Example of Plotting Simulation Vector Data

Use this function to import power data and save the 'gvArrayData.mat' file. Once that file is created, future calls to GV can use the standard commands (i.e., gvr, gvRun, gv.Run).

function gvObj = gvImportDsPower
%% gvImportDsPower
% Purpose: This function imports power data from a DynaSim simulation to a gv
% hypercube, enabling each simulation point to hold the vector data of
% frequencies, instead of just a scalar, for each simulation. Importantly, one
% should keep the power data in a separate folder, so it is not imported with
% the rest of the data initially.
%
% Implementation:
%   1) Add a new axis to the gvArray hypercube for the vector data, in this case  
%      the frequencies for the fft power.
%   2) Import the power data and store in a separate gvArray hypercube.
%   3) Merge the gvArray hypercube objects
%
% Author: Erik Roberts, 2018

%% ---- Edit ----
powerFnName = 'gvCalcPower'; % the function used to calculate the power
powerResultsPath = fullfile(pwd, 'power_results'); % the path to the directory storing the power results
%% --------------

% make gv object
gvObj = gv();

% import ds data
gvObj.importDsData('saveBool', 0);

% grab the axis metadata which would be discarded
axismeta = gvObj.model.data.dsData.axis(1).axismeta;

%% power freq axis
% Add freq axis to original ds data in gv hypercube. All the scalar simulation
% data will be stored in the first value of the frequency axis. The rest of the
% frequencies will be empty for other data types besides power.

% add new axes for power
gvObj.model.data.dsData.axis(end+1) = gvArrayAxis;

% add axis info
gvObj.model.data.dsData.axis(end).name = 'freq';
gvObj.model.data.dsData.axis(end).values = 1;

%% power data
% import ds power data separately
powerResults = dsImportResults(powerResultsPath, 'import_scope','custom', 'func',powerFnName, 'as_cell',1);

nFreqs = length(powerResults{1});
freqs = 1:nFreqs;

%% sim IDS
analysisFnAxInd = contains(gvObj.model.data.dsData.axisNames, 'analysisFn');
simIDind = contains(gvObj.model.data.dsData.axis(1).values, 'simID');
simIDs = sliceAnyDim(gvObj.model.data.dsData.data, simIDind, analysisFnAxInd);

simIDs = cell2mat(simIDs);

simIDsSize = size(simIDs);
simIDsSize(end+1) = nFreqs; % for later reshape

simIDs = simIDs(:); % reshape to col vector

%% get axis names and vals
axis_names = gvObj.model.data.dsData.exportAxisNames;
axis_vals = gvObj.model.data.dsData.exportAxisVals;
axis_vals{1} = {'power'};
axis_vals{end} = freqs;

%% make new data of correct dims
data = zeros(length(simIDs), nFreqs); % col vector

nSims = length(powerResults);
for simID = 1:nSims
  if ~isempty(powerResults{simID})
    data(simIDs == simID, :) = powerResults{simID};
  else
    data(simIDs == simID, :) = nan;
  end
end

% reshape the data
data = reshape(data, simIDsSize);

%% add data to new gvArray obj
tempObj = gvArray;
tempObj = tempObj.importData(data, axis_vals, axis_names);

%% merge objects
gvObj.model.data.dsData.merge(tempObj);

%% add axis metadata
% this was droppedin merge

axismeta.dataType{end+1} = 'numeric';
gvObj.model.data.dsData.axis(1).axismeta = axismeta;

%% save gvArrayData
dynasimData = gvObj.model.data.dsData;
filePath = fullfile(pwd, 'gvArrayData.mat');
save(filePath, 'dynasimData') % save gvArray obj

%% run
gvObj.run();

end