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.

Best practices for model execution

PySM 3, in order to have the same behaviour of PySM 2, uses hp.ud_grade to change the resolution of the map if the HEALPix \(N_{side}\) of the input templates is different from the requested output resolution, specified in the emission class (subclass of Model) or in the Sky class.

hp.ud_grade generally creates artifacts in the spectra and should be avoided unless the specific application can tolerate that.

Therefore we recommend to execute all PySM 2 derived models (e.g. d0 to d8) at their native \(N_{side}\) of 512, and then use the output_nside parameter of apply_smoothing_and_coord_transform() to transform to the target resolution, whether higher or lower, in Spherical Harmonics domain.

PySM 3 native models (e.g. d9 to d11 and s4 to s6) instead have precomputed templates (generated in Spherical Harmonics domain) from \(N_{side}\) 2048 to 8192. Therefore we recommend to execute them at 2048 if the target \(N_{side}\) is 1024 or lower and at 2*\(N_{side}\) if the target is 2048 or higher, except 8192 which is the highest possible resolution.

There are some exceptions, so always check in the documentation or the max_nside parameter of the models, for example the d12 model, even if native to PySM 3, uses externally provided maps which are only available at \(N_{side}\)=2048.


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:


Run PySM with MPI support

PySM 3 is capable of running across multiple nodes using a MPI communicator.

See the details in the MPI section of the tutorial.

In order to run in parallel with MPI, it also needs a functioning MPI environment and:

  • 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 .

Install the requirements for testing with:

pip install -e .[test]

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



CIB maps correction based on SPT data


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.

get_pysm_emission(preset_string, nside)

Get one of PySM preset emissions

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[, max_nside, ...])

Lensed CMB

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

Load one or a set of 3 CMB maps

COLines(nside[, max_nside, ...])

Class defining attributes for CO line emission.

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[, max_nside, map_dist])

This is the template object for PySM objects.

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

This is a model for modified black body emission.

ModifiedBlackBodyLayers(map_layers, ...[, ...])

Modified Black Body model with multiple layers

ModifiedBlackBodyRealization(largescale_alm, ...)

Modified Black Body model with stochastic small scales


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.

PowerLawRealization(largescale_alm, ...[, ...])

PowerLaw model with stochastic small scales

Sky([nside, max_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

WebSkyCIB(nside[, websky_version, ...])

PySM component interpolating between precomputed maps

WebSkyCMB(nside[, max_nside, ...])

The input is assumed to be in uK_CMB

WebSkyRadioGalaxies(nside[, websky_version, ...])

Load and interpolate WebSky CIB maps

WebSkySZ(nside[, version, sz_type, ...])


Class Inheritance Diagram

Inheritance diagram of pysm3.models.cmb.CMBLensed, pysm3.models.cmb.CMBMap, pysm3.models.co_lines.COLines, 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.dust_layers.ModifiedBlackBodyLayers, pysm3.models.dust_realization.ModifiedBlackBodyRealization, pysm3.models.power_law.PowerLaw, pysm3.models.power_law_realization.PowerLawRealization,, pysm3.models.spdust.SpDust, pysm3.models.spdust.SpDustPol, pysm3.models.websky.WebSkyCIB, pysm3.models.websky.WebSkyCMB, pysm3.models.websky.WebSkyRadioGalaxies, pysm3.models.websky.WebSkySZ