baseline_heuristic

lombscargle.heuristics.baseline_heuristic(n_samples, baseline, samples_per_peak=5, nyquist_factor=5, minimum_frequency=None, maximum_frequency=None)

Use a heuristic to compute a frequency grid.

Note that this assumes that the baseline is much larger than the oscillation period, such that the peak widths are driven by the baseline. If you are searching for periods longer than the baseline of your observations, this will not perform well.

Even with a large baseline, be aware that this heuristic is based on the concept of “average Nyquist frequency”, which is fundamentally incorrect for irregularly-sampled data. The nyquist_factor provides a fudge factor that allows sampling of higher frequencies.

Parameters:

n_samples : int

the number of samples in your dataset. This is used to guess at the maximum frequency if maximum_frequency is not provided.

baseline : float

the difference between the maximum and minimum observation times. This is used to estimate the peak width, and thus choose the frequency spacing.

samples_per_peak : float (optional, default=5)

The approximate number of desired samples across the typical peak width

nyquist_factor : float (optional, default=5)

The multiple of the average nyquist frequency used to choose the maximum frequency if maximum_frequency is not provided.

minimum_frequency : float (optional)

If specified, then use this minimum frequency rather than one chosen based on the size of the baseline.

maximum_frequency : float (optional)

If specified, then use this maximum frequency rather than one chosen based on the average nyquist frequency.

Returns:

freq : ndarray

an array freq_min + freq_spacing * np.arange(N_freq).