There's been various concerns about the SHAMs I've made being done correctly. I'm going to take a look at the clustering of them all in the same notebook for easy comparison.

March 1st 2019 : Same as above, but just doing as another notebook for posterity



In [7]:

    
import numpy as np
import astropy
from halotools.mock_observables import tpcf



In [8]:

    
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()



In [9]:

    
%%bash
ls /home/users/swmclau2/scratch/*.hdf5 -ltr









    



-rw-r--r-- 1 swmclau2 kipac   28562856 Dec 18  2017 /home/users/swmclau2/scratch/catalog_ab_halo_vmax@mpeak_shuffled.hdf5
-rw-r--r-- 1 swmclau2 kipac  794838268 Dec 20  2017 /home/users/swmclau2/scratch/abmatched_halos.hdf5
-rw-r--r-- 1 swmclau2 kipac    8684944 Dec 28  2017 /home/users/swmclau2/scratch/catalog_gama_downsampled_mag_16.hdf5
-rw-r--r-- 1 swmclau2 kipac    1205220 Apr 25  2018 /home/users/swmclau2/scratch/test_trainer.hdf5
-rw-r--r-- 1 swmclau2 kipac   46840815 Apr 25  2018 /home/users/swmclau2/scratch/PearceRedMagicWpCosmo.hdf5
-rw-r--r-- 1 swmclau2 kipac    8612467 May  6  2018 /home/users/swmclau2/scratch/PearceRedMagicWpCosmoTest.hdf5
-rw-r--r-- 1 swmclau2 kipac    1168099 May  9  2018 /home/users/swmclau2/scratch/TestNansTrainer.hdf5
-rw-r--r-- 1 swmclau2 kipac   41309655 May  9  2018 /home/users/swmclau2/scratch/PearceRedMagicWpCosmo2.hdf5
-rw-r--r-- 1 swmclau2 kipac   82607498 Jun 22  2018 /home/users/swmclau2/scratch/PearceRedMagicXiCosmo.hdf5
-rw-r--r-- 1 swmclau2 kipac    1072688 Jul  1  2018 /home/users/swmclau2/scratch/PearceRedMagicXiChinchillaTest.hdf5
-rw-r--r-- 1 swmclau2 kipac   14526166 Jul  7  2018 /home/users/swmclau2/scratch/PearceRedMagicXiCosmoTest.hdf5
-rw-r--r-- 1 swmclau2 kipac    4466663 Jul  8  2018 /home/users/swmclau2/scratch/PearceRedMagicXiChinchilla.hdf5
-rw-r--r-- 1 swmclau2 kipac   86786023 Oct 10 14:06 /home/users/swmclau2/scratch/PearceRedMagicXiCosmoFixedNd.hdf5
-rw-r--r-- 1 swmclau2 kipac   14138633 Oct 26 17:35 /home/users/swmclau2/scratch/PearceRedMagicXiCosmoFixedNd_Test.hdf5
-rw-r--r-- 1 swmclau2 kipac   35284192 Feb  8 11:36 /home/users/swmclau2/scratch/catalog_ab_halo_vmax@mpeak.hdf5
-rw-r--r-- 1 swmclau2 kipac   35284192 Feb  8 13:29 /home/users/swmclau2/scratch/catalog_ab_halo_mpeak.hdf5
-rw-r--r-- 1 swmclau2 kipac 8061416824 Feb 12 14:57 /home/users/swmclau2/scratch/catalog_ab_halo_mpeak_large.hdf5
-rw-r--r-- 1 swmclau2 kipac   28564192 Feb 12 16:13 /home/users/swmclau2/scratch/catalog_ab_halo_mpeak_shuffled.hdf5



In [10]:

    
scratch_path = '/home/users/swmclau2/scratch/'



In [15]:

    
simname = 'darksky'

vpeak_cat = astropy.table.Table.read(scratch_path + 'catalog_ab_halo_vmax@mpeak.hdf5', format = 'hdf5',\
                                    path = 'halo_vmax@mpeak_catalog')
mpeak_cat = astropy.table.Table.read(scratch_path + 'catalog_ab_halo_mpeak.hdf5', format = 'hdf5',\
                                     path = 'halo_mpeak_catalog')
shuffled_cat_mpeak = astropy.table.Table.read(scratch_path+'catalog_ab_halo_mpeak_shuffled.hdf5', format = 'hdf5',\
                                       path = 'halo_mpeak_shuffled')



In [16]:

    
rbins = np.logspace(-1, 1.5, 15)
rpoints = (rbins[1:]+rbins[:-1])/2



In [17]:

    
pos = np.c_[vpeak_cat['halo_x'], vpeak_cat['halo_y'],vpeak_cat['halo_z']]
vpeak_xi = tpcf(pos, rbins, period=1000.0)



In [18]:

    
pos = np.c_[mpeak_cat['halo_x'], mpeak_cat['halo_y'],mpeak_cat['halo_z']]
mpeak_xi = tpcf(pos, rbins, period=1000.0)

pos = np.c_[shuffled_cat['halo_x'], shuffled_cat['halo_y'],shuffled_cat['halo_z']] shuffled_xi = tpcf(pos, rbins, period=1000.0)



In [22]:

    
pos = np.c_[shuffled_cat_mpeak['halo_x'], shuffled_cat_mpeak['halo_y'],shuffled_cat_mpeak['halo_z']]
shuffled_xi_mpeak = shuffled_xi = tpcf(pos, rbins, period=1000.0)

pos = np.c_[halocat.halo_table['halo_x'], halocat.halo_table['halo_y'],halocat.halo_table['halo_z']] rand_idxs = np.random.choice(pos.shape[0], len(final_catalog)*2) halo_xi = tpcf(pos, rbins, period=1000.0)



In [23]:

    
plt.plot(rpoints, vpeak_xi, label = 'Vpeak')
plt.plot(rpoints, mpeak_xi, label = 'Mpeak')
plt.plot(rpoints, shuffled_xi, label = 'Shuffled')
plt.plot(rpoints, shuffled_xi_mpeak, label = 'Shuffled Mpeak')

plt.loglog();
plt.legend(loc='best')
plt.xlabel('r [Mpc]')
plt.ylabel('xi')









    Out[23]:





<matplotlib.text.Text at 0x7f610ee91bd0>



In [24]:

    
plt.plot(rpoints, vpeak_xi/shuffled_xi, label = 'Vpeak/Shuffled')
plt.plot(rpoints, mpeak_xi/shuffled_xi, label = 'Mpeak/Shuffled')
plt.plot(rpoints, shuffled_xi_mpeak/shuffled_xi, label = 'Shuffled Mpeak/Shuffled')

plt.xscale('log');
plt.legend(loc='best')
plt.xlabel('r [Mpc]')
plt.ylabel('xi')









    Out[24]:





<matplotlib.text.Text at 0x7f6104fe8350>



In [25]:

    
plt.plot(rpoints, vpeak_xi/shuffled_xi_mpeak, label = 'Vpeak/Shuffled Mpeak')
plt.plot(rpoints, mpeak_xi/shuffled_xi_mpeak, label = 'Mpeak/Shuffled Mpeak')
plt.plot(rpoints, shuffled_xi/shuffled_xi_mpeak, label = 'Shuffled/Shuffled Mpeak')

plt.xscale('log');
plt.legend(loc='best')
plt.xlabel('r [Mpc]')
plt.ylabel('xi')









    Out[25]:





<matplotlib.text.Text at 0x7f610ece3350>



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