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kouichi-c-nakamurakouichi-c-nakamura
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Merge remote-tracking branch 'upstream/master'
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‎.gitignore‎

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*.asv
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.DS_Store
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*.DS_Store
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*.m~

‎Browsing Functions/ObliquesSliceFigure.m‎

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‎Browsing Functions/aggregateAcr.m‎

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function acrOut = aggregateAcr(acrIn, varargin)
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% function acrOut = aggregateAcr(acrIn, varargin)
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%
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% Translate voxel acronyms into a useful list to split analysis by.
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%
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% e.g.:
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% >> acrIn = {'VISp5', 'VISp6a', 'MB', 'VISpm1', 'MB', 'LP', 'VISp5', 'LGd', 'VISp'};
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% >> acrOut = aggregateAcr(acrIn); acrOut
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% acrOut =
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% 1×9 cell array
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% {'VISp'} {'VISp'} {[NaN]} {'VISpm'} {[NaN]} {'LP'} {'VISp'} {'LGd'} {'VISp'}
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% Optional input argument aggType can be 1 for a short list of high-level
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% regions, or 2 [default] for a long list of sensible regions (manually
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% selected by N.A.S.). E.g.
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% >> acrOut = aggregateAcr(acrIn, 1); acrOut
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% acrOut =
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% 1×9 cell array
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% {'Isocortex'} {'Isocortex'} {'MB'} {'Isocortex'} {'MB'} {'TH'} {'Isocortex'} {'TH'} {'Isocortex'}
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% This function will return NaN for the acronyms that don't have a parent
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% in the list or that aren't valid acronyms. To find these in the output,
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% use:
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% >> cellfun(@(x)any(isnan(x)), acrOut)
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% If you are calling this function many times, you can speed up execution
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% by providing the tree-pair representation of the structure tree, which is
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% a bit slow to calculate. Provide this as the third input argument. You
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% can make it like this:
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% >> [~, tp] = makeSTtree(st);
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st = loadStructureTree();
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if nargin>2
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tp = varargin{2};
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else
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% see doc of this function to know what tp is
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[~, tp] = makeSTtree(st);
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end
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if nargin>1
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aggType = varargin{1};
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if isempty(aggType); aggType = 2; end
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else
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aggType = 2;
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end
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aggList = getAggList(aggType);
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aggTP = tp(ismember(tp(:,1), aggList),:);
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% aggTP now contains only children of desired list
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% since these lists are chosen such that every entry of structure tree only
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% has one (at most) parent on this list, the second column now contains
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% just one entry for each of our desired areas.
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% we also want exact matches so let's just add those in here.
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aggTP = [aggTP; horzcat(aggList', aggList', zeros(size(aggList))')];
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if ~iscell(acrIn) % can be either a single acronym string, or a cell array
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acrIn = {acrIn};
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end
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allAcr = st.acronym;
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allId = st.id;
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notAnAcr = ~ismember(acrIn, allAcr);
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if any(notAnAcr)
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firstBadAcr = acrIn{find(notAnAcr,1)};
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warning(...
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sprintf('One or more of your acronyms didn''t parse. One that didn''t is ''%s''.', ...
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firstBadAcr));
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end
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acrInClean = acrIn(~notAnAcr); % only process the good ones, the rest we set to NaN later
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idIn = cellfun(@(x)allId(strcmp(allAcr,x)), acrInClean);
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tpLoc = arrayfun(@(x)find(aggTP(:,2)==x), idIn, 'uni', false);
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noMatch = cellfun(@isempty, tpLoc);
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multipleMatch = cellfun(@(x)numel(x)>1, tpLoc);
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if any(multipleMatch)
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% -- todo -- report one that is bad
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warning('Some acronyms appear to be children of two or more parents in the list. This probably means your aggList is badly constructed. Will return the first match.');
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tpLoc = cellfun(@(x)x(1), tpLoc, 'uni', false);
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end
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% not throwing a warning here because this is probably common/normal, e.g.
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% voxels that are "MB" or "root" or other things.
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% These are returned as NaN.
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tpLocClean = tpLoc(~noMatch);
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tpLocClean = cell2mat(tpLocClean);
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aggParent = aggTP(tpLocClean,1);
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aggOut = arrayfun(@(x)allAcr{allId==x}, aggParent, 'uni', false);
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% now put in NaN's for the ones that didn't work
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acrOut1 = cell(size(tpLoc));
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acrOut1(noMatch) = {NaN};
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acrOut1(~noMatch) = aggOut;
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acrOut = cell(size(acrIn));
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acrOut(notAnAcr) = {NaN};
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acrOut(~notAnAcr) = acrOut1;
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function aggList = getAggList(aggType)
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switch aggType
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case 1 % 10 top-level divisions: {'Isocortex', 'HPF', 'OLF', 'CTXsp', 'CNU', 'TH', 'HY', 'MB', 'HB', 'CB'}
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aggList = [315 1089 698 703 623 549 1097 313 1065 512];
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case 2 % 316 manually selected regions
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aggList = [184 985 993 353 329 337 345 369 361 182305689 ...
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378 1057 677 1011 1002 1027 1018 402 394 ...
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409 385 425 533 312782574 312782628 39 48 972 44 723 ...
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731 738 746 104 111 119 894 879 886 312782546 417 ...
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541 922 895 507 151 159 597 605 814 961 619 639 647 ...
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788 566 382 423 463 726 982 19 918 926 843 1037 1084 ...
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502 484682470 589508447 484682508 583 952 966 131 295 ...
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319 780 672 56 998 754 250 258 266 310 333 23 292 536 ...
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1105 403 1022 1031 342 298 564 596 581 351 629 685 718 ...
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725 733 741 563807435 406 609 1044 475 170 218 1020 1029 ...
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325 560581551 255 127 64 1120 1113 155 59 362 366 1077 ...
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149 15 181 560581559 189 599 907 575 930 560581563 262 ...
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27 563807439 178 321 483 186 390 38 30 118 ...
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223 72 263 272 830 452 523 1109 126 133 347 286 338 ...
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576073699 689 88 210 491 525 557 515 980 1004 63 693 ...
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946 194 226 364 576073704 173 470 614 797 302 4 580 271 ...
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874 381 749 607344830 246 128 294 795 215 531 ...
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628 634 706 1061 549009203 616 214 35 549009211 975 115 ...
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606826663 757 231 66 75 58 374 1052 12 100 197 591 872 ...
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612 7 867 398 280 880 599626927 898 931 1093 318 534 574 ...
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621 549009215 549009219 549009223 549009227 679 147 162 ...
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604 146 238 350 358 207 96 101 711 1039 903 642 651 429 ...
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437 445 589508451 653 661 135 839 1048 372 83 136 106 ...
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203 235 307 395 852 859 938 177 169 995 1069 209 202 225 ...
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217 765 773 781 206 230 222 912 976 984 1091 936 944 951 ...
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957 968 1007 1056 1064 1025 1033 1041 1049 989 91 846 ...
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589508455];
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end

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