Python AccumDict.rup_data方法代码示例

# 需要导入模块: from openquake.baselib.general import AccumDict [as 别名]
# 或者: from openquake.baselib.general.AccumDict import rup_data [as 别名]
def compute_ruptures(sources, src_filter, gsims, monitor):
    """
    :param sources:
        List of commonlib.source.Source tuples
    :param src_filter:
        a source site filter
    :param gsims:
        a list of GSIMs for the current tectonic region model
    :param monitor:
        monitor instance
    :returns:
        a dictionary src_group_id -> [Rupture instances]
    """
    # NB: by construction each block is a non-empty list with
    # sources of the same src_group_id
    grp_id = sources[0].src_group_id
    trt = sources[0].tectonic_region_type
    eb_ruptures = []
    calc_times = []
    rup_mon = monitor('filtering ruptures', measuremem=False)
    num_samples = monitor.samples
    num_events = 0

    # Compute and save stochastic event sets
    for src, s_sites in src_filter(sources):
        t0 = time.time()
        if s_sites is None:
            continue
        max_dist = src_filter.integration_distance[trt]
        rupture_filter = functools.partial(
            filter_sites_by_distance_to_rupture,
            integration_distance=max_dist, sites=s_sites)
        num_occ_by_rup = sample_ruptures(
            src, monitor.ses_per_logic_tree_path, num_samples,
            monitor.seed)
        # NB: the number of occurrences is very low, << 1, so it is
        # more efficient to filter only the ruptures that occur, i.e.
        # to call sample_ruptures *before* the filtering
        for ebr in build_eb_ruptures(
                src, num_occ_by_rup, rupture_filter, monitor.seed, rup_mon):
            eb_ruptures.append(ebr)
            num_events += ebr.multiplicity
        dt = time.time() - t0
        calc_times.append((src.id, dt))
    res = AccumDict({grp_id: eb_ruptures})
    res.num_events = num_events
    res.calc_times = calc_times
    res.rup_data = {grp_id: calc.RuptureData(trt, gsims).to_array(eb_ruptures)}
    return res

# 需要导入模块: from openquake.baselib.general import AccumDict [as 别名]
# 或者: from openquake.baselib.general.AccumDict import rup_data [as 别名]
def compute_ruptures(sources, sitecol, siteidx, rlzs_assoc, monitor):
    """
    :param sources:
        List of commonlib.source.Source tuples
    :param sitecol:
        a :class:`openquake.hazardlib.site.SiteCollection` instance
    :param siteidx:
        always equal to 0
    :param rlzs_assoc:
        a :class:`openquake.commonlib.source.RlzsAssoc` instance
    :param monitor:
        monitor instance
    :returns:
        a dictionary src_group_id -> [Rupture instances]
    """
    assert siteidx == 0, (
        'siteidx can be nonzero only for the classical_tiling calculations: '
        'tiling with the EventBasedRuptureCalculator is an error')
    # NB: by construction each block is a non-empty list with
    # sources of the same src_group_id
    src_group_id = sources[0].src_group_id
    oq = monitor.oqparam
    trt = sources[0].tectonic_region_type
    max_dist = oq.maximum_distance[trt]
    cmaker = ContextMaker(rlzs_assoc.gsims_by_grp_id[src_group_id])
    params = sorted(cmaker.REQUIRES_RUPTURE_PARAMETERS)
    rup_data_dt = numpy.dtype(
        [('rupserial', U32), ('multiplicity', U16),
         ('numsites', U32), ('occurrence_rate', F32)] + [
            (param, F32) for param in params])
    eb_ruptures = []
    rup_data = []
    calc_times = []
    rup_mon = monitor('filtering ruptures', measuremem=False)
    num_samples = rlzs_assoc.samples[src_group_id]

    # Compute and save stochastic event sets
    for src in sources:
        t0 = time.time()
        s_sites = src.filter_sites_by_distance_to_source(max_dist, sitecol)
        if s_sites is None:
            continue
        rupture_filter = functools.partial(
            filter_sites_by_distance_to_rupture,
            integration_distance=max_dist, sites=s_sites)
        num_occ_by_rup = sample_ruptures(
            src, oq.ses_per_logic_tree_path, num_samples,
            rlzs_assoc.seed)
        # NB: the number of occurrences is very low, << 1, so it is
        # more efficient to filter only the ruptures that occur, i.e.
        # to call sample_ruptures *before* the filtering
        for ebr in build_eb_ruptures(
                src, num_occ_by_rup, rupture_filter, oq.random_seed, rup_mon):
            nsites = len(ebr.indices)
            try:
                rate = ebr.rupture.occurrence_rate
            except AttributeError:  # for nonparametric sources
                rate = numpy.nan
            rc = cmaker.make_rupture_context(ebr.rupture)
            ruptparams = tuple(getattr(rc, param) for param in params)
            rup_data.append((ebr.serial, len(ebr.etags), nsites, rate) +
                            ruptparams)
            eb_ruptures.append(ebr)
        dt = time.time() - t0
        calc_times.append((src.id, dt))
    res = AccumDict({src_group_id: eb_ruptures})
    res.calc_times = calc_times
    res.rup_data = numpy.array(rup_data, rup_data_dt)
    res.trt = trt
    return res

# 需要导入模块: from openquake.baselib.general import AccumDict [as 别名]
# 或者: from openquake.baselib.general.AccumDict import rup_data [as 别名]
def compute_ruptures(sources, sitecol, gsims, monitor):
    """
    :param sources:
        List of commonlib.source.Source tuples
    :param sitecol:
        a :class:`openquake.hazardlib.site.SiteCollection` instance
    :param gsims:
        a list of GSIMs for the current tectonic region model
    :param monitor:
        monitor instance
    :returns:
        a dictionary src_group_id -> [Rupture instances]
    """
    # NB: by construction each block is a non-empty list with
    # sources of the same src_group_id
    src_group_id = sources[0].src_group_id
    trt = sources[0].tectonic_region_type
    max_dist = monitor.maximum_distance[trt]
    cmaker = ContextMaker(gsims)
    params = sorted(cmaker.REQUIRES_RUPTURE_PARAMETERS)
    rup_data_dt = numpy.dtype(
        [('rupserial', U32), ('multiplicity', U16),
         ('numsites', U32), ('occurrence_rate', F64)] + [
            (param, F64) for param in params])
    eb_ruptures = []
    rup_data = []
    calc_times = []
    rup_mon = monitor('filtering ruptures', measuremem=False)
    num_samples = monitor.samples
    num_events = 0

    # Compute and save stochastic event sets
    for src in sources:
        t0 = time.time()
        s_sites = src.filter_sites_by_distance_to_source(max_dist, sitecol)
        if s_sites is None:
            continue
        rupture_filter = functools.partial(
            filter_sites_by_distance_to_rupture,
            integration_distance=max_dist, sites=s_sites)
        num_occ_by_rup = sample_ruptures(
            src, monitor.ses_per_logic_tree_path, num_samples,
            monitor.seed)
        # NB: the number of occurrences is very low, << 1, so it is
        # more efficient to filter only the ruptures that occur, i.e.
        # to call sample_ruptures *before* the filtering
        for ebr in build_eb_ruptures(
                src, num_occ_by_rup, rupture_filter, monitor.seed, rup_mon):
            nsites = len(ebr.indices)
            try:
                rate = ebr.rupture.occurrence_rate
            except AttributeError:  # for nonparametric sources
                rate = numpy.nan
            rc = cmaker.make_rupture_context(ebr.rupture)
            ruptparams = tuple(getattr(rc, param) for param in params)
            rup_data.append((ebr.serial, ebr.multiplicity, nsites, rate) +
                            ruptparams)
            eb_ruptures.append(ebr)
            num_events += ebr.multiplicity
        dt = time.time() - t0
        calc_times.append((src.id, dt))
    res = AccumDict({src_group_id: eb_ruptures})
    res.num_events = num_events
    res.calc_times = calc_times
    res.rup_data = numpy.array(rup_data, rup_data_dt)
    res.trt = trt
    return res