def test_add_spatial_single(test_mp):
scen = Scenario(test_mp, **SCENARIO['dantzig'], version='new')
data = {'country': 'Austria'}
scen.add_spatial_sets(data)
exp = ['World', 'Austria']
obs = scen.set('node')
npt.assert_array_equal(obs, exp)
exp = ['World', 'global', 'country']
obs = scen.set('lvl_spatial')
npt.assert_array_equal(obs, exp)
exp = [['country', 'Austria', 'World']]
obs = scen.set('map_spatial_hierarchy')
npt.assert_array_equal(obs, exp)
def test_add_spatial_multiple(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
data = {'country': ['Austria', 'Germany']}
scen.add_spatial_sets(data)
exp = ['World', 'Austria', 'Germany']
obs = scen.set('node')
npt.assert_array_equal(obs, exp)
exp = ['World', 'global', 'country']
obs = scen.set('lvl_spatial')
npt.assert_array_equal(obs, exp)
exp = [['country', 'Austria', 'World'], ['country', 'Germany', 'World']]
obs = scen.set('map_spatial_hierarchy')
npt.assert_array_equal(obs, exp)
def test_add_spatial_multiple(test_mp):
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
data = {"country": ["Austria", "Germany"]}
scen.add_spatial_sets(data)
exp = ["World", "Austria", "Germany"]
obs = scen.set("node")
npt.assert_array_equal(obs, exp)
exp = ["World", "global", "country"]
obs = scen.set("lvl_spatial")
npt.assert_array_equal(obs, exp)
exp = [["country", "Austria", "World"], ["country", "Germany", "World"]]
obs = scen.set("map_spatial_hierarchy")
npt.assert_array_equal(obs, exp)
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_add_spatial_hierarchy(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
data = {'country': {'Austria': {'state': ['Vienna', 'Lower Austria']}}}
scen.add_spatial_sets(data)
exp = ['World', 'Vienna', 'Lower Austria', 'Austria']
obs = scen.set('node')
npt.assert_array_equal(obs, exp)
exp = ['World', 'global', 'state', 'country']
obs = scen.set('lvl_spatial')
npt.assert_array_equal(obs, exp)
exp = [
['state', 'Vienna', 'Austria'],
['state', 'Lower Austria', 'Austria'],
['country', 'Austria', 'World'],
]
obs = scen.set('map_spatial_hierarchy')
npt.assert_array_equal(obs, exp)
def test_add_spatial_hierarchy(test_mp):
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
data = {"country": {"Austria": {"state": ["Vienna", "Lower Austria"]}}}
scen.add_spatial_sets(data)
exp = ["World", "Vienna", "Lower Austria", "Austria"]
obs = scen.set("node")
npt.assert_array_equal(obs, exp)
exp = ["World", "global", "state", "country"]
obs = scen.set("lvl_spatial")
npt.assert_array_equal(obs, exp)
exp = [
["state", "Vienna", "Austria"],
["state", "Lower Austria", "Austria"],
["country", "Austria", "World"],
]
obs = scen.set("map_spatial_hierarchy")
npt.assert_array_equal(obs, exp)
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_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_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)
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 create_timeseries_df(results: message_ix.Scenario) -> message_ix.Scenario:
logger.info('Create timeseries')
results.check_out(timeseries_only=True)
for var in ['ACT', 'CAP', 'CAP_NEW', 'EMISS']:
df = group_data(var, results)
if var != 'EMISS':
df['variable'] = ([
f'{df.loc[i, "technology"]}|{df.loc[i, "variable"]}'
for i in df.index
])
else:
df['variable'] = [
f'{df.loc[i, "emission"]}|{df.loc[i, "variable"]}'
for i in df.index
]
df['node'] = 'World' # TODO: wenn #6 gelöst, dann implementieren
df = df.rename(columns={'node': 'region'})
ts = pd.pivot_table(df,
values='lvl',
index=['region', 'variable', 'unit'],
columns=['year']).reset_index(drop=False)
results.add_timeseries(ts)
results.commit('timeseries added')
return results
def test_vintage_and_active_years(test_mp):
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
years = [2000, 2010, 2020]
scen.add_horizon(year=years, firstmodelyear=2010)
obs = scen.vintage_and_active_years()
exp = pd.DataFrame(
{
"year_vtg": (2000, 2000, 2010, 2010, 2020),
"year_act": (2010, 2020, 2010, 2020, 2020),
}
)
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# Add a technology, its lifetime, and period durations
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
scen.add_par(
"duration_period", pd.DataFrame({"unit": "???", "value": 10, "year": years})
)
scen.add_par(
"technical_lifetime",
pd.DataFrame(
{
"node_loc": "foo",
"technology": "bar",
"unit": "???",
"value": 20,
"year_vtg": years,
}
),
)
# part is before horizon
obs = scen.vintage_and_active_years(ya_args=("foo", "bar", "2000"))
exp = pd.DataFrame({"year_vtg": (2000,), "year_act": (2010,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
obs = scen.vintage_and_active_years(
ya_args=("foo", "bar", "2000"), in_horizon=False
)
exp = pd.DataFrame({"year_vtg": (2000, 2000), "year_act": (2000, 2010)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# fully in horizon
obs = scen.vintage_and_active_years(ya_args=("foo", "bar", "2010"))
exp = pd.DataFrame({"year_vtg": (2010, 2010), "year_act": (2010, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# part after horizon
obs = scen.vintage_and_active_years(ya_args=("foo", "bar", "2020"))
exp = pd.DataFrame({"year_vtg": (2020,), "year_act": (2020,)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# Advance the first model year
scen.add_cat("year", "firstmodelyear", years[-1], is_unique=True)
# Empty data frame: only 2000 and 2010 valid year_act for this node/tec;
# but both are before the first model year
obs = scen.vintage_and_active_years(
ya_args=("foo", "bar", years[0]), in_horizon=True
)
pdt.assert_frame_equal(pd.DataFrame(columns=["year_vtg", "year_act"]), obs)
# Exception is raised for incorrect arguments
with pytest.raises(ValueError, match="3 arguments are required if using `ya_args`"):
scen.vintage_and_active_years(ya_args=("foo", "bar"))
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_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(quiet=True)
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(quiet=True)
assert np.isclose(scen2.var("OBJ")["lvl"], scen1.var("OBJ")["lvl"])
def test_years_active_extend3(test_mp):
test_mp.add_unit("year")
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
scen.add_horizon(year=years, firstmodelyear=2010)
scen.add_set("year", [1992])
scen.add_par("duration_period", "1992", 2, "y")
scen.add_par("duration_period", "1995", 3, "y")
scen.add_par(
"technical_lifetime",
pd.DataFrame(
dict(
node_loc="foo",
technology="bar",
unit="year",
value=[20],
year_vtg=1990,
),
),
)
obs = scen.years_active("foo", "bar", 1990)
assert obs == [1990, 1992, 1995, 2000, 2005]
def make_westeros(mp, emissions=False, solve=False):
"""Return an :class:`message_ix.Scenario` for the Westeros model.
This is the same model used in the ``westeros_baseline.ipynb`` tutorial.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
emissions : bool, optional
If True, the ``emissions_factor`` parameter is also populated for CO2.
solve : bool, optional
If True, the scenario is solved.
"""
scen = Scenario(mp, version='new', **SCENARIO['westeros'])
# Sets
history = [690]
model_horizon = [700, 710, 720]
scen.add_horizon({
'year': history + model_horizon,
'firstmodelyear': model_horizon[0]
})
country = 'Westeros'
scen.add_spatial_sets({'country': country})
sets = {
'technology': 'coal_ppl wind_ppl grid bulb'.split(),
'mode': ['standard'],
'level': 'secondary final useful'.split(),
'commodity': 'electricity light'.split(),
}
for name, values in sets.items():
scen.add_set(name, values)
# Parameters — copy & paste from the tutorial notebook
gdp_profile = pd.Series([1., 1.5, 1.9],
index=pd.Index(model_horizon, name='Time'))
demand_per_year = 40 * 12 * 1000 / 8760
light_demand = pd.DataFrame({
'node': country,
'commodity': 'light',
'level': 'useful',
'year': model_horizon,
'time': 'year',
'value': (100 * gdp_profile).round(),
'unit': 'GWa',
})
scen.add_par("demand", light_demand)
year_df = scen.vintage_and_active_years()
vintage_years, act_years = year_df['year_vtg'], year_df['year_act']
base = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'mode': 'standard',
'time': 'year',
'unit': '-',
}
base_input = make_df(base, node_origin=country, time_origin='year')
base_output = make_df(base, node_dest=country, time_dest='year')
bulb_out = make_df(base_output,
technology='bulb',
commodity='light',
level='useful',
value=1.0)
scen.add_par('output', bulb_out)
bulb_in = make_df(base_input,
technology='bulb',
commodity='electricity',
level='final',
value=1.0)
scen.add_par('input', bulb_in)
grid_efficiency = 0.9
grid_out = make_df(base_output,
technology='grid',
commodity='electricity',
level='final',
value=grid_efficiency)
scen.add_par('output', grid_out)
grid_in = make_df(base_input,
technology='grid',
commodity='electricity',
level='secondary',
value=1.0)
scen.add_par('input', grid_in)
coal_out = make_df(base_output,
technology='coal_ppl',
commodity='electricity',
level='secondary',
value=1.)
scen.add_par('output', coal_out)
wind_out = make_df(base_output,
technology='wind_ppl',
commodity='electricity',
level='secondary',
value=1.)
scen.add_par('output', wind_out)
base_capacity_factor = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'time': 'year',
'unit': '-',
}
capacity_factor = {
'coal_ppl': 1,
'wind_ppl': 1,
'bulb': 1,
}
for tec, val in capacity_factor.items():
df = make_df(base_capacity_factor, technology=tec, value=val)
scen.add_par('capacity_factor', df)
base_technical_lifetime = {
'node_loc': country,
'year_vtg': model_horizon,
'unit': 'y',
}
lifetime = {
'coal_ppl': 20,
'wind_ppl': 20,
'bulb': 1,
}
for tec, val in lifetime.items():
df = make_df(base_technical_lifetime, technology=tec, value=val)
scen.add_par('technical_lifetime', df)
base_growth = {
'node_loc': country,
'year_act': model_horizon,
'time': 'year',
'unit': '-',
}
growth_technologies = [
"coal_ppl",
"wind_ppl",
]
for tec in growth_technologies:
df = make_df(base_growth, technology=tec, value=0.1)
scen.add_par('growth_activity_up', df)
historic_demand = 0.85 * demand_per_year
historic_generation = historic_demand / grid_efficiency
coal_fraction = 0.6
base_capacity = {
'node_loc': country,
'year_vtg': history,
'unit': 'GWa',
}
base_activity = {
'node_loc': country,
'year_act': history,
'mode': 'standard',
'time': 'year',
'unit': 'GWa',
}
old_activity = {
'coal_ppl': coal_fraction * historic_generation,
'wind_ppl': (1 - coal_fraction) * historic_generation,
}
for tec, val in old_activity.items():
df = make_df(base_activity, technology=tec, value=val)
scen.add_par('historical_activity', df)
act_to_cap = {
# 20 year lifetime
'coal_ppl': 1 / 10 / capacity_factor['coal_ppl'] / 2,
'wind_ppl': 1 / 10 / capacity_factor['wind_ppl'] / 2,
}
for tec in act_to_cap:
value = old_activity[tec] * act_to_cap[tec]
df = make_df(base_capacity, technology=tec, value=value)
scen.add_par('historical_new_capacity', df)
rate = [0.05] * len(model_horizon)
unit = ['-'] * len(model_horizon)
scen.add_par("interestrate", model_horizon, rate, unit)
base_inv_cost = {
'node_loc': country,
'year_vtg': model_horizon,
'unit': 'USD/GWa',
}
# in $ / kW
costs = {
'coal_ppl': 500,
'wind_ppl': 1500,
'bulb': 5,
}
for tec, val in costs.items():
df = make_df(base_inv_cost, technology=tec, value=val)
scen.add_par('inv_cost', df)
base_fix_cost = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'unit': 'USD/GWa',
}
# in $ / kW
costs = {
'coal_ppl': 30,
'wind_ppl': 10,
}
for tec, val in costs.items():
df = make_df(base_fix_cost, technology=tec, value=val)
scen.add_par('fix_cost', df)
base_var_cost = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'mode': 'standard',
'time': 'year',
'unit': 'USD/GWa',
}
# in $ / MWh
costs = {
'coal_ppl': 30,
'grid': 50,
}
for tec, val in costs.items():
df = make_df(base_var_cost, technology=tec, value=val)
scen.add_par('var_cost', df)
scen.commit('basic model of Westerosi electrification')
scen.set_as_default()
if emissions:
scen.check_out()
# Introduce the emission species CO2 and the emission category GHG
scen.add_set('emission', 'CO2')
scen.add_cat('emission', 'GHG', 'CO2')
# we now add CO2 emissions to the coal powerplant
base_emission_factor = {
'node_loc': country,
'year_vtg': vintage_years,
'year_act': act_years,
'mode': 'standard',
'unit': 'USD/GWa',
}
emission_factor = make_df(base_emission_factor,
technology='coal_ppl',
emission='CO2',
value=100.)
scen.add_par('emission_factor', emission_factor)
scen.commit('Added emissions sets/params to Westeros model.')
if solve:
scen.solve()
return scen
def test_reporter_no_solution(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig"])
pytest.raises(RuntimeError, Reporter.from_scenario, scen)
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_cat_all(test_mp):
scen = Scenario(test_mp, *msg_args)
df = scen.cat('technology', 'all')
npt.assert_array_equal(df, [
'canning_plant', 'transport_from_seattle', 'transport_from_san-diego'
])
def test_years_active(test_mp):
scen = Scenario(test_mp, *msg_multiyear_args)
df = scen.years_active('seattle', 'canning_plant', '2020')
npt.assert_array_equal(df, [2020, 2030])
def test_cat_list(test_mp):
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
# cat_list() returns default 'year' categories in a new message_ix.Scenario
exp = ["firstmodelyear", "lastmodelyear", "initializeyear_macro"]
assert exp == scen.cat_list("year")
def test_excel_read_write(test_mp):
fname = 'test_excel_read_write.xlsx'
scen1 = Scenario(test_mp, *msg_args)
scen1.to_excel(fname)
scen2 = Scenario(test_mp, model='foo', scenario='bar', version='new')
scen2.read_excel(fname)
exp = scen1.par('input')
obs = scen2.par('input')
pdt.assert_frame_equal(exp, obs)
scen1.solve()
scen2.commit('foo') # must be checked in
scen2.solve()
exp = scen1.var('OBJ')['lvl']
obs = scen2.var('OBJ')['lvl']
assert exp == obs
os.remove(fname)
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_vintage_and_active_years_with_lifetime(test_mp):
scen = Scenario(test_mp, *msg_args, version='new')
years = ['2000', '2010', '2020']
scen.add_horizon({'year': years, 'firstmodelyear': '2010'})
scen.add_set('node', 'foo')
scen.add_set('technology', 'bar')
scen.add_par('duration_period',
pd.DataFrame({
'unit': '???',
'value': 10,
'year': years
}))
scen.add_par(
'technical_lifetime',
pd.DataFrame({
'node_loc': 'foo',
'technology': 'bar',
'unit': '???',
'value': 20,
'year_vtg': years,
}))
# part is before horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2000'))
exp = pd.DataFrame({'year_vtg': (2000, ), 'year_act': (2010, )})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2000'),
in_horizon=False)
exp = pd.DataFrame({'year_vtg': (2000, 2000), 'year_act': (2000, 2010)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# fully in horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2010'))
exp = pd.DataFrame({'year_vtg': (2010, 2010), 'year_act': (2010, 2020)})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
# part after horizon
obs = scen.vintage_and_active_years(ya_args=('foo', 'bar', '2020'))
exp = pd.DataFrame({'year_vtg': (2020, ), 'year_act': (2020, )})
pdt.assert_frame_equal(exp, obs, check_like=True) # ignore col order
def test_years_active(test_mp):
test_mp.add_unit("year")
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
# First period length is immaterial
duration = [1900, 5, 5, 5, 5, 10, 10]
scen.add_horizon(year=years, firstmodelyear=years[-1])
scen.add_par(
"duration_period", pd.DataFrame(zip(years, duration), columns=["year", "value"])
)
# 'bar' built in period '1995' with 25-year lifetime:
# - is constructed in 1991-01-01.
# - by 1995-12-31, has operated 5 years.
# - operates until 2015-12-31. This is within the period '2020'.
scen.add_par(
"technical_lifetime",
pd.DataFrame(
dict(
node_loc="foo",
technology="bar",
unit="year",
value=25,
year_vtg=years[1],
),
index=[0],
),
)
result = scen.years_active("foo", "bar", years[1])
# Correct return type
assert isinstance(result, list)
assert isinstance(result[0], int)
# Years 1995 through 2020
npt.assert_array_equal(result, years[1:-1])
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_years_active_extend(message_test_mp):
scen = Scenario(message_test_mp, **SCENARIO["dantzig multi-year"])
# Existing time horizon
years = [1963, 1964, 1965]
result = scen.years_active("seattle", "canning_plant", years[1])
npt.assert_array_equal(result, years[1:])
# Add years to the scenario
years.extend([1993, 1995])
scen.check_out()
scen.add_set("year", years[-2:])
scen.add_par("duration_period", "1993", 28, "y")
scen.add_par("duration_period", "1995", 2, "y")
# technical_lifetime of seattle/canning_plant/1964 is 30 years.
# - constructed in 1964-01-01.
# - by 1964-12-31, has operated 1 year.
# - by 1965-12-31, has operated 2 years.
# - operates until 1993-12-31.
# - is NOT active within the period '1995' (1994-01-01 to 1995-12-31)
result = scen.years_active("seattle", "canning_plant", 1964)
npt.assert_array_equal(result, years[1:-1])
def make_dantzig(mp, solve=False, multi_year=False, **solve_opts):
"""Return an :class:`message_ix.Scenario` for Dantzig's canning problem.
Parameters
----------
mp : ixmp.Platform
Platform on which to create the scenario.
solve : bool, optional
If True, the scenario is solved.
multi_year : bool, optional
If True, the scenario has years 1963--1965 inclusive. Otherwise, the
scenario has the single year 1963.
"""
# add custom units and region for timeseries data
mp.add_unit('USD/case')
mp.add_unit('case')
mp.add_region('DantzigLand', 'country')
# initialize a new (empty) instance of an `ixmp.Scenario`
scen = Scenario(
mp,
model=SCENARIO['dantzig']['model'],
scenario='multi-year' if multi_year else 'standard',
annotation="Dantzig's canning problem as a MESSAGE-scheme Scenario",
version='new')
# Sets
# NB commit() is refused if technology and year are not given
t = ['canning_plant', 'transport_from_seattle', 'transport_from_san-diego']
sets = {
'technology': t,
'node': 'seattle san-diego new-york chicago topeka'.split(),
'mode': 'production to_new-york to_chicago to_topeka'.split(),
'level': 'supply consumption'.split(),
'commodity': ['cases'],
}
for name, values in sets.items():
scen.add_set(name, values)
scen.add_horizon({'year': [1962, 1963], 'firstmodelyear': 1963})
# Parameters
par = {}
demand = {
'node': 'new-york chicago topeka'.split(),
'value': [325, 300, 275]
}
par['demand'] = make_df(pd.DataFrame.from_dict(demand),
commodity='cases',
level='consumption',
time='year',
unit='case',
year=1963)
b_a_u = {'node_loc': ['seattle', 'san-diego'], 'value': [350, 600]}
par['bound_activity_up'] = make_df(pd.DataFrame.from_dict(b_a_u),
mode='production',
technology='canning_plant',
time='year',
unit='case',
year_act=1963)
par['ref_activity'] = par['bound_activity_up'].copy()
input = pd.DataFrame(
[
['to_new-york', 'seattle', 'seattle', t[1]],
['to_chicago', 'seattle', 'seattle', t[1]],
['to_topeka', 'seattle', 'seattle', t[1]],
['to_new-york', 'san-diego', 'san-diego', t[2]],
['to_chicago', 'san-diego', 'san-diego', t[2]],
['to_topeka', 'san-diego', 'san-diego', t[2]],
],
columns=['mode', 'node_loc', 'node_origin', 'technology'])
par['input'] = make_df(input,
commodity='cases',
level='supply',
time='year',
time_origin='year',
unit='case',
value=1,
year_act=1963,
year_vtg=1963)
output = pd.DataFrame(
[
['supply', 'production', 'seattle', 'seattle', t[0]],
['supply', 'production', 'san-diego', 'san-diego', t[0]],
['consumption', 'to_new-york', 'new-york', 'seattle', t[1]],
['consumption', 'to_chicago', 'chicago', 'seattle', t[1]],
['consumption', 'to_topeka', 'topeka', 'seattle', t[1]],
['consumption', 'to_new-york', 'new-york', 'san-diego', t[2]],
['consumption', 'to_chicago', 'chicago', 'san-diego', t[2]],
['consumption', 'to_topeka', 'topeka', 'san-diego', t[2]],
],
columns=['level', 'mode', 'node_dest', 'node_loc', 'technology'])
par['output'] = make_df(output,
commodity='cases',
time='year',
time_dest='year',
unit='case',
value=1,
year_act=1963,
year_vtg=1963)
# Variable cost: cost per kilometre × distance (neither parametrized
# explicitly)
var_cost = pd.DataFrame(
[
['to_new-york', 'seattle', 'transport_from_seattle', 0.225],
['to_chicago', 'seattle', 'transport_from_seattle', 0.153],
['to_topeka', 'seattle', 'transport_from_seattle', 0.162],
['to_new-york', 'san-diego', 'transport_from_san-diego', 0.225],
['to_chicago', 'san-diego', 'transport_from_san-diego', 0.162],
['to_topeka', 'san-diego', 'transport_from_san-diego', 0.126],
],
columns=['mode', 'node_loc', 'technology', 'value'])
par['var_cost'] = make_df(var_cost,
time='year',
unit='USD/case',
year_act=1963,
year_vtg=1963)
for name, value in par.items():
scen.add_par(name, value)
if multi_year:
scen.add_set('year', [1964, 1965])
scen.add_par('technical_lifetime', ['seattle', 'canning_plant', 1964],
3, 'y')
if solve:
# Always read one equation. Used by test_core.test_year_int.
scen.init_equ('COMMODITY_BALANCE_GT',
['node', 'commodity', 'level', 'year', 'time'])
solve_opts['equ_list'] = solve_opts.get('equ_list', []) \
+ ['COMMODITY_BALANCE_GT']
scen.commit('Created a MESSAGE-scheme version of the transport problem.')
scen.set_as_default()
if solve:
scen.solve(**solve_opts)
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_years_active_extended2(test_mp):
test_mp.add_unit("year")
scen = Scenario(test_mp, **SCENARIO["dantzig"], version="new")
scen.add_set("node", "foo")
scen.add_set("technology", "bar")
# Periods of uneven length
years = [1990, 1995, 2000, 2005, 2010, 2020, 2030]
# First period length is immaterial
duration = [1900, 5, 5, 5, 5, 10, 10]
scen.add_horizon(year=years, firstmodelyear=years[-1])
scen.add_par(
"duration_period", pd.DataFrame(zip(years, duration), columns=["year", "value"])
)
# 'bar' built in period '2020' with 10-year lifetime:
# - is constructed in 2011-01-01.
# - by 2020-12-31, has operated 10 years.
# - operates until 2020-12-31. This is within the period '2020'.
# The test ensures that the correct lifetime value is retrieved,
# i.e. the lifetime for the vintage 2020.
scen.add_par(
"technical_lifetime",
pd.DataFrame(
dict(
node_loc="foo",
technology="bar",
unit="year",
value=[20, 20, 20, 20, 20, 10, 10],
year_vtg=years,
),
),
)
result = scen.years_active("foo", "bar", years[-2])
# Correct return type
assert isinstance(result, list)
assert isinstance(result[0], int)
# Years 2020
npt.assert_array_equal(result, years[-2])