A Python compressed low-$\ell$ Planck likelihood for temperature and
  polarization
  We present Planck-low-py, a binned low-$\ell$ temperature and E-mode
polarization likelihood, as an option to facilitate ease of use of the Planck
2018 large-scale data in joint-probe analysis and forecasting. It is written in
Python and compresses the $\ell<30$ temperature and polarization angular power
spectra information from Planck into two log-normal bins in temperature and
three in polarization. These angular scales constrain the optical depth to
reionization and provide a lever arm to constrain the tilt of the primordial
power spectrum. We show that cosmological constraints on $\Lambda$CDM model
parameters using Planck-low-py are consistent with those derived with the full
Commander and SimAll likelihoods from the Planck legacy release.
