def test_excel_products_lca():
ei = ExcelImporter(os.path.join(EXCEL_FIXTURES_DIR, "with_products.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
ei.apply_strategies()
ei.match_database()
ei.write_database()
lca = LCA({('Product example', 'B'): 1})
lca.lci()
keys = {('Product example', 'B'), ('Product example', 'C'),
('Product example', 'E')}
for key in lca.product_dict:
assert key in keys
keys = {('Product example', 'A'), ('Product example', 'C'),
('Product example', 'D')}
for key in lca.activity_dict:
assert key in keys
for value in lca.supply_array:
assert np.allclose(value, 1) or np.allclose(
value, 0.539) or np.allclose(value, 0.539 * 0.00805)
def test_parameterized_import():
ei = ExcelImporter(os.path.join(EXCEL_FIXTURES_DIR, "basic_example.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
ei.apply_strategies()
ei.match_database()
assert ei.data == expected
def test_example_notebook():
ei = ExcelImporter(os.path.join(EXCEL_FIXTURES_DIR, "sample_activities_with_variables.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
convert_activity_parameters_to_list,
]
ei.apply_strategies()
ei.match_database(fields=['name'])
ei.write_database()
def test_excel_products_lca():
ei = ExcelImporter(os.path.join(EXCEL_FIXTURES_DIR, "with_products.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
print(ei.data)
ei.apply_strategies()
ei.match_database()
ei.write_database()
fu, data_objs, _ = prepare_lca_inputs({("Product example", "B"): 1})
lca = LCA(fu, data_objs=data_objs)
lca.lci()
keys = {
get_id(("Product example", "B")),
get_id(("Product example", "C")),
get_id(("Product example", "E")),
}
for key in lca.dicts.product:
assert key in keys
keys = {
get_id(("Product example", "A")),
get_id(("Product example", "C")),
get_id(("Product example", "D")),
}
for key in lca.dicts.activity:
assert key in keys
for value in lca.supply_array:
assert (np.allclose(value, 1) or np.allclose(value, 0.539)
or np.allclose(value, 0.539 * 0.00805))
def test_parameterized_import():
ei = ExcelImporter(
os.path.join(EXCEL_FIXTURES_DIR,
"sample_activities_with_variables.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
ei.apply_strategies()
ei.match_database()
assert ei.project_parameters == [{"amount": 0.25, "name": "PCB_area"}]
expected = [
{
"amount": 0.2,
"maximum": 1.0,
"minimum": 0.0,
"name": "PCB_cap_mass_film",
"uncertainty type": 4.0,
"unit": "kilogram",
},
{
"amount": 0.2,
"maximum": 1.0,
"minimum": 0.0,
"name": "PCB_cap_mass_SMD",
"uncertainty type": 4.0,
"unit": "kilogram",
},
{
"amount": 0.2,
"maximum": 1.0,
"minimum": 0.0,
"name": "PCB_cap_mass_Tantalum",
"uncertainty type": 4.0,
"unit": "kilogram",
},
]
assert ei.database_parameters == expected
expected = [
{
"arbitrary":
"metadata",
"code":
"mpcb",
"comment":
"something important here maybe?",
"database":
"PCB",
"exchanges": [
{
"amount": 0.0,
"database": "PCB",
"formula": "PCB_area * 2",
"location": "GLO",
"name": "unmounted printed circuit board",
"type": "technosphere",
"unit": "square meter",
},
{
"amount": 0.0,
"database": "PCB",
"formula": "PCB_mass_total",
"location": "GLO",
"name": "mounted printed circuit board",
"type": "production",
"unit": "kilogram",
},
],
"location":
"GLO",
"name":
"mounted printed circuit board",
"parameters": {
"PCB_mass_total": {
"amount":
0.6,
"group":
"alpha group!",
"formula":
"PCB_cap_mass_film + "
"PCB_cap_mass_SMD + "
"PCB_cap_mass_Tantalum",
}
},
"production amount":
0.0,
"reference product":
"mounted printed circuit board",
"type":
"process",
"unit":
"kilogram",
"worksheet name":
"PCB inventory",
},
{
"categories": ("electronics", "board"),
"code":
"45cb34db4147e510a2561cceec541f6b",
"comment":
"one input",
"database":
"PCB",
"exchanges": [{
"amount": 1.0,
"database": "PCB",
"location": "GLO",
"name": "unmounted printed circuit board",
"type": "production",
"uncertainty type": 0,
"unit": "square meter",
}],
"location":
"GLO",
"name":
"unmounted printed circuit board",
"production amount":
1.0,
"reference product":
"unmounted printed circuit board",
"type":
"process",
"unit":
"square meter",
"worksheet name":
"PCB inventory",
},
]
assert ei.data == expected
def test_parameterized_import():
ei = ExcelImporter(
os.path.join(EXCEL_FIXTURES_DIR,
"sample_activities_with_variables.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
ei.apply_strategies()
ei.match_database()
assert ei.project_parameters == [{'amount': 0.25, 'name': 'PCB_area'}]
expected = [{
'amount': 0.2,
'maximum': 1.0,
'minimum': 0.0,
'name': 'PCB_cap_mass_film',
'uncertainty type': 4.0,
'unit': 'kilogram'
}, {
'amount': 0.2,
'maximum': 1.0,
'minimum': 0.0,
'name': 'PCB_cap_mass_SMD',
'uncertainty type': 4.0,
'unit': 'kilogram'
}, {
'amount': 0.2,
'maximum': 1.0,
'minimum': 0.0,
'name': 'PCB_cap_mass_Tantalum',
'uncertainty type': 4.0,
'unit': 'kilogram'
}]
assert ei.database_parameters == expected
expected = \
[{'arbitrary': 'metadata',
'code': 'mpcb',
'comment': 'something important here maybe?',
'database': 'PCB',
'exchanges': [{'amount': 0.0,
'database': 'PCB',
'formula': 'PCB_area * 2',
'location': 'GLO',
'name': 'unmounted printed circuit board',
'type': 'technosphere',
'unit': 'square meter'},
{'amount': 0.0,
'database': 'PCB',
'formula': 'PCB_mass_total',
'location': 'GLO',
'name': 'mounted printed circuit board',
'type': 'production',
'unit': 'kilogram'}],
'location': 'GLO',
'name': 'mounted printed circuit board',
'parameters': {'PCB_mass_total': {'amount': 0.6,
'group': 'alpha group!',
'formula': 'PCB_cap_mass_film + '
'PCB_cap_mass_SMD + '
'PCB_cap_mass_Tantalum'}},
'production amount': 0.0,
'reference product': 'mounted printed circuit board',
'type': 'process',
'unit': 'kilogram',
'worksheet name': 'PCB inventory'},
{'categories': ('electronics', 'board'),
'code': '45cb34db4147e510a2561cceec541f6b',
'comment': 'one input',
'database': 'PCB',
'exchanges': [{'amount': 1.0,
'database': 'PCB',
'location': 'GLO',
'name': 'unmounted printed circuit board',
'type': 'production',
'uncertainty type': 0,
'unit': 'square meter'}],
'location': 'GLO',
'name': 'unmounted printed circuit board',
'production amount': 1.0,
'reference product': 'unmounted printed circuit board',
'type': 'process',
'unit': 'square meter',
'worksheet name': 'PCB inventory'}]
assert ei.data == expected
def test_excel_products_import():
ei = ExcelImporter(os.path.join(EXCEL_FIXTURES_DIR, "with_products.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
ei.apply_strategies()
ei.match_database()
expected = [
{
"code":
"A",
"database":
"Product example",
"exchanges": [
{
"amount": 1.0,
"database": "Product example",
"input": ("Product example", "B"),
"loc": 1.0,
"location": "RNA",
"name": "Acetic acid",
"type": "production",
"uncertainty type": 0,
"unit": "kilogram",
},
{
"amount": 0.539,
"database": "Product example",
"input": ("Product example", "E"),
"loc": 0.539,
"location": "RNA",
"name": "Methanol",
"type": "technosphere",
"uncertainty type": 0,
"unit": "kilogram",
},
],
"location":
"RNA",
"name":
"Acetic acid, at plant",
"production amount":
1.0,
"reference product":
"Acetic acid",
"type":
"process",
"unit":
"kilogram",
"worksheet name":
"first process",
},
{
"code": "B",
"database": "Product example",
"exchanges": [],
"location": "RNA",
"name": "Acetic acid",
"type": "product",
"unit": "kilogram",
"worksheet name": "first process",
},
{
"categories": ("Utilities", "Utilities"),
"code": "C",
"database": "Product example",
"exchanges": [],
"location": "RNA",
"name": "Electricity, at Grid, US, 2008",
"unit": "kilowatt hour",
"worksheet name": "other processes",
},
{
"code":
"D",
"database":
"Product example",
"exchanges": [
{
"amount": 1.0,
"database": "Product example",
"input": ("Product example", "E"),
"location": "RNA",
"name": "Methanol",
"type": "production",
"uncertainty type": 0,
"unit": "kilogram",
},
{
"amount": 0.00805,
"categories": ("Utilities", "Utilities"),
"database": "Product example",
"input": ("Product example", "C"),
"location": "RNA",
"name": "Electricity, at Grid, US, 2008",
"type": "technosphere",
"uncertainty type": 0,
"unit": "kilowatt hour",
},
],
"location":
"RNA",
"name":
"Methanol, at plant",
"production amount":
1.0,
"reference product":
"Methanol",
"unit":
"kilogram",
"worksheet name":
"other processes",
},
{
"code": "E",
"database": "Product example",
"exchanges": [],
"location": "RNA",
"name": "Methanol",
"type": "product",
"unit": "kilogram",
"worksheet name": "other processes",
},
]
assert ei.data == expected
def extract_excel_data(self, excelfilepath):
print(excelfilepath)
sp = ExcelImporter(excelfilepath)
if not migrations:
create_core_migrations()
if 'biosphere3' not in databases:
create_default_biosphere3()
# link the biosphere exchanges
sp.apply_strategies(verbose=False)
# make the internal links
sp.match_database(fields=["name", "unit", "location"])
# make the links to existing data
still_unlinked = link_iterable_by_fields(
sp.data,
self.db,
fields=["reference product", "name", "unit", "location"])
still_unlinked = link_iterable_by_fields(
still_unlinked, self.db, fields=["name", "unit", "location"])
# Change GLO to RoW
if still_unlinked:
for x in still_unlinked:
for exc in x.get('exchanges', []):
if not exc.get("input"):
if exc.get('location') == 'GLO':
exc['location'] = 'RoW'
still_unlinked = link_iterable_by_fields(
still_unlinked, self.db, fields=["name", "unit", "location"])
# Make processes less specific, by snipping off trailing descriptions (e.g. ', at user', ', metallurgical' etc.)
if still_unlinked:
attempts = 2
for i in range(attempts):
for x in still_unlinked:
for exc in x.get('exchanges', []):
if not exc.get("input"):
if exc['name'].rfind(',') != -1:
exc['name'] = exc['name'][:exc['name'].
rfind(',')]
still_unlinked = link_iterable_by_fields(
still_unlinked,
self.db,
fields=["name", "unit", "location"])
# Try switching despecified items back to GLO
if still_unlinked:
for x in still_unlinked:
for exc in x.get('exchanges', []):
if not exc.get("input"):
if exc.get('location') == 'RoW':
exc['location'] = 'GLO'
still_unlinked = link_iterable_by_fields(
still_unlinked, self.db, fields=["name", "unit", "location"])
if sp.statistics()[2] != 0:
fp = sp.write_excel()
print(fp)
assert sp.statistics()[2] == 0, "Unlinked exchanges"
fix_products_and_locations_external(sp.data, self.db)
self.db.extend(sp.data)
self.database_names.append(sp.db_name)
def test_excel_products_import():
ei = ExcelImporter(os.path.join(EXCEL_FIXTURES_DIR, "with_products.xlsx"))
ei.strategies = [
csv_restore_tuples,
csv_restore_booleans,
csv_numerize,
csv_drop_unknown,
csv_add_missing_exchanges_section,
normalize_units,
set_code_by_activity_hash,
assign_only_product_as_production,
link_technosphere_by_activity_hash,
drop_falsey_uncertainty_fields_but_keep_zeros,
convert_uncertainty_types_to_integers,
]
ei.apply_strategies()
ei.match_database()
expected = [{
'code':
'A',
'database':
'Product example',
'exchanges': [{
'amount': 1.0,
'database': 'Product example',
'input': ('Product example', 'B'),
'loc': 1.0,
'location': 'RNA',
'name': 'Acetic acid',
'type': 'production',
'uncertainty type': 0,
'unit': 'kilogram'
}, {
'amount': 0.539,
'database': 'Product example',
'input': ('Product example', 'E'),
'loc': 0.539,
'location': 'RNA',
'name': 'Methanol',
'type': 'technosphere',
'uncertainty type': 0,
'unit': 'kilogram'
}],
'location':
'RNA',
'name':
'Acetic acid, at plant',
'production amount':
1.0,
'reference product':
'Acetic acid',
'type':
'process',
'unit':
'kilogram',
'worksheet name':
'first process'
}, {
'code': 'B',
'database': 'Product example',
'exchanges': [],
'location': 'RNA',
'name': 'Acetic acid',
'type': 'product',
'unit': 'kilogram',
'worksheet name': 'first process'
}, {
'categories': ('Utilities', 'Utilities'),
'code': 'C',
'database': 'Product example',
'exchanges': [],
'location': 'RNA',
'name': 'Electricity, at Grid, US, 2008',
'unit': 'kilowatt hour',
'worksheet name': 'other processes'
}, {
'code':
'D',
'database':
'Product example',
'exchanges': [{
'amount': 1.0,
'database': 'Product example',
'input': ('Product example', 'E'),
'location': 'RNA',
'name': 'Methanol',
'type': 'production',
'uncertainty type': 0,
'unit': 'kilogram'
}, {
'amount': 0.00805,
'categories': ('Utilities', 'Utilities'),
'database': 'Product example',
'input': ('Product example', 'C'),
'location': 'RNA',
'name': 'Electricity, at Grid, US, 2008',
'type': 'technosphere',
'uncertainty type': 0,
'unit': 'kilowatt hour'
}],
'location':
'RNA',
'name':
'Methanol, at plant',
'production amount':
1.0,
'reference product':
'Methanol',
'unit':
'kilogram',
'worksheet name':
'other processes'
}, {
'code': 'E',
'database': 'Product example',
'exchanges': [],
'location': 'RNA',
'name': 'Methanol',
'type': 'product',
'unit': 'kilogram',
'worksheet name': 'other processes'
}]
assert ei.data == expected
