Esempio n. 1
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def make_figure_effect_by_mag(extractors, what):
    """
    $ oq plot "effect_by_mag?"
    """
    # NB: matplotlib is imported inside since it is a costly import
    import matplotlib.pyplot as plt
    [ex] = extractors
    gsims_by_trt = ex.get('gsims_by_trt', asdict=True)
    mags = ex.get('source_mags').array
    try:
        effect = ex.get('effect')
    except KeyError:
        onesite = ex.get('sitecol').one()
        maximum_distance = MagDepDistance(ex.oqparam.maximum_distance)
        imtls = ex.oqparam.imtls
        ebm = get_effect_by_mag(mags, onesite, gsims_by_trt, maximum_distance,
                                imtls)
        effect = numpy.array(list(ebm.values()))
    fig, ax = plt.subplots()
    trti = 0
    for trt in gsims_by_trt:
        ax.plot(mags, effect[:, -1, trti], label=trt)
        ax.set_xlabel('Mag')
        ax.set_ylabel('Intensity')
        ax.set_title('Effect at maximum distance')
        trti += 1
    ax.legend()
    return plt
Esempio n. 2
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 def test_single_site(self):
     # NB: the performance of get_mean_std is totally dominated by two
     # concomitant factors:
     # 1) source splitting (do not split the area source)
     # 2) collect the contexts in a single array
     # together they give a 200x speedup
     # numba is totally useless
     site = Site(Point(0, 0),
                 vs30=760.,
                 z1pt0=48.0,
                 z2pt5=0.607,
                 vs30measured=True)
     sitecol = SiteCollection([site])
     imtls = {"PGA": [.123]}
     for period in numpy.arange(.1, 1.3, .1):
         imtls['SA(%.2f)' % period] = [.123]
     assert len(imtls) == 13  # 13 periods
     oq = unittest.mock.Mock(imtls=DictArray(imtls),
                             maximum_distance=MagDepDistance.new('300'))
     mon = Monitor()
     hcurve = calc_hazard_curve(sitecol, asource, [ExampleA2021()], oq, mon)
     for child in mon.children:
         print(child)
     got = hcurve.array[:, 0]
     exp = [
         0.103379, 0.468937, 0.403896, 0.278772, 0.213645, 0.142985,
         0.103438, 0.079094, 0.062861, 0.051344, 0.04066, 0.031589, 0.024935
     ]
     numpy.testing.assert_allclose(got, exp, atol=1E-5)
Esempio n. 3
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 def test_international_date_line(self):
     # from a bug affecting a calculation in New Zealand
     fname = gettemp(characteric_source)
     [[src]] = nrml.to_python(fname)
     os.remove(fname)
     maxdist = MagDepDistance.new('200')
     sitecol = SiteCollection([
         Site(location=Point(176.919, -39.489),
              vs30=760, vs30measured=True, z1pt0=100, z2pt5=5)])
     srcfilter = SourceFilter(sitecol, maxdist)
     sites = srcfilter.get_close_sites(src)
     self.assertIsNotNone(sites)
Esempio n. 4
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    def __init__(self, trt, gsims, param=None, monitor=Monitor()):
        param = param or {}  # empty in the gmpe-smtk
        self.af = param.get('af', None)
        self.max_sites_disagg = param.get('max_sites_disagg', 10)
        self.collapse_level = param.get('collapse_level', False)
        self.trt = trt
        self.gsims = gsims
        self.single_site_opt = numpy.array(
            [hasattr(gsim, 'get_mean_std1') for gsim in gsims])
        self.maximum_distance = (param.get('maximum_distance')
                                 or MagDepDistance({}))
        self.investigation_time = param.get('investigation_time')
        self.trunclevel = param.get('truncation_level')
        self.num_epsilon_bins = param.get('num_epsilon_bins', 1)
        self.grp_id = param.get('grp_id', 0)
        self.effect = param.get('effect')
        self.task_no = getattr(monitor, 'task_no', 0)
        for req in self.REQUIRES:
            reqset = set()
            for gsim in gsims:
                reqset.update(getattr(gsim, 'REQUIRES_' + req))
            setattr(self, 'REQUIRES_' + req, reqset)
        # self.pointsource_distance is a dict mag -> dist, possibly empty
        psd = param.get('pointsource_distance')
        if hasattr(psd, 'ddic'):
            self.pointsource_distance = psd.ddic.get(trt, {})
        else:
            self.pointsource_distance = {}
        if 'imtls' in param:
            self.imtls = param['imtls']
        elif 'hazard_imtls' in param:
            self.imtls = DictArray(param['hazard_imtls'])
        else:
            self.imtls = {}
        self.imts = [imt_module.from_string(imt) for imt in self.imtls]
        self.reqv = param.get('reqv')
        if self.reqv is not None:
            self.REQUIRES_DISTANCES.add('repi')
        self.mon = monitor
        self.ctx_mon = monitor('make_contexts', measuremem=False)
        self.loglevels = DictArray(self.imtls) if self.imtls else {}
        self.shift_hypo = param.get('shift_hypo')
        with warnings.catch_warnings():
            # avoid RuntimeWarning: divide by zero encountered in log
            warnings.simplefilter("ignore")
            for imt, imls in self.imtls.items():
                if imt != 'MMI':
                    self.loglevels[imt] = numpy.log(imls)

        # instantiate monitors
        self.gmf_mon = monitor('computing mean_std', measuremem=False)
        self.poe_mon = monitor('get_poes', measuremem=False)
Esempio n. 5
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    def __init__(self, trt, gsims, param=None, monitor=Monitor()):
        param = param or {}
        self.af = param.get('af', None)
        self.max_sites_disagg = param.get('max_sites_disagg', 10)
        self.split_sources = param.get('split_sources', True)
        self.collapse_level = param.get('collapse_level', False)
        self.point_rupture_bins = param.get('point_rupture_bins', 20)
        self.trt = trt
        self.gsims = gsims
        self.maximum_distance = (param.get('maximum_distance')
                                 or MagDepDistance({}))
        self.trunclevel = param.get('truncation_level')
        self.effect = param.get('effect')
        for req in self.REQUIRES:
            reqset = set()
            for gsim in gsims:
                reqset.update(getattr(gsim, 'REQUIRES_' + req))
            setattr(self, 'REQUIRES_' + req, reqset)
        # self.pointsource_distance is a dict mag -> dist, possibly empty
        if param.get('pointsource_distance'):
            self.pointsource_distance = param['pointsource_distance'][trt]
        else:
            self.pointsource_distance = {}
        self.filter_distance = 'rrup'
        if 'imtls' in param:
            self.imtls = param['imtls']
        elif 'hazard_imtls' in param:
            self.imtls = DictArray(param['hazard_imtls'])
        else:
            self.imtls = {}
        self.imts = [imt_module.from_string(imt) for imt in self.imtls]
        self.reqv = param.get('reqv')
        if self.reqv is not None:
            self.REQUIRES_DISTANCES.add('repi')
        self.mon = monitor
        self.ctx_mon = monitor('make_contexts', measuremem=False)
        self.loglevels = DictArray(self.imtls)
        self.shift_hypo = param.get('shift_hypo')
        with warnings.catch_warnings():
            # avoid RuntimeWarning: divide by zero encountered in log
            warnings.simplefilter("ignore")
            for imt, imls in self.imtls.items():
                if imt != 'MMI':
                    self.loglevels[imt] = numpy.log(imls)

        # instantiate monitors
        self.gmf_mon = monitor('computing mean_std', measuremem=False)
        self.poe_mon = monitor('get_poes', measuremem=False)
Esempio n. 6
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 def test_two_sites(self):
     site1 = Site(Point(0, 0),
                  vs30=760.,
                  z1pt0=48.0,
                  z2pt5=0.607,
                  vs30measured=True)
     site2 = Site(Point(0, 0.5),
                  vs30=760.,
                  z1pt0=48.0,
                  z2pt5=0.607,
                  vs30measured=True)
     sitecol = SiteCollection([site1, site2])
     srcfilter = SourceFilter(sitecol, MagDepDistance.new('200'))
     imtls = {"PGA": [.123]}
     for period in numpy.arange(.1, .5, .1):
         imtls['SA(%.2f)' % period] = [.123]
     assert len(imtls) == 5  # 5 periods
     gsim_by_trt = {'Stable Continental Crust': ExampleA2021()}
     hcurves = calc_hazard_curves([asource], srcfilter, DictArray(imtls),
                                  gsim_by_trt)
     print(hcurves)
Esempio n. 7
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    def test_point_sources(self):
        sources = [
            openquake.hazardlib.source.PointSource(
                source_id='point1', name='point1',
                tectonic_region_type=const.TRT.ACTIVE_SHALLOW_CRUST,
                mfd=openquake.hazardlib.mfd.EvenlyDiscretizedMFD(
                    min_mag=4, bin_width=1, occurrence_rates=[5]
                ),
                nodal_plane_distribution=openquake.hazardlib.pmf.PMF([
                    (1, openquake.hazardlib.geo.NodalPlane(strike=0.0,
                                                           dip=90.0,
                                                           rake=0.0))
                ]),
                hypocenter_distribution=openquake.hazardlib.pmf.PMF([(1, 10)]),
                upper_seismogenic_depth=0.0,
                lower_seismogenic_depth=10.0,
                magnitude_scaling_relationship=
                openquake.hazardlib.scalerel.PeerMSR(),
                rupture_aspect_ratio=2,
                temporal_occurrence_model=PoissonTOM(1.),
                rupture_mesh_spacing=1.0,
                location=Point(10, 10)
            ),
            openquake.hazardlib.source.PointSource(
                source_id='point2', name='point2',
                tectonic_region_type=const.TRT.ACTIVE_SHALLOW_CRUST,
                mfd=openquake.hazardlib.mfd.EvenlyDiscretizedMFD(
                    min_mag=4, bin_width=2, occurrence_rates=[5, 6, 7]
                ),
                nodal_plane_distribution=openquake.hazardlib.pmf.PMF([
                    (1, openquake.hazardlib.geo.NodalPlane(strike=0,
                                                           dip=90,
                                                           rake=0.0)),
                ]),
                hypocenter_distribution=openquake.hazardlib.pmf.PMF([(1, 10)]),
                upper_seismogenic_depth=0.0,
                lower_seismogenic_depth=10.0,
                magnitude_scaling_relationship=
                openquake.hazardlib.scalerel.PeerMSR(),
                rupture_aspect_ratio=2,
                temporal_occurrence_model=PoissonTOM(1.),
                rupture_mesh_spacing=1.0,
                location=Point(10, 11)
            ),
        ]
        sites = [openquake.hazardlib.site.Site(Point(11, 10), 1, 2, 3),
                 openquake.hazardlib.site.Site(Point(10, 16), 2, 2, 3),
                 openquake.hazardlib.site.Site(Point(10, 10.6, 1), 3, 2, 3),
                 openquake.hazardlib.site.Site(Point(10, 10.7, -1), 4, 2, 3)]
        sitecol = openquake.hazardlib.site.SiteCollection(sites)
        gsims = {const.TRT.ACTIVE_SHALLOW_CRUST: SadighEtAl1997()}
        truncation_level = 1
        imts = {'PGA': [0.1, 0.5, 1.3]}
        s_filter = SourceFilter(sitecol, MagDepDistance.new('30'))
        result = calc_hazard_curves(
            sources, s_filter, imts, gsims, truncation_level)['PGA']
        # there are two sources and four sites. The first source contains only
        # one rupture, the second source contains three ruptures.
        #
        # the first source has 'maximum projection radius' of 0.707 km
        # the second source has 'maximum projection radius' of 500.0 km
        #
        # the epicentral distances for source 1 are: [ 109.50558394,
        # 667.16955987,   66.71695599,   77.83644865]
        # the epicentral distances for source 2 are: [ 155.9412148 ,
        # 555.97463322,   44.47797066,   33.35847799]
        #
        # Considering that the source site filtering distance is set to 30 km,
        # for source 1, all sites have epicentral distance larger than
        # 0.707 + 30 km. This means that source 1 ('point 1') is not considered
        # in the calculation because too far.
        # for source 2, the 1st, 3rd and 4th sites have epicentral distances
        # smaller than 500.0 + 30 km. This means that source 2 ('point 2') is
        # considered in the calculation for site 1, 3, and 4.
        #
        # JB distances for rupture 1 in source 2 are: [ 155.43860273,
        #  555.26752644,   43.77086388,   32.65137121]
        # JB distances for rupture 2 in source 2 are: [ 150.98882575,
        #  548.90356541,   37.40690285,   26.28741018]
        # JB distances for rupture 3 in source 2 are: [ 109.50545819,
        # 55.97463322,    0.        ,    0.        ]
        #
        # Considering that the rupture site filtering distance is set to 30 km,
        # rupture 1 (magnitude 4) is not considered because too far, rupture 2
        # (magnitude 6) affect only the 4th site, rupture 3 (magnitude 8)
        # affect the 3rd and 4th sites.

        self.assertEqual(result.shape, (4, 3))  # 4 sites, 3 levels
        numpy.testing.assert_allclose(result[0], 0)  # no contrib to site 1
        numpy.testing.assert_allclose(result[1], 0)  # no contrib to site 2

        # test that depths are kept after filtering (sites 3 and 4 remain)
        s_filter = SourceFilter(sitecol, MagDepDistance.new('100'))
        numpy.testing.assert_array_equal(
            s_filter.get_close_sites(sources[0]).depths, ([1, -1]))
Esempio n. 8
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    def test_bounding_box(self):
        maxdist = MagDepDistance.new('400')

        aae(maxdist('ANY_TRT'), 400)
        bb = maxdist.get_bounding_box(0, 10, 'ANY_TRT')
        aae(bb, [-3.6527738, 6.40272, 3.6527738, 13.59728])
Esempio n. 9
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from openquake.hazardlib import read_input, valid
from openquake.hazardlib.cross_correlation import BakerJayaram2008
from openquake.hazardlib.calc.filters import MagDepDistance

OVERWRITE_EXPECTED = False

CWD = os.path.dirname(__file__)
SOURCES_XML = os.path.join(CWD, 'data', 'sm01.xml')
GSIM_XML = os.path.join(CWD, 'data', 'lt02.xml')
PARAM = dict(source_model_file=SOURCES_XML,
             gsim_logic_tree_file=GSIM_XML,
             sites=[(0, -0.8)],
             reference_vs30_value=600,
             reference_depth_to_2pt5km_per_sec=5,
             reference_depth_to_1pt0km_per_sec=100,
             maximum_distance=MagDepDistance.new('200'),
             rupture_mesh_spacing=5.,
             width_of_mfd_bin=1.,
             investigation_time=1,
             truncation_level=3,
             cross_correl=BakerJayaram2008(),
             imtls={"SA(0.05)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.075)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.1)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.15)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.2)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.25)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.3)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.5)": valid.logscale(0.005, 2.13, 45),
                    "SA(0.75)": valid.logscale(0.005, 2.13, 45),
                    "SA(1.0)": valid.logscale(0.005, 2.13, 45),
Esempio n. 10
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def main(params):
    # example with 2x2=4 realizations with weights .36, .24, .24, .16
    inp = read_input(params)
    print(inp)
    print(inp.gsim_lt.get_rlzs_by_gsim_trt())
    acc = AccumDict(accum=[])
    ebrs = sample_ebruptures(inp.groups, inp.cmakerdict)
    ne = sum(ebr.n_occ for ebr in ebrs)
    print('There are %d ruptures and %d events' % (len(ebrs), ne))
    df = get_ebr_df(ebrs, inp.cmakerdict)
    print(df.groupby('rlz').count())  # there are 8, 9, 11, 8 events per rlz
    for ebr in ebrs:
        cmaker = inp.cmakerdict[ebr.rupture.tectonic_region_type]
        gmf_dict, _ = GmfComputer(ebr, inp.sitecol, cmaker).compute_all()
        acc += gmf_dict
    print(pandas.DataFrame(acc))


if __name__ == '__main__':
    params = dict(maximum_distance=MagDepDistance.new('200'),
                  imtls={'PGA': [0]},
                  source_model_file="source_model.xml",
                  area_source_discretization=10,
                  ses_seed=24,
                  ses_per_logic_tree_path=20,
                  investigation_time=1,
                  site_model_file="site_model.csv",
                  gsim_logic_tree_file="gmpe_logic_tree.xml")
    main(params)