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AlignSummary.m
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classdef AlignSummary
% this class stores aggregate statistics regarding event timing and is
% typically generated and stored within an AlignInfo instance. Its purpose
% is to store this information encapsulated from AlignInfo data.
% AlignSummary objects are generated by a pair of AlignDescriptor and
% ConditionDescriptor objects, because they generate a separate set of
% aggregate event occurrence statistics for each condition
properties(SetAccess=protected)
% AlignDescriptor object from which this was generated
alignDescriptor
% ConditionDescriptor object from which this was generated
conditionDescriptor
end
properties
timeUnitName = '';
end
properties(Dependent, SetAccess=protected)
minTimeDelta
nConditions
nMarks
nOccurrencesByMark
nIntervals
nOccurrencesByInterval
end
properties(SetAccess=protected)
nTrials = 0;
startAgg = EventAccumulator();
stopAgg = EventAccumulator();
markAgg = {}; % nMarks x 1 cell of nOccurrences x 1 EventAccumulators
intervalStartAgg = {}; % nIntervals x 1 cell of nOccurrences x 1 EventAccumulators
intervalStopAgg = {}; % nintervals x 1 cell of nOccurrences x 1 EventAccumulators
% nConditions x 1 numeric array indicating how many trials
% contribute to the statistics above (useful when aggregating)
nTrialsByCondition = 0;
startAggC = []; % nConditions x 1 EventAccumulators
stopAggC = []; % nConditions x 1 EventAccumulators
markAggC = {}; % nMarks x 1 cell of nConditions x nOccurrences EventAccumulators
intervalStartAggC = {} % nIntervals x 1 cell of nConditions x nOccurrences EventAccumulators
intervalStopAggC = {}; % nIntervals x 1 cell of nConditions x nOccurrences EventAccumulators
% these are build by initialize()
labelInfo
intervalInfo
end
properties(Dependent)
startMin
startMax
startMean
startMedian
startBins
startDist
stopMin
stopMax
stopMean
stopMedian
stopBins
stopDist
% nMark x 1 cell array with nOccurrences x 1 vectors
markMax
markMin
markMean
markMedian
% nInterval x 1 cell array with nOccurrences x 1 vectors
intervalStartMax
intervalStartMin
intervalStartMean
intervalStartMedian
% nInterval x 1 cell array of nOccurrences x 1 vectors
intervalStopMax
intervalStopMin
intervalStopMean
intervalStopMedian
% all times are relative to zero
% nConditions x 1 vector of earliest start times by condition
startMinByCondition
% nConditions x 1 vector of latest start times by condition
startMaxByCondition
% nConditions x 1 vector of mean start times by condition
startMeanByCondition
startMedianByCondition
% nConditions x 1 vector of earliest stop times by condition
stopMinByCondition
% nConditions x 1 vector of earliest stop times by condition
stopMaxByCondition
% nConditions x 1 vector of mean stop times by condition
stopMeanByCondition
stopMedianByCondition
% nMarks x 1 cell with nConditions x nOccurrences matrices of
% latest occurrence time(s) of that mark event (relative to the zero event)
markMaxByCondition
% nMarks x 1 cell with nConditions x nOccurrences matrices of
% earliest occurrence time(s) of that mark event (relative to the zero event)
markMinByCondition
% nMarks x 1 cell with nConditions x nOccurrences matrices of
% mean occurrence time(s) of that mark event (relative to the zero event)
markMeanByCondition
markMedianByCondition
% nIntervals x 1 cell with nConditions x nOccurrences matrices of
% latest occurrence time of the start of each occurrence of that interval
% (relative to the zero event)
intervalStartMaxByCondition
intervalStartMinByCondition
intervalStartMeanByCondition
intervalStartMedianByCondition
intervalStopMaxByCondition
intervalStopMinByCondition
intervalStopMeanByCondition
intervalStopMedianByCondition
end
methods
function as = AlignSummary()
end
end
methods % Simple dependent properties
function n = get.nConditions(as)
n = numel(as.nTrialsByCondition);
end
function v = get.minTimeDelta(as)
v = as.alignDescriptor.minTimeDelta;
end
function n = get.nMarks(as)
n = numel(as.markAgg);
end
function nvec = get.nOccurrencesByMark(as)
% markMean is nMark x 1 cell array with nOccurrences x 1 vectors
nvec = cellfun(@numel, as.markAgg);
end
function n = get.nIntervals(as)
n = numel(as.intervalStartAgg);
end
function nvec = get.nOccurrencesByInterval(as)
% intervalStartMean is nMark x 1 cell array with nOccualignSummaryAggregated{iAlign}rrences x 1 vectors
nvec = cellfun(@numel, as.intervalStartAgg);
end
function v = get.startMin(as)
v = as.startAgg.min;
end
function v = get.startMax(as)
v = as.startAgg.max;
end
function v = get.startMean(as)
v = as.startAgg.mean;
end
function v = get.startMedian(as)
v = as.startAgg.median;
end
function v = get.startBins(as)
v = as.startAgg.bins;
end
function v = get.startDist(as)
v = as.startAgg.counts;
end
function v = get.stopMin(as)
v = as.stopAgg.min;
end
function v = get.stopMax(as)
v = as.stopAgg.max;
end
function v = get.stopMean(as)
v = as.stopAgg.mean;
end
function v = get.stopMedian(as)
v = as.stopAgg.median;
end
function v = get.stopBins(as)
v = as.stopAgg.bins;
end
function v = get.stopDist(as)
v = as.stopAgg.counts;
end
function v = get.markMin(as)
v = cellfun(@EventAccumulator.min_array, as.markAgg, 'UniformOutput', false);
end
function v = get.markMax(as)
v = cellfun(@EventAccumulator.max_array, as.markAgg, 'UniformOutput', false);
end
function v = get.markMean(as)
v = cellfun(@EventAccumulator.mean_array, as.markAgg, 'UniformOutput', false);
end
function v = get.markMedian(as)
v = cellfun(@EventAccumulator.median_array, as.markAgg, 'UniformOutput', false);
end
function v = get.intervalStartMin(as)
v = cellfun(@EventAccumulator.min_array, as.intervalStartAgg, 'UniformOutput', false);
end
function v = get.intervalStartMax(as)
v = cellfun(@EventAccumulator.max_array, as.intervalStartAgg, 'UniformOutput', false);
end
function v = get.intervalStartMean(as)
v = cellfun(@EventAccumulator.mean_array, as.intervalStartAgg, 'UniformOutput', false);
end
function v = get.intervalStartMedian(as)
v = cellfun(@EventAccumulator.median_array, as.intervalStartAgg, 'UniformOutput', false);
end
function v = get.intervalStopMin(as)
v = cellfun(@EventAccumulator.min_array, as.intervalStopAgg, 'UniformOutput', false);
end
function v = get.intervalStopMax(as)
v = cellfun(@EventAccumulator.max_array, as.intervalStopAgg, 'UniformOutput', false);
end
function v = get.intervalStopMean(as)
v = cellfun(@EventAccumulator.mean_array, as.intervalStopAgg, 'UniformOutput', false);
end
function v = get.intervalStopMedian(as)
v = cellfun(@EventAccumulator.median_array, as.intervalStopAgg, 'UniformOutput', false);
end
% By Condition
function v = get.startMinByCondition(as)
v = EventAccumulator.min_array(as.startAggC);
end
function v = get.startMaxByCondition(as)
v = EventAccumulator.max_array(as.startAggC);
end
function v = get.startMeanByCondition(as)
v = EventAccumulator.mean_array(as.startAggC);
end
function v = get.startMedianByCondition(as)
v = EventAccumulator.median_array(as.startAggC);
end
function v = get.stopMinByCondition(as)
v = EventAccumulator.min_array(as.stopAggC);
end
function v = get.stopMaxByCondition(as)
v = EventAccumulator.max_array(as.stopAggC);
end
function v = get.stopMeanByCondition(as)
v = EventAccumulator.mean_array(as.stopAggC);
end
function v = get.stopMedianByCondition(as)
v = EventAccumulator.median_array(as.stopAggC);
end
function v = get.markMinByCondition(as)
v = cellfun(@EventAccumulator.min_array, as.markAggC, 'UniformOutput', false);
end
function v = get.markMaxByCondition(as)
v = cellfun(@EventAccumulator.max_array, as.markAggC, 'UniformOutput', false);
end
function v = get.markMeanByCondition(as)
v = cellfun(@EventAccumulator.mean_array, as.markAggC, 'UniformOutput', false);
end
function v = get.markMedianByCondition(as)
v = cellfun(@EventAccumulator.median_array, as.markAggC, 'UniformOutput', false);
end
function v = get.intervalStartMinByCondition(as)
v = cellfun(@EventAccumulator.min_array, as.intervalStartAggC, 'UniformOutput', false);
end
function v = get.intervalStartMaxByCondition(as)
v = cellfun(@EventAccumulator.max_array, as.intervalStartAggC, 'UniformOutput', false);
end
function v = get.intervalStartMeanByCondition(as)
v = cellfun(@EventAccumulator.mean_array, as.intervalStartAggC, 'UniformOutput', false);
end
function v = get.intervalStartMedianByCondition(as)
v = cellfun(@EventAccumulator.median_array, as.intervalStartAggC, 'UniformOutput', false);
end
function v = get.intervalStopMinByCondition(as)
v = cellfun(@EventAccumulator.min_array, as.intervalStopAggC, 'UniformOutput', false);
end
function v = get.intervalStopMaxByCondition(as)
v = cellfun(@EventAccumulator.max_array, as.intervalStopAggC, 'UniformOutput', false);
end
function v = get.intervalStopMeanByCondition(as)
v = cellfun(@EventAccumulator.mean_array, as.intervalStopAggC, 'UniformOutput', false);
end
function v = get.intervalStopMedianByCondition(as)
v = cellfun(@EventAccumulator.median_array, as.intervalStopAggC, 'UniformOutput', false);
end
end
% constructor methods and aggregation
methods(Static)
function as = buildEmptyFromConditionAlignDescriptor(conditionDescriptor, alignDescriptor, start, stop)
if nargin < 3
start = [];
end
if nargin < 4
stop = [];
end
as = AlignSummary();
as.alignDescriptor = alignDescriptor;
as.conditionDescriptor = conditionDescriptor;
delta = alignDescriptor.minTimeDelta;
as.nTrials = 0;
as.startAgg = EventAccumulator(start, delta);
as.stopAgg = EventAccumulator(stop, delta);
as.markAgg = cell(alignDescriptor.nMarks);
as.intervalStartAgg = cell(alignDescriptor.nIntervals);
as.intervalStopAgg = cell(alignDescriptor.nIntervals);
as.nTrialsByCondition = zeros(conditionDescriptor.nConditions, 1);
as.startAggC = EventAccumulator(start, delta);
as.stopAggC = EventAccumulator(start, delta);
for i = 1:conditionDescriptor.nConditions
as.startAggC(i, 1) = EventAccumulator(start, delta);
as.stopAggC(i, 1) = EventAccumulator(stop, delta);
end
as.markAggC = cell(alignDescriptor.nMarks);
as.intervalStartAggC = cell(alignDescriptor.nIntervals);
as.intervalStopAggC = cell(alignDescriptor.nIntervals);
end
% factory constructor method used by TDCA
function as = buildFromConditionAlignInfo(conditionInfo, alignInfo)
% build from a set of conditionInfo and alignInfo instances
as = AlignSummary();
as.alignDescriptor = AlignDescriptor.fromAlignDescriptor(alignInfo);
% fix value lists to their current values before converting to
% condition descriptor
% this withValid step is probably not necessary as TDCA usually coordinates conditionInfo.manualInvalid
% to account for trials marked invalid by the alignInfo, but just in case.
valid = alignInfo.valid & conditionInfo.valid;
conditionInfo = conditionInfo.fixAllValueLists('withValid', valid);
as.conditionDescriptor = ConditionDescriptor.fromConditionDescriptor(conditionInfo);
% request aligned, ungrouped data for start/stop/marks/intervals
[startData, stopData] = alignInfo.getStartStopRelativeToZeroByTrial();
markData = alignInfo.getAlignedMarkData(); % nMarks cell with nTrials x nOccurrences
[intervalStartData, intervalStopData] = alignInfo.getAlignedIntervalData(); % nIntervals cell with nTrials x nOccurrences
% total number of valid trials contributing
as.nTrials = nnz(alignInfo.valid);
% compute summary statistics for all trials
as.startAgg = EventAccumulator(startData, as.minTimeDelta);
as.stopAgg = EventAccumulator(stopData, as.minTimeDelta);
% compute summary statistics for EACH occurrence of EACH
% mark and interval event, nless markAggregateAllOccurrences is
% true)
as.markAgg = cellfun(@(d, aggAllOccur) EventAccumulator.constructForEachColumn(d, as.minTimeDelta, aggAllOccur), ...
markData, num2cell(alignInfo.markAggregateAllOccurrences), 'UniformOutput', false);
as.intervalStartAgg = cellfun(@(d) EventAccumulator.constructForEachColumn(d, as.minTimeDelta), intervalStartData, 'UniformOutput', false);
as.intervalStopAgg = cellfun(@(d) EventAccumulator.constructForEachColumn(d, as.minTimeDelta), intervalStopData, 'UniformOutput', false);
% group the data by condition into a flattened nConditions x 1
% array
as.nTrialsByCondition = cellfun(@numel, conditionInfo.listByCondition(:));
[startGrouped, stopGrouped] = conditionInfo.groupElementsFlattened(startData, stopData);
% compute summary statistics for each condition separately
temp = cellfun(@(d) EventAccumulator.constructForEachColumn(d, as.minTimeDelta), startGrouped, 'UniformOutput', false);
as.startAggC = cat(1, temp{:}); % only new versions of Matlab support cellfun uniform output with objects
temp = cellfun(@(d) EventAccumulator.constructForEachColumn(d, as.minTimeDelta), stopGrouped, 'UniformOutput', false);
as.stopAggC = cat(1, temp{:});
% aggregate within each condition for
as.markAggC = aggMultipleEventByCondition(markData, conditionInfo, alignInfo.markAggregateAllOccurrences);
as.intervalStartAggC = aggMultipleEventByCondition(intervalStartData, conditionInfo);
as.intervalStopAggC = aggMultipleEventByCondition(intervalStopData, conditionInfo);
as = as.initialize();
function agg = aggMultipleEventByCondition(dataCell, ci, aggregateAllOccurrences)
% dataCell is a nDistinctEvents (e.g. nMarks, nIntervals) cell,
% each of which is an nTrials x nOccurrences, to be grouped
% into conditions by ci
%
% agg is nDistinctEvents x 1 cell of nConditions x nOccurrences matrix
nEvents = numel(dataCell);
agg = cell(nEvents, 1);
if nargin < 3
aggregateAllOccurrences = false(size(dataCell));
end
if nEvents == 0
return;
end
for iE = 1:nEvents
dataGrouped = ci.groupElementsFlattened(dataCell{iE});
aggAllOccur = aggregateAllOccurrences(iE);
out = cellfun(@(d) EventAccumulator.constructForEachColumn(d, as.minTimeDelta, aggAllOccur)', ...
dataGrouped, 'UniformOutput', false); % nConditions x 1 cell of 1 x nOccurrences arrays
agg{iE} = cat(1, out{:}); % nConditions x nOccurrences
end
end
end
function as = buildByAggregation(alignSummarySet, varargin)
% build a new AlignSummary object by aggregating over multiple
% AlignSummary objects, shifting error ranges and means as
% appropriate
p = inputParser();
p.addParameter('aggregateMarks', true, @islogical);
p.addParameter('aggregateIntervals', true, @islogical);
p.addParameter('skipConditions', false, @islogical); % for internal use only, will not call initialize
p.parse(varargin{:});
if isempty(alignSummarySet)
as = [];
return;
end
if iscell(alignSummarySet)
set = makecol([alignSummarySet{:}]);
else
set = makecol(alignSummarySet);
end
set1 = set(1);
as = AlignSummary();
as.alignDescriptor = set1.alignDescriptor;
as.timeUnitName = set1.timeUnitName;
if ~p.Results.aggregateMarks
as.alignDescriptor = as.alignDescriptor.clearMarks();
end
if ~p.Results.aggregateIntervals
as.alignDescriptor = as.alignDescriptor.clearIntervals();
end
as.conditionDescriptor = set1.conditionDescriptor;
% TODO could check that all alignDescriptors and
% conditionDescriptors are equivalent here, but would be slow
aggNTrials = makecol([set.nTrials]); % used in nested functions below
% add up trial counts
as.nTrials = sum(aggNTrials);
as.startAgg = EventAccumulator.aggregate(set.startAgg);
as.stopAgg = EventAccumulator.aggregate(set.stopAgg);
if p.Results.aggregateMarks
as.markAgg = cell(set1.nMarks, 1);
for iM = 1:set1.nMarks
args = arrayfun(@(as) as.markAgg{iM}, set, 'UniformOutput', false);
as.markAgg{iM} = EventAccumulator.aggregate(args{:});
end
end
if p.Results.aggregateIntervals
[as.intervalStartAgg, as.intervalStopAgg] = deal(cell(set1.nIntervals, 1));
for iM = 1:set1.nIntervals
args = arrayfun(@(as) as.intervalStartAgg{iM}, set, 'UniformOutput', false);
as.intervalStartAgg{iM} = EventAccumulator.aggregate(args{:});
args = arrayfun(@(as) as.intervalStopAgg{iM}, set, 'UniformOutput', false);
as.intervalStopAgg{iM} = EventAccumulator.aggregate(args{:});
end
end
if ~p.Results.skipConditions
% nConditions x nSummary matrix of trial counts for doing
% proper re-weighting
nTrialsByConditionMat = cat(2, [set.nTrialsByCondition]);
% add up trial counts
as.nTrialsByCondition = sum(nTrialsByConditionMat, 2);
args = arrayfun(@(as) as.startAggC, set, 'UniformOutput', false);
as.startAggC = EventAccumulator.aggregate(args{:});
args = arrayfun(@(as) as.stopAggC, set, 'UniformOutput', false);
as.stopAggC = EventAccumulator.aggregate(args{:});
if p.Results.aggregateMarks
as.markAggC = cell(set1.nMarks, 1);
for iM = 1:set1.nMarks
args = arrayfun(@(as) as.markAggC{iM}, set, 'UniformOutput', false);
as.markAggC{iM} = EventAccumulator.aggregate(args{:});
end
end
if p.Results.aggregateIntervals
[as.intervalStartAggC, as.intervalStopAggC] = deal(cell(set1.nIntervals, 1));
for iM = 1:set1.nIntervals
args = arrayfun(@(as) as.intervalStartAggC{iM}, set, 'UniformOutput', false);
as.intervalStartAggC{iM} = EventAccumulator.aggregate(args{:});
args = arrayfun(@(as) as.intervalStopAggC{iM}, set, 'UniformOutput', false);
as.intervalStopAggC{iM} = EventAccumulator.aggregate(args{:});
end
end
as = as.initialize();
end
end
function as = aggregateByConcatenatingConditionsAlongNewAxis(alignSummarySet, newConditionDescriptor, varargin)
p = inputParser();
p.addParameter('aggregateMarks', true, @islogical);
p.addParameter('aggregateIntervals', true, @islogical);
p.parse(varargin{:});
% let aggregation handle the non-condition specific values
as = AlignSummary.buildByAggregation(alignSummarySet, p.Results);
minTimeDelta = max(arrayfun(@(as) as.minTimeDelta, alignSummarySet));
if iscell(alignSummarySet)
set = makecol([alignSummarySet{:}]);
else
set = makecol(alignSummarySet);
end
% update the condtion descriptor with the new version
as.conditionDescriptor = newConditionDescriptor;
args = arrayfun(@(as) as.nTrialsByCondition, set, 'UniformOutput', false);
as.nTrialsByCondition = cat(1, args{:});
args = arrayfun(@(as) as.startAggC, set, 'UniformOutput', false);
as.startAggC = cat(1, args{:});
args = arrayfun(@(as) as.stopAggC, set, 'UniformOutput', false);
as.stopAggC = cat(1, args{:});
if p.Results.aggregateMarks
as.markAggC = cell(as.nMarks, 1);
for i = 1:as.nMarks
args = arrayfun(@(as) as.markAggC{i}, set, 'UniformOutput', false);
as.markAggC{i} = vertcatPadCols(args{:});
end
end
if p.Results.aggregateIntervals
[as.intervalStartAggC, as.intervalStopAggC] = deal(cell(as.nIntervals, 1));
for i = 1:as.nIntervals
args = arrayfun(@(as) as.intervalStartAggC{i}, set, 'UniformOutput', false);
as.intervalStartAggC{i} = vertcatPadCols(args{:});
args = arrayfun(@(as) as.intervalStopAggC{i}, set, 'UniformOutput', false);
as.intervalStopAggC{i} = vertcatPadCols(args{:});
end
end
as = as.initialize();
function out = vertcatPadCols(varargin)
nC = cellfun(@(x) size(x, 2), varargin);
newC = max(nC);
for j = 1:numel(varargin)
varargin{j} = cat(2, varargin{j}, repmat(EventAccumulator([], minTimeDelta), size(varargin{j}, 1), newC - nC(j)));
end
out = cat(1, varargin{:});
end
end
function as = aggregateByConcatenatingConditionsAlongExistingAxis(alignSummarySet, newConditionDescriptor, catAxis, varargin)
p = inputParser();
p.addParameter('aggregateMarks', true, @islogical);
p.addParameter('aggregateIntervals', true, @islogical);
p.parse(varargin{:});
% let aggregation handle the non-condition specific values
as = AlignSummary.buildByAggregation(alignSummarySet, p.Results);
if iscell(alignSummarySet)
set = makecol([alignSummarySet{:}]);
else
set = makecol(alignSummarySet);
end
origConditionSizes = arrayfun(@(as) as.conditionDescriptor.conditionsSize, alignSummarySet, 'UniformOutput', false);
% update the condtion descriptor with the new version
as.conditionDescriptor = newConditionDescriptor;
args = arrayfun(@(as) as.nTrialsByCondition, set, 'UniformOutput', false);
as.nTrialsByCondition = catAlong(args{:});
args = arrayfun(@(as) as.startAggC, set, 'UniformOutput', false);
as.startAggC = catAlong(args{:});
args = arrayfun(@(as) as.stopAggC, set, 'UniformOutput', false);
as.stopAggC = catAlong(args{:});
if p.Results.aggregateMarks
as.markAggC = cell(as.nMarks, 1);
for i = 1:as.nMarks
args = arrayfun(@(as) as.markAggC{i}, set, 'UniformOutput', false);
as.markAggC{i} = catAlongPadCols(args{:});
end
end
if p.Results.aggregateIntervals
[as.intervalStartAggC, as.intervalStopAggC] = deal(cell(as.nIntervals, 1));
for i = 1:as.nIntervals
args = arrayfun(@(as) as.intervalStartAggC{i}, set, 'UniformOutput', false);
as.intervalStartAggC{i} = catAlongPadCols(args{:});
args = arrayfun(@(as) as.intervalStopAggC{i}, set, 'UniformOutput', false);
as.intervalStopAggC{i} = catAlongPadCols(args{:});
end
end
as = as.initialize();
function out = catAlong(varargin)
for j = 1:numel(varargin)
varargin{j} = reshape(varargin{j}, origConditionSizes{j});
end
out = cat(catAxis, varargin{:});
out = out(:);
end
function out = catAlongPadCols(varargin)
% pad out each arg to the max number of columns (for marks,
% intervals that have different nOccurrences)
nC = cellfun(@(x) size(x, 2), varargin);
newC = max(nC);
for j = 1:numel(varargin)
varargin{j} = cat(2, varargin{j}, repmat(EventAccumulator([], minTimeDelta), size(varargin{j}, 1), newC - nC(j)));
end
for j = 1:numel(varargin)
varargin{j} = reshape(varargin{j}, origConditionSizes{j});
end
out = cat(catAxis, varargin{:});
out = out(:);
end
end
% function as = buildForSelectedConditions(aso, cmask)
% % build a new AlignSummary object by including only selected
% % conditions
% error('Not fully implemented');
%
% as = AlignSummary();
% as.alignDescriptor = aso.alignDescriptor;
% as.conditionDescriptor = aso.conditionDescriptor;
%
% % mask all of the byConditions directly by cmask
% as.nTrialsByCondition = aso.nTrialsByCondition(cmask);
% as.startMinByCondition = aso.startMinByCondition(cmask);
% as.startMaxByCondition = aso.startMaxByCondition(cmask);
% as.startMeanByCondition = as.startMeanByCondition(cmask);
% as.stopMinByCondition = as.stopMinByCondition(cmask);
% as.stopMaxByCondition = as.stopMaxByCondition(cmask);
% as.stopMeanByCondition = as.stopMeanByCondition(cmask);
%
% applyCmaskFn = @(in) cellfun(@(e) e(cmask, :);
% as.markMaxByCondition = as.markMaxByCondition(cmask);
% as.markMinByCondition = as.markMinByCondition(cmask);
% as.markMeanByCondition = as.markMeanByCondition(cmask);
% as.intervalStartMaxByCondition = as.intervalStartMaxByCondition(cmask);
% as.intervalStartMinByCondition = as.intervalStartMinByCondition(cmask);
% as.intervalStartMeanByCondition = as.intervalStartMeanByCondition(cmask);
% as.intervalStopMaxByCondition = as.intervalStopMaxByCondition(cmask);
% as.intervalStopMinByCondition = as.intervalStopMinByCondition(cmask);
% as.intervalStopMeanByCondition = as.intervalStopMeanByCondition(cmask);
%
% % aggregate the selected conditions into the primary statistics
% aggNTrials = as.nTrialsByCondition; % used in nested functions below
% as.nTrials = sum(as.nTrialsByCondition);
%
% [as.startMin, as.startMax, as.startMean] = ...
% aggregateSingleEventStats(as.startMinByCondition, as.startMaxByCondition, as.startMeanByCondition, aggNTrials);
% [as.stopMin, as.stopMax, as.stopMean] = ...
% aggregateSingleEventStats(as.stopMinByCondition, as.stopMaxByCondition, as.stopMeanByCondition, aggNTrials);
%
% % aggregate
% % markMaxByCondition is nMarks x 1 of nConditions x nOccurrences,
% % cat(1, as.markMaxByCondition{:})' is nOccurrences x nConditions
% % nSummary cell arrays. Contents are nOccurrences x 1 vectors
% [as.markMax, as.markMin, as.markMean] = aggregateMultipleEventStats(...
% cat(2, set.markMax), cat(2, set.markMin), ...
% cat(2, set.markMean), aggNTrials);
%
% [as.intervalStartMax, as.intervalStartMin, as.intervalStartMean] = aggregateMultipleEventStats(...
% cat(2, set.intervalStartMax), cat(2, set.intervalStartMin), ...
% cat(2, set.intervalStartMean), aggNTrials);
%
% [as.intervalStopMax, as.intervalStopMin, as.intervalStopMean] = aggregateMultipleEventStats(...
% cat(2, set.intervalStopMax), cat(2, set.intervalStopMin), ...
% cat(2, set.intervalStopMean), aggNTrials);
%
% as = as.initialize();
%
% function [maxNew, minNew, meanNew] = aggregateSingleEventStats(...
% maxData, minData, meanData, nTrialsData)
% minNew = nanmin(minData);
% maxNew = nanmax(maxData);
% meanNew = nansum(makecol(meanData) .* makecol(nTrialsData)) / sum(nTrialsData);
% end
%
% function [maxNew, minNew, meanNew] = aggregateMultipleEventStats(...
% maxData, minData, meanData, nTrialsData)
% % max/min/meanData are nDistinctEvents x nSummary cells of
% % nOccurrences vectors.
% %
% % nTrialsData is nSummary x 1 vector
% %
% % max/min/meanNew are nDistinctEvents cells of nOccurrences
% % vectors
% nDistinctEvents = size(maxData, 1);
% nSummary = size(maxData, 2);
% nOccurrences = cellfun(@numel, maxData);
% maxOccurrencesByEvent = max(nOccurrences, [], 2);
%
% [maxNew, minNew, meanNew] = deal(cell(nDistinctEvents, 1));
%
% for iEv = 1:nDistinctEvents
% padToLength = @(vec) [makecol(vec); ...
% nan(maxOccurrencesByEvent(iEv)-numel(vec), 1)];
% % *Mat is maxOccurrencesByEvent x nSummary
% maxMat = cell2mat(cellfun(padToLength, maxData(iEv, :), ...
% 'UniformOutput', false));
% minMat = cell2mat(cellfun(padToLength, minData(iEv, :), ...
% 'UniformOutput', false));
% meanMat = cell2mat(cellfun(padToLength, meanData(iEv, :), ...
% 'UniformOutput', false));
%
% % cell elements are maxOccurrencesByEvent x 1 vectors
% maxNew{iEv} = nanmax(maxMat, [], 2);
% minNew{iEv} = nanmin(minMat, [], 2);
%
% % note that we use nanmean to compute the weighted sum
% % because nansum returns 0 when all are NaN
% meanNew{iEv} = nanmean(bsxfun(@times, meanMat, makerow(nTrialsData)), 2) / ...
% sum(nTrialsData / nSummary);
% end
% end
% end
end
% manipulations of AlignSummary that yield new AlignSummary objects
methods
function as = combineSetsOfConditions(as, newConditionDescriptor, conditionIdxCell)
as.warnIfNoArgOut(nargout);
% recombine conditions, for each entry of conditionIdxCell,
% collect stats across the condition indices inside
as.conditionDescriptor = newConditionDescriptor;
function out = aggregateRows(eamat)
nRowsNew = numel(conditionIdxCell);
nCols = size(eamat, 2);
out(nRowsNew, nCols) = EventAccumulator();
for iRNew = 1:nRowsNew
for iC = 1:nCols
% need to grab the appropriate rows
args = num2cell(eamat(conditionIdxCell{iRNew}, iC));
out(iRNew, iC) = EventAccumulator.aggregate(args{:});
end
end
end
as.startAggC = aggregateRows(as.startAggC);
as.stopAggC = aggregateRows(as.stopAggC);
as.markAggC = cellfun(@aggregateRows, as.markAggC, 'UniformOutput', false);
as.intervalStartAggC = cellfun(@aggregateRows, as.intervalStartAggC, 'UniformOutput', false);
as.intervalStopAggC = cellfun(@aggregateRows, as.intervalStopAggC, 'UniformOutput', false);
% must do this last as above calculations depend on nTrials
% field
as.nTrialsByCondition = cellfun(@(idx) sum(as.nTrialsByCondition(idx)), conditionIdxCell);
as = as.initialize();
end
function as = setConditionDescriptor(as, newConditionDescriptor)
% replace condition descriptor with one with the same number of
% conditions
as.warnIfNoArgOut(nargout);
assert(as.conditionDescriptor.nConditions == newConditionDescriptor.nConditions, 'Number of conditions do not match');
% recombine conditions, for each entry of conditionIdxCell,
% collect stats across the condition indices inside
as.conditionDescriptor = newConditionDescriptor;
end
% function as = manualSliceTimeWindow(as, tMin, tMax)
% % reslice a window of time out of this align summary, used by
% % pset's method by the same name
%
% function v = constrainToWindow(v)
% if v < tMin
% v = tMin;
% elseif v > tMax
% v = tMax;
% end
% end
%
% constrainToWindowCell = @(c) cellfun(@(v) filterWindow, c, 'UniformOutput', false);
% as.startMin = filterWindow(as.startMin);
% as.startMax = filterWindow(as.startMax);
% as.startMean = filterWindow(as.startMean);
%
% as.stopMin = filterWindow(as.stopMin);
% as.stopMax = filterWindow(as.stopMax);
% as.stopMean = filterWindow(as.stopMean);
%
% % nMark x 1 cell array with nOccurrences x 1 vectors
% as.markMax = filterWindowCell(as.markMax);
% as.markMin = filterWindowCell(as.markMin);
% as.markMean = filterWindowCell(as.markMean);
%
% % nInterval x 1 cell array with nOccurrences x 1 vectors
% as.intervalStartMax = filterWindowCell(as.intervalStartMax);
% as.intervalStartMin = filterWindowCell(as.intervalStartMin);
% as.intervalStartMean = filterWindowCell(as.intervalStartMean);
%
% % nInterval x 1 cell array of nOccurrences x 1 vectors
% as.intervalStopMax = filterWindowCell(as.intervalStopMax);
% as.intervalStopMin = filterWindowCell(as.intervalStopMin);
% as.intervalStopMean = filterWindowCell(as.intervalStopMean);
%
% function v = nanOutsideWindow(v)
% v(v < tMin | v > tMax) = NaN;
% end
%
% function v = nanOutsideWindowCell(c, nOccurrencesKeepCell)
% for i = 1:numel(c)
% v = c{i};
% v(v < tMin | v > tMax) = NaN;
% c{i} = v(:, 1:nOccurrencesKeepCell{i});
% end
% end
%
% % nConditions x 1 vector of earliest start times by condition
% as.startMinByCondition = nanOutsideWindow(as.startMinByCondition);
% as.startMaxByCondition = nanOutsideWindow(as.startMaxByCondition);
% as.startMeanByCondition = nanOutsideWindow(as.startMeanByCondition);
% as.stopMinByCondition = nanOutsideWindow(as.stopMinByCondition);
% as.stopMaxByCondition = nanOutsideWindow(as.stopMaxByCondition);
% as.stopMeanByCondition = nanOutsideWindow(as.stopMeanByCondition);
%
% % nMarks x 1 cell with nConditions x nOccurrences matrices
% % the nOccurrences dependent properties will automatically
% % update due to the filtering operations above, so we'll
% as.markMaxByCondition = nanOutsideWindowCell(as.markMaxByCondition, as.nOccurrencesByMark);
% as.markMinByCondition = nanOutsideWindowCell(as.markMinByCondition);
% as.markMeanByCondition = nanOutsideWindowCell(markMeanByCondition,
%
% % nIntervals x 1 cell with nConditions x nOccurrences matrices of
% % latest occurrence time of the start of each occurrence of that interval
% % (relative to the zero event)
% intervalStartMaxByCondition
% intervalStartMinByCondition
% intervalStartMeanByCondition
% intervalStopMaxByCondition
% intervalStopMinByCondition
% intervalStopMeanByCondition
% end
end
methods(Access=protected) % Builds internal properties at construction time
function as = initialize(as)
assert(nargout == 1);
as = as.buildLabelInfo();
as = as.buildIntervalInfo();
end
function as = buildLabelInfo(as, varargin)
% build a list of events to mark on the time axis or on data by
% aggregating across the start, stop, zero, and marks.
% each mark will be expanded so that one label will be
% generated for each mark
assert(nargout == 1);
% optionally provide time window which filters which labels will be included
p = inputParser();
p.addParameter('timeWindow', [], @isvector);
p.parse(varargin{:});
timeWindow = p.Results.timeWindow;
ad = as.alignDescriptor;
nConditions = as.conditionDescriptor.nConditions;
info = struct('name', {}, 'time', {}, 'min', {}, 'max', {}, 'appear', {}, 'fixed', {});
counter = 1;
% include the zero event provided that it lies within the start/stop window
if isequal(ad.zeroMark, true) || (ad.nMarks == 0 && ad.nIntervals == 0)
info(counter).name = ad.zeroLabel;
info(counter).nameShort = ad.zeroLabelShort;
info(counter).dist = [];
info(counter).bins = [];
info(counter).time = 0;
info(counter).min = 0;
info(counter).max = 0;
info(counter).timeByCondition = zeros(nConditions, 1);
info(counter).minByCondition = zeros(nConditions, 1);
info(counter).maxByCondition = zeros(nConditions, 1);
info(counter).appear = ad.zeroAppear;
info(counter).fixed = true;
info(counter).occurrence = 1;
counter = counter + 1;
end
% label each of the event marks that are fixed with respect to the zero event
markMin = as.markMin;
markMax = as.markMax;
markMedian = as.markMedian;
markMinByCondition = as.markMedianByCondition;
markMaxByCondition = as.markMaxByCondition;
markMedianByCondition = as.markMedianByCondition;
for iMark = 1:ad.nMarks
if ~ad.markShowOnAxis(iMark), continue; end
% loop over each occurrence of the mark
for iOccur = 1:as.nOccurrencesByMark(iMark)
info(counter).name = ad.markLabels{iMark};
info(counter).nameShort = ad.markLabelsShort{iMark};
info(counter).dist = as.markAgg{iMark}(iOccur).counts;
info(counter).bins = as.markAgg{iMark}(iOccur).bins;
info(counter).time = markMedian{iMark}(iOccur);
info(counter).min = markMin{iMark}(iOccur);
info(counter).max = markMax{iMark}(iOccur);
if ~isempty(markMedianByCondition) && ~isempty(markMedianByCondition{iMark})
info(counter).timeByCondition = markMedianByCondition{iMark}(:, iOccur);
info(counter).minByCondition = markMinByCondition{iMark}(:, iOccur);
info(counter).maxByCondition = markMaxByCondition{iMark}(:, iOccur);
else
info(counter).timeByCondition = [];
info(counter).minByCondition = [];
info(counter).maxByCondition = [];
end
info(counter).appear = ad.markAppear{iMark};
info(counter).fixed = ad.isMarkFixedTime(iMark);
info(counter).occurrence = iOccur;
counter = counter + 1;
end
end
if ~isempty(timeWindow)
times = [info.time];
timeMask = times >= timeWindow(1) & times <= timeWindow(2);
info = info(timeMask);
end
for i = 1:numel(info)
if isempty(info(i).appear)
info(i).appear = AppearanceSpec();
end
end
as.labelInfo = makecol(info);
end
function as = buildIntervalInfo(as, varargin)
assert(nargout == 1);
ad = as.alignDescriptor;
info = struct('name', {}, 'startTime', {}, 'startMin', {}, 'startMax', {}, 'appear', {}, 'fixed', {});
counter = 1;
intervalStartMin = as.intervalStartMin;
intervalStartMax = as.intervalStartMax;
intervalStartMedian = as.intervalStartMedian;
intervalStopMin = as.intervalStopMin;
intervalStopMax = as.intervalStopMax;
intervalStopMedian = as.intervalStopMedian;
intervalStartMinByCondition = as.intervalStartMinByCondition;
intervalStartMaxByCondition = as.intervalStartMaxByCondition;
intervalStartMedianByCondition = as.intervalStartMedianByCondition;
intervalStopMinByCondition = as.intervalStopMinByCondition;
intervalStopMaxByCondition = as.intervalStopMaxByCondition;