I've been given a new task to study how scale dependent bias varias as a function of HOD params and



In [1]:

    
from pearce.mocks.kittens import cat_dict
import numpy as np
from scipy.stats import binned_statistic, linregress



In [2]:

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



In [3]:

    
sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})



In [4]:

    
cosmo_params = {'simname':'chinchilla', 'Lbox':400.0, 'scale_factors':[0.81120]}



In [5]:

    
cat = cat_dict[cosmo_params['simname']](**cosmo_params)#construct the specified catalog!



In [6]:

    
cat.load(0.81120, tol = 0.01, HOD='redMagic', particles = True)#, hod_kwargs = {'sec_haloprop_key':'halo_log_nfw_conc'})#, hod_kwargs={'split': 0.5})



In [7]:

    
chain_fname = '/u/ki/swmclau2/des/PearceMCMC/200_walkers_50000_steps_chain_redmagic_bias_z0.23.npy'
chain = np.genfromtxt(chain_fname)

chain = chain[chain[:,0] >=-1.0] chain = chain[chain[:, -1] <=0.5]



In [8]:

    
n_walkers = 200
n_params = chain.shape[1]
n_burn = 30000
chain = chain[n_walkers*n_burn:, :]
print chain.shape









    



(4000000, 6)



In [9]:

    
ordered_param_names = param_names = ['logMmin', 'f_c', 'logM0', 'sigma_logM',  'logM1',   'alpha']

ordered_param_names = ['mean_occupation_satellites_assembias_param1','logMmin', 'mean_occupation_centrals_assembias_param1','logM1', 'logM0','sigma_logM', 'alpha', 'f_c']



In [10]:

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

# biases = [] indicies = np.random.choice(chain.shape[0], size = 1000, replace = False) for i, row in enumerate(chain[indicies]): print i hod_params = dict(zip(ordered_param_names, row)) cat.populate(hod_params) bias = cat.calc_bias(rbins, use_corrfunc = False) biases.append(bias) #plt.plot(rbc, bias, alpha = 0.1, color = 'b')

xis = [] indicies = np.random.choice(chain.shape[0], size = 100, replace = False) for i, row in enumerate(chain[indicies]): print i hod_params = dict(zip(ordered_param_names, row)) hod_params['mean_occupation_centrals_assembias_param1'] = -1.0 hod_params['mean_occupation_satellites_assembias_param1'] = 0.0 cat.populate(hod_params) xi = cat.calc_xi(rbins, use_corrfunc = False, do_jackknife=False) print xi xis.append(xi) #plt.plot(rbc, bias, alpha = 0.1, color = 'b')

for xi in xis: plt.plot(rbc, xi/xi_mm, alpha = 0.1, color = 'b') plt.xscale('log') plt.xlabel(r'$r$ [Mpc]') plt.ylabel(r'$\xi_{gg}/\xi_{mm}(r)$') plt.ylim([0.0, 2.0]) plt.xlim([1e0, 40]) plt.title(r"$\mathcal{A}_{cen}$ = %.1f, $\mathcal{A}_{sat}$ = %.1f"%(hod_params['mean_occupation_centrals_assembias_param1'],hod_params['mean_occupation_satellites_assembias_param1'])) plt.show()

xis = [] indicies = np.random.choice(chain.shape[0], size = 100, replace = False) for i, row in enumerate(chain[indicies]): ab_xis = [] print i for a_cen, a_sat in [(0.0, 0.0), (1.0, 0.0), (-1.0, 0.0), (0.0, 1.0), (0.0, -1.0)]: hod_params = dict(zip(ordered_param_names, row)) hod_params['mean_occupation_centrals_assembias_param1'] = a_cen hod_params['mean_occupation_satellites_assembias_param1'] = a_sat cat.populate(hod_params) xi = cat.calc_xi(rbins, use_corrfunc = False, do_jackknife=False) ab_xis.append(xi) xis.append(ab_xis) #plt.plot(rbc, bias, alpha = 0.1, color = 'b')

for j, xi in enumerate(xis): for i, (ab_xi, c) in enumerate(zip(xi, ['b', 'r', 'g', 'm', 'k']) ): if i > 3: continue if j == 0: if i == 0: plt.plot(rbc, ab_xi/xi_mm, color = c,label = 'No AB') if i==1: plt.plot(rbc, ab_xi/xi_mm, color = c,label = 'Positive AB') if i == 2: plt.plot(rbc, ab_xi/xi_mm, color = c,label = 'Negative AB') else: plt.plot(rbc, ab_xi/xi_mm, alpha = 0.05, color = c) plt.xscale('log') plt.xlabel(r'$r$ [Mpc]') plt.ylabel(r'$\xi_{gg}/\xi_{mm}(r)$') plt.ylim([0.0, 2.0]) plt.xlim([1e0, 40]) plt.legend(loc='best') plt.title(r"Maximal AB Values") plt.show()



In [11]:

    
xis = []
indicies = np.random.choice(chain.shape[0], size = 1000, replace = False)
for i, row in enumerate(chain[indicies]):
    print i
    try:
        hod_params = dict(zip(ordered_param_names, row))
        cat.populate(hod_params)
        xi = cat.calc_xi(rbins, use_corrfunc = False, do_jackknife=False)
        xis.append(xi)
    except MemoryError:
        continue
    #plt.plot(rbc, bias, alpha = 0.1, color = 'b')









    



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---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-11-b5744e8e7df8> in <module>()
      5     try:
      6         hod_params = dict(zip(ordered_param_names, row))
----> 7         cat.populate(hod_params)
      8         xi = cat.calc_xi(rbins, use_corrfunc = False, do_jackknife=False)
      9         xis.append(xi)

/u/ki/swmclau2/.local/lib/python2.7/site-packages/pearce/mocks/cat.pyc in populate(self, params, min_ptcl)
    707         # might be able to check is model has_attr mock.
    708         if self.populated_once:
--> 709             self.model.mock.populate(Num_ptcl_requirement=min_ptcl)
    710         else:
    711             self.model.populate_mock(self.halocat, Num_ptcl_requirement=min_ptcl)

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/halotools-0.6.dev4681-py2.7-linux-x86_64.egg/halotools/empirical_models/factories/hod_mock_factory.pyc in populate(self, seed, **kwargs)
    326             for halocatkey in self.additional_haloprops:
    327                 self.galaxy_table[halocatkey][gal_type_slice] = np.repeat(
--> 328                     self.halo_table[halocatkey], self._occupation[gal_type], axis=0)
    329 
    330         self.galaxy_table['x'] = self.galaxy_table['halo_x']

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc in repeat(a, repeats, axis)
    396 
    397     """
--> 398     return _wrapfunc(a, 'repeat', repeats, axis=axis)
    399 
    400 

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc in _wrapfunc(obj, method, *args, **kwds)
     55 def _wrapfunc(obj, method, *args, **kwds):
     56     try:
---> 57         return getattr(obj, method)(*args, **kwds)
     58 
     59     # An AttributeError occurs if the object does not have

KeyboardInterrupt:



In [ ]:

    
for xi in xis:
        plt.plot(rbc, xi, alpha = 0.01, color = 'g')

plt.loglog()
plt.xlabel(r'$r$ [Mpc]')
plt.ylabel(r'$\xi_{gg}(r)$')
#plt.ylim([-5, 20])
plt.xlim([1e0, 40])
plt.title(r"Allowed biases from Buzzard Y3 $w(\theta)$")
plt.show()



In [12]:

    
xi_mm = cat.calc_xi_mm(rbins)









    



/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/halotools-0.6.dev4681-py2.7-linux-x86_64.egg/halotools/mock_observables/two_point_clustering/clustering_helpers.py:134: UserWarning: 
 `sample1` exceeds `max_sample_size` 
downsampling `sample1`...
  warn(msg)






    



---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-12-801fd1ee18a8> in <module>()
----> 1 xi_mm = cat.calc_xi_mm(rbins)

/u/ki/swmclau2/.local/lib/python2.7/site-packages/pearce/mocks/cat.pyc in calc_xi_mm(self, rbins, n_cores, use_corrfunc)
    682         else:
    683             xi_all = tpcf(pos / self.h, rbins, period=self.Lbox / self.h, num_threads=n_cores,
--> 684                           estimator='Landy-Szalay')
    685 
    686         #cache, so we don't ahve to repeat this calculation several times.

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/halotools-0.6.dev4681-py2.7-linux-x86_64.egg/halotools/mock_observables/two_point_clustering/tpcf.pyc in tpcf(sample1, rbins, sample2, randoms, period, do_auto, do_cross, estimator, num_threads, max_sample_size, approx_cell1_size, approx_cell2_size, approx_cellran_size, RR_precomputed, NR_precomputed, seed)
    362     D1D1, D1D2, D2D2 = _pair_counts(sample1, sample2, rbins, period,
    363         num_threads, do_auto, do_cross, _sample1_is_sample2,
--> 364         approx_cell1_size, approx_cell2_size)
    365 
    366     # count random pairs

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/halotools-0.6.dev4681-py2.7-linux-x86_64.egg/halotools/mock_observables/two_point_clustering/tpcf.pyc in _pair_counts(sample1, sample2, rbins, period, num_threads, do_auto, do_cross, _sample1_is_sample2, approx_cell1_size, approx_cell2_size)
    121             num_threads=num_threads,
    122             approx_cell1_size=approx_cell1_size,
--> 123             approx_cell2_size=approx_cell1_size)
    124         D1D1 = np.diff(D1D1)
    125     else:

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/halotools-0.6.dev4681-py2.7-linux-x86_64.egg/halotools/mock_observables/pair_counters/npairs_3d.pyc in npairs_3d(sample1, sample2, rbins, period, verbose, num_threads, approx_cell1_size, approx_cell2_size)
    144 
    145     if num_threads > 1:
--> 146         pool = multiprocessing.Pool(num_threads)
    147         result = pool.map(engine, cell1_tuples)
    148         counts = np.sum(np.array(result), axis=0)

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/multiprocessing/__init__.pyc in Pool(processes, initializer, initargs, maxtasksperchild)
    230     '''
    231     from multiprocessing.pool import Pool
--> 232     return Pool(processes, initializer, initargs, maxtasksperchild)
    233 
    234 def RawValue(typecode_or_type, *args):

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/multiprocessing/pool.pyc in __init__(self, processes, initializer, initargs, maxtasksperchild)
    157         self._processes = processes
    158         self._pool = []
--> 159         self._repopulate_pool()
    160 
    161         self._worker_handler = threading.Thread(

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/multiprocessing/pool.pyc in _repopulate_pool(self)
    221             w.name = w.name.replace('Process', 'PoolWorker')
    222             w.daemon = True
--> 223             w.start()
    224             debug('added worker')
    225 

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/multiprocessing/process.pyc in start(self)
    128         else:
    129             from .forking import Popen
--> 130         self._popen = Popen(self)
    131         _current_process._children.add(self)
    132 

/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/multiprocessing/forking.pyc in __init__(self, process_obj)
    119             self.returncode = None
    120 
--> 121             self.pid = os.fork()
    122             if self.pid == 0:
    123                 if 'random' in sys.modules:

OSError: [Errno 12] Cannot allocate memory



In [ ]:

    
plt.plot(rbc, xi_mm)
plt.plot(rbc, xi)
plt.loglog();



In [ ]:

    
for xi in xis:
        plt.plot(rbc, xi/xi_mm, alpha = 0.01, color = 'b')

plt.xscale('log')
plt.xlabel(r'$r$ [Mpc]')
plt.ylabel(r'$\xi_{gg}/\xi_{mm}(r)$')
#plt.ylim([-5, 20])
plt.xlim([1e0, 40])
plt.title(r"Allowed biases from AB chains")
plt.show()



In [ ]:

    
xis = np.array(xis)
xis.shape



In [ ]:

    
plt.hist(np.sqrt(xis[:,-1]/xi_mm[-1]), color = 'g', bins =10, normed = True);
plt.xlabel(r"$b$")
plt.ylabel(r"P($b$)")
plt.title("Posterior histrogram of linear biases AB chain")

colors = sns.diverging_palette(80, 190,l= 80, n=N) sns.palplot(colors)

fig = plt.figure(figsize=(10,8)) for label, value, c in zip(varied_param_vals, bias_vals, colors): plt.plot(rbc, value, label = r'$\log{M_{min}}= %.1f$'%label, color = c) plt.xscale('log') plt.legend(loc = 'best') plt.xlabel(r'$r$ [Mpc]') plt.ylabel(r'$b(r)$') plt.title(r'Bias as a function of Central Log M Min $\log{M_{min}}$')



In [ ]: