[590] | 1 | % 'check_peaklocking': estimte peaklocking error in a civ field series TODO: UPDATE |
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| 2 | %------------------------------------------------------------------------ |
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| 3 | % function ParamOut=check_peaklocking(Param) |
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| 4 | % |
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| 5 | %%%%%%%%%%% GENERAL TO ALL SERIES ACTION FCTS %%%%%%%%%%%%%%%%%%%%%%%%%%% |
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| 6 | % |
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| 7 | % This function is used in four modes by the GUI series: |
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| 8 | % 1) config GUI: with no input argument, the function determine the suitable GUI configuration |
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| 9 | % 2) interactive input: the function is used to interactively introduce input parameters, and then stops |
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| 10 | % 3) RUN: the function itself runs, when an appropriate input structure Param has been introduced. |
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| 11 | % 4) BATCH: the function itself proceeds in BATCH mode, using an xml file 'Param' as input. |
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| 12 | % |
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| 13 | % This function is used in four modes by the GUI series: |
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| 14 | % 1) config GUI: with no input argument, the function determine the suitable GUI configuration |
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| 15 | % 2) interactive input: the function is used to interactively introduce input parameters, and then stops |
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| 16 | % 3) RUN: the function itself runs, when an appropriate input structure Param has been introduced. |
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| 17 | % 4) BATCH: the function itself proceeds in BATCH mode, using an xml file 'Param' as input. |
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| 18 | % |
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| 19 | %OUTPUT |
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| 20 | % GUI_input=list of options in the GUI series.fig needed for the function |
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| 21 | % |
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| 22 | %INPUT: |
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| 23 | % In run mode, the input parameters are given as a Matlab structure Param copied from the GUI series. |
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| 24 | % In batch mode, Param is the name of the corresponding xml file containing the same information |
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| 25 | % In the absence of input (as activated when the current Action is selected |
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| 26 | % in series), the function ouput GUI_input set the activation of the needed GUI elements |
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| 27 | % |
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| 28 | % Param contains the elements:(use the menu bar command 'export/GUI config' in series to see the current structure Param) |
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| 29 | % .InputTable: cell of input file names, (several lines for multiple input) |
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| 30 | % each line decomposed as {RootPath,SubDir,Rootfile,NomType,Extension} |
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| 31 | % .OutputSubDir: name of the subdirectory for data outputs |
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| 32 | % .OutputDirExt: directory extension for data outputs |
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| 33 | % .Action: .ActionName: name of the current activated function |
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| 34 | % .ActionPath: path of the current activated function |
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| 35 | % .IndexRange: set the file or frame indices on which the action must be performed |
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| 36 | % .FieldTransform: .TransformName: name of the selected transform function |
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| 37 | % .TransformPath: path of the selected transform function |
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| 38 | % .TransformHandle: corresponding function handle |
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| 39 | % .InputFields: sub structure describing the input fields withfields |
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| 40 | % .FieldName: name of the field |
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| 41 | % .VelType: velocity type |
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| 42 | % .FieldName_1: name of the second field in case of two input series |
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| 43 | % .VelType_1: velocity type of the second field in case of two input series |
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| 44 | % .ProjObject: %sub structure describing a projection object (read from ancillary GUI set_object) |
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| 45 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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[810] | 46 | |
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| 47 | %======================================================================= |
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[977] | 48 | % Copyright 2008-2017, LEGI UMR 5519 / CNRS UGA G-INP, Grenoble, France |
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[810] | 49 | % http://www.legi.grenoble-inp.fr |
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| 50 | % Joel.Sommeria - Joel.Sommeria (A) legi.cnrs.fr |
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| 51 | % |
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| 52 | % This file is part of the toolbox UVMAT. |
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| 53 | % |
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| 54 | % UVMAT is free software; you can redistribute it and/or modify |
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| 55 | % it under the terms of the GNU General Public License as published |
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| 56 | % by the Free Software Foundation; either version 2 of the license, |
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| 57 | % or (at your option) any later version. |
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| 58 | % |
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| 59 | % UVMAT is distributed in the hope that it will be useful, |
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| 60 | % but WITHOUT ANY WARRANTY; without even the implied warranty of |
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| 61 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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| 62 | % GNU General Public License (see LICENSE.txt) for more details. |
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| 63 | %======================================================================= |
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| 64 | |
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[590] | 65 | function ParamOut=check_peaklocking(Param) |
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| 66 | |
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| 67 | %% set the input elements needed on the GUI series when the action is selected in the menu ActionName |
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| 68 | if ~exist('Param','var') % case with no input parameter |
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| 69 | ParamOut={'AllowInputSort';'off';...% allow alphabetic sorting of the list of input files (options 'off'/'on', 'off' by default) |
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| 70 | 'WholeIndexRange';'off';...% prescribes the file index ranges from min to max (options 'off'/'on', 'off' by default) |
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| 71 | 'NbSlice';'on'; ...%nbre of slices ('off' by default) |
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| 72 | 'VelType';'two';...% menu for selecting the velocity type (options 'off'/'one'/'two', 'off' by default) |
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| 73 | 'FieldName';'off';...% menu for selecting the field (s) in the input file(options 'off'/'one'/'two', 'off' by default) |
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| 74 | 'FieldTransform'; 'off';...%can use a transform function |
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| 75 | 'ProjObject';'on';...%can use projection object(option 'off'/'on', |
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| 76 | 'Mask';'off';...%can use mask option (option 'off'/'on', 'off' by default) |
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| 77 | 'OutputDirExt';'.pklock';...%set the output dir extension |
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| 78 | ''}; |
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| 79 | return |
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| 80 | end |
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| 81 | |
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| 82 | %%%%%%%%%%%% STANDARD PART %%%%%%%%%%%% |
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| 83 | %% select different modes, RUN, parameter input, BATCH |
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| 84 | % BATCH case: read the xml file for batch case |
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| 85 | if ischar(Param) |
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| 86 | Param=xml2struct(Param); |
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| 87 | checkrun=0; |
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| 88 | % RUN case: parameters introduced as the input structure Param |
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| 89 | else |
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| 90 | hseries=guidata(Param.hseries);%handles of the GUI series |
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| 91 | if isfield(Param,'Specific')&& strcmp(Param.Specific,'?') |
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| 92 | checkrun=1;% will only search interactive input parameters (preparation of BATCH mode) |
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| 93 | else |
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| 94 | checkrun=2; % indicate the RUN option is used |
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| 95 | end |
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| 96 | end |
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| 97 | ParamOut=Param; %default output |
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| 98 | OutputDir=[Param.OutputSubDir Param.OutputDirExt]; |
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| 99 | |
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| 100 | %% root input file(s) and type |
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| 101 | RootPath=Param.InputTable(:,1); |
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| 102 | RootFile=Param.InputTable(:,3); |
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| 103 | SubDir=Param.InputTable(:,2); |
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| 104 | NomType=Param.InputTable(:,4); |
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| 105 | FileExt=Param.InputTable(:,5); |
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| 106 | [filecell,i1_series,i2_series,j1_series,j2_series]=get_file_series(Param); |
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| 107 | %%%%%%%%%%%% |
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| 108 | % The cell array filecell is the list of input file names, while |
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| 109 | % filecell{iview,fileindex}: |
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| 110 | % iview: line in the table corresponding to a given file series |
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| 111 | % fileindex: file index within the file series, |
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| 112 | % i1_series(iview,ref_j,ref_i)... are the corresponding arrays of indices i1,i2,j1,j2, depending on the input line iview and the two reference indices ref_i,ref_j |
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| 113 | % i1_series(iview,fileindex) expresses the same indices as a 1D array in file indices |
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| 114 | %%%%%%%%%%%% |
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| 115 | NbSlice=1;%default |
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| 116 | if isfield(Param.IndexRange,'NbSlice')&&~isempty(Param.IndexRange.NbSlice) |
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| 117 | NbSlice=Param.IndexRange.NbSlice; |
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| 118 | end |
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| 119 | nbview=1;%number of input file series (lines in InputTable) |
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| 120 | nbfield_j=size(i1_series{1},1); %nb of fields for the j index (bursts or volume slices) |
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| 121 | nbfield_i=size(i1_series{1},2); %nb of fields for the i index |
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| 122 | nbfield=nbfield_j*nbfield_i; %total number of fields |
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| 123 | nbfield_i=floor(nbfield/NbSlice);%total number of indexes in a slice (adjusted to an integer number of slices) |
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| 124 | nbfield=nbfield_i*NbSlice; %total number of fields after adjustement |
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| 125 | |
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| 126 | %determine the file type on each line from the first input file |
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| 127 | ImageTypeOptions={'image','multimage','mmreader','video'}; |
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| 128 | NcTypeOptions={'netcdf','civx','civdata'}; |
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| 129 | for iview=1:nbview |
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| 130 | if ~exist(filecell{iview,1}','file') |
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[668] | 131 | disp_uvmat('ERROR',['the first input file ' filecell{iview,1} ' does not exist'],checkrun) |
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[590] | 132 | return |
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| 133 | end |
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[784] | 134 | [FileInfo{iview},MovieObject{iview}]=get_file_info(filecell{iview,1}); |
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[783] | 135 | FileType{iview}=FileInfo{iview}.FileType; |
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[590] | 136 | CheckImage{iview}=~isempty(find(strcmp(FileType{iview},ImageTypeOptions)));% =1 for images |
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| 137 | CheckNc{iview}=~isempty(find(strcmp(FileType{iview},NcTypeOptions)));% =1 for netcdf files |
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| 138 | if ~isempty(j1_series{iview}) |
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| 139 | frame_index{iview}=j1_series{iview}; |
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| 140 | else |
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| 141 | frame_index{iview}=i1_series{iview}; |
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| 142 | end |
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| 143 | end |
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| 144 | |
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| 145 | %% calibration data and timing: read the ImaDoc files |
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| 146 | %none |
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| 147 | |
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| 148 | %% coordinate transform or other user defined transform |
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| 149 | % none |
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| 150 | |
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| 151 | %%%%%%%%%%%% END STANDARD PART %%%%%%%%%%%% |
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| 152 | % EDIT FROM HERE |
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| 153 | |
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| 154 | %% check the validity of ctinput file types |
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| 155 | %none |
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| 156 | |
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| 157 | %% Set field names and velocity types |
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| 158 | InputFields{1}=[];%default (case of images) |
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| 159 | if isfield(Param,'InputFields') |
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| 160 | InputFields{1}=Param.InputFields; |
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| 161 | end |
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| 162 | % only one input fieldseries |
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| 163 | |
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| 164 | %% Initiate output fields |
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| 165 | %initiate the output structure as a copy of the first input one (reproduce fields) |
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| 166 | [DataOut,tild,errormsg] = read_field(filecell{1,1},FileType{1},InputFields{1},1); |
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| 167 | if ~isempty(errormsg) |
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[668] | 168 | disp_uvmat('ERROR',['error reading ' filecell{1,1} ': ' errormsg],checkrun) |
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[590] | 169 | return |
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| 170 | end |
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| 171 | time_1=[]; |
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| 172 | if isfield(DataOut,'Time') |
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| 173 | time_1=DataOut.Time(1); |
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| 174 | end |
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| 175 | if CheckNc{iview} |
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| 176 | if isempty(strcmp('Conventions',DataOut.ListGlobalAttribute)) |
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| 177 | DataOut.ListGlobalAttribute=['Conventions' DataOut.ListGlobalAttribute]; |
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| 178 | end |
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| 179 | DataOut.Conventions='uvmat'; |
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| 180 | DataOut.ListGlobalAttribute=[DataOut.ListGlobalAttribute {Param.Action}]; |
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| 181 | ActionKey='Action'; |
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| 182 | while isfield(DataOut,ActionKey) |
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| 183 | ActionKey=[ActionKey '_1']; |
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| 184 | end |
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| 185 | DataOut.(ActionKey)=Param.Action; |
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| 186 | DataOut.ListGlobalAttribute=[DataOut.ListGlobalAttribute {ActionKey}]; |
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| 187 | if isfield(DataOut,'Time') |
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| 188 | DataOut.ListGlobalAttribute=[DataOut.ListGlobalAttribute {'Time','Time_end'}]; |
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| 189 | end |
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| 190 | end |
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| 191 | |
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| 192 | %%%%%%%%%%%%%%%% loop on field indices %%%%%%%%%%%%%%%% |
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| 193 | index_slice=1:nbfield;% select the file indices |
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| 194 | for index=index_slice |
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| 195 | if checkrun |
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| 196 | update_waitbar(hseries.Waitbar,index/(nbfield)) |
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| 197 | stopstate=get(hseries.RUN,'BusyAction'); |
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| 198 | else |
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| 199 | stopstate='queue'; |
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| 200 | end |
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| 201 | if isequal(stopstate,'queue')% enable STOP command |
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| 202 | Data=cell(1,nbview);%initiate the set Data; |
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| 203 | nbtime=0; |
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| 204 | dt=[]; |
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| 205 | %%%%%%%%%%%%%%%% loop on views (input lines) %%%%%%%%%%%%%%%% |
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| 206 | for iview=1:nbview |
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| 207 | % reading input file(s) |
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| 208 | [Data{iview},tild,errormsg] = read_field(filecell{iview,index},FileType{iview},InputFields{iview},frame_index{iview}(index)); |
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| 209 | if ~isempty(errormsg) |
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| 210 | errormsg=['time_series / read_field / ' errormsg]; |
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| 211 | display(errormsg) |
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| 212 | break |
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| 213 | end |
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| 214 | end |
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| 215 | if isempty(errormsg) |
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| 216 | Field=Data{1}; % default input field structure |
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| 217 | % coordinate transform (or other user defined transform) |
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| 218 | % none |
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| 219 | |
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| 220 | %field projection on an object |
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| 221 | if Param.CheckObject |
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| 222 | [Field,errormsg]=proj_field(Field,Param.ProjObject); |
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| 223 | if ~isempty(errormsg) |
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| 224 | msgbox_uvmat('ERROR',['time_series / proj_field / ' errormsg]) |
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| 225 | return |
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| 226 | end |
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| 227 | end |
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| 228 | |
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| 229 | % initiate the time series at the first iteration |
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[592] | 230 | if index==1 |
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[590] | 231 | % stop program if the first field reading is in error |
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| 232 | if ~isempty(errormsg) |
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[668] | 233 | disp_uvmat('ERROR',['time_series / sub_field / ' errormsg],checkrun) |
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[590] | 234 | return |
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| 235 | end |
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| 236 | DataOut=Field;%default |
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| 237 | DataOut.NbDim=Field.NbDim+1; %add the time dimension for plots |
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| 238 | nbvar=length(Field.ListVarName); |
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| 239 | if nbvar==0 |
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[668] | 240 | disp_uvmat('ERROR','no input variable selected',checkrun) |
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[590] | 241 | return |
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| 242 | end |
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| 243 | testsum=2*ones(1,nbvar);%initiate flag for action on each variable |
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| 244 | if isfield(Field,'VarAttribute') % look for coordinate and flag variables |
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| 245 | for ivar=1:nbvar |
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| 246 | if length(Field.VarAttribute)>=ivar && isfield(Field.VarAttribute{ivar},'Role') |
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| 247 | var_role=Field.VarAttribute{ivar}.Role;%'role' of the variable |
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| 248 | if isequal(var_role,'errorflag') |
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[668] | 249 | disp_uvmat('ERROR','do not handle error flags in time series',checkrun) |
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[590] | 250 | return |
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| 251 | end |
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| 252 | if isequal(var_role,'warnflag') |
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| 253 | testsum(ivar)=0; % not recorded variable |
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| 254 | eval(['DataOut=rmfield(DataOut,''' Field.ListVarName{ivar} ''');']);%remove variable |
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| 255 | end |
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| 256 | if isequal(var_role,'coord_x')| isequal(var_role,'coord_y')|... |
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| 257 | isequal(var_role,'coord_z')|isequal(var_role,'coord') |
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| 258 | testsum(ivar)=1; %constant coordinates, record without time evolution |
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| 259 | end |
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| 260 | end |
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| 261 | % check whether the variable ivar is a dimension variable |
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| 262 | DimCell=Field.VarDimName{ivar}; |
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| 263 | if ischar(DimCell) |
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| 264 | DimCell={DimCell}; |
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| 265 | end |
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| 266 | if numel(DimCell)==1 && isequal(Field.ListVarName{ivar},DimCell{1})%detect dimension variables |
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| 267 | testsum(ivar)=1; |
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| 268 | end |
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| 269 | end |
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| 270 | end |
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| 271 | for ivar=1:nbvar |
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| 272 | if testsum(ivar)==2 |
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| 273 | eval(['DataOut.' Field.ListVarName{ivar} '=[];']) |
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| 274 | end |
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| 275 | end |
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| 276 | DataOut.ListVarName=[{'Time'} DataOut.ListVarName]; |
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| 277 | end |
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| 278 | |
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| 279 | % add data to the current field |
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| 280 | for ivar=1:length(Field.ListVarName) |
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| 281 | VarName=Field.ListVarName{ivar}; |
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| 282 | VarVal=Field.(VarName); |
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| 283 | if testsum(ivar)==2% test for recorded variable |
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| 284 | if isempty(errormsg) |
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| 285 | if isequal(Param.ProjObject.ProjMode,'inside')% take the average in the domain for 'inside' mode |
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| 286 | if isempty(VarVal) |
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[668] | 287 | disp_uvmat('ERROR',['empty result at frame index ' num2str(i1_series{iview}(index))],checkrun) |
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[590] | 288 | return |
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| 289 | end |
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| 290 | VarVal=mean(VarVal,1); |
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| 291 | end |
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| 292 | VarVal=shiftdim(VarVal,-1); %shift dimension |
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| 293 | DataOut.(VarName)=cat(1,DataOut.(VarName),VarVal);%concanete the current field to the time series |
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| 294 | else |
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| 295 | DataOut.(VarName)=cat(1,DataOut.(VarName),0);% put each variable to 0 in case of input reading error |
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| 296 | end |
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| 297 | elseif testsum(ivar)==1% variable representing fixed coordinates |
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| 298 | VarInit=DataOut.(VarName); |
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| 299 | if isempty(errormsg) && ~isequal(VarVal,VarInit) |
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[668] | 300 | disp_uvmat('ERROR',['time series requires constant coordinates ' VarName],checkrun) |
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[590] | 301 | return |
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| 302 | end |
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| 303 | end |
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| 304 | end |
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[592] | 305 | |
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[590] | 306 | end |
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| 307 | end |
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| 308 | end |
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| 309 | %%%%%%% END OF LOOP WITHIN A SLICE |
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| 310 | |
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| 311 | %remove time for global attributes if exists |
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| 312 | Time_index=find(strcmp('Time',DataOut.ListGlobalAttribute)); |
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| 313 | if ~isempty(Time_index) |
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| 314 | DataOut.ListGlobalAttribute(Time_index)=[]; |
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| 315 | end |
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| 316 | DataOut.Conventions='uvmat'; |
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| 317 | for ivar=1:numel(DataOut.ListVarName) |
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| 318 | VarName=DataOut.ListVarName{ivar}; |
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| 319 | eval(['DataOut.' VarName '=squeeze(DataOut.' VarName ');']) %remove singletons |
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| 320 | end |
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| 321 | |
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| 322 | % add time dimension |
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| 323 | for ivar=1:length(Field.ListVarName) |
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| 324 | DimCell=Field.VarDimName(ivar); |
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| 325 | if testsum(ivar)==2%variable used as time series |
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| 326 | DataOut.VarDimName{ivar}=[{'Time'} DimCell]; |
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| 327 | elseif testsum(ivar)==1 |
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| 328 | DataOut.VarDimName{ivar}=DimCell; |
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| 329 | end |
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| 330 | end |
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| 331 | indexremove=find(~testsum); |
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| 332 | if ~isempty(indexremove) |
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| 333 | DataOut.ListVarName(1+indexremove)=[]; |
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| 334 | DataOut.VarDimName(indexremove)=[]; |
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| 335 | if isfield(DataOut,'Role') && ~isempty(DataOut.Role{1})%generaliser aus autres attributs |
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| 336 | DataOut.Role(1+indexremove)=[]; |
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| 337 | end |
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| 338 | end |
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| 339 | |
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| 340 | %shift variable attributes |
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| 341 | if isfield(DataOut,'VarAttribute') |
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| 342 | DataOut.VarAttribute=[{[]} DataOut.VarAttribute]; |
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| 343 | end |
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| 344 | DataOut.VarDimName=[{'Time'} DataOut.VarDimName]; |
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| 345 | DataOut.Action=Param.Action;%name of the processing programme |
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| 346 | test_time=diff(DataOut.Time)>0;% test that the readed time is increasing (not constant) |
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| 347 | if ~test_time |
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| 348 | DataOut.Time=1:filecounter; |
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| 349 | end |
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| 350 | |
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| 351 | % display nbmissing |
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| 352 | if ~isequal(nbmissing,0) |
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[668] | 353 | disp_uvmat('WARNING',[num2str(nbmissing) ' files skipped: missing files or bad input, see command window display'],checkrun) |
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[590] | 354 | end |
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| 355 | |
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| 356 | %name of result file |
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| 357 | OutputFile=fullfile_uvmat(RootPath{1},OutputDir,RootFile{1},FileExtOut,NomTypeOut,i1_series{1}(1),i1_series{1}(end),i_slice,[]); |
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| 358 | errormsg=struct2nc(OutputFile,DataOut); %save result file |
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| 359 | if isempty(errormsg) |
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| 360 | display([OutputFile ' written']) |
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| 361 | else |
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[668] | 362 | disp_uvmat('ERROR',['error in Series/struct2nc: ' errormsg],checkrun) |
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[590] | 363 | end |
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| 364 | |
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| 365 | return |
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| 366 | |
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| 367 | %%%%%%%%%%%%%%%%%% END%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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[583] | 368 | %evaluation of peacklocking errors |
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| 369 | %use splinhist: give spline coeff cc for a smooth histo (call spline4) |
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| 370 | %use histsmooth(x,cc): calculate the smooth histo for any value x |
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| 371 | %use histder(x,cc): calculate the derivative of the smooth histo |
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| 372 | global hfig1 hfig2 hfig3 |
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| 373 | global nbb Uval Vval Uhist Vhist % nbb resolution of the histogram nbb=10: 10 values in unity interval |
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| 374 | global xval xerror yval yerror |
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| 375 | |
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| 376 | set(handles.vector_y,'Value',1)% trigger the option Uhist on the interface |
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| 377 | set(handles.Vhist_input,'Value',1) |
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| 378 | set(handles.cm_switch,'Value',0) % put the switch to 'pixel' |
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| 379 | |
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| 380 | %adjust the extremal values of the histogram in U with respect to integer |
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| 381 | %values |
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| 382 | minimU=round(min(Uval)-0.5)+0.5; %first value of the histogram with integer bins |
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| 383 | maximU=round(max(Uval)-0.5)+0.5; |
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| 384 | minim_fin=(minimU-0.5+1/(2*nbb)); % first bin valueat the beginning of an integer interval |
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| 385 | maxim_fin=(maximU+0.5-1/(2*nbb)); % last integer value |
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| 386 | nb_bin_min= round(-(minim_fin - min(Uval))*nbb); % nbre of bins added below |
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| 387 | nb_bin_max=round((maxim_fin -max(Uval))*nbb); %nbre of bins added above |
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| 388 | Uval=[minim_fin:(1/nbb):maxim_fin]; |
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| 389 | histu_min=zeros(nb_bin_min,1); |
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| 390 | histu_max=zeros(nb_bin_max,1); |
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| 391 | Uhist=[histu_min; Uhist ;histu_max]; % column vector |
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| 392 | |
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| 393 | %adjust the extremal values of the histogram in V |
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| 394 | minimV=round(min(Vval-0.5)+0.5); |
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| 395 | maximV=round(max(Vval-0.5)+0.5); |
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| 396 | minim_fin=minimV-0.5+1/(2*nbb); % first bin valueat the beginning of an integer interval |
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| 397 | maxim_fin=maximV+0.5-1/(2*nbb); % last integer value |
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| 398 | nb_bin_min=round((min(Vval) - minim_fin)*nbb); % nbre of bins added below |
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| 399 | nb_bin_max=round((maxim_fin -max(Vval))*nbb); |
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| 400 | Vval=[minim_fin:(1/nbb):maxim_fin]; |
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| 401 | histu_min=zeros(nb_bin_min,1); |
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| 402 | histu_max=zeros(nb_bin_max,1); |
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| 403 | Vhist=[histu_min; Vhist ;histu_max]; % column vector |
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| 404 | |
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| 405 | % RUN_histo_Callback(hObject, eventdata, handles) |
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| 406 | % %adjust the histogram to integer values: |
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| 407 | |
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| 408 | %histoU and V |
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| 409 | [Uhistinter,xval,xerror]=peaklock(nbb,minimU,maximU,Uhist); |
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| 410 | [Vhistinter,yval,yerror]=peaklock(nbb,minimV,maximV,Vhist); |
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| 411 | |
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| 412 | % selection of value ranges such that histo>=10 (enough statistics) |
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| 413 | Uval_ind=find(Uhist>=10); |
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| 414 | ind_min=min(Uval_ind); |
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| 415 | ind_max=max(Uval_ind); |
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| 416 | U_min=Uval(ind_min);% minimum allowed value |
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| 417 | U_max=Uval(ind_max);%maximum allowed value |
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| 418 | |
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| 419 | % selection of value ranges such that histo>=10 (enough statistics) |
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| 420 | Vval_ind=find(Vhist>=10); |
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| 421 | ind_min=min(Vval_ind); |
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| 422 | ind_max=max(Vval_ind); |
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| 423 | V_min=Vval(ind_min);% minimum allowed value |
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| 424 | V_max=Vval(ind_max);%maximum allowed value |
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| 425 | |
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| 426 | figure(4)% plot U histogram with smoothed one |
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| 427 | plot(Uval,Uhist,'b') |
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| 428 | grid on |
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| 429 | hold on |
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| 430 | plot(Uval,Uhistinter,'r'); |
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| 431 | hold off |
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| 432 | |
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| 433 | figure(5)% plot V histogram with smoothed one |
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| 434 | plot(Vval,Vhist,'b') |
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| 435 | grid on |
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| 436 | hold on |
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| 437 | plot(Vval,Vhistinter,'r'); |
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| 438 | hold off |
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| 439 | |
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| 440 | figure(6)% plot pixel error in two subplots |
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| 441 | hfig4=subplot(2,1,1); |
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| 442 | hfig5=subplot(2,1,2); |
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| 443 | axes(hfig4) |
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| 444 | plot(xval,xerror) |
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| 445 | axis([U_min U_max -0.4 0.4]) |
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| 446 | xlabel('velocity u (pix)') |
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| 447 | ylabel('peaklocking error (pix)') |
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| 448 | grid on |
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| 449 | axes(hfig5) |
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| 450 | plot(yval,yerror) |
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| 451 | axis([V_min V_max -0.4 0.4]); |
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| 452 | xlabel('velocity v (pix)') |
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| 453 | ylabel('peaklocking error (pix)') |
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| 454 | grid on |
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| 455 | |
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| 456 | |
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| 457 | |
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| 458 | |
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| 459 | |
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| 460 | |
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| 461 | |
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| 462 | |
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| 463 | |
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| 464 | %'peaklock': determines peacklocking errors from velocity histograms. |
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| 465 | %------------------------------------------------------- |
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| 466 | %first smooth the input histogram 'histu' in such a way that the integral over |
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| 467 | %n-n+1 is preserved, then deduce the peaklocking 'error' function of the pixcel displacement 'x'. |
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| 468 | % |
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| 469 | % [histinter,x,error]=peaklock(nbb,minim,maxim,histu) |
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| 470 | %OUTPUT: |
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| 471 | %histinter: smoothed interpolated histogram |
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| 472 | % x: vector of displacement values. |
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| 473 | % error: vector of estimated errors corresponding to x |
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| 474 | %INPUT: |
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| 475 | %histu=vector representing the values of histogram of measured velocity ; |
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| 476 | %minim, maxim: extremal values of the measured velocity (absica for histu) |
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| 477 | %nbb: number of bins inside each integer interval for the histograms |
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| 478 | %SUBROUTINES INCLUDED: |
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| 479 | %spline4.m% spline interpolation at 4th order |
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| 480 | %splinhist.m: give spline coeff cc for a smooth histo (call spline4) |
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| 481 | %histsmooth.m(x,cc): calculate the smooth histo for any value x |
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| 482 | %histder.m(x,cc): calculate the derivative of the smooth histo |
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| 483 | function [histinter,x,error]=peaklock(nbb,minim,maxim,histu) |
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| 484 | |
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| 485 | nint=maxim-minim+1 |
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| 486 | xfin=[minim-0.5+1/(2*nbb):(1/nbb):maxim+0.5-(1/(2*nbb))]; |
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| 487 | histo=(reshape(histu,nbb,nint));%extract values with x between integer -1/2 integer +1/2 |
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| 488 | Integ=sum(histo)/nbb; %integral of the pdf on each integer bin |
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| 489 | [histinter,cc]=splinhist(Integ,minim,nbb); |
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| 490 | histx=reshape(histinter,nbb,nint); |
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| 491 | xint=[minim:1:maxim]; |
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| 492 | x=zeros(nbb,nint); |
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| 493 | %determination of the displacement x(j,:) |
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| 494 | %j=1 |
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| 495 | delx=histo(1,:)./histsmooth(-0.5*ones(1,nint),cc)/nbb; |
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| 496 | %del(1,:)=delx; |
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| 497 | x(1,:)=-0.5+delx-(delx.*delx/2).*histder(-0.5*ones(1,nint),cc); |
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| 498 | %histx(1,:)=histsmooth(x(j-1,:),cc); |
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| 499 | for j=2:nbb |
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| 500 | delx=histo(j,:)./histsmooth(x(j-1,:),cc)/nbb; |
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| 501 | %delx=delx.*(delx<3*ones(1,nint)/nbb)+3*ones(1,nint)/nbb.*~(delx <3*ones(1,nint)/nbb) |
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| 502 | x(j,:)=x(j-1,:)+delx-(delx.*delx/2).*histder(x(j-1,:),cc); |
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| 503 | end |
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| 504 | %reshape |
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| 505 | xint=ones(nbb,1)*xint; |
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| 506 | x=x+xint; |
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| 507 | x=reshape(x,1,nbb*nint); |
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| 508 | error=xfin+1/(2*nbb)-x; |
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| 509 | |
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| 510 | %------------------------------------------------------- |
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| 511 | % --- determine the spline coefficients cc for the interpolated histogram. |
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| 512 | %------------------------------------------------- |
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| 513 | function [histsmooth,cc]= splinhist(Integ,mini,nbb) |
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| 514 | % provides a smooth histogramm histmooth, which remains always positive, |
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| 515 | % and is such that its sum over each integer bin [i-1/2 i+1/2] is equal to |
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| 516 | % Integ(i). The function determines histmooth as the exponential of a 4th |
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| 517 | % order spline function and adjust the cefficients by a Newton method to |
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| 518 | % fit the integral conditions Integ |
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| 519 | % histmooth is determined at the abscissa |
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| 520 | % xfin=[mini-0.5+1/(2*n):(1/n):maxi+0.5-(1/(2*n))] (maxi=mini+size(aa)-1) |
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| 521 | %cc(1-5,i) provides the spline coefficients |
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| 522 | |
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| 523 | % order 0 |
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| 524 | siz=size(Integ); |
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| 525 | nint=siz(2); |
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| 526 | izero=find(Integ==0); %indices of zero elements |
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| 527 | inonzero=find(Integ); |
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| 528 | Integ(izero)=min(Integ(inonzero)); |
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| 529 | aa=log(Integ);%initial guess for a coeff |
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| 530 | spli=spline4(aa,mini,nbb); %appel à la fonction spline4 |
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| 531 | histsmooth=exp(spli); |
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| 532 | |
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| 533 | S=(sum(reshape(histsmooth,nbb,nint)))/nbb;% integral of the fit histsmooth on ]i-1/2 i+1/2[ |
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| 534 | epsilon=max(abs(Integ-S)); |
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| 535 | iter=0; |
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| 536 | while epsilon > 0.000001 & iter<10 |
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| 537 | ident=eye(nint); |
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| 538 | dSda=ones(nint); |
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| 539 | for j=1:nint% determination of the jacobian matrix dSda |
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| 540 | dhistda=spline4(ident(j,:),mini,nbb); |
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| 541 | expdhistda=dhistda.*histsmooth; |
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| 542 | dSda(j,:)=(sum(reshape(expdhistda,nbb,nint)))/nbb; |
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| 543 | end |
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| 544 | aa=aa+(Integ-S)*inv(dSda);%new estimate of coefficients aa by linear interpolation |
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| 545 | [spli,bb]=spline4(aa,mini,nbb);% new fit histsmooth |
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| 546 | histsmooth=exp(spli); |
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| 547 | S=(sum(reshape(histsmooth,nbb,nint)))/nbb;% integral of the fit histsmooth on ]i-1/2 i+1/2[ |
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| 548 | epsilon=max(abs(Integ-S)); |
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| 549 | iter=iter+1; |
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| 550 | end |
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| 551 | if iter==10, errordlg('splinhist did not converge after 10 iterations'),end |
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| 552 | cc(1,:)=aa; |
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| 553 | cc(2,:)=bb(1,:); |
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| 554 | cc(3,:)=bb(2,:); |
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| 555 | cc(4,:)=bb(3,:); |
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| 556 | cc(5,:)=bb(4,:); |
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| 557 | |
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| 558 | %------------------------------------------------------- |
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| 559 | % --- determine the 4th order spline coefficients from the function values aa. |
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| 560 | %------------------------------------------------- |
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| 561 | function [histsmooth,bb]= spline4(aa,mini,n) |
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| 562 | % spline interpolation at 4th order |
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| 563 | %aa=vector of values of a function at integer abscissa, starting at mini |
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| 564 | %n=number of subdivisions for the interpolated function |
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| 565 | % histmooth =interpolated values at absissa |
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| 566 | % xfin=[mini-0.5+1/(2*n):(1/n):maxi+0.5-(1/(2*n))] (maxi=mini+size(aa)-1) |
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| 567 | %bb=[b(i);c(i);d(i); e(i)] matrix of spline coeff |
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| 568 | L1=[1/2 1/4 1/8 1/16;1 1 3/4 1/2;0 2 3 3;0 0 6 12]; |
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| 569 | L2=[-1/2 1/4 -1/8 1/16;1 -1 3/4 -1/2;0 2 -3 3;0 0 6 -12]; |
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| 570 | M=inv(L2)*L1; |
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| 571 | [V,D]=eig(M); |
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| 572 | F=-inv(V)*inv(L2)*[1 ;0 ;0;0]; |
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| 573 | a1rev=[1 -1/D(1,1)]; |
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| 574 | b1rev=[F(1)/D(1,1)]; |
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| 575 | a2rev=[1 -1/D(2,2)]; |
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| 576 | b2rev=[F(2)/D(2,2)]; |
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| 577 | a3=[1 -D(3,3)]; |
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| 578 | b3=[F(3)]; |
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| 579 | a4=[1 -D(4,4)]; |
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| 580 | b4=[F(4)]; |
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| 581 | |
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| 582 | %data |
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| 583 | % n=10;% résolution de la pdf: nbre de points par unite de u |
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| 584 | % mini=-10.0;%general mini=uint16(min(values)-1 CHOOSE maxi-mini+1 EVEN |
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| 585 | % maxi=9.0; % general maxi=uint16(max(values))+1 |
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| 586 | %nint=double(maxi-mini+1); % nombre d'intervals entiers EVEN! |
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| 587 | siz=size(aa); |
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| 588 | nint=siz(2); |
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| 589 | maxi=mini+nint-1; |
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| 590 | npdf=nint*n;% nbre total d'intervals à introduire dans la pdf: hist(u,npdf) |
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| 591 | %simulation de pdf |
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| 592 | xfin=[mini-0.5+1/(2*n):(1/n):maxi+0.5-(1/(2*n))];% valeurs d'interpolation: we take n values in each integer interval |
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| 593 | %histolin=exp(-(xfin-1).*(xfin-1)).*(2+cos(10*(xfin-1)));% simulation d'une pdf |
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| 594 | %histo=log(histolin); |
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| 595 | %histo=sin(2*pi*xfin); |
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| 596 | %histextract=(reshape(histo,n,nint)); |
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| 597 | %aa=sum(histextract)/n %integral of the pdf on each integer bin |
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| 598 | IP=[0 diff(aa)]; |
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| 599 | Irev=zeros(size(aa)); |
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| 600 | for i=1:nint |
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| 601 | Irev(i)=aa(end-i+1); |
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| 602 | end |
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| 603 | IPrev=[0 diff(Irev)]; |
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| 604 | |
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| 605 | %get the spline coelfficients a_d, using filter on the eigen vectors A,B,C |
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| 606 | Arev=filter(b1rev,a1rev,IPrev); |
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| 607 | Brev=filter(b2rev,a2rev,IPrev); |
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| 608 | C=filter(b3,a3,IP); |
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| 609 | D=filter(b4,a4,IP); |
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| 610 | A=zeros(size(Arev)); |
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| 611 | B=zeros(size(Brev)); |
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| 612 | for i=1:nint |
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| 613 | A(i)=Arev(end-i+1); |
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| 614 | B(i)=Brev(end-i+1); |
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| 615 | end |
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| 616 | %Matr=V*[A;B;C;D]; |
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| 617 | bb=V*[A;B;C;D]; |
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| 618 | %b=Matr(1,:); |
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| 619 | %c=Matr(2,:); |
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| 620 | %d=Matr(3,:); |
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| 621 | %e=Matr(4,:); |
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| 622 | %a=aa; |
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| 623 | |
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| 624 | %calculate the interpolation using the spline coefficients a-d |
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| 625 | %xextract=(reshape(xfin,n,nint));% |
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| 626 | chi=xfin+1/(2*n)-min(xfin)-double(int16(xfin+(1/(2*n))-min(xfin)))-0.5;% decimal part |
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| 627 | chi2=chi.*chi; |
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| 628 | chi3=chi2.*chi; |
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| 629 | chi4=chi3.*chi; |
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| 630 | avec=reshape(ones(n,1)*aa,1,n*nint); |
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| 631 | bvec=reshape(ones(n,1)*bb(1,:),1,n*nint); |
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| 632 | cvec=reshape(ones(n,1)*bb(2,:),1,n*nint); |
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| 633 | dvec=reshape(ones(n,1)*bb(3,:),1,n*nint); |
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| 634 | evec=reshape(ones(n,1)*bb(4,:),1,n*nint); |
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| 635 | histsmooth=avec+bvec.*chi+cvec.*chi2+dvec.*chi3+evec.*chi4; |
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| 636 | |
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| 637 | %------------------------------------------------------- |
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| 638 | % --- determine the interpolated histogram at points chi from the spline ceff cc. |
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| 639 | %------------------------------------------------- |
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| 640 | function histx= histsmooth(chi,cc) |
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| 641 | % provides the value of the interpolated histogram at values chi=x-i |
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| 642 | %(difference with the mnearest integer) |
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| 643 | % cc(5,size(chi)) is the set of spline coefficients obtained by splinhist |
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| 644 | chi2=chi.*chi; |
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| 645 | chi3=chi2.*chi; |
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| 646 | chi4=chi3.*chi; |
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| 647 | histx=exp(cc(1,:)+cc(2,:).*chi+cc(3,:).*chi2+cc(4,:).*chi3+cc(5,:).*chi4); |
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| 648 | |
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| 649 | %------------------------------------------------------- |
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| 650 | % --- determine the derivative p'/p of the interpolated histogram at points chi from the spline ceff cc. |
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| 651 | %------------------------------------------------- |
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| 652 | function histder= histder(chi,cc) |
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| 653 | % provides the logarithmique derivative p'/p of the interpolated histogram |
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| 654 | %at values chi=x-i |
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| 655 | %(difference with the nearest integer) |
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| 656 | % cc(5,size(chi)) is the set of spline coefficients obtained by splinhist |
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| 657 | chi2=chi.*chi; |
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| 658 | chi3=chi2.*chi; |
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| 659 | chi4=chi3.*chi; |
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[810] | 660 | histder=cc(2,:)+2*cc(3,:).*chi+3*cc(4,:).*chi2+4*cc(5,:).*chi3; |
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