MS2=False,
bins=50,
color='k',
label=''):
check_for_mass_key(mass_key)
mass = np.array(m2.get_col_halo_table(MS_table, mass_key))
if MS2 == False:
mass_conv = 8.6E8
else:
mass_conv = 6.885E6
if no_h:
mass_conv /= cosmo['h']
mass = np.log10(mass * mass_conv)
hist_ret = plt.hist(mass, bins, color=color, label=label)
return hist_ret
if __name__ == '__main__':
a = m2.read_halo_table_ascii(
"/Volumes/DATA/Millennium Data/MillenniumII/snap6.txt",
"0,1,2,3,4,5,6,7,8,9,x,x,x,x,x,15,16,17,x,x,20")
plot_mass_histogram(a,
mass_key='fof_np',
no_h=True,
MS2=True,
color='k',
label='no_h')
plot_mass_histogram(a, mass_key='fof_np', no_h=False, MS2=True, color='r')
plt.rc('text', usetex=True)
plt.xlabel('Mass (M$_\\odot$)')
plt.show()
##function plots Millennium or MillenniumII structures
def plot_position_scatter(MS_table, pos_key1 = 'x', pos_key2 = 'y'):
check_for_pos_key(pos_key1)
check_for_pos_key(pos_key2)
pos1 = m2.get_col_halo_table(MS_table, pos_key1)
pos2 = m2.get_col_halo_table(MS_table, pos_key2)
plt.plot(pos1, pos2, '*', markersize = .96)
plt.xlabel('{0} [Mpc / h]'.format(pos_key1))
plt.ylabel('{0} [Mpc / h]'.format(pos_key2))
def plot_mass_histogram(MS_table, mass_key = 'fof_np', no_h = True, MS2 = False, bins = 50, color = 'k', label = ''):
check_for_mass_key(mass_key)
mass = np.array(m2.get_col_halo_table(MS_table, mass_key))
if MS2 == False:
mass_conv = 8.6E8
else:
mass_conv = 6.885E6
if no_h:
mass_conv /= cosmo['h']
mass = np.log10(mass * mass_conv)
hist_ret = plt.hist(mass, bins, color = color, label = label)
return hist_ret
if __name__ == '__main__':
a = m2.read_halo_table_ascii("/Volumes/DATA/Millennium Data/MillenniumII/snap6.txt", "0,1,2,3,4,5,6,7,8,9,x,x,x,x,x,15,16,17,x,x,20")
plot_mass_histogram(a, mass_key = 'fof_np', no_h = True, MS2 = True, color = 'k', label = 'no_h')
plot_mass_histogram(a, mass_key = 'fof_np', no_h = False, MS2 = True, color = 'r')
plt.rc('text', usetex = True)
plt.xlabel('Mass (M$_\\odot$)')
plt.show()
import numpy as np
import MillenniumII as m2
import os
import Correlation_Func as cf
import Standard_Plots as sp
if __name__ == '__main__':
curr = '{0}/'.format(os.getcwd())
filen = 'Mstar_cat_snap31-MS2.txt'
xi_m_m_filen = 'xi_m_m_31.txt'
h = 0.73
mass_conv = 6.9E6 / h #Converts to Solar Masses
#Read in xi_m_m
xi_m_m_corr = cf.read_corr_file(xi_m_m_filen)
#Read in sample
sample = m2.read_halo_table_ascii('{0}{1}'.format(curr, filen), '7,8,9,17')
print 'Plotting position scatter plot of sample file: {0}'.format(filen)
sp.plot_position_scatter(sample, 'x', 'y')
plt.show()
print np.median(np.array(m2.get_col_halo_table(sample, 'fof_np')) * mass_conv)
sample_auto_corr = cf.calc_auto_corr(sample, MS2 = True)
#sample_cross_corr = calc_cross_corr(sample)
plt.loglog(sample_auto_corr['data'].r, sample_auto_corr['data'].cf, label = 'sample')
plt.loglog(xi_m_m_corr['data'].r, xi_m_m_corr['data'].cf, label = '$\\xi_{mm}$')
print sample_auto_corr['data'].cf
print xi_m_m_corr['data'].cf
plt.plot(sample_auto_corr['data'].r, np.sqrt(sample_auto_corr['data'].cf / xi_m_m_corr['data'].cf))
plt.legend()
plt.rc('text', usetex = True)
plt.xlabel('r [Mpc / h]')
plt.ylabel('$\\xi(r)$')
import MillenniumII as ml
import os
from subprocess import check_output
from IO import readfile
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
home = '{0}/'.format(os.environ['HOME'])
direc = '{0}/Projects/age-clustering/plots/'.format(home)
ifilearr = ['sig_massloss.txt', 'no_sig_massloss.txt']
for ifile in ifilearr:
data = ml.read_halo_table_ascii('{0}{1}'.format(direc, ifile), 'x,x,x,x,x,x,x,7,8,9,x,x,x,x,x,x,x,x,x,x,x,x,x,x')
x = np.array(ml.get_col_halo_table(data,'x'))
y = np.array(ml.get_col_halo_table(data, 'y'))
z = np.array(ml.get_col_halo_table(data, 'z'))
out_halo_name = '{0}halo_{1}'.format(direc, ifile)
ml.write_halo_table_ascii(out_halo_name, data, fmt = '0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20')
xiauto_file = '{0}xi_{1}'.format(direc, ifile)
#check_output(['2pt-autocorrelation', out_halo_name, xiauto_file])
#xi_auto = readfile(xiauto_file, 3, ',', 2)
plt.plot(x, y, '+', markersize = 0.9, label = ifile)
plt.show()
#plt.loglog(xi_auto[0],xi_auto[1], label = ifile)
#plt.legend()
#plt.show()
#haloid, subhaloid, treeid, descendantid, lastprogenitorid, snapnum, sub_np, x, y, z, firsthaloinfofgroupid, radii, fofid, fof_np, firstsubhaloid, max_tree_mass, max_tree_mass_snap, min_mass_root_max, min_mass_root_max_snap, assem_gao, assem_jp, form_gao, form_jp, merg
#check_output(['2pt-crosscorrelation', out_halo_name, halos_filename, xi_cross_filename])
import numpy as np
import MillenniumII as m2
import os
import Correlation_Func as cf
import Standard_Plots as sp
if __name__ == '__main__':
curr = '{0}/'.format(os.getcwd())
filen = 'Mstar_cat_snap31-MS2.txt'
xi_m_m_filen = 'xi_m_m_31.txt'
h = 0.73
mass_conv = 6.9E6 / h #Converts to Solar Masses
#Read in xi_m_m
xi_m_m_corr = cf.read_corr_file(xi_m_m_filen)
#Read in sample
sample = m2.read_halo_table_ascii('{0}{1}'.format(curr, filen), '7,8,9,17')
print 'Plotting position scatter plot of sample file: {0}'.format(filen)
sp.plot_position_scatter(sample, 'x', 'y')
plt.show()
print np.median(
np.array(m2.get_col_halo_table(sample, 'fof_np')) * mass_conv)
sample_auto_corr = cf.calc_auto_corr(sample, MS2=True)
#sample_cross_corr = calc_cross_corr(sample)
plt.loglog(sample_auto_corr['data'].r,
sample_auto_corr['data'].cf,
label='sample')
plt.loglog(xi_m_m_corr['data'].r,
xi_m_m_corr['data'].cf,
label='$\\xi_{mm}$')
print sample_auto_corr['data'].cf
print xi_m_m_corr['data'].cf