EffectiveHalos
EffectiveHalos is a fast Python code providing models of the real-space matter power spectrum, based a combination of the Halo Model and Effective Field Theory, which are 1% accurate up to k = 1 h/Mpc, across a range of cosmologies, including those with massive neutrinos. It can additionally compute accurate halo count covariances (including a model of halo exclusion), both alone and in combination with the matter power spectrum.
This is based on the work of Philcox, Spergel & Villaescusa-Navarro (2020), and full documentation is availble on ReadTheDocs.
Authors
Main Authors
- Oliver Philcox (Princeton)
Collaborators
- David Spergel (Princeton / CCA)
- Francisco Villaescusa-Navarro (Princeton / CCA)
Installation
EffectiveHalos can be simply installed using pip:
pip install EffectiveHalos (--user)
Note that you will need a CLASS installation, including the 'classy' Python wrapper, to run EffectiveHalos.
Basic Usage
To compute a matter power spectrum in EffectiveHalos, simply run the following:
from EffectiveHalos import *
import numpy as np
## Parameters
z = 0. # redshift
cs2 = 8. # effective speed of sound (should be calibrated from simulations)
R = 1. # smoothing scale (should be calibrated from simulations)
k = np.arange(0.01, 1., 0.005) # wavenumbers in h/Mpc
## Load general classes
cosmology = Cosmology(z, 'Planck18') # use Planck 2018 cosmology
mass_function = MassFunction(cosmology, 'Bhattacharya') # Bhattacharya 2010 mass function
halo_physics = HaloPhysics(cosmology, mass_function, 'Duffy', 'NFW') # Duffy 08 concentration relation, NFW halo profiles
## Load HaloModel class
halo_model = HaloModel(cosmology, mass_function, halo_physics, k)
## Compute the power spectrum in both Effective and Standard Halo Models
power_spectrum_EHM = halo_model.halo_model(cs2, R)
power_spectrum_SHM = halo_model.halo_model(cs2, R, 'Linear', 0, 0, 0)
This generates an estimate for the matter power spectrum in a few seconds. Let's plot this:
A full tutorial can be found here.
New for version 1.1: Accurate models for the power spectrum in massive neutrino cosmologies.
Metadata
Release files for EffectiveHalos 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| EffectiveHalos-1.1.0.tar.gz | 25.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| EffectiveHalos-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 55.8 kB
Release files / EffectiveHalos-1.1.0.tar.gz
| Download URL | EffectiveHalos-1.1.0.tar.gz |
|---|---|
| Size | 25.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / EffectiveHalos-1.1.0-py3-none-any.whl
| Download URL | EffectiveHalos-1.1.0-py3-none-any.whl |
|---|---|
| Size | 30.6 kB |
| Tags | Python 3 |
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| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.6.8
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