This is a set of python tools dedicated to CMB lensing and CMB delensing, by Julien Carron.

This code is essentially always using the flat-sky approximation. For similar tools in curved-sky geometry see plancklens

Installation: in the repo directory,

 pip install -e . [--user]

This code uses pyFFTW by default for FFTs, based on FFTW. Sometimes it is simplest to work in a conda environment and install all this with

conda install -c conda-forge pyfftw

Main features are:

• Maximum a posterior estimation of CMB lensing deflection maps from temperature and/or polarization maps.
  (See https://arxiv.org/abs/1704.08230 by J.Carron and A. Lewis)
• Wiener filtering of masked CMB data and allowing for inhomogenous noise, including lensing deflections, using a multigrid preconditioner.
  (Described in the same reference)
• Fast and accurate simulation libraries for lensed CMB skies, and standard quadratic estimator lensing reconstruction tools.
  (See https://arxiv.org/abs/1611.01446 by J. Peloton et al.)
• CMB internal delensing tools, including internal delensing biases calculation for temperature and/or polarization maps.
  (See https://arxiv.org/abs/1701.01712 by J. Carron, A. Lewis and A. Challinor)

Several parts were directly adapted from or inspired by qcinv qcinv and quicklens by Duncan Hanson, many thanks to him.

To use the GPU implementation of some of the routines, you will need pyCUDA

An ipython notebook 'demo_basics.ipynb' covers the simple aspects of building simulation librairies.

The notebook 'demo_lensit.ipynb' shows an example of iterative lensing map reconstruction for a configuration roughly in line with CMB Stage IV specifications.

Other example and tests scripts might follow, or you may just write to me.