Exemple #1
0
def ffconvert(fname, limit_states, ff, min_iml=1E-10):
    """
    Convert a fragility function into a numpy array plus a bunch
    of attributes.

    :param fname: path to the fragility model file
    :param limit_states: expected limit states
    :param ff: fragility function node
    :returns: a pair (array, dictionary)
    """
    with context(fname, ff):
        ffs = ff[1:]
        imls = ff.imls
    nodamage = imls.attrib.get('noDamageLimit')
    if nodamage == 0:
        # use a cutoff to avoid log(0) in GMPE.to_distribution_values
        logging.warn(
            'Found a noDamageLimit=0 in %s, line %s, '
            'using %g instead', fname, ff.lineno, min_iml)
        nodamage = min_iml
    with context(fname, imls):
        attrs = dict(format=ff['format'], imt=imls['imt'], nodamage=nodamage)

    LS = len(limit_states)
    if LS != len(ffs):
        with context(fname, ff):
            raise InvalidFile('expected %d limit states, found %d' %
                              (LS, len(ffs)))
    if ff['format'] == 'continuous':
        minIML = float(imls['minIML'])
        if minIML == 0:
            # use a cutoff to avoid log(0) in GMPE.to_distribution_values
            logging.warn('Found minIML=0 in %s, line %s, using %g instead',
                         fname, imls.lineno, min_iml)
            minIML = min_iml
        attrs['minIML'] = minIML
        attrs['maxIML'] = float(imls['maxIML'])
        array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
        for i, ls, node in zip(range(LS), limit_states, ff[1:]):
            if ls != node['ls']:
                with context(fname, node):
                    raise InvalidFile('expected %s, found' % (ls, node['ls']))
            array['mean'][i] = node['mean']
            array['stddev'][i] = node['stddev']
    elif ff['format'] == 'discrete':
        attrs['imls'] = ~imls
        valid.check_levels(attrs['imls'], attrs['imt'])
        num_poes = len(attrs['imls'])
        array = numpy.zeros((LS, num_poes))
        for i, ls, node in zip(range(LS), limit_states, ff[1:]):
            with context(fname, node):
                if ls != node['ls']:
                    raise InvalidFile('expected %s, found' % (ls, node['ls']))
                poes = (~node if isinstance(~node, list) else
                        valid.probabilities(~node))
                if len(poes) != num_poes:
                    raise InvalidFile('expected %s, found' %
                                      (num_poes, len(poes)))
                array[i, :] = poes
    # NB: the format is constrained in nrml.FragilityNode to be either
    # discrete or continuous, there is no third option
    return array, attrs
Exemple #2
0
def ffconvert(fname, limit_states, ff, min_iml=1E-10):
    """
    Convert a fragility function into a numpy array plus a bunch
    of attributes.

    :param fname: path to the fragility model file
    :param limit_states: expected limit states
    :param ff: fragility function node
    :returns: a pair (array, dictionary)
    """
    with context(fname, ff):
        ffs = ff[1:]
        imls = ff.imls
    nodamage = imls.attrib.get('noDamageLimit')
    if nodamage == 0:
        # use a cutoff to avoid log(0) in GMPE.to_distribution_values
        logging.warning('Found a noDamageLimit=0 in %s, line %s, '
                        'using %g instead', fname, ff.lineno, min_iml)
        nodamage = min_iml
    with context(fname, imls):
        attrs = dict(format=ff['format'],
                     imt=imls['imt'],
                     id=ff['id'],
                     nodamage=nodamage)

    LS = len(limit_states)
    if LS != len(ffs):
        with context(fname, ff):
            raise InvalidFile('expected %d limit states, found %d' %
                              (LS, len(ffs)))
    if ff['format'] == 'continuous':
        minIML = float(imls['minIML'])
        if minIML == 0:
            # use a cutoff to avoid log(0) in GMPE.to_distribution_values
            logging.warning('Found minIML=0 in %s, line %s, using %g instead',
                            fname, imls.lineno, min_iml)
            minIML = min_iml
        attrs['minIML'] = minIML
        attrs['maxIML'] = float(imls['maxIML'])
        array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
        for i, ls, node in zip(range(LS), limit_states, ff[1:]):
            if ls != node['ls']:
                with context(fname, node):
                    raise InvalidFile('expected %s, found' %
                                      (ls, node['ls']))
            array['mean'][i] = node['mean']
            array['stddev'][i] = node['stddev']
    elif ff['format'] == 'discrete':
        attrs['imls'] = ~imls
        valid.check_levels(attrs['imls'], attrs['imt'], min_iml)
        num_poes = len(attrs['imls'])
        array = numpy.zeros((LS, num_poes))
        for i, ls, node in zip(range(LS), limit_states, ff[1:]):
            with context(fname, node):
                if ls != node['ls']:
                    raise InvalidFile('expected %s, found' %
                                      (ls, node['ls']))
                poes = (~node if isinstance(~node, list)
                        else valid.probabilities(~node))
                if len(poes) != num_poes:
                    raise InvalidFile('expected %s, found' %
                                      (num_poes, len(poes)))
                array[i, :] = poes
    # NB: the format is constrained in nrml.FragilityNode to be either
    # discrete or continuous, there is no third option
    return array, attrs