from __future__ import unicode_literals import numpy as np from astropy.io import fits from scipy import signal from pylab import * # This program requires the matplotlib python library for the plots. import math import matplotlib #matplotlib.rcParams['text.usetex'] = True #matplotlib.rcParams['text.latex.unicode'] = True import matplotlib.pyplot as plt from astropy.table import Table import matplotlib.path as mpath #from matplotlib import rc def custom_marker(): # u = np.array([ [2.444,7.553], # [0.513,7.046], # [-1.243,5.433], # [-2.353,2.975], # [-2.578,0.092], # [-2.075,-1.795], # [-0.336,-2.870], # [2.609,-2.016] ]) # u[:,0] -= 0.098 # codes = [1] + [2]*(len(u)-2) + [2] # u = np.append(u, -u[::-1], axis=0) # codes += codes star1 = mpath.Path.unit_regular_star(5, innerCircle=0.4) star2 = mpath.Path.unit_regular_star(5, innerCircle=0.4) star3 = mpath.Path.unit_regular_star(5, innerCircle=0.4) # star = mpath.Path.unit_regular_asterisk(5) # circle = mpath.Path.unit_circle() # verts = np.concatenate([star2.vertices[::-1, ...]*1.5, star.vertices, star3.vertices*-0.5]) factor1 = 5.0 factor2 = 2.0 factor3 = 1.0 verts = np.concatenate([star1.vertices[::-1,...] * factor1, star2.vertices * factor2, star3.vertices[::-1,...] * factor3]) codes = np.concatenate([star1.codes, star2.codes, star3.codes]) return mpath.Path(verts, codes) # use to make matplotlib-old like style plt.style.use('classic') font = {'family': 'serif', 'color': 'black', 'weight': 'normal', 'size': 16, } imbh_lit = [ ['587742610270847183',70e3,6.5e8], ['587729160057127189',107e3,1.32e9], ['587739720846934480',111e3,1.74e9], ['587725818021871671',152e3,2.36e9], ['587724240687792193',202e3,7.26e8], ] imbh_lit = np.array(imbh_lit) M_sph_arr =[] M_bh_arr =[] M_sph_arr_J =[] M_bh_arr_J =[] M_sph_arr_GS13_sc =[] M_bh_arr_GS13_sc =[] M_sph_arr_GS13_s =[] M_bh_arr_GS13_s =[] M_sph_arr_imbh =[] M_bh_arr_imbh =[] M_sph_arr_imbh_other =[] M_bh_arr_imbh_other =[] M_sph_arr_imbh_confirmed =[] M_sph_arr_imbh_confirmed_err =[] M_bh_arr_imbh_confirmed =[] x_borders = np.array([1e9, 1e11]) line_1 = 10**(2.22*np.log(x_borders/2e10)/math.log(10)+7.89) x_borders2 = np.array([2e10, 1e12]) line_2 = 10**(0.97*np.log(x_borders2/3e11)/math.log(10)+9.27) f = open('table2.dat') for content in f.readlines(): splitted_arr = content.split('|') M_sph_arr.append(float(splitted_arr[9])*1e9) M_bh_arr.append(float(splitted_arr[2])*1e5) f.close() f = open('scott13') for content in f.readlines(): splitted_arr = content.split('&') M_bh_col = splitted_arr[3] M_bh_col_arr = M_bh_col.split('$') M_sph_col = splitted_arr[6] M_sph_col_arr = M_sph_col.split('$') M_sph_arr_GS13_s.append(float(M_sph_col_arr[0])*1e10) M_bh_arr_GS13_s.append(float(M_bh_col_arr[0])*1e8) f.close() hdulist = fits.open('asu(2).fit') data = hdulist[1].data ''' for content in f.readlines(): splitted_arr = content.split(' ') print splitted_arr[15] M_bh_arr_J.append(10**float(splitted_arr[2])) #M_sph_arr_J.append(float(splitted_arr[16])) f.close() ''' M_sun_K = 3.3 M_sun_r = 4.76 f = open('decomp_res_v2') for content in f.readlines(): splitted_arr = content.split(',') M_sun = M_sun_K if 'R' in splitted_arr[8]: M_sun = M_sun_r Luminosity = 10**(0.4*(M_sun - float(splitted_arr[1])-float(splitted_arr[7]))) Mass = Luminosity * float(splitted_arr[4]) print Mass if int(splitted_arr[6]) is not 1: M_sph_arr_imbh.append(Mass) M_bh_arr_imbh.append(float(splitted_arr[5])) ''' text(Mass, float(splitted_arr[5]), splitted_arr[9], verticalalignment='bottom', horizontalalignment='left', color='red', fontsize=5) ''' else: M_sph_arr_imbh_confirmed.append(Mass) M_bh_arr_imbh_confirmed.append(float(splitted_arr[5])) M_sph_arr_imbh_confirmed_err.append(Mass*float(splitted_arr[10])) ''' text(Mass, float(splitted_arr[5]), splitted_arr[9], verticalalignment='bottom', horizontalalignment='left', color='green', fontsize=5) ''' f.close() f = open('decomp_res') for content in f.readlines(): splitted_arr = content.split(',') M_sun = M_sun_K if 'R' in splitted_arr[8]: M_sun = M_sun_r Luminosity = 10**(0.4*(M_sun - float(splitted_arr[1])-float(splitted_arr[7]))) Mass = Luminosity * float(splitted_arr[4]) print Mass M_sph_arr_imbh_other.append(Mass) M_bh_arr_imbh_other.append(float(splitted_arr[5])) f.close() t_LTR = Table.read('../../data/mbh_data.txt', format='ascii') print t_LTR['MBH'].shape t_UCD = t_LTR[t_LTR['objtype'] == 'UCD'] t_cE = t_LTR[t_LTR['objtype'] == 'cE'] t_other = t_LTR[(t_LTR['objtype'] != 'cE') & (t_LTR['objtype'] != 'UCD')] X_arr = np.array(t_LTR['Mstar']) Y_arr = np.array(t_LTR['MBH']) #ltr, = plt.quiver(X_arr, Y_arr,X_arr*0, Y_arr/4) lolims_UCD = np.zeros(shape=len(t_UCD), dtype=bool) lolims_UCD[t_UCD['errMBHp'] == 0.0] = True err_m = t_UCD['errMBHm'] err_m[lolims_UCD == True] = t_UCD['MBH'][lolims_UCD == True] * 0.1 plt.errorbar(t_UCD['Mstar'], t_UCD['MBH'], xerr=t_UCD['errMstar'], yerr=[err_m,t_UCD['errMBHp']],linestyle='none',uplims=lolims_UCD, color='magenta') lit_UCD, = plt.loglog(t_UCD['Mstar'], t_UCD['MBH'], linestyle='none',marker='o', color='magenta', markeredgewidth=0.0, markersize=5) lolims_cE = np.zeros(shape=len(t_cE), dtype=bool) lolims_cE[t_cE['errMBHp'] == 0.0] = True plt.errorbar(t_cE['Mstar'], t_cE['MBH'], xerr=t_cE['errMstar'], yerr=[t_cE['errMBHm'],t_cE['errMBHp']],linestyle='none',uplims=lolims_cE, color='gray') lit_cE, = plt.loglog(t_cE['Mstar'], t_cE['MBH'], linestyle='none',marker='o', color='gray', markeredgewidth=0.0, markersize=5) shifts = {'NGC5102':{'dx':1.2, 'dy':0.8}, 'NGC5206':{'dx':1.2, 'dy':0.70}, 'NGC4395':{'dx':0.45, 'dy':1.3}, 'NGC205':{'dx':1.2, 'dy':0.75}, 'NGC404':{'dx':0.22, 'dy':1.05}, 'Messier32':{'dx':1.15, 'dy':0.75},'NGC4486B':{'dx':1.15, 'dy':0.75},'M32':{'dx':1.15, 'dy':0.75}} for itm in t_cE: (dx, dy) = (1. , 1.) if itm['Object'] in shifts.keys(): (dx, dy) = (shifts[itm['Object']]['dx'], shifts[itm['Object']]['dy']) plt.text(itm['Mstar']*dx, itm['MBH']*dy, itm['Object'], verticalalignment='bottom', horizontalalignment='left', color='gray', fontsize=10) lolims_other = np.zeros(shape=len(t_other), dtype=bool) lolims_other[t_other['errMBHm'] == 0] = True lolims_other[( t_other['MBH'] / (t_other['errMBHm'] + 1.0) < 1.2)] = True err_m = t_other['errMstar'].copy() err_m[t_other['errMstar'] == 0] = t_other['Mstar'][t_other['errMstar'] == 0] * 0.2 xuplims = np.zeros(shape=len(t_other), dtype=bool) xuplims[t_other['errMstar'] == 0] = True bh_m_err = t_other['errMBHm'] bh_m_err_p = t_other['errMBHp'] bh_m_err[lolims_other == True] = t_other['MBH'][lolims_other == True] * 0.4 bh_m_err_p[lolims_other == True] = t_other['MBH'][lolims_other == True] * 0 print xuplims, lolims_other plt.errorbar(t_other['Mstar'], t_other['MBH'], xerr=err_m, yerr=[bh_m_err,bh_m_err_p],linestyle='none',uplims=lolims_other,xuplims=xuplims, color='peru') lit_other, = plt.loglog(t_other['Mstar'], t_other['MBH'], linestyle='none',marker='o', color='peru', markeredgewidth=0.0, markersize=5) for itm in t_other: (dx, dy) = (1. , 1.) if itm['Object'] in shifts.keys(): (dx, dy) = (shifts[itm['Object']]['dx'], shifts[itm['Object']]['dy']) plt.text(itm['Mstar']*dx, itm['MBH']*dy, itm['Object'], verticalalignment='bottom', horizontalalignment='left', color='peru', fontsize=10) plt_graham, = plt.loglog(M_sph_arr, M_bh_arr, 'bo', markeredgewidth=0.0, markersize=5) #plt.loglog(M_sph_arr_GS13_sc, M_bh_arr_GS13_sc, 'ro', markeredgewidth=0.0, markersize=5, label='GS15 core-Sersic') #plt.loglog(M_sph_arr_imbh_other, M_bh_arr_imbh_other, 'k+', markeredgewidth=2.0, markersize=7) plt_graham_scott, = plt.loglog(M_sph_arr_GS13_s, M_bh_arr_GS13_s, 'go', markeredgewidth=0.0, markersize=5) plt_fourstar, = plt.loglog(M_sph_arr_imbh_other, M_bh_arr_imbh_other, 'k*', markeredgewidth=0.0, markersize=10) yerr = np.array(M_sph_arr_imbh)*0+3e4 xerr = np.array(M_bh_arr_imbh)*0+1e8 #errorbar(M_sph_arr_imbh_confirmed, M_bh_arr_imbh_confirmed, yerr=0, xerr=M_sph_arr_imbh_confirmed_err,fmt='.', ecolor='g', capthick=2) plt_literature, = plt.loglog(imbh_lit[:,2], imbh_lit[:,1], color='red', linestyle='none', marker='*', markeredgecolor='maroon', markeredgewidth=1.5, alpha=0.5, markersize=17) plt_imbh, = plt.loglog(M_sph_arr_imbh_confirmed, M_bh_arr_imbh_confirmed, color='green', linestyle='none', marker='*', markeredgecolor='k', markeredgewidth=1.5, alpha=0.5, markersize=17) plt.loglog(x_borders, line_1, '--', color='gray', lw=2) #plt.loglog(x_borders2, line_2, 'r') plt.tick_params(which='major',length=10) plt.tick_params(which='minor',length=5) plt.ylabel(r"$M_{\rm BH}$, M$_{\odot}$", fontdict=font) plt.xlabel(r"$M_{\rm bulge}$, M$_{\odot}$", fontdict=font) legend_plots = [plt_imbh, plt_literature, plt_fourstar, plt_graham, plt_graham_scott,lit_UCD,lit_cE,lit_other] legend_labels = ['IMBH (FourStar + HST)', 'IMBH (literature)', 'IMBH candidates (FourStar)', 'Graham et al. 2015', 'Graham & Scott 2015','UCD (literature)','cE (literature)','Other galaxies (literature)'] plt.legend(legend_plots, legend_labels, numpoints=1, loc='upper left', prop={'size': 11}) #plt.xlim(1.5e8,2e12) plt.xlim(1.5e7,2e12) plt.savefig('../../paper/diagrams/SR_kcorr_LT.pdf',dpi=300, bbox_inches='tight') plt.close()