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