def test_true_value_allocation_factors(no_allocation):
dataset = {'exchanges': [{
'name': 'first',
'type': 'reference product',
'amount': 4,
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'name': 'second',
'type': 'reference product',
'amount': 10,
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'name': 'third',
'type': 'biosphere',
'amount': 30
}]}
obj, lst = economic_allocation(dataset)
# Allocation by revenue; revenue is (4 * 2.5 = 10, 2 * 10 = 20) = (1/3, 2/3)
assert [x[0] for x in lst] == [1/3, 2/3]
def test_true_value_allocation_factors(no_allocation):
dataset = {
'exchanges': [{
'name': 'first',
'type': 'reference product',
'amount': 4,
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'name': 'second',
'type': 'reference product',
'amount': 10,
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'name': 'third',
'type': 'biosphere',
'amount': 30
}]
}
obj, lst = economic_allocation(dataset)
# Allocation by revenue; revenue is (4 * 2.5 = 10, 2 * 10 = 20) = (1/3, 2/3)
assert [x[0] for x in lst] == [1 / 3, 2 / 3]
def test_normal_true_value_allocation():
dataset = {'exchanges': [{
'name': 'first',
'type': 'reference product',
'amount': 4,
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'name': 'second',
'type': 'reference product',
'amount': 10,
'properties': [{
'name': 'price',
'amount': 4
}, {
'name': 'true value relation',
'amount': 7.5
}]
}, {
'name': 'third',
'type': 'reference product',
'amount': 10,
'properties': [{
'name': 'price',
'amount': 5
}, {
'name': 'true value relation',
'amount': 2.5
}]
}, {
'name': 'fourth',
'type': 'biosphere',
'amount': 100
}]}
# Total revenue is 100, of which 90 has true value
# Total true value is 100, of which 75 comes from ``second``
get_biosphere_exchange = lambda x: [exc for exc in x['exchanges'] if exc['name'] == 'fourth'][0]
for ds in economic_allocation(dataset):
assert len(ds['exchanges']) == 4
assert sum(1 for exc in ds['exchanges'] if exc['type'] == 'dropped product') == 2
assert sum(1 for exc in ds['exchanges'] if exc['amount'] == 0) == 2
rp = get_single_reference_product(ds)
bio_exchange = get_biosphere_exchange(ds)
assert rp['type'] == 'reference product'
assert rp['amount'] == 1
if rp['name'] == 'first':
assert bio_exchange['amount'] == 100 / 4 * (4 * 2.5 / 100)
elif rp['name'] == 'second':
assert bio_exchange['amount'] == (7.5 * 10) / (7.5 * 10 + 2.5 * 10) * 90 / 100 * 100 / 10
else:
assert bio_exchange['amount'] == (2.5 * 10) / (7.5 * 10 + 2.5 * 10) * 90 / 100 * 100 / 10
def test_economic_allocation_outputs(no_allocation):
dataset = {
'exchanges': [{
'type': 'reference product',
'name': 'foo',
'amount': 2,
'properties': [{
'name': 'price',
'amount': 14
}]
}]
}
expected = {
'type': 'reference product',
'name': 'foo',
'amount': 2,
'properties': [{
'name': 'price',
'amount': 14
}]
}
obj, lst = economic_allocation(dataset)
assert obj is dataset
assert list(lst) == [(1, expected)]
def test_economic_allocation_outputs(no_allocation):
dataset = {
'exchanges': [{
'type': 'reference product',
'name': 'foo',
'amount': 2,
'properties': [{
'name': 'price',
'amount': 14
}]
}]
}
expected = {
'type': 'reference product',
'name': 'foo',
'amount': 2,
'properties': [{
'name': 'price',
'amount': 14
}]
}
obj, lst = economic_allocation(dataset)
assert obj is dataset
assert list(lst) == [(1, expected)]
def test_normal_true_value_allocation():
dataset = {
'exchanges': [{
'name': 'first',
'type': 'reference product',
'amount': 4,
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'name':
'second',
'type':
'reference product',
'amount':
10,
'properties': [{
'name': 'price',
'amount': 4
}, {
'name': 'true value relation',
'amount': 7.5
}]
}, {
'name':
'third',
'type':
'reference product',
'amount':
10,
'properties': [{
'name': 'price',
'amount': 5
}, {
'name': 'true value relation',
'amount': 2.5
}]
}, {
'name': 'fourth',
'type': 'biosphere',
'amount': 100
}]
}
# Total revenue is 100, of which 90 has true value
# Total true value is 100, of which 75 comes from ``second``
get_biosphere_exchange = lambda x: [
exc for exc in x['exchanges'] if exc['name'] == 'fourth'
][0]
for ds in economic_allocation(dataset):
assert len(ds['exchanges']) == 4
assert sum(1 for exc in ds['exchanges']
if exc['type'] == 'dropped product') == 2
assert sum(1 for exc in ds['exchanges'] if exc['amount'] == 0) == 2
rp = get_single_reference_product(ds)
bio_exchange = get_biosphere_exchange(ds)
assert rp['type'] == 'reference product'
assert rp['amount'] == 1
if rp['name'] == 'first':
assert bio_exchange['amount'] == 100 / 4 * (4 * 2.5 / 100)
elif rp['name'] == 'second':
assert bio_exchange['amount'] == (7.5 * 10) / (
7.5 * 10 + 2.5 * 10) * 90 / 100 * 100 / 10
else:
assert bio_exchange['amount'] == (2.5 * 10) / (
7.5 * 10 + 2.5 * 10) * 90 / 100 * 100 / 10
def test_allocate_cogeneration_dataset(cogen):
"""Cogeneration dataset produces:
1. heat, district or industrial, natural gas
* Reference product
* Price: 0.0106 €/MJ
* True value: 0.184213553594 €/MJ
* Amount: 4.1 MJ
* Revenue: 0.04346 €
* True value revenue: 0.7552755697353999 €
2. electricity, high voltage
* Allocatable byproduct
* Price: 0.0977 €/kWh
* True value: 3.6 €/kWh
* Amount: 1 kWh
* Revenue: 0.0977 €
* True value revenue: 3.6 €
Allocation factors: 0.3078775857183338, 0.6921224142816661
TV allocation factors: 0.17341625291951063, 0.8265837470804893
Elementary exchange: 0.622135922330097 kg Carbon dioxide, fossil
Input: 6.47766990291262E-10 unit gas power plant, 100MW electrical
Input: 0.298730395817774 m3 natural gas, high pressure
This real world dataset does not test for a mix of true-value and non-true-value exchanges, but this is tested in the artificial test cases above.
"""
datasets = economic_allocation(cogen)
assert len(datasets) == 2
for ds in datasets:
assert len(ds['exchanges']) == 5
for key, value in list(cogen.items()):
if key == 'exchanges':
continue
else:
assert ds[key] == value
rp = get_single_reference_product(ds)
if rp['name'] == 'heat, district or industrial, natural gas':
expected = {
'heat, district or industrial, natural gas': {
'amount': 1,
'type': 'reference product',
},
'electricity, high voltage': {
'amount': 0,
'type': 'dropped product',
},
'Carbon dioxide, fossil': {
'amount': 0.17341625291951063 * 0.622135922330097 / 4.1,
},
'gas power plant, 100MW electrical': {
'amount': 0.17341625291951063 * 6.47766990291262E-10 / 4.1,
},
'natural gas, high pressure': {
'amount': 0.17341625291951063 * 0.298730395817774 / 4.1,
},
}
else:
expected = {
'heat, district or industrial, natural gas': {
'amount': 0,
'type': 'dropped product',
},
'electricity, high voltage': {
'amount': 1,
'type': 'reference product',
},
'Carbon dioxide, fossil': {
'amount': 0.8265837470804893 * 0.622135922330097,
},
'gas power plant, 100MW electrical': {
'amount': 0.8265837470804893 * 6.47766990291262E-10,
},
'natural gas, high pressure': {
'amount': 0.8265837470804893 * 0.298730395817774,
},
}
for exc in ds['exchanges']:
print((exc['name']))
for key, value in list(expected[exc['name']].items()):
if isinstance(value, Number):
assert allclose(exc[key], value)
else:
assert exc[key] == value
def test_allocation_function_output_valid(cogen):
for new_ds in economic_allocation(cogen):
assert dataset_schema(new_ds)
def test_allocate_cogeneration_dataset(cogen):
"""Cogeneration dataset produces:
1. heat, district or industrial, natural gas
* Reference product
* Price: 0.0106 €/MJ
* True value: 0.184213553594 €/MJ
* Amount: 4.1 MJ
* Revenue: 0.04346 €
* True value revenue: 0.7552755697353999 €
2. electricity, high voltage
* Allocatable byproduct
* Price: 0.0977 €/kWh
* True value: 3.6 €/kWh
* Amount: 1 kWh
* Revenue: 0.0977 €
* True value revenue: 3.6 €
Allocation factors: 0.3078775857183338, 0.6921224142816661
TV allocation factors: 0.17341625291951063, 0.8265837470804893
Elementary exchange: 0.622135922330097 kg Carbon dioxide, fossil
Input: 6.47766990291262E-10 unit gas power plant, 100MW electrical
Input: 0.298730395817774 m3 natural gas, high pressure
This real world dataset does not test for a mix of true-value and non-true-value exchanges, but this is tested in the artificial test cases above.
"""
datasets = economic_allocation(cogen)
assert len(datasets) == 2
for ds in datasets:
assert len(ds['exchanges']) == 5
for key, value in cogen.items():
if key == 'exchanges':
continue
else:
assert ds[key] == value
rp = get_single_reference_product(ds)
if rp['name'] == 'heat, district or industrial, natural gas':
expected = {
'heat, district or industrial, natural gas': {
'amount': 1,
'type': 'reference product',
},
'electricity, high voltage': {
'amount': 0,
'type': 'dropped product',
},
'Carbon dioxide, fossil': {
'amount': 0.17341625291951063 * 0.622135922330097 / 4.1,
},
'gas power plant, 100MW electrical': {
'amount': 0.17341625291951063 * 6.47766990291262E-10 / 4.1,
},
'natural gas, high pressure': {
'amount': 0.17341625291951063 * 0.298730395817774 / 4.1,
},
}
else:
expected = {
'heat, district or industrial, natural gas': {
'amount': 0,
'type': 'dropped product',
},
'electricity, high voltage': {
'amount': 1,
'type': 'reference product',
},
'Carbon dioxide, fossil': {
'amount': 0.8265837470804893 * 0.622135922330097,
},
'gas power plant, 100MW electrical': {
'amount': 0.8265837470804893 * 6.47766990291262E-10,
},
'natural gas, high pressure': {
'amount': 0.8265837470804893 * 0.298730395817774,
},
}
for exc in ds['exchanges']:
print(exc['name'])
for key, value in expected[exc['name']].items():
if isinstance(value, Number):
assert allclose(exc[key], value)
else:
assert exc[key] == value
def test_allocation_function_output_valid(cogen):
for new_ds in economic_allocation(cogen):
assert dataset_schema(new_ds)
def test_normal_economic_allocation():
dataset = {
'exchanges': [{
'name': 'first',
'type': 'reference product',
'amount': 4,
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'name': 'second',
'type': 'reference product',
'amount': 10,
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'name': 'third',
'type': 'biosphere',
'amount': 60
}]
}
# Allocation by revenue; revenue is (4 * 2.5 = 10, 2 * 10 = 20) = (1/3, 2/3)
# So biosphere amount is (20, 40)
# Normalize to production of 1: 20 / 4, 40 / 10 = (5, 4)
expected = [{
'exchanges': [{
'amount': 1.0,
'name': 'first',
'type': 'reference product',
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'amount': 0.0,
'name': 'second',
'type': 'dropped product',
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'amount': 5,
'name': 'third',
'type': 'biosphere',
}]
}, {
'exchanges': [{
'amount': 1.0,
'name': 'second',
'type': 'reference product',
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'type': 'dropped product',
'amount': 0.0,
'name': 'first',
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'type': 'biosphere',
'name': 'third',
'amount': 4
}]
}]
assert economic_allocation(dataset) == expected
def test_allocation_cardboard(cardboard):
"""Cardboard dataset produces:
1. corrugated board box
* Reference product
* Price: 0.843326883 €/kg
* Amount: 1000 kg
* Revenue: 843.326883 €
2. residual softwood, wet
* Allocatable byproduct
* Price: 19.4156914399732 €/m3
* Amount: 0.00209150326797386 m3
* Revenue: 0.04060798209667605 €
Allocation factors: 0.9999518501927914, 4.814980720846648e-05
Elementary exchange: 0.000163 kg Propane
Elementary exchange: 0.03 kg Nitrogen oxides
Input: 5.95 kg potato starch
Input: 19.9481865284974 m3 natural gas, low pressure
Waste: 0.046 kg waste mineral oil
"""
datasets = economic_allocation(cardboard)
assert len(datasets) == 2
for ds in datasets:
assert len(ds['exchanges']) == 7
for key, value in list(cardboard.items()):
if key == 'exchanges':
continue
else:
assert ds[key] == value
rp = get_single_reference_product(ds)
if rp['name'] == 'corrugated board box':
expected = {
'corrugated board box': {
'amount': 1,
'type': 'reference product',
},
'residual softwood, wet': {
'amount': 0,
'type': 'dropped product',
},
'waste mineral oil': {
'amount': 0.9999518501927914 * 0.046 / 1000,
},
'natural gas, low pressure': {
'amount': 0.9999518501927914 * 19.9481865284974 / 1000,
},
'potato starch': {
'amount': 0.9999518501927914 * 5.95 / 1000,
},
'Propane': {
'amount': 0.9999518501927914 * 0.000163 / 1000,
},
'Nitrogen oxides': {
'amount': 0.9999518501927914 * 0.03 / 1000
}
}
else:
expected = {
'corrugated board box': {
'amount': 0,
'type': 'dropped product',
},
'residual softwood, wet': {
'amount': 1,
'type': 'reference product',
},
'waste mineral oil': {
'amount':
4.814980720846648e-05 * 0.046 / 0.00209150326797386,
},
'natural gas, low pressure': {
'amount':
4.814980720846648e-05 * 19.9481865284974 /
0.00209150326797386,
},
'potato starch': {
'amount':
4.814980720846648e-05 * 5.95 / 0.00209150326797386,
},
'Propane': {
'amount':
4.814980720846648e-05 * 0.000163 / 0.00209150326797386,
},
'Nitrogen oxides': {
'amount':
4.814980720846648e-05 * 0.03 / 0.00209150326797386
}
}
for exc in ds['exchanges']:
print((exc['name']))
for key, value in list(expected[exc['name']].items()):
if isinstance(value, Number):
assert allclose(exc[key], value)
else:
assert exc[key] == value
def test_normal_economic_allocation():
dataset = {'exchanges': [{
'name': 'first',
'type': 'reference product',
'amount': 4,
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'name': 'second',
'type': 'reference product',
'amount': 10,
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'name': 'third',
'type': 'biosphere',
'amount': 60
}]}
# Allocation by revenue; revenue is (4 * 2.5 = 10, 2 * 10 = 20) = (1/3, 2/3)
# So biosphere amount is (20, 40)
# Normalize to production of 1: 20 / 4, 40 / 10 = (5, 4)
expected = [{
'exchanges': [{
'amount': 1.0,
'name': 'first',
'type': 'reference product',
'properties': [{
'name': 'price',
'amount': 2.5}]
}, {
'amount': 0.0,
'name': 'second',
'type': 'dropped product',
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'amount': 5,
'name': 'third',
'type': 'biosphere',
}]
}, {
'exchanges': [{
'amount': 1.0,
'name': 'second',
'type': 'reference product',
'properties': [{
'name': 'price',
'amount': 2
}]
}, {
'type': 'dropped product',
'amount': 0.0,
'name': 'first',
'properties': [{
'name': 'price',
'amount': 2.5
}]
}, {
'type': 'biosphere',
'name': 'third',
'amount': 4
}]
}]
assert economic_allocation(dataset) == expected
def test_allocation_cardboard(cardboard):
"""Cardboard dataset produces:
1. corrugated board box
* Reference product
* Price: 0.843326883 €/kg
* Amount: 1000 kg
* Revenue: 843.326883 €
2. residual softwood, wet
* Allocatable byproduct
* Price: 19.4156914399732 €/m3
* Amount: 0.00209150326797386 m3
* Revenue: 0.04060798209667605 €
Allocation factors: 0.9999518501927914, 4.814980720846648e-05
Elementary exchange: 0.000163 kg Propane
Elementary exchange: 0.03 kg Nitrogen oxides
Input: 5.95 kg potato starch
Input: 19.9481865284974 m3 natural gas, low pressure
Waste: 0.046 kg waste mineral oil
"""
datasets = economic_allocation(cardboard)
assert len(datasets) == 2
for ds in datasets:
assert len(ds['exchanges']) == 7
for key, value in cardboard.items():
if key == 'exchanges':
continue
else:
assert ds[key] == value
rp = get_single_reference_product(ds)
if rp['name'] == 'corrugated board box':
expected = {
'corrugated board box': {
'amount': 1,
'type': 'reference product',
},
'residual softwood, wet': {
'amount': 0,
'type': 'dropped product',
},
'waste mineral oil': {
'amount': 0.9999518501927914 * 0.046 / 1000,
},
'natural gas, low pressure': {
'amount': 0.9999518501927914 * 19.9481865284974 / 1000,
},
'potato starch': {
'amount': 0.9999518501927914 * 5.95 / 1000,
},
'Propane': {
'amount': 0.9999518501927914 * 0.000163 / 1000,
},
'Nitrogen oxides': {
'amount': 0.9999518501927914 * 0.03 / 1000
}
}
else:
expected = {
'corrugated board box': {
'amount': 0,
'type': 'dropped product',
},
'residual softwood, wet': {
'amount': 1,
'type': 'reference product',
},
'waste mineral oil': {
'amount': 4.814980720846648e-05 * 0.046 / 0.00209150326797386,
},
'natural gas, low pressure': {
'amount': 4.814980720846648e-05 * 19.9481865284974 / 0.00209150326797386,
},
'potato starch': {
'amount': 4.814980720846648e-05 * 5.95 / 0.00209150326797386,
},
'Propane': {
'amount': 4.814980720846648e-05 * 0.000163 / 0.00209150326797386,
},
'Nitrogen oxides': {
'amount': 4.814980720846648e-05 * 0.03 / 0.00209150326797386
}
}
for exc in ds['exchanges']:
print(exc['name'])
for key, value in expected[exc['name']].items():
if isinstance(value, Number):
assert allclose(exc[key], value)
else:
assert exc[key] == value