def sobol_pandas(gsa_in_lca, first, total):
lca = gsa_in_lca.lca
ind_reverse_act = 0
ind_reverse_prod = 1
ind_reverse_bio = 2
ind_db_name = 0
ind_act_code = 1
sum_first = np.sum(first)
sum_total = np.sum(total)
normalized_first = np.array(
[first[i] / sum_first for i in range(len(first))])
normalized_total = np.array(
[total[i] / sum_total for i in range(len(total))])
# get_act_prod_bio = lambda ind_reverse, p:
activities, products_flows = [], []
for input_ in gsa_in_lca.inputs:
#Technosphere activities, columns
act_mask_tech = gsa_in_lca.inputs_dict[input_]['tech_params_where']
act_pos_tech = lca.tech_params[act_mask_tech]['col']
act_tech = [ bw.Database(lca.reverse_dict()[ind_reverse_act][p][ind_db_name]).get(lca.reverse_dict()[ind_reverse_act][p][ind_act_code])['name'] \
for p in act_pos_tech]
#Products, rows
prod_mask = gsa_in_lca.inputs_dict[input_]['tech_params_where']
prod_pos = lca.tech_params[prod_mask]['row']
products = [ bw.Database(lca.reverse_dict()[ind_reverse_prod][p][ind_db_name]).get(lca.reverse_dict()[ind_reverse_prod][p][ind_act_code])['name'] \
for p in prod_pos ]
#Technosphere activities wrt biosphere flows, columns
act_mask_bio = gsa_in_lca.inputs_dict[input_]['bio_params_where']
act_pos_bio = lca.bio_params[act_mask_bio]['col']
act_bio = [ bw.Database(lca.reverse_dict()[ind_reverse_act][p][ind_db_name]).get(lca.reverse_dict()[ind_reverse_act][p][ind_act_code])['name'] \
for p in act_pos_bio]
#Biosphere flows, rows
bio_mask = gsa_in_lca.inputs_dict[input_]['bio_params_where']
bio_pos = lca.bio_params[bio_mask]['row']
bio_flows = [ bw.Database(lca.reverse_dict()[ind_reverse_bio][p][ind_db_name]).get(lca.reverse_dict()[ind_reverse_bio][p][ind_act_code])['name'] \
for p in bio_pos]
activities += act_tech + act_bio
products_flows += products + bio_flows
data = {
'Products or flows': products_flows + list(gsa_in_lca.parameters),
'Activities': activities + list(gsa_in_lca.parameters),
'First': first,
'Total': total,
'Normalized first': normalized_first,
'Normalized total': normalized_total
}
# Creates pandas DataFrame
df = pd.DataFrame(data)
gsa_in_lca.sobol_df = df
def test_exchange_interface(qtbot, ab_app):
flow = bw.Database(bw.config.biosphere).random()
db = bw.Database("testdb")
act_key = ("testdb", "act_unc")
db.write({
act_key: {
"name":
"act_unc",
"unit":
"kilogram",
"exchanges": [
{
"input": act_key,
"amount": 1,
"type": "production"
},
{
"input": flow.key,
"amount": 2,
"type": "biosphere"
},
]
}
})
act = bw.get_activity(act_key)
exc = next(e for e in act.biosphere())
interface = get_uncertainty_interface(exc)
assert isinstance(interface, ExchangeUncertaintyInterface)
assert interface.amount == 2
assert interface.uncertainty_type == UndefinedUncertainty
assert interface.uncertainty == {}
def add_non_fossil_co2_flows_to_storage():
"""Add a new flow to the biosphere: Non-fossil CO2 to storage.
Add this biosphere flow to LCIA methods where it is suitable.
"""
from peewee import IntegrityError
biosphere = bw.Database('biosphere3')
new_flow = biosphere.new_activity(
'CO2 to geological storage, non-fossil', **{
'name': 'CO2 to geological storage, non-fossil',
'unit': 'kilogram',
'type': 'storage',
'categories': ('geological storage', )
})
try:
new_flow.save()
except IntegrityError as e:
print(
"Database Error (flow is likely to be present already): {}".format(
e))
print("Added new flow: {}".format(new_flow))
co2_to_soil = [
x for x in bw.Database("biosphere3")
if ("Carbon dioxide, to soil or biomass stock" in str(x)
and "('soil',)" in str(x))
][0]
print("Use {} as a template for the characterization factors.".format(
co2_to_soil))
for cat in lcia_methods:
method = bw.Method(lcia_methods[cat])
method_data = method.load()
# first make sure we don't already have the flow included:
if [x for x in method_data if new_flow.key[1] in x[0][1]]:
print('Flow already present- you must have run this code already.')
continue
else:
try:
characterized_flow = [
x for x in method_data if co2_to_soil.key[1] in x[0][1]
][0]
except:
continue
method_data.extend([(new_flow.key, characterized_flow[1])])
print('Flow added to method: {}'.format(method.name))
print('Characterisation factor: {}'.format(characterized_flow[1]))
orig_name = [x for x in method.name]
new_method = bw.Method(tuple(orig_name + ['CO2 storage']))
new_method.register()
new_method.write(method_data)
new_method.process()
def relink_database(self, db_name: str, parent: QObject) -> None:
"""Relink technosphere exchanges within the given database."""
db = bw.Database(db_name)
depends = db.find_dependents()
options = [(depend, bw.databases.list) for depend in depends]
dialog = DatabaseLinkingDialog.relink_sqlite(db_name, options, parent)
if dialog.exec_() == DatabaseLinkingDialog.Accepted:
# Now, start relinking.
for old, new in dialog.relink.items():
other = bw.Database(new)
relink_exchanges_existing_db(db, old, other)
signals.database_changed.emit(db_name)
signals.databases_changed.emit()
def lookup_geothermal():
db_geothe = bw.Database("geothermal energy")
db_ecoinv = bw.Database("ecoinvent 3.5 cutoff")
db_biosph = bw.Database("biosphere3")
#needed to exclude print statements from the search function
with open(os.devnull, "w") as f, contextlib.redirect_stdout(f):
wellhead = db_geothe.search("geothermal wellhead")[0].key
diesel = db_ecoinv.search(
"market diesel, burned diesel-electric generating set")[1].key
steel = db_ecoinv.search("market steel low alloyed")[0].key
cement = db_ecoinv.search("market cement portland",
filter={"location": "ROW"})[0].key
water = db_ecoinv.search("market tap water",
filter={"location": "ROW"})[0].key
drilling_mud = db_geothe.search("drilling mud")[0].key
drill_wst = db_ecoinv.search("market drilling waste")[0].key
wells_closr = db_ecoinv.search("deep well closure")[0].key
coll_pipe = db_geothe.search("collection pipelines")[0].key
plant = db_geothe.search(
"geothermal plant, double flash (electricity)")[0].key
ORC_fluid = db_ecoinv.search("market perfluoropentane")[0].key
ORC_fluid_wst = db_ecoinv.search("treatment perfluoropentane")[0].key
diesel_stim = db_ecoinv.search(
"market diesel, burned diesel-electric generating set")[0].key
co2 = db_biosph.search("Carbon dioxide, fossil")[0].key
electricity_prod_conventional = db_geothe.search(
"electricity production, geothermal, conventional")[0].key
electricity_prod_enhanced = db_geothe.search(
"electricity production, geothermal, enhanced")[0].key
return wellhead, \
diesel, \
steel, \
cement, \
water, \
drilling_mud, \
drill_wst, \
wells_closr, \
coll_pipe, \
plant, \
ORC_fluid, \
ORC_fluid_wst,\
diesel_stim, \
co2, \
electricity_prod_conventional, \
electricity_prod_enhanced
def store_database_as_package(db_name: str, directory: str = None) -> bool:
""" Attempt to use `bw.BW2Package` to save the given database as an
isolated package that can be shared with others.
Returns a boolean signifying success or failure.
"""
if db_name not in bw.databases:
return False
metadata = bw.databases[db_name]
db = bw.Database(db_name)
directory = directory or bw.projects.output_dir
output_dir = Path(directory)
if output_dir.suffix == ".bw2package":
out_file = output_dir
else:
out_file = output_dir / "{}.bw2package".format(db.filename)
# First, ensure the metadata on the database is up-to-date.
modified = dt.strptime(metadata["modified"], "%Y-%m-%dT%H:%M:%S.%f")
if "processed" in metadata:
processed = dt.strptime(metadata["processed"], "%Y-%m-%dT%H:%M:%S.%f")
if processed < modified:
db.process()
else:
db.process()
# Now that processing is done, perform the export.
ABPackage.unrestricted_export(db, out_file)
return True
def sync(self):
# code below is based on the assumption that bw uses utc timestamps
tz = datetime.datetime.now(datetime.timezone.utc).astimezone()
time_shift = -tz.utcoffset().total_seconds()
data = []
for name in natural_sort(bw.databases):
dt = bw.databases[name].get("modified", "")
if dt:
dt = arrow.get(dt).shift(seconds=time_shift).humanize()
# final column includes interactive checkbox which shows read-only state of db
database_read_only = project_settings.db_is_readonly(name)
data.append({
"Name":
name,
"Depends":
", ".join(bw.databases[name].get("depends", [])),
"Modified":
dt,
"Records":
len(bw.Database(name)),
"Read-only":
database_read_only,
})
self.dataframe = pd.DataFrame(data, columns=self.HEADERS)
def conveniently_tag_database(
fg_db_to_tag='',
label='',
):
'''
ESA: LEGACY, use not recommended, use instead: conveniently_tag_database_v2
Auxiliary function to conveniently assign new tag labels to a foreground database, for group analysis.
Select a forground database to tag, via fg_db_to_tag;
Then define the label name, via label
Returns the tagged database as a new dictionnnary, to be checked, and then re-written in database
Usage: new_data = conveniently_tag_database('fg_database', 'label_name')
'''
db = bw2.Database(fg_db_to_tag)
data = db.load()
new_data = {}
print('There are %i items to be tagged, one by one' % (len(data)))
val = input("Do you want to proceed (Y/N) ? ")
if val != 'Y':
print('Okay, we stop here')
else:
print(
"Lets proceed! Type 'skip' in order to not tag the given activity")
for pro_tpl, pro in data.items():
val = input(pro['name'] + "... to be in the group called... ? ")
if val == 'skip':
# need to pop the key if it was defined previously
pro.pop(label, 'label was not present')
if val != 'skip':
pro[label] = val
new_data[pro_tpl] = pro
return new_data
def delete_database(self, name):
ok = QtWidgets.QMessageBox.question(None, "Delete database?", (
"Are you sure you want to delete database '{}'? It has {} activity datasets"
).format(name, len(bw.Database(name))))
if ok:
del bw.databases[name]
self.change_project(bw.projects.current, reload=True)
def sync(self, name, data=None):
self.database_name = name
if not data:
self.database = bw.Database(name)
self.database.order_by = 'name'
self.database.filters = {'type': 'process'}
data = itertools.islice(self.database, 0, self.MAX_LENGTH)
self.setRowCount(min(len(self.database), self.MAX_LENGTH))
self.setHorizontalHeaderLabels(self.HEADERS)
for row, ds in enumerate(data):
for col, value in self.COLUMNS.items():
if value == "key":
self.setItem(
row, col,
ABTableItem(str(ds.key), key=ds.key, color=value))
elif value == "location":
self.setItem(
row, col,
ABTableItem(str(ds.get(value, '')),
key=ds.key,
color=value))
else:
self.setItem(
row, col,
ABTableItem(ds.get(value, ''), key=ds.key,
color=value))
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 report_direct_emissions(self):
"""
Report the direct (exhaust) emissions of the LDV fleet.
"""
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:
for var in (df.loc[(year, region)]
.index.get_level_values(0)
.unique()):
for act, share in self._act_from_variable(
var, db, year, region).items():
for ex in act.biosphere():
result[(year, region, ex["name"])] = (
result.get((year, region, ex["name"]), 0)
+ ex["amount"] * share * df.loc[(year, region, var), "value"])
df_result = pd.Series(result)
print("Calculation took {} seconds.".format(time.time() - start))
return df_result * 1e9 # kg
def copy_to_db(self, activity_key):
origin_db = activity_key[0]
activity = bw.get_activity(activity_key)
# TODO: Exclude read-only dbs from target_dbs as soon as they are implemented
available_target_dbs = sorted(set(bw.databases).difference(
{'biosphere3', origin_db}
))
if not available_target_dbs:
QtWidgets.QMessageBox.information(
None,
"No target database",
"No valid target databases available. Create a new database first."
)
else:
target_db, ok = QtWidgets.QInputDialog.getItem(
None,
"Copy activity to database",
"Target database:",
available_target_dbs,
0,
False
)
if ok:
new_code = self.generate_copy_code((target_db, activity['code']))
activity.copy(code=new_code, database=target_db)
# only process database immediatly if small
if len(bw.Database(target_db)) < 200:
bw.databases.clean()
signals.database_changed.emit(target_db)
signals.databases_changed.emit()
def random_graph(self) -> None:
""" Show graph for a random activity in the currently loaded database."""
if self.selected_db:
self.new_graph(bw.Database(self.selected_db).random().key)
else:
QtWidgets.QMessageBox.information(None, "Not possible.",
"Please load a database first.")
def new_activity(self, database_name: str) -> None:
name, ok = QtWidgets.QInputDialog.getText(
self.window,
"Create new technosphere activity",
"Please specify an activity name:" + " " * 10,
)
if ok and name:
data = {
"name": name,
"reference product": name,
"unit": "unit",
"type": "process"
}
new_act = bw.Database(database_name).new_activity(
code=uuid.uuid4().hex, **data)
new_act.save()
production_exchange = new_act.new_exchange(input=new_act,
amount=1,
type="production")
production_exchange.save()
bw.databases.set_modified(database_name)
AB_metadata.update_metadata(new_act.key)
signals.open_activity_tab.emit(new_act.key)
signals.database_changed.emit(database_name)
signals.databases_changed.emit()
def run_forwast(self):
"""
adapted from pjamesjoyce/lcopt
"""
response = requests.get(self.forwast_url)
forwast_zip = zipfile.ZipFile(io.BytesIO(response.content))
import_signals.download_complete.emit()
with tempfile.TemporaryDirectory() as tempdir:
if not import_signals.cancel_sentinel:
forwast_zip.extractall(tempdir)
import_signals.unarchive_finished.emit()
if not import_signals.cancel_sentinel:
import_signals.extraction_progress.emit(0, 0)
import_signals.strategy_progress.emit(0, 0)
import_signals.db_progress.emit(0, 0)
bw.BW2Package.import_file(
os.path.join(tempdir, 'forwast.bw2package'))
if self.db_name != 'forwast':
bw.Database('forwast').rename(self.db_name)
if not import_signals.cancel_sentinel:
import_signals.extraction_progress.emit(1, 1)
import_signals.strategy_progress.emit(1, 1)
import_signals.db_progress.emit(1, 1)
import_signals.finished.emit()
else:
self.delete_canceled_db()
def freezeParams(db_name, **params):
"""
Freeze parameters values in all exchanges amounts of a DB.
The formulas are computed and the 'amount' attributes are set with the result.
This enables parametric datasets to be used by standard, non parametric tools of Brightway2.
"""
db = bw.Database(db_name)
with DbContext(db):
for act in db:
for exc in act.exchanges():
amount = _getAmountOrFormula(exc)
# Amount is a formula ?
if isinstance(amount, Basic):
replace = [(name, value)
for name, value in _completeParamValues(
params, setDefaults=True).items()]
val = amount.subs(replace).evalf()
with ExceptionContext(val):
val = float(val)
print("Freezing %s // %s : %s => %d" %
(act, exc['name'], amount, val))
# Update in DB
exc["amount"] = val
exc.save()
def superstructure_from_databases(cls, databases: List[str],
superstructure: Optional[str] = None) -> 'Builder':
"""Given a list of database names and the name of the superstructure,
upgrade or create the superstructure database.
"""
assert len(databases) >= 1, "At least one database should be included"
assert len(databases) == len(set(databases)), "Duplicates are not allowed in the databases"
assert all(db in bw.databases for db in databases), "All databases must exist in the project"
if superstructure is None:
# Default to first db in list if no name is given
superstructure, databases = databases[0], databases[1:]
elif superstructure not in bw.databases:
db = bw.Database(superstructure)
db.register()
elif superstructure in databases:
databases.remove(superstructure)
print("Superstructure: {}, deltas: {}".format(superstructure, ", ".join(databases)))
builder = cls.initialize(superstructure, databases)
print("Amount of activities in superstructure: {}".format(len(builder.unique_codes)))
builder.find_missing_activities()
print("Total amount of activities in superstructure: {}".format(len(builder.unique_codes)))
if builder.missing_activities:
print("Storing {} new activities for superstructure.".format(len(builder.missing_activities)))
builder.expand_superstructure_activities()
print("Amount of exchanges in superstructure: {}".format(len(builder.unique_indexes)))
builder.find_missing_exchanges()
print("Total amount of exchanges in superstructure: {}".format(len(builder.unique_indexes)))
if builder.missing_exchanges:
print("Storing {} new exchanges for superstructure.".format(len(builder.missing_exchanges)))
builder.expand_superstructure_exchanges()
return builder
def uslci_db(self):
"""
Orignal repository were downloaded from the U.S. Federal LCA Commons|Ag data Commons platform.
Note Biosteam_lca leverages ecospold2 importer and excel importer. For converting from json format to ecospold format, the Openlca Data Converter were used. Pre-processed database are saved under biosteam_lca-database ddirectory.
**References**
[1] USDA National Agricultural Library. (2015). LCA Commons. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1173236. Accessed 2020-03-23.
[2] Michael Srocka, Juliane Franze, Andreas Ciroth, January 2010. Documentation openLCA format converter V2. Data Conversion from EcoSpold02 to ILCD. GreenDeltaTC GmbH Berlin
"""
db_name = 'us_lci'
lci_import = importers.SingleOutputEcospold2Importer(
os.path.join(self.dirpath, 'US_lci'), db_name)
lci_import.apply_strategies()
lci_import.migrate('unusual-units')
lci_import.migrate('default-units')
#linking the biosphere flows by their names, units, and categories
link_iter = functools.partial(strategies.link_iterable_by_fields,
other=bw2.Database(bw2.config.biosphere),
kind='biosphere')
lci_import.apply_strategy(link_iter)
try:
self.inspect(lci_import, db_name)
except:
pass
sp = lci_import
return sp
def test_succceed_open_activity(ab_app):
""" Create a tiny test database with a production activity
"""
assert bw.projects.current == "pytest_project"
db = bw.Database("testdb")
act_key = ("testdb", "act1")
db.write({
act_key: {
"name": "act1",
"unit": "kilogram",
"exchanges": [{
"input": act_key,
"amount": 1,
"type": "production"
}]
}
})
activities_tab = ab_app.main_window.right_panel.tabs["Activity Details"]
# Select the activity and emit signal to trigger opening the tab
act = bw.get_activity(act_key)
signals.open_activity_tab.emit(act_key)
assert len(activities_tab.tabs) == 1
assert act_key in activities_tab.tabs
# Current index of QTabWidget is changed by opening the tab
index = activities_tab.currentIndex()
assert act.get("name") == activities_tab.tabText(index)
def replace(parameters, gt_model):
# CONVENTIONAL GEOTHERMAL
parameters.static()
gt_model.run(parameters)
params_sta_conv = gt_model.array_io
#Lookup activities
_, _, _, _, _, _, _, _, _, _, _, _, _, _, electricity_prod_conventional, _, = lookup_geothermal(
)
act = bw.get_activity(electricity_prod_conventional)
if not bw.Database("geothermal energy").search(act["name"] + " zeros"):
act.copy(name=act["name"] + " (zeros)")
# Delete all exchanges
for exc in act.exchanges():
exc.delete()
# Insert new exchanges
for inp in params_sta_conv:
if inp['input_db'] != "biosphere3":
print(inp)
# act.new_exchange(input = (inp['input_db'],inp['input_code']), amount = float(inp['amount']), type= "technosphere").save()
else:
print(type(tuple((str(inp['input_db']), str(inp['input_code'])))))
print(float(inp['amount']))
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 add_unit_score_exchange_and_cf(method, biosphere='biosphere3'):
""" Add unit score biosphere exchanges and cfs to biosphere and methods.
Allows the storing of LCIA results in the B matrix for LCI datasets. Makes
changes inplace and does not return anything.
Parameters
----------
method: tuple
Identification of the LCIA method, using Brightway2 tuple identifiers
biosphere: str, default `biosphere3`
Name of the biosphere database where biosphere exchanges are stored
Note
----
This function is invoked directly by the DatabaseAggregator
"""
if method not in bw.methods:
raise ValueError("Method {} not in registered methods".format(method))
if biosphere not in bw.databases:
raise ValueError(
"Database {} not in registered databases".format(biosphere))
m = bw.Method(method)
ef_code = m.get_abbreviation()
ef_name = 'Unit impact for {}'.format(method)
# Add to biosphere database, skip if already present
try:
ef = bw.get_activity((biosphere, ef_code))
assert ef['name'] == ef_name
except:
ef = bw.Database(biosphere).new_activity(code=ef_code)
ef['name'] = ef_name
ef['unit'] = m.metadata['unit']
ef['categories'] = ('undefined', )
ef['exchanges']: []
ef['type'] = 'unit impact exchange'
ef.save()
try:
bw.mapping[(biosphere, ef_code)]
except KeyError:
print("Manually added {} to mapping".format(ef_code))
bw.mapping.add((biosphere, ef_code))
# Add to associated method, skip if already present
loaded_method = m.load()
try:
existing_cf = [
cf_tuple for cf_tuple in loaded_method
if cf_tuple[0] == (biosphere, ef_code)
][0]
assert existing_cf[1] == 1
except:
loaded_method.append(((biosphere, m.get_abbreviation()), 1))
bw.Method(method).write(loaded_method)
def _relink_exchanges(data: list, other: str) -> list:
other = bw.Database(other)
if len(other) == 0:
raise StrategyError("Cannot link to empty database")
act = other.random()
is_technosphere = act.get("type", "process") == "process"
kind = TECHNOSPHERE_TYPES if is_technosphere else BIOSPHERE_TYPES
return link_iterable_by_fields(data, other=other, kind=kind)
def delete_database(self, name):
ok = self.window.confirm(
("Are you sure you want to delete database '{}'? "
"It has {} activity datasets").format(name,
len(bw.Database(name))))
if ok:
del bw.databases[name]
self.change_project(bw.projects.current, reload=True)
def ensure_sqlite_indices(self):
"""
- fix for https://github.com/LCA-ActivityBrowser/activity-browser/issues/189
- also see bw2data issue: https://bitbucket.org/cmutel/brightway2-data/issues/60/massive-sqlite-query-performance-decrease
"""
if bw.databases and not sqlite3_lci_db._database.get_indexes('activitydataset'):
print('creating missing sqlite indices')
bw.Database(list(bw.databases)[-1])._add_indices()
def test_fail_open_biosphere(ab_app):
""" Specifically fail to open an activity tab for a biosphere flow
"""
assert bw.projects.current == "pytest_project"
activities_tab = ab_app.main_window.right_panel.tabs["Activity Details"]
# Select any biosphere activity and emit signal to trigger opening the tab
biosphere_flow = bw.Database("biosphere3").random()
signals.open_activity_tab.emit(biosphere_flow.key)
assert len(activities_tab.tabs) == 0
def random_graph(self) -> None:
""" Show graph for a random activity in the currently loaded database."""
if self.selected_db:
method = bw.methods.random()
act = bw.Database(self.selected_db).random()
demand = {act: 1.0}
self.update_sankey(demand, method)
else:
QtWidgets.QMessageBox.information(None, "Not possible.", "Please load a database first.")
def is_technosphere_db(db_name: str) -> bool:
"""Returns True if database describes the technosphere, False if it describes a biosphere."""
if not db_name in bw.databases:
raise KeyError("Not an existing database:", db_name)
act = bw.Database(db_name).random()
if act is None or act.get("type", "process") == "process":
return True
else:
return False
def setup_fixtures(request):
print('RUNNING SETUP FIXTURE')
if FULL_SETUP:
bw2.projects.purge_deleted_directories()
if bw2_project_exists(IMPORT_PROJECT_NAME):
bw2.projects.delete_project(name=IMPORT_PROJECT_NAME,
delete_dir=True)
if bw2_project_exists(TEST_BW_PROJECT_NAME):
bw2.projects.delete_project(name=TEST_BW_PROJECT_NAME,
delete_dir=True)
if bw2_project_exists(DEFAULT_BIOSPHERE_PROJECT):
bw2.projects.set_current(DEFAULT_BIOSPHERE_PROJECT)
bw2.projects.copy_project(IMPORT_PROJECT_NAME, switch=True)
else:
bw2.projects.set_current(IMPORT_PROJECT_NAME)
bw2.bw2setup()
script_path = os.path.dirname(os.path.realpath(__file__))
ecospold_folder = os.path.join("tests", "assets", "datasets")
ecospold_path = os.path.join(script_path, ecospold_folder)
print(ecospold_path)
ei = bw2.SingleOutputEcospold2Importer(ecospold_path,
"Ecoinvent3_3_cutoff")
ei.apply_strategies()
ei.statistics()
ei.write_database()
bw2.projects.copy_project(TEST_BW_PROJECT_NAME, switch=True)
test_db = bw2.Database(TEST_BW_DB_NAME)
test_db.write(TEST_MODEL_DATABASE)
bw2.projects.set_current('default')
def teardown_fixtures():
print('TEAR IT DOWN!!')
print('cleaning up brightway')
bw2.projects.set_current('default')
if bw2_project_exists(TEST_BW_PROJECT_NAME):
bw2.projects.delete_project(
name=TEST_BW_PROJECT_NAME) #, delete_dir=True)
#bw2.projects.purge_deleted_directories()
if bw2_project_exists(IMPORT_PROJECT_NAME):
bw2.projects.delete_project(name=IMPORT_PROJECT_NAME)
shutil.rmtree(os.path.join(script_path, "tests", TEST_FOLDER))
request.addfinalizer(teardown_fixtures)
