In [1]:

    

%matplotlib inline
from matplotlib import pyplot as plt


    

In [2]:

    

import h5py
from os import path
import numpy as np


    

    




/home/users/swmclau2/.local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [3]:

    

aemulator_dir = '/home/users/swmclau2/scratch/Aemulators/'


    

In [4]:

    

ds_hsab = h5py.File(path.join(aemulator_dir, 'ds_hsab/PearceDsHSABCosmo.hdf5'), 'r')


    

In [5]:

    

ds_hsab_test = h5py.File(path.join(aemulator_dir, 'ds_hsab_test/PearceDsHSABCosmoTest.hdf5'), 'r')


    

In [6]:

    

ds_hsab_test2 = h5py.File(path.join(aemulator_dir, 'ds_hsab_test2/PearceDsHSABCosmoTest.hdf5'), 'r')


    

In [7]:

    

ds_hsab.attrs['cosmo_param_names']


    

    
Out[7]:





array(['ombh2', 'omch2', 'w0', 'ns', 'H0', 'Neff', 'sigma8'], dtype='|S6')

In [8]:

    

ds_hsab_test2.attrs['cosmo_param_names']


    

    
Out[8]:





array(['ombh2', 'omch2', 'w0', 'ns', 'H0', 'Neff', 'sigma8'], dtype='|S6')

In [9]:

    

ds_hsab_data = np.zeros((40, 1000, 18))
for i in xrange(40):
    ds_hsab_data[i] = ds_hsab['cosmo_no_%02d'%i]['a_1.000']['obs'][()]


    

In [10]:

    

ds_hsab_test_data = np.zeros((35, 100, 18))
for i in xrange(35):
    ds_hsab_test_data[i] = ds_hsab_test['cosmo_no_%02d'%i]['a_1.000']['obs'][()]


    

In [11]:

    

ds_hsab_test2_data = np.zeros((35, 100, 18))
for i in xrange(35):
    ds_hsab_test2_data[i] = ds_hsab_test2['cosmo_no_%02d'%i]['a_1.000']['obs'][()]


    

In [12]:

    

r_bins = np.logspace(-1, 1.6, 19)
rbc = (r_bins[1:] +r_bins[:-1])/2.0


    

In [39]:

    

N = 5
for i in xrange(0,35,N):
    plt.plot(rbc, ds_hsab_test2_data[i:i+N].mean(axis = (0,1)), label = i)

denom = ds_hsab_data.mean(axis = (0,1))
plt.legend(loc='best')
plt.plot(rbc, denom, ls = '--', lw=10)
plt.xscale('log')


    

In [14]:

    

np.any(np.all(ds_hsab_data==0.0, axis = 2))


    

    
Out[14]:





False

In [16]:

    

np.any(np.all(ds_hsab_test2_data==0.0, axis = 2))


    

    
Out[16]:





False

In [14]:

    

from pearce.mocks.kittens import TrainingBox


    

In [15]:

    

cat = TrainingBox(0)


    

In [16]:

    

cat.load(1.0, HOD='hsabZheng07', particles=True, hod_kwargs={'sec_haloprop_key':'halo_local_density_10'})


    

In [31]:

    

ds_hsab.attrs['hod_param_vals'][1]


    

    
Out[31]:





array([-0.42542543, 13.08648649,  0.22027027,  0.68968969, 14.0963964 ,
        1.24534535,  0.52702703])

In [32]:

    

hod_pnames = ds_hsab.attrs['hod_param_names']
hod_dict = dict(zip(hod_pnames, ds_hsab.attrs['hod_param_vals'][1]))


    

In [33]:

    

hod_dict


    

    
Out[33]:





{'alpha': 1.2453453453453454,
 'conc_gal_bias': 0.527027027027027,
 'logM0': 13.086486486486486,
 'logM1': 14.096396396396395,
 'mean_occupation_centrals_assembias_param1': -0.4254254254254254,
 'mean_occupation_satellites_assembias_param1': 0.6896896896896896,
 'sigma_logM': 0.2202702702702703}

In [34]:

    

ds_hsab_data.shape


    

    
Out[34]:





(40, 1000, 18)

In [35]:

    

cat.populate(hod_dict, min_ptcl=100)


    

In [ ]:

    

ds = cat.calc_ds(r_bins, n_cores = 1)


    

In [ ]:

    

ds


    

In [ ]:

    

denom = ds_hsab_data.mean(axis = (0,1))
plt.plot(rbc, ds_hsab_data[0,1])
plt.plot(rbc, np.log10(ds) )

plt.xscale('log')


    

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