def lca(self, method=None, amount=1.):
"""Shortcut to construct an LCA object for this activity."""
from bw2calc import LCA
lca = LCA({self: amount}, method=method)
lca.lci()
if method is not None:
lca.lcia()
lca.fix_dictionaries()
return lca
def lca(self, method=None, amount=None):
"""Shortcut to construct an LCA object for this exchange **input**.
Uses the exchange amount if no other amount is provided."""
from bw2calc import LCA
if amount is None:
amount = self['amount']
lca = LCA({self.input: amount}, method=method)
lca.lci()
if method is not None:
lca.lcia()
lca.fix_dictionaries()
return lca
def lci_matrices_to_excel(database_name, include_descendants=True):
"""Fake docstring"""
from bw2calc import LCA
print("Starting Excel export. This can be slow for large matrices!")
safe_name = safe_filename(database_name, False)
filepath = os.path.join(projects.output_dir, safe_name + ".xlsx")
lca = LCA({Database(database_name).random(): 1})
lca.load_lci_data()
lca.fix_dictionaries()
if not include_descendants:
lca.activity_dict = {
key: value
for key, value in lca.activity_dict.items()
if key[0] == database_name
}
# Drop biosphere flows with zero references
# TODO: This will ignore (-1 + 1 = 0) references
lca.biosphere_dict = {
key: value
for key, value in lca.biosphere_dict.items()
if lca.biosphere_matrix[lca.biosphere_dict[key], :].sum() != 0
}
workbook = xlsxwriter.Workbook(filepath)
bold = workbook.add_format({'bold': True})
print("Sorting objects")
sorted_activity_keys = sorted([(Database.get(key).get("name")
or u"Unknown", key)
for key in lca.activity_dict])
sorted_product_keys = sorted([(Database.get(key).get("name")
or u"Unknown", key)
for key in lca.product_dict])
sorted_bio_keys = sorted([(Database.get(key).get("name")
or u"Unknown", key)
for key in lca.biosphere_dict])
tm_sheet = workbook.add_worksheet('technosphere')
tm_sheet.set_column('A:A', 50)
data = Database(database_name).load()
# Labels
for index, data in enumerate(sorted_activity_keys):
tm_sheet.write_string(0, index + 1, data[0])
for index, data in enumerate(sorted_product_keys):
tm_sheet.write_string(index + 1, 0, data[0])
print("Entering technosphere matrix data")
coo = lca.technosphere_matrix.tocoo()
# Translate row index to sorted product index
act_dict = {obj[1]: idx for idx, obj in enumerate(sorted_activity_keys)}
pro_dict = {obj[1]: idx for idx, obj in enumerate(sorted_product_keys)}
bio_dict = {obj[1]: idx for idx, obj in enumerate(sorted_bio_keys)}
pro_lookup = {v: pro_dict[k] for k, v in lca.product_dict.items()}
bio_lookup = {v: bio_dict[k] for k, v in lca.biosphere_dict.items()}
act_lookup = {v: act_dict[k] for k, v in lca.activity_dict.items()}
# Matrix values
for row, col, value in zip(coo.row, coo.col, coo.data):
tm_sheet.write_number(pro_lookup[row] + 1, act_lookup[col] + 1, value)
bm_sheet = workbook.add_worksheet('biosphere')
bm_sheet.set_column('A:A', 50)
data = Database(database_name).load()
# Labels
for index, data in enumerate(sorted_activity_keys):
bm_sheet.write_string(0, index + 1, data[0])
for index, data in enumerate(sorted_bio_keys):
bm_sheet.write_string(index + 1, 0, data[0])
print("Entering biosphere matrix data")
coo = lca.biosphere_matrix.tocoo()
# Matrix values
for row, col, value in zip(coo.row, coo.col, coo.data):
bm_sheet.write_number(bio_lookup[row] + 1, act_lookup[col] + 1, value)
COLUMNS = (u"Index", u"Name", u"Reference product", u"Unit", u"Categories",
u"Location")
tech_sheet = workbook.add_worksheet('technosphere-labels')
tech_sheet.set_column('B:B', 60)
tech_sheet.set_column('C:C', 30)
tech_sheet.set_column('D:D', 15)
tech_sheet.set_column('E:E', 30)
print("Writing metadata")
# Header
for index, col in enumerate(COLUMNS):
tech_sheet.write_string(0, index, col, bold)
tech_sheet.write_comment(
'C1',
"Only for ecoinvent 3, where names =/= products.",
)
for index, data in enumerate(sorted_activity_keys):
obj = Database.get(data[1])
tech_sheet.write_number(index + 1, 0, index + 1)
tech_sheet.write_string(index + 1, 1, obj.get(u'name') or u'Unknown')
tech_sheet.write_string(index + 1, 2,
obj.get(u'reference product') or u'')
tech_sheet.write_string(index + 1, 3, obj.get(u'unit') or u'Unknown')
tech_sheet.write_string(index + 1, 4,
u" - ".join(obj.get(u'categories') or []))
tech_sheet.write_string(index + 1, 5,
obj.get(u'location') or u'Unknown')
COLUMNS = (
u"Index",
u"Name",
u"Unit",
u"Categories",
)
bio_sheet = workbook.add_worksheet('biosphere-labels')
bio_sheet.set_column('B:B', 60)
bio_sheet.set_column('C:C', 15)
bio_sheet.set_column('D:D', 30)
# Header
for index, col in enumerate(COLUMNS):
bio_sheet.write_string(0, index, col, bold)
for index, data in enumerate(sorted_bio_keys):
obj = Database.get(data[1])
bio_sheet.write_number(index + 1, 0, index + 1)
bio_sheet.write_string(index + 1, 1, obj.get(u'name') or u'Unknown')
bio_sheet.write_string(index + 1, 2, obj.get(u'unit') or u'Unknown')
bio_sheet.write_string(index + 1, 3,
u" - ".join(obj.get(u'categories') or []))
workbook.close()
return filepath
def lci_matrices_to_matlab(database_name):
from bw2calc import LCA
lca = LCA({Database(database_name).random(): 1})
lca.lci()
lca.fix_dictionaries()
ra, rp, rb = lca.reverse_dict()
safe_name = safe_filename(database_name, False)
scipy.io.savemat(
os.path.join(projects.output_dir, safe_name + ".mat"),
{
"technosphere": lca.technosphere_matrix,
"biosphere": lca.biosphere_matrix
},
)
workbook = xlsxwriter.Workbook(os.path.join(dirpath, safe_name + ".xlsx"))
bold = workbook.add_format({"bold": True})
COLUMNS = ("Index", "Name", "Reference product", "Unit", "Categories",
"Location")
tech_sheet = workbook.add_worksheet("technosphere")
tech_sheet.set_column("B:B", 60)
tech_sheet.set_column("C:C", 30)
tech_sheet.set_column("D:D", 15)
tech_sheet.set_column("E:E", 30)
# Header
for index, col in enumerate(COLUMNS):
tech_sheet.write_string(0, index, col, bold)
tech_sheet.write_comment(
"C1",
"Only for ecoinvent 3, where names =/= products.",
)
data = Database(database_name).load()
for index, key in sorted(ra.items()):
tech_sheet.write_number(index + 1, 0, index + 1)
tech_sheet.write_string(index + 1, 1, data[key].get("name")
or "Unknown")
tech_sheet.write_string(index + 1, 2,
data[key].get("reference product") or "")
tech_sheet.write_string(index + 1, 3, data[key].get("unit")
or "Unknown")
tech_sheet.write_string(index + 1, 4,
" - ".join(data[key].get("categories") or []))
tech_sheet.write_string(index + 1, 5, data[key].get("location")
or "Unknown")
COLUMNS = (
"Index",
"Name",
"Unit",
"Categories",
)
biosphere_dicts = {}
bio_sheet = workbook.add_worksheet("biosphere")
bio_sheet.set_column("B:B", 60)
bio_sheet.set_column("C:C", 15)
bio_sheet.set_column("D:D", 30)
# Header
for index, col in enumerate(COLUMNS):
bio_sheet.write_string(0, index, col, bold)
for index, key in sorted(rb.items()):
if key[0] not in biosphere_dicts:
biosphere_dicts[key[0]] = Database(key[0]).load()
obj = biosphere_dicts[key[0]][key]
bio_sheet.write_number(index + 1, 0, index + 1)
bio_sheet.write_string(index + 1, 1, obj.get("name", "Unknown"))
bio_sheet.write_string(index + 1, 2, obj.get("unit", "Unknown"))
bio_sheet.write_string(index + 1, 3,
" - ".join(obj.get("categories", [])))
workbook.close()
return dirpath
