def set_variables(self):
"""
Function that loads and sets the necessary variables.
"""
root_dir = os.path.dirname(os.path.realpath(__file__))
self.scratch_dir = os.path.join(root_dir, 'scratch')
self.input_dir = os.path.join(root_dir, 'input_data')
os.chdir(self.scratch_dir)
# Input data BIOSAFE
self.legal_weights = pd.read_csv(self.pjoin(self.input_dir,
'legalWeights.csv'),
index_col=0)
self.links_law = pd.read_csv(self.pjoin(self.input_dir,
'linksLaw.csv'),
index_col=0)
self.links_eco1 = pd.read_csv(self.pjoin(self.input_dir,
'linksEco.csv'),
index_col=0)
self.lut = pd.read_excel(self.pjoin(self.input_dir,
'BIOSAFE_20190711.xlsx'),
sheet_name='lut_RWES').fillna(method='ffill')
# this lookup table (lut) has:
# ecotope codes of BIOSAFE in the 1st column: oldEcotope
# aggregated/translated ectotopes in 2nd column: newEcotope
# Ecotopes used in Virtual River
self.vr_eco = pd.read_csv(self.pjoin(self.input_dir,
'VR_ecotopes.csv'))
# Aggregate BIOSAFE ecotopes into RWES ecotopes
self.links_eco2 = bsf.aggregateEcotopes(self.links_eco1, self.lut)
return
legalWeights, linksLaw, linksEco = bsIO.from_csv(bio_dir)
speciesPresence = pd.DataFrame(np.random.randint(2, size=len(linksLaw)),\
columns=['speciesPresence'], \
index=linksLaw.index)
ecotopeArea = pd.DataFrame(np.ones(82) * 1e5,\
columns = ['area_m2'],\
index = linksEco.columns.values[0:-1])
bs = biosafe.biosafe(legalWeights, linksLaw, linksEco, speciesPresence,
ecotopeArea)
excel_file = os.path.join(bio_dir, 'BIOSAFE_20150629.xlsx')
lut1 = pd.read_excel(excel_file, sheetname='lut_RWES').fillna(method='ffill')
# this lookup table has:
# ecotope codes of BIOSAFE in the first column: oldEcotope
# aggregated/translated ectotopes in the second column: newEcotope
linksEco1 = biosafe.aggregateEcotopes(linksEco, lut1)
bs.linksEco = linksEco1
#%% Initialize cost module
# Read input maps
dike_length = pcr.readmap(os.path.join(cost_dir, 'dike_length.map'))
dike_raise_sections = pcr.readmap(
os.path.join(cost_dir, 'dike_raise_sections.map'))
pollution_zones = pcr.readmap(os.path.join(cost_dir, 'pollution_zones.map'))
smoothing_cost_classes = pcr.readmap(
os.path.join(cost_dir, 'smoothing_cost_classes.map'))
dem = pcr.readmap(os.path.join(ref_map_dir, 'dem.map'))
# Read input distributions
road_distr = ec.read_distribution(cost_dir, 'roads')
smoothing_distr = ec.read_distribution(cost_dir, 'smoothing')