SciCodePile/SciCode-Domain-Code

1"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
2"Neurology","ChristianGaser/cat12","cat_conf_opts.m",".m","24921","381","function opts = cat_conf_opts(expert)3% Configuration file for CAT SPM options4%5% ______________________________________________________________________6%7% Christian Gaser, Robert Dahnke8% Structural Brain Mapping Group (https://neuro-jena.github.io)9% Departments of Neurology and Psychiatry10% Jena University Hospital11% ______________________________________________________________________12% $Id$13%#ok<*AGROW>14 15if ~exist('expert','var')16  expert = 0; % switch to de/activate further GUI options17end18 19 20%------------------------------------------------------------------------21% various options for estimating the segmentations22%------------------------------------------------------------------------23 24 25% tpm:26%------------------------------------------------------------------------27tpm         = cfg_files;28tpm.tag     = 'tpm';29tpm.name    = 'Tissue Probability Map';30tpm.filter  = 'image';31tpm.ufilter = '.*';32tpm.def     =  @(val)cat_get_defaults('opts.tpm', val{1});33tpm.help    = {34  'Select the tissue probability image that includes 6 tissue probability classes for (1) grey matter, (2) white matter, (3) cerebrospinal fluid, (4) bone, (5) non-brain soft tissue, and (6) the background.  CAT uses the TPM only for the initial SPM segmentation.  Hence, it is more independent and allows accurate and robust processing even with the standard TPM in case of strong anatomical differences, e.g. very old/young brains.  Nevertheless, for children data we recommend to use customized TPMs created using the Template-O-Matic toolbox. '35  ''36  'The default tissue probability maps are modified versions of the ICBM Tissue Probabilistic Atlases.  These tissue probability maps are kindly provided by the International Consortium for Brain Mapping, John C. Mazziotta and Arthur W. Toga. http://www.loni.ucla.edu/ICBM/ICBM_TissueProb.html.'37  ''38  'The original data are derived from 452 T1-weighted scans, which were aligned with an atlas space, corrected for scan inhomogeneities, and classified into grey matter, white matter and cerebrospinal fluid.  These data were then affine registered to the MNI space and down-sampled to 1.5 mm resolution.  Rather than assuming stationary prior probabilities based upon mixing proportions, additional information is used, based on other subjects'' brain images.  Priors are usually generated by registering a large number of subjects together, assigning voxels to different tissue types and averaging tissue classes over subjects.  The algorithm used here will employ these priors for the first initial segmentation and normalization.  Six tissue classes are used: grey matter, white matter, cerebrospinal fluid, bone, non-brain soft tissue and air outside of the head and in nose, sinus and ears.  These maps give the prior probability of any voxel in a registered image being of any of the tissue classes - irrespective of its intensity.  The model is refined further by allowing the tissue probability maps to be deformed according to a set of estimated parameters.  This allows spatial normalisation and segmentation to be combined into the same model.  Selected tissue probability map must be in multi-volume nifti format and contain all six tissue priors. '39  ''40};41tpm.num     = [1 1];42tpm.def     =  @(val)cat_get_defaults('opts.tpm', val{:});43 44 45% ngaus:46%------------------------------------------------------------------------47% The default of SPM12 [GM,WM,CSF,bone,head tissue,BG] was [1,1,2,3,4,2]48% and works very well for most data and the segmentation did not benefit by 49% more classes. There are no systematic effects for interferences or 50% special anatomical properties (e.g. WMHs)!51%------------------------------------------------------------------------52ngaus         = cfg_entry;53ngaus.tag     = 'ngaus';54ngaus.name    = 'Gaussians per class';55ngaus.strtype = 'n';56ngaus.num     = [1 6];57ngaus.def     = @(val)cat_get_defaults('opts.ngaus', val{:});58ngaus.help    = {59  'The number of Gaussians used to represent the intensity distribution for each tissue class can be greater than one.  In other words, a tissue probability map may be shared by several clusters.  The assumption of a single Gaussian distribution for each class does not hold for a number of reasons.  In particular, a voxel may not be purely of one tissue type, and instead contain signal from a number of different tissues (partial volume effects).  Some partial volume voxels could fall at the interface between different classes, or they may fall in the middle of structures such as the thalamus, which may be considered as being either grey or white matter.  Various other image segmentation approaches use additional clusters to model such partial volume effects.  These generally assume that a pure tissue class has a Gaussian intensity distribution, whereas intensity distributions for partial volume voxels are broader, falling between the intensities of the pure classes.  Unlike these partial volume segmentation approaches, the model adopted here simply assumes that the intensity distribution of each class may not be Gaussian, and assigns belonging probabilities according to these non-Gaussian distributions.  Typical numbers of Gaussians could be one to three for grey and white matter, two for CSF, three for bone, four for other soft tissues and two for air (background).'60  ''61  ...'Note that if any of the Num. Gaussians is set to non-parametric, then a non-parametric approach will be used to model the tissue intensities.  This may work for some images (eg CT), but not others - and it has not been optimised for multi-channel data. Note that it is likely to be especially problematic for images with poorly behaved intensity histograms due to aliasing effects that arise from having discrete values on the images.'62  ...''63};64 65 66biasacc         = cfg_menu;67biasacc.tag     = 'biasacc';68biasacc.name    = 'Strength of SPM Inhomogeneity Correction';69biasacc.def     = @(val)cat_get_defaults('opts.biasstr', val{:});70biasacc.labels  = {'light','medium','strong','heavy'};71biasacc.values  = {0.25 0.50 0.75 1.00};72biasacc.help    = {73  'Strength of the SPM inhomogeneity (bias) correction that simultaneously controls the SPM biasreg, biasfwhm, samp (resolution), and tol (iteration) parameter.  Modify this value only if you experience any problems!  Use smaller values for slighter corrections (e.g. in synthetic contrasts without visible bias) and higher values for stronger corrections (e.g. in 3 or 7 Tesla data with strong visible bias).  Stronger corrections often improve cortical results but can also cause overcorrection in larger GM structures such as the subcortical structurs, thalamus, or amygdala and will take longer.  Bias correction is further controlled by the Affine Preprocessing (APP). '74  ''75};76 77%------------------------------------------------------------------------78% Bias correction79%------------------------------------------------------------------------80biasstr         = cfg_menu;81biasstr.tag     = 'biasstr';82biasstr.name    = 'Strength of SPM Inhomogeneity Correction';83biasstr.def     = @(val)cat_get_defaults('opts.biasstr', val{:});84if ~expert85  biasstr.labels  = {'light','medium','strong'};86  biasstr.values  = {0.25 0.50 0.75};87  %biasstr.labels  = {'ultralight','light','medium','strong','heavy'};88  %biasstr.values  = {0 0.25 0.50 0.75 1};89  biasstr.help    = {90    'Strength of the SPM inhomogeneity (bias) correction that simultaneously controls the SPM biasreg and biasfwhm parameter.  Modify this value only if you experience any problems!  Use smaller values for slighter corrections (e.g. in synthetic contrasts without visible bias) and higher values for stronger corrections (e.g. in 3 or 7 Tesla data with strong visible bias).  Bias correction is further controlled by the Affine Preprocessing (APP). '91    ''92  };93elseif expert>=194  biasstr.labels  = {'ultralight (eps)','light (0.25)','medium (0.50)','strong (0.75)','heavy (1.00)'};95  biasstr.values  = {eps 0.25 0.50 0.75 1};96  biasstr.help = {97    'Strength of the SPM inhomogeneity (bias) correction that simultaneously controls the SPM biasreg and biasfwhm parameter.  Modify this value only if you experience any problems!  Use smaller values (>0) for slighter corrections (e.g. in synthetic contrasts without visible bias) and higher values (<=1) for stronger corrections (e.g. in 7 Tesla data).  Bias correction is further controlled by the Affine Preprocessing (APP). '98    ''99    '  biasreg	  =  min(  10 , max(  0 , 10^-(biasstr*2 + 2) )) '100    '  biasfwhm	  =  min( inf , max( 30 , 30 + 60*(1-biasstr) )) '101    ''102    '                  biasstr   biasfwhm   biasreg'103    '  ultralight:     eps       90         0.0100 '104    '  light:          0.25      75         0.0032 '105    '  medium:         0.50      60         0.0010 '106    '  strong:         0.75      45         0.0003 '107    '  heavy:          1.00      30         0.0001 '108  };109%{110elseif expert==2111  biasstr.labels  = {'use SPM bias parameter (0)','ultralight (eps)','light (0.25)','medium (0.50)','strong (0.75)','heavy (1.00)'};112  biasstr.values  = {0 eps 0.25 0.50 0.75 1};113  biasstr.help = {114    'Strength of the SPM inhomogeneity (bias) correction that simultaneously controls the SPM biasreg and biasfwhm parameter.  Modify this value only if you experience any problems!  Use smaller values (>0) for slighter corrections (e.g. in synthetic contrasts without visible bias) and higher values (<=1) for stronger corrections (e.g. in 7 Tesla data).  The value 0 will use the original SPM biasreg and biasfwhm parameter of the cat_defaults file.  Bias correction is further controlled by the Affine Preprocessing (APP). '115    ''116    '  biasreg	  =  min(  10 , max(  0 , 10^-(biasstr*2 + 2) )) '117    '  biasfwhm	  =  min( inf , max( 30 , 30 + 60*(1-biasstr) )) '118    ''119    '                  biasstr   biasfwhm   biasreg'120    '  SPM parameter:  -         -          -      '121    '  ultralight:     eps       90         0.0100 '122    '  light:          0.25      75         0.0032 '123    '  medium:         0.50      60         0.0010 '124    '  strong:         0.75      45         0.0003 '125    '  heavy:          1.00      30         0.0001 '126  };127%}128end129  130 131% biasreg: 132%------------------------------------------------------------------------133% Test on the BWP and real data demonstrate that 0.001 mm works best in134% average, whereas some image benefit by more regularisation (0.01) and strong135% bias requires less regularisation (0.0001). There are no special cases136% that benefit by a regularisation >0.01 or <0.0001! Hence, I removed137% these entries (RD 2017-03).138%------------------------------------------------------------------------139biasreg        = cfg_menu;140biasreg.tag    = 'biasreg';141biasreg.name   = 'Bias regularisation';142biasreg.def    = @(val)cat_get_defaults('opts.biasreg', val{:});143if 0144  biasreg.labels = {'No regularisation (0)','Extremely light regularisation (0.00001)','Very light regularisation (0.0001)','Light regularisation (0.001)','Medium regularisation (0.01)','Heavy regularisation (0.1)','Very heavy regularisation (1)','Extremely heavy regularisation (10)'};145  biasreg.values = {0, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1.0, 10};146else147  biasreg.labels = {'Very light regularisation (0.0001)','Light regularisation (0.001)','Medium regularisation (0.01)'};148  biasreg.values = {0.0001, 0.001, 0.01};149end150biasreg.help   = {151  'Regularisation of the SPM bias field.  This parameter is controlled by the biasreg parameter if biasstr>0. Test on the BWP and real data showed that optimal corrections was in range of 0.01 and 0.0001.'152  ''153  'MR images are usually corrupted by a smooth, spatially varying artefact that modulates the intensity of the image (bias).  These artefact, although not usually a problem for visual inspection, can impede automated processing of the images.  An important issue relates to the distinction between intensity variations that arise because of bias artifact due to the physics of MR scanning, and those that arise due to different tissue properties.  The objective is to model the latter by different tissue classes, while modelling the former with a bias field.  We know a priori that intensity variations due to MR physics tend to be spatially smooth, whereas those due to different tissue types tend to contain more high frequency information.  A more accurate estimate of a bias field can be obtained by including prior knowledge about the distribution of the fields likely to be encountered by the correction algorithm.  For example, if it is known that there is little or no intensity non-uniformity, then it would be wise to penalise large values for the intensity non-uniformity parameters.  This regularisation can be placed within a Bayesian context, whereby the penalty incurred is the negative logarithm of a prior probability for any particular pattern of non-uniformity.  Knowing what works best should be a matter of empirical exploration.  For example, if your data has very little intensity non-uniformity artifact, then the bias regularisation should be increased.  This effectively tells the algorithm that there is very little bias in your data, so it does not try to model it.  '154  ''155};156 157 158% biasfwhm:159%------------------------------------------------------------------------160% Test on the BWP and real data demonstrate that 60 mm works best for most161% datasets. Only 7 Tesla data need further adaptation, larger filter size is162% normaly not required (RD 2017-03)!163%  - 30-40 mm: low filter size for very strong fields (e.g. 7 or 3 Tesla data)164%  - 50-60 mm: medium filter size works best for >95% of the data, and do not overfit in case of low bias 165%  - 70-90 mm: high filter size in case of low bias data166%  -   >90 mm: better to avoid that, because there is mostly a low bias in the data that is normally not visible 167%------------------------------------------------------------------------168biasfwhm        = cfg_menu;169biasfwhm.tag    = 'biasfwhm';170biasfwhm.name   = 'Bias FWHM';171if 0172  biasfwhm.labels = {'30mm cutoff','40mm cutoff','50mm cutoff','60mm cutoff','70mm cutoff','80mm cutoff','90mm cutoff','100mm cutoff','110mm cutoff','120mm cutoff','130mm cutoff','140mm cutoff','150mm cutoff','No correction'};173  biasfwhm.values = {30,40,50,60,70,80,90,100,110,120,130,140,150,Inf};174else175  biasfwhm.labels = {'30mm cutoff','40mm cutoff','50mm cutoff','60mm cutoff','70mm cutoff','80mm cutoff','90mm cutoff'};176  biasfwhm.values = {30,40,50,60,70,80,90};177end178biasfwhm.def    = @(val)cat_get_defaults('opts.biasfwhm', val{:});179biasfwhm.help   = {180  'FWHM of Gaussian smoothness of bias.  This parameter is controlled by the biasreg parameter if biasstr>0.  Test on the BWP and real data showed that 50 to 60 mm works best for nearly all datasets and only some 7 Tesla scans require further adaptation! '181  '  30-40 mm:  low filter size for very strong fields (e.g. 7 or 3 Tesla data) '182  '  50-60 mm:  medium filter size works best for >95% of the data, and do not overfit in case of low bias '183  '  70-90 mm:  high filter size in case of low bias data '184  ''185  'If your intensity non-uniformity is very smooth, then choose a large FWHM.  This will prevent the algorithm from trying to model out intensity variation due to different tissue types.  The model for intensity non-uniformity is one of i.i.d. Gaussian noise that has been smoothed by some amount, before taking the exponential.  Note also that smoother bias fields need fewer parameters to describe them.  This means that the algorithm is faster for smoother intensity non-uniformities.  '186  ''187};188 189biasspm        = cfg_branch;190biasspm.tag    = 'spm';191biasspm.name   = 'Original SPM bias correction parameter';192biasspm.val    = {biasfwhm biasreg};193biasspm.help   = {194  'SPM bias correction parameter biasfwhm and biasreg.' 195}; 196 197bias        = cfg_choice;198bias.tag    = 'bias';199bias.name   = 'Biascorrection parameter';200if cat_get_defaults('opts.biasstr')>0201  bias.val = {biasstr};202else203  bias.val = {biasspm};204end205bias.values = {biasstr biasspm};206bias.help   = {207  'Bias correction parameters.' 208}; 209 210 211 212 213 214 215 216% warpreg: 217%------------------------------------------------------------------------218% no useful changes in the following testcases:219%   [0 0.001  0.5   0.05 0.2]   % the default setting220%   [0 0.0001 0.001 0.01 0.1]   % lower  initial regularision with stepwise increasement 221%   [0 0.001  0.01  0.1  0.2]   % low    initial regularision with stepwise increasement 222%   [0.5 0.4  0.3   0.2  0.1]   % medium initial regularision with stepwise decreasment 223%   [1.0 0.8  0.6   0.4  0.2]   % 224%   [0.0 0.8  0.2   0.8  0.2]   % 225%------------------------------------------------------------------------226warpreg         = cfg_entry;227warpreg.def     = @(val)cat_get_defaults('opts.warpreg', val{:});228warpreg.tag     = 'warpreg';229warpreg.name    = 'Warping Regularisation';230warpreg.strtype = 'r';231warpreg.num     = [1 5];232warpreg.help    = {233  'The objective function for registering the tissue probability maps to the image to process, involves minimising the sum of two terms.  One term gives a function of how probable the data is given the warping parameters.  The other is a function of how probable the parameters are, and provides a penalty for unlikely deformations.  Smoother deformations are deemed to be more probable.  The amount of regularisation determines the tradeoff between the terms.  Pick a value around one.  However, if your normalised images appear distorted, then it may be an idea to increase the amount of regularisation (by an order of magnitude).  More regularisation gives smoother deformations, where the smoothness measure is determined by the bending energy of the deformations. '234  ''235};236 237 238% affreg239%------------------------------------------------------------------------240% no large differences 241% - mni was most stable242% - rigid did not work in ~20% of the cases (and is of course not meaningful here)243% - subj and none led to identical results 244% - no registration only for animals245%------------------------------------------------------------------------246affreg        = cfg_menu;247affreg.tag    = 'affreg';248affreg.name   = 'Affine Regularisation';249affreg.help   = {250  'The procedure is a local optimisation, so it needs reasonable initial starting estimates.  Images should be placed in approximate alignment using the Display function of SPM before beginning.  A Mutual Information affine registration with the tissue probability maps (D''Agostino et al, 2004) is used to achieve approximate alignment.  Note that this step does not include any model for intensity non-uniformity.  This means that if the procedure is to be initialised with the affine registration, then the data should not be too corrupted with this artifact.  If there is a lot of intensity non-uniformity, then manually position your image in order to achieve closer starting estimates, and turn off the affine registration.  Affine registration into a standard space can be made more robust by regularisation (penalising excessive stretching or shrinking).  The best solutions can be obtained by knowing the approximate amount of stretching that is needed (e.g. ICBM templates are slightly bigger than typical brains, so greater zooms are likely to be needed). For example, if registering to an image in ICBM/MNI space, then choose this option.  If registering to a template that is close in size, then select the appropriate option for this.'251  ''252};253if expert254  affreg.labels = {'ICBM space template - European brains','ICBM space template - East Asian brains','No regularisation','No Affine Registration'};255  affreg.values = {'mni','eastern','none',''};256  affreg.help   = [affreg.help {257    'No affine registration was added for processing of animals, where registration may fail!'258    ''259  }];260else261  affreg.labels = {'ICBM space template - European brains','ICBM space template - East Asian brains','No regularisation'};262  affreg.values = {'mni','eastern','none'};263end264affreg.def    = @(val)cat_get_defaults('opts.affreg', val{:});265 266 267 268% samp:269%------------------------------------------------------------------------270% Surprisingly, there is no systematical advantage in using higher271% resolution! Only very slightly in single cases, e.g. 7 Tesla. 272%------------------------------------------------------------------------273samp         = cfg_entry;274samp.tag     = 'samp';275samp.name    = 'Sampling distance';276samp.strtype = 'r';277samp.num     = [1 1];278samp.def     = @(val)cat_get_defaults('opts.samp', val{:});279samp.help    = {280  'This encodes the approximate distance between sampled points when estimating the model parameters.  Smaller values use more of the data, but the procedure is slower and needs more memory.  Determining the ""best"" setting involves a compromise between speed and accuracy.'281  ''282};283 284% SPM processing accuracy285tol         = cfg_menu;286tol.tag     = 'tol';287tol.name    = 'SPM iteration accuracy';288tol.help    = { ...289    'Parameter to control the iteration stop criteria of SPM preprocessing functions. In most cases the standard value is good enough for the initialization in CAT. However, some images with servere (local) inhomogeneities or atypical anatomy may benefit by further iterations. '290  };291tol.def    = @(val)cat_get_defaults('opts.tol', val{:}); 292tol.labels = {'average (default)' 'high (slow)' 'ultra high (very slow)'};293tol.values = {1e-4 1e-8 1e-16};294if expert>1 % developer295  tol.labels = [{'ultra low (superfast)' 'low (fast)'} tol.labels {'insane'}];296  tol.values = [{1e-1 1e-2} tol.values {1e-32}];297end298 299% single parameter 300accspm        = cfg_branch;301accspm.tag    = 'spm';302accspm.name   = 'Original SPM accuracy parameter';303accspm.val    = {samp tol};304accspm.help   = {305  'Official SPM resolution parameter ""samp"" and internal SPM iteration parameter ""tol"".' 306}; 307 308% combined SPM processing accuracy parameter309accstr         = cfg_menu;310accstr.tag     = 'accstr';311accstr.name    = 'SPM processing accuracy';312accstr.help    = { ...313    'Parameter to control the accuracy of SPM preprocessing functions. In most images the standard accuracy is good enough for the initialization in CAT. However, some images with severe (local) inhomogeneities or atypical anatomy may benefit by additional iterations and higher resolution. '314  };315accstr.labels = {'average (default)' 'high (slow)' 'ulta high (very slow)'};316accstr.values = {0.5 0.75 1.0};317accstr.def    = @(val)cat_get_defaults('opts.accstr', val{:}); % no cat_defaults entry318if expert319  %accstr.labels = [{'ultra low (superfast)' 'low (fast)'} accstr.labels];320  %accstr.values = [{0 0.25} accstr.values];321  accstr.help   = [accstr.help; 322   {''323   ['Overview of parameters: ' ...324    '  accstr:  0.50   0.75   1.00' ...325    '  samp:    3.00   2.00   1.00 (in mm)' ...326    '  tol:     1e-4   1e-8   1e-16' ...327    '' ...328    'SPM default is samp = 3 mm with tol = 1e-4. ']}];329end330 331% single parameter332acc        = cfg_choice;333acc.tag    = 'acc';334acc.name   = 'SPM preprocessing accuracy parameters';335if cat_get_defaults('opts.accstr')>0336  acc.val  = {accstr};337else338  acc.val  = {accspm};339end340acc.values = {accstr accspm};341acc.help   = {342  'Choose between single or combined SPM preprocessing accuracy parameters.' 343}; 344 345 346redspmres         = cfg_entry;347redspmres.tag     = 'redspmres';348redspmres.name    = 'SPM preprocessing output resolution limit';349redspmres.strtype = 'r';350redspmres.num     = [1 1];351redspmres.def     = @(val)cat_get_defaults('opts.redspmres', val{:});352redspmres.help    = {'Limit SPM preprocessing resolution to improve robustness and performance. Use 0 to process data in the full internal resolution.' ''};353 354 355%------------------------------------------------------------------------356opts      = cfg_branch;357opts.tag  = 'opts';358opts.name = 'Options for initial SPM12 preprocessing';359opts.help = {360    'CAT uses the Unified Segmentation of SPM12 for initial registration, bias correction, and segmentation.  The parameters used here were optimized for a variety of protocols and anatomies.  Only in case of strong inhomogeneity of high-field MR scanners we recommend to increase the biasstr parameter.  For children data we recommend to use customized TPMs created by the Template-O-Matic toolbox. '  361    ''362  };363if expert>1364  opts.val  = {tpm,affreg,ngaus,warpreg,bias,acc,redspmres};365  opts.help = [opts.help; {366    'Increasing the initial sampling resolution to 1.5 or 1.0 mm may help in some cases of strong inhomogeneity but in general it only increases processing time.'367    ''368    'Strength of the bias correction ""biasstr"" controls the biasreg and biasfwhm parameter if biasstr>0!'369    ''370  }];371elseif expert==1372  opts.val  = {tpm,affreg,biasstr,accstr};373  opts.help = [opts.help; {374    'Increasing the initial sampling resolution to 1.5 or 1.0 mm ma help in some cases of strong inhomogeneity but in general it only increases processing time.'375    ''376  }];377else378  opts.val  = {tpm,affreg,biasacc};379   380end381 382","MATLAB"
383"Neurology","ChristianGaser/cat12","cat_io_handle_pre.m",".m","1621","38","function FO = cat_io_handle_pre(F,pre,addpre,existfile)384% Remove all known cat prefix types from a filename (and check if this file exist). 385% ______________________________________________________________________386%387% Christian Gaser, Robert Dahnke388% Structural Brain Mapping Group (https://neuro-jena.github.io)389% Departments of Neurology and Psychiatry390% Jena University Hospital391% ______________________________________________________________________392% $Id$393 394  if nargin<4, existfile = 1; end395  if nargin<3, addpre    = 0; end396 397  [pp,ff,ee] = fileparts(F); 398 399  if ~addpre400    prefix{1} = {'r','m','e'};401    prefix{2} = {'ma','mb','mc','mv','mn','mi','ml', ...                     % modifications of T402                 'pa','pb','p0','p1','p2','p3','p4','p5','pf','pp' ...  % segmentations of T403                 'en','eb','ev','ei','ej','em', ...                     % error maps404                 't1','t2','t3','t4'};                                  % thickness and distaces405    prefix{3} = {'ra0','ra1','rw0','rw1','rwa','rwb','rwj', ...406                                   'rd0','rd1','rda','rdb','rdj', ...407                       'rcs','lcs','bcs','acs','hcs'};408    prefix{4} = {'ercs','elcs','ebcs','eacs','ehcs'};409 410    for pf=1:numel(prefix)411      if numel(ff)>pf+1 && any(strcmp(ff(1:pf),prefix{pf})) && ...412        (~existfile || exist(fullfile(pp,[ff(pf+1:end) ee]),'file'))413         FN = cat_io_handle_pre(fullfile(pp,[ff(pf+1:end) ee]),'',addpre,existfile); 414         if (~existfile || exist(FN,'file')), [ppn,ffn] = fileparts(FN); ff=ffn; end415      end416    end417  end418  419  FO = fullfile(pp,[pre ff ee]);420end","MATLAB"
421"Neurology","ChristianGaser/cat12","cat_io_matlabversion.m",".m","867","33","function year = cat_io_matlabversion(varargin)422% return Matlab version year423% ______________________________________________________________________424%425% Christian Gaser, Robert Dahnke426% Structural Brain Mapping Group (https://neuro-jena.github.io)427% Departments of Neurology and Psychiatry428% Jena University Hospital429% ______________________________________________________________________430% $Id$431 432 433  if strcmpi(spm_check_version,'octave')434    year = 20201;435    return436  end437  438  vers = version;439  [t1,t2,t3,year] = regexp(vers,'\(R.....\)');440  switch year{1}(end-1)441    case 'a',  year = [year{1}(3:end-2) '1'];442    case 'b',  year = [year{1}(3:end-2) '2'];443    otherwise, year = [year{1}(3:end-2) '0'];444  end445  year = str2double(year);446  447  if nargin==1448    year = varargin{1}==year;449  end450  if nargin==2451    year = varargin{1}<=year && year<=varargin{1};452  end453end","MATLAB"
454"Neurology","ChristianGaser/cat12","optimizer3d.h",".h","682","14","/* $Id$ */455/* (c) John Ashburner (2007) */456extern void fmg3(int n0[], float *a0, float *b0, int rtype, double param[], int c, int nit,457                 float *u0, float *scratch);458extern void cgs3(int dm[], float A[], float b[], int rtype, double param[], double tol, int nit,459                 float x[], float r[], float p[], float Ap[]);460extern void resize(int na[], float *a, int nc[], float *c, float *b);461extern float norm(int m, float a[]);462extern void LtLf_be(int dm[], float f[], double s[], float g[]);463extern void LtLf_me(int dm[], float f[], double s[], float g[]);464extern void LtLf_le(int dm[], float f[], double s[], float g[]);465extern int fmg3_scratchsize(int n0[]);466 467","Unknown"
468"Neurology","ChristianGaser/cat12","cat_io_json.m",".m","632","34","function S = cat_io_json(varargin)469%cat_io_json. Read json file.   470 471  S = struct();472 473  if nargin == 1474    if isstruct(varargin{1})475      % job case476      files = job.files; 477    else478      % just a file479      files = cellstr(varargin{1});480    end481  end482 483  for fi = 1:numel(files)484    if ~exist(files{fi},'file')485      cat_io_cprintf('err','ERROR: Miss ""%s""\n',files{fi})486    else487      fid = fopen(files{fi}); 488      raw = fread(fid,inf); 489      str = char(raw'); 490      fclose(fid); 491      val = jsondecode(str);492 493      if fi == 1494        S = val; 495      else496        S = cat_io_mergeStruct(S,val); 497      end498    end499  end500end501  ","MATLAB"
502"Neurology","ChristianGaser/cat12","cat_stat_nanmedian.m",".m","2576","89","function out = cat_stat_nanmedian(in, dim)503% ----------------------------------------------------------------------504% Median, not considering NaN values. Similar usage like median() or 505% MATLAB nanmedian of the statistic toolbox. Process input as double506% due to errors in large single arrays and set data class of ""out"" 507% to the data class of ""in"" at the end of the processing,508% Use dim==0 to evaluate in(:) in case of dimension selection 509% (e.g., in(:,:,:,2) ).510%511% out = cat_stat_nanmedian(in,dim)512%513% Example 1:514%   a = rand(4,6,3); 515%   a(rand(size(a))>0.5)=nan; 516%   av = cat_stat_nanmedian(a,3); 517%   am = nanmedian(a,3); % of the statistical toolbox ...518%   fprintf('%0.4f %0.4f\n',([av(:),am(:)])');519%520% Example 2 - special test call of example 1:521%   cat_stat_nanmedian('test')522%523% See also cat_stat_nansum, cat_stat_nanstd, cat_stat_nanmean.524% ______________________________________________________________________525%526% Christian Gaser, Robert Dahnke527% Structural Brain Mapping Group (https://neuro-jena.github.io)528% Departments of Neurology and Psychiatry529% Jena University Hospital530% ______________________________________________________________________531% $Id$532 533  if nargin < 1534    help cat_stat_nanmedian;535    return;536  end;537  538  if ischar(in) && strcmp(in,'test')539    a = rand(4,6,3); 540    a(rand(size(a))>0.5)=nan; 541    av = cat_stat_nanmedian(a,3); 542    am = nanmedian(a,3); % of the statistical toolbox ...543    fprintf('%0.4f %0.4f\n',([av(:),am(:)])');544    out = nanmean(av(:) - am(:)); 545    return; 546  end547  548  if nargin < 2549    if size(in,1) ~= 1550      dim = 1;551    elseif size(in,2) ~= 1552      dim = 2;553    else 554      dim = 3; 555    end;556  end;557    558  if dim == 0 559    in  = in(:); 560    dim = 1; 561  end562  563  sz  = size(in);564 565  if isempty(in), out = nan; return; end566  tp = class(in);567  in = double(in); % single failed in large arrays568   569  % reduce to 2d matrix570  pm = [dim:max(length(size(in)),dim) 1:dim-1];571  in = reshape(permute(in,pm),size(in,dim),prod(sz)/size(in,dim));572 573  in = sort(in,1);574  s  = size(in,1) - sum(isnan(in));575  576  % estimate median in loop577  out = zeros(size(s));578  for i = 1:length(s)579    if s(i)>0, out(i) = cat_stat_nanmean([in(floor((s(i)+1)/2),i),in(ceil((s(i)+1)/2),i)]); else out(i)=nan; end580  end581  582  % estimate median as matrix ... doesn't work :/583  %out = nan(size(s)); si=1:numel(s);584  %out(s>0) = nanmean( [ in(([floor((s(s>0)+1)/2);si(s>0)])'); in(ceil((s(s>0)+1)/2),si(s>0)) ] );585  586  % correct for permutation587  sz(dim) = 1; out = ipermute(reshape(out,sz(pm)),pm);588  589  eval(sprintf('out = %s(out);',tp));590end","MATLAB"
591"Neurology","ChristianGaser/cat12","cat_io_mergeStruct.m",".m","2571","93","function S=cat_io_mergeStruct(S,SN,ri,id)592% _________________________________________________________________________593% Merge to structures 'S' and 'SN'. 594%595%   S = cat_io_mergeStruct(S,SN[,[],id])596% 597%   where id>0 updates elements S(id)  598%   599% WARNING:600%   This function is still in developent! Be careful by using it, due to601%   unexpected behaviour. Updating of structures is a complex topic with 602%   many subcases and here only a simple alignment is used!603%604% ______________________________________________________________________605%606% Christian Gaser, Robert Dahnke607% Structural Brain Mapping Group (https://neuro-jena.github.io)608% Departments of Neurology and Psychiatry609% Jena University Hospital610% ______________________________________________________________________611% $Id$612 613  % check input614  maxri = 20; 615  if ~exist('id','var') || isempty(id), id=0; end616  if ~exist('ri','var') || isempty(ri), ri=0; end617  618  SFN  = fieldnames(S);619  SNFN = fieldnames(SN);620 621  %% add new empty fields in additional element in S622  NSFN = setdiff(SNFN,SFN);623  if ~isempty(NSFN)624    ne   = numel(S)+1; 625    for ni = 1:numel(NSFN)626      if isnumeric(SN(1).(NSFN{ni})) 627        S(ne).(NSFN{ni}) = [];628      elseif islogical(SN(1).(NSFN{ni}))629        S(ne).(NSFN{ni}) = false; 630      elseif ischar(SN(1).(NSFN{ni}))631        S(ne).(NSFN{ni}) = '';632      elseif isstruct(SN(1).(NSFN{ni}))633        if ri<maxri634          Stmp = cat_io_mergeStruct(struct(),SN(1).(NSFN{ni})(1),ri+1);635        else636          Stmp = struct(); 637        end638        S(ne).(NSFN{ni}) = Stmp(1);639      elseif iscell(SN(1).(NSFN{ni}))640        S(ne).(NSFN{ni}) = {};641      end642    end643    S(ne) = []; 644  end645 646  %%647  NSSFN = setdiff(SFN,SNFN);648  if ~isempty(NSSFN)649    sne   = numel(SN) + 1; 650    for ni = 1:numel(NSSFN)651      if isnumeric(S(1).(NSSFN{ni})) 652        SN(sne).(NSSFN{ni}) = [];653      elseif islogical(S(1).(NSSFN{ni}))654        SN(sne).(NSSFN{ni}) = false(0);655      elseif ischar(S(1).(NSSFN{ni}))656        SN(sne).(NSSFN{ni}) = '';657      elseif isstruct(S(1).(NSSFN{ni}))658        if ri<maxri && ~isempty(fieldnames(S))659          Stmp = cat_io_mergeStruct(struct(),S(1).(NSSFN{ni})(1),ri+1);660        else661          Stmp = struct(); 662        end663        SN(sne).(NSSFN{ni}) = Stmp(1);664      elseif iscell(S(1).(NSSFN{ni}))665        SN(sne).(NSSFN{ni}) = {};666      end667    end668    SN(sne) = [];669  end670 671  %%672  S   = orderfields(S);673  SN  = orderfields(SN);674  675  ns  = numel(S); 676  for sni=1:numel(SN)677    if id==0678      S(ns + sni) = SN(sni);679    else680      S(id + sni - 1) = SN(sni);681    end682  end683end","MATLAB"
684"Neurology","ChristianGaser/cat12","cat_surf_render2.m",".m","130198","3207","function varargout = cat_surf_render2(action,varargin)685% Display a surface mesh & various utilities686% FORMAT H = cat_surf_render2('Disp',M,'PropertyName',propertyvalue)687% M        - a GIfTI filename/object or patch structure688% H        - structure containing handles of various objects689% Opens a new figure unless a 'parent' Property is provided with an axis690% handle.691%692% FORMAT H = cat_surf_render2(M)693% Shortcut to previous call format.694%695% FORMAT H = cat_surf_render2('view',VIEW)696% FORMAT H = cat_surf_render2('view',H,VIEW)697% Camera line of sight698% VIEW     - [az,el] set the azimuth and elevation angles of the camera699%          - ['left'|'right'|'top'|'bottom'|'front'|'back'] 700% H        - structure containing handles of various objects701%702% FORMAT H = cat_surf_render2('clim',LIMITS)703% FORMAT H = cat_surf_render2('clim',H,LIMITS)704% Set colormap limits.705% H        - structure containing handles of various objects706% LIMITS   - [cmin cmax] lower and upper limit  707% LIMITS   - ['p1'|'p2'|'p5'] settings for 99, 98, and 95 percent    708% 709% FORMAT H = cat_surf_render2('ContextMenu',AX)710% AX       - axis handle or structure returned by cat_surf_render2('Disp',...)711%712% FORMAT H = cat_surf_render2('Overlay',AX,P)713% AX       - axis handle or structure given by cat_surf_render2('Disp',...)714% P        - data to be overlayed on mesh (see spm_mesh_project)715%716% FORMAT H = cat_surf_render2('ColourBar',AX,MODE)717% AX       - axis handle or structure returned by cat_surf_render2('Disp',...)718% MODE     - {['on'],'off'}719%720% FORMAT H = cat_surf_render2('Clim',AX,[mn mx])721% AX       - axis handle or structure given by cat_surf_render2('Disp',...)722% mn mx    - min/max of range723%724% FORMAT H = cat_surf_render2('Clip',AX,[mn mx])725% AX       - axis handle or structure given by cat_surf_render2('Disp',...)726% mn mx    - min/max of clipping range727%728% FORMAT H = cat_surf_render2('ColourMap',AX,MAP)729% AX       - axis handle or structure returned by cat_surf_render2('Disp',...)730% MAP      - a colour map matrix731%732% FORMAT MAP = cat_surf_render2('ColourMap',AX)733% Retrieves the current colourmap.734%735% FORMAT H = cat_surf_render2('Underlay',AX,P)736% AX       - axis handle or structure given by cat_surf_render2('Disp',...)737% P        - data (curvature) to be underlayed on mesh (see spm_mesh_project)738%739% FORMAT H = cat_surf_render2('Clim',AX, range)740% range    - range of colour scaling741%742% FORMAT H = cat_surf_render2('SaveAs',AX, filename)743% filename - filename744%745% FORMAT cat_surf_render2('Register',AX,hReg)746% AX       - axis handle or structure returned by cat_surf_render2('Disp',...)747% hReg     - Handle of HandleGraphics object to build registry in.748% See spm_XYZreg for more information.749%__________________________________________________________________________750% Copyright (C) 2010-2011 Wellcome Trust Centre for Neuroimaging751% ______________________________________________________________________752%753% Christian Gaser, Robert Dahnke754% Structural Brain Mapping Group (https://neuro-jena.github.io)755% Departments of Neurology and Psychiatry756% Jena University Hospital757% ______________________________________________________________________758% based on spm_mesh_render.m759% $Id$760 761%#ok<*ASGLU>762%#ok<*INUSL>763%#ok<*INUSD>764%#ok<*TRYNC>765 766 767 768 769% we need to hide some options ...770expert = 2; %cat_get_defaults('extopts.expertgui'); 771try772  cat_get_defaults('print.dpi');  773catch774  cat_get_defaults('print.dpi',150); 775end776try777  cat_get_defaults('print.type');  778catch779  cat_get_defaults('print.type','.png'); 780end781 782 783 784%-Input parameters785%--------------------------------------------------------------------------786if ~nargin, action = 'Disp'; end787 788if ~ischar(action)789    varargin = {action varargin{:}};790    action   = 'Disp';791end792 793if nargout, varargout = {[]}; end794 795%-Action796%--------------------------------------------------------------------------797switch lower(action)798    799    %-Display800    %======================================================================801    case 'disp'802        if isempty(varargin)803            [M, sts] = spm_select(1,'any','Select surface mesh file');804            if ~sts, return; end805            varargin{1} = cellstr(M);806        else807            M = varargin{1};808        end809 810      811        %%812        %-Figure & Axis813        %------------------------------------------------------------------814        O = getOptions(varargin{2:end});815        if isfield(O,'parent')816            H.issubfigure = 1; 817            H.axis   = O.parent;818            H.figure = ancestor(H.axis,'figure');819            % this brings the figure to the foreground :/820            %figure(H.figure); axes(H.axis);821        else822            H.issubfigure = 0;823            H.figure = figure('Color',[1 1 1]);824            H.axis   = axes('Parent',H.figure,'Visible','off');825        end826        if isfield(O,'pcdata'), O.pcdata = cellstr(O.pcdata); end827        if isfield(O,'pmesh'),  O.pmesh  = cellstr(O.pmesh); end828        renderer = get(H.figure,'Renderer');829        830        %{831        try832          % warning to error833          val = feval('set',H.figure,'Renderer','OpenGL');834          if isempty(val)==0 835              error('OpenGLerr')836          end837        catch 838          cat_io_cprintf('err','OpenGL error - cannot display surface\n');839          return840        end841        %}842      if ~isstruct(varargin{1}) && ~isa(varargin{1},'gifti')843        % surface info844        if nargin>=3845          sinfo = cat_surf_info(varargin{3}); 846        elseif nargin>=1847          if ischar(varargin{1})848            %%849            for i=1:size(varargin{1},1)850              [pp,ff,ee] = spm_fileparts(varargin{1}(i,:)); 851              varargin{1}(i,:) = fullfile(pp,[ff strrep(ee,'.dat','.gii')]);852            end853          else854            for i=1:numel(varargin{1})855              [pp,ff,ee] = spm_fileparts(varargin{1}{i}); 856              varargin{1}{i} = fullfile(pp,[ff strrep(ee,'.dat','.gii')]);857            end858          end859          sinfo = cat_surf_info(varargin{1}); 860        else861          sinfo = cat_surf_info(M);862        end863        if ischar(varargin{1})864          sinfo(1).Pmesh = varargin{1};865        end866       867        %%868        labelmap = zeros(0); labelnam = cell(0); labelmapclim = zeros(1,2); labeloid = zeros(0); labelid = zeros(0); nid=1;869        for pi=1:numel(sinfo)870 871            if ~exist(sinfo(pi).fname,'file')872              error('cat_surf_render:nofile','The file ""%s"" does not exist!',sinfo(pi).fname); 873            end874            875            H.filename{pi} = sinfo(pi).fname; 876            877            % load mesh878            [pp,ff,ee] = spm_fileparts(sinfo(pi).Pmesh);879            switch ee880                case '.gii'881                    S  = gifti(sinfo(pi).Pmesh); 882                otherwise883                    S  = gifti(cat_io_FreeSurfer('read_surf',sinfo(pi).Pmesh));884            end885            886            887            if ~isfield(S,'cdata') && ~isempty(O) && isfield(O,'pcdata')888              % load texture889              [pp,ff,ee] = spm_fileparts(sinfo(pi).Pdata);890              switch sinfo(pi).ee891                  case '.gii'892                      cdata  = gifti(O.pcdata{pi}); 893                      cdatap = export(cdata,'patch');894                      if isfield(cdatap,'facevertexcdata')895                        cdata = cdatap.facevertexcdata; 896                      elseif isfield(cdata,'cdata')897                        if isnumeric(cdata.cdata)898                          cdata = cdata.cdata; 899                        else900                          fname = cdata.cdata.fname; 901                          fid = fopen(fname, 'r', 'b') ;902                          if (fid < 0)903                             str = sprintf('could not open curvature file %s', fname) ;904                             error(str) ;905                          end906                          cdata = fread(fid, 'double') ;907                          fclose(fid);908                        909                        end910                      end911                      if isnumeric(cdata) 912                        labelmapclim = [min(cdata) max(cdata)];913                      else914                        if isfield(cdata,'cdata')915                          labelmapclim = [min(cdata.cdata) max(cdata.cdata)];916                        elseif  isfield(cdata,'vertices')917                          labelmapclim = [0 0];918                          cdata = zeros(size(cdata.vertices,1),1,'single');919                        else920                          labelmapclim = [];921                          cdata = [];922                        end923                      end924                  case '.annot' 925                      %%926                      [fsv,cdatao,colortable] = cat_io_FreeSurfer('read_annotation',O.pcdata{pi}); clear fsv;927                      cdata   = zeros(size(cdatao));928                      entries = round(unique(cdatao));929 930                      for ei = 1:numel(entries)931                        cid = find( labeloid == entries(ei) ,1); % previous imported label?932                        if ~isempty(cid) % previous imported label933                          cdata( round(cdatao) == entries(ei) ) = labelid(cid);  934                        else % new label > new entry935                          id = find( colortable.table(:,5)==entries(ei) , 1);936                          if ~isempty(id)937                            cdata( round(cdatao) == entries(ei) ) = nid;  938                            labelmap(nid,:) = colortable.table(id,1:3)/255;939                            labelnam(nid)   = colortable.struct_names(id);940                            labeloid(nid)   = entries(ei);941                            labelid(nid)    = nid;942                            labelmapclim(2) = nid;943                            nid             = nid+1;944                          end945                        end946                      end947 948                  otherwise949                      St = gifti(struct('cdata',cat_io_FreeSurfer('read_surf_data',sinfo(pi).Pdata)));950                      cdata = St.cdata; clear St; 951                      labelmapclim = [min(cdata) max(cdata)];952              end953              S.cdata = cdata; clear cdata; 954              S = export(S,'patch');955            elseif isfield(S,'cdata') 956              S = export(S,'patch');957              labelmapclim = [min(S.facevertexcdata) max(S.facevertexcdata)];958            end959            % ignore this warning writing gifti with int32 (eg. cat_surf_createCS:580 > gifti/subsref:45)960            warning off MATLAB:subscripting:noSubscriptsSpecified961            % flip faces in case of defect surfaces962            if mean(max(S.vertices + spm_mesh_normals(S)) - max(S.vertices))>0963            %if strcmp(sinfo(1).texture,'defects'), 964              S.faces = S.faces(:,[2,1,3]); 965            end966            % use original colormap from annot file otherwise use jet967            if ~strcmp(sinfo(pi).ee,'.annot')968              labelmap = jet; 969            end970            971            972            % Patch973            %  ----------------------------------------------------------974            P = struct('vertices',S.vertices, 'faces',double(S.faces));975            H.patch(pi) = patch(P,...976              'FaceColor',        [0.6 0.6 0.6],...977              'EdgeColor',        'none',...978              'FaceLighting',     'gouraud',...979              'SpecularStrength', 0.0,... 0.7980              'AmbientStrength',  0.4,... 0.1981              'DiffuseStrength',  0.6,... 0.7982              'SpecularExponent', 10,...983              'Clipping',         'off',...984              'DeleteFcn',        {@myDeleteFcn, renderer},...985              'Visible',          'off',...986              'Tag',              'CATSurfRender',...987              'Parent',           H.axis);988            setappdata(H.patch(pi),'patch',P);989 990            %% -Label connected components of the mesh991            %------------------------------------------------------------------992            C = spm_mesh_label(P);993            setappdata(H.patch(pi),'cclabel',C);994 995            %-Compute mesh curvature996            %------------------------------------------------------------------997            curv = spm_mesh_curvature(P); %$ > 0;998            setappdata(H.patch(pi),'curvature',curv);999 1000            %-Apply texture to mesh1001            %------------------------------------------------------------------1002            if isfield(S,'cdata')1003                T = S.cdata;1004            elseif isfield(S,'facevertexcdata')1005                T = S.facevertexcdata;1006            else1007                T = [];1008            end1009            try1010              updateTexture(H,T,pi);1011            end1012            1013            H.cdata = T; % remove this later ...1014            clear S P; 1015        end1016        H.sinfo = sinfo; 1017      else1018        labelmap = jet; 1019        if 0 %flip1020          S.vertices = [varargin{1}.vertices(:,2) varargin{1}.vertices(:,1) varargin{1}.vertices(:,3)]; 1021        else1022          S.vertices = varargin{1}.vertices;1023        end1024        S.faces    = varargin{1}.faces; 1025        if isfield(varargin{1},'facevertexcdata'), S.cdata = varargin{1}.facevertexcdata; end1026        S = gifti(S);1027        S = export(S,'patch'); 1028        warning off;1029        curv = spm_mesh_curvature(S); %$ > 0;1030        warning on; 1031        1032        labelnam = cell(0); %labelmapclim = zeros(1,2); labeloid = zeros(0); labelid = zeros(0); nid=1;1033        P = struct('vertices',S.vertices, 'faces',double(S.faces));1034            H.patch(1) = patch(P,...1035              'FaceColor',        [0.6 0.6 0.6],...1036              'EdgeColor',        'none',...1037              'FaceLighting',     'gouraud',...1038              'SpecularStrength', 0.0,... 0.71039              'AmbientStrength',  0.4,... 0.11040              'DiffuseStrength',  0.6,... 0.71041              'SpecularExponent', 10,...1042              'Clipping',         'off',...1043              'DeleteFcn',        {@myDeleteFcn, renderer},...1044              'Visible',          'off',...1045              'Tag',              'CATSurfRender',...1046              'Parent',           H.axis);1047            setappdata(H.patch(1),'patch',P);1048            C = spm_mesh_label(P);1049            setappdata(H.patch(1),'cclabel',C);1050            setappdata(H.patch(1),'curvature',curv);1051        1052            if isfield(varargin{1},'cdata')1053                try1054                  T = gifti(varargin{1}.cdata);1055                catch1056                  T = gifti(varargin{1});1057                end1058                T = T.cdata;1059            elseif isfield(varargin{1},'facevertexcdata')1060                try1061                  T = gifti(varargin{1}.facevertexcdata);1062                catch1063                  T = gifti(varargin{1});1064                end1065                T = T.cdata;1066            else1067                T = [];1068            end1069            try1070              warning off;1071              updateTexture(H,T,1);1072              warning on;1073            catch 1074              warning on; 1075            end1076            labelmapclim = [min(T) max(T)];1077            H.filename{1} = ''; 1078            H.cdata = T; 1079            sinfo   = cat_surf_info('');1080            H.sinfo = sinfo; 1081            1082            if mean(max(S.vertices + spm_mesh_normals(S)) - max(S.vertices))>01083            %if strcmp(sinfo(1).texture,'defects'), 1084              S.faces = S.faces(:,[2,1,3]); 1085            end1086            %if strcmp(sinfo(1).texture,'defects'), S.faces = S.faces(:,[2,1,3]); end1087      end1088 1089 1090        %% -Set viewpoint, light and manipulation options1091        %------------------------------------------------------------------1092        axis(H.axis,'image');1093        axis(H.axis,'off');1094        material(H.figure,'dull');1095 1096        % default lighting1097        if 1 && ismac, H.catLighting = 'inner'; else H.catLighting = 'cam'; end1098 1099        1100        [caz,cel]  = view;1101        H.light(1) = camlight('headlight','infinite'); 1102        set(H.light(1),'Parent',H.axis,'Tag','camlight'); 1103        switch H.catLighting1104          case 'inner'1105            % switch off local light (camlight)1106            set(H.light(1),'visible','off','parent',H.axis);1107 1108            % set inner light1109            H.light(2) = light('Position',[0 0 0],'parent',H.axis,'Tag','centerlight'); 1110            for pi=1:numel(H.patch)1111              set(H.patch(pi),'BackFaceLighting','unlit');1112            end1113        end1114       1115        1116        if ~H.issubfigure1117          H.rotate3d = rotate3d(H.axis);1118          set(H.rotate3d,'Enable','on');1119          set(H.rotate3d,'ActionPostCallback',{@myPostCallback, H});1120        end1121 1122 1123        %-Store handles1124        %------------------------------------------------------------------1125        setappdata(H.axis,'handles',H);1126        for pi=1:numel(H.patch)1127          set(H.patch(pi),'Visible','on');1128          setappdata(H.patch(pi),'clip',[false NaN NaN]);1129        end1130 1131 1132        for pi=1:numel(H.patch)1133          setappdata(H.patch(pi),'colourmap',labelmap); 1134        end1135        try1136          cat_surf_render2('clim',H.axis,labelmapclim); % RD20221129: Sometimes problems in fast surfaces 1137        end1138        colormap(labelmap); try caxis(labelmapclim); end1139 1140        if numel(labelnam)>01141          %%1142          H = cat_surf_render2('ColorBar',H.axis,'on'); 1143          labelnam2 = [{''} labelnam]; for lni=1:numel(labelnam2),labelnam2{lni} = [' ' labelnam2{lni} ' ']; end1144 1145          labellength = min(100,max(cellfun('length',labelnam2))); 1146          ss = max(1,round(diff(labelmapclim+1)/60)); 1147          ytick = labelmapclim(1):ss:labelmapclim(2);1148          1149          set(H.colourbar,'ytick',ytick,'yticklabel',labelnam2(1:ss:end),...1150            'Position',[max(0.75,0.98-0.008*labellength) 0.05 0.02 0.9]);1151          try, set(H.colourbar,'TickLabelInterpreter','none'); end1152          set(H.axis,'Position',[0.1 0.1 min(0.6,0.98-0.008*labellength - 0.2) 0.8])1153          1154          H.labelmap = struct('colormap',labelmap,'ytick',ytick,'labelnam2',{labelnam2});1155          setappdata(H.axis,'handles',H);1156        end1157 1158 1159        % annotation for colormap ...1160        %if ~H.issubfigure1161        %  [pp,ff,ee] = fileparts(H.filename{1}); 1162        %  H.text = annotation('textbox','string',[ff ee],'position',[0.0,0.97,0.2,0.03],'LineStyle','none','Interpreter','none');1163        %end1164 1165        1166        %-Add context menu1167        %------------------------------------------------------------------1168        if ~isfield(O,'parent')1169          %try1170            cat_surf_render2('ContextMenu',H);1171          %end1172        end1173        1174        if strcmp(renderer,'painters')1175          1176        end1177        1178        % set default view1179        cat_surf_render2('view',H,[   0  90]);1180        if numel(H.patch)==11181          if strcmp(H.sinfo(1).side,'lh'); cat_surf_render2('view',H,[ -90   0]);  end1182          if strcmp(H.sinfo(1).side,'rh'); cat_surf_render2('view',H,[  90   0]);  end1183          %if strcmp(H.sinfo(1).side,'ch'); cat_surf_render2('view',H,[  0   90]);  end1184        end 1185        1186        % remember this zoom level1187        axis vis3d; %zoom(1.15);1188        zoom reset1189        1190        1191    %-Context Menu1192    %======================================================================1193    case 'contextmenu'1194        1195        if isempty(varargin), varargin{1} = gca; end1196        H = getHandles(varargin{1});1197        sinfo1 = cat_surf_info(H.filename);1198        if ~isempty(get(H.patch(1),'UIContextMenu')), return; end1199        1200        cmenu = uicontextmenu('Callback',{@myMenuCallback, H});

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