+
−% CONSOLIDATE resamples all input AOs onto the same time grid.
+
−%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
−%
+
−% CONSOLIDATE resamples all input AOs onto the same time grid and truncates all
+
−%             time-series to start at the maximum start time of the inputs and end
+
−%             at the minimum stop time of the inputs.
+
−% 
+
−% ALGORITHM:
+
−%             1) Drop duplicate samples (ao/dropduplicates)
+
−%             2) Interpolate missing samples (ao/interpmissing)
+
−%             3) Fix uneven sample rate using interpolate (ao/fixfs)
+
−%             4) Resample to same fs, either max or specified (ao/resample
+
−%                or ao/interp depending on ratio of old and new sample
+
−%                rate)
+
−%             5) Truncate all vectors to minimum overlap of time-series
+
−%                (ao/split)
+
−%             6) Resample on to the same timing grid (ao/interp)
+
−%             7) Truncate all vectors to same number of samples to correct for 
+
−%                any rounding errors in previous steps (ao/select)
+
−%
+
−% CALL:       >> bs = consolidate(as)
+
−%
+
−% INPUTS:     as  - array of at least two time-series analysis objects
+
−%             pl  - parameter list (see below)
+
−%
+
−% OUTPUTS:    bs  - array of analysis objects, one for each input
+
−%
+
−% <a href="matlab:utils.helper.displayMethodInfo('ao', 'consolidate')">Parameters Description</a>
+
−%
+
−% VERSION:     $Id: consolidate.m,v 1.32 2011/04/08 08:56:13 hewitson Exp $
+
−%
+
−%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
−
+
−%           't'     - specify a new time vector to resample on to. This
+
−%                     will be truncated to fit within the maximum start
+
−%                     time and minimum stop time of the inputs.
+
−%      or
+
−
+
−function varargout = consolidate(varargin)
+
−
+
−  % Check if this is a call for parameters
+
−  if utils.helper.isinfocall(varargin{:})
+
−    varargout{1} = getInfo(varargin{3});
+
−    return
+
−  end
+
−
+
−  import utils.const.*
+
−  utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
+
−  
+
−  % Collect input variable names
+
−  in_names = cell(size(varargin));
+
−  for ii = 1:nargin,in_names{ii} = inputname(ii);end
+
−
+
−  % Collect all AOs and plists
+
−  [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
+
−  [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+
−
+
−  if numel(as) < 2
+
−    error('### Consolidate requires at least two time-series AOs to work.');
+
−  end
+
−  
+
−  if nargout == 0
+
−    error('### Consolidate cannot be used as a modifier. Please give an output variable.');
+
−  end
+
−  
+
−  % Decide on a deep copy or a modify
+
−  bs = copy(as, nargout);
+
−  na = numel(bs);
+
−
+
−  % Combine plists
+
−  pl = parse(pl, getDefaultPlist);
+
−
+
−  % Get only tsdata AOs
+
−  inhists = [];
+
−  for j=1:na
+
−    if ~isa(bs(j).data, 'tsdata')
+
−      bs(j) = [];
+
−      warning('!!! Skipping AO %s - it''s not a time-series AO.', bs(j).name);
+
−    else
+
−      % gather the input history objects
+
−      inhists = [inhists bs(j).hist];
+
−    end
+
−  end
+
−
+
−  % If fs is specified, use it. Otherwise, use max of all
+
−  % input AOs.
+
−  fs = find(pl, 'fs');
+
−  if isempty(fs)
+
−    % compute max fs
+
−    fs = 0;
+
−    for j=1:na
+
−      if bs(j).data.fs > fs
+
−        fs = bs(j).data.fs;
+
−      end
+
−    end
+
−  end
+
−  utils.helper.msg(msg.PROC2, 'resampling all time-series to an fs of %f', fs);
+
−  
+
−  %----------------- Drop all repeated samples
+
−  utils.helper.msg(msg.PROC1, 'drop duplicates');
+
−  for j=1:na
+
−    utils.helper.msg(msg.PROC2, 'processing %s', bs(j).name);
+
−    dropduplicates(bs(j),pl);
+
−  end
+
−
+
−  %----------------- Interpolate all missing samples
+
−  utils.helper.msg(msg.PROC1, 'interpolate missing samples');
+
−  for j=1:na
+
−    utils.helper.msg(msg.PROC2, 'processing %s', bs(j).name);    
+
−    interpmissing(bs(j),pl.pset('method', find(pl, 'interp_method')));
+
−  end
+
−
+
−  
+
−  %----------------- Fix uneven sampling
+
−  utils.helper.msg(msg.PROC1, 'fixing uneven sample rates');
+
−  for j=1:na
+
−    utils.helper.msg(msg.PROC2, 'processing %s', bs(j).name);
+
−    fixfs(bs(j),pl.pset('method', find(pl, 'fixfs_method')));
+
−  end
+
−  %----------------- Resample all vectors to same fs
+
−  utils.helper.msg(msg.PROC1, 'resample to same fs');
+
−
+
−  for j=1:na
+
−    % Check the resampling factor
+
−    [P,Q] = utils.math.intfact(fs,bs(j).data.fs);
+
−    if P > 100 || Q > 100
+
−      utils.helper.msg(msg.PROC2, 'resampling factor too high [%g/%g]. Trying interpolation', P, Q);
+
−      N  = length(bs(j).data.getX);
+
−      t  = linspace(0, (P*N/Q-1)/fs, P*N/Q);
+
−      interp(bs(j), plist('vertices', t));
+
−    else
+
−      resample(bs(j), plist('fsout', fs));
+
−    end
+
−  end
+
−
+
−  %---------------- Time properties of AOs
+
−  % Find max start time
+
−  start = 0;
+
−  for j=1:na
+
−    dstart = bs(j).data.t0.utc_epoch_milli/1000 + bs(j).data.getX(1);
+
−    if dstart > start
+
−      start = dstart;
+
−    end
+
−  end
+
−
+
−  % Find min stop time
+
−  stop = 1e20;
+
−  for j=1:na
+
−    dstop = floor(bs(j).data.t0.utc_epoch_milli/1000 + bs(j).data.getX(end));
+
−    if dstop < stop
+
−      stop = dstop;
+
−    end
+
−  end
+
−
+
−  %----------------- Truncate all vectors
+
−  utils.helper.msg(msg.PROC1, 'truncate all vectors');
+
−  utils.helper.msg(msg.PROC2, 'truncating vectors on interval [%.4f,%.4f]', start, stop);
+
−
+
−  % split each ao
+
−  bs = split(bs, plist('timespan', timespan(start, stop)));
+
−  
+
−  %----------------- Resample all vectors on to the same grid
+
−  utils.helper.msg(msg.PROC1, 'resample to same grid');
+
−  % compute new time grid
+
−  
+
−  % get the grid from the first AO
+
−  for j=1:na
+
−    toff = start - bs(j).t0.utc_epoch_milli/1000;
+
−    N = length(bs(j).data.getX);
+
−    t = linspace(toff, toff+(N-1)/fs, N);
+
−    interp(bs(j), plist('vertices', t));
+
−  end
+
−  
+
−  % Now ensure that we have the same data length
+
−  ns = realmax;
+
−  for jj=1:na
+
−    if len(bs(jj)) < ns
+
−      ns = len(bs(jj));
+
−    end
+
−  end
+
−  
+
−  bs = select(bs, 1:ns);
+
−  
+
−  nsecs = [];
+
−  for j=1:na
+
−    if isempty(nsecs)
+
−      nsecs = bs(j).data.nsecs;
+
−    end
+
−    if nsecs ~= bs(j).data.nsecs
+
−      error('### Something went wrong with the truncation. Vectors don''t span the same time period.');
+
−    end
+
−  end
+
−
+
−  %----------------- Set history on output AOs
+
−
+
−  for j=1:na
+
−    bs(j).name = sprintf('%s(%s)', mfilename, ao_invars{j});
+
−    bs(j).addHistory(getInfo('None'), pl, ao_invars(j), inhists(j));
+
−  end
+
−
+
−  % Set output
+
−  if nargout == numel(bs)
+
−    % List of outputs
+
−    for ii = 1:numel(bs)
+
−      varargout{ii} = bs(ii);
+
−    end
+
−  else
+
−    % Single output
+
−    varargout{1} = bs;
+
−  end
+
−end
+
−
+
−%--------------------------------------------------------------------------
+
−% Get Info Object
+
−%--------------------------------------------------------------------------
+
−function ii = getInfo(varargin)
+
−  if nargin == 1 && strcmpi(varargin{1}, 'None')
+
−    sets = {};
+
−    pl   = [];
+
−  else
+
−    sets = {'Default'};
+
−    pl   = getDefaultPlist;
+
−  end
+
−  % Build info object
+
−  ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: consolidate.m,v 1.32 2011/04/08 08:56:13 hewitson Exp $', sets, pl);
+
−  ii.setModifier(false);
+
−  ii.setArgsmin(2);
+
−end
+
−
+
−%--------------------------------------------------------------------------
+
−% Get Default Plist
+
−%--------------------------------------------------------------------------
+
−
+
−function plout = getDefaultPlist()
+
−  persistent pl;  
+
−  if exist('pl', 'var')==0 || isempty(pl)
+
−    pl = buildplist();
+
−  end
+
−  plout = pl;  
+
−end
+
−
+
−function pl_default = buildplist()
+
−  pl_default = combine(...
+
−    plist({'fs','The target sampling frequency for consolidate'}, paramValue.EMPTY_DOUBLE),...
+
−    plist({'interp_method', 'The method for the interpolation step'}, {2, {'nearest', 'linear', 'spline', 'cubic'}, paramValue.SINGLE}), ...
+
−    plist({'fixfs_method', 'The method for the fixfs step'}, {1, {'Time', 'Samples'}, paramValue.SINGLE}), ...
+
−    ao.getInfo('dropduplicates').plists,...
+
−    ao.getInfo('interpmissing').plists,...
+
−    ao.getInfo('fixfs').plists);
+
−end
+
−