def irma_percen(irma_tc_s, irma_tc_n, exp_s, exp_n):
""" Plot irma damage in % in lesser antilles and TCA. """
if_exp = ImpactFuncSet()
if_em = IFTropCyclone()
if_em.set_emanuel_usa()
if_exp.append(if_em)
fig, axs = plt.subplots(1,
2,
figsize=(16, 20),
subplot_kw=dict(projection=ccrs.PlateCarree()),
squeeze=True,
sharex=False,
sharey=False)
for idx, axis in enumerate(axs.flatten()):
grid = axis.gridlines(draw_labels=True, alpha=0.2)
grid.xlabels_top = grid.ylabels_right = False
grid.xformatter = LONGITUDE_FORMATTER
grid.yformatter = LATITUDE_FORMATTER
# south
plot_percen(irma_tc_s, exp_s, if_exp, axs[0])
# nord
im = plot_percen(irma_tc_n, exp_n, if_exp, axs[1])
plt.subplots_adjust(wspace=0.14)
fig.subplots_adjust(right=0.88)
cbar_ax = fig.add_axes([0.885, 0.4285, 0.03, 0.148])
fig.colorbar(im, cax=cbar_ax, orientation='vertical', label='% Damage')
return fig
def test_calc_sector_total_impact(self):
"""Test running total impact calculations."""
sup = SupplyChain()
sup.read_wiod16(year='test',
range_rows=(5, 117),
range_cols=(4, 116),
col_iso3=2,
col_sectors=1)
# Tropical cyclone over Florida and Caribbean
hazard = Hazard('TC')
hazard.read_mat(HAZ_TEST_MAT)
# Read demo entity values
# Set the entity default file to the demo one
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
exp.check()
exp.gdf.region_id = 840 #assign right id for USA
exp.assign_centroids(hazard)
impf_tc = IFTropCyclone()
impf_tc.set_emanuel_usa()
impf_set = ImpactFuncSet()
impf_set.append(impf_tc)
impf_set.check()
sup.calc_sector_direct_impact(hazard, exp, impf_set)
sup.calc_indirect_impact(io_approach='ghosh')
sup.calc_total_impact()
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.total_impact.shape)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.total_aai_agg.shape)
def init_if(if_name_or_instance, param_dict, df_out = pd.DataFrame(index=[0])):
""" create an ImpactFunc based on the parameters in param_dict using the
method specified in if_parameterisation_name and document it in df_out.
Parameters:
if_name_or_instance (str or ImpactFunc): method of impact function
parameterisation e.g. 'emanuel' or an instance of ImpactFunc
param_dict: dict of parameter_names and values
e.g. {'v_thresh': 25.7, 'v_half': 70, 'scale': 1}
or e.g. {'mdd_shift': 1.05, 'mdd_scale': 0.8,
'paa_shift': 1, paa_scale': 1}
Returns:
ImpactFunc: The Impact function based on the parameterisation
df_out: Output DataFrame with headers of columns defined and with
first row (index=0) defined with values. The impact function
parameters from param_dict are represented here.
"""
ImpactFunc_final = None
if isinstance(if_name_or_instance,str):
if if_name_or_instance == 'emanuel':
ImpactFunc_final = IFTropCyclone()
ImpactFunc_final.set_emanuel_usa(**param_dict)
ImpactFunc_final.haz_type = 'TC'
ImpactFunc_final.id = 1
df_out['impact_function'] = if_name_or_instance
elif isinstance(if_name_or_instance,impact_funcs.ImpactFunc):
ImpactFunc_final = change_if(if_name_or_instance, param_dict)
df_out['impact_function'] = ('given_' +
ImpactFunc_final.haz_type +
str(ImpactFunc_final.id))
for key, val in param_dict.items():
df_out[key] = val
return ImpactFunc_final, df_out
def calc_imp(expo_dict, tc_dict, data_dir):
""" Compute impacts of TCs in every island group. """
try:
abs_path = os.path.join(data_dir, 'imp_isl.p')
with open(abs_path, 'rb') as f:
imp_dict = pickle.load(f)
print('Loaded imp_isl:', len(imp_dict))
except FileNotFoundError:
if_exp = ImpactFuncSet()
if_em = IFTropCyclone()
if_em.set_emanuel_usa()
if_exp.add_func(if_em)
imp_dict = dict()
for isl_iso in expo_dict:
imp = Impact()
imp.calc(expo_dict[isl_iso], if_exp, tc_dict[isl_iso])
imp_dict[isl_iso] = imp
save(os.path.join(data_dir, 'imp_isl.p'), imp_dict)
return imp_dict
def plot_right(irma_tc, exp, ax, scale_pos, plot_line=False):
""" Plot irma damage in USD. """
if_exp = ImpactFuncSet()
if_em = IFTropCyclone()
if_em.set_emanuel_usa()
if_exp.append(if_em)
imp_irma = Impact()
imp_irma.calc(exp, if_exp, irma_tc)
extent = [
exp.longitude.min() - BUFFER_DEG,
exp.longitude.max() + BUFFER_DEG,
exp.latitude.min() - BUFFER_DEG,
exp.latitude.max() + BUFFER_DEG
]
ax.set_extent((extent))
u_plot.add_shapes(ax)
sel_pos = np.argwhere(imp_irma.eai_exp > 0)[:, 0]
hex_bin = ax.hexbin(imp_irma.coord_exp[sel_pos, 1],
imp_irma.coord_exp[sel_pos, 0],
C=imp_irma.eai_exp[sel_pos],
reduce_C_function=np.average,
transform=ccrs.PlateCarree(),
gridsize=2000,
norm=LogNorm(vmin=MIN_VAL, vmax=MAX_VAL),
cmap='YlOrRd',
vmin=MIN_VAL,
vmax=MAX_VAL)
ax.set_title('')
ax.grid(False)
add_cntry_names(ax, extent)
scale_bar(ax, scale_pos, 10)
if plot_line:
x1, y1 = [-64.57, -64.82], [18.28, 18.47]
ax.plot(x1, y1, linewidth=1.0, color='grey', linestyle='--')
return hex_bin
def calc_sector_direct_impact(self):
"""Test running direct impact calculations."""
sup = SupplyChain()
sup.read_wiod16(year='test',
range_rows=(5, 117),
range_cols=(4, 116),
col_iso3=2,
col_sectors=1)
# Tropical cyclone over Florida and Caribbean
hazard = Hazard('TC')
hazard.read_mat(HAZ_TEST_MAT)
# Read demo entity values
# Set the entity default file to the demo one
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
exp.check()
exp.gdf.region_id = 840 #assign right id for USA
exp.assign_centroids(hazard)
impf_tc = IFTropCyclone()
impf_tc.set_emanuel_usa()
impf_set = ImpactFuncSet()
impf_set.append(impf_tc)
impf_set.check()
subsecs = list(range(10)) + list(range(15, 25))
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec=subsecs)
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.reg_dir_imp[0], 'USA')
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, subsecs].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[subsecs].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='manufacturing')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.reg_dir_imp[0], 'USA')
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(4, 23)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(4, 23)].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='agriculture')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(0, 1)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(0, 1)].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='mining')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(3, 4)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(3, 4)].sum(),
places=3)
sup.calc_sector_direct_impact(hazard,
exp,
impf_set,
selected_subsec='service')
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.direct_impact.shape)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.direct_aai_agg.shape)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[sup.reg_pos['USA']].sum(),
places=3)
self.assertAlmostEqual(sup.direct_impact.sum(),
sup.direct_impact[:, range(26, 56)].sum(),
places=3)
self.assertAlmostEqual(sup.direct_aai_agg.sum(),
sup.direct_aai_agg[range(26, 56)].sum(),
places=3)