Cosmic Linear Anisotropy Solving System with Machine Learning Accelerated and Accurate CMB, LSS, and Halo Model Observables Computations.

To install the code (copy and paste into your terminal):

pip install classy_szfast && git clone https://github.com/CLASS-SZ/class_sz && cd class_sz && pip install -e . && source set_class_sz_env.sh

• Tip 1: Check here (this is important).

• Tip 2: Are you a Mac M1 user? Check here.

• Tip 3: Try to install from source (use tip 1 and/or 2 from above if needed).

• Tip 4: Does the problem seem to be related to tensorflow or cosmopower? Check here

• Tip 5: If it still crashes, open an issue and get in touch (we will aim to respond within 24h).

• Tip 6: We strongly encourage to use a python virtual environment:

$ module load python # if you are on a computing cluster
$ VENVDIR=/path/to/wherever/you/want/to/store/your/venvs
$ python -m venv --system-site-packages $VENVDIR/name-of-your-venv
$ source $VENVDIR/name-of-your-venv/bin/activate
$ pip install classy_szfast && git clone https://github.com/CLASS-SZ/class_sz && cd class_sz && pip install -e . && source set_class_sz_env.sh

We release example notebooks.

You may also want to take a look at a brief summary from (B. Bolliet, A. Kusiak, F. McCarthy, et al 2023).

Looking to modify the code, or to understand how it works? check here. We are working on re-factoring the code so it becomes more modular and user friendly. Stay tuned!

Have a look at the notebooks, there are loads of examples.

The idea is:

from classy_sz import Class as Class_sz
class_sz = Class_sz()
class_sz.set({'output':'tSZ_1h, tSZ_gal_1h,tSZ_gal_1h'}) # ask for cross-correlations, tsz, etc.
class_sz.compute()

You can compute linear bias tracer power spectra by calling:

from classy_sz import Class as Class_sz
class_sz = Class_sz()
class_sz.set({'output':'gal_gal_hf',
             'non_linear': 'hmcode'
             }) 
class_sz.compute()

See these notebooks to get started.

To run the emulator-based computations, the idea is to change:

class_sz.compute()

to:

class_sz.compute_class_szfast()

In a bit more details, say you are interested in CMB $C_\ell$'s:

from classy_sz import Class as Class_sz

cosmo_params = {
'omega_b': 0.02242,
'omega_cdm':  0.11933,
'H0': 67.66, 
'tau_reio': 0.0561,
'ln10^{10}A_s': 3.047,
'n_s': 0.9665,

'N_ncdm': 1,
'N_ur': 2.0328,
'm_ncdm': 0.06    
}
class_sz = Class_sz()
class_sz.set(cosmo_params)
class_sz.set({
'output':'tCl,lCl,pCl',
'skip_background_and_thermo': 1, # do you want exact solution for background? yes: 1, no: 0 (if "no" you can access exact background quantities via emulators).
})

class_sz.compute_class_szfast()

lensed_cls = class_sz.lensed_cl()
l_fast = lensed_cls['ell']
cl_tt_fast = lensed_cls['tt']
cl_ee_fast = lensed_cls['ee']
cl_te_fast = lensed_cls['te']
cl_pp_fast = lensed_cls['pp']

CLASS_SZ is as fast as it gets, with full parallelization, implementation of high-accuracy neural network emulators, and Fast Fourier Transforms.

CLASS_SZ has been built as an extension of Julien Lesgourgues's CLASS code, therefore the halo model and LSS calculations (essentially based on distances and matter clustering) are always consistent with the cosmological model computed by CLASS. We are doing our best to keep up with CLASS version updates. We are currently working on updating to CLASS v3.

CLASS_SZ is initially based on Eiichiro Komatsu’s Fortran code SZFAST.

CLASS_SZ's outputs are regularly cross-checked with other CMBxLSS codes, such as:

• cosmocnc
• hmvec
• ccl
• HaloGen
• yxg
• halomodel_cib_tsz_cibxtsz

The class_sz code is public.

If you use it, please cite:

• CLASS_SZ: I Overview (Bolliet et al. 2024)
• Projected-field kinetic Sunyaev-Zel'dovich Cross-correlations: halo model and forecasts (Bolliet et al. 2023)

If you use accelerated computations, please cite:

• High-accuracy emulators for observables in LCDM, Neff+LCDM, Mnu+LCDM and wCDM cosmologies (Bolliet et al. 2023)
• COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys (Spurio Mancini et al. 2021)

If you use thermal SZ power spectrum and cluster counts calculations, please cite:

• Including massive neutrinos in thermal Sunyaev Zeldovich power spectrum and cluster counts analyses (Bolliet et al. 2020)
• Dark Energy from the Thermal Sunyaev Zeldovich Power Spectrum (Bolliet et al. 2017)
• The Sunyaev-Zel'dovich angular power spectrum as a probe of cosmological parameters (Komatsu and Seljak, 2002)

In all these cases, please also cite the original CLASS papers:

• CLASS I: Overview (Lesgourgues, 2011)
• CLASS II: Approximation schemes (Blas, Lesgourgues, Tram, 2011)

As well as other references listed here: http://class-code.net

If you are a developer, you will need to modify the C code and the python wrapper.

CLASS_SZ functionalities are located in the files:

• source/class_sz.c for the main CLASS_SZ functions,
• tools/class_sz_tools.c for other useful routines,
• source/class_sz_clustercounts.c for tSZ cluster counts. Since March 2024, CLASS_SZ cluster counts calculations are superseded by cosmocnc (Zubeldia & Bolliet 2024).

And importantly, in the python and cython files:

• python/classy.pyx for the Python wrapper,
• classy_szfast/classy_szfast.py for the Python wrapper for the emulators,
• classy_szfast/classy_sz.py for the Python wrapper for cobaya,
• classy_szfast/cosmosis_classy_szfast_interface.py for the Python wrapper for cosmosis.

To install the C executable, so you can run the C code, you should install from source and compile:

Clean up and compile:

$ git clone https://github.com/CLASS-SZ/class_sz
$ cd class_sz/class-sz/python
$ git clone https://github.com/CLASS-SZ/classy_szfast
$ cd ..
$ chmod +x select_makefile.sh
$ ./select_makefile.sh
$ chmod +x download_emulators.sh
$ ./download_emulators.sh
$ make clean
$ make -j
$ export PYTHONPATH=$(pwd)/python/classy_szfast:$PYTHONPATH
$ cd ..
$ source set_class_sz_env.sh

The -j flag speeds up the compilation process by using multiple cores.

If this crashes, check the tips at the top of the page.

If it installes, run the C code with many power spectra output:

$ ./class_sz class_sz_test.ini

The .ini files are the parameter files.

If you want to run CLASS and not do the class_sz part, you can! For example:

$ ./class_sz explanatory.ini

This will just run the standard CLASS code and its calculations. All depends on what output you request: if you request a class_sz observable or not.

It is often the case that some libraries are not found. In general, setting the following paths appropriately should solve your issues:

export LIBRARY_PATH=/path/to/your/libs:path/to/gsl:path/to/fftw:$LIBRARY_PATH
export C_INCLUDE_PATH=/path/to/your/includes:path/to/gsl:path/to/fftw:$C_INCLUDE_PATH
export DYLD_LIBRARY_PATH="/path/to/your/libs:$DYLD_LIBRARY_PATH" # (Mac M1 users only)

To ensure these paths are set every time you open a terminal, you can add these lines to your ~/.bashrc or ~/.bash_profile file automatically using the echo command.

For ~/.bashrc (common for most Linux systems):

echo -e "\n# Set library paths for class_sz\nexport LIBRARY_PATH=/path/to/your/libs:path/to/gsl/:path/to/fftw/:\$LIBRARY_PATH\nexport C_INCLUDE_PATH=/path/to/your/includes:path/to/gsl:path/to/fftw:\$C_INCLUDE_PATH\nexport DYLD_LIBRARY_PATH=\"/path/to/your/libs:\$DYLD_LIBRARY_PATH\" # (Mac M1 users only)" >> ~/.bashrc

To apply the changes immediately:

source ~/.bashrc

For ~/.bash_profile (common for macOS):

echo -e "\n# Set library paths for class_sz\nexport LIBRARY_PATH=/path/to/your/libraries:path/to/gsl/:path/to/fftw/:\$LIBRARY_PATH\nexport C_INCLUDE_PATH=path/to/gsl/:path/to/fftw/:\$C_INCLUDE_PATH\nexport DYLD_LIBRARY_PATH=\"/path/to/your/libraries:\$DYLD_LIBRARY_PATH\" # (Mac M1 users only)" >> ~/.bash_profile

To apply the changes immediately:

source ~/.bash_profile

Use module load, module show to get GSL and FFTW. At NERSC/Cori/Perlmutter, the code works with gsl/2.7. (There seems to be a problematic behavior during job submission with gsl/2.5.)

For Monte Carlo analyses, we also recall that Mpi4py needs to be correctly installed. Follow: Cobaya MPI Installation Guide.

At NERSC, these commands may help for mpi4py:

MPICC="cc -shared" pip install --force-reinstall --no-cache-dir --no-binary=mpi4py mpi4py

To install the new version of CLASS_SZ, you will need TensorFlow (needed for the Cosmopower emulators). On M1/M2, make sure you have the arm64 version of conda (if not, you need to remove your entire conda and install the arm64 version for Apple Silicon).

This video might be helpful: Installing TensorFlow on M1 Mac.

Then you can follow the standard TensorFlow installation recipe for M1, e.g., Medium Article or the Apple Developer Forums.

The following line should fix most issues:

$ conda install -c apple tensorflow-deps

If issues seem to be related to cosmopower, you can try the following commands before installing class_sz:

$ module load python
$ python3 -m venv /path/to/your/venv
$ source /path/to/your/venv/bin/activate
#
$ pip install --upgrade pip
#
$ module swap PrgEnv-${PE_ENV,,} PrgEnv-gnu
$ MPICC="cc -shared" pip install --force-reinstall --no-cache-dir --no-binary=mpi4py mpi4py
#
$ pip install numpy scipy
# these below are probably not needed for cosmopower, but we keep them here as they may solve dependicies.
$ pip install healpy camb
$ pip install astropy h5py setuptools "iminuit>=2.0.0" cachetools matplotlib
$ pip install hankl
$ pip install tf-keras cosmopower mcfit
$ pip install -U --force-reinstall charset-normalizer
$ python3 -c 'import cosmopower as cp'

And then install class_sz (see top of page).

(Thanks to V. Irsic and R. de Belsunce for this.)

See Makefile_preM1mac for an example makefile for older Macs (without the M1 chip). Some key points include adding paths involving libomp to LDFLAG and INCLUDES. In python/setup.py, you may also want to modify the extra_link_args list to contain '-lomp' instead of '-lgomp' and add the libomp library path as well to that list. For example, extra_link_args=['-lomp', '-lgsl','-lfftw3','-lgslcblas', '-L/usr/local/opt/libomp/lib/'].

This makefile is not maintained anymore but we keep it for reference. If you need to run class_sz on a pre-M1 Mac and have serious issues, please contact us.