def lnlike(theta):
model = Zheng07(threshold = -20.)
model.param_dict['logM0'] = theta[0]
model.param_dict['sigma_logM'] = np.exp(theta[1])
model.param_dict['logMmin'] = theta[2]
model.param_dict['alpha'] = theta[3]
model.param_dict['logM1'] = theta[4]
model.populate_mock()
r , xir_model = model.mock.compute_galaxy_clustering()
res = xir_data - xir_model
return -0.5*np.sum(np.dot(np.dot(res , inv_c) , res))
def __init__(self):
self.model = Zheng07(threshold = -21.5)
import numpy as np
import abcpmc
import matplotlib.pyplot as plt
from interruptible_pool import InterruptiblePool
import time
plt.switch_backend("Agg")
from halotools.empirical_models import Zheng07
model = Zheng07(threshold = -21.5)
print 'Data HOD Parameters ', model.param_dict
n_avg = 5
avg_nz, avg_corr = 0., 0.
for i in xrange(n_avg):
model.populate_mock()
# number density
avg_nz += model.mock.number_density
# 6th element of xi(r) array
r, xi_r = model.mock.compute_galaxy_clustering(N_threads=1)
try:
avg_xi += xi_r
except NameError:
avg_xi = xi_r
def __init__(self):
self.model = Zheng07()
import time
plt.switch_backend("Agg")
from halotools.empirical_models import Zheng07
from astropy.table import Table
import corner
from scipy.stats import norm, gamma
from scipy.stats import multivariate_normal
from scipy.spatial import cKDTree
import abcpmc
import seaborn as sns
sns.set_style("white")
np.random.seed()
from abcpmc import mpi_util
from scipy.stats import ks_2samp
model = Zheng07(threshold=-20.)
print 'Data HOD Parameters ', model.param_dict
"""data and covariance"""
#We shouldn't treat a mean of mocks as a data!
mock_nbar = np.loadtxt("nbar_Mr20.dat")
#data_nbar = np.mean(mock_nbar)
#mocks_xir = np.loadtxt("xir.dat")
#data_xir = np.mean(mocks_xir , axis = 0)
model.populate_mock()
data_nbar = model.mock.number_density
data_xir = model.mock.compute_galaxy_clustering()[1]
np.savetxt("xir_Mr20.dat", data_xir)
data = [data_nbar, data_xir]
ax.plot(mass, input_hod_centrals, color='red', linewidth = 1.)
plt.loglog()
plt.xlim(xmin=1e11, xmax=1e14)
plt.ylim(ymin=5e-3, ymax=10)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
fig4.savefig('/home/mj/public_html/abcpaper/hod_centrals.pdf',
bbox_extra_artists=[xlabel, ylabel], bbox_inches='tight')
"""
fig5 = plt.figure()
ax = fig5.add_subplot(111)
xlabel = ax.set_xlabel('R [Mpc]', fontsize=20)
ylabel = ax.set_ylabel(r'$\xi_{\rm gg}$', fontsize=25)
title = ax.set_title(r'tpcf of galaxies with $M_{r} > -20$', fontsize=25)
m = Zheng07()
m.populate_mock()
rr = m.compute_galaxy_clustering()[0]
xir = np.loadtxt("xir_Mr20.dat")
xir_cov = np.loadtxt("clustering_covariance_Mr20.dat")
ax.errorbar(rr,
xir,
yerr=np.diag(xir_cov)**.5,
fmt="ok",
ms=1,
capsize=2,
alpha=1.)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.xlim(xmin=0.1, xmax=15)
ax.set_yscale("log")
from halotools.empirical_models import Zheng07, model_defaults
from halotools.mock_observables import wp
from halotools.mock_observables.clustering import tpcf
from halotools.empirical_models.mock_helpers import (three_dim_pos_bundle,
infer_mask_from_kwargs)
from halotools.mock_observables.clustering import wp
from halotools.sim_manager import supported_sims
import matplotlib.pyplot as plt
plt.switch_backend("Agg")
import time
import numpy as np
model = Zheng07()
xir = []
for i in range(500):
model.populate_mock()
xir.append(model.mock.compute_galaxy_clustering()[1])
covar = np.cov(np.array(xir).T)
np.savetxt("clustering_covariance_Mr20.dat", covar)
"""
a = time.time()
model.mock.compute_galaxy_clustering()
print time.time() - a
rbins = model_defaults.default_rbins
rbin_centers = (rbins[1:] + rbins[:-1])/2.
cat = supported_sims.HaloCatalog()
l = cat.Lbox
print l
p_bins = np.linspace(0,l/2,200)
mask = infer_mask_from_kwargs(model.mock.galaxy_table)