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CLASS-PT

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Nonlinear perturbation theory extension of the Boltzmann code CLASS

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# CLASS-PT: nonlinear perturbation theory extension of the Boltzmann code CLASS

This is a modification of the CLASS code that computes the non-linear power spectra of dark matter and biased tracers in one-loop cosmological perturbation theory, for both Gaussian and non-Gaussian initial conditions.

This version is based on [CLASS v3](https://github.com/lesgourg/class_public) and includes all upstream improvements (updated BBN, Halofit/HMcode, spectral distortions, etc.). A GitHub Actions workflow automatically checks for new upstream CLASS releases and opens a pull request when updates are available.

CLASS-PT can be interfaced with the MCMC sampler [MontePython](https://github.com/brinckmann/montepython_public) using the custom-built likelihoods found [here](https://github.com/oliverphilcox/full_shape_likelihoods).

# Getting started

Read [these instructions](https://github.com/Michalychforever/CLASS-PT/blob/master/instructions.pdf) for the installation details. See also this [troubleshooting](https://github.com/Michalychforever/CLASS-PT/blob/master/troubleshooting.rst) guide.

The installation instructions for CLASS can be found on the official code [webpage](https://github.com/lesgourg/class_public).

## Dependencies

- C/C++ compiler (gcc/g++ recommended, with `-fopenmp` support)
- [OpenBLAS](https://www.openblas.net/) (path configured in Makefile via `OPENBLAS`)
- Python 3 with NumPy and Cython (for the Python wrapper)

## Compilation

First, edit the `Makefile` to set the `OPENBLAS` variable to the path of `libopenblas.a` on your system.

Then ensure the OpenBLAS and GCC shared libraries are on your library path:
```bash
export LD_LIBRARY_PATH=/path/to/openblas/lib:/path/to/gcc/lib64:$LD_LIBRARY_PATH
```

Build and install:
```bash
# Build the CLASS executable and static library
make class

# Build and install the Python wrapper
make classy

# Or build the wrapper in-place (for development)
cd python && python setup.py build_ext --inplace && cd ..

# Clean build artifacts
make clean
```

After installation, verify:
```python
from classy import Class
c = Class()
c.set({'output': 'mPk'})
c.compute()
print(c.pk(0.1, 0))  # should print a positive number
```

## Quick example

Once you are all set, check out this [jupyter notebook](https://github.com/Michalychforever/CLASS-PT/blob/master/notebooks/nonlinear_pt.ipynb) for examples of working sessions. Here's a simple example of computing the galaxy power spectrum multipoles with CLASS-PT:

```python
# Import modules
from classy import Class
import numpy as np

# Set usual CLASS parameters
z_pk = 0.5
cosmo = Class()
cosmo.set({'A_s':2.089e-9,
           'n_s':0.9649,
           'tau_reio':0.052,
           'omega_b':0.02237,
           'omega_cdm':0.12,
           'h':0.6736,
           'YHe':0.2425,
           'N_ur':2.0328,
           'N_ncdm':1,
           'm_ncdm':0.06,
           'z_pk':z_pk
          })
# Set additional CLASS-PT settings
cosmo.set({'output':'mPk',
           'non linear':'PT',
           'IR resummation':'Yes',
           'Bias tracers':'Yes',
           'cb':'Yes', # use CDM+baryon spectra
           'RSD':'Yes',
           'AP':'Yes', # Alcock-Paczynski effect
           'Omfid':'0.31', # fiducial Omega_m
           'PNG':'No' # single-field inflation PNG
         })
cosmo.compute()

# Define some wavenumbers and compute spectra
khvec = np.logspace(-3,np.log10(1),1000) # array of k in 1/Mpc
cosmo.initialize_output(khvec, z_pk, len(khvec))

# Define nuisance parameters and extract outputs
b1, b2, bG2, bGamma3, cs0, cs2, cs4, Pshot_nbar, a0_nbar, a2_nbar, b4 = 2., -1., 0.1, -0.1, 0., 30., 0., 3000., 0., 0., 10.
pk_g0 = cosmo.pk_gg_l0(b1, b2, bG2, bGamma3, cs0, Pshot_nbar, a0_nbar, a2_nbar, b4)
pk_g2 = cosmo.pk_gg_l2(b1, b2, bG2, bGamma3, cs2, a2_nbar, b4)
pk_g4 = cosmo.pk_gg_l4(b1, b2, bG2, bGamma3, cs4, b4)
```

You can also use the Mathematica notebook *'read_tables.nb'* to read the code output. We also provide a technical summary of the fNL implementations [here](https://github.com/Michalychforever/CLASS-PT/blob/master/notebooks/summary_orthogonal_github.ipynb).

# Using the code

You can use CLASS-PT freely, provided that in your publications you cite at least the code paper [arXiv:2004.10607](https://arxiv.org/abs/2004.10607). Feel free to cite the other companion papers devoted to new large-scale structure analysis methodologies!

# Authors
- [Mikhail (Misha) Ivanov](mailto:ivanov@ias.edu)
- [Anton Chudaykin](mailto:anton.chudaykin@unige.ch)
- Marko Simonovic
- Oliver Philcox
- Giovanni Cabass

CLASS-PT

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