def test_validate_directional():
# test that validation works as expected with directional data
assert validate(df.rename(region={'Europe': 'Austria>Germany'}))
assert not validate(df.rename(region={'Europe': 'Austria>foo'}))
# test that directional data with more than one `>` fails
assert not validate(df.rename(region={'Europe': 'Austria>Italy>France'}))
def test_validate_subannual_datetime_as_subannual():
# test that validation works as expected with continuous time as subannual
assert validate(IamDataFrame(TEST_DF, subannual='01-01 00:00+01:00'))
# assert that missing timezone fails
assert not validate(IamDataFrame(TEST_DF, subannual='01-01 00:00'))
# assert that wrong timezone fails
assert not validate(IamDataFrame(TEST_DF, subannual='01-01 00:00+02:00'))
# assert that value not castable to datetime fails
assert not validate(IamDataFrame(TEST_DF, subannual='01-32 00:00+01:00'))
def _generate_yearly_values(input_file):
data_wrapper = dr.load_gdx_file(input_file, DEF_GAMS_DIR)
dt.generate_load_demand_series(data_wrapper)
dt.generate_primary_energy_values(data_wrapper)
dt.generate_final_energy_values(data_wrapper)
dt.generate_capacity_values(data_wrapper)
dt.generate_transport_capacity_values(data_wrapper)
dt.generate_storage_capacity_values(data_wrapper)
dt.generate_emissions_values(data_wrapper)
# dt.generate_additional_emissions_values(data_wrapper) - currently not in nomenclature
dt.generate_secondary_energy(data_wrapper)
dt.generate_exogenous_costs(data_wrapper)
dt.generate_co2_prices(data_wrapper)
data_wrapper.generate_idataframe(True)
nomenclature.validate(data_wrapper.idataframe)
return data_wrapper
def test_validate_time_entry():
df_sub = TEST_DF.rename(columns={'year': 'subannual'}).\
replace([2005, 2010], value=['2005-06-17 00:00+01:00',
'2010-07-21 12:00+01:00'])
assert validate(IamDataFrame(df_sub))
def test_validate_subannual_months():
# test that validation works as expected with months
# (and representative timeslices generally)
assert validate(IamDataFrame(TEST_DF, subannual='January'))
assert not validate(IamDataFrame(TEST_DF, subannual='foo'))
def test_validate_fail():
# test that simple validation fails on variable and region dimension
assert not (validate(df.rename(variable={'Primary Energy': 'foo'})))
assert not (validate(df.rename(region={'Europe': 'foo'})))
def test_validate():
# test simple validation
assert validate(df)
def test_validate_unit_entry():
assert not (validate(df.rename(unit={'EJ/yr': 'MWh'})))
def _generate_load_factors(input_file):
data_wrapper_series = dr.load_gdx_file(input_file, DEF_GAMS_DIR)
dt.generate_load_factors(data_wrapper_series)
data_wrapper_series.generate_idataframe_renewable_series()
nomenclature.validate(data_wrapper_series.idataframe)
return data_wrapper_series
import pyam
# Read SCOPE output file
TEST_DF = pd.read_excel("scope_sd_20201120_test_data.xlsx")
# Create dictionary and convert regions with iso_mapping into IAMC format
TEST_DICT = TEST_DF.to_dict()
DICT_REGIO = {}
DICT_REGIO["Region"] = {}
for i in range(len(TEST_DICT["Region"].keys())):
try:
DICT_REGIO["Region"][i] = nc.iso_mapping[TEST_DICT["Region"][i]]
except KeyError:
DICT_REGIO["Region"][i] = TEST_DICT["Region"][i]
NEW_DICT = {}
NEW_DICT["Model"] = TEST_DICT["Model"]
NEW_DICT["Scenario"] = TEST_DICT["Scenario"]
NEW_DICT["Region"] = DICT_REGIO["Region"]
NEW_DICT["Variable"] = TEST_DICT["Variable"]
NEW_DICT["Unit"] = TEST_DICT["Unit"]
NEW_DICT["Subannual"] = TEST_DICT["Subannual"]
NEW_DICT[2050] = TEST_DICT[2050]
NEW_DF = pd.DataFrame.from_dict(NEW_DICT)
# Save dict into new excel file
NEW_DF.to_excel("scope_sd_20201120_test_data_validated.xlsx")
# Check the created excel file with the openENTRANCE nomenclature
nc.validate("scope_sd_20201120_test_data_validated.xlsx")
#delete lines with 0 units
sheet = sheet[sheet.NumberUnits > 0]
# converting region names from plan4eu to OpenEntrance
sheet = sheet.replace({"Zone": plan4euRegDict})
sheet = sheet.replace({"Name": vardict['technos']})
iamc_region = sheet['Zone']
iamc_techno = sheet['Name']
for name_plan4eu in vardict[key].keys():
iamc_value = sheet[name_plan4eu]
iamc_unit = vardict[key][name_plan4eu]['unit']
iamc_variable = vardict[key][name_plan4eu]['variable'] + iamc_techno
#conversion needed?
if name_plan4eu in vardict['conversion'].keys():
if vardict['conversion'][name_plan4eu] == 'DivideByMaxPower':
iamc_value = iamc_value / sheet['MaxPower']
iamc = pd.DataFrame({
'Model': cfg['model'],
'Scenario': cfg['scenario'],
'Region': iamc_region,
'Variable': iamc_variable,
'Unit': iamc_unit,
'2050': iamc_value
})
iamc.to_csv(outputfile, index=False, mode='a', header=False)
nc.validate(outputfile)
