diff --git a/App/src/TestFittingModule1.cpp b/App/src/TestFittingModule1.cpp
index 9fa45e36baeb4e605664450982c9ce7a2a6f9a2e..6859a902a8c7b454c62db684e17f234d915558fb 100644
--- a/App/src/TestFittingModule1.cpp
+++ b/App/src/TestFittingModule1.cpp
@@ -77,8 +77,8 @@ void TestFittingModule1::execute()
//m_fitSuite->setMinimizer( MinimizerFactory::createMinimizer("Scan") );
m_fitSuite->attachObserver( FitSuiteObserverFactory::createPrintObserver() );
- m_fitSuite->attachObserver( FitSuiteObserverFactory::createDrawObserver() );
- m_fitSuite->attachObserver( FitSuiteObserverFactory::createTreeObserver() );
+ //m_fitSuite->attachObserver( FitSuiteObserverFactory::createDrawObserver() );
+ //m_fitSuite->attachObserver( FitSuiteObserverFactory::createTreeObserver() );
m_fitSuite->runFit();
}
diff --git a/Doc/UserManual/Examples.tex b/Doc/UserManual/Examples.tex
index 2eb0e916fcf59b9111ae02cb9a82fd8cde698af9..81930afdcacdf55a9a2b8e143596587c7f76d64b 100644
--- a/Doc/UserManual/Examples.tex
+++ b/Doc/UserManual/Examples.tex
@@ -1,40 +1,9 @@
%\newpage
-\chapter{Examples}
+\chapter{Examples} \label{Exampleschap}
%\section{Examples}
-%\subsection{Hello listing}
-
-%\begin{lstlisting}[language=python, style=eclipse]
-%mAmbience = MaterialManager.getHomogeneousMaterial("Air", 1.0, 0.0 )
-%mSubstrate = MaterialManager.getHomogeneousMaterial("Substrate", 1.0-6e-6, 2e-8 )
-%n_particle = complex(1.0-6e-4, 2e-8)
-%cylinder_ff = FormFactorCylinder(5*nanometer, 5*nanometer)
-%cylinder = Particle(n_particle, cylinder_ff)
-%prism_ff = FormFactorPrism3(5*nanometer, 5*nanometer)
-%prism = Particle(n_particle, prism_ff)
-%particle_decoration = ParticleDecoration()
-%particle_decoration.addParticle(cylinder, 0.0, 0.5)
-%particle_decoration.addParticle(prism, 0.0, 0.5)
-%interference = InterferenceFunctionNone()
-%particle_decoration.addInterferenceFunction(interference)
-%# air layer with particles and substrate form multi layer
-%air_layer = Layer(mAmbience)
-%air_layer_decorator = LayerDecorator(air_layer, particle_decoration)
-%substrate_layer = Layer(mSubstrate, 0)
-%multi_layer = MultiLayer()
-%multi_layer.addLayer(air_layer_decorator)
-%multi_layer.addLayer(substrate_layer)
-
-%# build and run experiment
-%simulation = Simulation()
-%simulation.setDetectorParameters(100,-1.0*degree, 1.0*degree, 100, 0.0*degree 2.0*degree, True)
-%simulation.setBeamParameters(1.0*angstrom, -0.2*degree, 0.0*degree)
-%simulation.setSample(multi_layer)
-%simulation.runSimulation()
-%\end{lstlisting}
-
\section{General methodology}
-A simulation of GISAXS using BornAgain platform can be decomposed into the following points:
+A simulation of GISAXS using \BornAgain\ platform can be decomposed into the following points:
\begin{itemize}
\item Definition of the materials by specifying their names and their
refractive indices,
@@ -72,7 +41,7 @@ roughnesses (equal to 0 by default) and the refractive index of the
material. We do not define any dimensions in the $x$, $y$
directions. And, except for roughness, the layer's vertical boundaries are plane and
perpendicular to the $z$-axis. There is also no limitation to the
-number of layers that could be defined in BornAgain.\\
+number of layers that could be defined in \BornAgain.\\
\noindent {\huge\danger}
\colorbox{Lightgray}{\parbox{\dimexpr\linewidth-8\fboxsep}
@@ -81,7 +50,7 @@ When assembling the sample, the layers are defined from top to
bottom. So in most cases the first layer will be the air layer.}}\\
\noindent The particles are characterized by their form factors (\textit{i.e.} the Fourier transform of the shape function - see the list of form factors implemented
- in BornAgain) and
+ in \BornAgain) and
the refractive index of the composing material. The number of input parameters for the form
factor depends on the
particle symmetry; it ranges from one parameter for a sphere (its
@@ -130,11 +99,10 @@ parameter like, for example, \texttt{20.0*Units::micrometer} in C++.
\subsection{Programs}
-\noindent \smallpencil \colorbox{Lightgray}{\parbox{\dimexpr\linewidth-8\fboxsep}
-{\underline{Programming}: The examples presented in the next
+\MakeRemark{Programming}{The examples presented in the next
paragraphs are written in C++ or Python. For tutorials about these
programming languages, the users are referred to
- \cite{Cppref} and \cite{Pythonref} respectively.}}\\
+ \cite{Cppref} and \cite{Pythonref} respectively.}\\
\noindent Note about the version of C++ and Python to run the
examples.\\
@@ -161,7 +129,7 @@ modules (lines~\ref{import_begin}-\ref{import_end}). For example,
line~\ref{import_numpy} imports NumPy, which
is a fundamental package for scientific computing with Python
(\url{http://www.numpy.org/}). In particular, line~\ref{import_end}
-imports the features of BornAgain software.\\
+imports the features of \BornAgain\ software.\\
\noindent Finally line~\ref{def_function} marks the beginning of the
function to define and run the simulation.
@@ -221,7 +189,7 @@ prism = Particle(n_particle, prism_ff) @\label{particlesprism2}@
\end{lstlisting}
\noindent We implement two different shapes of particles: cylinders and
- prisms (\textit{i.e.} elongated particles with a constant equilateral triangular cross section).\\ All particles implemented in BornAgain are defined by their
+ prisms (\textit{i.e.} elongated particles with a constant equilateral triangular cross section).\\ All particles implemented in \BornAgain\ are defined by their
form factors, their sizes and the refractive index of the material
they are made of. Here, for the
cylindrical particle, we input its radius and its height. For the prism,
@@ -368,7 +336,7 @@ are the minimum and maximum values respectively of $\phi_f$, which is the in-pla
the number of points in the range of variations of the exit angle
$\alpha_f$ measured from the $x,y$-plane in the $z$-direction,\\ \texttt{alpha\_f\_min=0.0*degree} and \texttt{alpha\_f\_max=2.0*degree}
are the minimum and maximum values respectively of $\alpha_f$,\\
-\texttt{isgisaxs\_style=True} (default value = False) is a boolean
+\texttt{isgisaxs\_style=True} (default value = \texttt{False}) is a boolean
used to characterise the structure of the output data. If
\texttt{isgisaxs\_style=True}, the output data is binned at constant
values of the sine of the output angles, $\alpha_f$ and $\phi_f$, otherwise it is binned
@@ -382,7 +350,7 @@ at constant values of these two angles.\\
grazing angle on the surface of the sample,
\texttt{phi\_i=0.0*degree} is the in-plane
direction of the incident beam (measured with respect to the
-$x$-axis). Note that in Fig.\ref{fig:multil3d} $\alpha_i=\alpha_0$ and $\phi_i=\phi_0$.\\
+$x$-axis). Note that in Fig.~\ref{fig:multil3d} $\alpha_i=\alpha_0$ and $\phi_i=\phi_0$.\\
\noindent \underline{Remark}: Note that, except for
\texttt{isgisaxs\_style}, there are no default values implemented for the
@@ -404,7 +372,7 @@ return GetOutputData(simulation) @\label{outputdata}@%arr = GetOutputData(simula
as a function of outgoing angles $\alpha_f$ and $\phi_f$ for further
uses (plots, fits,\ldots) as a NumPy array containing
\texttt{n\_phi}$\times$\texttt{n\_alpha}
-datapoints. Some options are provided by BornAgain. For example, figure~\ref{fig:output_ex1} shows the two-dimensional
+datapoints. Some options are provided by \BornAgain. For example, figure~\ref{fig:output_ex1} shows the two-dimensional
contourplot of the intensity as a function of $\alpha_f$ and
$\phi_f$.
diff --git a/Doc/UserManual/Fitting.tex b/Doc/UserManual/Fitting.tex
index d13aebaceee7ae83dab9aa66bf9390e5691b6efd..3ebd8013100d34a22bb89445011006545f04ef83 100644
--- a/Doc/UserManual/Fitting.tex
+++ b/Doc/UserManual/Fitting.tex
@@ -1,5 +1,5 @@
\newpage
-\chapter{Fitting}
+\chapter{Fitting} \label{Fittingchap}
% Minuit Library:
% Migrad Algorithm ("Minuit", "Migrad"),
@@ -15,7 +15,7 @@
% Minimize Algorithm ("Minuit2", "Minimize")
% Scan Algorithm ("Minuit2", "Scan")
% Fumili2 Algorithm ("Minuit2", "Fumili2")
-% GSL Library: (Only available if GSL and MathMore are avaiable too)
+% GSL Library: (Only available if GSL and MathMore are available too)
% Fletcher-Reeves Conjugate Gradient Algorithm ("GSLMultiMin", "conjugatefr")
% Polak-Ribiere Conjugate Gradient Algorithm ("GSLMultiMin", "conjugatepr")
% BFGS Conjugate Gradient Algorithm ("GSLMultiMin", "bfgs2")
@@ -23,29 +23,20 @@
% Simulated Annealing Algorithm ("GSLSimAn", "")
In addition to the simulation of grazing incidence
-x-ray and neutron scattering by
-multilayered samples, BornAgain also offers the option to
+X-ray and neutron scattering by
+multilayered samples, \BornAgain\ also offers the option to
fit a selection of simulated sample parameters to experimental data. This aspect
-of the software is discussed in this chapter.
+of the software is discussed in the following chapter.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Short description of fitting theory}
-These features of BornAgain deal with estimating the optimum parameters
+These features of \BornAgain\ deal with estimating the optimum parameters
in the simulation by minimizing the difference ($\chi^2$) between theory and experimental data.\\
-\textbf{From Minuit user's guide}: Minuit is usually used to find the ``best'' values of a set of
-parameters, where ``best'' is defined as those values which minimize a
-given function, FCN. The width of the function minimum in some
-neighbourhood of the minimum, gives information about the uncertainty
-in the best parameter values. An important feature of Minuit is that
-it offers several tools to analyze the parameter errors.
-
-
-\noindent \smallpencil \colorbox{Lightgray}{\parbox{\dimexpr\linewidth-8\fboxsep}
-{\underline{Theory}: Users wanting to find out more about minimization (also called
-maximization or optimization methods) are referred to \ldots.}}\\
+\MakeRemark{Theory}{Users wanting to find out more about minimization (also called
+maximization or optimization methods) are referred to \ldots.}\\
%The conjugate gradient and BFGS methods are described in detail in the following book,
% R. Fletcher, Practical Methods of Optimization (Second Edition) Wiley (1987), ISBN 0471915475.
@@ -58,158 +49,181 @@ maximization or optimization methods) are referred to \ldots.}}\\
Local minimum, multiple minima
+Multidimensional fit = fit using multiple parameters??
+
+Local or global minimization
%\url{http://seal.web.cern.ch/seal/MathLibs/Minuit2/html/}
%\url{http://seal.web.cern.ch/seal/documents/minuit/mntutorial.pdf}
%\url{http://root.cern.ch/root/html/MATH_MINUIT2_Index.html}
-A detailed tutorial of Minuit software can be downloaded at the
-following address \url{http://seal.web.cern.ch/seal/documents/minuit/mnusersguide.pdf}.
-%\cite{MinuitRoot}.
-
-Local or global minimization
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Implementation in BornAgain}
-BornAgain fitting features include...\\
+\section{Implementation in \BornAgain}
+\BornAgain\ fitting features include...\\
Different geometries, number of fit parameters, variety of minimizers\\
and minimization strategies.\\
-
-Minimizer belongs to MinimizerFactory (interacts with Root)
-
+A detailed description of the architecture of Fit folder is given in
+\ldots \\
Variable parameters: customize lists, inputs required, default values
-Methods: implemented list, possibility to add your own.
+FitSuite: m\_minimizer, m\_is\_last\_iteration
-Set/create minimizer : name + algorithm + option (part of Minimizer
-factory). The minimizers can be chosen from the following list:
+\textbf{From Minuit user's guide}: Minuit is usually used to find the ``best'' values of a set of
+parameters, where ``best'' is defined as those values which minimize a
+given function, FCN. The width of the function minimum in some
+neighbourhood of the minimum, gives information about the uncertainty
+in the best parameter values. An important feature of Minuit is that
+it offers several tools to analyze the parameter errors.\\
-FitSuite: m\_minimizer, m\_is\_last\_iteration
+A detailed tutorial of Minuit software can be downloaded at the
+following address \url{http://seal.web.cern.ch/seal/documents/minuit/mnusersguide.pdf}.
+%\cite{MinuitRoot}.
\textbf{Questions}
\begin{itemize}
+\item background noise, intensity threshold, beam intensity
\item Is it possible to fit on the particle kind (cylinder or prism or
\ldots)?
\item Fit using several experimental runs?
+\textbf{ Software Scatter :The program allows to automatically fit large number of data files such as files from timeresolved
+studies. A requirement is that subsequent scattering curves do not differ too much, so
+that the fitted values of the first curve can be used as starting parameters for a fit to the next
+curve. Before starting a fit series, choose the first data file and perform a fit so that the
+parameter values are set to an optimal start value. The first/last point, qmin/max and Imin for
+the fit will apply to all data sets.}
\item Number of degrees of freedom?
\item Weighted fits?
-\item Errorbars, epsilon for standard error
+\item Errorbars, epsilon for standard error - interpretation of values
+ and associated errors
\item Normalisation of the data (in order to be compared)
\item Constraints between parameters
\item Quick fit option
\item Possible exponential decrease of the form factor
-\item fit of 1D plots?
+\item fit of 1D plots - possible
+\item interrupt fit
\end{itemize}
-%To run tests: python TestFit.py\\
-%List of tests\\
-%TestFit01 - Two parameter fit using variety of minimizers. Geometry: cylinders in the air.\\
-%TestFit02 - Fitting using sample builder\\
-
-
-Class TestFittingModule1:
-\begin{itemize}
-\item mp\_real\_data (experimental data),
-\item mp\_simulated\_data,
-\item mp\_simulation,
-\item mp\_sample,
-\item mp\_fitsuite: \texttt{addSimulationAndRealdData}: link a given set of experimental data with a
- particular set of simulated data in order to proceed to a fit. It is
- therefore possible to generate a batch of different numerical
- samples to test different configurations in order to obtain the best
- fit by playing with the number of layers or the shape of particles
- (different form factors). What about \texttt{chi2\_module} by default?
-\end{itemize}
-
-\texttt{attachObserver} uses
-\texttt{FitSuiteObserverFactory::CreatePrintObserver()} or
-\texttt{FitSuiteObserverFactory::CreateDrawObserver()} or
-\texttt{FitSuiteObserverFactory::CreateTreeObserver()}.
-
-
-%runFit()
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\subsubsection{General procedure to fit / for fitting}
+\subsubsection{General fitting procedure}
-Format of input (experimental data) - requirements (structure,
+Different steps:
+\begin{itemize}
+\item Creation of sample: multilayered sample, beam ,detector
+\item Parameters to fit
+\item Real (experimental) data (reduced, scaled, format)
+\item Format of input (experimental data) - requirements (structure,
normalisation, values...)
-Experimental data can be entered by importing it from ASCII files created by other applications.
-%Therefore the number of layers cannot be used as a fitting parameter
-
-A fit is run for a fixed value of \textbf{the number of layers
- constituting the sample}, the \textbf{input beam wavelength}.
-
-Input = matrix of intensity as function of ?
-
-Generation of the numerical model (cf other examples or detailed
-description or only script given and description of main steps)
-
-\textbf{Structure of fitting module in BornAgain.}
+\item Fit: algorithm, weights
+\end{itemize}
+Experimental data can be imported from ASCII files created by other applications.
+%Therefore the number of layers cannot be used as a fitting parameter
-\textbf{Structure of Fit folder (sources *.cpp):}
-\begin{itemize}
-\item FitObject.cpp
-\item FitSuiteObjects.cpp
-\item MinimizerFactory.cpp
-\item ROOTMinimizer.cpp
-\item FitParameter.cpp
-\item FitSuiteParameters.cpp
-\item MinimizerScan.cpp
-\item ROOTMinimizerHelper.cpp
-\item FitParameterLinked.cpp
-\item FitSuitePrintObserver.cpp
-\item MinimizerTest.cpp
-\item FitSuite.cpp
-\item FitSuiteStrategies.cpp
-\item ROOTGSLNLSMinimizer.cpp
-\item FitSuiteFunctions.cpp
-\item IFitSuiteStrategy.cpp
-\item ROOTGSLSimAnMinimizer.cpp
-\end{itemize}
+Input = two-dimensional matrix of intensity as function of ...
-\textbf{Structure of Fit folder (headers *.h):}
+The class \texttt{FitSuite} contains the main functionalities to be used
+for the fit:
\begin{itemize}
-\item AttFitting.h
-\item FitSuite.h
-\item FitSuiteStrategies.h
-\item MinimizerTest.h
-\item ROOTMinimizerHelper.h
-\item AttLimits.h
-\item FitSuiteFunctions.h
-\item IFitSuiteStrategy.h
-\item ROOTGSLNLSMinimizer.h
-\item FitObject.h
-\item FitSuiteObjects.h
-\item IMinimizer.h
-\item ROOTGSLSimAnMinimizer.h
-\item FitParameter.h
-\item FitSuiteParameters.h
-\item MinimizerFactory.h
-\item ROOTMinimizer.h
-\item FitParameterLinked.h
-\item FitSuitePrintObserver.h
-\item MinimizerScan.h
-\item ROOTMinimizerFunction.h
+\item \texttt{addFitParameter}: choice of fitting parameters,
+\item \texttt{addFitStrategy}: choice of a fitting strategy
+ (\textbf{give description}),
+\item Adds pair of (simulation, real data) for consecutive simulation
+\texttt{addSimulationAndRealData(simulation, real\_data, chi2\_module)}
+\item \texttt{runFit()} : run the fit,
+\item \texttt{initPrint(print\_every\_nth)} to print intermediate
+ results or \texttt{printResults()} to print the final ones
\end{itemize}
-\myparagraph{\underline{Choice of parameters to be fitted}}
-In principle, every parameter used in the construction of the sample
-can be used as a fit parameter. For example
+FitSuite::FitSuite() : m\_minimizer(0), m\_is\_last\_iteration(false)
+ m\_function\_chi2.init(this);
+ m\_function\_gradient.init(this);
+
+getNCalls()
+
+ // running minimization using strategies
+ m\_fit\_strategies.minimize();
+
+%\textbf{Structure of fitting module in \BornAgain.}
+%\textbf{Structure of Fit folder (sources *.cpp):}
+%\begin{itemize}
+%\item FitObject.cpp
+%\item FitSuiteObjects.cpp
+%\item MinimizerFactory.cpp
+%\item ROOTMinimizer.cpp
+%\item FitParameter.cpp
+%\item FitSuiteParameters.cpp
+%\item MinimizerScan.cpp
+%\item ROOTMinimizerHelper.cpp
+%\item FitParameterLinked.cpp
+%\item FitSuitePrintObserver.cpp
+%\item MinimizerTest.cpp
+%\item FitSuite.cpp: addFitParameter, addSimulationAndRealData
+%\item FitSuiteStrategies.cpp
+%\item ROOTGSLNLSMinimizer.cpp
+%\item FitSuiteFunctions.cpp
+%\item IFitSuiteStrategy.cpp
+%\item ROOTGSLSimAnMinimizer.cpp
+%\end{itemize}
+
+%\textbf{Structure of Fit folder (headers *.h):}
+%\begin{itemize}
+%\item AttFitting.h
+%\item FitSuite.h
+%\item FitSuiteStrategies.h
+%\item MinimizerTest.h
+%\item ROOTMinimizerHelper.h
+%\item AttLimits.h
+%\item FitSuiteFunctions.h
+%\item IFitSuiteStrategy.h
+%\item ROOTGSLNLSMinimizer.h
+%\item FitObject.h
+%\item FitSuiteObjects.h
+%\item IMinimizer.h
+%\item ROOTGSLSimAnMinimizer.h
+%\item FitParameter.h
+%\item FitSuiteParameters.h
+%\item MinimizerFactory.h
+%\item ROOTMinimizer.h
+%\item FitParameterLinked.h
+%\item FitSuitePrintObserver.h
+%\item MinimizerScan.h
+%\item ROOTMinimizerFunction.h
+%\end{itemize}
+
+\subsubsection{Building the sample}
+This step is similar / identical for any simulation using \BornAgain. It
+corresponds to first characterizing the geomerey of the system: the particles (shapes, sizes, refractive
+indices), the different layers (thickness,
+order, refractive index, possible roughness of the interface), the
+interference between the particles and the way they are distributed in
+the layers (buried particles or particles sitting on top of a
+layer). Then we specify the parameters of the input beam and of the
+output detector.
+
+\subsubsection{Loading real data}
+By ``real'' data, we mean the system to which the fitting model will
+be compared to. It usually refers to experimental data.
+
+\subsubsection{Choice of parameters to be fitted}
+A fit is run for a fixed value of \textbf{the number of layers
+ constituting the sample}, the \textbf{input beam wavelength}. In principle, every parameter used in the construction of the sample
+can be used as a fit parameter. For example,
\begin{table}[h]
\centering
\begin{tabular}{|@{}l||l@{}|}
\hline
-\textbf{Particles} & dimensions: height, radius, length, width, \\
- & half side length, thickness, \textbf{index of refraction}
- height\_aspect\_ratio, \textbf{orientation?} \\
-& (depending on the shape) \\
-& inteference (density, proportion, width, distance,\\
-& probability,
-dispersion radius) \\
+\textbf{Particles} & dimensions (depending on the shape): height, radius, length, width, \\
+ & half side length, thickness,
+ inclination of the lateral faces,
+ \textbf{index of refraction}, height
+ aspect ratio, \textbf{orientation?}\\
+& position ($x, ,y, z$), depth, abundance \\
+& interference (density, proportion, width, distance, probability\\
+& dispersion radius) \\
\hline
-\textbf{Lattice} & \\
+\textbf{Lattice} & lengths of axis\\
\hline
\textbf{Layers} & roughness, thickness, \textbf{index of refraction} \\
\hline
@@ -219,61 +233,31 @@ dispersion radius) \\
\hline
\end{tabular}
\label{table:fitting_parameters}
-\caption{List of parameters that can be defined as fitting parameters.}
+\caption{List of parameters that can be used as fitting parameters.}
\end{table}
-The heights of different particles can be associated to two different
-fitting parameters and optimized / minimized separately.
-
-
-m\_cylinder\_ratio, m\_cylinder\_height
-m\_prism3\_half\_side, m\_prism3\_height
-
- *Normalizer/scale
- *Normalizer/shift
+\texttt{RegisterParameter}
- *SampleBuilder/particle\_probability1
- *SampleBuilder/particle\_radius1
- *SampleBuilder/dispersion\_radius1
- *SampleBuilder/height\_aspect\_ratio1
+%m\_cylinder\_ratio
+% *Normalizer/scale, *Normalizer/shift
+% *SampleBuilder/particle\_probability1
+% *SampleBuilder/interf\_distance, *SampleBuilder/interf\_width, height\_aspect\_ratio
+% */lattice\_length\_a, */lattice\_length\_c
+%*/nanoparticle\_radius
+%*/sigma\_nanoparticle\_radius
+% */meso\_height, */meso\_radius
+%*/sigma\_meso\_height, \_meso\_radius, \_lattice\_length\_a
+%*/surface\_filling\_ratio
+%*/roughness
- *SampleBuilder/interf\_distance
- *SampleBuilder/interf\_width
- *SampleBuilder/height\_aspect\_ratio
-
- */lattice\_length\_a
- */lattice\_length\_c
- */nanoparticle\_radius",
- */sigma\_nanoparticle\_radius
- */meso\_height
- */meso\_radius
- */sigma\_meso\_height
- */sigma\_meso\_radius
- */sigma\_lattice\_length\_a
- */surface\_filling\_ratio
- */roughness
-
-
-In BornAgain, the parameters used for the fit are specified using the
-function \texttt{addFitParameter} with the following syntax:
+If the sample contains different types of particles, the heights of different particles can be associated to two different
+fitting parameters and optimized / minimized separately.\\
-\begin{lstlisting}[language=C++, style=eclipse,numbers=none]
-m\_fitSuite->addFitParameter("*height", 4.*Units::nanometer,
-0.04*Units::nanometer, AttLimits::lowerLimited(0.01) );
-m\_fitSuite->addFitParameter("*radius", 6.*Units::nanometer,
-0.06*Units::nanometer, AttLimits::lowerLimited(0.01) );
+In \BornAgain, the parameters used for the fit are specified using the
+function \texttt{addFitParameter} with one of the following two syntaxes
-m\_fitSuite->addFitParameter("*SampleBuilder/m\_cylinder\_height",4*Units::nanometer, 0.01*Units::nanometer,AttLimits::lowerLimited(0.01) );
-
-m\_fitSuite->addFitParameter("*SampleBuilder/m\_cylinder\_radius",
- 6*Units::nanometer, 0.01*Units::nanometer, AttLimits::lowerLimited(0.01) );
-
-m\_fitSuite->addFitParameter("*SampleBuilder/m\_prism3\_half\_side", 4*Units::nanometer, 0.01*Units::nanometer, AttLimits::lowerLimited(0.01) );
-\end{lstlisting}
-
-There are two possible syntaxes:
\begin{itemize}
\item First option: \\ \texttt{m\_fitSuite->addFitParameter(name, value, step, attlim, error);},
where \texttt{value}, \texttt{step} and \texttt{error} are double values corresponding to the ... respectively.
@@ -281,9 +265,8 @@ where \texttt{value}, \texttt{step} and \texttt{error} are double values corresp
\texttt{m\_fitSuite->addFitParameter(name, value, attlim, error);}
\end{itemize}
-\texttt{attlim} is , \texttt{name} is ...
+\texttt{attlim} is , \texttt{name} is ... The path used corresponds to ...
By default the input value of \texttt{error} is 0.
-
\texttt{AttLimits} can be
\begin{itemize}
@@ -291,34 +274,71 @@ By default the input value of \texttt{error} is 0.
\item lowerLimited(double value),
\item limited(double min value, double max value).
\end{itemize}
-The unit of \texttt{AttLimits} is identical to the one used to characterize the
+The unit of \texttt{AttLimits} is identical to the one used to characterize the
parameter.
-Number of iterations
-Interrupt fit
-Procedure: initially choose a small number of fitting parameters
+\begin{lstlisting}[language=python, style=eclipse,numbers=none]
+FitSuite().addFitParameter("*height", 4.*nanometer, 0.04*Units::nanometer, AttLimits::lowerLimited(0.01) )
+\end{lstlisting}
-\textbf{ Software Scatter :The program allows to automatically fit large number of data files such as files from timeresolved
-studies. A requirement is that subsequent scattering curves do not differ too much, so
-that the fitted values of the first curve can be used as starting parameters for a fit to the next
-curve. Before starting a fit series, choose the first data file and perform a fit so that the
-parameter values are set to an optimal start value. The first/last point, qmin/max and Imin for
-the fit will apply to all data sets.}
-Background noise, intensity threshold.
+
+\MakeRemark{Advice}{Initially choose a small number of fitting
+ parameters.}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\subsubsection{Associating real and numerical data}
+The function \texttt{addSimulationAndRealdData} links a given set of real data with a
+ particular set of simulated data in order to proceed to a fit. It is
+ therefore possible to generate a batch of different numerical
+ samples to test different configurations in order to obtain the best
+ fit by playing with the number of layers or the shape of particles
+ (different form factors).
+
+There are two possible syntaxes:
+\begin{lstlisting}[language=python, style=eclipse,numbers=none]
+FitSuite().addSimulationAndRealData(simulation, real_data,
+chi2_module)
+
+FitSuite().addSimulationAndRealData(simulation, real_data)
+\end{lstlisting}
+
+
+What about \texttt{chi2\_module} by default?
+
+setChiSquaredFunction: SquaredFunctionWithSystematicError, SquaredFunctionDefault(), SquaredFunctionWhichOnlyWorks(),
+\texttt{SquaredFunctionWithGaussianError(m\_sigma)}
+,SquaredFunctionWithSystematicError(0.08)
+chiModule.setChiSquaredFunction(SquaredFunctionDefault()) ( isgisaxs
+uses epsilon=0, which correspond to our SquaredFunctionDefault).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\subsubsection{Fitting methods implemented}
+\subsubsection{Choice of fitting method}
+Methods: implemented list, possibility to add your own.
+
+Number of iterations
+
+Minimizer belongs to MinimizerFactory (interacts with Root)
+
+Set/create minimizer : name + algorithm + option (part of Minimizer
+factory). The minimizers can be chosen from the following list:
+
\textbf{Is there any default method implemented?}
-Different minimizers from Root library can be used in BornAgain. They are listed in
+Normalisation of data ?
+
+
+Different minimizers from Root library can be used in \BornAgain. They are listed in
Table~\ref{table:fit_minimizers}. Users can also add their own by
implementing their definition in the \texttt{Catalogue} contained in
program \texttt{MinimizerFactory}.
Minuit user's manual describes which minimizer to use in order to best
fit our data\ldots \cite{MinuitRoot}.
+SET STRATEGY chosen using...
+How to change options of algorithms.
+
\begin{table}[h]
\centering
\begin{tabular}{@{}ll@{}}
@@ -339,16 +359,12 @@ Genetic & \\ %TMVA Toolkit for Multivariate Data Analysis with ROOT
\hline
\end{tabular}
\label{table:fit_minimizers}
-\caption{List of fitting minimizers implemented in BornAgain.}
+\caption{List of fitting minimizers implemented in \BornAgain.}
\end{table}
+Give general syntax
-
-Minuit2, originally developed in the SEAL project, is now distributed
-within ROOT. The classes have been moved inside the namespace
-ROOT::Minuit2.
-
Minuit is conceived as a tool to find the minumum value of a
multi-parameter function and analyze the shape of the function around
the minimum. The principal application is foreseen for statistical
@@ -362,31 +378,40 @@ Minuit2, optional package in the ROOT framework, is a numerical minimization com
originally written in C++ programming language. The program searches for minima in a user-defined function with
respect to one or more parameters using several different methods as
specified by the user.
+%MIGRAD
+%This is the best minimizer for nearly all functions. It is a variable-metric method with inexact line search, a stable metric updating scheme, and checks for positive-definiteness. It will run faster if you SET STRATEGY 0 and will be more reliable if you SET STRATEGY 2 (although the latter option may not help much). Its main weakness is that it depends heavily on knowledge of the first derivatives, and fails miserably if they are very inaccurate. If first derivatives are a problem, they can be calculated analytically inside FCN (see elsewhere in this writeup) or if this is not feasible, the user can try to improve the accuracy of Minuit's numerical approximation by adjusting values using the SET EPS and/or SET STRATEGY commands (see Floating Point Precision and SET STRATEGY).
+%SCAN
+%This is not intended to minimize, and just scans the function, one parameter at a time. It does however retain the best value after each scan, so it does some sort of highly primitive minimization.
+%SIMPLEX
+%This genuine multidimensional minimization routine is usually much slower than MIGRAD, but it does not use first derivatives, so it should not be so sensitive to the precision of the FCN calculations, and is even rather robust with respect to gross fluctuations in the function value. However, it gives no reliable information about parameter errors, no information whatsoever about parameter correlations, and worst of all cannot be expected to converge accurately to the minimum in a finite time. Its estimate of EDM is largely fantasy, so it would not even know if it did converge.
+%Minuit2, originally developed in the SEAL project, is now distributed
+%within ROOT. The classes have been moved inside the namespace
+%ROOT::Minuit2.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Outputs}
log file, how to interpret the data, how to save the estimated parameters.
-Customize using \texttt{Observer}.
-See FitGISAXS, IsGISAXS, Fish.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Examples}
-in C++ or Python?
-TestPyFit: testfit01.py, testfit02.py
-FitSuite
-
-Observer
-
-Screenshot after running the program
-Graph?
-Simulation
-Real data
-
-
-Difference between TestFit and TestPyFit
-
-Different steps to follow - order of execution
+Output = two-dimensional matrix + errors $\chi^2$
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Example in Python}
+
+In this example we use a simple sample geometry: cylindrical and
+prismatic particles
+in air layer, deposited on a substrate layer, with no interference
+between them. We consider 4 fitting parameters: radius and height of cylinders and the
+side length and height of prisms. Our "real" data is a 2D intensity
+map obtained from the simulation of the same geometry with a fixed
+value of 5 nanometers for the height of both particle shapes as well
+as for the radius of the cylinders and the half side length of the
+prisms' triangular basis. Then we run our minimization consequently
+using ... as a minimization engine, starting with a cylinder's height
+of 4~nm, a cylinder's radius of 6~nm, a prism's half side of ...~nm,
+and prism3\_length = ...nm as initial fit parameter values.
+
+\MakeRemark{Order of steps}{ The first two steps could be interchanged.}
%%%%%%%%%%%%%%%%%%%%%
\myparagraph{\underline{First step:} Initializing the simulation}
+The details about the multilayered sample are given in Section%~\ref{}.
definition of the detector's
parameters \texttt{setDetectorParameters} and the beam's parameters
@@ -395,40 +420,152 @@ Different steps to follow - order of execution
%%%%%%%%%%%%%%%%%%%%%
\myparagraph{\underline{Generating the sample}}
-Layers, particles,
+\begin{lstlisting}[language=python, style=eclipseboxed, name=exfit,nolol]
+import sys, os, numpy
+import math @\label{import_libmath}@
-%%%%%%%%%%%%%%%%%%%%%
-\myparagraph{\underline{Choice of parameters to be fitted}}
+sys.path.append(os.path.abspath(
+ os.path.join(os.path.split(__file__)[0],
+ '..', '..', '..', 'lib')))
-choice of parameters
- to be used for minimization \texttt{addFitParameter}.
+from libBornAgainCore import * @\label{import_corelib}@
+from libBornAgainFit import * @\label{import_fitlib}@
+
+# values we want to find
+cylinder_height = 5.0*nanometer @\label{cylheightini}@
+cylinder_radius = 5.0*nanometer @\label{cylradini}@
+prism3_half_side = 5.0*nanometer @\label{prismhlfini}@
+prism3_height = 5.0*nanometer @\label{prismhightini}@
+\end{lstlisting}
+
+\begin{lstlisting}[language=python, style=eclipseboxed, name=exfit,nolol]
+# ----------------------------------
+# create sample : cylinders and prisms in the air on substrate layer
+# ----------------------------------
+def buildSample():
+ # defining materials
+ mAmbience = MaterialManager.getHomogeneousMaterial("Air", 0.0, 0.0 )
+ mSubstrate = MaterialManager.getHomogeneousMaterial("Substrate", 6e-6, 2e-8 )
+ # collection of particles
+ n_particle = complex(6e-4, 2e-8)
+ cylinder_ff = FormFactorCylinder(cylinder_height, cylinder_radius) @\label{fit_cylff}@
+ cylinder = Particle(n_particle, cylinder_ff)
+ prism_ff = FormFactorPrism3(prism3_height, prism3_half_side) @\label{fit_prismff}@
+ prism = Particle(n_particle, prism_ff)
+ particle_decoration = ParticleDecoration()
+ particle_decoration.addParticle(cylinder, 0.0, 0.5)
+ particle_decoration.addParticle(prism,0.0, 0.5)
+ interference = InterferenceFunctionNone()
+ particle_decoration.addInterferenceFunction(interference)
+ # air layer with particles and substrate form multi layer
+ air_layer = Layer(mAmbience)
+ air_layer_decorator = LayerDecorator(air_layer, particle_decoration)
+ substrate_layer = Layer(mSubstrate, 0)
+ multi_layer = MultiLayer()
+ multi_layer.addLayer(air_layer_decorator)
+ multi_layer.addLayer(substrate_layer)
+ return multi_layer
+
+# ----------------------------------
+# create sample : input beam and detector - characteristics
+# ----------------------------------
+def createSimulation():
+ simulation = Simulation()
+ simulation.setDetectorParameters(100, 0.0*degree, 2.0*degree,100 , 0.0*degree, 2.0*degree)
+ simulation.setBeamParameters(1.0*angstrom, 0.2*degree, 0.0*degree)
+ simulation.setBeamIntensity(1e10) @\label{fit_beamintensity}@
+ return simulation
+\end{lstlisting}
+
+\begin{lstlisting}[language=python, style=eclipseboxed, name=exfit,nolol]
+# ----------------------------------
+# read "real" data from file
+# ----------------------------------
+def GetRealData():
+ real_data =
+ OutputDataIOFactory.getOutputData('Refdata_fitcylinderprisms.txt') @\label{fit_input_realdata}@
+ return real_data
+\end{lstlisting}
+
+\texttt{'Refdata\_fitcylinderprisms.txt'}
+
+\begin{lstlisting}[language=python, style=eclipseboxed,
+ name=exfit,nolol]
+# ----------------------------------
+# run fitting
+# ----------------------------------
+def run_fitting():
+ sample = buildSample() @\label{}@
+ simulation = createSimulation() @\label{}@
+ simulation.setSample(sample) @\label{}@
+
+ # get the real data, which is simply results of our simulation with default values
+ real_data = GetRealData() @\label{}@
+
+ # run the simulation
+ simulation.runSimulation() @\label{}@
+
+ # linking real and numerical (to be fitted) data
+ fitSuite = FitSuite() @\label{}@
+ fitSuite.addSimulationAndRealData(simulation, real_data) @\label{}@
+\end{lstlisting}
%%%%%%%%%%%%%%%%%%%%%
-\myparagraph{\underline{Running the simulation / the fit}}
+\myparagraph{\underline{Choice of fitting minimizer}}
+
+\begin{lstlisting}[language=python, style=eclipseboxed,
+ name=exfit,nolol]
+ # setting fitting minimizer
+ fitSuite.setMinimizer(
+ MinimizerFactory.createMinimizer("Minuit2","Migrad") ) @\label{}@
+\end{lstlisting}
-Normalisation ?
-Output = two-dimensional matrix.
+%%%%%%%%%%%%%%%%%%%%%
+\myparagraph{\underline{Choice of numerical parameters to be fitted}}
+
+\begin{lstlisting}[language=python, style=eclipseboxed,
+ name=exfit,nolol]
+ # setting fitting parameters
+ fitSuite.addFitParameter("*FormFactorCylinder/height",
+ 4.*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) ) @\label{}@
+ fitSuite.addFitParameter("*FormFactorCylinder/radius",
+ 6.*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) ) @\label{}@
+ fitSuite.addFitParameter("*FormFactorPrism3/height", 4.*nanometer,
+ 0.01*nanometer, AttLimits.lowerLimited(0.01) ) @\label{}@
+ fitSuite.addFitParameter("*FormFactorPrism3/half_side",
+ 6*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) ) @\label{}@
+\end{lstlisting}
-How to run the fit: using the function \texttt{fit}
+choice of parameters
+ to be used for minimization \texttt{addFitParameter}.
+%%%%%%%%%%%%%%%%%%%%%
+\myparagraph{\underline{Running the fit}}
+\begin{lstlisting}[language=python, style=eclipseboxed, name=exfit,nolol]
+ # run fit @\label{}@
+ fitSuite.runFit()
+ # print fit results
+ fitSuite.printResults()
+
+# ----------------------------------
+# main()
+# ----------------------------------
+if __name__ == '__main__':
+ run_fitting()
+\end{lstlisting}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+After running the fit, the following text should be displayed on your
+screen (generated by using \texttt{fitsuite.PrintResults})
\begin{lstlisting}[caption={Python script of fitting example},
label=script_exfit1,captionpos=b,escapeinside={@}{@} ,language=python,style=eclipse, numbers= none,frame = leftline ,
framerule = 2mm ,
rulecolor = \color{lightgrey},
breaklines = true]
-# In this test we are using simple geometry: cylinders without interference in
-# air layer with two parameters (radius and height of cylinders), describing
-# the sample. Our "real" data is 2D intensity map obtained from the simulation of
-# the same geometry with fixed values height = 5nm and radius = 5nm.
-# Then we run our minimization consequently using different minimization engines,
-# with height=4nm, radius=6nm as starting fit parameter values.
-import sys
-import os
-import numpy
-import time
+import sys, os, numpy
+import math
sys.path.append(os.path.abspath(
os.path.join(os.path.split(__file__)[0],
@@ -437,148 +574,114 @@ sys.path.append(os.path.abspath(
from libBornAgainCore import *
from libBornAgainFit import *
-# sample parameters we are going to find
-cylinder_height = 5*nanometer
-cylinder_radius = 5*nanometer
-
-# minimizer name and type of minimization algorithm
-Minimizers = [
- ("Minuit2","Migrad"),
- ("Minuit2","Fumili"),
- ("GSLMultiMin","BFGS"),
- ("GSLMultiMin","SteepestDescent"),
- ("GSLMultiFit",""),
-# ("GSLSimAn","")
-]
-
-# -----------------------------------------------------------------------------
-# run several minimization rounds using different minimizers
-# -----------------------------------------------------------------------------
-def runTest():
- #print "**********************************************************************"
- #print "* Starting TestFit01 *"
- #print "**********************************************************************"
- nTest=0
- status = "OK"
- for m in Minimizers:
- minimizer_name = m[0]
- minimizer_algorithm = m[1]
- print "Minimizer {0:-2d} {1:}({2:})".format(nTest, minimizer_name, minimizer_algorithm)
- result_ok = run_fitting(minimizer_name, minimizer_algorithm)
- nTest+=1
- if not result_ok: status = "FAILED"
-
- return "TestFit01", "Two parameters fit using variety of minimizers.", status
-
-# -----------------------------------------------------------------------------
-# run fitting specified minimizer
-# -----------------------------------------------------------------------------
-def run_fitting(minimizer_name, minimizer_algorithm):
- sample = buildSample()
- simulation = createSimulation()
- simulation.setSample(sample)
-
- # creating real data, which is simply results of our simulation with default values
- simulation.runSimulation()
- real_data = simulation.getOutputDataClone()
-
- # setting fit suite
- fitSuite = FitSuite()
- fitSuite.setMinimizer( MinimizerFactory.createMinimizer(minimizer_name, minimizer_algorithm) )
- fitSuite.addFitParameter("*height", 4.*nanometer, 0.04*nanometer, AttLimits.lowerLimited(0.01) )
- fitSuite.addFitParameter("*radius", 6.*nanometer, 0.06*nanometer, AttLimits.lowerLimited(0.01) )
- fitSuite.addSimulationAndRealData(simulation, real_data)
-
- # run fit
- start_time = time.time()
- fitSuite.runFit()
- real_time = time.time() - start_time
-
- height_found = fitSuite.getMinimizer().getValueOfVariableAtMinimum(0)
- height_diff = abs(height_found - cylinder_height)/cylinder_height
- radius_found = fitSuite.getMinimizer().getValueOfVariableAtMinimum(1)
- radius_diff = abs(radius_found - cylinder_radius)/cylinder_radius
-
- print " RealTime : {0:.3f} sec".format(real_time)
- print " NCalls : {0:<5d}".format(fitSuite.getNCalls())
- print ' par1 : {0:.4f} ({1:.3g}) '.format(height_found, height_diff)
- print ' par2 : {0:.4f} ({1:.3g}) '.format(radius_found, radius_diff)
-
- diff = 1.0e-02
- isSuccess = True
- if( (height_diff > diff) or (radius_diff > diff) ) : isSuccess=False
- return isSuccess
-
-# -----------------------------------------------------------------------------
-# create cylinders in the air
-# -----------------------------------------------------------------------------
-def buildSample():
+# values we want to find
+cylinder_height = 5.0*nanometer
+cylinder_radius = 5.0*nanometer
+prism3_half_side = 5.0*nanometer
+prism3_height = 5.0*nanometer
+
+# ----------------------------------
+# create sample : cylinders and prisms in the air on substrate layer
+# ----------------------------------
+def buildSample():
+ # defining materials
+ mAmbience = MaterialManager.getHomogeneousMaterial("Air", 0.0, 0.0 )
+ mSubstrate = MaterialManager.getHomogeneousMaterial("Substrate", 6e-6, 2e-8 )
+ # collection of particles
+ n_particle = complex(6e-4, 2e-8)
cylinder_ff = FormFactorCylinder(cylinder_height, cylinder_radius)
- n_particle = complex(1.0-6e-4, 2e-8)
cylinder = Particle(n_particle, cylinder_ff)
- interference = InterferenceFunctionNone()
-
+ prism_ff = FormFactorPrism3(prism3_height, prism3_half_side)
+ prism = Particle(n_particle, prism_ff)
particle_decoration = ParticleDecoration()
- particle_decoration.addParticle(cylinder)
+ particle_decoration.addParticle(cylinder, 0.0, 0.5)
+ particle_decoration.addParticle(prism,0.0, 0.5)
+ interference = InterferenceFunctionNone()
particle_decoration.addInterferenceFunction(interference)
-
- mAmbience = MaterialManager.getHomogeneousMaterial("Air", 1.0, 0.0 )
+ # air layer with particles and substrate form multi layer
air_layer = Layer(mAmbience)
air_layer_decorator = LayerDecorator(air_layer, particle_decoration)
+ substrate_layer = Layer(mSubstrate, 0)
multi_layer = MultiLayer()
multi_layer.addLayer(air_layer_decorator)
-
+ multi_layer.addLayer(substrate_layer)
return multi_layer
+# ----------------------------------
+# create sample : input beam and detector - characteristics
+# ----------------------------------
def createSimulation():
- simulation = Simulation();
- simulation.setDetectorParameters(100, 0.0*degree, 2.0*degree,100 , 0.0*degree, 2.0*degree);
- simulation.setBeamParameters(1.0*angstrom, -0.2*degree, 0.0*degree);
- simulation.setBeamIntensity(1e10);
+ simulation = Simulation()
+ simulation.setDetectorParameters(100, 0.0*degree, 2.0*degree,100 , 0.0*degree, 2.0*degree)
+ simulation.setBeamParameters(1.0*angstrom, 0.2*degree, 0.0*degree)
+ simulation.setBeamIntensity(1e10) # to leave or not
return simulation
-#-------------------------------------------------------------
+# ----------------------------------
+# read "real" data from file
+# ----------------------------------
+def GetRealData():
+ real_data = OutputDataIOFactory.getOutputData('Refdata_fitcylinderprisms.txt')
+ return real_data
+
+# ----------------------------------
+# run fitting
+# ----------------------------------
+def run_fitting():
+ sample = buildSample()
+ simulation = createSimulation()
+ simulation.setSample(sample)
+
+ # get the real data, which is simply results of our simulation with default values
+ real_data = GetRealData()
+
+ # run the simulation
+ simulation.runSimulation()
+
+ # linking real and numerical (to be fitted) data
+ fitSuite = FitSuite()
+ fitSuite.addSimulationAndRealData(simulation, real_data)
+
+ # setting fitting minimizer
+ fitSuite.setMinimizer( MinimizerFactory.createMinimizer("Minuit2","Migrad") )
+
+ # setting fitting parameters
+ fitSuite.addFitParameter("*FormFactorCylinder/height", 4.*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) )
+ fitSuite.addFitParameter("*FormFactorCylinder/radius", 6.*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) )
+ fitSuite.addFitParameter("*FormFactorPrism3/height", 4.*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) )
+ fitSuite.addFitParameter("*FormFactorPrism3/half_side", 6*nanometer, 0.01*nanometer, AttLimits.lowerLimited(0.01) )
+
+ # run fit
+ fitSuite.runFit()
+
+ # print fit results
+ fitSuite.printResults()
+
+# ----------------------------------
# main()
-#-------------------------------------------------------------
+# ----------------------------------
if __name__ == '__main__':
- name,description,status = runTest()
- print name,description,status
- if("FAILED" in status) : exit(1)
+ run_fitting()
+
\end{lstlisting}
-Output of functional test: fit module 2 params
-
-TestFittingModule1.cpp with "Minuit2", "Migrad"
-
-\begin{lstlisting}[style=eclipse,numbers=none]
-FitSuitePrintObserver::update() -> Info. NCall:0 NStrategy:0 Chi2:6.26500281e+09
-Time spend since last call, cpu:0.03 sec, wall time 0.03sec
- # 0 *height 4.00000000e+00 lim(0.01,)
- # 1 *radius 6.00000000e+00 lim(0.01,)
-FitSuitePrintObserver::update() -> Info. NCall:20 NStrategy:0 Chi2:9.29429839e+07
-Time spend since last call, cpu:1.27 sec, wall time 4.72sec
- # 0 *height 4.51488966e+00 lim(0.01,)
- # 1 *radius 4.68705140e+00 lim(0.01,)
-FitSuitePrintObserver::update() -> Info. NCall:40 NStrategy:0 Chi2:9.74308159e+05
-Time spend since last call, cpu:0.97 sec, wall time 1.05sec
- # 0 *height 4.94285364e+00 lim(0.01,)
- # 1 *radius 5.07666127e+00 lim(0.01,)
-FitSuitePrintObserver::update() -> Info. NCall:60 NStrategy:0 Chi2:7.91181985e+00
-Time spend since last call, cpu:1.00 sec, wall time 1.07sec
- # 0 *height 4.99977322e+00 lim(0.01,)
- # 1 *radius 4.99985065e+00 lim(0.01,)
-FitSuitePrintObserver::update() -> Info. NCall:80 NStrategy:0 Chi2:1.01820395e-02
-Time spend since last call, cpu:0.98 sec, wall time 1.05sec
- # 0 *height 5.00000020e+00 lim(0.01,)
- # 1 *radius 5.00000004e+00 lim(0.01,)
-
-FitSuiteObserverPrint::update() -> Info. Printing results
+\newpage
+
+fit with 4 parameters
+
+\begin{lstlisting}[caption={Output of fit using Python script},
+ label=script_exfit1,captionpos=b,escapeinside={@}{@} ,style=eclipse, numbers= none,%frame = leftline,
+ %framerule = 2mm ,
+ %rulecolor = \color{lightgrey},
+ breaklines = true]
--- FitSuite::printResults --------------------------
- Chi2:1.19798858e-02 chi2.NCall:85 grad.NCall:0,0,0 (neval, ngrad, total)
- # 0 *height 4.99999992e+00 lim(0.01,)
- # 1 *radius 5.00000004e+00 lim(0.01,)
+ Chi2:6.06100495e-07 chi2.NCall:159 grad.NCall:0,0,0 (neval, ngrad, total)
+ # 0 *FormFactorCylinder/height 4.99997446e+00 lim(0.01,)
+ # 1 *FormFactorCylinder/radius 5.00001015e+00 lim(0.01,)
+ # 2 *FormFactorPrism3/height 5.00000526e+00 lim(0.01,)
+ # 3 *FormFactorPrism3/half_side 4.99998321e+00 lim(0.01,)
-----------------------------------------------------
MinimizerType : Minuit2
MinimizerAlgorithm : Migrad
@@ -594,18 +697,21 @@ FitSuiteObserverPrint::update() -> Info. Printing results
Status : 0 'OK, valid minimum'
IsValidError : 0 'No detailed error validation'
CovMatrixStatus : 3 'full accurate'
- NCalls : 85
- MinValue : 1.01756899e-02
- Edm : 4.39332982e-12
+ NCalls : 159
+ MinValue : 2.11285899e-07
+ Edm : 2.02968770e-08
--- Variables ---------------------------------------
- NumberOfVariables : 2 (free), 2 (total)
+ NumberOfVariables : 4 (free), 4 (total)
Errors : yes, see below
Npar Name Value Error GlobalCC
- 0 *height 5.000000e+00 1.109913e-04 1.208255e-01
- 1 *radius 5.000000e+00 8.941040e-05 1.208255e-01
+ 0 *FormFactorCylinder/height 4.999974e+00 2.545638e-01 5.698257e-01
+ 1 *FormFactorCylinder/radius 5.000010e+00 1.336151e-01 2.309216e-02
+ 2 *FormFactorPrism3/height 5.000005e+00 1.730972e-01 5.697503e-01
+ 3 *FormFactorPrism3/half_side 4.999983e+00 3.100541e-01 2.897434e-02
--- Correlations-------------------------------------
- 1.000000e+00 -1.208255e-01
- -1.208255e-01 1.000000e+00
-fitting1 : Real Time = 8.33 seconds Cpu Time = 4.58 seconds
+ 1.000000e+00 7.770605e-03 -5.695874e-01 1.079534e-02
+ 7.770605e-03 1.000000e+00 -1.702212e-03 -2.134996e-02
+ -5.695874e-01 -1.702212e-03 1.000000e+00 7.135700e-03
+ 1.079534e-02 -2.134996e-02 7.135700e-03 1.000000e+00
\end{lstlisting}
diff --git a/Doc/UserManual/UserManual.tex b/Doc/UserManual/UserManual.tex
index 9de3acd59f13d59ba061fe69c8eda1d3ce3024de..ba7dcff34f5a3fe4f89eefb8f90fcac98391d3e5 100644
--- a/Doc/UserManual/UserManual.tex
+++ b/Doc/UserManual/UserManual.tex
@@ -146,15 +146,16 @@ keepaspectratio]{results2_2.png}%
\maketitle
\tableofcontents
-%\lstlistoflistings
+\lstlistoflistings
%\listoffigures
-%\listoftables
+\listoftables
-\input{Introduction}
-\input{QuickStart}
-\input{SoftwareArchitecture}
-\input{Installation}
+%\input{Introduction}
+%\input{QuickStart}
+%\input{SoftwareArchitecture}
+%\input{Installation}
\input{Examples}
+%\input{Fitting}
%List of notations\\ Bugs\\ License agreement\\ Directory layout \\ FAQ \\ Future development.
diff --git a/Doc/UserManual/lstcustom.sty b/Doc/UserManual/lstcustom.sty
index 4f226b20eb7e4478c36f8bc096b2e1089b15feb7..37733b6cd950801f013a3d79c76f33be8b2964f1 100644
--- a/Doc/UserManual/lstcustom.sty
+++ b/Doc/UserManual/lstcustom.sty
@@ -18,7 +18,7 @@
context, ERROR, WARNING, INFO, enum},
morecomment=[l]{//},
morecomment=[s]{/*}{*/},
- % FIXME: listings does not see guillemots, so any regions with them wont work
+ % FIXME: listings does not see guillemets, so any regions with them wont work
morecomment=[s]{«REM»}{«ENDREM»},
morecomment=[s]{«REM}{»},
morestring=[s]{'}{'},
@@ -262,16 +262,16 @@ breaklines = true
stringstyle=\color{darkblue},
numberstyle=\color{darkgrey}\lstfontfamily,
emph={Simulation,MaterialManager,MultiLayer,Layer,Particle,ParticleDecoration},
- emphstyle=\color{dockerblue},%dred}, %darkpink},
- % emphstyle=\color{red},
- backgroundcolor=\color{lightgrey},
+ emphstyle=\color{dockerblue},
+ backgroundcolor=\color{lightlightgrey},
morecomment=[s][\color{lightblue}]{/**}{*/},
showstringspaces=false,
- numbers=left,%none,
+ numbers=left,
emphstyle={[2]\color{blue}},
- frame=shadowbox,
+ frame=single,
firstnumber=auto,
rulesepcolor=\color{darkgrey},
+rulecolor=\color{darkgrey},
breaklines = true
%showspaces=true,
%showtabs=true,
diff --git a/Fit/Factory/src/FitSuite.cpp b/Fit/Factory/src/FitSuite.cpp
index 1bde9dfeda7c00d47a7e479b7ea7355f05f00ea9..485312ef8747c84387c9eafb3c401cf622fa933a 100644
--- a/Fit/Factory/src/FitSuite.cpp
+++ b/Fit/Factory/src/FitSuite.cpp
@@ -102,7 +102,7 @@ void FitSuite::runFit()
// running minimization using strategies
m_fit_strategies.minimize();
- // seting parameters to the optimum values found by the minimizer
+ // setting parameters to the optimum values found by the minimizer
m_fit_parameters.setValues(m_minimizer->getValueOfVariablesAtMinimum());
// calling observers to let them to get results
@@ -123,7 +123,7 @@ void FitSuite::minimize()
// initializing minimizer's parameters with the list of local fit parameters
m_minimizer->setParameters(m_fit_parameters);
- // setting number of free parameters for propper chi2 normalization
+ // setting number of free parameters for proper chi2 normalization
m_fit_objects.setNfreeParameters((int)m_fit_parameters.getNfreeParameters());
// minimizing