Metadata-Version: 2.1
Name: planck-2020-lollipop
Version: 4.1.0
Summary: A cobaya low-ell likelihood polarized for planck
Home-page: https://github.com/planck-npipe/lollipop
Author: Matthieu Tristram
License: GNU license
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: astropy
Requires-Dist: cobaya >=3.0

LoLLiPoP: Low-L Likelihood Polarized for Planck
================================================
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``Lollipop`` is a Planck low-l polarization likelihood based on cross-power-spectra for which the
bias is zero when the noise is uncorrelated between maps. It uses the approximation presented in
[Hamimeche & Lewis (2008)](https://arxiv.org/abs/0801.0554), modified as described in [Mangilli et
al. (2015)](https://arxiv.org/abs/1503.01347) to apply to cross-power spectra.  This version is
based on the Planck PR4 data. Cross-spectra are computed on the CMB maps from Commander component
separation applied on each detset-split Planck frequency maps.

It was previously applied and described in
- [Planck Collaboration Int. XLVII (2016)](https://arxiv.org/abs/1605.03507) for investigating the
  reionization history,
- [Tristram et al. (2020)](https://arxiv.org/abs/2010.01139) for estimating constraints on the
  tensor-to-scalar ratio.

It is interfaced with the ``cobaya`` MCMC sampler.

Requirements
------------
* Python >= 3.5
* `numpy`
* `astropy`

Install
-------

The easiest way to install the `Lollipop` likelihood is *via* `pip`

```shell
pip install planck-2020-lollipop [--user]
```

If you plan to dig into the code, it is better to clone this repository to some location

```shell
git clone https://github.com/planck-npipe/lollipop.git /where/to/clone
```

Then you can install the `Lollipop` likelihoods and its dependencies *via*

```shell
pip install -e /where/to/clone
```

The ``-e`` option allow the developer to make changes within the `Lollipop` directory without having
to reinstall at every changes. If you plan to just use the likelihood and do not develop it, you can
remove the ``-e`` option.

Installing Lollipop likelihood data
-----------------------------------

You should use the `cobaya-install` binary to automatically download the data needed by the
`lollipop.lowlE` or `lollipop.lowlB` or `lollipop.lowlEB` likelihoods

```shell
cobaya-install /where/to/clone/examples/test_lollipop.yaml -p /where/to/put/packages
```

Data and code such as [CAMB](https://github.com/cmbant/CAMB) will be downloaded and installed within
the ``/where/to/put/packages`` directory. For more details, you can have a look to `cobaya`
[documentation](https://cobaya.readthedocs.io/en/latest/installation_cosmo.html).


Likelihood versions
-------------------

* ``lowlE``
* ``lowlB``
* ``lowlEB``


