hazimp.jobs.vulnerability_model

Title: vulnerability_model.py

Author: Ben Cooper, ben.cooper@ga.gov.au

Description: TODO:

Copyright 2012 by Geoscience Australia

Module Contents

Classes

VulnerabilitySet	A set of vulnerability functions for a given intensity measure
VulnerabilityFunction	A vulnerability function defined by a specified set of points on a curve.
RealisedVulnerabilityCurves	Respresents a collection of vulnerability curves associated with an

Functions

vuln_sets_from_xml_file(filenames: list) → dict	Load a GEM NRML vulnerability file in the format described in
vuln_sets_from_xml_node(xml_node)	Load in the vulnerability sets from an xml node.
ratio_cutoff(ratio)	As VulnerabilityFunction.sample may return values outside of a ratio,

Attributes

LOGGER
DEFAULTLOSS

hazimp.jobs.vulnerability_model.LOGGER

hazimp.jobs.vulnerability_model.DEFAULTLOSS = 0

hazimp.jobs.vulnerability_model.vuln_sets_from_xml_file(filenames: list) → dict

Load a GEM NRML vulnerability file in the format described in resources/nrml/schema/risk/vulnerability.xsd

Args: :param filenames: The names of the xml files.

Returns:

A dictionary of Vulnerability Sets.

hazimp.jobs.vulnerability_model.vuln_sets_from_xml_node(xml_node)

Load in the vulnerability sets from an xml node.

Parameters:

• vulnerability_models – Dictionary to store vulnerability models.

• xml_node – The root node of the vulnerability xml file.

Returns:

A dictionary of Vulnerability Sets.

class hazimp.jobs.vulnerability_model.VulnerabilitySet(intensity_measure_level, intensity_measure_type, vulnerability_set_id, asset_category, loss_category, vulnerability_functions, default_loss)

Bases: object

A set of vulnerability functions for a given intensity measure level. All vulnerability functions have the same intensity measure (x-axis) and loss category (y-axis). Each vulnerability function represents a class of assets, such as brick buildings etc.

Methods: - calc_mean - return mean loss and sigma for the given function id - sample - return a sample for the given function id

Constructor input: - intensity_measure_level - a set of points for the x axis of the mean loss

curve (common to all functions)

• intensity_measure_type - type of intensity measure that the intensity measure level specifies (‘MMI’ is the only supported value)

• vulnerability_functions - a dictionary of VulnerabilityFunction objects where the function id is the key

Class method: - from_xml - construct a Vulnerability_Set object from a NRML vulnerability

file

__repr__(self)

Return repr(self).

build_realised_vuln_curves(self, vulnerability_function_ids, variability_method=None)

Given a list of vulnerability_function_IDs return the actual vulnerability curves, as a realised vulnerabitly curves instance.

Currently this just returns a mean curve.

Parmas vulnerability_function_IDs:

A list of the vuln. functions. The list dimension is asset.

Parmas variability_method:

How the vulnerability function is sampled.

Returns:

A realised vulnerabitly curves instance. Use this to calc the loss ratio.

calc_mean(self, func_id, intensity)

A wrapper for VulnerabilityFunction.calc_mean. Lookup the specified vulnerability function in the set and run calc_mean for that function.

sample(self, func_id, mean, sigma)

A wrapper for VulnerabilityFunction.sample. Lookup the specified vulnerability function in the set and run sample for that function.

Parameters:

• func_id (str) – Then name of the vulnerability function set

• mean (float) – Mean value of the selected vulnerability function

• sigma (float) – the coefficient of variation of the selected vunlerability function

hazimp.jobs.vulnerability_model.ratio_cutoff(ratio)

As VulnerabilityFunction.sample may return values outside of a ratio, ensure that the given ratio array is between 0 and 1 by setting values outside these bounds to the bounds.

Parameters:

ratio (float) – the value to be tested.

Returns:

1 if ratio > 1.0, 0 if ratio < 0.0, or the value of ratio otherwise.

class hazimp.jobs.vulnerability_model.VulnerabilityFunction(func_id, mean_loss, coefficient_of_variation, distribution)

Bases: object

A vulnerability function defined by a specified set of points on a curve.

Methods: calc_mean - return mean loss and sigma based on the given set of points sample - return a sample based on the specified probabilistic distribution

get_loss(self, variability_method=None)

Get the actual loss for a curve.

Currently this just returns a mean curve.

Parameters:

variability_method – How the vulnerability function is sampled.

Returns:

The loss y-axis values, sampled using the supplied method.

calc_mean(self, intensity)

Calculate mean loss ratio and sigma based on the specified points on the curve:

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Mean loss ratio | +

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Intensity measure level

For a given intensity, mean loss and sigma is determined by linearly interpolating the points on the curve.

Note that sigma is calculated as cv * mean loss as cv = mean loss/sigma

classmethod from_xml_node(cls, xml_node)

Load in a vulnerability function from an xml node.

Parameters:

xml_node – The root node of the vulnerability xml file.

Returns:

A vulnerability function.

class hazimp.jobs.vulnerability_model.RealisedVulnerabilityCurves(intensity_measure_type, loss_category_type, intensity_measure_level, loss_per_asset, vulnerability_set_id, default_loss)

Bases: object

Respresents a collection of vulnerability curves associated with an exposure set.

There is one vulnerability curve per asset

look_up(self, intensity)

Given an intensity use the curve to determine the loss ratio.

Parameters:

intensity – An array intensity measures. Dimensions(asset, …)

Returns:

A loss value. loss_category_type describes type of loss.