PySM Documentation

PySM 3 generates full-sky simulations of Galactic foregrounds in intensity and polarization relevant for CMB experiments. The components simulated are: thermal dust, synchrotron, AME, free-free, and CMB at a given HEALPix \(N_{side}\), with an option to integrate over a bandpass and to smooth with a given beam.

There is scope for a few options for the model for each component, attempting to be consistent with current data.

Currently much of the available data is limited in resolution at degree-scale. We therefore make efforts to provide reasonable small-scale simulations to extend the data to higher multipoles. The details of the procedures developed can be found in the accompanying paper.

If you are using this code please cite the PySM 2 paper Thorne at al

This code is based on the large-scale Galactic part of Planck Sky Model (Delabrouille 2012) code and uses some of its inputs.


Each model is identified with a letter and a number, the letter indicates the kind of emission and the number the type of model, generally in order of complexity starting at 1. For example for dust we start with d1 based on Planck commander results, d4 has 2 dust populations and d6 implements a model of dust frequency decorrelation.

For example free-free:

f1: We model the free-free emission using the analytic model assumed in the Commander fit to the Planck 2015 data (Draine 2011 Physics of the Interstellar and Intergalactic Medium) to produce a degree-scale map of free-free emission at 30 GHz. We add small scales to this using a procedure outlined in the accompanying paper. This map is then scaled in frequency by applying a spatially constant power law index of -2.14.


PySM is written in pure Python, multi-threading and Just-In-Time compilation are provided by numba. The required packages are:

  • healpy

  • numba

  • toml

  • astropy

  • importlib_metadata just for Python 3.7

How many threads are used by numba can be controlled by setting:


For debugging purposes, you can also completely disable the numba JIT compilation and have the code just use plain Python, which is slower, but easier to debug:


In order to run in parallel with MPI, it also needs:

  • mpi4py

MPI-Distributed smoothing (optional) requires libsharp, it is easiest to install the conda package:

conda install -c conda-forge libsharp=*=*openmpi*

It also has a mpich version:

conda install -c conda-forge libsharp=*=*mpich*


The easiest way to install the last release is to use conda:

conda install -c conda-forge pysm3

or pip:

pip install pysm3

See the changelog on Github for details about what is included in each release.

Install at NERSC

Optionally replace with a newer anaconda environment:

module load python/3.7-anaconda-2019.10
conda create -c conda-forge -n pysm3 pysm3 python=3.7 ipython
conda activate pysm3
module unload python

Development install

The development version is available in the main branch of the GitHub repository, you can clone and install it with:

pip install .

Create a development installation with:

pip install -e .

Execute the unit tests with:


Configure verbosity

PySM uses the logging module to configure its verbosity, by default it will only print warnings and errors, to configure logging you can access the “pysm3” logger with:

import logging
log = logging.getLogger("pysm3")

configure the logging level:


redirect the logs to the console:

handler = logging.StreamHandler()

or customize their format:

log_format="%(name)s - %(levelname)s - %(message)s"
formatter = logging.Formatter(log_format)

For more details see the Python documentation.


All the tutorials are Jupyter Notebooks and can be accessed from the repository:



pysm3 Package



Apply smoothing and coordinate rotation to an input map

bandpass_unit_conversion(freqs[, weights, ...])

Unit conversion from input to output unit given a bandpass


Function to check that the input to Model.get_emission is a np.ndarray.

mpi_smoothing(input_map, fwhm, map_dist)

normalize_weights(freqs, weights)

Normalize bandpass weights to support integration "in K_RJ"

read_map(path, nside[, unit, field, map_dist])

Wrapper of healpy.read_map for PySM data.


Run the tests for the package.


Get the version string for the named package.



A warning class to indicate a deprecated feature.

CMBLensed(nside, cmb_spectra[, cmb_seed, ...])

Lensed CMB

CMBMap(nside[, map_IQU, map_I, map_Q, ...])


CurvedPowerLaw(map_I, freq_ref_I, ...[, ...])

This function initialzes the power law model of synchrotron emission.

DecorrelatedModifiedBlackBody([map_I, ...])

See parent class for other documentation.

HensleyDraine2017(map_I, map_Q, map_U, ...)

This is a model for modified black body emission.

InterpolatingComponent(path, input_units, nside)

PySM component interpolating between precomputed maps

MapDistribution([pixel_indices, mpi_comm, ...])

Define how a map is distributed

Model(nside[, map_dist])

This is the template object for PySM objects.

ModifiedBlackBody(map_I, map_Q, map_U, ...)

This is a model for modified black body emission.


The package was not found.

PowerLaw(map_I, freq_ref_I, map_pl_index, nside)

This is a model for a simple power law synchrotron model.

Sky([nside, preset_strings, ...])

Sky is the main interface to PySM

SpDust(map_I, freq_ref_I, emissivity, ...[, ...])

Implementation of the SpDust2 code of (Ali-Haimoud et al 2012) evaluated for a Cold Neutral Medium.

SpDustPol(map_I, freq_ref_I, emissivity, ...)

SpDust2 model with Polarized emission

Class Inheritance Diagram

Inheritance diagram of pysm3.models.cmb.CMBLensed, pysm3.models.cmb.CMBMap, pysm3.models.power_law.CurvedPowerLaw, pysm3.models.dust.DecorrelatedModifiedBlackBody, pysm3.models.hd2017.HensleyDraine2017, pysm3.models.interpolating.InterpolatingComponent, pysm3.distribution.MapDistribution, pysm3.models.template.Model, pysm3.models.dust.ModifiedBlackBody, pysm3.models.power_law.PowerLaw,, pysm3.models.spdust.SpDust, pysm3.models.spdust.SpDustPol