def _sum_variables_and_add_scores(self, market, variables):
"""
Sum the variables that belong to the market
and calculate the LCA scores for all years,
regions and methods.
"""
df = self.data[self.data.Variable.isin(variables)]\
.groupby(["Region", "Year"])\
.sum()
df.reset_index(inplace=True)
df["Market"] = market
# add methods dimension & score column
methods_df = pd.DataFrame({"Method": self.methods, "Market": market})
df = df.merge(methods_df)
df.loc[:, "score"] = 0.
df.loc[:, "score_direct"] = 0.
df.set_index(["Year", "Region", "Method"], inplace=True)
# calc score
for year in self.years:
db = bw.Database(eidb_label(self.model, self.scenario, year))
# database indexes of powerplants
pps = [
pp for pp in db
if pp["unit"] == "kilowatt hour" and "market" not in pp["name"]
]
lca = bw.LCA({pps[0]: 1})
lca.lci()
pp_idxs = [lca.activity_dict[pp.key] for pp in pps]
for region in self.regions:
# find activity
act = [
a for a in db
if a["name"] == market and a["location"] == region
][0]
# create first lca object
lca = bw.LCA({act: 1}, method=self.methods[0])
# build inventories
lca.lci()
for method in self.methods:
lca.switch_method(method)
lca.lcia()
df.at[(year, region, method), "score"] = lca.score
res_vec = np.squeeze(
np.asarray(lca.characterized_inventory.sum(axis=0)))
df.at[(year, region, method),
"score_direct"] = np.sum(res_vec[pp_idxs])
df["total_score"] = df["score"] * df["value"] * 2.8e11 # EJ -> kWh
df["total_score_direct"] = df["score_direct"] * df[
"value"] * 2.8e11 # EJ -> kWh
return df
def _get_material_bioflows_for_bev(self):
"""
Obtain bioflow ids for *interesting* materials.
These are the top bioflows in the ILCD materials
characterization method for an BEV activity.
"""
method = ('ILCD 2.0 2018 midpoint',
'resources', 'minerals and metals')
year = self.years[0]
act_str = "transport, passenger car, fleet average, battery electric, {}".format(year)
# upstream material demands are the same for all regions
# so we can use GLO here
act = Activity(
Act.get((Act.name == act_str)
& (Act.database == eidb_label(
self.model, self.scenario, year))
& (Act.location == "EUR")))
lca = bw.LCA({act: 1}, method=method)
lca.lci()
lca.lcia()
inv_bio = {value: key for key, value in lca.biosphere_dict.items()}
ca = ContributionAnalysis()
ef_contrib = ca.top_emissions(lca.characterized_inventory)
return [inv_bio[int(el[1])] for el in ef_contrib]
def report_tech_LCA(self, year):
"""
For each REMIND technology, find a set of activities in the region.
Use ecoinvent tech share file to determine the shares of technologies
within the REMIND proxies.
"""
tecf = pd.read_csv(DATA_DIR/"powertechs.csv", index_col="tech")
tecdict = tecf.to_dict()["mif_entry"]
db = bw.Database("_".join(["ecoinvent", self.scenario, str(year)]))
result = self._cartesian_product({
"region": self.regions,
"tech": list(tecdict.keys()),
"method": self.methods
}).sort_index()
for region in self.regions:
# read the ecoinvent techs for the entries
shares = self.supplier_shares(db, region)
for tech, acts in shares.items():
# calc LCA
lca = bw.LCA(acts, self.methods[0])
lca.lci()
for method in self.methods:
lca.switch_method(method)
lca.lcia()
result.at[(region, tech, method), "score"] = lca.score
return result
def setup_lca_model_oases(path_base,
num_params=None,
write_dir_name=None,
flag_generate_scores_dict=False):
# LCA model
bw.projects.set_current("GSA for oases")
co = bw.Database("CH consumption 1.0")
demand_act = [
act for act in co if "ch hh average consumption" in act["name"]
]
assert len(demand_act) == 1
demand_act = demand_act[0]
demand = {demand_act: 1}
method = ("IPCC 2013", "climate change", "GWP 100a")
# num_params
if num_params is None:
lca = bw.LCA(demand, method)
lca.lci()
lca.lcia()
print("LCA score is {}".format(lca.score))
all_uncertain_params = lca.tech_params[
lca.tech_params["uncertainty_type"] > 1]
num_params = len(all_uncertain_params)
print("Total number of uncertain exchanges is {}".format(num_params))
# Define some variables
if write_dir_name is None:
write_dir_name = "oases_gsa_gwp_{}".format(num_params)
write_dir = path_base / write_dir_name
if flag_generate_scores_dict:
model = LCAModel(demand, method, write_dir) # generate scores_dict
del model
model = LCAModel(demand, method, write_dir, num_params=num_params)
gsa_seed = 92374523
return model, write_dir, gsa_seed
def __init__(self, cs_name):
if cs_name not in bw.calculation_setups:
raise ValueError(
"{} is not a known `calculation_setup`.".format(cs_name))
self.cs_name = cs_name
cs = bw.calculation_setups[cs_name]
self.seed = None
self.cf_rngs = {}
self.CF_rng_vectors = {}
self.include_technosphere = True
self.include_biosphere = True
self.include_cfs = True
self.include_parameters = True
self.param_rng = None
self.param_cols = ["input", "output", "type"]
self.tech_rng: Optional[Union[MCRandomNumberGenerator,
np.ndarray]] = None
self.bio_rng: Optional[Union[MCRandomNumberGenerator,
np.ndarray]] = None
self.cf_rng: Optional[Union[MCRandomNumberGenerator,
np.ndarray]] = None
# functional units
self.func_units = cs['inv']
self.rev_fu_index = {i: fu for i, fu in enumerate(self.func_units)}
# activities
self.activity_keys = [list(fu.keys())[0] for fu in self.func_units]
self.activity_index = {
key: index
for index, key in enumerate(self.activity_keys)
}
self.rev_activity_index = {v: k for k, v in self.activity_keys}
# self.fu_index = {k: i for i, k in enumerate(self.activity_keys)}
# methods
self.methods = cs['ia']
self.method_index = {m: i for i, m in enumerate(self.methods)}
self.rev_method_index = {v: k for k, v in self.method_index.items()}
# todo: get rid of the below
self.func_unit_translation_dict = {
str(bw.get_activity(list(func_unit.keys())[0])): func_unit
for func_unit in self.func_units
}
self.func_key_dict = {
m: i
for i, m in enumerate(self.func_unit_translation_dict.keys())
}
self.func_key_list = list(self.func_key_dict.keys())
self.results = []
self.lca = bw.LCA(demand=self.func_units_dict, method=self.methods[0])
def get_lca(fu, method):
"""Calculates a non-stochastic LCA and returns a the LCA object."""
lca = bw.LCA(fu, method=method)
lca.lci()
lca.lcia()
print('Non-stochastic LCA score:', lca.score)
# add reverse dictionaries
lca.activity_dict_rev, lca.product_dict_rev, lca.biosphere_dict_rev = lca.reverse_dict()
return lca
def static_calc(flow, amount, methods, factorize=False):
"""Establish static calculation basis. By default no factorization."""
# if not isinstance(flow, Mapping):
# raise ValueError("Flow must be a dictionary")
for key in flow:
if not key:
raise ValueError("Invalid dictionary")
lca = bw2.LCA({flow: amount}, method=methods)
lca.lci()
if factorize:
lca.decompose_technosphere()
lca.lcia()
return lca.score
def doLCA(oActivity, aoMethods, HHV=None):
if HHV:
functional_unit = {oActivity: 1 / HHV}
else:
functional_unit = {oActivity: 1}
oLCA = bw.LCA(functional_unit, aoMethods[0])
oLCA.lci()
oLCA.lcia()
dScores = {aoMethods[0]: oLCA.score}
for oMethod in aoMethods[1:]:
oLCA.switch_method(oMethod)
oLCA.lcia()
dScores[oMethod] = oLCA.score
return dScores, oLCA
def report_endpoint(self):
"""
*DEPRECATED*
Report the surplus extraction costs for the scenario.
:return: A `pandas.Series` containing extraction costs
with index `year` and `region`.
"""
indicatorgroup = 'ReCiPe Endpoint (H,A) (obsolete)'
endpoint_methods = [
m for m in bw.methods if m[0] == indicatorgroup and m[2] == "total"
and not m[1] == "total"
]
df = self.data[self.data.Variable.isin(self.variables)]
df.set_index(["Year", "Region", "Variable"], inplace=True)
start = time.time()
result = {}
# calc score
for year in self.years:
db = bw.Database(eidb_label(self.model, self.scenario, year))
for region in self.regions:
# create large lca demand object
demand = [
self._act_from_variable(var,
db,
year,
region,
scale=df.loc[(year, region, var),
"value"])
for var in (df.loc[(
year, region)].index.get_level_values(0).unique())
]
# flatten dictionaries
demand = {k: v for item in demand for k, v in item.items()}
lca = bw.LCA(demand, method=endpoint_methods[0])
# build inventories
lca.lci()
for method in endpoint_methods:
lca.switch_method(method)
lca.lcia()
# 6% discount for monetary endpoint
factor = 1e9 * 1.06 ** (year - 2013) \
if "resources" == method[1] else 1e9
result[(year, region, method)] = lca.score * factor
df_result = pd.Series(result)
print("Calculation took {} seconds.".format(time.time() - start))
return df_result # billion pkm
def report_LDV_LCA(self):
"""
Report per-drivetrain impacts along the given dimension.
Both per-pkm as well as total numbers are given.
:return: a dataframe with impacts for the REMIND EDGE-T
transport sector model. Levelized impacts (per pkm) are
found in the column `score_pkm`, total impacts in `total_score`.
:rtype: pandas.DataFrame
"""
df = self.data[self.data.Variable.isin(self.variables)]
df.loc[:, "score_pkm"] = 0.
# add methods dimension & score column
methods_df = pd.DataFrame({"Method": self.methods, "score_pkm": 0.})
df = df.merge(methods_df, "outer") # on "score_pkm"
df.set_index(["Year", "Region", "Variable", "Method"], inplace=True)
start = time.time()
# calc score
for year in self.years:
# find activities which at the moment do not depend
# on regions
db = bw.Database(eidb_label(self.model, self.scenario, year))
for region in self.regions:
for var in (df.loc[(year, region)]
.index.get_level_values(0)
.unique()):
demand = self._act_from_variable(var, db, year, region)
lca = bw.LCA(demand,
method=self.methods[0])
# build inventories
lca.lci()
if "_LowD" in self.scenario:
fct = max(1 - (year - 2020)/15 * 0.15, 0.85)
else:
fct = 1.
for method in self.methods:
lca.switch_method(method)
lca.lcia()
df.loc[(year, region, var, method),
"score_pkm"] = lca.score * fct
print("Calculation took {} seconds.".format(time.time() - start))
df["total_score"] = df["value"] * df["score_pkm"] * 1e9
return df[["total_score", "score_pkm"]]
def report_materials(self):
"""
Report the material demand of the LDV fleet for all regions and years.
:return: A `pandas.Series` with index `year`, `region` and `material`.
"""
# materials
bioflows = self._get_material_bioflows_for_bev()
df = self.data[self.data.Variable.isin(self.variables)]
df.set_index(["Year", "Region", "Variable"], inplace=True)
start = time.time()
result = {}
# calc score
for year in self.years:
db = bw.Database(eidb_label(self.model, self.scenario, year))
for region in self.regions:
# create large lca demand object
demand = [
self._act_from_variable(
var, db, year, region,
scale=df.loc[(year, region, var), "value"])
for var in (df.loc[(year, region)]
.index.get_level_values(0)
.unique())]
# flatten dictionaries
demand_flat = {}
for item in demand:
for act, val in item.items():
demand_flat[act] = val + demand_flat.get(act, 0)
lca = bw.LCA(demand_flat)
# build inventories
lca.lci()
for code in bioflows:
result[(
year, region,
bw.get_activity(code)["name"].split(",")[0]
)] = (
lca.inventory.sum(axis=1)[
lca.biosphere_dict[code], 0]
)
df_result = pd.Series(result)
print("Calculation took {} seconds.".format(time.time() - start))
return df_result * 1e9 # kg
def report_midpoint_to_endpoint(self):
"""
*DEPRECATED*
Report midpoint impacts for the full fleet for each scenario.
:return: A `pandas.Series` containing impacts
with index `year`,`region` and `method`.
"""
methods = [
m for m in bw.methods
if m[0] == "ReCiPe Endpoint (H,A) (obsolete)" and m[2] != "total"
]
df = self.data[self.data.Variable.isin(self.variables)]
df.set_index(["Year", "Region", "Variable"], inplace=True)
start = time.time()
result = {}
# calc score
for year in self.years:
db = bw.Database(eidb_label(self.model, self.scenario, year))
for region in self.regions:
# create large lca demand object
demand = [
self._act_from_variable(var,
db,
year,
region,
scale=df.loc[(year, region, var),
"value"])
for var in (df.loc[(
year, region)].index.get_level_values(0).unique())
]
# flatten dictionaries
demand = {k: v for item in demand for k, v in item.items()}
lca = bw.LCA(demand, method=self.methods[0])
# build inventories
lca.lci()
for method in methods:
lca.switch_method(method)
lca.lcia()
factor = 1e9
result[(year, region, method)] = lca.score * factor
df_result = pd.Series(result)
print("Calculation took {} seconds.".format(time.time() - start))
return df_result # billion pkm
def LCA_to_df(datasets,
cats=['CC', 'R_Total'],
amount=1,
names=['name', 'location']):
"""Calcuate a LCA for a list of datasets for a list of methods
and return a pandas dataframe.
"""
results = {}
index_dict = {}
# create lca object
if datasets and cats:
lca = bw.LCA({datasets[0]: 1}, method=lcia_methods[cats[0]])
lca.lci()
lca.lcia()
else:
raise ValueError(
"No datasets or impact categories found." +
"Provide at least one dataset and one impact category.")
for ds in datasets:
index_dict[ds['code']] = tuple(ds[i] for i in names)
for cat in cats:
print(cat)
lca.switch_method(lcia_methods[cat])
results[cat] = {}
for dataset in pyprind.prog_bar(datasets):
lca.redo_lcia({dataset: amount})
results[cat][dataset['code']] = lca.score
# We group all energy into one category:
for cat in category_group:
if cat in results.keys():
for key in results[cat].keys():
if 'CED' not in results.keys():
results['CED'] = {}
try:
results['CED'][key] += results[cat][key]
except KeyError:
results['CED'][key] = results[cat][key]
del results[cat]
return pd.DataFrame(results).rename(index=index_dict)
def _sum_variables_and_add_scores(self, market, variables):
"""
Sum the variables that belong to the market
and calculate the LCA scores for all years,
regions and methods.
"""
df = self.data[self.data.Variable.isin(variables)]\
.groupby(["Region", "Year"])\
.sum()
df.reset_index(inplace=True)
df["market"] = market
# add methods dimension & score column
methods_df = pd.DataFrame({"method": self.methods, "market": market})
df = df.merge(methods_df)
df.loc[:, "score"] = 0.
# calc score
for year in self.years:
db = bw.Database(eidb_label(self.model, self.scenario, year))
for region in self.regions:
# import ipdb;ipdb.set_trace()
# find activity
act = [a for a in db if a["name"] == market and
a["location"] == region][0]
# create first lca object
lca = bw.LCA({act: 1}, method=df.method[0])
# build inventories
lca.lci()
df_slice = df[(df.Year == year) &
(df.Region == region)]
def get_score(method):
lca.switch_method(method)
lca.lcia()
return lca.score
df_slice.loc[:, "score"] = df_slice.apply(
lambda row: get_score(row["method"]), axis=1)
df.update(df_slice)
df["total_score"] = df["score"] * df["value"] * 2.8e11 # EJ -> kWh
return df
def __init__(self, func_unit, method, write_dir):
self.lca = bw.LCA(func_unit, method)
self.lca.lci()
self.lca.lcia()
self.write_dir = write_dir
self.make_dirs()
# self.uncertain_tech_params_where = np.where(self.lca.tech_params['uncertainty_type'] > 1)[0]
# self.uncertain_tech_params = self.lca.tech_params[self.uncertain_tech_params_where]
self.uncertain_tech_params_where = self.get_LSA_params(
var_threshold=0) # TODO change the threshold
self.uncertain_tech_params = self.lca.tech_params[
self.uncertain_tech_params_where]
self.num_params = self.__num_input_params__()
self.influential_params = []
self.choices = uncertainty_choices
self.mc = MCRandomNumberGenerator(self.uncertain_tech_params)
def setup_lca_model_paper(path_base,
num_params=None,
write_dir=None,
flag_generate_scores_dict=False):
# LCA model
bw.projects.set_current("GSA for paper")
co = bw.Database("CH consumption 1.0")
demand_act = [act for act in co if "Food" in act["name"]]
assert len(demand_act) == 1
demand_act = demand_act[0]
demand = {demand_act: 1}
method = ("IPCC 2013", "climate change", "GWP 100a")
# num_params
if num_params is None:
lca = bw.LCA(demand, method)
lca.lci()
lca.lcia()
print("LCA score is {}".format(lca.score))
all_uncertain_params = lca.tech_params[
lca.tech_params["uncertainty_type"] > 1]
num_params = len(all_uncertain_params)
print("Total number of uncertain exchanges is {}".format(num_params))
# Define some variables
if write_dir is None:
# path_base = Path('/data/user/kim_a/paper_gsa')
write_dir = path_base / "lca_model_food_{}".format(num_params)
if flag_generate_scores_dict:
model = LCAModel(demand, method, write_dir) # generate scores_dict
del model
model = LCAModel(
demand,
method,
write_dir,
num_params=num_params,
uncertain_exchanges_types=["tech"],
)
# gsa_seed = 92374523
gsa_seed = 6000814
return model, write_dir, gsa_seed
def __init__(self, cs_name):
try:
cs = bw.calculation_setups[cs_name]
except KeyError:
raise ValueError(
"{} is not a known `calculation_setup`.".format(cs_name))
self.func_units = cs['inv']
self.methods = cs['ia']
self.lca = bw.LCA(demand=self.all, method=self.methods[0])
self.lca.lci(factorize=True)
self.method_matrices = []
self.results = np.zeros((len(self.func_units), len(self.methods)))
# contribution matrices
self.process_contributions = np.zeros(
(len(self.func_units), len(self.methods),
self.lca.technosphere_matrix.shape[0]))
self.elementary_flow_contributions = np.zeros(
(len(self.func_units), len(self.methods),
self.lca.biosphere_matrix.shape[0]))
(self.rev_activity_dict, self.rev_product_dict,
self.rev_biosphere_dict) = self.lca.reverse_dict()
for method in self.methods:
self.lca.switch_method(method)
self.method_matrices.append(self.lca.characterization_matrix)
for row, func_unit in enumerate(self.func_units):
self.lca.redo_lci(func_unit)
for col, cf_matrix in enumerate(self.method_matrices):
self.lca.characterization_matrix = cf_matrix
self.lca.lcia_calculation()
self.results[row, col] = self.lca.score
self.process_contributions[
row, col] = self.lca.characterized_inventory.sum(axis=0)
self.elementary_flow_contributions[row, col] = np.array(
self.lca.characterized_inventory.sum(axis=1)).ravel()
def report_tech_LCA(self, year):
"""
For each REMIND technology, find a set of activities in the region.
Use ecoinvent tech share file to determine the shares of technologies
within the REMIND proxies.
"""
tecf = pd.read_csv(DATA_DIR / "powertechs.csv", index_col="tech")
tecdict = tecf.to_dict()["mif_entry"]
db = bw.Database(eidb_label(self.model, self.scenario, year))
result = self._cartesian_product({
"region": self.regions,
"tech": list(tecdict.keys()),
"method": self.methods
}).sort_index()
for region in self.regions:
# read the ecoinvent techs for the entries
shares = self.supplier_shares(db, region)
for tech, acts in shares.items():
# calc LCA
lca = bw.LCA(acts, self.methods[0])
lca.lci()
for method in self.methods:
lca.switch_method(method)
lca.lcia()
result.at[(region, tech, method), "score"] = lca.score
res_vec = np.squeeze(
np.asarray(lca.characterized_inventory.sum(axis=0)))
result.at[(region, tech, method),
"score_direct"] = np.sum(res_vec[pp_idxs])
return result
def __init__(self,
up_db_name,
agg_db_name,
database_type='LCIA',
method_list=[],
biosphere='biosphere3',
overwrite=False):
assert up_db_name in bw.databases, "Source database does not exist"
if agg_db_name in bw.databases and not overwrite:
warnings.warn(
"A database named {} already exists, set `overwrite` to True to overwrite"
.format(agg_db_name))
return
self.source = bw.Database(up_db_name)
self.new_name = agg_db_name
self.biosphere = biosphere
self.lca = bw.LCA({self.source.random(): 1})
self.lca.lci(factorize=True)
self.database_type = database_type
self.methods = method_list
if self.database_type not in ['LCI', 'LCIA']:
raise ValueError(
'{} is not a valid database type, should be "LCI" or "LCIA"'.
format(self.database_type))
if self.database_type == "LCIA":
if not self.methods:
raise ValueError(
"Need to pass a list of method identifiers to create an LCIA score database, none passed"
)
for m in self.methods:
if any([(self.biosphere, bw.Method(m).get_abbreviation())
not in [cf[0] for cf in bw.Method(m).load()],
(self.biosphere, bw.Method(m).get_abbreviation())
not in bw.Database(self.biosphere)]):
add_unit_score_exchange_and_cf(m, biosphere)
self.C_matrices = {}
def __init__(self, parent=None):
super().__init__(parent)
self.label = QtWidgets.QLabel('hello')
self.grid_lay = QtWidgets.QGridLayout()
self.grid_lay.addWidget(QtWidgets.QLabel('Activity: '), 0, 0)
self.grid_lay.addWidget(QtWidgets.QLabel('Method: '), 1, 0)
self.cs = self.window().left_panel.cs_tab.list_widget.name
self.func_units = bw.calculation_setups[self.cs]['inv']
self.func_units = [{bw.get_activity(k): v
for k, v in fu.items()} for fu in self.func_units]
self.methods = bw.calculation_setups[self.cs]['ia']
self.func_unit_cb = QtWidgets.QComboBox()
self.func_unit_cb.addItems(
[list(fu.keys())[0].__repr__() for fu in self.func_units])
self.method_cb = QtWidgets.QComboBox()
self.method_cb.addItems([m.__repr__() for m in self.methods])
self.grid_lay.addWidget(self.func_unit_cb, 0, 1)
self.grid_lay.addWidget(self.method_cb, 1, 1)
self.reload_pb = QtWidgets.QPushButton('Reload')
self.reload_pb.clicked.connect(self.new_sankey)
self.grid_lay.addWidget(self.reload_pb, 2, 0)
self.close_pb = QtWidgets.QPushButton('Close')
self.close_pb.clicked.connect(self.switch_to_main)
self.grid_lay.setColumnStretch(4, 1)
self.grid_lay.addWidget(self.close_pb, 0, 5)
self.color_attr_cb = QtWidgets.QComboBox()
self.color_attr_cb.addItems(['flow', 'location', 'name'])
self.grid_lay.addWidget(QtWidgets.QLabel('color by: '), 0, 2)
self.grid_lay.addWidget(self.color_attr_cb, 0, 3)
self.grid_lay.addWidget(QtWidgets.QLabel('cutoff: '), 1, 2)
self.cutoff_sb = QtWidgets.QDoubleSpinBox()
self.cutoff_sb.setRange(0.0, 1.0)
self.cutoff_sb.setSingleStep(0.001)
self.cutoff_sb.setDecimals(4)
self.cutoff_sb.setValue(0.005)
self.cutoff_sb.setKeyboardTracking(False)
self.grid_lay.addWidget(self.cutoff_sb, 1, 3)
self.hlay = QtWidgets.QHBoxLayout()
self.hlay.addLayout(self.grid_lay)
# qt js interaction
self.bridge = Bridge()
self.bridge.viewer_waiting.connect(self.send_json)
self.bridge.link_clicked.connect(self.expand_sankey)
self.channel = QtWebChannel.QWebChannel()
self.channel.registerObject('bridge', self.bridge)
self.view = QtWebEngineWidgets.QWebEngineView()
self.view.page().setWebChannel(self.channel)
html = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'activity-browser-sankey.html')
self.url = QtCore.QUrl.fromLocalFile(html)
wait_html = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'spinner.html')
self.wait_url = QtCore.QUrl.fromLocalFile(wait_html)
self.view.load(self.wait_url)
self.vlay = QtWidgets.QVBoxLayout()
self.vlay.addLayout(self.hlay)
self.vlay.addWidget(self.view)
self.setLayout(self.vlay)
# sankey
demand_all = dict(collections.ChainMap(*self.func_units))
self.lca = bw.LCA(demand_all, bw.methods.random())
self.lca.lci()
self.lca.lcia()
self.new_sankey()
self.func_unit_cb.currentIndexChanged.connect(self.new_sankey)
self.method_cb.currentIndexChanged.connect(self.new_sankey)
self.color_attr_cb.currentIndexChanged.connect(self.update_colors)
self.cutoff_sb.valueChanged.connect(self.new_sankey)
# connections
sankeysignals.calculating_gt.connect(self.busy_indicator)
sankeysignals.initial_sankey_ready.connect(self.draw_sankey)
def __init__(self, cs_name):
if cs_name not in bw.calculation_setups:
raise ValueError(
"{} is not a known `calculation_setup`.".format(cs_name))
self.cs_name = cs_name
self.cs = bw.calculation_setups[cs_name]
self.seed = None
self.cf_rngs = {}
self.CF_rng_vectors = {}
self.include_technosphere = True
self.include_biosphere = True
self.include_cfs = True
self.include_parameters = True
self.param_rng = None
self.param_cols = ["row", "col", "type"]
self.tech_rng: Optional[Union[MCRandomNumberGenerator,
np.ndarray]] = None
self.bio_rng: Optional[Union[MCRandomNumberGenerator,
np.ndarray]] = None
self.cf_rng: Optional[Union[MCRandomNumberGenerator,
np.ndarray]] = None
# functional units
self.func_units = self.cs['inv']
self.rev_fu_index = {i: fu for i, fu in enumerate(self.func_units)}
# activities
self.activity_keys = [list(fu.keys())[0] for fu in self.func_units]
self.activity_index = {
key: index
for index, key in enumerate(self.activity_keys)
}
self.rev_activity_index = {
index: key
for index, key in enumerate(self.activity_keys)
}
# previously: self.rev_activity_index = {v: k for k, v in self.activity_keys}
# self.fu_index = {k: i for i, k in enumerate(self.activity_keys)}
# methods
self.methods = self.cs['ia']
self.method_index = {m: i for i, m in enumerate(self.methods)}
self.rev_method_index = {i: m for i, m in enumerate(self.methods)}
# previously: self.rev_method_index = {v: k for k, v in self.method_index.items()}
# self.rev_method_index = {v: k for k, v in self.method_index.items()}
self.func_unit_translation_dict = {
str(bw.get_activity(list(func_unit.keys())[0])): func_unit
for func_unit in self.func_units
}
if len(self.func_unit_translation_dict) != len(self.func_units):
self.func_unit_translation_dict = {}
for fu in self.func_units:
act = bw.get_activity(next(iter(fu)))
self.func_unit_translation_dict["{} {}".format(act,
act[0])] = fu
self.func_key_dict = {
m: i
for i, m in enumerate(self.func_unit_translation_dict.keys())
}
self.func_key_list = list(self.func_key_dict.keys())
# GSA calculation variables
self.A_matrices = list()
self.B_matrices = list()
self.CF_dict = defaultdict(list)
self.parameter_exchanges = list()
self.parameters = list()
self.parameter_data = defaultdict(dict)
self.results = list()
self.lca = bw.LCA(demand=self.func_units_dict, method=self.methods[0])
#local files
from pygsa.constants import *
from pygsa.saindices.sobol_indices import sobol_indices
###1. LCA model
bw.projects.set_current('Sobol indices')
method = ('IPCC 2013', 'climate change', 'GWP 100a')
ei = bw.Database('ecoinvent 3.5 cutoff')
bs = bw.Database('biosphere3')
demand_act = [act for act in ei if 'market for electricity, high voltage'==act['name'] and 'CH' in act['location']][0]
demand_amt = 1
demand = {demand_act: demand_amt}
lca = bw.LCA(demand,method)
lca.lci()
lca.lcia()
q_low = (1-THREE_SIGMA_Q)/2
q_high = (1+THREE_SIGMA_Q)/2
A = lca.technosphere_matrix
B = lca.biosphere_matrix
c = sum(lca.characterization_matrix)
lca.build_demand_array()
d = lca.demand_array
cB = c*B
score_initial = cB*spsolve(A,d) #run it before MC to factorize matrix A
m for m in bw.methods if 'ReCiPe' in m[0] and 'Midpoint (E)' in m[0]
and 'w/o LT' not in m[0] and ' no LT' not in m[0] and 'V1.13' in m[0]
]
catalyst_production_db.write(catalyst_data)
print('==========')
Ni_Al2O3_prod = [
act for act in catalyst_production_db if 'Ni/Al2O3' in act['name']
][0]
Ni_Al2O3_prod_exc_list = [exc for exc in Ni_Al2O3_prod.exchanges()]
method1 = recipe_mid[6]
function_unit = {Ni_Al2O3_prod: 1}
lca = bw.LCA(function_unit, method1)
lca.lci()
lca.lcia()
print(lca.demand)
print(lca.method)
print('The {} process accounts for {:f} {}'.format(
list(function_unit.keys())[0]['name'], lca.score,
bw.methods.get(method1).get('unit')))
# out：The Ni/Al2O3 Production process accounts for 26.836102 kg CO2-Eq
print('==========')
ca = bwa.ContributionAnalysis()
# print(ca.annotated_top_processes(lca, limit = 0.1, limit_type = 'percent'))
# print('==========')
print(lca.top_activities())
def run_analyses(self,
demand_item,
demand_item_code,
amount=1,
methods=[('IPCC 2013', 'climate change', 'GWP 100a')],
top_processes=10,
gt_cutoff=0.01,
pie_cutoff=0.05):
ready = self.setup_bw2()
name = self.bw2_database_name
if ready:
if name in bw2.databases:
del bw2.databases[name]
print('Rewriting database ({}) ...'.format(name))
else:
print('Writing database ({})...'.format(
name)) # pragma: no cover
new_db = bw2.Database(name)
new_db.write(self.bw2_database)
new_db.process()
#print ('trying to get {}'.format(demand_item_code))
product_demand = new_db.get(demand_item_code)
if product_demand is not False:
fu = {product_demand: amount}
parameter_sets = self.modelInstance.evaluated_parameter_sets
ts = time.time()
ts_format = datetime.datetime.fromtimestamp(ts).strftime(
'%Y-%m-%d %H:%M:%S')
result_dict = {
'settings': {
'pie_cutoff':
pie_cutoff,
'methods': [str(method) for method in methods],
'method_names':
[', '.join(method[1:]) for method in methods],
'method_units':
[bw2.methods[method]['unit'] for method in methods],
'item':
demand_item,
'item_code':
demand_item_code,
'amount':
amount,
'ps_names': [name for name in parameter_sets.keys()],
'item_unit':
product_demand['unit'],
'timestamp':
ts_format,
}
}
result_sets = []
#for each parameter set in the model run the analysis
for n, (parameter_set_name,
parameter_set) in enumerate(parameter_sets.items()):
# update the parameter_set values
print('\nAnalysis {}\n'.format(n + 1))
self.update_exchange_amounts(new_db, parameter_set)
initial_method = methods[0]
# run the LCA
lca = bw2.LCA(fu, initial_method)
lca.lci(factorize=True)
lca.lcia()
ps_results = []
for method in methods:
lca.switch_method(method)
lca.redo_lcia(fu)
unit = bw2.methods[method]['unit']
score = lca.score
#print('Analysis for {} {} of {}, using {}'.format(amount, product_demand['unit'], product_demand['name'], method))
#print ('{:.3g} {}'.format(score, unit))
method_dict = {
o[0]: o[1]
for o in bw2.Method(method).load()
}
default_tag = "other"
label = "lcopt_type"
type_graph = [
recurse_tagged_database(key, amount, method_dict,
lca, label, default_tag)
for key, amount in fu.items()
]
# type_result = aggregate_tagged_graph(type_graph)
# for k,v in type_result.items():
# print('{}\t\t{}'.format(k,v))
label = "name"
foreground_graph = [
recurse_tagged_database(key, amount, method_dict,
lca, label, default_tag)
for key, amount in fu.items()
]
foreground_result = aggregate_tagged_graph(
foreground_graph)
#for k,v in foreground_result.items():
# print('{}\t\t{}'.format(k,v))
recursed_graph = self.multi_recurse(
deepcopy(type_graph[0]))
dropped_graph = self.drop_level_recurse(
deepcopy(type_graph[0]))
result_set = {
'ps_name': parameter_set_name,
'method': str(method),
'unit': unit,
'score': score,
'foreground_results': foreground_result,
'graph': recursed_graph,
'dropped_graph': dropped_graph,
'original_graph': str(type_graph[0])
}
ps_results.append(result_set)
result_sets.append(ps_results)
result_dict['results'] = result_sets
return result_dict
def contribution_LCA_to_df(datasets,
cats=['CC', 'R_Total'],
amount=1,
names=['name', 'location']):
"""Calculate foreground contribution LCA of a list of datasets
and return a multi-index dataframe.
"""
results = {}
codes = {}
index_dict = {}
if datasets and cats:
lca = bw.LCA({datasets[0]: 1}, method=lcia_methods[cats[0]])
lca.lci()
lca.lcia()
else:
raise ValueError(
"No datasets or impact categories found." +
"Provide at least one dataset and one impact category.")
for ds in datasets:
index_dict[ds['code']] = tuple(ds[i] for i in names)
for cat in cats:
lca.switch_method(lcia_methods[cat])
cf_dict = dict(bw.Method(lcia_methods[cat]).load())
codes[cat] = {}
results[cat] = {}
for dataset in datasets:
for exc in dataset.technosphere():
existing_value = 0
if (dataset['code'], exc.input['name']) in results[cat].keys():
existing_value = results[cat][(dataset['code'],
exc.input['name'])]
if exc['amount'] == 0:
continue
if exc['input'] in codes[cat]:
results[cat][(dataset['code'], exc.input['name'])] = \
amount * codes[cat][exc['input']]*exc['amount'] + existing_value
else:
lca.redo_lcia({exc.input: exc['amount']})
results[cat][(dataset['code'],exc.input['name'])] = \
lca.score * amount + existing_value
codes[cat][exc['input']] = (lca.score / exc['amount'])
for exc in dataset.biosphere():
# Not all flows are characterized
if exc.input in cf_dict:
existing_value = 0
if (dataset['code'],
exc.input['name']) in results[cat].keys():
existing_value = results[cat][(dataset['code'],
exc.input['name'])]
results[cat][(dataset['code'],exc.input['name'])] = \
amount * exc['amount'] * cf_dict[exc.input] + existing_value
for cat in category_group:
if cat in results.keys():
for key in results[cat].keys():
if 'CED' not in results.keys():
results['CED'] = {}
try:
results['CED'][key] += results[cat][key]
except KeyError:
results['CED'][key] = results[cat][key]
del results[cat]
return pd.DataFrame(results).unstack().sort_index(axis=1).rename(
index=index_dict)
def _construct_lca(self):
return bw.LCA(demand=self.func_units_dict, method=self.methods[0])
print(year)
utils_update.dbUpdate_ElecAluLiq(bw_db_name=bw_db_name, year=year)
utils_update.dbUpdate_EnerAlumina(bw_db_name=bw_db_name, year=year)
utils_update.dbUpdate_cons_mix(bw_db_name=bw_db_name,
year=year,
mineral='bauxite')
utils_update.dbUpdate_cons_mix(bw_db_name=bw_db_name,
year=year,
mineral='alumina')
utils_update.dbUpdate_aluminum_global_production(bw_db_name=bw_db_name,
year=year)
for method in mining_recipe_method:
Method = bw.Method(method)
alu_cons_act = bw_db.get('market for consumption of aluminium')
functional_unit = {alu_cons_act: 1}
lca = bw.LCA(functional_unit, method)
lca.lci(
) # Builds matrices, solves the system, generates an LCI matrix.
lca.lcia()
#Get activity contribution
temp_dtf = pd.DataFrame(
[(tmp_list[0], tmp_list[0] / lca.score, tmp_list[1],
str(tmp_list[2])) for tmp_list in lca.top_activities()],
columns=['Score', "Rel_score", 'Supply', 'Activity'])
temp_dtf = temp_dtf.join(
pd.DataFrame(
{
'Year': year,
'Country': alu_cons_act['location'],
'Method_name': method[0],
'Midpoint': method[1],
def _construct_lca(self) -> bw.LCA:
return bw.LCA(demand=self.func_units_dict,
method=self.methods[0],
presamples=[self.package.path])
def get_median_act(bw_db_name,
name_to_include,
name_to_exclude,
unit,
ia_method,
location_keywords=None):
'''
Function that return the median case of LCA scores of activiies specified by keywords
Attributes:
bw_db_name is the name of the Brightway database to update.
name_to_include is a list of strings that should be individually contained in the activity name
name_to_exclude is a list of strings that should not be individually contained in the activity name
location_keywords is a list of strings that should be individually contained in the activity name
None specified no location
unit is the string of activity unit.
ia_method is a bw Impact Assessment method
'''
bw_db = bw.Database(bw_db_name)
#Get the list of all potential activities
if (location_keywords == None):
act_list = [
act for act in bw_db
if all(keyword in act['name']
for keyword in name_to_include) and all(
keyword not in act['name']
for keyword in name_to_exclude) and unit in act['unit']
]
else:
act_list = [
act for act in bw_db
if all(keyword in act['name']
for keyword in name_to_include) and all(
keyword not in act['name']
for keyword in name_to_exclude)
and any(keyword == act['location']
for keyword in location_keywords) and unit in act['unit']
]
#Check the number of activities
if len(act_list) == 0:
raise KeyError("No activities found")
else:
dtf = pd.DataFrame()
for act in act_list:
try:
lca_calc = bw.LCA({act: 1}, ia_method)
except:
raise StopIteration('LCA calculations do not work')
lca_calc.lci()
lca_calc.lcia()
new_row = pd.DataFrame(
{
'Activity': act['name'],
'Activity Location': act['location'],
'Score': lca_calc.score,
'Code': act['code']
},
index=[0])
dtf = dtf.append(new_row, ignore_index=True)
dtf.sort_values(by='Score', inplace=True)
#Get the index of the median. If odd number of activities, exact one. Otherwise, the closest lower score
return {
'Median': dtf[dtf['Score'] <= dtf['Score'].median()].iloc[-1].Code
}
def report_LDV_LCA(self):
"""
Report per-drivetrain impacts along the given dimension.
Both per-pkm as well as total numbers are given.
:return: a dataframe with impacts for the REMIND EDGE-T
transport sector model. Levelized impacts (per pkm) are
found in the column `score_pkm`, total impacts in `total_score`.
:rtype: pandas.DataFrame
"""
df = self.data[self.data.Variable.isin(self.variables)]
df.loc[:, "score_pkm"] = 0.
df.loc[:, "score_pkm_direct"] = 0.
# add methods dimension & score column
methods_df = pd.DataFrame({"Method": self.methods, "score_pkm": 0.})
df = df.merge(methods_df, "outer") # on "score_pkm"
df.set_index(["Year", "Region", "Variable", "Method"], inplace=True)
start = time.time()
# calc score
for year in self.years:
# find activities which at the moment do not depend
# on regions
db = bw.Database(eidb_label(self.model, self.scenario, year))
fleet_acts = [
a for a in db if a["name"].startswith(
"transport, passenger car, fleet average")
]
lca = bw.LCA({fleet_acts[0]: 1})
lca.lci()
fleet_idxs = [lca.activity_dict[a.key] for a in fleet_acts]
for region in self.regions:
for var in (df.loc[(
year, region)].index.get_level_values(0).unique()):
demand = self._act_from_variable(var, db, year, region)
if not demand:
continue
lca = bw.LCA(demand, method=self.methods[0])
# build inventories
lca.lci()
## this is a workaround to correct for higher loadfactors in the LowD scenarios
if "_LowD" in self.scenario:
fct = max(1 - (year - 2020) / 15 * 0.15, 0.85)
else:
fct = 1.
for method in self.methods:
lca.switch_method(method)
lca.lcia()
df.at[(year, region, var, method),
"score_pkm"] = lca.score * fct
res_vec = np.squeeze(
np.asarray(
lca.characterized_inventory.sum(axis=0)))
df.at[(year, region, var, method), "score_pkm_direct"] = \
np.sum(res_vec[fleet_idxs]) * fct
print("Calculation took {} seconds.".format(time.time() - start))
df["total_score"] = df["value"] * df["score_pkm"] * 1e9
df["total_score_direct"] = df["value"] * df["score_pkm_direct"] * 1e9
return df[[
"total_score", "total_score_direct", "score_pkm",
"score_pkm_direct"
]]
