In [1]:

    

import matplotlib
#matplotlib.use('Agg')
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()


    

In [2]:

    

import numpy as np
from pearce.mocks.kittens import TrainingBox, MDHR


    

In [3]:

    

cat = TrainingBox(boxno=0, system = 'ki-ls')


    

In [4]:

    

cat.load(1.0, HOD = 'corrZheng07', biased_satellites = True, hod_kwargs={'sec_haloprop_key': 'halo_local_density_10'})


    

    




/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/phase_space_models/analytic_models/satellites/nfw/biased_nfw_phase_space.py:197: UserWarning: You have selected 99 bins to digitize host halo concentration 
and 11 bins to digitize the galaxy bias parameter.
To populate mocks, the BiasedNFWPhaseSpace class builds a lookup table with shape (99, 11, 101),
one entry for every numerical solution to the Jeans equation.
Using this fine of a binning requires a long pre-computation of 109989 integrals
.Make sure you actually need to use so many bins
  warn(lookup_table_performance_warning.format(*args))
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/phase_space_models/analytic_models/satellites/nfw/biased_nfw_phase_space.py:197: UserWarning: You have selected 99 bins to digitize host halo concentration 
and 11 bins to digitize the galaxy bias parameter.
To populate mocks, the BiasedNFWPhaseSpace class builds a lookup table with shape (99, 11, 101),
one entry for every numerical solution to the Jeans equation.
Using this fine of a binning requires a long pre-computation of 109989 integrals
.Make sure you actually need to use so many bins
  warn(lookup_table_performance_warning.format(*args))
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/factories/hod_model_factory.py:741: UserWarning: 

The param_dict key sigma_logM appears in more than one component model.
This is permissible, but if you are seeing this message you should be sure you understand it.
In particular, double-check that this parameter does not have conflicting meanings across components.

If you do not wish to see this message every time you instantiate, 
simply attach a _suppress_repeated_param_warning attribute 
to any of your component models and set this variable to ``True``.

  warn(msg % key)
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/factories/hod_model_factory.py:741: UserWarning: 

The param_dict key logMmin appears in more than one component model.
This is permissible, but if you are seeing this message you should be sure you understand it.
In particular, double-check that this parameter does not have conflicting meanings across components.

If you do not wish to see this message every time you instantiate, 
simply attach a _suppress_repeated_param_warning attribute 
to any of your component models and set this variable to ``True``.

  warn(msg % key)
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/factories/hod_model_factory.py:741: UserWarning: 

The param_dict key mean_occupation_centrals_assembias_corr1 appears in more than one component model.
This is permissible, but if you are seeing this message you should be sure you understand it.
In particular, double-check that this parameter does not have conflicting meanings across components.

If you do not wish to see this message every time you instantiate, 
simply attach a _suppress_repeated_param_warning attribute 
to any of your component models and set this variable to ``True``.

  warn(msg % key)

In [5]:

    

cat.halocat.halo_table.colnames


    

    
Out[5]:





['halo_upid',
 'halo_vx',
 'halo_y',
 'halo_x',
 'halo_z',
 'halo_vy',
 'halo_vz',
 'halo_rs',
 'halo_rvir',
 'halo_mvir',
 'halo_id',
 'halo_nfw_conc',
 'halo_hostid',
 'halo_local_density_1',
 'halo_local_density_5',
 'halo_local_density_10',
 'halo_mvir_host_halo']

In [6]:

    

cat.model.param_dict


    

    
Out[6]:





{'alpha': 1.06,
 'conc_gal_bias': 1.0,
 'logM0': 11.38,
 'logM1': 13.31,
 'logMmin': 12.02,
 'mean_occupation_centrals_assembias_corr1': 0.5,
 'mean_occupation_satellites_assembias_corr1': 0.5,
 'sigma_logM': 0.26}

In [7]:

    

print cat.model.param_dict.keys()


    

    




['mean_occupation_centrals_assembias_corr1', 'logMmin', 'logM0', 'sigma_logM', 'mean_occupation_satellites_assembias_corr1', 'logM1', 'alpha', 'conc_gal_bias']

In [8]:

    

default_params = cat.model.param_dict
default_params['mean_occupation_centrals_assembias_corr1'] = 0.0
default_params['mean_occupation_satellites_assembias_corr1'] = 0.0
#default_params['mean_occupation_centrals_assembias_split1'] = 0.5
#default_params['mean_occupation_satellites_assembias_split1'] = 0.5


    

In [9]:

    

default_params['sigma_logM'] = 0.5
default_params['logMmin'] = 13.5


    

In [10]:

    

cat.model.model_dictionary['centrals_occupation'].sec_haloprop_key


    

    
Out[10]:





'halo_local_density_10'

In [11]:

    

r_bins = np.logspace(-1.1, 1.6, 19)
rbc = (r_bins[1:]+r_bins[:-1])/2.0


    

In [12]:

    

#default_y = np.zeros((18,))
#for i in xrange(N):
#    print i,
cat.populate(default_params)
default_y= cat.calc_xi(r_bins)
#default_y/=N


    

    




/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/phase_space_models/analytic_models/monte_carlo_helpers.py:205: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  self.rad_prof_func_table_indices[digitized_param_list]
/u/ki/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5005-py2.7-linux-x86_64.egg/halotools/empirical_models/phase_space_models/analytic_models/monte_carlo_helpers.py:522: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  self.rad_prof_func_table_indices[digitized_param_list]

In [26]:

    

N = 10
cmap = sns.color_palette("GnBu_d", N)


    

In [27]:

    

pname = 'conc_gal_bias'
l, h = -2, 2
#l, h = 0.0, 1.0

d = default_params.copy()
for v, c in zip(np.logspace(l,h,N), cmap):
    d[pname] = v
    #y = np.zeros((18,))
    #for i in xrange(N):
       # print i,
    cat.populate(d)
    y= cat.calc_xi(r_bins)
    print v, (y/default_y)[0]
    #y/=N
    plt.plot(rbc, y/default_y, label = v, c = c)
    
plt.xscale('log')
#plt.loglog()
plt.legend(loc = 'best')
plt.show();


    

    




0.01 0.12442069731470948
0.027825594022071243 0.1256626187317996
0.0774263682681127 0.1274281264460682
0.21544346900318834 1.0137732660150431
0.5994842503189409 1.0175011343859999
1.6681005372000592 1.974100464883608
4.6415888336127775 3.2108381591481554
12.915496650148826 3.3118969647137337
35.93813663804626 3.3195622537815006
100.0 3.302926171204394






    

In [28]:

    

d['conc_gal_bias'] = 50.0


    

In [29]:

    

cat.populate(d)


    

In [30]:

    

sat_cut = cat.model.mock.galaxy_table['gal_type'] == 'satellites'


    

In [31]:

    

cat.model.mock.galaxy_table.colnames


    

    
Out[31]:





['halo_upid',
 'halo_num_centrals',
 'halo_hostid',
 'halo_local_density_10',
 'halo_y',
 'halo_x',
 'halo_id',
 'halo_vx',
 'halo_vy',
 'halo_vz',
 'halo_num_satellites',
 'conc_NFWmodel',
 'halo_rvir',
 'halo_z',
 'halo_mvir',
 'conc_gal_bias',
 'gal_type',
 'vx',
 'conc_galaxy',
 'host_centric_distance',
 'vy',
 'y',
 'x',
 'vz',
 'z']

In [32]:

    

plt.hist(cat.model.mock.galaxy_table[sat_cut]['conc_galaxy']*1.0/cat.model.mock.galaxy_table[sat_cut]['conc_NFWmodel'])
plt.yscale('log')


    

In [33]:

    

set(cat.model.mock.galaxy_table['gal_type'])


    

    
Out[33]:





{'centrals', 'satellites'}

In [34]:

    

cat.model._input_model_dictionary['satellites_profile']._conc_NFWmodel_lookup_table_min,\
cat.model._input_model_dictionary['satellites_profile']._conc_NFWmodel_lookup_table_max


    

    
Out[34]:





(1, 99)

In [35]:

    

plt.hist(cat.halocat.halo_table['halo_nfw_conc'][np.isfinite(cat.halocat.halo_table['halo_nfw_conc'])][:1000])
plt.yscale('log')


    

In [36]:

    

finite_idxs = np.isfinite(cat.halocat.halo_table['halo_nfw_conc'])
np.sum(cat.halocat.halo_table[finite_idxs]['halo_nfw_conc']>100)*1.0/finite_idxs.shape[0]


    

    
Out[36]:





0.002210009806906087

In [37]:

    

plt.hist(cat.model.mock.galaxy_table['conc_galaxy'])
plt.yscale('log')


    

In [ ]: