Loading & getting MFLike likelihood from cobaya

Loading & getting MFLike likelihood from cobaya#

In this first tutorial, we will see how to instantiate mflike from cobaya interface and how we can retrieve the likelihood. This notebook is currently using the following modules:

Hide code cell source

 
import camb
import cobaya
import matplotlib as mpl
import numpy as np

print("     Numpy :", np.__version__)
print("Matplotlib :", mpl.__version__)
print("      CAMB :", camb.__version__)
print("    Cobaya :", cobaya.__version__)

     Numpy : 2.4.6
Matplotlib : 3.10.9
      CAMB : 1.6.6
    Cobaya : 3.6.2

Installing MFLike likelihood data#

We first start by declaring the likelihood within a dict object. Most of the likelihood’s options are set by default. Here we only set the input sacc file and the associated covariance file.

mflike_input_file = dict(
    input_file="LAT_simu_sacc_00044.fits", cov_Bbl_file="data_sacc_w_covar_and_Bbl.fits"
)
mflike_config = {"mflike.TTTEEE": mflike_input_file}

These files do not come with the mflike likelihood and we need to download and to install data needed by mflike. Here we will do it programatically in such a way we can use this notebook without any prerequisites. Code and data will be stored in the /tmp directory for the tutorial purpose but feel free to change it to whatever location you want. By the way the next command will take some times to proceed.

import os
import tempfile

from cobaya.install import install
from cobaya.tools import resolve_packages_path

packages_path = resolve_packages_path() or os.path.join(tempfile.gettempdir(), "LAT_packages")
install({"likelihood": mflike_config}, path=packages_path, no_set_global=True, skip_global=True);

[install] Installing external packages at '/tmp/LAT_packages'

================================================================================
likelihood:mflike.TTTEEE
================================================================================
[install] Checking if dependencies have already been installed...
[install] External dependencies for this component already installed.
[install] Doing nothing.

================================================================================
* Summary * 
================================================================================

[install] All requested components' dependencies correctly installed at /tmp/LAT_packages

Loading MFLike via cobaya#

Given a successful installation of the data, we then need to set the default values of CMB, foreground and nuisance parameters.

cosmo_params = {
    "cosmomc_theta": 0.0104092,
    "As": 1e-10 * np.exp(3.044),
    "ombh2": 0.02237,
    "omch2": 0.1200,
    "ns": 0.9649,
    "Alens": 1.0,
    "tau": 0.0544,
}

fg_params = {
    "a_tSZ": 3.30,
    "a_kSZ": 1.60,
    "a_p": 6.90,
    "beta_p": 2.20,
    "a_c": 4.90,
    "beta_c": 2.20,
    "a_s": 3.10,
    "T_d": 9.60,
    "a_gtt": 2.80,
    "a_gte": 0.10,
    "a_gee": 0.10,
    "a_psee": 0.003,
    "a_pste": 0.042,
    "xi": 0.10,
    "beta_s": -2.5,  # beta radio
    "alpha_s": 1,  # alpha radio
    "T_effd": 19.6,  # effective galactic dust temperature
    "beta_d": 1.5,  # beta galactic dust
    "alpha_dT": -0.6,  # galactic dust ell slope for T
    "alpha_dE": -0.4,  # galactic dust ell slope for E
    "alpha_tSZ": 0.0,  # tSZ ell slope
    "alpha_p": 1,  # CIB poisson ell slope
}

nuisance_params = {
    # only ideal values for now
    "bandint_shift_LAT_93": 0,
    "bandint_shift_LAT_145": 0,
    "bandint_shift_LAT_225": 0,
    "calT_LAT_93": 1,
    "calE_LAT_93": 1,
    "calT_LAT_145": 1,
    "calE_LAT_145": 1,
    "calT_LAT_225": 1,
    "calE_LAT_225": 1,
    "cal_LAT_93": 1,
    "cal_LAT_145": 1,
    "cal_LAT_225": 1,
    "calG_all": 1,
    "alpha_LAT_93": 0,
    "alpha_LAT_145": 0,
    "alpha_LAT_225": 0,
}

We finally put everything into a dictionary to push it into cobaya configuration system. Here for the purpose of this tutorial, we use camb as Boltzmann solver with minimal settings. Another configuration high_accuracy_settings is also provided and you can switch within the info object.
The likelihood also depends on the foreground model, the default model for which can also be obtained from mflike.BandpowerForeground.

minimal_settings = dict(lens_potential_accuracy=1)

high_accuracy_settings = dict(
    lens_potential_accuracy=8,
    lens_margin=2050,
    AccuracyBoost=2.0,
    lSampleBoost=2.0,
    lAccuracyBoost=2.0,
    kmax=10,
    k_per_logint=130,
    nonlinear=True,
    DoLateRadTruncation=False,
)

info = {
    "params": cosmo_params | fg_params | nuisance_params,
    "likelihood": mflike_config,
    "theory": {"camb": {"extra_args": minimal_settings}, "mflike.BandpowerForeground": None},
    "packages_path": packages_path,
}

We will now get the model given the above parameters

from cobaya.model import get_model

model = get_model(info)

[camb] `camb` module loaded successfully from /home/docs/checkouts/readthedocs.org/user_builds/lat-mflike/envs/latest/lib/python3.11/site-packages/camb
Warning: The FITS format without the 'sacc_ordering' column is deprecated
Assuming data rows are in the correct order as it was before version 1.0.
Warning: The FITS format without the 'sacc_ordering' column is deprecated
Assuming data rows are in the correct order as it was before version 1.0.
[mflike.ttteee] Number of bins used: 3087
[mflike.ttteee] Initialized!

To get a direct acces to the MFLike likelihood instance, we can retrieve it from the collection of likelihoods of cobaya given its name (so far there is only our likelihood)

mflike = model.likelihood["mflike.TTTEEE"]

We can now play with the different data (Bbl, inverted covariance matrix) and we can also call the different likelihood methods.