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_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
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
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 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('$\\xi(r)$') plt.show()
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]')