text:
function obj = coef_f_q_sym_pTop_func_new(in1,in2,in3)
coefs_tq1_1 = in2(1);
coefs_tq1_2 = in2(4);
coefs_tq1_3 = in2(7);
coefs_tq1_4 = in2(10);
coefs_tq1_5 = in2(13);
coefs_tq1_6 = in2(16);
coefs_tq1_7 = in2(19);
coefs_tq1_8 = in2(22);
coefs_tq1_9 = in2(25);
coefs_tq2_1 = in2(2);
coefs_tq2_2 = in2(5);
coefs_tq2_3 = in2(8);
coefs_tq2_4 = in2(11);
coefs_tq2_5 = in2(14);
coefs_tq2_6 = in2(17);
coefs_tq2_7 = in2(20);
coefs_tq2_8 = in2(23);
coefs_tq2_9 = in2(26);
coefs_tq3_1 = in2(3);
coefs_tq3_2 = in2(6);
coefs_tq3_3 = in2(9);
coefs_tq3_4 = in2(12);
coefs_tq3_5 = in2(15);
coefs_tq3_6 = in2(18);
coefs_tq3_7 = in2(21);
coefs_tq3_8 = in2(24);
coefs_tq3_9 = in2(27);
coefs_tq1_10 = in2(28);
coefs_tq1_11 = in2(31);
coefs_tq2_10 = in2(29);
coefs_tq2_11 = in2(32);
coefs_tq3_10 = in2(30);
coefs_tq3_11 = in2(33);
coef_Jacob1_qt_syms1 = in3(1);
coef_Jacob1_qt_syms2 = in3(5);
coef_Jacob1_qt_syms3 = in3(9);
coef_Jacob1_qt_syms4 = in3(13);
coef_Jacob1_qt_syms5 = in3(17);
coef_Jacob1_qt_syms6 = in3(21);
coef_Jacob1_qt_syms7 = in3(25);
coef_Jacob1_qt_syms8 = in3(29);
coef_Jacob1_qt_syms9 = in3(33);
coef_Jacob2_qt_syms1 = in3(2);
coef_Jacob2_qt_syms2 = in3(6);
coef_Jacob2_qt_syms3 = in3(10);
coef_Jacob2_qt_syms4 = in3(14);
coef_Jacob2_qt_syms5 = in3(18);
coef_Jacob2_qt_syms6 = in3(22);
coef_Jacob2_qt_syms7 = in3(26);
coef_Jacob2_qt_syms8 = in3(30);
coef_Jacob2_qt_syms9 = in3(34);
coef_Jacob3_qt_syms1 = in3(3);
coef_Jacob3_qt_syms2 = in3(7);
coef_Jacob3_qt_syms3 = in3(11);
coef_Jacob3_qt_syms4 = in3(15);
coef_Jacob3_qt_syms5 = in3(19);
coef_Jacob3_qt_syms6 = in3(23);
coef_Jacob3_qt_syms7 = in3(27);
coef_Jacob3_qt_syms8 = in3(31);
coef_Jacob3_qt_syms9 = in3(35);
coef_Jacob4_qt_syms1 = in3(4);
coef_Jacob4_qt_syms2 = in3(8);
coef_Jacob4_qt_syms3 = in3(12);
coef_Jacob4_qt_syms4 = in3(16);
coef_Jacob4_qt_syms5 = in3(20);
coef_Jacob4_qt_syms6 = in3(24);
coef_Jacob4_qt_syms7 = in3(28);
coef_Jacob4_qt_syms8 = in3(32);
coef_Jacob4_qt_syms9 = in3(36);
coef_Jacob1_qt_syms10 = in3(37);
coef_Jacob1_qt_syms11 = in3(41);
coef_Jacob1_qt_syms12 = in3(45);
coef_Jacob1_qt_syms13 = in3(49);
coef_Jacob1_qt_syms14 = in3(53);
coef_Jacob1_qt_syms15 = in3(57);
coef_Jacob1_qt_syms16 = in3(61);
coef_Jacob1_qt_syms17 = in3(65);
coef_Jacob1_qt_syms18 = in3(69);
coef_Jacob1_qt_syms19 = in3(73);
coef_Jacob1_qt_syms20 = in3(77);
coef_Jacob1_qt_syms21 = in3(81);
coef_Jacob1_qt_syms22 = in3(85);
coef_Jacob1_qt_syms23 = in3(89);
coef_Jacob1_qt_syms24 = in3(93);
coef_Jacob1_qt_syms25 = in3(97);
coef_Jacob1_qt_syms26 = in3(101);
coef_Jacob1_qt_syms27 = in3(105);
coef_Jacob1_qt_syms28 = in3(109);
coef_Jacob1_qt_syms29 = in3(113);
coef_Jacob1_qt_syms30 = in3(117);
coef_Jacob1_qt_syms31 = in3(121);
coef_Jacob1_qt_syms32 = in3(125);
coef_Jacob1_qt_syms33 = in3(129);
coef_Jacob1_qt_syms34 = in3(133);
coef_Jacob1_qt_syms35 = in3(137);
coef_Jacob1_qt_syms36 = in3(141);
coef_Jacob2_qt_syms10 = in3(38);
coef_Jacob2_qt_syms11 = in3(42);
coef_Jacob2_qt_syms12 = in3(46);
coef_Jacob2_qt_syms13 = in3(50);
coef_Jacob2_qt_syms14 = in3(54);
coef_Jacob2_qt_syms15 = in3(58);
coef_Jacob2_qt_syms16 = in3(62);
coef_Jacob2_qt_syms17 = in3(66);
coef_Jacob2_qt_syms18 = in3(70);
coef_Jacob2_qt_syms19 = in3(74);
coef_Jacob2_qt_syms20 = in3(78);
coef_Jacob2_qt_syms21 = in3(82);
coef_Jacob2_qt_syms22 = in3(86);
coef_Jacob2_qt_syms23 = in3(90);
coef_Jacob2_qt_syms24 = in3(94);
coef_Jacob2_qt_syms25 = in3(98);
coef_Jacob2_qt_syms26 = in3(102);
coef_Jacob2_qt_syms27 = in3(106);
coef_Jacob2_qt_syms28 = in3(110);
coef_Jacob2_qt_syms29 = in3(114);
coef_Jacob2_qt_syms30 = in3(118);
coef_Jacob2_qt_syms31 = in3(122);
coef_Jacob2_qt_syms32 = in3(126);
coef_Jacob2_qt_syms33 = in3(130);
coef_Jacob2_qt_syms34 = in3(134);
coef_Jacob2_qt_syms35 = in3(138);
coef_Jacob2_qt_syms36 = in3(142);
coef_Jacob3_qt_syms10 = in3(39);
coef_Jacob3_qt_syms11 = in3(43);
coef_Jacob3_qt_syms12 = in3(47);
coef_Jacob3_qt_syms13 = in3(51);
coef_Jacob3_qt_syms14 = in3(55);
coef_Jacob3_qt_syms15 = in3(59);
coef_Jacob3_qt_syms16 = in3(63);
coef_Jacob3_qt_syms17 = in3(67);
coef_Jacob3_qt_syms18 = in3(71);
coef_Jacob3_qt_syms19 = in3(75);
coef_Jacob3_qt_syms20 = in3(79);
coef_Jacob3_qt_syms21 = in3(83);
coef_Jacob3_qt_syms22 = in3(87);
coef_Jacob3_qt_syms23 = in3(91);
coef_Jacob3_qt_syms24 = in3(95);
coef_Jacob3_qt_syms25 = in3(99);
coef_Jacob3_qt_syms26 = in3(103);
coef_Jacob3_qt_syms27 = in3(107);
coef_Jacob3_qt_syms28 = in3(111);
coef_Jacob3_qt_syms29 = in3(115);
coef_Jacob3_qt_syms30 = in3(119);
coef_Jacob3_qt_syms31 = in3(123);
coef_Jacob3_qt_syms32 = in3(127);
coef_Jacob3_qt_syms33 = in3(131);
coef_Jacob3_qt_syms34 = in3(135);
coef_Jacob3_qt_syms35 = in3(139);
coef_Jacob3_qt_syms36 = in3(143);
coef_Jacob4_qt_syms10 = in3(40);
coef_Jacob4_qt_syms11 = in3(44);
coef_Jacob4_qt_syms12 = in3(48);
coef_Jacob4_qt_syms13 = in3(52);
coef_Jacob4_qt_syms14 = in3(56);
coef_Jacob4_qt_syms15 = in3(60);
coef_Jacob4_qt_syms16 = in3(64);
coef_Jacob4_qt_syms17 = in3(68);
coef_Jacob4_qt_syms18 = in3(72);
coef_Jacob4_qt_syms19 = in3(76);
coef_Jacob4_qt_syms20 = in3(80);
coef_Jacob4_qt_syms21 = in3(84);
coef_Jacob4_qt_syms22 = in3(88);
coef_Jacob4_qt_syms23 = in3(92);
coef_Jacob4_qt_syms24 = in3(96);
coef_Jacob4_qt_syms25 = in3(100);
coef_Jacob4_qt_syms26 = in3(104);
coef_Jacob4_qt_syms27 = in3(108);
coef_Jacob4_qt_syms28 = in3(112);
coef_Jacob4_qt_syms29 = in3(116);
coef_Jacob4_qt_syms30 = in3(120);
coef_Jacob4_qt_syms31 = in3(124);
coef_Jacob4_qt_syms32 = in3(128);
coef_Jacob4_qt_syms33 = in3(132);
coef_Jacob4_qt_syms34 = in3(136);
coef_Jacob4_qt_syms35 = in3(140);
coef_Jacob4_qt_syms36 = in3(144);
pinvG1_1 = in1(1);
pinvG1_2 = in1(4);
pinvG1_3 = in1(7);
pinvG2_1 = in1(2);
pinvG2_2 = in1(5);
pinvG2_3 = in1(8);
pinvG3_1 = in1(3);
pinvG3_2 = in1(6);
pinvG3_3 = in1(9);
obj = 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author: zarathustr

repo: LibQPEP

save_path: github-repos/MATLAB/zarathustr-LibQPEP


text:
function coords = getCurROIcoords(view)
%
% coords = getCurROIcoords(view)
%
% Gets ROI.coords from currently selected ROI.  Error dialog if no
% selectedROI.
%
% djh 4/24/98

if view.selectedROI
  coords = view.ROIs(view.selectedROI).coords;
else
  myErrorDlg('No ROI selected');
end

author: vistalab

repo: vistasoft

save_path: github-repos/MATLAB/vistalab-vistasoft


text:
function [f]=ref_idwiltii_1(coef,g,a,M)
%REF_IDWILTII_1  Reference IDWILTII by IDGT type II
%

%   Author : Peter L. Søndergaard

L=size(g,1);
N=L/a;
W=size(coef,2);

coef=reshape(coef,M*2,N/2,W);

coef2=zeros(2*M,N,W);

if 0

  % --- loop version ---
  for n=0:N/2-1

    % m=0
    coef2(1,2*n+1,:) = coef(1,n+1,:);

    % m odd
    for m=1:2:M-1
      coef2(m+1,2*n+1,:)     = -i/sqrt(2)*coef(m+1,n+1,:);
      coef2(2*M-m+1,2*n+1,:) = -i/sqrt(2)*coef(m+1,n+1,:);

      coef2(m+1,2*n+2,:)     =  1/sqrt(2)*coef(M+m+1,n+1,:);
      coef2(2*M-m+1,2*n+2,:) = -1/sqrt(2)*coef(M+m+1,n+1,:);
    end;

    % m even
    for m=2:2:M-1
      coef2(m+1,2*n+1,:)     =  1/sqrt(2)*coef(m+1,n+1,:);
      coef2(2*M-m+1,2*n+1,:) = -1/sqrt(2)*coef(m+1,n+1,:);

      coef2(m+1,2*n+2,:)     = -i/sqrt(2)*coef(M+m+1,n+1,:);
      coef2(2*M-m+1,2*n+2,:) = -i/sqrt(2)*coef(M+m+1,n+1,:);
    end;

    % m=nyquest
    if mod(M,2)==0
      coef2(M+1,2*n+2,:) = -i*coef(M+1,n+1,:);
    else
      coef2(M+1,2*n+1,:) = -i*coef(M+1,n+1,:);
    end;

  end;

else

  % --- Vector version ---
  % First and middle modulation are transferred unchanged.
  coef2(1,1:2:N,:) = coef(1,:,:);

  coef2(2:2:M,1:2:N,:)        = -i/sqrt(2)*coef(2:2:M,:,:);
  coef2(2*M:-2:M+2,1:2:N,:)   = -i/sqrt(2)*coef(2:2:M,:,:);

  coef2(2:2:M,2:2:N,:)        =  1/sqrt(2)*coef(M+2:2:2*M,:,:);
  coef2(2*M:-2:M+2,2:2:N,:)   = -1/sqrt(2)*coef(M+2:2:2*M,:,:);

  if M>2
    coef2(3:2:M,1:2:N,:)        = 1/sqrt(2)*coef(3:2:M,:,:);
    coef2(2*M-1:-2:M+2,1:2:N,:) = -1/sqrt(2)*coef(3:2:M,:,:);

    coef2(3:2:M,2:2:N,:)        = -i/sqrt(2)*coef(M+3:2:2*M,:,:);
    coef2(2*M-1:-2:M+2,2:2:N,:) = -i/sqrt(2)*coef(M+3:2:2*M,:,:);
  end;

  if mod(M,2)==0
    coef2(M+1,2:2:N,:) = -i*coef(M+1,:,:);
  else
    coef2(M+1,1:2:N,:) = -i*coef(M+1,:,:);
  end;

end;

f=ref_igdgt(reshape(coef2,2*M*N,W),g,a,2*M,.5,0,0);

%if norm(imag(f(:)))<1e-10
%  f=real(f);
%end;

author: ltfat

repo: ltfat

save_path: github-repos/MATLAB/ltfat-ltfat


text:
% requires:
% - moxunit: https://github.com/MOxUnit/MOxUnit
% - bids-matlab

addpath(fullfile(pwd, '..'));

input_file = fullfile(pwd, '..', 'input', 'spreadsheet_to_convert.xlsx');
files_out = bids_spreadsheet2participants(input_file, 'ignore', 'comment', 'export', pwd);

json_expected = jsonread(fullfile(pwd, 'data', 'participants.json'));
json_actual = jsonread(fullfile(pwd, 'participants.json'));
% uses moxunit assertEqual
assertEqual(json_actual, json_expected);

tsv_expected  = bids.util.tsvread(fullfile(pwd, 'data', 'participants.tsv'));
tsv_actual  = bids.util.tsvread(fullfile(pwd, 'participants.tsv'));
assertEqual(tsv_actual, tsv_expected);

author: bids-standard

repo: bids-starter-kit


text:
%kirotate 'Rotate Object by Arbitrary Angle '
% This MatLab function was automatically generated by a converter (KhorosToMatLab) from the Khoros irotate.pane file
%
% Parameters:
% InputFile: i 'Input ', required: 'Input data object'
% Toggle: resize 'Resize to fit', default: 0: 'resize to fit rotated image'
% Toggle: planes 'Process by planes', default: 0: 'Process data by WxH planes (fast, but only good for smaller objects) instead of small prisms down D (slow, but works for huge objects)'
% OutputFile: o 'Output', required: 'Output data object'
% Toggle: wctr 'W center', default: 0: 'center of rotation in center of W coordinate span'
% Toggle: hctr 'H center', default: 0: 'center of rotation in center of H coordinate span'
%
% Example: o = kirotate(i, {'i','';'resize',0;'planes',0;'o','';'wctr',0;'hctr',0})
%
% Khoros helpfile follows below:
%
%  PROGRAM
% irotate - Rotate Object by Arbitrary Angle
%
%  DESCRIPTION
% .I irotate
% is used to apply a rotation operation to each WxH plane in the value segment
% of a data object. The is equivalent to rotating the data about an axis
% that lies parallel to the DEPTH axis.
%
% Interpolation of output data from the input data is bilinear.
%
% Each of the (DxE) WxH planes is rotated before being stored in the output
% object.
%
% If rotation about an axis parallel to an axis other than DEPTH is desired,
% it is necessary to use the kaxis
% program to reorient the data such that the desired axis becomes the DEPTH axis
% before applying irotate. This limitation is due to computational complexity and
% performance issues, particularly when dealing with large data sets.
%
% If the input object has a map, the data is pulled through the map prior to
% rotation, and the output object will have no map.
%
% The arbitrary (floating point) rotation angle is CW for a positive angle, and
% should be specified in degrees.
%
% The center of rotation is used to specify that WxH point in the input object
% about which the rotation is to take place. The -wctr and -hctr flags can be used
% to ask irotate to automatically place the center of rotation at the center
% of the WxH plane.
%
% The -resize flag will causes the output object dimensions to be increased or
% decreased sufficiently to just contain all of the input data even after
% rotation.
% When this flag is used, the W and H dimensions may increase or decrease by a
% factor of up to sqrt(2).  If -resize is selected and the center of rotation is
% not in the center of the WxH plane, the resulting rotated data will not
% be centered in the output data.
%
% The -planes flag is used to change the way the data is accessed for processing.
% If the data set is small enough that individual WxH planes of data
% will fit in memory, then use of the -planes
% flag will cause processing to be done on a plane-by-plane basis; this is
% generally "much faster\fR than the default method (orders of magnitude).
% If -planes is "not\fR specified, then
% processing will occur in prisms down the D axis, which is much slower but will
% work on data sets of any size. If you have lots of memory, you may be able to
% get away with using -planes even for quite large data sets, say around 2Kx2K
% or more points per plane.
%
% The value given to those data points in the output that do not map to
% data in the input object is controlled by the padding behavior. If padding by
% zero is selected, then those areas will be set to zero. If padding by a
% specific pad value is selected, then that value will appear in areas that
% don't map to the input data. If padding by the pad value attribute of the
% input object is selected, then the pad value from the input object
% attribute KPDS_VALUE_PAD_VALUE is retrieved and is placed in areas that
% don't map to the input data.
%
% If the input object has a mask, a new mask will be computed for the output
% object indicating which data points contain reliable data.
% If a mask is present, then the output mask will be computed
% with value 1 if there was a full set of valid data points in the input from
% which to compute the output value. If any of the needed input points was
% marked as invalid,
% then the output mask value is set to 0, marking that output point as invalid.
% For regions of the output that do not map to data in the input object, the
% mask can be set to valid or invalid, depending on the setting of the
% -padvalid argument.
%
% If the input object has a location data segment and the location data is
% anything other than KUNIFORM, then
% .I irotate
% will complain and ask you to use klocxform instead.
%
%
%
%  EXAMPLES
%
%  "SEE ALSO"
% igeowarp(1,) klocxform(1)
%
%  RESTRICTIONS
%
% If
% .I irotate
% is too slow for your application, be sure to read about the -planes option
% to see if it can be used. If so, processing may occur several orders of
% magnitude faster.
%
%  REFERENCES
%
%  COPYRIGHT
% Copyright (C) 1993 - 1997, Khoral Research, Inc. ("KRI")  All rights reserved.
%

function varargout = kirotate(varargin)
if nargin ==0
  Inputs={};arglist={'',''};
elseif nargin ==1
  Inputs=varargin{1};arglist={'',''};
elseif nargin ==2
  Inputs=varargin{1}; arglist=varargin{2};
else error('Usage: [out1,..] = kirotate(Inputs,arglist).');
end
if size(arglist,2)~=2
  error('arglist must be of form {''ParameterTag1'',value1;''ParameterTag2'',value2}')
 end
narglist={'i', '__input';'resize', 0;'planes', 0;'o', '__output';'wctr', 0;'hctr', 0};
maxval={0,0,0,0,0,0};
minval={0,0,0,0,0,0};
istoggle=[0,1,1,0,1,1];
was_set=istoggle * 0;
paramtype={'InputFile','Toggle','Toggle','OutputFile','Toggle','Toggle'};
% identify the input arrays and assign them to the arguments as stated by the user
if ~iscell(Inputs)
Inputs = {Inputs};
end
NumReqOutputs=1; nextinput=1; nextoutput=1;
  for ii=1:size(arglist,1)
  wasmatched=0;
  for jj=1:size(narglist,1)
   if strcmp(arglist{ii,1},narglist{jj,1})  % a given argument was matched to the possible arguments
     wasmatched = 1;
     was_set(jj) = 1;
     if strcmp(narglist{jj,2}, '__input')
      if (nextinput > length(Inputs))
        error(['Input ' narglist{jj,1} ' has no corresponding input!']);
      end
      narglist{jj,2} = 'OK_in';
      nextinput = nextinput + 1;
     elseif strcmp(narglist{jj,2}, '__output')
      if (nextoutput > nargout)
        error(['Output nr. ' narglist{jj,1} ' is not present in the assignment list of outputs !']);
      end
      if (isempty(arglist{ii,2}))
        narglist{jj,2} = 'OK_out';
      else
        narglist{jj,2} = arglist{ii,2};
      end

      nextoutput = nextoutput + 1;
      if (minval{jj} == 0)
         NumReqOutputs = NumReqOutputs - 1;
      end
     elseif isstr(arglist{ii,2})
      narglist{jj,2} = arglist{ii,2};
     else
        if strcmp(paramtype{jj}, 'Integer') & (round(arglist{ii,2}) ~= arglist{ii,2})
            error(['Argument ' arglist{ii,1} ' is of integer type but non-integer number ' arglist{ii,2} ' was supplied']);
        end
        if (minval{jj} ~= 0 | maxval{jj} ~= 0)
          if (minval{jj} == 1 & maxval{jj} == 1 & arglist{ii,2} < 0)
            error(['Argument ' arglist{ii,1} ' must be bigger or equal to zero!']);
          elseif (minval{jj} == -1 & maxval{jj} == -1 & arglist{ii,2} > 0)
            error(['Argument ' arglist{ii,1} ' must be smaller or equal to zero!']);
          elseif (minval{jj} == 2 & maxval{jj} == 2 & arglist{ii,2} <= 0)
            error(['Argument ' arglist{ii,1} ' must be bigger than zero!']);
          elseif (minval{jj} == -2 & maxval{jj} == -2 & arglist{ii,2} >= 0)
            error(['Argument ' arglist{ii,1} ' must be smaller than zero!']);
          elseif (minval{jj} ~= maxval{jj} & arglist{ii,2} < minval{jj})
            error(['Argument ' arglist{ii,1} ' must be bigger than ' num2str(minval{jj})]);
          elseif (minval{jj} ~= maxval{jj} & arglist{ii,2} > maxval{jj})
            error(['Argument ' arglist{ii,1} ' must be smaller than ' num2str(maxval{jj})]);
          end
        end
     end
     if ~strcmp(narglist{jj,2},'OK_out') &  ~strcmp(narglist{jj,2},'OK_in')
       narglist{jj,2} = arglist{ii,2};
     end
   end
   end
   if (wasmatched == 0 & ~strcmp(arglist{ii,1},''))
        error(['Argument ' arglist{ii,1} ' is not a valid argument for this function']);
   end
end
% match the remaining inputs/outputs to the unused arguments and test for missing required inputs
 for jj=1:size(narglist,1)
     if  strcmp(paramtype{jj}, 'Toggle')
        if (narglist{jj,2} ==0)
          narglist{jj,1} = '';
        end;
        narglist{jj,2} = '';
     end;
     if  ~strcmp(narglist{jj,2},'__input') && ~strcmp(narglist{jj,2},'__output') && istoggle(jj) && ~ was_set(jj)
          narglist{jj,1} = '';
          narglist{jj,2} = '';
     end;
     if strcmp(narglist{jj,2}, '__input')
      if (minval{jj} == 0)  % meaning this input is required
        if (nextinput > size(Inputs))
           error(['Required input ' narglist{jj,1} ' has no corresponding input in the list!']);
        else
          narglist{jj,2} = 'OK_in';
          nextinput = nextinput + 1;
        end
      else  % this is an optional input
        if (nextinput <= length(Inputs))
          narglist{jj,2} = 'OK_in';
          nextinput = nextinput + 1;
        else
          narglist{jj,1} = '';
          narglist{jj,2} = '';
        end;
      end;
     else
     if strcmp(narglist{jj,2}, '__output')
      if (minval{jj} == 0) % this is a required output
        if (nextoutput > nargout & nargout > 1)
           error(['Required output ' narglist{jj,1} ' is not stated in the assignment list!']);
        else
          narglist{jj,2} = 'OK_out';
          nextoutput = nextoutput + 1;
          NumReqOutputs = NumReqOutputs-1;
        end
      else % this is an optional output
        if (nargout - nextoutput >= NumReqOutputs)
          narglist{jj,2} = 'OK_out';
          nextoutput = nextoutput + 1;
        else
          narglist{jj,1} = '';
          narglist{jj,2} = '';
        end;
      end
     end
  end
end
if nargout
   varargout = cell(1,nargout);
else
  varargout = cell(1,1);
end
global KhorosRoot
if exist('KhorosRoot') && ~isempty(KhorosRoot)
w=['"' KhorosRoot];
else
if ispc
  w='"C:\Program Files\dip\khorosBin\';
else
[s,w] = system('which cantata');
w=['"' w(1:end-8)];
end
end
[varargout{:}]=callKhoros([w 'irotate"  '],Inputs,narglist);

author: aludnam

repo: MATLAB

save_path: github-repos/MATLAB/aludnam-MATLAB


text:
function [fp, fm] = partition(f)
% PARTITION   Partition a DISKFUN into its even/periodic odd/anti-periodic
% parts.
%
% [FP, FM] = partition(F) partitions F into two diskfuns FP & FM with the
% following properties:
%
%   FP has a CDR decomposition such that C is even and R is pi periodic.
%   FM has a CDR decomposition such that C is odd and R is pi
%   anti-periodic.
%
% See also DISKFUN/COMBINE.

% Copyright 2017 by The University of Oxford and The Chebfun Developers.
% See http://www.chebfun.org/ for Chebfun information.

if ( ~isa(f,'diskfun') )
    error('DISKFUN:partition:unknown',['Undefined function ''partition'' for ' ...
        'input argument of type %s.'], class(f));
end

if ( isempty(f) )
    fp = diskfun();
    fm = diskfun();
    return
end

% Do the even-pi-periodic case first.
id = f.idxPlus;

if isempty(id)
    fp = diskfun();
else
    fp = f;
    fp.cols = fp.cols(:, id);
    fp.rows = fp.rows(:, id);
    fp.pivotValues = fp.pivotValues(id, :);
    fp.pivotLocations = fp.pivotLocations(id, :);
    fp.idxPlus = 1:length(id);
    fp.idxMinus = [];
end

% Now the odd case.
id = f.idxMinus;

if ( isempty(id) )
    fm = diskfun();
else
    fm = f;
    fm.cols = fm.cols(:, id);
    fm.rows = fm.rows(:, id);
    fm.pivotValues = fm.pivotValues(id);
    fm.pivotLocations = fm.pivotLocations(id, :);
    fm.idxMinus = 1:length(id);
    fm.idxPlus = [];
    fm.nonZeroPoles = 0;
end

end

author: chebfun

repo: chebfun

save_path: github-repos/MATLAB/chebfun-chebfun


text:
%VGG_SEGMENT_MS  Mean shift image segmentation
%
%   S = vgg_segment_ms(A, h_s, h_r, min_sz[, W])
%
% Segmentation of an image using mean shift.
%
% Uses EDISON code by Chris Christoudias and Bogdan Georgescu, downloaded
% from http://www.caip.rutgers.edu/riul/research/code/EDISON/index.html,
% and based on the following papers:
%   [1] D. Comanicu, P. Meer: "Mean shift: A robust approach toward feature
%   space analysis". IEEE Trans. Pattern Anal. Machine Intell., May 2002.
%   [2] C. Christoudias, B. Georgescu, P. Meer: "Synergism in low level
%   vision". 16th International Conference of Pattern Recognition, Track 1
%   - Computer Vision and Robotics, Quebec City, Canada, August 2001.
%
%IN:
%   A - HxWx3 uint8 image for segmentation.
%   h_s - scalar parameter on.
%   h_r - scalar parameter on.
%   min_sz - scalar indicating the minimum number of pixels per segment.
%   W - HxW single matrix containing weights from edge detection for
%       synergism with segmentation. See [2]. Default: uniform weight.
%
%OUT:
%   S - HxW uint32 segmentation matrix, each value of which gives the index
%       of the region said pixel belongs to, from 1 to num_segments.

% $Id: vgg_segment_ms.m,v 1.1 2007/12/10 10:59:31 ojw Exp $

function varargout = vgg_segment_ms(varargin)
funcName = mfilename;
sd = 'seg_ms/';
sourceList = {['-I' sd], [funcName '.cxx'], [sd 'msImageProcessor.cpp'],...
              [sd 'ms.cpp'], [sd 'rlist.cpp'], [sd 'RAList.cpp'],...
              [sd 'msSys.cpp']};
vgg_mexcompile_script; % Compilation happens in this script
return

repo: SelfExSR


text:
function c3 = sandia_sgmgg_coef_inc2 ( m, n1, s1, c1, s2 )

%*****************************************************************************80
%
%% SANDIA_SGMGG_COEF_INC2 computes tentative coefficient changes.
%
%  Discussion:
%
%    An active set S1 of N1 sparse grid indices is given, each of
%    size M.
%
%    The coefficient C1 of each sparse grid index is also given.
%
%    A candidate sparse grid index S2 is provided.
%
%    This function determines the N+1 coefficients that would be
%    appropriate if the candidate S2 was added to the active set
%    as the (N+1)-st item.
%
%  Example:
%
%    Input:
%
%      +1 * {0,2}
%      -1 * {0,1}  +1 * {1,1}
%                  -1 * {1,0}  +1 * {2,0}
%
%    Add {3,0}
%
%    Output:
%
%      +1 * {0,2}
%      -1 * {0,1}  +1 * {1,1}
%                  -1 * {1,0}   0 * {2,0}  +1 * {3,0}
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    29 August 2011
%
%  Author:
%
%    John Burkardt
%
%  Parameters:
%
%    Input, integer M, the dimension of the vector.
%
%    Input, integer N1, the number of points in the active set.
%
%    Input, integer S1(M,N1), the indices for the active set.
%
%    Input, integer C1(N1), the coefficients for the active set.
%
%    Input, integer S2(M), the indices for the candidate.
%
%    Output, integer C3(N1+1), the coefficients for the active set
%    plus the candidate.
%

%
%  During the calculation, we may try to update coefficients of inactive
%  index sets.  By the end of the calculation, all these inactive index
%  sets should have accumulated total coefficients of 0 again.  As a check,
%  we temporarily set aside space for these objects, and check, at the end,
%  that the coefficients are zero.
%
  n4 = 0;
  c4 = zeros(n1,1);
  s4 = zeros(m,n1);
%
%  Copy C1.
%
  c3(1:n1) = c1(1:n1);
  c3(n1+1) = 1;
%
%  Consider the effect of the new item S2 on each of the current
%  items in the active set S1.
%
  for j = 1 : n1
%
%  Determine S, the element-wise minimum of the J-th item in S1 versus S2.
%
    k = j;

    for i = 1 : m

      if ( s2(i) < s1(i,j) )
        s(i) = s2(i);
        k = - 1;
      else
        s(i) = s1(i,j);
      end

    end
%
%  If S = S1(*,J), K is J.
%
    if ( k ~= -1 )

      c3(k) = c3(k) - c1(j);
%
%  If S is equal to an element of the active set, we set K to that index.
%
    else

      for j2 = 1 : n1

        k = j2;

        for i2 = 1 : m

          if ( s1(i2,j2) ~= s(i2) )
            k = - 1;
            break
          end

        end

        if ( k ~= - 1 )
          c3(k) = c3(k) - c1(j);
          break
        end

      end

    end
%
%  If S is equal to an element of the inactive set, set K to that index.
%
    if ( k == -1 )

      for j2 = 1 : n4

        k = j2;

        for i2 = 1 : m

          if ( s4(i2,j2) ~= s(i2) )
            k = - 1;
            break
          end

        end

        if ( k ~= - 1 )
          c4(k) = c4(k) - c1(j);
          break
        end

      end

    end
%
%  S is not equal to S1(*,J), or any element of S1, or any element of S4.
%  Add S to the set of elements S4.
%
    if ( k == -1 )
      n4 = n4 + 1;
      k = n4;
      c4(k) = 0;
      s4(1:m,k) = s(1:m);
      c4(k) = c4(k) - c1(j);
    end

  end
%
%  At the end, the C4(1:N4) should all be zero.
%
  if ( any ( c4(1:n4) ~= 0 ) )
    fprintf ( 1, '\n' );
    fprintf ( 1, 'SANDIA_SGMGG_COEF_INC2 - Fatal error!\n' );
    fprintf ( 1, '  Some inactive indices were assigned a nonzero coefficient.\n' );
    i4mat_transpose_print ( m, n4, s4, '  S4:' );
    i4vec_print ( n4, c4, '  C4:' );
    error ( 'SANDIA_SGMGG_COEF_INC2 - Fatal error!' );
  end

  return
end

author: johannesgerer

repo: jburkardt-m