pyhmcode 1.0.0

Python interface for HMCode

pyhmcode

This library provides a convenient interface to Alexander Mead's library, which underlies the HMCode and HMx codes to predict non-linear power spectra. For details on the Fortran version, refer to https://github.com/alexander-mead/HMcode. The pyhmcode interface uses the excellent f90wrap library to generate the python interface. This allows access to virtually all functionality in the library, although to speed up compilation, the interface is limited to commonly-used subroutines. If you use pyhmcode, please cite Tröster, Mead et al. 2021 and the relevant papers describing the model:

• HMCode2015: Mead et al. (2015; https://arxiv.org/abs/1505.07833)
• HMCode2016: Mead et al. (2016; https://arxiv.org/abs/1602.02154)
• HMCode2020: Mead et al. (2021; https://arxiv.org/abs/2009.01858)
• HMx: Mead, Tröster et al. (2020; https://arxiv.org/abs/2005.00009)

Installation

pyhmcode is pip-installable, so

pip install pyhmcode

should do the trick. For more control over the installation and access to the alternative interface, as well as CosmoSIS support, the repository can be cloned with

git clone --recursive https://github.com/tilmantroester/pyhmcode

Alternative interface and CosmoSIS support

There is an alternative interface to compute powerspectra in the powerspectrum_interface subdirectory. This interface uses ctypes and a thin Fortran wrapper to access the main functionality of HMCode and HMx, namely predicting the non-linear power spectra. Installation proceeds by

cd powerspectrum_interface
pip install .

This installs the pyhmx python module, which in turn is interfaced with CosmoSIS using the cosmosis_interface.py module.

Demos

The notebooks and example subdirectories include a number of examples on how to use the the interfaces. A basic example (from examples/basic_example.py) showing the use of pyhmcode to generate non-linear power specta, as well as its integration with CCL is shown below.

import pyhmcode
import pyhmcode.halo_profile_utils

# We use CCL to generate the linear power spectrum
import pyccl as ccl

import numpy as np


ccl_cosmology = ccl.CosmologyVanillaLCDM()

k = np.logspace(-4, 1.5, 100)
a = np.linspace(1/(1+6), 1, 10)
z = 1/a - 1

pofk_lin = np.array([ccl.linear_matter_power(ccl_cosmology, k=k, a=a_)
                     for a_ in a])

# CCL uses units of Mpc, while pyhmcode uses Mpc/h. Hence we need to convert
# the units here.
h = ccl_cosmology["h"]
k = k/h
pofk_lin = pofk_lin * h**3

# Create the pyhmcode cosmology object. Beside the cosmology parameters, it
# also holds the linear power spectrum.
hmcode_cosmology = pyhmcode.halo_profile_utils.ccl2hmcode_cosmo(
                        ccl_cosmo=ccl_cosmology,
                        pofk_lin_k_h=k,
                        pofk_lin_z=z[::-1],
                        pofk_lin=pofk_lin[::-1],
                        log10_T_heat=7.8)

# Create the halo model object, which holds information on the specific halo
# model to use. E.g., the HMCode or HMx version.
hmcode_model = pyhmcode.Halomodel(
                    pyhmcode.HMx2020_matter_pressure_w_temp_scaling)

# Now we can compute the non-linear power spectrum, given the cosmology,
# halo model, and a list of fields.
hmcode_pofk = pyhmcode.calculate_nonlinear_power_spectrum(
                                    cosmology=hmcode_cosmology,
                                    halomodel=hmcode_model, 
                                    fields=[pyhmcode.field_matter,
                                            pyhmcode.field_electron_pressure])

# The output of calculate_nonlinear_power_spectrum has
# shape (n_field, n_field, n_z, n_k).
matter_matter_pofk = hmcode_pofk[0, 0]
matter_electron_pressure_pofk = hmcode_pofk[0, 1]

# We can also use the halo profiles from HMCode or HMx and use them in another
# halo model code.
profile_generator = pyhmcode.halo_profile_utils.HMxProfileGenerator(
                        hmcode_cosmology,
                        a_arr=a, k_arr=k,
                        fields=[pyhmcode.field_matter,
                                pyhmcode.field_cdm,
                                pyhmcode.field_electron_pressure],
                        add_diffuse=False)

# Here we use the halo profile in the CCL halo model framework. 
# First setup the CCL halo model specification
mass_def = ccl.halos.MassDef("vir", 'matter')
hmf = ccl.halos.MassFuncSheth99(ccl_cosmology, mass_def=mass_def,
                                mass_def_strict=False, use_delta_c_fit=True)
hbf = ccl.halos.HaloBiasSheth99(ccl_cosmology, mass_def=mass_def,
                                mass_def_strict=False, use_delta_c_fit=True)
hmc = ccl.halos.HMCalculator(ccl_cosmology, hmf, hbf, mass_def)

# Compute the CCL halo model power spectrum, using the halo profile from HMx
ccl_halomodel_pofk = ccl.halos.halomod_Pk2D(
                            cosmo=ccl_cosmology,
                            hmc=hmc, 
                            prof=profile_generator.matter_profile,
                            normprof1=False,
                            a_arr=a, lk_arr=np.log(k*h))