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gwsample-generation [2022/05/19 09:43]
theoastro 		gwsample-generation [2022/05/19 09:53] (current)
theoastro
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 ==== GW sample generation ==== ==== GW sample generation ====
 +In order to generate EOS samples, we need a posterior probability file computed for a certain EOS set. Here, we use the ''15nsat_cse_uniform_R14'' EOS folder (get tar.gz) and its respective probability file, which can be found [[https://zenodo.org/record/6106130#.YoYSi3VByV5|here]]. 
  
   import numpy as np   import numpy as np
   import pandas as pd   import pandas as pd
   import bilby   import bilby
- 
   # load posterior file   # load posterior file
-  eos_post = np.loadtxt('/home/nina/15_nmma_tutorial/06_GWEM-  +  eos_post = np.loadtxt('./posterior_probability_files/Astro/15nsat_cse_uniform_R14/posterior_probability.txt')
- resampling/posterior_probability_files/Astro/15nsat_cse_uniform_R14/posterior_probability.txt') +
   npts = 150000    npts = 150000 
   Neos = 5000   Neos = 5000
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   params_low =  [1.001398, 1.001398, 1]   params_low =  [1.001398, 1.001398, 1]
   params_high = [2.2,      2.2,     75]   params_high = [2.2,      2.2,     75]
- 
   # 1) create dummy EOS samples with eos_post from nature paper   # 1) create dummy EOS samples with eos_post from nature paper
   EOS_raw = np.arange(0, Neos)  # the gwem_resampling will add one to this   EOS_raw = np.arange(0, Neos)  # the gwem_resampling will add one to this
   EOS_samples = np.random.choice(EOS_raw, p=eos_post, size=npts, replace=True)   EOS_samples = np.random.choice(EOS_raw, p=eos_post, size=npts, replace=True)
- 
   # 2) generate samples for masses and distance   # 2) generate samples for masses and distance
   mass_1 = np.random.uniform(params_low[0], params_high[0], size=npts)   mass_1 = np.random.uniform(params_low[0], params_high[0], size=npts)
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   chirp_mass = bilby.gw.conversion.component_masses_to_chirp_mass(mass_1, mass_2)   chirp_mass = bilby.gw.conversion.component_masses_to_chirp_mass(mass_1, mass_2)
   lum_distance = np.random.uniform(params_low[2], params_high[2], size=npts)   lum_distance = np.random.uniform(params_low[2], params_high[2], size=npts)
- 
   # 3) create pandas dataframe   # 3) create pandas dataframe
   dataset = pd.DataFrame({'mass_1': mass_1, 'mass_2': mass_2, 'chirp_mass': chirp_mass, 'mass_ratio': mass_ratio, 'luminosity_distance': lum_distance, 'EOS': EOS_samples})   dataset = pd.DataFrame({'mass_1': mass_1, 'mass_2': mass_2, 'chirp_mass': chirp_mass, 'mass_ratio': mass_ratio, 'luminosity_distance': lum_distance, 'EOS': EOS_samples})
- 
   # 4) save GWsamples.dat file   # 4) save GWsamples.dat file
-  dataset.to_csv('GWsamples_new.dat', index=False, sep=' ')+  dataset.to_csv('GWsamples_new.dat', index=False, sep='  ')
Last modified: le 2022/05/19 09:43

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