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,collapse_map,bounding_box,log_scale,exposure_model,marker_size,export_map_to_csv): exposure_path = exposure_model agg_collapses = True if plotting_type == 1: agg_collapses = False data = parsecm.parse_collapse_maps(collapse_map,agg_collapses,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]) collapses = np.array(data[1][1]) plot_single_map(locations,collapses,box,log_scale,marker_size,'Aggregated Collapses per location',1) if plotting_type == 1 or plotting_type == 2: individualCollapses = data[0] idTaxonomies = np.array(parsee.extractIDTaxonomies(exposure_path,False)) uniqueTaxonomies = extract_unique_taxonomies(idTaxonomies[:,1]) collapsesTaxonomies = np.zeros((len(uniqueTaxonomies))) for i in range(len(uniqueTaxonomies)): locations,collapses = processLosses(uniqueTaxonomies[i],idTaxonomies,individualCollapses) collapsesTaxonomies[i] = sum(collapses) if locations.shape[0] > 0: plot_single_map(locations,collapses,box,log_scale,marker_size,'Collapse map for '+uniqueTaxonomies[i],i+2) plot_pie_chart_losses(uniqueTaxonomies,collapsesTaxonomies)
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) 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, collapse_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_collapses = True if plotting_type == 1: agg_collapses = False data = parsecm.parse_collapse_maps(collapse_map, agg_collapses, 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]) collapses = np.array(data[1][1]) plot_single_map(locations, collapses, box, log_scale, marker_size, 'Aggregated Collapses per location', 1) if plotting_type == 1 or plotting_type == 2: individualCollapses = data[0] idTaxonomies = np.array( parsee.extractIDTaxonomies(exposure_path, False)) uniqueTaxonomies = extract_unique_taxonomies(idTaxonomies[:, 1]) collapsesTaxonomies = np.zeros((len(uniqueTaxonomies))) for i in range(len(uniqueTaxonomies)): locations, collapses = processLosses(uniqueTaxonomies[i], idTaxonomies, individualCollapses) collapsesTaxonomies[i] = sum(collapses) if locations.shape[0] > 0: plot_single_map(locations, collapses, box, log_scale, marker_size, 'Collapse map for ' + uniqueTaxonomies[i], i + 2) plot_pie_chart_losses(uniqueTaxonomies, collapsesTaxonomies)
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)