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comparison m-toolbox/classes/@ao/fixfs.m @ 0:f0afece42f48

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Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % FIXFS resamples the input time-series to have a fixed sample rate.
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % FIXFS resamples the input time-series to have a fixed sample rate.
5 %
6 % The new sampling grid is computed from the specified sample rate. If no
7 % sample rate is specified, the target is taken from a fit to the input tsdata
8 % object. The new sampling grid starts at the time returned from the fit
9 % (unless specified) and contains the same number of points or spans the
10 % same time as specified.
11 %
12 % CALL: bs = fixfs(a1,a2,a3,...,pl)
13 % bs = fixfs(as,pl)
14 % bs = as.fixfs(pl)
15 %
16 % INPUTS: aN - input analysis objects
17 % as - input analysis objects array
18 % pl - input parameter list
19 %
20 % OUTPUTS: bs - array of analysis objects, one for each input
21 %
22 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'fixfs')">Parameters Description</a>
23 %
24 % $Id: fixfs.m,v 1.40 2011/08/23 13:48:48 hewitson Exp $
25 %
26 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
27
28
29 function varargout = fixfs(varargin)
30
31 % Check if this is a call for parameters
32 if utils.helper.isinfocall(varargin{:})
33 varargout{1} = getInfo(varargin{3});
34 return
35 end
36
37 import utils.const.*
38 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
39
40 % Collect input variable names
41 in_names = cell(size(varargin));
42 for ii = 1:nargin,in_names{ii} = inputname(ii);end
43
44 % Collect all AOs and plists
45 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
46 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
47
48 % Decide on a deep copy or a modify
49 bs = copy(as, nargout);
50
51 % combine plists
52 pl = parse(pl, getDefaultPlist());
53
54
55 % Get fs
56 t0s = -1; % Please keep the -1. At the moment we don' use the t0.
57 fss = find(pl, 'fs');
58 method = find(pl, 'method');
59 interp = find(pl, 'interpolation');
60 alias = find(pl, 'filter');
61
62 if numel(fss) ~= 1 && numel(fss) < numel(as)
63 error('### Please specify either a no sample rate, a single sample rate, or one for each input time-series.');
64 end
65
66 % Get only tsdata AOs
67 for jj = 1:numel(bs)
68 if isa(bs(jj).data, 'tsdata')
69 % record input hist
70 hin = bs(jj).hist;
71 bs(jj).timeshift;
72 utils.helper.msg(msg.PROC1, 'fixing AO: %s', bs(jj).name);
73 %------------- Fit sample rate and t0
74 [ffs, ft0, unevenly] = tsdata.fitfs(bs(jj).data.getX);
75 %---------------- Get target sample rate
76 if numel(fss) > 1
77 fs = fss(jj);
78 else
79 fs = fss;
80 end
81 if fs < 0
82 utils.helper.msg(msg.PROC1, 'using sample rate from fit: %f', ffs);
83 fs = ffs;
84 end
85 %---------------- Get target start time
86 if numel(t0s) > 1
87 t0 = t0s(jj);
88 else
89 t0 = t0s;
90 end
91 if t0 < 0
92 utils.helper.msg(msg.PROC1, 'using start time from fit: %f', ft0);
93 t0 = ft0;
94 end
95 if unevenly % then the fitted t0 is empty so we need to get it from the first input datum
96 t0 = bs(jj).x(1);
97 end
98 %-------------- Compute new grid
99 switch lower(method)
100 case 'samples'
101 N = length(bs(jj).data.y);
102 t = linspace(t0, t0+(N-1)/fs, N);
103 case 'time'
104 Nsecs = bs(jj).data.nsecs;
105 t = t0 + [0:1/fs:Nsecs-1/fs].';
106 otherwise
107 error('### Unknown interpolation method. Do you want to preserve data duration or number of samples?');
108 end
109 %-------------- Antialiasing filter
110 switch lower(alias)
111 case 'iir'
112 utils.helper.msg(msg.PROC1, 'applying iir antialising filter');
113 pl = plist('type', 'lowpass',...
114 'order', 8,...
115 'fs', bs(jj).data.fs,...
116 'fc', 0.9*(fs/2));
117 f = miir(pl);
118 filtfilt(bs(jj),f);
119 case 'fir'
120 utils.helper.msg(msg.PROC1, 'applying fir antialising filter');
121 pl = plist('type', 'lowpass',...
122 'order', 64,...
123 'fs', bs(jj).data.fs,...
124 'fc', 0.9*(fs/2));
125 f = mfir(pl);
126 filter(bs(jj),f);
127 case 'off'
128 otherwise
129 error('### Unknown filtering method. Please choose: ''iir'', ''fir'' or ''off'' ');
130 end
131 %-------------- Interpolate
132 bs(jj).interp(plist('vertices', t, 'method', interp));
133 % Set name
134 bs(jj).name = sprintf('%s(%s)', mfilename, ao_invars{jj});
135 % Add history
136 bs(jj).addHistory(getInfo, pl, ao_invars(jj), hin);
137 % clear errors
138 bs(jj).clearErrors;
139 else
140 warning('!!! Skipping AO %s - it''s not a time-series AO.', ao_invars{jj});
141 bs(jj) = [];
142 end
143 end
144
145 % Set output
146 if nargout == numel(bs)
147 % List of outputs
148 for ii = 1:numel(bs)
149 varargout{ii} = bs(ii);
150 end
151 else
152 % Single output
153 varargout{1} = bs;
154 end
155 end
156
157 %--------------------------------------------------------------------------
158 % Get Info Object
159 %--------------------------------------------------------------------------
160 function ii = getInfo(varargin)
161 if nargin == 1 && strcmpi(varargin{1}, 'None')
162 sets = {};
163 pl = [];
164 else
165 sets = {'Default'};
166 pl = getDefaultPlist;
167 end
168 % Build info object
169 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: fixfs.m,v 1.40 2011/08/23 13:48:48 hewitson Exp $', sets, pl);
170 end
171
172 %--------------------------------------------------------------------------
173 % Get Default Plist
174 %--------------------------------------------------------------------------
175 function plout = getDefaultPlist()
176 persistent pl;
177 if exist('pl', 'var')==0 || isempty(pl)
178 pl = buildplist();
179 end
180 plout = pl;
181 end
182
183 function pl = buildplist()
184 pl = plist();
185
186 % Fs
187 p = param({'fs', 'The target sampling frequency.'}, {1, {-1}, paramValue.OPTIONAL});
188 pl.append(p);
189
190 % Method
191 p = param({'method','Choose if the new data should span the same time or preserve the number of samples (time/samples)'},...
192 {1, {'time', 'samples'}, paramValue.SINGLE});
193 pl.append(p);
194
195 % Filter
196 p = param({'filter','Specify options for the antialiasing filter.'},{3, {'iir', 'fir', 'off'}, paramValue.SINGLE});
197 pl.append(p);
198
199 % Interpolation
200 pli = ao.getInfo('interp').plists;
201 p = setKey(pli.params(pli.getIndexForKey('method')), 'interpolation');
202 pl.append(p);
203
204 end
205
206 % Parameter list:
207 % 'fs' - specify the target sample rate. Either a single value
208 % for all input time-series, or a vector of values, one
209 % for each input. To take a fitted value from the data,
210 % specify a sample rate of -1.
211 % e.g.: fs = [1 2 -1 4] to work on 4 input time-series
212 % [default: take from data]
213 %
214 % 'method' - Choose the behaviour
215 % 'Time' - new data span the same time [default]
216 % 'Samples' - new data preserves number of samples
217 %
218 % 'filter' - specify if antialising filter is applied
219 % 'off' - no filter applied [default]
220 % 'iir' - 8th order iir filter at fc = fs/2
221 % filter is applied forward and backward (filtfilt)
222 % 'fir' - 64th order fir filter at fc = fs/2
223 % filter is applied only forward
224 %
225 % 'interpolation' - specify interpolation method as for interp method
226 % 'nearest' -
227 % 'linear' -
228 % 'spline' - default
229 % 'cubic' -