| [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter2(SP, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter)
|
function [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter2(SP, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter)
% % Nth_oct_time_filter2: Calculates the Nth octave center frequencies, sound levels, peak levels, and time records
% %
% % Syntax:
% %
% % [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter2(SP, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter);
% %
% % **********************************************************************
% %
% % Description
% %
% % This program applies Nth octave band filters to the input time record.
% % The program outputs the center frequency bands, the time average rms
% % values, the peak values, and band filtered time records for each
% % Nth octave band respectively.
% %
% % Nth_octdsgn computes the filter coefficients using a 3rd order
% % butterworth filter for an Nth octave band filter according to
% % ANSI S1.11.
% %
% % To optimize filter stability, this program uses iterative downsampling
% % to make the sampling rate reasonable before applying the third octave
% % Butterworth filters.
% %
% % There are two options for the downsampling filters to optimize
% % performance for continuous signals or for impulsive signals.
% % For continuous noise the time domain does not have significant
% % impulses; however, for impulsive time records there are often very
% % large impulses with distinctive peaks.
% %
% % There are two antialiasing filters and interpolation schemes available.
% % The first program is the built-in Matlab "resample" progam which
% % uses a Kaiser window fir filter for antialising and uses an unknown
% % interpolation method. The second program available for downsampling
% % is bessel_down_sample which uses a Bessel filter for antialiasing
% % and uses interp with the cubic spline option for interpolation.
% %
% % The resample function has good antialising up to the Nyquist frequency;
% % however, it has significant ringing effect when there are impulses.
% % The bessel_down_sample function has good antialising; however, there is
% % excessive attenuation near the Nyquist frequency.
% % The bessel_down_sample function experiences no ringing due to impulses
% % so it is very useful for peak estimation.
% %
% %
% % To avoid phase shift, the filtfilt Matlab program can
% % be used to implement the one-Nth octave filters.
% %
% %
% % The input and output variables are described in more detail in the
% % sections below respectively.
% %
% % **********************************************************************
% %
% % Input Variables
% %
% % SP=randn(10, 50000);
% % % (Pa) is the time record of the sound pressure
% % % default is SP=rand(1, 50000);
% %
% % Fs=50000; % (Hz) is the sampling rate of the time record.
% % % default is Fs=50000; Hz.
% %
% % num_x_filter=2; % This is the number of times the time record
% % % should be filtered.
% % % default is num_x_filter=2;
% %
% % N=3; % is the number of frequency bands per octave.
% % % Can be any number > 0.
% % % Default is 3 for third octave bands.
% %
% % fc=[200, 250]; % (Hz) is an array of center frequencies for the third
% % % octave band filters
% % %
% % % if empty the third octave bands from 20 to 20000 Hz
% % % are used
% % %
% % % default is fc = [ 20, 25, 31.5, 40, 50, 63, ...
% % % 80, 100, 125, 160, 200, 250, 315, 400, 500, ...
% % % 630, 800, 1000, 1250, 1600, 2000, 2500, 3150, ...
% % % 4000, 5000, 6300, 8000, 10000, ...
% % % 12500, 16000, 20000];
% %
% % sensor=1; % Constant integer input for selecting the sensor type
% % % 1 is for acoustic microphone Pref=20E-6 (Pa)
% % %
% % % 2 is for accelerometer output is in same
% % % units as the input (m/s^2)
% % %
% % % 3 generic sensor multiply by 1: output is in same
% % % units as the input
% % %
% % % default is sensor=1; For a microphone
% %
% % settling_time=0.1; % (seconds) Time requiered for the filter to settle
% % % usually 0.1 seconds or less.
% % % This quantity is usually frequency dependent.
% %
% % filter_program=1; % 1 is for using the filter progam otherwise the
% % % filtfilt program is used.
% % % filter.m runs faster and may settle
% % % more quickly.
% % % filtfilt.m is used to remove phase shift.
% % % default is filter_program=1 using filter progam.
% %
% % resample_filter=1; % type of filter to use when resampling
% % % 1 resample function Kaiser window fir filter
% % % 2 Bessel filter
% % % otherwise resample function Kaiser window fir
% % % filter
% % % default is resample_filter=1; (Kaiser window)
% %
% %
% % **********************************************************************
% %
% % Output Variables
% %
% % fc_out % (Hz) array of center frequencies
% %
% % SP_levels % (units) sound pressure or other sensor metric for
% % % each channel and each frequency band
% %
% % SP_peak_levels % (units) Maximum of the absolute value of the peak
% % % values for each frequency band
% %
% % SP_bands % Time record for each mic channel and for each
% % % frequency band after filtering
% %
% %
% % **********************************************************************
% %
%
% Example='1';
%
% SP=randn(50000, 1); % SP is the data variables in linear units such as
% % (Pa)
%
% Fs=50000; % (Hz) Sampling rate
%
% num_x_filter=2; % Number of times to filter the data. Minimum
% % value is 1. Typically a value of 2 to 10 at low
% % frequencies (Fc < 100), num_x_filter=10 has a
% % significant phase shift.
%
% N=3; % Number of bands per octave.
%
% fc=[]; % (Hz) Center frequency of the third-octave band
%
% sensor=1; % acoustic microphone
% % output is in dB
%
% settling_time=1; % (seconds) Time requiered for the filter to settle
% % usually 0.1 seconds or less.
% % This quantity is usually frequency dependent.
%
% filter_program=1; % 1 is for using the filter progam otherwise the
% % filtfilt program is used.
% % default is filter_program=1 using filter progam.
%
% resample_filter=1;
%
% [fc_out, SP_levels, SP_peak_levels, SP_bands]=Nth_oct_time_filter2(SP, Fs, num_x_filter, N, fc, sensor, settling_time, filter_program, resample_filter);
%
% %
%
% Example='2';
%
% % Compare the spectra of white noise, pink noise, and brown noise.
% %
%
% x1 = spatialPattern([1,500000],0); % white noise has a linearly
% % increasing spectrum
%
% x2 = spatialPattern([1,500000],-1); % pink noise has a constant
% % spectrum
%
% x3 = spatialPattern([1,500000],-2); % brown noise has a linearly
% % increasing spectra
%
% Fs=50000; % (Hz) Sampling rate
%
% num_x_filter=2; % Number of times to filter the data.
%
% N=3; % number of bands per octave.
%
% min_f=20; % is the minimum frequency band to calculate (Hz).
% max_f=20000; % is the maximum frequency band to calculate (Hz).
%
% [fc] = nth_freq_band(N, min_f, max_f);
%
% sensor=1;
%
% settling_time=1;
%
% filter_program=1;
%
% resample_filter=1;
%
% [fc_out1, SP_levels1]=Nth_oct_time_filter(x1, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
% [fc_out2, SP_levels2]=Nth_oct_time_filter(x2, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
% [fc_out3, SP_levels3]=Nth_oct_time_filter(x3, Fs, num_x_filter, N, min_f, max_f, sensor, settling_time, filter_program, resample_filter);
%
% % Plot the results
% figure(1);
% semilogx(fc_out1, SP_levels1, 'color', [1 1 1], 'linewidth', 2, 'marker', 's', 'MarkerSize', 8);
% hold on;
% semilogx(fc_out2, SP_levels2, 'color', [1 0.6 0.784], 'linewidth', 2, 'linestyle', '--', 'marker', 'o', 'MarkerSize', 8);
% semilogx(fc_out3, SP_levels3, 'color', [0.682 0.467 0], 'linewidth', 2, 'linestyle', ':', 'marker', 'x', 'MarkerSize', 12);
% set(gca, 'color', 0.7*[1 1 1]);
% legend({'White Noise', 'Pink Noise', 'Brown Noise'}, 'location', 'SouthEast');
% xlabel('Frequency Hz', 'Fontsize', 28);
% ylabel('Sound Pressure Level (dB ref. 20 \mu Pa)', 'Fontsize', 28);
% title('Classical Third Octave Band Spectra', 'Fontsize', 40);
% set(gca, 'Fontsize', 20);
%
% % **********************************************************************
% %
% % References
% %
% % 1) ANSI S1.11-1986 American National Stadard Specification for
% % Octave-Band and Fractional-Octave-Band Analog
% % and Digital Filters.
% %
% %
% % **********************************************************************
% %
% % Subprograms
% %
% % This program requires the Matlab Signal Processing Toolbox
% % This program is based on the Octave Toolbox by Christophe Couvreur
% % Matlab Central File Exchange ID 69
% %
% %
% %
% % List of Dependent Subprograms for
% % Nth_oct_time_filter2
% %
% % FEX ID# is the File ID on the Matlab Central File Exchange
% %
% %
% % Program Name Author FEX ID#
% % 1) bessel_antialias Edward L. Zechmann
% % 2) bessel_digital Edward L. Zechmann
% % 3) bessel_down_sample Edward L. Zechmann
% % 4) convert_double Edward L. Zechmann
% % 5) filter_settling_data3 Edward L. Zechmann
% % 6) geospace Edward L. Zechmann
% % 7) LMSloc Alexandros Leontitsis 801
% % 8) match_height_and_slopes2 Edward L. Zechmann
% % 9) moving Aslak Grinsted 8251
% % 10) Nth_octdsgn Edward L. Zechmann
% % 11) remove_filter_settling_data Edward L. Zechmann
% % 12) resample_interp3 Edward L. Zechmann
% % 13) rms_val Edward L. Zechmann
% % 14) sub_mean Edward L. Zechmann
% %
% %
% %
% % **********************************************************************
% %
% % Program was written by Edward L. Zechmann
% %
% % date 7 December 2008 Copied content of Nth_oct_time_filter
% % and modified code to use fc as an input
% % variable.
% %
% % modified 8 December 2008 Updated Comments
% %
% % modified 10 December 2008 Updated Comments
% %
% % modified 16 December 2008 Generlaized program to Nth Octave Bands
% %
% % modified 22 December 2008 Updated Comments. Finished Upgrade
% %
% % modified 5 January 2008 Added sub_mean to remove running
% % average using a time constant at one-
% % half the lowest center frequency.
% %
% % modified 26 March 2009 Fixed a bug in initilizing SPtrunc2
% % Fixed a bug in initializing num_pts2
% %
% % modified 6 October 2009 Updated comments
% %
% % modified 31 December 2009 Added warnings for frequency
% % resolution issues.
% %
% % modified 2 July 2010 Modified filter settling data program
% % Updated the filter settling data
% % removal. Updated Comments
% %
% % modified 4 August 2010 Updated Comments
% %
% % modified 9 January 2012 Type cast the ouput Bc and Ac of
% % Nth_octdsg to double precision.
% %
% % modified 15 March 2012 Changed the range_size to 8 and the
% % downsample_factor to 20 to improve
% % accuaracy of low frueqnecies when
% % processing short time records.
% %
% %
% % **********************************************************************
% %
% % Please feel free to modify this code.
% %
% % See Also: Nth_oct_time_filter, octave, resample, filter, filtfilt
% %
% % See also: Octave Toolbox, resample, filter_settling_data3
% %
% % Octave toolbox on Matlab Central File Exchange ID 69
% % Author Christophe Couvreur, Faculte Polytechnique de Mons (Belgium)
% % couvreur@thor.fpms.ac.beare
% %
if (nargin < 1 || isempty(SP)) || ~isnumeric(SP)
SP=rand(1, 50000);
end
% Make the data have the correct data type and size
[SP]=convert_double(SP);
[num_pts, num_mics]=size(SP);
if num_mics > num_pts
SP=SP';
[num_pts, num_mics]=size(SP);
end
SP=convert_double(SP);
if (nargin < 2 || isempty(Fs)) || ~isnumeric(Fs)
Fs=50000;
end
if (nargin < 3 || isempty(num_x_filter)) || ~isnumeric(num_x_filter)
num_x_filter=2;
end
if (nargin < 4 || (isempty(N)) || ~isnumeric(N))
N=20;
end
if (nargin < 5 || isempty(fc)) || ~isnumeric(fc)
fc = [ 20, 25, 31.5, 40, 50, 63, 80, 100, 125, 160, ...
200, 250, 315, 400, 500, 630, 800, 1000, 1250, 1600, ...
2000, 2500, 3150, 4000, 5000, 6300, 8000, 10000, ...
12500, 16000, 20000];
end
if (nargin < 6 || isempty(sensor)) || ~isnumeric(sensor)
sensor=1;
end
if (nargin < 7 || isempty(settling_time)) || ~isnumeric(settling_time)
settling_time=0.1;
end
if (nargin < 8 || (isempty(filter_program)) || ~isnumeric(filter_program))
filter_program=1;
end
if (nargin < 9 || isempty(resample_filter)) || ~isnumeric(resample_filter)
resample_filter=1;
end
if ~isequal(resample_filter, 2)
resample_filter=1;
end
% Remove the running average from the signal.
% The time constant should be less than half the lowest frequency to
% resolve.
[SP]=sub_mean(SP, Fs, 0);
[num_pts, num_mics]=size(SP);
if num_mics > num_pts
SP=SP';
[num_pts, num_mics]=size(SP);
end
% By hard coding, a 3rd order butterworth filter is used.
n=3;
% Make sure center frequencies are positive.
fc=fc(fc > 0);
% sort the center frequencies to categorize the center frequncies into
% ranges for resampling.
[fc ix]=sort(fc);
fc_out=fc;
% count the frequency bands
num_bands=length(fc);
% srr is the sampling rate ratio
srr=Fs./fc;
% initialize the regime to ones.
regime=zeros(num_bands, 1);
% Using a small range size keeps
% increases the likelihood that the filters will be stable.
% In testing, using range_size=2, 4, or 8 resulted in nearly identical
% sound pressure levels; however, range_size=2 is more likely to result in
% a stable filter.
%
% Further testing shows that the resampling techniques employed in this
% program do not work well with short time records.
%
% So a range_size of 8 works better with short time records.
%
upsample_factor=5;
if isequal(resample_filter, 1)
% resample function works great at small and large range sizes.
range_size=8;
downsample_factor=20;
else
% Bessel filters have too much attenuation at high frequencies so use a
% larger range size; however, the filters may be less stable.
range_size=8;
downsample_factor=20;
end
%
% For range_size=2; and downsample_factor=5; the regime follows the pattern
%
% srr range Ouput type rf value regime number
% srr <= 2 output zeros 1
% 2 < srr <= 5 upsample rf=4 2
% 5 < srr <= 10 do nothing rf=1 3
% 10 < srr <= 20 downsample rf=1/2 4
% 20 < srr <= 40 downsample rf=1/4 5
% 40 < srr <= 80 downsample rf=1/8 6
% ...
% ... iterative downsampling until the frequency
% ... resolution limit is exceeded
% ...
% fc <= 4/T output zeros 0
%
% For range_size=4; and downsample_factor=5; the regime follows the pattern
%
% srr range Ouput type rf value regime number
% srr <= 2 output zeros 1
% 2 < srr <= 5 upsample rf=4 2
% 5 < srr <= 20 do nothing rf=1 3
% 20 < srr <= 80 downsample rf=1/4 4
% 80 < srr <= 320 downsample rf=1/16 5
% 320 < srr <= 1280 downsample rf=1/64 6
% ...
% ... iterative downsampling until the frequency
% ... resolution limit is exceeded
% ...
% fc <= 4/T output zeros 0
%
% For range_size=8; and downsample_factor=5; the regime follows the pattern
%
% srr range Ouput type rf value regime number
% srr <= 2 output zeros 1
% 2 < srr <= 5 upsample rf=4 2
% 5 < srr <= 40 do nothing rf=1 3
% 40 < srr <= 320 downsample rf=1/8 4
% 320 < srr <= 2560 downsample rf=1/64 5
% 2560 < srr <= 20480 downsample rf=1/512 6
% ...
% ... iterative downsampling until the frequency
% ... resolution limit is exceeded
% ...
% fc <= 4/T output zeros 0
% Determine the regime for each frequency band
%
for e1=1:num_bands;
if srr(e1) <= 2
% Output Zeros since Center Frequency exceeds the Nyquist Frequency
regime(e1)=1;
warning(['Center frequency above Nyquist frequency: Outputting zeros for fc = ' num2str(fc(e1)), ' Hz.']);
elseif fc(e1) <= 4*Fs/(num_pts)
% Output Zeros since Center Frequency is below the lower
% frequency resolution limit
regime(e1)=0;
warning(['Length of data too short to resolve center frequency: Outputting zeros for fc = ', num2str(fc(e1)), ' Hz.']);
elseif (2 < srr(e1)) && logical(srr(e1) <= upsample_factor)
% The signal is upsampled
regime(e1)=2;
elseif logical(srr(e1) > downsample_factor)
% The signal is downsampled
regime(e1)=ceil(log(srr(e1)/downsample_factor)./log(range_size))+2;
else
regime(e1)=3;
end
end
% set the reference sensor value
switch sensor
case 1
% reference sound pressure
Pref=20*10^(-6); % Pa
case 2
% reference acceleration
Pref=1; % m/s^2
case 3
Pref=1;
otherwise
Pref=1;
end
% Initialize the output variables to reasonable values
SP_levels=0;
SP_bands=0;
SP_peak_levels=0;
cregime=0;
% Initialize the previous channel index
pe1=0;
max_num_settling_pts=500000;
% Calculate the sound pressure levels, peak levels, and band time records
if logical(num_mics > 0) && ((logical(num_bands > 0) && logical(num_pts > 0)))
SP_levels=zeros(num_mics, num_bands);
SP_peak_levels=zeros(num_mics, num_bands);
% The time record in each band is only initialized if
% it is output. This saves memory and reduces processing time.
if nargout > 2
SP_bands=zeros(num_mics, num_bands, num_pts);
end
for e1=1:num_mics;
for e2=num_bands:(-1):1;
% Set the third octave center frequency
fc2=fc(e2);
% initialize the flag which determines whether to output zeros
flag2=0;
% Determine whether to output zeros.
% Initialize the data variable SP_trunc2.
if isequal(regime(e2), 0) || isequal(regime(e2), 1)
flag2=1;
else
if ~isequal(e1, pe1) || logical(cregime > regime(e2)) || ~isequal(exist('SP_trunc2', 'var'), 1)
SP_trunc2=SP(:, e1);
else
if cregime < 3 && regime(e2) >=3
SP_trunc2=SP(:, e1);
end
end
end
settling_time2=max(settling_time, 10/fc2);
if settling_time2*Fs > max_num_settling_pts
settling_time2=max_num_settling_pts/Fs;
end
if isequal(flag2, 0)
Fs2=Fs;
% Resample the time record if necessary
if regime(e2) > 1
if isequal(regime(e2), 2)
% set the resample rate
%upsample_factor=downsample_factor;
Fs2=Fs*upsample_factor;
% upsample if necessary
if ~isequal(cregime, regime(e2)) || ~isequal(e1, pe1)
% Adding filter settling data
num_data_pts=length(SP_trunc2);
[y2, num_pts_se]=filter_settling_data3(Fs, SP_trunc2, settling_time2);
if isequal(resample_filter, 1)
SP_trunc2=resample(y2, upsample_factor, 1);
else
[SP_trunc2]=resample_interp3(y2, -1+(1:length(y2)), 1/upsample_factor);
end
% Remove the settling data from the time
% record.
[SP_trunc2]=remove_filter_settling_data(SP_trunc2, upsample_factor, num_pts_se, num_data_pts, 1);
end
elseif regime(e2) > 3
Fs2=Fs/(range_size^(regime(e2)-3));
% Down Sample if necessary
if ~isequal(cregime, regime(e2)) || ~isequal(e1, pe1)
% determine the amount of down sampling
if cregime >= 3 && logical(regime(e2) > cregime)
num_iter=regime(e2)-cregime;
else
num_iter=regime(e2)-3;
end
for e3=1:num_iter;
% Calculate the sampling rate for selecting
% the filter settling data
Fs22=Fs/(range_size^(regime(e2)-3+e3-num_iter-1));
% Adding filter settling data
num_data_pts=length(SP_trunc2);
[y2, num_pts_se]=filter_settling_data3(Fs22, SP_trunc2, settling_time2);
% Downsample the time record with the
% preappended and postappended settling data.
if isequal(resample_filter, 1)
SP_trunc2=resample(y2, 1, range_size);
else
[SP_trunc2]=bessel_down_sample(y2, Fs22*range_size, Fs22, settling_time2);
end
% Remove the settling data from the time
% record.
[SP_trunc2]=remove_filter_settling_data(SP_trunc2, range_size, num_pts_se, num_data_pts, 0);
end
end
end
end
% transfer the time record to a buffer variable
SP_trunc22=SP_trunc2;
% Calculate the 1/3 octave band filter coefficients
[Bc, Ac]=Nth_octdsgn(Fs2, fc2, N, n);
% Determine the size of data needed to keep the filter from failing
flag3=0;
[data_pts, num_chans]=size(SP_trunc22);
filtorder=max([size(Bc, 2), size(Ac, 2)]);
% If there is too little data then the filter will fail
% replicate the time record several times so that there is
% enough data to keep the filter from failing.
if data_pts < 5*filtorder
flag3=1;
num_reps=ceil(5*filtorder/data_pts);
SP_trunc22 = repmat(SP_trunc22, 1, num_reps);
end
% Calculate, Preappend and postappend the filter settling data.
num_data_pts=length(SP_trunc22);
[SP_trunc22, num_pts_se]=filter_settling_data3(Fs2, SP_trunc22, settling_time2);
% Apply the 1/3 octave bandpass filter to the data.
for e3=1:num_x_filter;
if isequal(filter_program, 1)
SP_trunc22 = filter(Bc, Ac, SP_trunc22);
else
SP_trunc22 = filtfilt(Bc, Ac, SP_trunc22);
end
end
% Remove the settling data from the time
% record.
[SP_trunc22]=remove_filter_settling_data(SP_trunc22, 1, num_pts_se, num_data_pts, 2);
% Remove any replicated data used to keep the filter from
% failing.
if flag3 == 1
SP_trunc22=SP_trunc22(1:data_pts, 1:num_chans);
end
% Only concatenate the 1/nth octave band time records
% if the output variable exists.
if nargout > 2
% resample the output time record to the original
% sampling rate
if regime(e2) == 2
% Adding filter settling data
num_data_pts=length(SP_trunc22);
[y2, num_pts_se]=filter_settling_data3(Fs*upsample_factor, SP_trunc22, settling_time2);
% Downsample the time record to reduce the sampling
% rate.
if isequal(resample_filter, 1)
SP_trunc22=resample(y2, 1, upsample_factor);
else
[SP_trunc22]=resample_interp3(y2, -1+(1:length(y2)), upsample_factor);
end
% Remove the settling data from the time
% record.
[SP_trunc22]=remove_filter_settling_data(SP_trunc22, upsample_factor, num_pts_se, num_data_pts, 0);
elseif regime(e2) > 3
% Calculate the down sampling factor applied to
% the data. This will be used to upsample the data
% to the original samling rate.
%
% This downsampling factor is called the reduction
% factor.
reduction_factor=(range_size^(regime(e2)-3));
% Adding filter settling data
num_data_pts=length(SP_trunc22);
[y2, num_pts_se]=filter_settling_data3(Fs/reduction_factor, SP_trunc22, settling_time2);
% Upsample the time record to the original
% sampling rate.
if isequal(resample_filter, 1)
SP_trunc22=resample(y2, reduction_factor, 1);
else
[SP_trunc22]=resample_interp3(y2, -1+(1:length(y2)), 1/reduction_factor);
end
% Remove the settling data from the time
% record.
[SP_trunc22]=remove_filter_settling_data(SP_trunc22, reduction_factor, num_pts_se, num_data_pts, 1);
end
% Append the time record for the microphone channel
% to the output band.
num_pts2=min([length(SP_trunc22) num_pts]);
for e3=1:num_pts2;
SP_bands(e1, ix(e2), e3)=SP_trunc22(e3);
end
end
% Calculate the levels and peak levels.
switch sensor
case 1
SP_levels(e1, ix(e2))=10*log10((norm(SP_trunc22)./sqrt(length(SP_trunc22))./Pref).^2);
SP_peak_levels(e1, ix(e2))=20*log10(max(abs(SP_trunc22))./Pref);
case 2
SP_levels(e1, ix(e2))=norm(SP_trunc22)./sqrt(length(SP_trunc22));
SP_peak_levels(e1, ix(e2))=max(abs(SP_trunc22));
case 3
SP_levels(e1, ix(e2))=norm(SP_trunc22)./sqrt(length(SP_trunc22));
SP_peak_levels(e1, ix(e2))=max(abs(SP_trunc22));
otherwise
SP_levels(e1, ix(e2))=norm(SP_trunc22)./sqrt(length(SP_trunc22));
SP_peak_levels(e1, ix(e2))=max(abs(SP_trunc22));
end
else
%
% If the center frequency is greater than the
% Nyquist freuqency or less than the minimum frequency
% limit then output a zero time record and indicate
% that the levels and peak levels are null.
%
if isequal(exist('SP_trunc22', 'var'), 1)
num_pts2=min([length(SP_trunc22) num_pts]);
else
num_pts2=num_pts;
end
if nargout > 3
for e3=1:num_pts2;
SP_bands(e1, ix(e2), e3)=0;
end
end
switch sensor
case 1
SP_levels(e1, ix(e2))=-10^15;
SP_peak_levels(e1, ix(e2))=-10^15;
case 2
SP_levels(e1, ix(e2))=0;
SP_peak_levels(e1, ix(e2))=0;
case 3
SP_levels(e1, ix(e2))=0;
SP_peak_levels(e1, ix(e2))=0;
otherwise
SP_levels(e1, ix(e2))=0;
SP_peak_levels(e1, ix(e2))=0;
end
end
% Set the previous hcannel index.
pe1=e1;
% Set the previous regime number.
cregime=regime(e2);
end
end
end
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