Returns only the mean values.

        See documentation for method `GroundShakingIntensityModel` in
        :class:~`openquake.hazardlib.gsim.base.GSIM`
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def _get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        # distances
        distsl = copy.copy(dists)
        distsl.rjb, distsl.rrup = \
            utils.get_equivalent_distances_east(rup.mag, dists.repi)
        #
        # Pezeshk et al. 2011 - Rrup
        mean1, stds1 = super().get_mean_and_stddevs(sites, rup, distsl, imt,
                                                    stddev_types)
        mean1 = self.apply_correction_to_BC(mean1, imt, distsl)
        #
        # Atkinson 2008 - Rjb
        gmpe = Atkinson2008prime()
        mean2, stds2 = gmpe.get_mean_and_stddevs(sites, rup, distsl, imt,
                                                 stddev_types)
        #
        # Silva et al. 2002 - Rjb
        gmpe = SilvaEtAl2002SingleCornerSaturation()
        mean4, stds4 = gmpe.get_mean_and_stddevs(sites, rup, distsl, imt,
                                                 stddev_types)
        mean4 = self.apply_correction_to_BC(mean4, imt, distsl)
        #
        # Silva et al. 2002 - Rjb
        gmpe = SilvaEtAl2002DoubleCornerSaturation()
        mean5, stds5 = gmpe.get_mean_and_stddevs(sites, rup, distsl, imt,
                                                 stddev_types)
        mean5 = self.apply_correction_to_BC(mean5, imt, distsl)
        #
        # distances
        distsl.rjb, distsl.rrup = \
            utils.get_equivalent_distances_east(rup.mag, dists.repi, ab06=True)
        #
        # Atkinson and Boore 2006 - Rrup
        gmpe = AtkinsonBoore2006Modified2011()
        mean3, stds3 = gmpe.get_mean_and_stddevs(sites, rup, distsl, imt,
                                                 stddev_types)
        # Computing adjusted mean and stds
        mean_adj = mean1*0.2 + mean2*0.2 + mean3*0.2 + mean4*0.2 + mean5*0.2

        # Note that in this case we do not apply a triangular smoothing on
        # distance as explained at page 996 of Atkinson and Adams (2013)
        # for the calculation of the standard deviation
        stds_adj = np.log(np.exp(stds1)*0.2 + np.exp(stds2)*0.2 +
                          np.exp(stds3)*0.2 + np.exp(stds4)*0.2 +
                          np.exp(stds5)*0.2)
        #
        return mean_adj, stds_adj
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def mse(X):
    '''  '''
    return np.mean(np.square(np.abs(np.fft.fft(X, axis=1))), axis=1)
1325234
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
                   for stddev_type in stddev_types)

        C = self.COEFFS[imt]
        mean = self._get_mean(
            C, rup.mag, rup.rake, rup.dip, dists.rrup, dists.rjb
        )
        stddevs = self._get_stddevs(C, rup.mag, stddev_types, dists.rrup.size)

        return mean, stddevs
688655
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        # extracting dictionary of coefficients specific to required
        # intensity measure type.
        C = self.COEFFS[imt]

        mean = (self._get_magnitude_scaling_term(C, rup.mag) +
                self._get_distance_scaling_term(C, rup.mag, dists.rrup) +
                self._get_style_of_faulting_term(C, rup.rake) +
                self._get_site_scaling_term(C, sites.vs30))

        stddevs = self._get_stddevs(imt,
                                    rup.mag,
                                    len(dists.rrup),
                                    stddev_types)
        return mean, stddevs
293024
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        # extracting dictionary of coefficients specific to required
        # intensity measure type.

        C = self.COEFFS[imt]
        mean = (self._get_magnitude_scaling(C, rup.mag) +
                self._get_distance_scaling(C, dists, rup.mag) +
                self._get_site_term(C, sites.vs30))

        # Mean is returned in terms of m/s^2. Need to convert to g
        mean -= np.log(g)
        stddevs = self.get_stddevs(C, sites.vs30.shape, stddev_types)
        return mean + self.adjustment_factor, stddevs
461994
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        mean = self._get_mean(sites, rup, dists, imt, stddev_types)
        stddevs = [np.ones(len(dists.rrup))*get_sigma(imt)]
        return mean, stddevs
547341
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        mean, stddevs = super().get_mean_and_stddevs(
            sites, rup, dists, imt, stddev_types)

        idx_rock = sites.vs30 >= self.ROCK_VS30
        idx_soil = sites.vs30 < self.ROCK_VS30

        mean[idx_rock] += 0.275
        mean[idx_soil] += 0.31

        return mean, stddevs
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def Mean(self):
        
        mu = 0.0
        for x, p in self.d.iteritems():
            mu += p * x
        return mu
391667
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        mean, stds = self._get_mean_and_stddevs(sites, rup, dists, imt,
                                                stddev_types)
        stddevs = [np.ones(len(dists.repi))*get_sigma(imt)]
        return mean, stddevs
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def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        # pylint: disable=too-many-arguments
        

        # extract dictionary of coefficients specific to required
        # intensity measure type
        coeffs = self.COEFFS[imt]
        coeffs.update(self.CONSTS)

        # equation (1) is in terms of common logarithm
        log_mean = (self._compute_magnitude(rup, coeffs) +
                    self._compute_distance(dists, coeffs) +
                    self._get_site_amplification(sites, coeffs) +
                    self._get_mechanism(rup, coeffs))
        # so convert to g and thence to the natural logarithm
        mean = log_mean*np.log(10.0) - np.log(g)

        # convert standard deviations from common to natural logarithm
        log_stddevs = self._get_stddevs(coeffs, stddev_types, len(sites.vs30))
        stddevs = log_stddevs*np.log(10.0)

        return mean, stddevs
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def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        C = self.COEFFS[imt]
        C_SIG = self.SIGMA_COEFFS[imt]

        mean = (self.get_magnitude_scaling_term(C, rup.mag) +
                self.get_distance_scaling_term(C, dists.rhypo))

        std_devs = self.get_stddevs(C_SIG, stddev_types, len(dists.rhypo))

        #: Mean ground motions initially returned in cm/s/s (for PGA, SA)
        #: and cm/s for PGV
        if not imt.name == "PGV":
            # Convert mean from log(cm/s/s) to g
            mean = np.log(np.exp(mean) / (100. * g))

        return mean, std_devs
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def get_stddevs(self, mean, stddev_types):
        
        mean = np.exp(mean)
        sigma = 0.39 + np.zeros(mean.shape)
        sigma[mean < 0.068] = 0.55
        idx = np.logical_and(mean >= 0.068, mean <= 0.21)
        sigma[idx] = 0.173- 0.140 * np.log(mean[idx])
        stddevs = []
        for stddev in stddev_types:
            if stddev == const.StdDev.TOTAL:
                stddevs.append(sigma)
        return stddevs
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def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        # extracting dictionary of coefficients specific to required
        # intensity measure type.
        C = self.COEFFS[imt]
        # Implements mean model (equation 12)
        mean = (self._compute_magnitude(rup, C) +
                self._compute_distance(dists, C) +
                self._get_site_amplification(sites, rup, C) +
                self._get_mechanism(rup, C))

        stddevs = self._get_stddevs(rup, np.exp(mean), stddev_types, sites)

        return mean, stddevs
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def mean(self):
        
        return self._constructor(self.values.mean(axis=self.baseaxes, keepdims=True))
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def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
        
        return self.kwargs[str(imt)].get_mean_and_stddevs(
            sctx, rctx, dctx, imt, stddev_types)
252611
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
        
        assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
                   for stddev_type in stddev_types)

        # GMPE differentiates strike-slip, reverse and normal ruptures,
        # but combines normal and strike-slip into one category. See page 180.
        is_reverse = (45 <= rup.rake <= 135)

        stddevs = [numpy.zeros_like(sites.vs30) for _ in stddev_types]
        means = numpy.zeros_like(sites.vs30)

        [rocks_i] = (sites.vs30 > self.ROCK_VS30).nonzero()
        if len(rocks_i):
            rrup = dists.rrup.take(rocks_i)
            mean_rock = self._get_mean_rock(rup.mag, rup.rake, rrup,
                                            is_reverse, imt)
            means.put(rocks_i, mean_rock)
            for stddev_arr in stddevs:
                stddev_rock = self._get_stddev_rock(rup.mag, imt)
                stddev_arr.put(rocks_i, stddev_rock)

        [soils_i] = (sites.vs30 <= self.ROCK_VS30).nonzero()
        if len(soils_i):
            rrup = dists.rrup.take(soils_i)
            mean_soil = self._get_mean_deep_soil(rup.mag, rup.rake, rrup,
                                                 is_reverse, imt)
            means.put(soils_i, mean_soil)
            for stddev_arr in stddevs:
                stddev_soil = self._get_stddev_deep_soil(rup.mag, imt)
                stddev_arr.put(soils_i, stddev_soil)

        return means, stddevs
708026
def _compute_mean(self, C, g, mag, hypo_depth, rrup, vs30, pga_rock, imt):
        
        if hypo_depth > 100:
            hypo_depth = 100
        delta = 0.00724 * 10 ** (0.507 * mag)
        R = np.sqrt(rrup ** 2 + delta ** 2)

        s_amp = self._compute_soil_amplification(C, vs30, pga_rock, imt)

        mean = (
            # 1st term
            C['c1'] + C['c2'] * mag +
            # 2nd term
            C['c3'] * hypo_depth +
            # 3rd term
            C['c4'] * R -
            # 4th term
            g * np.log10(R) +
            # 5th, 6th and 7th terms
            s_amp
        )

        return mean