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The Python module that used to measure the environment-dependent Wavelet Power Spectrum (env-WPS) of the cosmic density field.

Home Page: https://github.com/WangYun1995/WPSmesh

Python 100.00%
baryons cosmological-simulations large-scale-structure-cosmology python3 wavelet-analysis dark-matter continuous-wavelet-transform

wpsmesh's Introduction

WPSmesh

The Python module WPSmesh is built upon the nbodykit. We use it to measure the environment-dependent Wavelet Power Spectrum (env-WPS) of the cosmic density field.

The contents of WPSs(_sub).npz

>>import numpy as np

# Specify the path
>>path = '/.../.../data/hydro/TNG300/'

# Load .npz files
>>WPSs     = np.load(path+WPSs.npz)      # measured from the full volume
>>WPSs_sub = np.load(path+WPSs_sub.npz)  # measured from the 8 sub-volumes

# View all the constituent files
>>WPSs.files
>>['k_pseu', 'f_vol', 'env_WPS', 'global_WPS']
>>WPSs_sub.files
>>['k_pseu', 'f_vol_sub', 'env_WPS_sub', 'global_WPS_sub']

>>k         = WPSs['k_pseu']     # The pseudo wavenumber, shape: (25,), unit: h/Mpc
>>fvol      = WPSs['f_vol']      # The volume fraction of the local density environment, shape: (8,)
>>envWPS    = WPSs['env_WPS']    # The total matter env-WPS, shape: (25,8), unit: (Mpc/h)^3
>>globalWPS = WPSs['global_WPS'] # The total matter global-WPS, shape: (25,), unit: (Mpc/h)^3

>>fvol_sub      = WPSs_sub['f_vol_sub']      # The volume fraction of the local density environment, shape: (8,8)
>>envWPS_sub    = WPSs_sub['env_WPS_sub']    # The total matter env-WPS, shape: (25,8,8), unit: (Mpc/h)^3
>>globalWPS_sub = WPSs_sub['global_WPS_sub'] # The total matter global-WPS, shape: (25,8), unit: (Mpc/h)^3

References

  • Wang, Yun, and Ping He. "How do baryonic effects on the cosmic matter distribution vary with scale and local density environment?" Monthly Notices of the Royal Astronomical Society, Volume 528, Issue 2, February 2024, Pages 3797โ€“3808.
  • Hand, Nick, et al. "nbodykit: An open-source, massively parallel toolkit for large-scale structure." The Astronomical Journal 156.4 (2018): 160.

Acknowledgement

We especially thank Dr. Yu Feng and Dr. Simon Foreman for their help.

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