plancklens is is a python code for cosmology containing most of Planck 2018 CMB lensing pipeline, by Julien Carron on behalf of the Planck collaboration (publication here.) Some numerical parts are written in Fortran. Portions of it (structure and code) have been directly adapted from pre-existing work by Duncan Hanson.

This package may be used for:

• lensing (as well as other types of anisotropies) reconstruction on actual Planck data or other data/sims
• or simply for calculation of responses, reconstruction noise levels and biases for forecasts or other analytical work

After cloning the repository, build an editable installation with

pip install -e . [--user]

The –-user is required only if you don’t have write permission to your main python installation. A fortran compiler is required for a successful installation.

Alternatively, you can use the Dockerfile to install the code in a separate container (see below).

The Dockerfile allows you to install plancklens and lenspyx in a Docker container and to run Jupyter. This image is based on the jupyter/scipy-notebook image. After installing docker on your machine, go in the plancklens repository and build the image with

docker build -t plancklens .

You can then start the container with

docker run -it -p 8888:8888 plancklens

Visiting http://<hostname>:8888/?token=<token> in a browser loads JupyterLab, where:

`hostname` is the name of the computer running Docker

`token` is the secret token printed in the console.

If you only wish to run a terminal within the container type

docker run -it plancklens /bin/sh

This code contains most of the Planck 2018 lensing pipeline. In particular it is possible to reproduce the published map and band-powers basically exactly. Some more detailed parts of the pipeline have been left out or are not yet translated to python 3. This is the case notably of the band-powers likelihood code.

The code used to produce lensed CMB skies is the stand-alone pip package lenspyx (with big speed improvement expected soon)

• To obtain analytical reconstruction noise curve and responses, you might want to check n0s.py especially the commented get_N0 function

• To use the examples lensing reconstruction parameter files, you will need further to define the environment variable $PLENS to some place safe to write. Details on the structure of a parameter file are given in this one: idealized_example.py

• In order to reproduce the 2018 lensing maps and spectrum band-powers, one may use the provided smicadx12_planck2018.py parameter file.

• The basics on how to use parameter files can be found in this jupyter notebook. Some details on the numerical computations are collected in this document. You might also need to check out the plancklens documentation (with some bits in progress).

Generally, in plancklens a QE is often described by a short string.

For example 'ptt' stands for lensing (or lensing gradient mode) from temperature x temperature.

Anisotropy source keys are a one-letter string including

'p' (lensing gradient)

'x' (lensing curl)

's' (point sources)

'f' (modulation field)

'a' (polarization rotation)

Typical keys include then:

'ptt', 'xtt', 'stt', 'ftt' for the corresponding QEs from temperature only

'p_p', 'x_p', 'f_p', 'a_p' for the corresponding QEs from polarization only (combining EE EB and BB if relevant)

'p', 'x', 'f', 'a', 'f' ... for the MV (or GMV) combination

'p_eb', ... for the EB estimator (this is the symmetrized version  ('peb' + 'pbe') / 2  so that E and B appear each once on the gradient and inverse-variance filtered leg)

• Jan 14 2021: important bug fix in fortran N1 file affecting EB TB and TE. The n1 module must be rebuilt.