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 Ć 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 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 = {}
# Common values
common = dict(
commodity="cases",
year=1963,
year_vtg=1963,
year_act=1963,
time="year",
time_dest="year",
time_origin="year",
)
par["demand"] = make_df(
"demand",
**common,
node=["new-york", "chicago", "topeka"],
level="consumption",
value=[325, 300, 275],
unit="case",
)
par["bound_activity_up"] = make_df(
"bound_activity_up",
**common,
node_loc=["seattle", "san-diego"],
mode="production",
technology="canning_plant",
value=[350, 600],
unit="case",
)
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",
**input,
**common,
level="supply",
value=1,
unit="case",
)
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", **output, **common, value=1, unit="case")
# Variable cost: cost per kilometre Ć 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",
**var_cost,
**common,
unit="USD/case")
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:
solve_opts.setdefault("quiet", True)
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
