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/* $Id: vector_utils.h,v 1.1 2013-07-24 07:44:56 cgarcia Exp $
*
* This file is part of the MOSCA library
* Copyright (C) 2013 European Southern Observatory
*
* 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 2 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, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/*
* $Author: cgarcia $
* $Date: 2013-07-24 07:44:56 $
* $Revision: 1.1 $
* $Name: not supported by cvs2svn $
*/
#ifndef VECTOR_UTILS_H
#define VECTOR_UTILS_H
#include <vector>
#include "cpl_polynomial.h"
#include "gsl/gsl_bspline.h"
namespace mosca
{
template<typename T>
void vector_divide(std::vector<T>& input,
std::vector<T>& input_err,
const std::vector<int>& dividend);
template<typename T>
void vector_smooth(std::vector<T>& input,
std::vector<T>& input_err,
size_t smooth_size);
template<typename T>
void vector_smooth(std::vector<T>& input,
size_t smooth_size);
//TODO: Document that smooth_size is actually half the size
template<typename T>
void vector_smooth(std::vector<T>& input,
const std::vector<bool>& mask,
size_t smooth_size);
//TODO: Generalize this to cubicspline_fit. Fit X vs Y. Currently it is a particular
//case in which the X points are asumed to equispaced.
class vector_cubicspline
{
public:
vector_cubicspline();
~vector_cubicspline();
/* This will fit a spline fit to the points in X,Y space given by:
* X: a regular spaced vector from 0 to input.size() -1
* Y: the values in parameter input
* This won't work if the input values represent a non-linear trend.
* It modifies the yval vector with the fit.
*/
template<typename T>
void fit(std::vector<T>& yval, size_t& nknots);
/* This will fit a spline fit to the points in X,Y space given by:
* X: a regular spaced vector from 0 to input.size() -1
* Y: the values in parameter input
* This won't work if the input values represent a non-linear trend.
* It modifies the yval vector with the fit.
*/
template<typename T>
void fit(std::vector<T>& yval, const std::vector<bool>& mask, size_t& nknots);
/* This will fit a spline fit to the points in X,Y space given by
* xval, yval.
* It modifies the yval vector with the fit.
* The nknots are set uniformly in the x_min_knot, x_max_knot range.
* If they are the same, the minimum and maximum values of xval are used.
* The fit cannot be evaluated outside the knots range.
* If xval has values outside the knots range, yval is set to 0
* The mask specifies which vector values are valid (mask == 1)
*/
template<typename T>
void fit(const std::vector<T>& xval, std::vector<T>& yval,
const std::vector<bool>& mask,
size_t& nknots,
double x_min_knot = 0, double x_max_knot = 0);
/* This evaluates the fitting at point xi. Take into account that if
* the fit version with just an yval vector is used, then this xi is
* in the scale of 0 to input.size() -1
*/
double eval(double xi) const;
private:
void m_clear_fit();
gsl_bspline_workspace * m_bspline_workspace;
gsl_matrix * m_covar;
gsl_vector *m_spline_coeffs;
gsl_vector *m_basis;
double m_x_min_knot;
double m_x_max_knot;
};
class vector_polynomial
{
public:
vector_polynomial();
~vector_polynomial();
/* This will fit a polynomial fit to the points in X,Y space given by:
* X: a regular spaced vector from 0 to yval.size() -1
* Y: the values in parameter input
* This won't work if the input values represent a non-linear trend
* It modifies the yval vector with the fit.
*/
template<typename T>
void fit(std::vector<T>& yval, size_t& polyorder);
/* This will fit a polynomial fit to the points in X,Y space given by:
* X: a regular spaced vector from 0 to yval.size() -1
* Y: the values in parameter input
* This won't work if the input values represent a non-linear trend
* It modifies the yval vector with the fit.
* The points where mask is not true are excluded.
*/
template<typename T>
void fit(std::vector<T>& yval,
const std::vector<bool>& mask, size_t& polyorder);
/* This will fit a polynomial fit to the points in X,Y space given by
* xval, yval.
* It modifies the yval vector with the fit.
* TODO: Document that polyorder is modified
*/
template<typename T>
void fit(const std::vector<T>& xval, std::vector<T>& yval,
const std::vector<bool>& mask,
size_t& polyorder);
/* This evaluates the fitting at point xi. Take into account that if
* the fit version with just an yval vector is used, then this xi is
* in the scale of 0 to input.size() -1
*/
double eval(double xi) const;
private:
void m_clear_fit();
cpl_polynomial * m_pol_fit;
};
}
#include "vector_utils.tcc"
#endif
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