Fix Sphinx warning in gammapy/time/period.py
@@ -22,7 +22,7 @@ def robust_periodogram(time, flux, flux_err=np.array([None]), periods=np.array([
The significance of a periodogram peak can be evaluated in terms of a false alarm probability.
It can be computed with the `~false_alarm_probability`-method of `~astropy`, assuming Gaussian white noise light curves.
- For an introduction to the false alarm probability of periodogram peaks, see [3]_.
+ For an introduction to the false alarm probability of periodogram peaks, see :ref:`stats-lombscargle`.
The periodogram is biased by measurement errors, high order modes and sampling of the light curve.
To evaluate the impact of the sampling, compute the spectral window function with the `astropy.stats.LombScargle`-class.
@@ -60,10 +60,10 @@ def robust_periodogram(time, flux, flux_err=np.array([None]), periods=np.array([
References
----------
- .. [1] Nikolay Mayorov (2015), "Robust nonlinear regression in scipy", `Link <http://scipy-cookbook.readthedocs.io/items/robust_regression.html>`_
+ .. [1] Nikolay Mayorov (2015), "Robust nonlinear regression in scipy",
+ see `here <http://scipy-cookbook.readthedocs.io/items/robust_regression.html>`__
.. [2] Thieler et at. (2016), "RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression",
- `Link <https://www.jstatsoft.org/article/view/v069i09>`_
- .. [3] Astropy docs, `Link <http://docs.astropy.org/en/stable/stats/lombscargle.html>`_
+ see `here <https://www.jstatsoft.org/article/view/v069i09>`__
"""
# set flux errors
if flux_err.any() == None:
