Code Duplication - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 95-95 lines in 2 locations

tests/test_scripts/test_solph/test_simple_model/test_simple_dispatch_one_explicit_timemode.py 1 location


from oemof.solph.flows import Flow


def test_dispatch_one_time_step(solver="cbc"):
    """Create an energy system and optimize the dispatch at least costs."""

    # ######################### create energysystem components ################
    # resource buses
    bgas = Bus(label="gas", balanced=False)

    # electricity and heat
    bel = Bus(label="b_el")
    bth = Bus(label="b_th")

    # an excess and a shortage variable can help to avoid infeasible problems
    excess_el = Sink(label="excess_el", inputs={bel: Flow()})

    # sources
    wind = Source(
        label="wind", outputs={bel: Flow(fix=0.5, nominal_capacity=66.3)}
    )

    # demands (electricity/heat)
    demand_el = Sink(
        label="demand_elec", inputs={bel: Flow(nominal_capacity=85, fix=0.3)}
    )

    demand_th = Sink(
        label="demand_therm", inputs={bth: Flow(nominal_capacity=40, fix=0.2)}
    )

    # combined heat and power plant (chp)
    pp_chp = Converter(
        label="pp_chp",
        inputs={bgas: Flow()},
        outputs={
            bel: Flow(nominal_capacity=30, variable_costs=42),
            bth: Flow(nominal_capacity=40),
        },
        conversion_factors={bel: 0.3, bth: 0.4},
    )

    # heatpump with a coefficient of performance (COP) of 3
    b_heat_source = Bus(label="b_heat_source")

    heat_source = Source(label="heat_source", outputs={b_heat_source: Flow()})

    cop = 3
    heat_pump = Converter(
        label="heat_pump",
        inputs={bel: Flow(), b_heat_source: Flow()},
        outputs={bth: Flow(nominal_capacity=10)},
        conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
    )

    energysystem = EnergySystem(timeincrement=[1])
    energysystem.add(
        bgas,
        bel,
        bth,
        excess_el,
        wind,
        demand_el,
        demand_th,
        pp_chp,
        b_heat_source,
        heat_source,
        heat_pump,
    )

    # ################################ optimization ###########################

    # create optimization model based on energy_system
    optimization_model = Model(energysystem=energysystem)

    # solve problem
    optimization_model.solve(solver=solver)

    # write back results from optimization object to energysystem
    optimization_model.results()

    # ################################ results ################################
    data = views.node(processing.results(model=optimization_model), "b_el")

    # generate results to be evaluated in tests
    results = data["sequences"].sum(axis=0).to_dict()

    print("DateTimeIndex:", data["sequences"].index)

    test_results = {
        (("wind", "b_el"), "flow"): 33,
        (("b_el", "demand_elec"), "flow"): 26,
        (("b_el", "excess_el"), "flow"): 5,
        (("b_el", "heat_pump"), "flow"): 3,
    }

    for key in test_results.keys():
        assert results[key] == pytest.approx(test_results[key], abs=0.5)


tests/test_scripts/test_solph/test_simple_model/test_simple_dispatch_one.py 1 location


from oemof.solph.flows import Flow


def test_dispatch_one_time_step(solver="cbc"):
    """Create an energy system and optimize the dispatch at least costs."""

    # ######################### create energysystem components ################
    # resource buses
    bgas = Bus(label="gas", balanced=False)

    # electricity and heat
    bel = Bus(label="b_el")
    bth = Bus(label="b_th")

    # an excess and a shortage variable can help to avoid infeasible problems
    excess_el = Sink(label="excess_el", inputs={bel: Flow()})

    # sources
    wind = Source(
        label="wind", outputs={bel: Flow(fix=0.5, nominal_capacity=66.3)}
    )

    # demands (electricity/heat)
    demand_el = Sink(
        label="demand_elec", inputs={bel: Flow(nominal_capacity=85, fix=0.3)}
    )

    demand_th = Sink(
        label="demand_therm", inputs={bth: Flow(nominal_capacity=40, fix=0.2)}
    )

    # combined heat and power plant (chp)
    pp_chp = Converter(
        label="pp_chp",
        inputs={bgas: Flow()},
        outputs={
            bel: Flow(nominal_capacity=30, variable_costs=42),
            bth: Flow(nominal_capacity=40),
        },
        conversion_factors={bel: 0.3, bth: 0.4},
    )

    # heatpump with a coefficient of performance (COP) of 3
    b_heat_source = Bus(label="b_heat_source")

    heat_source = Source(label="heat_source", outputs={b_heat_source: Flow()})

    cop = 3
    heat_pump = Converter(
        label="heat_pump",
        inputs={bel: Flow(), b_heat_source: Flow()},
        outputs={bth: Flow(nominal_capacity=10)},
        conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
    )

    energysystem = EnergySystem(timeincrement=[1])
    energysystem.add(
        bgas,
        bel,
        bth,
        excess_el,
        wind,
        demand_el,
        demand_th,
        pp_chp,
        b_heat_source,
        heat_source,
        heat_pump,
    )

    # ################################ optimization ###########################

    # create optimization model based on energy_system
    optimization_model = Model(energysystem=energysystem)

    # solve problem
    optimization_model.solve(solver=solver)

    # write back results from optimization object to energysystem
    optimization_model.results()

    # ################################ results ################################
    data = views.node(processing.results(model=optimization_model), "b_el")

    # generate results to be evaluated in tests
    results = data["sequences"].sum(axis=0).to_dict()

    print("DateTimeIndex:", data["sequences"].index)

    test_results = {
        (("wind", "b_el"), "flow"): 33,
        (("b_el", "demand_elec"), "flow"): 26,
        (("b_el", "excess_el"), "flow"): 5,
        (("b_el", "heat_pump"), "flow"): 3,
    }

    for key in test_results.keys():
        assert results[key] == pytest.approx(test_results[key], abs=0.5)


		@@ 27-121 (lines=95) @@
24		from oemof.solph.flows import Flow
25
26
27		def test_dispatch_one_time_step(solver="cbc"):
28		"""Create an energy system and optimize the dispatch at least costs."""
29
30		# ######################### create energysystem components ################
31		# resource buses
32		bgas = Bus(label="gas", balanced=False)
33
34		# electricity and heat
35		bel = Bus(label="b_el")
36		bth = Bus(label="b_th")
37
38		# an excess and a shortage variable can help to avoid infeasible problems
39		excess_el = Sink(label="excess_el", inputs={bel: Flow()})
40
41		# sources
42		wind = Source(
43		label="wind", outputs={bel: Flow(fix=0.5, nominal_capacity=66.3)}
44		)
45
46		# demands (electricity/heat)
47		demand_el = Sink(
48		label="demand_elec", inputs={bel: Flow(nominal_capacity=85, fix=0.3)}
49		)
50
51		demand_th = Sink(
52		label="demand_therm", inputs={bth: Flow(nominal_capacity=40, fix=0.2)}
53		)
54
55		# combined heat and power plant (chp)
56		pp_chp = Converter(
57		label="pp_chp",
58		inputs={bgas: Flow()},
59		outputs={
60		bel: Flow(nominal_capacity=30, variable_costs=42),
61		bth: Flow(nominal_capacity=40),
62		},
63		conversion_factors={bel: 0.3, bth: 0.4},
64		)
65
66		# heatpump with a coefficient of performance (COP) of 3
67		b_heat_source = Bus(label="b_heat_source")
68
69		heat_source = Source(label="heat_source", outputs={b_heat_source: Flow()})
70
71		cop = 3
72		heat_pump = Converter(
73		label="heat_pump",
74		inputs={bel: Flow(), b_heat_source: Flow()},
75		outputs={bth: Flow(nominal_capacity=10)},
76		conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
77		)
78
79		energysystem = EnergySystem(timeincrement=[1])
80		energysystem.add(
81		bgas,
82		bel,
83		bth,
84		excess_el,
85		wind,
86		demand_el,
87		demand_th,
88		pp_chp,
89		b_heat_source,
90		heat_source,
91		heat_pump,
92		)
93
94		# ################################ optimization ###########################
95
96		# create optimization model based on energy_system
97		optimization_model = Model(energysystem=energysystem)
98
99		# solve problem
100		optimization_model.solve(solver=solver)
101
102		# write back results from optimization object to energysystem
103		optimization_model.results()
104
105		# ################################ results ################################
106		data = views.node(processing.results(model=optimization_model), "b_el")
107
108		# generate results to be evaluated in tests
109		results = data["sequences"].sum(axis=0).to_dict()
110
111		print("DateTimeIndex:", data["sequences"].index)
112
113		test_results = {
114		(("wind", "b_el"), "flow"): 33,
115		(("b_el", "demand_elec"), "flow"): 26,
116		(("b_el", "excess_el"), "flow"): 5,
117		(("b_el", "heat_pump"), "flow"): 3,
118		}
119
120		for key in test_results.keys():
121		assert results[key] == pytest.approx(test_results[key], abs=0.5)
122

		@@ 27-121 (lines=95) @@
24		from oemof.solph.flows import Flow
25
26
27		def test_dispatch_one_time_step(solver="cbc"):
28		"""Create an energy system and optimize the dispatch at least costs."""
29
30		# ######################### create energysystem components ################
31		# resource buses
32		bgas = Bus(label="gas", balanced=False)
33
34		# electricity and heat
35		bel = Bus(label="b_el")
36		bth = Bus(label="b_th")
37
38		# an excess and a shortage variable can help to avoid infeasible problems
39		excess_el = Sink(label="excess_el", inputs={bel: Flow()})
40
41		# sources
42		wind = Source(
43		label="wind", outputs={bel: Flow(fix=0.5, nominal_capacity=66.3)}
44		)
45
46		# demands (electricity/heat)
47		demand_el = Sink(
48		label="demand_elec", inputs={bel: Flow(nominal_capacity=85, fix=0.3)}
49		)
50
51		demand_th = Sink(
52		label="demand_therm", inputs={bth: Flow(nominal_capacity=40, fix=0.2)}
53		)
54
55		# combined heat and power plant (chp)
56		pp_chp = Converter(
57		label="pp_chp",
58		inputs={bgas: Flow()},
59		outputs={
60		bel: Flow(nominal_capacity=30, variable_costs=42),
61		bth: Flow(nominal_capacity=40),
62		},
63		conversion_factors={bel: 0.3, bth: 0.4},
64		)
65
66		# heatpump with a coefficient of performance (COP) of 3
67		b_heat_source = Bus(label="b_heat_source")
68
69		heat_source = Source(label="heat_source", outputs={b_heat_source: Flow()})
70
71		cop = 3
72		heat_pump = Converter(
73		label="heat_pump",
74		inputs={bel: Flow(), b_heat_source: Flow()},
75		outputs={bth: Flow(nominal_capacity=10)},
76		conversion_factors={bel: 1 / 3, b_heat_source: (cop - 1) / cop},
77		)
78
79		energysystem = EnergySystem(timeincrement=[1])
80		energysystem.add(
81		bgas,
82		bel,
83		bth,
84		excess_el,
85		wind,
86		demand_el,
87		demand_th,
88		pp_chp,
89		b_heat_source,
90		heat_source,
91		heat_pump,
92		)
93
94		# ################################ optimization ###########################
95
96		# create optimization model based on energy_system
97		optimization_model = Model(energysystem=energysystem)
98
99		# solve problem
100		optimization_model.solve(solver=solver)
101
102		# write back results from optimization object to energysystem
103		optimization_model.results()
104
105		# ################################ results ################################
106		data = views.node(processing.results(model=optimization_model), "b_el")
107
108		# generate results to be evaluated in tests
109		results = data["sequences"].sum(axis=0).to_dict()
110
111		print("DateTimeIndex:", data["sequences"].index)
112
113		test_results = {
114		(("wind", "b_el"), "flow"): 33,
115		(("b_el", "demand_elec"), "flow"): 26,
116		(("b_el", "excess_el"), "flow"): 5,
117		(("b_el", "heat_pump"), "flow"): 3,
118		}
119
120		for key in test_results.keys():
121		assert results[key] == pytest.approx(test_results[key], abs=0.5)
122

oemof / oemof-solph

Code Duplication Length = 95-95 lines in 2 locations

tests/test_scripts/test_solph/test_simple_model/test_simple_dispatch_one_explicit_timemode.py 1 location

tests/test_scripts/test_solph/test_simple_model/test_simple_dispatch_one.py 1 location