Metadata-Version: 2.1
Name: EffectiveHalos
Version: 1.0.1
Summary: Combining the Halo Model and Perturbation Theory: A 1% Accurate Model to k = 1 h/Mpc
Home-page: https://effectivehalos.rtfd.io
Author: Oliver Philcox
Author-email: ohep2@alumni.cam.ac.uk
License: UNKNOWN
Description: # 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. 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 (in prep.), and full documentation is availble on [ReadTheDocs](https://EffectiveHalos.rtfd.io).
        
        ## 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](https://github.com/lesgourg/class_public) installation, including the 'classy' Python wrapper, to run EffectiveHalos.
        
        
        ## Basic Usage
        
        To compute a matter power spectrum in EffectiveHalos, simply run the following:
        
        ```python
        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:
        
        ![alt text](https://github.com/oliverphilcox/EffectiveHalos/blob/master/docs/ehm_tutorial_spec.png "Effective Halo Model Power Spectrum")
        
        A full tutorial can be found [here](https://effectivehalos.readthedocs.io/en/latest/Tutorial.html).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
