Curved-sky iterative CMB lensing reconstruction and bias calculation.
If you use delensalot for your work, please consider citing the ApJ publication CMB-S4: Iterative internal delensing and r constraints.
Download the project, navigate to the root folder and execute the command,
python setup.py install
Make sure that you have the latest plancklens and that you are using the plancklensdev branch
cd plancklens
git checkout plancklensdev
This is needed to give delensalot control over plancklens's mpi implementation, which we conveniently set up at this branch.
You will need to install jupyter for the tutorials found in first_steps/notebooks/, and possibly an ipykernel to create a jupyter-kernel out of the environment in which you install delensalot.
To run the tutorials with a jupyter kernel, you will have to install delensalot in it. Assuming you are using conda for your package management,
conda create --name delensalot
conda activate delensalot
Then, install your favourite packages,
conda install pip, numpy
Now, go to the delensalot directory, and install, including its requirements,
cd </path/to/delensalot>
python3 -m pip install -r requirements .
python3 setup.py develop
Eventually, create your kernel using the environment at which you just installed all packages,
python3 -m ipykernel install --user --name=delensalot
Frequent problems are
attrs. If there are errors related to the metamodel, make sure you haveattrs(notattr, which is a different package) installed and updated/upgraded (version 23.1.0 should do)astropy. If there are errors related to ducc0, chances are it's because of an oldastropyinstallation. Make sure you have the latestastropy.
delensalot comes with two handy functions to get you started very easily.
To get a delensed power spectrum, simply import delensalot and run anafast():
import delensalot
delensedmap = delensalot.anafast(obsmaps, lmax_cmb=lmax_cmb, beam=beam, itmax=itmax, noise=noise, verbose=True)
To get a delensed B map, simply import delensalot and run map2delblm():
import delensalot
delensedmap = delensalot.map2delblm(obsmaps, lmax_cmb=lmax_cmb, beam=beam, itmax=itmax, noise=noise, verbose=True)
here obsmaps is the observed Q and U map you may have... gotten from somewhere. Then all what is left to do is to tell delensalot about,
- the maximum \ell (
lmax_cmb) of your CMB map you'd like to use, - the beam (
sims_beam) of the transfer function of the observed maps, - how many iterations (
itmax) you'd like to perform, - and the noise level (
noise) of the observation.
If you are interested in the B-lensing template, instead use map2tempblm(),
import delensalot
Blenstemplate = delensalot.map2tempblm(obsmaps, lmax_cmb=lmax_cmb, beam=beam, itmax=itmax, noise=noise, verbose=True)
To run a configuration file <path-to-config/conf.py>, type in your favorite bash,
python3 run.py -r <path-to-config/conf.py>
delensalot supports MPI,
srun --nodes <nnodes> -n <taskspernode> python3 run.py -r <path-to-config/conf.py>
If you'd like to know the status of the analysis done with <path-to-config/conf.py>, run,
python3 run.py -s <path-to-config/conf.py>
delensalot supports interactive mode. See first_steps/notebooks/ for our tutorials.
Type python3 run.py [-h] for quickhelp,
usage: run.py [-h] [-p NEW] [-r RESUME] [-s STATUS] [-purgehashs PURGEHASHS]
delensalot entry point.
optional arguments:
-h, --help show this help message and exit
-r RESUME Absolute path to config file to run/resume.
-s STATUS Absolute path for the analysis to write a report.
uses
Documentation may be found [HERE]
delensalot can be computationally demanding.
We have parallelized the computations across the simulation index in most cases. Assuming you have MPI set up and srun is available, you can simply run MPI-supported delensalot via,
srun -MPI_paramX X -MPI_paramY Y python3 <path-to-delensalot>/run.py -r <path-to-config-file>
If you have troubles, your HPC-center can help.