def empty_measure(eco_clone):
"""Create an empty river measure."""
empty_map = pcr.scalar(mapIO.emptyMap(eco_clone.pcr_map))
empty_eco = measures.LegendMap(pcr.nominal(empty_map), eco_clone.legend_df)
settings = pd.DataFrame(
{1: ['lowering', 'dummy']},
index=['msr_type', 'ID'],
)
msr = measures.Measure(settings, empty_map, empty_map, empty_eco,
empty_map, empty_map, empty_map, empty_map)
return msr
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