

In [61]:

    
from pearce.mocks.kittens import TrainingBox



In [62]:

    
import numpy as np
from halotools.mock_observables import tpcf_one_two_halo_decomp



In [63]:

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



In [64]:

    
r_bins = np.logspace(-1.1, 1.6, 11)



In [65]:

    
rbc = (r_bins[1:]+r_bins[:-1])/2.0

xi = np.zeros((40, 10*3)) for i in xrange(40): print i cat = TrainingBox(i, system = 'ki-ls') cat.load(1.0, HOD='zheng07') cat.populate(min_ptcl = 100) #xi[i, :10] = cat.calc_xi(r_bins).squeeze()#, halo=True).squeeze() #gal_pos = np.vstack(np.array(cat.model.mock.galaxy_table['%s'%coord]) for coord in ['x', 'y', 'z']).T/cat.h #xi_1h, xi_2h = tpcf_one_two_halo_decomp(gal_pos, cat.model.mock.galaxy_table['halo_hostid'], r_bins, do_cross = False,\ estimator = 'Landy-Szalay', num_threads = 4, period = cat.Lbox/cat.h) #xi[i, 10:] = np.r_[xi_1h.squeeze(), xi_2h.squeeze()] #del cat

for boxno in xrange(40): cat = TrainingBox(boxno, system = 'ki-ls') cat.load_catalog(1.0) concs = cat.halocat.halo_table['halo_nfw_conc'] idxs = np.isfinite(concs) #print np.sum(~idxs)*1.0/idxs.shape[0] sns.distplot(np.log10(concs[idxs])) plt.title('Box %d'%boxno) plt.show()



In [136]:

    
boxno = 1
cat = TrainingBox(boxno, system = 'ki-ls')



In [137]:

    
cat.filenames









    Out[137]:





['/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_0.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_1.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_2.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_3.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_4.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_5.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_6.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_7.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_8.list',
 '/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b/outbgc2_9.list']



In [145]:

    
halodir









    Out[145]:





'/nfs/slac/g/ki/ki22/cosmo/beckermr/tinkers_emu/Box001/halos/m200b'



In [138]:

    
from os.path import join, dirname
from csv import DictReader, DictWriter

fieldnames = '#ID DescID M200b Vmax Vrms R200b Rs Np X Y Z VX VY VZ Parent_ID'.split(' ')

missed_keys = dict()
#cosmo_idx = 0
snapshot_idx = 9

halodir = dirname(cat.filenames[snapshot_idx])
outlist_fname = join(halodir, "out_%d.list"%snapshot_idx)

print cosmo_idx, snapshot_idx

outlist_rs = dict()
outlist_rvir = dict()
with open(outlist_fname) as csvfile:
    reader = DictReader(filter(lambda row: row[0]!='#' or row[:3]=='#ID', csvfile), delimiter = ' ')
    for row in reader:
        outlist_rs[row['#ID']] = row['Rs']
        outlist_rvir[row['#ID']] = row['R200b']

0 9



In [139]:

    
outlist_rs_arr = np.array(outlist_rs.values()).astype(float)
outlist_rvir_arr = np.array(outlist_rvir.values()).astype(float)

outlist_concs = outlist_rvir_arr/outlist_rs_arr



In [140]:

    
outlist_concs.shape









    Out[140]:





(9537295,)



In [141]:

    
cat.load(1.0)









    



/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/sim_manager/cached_halo_catalog.py:567: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.
  f = h5py.File(self.log_entry.fname)



In [142]:

    
len(cat.halocat.halo_table)









    



/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/sim_manager/halo_table_cache_log_entry.py:404: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.
  f = h5py.File(self.fname)
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/sim_manager/halo_table_cache_log_entry.py:221: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.
  f = h5py.File(self.fname)
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/sim_manager/halo_table_cache_log_entry.py:307: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.
  f = h5py.File(self.fname)






    Out[142]:





9537295



In [143]:

    
arr_idxs = np.isfinite(outlist_concs)
cat_idxs = np.isfinite(cat.halocat.halo_table['halo_nfw_conc'])



In [144]:

    
sns.distplot(np.log10(outlist_concs[arr_idxs]))
sns.distplot(np.log10(cat.halocat.halo_table['halo_nfw_conc'][cat_idxs]))
plt.title('Box %d'%boxno)









    Out[144]:





<matplotlib.text.Text at 0x7fdbfabe8710>



In [128]:

    
cat_rs = cat.halocat.halo_table['halo_rs']



In [129]:

    
so_rs, sc_rs = sorted(outlist_rs_arr), sorted(cat_rs)



In [130]:

    
plt.hist(so_rs, alpha = 0.3)
plt.hist(sc_rs, alpha = 0.3)
plt.loglog();



In [131]:

    
so_rs = np.array(so_rs)/1000



In [132]:

    
sc_rs = np.array(sc_rs)



In [135]:

    
so_rs[-100:], sc_rs[-100:]









    Out[135]:





(array([1.166551, 1.16876 , 1.170837, 1.172706, 1.173435, 1.175669,
        1.178514, 1.179364, 1.179552, 1.185505, 1.185936, 1.189598,
        1.191034, 1.194877, 1.195465, 1.195893, 1.201139, 1.201318,
        1.208251, 1.221391, 1.228048, 1.23174 , 1.233371, 1.233784,
        1.234081, 1.234843, 1.239313, 1.248351, 1.250539, 1.251836,
        1.254106, 1.272076, 1.274141, 1.276328, 1.278318, 1.282019,
        1.291203, 1.296845, 1.309911, 1.312583, 1.312787, 1.316627,
        1.31821 , 1.32156 , 1.326773, 1.328898, 1.335692, 1.335851,
        1.337527, 1.339705, 1.340078, 1.345723, 1.345782, 1.346101,
        1.346937, 1.352792, 1.361605, 1.363058, 1.366344, 1.368634,
        1.372277, 1.372996, 1.375814, 1.384911, 1.394169, 1.398047,
        1.406694, 1.413024, 1.416634, 1.418892, 1.420147, 1.435947,
        1.436082, 1.447801, 1.479046, 1.492542, 1.499214, 1.504311,
        1.506773, 1.519541, 1.521205, 1.534266, 1.584563, 1.58914 ,
        1.630004, 1.636454, 1.642735, 1.653295, 1.690613, 1.704998,
        1.770431, 1.786943, 1.79951 , 1.819427, 1.865483, 1.93627 ,
        1.93788 , 2.011709, 2.140498, 2.241241]),
 array([1.171111 , 1.172706 , 1.172861 , 1.175842 , 1.179552 , 1.180777 ,
        1.182597 , 1.18418  , 1.1882399, 1.189598 , 1.19321  , 1.194256 ,
        1.1949229, 1.1958929, 1.201318 , 1.204193 , 1.208251 , 1.208668 ,
        1.212522 , 1.214465 , 1.215181 , 1.221765 , 1.227957 , 1.231712 ,
        1.233863 , 1.234102 , 1.235334 , 1.23556  , 1.237025 , 1.25026  ,
        1.25059  , 1.25508  , 1.263268 , 1.272076 , 1.278318 , 1.282943 ,
        1.2862159, 1.286764 , 1.292182 , 1.296515 , 1.2979289, 1.3006281,
        1.312141 , 1.3193669, 1.3204399, 1.3263731, 1.3267729, 1.328258 ,
        1.33541  , 1.335692 , 1.339946 , 1.340078 , 1.3446541, 1.3447601,
        1.345782 , 1.347507 , 1.350225 , 1.352428 , 1.356171 , 1.359543 ,
        1.360836 , 1.371989 , 1.374221 , 1.376729 , 1.378118 , 1.3782581,
        1.398975 , 1.400375 , 1.4066939, 1.4132111, 1.414827 , 1.4166341,
        1.4359471, 1.4407729, 1.478751 , 1.479818 , 1.4934909, 1.497986 ,
        1.5043111, 1.505575 , 1.518742 , 1.526791 , 1.588054 , 1.590426 ,
        1.629212 , 1.6334821, 1.644296 , 1.652302 , 1.693099 , 1.7142559,
        1.770641 , 1.7730861, 1.8139969, 1.815874 , 1.865709 , 1.933868 ,
        1.935756 , 2.012833 , 2.1428368, 2.237226 ], dtype=float32))



In [ ]: