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_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_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 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 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 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 × 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