PhotometryTransform.cc

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// -*- LSST-C++ -*-
/*
 * This file is part of jointcal.
 *
 * Developed for the LSST Data Management System.
 * This product includes software developed by the LSST Project
 * (https://www.lsst.org).
 * See the COPYRIGHT file at the top-level directory of this distribution
 * for details of code ownership.
 *
 * 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 <https://www.gnu.org/licenses/>.
 */
 
#include "ndarray.h"
#include "Eigen/Core"
 
#include "lsst/afw/math/detail/TrapezoidalPacker.h"
 
#include "lsst/jointcal/Point.h"
#include "lsst/jointcal/PhotometryTransform.h"
 
namespace lsst {
namespace jointcal {
 
// ------------------ PhotometryTransformChebyshev helpers ---------------------------------------------------
 
namespace {
 
// To evaluate a 1-d Chebyshev function without needing to have workspace, we use the
// Clenshaw algorith, which is like going through the recurrence relation in reverse.
// The CoeffGetter argument g is something that behaves like an array, providing access
// to the coefficients.
template <typename CoeffGetter>
double evaluateFunction1d(CoeffGetter g, double x, std::size_t size) {
    double b_kp2 = 0.0, b_kp1 = 0.0;
    for (std::size_t k = (size - 1); k > 0; --k) {
        double b_k = g[k] + 2 * x * b_kp1 - b_kp2;
        b_kp2 = b_kp1;
        b_kp1 = b_k;
    }
    return g[0] + x * b_kp1 - b_kp2;
}
 
// This class imitates a 1-d array, by running evaluateFunction1d on a nested dimension;
// this lets us reuse the logic in evaluateFunction1d for both dimensions.  Essentially,
// we run evaluateFunction1d on a column of coefficients to evaluate T_i(x), then pass
// the result of that to evaluateFunction1d with the results as the "coefficients" associated
// with the T_j(y) functions.
struct RecursionArrayImitator {
    double operator[](Eigen::Index i) const {
        return evaluateFunction1d(coefficients[i], x, coefficients.getSize<1>());
    }
 
    RecursionArrayImitator(ndarray::Array<double const, 2, 2> const &coefficients_, double x_)
            : coefficients(coefficients_), x(x_) {}
 
    ndarray::Array<double const, 2, 2> coefficients;
    double x;
};
 
// Compute an affine transform that maps an arbitrary box to [-1,1]x[-1,1]
geom::AffineTransform makeChebyshevRangeTransform(geom::Box2D const &bbox) {
    return geom::AffineTransform(
            geom::LinearTransform::makeScaling(2.0 / bbox.getWidth(), 2.0 / bbox.getHeight()),
            geom::Extent2D(-(2.0 * bbox.getCenterX()) / bbox.getWidth(),
                           -(2.0 * bbox.getCenterY()) / bbox.getHeight()));
}
 
// Initialize a "unit" Chebyshev
ndarray::Array<double, 2, 2> _initializeChebyshev(size_t order, bool identity) {
    ndarray::Array<double, 2, 2> coeffs = ndarray::allocate(ndarray::makeVector(order + 1, order + 1));
    coeffs.deep() = 0.0;
    if (identity) {
        coeffs[0][0] = 1;
    }
    return coeffs;
}
}  // namespace
 
PhotometryTransformChebyshev::PhotometryTransformChebyshev(size_t order, geom::Box2D const &bbox,
                                                           bool identity)
        : _bbox(bbox),
          _toChebyshevRange(makeChebyshevRangeTransform(bbox)),
          _coefficients(_initializeChebyshev(order, identity)),
          _order(order),
          _nParameters((order + 1) * (order + 2) / 2) {}
 
PhotometryTransformChebyshev::PhotometryTransformChebyshev(ndarray::Array<double, 2, 2> const &coefficients,
                                                           geom::Box2D const &bbox)
        : _bbox(bbox),
          _toChebyshevRange(makeChebyshevRangeTransform(bbox)),
          _coefficients(coefficients),
          _order(coefficients.size() - 1),
          _nParameters((_order + 1) * (_order + 2) / 2) {}
 
void PhotometryTransformChebyshev::offsetParams(Eigen::VectorXd const &delta) {
    // NOTE: the indexing in this method and computeParameterDerivatives must be kept consistent!
    Eigen::VectorXd::Index k = 0;
    for (ndarray::Size j = 0; j <= _order; ++j) {
        ndarray::Size const iMax = _order - j;  // to save re-computing `i+j <= order` every inner step.
        for (ndarray::Size i = 0; i <= iMax; ++i, ++k) {
            _coefficients[j][i] -= delta[k];
        }
    }
}
 
namespace {
// The integral of T_n(x) over [-1,1]:
// https://en.wikipedia.org/wiki/Chebyshev_polynomials#Differentiation_and_integration
double integrateTn(int n) {
    if (n % 2 == 1)
        return 0;
    else
        return 2.0 / (1.0 - static_cast<double>(n * n));
}
 
Eigen::VectorXd computeIntegralTn(ndarray::Size order, double x) {
    // We need to compute through order+2, because the integral recurrance relation uses Tn+1(x).
    Eigen::VectorXd Tn(order + 2);
 
    // The nth chebyshev polynomial evaluated at x.
    Tn[0] = 1;
    Tn[1] = x;
    for (ndarray::Size i = 2; i <= order + 1; ++i) {
        Tn[i] = 2 * x * Tn[i - 1] - Tn[i - 2];
    }
 
    // The integral of the nth chebyshev polynomial at x.
    Eigen::VectorXd integralTn(order + 1);
    integralTn[0] = x;
    if (order > 0) {  // have to evaluate this separately from the recurrence relation below.
        integralTn[1] = 0.5 * x * x;
    }
    for (ndarray::Size i = 2; i <= order; ++i) {
        // recurrence relation: integral(Tn(x)) = n*T[n+1](x)/(n^2 - 1) - x*T[n](x)/(n-1)
        integralTn[i] = i * Tn[i + 1] / (i * i - 1) - x * Tn[i] / (i - 1);
    }
 
    return integralTn;
}
 
}  // namespace
 
double PhotometryTransformChebyshev::oneIntegral(double x, double y) const {
    geom::Point2D point = _toChebyshevRange(geom::Point2D(x, y));
 
    auto integralTnx = computeIntegralTn(_order, point.getX());
    auto integralTmy = computeIntegralTn(_order, point.getY());
 
    // NOTE: the indexing in this method and offsetParams must be kept consistent!
    // Roll up the x and y terms
    double result = 0;
    for (ndarray::Size j = 0; j <= _order; ++j) {
        ndarray::Size const iMax = _order - j;  // to save re-computing `i+j <= order` every inner step.
        for (ndarray::Size i = 0; i <= iMax; ++i) {
            result += _coefficients[j][i] * integralTnx[i] * integralTmy[j];
        }
    }
    return result;
}
 
double PhotometryTransformChebyshev::integrate(geom::Box2D const &bbox) const {
    double result = 0;
 
    result += oneIntegral(bbox.getMaxX(), bbox.getMaxY());
    result += oneIntegral(bbox.getMinX(), bbox.getMinY());
    result -= oneIntegral(bbox.getMaxX(), bbox.getMinY());
    result -= oneIntegral(bbox.getMinX(), bbox.getMaxY());
 
    // scale factor due to the change of limits in the integral
    return result / _toChebyshevRange.getLinear().computeDeterminant();
}
 
double PhotometryTransformChebyshev::integrate() const {
    double result = 0;
    double determinant = _bbox.getArea() / 4.0;
    for (ndarray::Size j = 0; j < _coefficients.getSize<0>(); j++) {
        for (ndarray::Size i = 0; i < _coefficients.getSize<1>(); i++) {
            result += _coefficients[j][i] * integrateTn(i) * integrateTn(j);
        }
    }
    return result * determinant;
}
 
double PhotometryTransformChebyshev::mean(geom::Box2D const &bbox) const {
    return integrate(bbox) / bbox.getArea();
}
 
double PhotometryTransformChebyshev::mean() const { return integrate() / _bbox.getArea(); }
 
Eigen::VectorXd PhotometryTransformChebyshev::getParameters() const {
    Eigen::VectorXd parameters(_nParameters);
    // NOTE: the indexing in this method and offsetParams must be kept consistent!
    Eigen::VectorXd::Index k = 0;
    for (ndarray::Size j = 0; j <= _order; ++j) {
        ndarray::Size const iMax = _order - j;  // to save re-computing `i+j <= order` every inner step.
        for (ndarray::Size i = 0; i <= iMax; ++i, ++k) {
            parameters[k] = _coefficients[j][i];
        }
    }
 
    return parameters;
}
 
double PhotometryTransformChebyshev::computeChebyshev(double x, double y) const {
    geom::Point2D p = _toChebyshevRange(geom::Point2D(x, y));
    return evaluateFunction1d(RecursionArrayImitator(_coefficients, p.getX()), p.getY(),
                              _coefficients.getSize<0>());
}
 
void PhotometryTransformChebyshev::computeChebyshevDerivatives(
        double x, double y, Eigen::Ref<Eigen::VectorXd> derivatives) const {
    geom::Point2D p = _toChebyshevRange(geom::Point2D(x, y));
    // Algorithm: compute all the individual components recursively (since we'll need them anyway),
    // then combine them into the final answer vectors.
    Eigen::VectorXd Tnx(_order + 1);
    Eigen::VectorXd Tmy(_order + 1);
    Tnx[0] = 1;
    Tmy[0] = 1;
    if (_order >= 1) {
        Tnx[1] = p.getX();
        Tmy[1] = p.getY();
    }
    for (ndarray::Size i = 2; i <= _order; ++i) {
        Tnx[i] = 2 * p.getX() * Tnx[i - 1] - Tnx[i - 2];
        Tmy[i] = 2 * p.getY() * Tmy[i - 1] - Tmy[i - 2];
    }
 
    // NOTE: the indexing in this method and offsetParams must be kept consistent!
    Eigen::VectorXd::Index k = 0;
    for (ndarray::Size j = 0; j <= _order; ++j) {
        ndarray::Size const iMax = _order - j;  // to save re-computing `i+j <= order` every inner step.
        for (ndarray::Size i = 0; i <= iMax; ++i, ++k) {
            derivatives[k] = Tmy[j] * Tnx[i];
        }
    }
}
 
}  // namespace jointcal
}  // namespace lsst