def build_map(plotting_type,risk_map,bounding_box,log_scale,exposure_model,marker_size,export_map_to_csv):
exposure_path = os.path.dirname(rmtk.__file__) + "/plotting/input_models/" + exposure_model
agg_losses = True
if plotting_type == 1:
agg_losses = False
data = parselm.parse_risk_maps(risk_map,agg_losses,export_map_to_csv)
box = define_bounding_box(bounding_box,data[0])
if plotting_type == 0 or plotting_type == 2:
locations = np.array(data[1][0])
losses = np.array(data[1][1])
plot_single_map(locations,losses,box,log_scale,marker_size,'Aggregated losses per location',1)
if plotting_type == 1 or plotting_type == 2:
individualLosses = data[0]
idTaxonomies = np.array(parsee.extractIDTaxonomies(exposure_path,False))
uniqueTaxonomies = extractUniqueTaxonomies(idTaxonomies[:,1])
lossesTaxonomies = np.zeros((len(uniqueTaxonomies)))
for i in range(len(uniqueTaxonomies)):
locations,losses = processLosses(uniqueTaxonomies[i],idTaxonomies,individualLosses)
lossesTaxonomies[i] = sum(losses)
if locations.shape[0] > 0:
plot_single_map(locations,losses,box,log_scale,marker_size,'Loss map for '+uniqueTaxonomies[i],i+2)
plot_pie_chart_losses(uniqueTaxonomies,lossesTaxonomies)
def build_map(plotting_type, risk_map, bounding_box, log_scale, exposure_model,
marker_size, export_map_to_csv):
exposure_path = exposure_model
agg_losses = True
if plotting_type == 1:
agg_losses = False
data = parselm.parse_risk_maps(risk_map, agg_losses, export_map_to_csv)
box = define_bounding_box(bounding_box, data[0])
if plotting_type == 0 or plotting_type == 2:
locations = np.array(data[1][0])
losses = np.array(data[1][1])
plot_single_map(locations, losses, box, log_scale, marker_size,
'Aggregated losses per location', 1)
if plotting_type == 1 or plotting_type == 2:
individualLosses = data[0]
idTaxonomies = np.array(
parsee.extractIDTaxonomies(exposure_path, False))
uniqueTaxonomies = extractUniqueTaxonomies(idTaxonomies[:, 1])
lossesTaxonomies = np.zeros((len(uniqueTaxonomies)))
for i in range(len(uniqueTaxonomies)):
locations, losses = processLosses(uniqueTaxonomies[i],
idTaxonomies, individualLosses)
lossesTaxonomies[i] = sum(losses)
if locations.shape[0] > 0:
plot_single_map(locations, losses, box, log_scale, marker_size,
'Loss map for ' + uniqueTaxonomies[i], i + 2)
plot_pie_chart_losses(uniqueTaxonomies, lossesTaxonomies)
def build_map(plotting_type, risk_map, bounding_box, log_scale, exposure_model,
marker_size, export_map_to_csv):
exposure_path = exposure_model
agg_losses = True
if plotting_type == 1:
agg_losses = False
data = parselm.parse_risk_maps(risk_map, agg_losses, export_map_to_csv)
meta_data = data[2]
box = define_bounding_box(bounding_box, data[0])
if plotting_type == 0 or plotting_type == 2:
locations = np.array(data[1][0])
losses = np.array(data[1][1])
plot_single_map(locations, losses, box, log_scale, marker_size,
'Aggregated losses per location', 1, meta_data)
if plotting_type == 1 or plotting_type == 2:
individualLosses = data[0]
idTaxonomies = np.array(
parsee.extractIDTaxonomies(exposure_path, False))
uniqueTaxonomies = extractUniqueTaxonomies(idTaxonomies[:, 1])
lossesTaxonomies = np.zeros((len(uniqueTaxonomies)))
for i in range(len(uniqueTaxonomies)):
locations, losses = processLosses(uniqueTaxonomies[i],
idTaxonomies, individualLosses)
lossesTaxonomies[i] = sum(losses)
nonzero_values = losses != 0
locations = locations[nonzero_values]
losses = losses[nonzero_values]
if locations.shape[0] > 0:
if meta_data['poE'] == 'None':
title = 'Scenario loss map for ' + uniqueTaxonomies[i]
else:
poe = str(
float(meta_data['poE']) * 100
) + '% in ' + meta_data['investigationTime'] + ' years'
title = 'Loss map (' + poe + ') for ' + uniqueTaxonomies[i]
plot_single_map(locations, losses, box, log_scale, marker_size,
title, i + 2, meta_data)
plot_pie_chart_losses(uniqueTaxonomies, lossesTaxonomies)
def build_map(plotting_type,risk_map,bounding_box,log_scale,exposure_model,marker_size,export_map_to_csv):
exposure_path = exposure_model
agg_losses = True
if plotting_type == 1:
agg_losses = False
data = parselm.parse_risk_maps(risk_map,agg_losses,export_map_to_csv)
meta_data = data[2]
box = define_bounding_box(bounding_box,data[0])
if plotting_type == 0 or plotting_type == 2:
locations = np.array(data[1][0])
losses = np.array(data[1][1])
plot_single_map(locations,losses,box,log_scale,marker_size,'Aggregated losses per location',1, meta_data)
if plotting_type == 1 or plotting_type == 2:
individualLosses = data[0]
idTaxonomies = np.array(parsee.extractIDTaxonomies(exposure_path,False))
uniqueTaxonomies = extractUniqueTaxonomies(idTaxonomies[:,1])
lossesTaxonomies = np.zeros((len(uniqueTaxonomies)))
for i in range(len(uniqueTaxonomies)):
locations,losses = processLosses(uniqueTaxonomies[i],idTaxonomies,individualLosses)
lossesTaxonomies[i] = sum(losses)
nonzero_values = losses != 0
locations = locations[nonzero_values]
losses = losses[nonzero_values]
if locations.shape[0] > 0:
if meta_data['poE'] == 'None':
title = 'Scenario loss map for ' + uniqueTaxonomies[i]
else:
poe = str(float(meta_data['poE'])*100) + '% in ' + meta_data['investigationTime'] + ' years'
title = 'Loss map ('+ poe + ') for '+uniqueTaxonomies[i]
plot_single_map(locations,losses,box,log_scale,marker_size,title,i+2, meta_data)
plot_pie_chart_losses(uniqueTaxonomies,lossesTaxonomies)
