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view m-toolbox/classes/@ao/diff.m @ 17:7afc99ec5f04 database-connection-manager
Update ao_model_retrieve_in_timespan
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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% DIFF differentiates the data in AO. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: DIFF differentiates the data in AO. The result is a data % series the same length as the input series. % In case of method 'diff' computes the difference between two samples, in which % case the resulting time object has the length of the input % series -1 sample. % CALL: bs = diff(a1,a2,a3,...,pl) % bs = diff(as,pl) % bs = as.diff(pl) % % INPUTS: aN - input analysis objects % as - input analysis objects array % pl - input parameter list % % OUTPUTS: bs - array of analysis objects, one for each input, % containing the differentiated data % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'diff')">Parameters Description</a> % % REFERENCES: % [1] L. Ferraioli, M. Hueller and S. Vitale, Discrete derivative % estimation in LISA Pathfinder data reduction, % <a % href="matlab:web('http://www.iop.org/EJ/abstract/0264-9381/26/9/094013/','-browser')">Class. Quantum Grav. 26 (2009) 094013.</a> % [2] L. Ferraioli, M. Hueller and S. Vitale, Discrete derivative % estimation in LISA Pathfinder data reduction % <a href="matlab:web('http://arxiv.org/abs/0903.0324v1','-browser')">http://arxiv.org/abs/0903.0324v1</a> % % VERSION: $Id: diff.m,v 1.36 2011/08/03 19:18:56 adrien Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % PARAMETERS: method - the method to use: [default: '3POINT'] % 'diff' - like MATLABs diff % compute difference between each two samples % xaxis will be integers from 1 to length % of resulting object % '2POINT' - 2 point derivative computed as % [y(i+1)-y(i)]./[x(i+1)-x(i)]. % '3POINT' - 3 point derivative. Compute derivative % at i as [y(i+1)-y(i-1)] / [x(i+1)-x(i-1)]. % For i==1, the output is computed as % [y(2)-y(1)]/[x(2)-x(1)]. The last sample % is computed as [y(N)-y(N-1)]/[x(N)-x(N-1)]. % '5POINT' - 5 point derivative. Compute derivative dx % at i as % [-y(i+2)+8*y(i+1)-8*y(i-1)+y(i-2)] / % [3*(x(i+2)-x(i-2))]. % For i==1, the output is computed as % [y(2)-y(1)]/[x(2)-x(1)]. The last sample % is computed as [y(N)-y(N-1)]/[x(N)-x(N-1)]. % 'ORDER2' - Compute derivative using a 2nd order % method. % 'ORDER2SMOOTH' - Compute derivative using a 2nd order % method with a parabolic fit to 5 % consecutive samples. % 'filter' - applies an IIR filter built from a % single pole at the chosen frequency. The % filter is applied forwards and backwards % (filtfilt) to achieve the desired f^2 % response. This only works for time-series % AOs. For this method, you can specify the % pole frequency with an additional parameter % 'f0' [default: 1/Nsecs] % 'FPS' - Calculates five points derivative using % utils.math.fpsder function. If you call % with this oprtion you may add also the % parameters: % 'ORDER' derivative order, supperted % values are: % 'ZERO', 'FIRST', 'SECOND' % 'COEFF' coefficient used for the % derivation. Refers to the fpsder help % for further details. % % function varargout = diff(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 = utils.helper.collect_objects(varargin(:), 'plist', in_names); % Decide on a deep copy or a modify bs = copy(as, nargout); % combine plists pl = parse(pl, getDefaultPlist()); % Extract method method = find(pl, 'method'); for jj = 1:numel(bs) % Diff can't work for cdata objects since we need x data if isa(bs(jj).data, 'cdata') error('### diff doesn''t work with cdata AOs since we need an x-data vector.'); end % Compute derivative with selected method switch lower(method) case 'diff' yunit = bs(jj).yunits; y = bs(jj).y; x = bs(jj).x; newX = x(1:end-1); % cut the last sample from the time series to make x and y same length dy = diff(y); bs(jj).data.setY(dy); bs(jj).data.setX(newX); bs(jj).setYunits(yunit); case '2point' x = bs(jj).data.getX; dx = diff(x); y = bs(jj).data.getY; dy = diff(y); z = dy./dx; bs(jj).data.setY(z); bs(jj).data.setX((x(1:end-1)+x(2:end))/2); bs(jj).data.setYunits(bs(jj).data.yunits./bs(jj).data.xunits); case '3point' x = bs(jj).data.getX; dx = diff(x); y = bs(jj).data.getY; z = zeros(size(y)); z(2:end-1) = (y(3:end)-y(1:end-2)) ./ (dx(2:end)+dx(1:end-1)); z(1) = (y(2)-y(1)) ./ (dx(1)); z(end) = 2*z(end-1)-z(end-2); bs(jj).data.setY(z); bs(jj).data.setYunits(bs(jj).data.yunits./bs(jj).data.xunits); case '5point' x = bs(jj).data.getX; dx = diff(x); y = bs(jj).data.getY; z = zeros(size(y)); z(1) = (y(2)-y(1)) ./ (dx(1)); z(2) = (y(3)-y(1))./(dx(2)+dx(1)); z(3:end-2) = (-y(5:end) + 8.*y(4:end-1) - 8.*y(2:end-3) + y(1:end-4)) ./ (3.*(x(5:end)-x(1:end-4))); z(end-1) = 2*z(end-2)-z(end-3); z(end) = 2*z(end-1)-z(end-2); bs(jj).data.setY(z); bs(jj).data.setYunits(bs(jj).data.yunits./bs(jj).data.xunits); case 'order2' x = bs(jj).data.getX; dx = diff(x); y = bs(jj).data.getY; z = zeros(size(y)); m = length(y); % y'(x1) z(1) = (1/dx(1)+1/dx(2))*(y(2)-y(1))+... dx(1)/(dx(1)*dx(2)+dx(2)^2)*(y(1)-y(3)); % y'(xm) z(m) = (1/dx(m-2)+1/dx(m-1))*(y(m)-y(m-1))+... dx(m-1)/(dx(m-1)*dx(m-2)+dx(m-2)^2)*(y(m-2)-y(m)); % y'(xi) (i>1 & i<m) dx1 = repmat(dx(1:m-2),1,1); dx2 = repmat(dx(2:m-1),1,1); y1 = y(1:m-2); y2 = y(2:m-1); y3 = y(3:m); z(2:m-1) = 1./(dx1.*dx2.*(dx1+dx2)).*... (-dx2.^2.*y1+(dx2.^2-dx1.^2).*y2+dx1.^2.*y3); bs(jj).data.setY(z); bs(jj).data.setYunits(bs(jj).data.yunits./bs(jj).data.xunits); case 'order2smooth' x = bs(jj).data.getX; y = bs(jj).data.getY; dx = diff(x); m = length(y); if max(abs(diff(dx)))>sqrt(eps(max(abs(dx)))) error('### The x-step must be constant for method ''ORDER2SMOOTH''') elseif m<5 error('### Length of y must be at least 5 for method ''ORDER2SMOOTH''.') end h = mean(dx); z = zeros(size(y)); % y'(x1) z(1) = sum(y(1:5).*[-54; 13; 40; 27; -26])/70/h; % y'(x2) z(2) = sum(y(1:5).*[-34; 3; 20; 17; -6])/70/h; % y'(x{m-1}) z(m-1) = sum(y(end-4:end).*[6; -17; -20; -3; 34])/70/h; % y'(xm) z(m) = sum(y(end-4:end).*[26; -27; -40; -13; 54])/70/h; % y'(xi) (i>2 & i<(N-1)) Dc = [2 1 0 -1 -2]; tmp = convn(Dc,y)/10/h; z(3:m-2) = tmp(5:m); bs(jj).data.setY(z); bs(jj).data.setYunits(bs(jj).data.yunits./bs(jj).data.xunits); case 'filter' error('### Comming with release 2.5'); case 'fps' order = find(pl, 'ORDER'); coeff = find(pl, 'COEFF'); x = bs(jj).data.getX; dx = x(2)-x(1); fs = 1/dx; y = bs(jj).data.getY; params = struct('ORDER', order, 'COEFF', coeff, 'FS', fs); z = utils.math.fpsder(y, params); bs(jj).data.setY(z); % setting units switch lower(order) case 'first' bs(jj).data.setYunits(bs(jj).data.yunits./bs(jj).data.xunits); case 'second' bs(jj).data.setYunits(bs(jj).data.yunits.*bs(jj).data.xunits.^(-2)); end otherwise error('### Unknown method for computing the derivative.'); end % name for this object bs(jj).name = sprintf('diff(%s)', ao_invars{jj}); % add history bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), bs(jj).hist); end % Clear the errors since they don't make sense anymore clearErrors(bs); % 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: diff.m,v 1.36 2011/08/03 19:18:56 adrien Exp $', sets, pl); end %-------------------------------------------------------------------------- % Get Default Plist %-------------------------------------------------------------------------- function plout = getDefaultPlist() persistent pl; if exist('pl', 'var')==0 || isempty(pl) pl = buildplist(); end plout = pl; end function pl = buildplist() pl = plist(); % Method p = param({'method',['The method to use. Choose between:<ul>', ... '' ... '<li>''2POINT'' - 2 point derivative computed as [y(i+1)-y(i)]./[x(i+1)-x(i)]', ... '</li>' ... '<li>''3POINT'' - 3 point derivative. Compute derivative dx at i as <br>', ... '<tt>[y(i+1)-y(i-1)] / [x(i+1)-x(i-1)]</tt><br>', ... 'For <tt>i==1</tt>, the output is computed as <tt>[y(2)-y(1)]/[x(2)-x(1)]</tt>.<br>', ... 'The last sample is computed as <tt>[y(N)-y(N-1)]/[x(N)-x(N-1)]</tt>', ... '</li>' ... '<li>''5POINT'' - 5 point derivative. Compute derivative dx at i as <br>', ... '<tt>[-y(i+2)+8*y(i+1)-8*y(i-1)+y(i-2)] / [3*(x(i+2)-x(i-2))]</tt><br>', ... 'For <tt>i==1</tt>, the output is computed as <tt>[y(2)-y(1)]/[x(2)-x(1)]</tt><br>', ... 'The last sample is computed as <tt>[y(N)-y(N-1)]/[x(N)-x(N-1)]</tt>', ... '</li>' ... '<li>''ORDER2'' - Compute derivative using a 2nd order method', ... '</li>' ... '<li>''ORDER2SMOOTH'' - Compute derivative using a 2nd order method<br>', ... 'with a parabolic fit to 5 consecutive samples', ... '</li>' ... '<li>''filter'' - applies an IIR filter built from a single pole at the chosen frequency.<br>', ... 'The filter is applied forwards and backwards (filtfilt) to achieve the desired f^2<br>', ... 'response. This only works for time-series AOs.<br>', ... 'For this method, you can specify the pole frequency with an additional parameter ''F0'' (see below):', ... '</li>'... '<li>''FPS'' - Calculates five points derivative using utils.math.fpsder.<br>', ... 'When calling with this option you may add also the parameters ''ORDER'' (see below)<br>', ... 'and ''COEFF'' (see below)' ... '</li>' ... ]}, {1, {'2POINT', '3POINT', '5POINT', 'ORDER2', 'ORDER2SMOOTH', 'FILTER', 'FPS'}, paramValue.SINGLE}); pl.append(p); % F0 p = param({'f0','The pole frequency for the ''filter'' method.'}, {1, {'1/Nsecs'}, paramValue.OPTIONAL}); pl.append(p); % Order p = param({'ORDER','Derivative order'}, {1, {'ZERO', 'FIRST', 'SECOND'}, paramValue.SINGLE}); pl.append(p); % Coeff p = param({'COEFF',['Coefficient used for the derivation. <br>', ... 'Refer to the <a href="matlab:doc(''utils.math.fpsder'')">fpsder help</a> for further details']}, paramValue.EMPTY_DOUBLE); pl.append(p); end