FILTER overides the filter function for analysis objects. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DESCRIPTION: FILTER overides the filter function for analysis objects. Applies the input digital IIR/FIR filter to the input analysis object. If the input analysis object contains a time-series (tsdata) then the filter is applied using the normal recursion algorithm. The output analysis object contains a tsdata object. If the input analysis object contains a frequency-series (fsdata) then the response of the filter is computed and then multiplied with the input frequency series. The output analysis object contains a frequency series. CALL: >> [b, filt] = filter(a,pl) >> [b, filt] = filter(a,filt,pl) >> b = filter(a,pl) INPUTS: pl - a parameter list a - input analysis object OUTPUTS: filt - a copy of the input filter object with the history values filled in. b - output analysis object containing the filtered data. PARAMETERS: filter - the filter object to use to filter the data VERSION: $Id: filter.m,v 1.20 2007/11/02 12:26:24 ingo Exp $ The following call returns a parameter list object that contains the default parameter values: >> pl = filter(ao, 'Params') HISTORY: 11-02-07 M Hewitson Creation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0001 function varargout = filter(varargin) 0002 % FILTER overides the filter function for analysis objects. 0003 % 0004 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0005 % 0006 % DESCRIPTION: FILTER overides the filter function for analysis objects. 0007 % Applies the input digital IIR/FIR filter to the input analysis 0008 % object. If the input analysis object contains a 0009 % time-series (tsdata) then the filter is applied using the normal 0010 % recursion algorithm. The output analysis object contains a tsdata 0011 % object. 0012 % 0013 % If the input analysis object contains a frequency-series (fsdata) 0014 % then the response of the filter is computed and then multiplied 0015 % with the input frequency series. The output analysis object 0016 % contains a frequency series. 0017 % 0018 % CALL: >> [b, filt] = filter(a,pl) 0019 % >> [b, filt] = filter(a,filt,pl) 0020 % >> b = filter(a,pl) 0021 % 0022 % INPUTS: pl - a parameter list 0023 % a - input analysis object 0024 % 0025 % OUTPUTS: filt - a copy of the input filter object with the 0026 % history values filled in. 0027 % b - output analysis object containing the filtered data. 0028 % 0029 % PARAMETERS: filter - the filter object to use to filter the data 0030 % 0031 % VERSION: $Id: filter.m,v 1.20 2007/11/02 12:26:24 ingo Exp $ 0032 % 0033 % The following call returns a parameter list object that contains the 0034 % default parameter values: 0035 % 0036 % >> pl = filter(ao, 'Params') 0037 % 0038 % HISTORY: 11-02-07 M Hewitson 0039 % Creation 0040 % 0041 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0042 0043 % Standard history variable 0044 ALGONAME = mfilename; 0045 VERSION = '$Id: filter.m,v 1.20 2007/11/02 12:26:24 ingo Exp $'; 0046 0047 %% Check if this is a call for parameters 0048 if nargin == 2 0049 if isa(varargin{1}, 'ao') && ischar(varargin{2}) 0050 in = char(varargin{2}); 0051 if strcmp(in, 'Params') 0052 varargout{1} = getDefaultPL(); 0053 return 0054 elseif strcmp(in, 'Version') 0055 varargout{1} = VERSION; 0056 return 0057 end 0058 end 0059 end 0060 0061 %% capture input variable names 0062 invars = {}; 0063 as = []; 0064 ps = []; 0065 fobj = []; 0066 % Initialise output 0067 bs = []; 0068 0069 for j=1:nargin 0070 if isa(varargin{j}, 'ao') 0071 invars = [invars cellstr(inputname(j))]; 0072 end 0073 if isa(varargin{j}, 'ao') 0074 as = [as varargin{j}]; 0075 end 0076 if isa(varargin{j}, 'plist') 0077 ps = [ps varargin{j}]; 0078 end 0079 if isa(varargin{j}, 'miir') || isa(varargin{j}, 'mfir') 0080 fobj = varargin{j}; 0081 end 0082 end 0083 0084 if isa(ps, 'plist') 0085 pl = combine(ps); 0086 else 0087 pl = plist(); 0088 end 0089 if isempty(fobj) 0090 fobj = find(pl, 'filter'); 0091 end 0092 0093 % check inputs 0094 if ~isa(fobj, 'miir') && ~isa(fobj, 'mfir') 0095 error('### the filter input should be an miir/mfir object.'); 0096 end 0097 0098 for j=1:numel(as) 0099 0100 % get input data 0101 a = as(j); 0102 d = a.data; 0103 0104 %--------- Time-series filter 0105 % 0106 if isa(d, 'tsdata') 0107 0108 % get input data 0109 fs = d.fs; 0110 0111 if isa(fobj, 'mfir') 0112 0113 % apply filter 0114 disp('* filtering with FIR filter...'); 0115 G = get(fobj, 'gain'); 0116 0117 % [fstruct, y] = ltpda_firfilter(struct(filt), d.x); 0118 coeffs = get(fobj, 'a'); 0119 Zi = get(fobj, 'histout'); 0120 [y, Zf] = filter(coeffs, 1, G.*d.x, Zi); 0121 0122 % remove group delay 0123 if isempty(find(pl, 'gdoff')) 0124 gd = floor(get(fobj, 'gd')); 0125 t = d.t(1:end-gd); 0126 y = y(1+gd:end); 0127 else 0128 t = d.t; 0129 end 0130 0131 % consolodate this structure with mfir class before converting. 0132 fobj = set(fobj, 'histout', Zf.'); 0133 0134 else %if isa(fobj, 'miir') 0135 0136 if fs ~= get(fobj, 'fs') 0137 warning('!!! Filter is designed for a different sample rate of data.'); 0138 % Adjust/redesign if this is a standard filter 0139 fobj = redesign(fobj, fs); 0140 end 0141 0142 % apply filter 0143 % [fstruct, y] = ltpda_iirfilter(struct(fobj), d.x, length(d.x)); 0144 0145 acoeffs = get(fobj, 'a'); 0146 bcoeffs = get(fobj, 'b'); 0147 Zi = get(fobj, 'histout'); 0148 [y, Zf] = filter(acoeffs, bcoeffs, d.x, Zi); 0149 t = d.t; 0150 % consolodate this structure with miir class before converting. 0151 % fobj = set(fobj, 'histin', fstruct.histin); 0152 % fobj = set(fobj, 'histout', fstruct.histout); 0153 fobj = set(fobj, 'histout', Zf); 0154 end 0155 0156 %----- Create output analysis object 0157 % make a new tsdata object 0158 ts = tsdata(t, y); 0159 ts = set(ts, 'name', sprintf('filter %s with %s', d.name, get(fobj,'name'))); 0160 ts = set(ts, 'xunits', d.xunits); 0161 ts = set(ts, 'yunits', d.yunits); 0162 ts = set(ts, 't0', d.t0); 0163 0164 % make a new history object 0165 pl = plist(); 0166 pl = append(pl, param('filter', fobj)); 0167 h = history(ALGONAME, VERSION, pl, a.hist); 0168 h = set(h, 'invars', invars); 0169 0170 % make output analysis object 0171 b = ao(ts, h); 0172 % name for this object 0173 if isempty(invars{1}) 0174 n1 = a.name; 0175 else 0176 n1 = invars{1}; 0177 end 0178 b = setnh(b, 'name', sprintf('%s(%s)', get(fobj,'name'), n1)); 0179 bs = [bs b]; 0180 0181 %--------- Frequency-series filter 0182 % 0183 elseif isa(d, 'fsdata') 0184 error('### I don''t work yet. Please code me up.'); 0185 0186 else 0187 error('### unknown data type.'); 0188 end 0189 end 0190 0191 % Reshape the ouput to the same size of the input 0192 bs = reshape(bs, size(as)); 0193 0194 if nargout == 1 0195 varargout{1} = bs; 0196 elseif nargout == 2 0197 varargout{1} = bs; 0198 varargout{2} = fobj; 0199 else 0200 error('### wrong number of output arguments.'); 0201 end 0202 0203 %% Get default params 0204 function pl_default = getDefaultPL() 0205 0206 pl_default = plist(param('filter', '')); 0207 pl_default = append(pl_default, 'gdoff' ,[]);