def assess_bio_effects(bio_mod, eco_ref, sections, ndff_species, maps_dir):
"""
Determine all effects for all measures.
"""
bs_ref = biosafe.spatialBiosafe(bio_mod,
eco_ref,
sections,
ndff_species,
params=['FI', 'TFI'],
toFiles=None)
FI_ref, TFI_ref = bs_ref.spatial()
TFI_ref.drop(['xcoor', 'ycoor'], axis=1, inplace=True)
TFI_ref['msr'] = 'reference'
FI_ref.drop(['xcoor', 'ycoor'], axis=1, inplace=True)
FI_ref['msr'] = 'reference'
FI_list, TFI_list = [FI_ref], [TFI_ref]
measure_names = [
f for f in os.listdir(maps_dir)
if os.path.isdir(os.path.join(maps_dir, f))
]
for msr_name in measure_names[:]:
print msr_name
measure_pathname = os.path.join(maps_dir, msr_name)
assert os.path.isdir(measure_pathname) == True
msr = measures.read_measure(measure_pathname)
eco_map = pcr.cover(msr.ecotopes.pcr_map, ecotopes.pcr_map)
msr_eco = measures.LegendMap(eco_map, ecotopes.legend_df)
bs_msr = biosafe.spatialBiosafe(bio_mod,
msr_eco,
sections,
ndff_species,
params=['FI', 'TFI'],
toFiles=None)
FI_msr, TFI_msr = bs_msr.spatial()
TFI_msr.drop(['xcoor', 'ycoor'], axis=1, inplace=True)
TFI_msr['msr'] = msr_name
FI_msr.drop(['xcoor', 'ycoor'], axis=1, inplace=True)
FI_msr['msr'] = msr_name
FI_list.append(FI_msr)
TFI_list.append(TFI_msr)
FI_all = pd.concat(FI_list)
TFI_all = pd.concat(TFI_list)
return FI_all, TFI_all
def assess_effects(cost_types, maps_dir, cost_correction):
"""
Determine all effects for all measures.
"""
measure_names = [
f for f in os.listdir(maps_dir)
if os.path.isdir(os.path.join(maps_dir, f))
]
cost_list = []
std_list = []
for msr_name in measure_names[:]:
print(msr_name)
measure_pathname = os.path.join(maps_dir, msr_name)
measure = read_measure(measure_pathname)
cost_all_types, std_all_types = assess_costs(cost_types, measure)
cost_list.append(cost_all_types)
std_list.append(std_all_types)
cost_all_msrs = pd.concat(cost_list)
std_all_msrs = pd.concat(std_list)
cost_all_msrs.index.name = 'id'
std_all_msrs.index.name = 'id'
return cost_all_msrs, std_all_msrs
# Set earthwork values
cost_input_ew = pd.read_csv(os.path.join(cost_dir, 'cost_ew.csv'),
index_col=0)
cost_correction = pd.read_csv(os.path.join(cost_dir,
'cost_correction.csv'),
index_col=0,
comment='#')
#%% Test the cost calculation
msr_name = 'sidechannel_everywhere'
label = 'everywhere'
msr_name = 'lowering_everywhere'
label = 'everywhere'
#msr_name = 'minemblowering_dummy-ID'
#msr_name = 'groynelowering_dummy-ID'
msr = read_measure(os.path.join(msr_map_dir, msr_name))
printing = True
dh = dem - msr.dem
vtot = pcr.maptotal(dh * 25**2)
pcr.aguila(dh)
### Smoothing
c_sm = CostSmoothing('dummy', smoothing_distr)
sm_cost, sm_std = c_sm.overview(msr)
if printing == True:
print(sm_cost)
print(sm_std)
### Earth work
c_ew = CostEarthwork(label, minemb_ext_use_distr, minemb_int_use_distr,
minemb_polluted_distr, dem, minemb, groyne,
pollution_zones, cost_input_ew)