Using LombScargle with units crashes when calling autopower()

Description

The LombScargle fails to compute autpower if Times and y values have been passed as Quantity to LombScargle . I’m using astropy Version: 4.1 and numpy Version: 1.18.1. See the snippet below

Code

window_ls = LombScargle(data["Time"] * u.s, np.ones(data["Time"].shape) * u.ct / u.s, fit_mean=False, nterms=1, center_data=False)
frequency_window, power_window = window_ls.autopower(minimum_frequency=minimum_frequency,
                                                         maximum_frequency=0, samples_per_peak=5)

Error

  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py", line 727, in to_value
    scale = self.unit._to(unit)
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/core.py", line 951, in _to
    f"'{self!r}' is not a scaled version of '{other!r}'")
astropy.units.core.UnitConversionError: 'Unit("s")' is not a scaled version of 'Unit(dimensionless)'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py", line 1080, in __int__
    return int(self.to_value(dimensionless_unscaled))
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py", line 730, in to_value
    value = self._to_value(unit, equivalencies)
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py", line 661, in _to_value
    equivalencies=equivalencies)
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/core.py", line 987, in to
    return self._get_converter(other, equivalencies=equivalencies)(value)
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/core.py", line 918, in _get_converter
    raise exc
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/core.py", line 904, in _get_converter
    self, other, self._normalize_equivalencies(equivalencies))
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/core.py", line 888, in _apply_equivalencies
    unit_str, other_str))
astropy.units.core.UnitConversionError: 's' (time) and '' (dimensionless) are not convertible

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/agurpide/scripts/swift_scripts/lomb_scargle.py", line 190, in <module>
    samples_per_peak=samples_per_peak)
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/timeseries/periodograms/lombscargle/core.py", line 306, in autopower
    maximum_frequency=maximum_frequency)
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/timeseries/periodograms/lombscargle/core.py", line 249, in autofrequency
    Nf = 1 + int(np.round((maximum_frequency - minimum_frequency) / df))
  File "/home/agurpide/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py", line 1082, in __int__
    raise TypeError('only dimensionless scalar quantities can be '
TypeError: only dimensionless scalar quantities can be converted to Python scalars

Problem

in astropy.timeseries.periodograms.lombscargle.core.py

        df = 1.0 / baseline / samples_per_peak

        if minimum_frequency is None:
            minimum_frequency = 0.5 * df

        if maximum_frequency is None:
            avg_nyquist = 0.5 * n_samples / baseline
            maximum_frequency = nyquist_factor * avg_nyquist

        Nf = 1 + int(np.round((maximum_frequency - minimum_frequency) / df))

The problem I believe is that while df is a Quantity, maximum_frequency and minimum_frequency are not and thus it crashes. Perhaps a solution could be to allow autopower to accept minimum_frequency and maximum_frequency as Quantity too.

[mhvk]

It does seem a case where the docs just have to be clearer - if you use quantities in constructing LS, you should use quantities throughout…

[adrn]

Not a timeseries maintainer, but a LombScargle user: This indeed looks like an oversight to me, and I think we should support this!