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lomb transforms a real-valued time series (from the specified input-file, or from the standard input if input-file is specified as "-"; input-file must be in text form) into a power spectrum (on the standard output), using a technique known as the Lomb periodogram.
The input is a text file containing a sampled time series, presented as two columns of numbers (the sample times and the sample values). The intervals between consecutive samples need not be uniform (in fact, this is the most significant advantage of the Lomb periodogram over other methods of power spectral density estimation). lomb writes the Lomb periodogram (the power spectral density estimate derived from the input time series) on the standard output, in two columns (frequency and power). If the units of the sample times in the input file are seconds, the units of the frequencies in the output are Hz.
Among many other applications, this program can be used to estimate heart rate power spectra, in combination with ihr(1) . The Lomb method is ideal for analysis of any time series with missing or noisy data (the noisy data may be removed from the time series and need not be replaced, as would be necessary if conventional PSD estimation algorithms were employed).
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Updated 8 March 2019