Index: /trunk/doc/ipptools/outline.tex =================================================================== --- /trunk/doc/ipptools/outline.tex (revision 5058) +++ /trunk/doc/ipptools/outline.tex (revision 5059) @@ -351,4 +351,15 @@ - update MasterDetrendRun +Missing Modules + + load from DVO + load PS astrom table + load guide-star table + imastro + mosastro + read WCS / write WCS + detrend-image selection + measure fringe pattern + \end{verbatim} \end{document} Index: /trunk/doc/pslib/ChangeLogSDRS.tex =================================================================== --- /trunk/doc/pslib/ChangeLogSDRS.tex (revision 5058) +++ /trunk/doc/pslib/ChangeLogSDRS.tex (revision 5059) @@ -1,3 +1,3 @@ -%%% $Id: ChangeLogSDRS.tex,v 1.171 2005-09-13 21:28:03 eugene Exp $ +%%% $Id: ChangeLogSDRS.tex,v 1.172 2005-09-16 01:56:17 eugene Exp $ \subsection{Changes from version 00 to version 01} @@ -793,2 +793,13 @@ \end{itemize} +\subsection{Changes from Revision 16 (13 Sept 2005) to present} + +\begin{itemize} +\item moved param constraints to \code{psMinConstrain} +\item changed type requirement on psMinimize functions to just F64 +\item changed psMinimize input arguments to accept weight not sigma +\item changed psPolynomial masks from char to psU8. +\item changed \code{psImage.col0,row0} changed from const. + +\end{itemize} + Index: /trunk/doc/pslib/psLibADD.tex =================================================================== --- /trunk/doc/pslib/psLibADD.tex (revision 5058) +++ /trunk/doc/pslib/psLibADD.tex (revision 5059) @@ -1,3 +1,3 @@ -%%% $Id: psLibADD.tex,v 1.85 2005-07-12 04:18:56 eugene Exp $ +%%% $Id: psLibADD.tex,v 1.86 2005-09-16 01:56:17 eugene Exp $ \documentclass[panstarrs]{panstarrs} @@ -324,18 +324,55 @@ \begin{itemize} -\item Perform the Robust Histogram Statistics algorithm above -\item Smooth the resulting histogram with a Gaussian with $\sigma_s$ = - 1 bin. +\item Perform the Robust Histogram Statistics algorithm above, + yielding an estimated standard deviation, $\sigma$. + +\item Generate a new histogram for the data sample setting a bin size, + $d\sigma$, based on the estimated standard deviation and the number + of data points in the inner 50 percentile ($N_{\rm 50}$) as follows: + +\begin{itemize} +\item let $dN = (\sigma / d\sigma) = 0.017 N_{50}$ +\item limit $dN$ to the range 1 to 5. +\item set the bin size $d\sigma = \sigma / dN$ +\end{itemize} + +\item Smooth the resulting histogram with a Gaussian with $\sigma_x$ = + 1 bin in this new histogram. \item Find the bin with the peak value in the range $\pm 2 \sigma$ of the robust histogram median. -\item Fit a Gaussian to the bins in the range $\pm 2 \sigma$ of - the robust histogram median. + +\item Fit a Gaussian to the bins in the range $\pm 20 \sigma$ of the + robust histogram median. Limit the fit range to the data range, if + the latter is less then $\pm 20 \sigma$. If the data range is small + compared to the estimated $\sigma$, fit at least 4 bins of the + hisgram centered on the robust histogram median. + \item The robust mean $\mbox{mean}_r$ is derived directly from the fitted Gaussian mean. \item The robust standard deviation, $\sigma_r$, is determined by subtracting the smoothing scale in quadrature: $\sigma_r^2 = - \sigma^2 - \sigma_s^2$ + \sigma_{\rm fit}^2 - \sigma_s^2$ \end{itemize} +To explain the choice of the histogram bin size: a histogram of a +Gaussian distribution with bin size $d\sigma$ will have approximately +$(2.35 \sigma/d\sigma)$ bins covering the range LQ to UQ. Thus, the +average number of points per bin ($N_{\rm bin}$) in that interval will +be $N_{50} / (2.35 \sigma/d\sigma)$. The value of $d\sigma$ should be +no larger than $\sigma$, regardless of the number of points, to avoid +too much undersampling. The value of $d\sigma$ should also be no +smaller than $5\sigma$, again regardless of the number of points, to +avoid excessive oversample. Intermediate to those two values, the bin +size is choosen to keep about 25 points per bin. Thus, the bin size +($d\sigma$) is set to about: +\[ +d\sigma = 2.35 \sigma (N_{\rm bin}/N_{50}) = (25 \times 2.35) (\sigma/N_{50}) +\] +With the limitation that $\sigma/d\sigma$ should be limited on one end +to the value 1, and the other to the value 5. The easiest way to set +this limit is to define dN to be: +\[ +dN = (\sigma / d\sigma) = (N_{50} / N_{\rm bin}) / 2.35 = 0.017 * N_{50} +\] \subsubsection{Histograms} Index: /trunk/doc/pslib/psLibSDRS.tex =================================================================== --- /trunk/doc/pslib/psLibSDRS.tex (revision 5058) +++ /trunk/doc/pslib/psLibSDRS.tex (revision 5059) @@ -1,3 +1,3 @@ -%%% $Id: psLibSDRS.tex,v 1.339 2005-09-15 20:53:06 price Exp $ +%%% $Id: psLibSDRS.tex,v 1.340 2005-09-16 01:56:17 eugene Exp $ \documentclass[panstarrs,spec]{panstarrs} @@ -3449,6 +3449,6 @@ const int numCols; ///< Number of columns in image const int numRows; ///< Number of rows in image. - const int col0; ///< Column position relative to parent. - const int row0; ///< Row position relative to parent. + int col0; ///< Column position relative to parent. + int row0; ///< Row position relative to parent. union { psS8 **S8; ///< Pointers to char data @@ -4488,5 +4488,5 @@ psF64 *coeff; ///< Coefficients psF64 *coeffErr; ///< Error in coefficients - char *mask; ///< Coefficient mask + psMaskType *mask; ///< Coefficient mask } psPolynomial1D; \end{datatype} @@ -4500,5 +4500,5 @@ psF64 **coeff; ///< Coefficients psF64 **coeffErr; ///< Error in coefficients - char **mask; ///< Coefficients mask + psMaskType **mask; ///< Coefficients mask } psPolynomial2D; \end{datatype} @@ -4669,6 +4669,21 @@ the minimization analysis. The maximum number of iterations is specified by \code{maxIter}, while the maximum tolerance for -convergence is \code{tol}. Four parameter vectors describe the -behavior of individual parameters. The \code{paramMask} vector +convergence is \code{tol}. The output information carried by the +structure consists of the value of the function at the minimum +(\code{value}), the number of iterations performed (\code{iter}) and +last change in tolerance before returning (\code{lastDelta}). + +\begin{datatype} +typedef struct { + const int maxIter; ///< Maximum number of iterations + const float tol; ///< Tolerance to reach + float value; ///< Value after minimization + int iter; ///< Actual number of iterations performed + float lastDelta; ///< Last change before quitting +} psMinimization; +\end{datatype} + +We define the \code{psMinConstrain} structure to define values which +constrain the allowed parameter values. The \code{paramMask} vector defines the free (0) or frozen (not 0) parameters. The \code{paramMin} defines the minimum allowed value for each parameter, @@ -4680,16 +4695,8 @@ swing is saturated to the limit (with the correct sign). Any of these parameter vectors may be set to \code{NULL}, in which case the concept -is ignored in the analysis. The output information carried by the -structure consists of the value of the function at the minimum -(\code{value}), the number of iterations performed (\code{iter}) and -last change in tolerance before returning (\code{lastDelta}). +is ignored in the analysis. \begin{datatype} typedef struct { - const int maxIter; ///< Maximum number of iterations - const float tol; ///< Tolerance to reach - float value; ///< Value after minimization - int iter; ///< Actual number of iterations performed - float lastDelta; ///< Last change before quitting psVector *paramMask; ///< valid / invalid parameters psVector *paramMax; ///< max allowed parameters @@ -4699,7 +4706,8 @@ \end{datatype} -The corresponding allocator is: +The corresponding allocators are: \begin{prototype} psMinimization *psMinimizationAlloc(int maxIter, float tol); +psMinConstrain *psMinConstrainAlloc(); \end{prototype} and the parameter vectors are initially set to \code{NULL}. @@ -4717,5 +4725,5 @@ Then \code{psMinimizeLMChi2} shall fit the specified function, -\code{func}, to a set of measurements, \code{x,y,yErr}, using the +\code{func}, to a set of measurements, \code{x,y,yWt}, using the Levenberg-Marquardt method: @@ -4724,7 +4732,8 @@ psImage *covar, psVector *params, + psMinConstrain *constrain, const psArray *x, const psVector *y, - const psVector *yErr, + const psVector *yWt, psMinimizeLMChi2Func func); \end{prototype} @@ -4755,16 +4764,16 @@ The measurement ordinates, \code{x}, shall consist of multiple vectors, each of which may be passed to the model \code{func}. If the -measurement coordinates, \code{y}, and errors, \code{yErr}, are not of +measurement coordinates, \code{y}, and weights, \code{yWt}, are not of the same length as the ordinates array, \code{x}, then the function shall generate a warning, and truncate the longest of the array/vectors to match the length of the shortest. The vectors -contained within the \code{x} array, and the \code{y} and \code{yErr} -vectors must be of type \code{psF32}. The \code{yErr} vector may be +contained within the \code{x} array, and the \code{y} and \code{yWt} +vectors must be of type \code{psF64}. The \code{yWt} vector may be \code{NULL}, in which case the errors shall be assumed to be identical. -\code{paramMask} must be of type \code{psMaskType}, while \code{params} -must be of type \code{psF32}. The \code{func} function must be valid -only for types \code{psF32}, \code{psF64}. +\code{paramMask} must be of type \code{psMaskType}, while +\code{params} must be of type \code{psF64}. The \code{func} function +must be valid only for type \code{psF64}. \begin{prototype} @@ -4785,27 +4794,4 @@ of the distances. This vector must be pre-allocated to the dimenstions of \code{params}. - -%% \subsubsubsection{Pre-defined Functions for LM} -%% -%% We define some commonly used functions for use with the LM -%% minimization, used for the purpose of performing $\chi^2$ fitting: -%% -%% \begin{prototype} -%% psMinimizeLMChi2Func psMinimizeLMChi2Gauss1D; -%% psMinimizeLMChi2Func psMinimizeLMChi2Gauss2D; -%% \end{prototype} -%% -%% \code{psMinimizeChi2LMGauss1D} shall take as \code{params}, the -%% normalization, center, and standard deviation of a Gaussian to be fit, -%% and as \code{x}, a vector containing a single value. It shall return -%% the value of the Gaussian at the value, and the derivatives -%% (\code{deriv}) with respect to each of the parameters. -%% -%% \code{psMinimizeChi2LMGauss2D} shall take, as \code{params}, the -%% normalization, center (two values), standard deviation (two values) -%% and position angle of a 2-dimensional Gaussian, and as \code{x}, a -%% vector containing a position, $(x,y)$. It shall return the value of -%% the 2-dimensional Gaussian at the specified point, along with the -%% derivatives with respect to each of the parameters. \subsubsection{Powell} @@ -4827,5 +4813,5 @@ bool psMinimizePowell(psMinimization *min, psVector *params, - const psVector *paramMask, + const psVector *paramMask, const psArray *coords, psMinimizePowellFunc func); @@ -4848,5 +4834,5 @@ bool psMinimizeChi2Powell(psMinimization *min, psVector *params, - const psVector *paramMask, + psMinConstrain *constrain, const psArray *coords, const psVector *value,