File LeastSqFitter1d.h

namespace lsst

Class for a simple mapping implementing a generic AstrometryTransform.

Remove all non-astronomical counts from the Chunk Exposure’s pixels.

Forward declarations for lsst::utils::Cache

For details on the Cache class, see the Cache.h file.

It uses a template rather than a pointer so that the derived classes can use the specifics of the transform. The class simplePolyMapping overloads a few routines.

A base class for image defects

Numeric constants used by the Integrate.h integrator routines.

Compute Image Statistics

Note

Gauss-Kronrod-Patterson quadrature coefficients for use in quadpack routine qng. These coefficients were calculated with 101 decimal digit arithmetic by L. W. Fullerton, Bell Labs, Nov 1981.

Note

The Statistics class itself can only handle lsst::afw::image::MaskedImage() types. The philosophy has been to handle other types by making them look like lsst::afw::image::MaskedImage() and reusing that code. Users should have no need to instantiate a Statistics object directly, but should use the overloaded makeStatistics() factory functions.

namespace meas
namespace astrom
namespace sip
template<class FittingFunc>
class LeastSqFitter1d
#include <LeastSqFitter1d.h>

Fit an lsst::afw::math::Function1 object to a set of data points in one dimension.

The class is templated over the kind of object to fit.

Input is a list of x ordinates for a set of points, the y coordinate, and the uncertainties, s. order is order of the polynomial to fit (e.g if the templated function is lsst::afw::math::PolynomialFunction1, then order=3 => fit a function of the form \(ax^2+bx+c\)

See

LeastSqFitter1d

Template Parameters
Parameters

  • x: Ordinate of points to fit

  • y: Co-ordinate of pionts to fit

  • s: 1 \(\sigma\) uncertainties in z

  • order: Polynomial order to fit

Public Functions

LeastSqFitter1d(const std::vector<double> &x, const std::vector<double> &y, const std::vector<double> &s, int order)

Fit a 1d polynomial to a set of data points z(x, y)

Template Parameters
Parameters

  • x: vector of x positions of data

  • y: vector of y positions of data

  • s: Vector of measured uncertainties in the values of z

  • order: Order of 2d function to fit

Eigen::VectorXd getParams()

Return the best fit parameters as an Eigen::Matrix.

Eigen::VectorXd getErrors()

Return the 1 sigma uncertainties in the best fit parameters as an Eigen::Matrix.

FittingFunc getBestFitFunction()

Return the best fit polynomial as a lsst::afw::math::Function1 object.

double valueAt(double x)

Calculate the value of the function at a given point.

std::vector<double> residuals()

Return a vector of residuals of the fit (i.e the difference between the input y values, and the value of the fitting function at that point.

double getChiSq()

Return a measure of the goodness of fit.

\[ \chi_r^2 = \sum \left( \frac{y_i - f(x_i)}{s} \right)^2 \]
.

double getReducedChiSq()

Return a measure of the goodness of fit.

\[ \chi_r^2 = \sum \left( \frac{y_i - f(x_i)}{s} \right)^2 \div (N-p) \]
Where \( N \) is the number of data points, and \( p \) is the number of parameters in the fit.

Private Functions

void initFunctions()

Initialise the array of functions. _funcArray[i] is a object of type math::Function1 of order _norder

double func1d(double value, int exponent)

Private Members

std::vector<double> _x
std::vector<double> _y
std::vector<double> _s
int _order
int _nData
Eigen::JacobiSVD<Eigen::MatrixXd> _svd
Eigen::VectorXd _par
std::vector<std::shared_ptr<FittingFunc>> _funcArray