solving
scen1.to_excel(fname)
# Writing to Excel when scenario has a solution
scen1.solve()
scen1.to_excel(fname)
scen2 = Scenario(message_test_mp,
model="foo",
scenario="bar",
version="new")
# Fails without init_items=True
with pytest.raises(ValueError, match="no set 'new_set'"):
scen2.read_excel(fname)
# Succeeds with init_items=True
scen2.read_excel(fname, init_items=True, commit_steps=True)
exp = scen1.par("input")
obs = scen2.par("input")
pdt.assert_frame_equal(exp, obs)
assert scen2.has_par("new_par")
assert float(scen2.par("new_par")["value"]) == 2
scen2.solve()
assert np.isclose(scen2.var("OBJ")["lvl"], scen1.var("OBJ")["lvl"])
def test_add_share_mode_lo(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig"]).clone()
scen.solve(quiet=True)
# data for share bound
def calc_share(s):
a = calculate_activity(
s, tec="transport_from_san-diego").loc["to_new-york"]
b = calculate_activity(s, tec="transport_from_san-diego").sum()
return a / b
exp = 1.05 * calc_share(scen)
# add share constraints
clone = scen.clone("foo", "baz", keep_solution=False)
clone.check_out()
clone.add_set("shares", "test-share")
clone.add_par(
"share_mode_lo",
pd.DataFrame(
{
"shares": "test-share",
"node_share": "san-diego",
"technology": "transport_from_san-diego",
"mode": "to_new-york",
"year_act": _year,
"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_new_timeseries_long_name64(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO['dantzig multi-year'])
scen = scen.clone(keep_solution=False)
scen.check_out(timeseries_only=True)
df = pd.DataFrame({
'region': [
'India',
],
'variable': [
('Emissions|CO2|Energy|Demand|Transportation|Aviation|'
'Domestic|Fre'),
],
'unit': [
'Mt CO2/yr',
],
'2012': [
0.257009,
]
})
scen.add_timeseries(df)
scen.commit('importing a testing timeseries')
def test_add_share_mode_lo(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO['dantzig']).clone()
scen.solve()
# data for share bound
def calc_share(s):
a = calculate_activity(
s, tec='transport_from_san-diego').loc['to_new-york']
b = calculate_activity(s, tec='transport_from_san-diego').sum()
return a / b
exp = 1.05 * calc_share(scen)
# add share constraints
clone = scen.clone('foo', 'baz', keep_solution=False)
clone.check_out()
clone.add_set('shares', 'test-share')
clone.add_par(
'share_mode_lo',
pd.DataFrame(
{
'shares': 'test-share',
'node_share': 'san-diego',
'technology': 'transport_from_san-diego',
'mode': 'to_new-york',
'year_act': _year,
'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_add_share_mode_up(test_mp):
scen = Scenario(test_mp, *msg_args).clone()
scen.solve()
# data for share bound
def calc_share(s):
a = calculate_activity(s,
tec='transport_from_seattle').loc['to_chicago']
b = calculate_activity(s, tec='transport_from_seattle').sum()
return a / b
exp = 0.95 * calc_share(scen)
# add share constraints
clone = scen.clone(scenario='share_mode_up', keep_solution=False)
clone.check_out()
clone.add_set('shares', 'test-share')
clone.add_par(
'share_mode_up',
pd.DataFrame(
{
'shares': 'test-share',
'node_share': 'seattle',
'technology': 'transport_from_seattle',
'mode': 'to_chicago',
'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_clone(tmpdir):
# Two local platforms
mp1 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp1")
mp2 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp2")
# A minimal scenario
scen1 = Scenario(mp1, model="model", scenario="scenario", version="new")
scen1.add_spatial_sets({"country": "Austria"})
scen1.add_set("technology", "bar")
scen1.add_horizon(year=[2010, 2020])
scen1.commit("add minimal sets for testing")
assert len(mp1.scenario_list(default=False)) == 1
# Clone
scen2 = scen1.clone(platform=mp2)
# Return type of ixmp.Scenario.clone is message_ix.Scenario
assert isinstance(scen2, Scenario)
# Close and re-open both databases
mp1.close_db() # TODO this should be done automatically on del
mp2.close_db() # TODO this should be done automatically on del
del mp1, mp2
mp1 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp1")
mp2 = ixmp.Platform(driver="hsqldb", path=tmpdir / "mp2")
# Same scenarios present in each database
assert all(
mp1.scenario_list(default=False) == mp2.scenario_list(default=False))
# Load both scenarios
scen1 = Scenario(mp1, "model", "scenario")
scen2 = Scenario(mp2, "model", "scenario")
# Contents are identical
assert all(scen1.set("node") == scen2.set("node"))
assert all(scen1.set("year") == scen2.set("year"))
def test_clone(tmpdir):
# Two local platforms
mp1 = ixmp.Platform(tmpdir / 'mp1', dbtype='HSQLDB')
mp2 = ixmp.Platform(tmpdir / 'mp2', dbtype='HSQLDB')
# A minimal scenario
scen1 = Scenario(mp1, model='model', scenario='scenario', version='new')
scen1.add_spatial_sets({'country': 'Austria'})
scen1.add_set('technology', 'bar')
scen1.add_horizon({'year': [2010, 2020]})
scen1.commit('add minimal sets for testing')
assert len(mp1.scenario_list(default=False)) == 1
# Clone
scen2 = scen1.clone(platform=mp2)
# Return type of ixmp.Scenario.clone is message_ix.Scenario
assert isinstance(scen2, Scenario)
# Close and re-open both databases
mp1.close_db() # TODO this should be done automatically on del
mp2.close_db() # TODO this should be done automatically on del
del mp1, mp2
mp1 = ixmp.Platform(tmpdir / 'mp1', dbtype='HSQLDB')
mp2 = ixmp.Platform(tmpdir / 'mp2', dbtype='HSQLDB')
# Same scenarios present in each database
assert all(
mp1.scenario_list(default=False) == mp2.scenario_list(default=False))
# Load both scenarios
scen1 = Scenario(mp1, 'model', 'scenario')
scen2 = Scenario(mp2, 'model', 'scenario')
# Contents are identical
assert all(scen1.set('node') == scen2.set('node'))
assert all(scen1.set('year') == scen2.set('year'))
def test_add_cat_unique(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig multi-year"])
scen2 = scen.clone(keep_solution=False)
scen2.check_out()
scen2.add_cat("year", "firstmodelyear", 1963, True)
assert [1963] == scen2.cat("year", "firstmodelyear")
def test_commodity_share_up(message_test_mp):
"""Origial Solution
----------------
lvl mode mrg node_loc technology
0 350.0 production 0.000 seattle canning_plant
1 50.0 to_new-york 0.000 seattle transport_from_seattle
2 300.0 to_chicago 0.000 seattle transport_from_seattle
3 0.0 to_topeka 0.036 seattle transport_from_seattle
4 600.0 production 0.000 san-diego canning_plant
5 275.0 to_new-york 0.000 san-diego transport_from_san-diego
6 0.0 to_chicago 0.009 san-diego transport_from_san-diego
7 275.0 to_topeka 0.000 san-diego transport_from_san-diego
Constraint Test
---------------
Seattle canning_plant production (original: 350) is limited to 50% of all
transport_from_san-diego (original: 550). Expected outcome: some increase
of transport_from_san-diego with some decrease of production in seattle.
"""
# data for share bound
def calc_share(s):
a = s.var("ACT",
filters={
"technology": ["canning_plant"],
"node_loc": ["seattle"]
})["lvl"][0]
b = calculate_activity(s, tec="transport_from_san-diego").sum()
return a / b
# common operations for both subtests
def add_data(s, map_df):
s.add_cat("technology", "share", "canning_plant")
s.add_cat("technology", "total", "transport_from_san-diego")
s.add_set("shares", "test-share")
s.add_set(
"map_shares_commodity_share",
pd.DataFrame(
{
"shares": "test-share",
"node_share": "seattle",
"node": "seattle",
"type_tec": "share",
"mode": "production",
"commodity": "cases",
"level": "supply",
},
index=[0],
),
)
s.add_set("map_shares_commodity_total", map_df)
s.add_par(
"share_commodity_up",
pd.DataFrame(
{
"shares": "test-share",
"node_share": "seattle",
"year_act": _year,
"time": "year",
"unit": "%",
"value": 0.5,
},
index=[0],
),
)
# initial data
scen = Scenario(message_test_mp, **SCENARIO["dantzig"]).clone()
scen.solve(quiet=True)
exp = 0.5
# check shares orig, should be bigger than expected bound
orig = calc_share(scen)
assert orig > exp
# add share constraints for modes explicitly
map_df = pd.DataFrame({
"shares": "test-share",
"node_share": "seattle",
"node": "san-diego",
"type_tec": "total",
"mode": ["to_new-york", "to_chicago", "to_topeka"],
"commodity": "cases",
"level": "supply",
})
clone = scen.clone(scenario="share_mode_list", keep_solution=False)
clone.check_out()
add_data(clone, map_df)
clone.commit("foo")
clone.solve()
# check shares new, should be lower than expected bound
obs = calc_share(clone)
assert obs <= exp
# check obj
orig_obj = scen.var("OBJ")["lvl"]
new_obj = clone.var("OBJ")["lvl"]
assert new_obj >= orig_obj
# add share constraints with mode == 'all'
map_df2 = pd.DataFrame(
{
"shares": "test-share",
"node_share": "seattle",
"node": "san-diego",
"type_tec": "total",
"mode": "all",
"commodity": "cases",
"level": "supply",
},
index=[0],
)
clone2 = scen.clone(scenario="share_all_modes", keep_solution=False)
clone2.check_out()
add_data(clone2, map_df2)
clone2.commit("foo")
clone2.solve()
# check shares new, should be lower than expected bound
obs2 = calc_share(clone2)
assert obs2 <= exp
# it should also be the same as the share with explicit modes
assert obs == obs2
# check obj
orig_obj = scen.var("OBJ")["lvl"]
new_obj = clone2.var("OBJ")["lvl"]
assert new_obj >= orig_obj
