correlated_fields.py

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2019 Max-Planck-Society
# Authors: Philipp Frank, Philipp Arras
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.

import numpy as np

from ..domain_tuple import DomainTuple
from ..domains.power_space import PowerSpace
from ..domains.unstructured_domain import UnstructuredDomain
from ..field import Field
from ..operators.adder import Adder
from ..operators.contraction_operator import ContractionOperator
from ..operators.distributors import PowerDistributor
from ..operators.endomorphic_operator import EndomorphicOperator
from ..operators.harmonic_operators import HarmonicTransformOperator
from ..operators.linear_operator import LinearOperator
from ..operators.operator import Operator
from ..operators.simple_linear_operators import VdotOperator, ducktape
from ..operators.value_inserter import ValueInserter
from ..sugar import from_global_data, full, makeDomain
from ..probing import StatCalculator


def _lognormal_moments(mean, sig):
    mean, sig = float(mean), float(sig)
    assert sig > 0
    logsig = np.sqrt(np.log((sig/mean)**2 + 1))
    logmean = np.log(mean) - logsig**2/2
    return logmean, logsig


class _lognormal_moment_matching(Operator):
    def __init__(self, mean, sig, key):
        key = str(key)
        logmean, logsig = _lognormal_moments(mean, sig)
        self._mean = mean
        self._sig = sig
        op = _normal(logmean, logsig, key).exp()
        self._domain = op.domain
        self._target = op.target
        self.apply = op.apply

    @property
    def mean(self):
        return self._mean

    @property
    def std(self):
        return self._sig


def _normal(mean, sig, key):
    return Adder(Field.scalar(mean)) @ (
        sig*ducktape(DomainTuple.scalar_domain(), None, key))


def _log_k_lengths(pspace):
    return np.log(pspace.k_lengths[1:])


def _logkl(power_space):
    power_space = DomainTuple.make(power_space)
    assert isinstance(power_space[0], PowerSpace)
    assert len(power_space) == 1
    logkl = _log_k_lengths(power_space[0])
    assert logkl.shape[0] == power_space[0].shape[0] - 1
    logkl -= logkl[0]
    logkl = np.insert(logkl, 0, 0)
    return logkl


class _SlopeRemover(EndomorphicOperator):
    def __init__(self, domain):
        self._domain = makeDomain(domain)
        assert len(self._domain) == 1
        assert isinstance(self._domain[0], PowerSpace)
        logkl = _logkl(self._domain)
        self._sc = logkl/float(logkl[-1])
        self._capability = self.TIMES | self.ADJOINT_TIMES

    def apply(self, x, mode):
        self._check_input(x, mode)
        x = x.to_global_data()
        if mode == self.TIMES:
            res = x - x[-1]*self._sc
        else:
            res = np.zeros(x.shape, dtype=x.dtype)
            res += x
            res[-1] -= (x*self._sc).sum()
        return from_global_data(self._tgt(mode), res)


class _TwoLogIntegrations(LinearOperator):
    def __init__(self, target):
        self._target = makeDomain(target)
        assert len(self._target) == 1
        assert isinstance(self._target[0], PowerSpace)
        self._domain = makeDomain(
            UnstructuredDomain((2, self.target.shape[0] - 2)))
        self._capability = self.TIMES | self.ADJOINT_TIMES
        if not isinstance(self._target[0], PowerSpace):
            raise TypeError
        logk_lengths = _log_k_lengths(self._target[0])
        self._logvol = logk_lengths[1:] - logk_lengths[:-1]

    def apply(self, x, mode):
        self._check_input(x, mode)
        if mode == self.TIMES:
            x = x.to_global_data()
            res = np.empty(self._target.shape)
            res[0] = 0
            res[1] = 0
            res[2:] = np.cumsum(x[1])
            res[2:] = (res[2:] + res[1:-1])/2*self._logvol + x[0]
            res[2:] = np.cumsum(res[2:])
            return from_global_data(self._target, res)
        else:
            x = x.to_global_data_rw()
            res = np.zeros(self._domain.shape)
            x[2:] = np.cumsum(x[2:][::-1])[::-1]
            res[0] += x[2:]
            x[2:] *= self._logvol/2.
            x[1:-1] += x[2:]
            res[1] += np.cumsum(x[2:][::-1])[::-1]
            return from_global_data(self._domain, res)


class _Normalization(Operator):
    def __init__(self, domain):
        self._domain = self._target = makeDomain(domain)
        assert len(self._domain) == 1
        assert isinstance(self._domain[0], PowerSpace)
        hspace = self._domain[0].harmonic_partner
        pd = PowerDistributor(hspace, power_space=self._domain[0])
        cst = pd.adjoint(full(pd.target, 1.)).to_global_data_rw()
        cst[0] = 0
        self._cst = from_global_data(self._domain, cst)
        self._specsum = _SpecialSum(self._domain)

    def apply(self, x):
        self._check_input(x)
        amp = x.exp()
        spec = (2*x).exp()
        # FIXME This normalizes also the zeromode which is supposed to be left
        # untouched by this operator
        return self._specsum(self._cst*spec)**(-0.5)*amp


class _SpecialSum(EndomorphicOperator):
    def __init__(self, domain):
        self._domain = makeDomain(domain)
        assert len(self._domain) == 1
        self._capability = self.TIMES | self.ADJOINT_TIMES

    def apply(self, x, mode):
        self._check_input(x, mode)
        return full(self._tgt(mode), x.sum())


class _Amplitude(Operator):
    def __init__(self, target, fluctuations, flexibility, asperity,
                 loglogavgslope, key):
        """
        fluctuations > 0
        flexibility > 0
        asperity > 0
        loglogavgslope probably negative
        """
        assert isinstance(fluctuations, Operator)
        assert isinstance(flexibility, Operator)
        assert isinstance(asperity, Operator)
        assert isinstance(loglogavgslope, Operator)
        target = makeDomain(target)
        assert len(target) == 1
        assert isinstance(target[0], PowerSpace)

        twolog = _TwoLogIntegrations(target)
        dt = twolog._logvol
        sc = np.zeros(twolog.domain.shape)
        sc[0] = sc[1] = np.sqrt(dt)
        sc = from_global_data(twolog.domain, sc)
        expander = VdotOperator(sc).adjoint
        sigmasq = expander @ flexibility

        dist = np.zeros(twolog.domain.shape, dtype=np.float64)
        dist[0] += 1
        scale = VdotOperator(from_global_data(twolog.domain,
                                              dist)).adjoint @ asperity

        shift = np.ones(scale.target.shape)
        shift[0] = dt**2/12.
        shift = from_global_data(scale.target, shift)
        scale = sigmasq*(Adder(shift) @ scale).sqrt()
        smooth = twolog @ (scale*ducktape(scale.target, None, key))

        tg = smooth.target
        noslope = _SlopeRemover(tg) @ smooth
        _t = VdotOperator(from_global_data(tg, _logkl(tg))).adjoint
        smoothslope = _t @ loglogavgslope + noslope

        normal_ampl = _Normalization(target) @ smoothslope
        vol = target[0].harmonic_partner.get_default_codomain().total_volume
        arr = np.zeros(target.shape)
        arr[1:] = vol
        expander = VdotOperator(from_global_data(target, arr)).adjoint
        mask = np.zeros(target.shape)
        mask[0] = vol
        adder = Adder(from_global_data(target, mask))
        op = adder @ ((expander @ fluctuations)*normal_ampl)
        self.apply = op.apply
        self.fluctuation_amplitude = fluctuations
        self._domain, self._target = op.domain, op.target


class CorrelatedFieldMaker:
    def __init__(self):
        self._a = []
        self._azm = None

    def add_fluctuations(self,
                         target,
                         fluctuations_mean,
                         fluctuations_stddev,
                         flexibility_mean,
                         flexibility_stddev,
                         asperity_mean,
                         asperity_stddev,
                         loglogavgslope_mean,
                         loglogavgslope_stddev,
                         prefix='',
                         index=None):
        fluctuations_mean = float(fluctuations_mean)
        fluctuations_stddev = float(fluctuations_stddev)
        flexibility_mean = float(flexibility_mean)
        flexibility_stddev = float(flexibility_stddev)
        asperity_mean = float(asperity_mean)
        asperity_stddev = float(asperity_stddev)
        loglogavgslope_mean = float(loglogavgslope_mean)
        loglogavgslope_stddev = float(loglogavgslope_stddev)
        prefix = str(prefix)
        assert fluctuations_stddev > 0
        assert fluctuations_mean > 0
        assert flexibility_stddev > 0
        assert flexibility_mean > 0
        assert asperity_stddev > 0
        assert asperity_mean > 0
        assert loglogavgslope_stddev > 0

        fluct = _lognormal_moment_matching(fluctuations_mean,
                                           fluctuations_stddev,
                                           prefix + 'fluctuations')
        flex = _lognormal_moment_matching(flexibility_mean, flexibility_stddev,
                                          prefix + 'flexibility')
        asp = _lognormal_moment_matching(asperity_mean, asperity_stddev,
                                         prefix + 'asperity')
        avgsl = _normal(loglogavgslope_mean, loglogavgslope_stddev,
                        prefix + 'loglogavgslope')
        amp = _Amplitude(target, fluct, flex, asp, avgsl, prefix + 'spectrum')
        if index is not None:
            self._a.insert(index, amp)
        else:
            self._a.append(amp)

    def finalize_from_op(self, zeromode, prefix=''):
        assert isinstance(zeromode, Operator)
        self._azm = zeromode
        hspace = makeDomain([dd.target[0].harmonic_partner for dd in self._a])
        foo = np.ones(hspace.shape)
        zeroind = len(hspace.shape)*(0,)
        foo[zeroind] = 0
        azm = Adder(from_global_data(hspace, foo)) @ ValueInserter(
            hspace, zeroind) @ zeromode

        n_amplitudes = len(self._a)
        ht = HarmonicTransformOperator(hspace, space=0)
        for i in range(1, n_amplitudes):
            ht = HarmonicTransformOperator(ht.target, space=i) @ ht

        pd = PowerDistributor(hspace, self._a[0].target[0], 0)
        for i in range(1, n_amplitudes):
            foo = PowerDistributor(pd.domain, self._a[i].target[0], space=i)
            pd = pd @ foo

        spaces = tuple(range(n_amplitudes))
        a = ContractionOperator(pd.domain, spaces[1:]).adjoint @ self._a[0]
        for i in range(1, n_amplitudes):
            co = ContractionOperator(pd.domain, spaces[:i] + spaces[(i + 1):])
            a = a*(co.adjoint @ self._a[i])

        return ht(azm*(pd @ a)*ducktape(hspace, None, prefix + 'xi'))

    def finalize(self,
                 offset_amplitude_mean,
                 offset_amplitude_stddev,
                 prefix='',
                 offset=None):
        """
        offset vs zeromode: volume factor
        """
        offset_amplitude_stddev = float(offset_amplitude_stddev)
        offset_amplitude_mean = float(offset_amplitude_mean)
        assert offset_amplitude_stddev > 0
        assert offset_amplitude_mean > 0
        if offset is not None:
            raise NotImplementedError
            offset = float(offset)
        azm = _lognormal_moment_matching(offset_amplitude_mean,
                                         offset_amplitude_stddev,
                                         prefix + 'zeromode')
        return self.finalize_from_op(azm, prefix)

    @property
    def amplitudes(self):
        return self._a

    @property
    def amplitude_total_offset(self):
        return self._azm

    @property
    def total_fluctuation(self):
        if len(self._a) == 0:
            raise NotImplementedError
        if len(self._a) == 1:
            return self._a[0].fluctuation_amplitude
        q = 1.
        for a in self._a:
            fl = a.fluctuation_amplitude
            q = q*(Adder(full(fl.target, 1.)) @ fl**2)
        return (Adder(full(q.target, -1.)) @ q).sqrt()

    def slice_fluctuation(self, space):
        if len(self._a) == 0:
            raise NotImplementedError
        assert space < len(self._a)
        if len(self._a) == 1:
            return self._a[0].fluctuation_amplitude
        q = 1.
        for j in range(len(self._a)):
            fl = self._a[j].fluctuation_amplitude
            if j == space:
                q = q*fl**2
            else:
                q = q*(Adder(full(fl.target, 1.)) @ fl**2)
        return q.sqrt()

    def average_fluctuation(self, space):
        if len(self._a) == 0:
            raise NotImplementedError
        assert space < len(self._a)
        if len(self._a) == 1:
            return self._a[0].fluctuation_amplitude
        return self._a[space].fluctuation_amplitude

    def offset_amplitude_realized(self, samples):
        res = 0.
        for s in samples:
            res += s.mean()**2
        return np.sqrt(res/len(samples))

    def total_fluctuation_realized(self, samples):
        res = 0.
        for s in samples:
            res = res + (s - s.mean())**2
        res = res/len(samples)
        return np.sqrt(res.mean())

    def average_fluctuation_realized(self, samples, space):
        ldom = len(samples[0].domain)
        assert space < ldom
        if ldom == 1:
            return self.total_fluctuation_realized(samples)
        spaces = ()
        for i in range(ldom):
            if i != space:
                spaces += (i,)
        res = 0.
        for s in samples:
            r = s.mean(spaces)
            res = res + (r - r.mean())**2
        res = res/len(samples)
        return np.sqrt(res.mean())

    def slice_fluctuation_realized(self, samples, space):
        ldom = len(samples[0].domain)
        assert space < ldom
        if ldom == 1:
            return self.total_fluctuation_realized(samples)
        res1 = 0.
        res2 = 0.
        for s in samples:
            res1 = res1 + s**2
            res2 = res2 + s.mean(space)**2
        res1 = res1/len(samples)
        res2 = res2/len(samples)
        res = res1.mean() - res2.mean()
        return np.sqrt(res)

    def stats(self, op, samples):
        sc = StatCalculator()
        for s in samples:
            sc.add(op(s.extract(op.domain)))
        return sc.mean.to_global_data(), sc.var.sqrt().to_global_data()

    def moment_slice_to_average(self, fluctuations_slice_mean, nsamples=1000):
        fluctuations_slice_mean = float(fluctuations_slice_mean)
        assert fluctuations_slice_mean > 0
        scm = 1.
        for a in self._a:
            m, std = a.fluctuation_amplitude.mean, a.fluctuation_amplitude.std
            mu, sig = _lognormal_moments(m, std)
            flm = np.exp(mu + sig*np.random.normal(size=nsamples))
            scm *= flm**2 + 1.
        scm = np.mean(np.sqrt(scm))
        return fluctuations_slice_mean/scm