| function dataout = iomega(datain, dt, datain_type, dataout_type) | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| % | |
| % IOMEGA is a MATLAB script for converting displacement, velocity, or | |
| % acceleration time-series to either displacement, velocity, or | |
| % acceleration times-series. The script takes an array of waveform data | |
| % (datain), transforms into the frequency-domain in order to more easily | |
| % convert into desired output form, and then converts back into the time | |
| % domain resulting in output (dataout) that is converted into the desired | |
| % form. | |
| % | |
| % Variables: | |
| % ---------- | |
| % | |
| % datain = input waveform data of type datain_type | |
| % | |
| % dataout = output waveform data of type dataout_type | |
| % | |
| % dt = time increment (units of seconds per sample) | |
| % | |
| % 1 - Displacement | |
| % datain_type = 2 - Velocity | |
| % 3 - Acceleration | |
| % | |
| % 1 - Displacement | |
| % dataout_type = 2 - Velocity | |
| % 3 - Acceleration | |
| % | |
| % | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| % Make sure that datain_type and dataout_type are either 1, 2 or 3 | |
| if (datain_type < 1 || datain_type > 3) | |
| error('Value for datain_type must be a 1, 2 or 3'); | |
| elseif (dataout_type < 1 || dataout_type > 3) | |
| error('Value for dataout_type must be a 1, 2 or 3'); | |
| end | |
| % Determine Number of points (next power of 2), frequency increment | |
| % and Nyquist frequency | |
| N = 2^nextpow2(max(size(datain))); | |
| df = 1/(N*dt); | |
| Nyq = 1/(2*dt); | |
| % Save frequency array | |
| iomega_array = 1i*2*pi*(-Nyq : df : Nyq-df); | |
| iomega_exp = dataout_type - datain_type; | |
| % Pad datain array with zeros (if needed) | |
| size1 = size(datain,1); | |
| size2 = size(datain,2); | |
| if (N-size1 ~= 0 && N-size2 ~= 0) | |
| if size1 > size2 | |
| datain = vertcat(datain,zeros(N-size1,1)); | |
| else | |
| datain = horzcat(datain,zeros(1,N-size2)); | |
| end | |
| end | |
| % Transform datain into frequency domain via FFT and shift output (A) | |
| % so that zero-frequency amplitude is in the middle of the array | |
| % (instead of the beginning) | |
| A = fft(datain); | |
| A = fftshift(A); | |
| % Convert datain of type datain_type to type dataout_type | |
| for j = 1 : N | |
| if iomega_array(j) ~= 0 | |
| A(j) = A(j) * (iomega_array(j) ^ iomega_exp); | |
| else | |
| A(j) = complex(0.0,0.0); | |
| end | |
| end | |
| % Shift new frequency-amplitude array back to MATLAB format and | |
| % transform back into the time domain via the inverse FFT. | |
| A = ifftshift(A); | |
| datain = ifft(A); | |
| % Remove zeros that were added to datain in order to pad to next | |
| % biggerst power of 2 and return dataout. | |
| if size1 > size2 | |
| dataout = real(datain(1:size1,size2)); | |
| else | |
| dataout = real(datain(size1,1:size2)); | |
| end | |
| return |