def plot_HI_gas_fraction_groups(plt, output_dir, obs_dir, mHI_halos_stacking): ################################### # Plots global mass densities fig = plt.figure(figsize=(5,4.5)) xtit="$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot}\,h^{-1})$" ytit="$\\rm log_{10}(M_{\\rm HI}/M_{\\rm halo})$" ax = fig.add_subplot(111) plt.subplots_adjust(bottom=0.15, left=0.15) prepare_ax(ax, 10, 15, -5, -0.5, xtit, ytit) #Predicted SMHM ind = np.where((mHI_halos_stacking > 0) & (xmf > 10.3)) xplot = xmf[ind] yplot = mHI_halos_stacking[ind]-xmf[ind] for i,j in zip (xplot,yplot): print i,j ax.plot(xplot,yplot, color='k', linestyle='solid', label='Shark') common.prepare_legend(ax, ['k']) common.savefig(output_dir, fig, "HI_groups_stacking.pdf")
def conduct_bad_and_good(x, data, filename): """Study BadAndGoodLS algorithm.""" r_1 = BadAndGoodLS(x, data, beta=0.01, gamma=40) r_1.fit() r_2 = BadAndGoodLS(x, data, beta=0.01, gamma=100) r_2.fit() r_3 = BadAndGoodLS(x, data, beta=0.01, gamma=200) r_3.fit() fig, ax = plt.subplots(1, 1, figsize=FIG_SIZE) ax.plot(x, data, 'o', label='Data') ax.plot(x, r_1.result, '-', label=r_1.name) ax.plot(x, r_2.result, '--', label=r_2.name) ax.plot(x, r_3.result, '-.', label=r_3.name) ax.legend(loc='best') ax.set_xlabel(r'$x$') ax.set_ylabel('Observations and fit') fig.tight_layout(pad=0.1) savefig(fig, filename) r_1.print_result() r_2.print_result() r_3.print_result()
def plot_cooling_rate(plt, outdir, med_tvir): fig = plt.figure(figsize=(5,5)) xmin, xmax, ymin, ymax = -1.8, 1.2, -1, 6 ax = fig.add_subplot(111) xtit="$\\rm log_{10}(T_{\\rm vir}/\\rm keV)$" ytit="$\\rm log_{10}(L_{\\rm cool}/ 10^{40}\\rm erg\,s^{-1})$" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) xleg= xmax - 0.2 * (xmax - xmin) yleg= ymax - 0.1 * (ymax - ymin) ax.text(xleg, yleg, 'z=0') ind = np.where(med_tvir[0, :] != 0) xplot = tfunc[ind] yplot = med_tvir[0, ind] errdn = med_tvir[1, ind] errup = med_tvir[2, ind] ax.errorbar(xplot,yplot[0],color='k', label="SHArk") ax.errorbar(xplot,yplot[0],yerr=[errdn[0],errup[0]], ls='None', mfc='None', ecolor = 'k', mec='k',marker='+',markersize=2) ax.plot(tfunc, 3.0 * tfunc + 1.9, 'r', linestyle='dashed', label='Anderson+15') common.prepare_legend(ax, ['r','k'], loc=2) common.savefig(outdir, fig, 'cooling_rate.pdf')
def plot_sft_efficiency(plt, outdir, redshifts, sfre, sfreH2, mhrat): fig = plt.figure(figsize=(9.5, 11)) xmin, xmax, ymin, ymax = 0, 10, -6, 0 # panel 1 ax = fig.add_subplot(311) plt.subplots_adjust(bottom=0.15, left=0.15) xtit = "$\\rm redshift$" ytit = "$\\rm log_{10}(SFR/M_{\\rm cold} Gyr^{-1})$" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) #note that only h^2 is needed because the volume provides h^3, and the SFR h^-1. ind = np.where(sfre > 0) ax.plot(redshifts[ind], np.log10(sfre[ind]), 'r', label='Shark') common.prepare_legend(ax, ['r']) #panel 2 ax = fig.add_subplot(312) ytit = "$\\rm log_{10}(SFE_{\\rm H_2}/Gyr^{-1})$" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) #note that only h^2 is needed because the volume provides h^3, and the SFR h^-1. ind = np.where(sfreH2 > 0) ax.plot(redshifts[ind], np.log10(sfreH2[ind]), 'r', label='Shark') common.prepare_legend(ax, ['r']) #panel 3 ax = fig.add_subplot(313) ytit = "$\\rm log_{10}(M_{\\rm mol}/M_{\\rm atom})$" common.prepare_ax(ax, xmin, xmax, -3, 2, xtit, ytit, locators=(0.1, 1, 0.1)) #note that only h^2 is needed because the volume provides h^3, and the SFR h^-1. ind = np.where(mhrat > 0) ax.plot(redshifts[ind], np.log10(mhrat[ind]), 'r', label='Shark') common.prepare_legend(ax, ['r']) common.savefig(outdir, fig, "cosmic_sfe.pdf")
def plot_baryon_fractions(plt, outdir, redshifts, mstar_dm, mcold_dm, mhot_dm, meje_dm, mbar_dm): fig = plt.figure(figsize=(9.5, 9.5)) xtit = "$\\rm redshift$" ytit = "$\\rm log_{10}(\\rho/\\rho_{\\rm m})$" ax = fig.add_subplot(111) common.prepare_ax(ax, 0, 10, -4, 0.5, xtit, ytit, locators=(0.1, 1, 0.1)) ax.plot(redshifts, mstar_dm, 'k', label='stars') ax.plot(redshifts, mcold_dm, 'b', label='ISM gas') ax.plot(redshifts, mhot_dm, 'r', label='halo gas') ax.plot(redshifts, meje_dm, 'g', label='ejected gas') ax.plot(redshifts, mbar_dm, 'm', label='total baryons') yplot = [0.1866920152, 0.1866920152] xplot = [0, 10] ax.plot(xplot, np.log10(yplot), 'k', linestyle='dashed', label='Universal $f_{\\rm baryon}$') common.prepare_legend(ax, ['k', 'b', 'r', 'g', 'm', 'k']) common.savefig(outdir, fig, "baryon_frac.pdf")
def plot_caustic_halos(plt, outdir, sats_vproj, sats_rproj, sats_type): fig = plt.figure(figsize=(9,9)) xtit = "$r/r_{\\rm vir}$" ytit = "$v_{\\rm r}/v_{\\rm vir}$" xmin, xmax, ymin, ymax = 0, 1.2, -5, 5 xleg = xmin + 0.02 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) ax = fig.add_subplot(111) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) #ind = np.where(sats_type == 2) #xdata = sats_rproj[ind] #ydata = sats_vproj[ind] #us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs) ind = np.where(sats_type == 2) xdata = sats_rproj[ind] ydata = sats_vproj[ind] ax.plot(xdata,ydata,'ro',markersize=0.7) #, **contour_kwargs) ind = np.where(sats_type == 1) xdata = sats_rproj[ind] ydata = sats_vproj[ind] ax.plot(xdata,ydata,'ko',markersize=0.7) #, **contour_kwargs) common.savefig(outdir, fig, 'caustic_groups.pdf')
def plot_curves(file_name, type_name, show_ylabel=True, show_legend=True): json_file = os.path.join(common.result_folder, file_name) xlabel = 'num_dexels' ylabel = 'time' with open(json_file, 'r') as f: database = json.load(f) # Get list of x entries x_array = set() for entries in database.values(): x_array = x_array | set(entry[xlabel] for entry in entries) x_array = sorted(x_array) # Get list of y entries y_arrays = {} for i, entries in enumerate(database.values()): for entry in entries: key = (entry['operation'], entry['radius_relative']) if key not in y_arrays: y_arrays[key] = numpy.zeros( (len(database.keys()), len(x_array))) j = x_array.index(entry[xlabel]) y_arrays[key][i, j] = entry[ylabel] / 1000 # Draw plot colors = itertools.cycle(('C0', 'C1', 'C2', 'C3')) markers = itertools.cycle(('o', '*', 'd', '^')) # ',', +', '.', 'o', '*') linestyles = itertools.cycle(('-', '--', '-.', ':')) # plt.figure() fig, ax = common.newfig(1) show_conv = (type_name == 'loglog') for key, y_array in sorted(y_arrays.items()): r = key[1] c = next(colors) e = key[0].title() name = '{}, $r = {}$'.format(e, r) common.region_plot(x_array, y_array, c, c, name, next(linestyles), next(markers), show_conv) if type_name == 'loglog': plt.loglog() ax.xaxis.set_major_formatter(plticker.LogFormatter(labelOnlyBase=True)) ax.xaxis.set_minor_formatter( plticker.LogFormatter(labelOnlyBase=False, minor_thresholds=(4, 0.5))) plt.xlabel(common.legends[xlabel], fontsize='large') if show_ylabel: plt.ylabel(common.legends[ylabel], fontsize='large') # if show_legend: plt.legend(loc='upper left') result_plot = os.path.join( common.result_folder, ylabel + '_' + os.path.splitext(file_name)[0] + '_' + type_name + '.pdf') # plt.savefig(result_plot) common.savefig(result_plot)
def myplot(tag3, ylabel): data = df[(df.tag1 != 'query') & (df.tag1 != 'ks') & (df.tag3 == tag3)] data = data.sort_values('var') for chain, group in data.groupby('tag1'): fig, ax = plt.subplots() for single, grp in group.groupby('tag2'): grp.plot(ax=ax, kind='line', x='var', y='value', label=single) ax.set_ylim((0, ax.yaxis.get_data_interval()[1])) ax.margins(y=0.1) ax.set_ylabel(ylabel) ax.set_xlabel(' ') cmn.savefig('top_' + chain + '_' + tag3, fig)
def plot_h1h2_gas_fraction(plt, output_dir, mhr_relation, mhr_relation_cen, mhr_relation_sat): fig = plt.figure(figsize=(5, 4.5)) ax = fig.add_subplot(111) xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" ytit = "$\\rm log_{10}(M_{\\rm H_2}/M_{\\rm HI})$" prepare_ax(ax, 8, 12, -3, 1.0, xtit, ytit) # Predicted SMHM ind = np.where(mhr_relation[0, :] != 0) xplot = xmf[ind] yplot = mhr_relation[0, ind] errdn = mhr_relation[1, ind] errup = mhr_relation[2, ind] ax.errorbar(xplot, yplot[0], color='k', label="all galaxies") ax.errorbar(xplot, yplot[0], yerr=[errdn[0], errup[0]], ls='None', mfc='None', ecolor='k', mec='k', marker='+', markersize=2) ind = np.where(mhr_relation_cen[0, :] != 0) xplot = xmf[ind] yplot = mhr_relation_cen[0, ind] ax.errorbar(xplot, yplot[0], color='b', linestyle='dotted', label="centrals") ind = np.where(mhr_relation_sat[0, :] != 0) xplot = xmf[ind] yplot = mhr_relation_sat[0, ind] ax.errorbar(xplot, yplot[0], color='r', linestyle='dashed', label="satelites") common.prepare_legend(ax, ['k', 'b', 'r', 'grey', 'grey']) common.savefig(output_dir, fig, "HIH2_gas_fraction.pdf")
def conduct(x, data, noise, filename): """Conduct one experiment with data.""" # Analyze the behavior of the non-normalized residuals first. lscf_def_noise = LSConservativeFormulation(x, data) lscf_def_noise.fit() fig, ax = plt.subplots(1, 2, figsize=FIG_SIZE) ax[0].bar(x, lscf_def_noise.residuals) ax[0].set_xlabel(r'$x$') ax[0].set_ylabel('Non-normalized residuals') kde = stats.gaussian_kde(lscf_def_noise.residuals) __, bins, __ = ax[1].hist(lscf_def_noise.residuals, density=True) xx = np.linspace(bins.min(), bins.max(), num=101) ax[1].plot(xx, kde(xx)) ax[1].set_xlabel(r'$R$') ax[1].set_ylabel(r'Residuals PDF') fig.tight_layout(pad=0.1) savefig(fig, 'residuals-' + filename) # Now fit and plot the fitting results. ols = OLS(x, data, noise) ols.fit() lscf = LSConservativeFormulation(x, data, noise) lscf.fit() lsbag = BadAndGoodLS(x, data, noise=noise, beta=6/len(x), gamma=50) lsbag.fit() ols.print_result() lscf.print_result() lsbag.print_result() fig, ax = plt.subplots(1, 1, figsize=FIG_SIZE) ax.plot(x, data, 'o', label='Data') ax.plot(x, ols.result, '-', label='LS, ordinary') ax.plot(x, lscf.result, '--', label='LS, extension 1') ax.plot(x, lsbag.result, '-.', label='LS, extension 2') ax.legend(loc='best') ax.set_xlabel(r'$x$') ax.set_ylabel('Observations and fit') fig.tight_layout(pad=0.1) savefig(fig, filename)
def plot_mass_cosmic_density(plt, outdir, redshifts, mcold, mHI, mH2): fig = plt.figure(figsize=(5, 4.5)) ax = fig.add_subplot(111) plt.subplots_adjust(bottom=0.15, left=0.15) xtit = "$\\rm Lookback\,time/Gyr$" ytit = "$\\rm log_{10}(\\Omega_{\\rm gas})$" common.prepare_ax(ax, 0, 13.5, -4, -2.7, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax2 = ax.twiny() ax2.set_xlim(ax.get_xlim()) new_tick_locations = np.array([0., 2., 4., 6., 8., 10., 12.]) ax2.set_xticks(new_tick_locations) ax2.set_xticklabels(us.redshift(new_tick_locations), fontsize=12) ax2.set_xlabel("redshift", fontsize=13) #note that only h^2 is needed because the volume provides h^3, and the SFR h^-1. ax.plot(us.look_back_time(redshifts), mcold + np.log10(Omegab) - np.log10(XH), 'k', label='total neutral ISM') ax.plot(us.look_back_time(redshifts), mHI + np.log10(Omegab) - np.log10(XH), 'b', linestyle='dotted', label='atomic') ax.plot(us.look_back_time(redshifts), mH2 + np.log10(Omegab) - np.log10(XH), 'r', linestyle='dashed', label='molecular') common.prepare_legend(ax, ['k', 'b', 'r'], loc=1) common.savefig(outdir, fig, "omega_neutral.pdf")
def plot_sizes_combined(plt, outdir, rcomb): fig = plt.figure(figsize=(5, 4.5)) # Total ################################## xtit = "$\\rm log_{10} (\\rm M_{\\rm stars, total}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm r_{\\rm 50, comb}/kpc)$" xmin, xmax, ymin, ymax = 8, 12, -0.5, 2 ax = fig.add_subplot(111) plt.subplots_adjust(bottom=0.15, left=0.15) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) #Predicted size-mass for disks ind = np.where(rcomb[0, 0, :] != 0) xplot = xmf[ind] yplot = rcomb[0, 0, ind] errdn = rcomb[0, 1, ind] errup = rcomb[0, 2, ind] ax.errorbar(xplot, yplot[0], yerr=[errdn[0], errup[0]], ls='None', mfc='None', ecolor='k', mec='k', marker='o', label="Shark disk+bulge combined") common.prepare_legend(ax, ['k', 'k', 'k'], loc=2) common.savefig(outdir, fig, 'sizes_combined.pdf')
def plot_scaling_z(plt, outdir, obsdir, snap, SFRMstar, R50Mstar, R50Mstar30, MBHMstar, SigmaMstar30, ZstarMstar, ZSFMstar, AgeSMstar, SFRMstar30, R50pMstar30): #define observaiton of the MS at z=0 to be plotted def obs_mainseq_z0(): #best fit from Davies et al. (2016) xdataD16 = [9.3, 10.6] ydataD16 = [-0.39, 0.477] ax.plot(xdataD16, ydataD16, color='b', linestyle='dashdot', linewidth=4, label='Davies+16') #SDSS z=0 relation lm, SFR = common.load_observation(obsdir, 'SFR/Brinchmann04.dat', (0, 1)) hobs = 0.7 #add cosmology correction plus IMF correction that goes into the stellar mass. corr_cos = np.log10(pow(hobs, 2) / pow(h0, 2)) - 0.09 # apply correction to both stellar mass and SFRs. ax.plot(lm[0:35] + corr_cos, SFR[0:35] + corr_cos, color='PaleVioletRed', linewidth=3, linestyle='dashed', label='Brinchmann+04') ax.plot(lm[36:70] + corr_cos, SFR[36:70] + corr_cos, color='PaleVioletRed', linewidth=5, linestyle='dotted') ax.plot(lm[71:len(SFR)] + corr_cos, SFR[71:len(SFR)] + corr_cos, color='PaleVioletRed', linewidth=5, linestyle='dotted') ########################### will plot main sequence for all stellar particles in the subhalo xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm SFR/M_{\odot} yr^{-1})$" xmin, xmax, ymin, ymax = 7, 12, -5, 1.5 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #observations z=0 if (z == 0): obs_mainseq_z0() #VR ind = np.where(SFRMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = SFRMstar[idx, 0, ind] errdn = SFRMstar[idx, 1, ind] errup = SFRMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'PaleVioletRed', 'k']) common.savefig(outdir, fig, "main_sequence_z_comp_obs.pdf") ########################### will plot main sequence for 30kpc aperture xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm SFR_{\\rm 30kpc}/M_{\odot} yr^{-1})$" xmin, xmax, ymin, ymax = 7, 12, -5, 1.5 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #observations z=0 if (z == 0): obs_mainseq_z0() #VR ind = np.where(SFRMstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = SFRMstar30[idx, 0, ind] errdn = SFRMstar30[idx, 1, ind] errup = SFRMstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'PaleVioletRed', 'k']) common.savefig(outdir, fig, "main_sequence_30kpc_z_comp_obs.pdf") ########################### will plot r50 vs stellar mass for all stellar particles in the subhalo #define observations first def plot_gama_size_mass(): m, r = common.load_observation(obsdir, 'SizesAndAM/rdisk_L16.dat', [0, 1]) ax.plot(m[0:36], r[0:36], linestyle='dotted', color='b', label='L16 disks') ax.plot(m[38:83], r[38:83], linestyle='dotted', color='b') ax.plot(m[85:128], r[85:129], linestyle='dotted', color='b') m, r = common.load_observation(obsdir, 'SizesAndAM/rbulge_L16.dat', [0, 1]) ax.plot(m[0:39], r[0:39], linestyle='dotted', color='darkgreen', label='L16 bulges') ax.plot(m[41:76], r[41:76], linestyle='dotted', color='darkgreen') ax.plot(m[78:115], r[78:115], linestyle='dotted', color='darkgreen') xtit = "$\\rm log_{10} (\\rm M_{\\star,tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,tot}/pkpc)$" xmin, xmax, ymin, ymax = 7, 12, -0.3, 2 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) if (z == 0): plot_gama_size_mass() #VR ind = np.where(R50Mstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = R50Mstar[idx, 0, ind] + 3.0 errdn = R50Mstar[idx, 1, ind] errup = R50Mstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'darkgreen', 'k'], loc='upper left') common.savefig(outdir, fig, "r50_Mstar_z_comp_obs.pdf") ################## will plot r50 vs stellar mass for quantities measured within 30kpc xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc}/pkpc)$" xmin, xmax, ymin, ymax = 7, 12, -0.3, 2 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) if (z == 0): plot_gama_size_mass() #VR ind = np.where(R50Mstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = R50Mstar30[idx, 0, ind] + 3.0 errdn = R50Mstar30[idx, 1, ind] errup = R50Mstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'darkgreen', 'k'], loc='upper left') common.savefig(outdir, fig, "r50_Mstar_30kpc_z_comp_obs.pdf") ################## will plot r50 vs stellar mass for quantities measured within 30kpc, but in this case the r50 is projected xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc,2D}/pkpc)$" xmin, xmax, ymin, ymax = 7, 12, -0.3, 2 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) if (z == 0): plot_gama_size_mass() #VR ind = np.where(R50pMstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = R50pMstar30[idx, 0, ind] + 3.0 errdn = R50pMstar30[idx, 1, ind] errup = R50pMstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'darkgreen', 'k'], loc='upper left') common.savefig(outdir, fig, "r50_projected_Mstar_30kpc_z_comp_obs.pdf") ########################### will plot stellar velocity dispersion vs. stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\sigma_{\\star,30kpc}/km s^{-1})$" xmin, xmax, ymin, ymax = 7, 12, 1, 3 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) #VR ind = np.where(SigmaMstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = SigmaMstar30[idx, 0, ind] errdn = SigmaMstar30[idx, 1, ind] errup = SigmaMstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) #observations if (z == 0): lm, sig, sigdn, sigup = common.load_observation( obsdir, 'StellarPops/vdS19-sigma.csv', [0, 1, 2, 3]) sig = np.log10(sig) sigdn = np.log10(sigdn) sigup = np.log10(sigup) common.errorbars(ax, lm, sig, sigdn, sigup, 'b', 'D', label='van de Sande+19') if idx == 0: common.prepare_legend(ax, ['k'], loc='upper left') common.savefig(outdir, fig, "vdisp_Mstar_30kpc_z_comp_obs.pdf") ############ will plot stellar metallicity-stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm Z_{\star}/Z_{\\odot})$" xmin, xmax, ymin, ymax = 7, 12, -2, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) plt.subplots_adjust(left=0.2) #VR ind = np.where(ZstarMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = ZstarMstar[idx, 0, ind] - log10zsun errdn = ZstarMstar[idx, 1, ind] errup = ZstarMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) #observations if (z == 0): lm, mz, mzdn, mzup = common.load_observation( obsdir, 'MZR/MSZR-Gallazzi05.dat', [0, 1, 2, 3]) common.errorbars(ax, lm[0:7], mz[0:7], mzdn[0:7], mzup[0:7], 'b', 'D', label='Kirby+13') common.errorbars(ax, lm[7:22], mz[7:22], mzdn[7:22], mzup[7:22], 'b', 'o', label='Gallazzi+05') if idx == 0: common.prepare_legend(ax, ['k', 'b', 'b'], loc='lower right') common.savefig(outdir, fig, "zstar_mstar_30kpc_z_comp_obs.pdf") ################ will plot star-forming gas metallicity vs. stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm Z_{\\rm SF,gas}/Z_{\\odot})$" xmin, xmax, ymin, ymax = 7, 12, -2, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) plt.subplots_adjust(left=0.2) if (z == 0): #MZR z=0 corrzsun = 8.69 #solar oxygen abundance in units of 12 + log(O/H) hobs = 0.72 #add cosmology correction plus IMF correction that goes into the stellar mass. corr_cos = np.log10(pow(hobs, 2) / pow(h0, 2)) - 0.09 lm, mz, mzdn, mzup = common.load_observation( obsdir, 'MZR/MMAdrews13.dat', [0, 1, 2, 3]) hobs = 0.7 #add cosmology correction plus IMF correction that goes into the stellar mass. corr_cos = np.log10(pow(hobs, 2) / pow(h0, 2)) - 0.09 common.errorbars(ax, lm + corr_cos, mz - corrzsun, mzdn - corrzsun, mzup - corrzsun, 'b', 's', label='Andrews+13') #correction for Tremonti is the same. lm, mz, mzdn, mzup = common.load_observation( obsdir, 'MZR/Tremonti04.dat', [0, 1, 2, 3]) common.errorbars(ax, lm + corr_cos, mz - corrzsun, mzdn - corrzsun, mzup - corrzsun, 'b', 'o', label="Tremonti+04") #VR ind = np.where(ZSFMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = ZSFMstar[idx, 0, ind] - log10zsun errdn = ZSFMstar[idx, 1, ind] errup = ZSFMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['k', 'b', 'b'], loc='lower right') common.savefig(outdir, fig, "zsfgas_mstar_30kpc_z_comp_obs.pdf") ################ will plot stellar ages vs stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm age_{\\star}/Gyr)$" xmin, xmax, ymin, ymax = 7, 12, 0, 1.1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) plt.subplots_adjust(left=0.2) #VR ind = np.where(AgeSMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = AgeSMstar[idx, 0, ind] errdn = AgeSMstar[idx, 1, ind] errup = AgeSMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) #observations if (z == 0): lm, age, agedn, ageup = common.load_observation( obsdir, 'StellarPops/vdS19-age.csv', [0, 1, 2, 3]) common.errorbars(ax, lm, age, agedn, ageup, 'b', 'D', label='van de Sande+19') if idx == 0: common.prepare_legend(ax, ['k']) common.savefig(outdir, fig, "starage_mstar_z_comp_obs.pdf") ################ will plot black hole mass vs stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm M_{\\rm BH}/M_{\odot})$" xmin, xmax, ymin, ymax = 7, 12, 6, 11 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) plt.subplots_adjust(left=0.2) if (z == 0): #BH-bulge relation mBH_M13, errup_M13, errdn_M13, mBH_power, mbulge_M13 = common.load_observation( obsdir, 'BHs/MBH_sigma_Mbulge_McConnelMa2013.dat', [0, 1, 2, 3, 7]) ind = np.where((mBH_M13 > 0) & (mbulge_M13 > 0)) xobs = np.log10(mbulge_M13[ind]) yobs = np.log10(mBH_M13[ind] * pow(10.0, mBH_power[ind])) lerr = np.log10( (mBH_M13[ind] - errdn_M13[ind]) * pow(10.0, mBH_power[ind])) herr = np.log10( (mBH_M13[ind] + errup_M13[ind]) * pow(10.0, mBH_power[ind])) ax.errorbar(xobs, yobs, yerr=[yobs - lerr, herr - yobs], ls='None', mfc='None', ecolor='r', mec='r', marker='^', label="McConnell & Ma 2013") #BH-bulge relation mBH_H04, errup_H04, errdn_H04, mbulge_H04 = common.load_observation( obsdir, 'BHs/MBH_sigma_Mbulge_HaeringRix2004.dat', [0, 1, 2, 4]) xobs = np.log10(mbulge_H04) yobs = xobs * 0. - 999. indx = np.where(mBH_H04 > 0) yobs[indx] = np.log10(mBH_H04[indx]) lerr = yobs * 0. - 999. indx = np.where((mBH_H04 - errdn_H04) > 0) lerr[indx] = np.log10(mBH_H04[indx] - errdn_H04[indx]) herr = yobs * 0. + 999. indx = np.where((mBH_H04 + errup_H04) > 0) herr[indx] = np.log10(mBH_H04[indx] + errup_H04[indx]) ax.errorbar(xobs, yobs, yerr=[yobs - lerr, herr - yobs], ls='None', mfc='None', ecolor='maroon', mec='maroon', marker='s', label="Haering+04") #VR ind = np.where(MBHMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = MBHMstar[idx, 0, ind] errdn = MBHMstar[idx, 1, ind] errup = MBHMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'k', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='k', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='k', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['k', 'r', 'maroon'], loc='upper left') common.savefig(outdir, fig, "blackhole_stellarmass_z_comp_obs.pdf")
def plot_SMHM_z(plt, outdir, z, massgal): fig = plt.figure(figsize=(9.7, 11.7)) xtit = "$\\rm log_{10} (\\rm M_{\\rm halo, DM}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" xmin, xmax, ymin, ymax = 10.5, 15, 7, 13 xleg = xmin + 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) #Moster et al. (2013) abundance matching SMHM relation M10 = 11.590 M11 = 1.195 N10 = 0.0351 N11 = -0.0247 beta10 = 1.376 beta11 = -0.826 gamma10 = 0.608 gamma11 = 0.329 subplots = (321, 322, 323, 324, 325, 326) all_labels = (('Shark', 'Moster+13', 'Behroozi+13'), ) for i, (z, subplot) in enumerate(zip(z, subplots)): labels = all_labels[0] # z=0 ################################## ax = fig.add_subplot(subplot) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) ax.tick_params(labelsize=13) ax.text(xleg, yleg, 'z=%s' % str(z)) #Predicted SMHM ind = np.where(massgal[i, 0, :] != 0) xplot = xmf[ind] yplot = massgal[i, 0, ind] errdn = massgal[i, 1, ind] errup = massgal[i, 2, ind] if not labels: ax.errorbar(xplot, yplot[0], color='k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='grey', interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='grey', interpolate=True) else: ax.errorbar(xplot, yplot[0], color='k', label=labels[0]) ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='grey', interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='grey', interpolate=True) M1 = pow(10.0, M10 + M11 * z / (z + 1)) N = N10 + N11 * z / (z + 1) beta = beta10 + beta11 * z / (z + 1) gamma = gamma10 + gamma11 * z / (z + 1) mh = pow(10.0, xmf) m = mh * 2 * N * pow((pow(mh / M1, -beta) + pow(mh / M1, gamma)), -1) if not labels: ax.plot(xmf, np.log10(m), 'r', linestyle='dashed', linewidth=3) else: ax.plot(xmf, np.log10(m), 'r', linestyle='dashed', linewidth=3, label=labels[1]) a = 1.0 / (1.0 + z) nu = np.exp(-4 * a * a) log_epsilon = -1.777 + (-0.006 * (a - 1)) * nu M1 = 11.514 + (-1.793 * (a - 1) - 0.251 * z) * nu alpha = -1.412 + 0.731 * nu * (a - 1) delta = 3.508 + (2.608 * (a - 1) - 0.043 * z) * nu gamma = 0.316 + (1.319 * (a - 1) + 0.279 * z) * nu Min = xmf - M1 fx = -np.log10(pow(10, alpha * Min) + 1.0) + delta * pow( np.log10(1 + np.exp(Min)), gamma) / (1 + np.exp(pow(10, -Min))) f = -0.3 + delta * pow(np.log10(2.0), gamma) / (1 + np.exp(1)) m = log_epsilon + M1 + fx - f if not labels: ax.plot(xmf, m, 'b', linestyle='dashdot', linewidth=3) else: ax.plot(xmf, m, 'b', linestyle='dashdot', linewidth=3, label=labels[2]) if labels: common.prepare_legend(ax, ['r', 'b', 'k'], loc=4) common.savefig(outdir, fig, 'SMHM_z.pdf')
def plot_BMHM_z(plt, outdir, massbar, massbar_inside): fig = plt.figure(figsize=(5, 6)) xtit = "" ytit = "" xmin, xmax, ymin, ymax = 10, 15, -1, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymin + 0.15 * (ymax - ymin) ax = fig.add_subplot(311) plt.subplots_adjust(left=0.17) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=0') #Predicted SMHM ind = np.where(massbar[0, 0, :] != 0) xplot = xmf[ind] yplot = massbar[0, 0, ind] errdn = massbar[0, 1, ind] errup = massbar[0, 2, ind] ax.errorbar(xplot, yplot[0], color='k', label="all baryons") ax.errorbar(xplot, yplot[0], yerr=[errdn[0], errup[0]], ls='None', mfc='None', ecolor='k', mec='k', marker='+', markersize=2) ind = np.where(massbar_inside[0, 0, :] != 0) xplot = xmf[ind] yplot = massbar_inside[0, 0, ind] errdn = massbar_inside[0, 1, ind] errup = massbar_inside[0, 2, ind] ax.plot(xplot, yplot[0], color='b', linestyle='dotted', label="inside halos") xline = [10.0, 15.0] yline = [0.0, 0.0] ax.plot(xline, yline, 'r', linestyle='dashed') common.prepare_legend(ax, ['b', 'k'], loc=2) # z=0.5 ################################## #ax = fig.add_subplot(222) #common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) #ax.text(xleg,yleg, 'z=0.5') #Predicted SMHM #ind = np.where(massbar[1,0,:] != 0) #xplot = xmf[ind] #yplot = massbar[1,0,ind] #errdn = massbar[1,1,ind] #errup = massbar[1,2,ind] #ax.errorbar(xplot,yplot[0],color='k', label="Shark") #ax.errorbar(xplot,yplot[0],yerr=[errdn[0],errup[0]], ls='None', mfc='None', ecolor = 'k', mec='k',marker='+',markersize=2) #ax.plot(xmf,xmf,'r', linestyle='dashed') # z=1 ################################## ax = fig.add_subplot(312) xtit = "" ytit = "$\\rm log_{10} (\\rm M_{\\rm bar}(\\Omega_{\\rm DM}/\\Omega_{\\rm b})/\\rm M_{\\rm halo, DM})$" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=1') # Predicted SMHM ind = np.where(massbar[2, 0, :] != 0) xplot = xmf[ind] yplot = massbar[2, 0, ind] errdn = massbar[2, 1, ind] errup = massbar[2, 2, ind] ax.errorbar(xplot, yplot[0], color='k', label="Shark") ax.errorbar(xplot, yplot[0], yerr=[errdn[0], errup[0]], ls='None', mfc='None', ecolor='k', mec='k', marker='+', markersize=2) ind = np.where(massbar_inside[2, 0, :] != 0) xplot = xmf[ind] yplot = massbar_inside[2, 0, ind] errdn = massbar_inside[2, 1, ind] errup = massbar_inside[2, 2, ind] ax.plot(xplot, yplot[0], color='b', linestyle='dotted') ax.plot(xline, yline, 'r', linestyle='dashed') # z=1 ################################## ax = fig.add_subplot(313) xtit = "$\\rm log_{10} (\\rm M_{\\rm halo, DM}/M_{\odot})$" ytit = "" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=2') #Predicted SMHM ind = np.where(massbar[3, 0, :] != 0) xplot = xmf[ind] yplot = massbar[3, 0, ind] errdn = massbar[3, 1, ind] errup = massbar[3, 2, ind] ax.errorbar(xplot, yplot[0], color='k', label="Shark") ax.errorbar(xplot, yplot[0], yerr=[errdn[0], errup[0]], ls='None', mfc='None', ecolor='k', mec='k', marker='+', markersize=2) ind = np.where(massbar_inside[3, 0, :] != 0) xplot = xmf[ind] yplot = massbar_inside[3, 0, ind] errdn = massbar_inside[3, 1, ind] errup = massbar_inside[3, 2, ind] ax.plot(xplot, yplot[0], color='b', linestyle='dotted') ax.plot(xline, yline, 'r', linestyle='dashed') common.savefig(outdir, fig, 'BMHM_z.pdf')
def plot_con_lambda(plt, outdir, con, lam, snap): xtit="$\\rm log_{10} (\\rm M_{\\rm tot}/M_{\odot})$" ytit="$\\rm concentration$" xmin, xmax, ymin, ymax = 8, 14, 0.5, 30 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.1 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) #Predicted HMF ind = np.where(con[0,idx,0,:] != 0.) xplot = xmf[ind] yplot = con[0,idx,0,ind] errdn = con[0,idx,1,ind] errup = con[0,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', label ='VR hosts') if idx > 0: ax.plot(xplot,yplot[0],'r') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True) ind = np.where(con[1,idx,0,:] != 0.) xplot = xmf[ind] yplot = con[1,idx,0,ind] errdn = con[1,idx,1,ind] errup = con[1,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', linestyle='dashed', label ='VR subh') if idx > 0: ax.plot(xplot,yplot[0],'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['r','r'], loc='upper left') common.savefig(outdir, fig, "con_mtot_z.pdf") xtit="$\\rm log_{10} (\\rm M_{\\rm tot}/M_{\odot})$" ytit="$\lambda_{\\rm Bullock}$" xmin, xmax, ymin, ymax = 8, 14, 0.01, 1 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.3 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) plt.subplots_adjust(left=0.15) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) ax.set_yscale("log") #Predicted HMF ind = np.where(lam[0,idx,0,:] != 0.) xplot = xmf[ind] yplot = lam[0,idx,0,ind] errdn = lam[0,idx,1,ind] errup = lam[0,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', label ='VR hosts') if idx > 0: ax.plot(xplot,yplot[0],'r') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True) ind = np.where(lam[1,idx,0,:] != 0.) xplot = xmf[ind] yplot = lam[1,idx,0,ind] errdn = lam[1,idx,1,ind] errup = lam[1,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', linestyle='dashed', label ='VR subh') if idx > 0: ax.plot(xplot,yplot[0],'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['r','r'], loc='upper center') common.savefig(outdir, fig, "lambda_mtot_z.pdf")
def plot_halomf_z(plt, outdir, obs_dir, snap, vol_eagle, hist, histsh): xtit="$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$" ytit="$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm halo}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 8, 14, -6, 1 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.1 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, subgn, mfofs, m200s, m200sm = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 3, 4, 6]) for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) #HMF calc HMF calculated by Sheth & Tormen (2001) lmp, dp = common.load_observation(obs_dir, 'hmf_calc_z%01d.dat'% z, [0, 7]) lmp_plot = np.log10(lmp) - np.log10(h0) dp_plot = np.log10(dp) + np.log10(pow(h0,3.)) if(idx == 0): ax.plot(lmp_plot,dp_plot,'b', label = 'HMF SMT01') if(idx > 0): ax.plot(lmp_plot,dp_plot,'b') #HMF from SUBFIND ind = np.where((mfofs > 0) & (subgn == 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(mfofs[ind]),bins=np.append(mbins,mupp)) histsfof = H ind = np.where((m200s > 0) & (subgn == 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(m200s[ind]),bins=np.append(mbins,mupp)) hists200 = H ind = np.where((m200sm > 0) & (subgn == 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(m200sm[ind]),bins=np.append(mbins,mupp)) hists200m = H y = histsfof[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', label ='EAGLE L25 SF $M_{\\rm FOF}$') if idx > 0: ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g') y = hists200[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed', label ='SF $M_{200\,crit}$') if idx > 0: ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed') y = hists200m[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dotted', label ='SF $M_{200\,mean}$') if idx > 0: ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dotted') #Predicted HMF y = hist[0,idx,:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind],y[ind],'r', linestyle='dashed', label ='VR $M_{200\,crit}$') if idx > 0: ax.plot(xmf[ind],y[ind],'r', linestyle='dashed') y = histsh[idx,:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind],y[ind],'r', label ='VR $M_{\\rm FOF}$') if idx > 0: ax.plot(xmf[ind],y[ind],'r') y = hist[1,idx,:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind],y[ind],'r', linestyle='dotted', label ='VR $M_{200\,mean}$') if idx > 0: ax.plot(xmf[ind],y[ind],'r', linestyle='dotted') if idx == 0: common.prepare_legend(ax, ['b','g','g','g','r','r','r']) common.savefig(outdir, fig, "halomf_z.pdf")
def plot_r200_z(plt, outdir, r200, snap): bin_it = functools.partial(us.wmedians, xbins=xmf) xtit="$\\rm log_{10} (\\rm M_{\\rm 200crit}/M_{\odot})$" ytit="$\\rm log_{10}(\\rm R_{\\rm 200crit}/pMpc)$" xmin, xmax, ymin, ymax = 8, 14, -3, 1 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.1 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, subgn, m200s, r200s = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 4, 5]) for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) #HMF from SUBFIND ind = np.where((m200s > 0) & (subgn == 0) & (sn == s) & (r200s > 0)) rplot = bin_it(x= np.log10(m200s[ind]), y = np.log10(r200s[ind])-3.0) ind = np.where(rplot[0,:] != 0.) xplot = xmf[ind] yplot = rplot[0,ind] errdn = rplot[1,ind] errup = rplot[2,ind] if idx == 0: ax.plot(xplot,yplot[0],'b', linestyle='dashed', label ='SF') if idx > 0: ax.plot(xplot,yplot[0],'b', linestyle='dashed') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True) #Predicted HMF ind = np.where(r200[0,idx,0,:] != 0.) xplot = xmf[ind] yplot = r200[0,idx,0,ind] errdn = r200[0,idx,1,ind] errup = r200[0,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', label ='VR') if idx > 0: ax.plot(xplot,yplot[0],'r') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True) if idx == 0: common.prepare_legend(ax, ['b','r']) common.savefig(outdir, fig, "r200M200_crit_z.pdf") xtit="$\\rm log_{10} (\\rm M_{\\rm 200mean}/M_{\odot})$" ytit="$\\rm log_{10}(\\rm R_{\\rm 200mean}/pMpc)$" xmin, xmax, ymin, ymax = 8, 14, -3, 1 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.1 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, subgn, m200s, r200s = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 6, 7]) for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) #HMF from SUBFIND ind = np.where((m200s > 0) & (subgn == 0) & (sn == s) & (r200s > 0)) rplot = bin_it(x= np.log10(m200s[ind]), y = np.log10(r200s[ind])-3.0) ind = np.where(rplot[0,:] != 0.) xplot = xmf[ind] yplot = rplot[0,ind] errdn = rplot[1,ind] errup = rplot[2,ind] if idx == 0: ax.plot(xplot,yplot[0],'b', linestyle='dashed', label ='SF') if idx > 0: ax.plot(xplot,yplot[0],'b', linestyle='dashed') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True) #Predicted HMF ind = np.where(r200[1,idx,0,:] != 0.) xplot = xmf[ind] yplot = r200[1,idx,0,ind] errdn = r200[1,idx,1,ind] errup = r200[1,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', label ='VR') if idx > 0: ax.plot(xplot,yplot[0],'r') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True) if idx == 0: common.prepare_legend(ax, ['b','r']) common.savefig(outdir, fig, "r200M200_mean_z.pdf")
def plot_rivmax_vmax_z(plt, outdir, rvmax, hist, snap, vol_eagle): bin_it = functools.partial(us.wmedians, xbins=xvf) xtit="$\\rm log_{10} (\\rm V_{\\rm max}/km s^{-1})$" ytit="$\\rm log_{10}(\\rm R_{\\rm V_{max}}/pMpc)$" xmin, xmax, ymin, ymax = 1, 3.2, -3, 1 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.1 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, subgn, rvs, vs = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 18, 19]) for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) #HMF from SUBFIND ind = np.where((rvs > 0) & (subgn == 0) & (sn == s) & (vs> 0)) rplot = bin_it(x= np.log10(vs[ind]), y = np.log10(rvs[ind])-3.0) ind = np.where((rvs > 0) & (subgn > 0) & (sn == s) & (vs> 0)) rplotsubh = bin_it(x= np.log10(vs[ind]), y = np.log10(rvs[ind])-3.0) ind = np.where(rplot[0,:] != 0.) xplot = xvf[ind] yplot = rplot[0,ind] errdn = rplot[1,ind] errup = rplot[2,ind] if idx == 0: ax.plot(xplot,yplot[0],'b', linestyle='solid', label ='SF hosts') if idx > 0: ax.plot(xplot,yplot[0],'b', linestyle='solid') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True) ind = np.where(rplotsubh[0,:] != 0.) xplot = xvf[ind] yplot = rplotsubh[0,ind] errdn = rplotsubh[1,ind] errup = rplotsubh[2,ind] if idx == 0: ax.plot(xplot,yplot[0],'b', linestyle='dashed', label ='SF subh') if idx > 0: ax.plot(xplot,yplot[0],'b', linestyle='dashed') #Predicted HMF ind = np.where(rvmax[0,idx,0,:] != 0.) xplot = xvf[ind] yplot = rvmax[0,idx,0,ind] errdn = rvmax[0,idx,1,ind] errup = rvmax[0,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', label ='VR hosts') if idx > 0: ax.plot(xplot,yplot[0],'r') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True) ind = np.where(rvmax[1,idx,0,:] != 0.) xplot = xvf[ind] yplot = rvmax[1,idx,0,ind] errdn = rvmax[1,idx,1,ind] errup = rvmax[1,idx,2,ind] if idx == 0: ax.plot(xplot,yplot[0],'r', linestyle='dashed', label ='VR subh') if idx > 0: ax.plot(xplot,yplot[0],'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['b','b','r','r'], loc='upper left') common.savefig(outdir, fig, "rvmax_vmax_z.pdf") xtit="$\\rm log_{10} (\\rm V_{\\rm max}/km s^{-1})$" ytit="$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm halo}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 1, 3.2, -6, 1 xleg = xmax - 0.2 * (xmax-xmin) yleg = ymax - 0.1 * (ymax-ymin) fig = plt.figure(figsize=(5,10)) idx = [0,1,2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg,yleg, 'z=%s' % (str(z))) #HMF from SUBFIND ind = np.where((rvs > 0) & (subgn == 0) & (sn == s) & (vs> 0)) H, bins_edges = np.histogram(np.log10(vs[ind]),bins=np.append(vbins,vupp)) histsvh = H ind = np.where((rvs > 0) & (subgn > 0) & (sn == s) & (vs> 0)) H, bins_edges = np.histogram(np.log10(vs[ind]),bins=np.append(vbins,vupp)) histsvsubh = H y = histsvh[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g', label ='EAGLE L25 SF hosts') if idx > 0: ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g') y = histsvsubh[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed', label ='SF subh') if idx > 0: ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed') #Predicted HMF y = hist[0,idx,:] ind = np.where(y != 0.) if idx == 0: ax.plot(xvf[ind],y[ind],'r', linestyle='solid', label ='VR hosts') if idx > 0: ax.plot(xvf[ind],y[ind],'r', linestyle='solid') y = hist[1,idx,:] ind = np.where(y != 0.) if idx == 0: ax.plot(xvf[ind],y[ind],'r', linestyle='dashed', label ='VR subh') if idx > 0: ax.plot(xvf[ind],y[ind],'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['g','g','r','r']) common.savefig(outdir, fig, "vmaxf_z.pdf")
def plot_lambda(plt, outdir, obsdir, lambdaH, lambda_jiang, lambda_mass, bt, ms, ssfr_z0): lambda_allstar = lambda_jiang[3,:] lambda_disk= lambda_jiang[0,:] lambda_star= lambda_jiang[1,:] lambda_gas = lambda_jiang[2,:] fig = plt.figure(figsize=(5,12)) xtit = "" ytit = "$\\rm log_{10}(\\lambda_{\\star})$" xmin, xmax, ymin, ymax = -3, 0, -4, 0 xleg = xmin + 0.02 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) cutms = 7.5 med = np.zeros(shape = (3, len(xlf))) ax = fig.add_subplot(411) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'all galaxies, stars+gas') ind = np.where((lambdaH > 0) & (lambda_allstar > 0) & (lambda_allstar < 10) & (ms > cutms)) if(len(lambdaH[ind]) > 0): #xdata = np.log10(lambdaH[ind]) #ydata = np.log10(lambda_allstar[ind]) #us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs) coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_allstar[ind])) ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3))) med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_allstar[ind]), xbins = xlf, low_numbers=True) ind = np.where((med[0,:] != 0) & (med[0,:] > -5)) xobs = xlf[ind] yobs = med[0,ind] yerrdn = med[1,ind] yerrup = med[2,ind] ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k') #ind = np.where((lambdaH > 0) & (lambda_allstar > 0) & (lambda_allstar < 10) & (ms > 10)) #med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_allstar[ind]), xbins = xlf, low_numbers=True) #ind = np.where((med[0,:] != 0) & (med[0,:] > -5)) #xobs = xlf[ind] #yobs = med[0,ind] #ax.plot(xobs, yobs[0], color='k',linestyle='dotted', label='$\\rm log_{10}(M_{\\star}/M_{\\odot}) > 10$') #ind = np.where((lambdaH > 0) & (lambda_allstar > 0) & (lambda_allstar < 10) & (ms > 7.5) & (ms < 9)) #med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_allstar[ind]), xbins = xlf, low_numbers=True) #ind = np.where((med[0,:] != 0) & (med[0,:] > -5)) #xobs = xlf[ind] #yobs = med[0,ind] #ax.plot(xobs, yobs[0], color='k',linestyle='dashed', label='$\\rm 7.5<log_{10}(M_{\\star}/M_{\\odot})<9$') ax = fig.add_subplot(412) ytit = "$\\rm log_{10}(\\lambda_{\\rm disk})$" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'disk-dominated, stars+gas') ind = np.where((lambdaH > 0) & (lambda_disk > 0) & (bt < 0.5) & (ms > cutms)) #xdata = np.log10(lambdaH[ind]) #ydata = np.log10(lambda_disk[ind]) #us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs) coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_disk[ind])) ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3))) med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_disk[ind]), xbins = xlf, low_numbers=True) ind = np.where((med[0,:] != 0) & (med[0,:] > -5)) xobs = xlf[ind] yobs = med[0,ind] yerrdn = med[1,ind] yerrup = med[2,ind] ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k') ax = fig.add_subplot(413) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'disk-dominated, stars') ind = np.where((lambdaH > 0) & (lambda_star > 0) & (bt < 0.5) & (ms > cutms)) xdata = np.log10(lambdaH[ind]) ydata = np.log10(lambda_star[ind]) us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs) coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_star[ind])) ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3))) med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_star[ind]), xbins = xlf, low_numbers=True) ind = np.where((med[0,:] != 0) & (med[0,:] > -5)) xobs = xlf[ind] yobs = med[0,ind] yerrdn = med[1,ind] yerrup = med[2,ind] ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k') ax = fig.add_subplot(414) xtit = "$\\rm log_{10}(\\lambda_{\\rm halo})$" common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xmin + 0.02 * (xmax - xmin), yleg, 'disk-dominated, gas') ind = np.where((lambdaH > 0) & (lambda_gas > 0) & (bt < 0.5) & (ms > cutms)) #xdata = np.log10(lambdaH[ind]) #ydata = np.log10(lambda_gas[ind]) #us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs) coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_gas[ind])) ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3))) med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_gas[ind]), xbins = xlf, low_numbers=True) ind = np.where((med[0,:] != 0) & (med[0,:] > -5)) xobs = xlf[ind] yobs = med[0,ind] yerrdn = med[1,ind] yerrup = med[2,ind] ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k') common.savefig(outdir, fig, 'lambda_relation.pdf')
def plot_halomf_z(plt, outdir, obsdir, z, h0, hist, histsh, plotz): xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm halo}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 10.1, 15, -6, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(7, 7)) subplots = (221, 222, 223, 224) idx = (0, 1, 2, 3) for subplot, idx, z, plot_this_z in zip(subplots, idx, z, plotz): ax = fig.add_subplot(subplot) if (idx == 0 or idx == 2): ytitplot = ytit else: ytitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytitplot, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #HMF calc HMF calculated by Sheth & Tormen (2001) lmp, dp = common.load_observation( obsdir, 'mf/HMF/mVector_PLANCK-SMT_z%s.dat' % str(z).replace('.', ''), [0, 7]) lmp_plot = np.log10(lmp) - np.log10(h0) dp_plot = np.log10(dp) + np.log10(pow(h0, 3.)) if idx == 0: ax.plot(lmp_plot, dp_plot, 'b', label='HMF calc') elif idx > 0: ax.plot(lmp_plot, dp_plot, 'b') #Predicted HMF if plot_this_z: y = hist[idx, :] ind = np.where(y < 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', label='HMF Shark') if idx > 0: ax.plot(xmf[ind], y[ind], 'r') y = histsh[idx, :] ind = np.where(y < 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='SHMF Shark') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['b', 'r', 'r']) common.savefig(outdir, fig, "halomf_z.pdf") xtit = "$\\rm log_{10}(\\rm M/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M}/{\\rm Mpc}^{-3})$" xmin, xmax, ymin, ymax = 8, 15, -6, 1.2 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 5)) idx = 0 ax = fig.add_subplot(111) ytitplot = ytit common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytitplot, locators=(0.1, 1, 0.1)) #lmp, dp = common.load_observation(obsdir, 'mf/HMF/mVector_PLANCK-SMT_z0_extended.dat', [0, 7]) #lmp_plot = np.log10(lmp) - np.log10(h0) #dp_plot = np.log10(dp) + np.log10(pow(h0,3.)) #ax.plot(lmp_plot,dp_plot,'k') #ax.plot(lmp_plot+np.log10(fb),dp_plot,'b', linestyle='dashed') y = hist[idx, :] ind = np.where((y < 0.) & (xmf > 10.3)) ax.plot(xmf[ind], y[ind], 'k') ax.plot(xmf[ind] + np.log10(fb), y[ind], 'b', linestyle='dashed') lm, p, dpdn, dpup = common.load_observation(obsdir, 'mf/SMF/GAMAII_BBD_GSMFs.dat', [0, 1, 2, 3]) xobs = lm indx = np.where(p > 0) yobs = np.log10(p[indx]) ydn = yobs - np.log10(p[indx] - dpdn[indx]) yup = np.log10(p[indx] + dpup[indx]) - yobs ax.errorbar(xobs[indx], yobs, ydn, yup, 'ro', label='Wright+17') smfdensity = common.load_observation( obsdir, 'Models/SharkVariations/SMF_FeedbackExperiment.dat', [0]) ynofeed = smfdensity[0:len(xsmf) - 1] yreio = smfdensity[len(xsmf):2 * len(xsmf) - 1] ystarf = smfdensity[2 * len(xsmf):3 * len(xsmf) - 1] yfinal = smfdensity[3 * len(xsmf):4 * len(xsmf) - 1] ind = np.where(ynofeed != 0) ax.plot(xsmf[ind], ynofeed[ind], 'DarkRed') ind = np.where(ystarf != 0) ax.plot(xsmf[ind], ystarf[ind], 'LightCoral') ind = np.where(yfinal != 0) ax.plot(xsmf[ind], yfinal[ind], 'Orange') common.prepare_legend(ax, ['r']) common.savefig(outdir, fig, "halomf_z0.pdf")
def plot_specific_am(plt, outdir, obsdir, sam_stars_disk, sam_gas_disk_atom, sam_gas_disk_mol, sam_halo, sam_bar, sam_stars): fig = plt.figure(figsize=(4.5,4.5)) xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\rm disk}/kpc\\, km s^{-1})$" xmin, xmax, ymin, ymax = 8, 11.5, 1.5, 5 xleg = xmax - 0.5 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) # choose type of selection: selec = 1 #disk-dominated galaxies s = 0 # LTG ################################## ax = fig.add_subplot(111) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'z=0 Shark-default', fontsize = 12) ind = np.where(sam_halo[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_halo[s,0,ind,selec] + 3.0 errdn = sam_halo[s,1,ind,selec] errup = sam_halo[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k',label="DM") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True) #Predicted sAM-mass for disks in disk=dominated galaxies ind = np.where(sam_stars[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_stars[s,0,ind,selec]+ 3.0 errdn = sam_stars[s,1,ind,selec] errup = sam_stars[s,2,ind,selec] ax.plot(xplot,yplot[0],color='r',linestyle='dashed',label="stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.25,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.25,interpolate=True) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_gas_disk_atom[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_gas_disk_atom[s,0,ind,selec] + 3.0 errdn = sam_gas_disk_atom[s,1,ind,selec] errup = sam_gas_disk_atom[s,2,ind,selec] ax.plot(xplot,yplot[0],color='b',linestyle='dotted',label="atomic ISM") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.25,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.25,interpolate=True) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_gas_disk_mol[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_gas_disk_mol[s,0,ind,selec] + 3.0 errdn = sam_gas_disk_mol[s,1,ind,selec] errup = sam_gas_disk_mol[s,2,ind,selec] ax.plot(xplot,yplot[0],color='g',linestyle='dashdot',label="molecular ISM") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.25,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.25,interpolate=True) common.prepare_legend(ax, ['k'], loc=2) common.savefig(outdir, fig, 'specific_am_onlyz0.pdf') fig = plt.figure(figsize=(9.5,9.5)) xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\rm disk}/kpc\\, km s^{-1})$" xmin, xmax, ymin, ymax = 8, 11.5, 1.5, 5 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) subplots = (221, 222, 223, 224) indz = (0, 1, 2, 3) # choose type of selection: selec = 1 #disk-dominated galaxies # LTG ################################## for z,s,p in zip(zlist, indz, subplots): ax = fig.add_subplot(p) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'z=%s' % str(z)) ind = np.where(sam_halo[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_halo[s,0,ind,selec] + 3.0 errdn = sam_halo[s,1,ind,selec] errup = sam_halo[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k',label="DM") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True) #Predicted sAM-mass for disks in disk=dominated galaxies ind = np.where(sam_stars[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_stars[s,0,ind,selec]+ 3.0 errdn = sam_stars[s,1,ind,selec] errup = sam_stars[s,2,ind,selec] ax.plot(xplot,yplot[0],color='r',label="stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.5,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.5,interpolate=True) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_gas_disk_atom[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_gas_disk_atom[s,0,ind,selec] + 3.0 errdn = sam_gas_disk_atom[s,1,ind,selec] errup = sam_gas_disk_atom[s,2,ind,selec] ax.plot(xplot,yplot[0],color='b',label="atomic ISM") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.5,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.5,interpolate=True) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_gas_disk_mol[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_gas_disk_mol[s,0,ind,selec] + 3.0 errdn = sam_gas_disk_mol[s,1,ind,selec] errup = sam_gas_disk_mol[s,2,ind,selec] ax.plot(xplot,yplot[0],color='g',label="molecular ISM") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.5,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.5,interpolate=True) common.prepare_legend(ax, ['k'], loc=2) common.savefig(outdir, fig, 'specific_am.pdf') #plot angular momentum components separately. fig = plt.figure(figsize=(15,5)) s = 0 #plot stars xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\star}/kpc\\, km s^{-1})$" xmin, xmax, ymin, ymax = 8, 11.5, 1.5, 4 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) ax = fig.add_subplot(141) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) jst, jmole, jatomic, jbar = common.load_observation(obsdir, 'Models/SharkVariations/AngularMomentum.dat', [0,1,2,3]) jsL18 = np.zeros(shape = (3, len(xmf))) jmolL18 = np.zeros(shape = (3, len(xmf))) jatomL18 = np.zeros(shape = (3, len(xmf))) jbarL18 = np.zeros(shape = (3, len(xmf))) i = 0 p =0 for j in range(0,len(jst)/2): jsL18[i,p] = jst[j] jmolL18[i,p] = jmole[j] jatomL18[i,p] = jatomic[j] jbarL18[i,p] = jbar[j] p = p + 1 if(p >= len(xmf)): p = 0 i = i +1 #Predicted sAM-mass for disks in disk=dominated galaxies ind = np.where(sam_stars[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_stars[s,0,ind,selec]+ 3.0 errdn = sam_stars[s,1,ind,selec] errup = sam_stars[s,2,ind,selec] ax.plot(xplot,yplot[0],color='r') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.35,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.35,interpolate=True) ind = np.where(sam_stars_disk[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_stars_disk[s,0,ind,selec]+ 3.0 errdn = sam_stars_disk[s,1,ind,selec] errup = sam_stars_disk[s,2,ind,selec] ax.plot(xplot,yplot[0],color='r', linestyle='dotted') ind = np.where(jsL18[0,:] != 0) xplot = xmf[ind] yplot = jsL18[0,ind]+ 3.0 errdn = jsL18[1,ind] errup = jsL18[2,ind] ax.plot(xplot,yplot[0],color='r',linestyle='dashed') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', linestyle='dashed', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', linestyle='dashed', alpha=0.2,interpolate=True) #Read observational data. ms, mserr, js, jserr = common.load_observation(obsdir, 'SizesAndAM/Posti18_AMdata.dat', [3,5,6,8]) errmdn = np.log10(ms) - np.log10(ms - mserr) errmup = np.log10(ms + mserr) - np.log10(ms) errjdn = np.log10(js) - np.log10(js - jserr) errjup = np.log10(js + jserr) - np.log10(js) ax.errorbar(np.log10(ms), np.log10(js), yerr=[errjdn,errjup], xerr=[errmdn,errmup],ls='None', mfc='None', ecolor = 'r', mec='r',marker='+',label="Posti+18") bt, mbO14, msO14, mgO14, jbO14, jsO14, jgO14, jmolO14 = common.load_observation(obsdir, 'SizesAndAM/Obreschkow14_FP.dat', [2,6,7,8,11,12,14,15]) ax.plot(msO14, jsO14, 'ro',fillstyle='none', label="Obreschkow+14") mgB17, msB17, errmsB17, mbB17, errmbB17, jgB17, errjgB17, jsB17, errjsB17, jbB17, errjbB17 = common.load_observation(obsdir, 'SizesAndAM/LITTLETHINGS_Butler16.dat', [1,3,4,5,6,7,8,9,10,11,12]) ax.errorbar(msB17, jsB17, yerr=[errjsB17,errjsB17], xerr=[errmsB17,errmsB17], ls='None', mfc='None', ecolor = 'r', mec='r',marker='s',label="Butler+16") msC17, mgC17, mbC17, errupmbC17, errdnmbC17, JstarC17, JgasC17, errupJgasC17, errdnJgasC17, jbarC17, errupjbarC17, errdnjbarC17 = common.load_observation(obsdir, 'SizesAndAM/Chowdhury17.dat', [1,2,5,6,7,8,8,10,11,15,16,17]) ax.plot(msC17, JstarC17-msC17, 'rp', fillstyle='none',label="Chowdhury+17") mbE17, jbE17 = common.load_observation(obsdir, 'SizesAndAM/Elson17.dat', [5,7]) mbE17 = np.log10(mbE17 * 1e8) jbE17 = np.log10(jbE17) #IDGalaxy Mstar errup errdn Mgas errup errdn Mbar errup errdn jstar errup errdn jgas errup errdn jbar errup errdn (msK18, errupmsK18, errdnmsK18, mgK18, errupmgK18, errdnmgK18, mbK18, errupmbK18, errdnmbK18, jsK18, errupjsK18, errdnjsK18, jgK18, errupjgK18, errdnjgK18, jbarK18, errupjbarK18, errdnjbarK18) = common.load_observation(obsdir, 'SizesAndAM/Kurapati18.dat', [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]) ax.errorbar(msK18, jsK18, yerr=[errdnjsK18,errupjsK18], xerr=[errdnmsK18,errupmsK18],ls='None', mfc='None', ecolor = 'r', mec='r',marker='*',label="Kurapati+18") common.prepare_legend(ax, ['r', 'r', 'r','r','r'], loc=2) #plot molecular gas xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\rm H_2}/kpc\\, km s^{-1})$" ax = fig.add_subplot(142) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_gas_disk_mol[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_gas_disk_mol[s,0,ind,selec] + 3.0 errdn = sam_gas_disk_mol[s,1,ind,selec] errup = sam_gas_disk_mol[s,2,ind,selec] ax.plot(xplot,yplot[0],color='g', label="ISM/stars AM transfer") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.35,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.35,interpolate=True) ind = np.where(jmolL18[0,:] != 0) xplot = xmf[ind] yplot = jmolL18[0,ind]+ 3.0 errdn = jmolL18[1,ind] errup = jmolL18[2,ind] ax.plot(xplot,yplot[0],color='g',linestyle='dashed', label="Lagos+18") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', linestyle='dashed', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', linestyle='dashed', alpha=0.2,interpolate=True) ax.plot(msO14, jmolO14, 'go',fillstyle='none') common.prepare_legend(ax, ['k'], loc=2) #plot atomic gas xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\rm HI}/kpc\\, km s^{-1})$" ax = fig.add_subplot(143) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_gas_disk_atom[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_gas_disk_atom[s,0,ind,selec] + 3.0 errdn = sam_gas_disk_atom[s,1,ind,selec] errup = sam_gas_disk_atom[s,2,ind,selec] ax.plot(xplot,yplot[0],color='b') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.35,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.35,interpolate=True) ind = np.where(jatomL18[0,:] != 0) xplot = xmf[ind] yplot = jatomL18[0,ind]+ 3.0 errdn = jatomL18[1,ind] errup = jatomL18[2,ind] ax.plot(xplot,yplot[0],color='b',linestyle='dashed') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', linestyle='dashed', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', linestyle='dashed', alpha=0.2,interpolate=True) ax.errorbar(msB17, jgB17, yerr=[errjgB17,errjgB17], xerr=[errmsB17,errmsB17,],ls='None', mfc='None', ecolor = 'b', mec='b',marker='s') ax.plot(msO14, jgO14, 'bo',fillstyle='none') ax.plot(msC17, JgasC17-mgC17, 'bp', fillstyle='none') ax.errorbar(msK18, jgK18, yerr=[errdnjgK18,errupjgK18], xerr=[errdnmsK18,errupmsK18],ls='None', mfc='None', ecolor = 'b', mec='b',marker='*') #plot total baryon xtit = "$\\rm log_{10} (\\rm M_{\\rm bar}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\rm bar}/kpc\\, km s^{-1})$" ax = fig.add_subplot(144) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(sam_bar[s,0,:,selec] != 0) xplot = xmf[ind] yplot = sam_bar[s,0,ind,selec] + 3.0 errdn = sam_bar[s,1,ind,selec] errup = sam_bar[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.25,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.25,interpolate=True) ind = np.where(jbarL18[0,:] != 0) xplot = xmf[ind] yplot = jbarL18[0,ind]+ 3.0 errdn = jbarL18[1,ind] errup = jbarL18[2,ind] ax.plot(xplot,yplot[0],color='k',linestyle='dashed') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', linestyle='dashed', alpha=0.1,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', linestyle='dashed', alpha=0.1,interpolate=True) ax.errorbar(mbB17, jbB17, yerr=[errjbB17,errjbB17], xerr=[errmbB17,errmbB17],ls='None', mfc='None', ecolor = 'k', mec='k',marker='s') ax.plot(mbO14, jbO14, 'ko',fillstyle='none') ax.errorbar(mbC17, jbarC17, yerr=[errdnjbarC17,errupjbarC17], xerr=[errdnmbC17,errupmbC17], ls='None', mfc='None', ecolor = 'k', mec='k',marker='p') ax.errorbar(mbK18, jbarK18, yerr=[errdnjbarK18,errupjbarK18], xerr=[errdnmbK18,errupmbK18],ls='None', mfc='None', ecolor = 'k', mec='k',marker='*') ax.plot(mbE17, jbE17, 'k^', fillstyle='none', label='Elson17') common.prepare_legend(ax, ['k'], loc=2) common.savefig(outdir, fig, 'specific_am_z0_components.pdf') for c in range (0,2): print 'will change selection' for i in range (0,3): print 'will change within the same sample' for x,y,z,a in zip(sam_stars_disk[s,i,:,c],sam_gas_disk_mol[s,i,:,c],sam_gas_disk_atom[s,i,:,c],sam_bar[s,i,:,c]): print x,y,z,a
def plot_specific_am_ratio(plt, outdir, obsdir, sam_ratio_halo_disk, sam_ratio_halo_gal, sam_ratio_halo_disk_gas, sam_vs_sam_halo_disk, sam_vs_sam_halo_gal, sam_vs_sam_halo_disk_gas): fig = plt.figure(figsize=(9.5,9.5)) xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm j_{\\star}/j_{\\rm halo}$)" xmin, xmax, ymin, ymax = 10, 15, -3, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) subplots = (221, 222, 223, 224) indz = (0, 1, 2, 3) # choose type of selection: selec = 1 #disk-dominated galaxies # LTG ################################## for z,s,p in zip(zlist, indz, subplots): ax = fig.add_subplot(p) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'z=%s' % str(z)) ind = np.where(sam_ratio_halo_gal[s,0,:,selec] != 0) xplot = xmfh[ind] yplot = sam_ratio_halo_gal[s,0,ind,selec] errdn = sam_ratio_halo_gal[s,1,ind,selec] errup = sam_ratio_halo_gal[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k',label="all stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True) ind = np.where(sam_ratio_halo_disk[s,0,:,selec] != 0) xplot = xmfh[ind] yplot = sam_ratio_halo_disk[s,0,ind,selec] errdn = sam_ratio_halo_disk[s,1,ind,selec] errup = sam_ratio_halo_disk[s,2,ind,selec] ax.plot(xplot,yplot[0],color='g',label="disk stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.2,interpolate=True) ind = np.where(sam_ratio_halo_disk_gas[s,0,:,selec] != 0) xplot = xmfh[ind] yplot = sam_ratio_halo_disk_gas[s,0,ind,selec] errdn = sam_ratio_halo_disk_gas[s,1,ind,selec] errup = sam_ratio_halo_disk_gas[s,2,ind,selec] ax.plot(xplot,yplot[0],color='b',label="disk gas") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True) common.prepare_legend(ax, ['k'], loc=2) common.savefig(outdir, fig, 'specific_am_ratio.pdf') selec = 0 #disk-dominated galaxies #plot specific AM vs. specific AM fig = plt.figure(figsize=(9,8)) xtit = "$\\rm log_{10} (\\rm j_{\\rm halo}/kpc\,km\,s^{-1})$" ytit = "$\\rm log_{10} (\\rm j_{\\star},j_{\\star,disk},j_{\\rm gas,disk}/kpc\,km\,s^{-1}$)" xmin, xmax, ymin, ymax = 1,6,1,6 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) subplots = (221, 222, 223, 224) indz = (0, 1, 2, 3) zinplot = (0, 0.5, 1, 2) # LTG ################################## for z,s,p in zip(zinplot, indz, subplots): ax = fig.add_subplot(p) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'z=%s' % str(z), fontsize=10) #if(s == 0): # ax.text(5.5,6.4,'Lagos+18',fontsize=14) ind = np.where(sam_vs_sam_halo_gal[s,0,:,selec] != 0) xplot = xlf[ind]+3 yplot = sam_vs_sam_halo_gal[s,0,ind,selec]+3 errdn = sam_vs_sam_halo_gal[s,1,ind,selec] errup = sam_vs_sam_halo_gal[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k',label="all stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True) ind = np.where(sam_vs_sam_halo_disk[s,0,:,selec] != 0) xplot = xlf[ind]+3 yplot = sam_vs_sam_halo_disk[s,0,ind,selec]+3 errdn = sam_vs_sam_halo_disk[s,1,ind,selec] errup = sam_vs_sam_halo_disk[s,2,ind,selec] ax.plot(xplot,yplot[0],color='g',label="disk stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.2,interpolate=True) ind = np.where(sam_vs_sam_halo_disk_gas[s,0,:,selec] != 0) xplot = xlf[ind]+3 yplot = sam_vs_sam_halo_disk_gas[s,0,ind,selec]+3 errdn = sam_vs_sam_halo_disk_gas[s,1,ind,selec] errup = sam_vs_sam_halo_disk_gas[s,2,ind,selec] ax.plot(xplot,yplot[0],color='b',label="disk gas") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True) xplot = [1,5] ax.plot(xplot,xplot,color='grey',linestyle='dotted') if(s == 0): common.prepare_legend(ax, ['k','g','b'], loc=2) common.savefig(outdir, fig, 'specific_am_halo_vs_galaxy.pdf') selec = 0 #all galaxies #plot specific AM vs. specific AM fig = plt.figure(figsize=(5,5)) xtit = "$\\rm log_{10} (\\rm j_{\\rm halo}/kpc\,km\,s^{-1})$" ytit = "$\\rm log_{10} (\\rm j_{\\star},j_{\\star,disk},j_{\\rm gas,disk}/kpc\,km\,s^{-1}$)" xmin, xmax, ymin, ymax = 1,6,1,6 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) s = 0 # LTG ################################## ax = fig.add_subplot(111) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'z=0', fontsize=10) #if(s == 0): # ax.text(5.5,6.4,'Lagos+18',fontsize=14) ind = np.where(sam_vs_sam_halo_gal[s,0,:,selec] != 0) xplot = xlf[ind]+3 yplot = sam_vs_sam_halo_gal[s,0,ind,selec]+3 errdn = sam_vs_sam_halo_gal[s,1,ind,selec] errup = sam_vs_sam_halo_gal[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k',label="all stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True) ind = np.where(sam_vs_sam_halo_disk[s,0,:,selec] != 0) xplot = xlf[ind]+3 yplot = sam_vs_sam_halo_disk[s,0,ind,selec]+3 errdn = sam_vs_sam_halo_disk[s,1,ind,selec] errup = sam_vs_sam_halo_disk[s,2,ind,selec] ax.plot(xplot,yplot[0],color='g',label="disk stars") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.2,interpolate=True) ind = np.where(sam_vs_sam_halo_disk_gas[s,0,:,selec] != 0) xplot = xlf[ind]+3 yplot = sam_vs_sam_halo_disk_gas[s,0,ind,selec]+3 errdn = sam_vs_sam_halo_disk_gas[s,1,ind,selec] errup = sam_vs_sam_halo_disk_gas[s,2,ind,selec] ax.plot(xplot,yplot[0],color='b',label="disk gas") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True) xplot = [1,5] ax.plot(xplot,xplot,color='grey',linestyle='dotted') common.prepare_legend(ax, ['k','g','b'], loc=2) common.savefig(outdir, fig, 'specific_am_halo_vs_galaxy_z0.pdf')
def plot_sizes(plt, outdir, obsdir, disk_size_cen, disk_size_sat, bulge_size, vmax_halo_gal): print 'sizes disk centrals' for i,j,p in zip(disk_size_cen[0,0,:],disk_size_cen[0,1,:],disk_size_cen[0,2,:]): print i,j,p print 'disk sizes satellites' for i,j,p in zip(disk_size_sat[0,0,:],disk_size_sat[0,1,:],disk_size_sat[0,2,:]): print i,j,p print 'sizes bulges' for i,j,p in zip(bulge_size[0,0,:],bulge_size[0,1,:],bulge_size[0,2,:]): print i,j,p rb, r16, r84 = common.load_observation(obsdir, 'Models/SharkVariations/SizeDisksAndBulges_OtherModels.dat', [0,1,2]) fig = plt.figure(figsize=(5,11.5)) xtit = "$\\rm log_{10} (\\rm M_{\\star,disk}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm r_{\\star,disk}/kpc)$" xmin, xmax, ymin, ymax = 8, 12, -0.1, 2 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) # LTG ################################## ax = fig.add_subplot(311) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(8.1,1.7,'disks of centrals',fontsize=12) #Predicted size-mass for disks in disk=dominated galaxies ind = np.where(disk_size_cen[0,0,:] != 0) xplot = xmf[ind] yplot = disk_size_cen[0,0,ind] errdn = disk_size_cen[0,1,ind] errup = disk_size_cen[0,2,ind] ax.plot(xplot,yplot[0],color='b',linestyle='solid',label="ISM/stars AM transfer") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.3,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.3,interpolate=True) rdisk_am = rb[30:59] rdisk_am16 = r16[30:59] rdisk_am84 = r84[30:59] ind = np.where(rdisk_am != 0) xplot = xmf[ind] yplot = rdisk_am[ind] errdn = rdisk_am16[ind] errup = rdisk_am84[ind] ax.plot(xplot,yplot,color='b',linestyle='dashed', label="Lagos+18") ax.fill_between(xplot,yplot,yplot-errdn, facecolor='b', linestyle='solid', alpha=0.4,interpolate=True) ax.fill_between(xplot,yplot,yplot+errup, facecolor='b', linestyle='solid', alpha=0.4,interpolate=True) #Lange et al. (2016) m,r = common.load_observation(obsdir, 'SizesAndAM/rdisk_L16.dat', [0,1]) ax.plot(m[0:36], r[0:36], linestyle='dotted',color='k') ax.plot(m[38:83], r[38:83], linestyle='dotted',color='k') ax.plot(m[85:128], r[85:129], linestyle='dotted',color='k') common.prepare_legend(ax, ['b','b'], bbox_to_anchor=(0.005, 0.62)) ax = fig.add_subplot(312) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(8.1,1.7,'disks of satellites',fontsize=12) #Predicted size-mass for disks in disk=dominated galaxies satellites ind = np.where(disk_size_sat[0,0,:] != 0) xplot = xmf[ind] yplot = disk_size_sat[0,0,ind] errdn = disk_size_sat[0,1,ind] errup = disk_size_sat[0,2,ind] ax.plot(xplot,yplot[0],color='g',linestyle='solid') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.3,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.3,interpolate=True) rdisk_am = rb[0:29] rdisk_am16 = r16[0:29] rdisk_am84 = r84[0:29] ind = np.where(rdisk_am != 0) xplot = xmf[ind] yplot = rdisk_am[ind] errdn = rdisk_am16[ind] errup = rdisk_am84[ind] ax.plot(xplot,yplot,color='g',linestyle='dashed') ax.fill_between(xplot,yplot,yplot-errdn, facecolor='g', linestyle='solid', alpha=0.4,interpolate=True) ax.fill_between(xplot,yplot,yplot+errup, facecolor='g', linestyle='solid', alpha=0.4,interpolate=True) #Lange et al. (2016) m,r = common.load_observation(obsdir, 'SizesAndAM/rdisk_L16.dat', [0,1]) ax.plot(m[0:36], r[0:36], linestyle='dotted',color='k',label="L16 50th, 68th, 90th") ax.plot(m[38:83], r[38:83], linestyle='dotted',color='k') ax.plot(m[85:128], r[85:129], linestyle='dotted',color='k') common.prepare_legend(ax, ['k'], bbox_to_anchor=(0.005, 0.67)) # ETGs ################################## xtit = "$\\rm log_{10} (\\rm M_{\\star,bulge}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm r_{\\star,bulge}/kpc)$" xmin, xmax, ymin, ymax = 8, 12, -0.35, 2 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) ax = fig.add_subplot(313) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(8.1,1.7,'bulges of all galaxies',fontsize=12) #Predicted size-mass for bulges in bulge-dominated systems ind = np.where(bulge_size[0,0,:] != 0) if(len(xmf[ind]) > 0): xplot = xmf[ind] yplot = bulge_size[0,0,ind] errdn = bulge_size[0,1,ind] errup = bulge_size[0,2,ind] ax.plot(xplot,yplot[0],color='r',linestyle='solid') ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.3,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.3,interpolate=True) rdisk_am = rb[60:99] rdisk_am16 = r16[60:99] rdisk_am84 = r84[60:99] ind = np.where(rdisk_am != 0) xplot = xmf[ind] yplot = rdisk_am[ind] errdn = rdisk_am16[ind] errup = rdisk_am84[ind] ax.plot(xplot,yplot,color='r',linestyle='dashed') ax.fill_between(xplot,yplot,yplot-errdn, facecolor='LightCoral', linestyle='solid', alpha=0.4,interpolate=True) ax.fill_between(xplot,yplot,yplot+errup, facecolor='LightCoral', linestyle='solid', alpha=0.4,interpolate=True) #Lange et al. (2016) m,r = common.load_observation(obsdir, 'SizesAndAM/rbulge_L16.dat', [0,1]) ax.plot(m[0:39], r[0:39], linestyle='dotted',color='k') ax.plot(m[41:76], r[41:76], linestyle='dotted',color='k') ax.plot(m[78:115], r[78:115], linestyle='dotted',color='k') common.savefig(outdir, fig, 'sizes_angular_momentum_model.pdf') fig = plt.figure(figsize=(9.5,9.5)) xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$" ytit = "$\\rm log_{10} (\\rm v_{\\rm max}/km s^{-1}$)" xmin, xmax, ymin, ymax = 10, 15, 1.3, 3.5 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) subplots = (221, 222, 223, 224) indz = (0, 1, 2, 3) # choose type of selection: selec = 0 #all galaxies # LTG ################################## for z,s,p in zip(zlist, indz, subplots): ax = fig.add_subplot(p) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, 'z=%s' % str(z)) ind = np.where(vmax_halo_gal[s,0,:,selec] != 0) xplot = xmfh[ind] yplot = vmax_halo_gal[s,0,ind,selec] errdn = vmax_halo_gal[s,1,ind,selec] errup = vmax_halo_gal[s,2,ind,selec] ax.plot(xplot,yplot[0],color='k',label="central subhalos") ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True) ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True) common.prepare_legend(ax, ['k'], loc=2) common.savefig(outdir, fig, 'vmax_vs_subhalo.pdf')
def plot_bt_resolve(plt, outdir, obsdir, ETGsmhalo, LTGsmhalo): resolve_obs_as_errorbars = functools.partial(_resolve_obs_as_errorbars, obsdir) fig = plt.figure(figsize=(5, 9)) xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$" ytit = "$\\rm frequency$" xmin, xmax, ymin, ymax = 11.5, 15, -0.05, 1.05 xleg = xmax - 0.5 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) # LTG ################################## ax = fig.add_subplot(311) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, '$\\rm all\, masses$') #Predicted size-mass for disks ind = np.where(ETGsmhalo[0, :] >= 0) xplot = xmf[ind] yplot = ETGsmhalo[0, ind] ax.plot(xplot, yplot[0], 'r', label='SHArk ETGs') ind = np.where(LTGsmhalo[0, :] >= 0) xplot = xmf[ind] yplot = LTGsmhalo[0, ind] ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='SHArk LTGs') #Baldry (Chabrier IMF), ['Baldry+2012, z<0.06'] resolve_obs_as_errorbars(ax, 'ETall_frac.txt', [0, 1, 2, 3], 'r', 'o', err_absolute=False, label='RESOLVE/ECO ETGs') resolve_obs_as_errorbars(ax, 'LTall_frac.txt', [0, 1, 2, 3], 'b', 's', err_absolute=False, label='RESOLVE/ECO LTGs') common.prepare_legend(ax, ['r', 'b', 'r', 'b'], loc=2, bbox_to_anchor=(0.0, 1.45)) # LTG ################################## ax = fig.add_subplot(312) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, '$M^{\\prime}_{\\rm bar}/M_{\odot} > 10^{10}$') #Predicted size-mass for disks ind = np.where(ETGsmhalo[1, :] >= 0) xplot = xmf[ind] yplot = ETGsmhalo[1, ind] ax.plot(xplot, yplot[0], 'r') ind = np.where(LTGsmhalo[1, :] >= 0) xplot = xmf[ind] yplot = LTGsmhalo[1, ind] ax.plot(xplot, yplot[0], 'b', linestyle='dashed') #Baldry (Chabrier IMF), ['Baldry+2012, z<0.06'] resolve_obs_as_errorbars(ax, 'EThimbary_frac.txt', [0, 1, 2, 3], 'r', 'o', err_absolute=False) resolve_obs_as_errorbars(ax, 'LThimbary_frac.txt', [0, 1, 2, 3], 'b', 's', err_absolute=False) # LTG ################################## ax = fig.add_subplot(313) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) ax.text(xleg, yleg, '$M^{\\prime}_{\\rm bar}/M_{\odot} < 10^{10}$') #Predicted size-mass for disks ind = np.where(ETGsmhalo[2, :] >= 0) xplot = xmf[ind] yplot = ETGsmhalo[2, ind] ax.plot(xplot, yplot[0], 'r') ind = np.where(LTGsmhalo[2, :] >= 0) xplot = xmf[ind] yplot = LTGsmhalo[2, ind] ax.plot(xplot, yplot[0], 'b', linestyle='dashed') #Baldry (Chabrier IMF), ['Baldry+2012, z<0.06'] resolve_obs_as_errorbars(ax, 'ETlowmbary_frac.txt', [0, 1, 2, 3], 'r', 'o', err_absolute=False) resolve_obs_as_errorbars(ax, 'LTlowmbary_frac.txt', [0, 1, 2, 3], 'b', 's', err_absolute=False) common.savefig(outdir, fig, 'bt_resolve.pdf')
def plot_scaling_z(plt, outdir, snap, SFRMstar, R50Mstar, R50Mstar30, MBHMstar, SigmaMstar30, ZstarMstar, ZSFMstar, AgeSMstar, SFRMstar30, R50pMstar30, ZNSFMstar): bin_it = functools.partial(us.wmedians, xbins=xmf) ########################### will plot main sequence for all stellar particles in the subhalo xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm SFR/M_{\odot} yr^{-1})$" xmin, xmax, ymin, ymax = 7, 12, -5, 1.5 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, mstot, sfr, r50, ms30, sfr30 = common.load_observation( '/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 29, 30, 26, 22, 24]) for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (sfr > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(sfr[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(SFRMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = SFRMstar[idx, 0, ind] errdn = SFRMstar[idx, 1, ind] errup = SFRMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "main_sequence_z.pdf") ########################### will plot main sequence for 30kpc aperture xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm SFR_{\\rm 30kpc}/M_{\odot} yr^{-1})$" xmin, xmax, ymin, ymax = 7, 12, -5, 1.5 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((ms30 > 0) & (sn == s) & (sfr30 > 0)) rplot = bin_it(x=np.log10(ms30[ind]), y=np.log10(sfr30[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(SFRMstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = SFRMstar30[idx, 0, ind] errdn = SFRMstar30[idx, 1, ind] errup = SFRMstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "main_sequence_30kpc_z.pdf") ########################### will plot r50 vs stellar mass for all stellar particles in the subhalo xtit = "$\\rm log_{10} (\\rm M_{\\star,tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm R_{\\rm 50}/pMpc)$" xmin, xmax, ymin, ymax = 7, 12, -3.3, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (r50 > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(r50[ind]) - 3.0) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(R50Mstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = R50Mstar[idx, 0, ind] errdn = R50Mstar[idx, 1, ind] errup = R50Mstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "r50_Mstar_z.pdf") ################## will plot r50 vs stellar mass for quantities measured within 30kpc sn, mstot, sfr, r50, r50p = common.load_observation( '/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE-REF.data', [2, 22, 30, 33, 34]) xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc}/pMpc)$" xmin, xmax, ymin, ymax = 7, 12, -3.3, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (r50 > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(r50[ind]) - 3.0) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(R50Mstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = R50Mstar30[idx, 0, ind] errdn = R50Mstar30[idx, 1, ind] errup = R50Mstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "r50_Mstar_30kpc_z.pdf") ################## will plot r50 vs stellar mass for quantities measured within 30kpc, but in this case the r50 is projected xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc,2D}/pMpc)$" xmin, xmax, ymin, ymax = 7, 12, -3.3, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (r50p > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(r50p[ind]) - 3.0) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(R50pMstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = R50pMstar30[idx, 0, ind] errdn = R50pMstar30[idx, 1, ind] errup = R50pMstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "r50_projected_Mstar_30kpc_z.pdf") ########################### will plot stellar velocity dispersion vs. stellar mass sn, mstot, sfr, r50, vs = common.load_observation( '/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE-REF.data', [2, 22, 30, 33, 25]) xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\\sigma_{\\star,30kpc}/km s^{-1})$" xmin, xmax, ymin, ymax = 7, 12, 1, 3 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) plt.subplots_adjust(left=0.2) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (vs > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(vs[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(SigmaMstar30[idx, 0, :] != 0.) xplot = xmf[ind] yplot = SigmaMstar30[idx, 0, ind] errdn = SigmaMstar30[idx, 1, ind] errup = SigmaMstar30[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "vdisp_Mstar_30kpc_z.pdf") ############ will plot stellar metallicity-stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm Z_{\star})$" xmin, xmax, ymin, ymax = 7, 12, -5, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, mstot, zsf, zs, age, znsf = common.load_observation( '/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 29, 33, 34, 35, 36]) for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (zs > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(zs[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(ZstarMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = ZstarMstar[idx, 0, ind] errdn = ZstarMstar[idx, 1, ind] errup = ZstarMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "zstar_mstar_z.pdf") ################ will plot star-forming gas metallicity vs. stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm Z_{\\rm SF,gas})$" xmin, xmax, ymin, ymax = 7, 12, -5, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (zsf > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(zsf[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(ZSFMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = ZSFMstar[idx, 0, ind] errdn = ZSFMstar[idx, 1, ind] errup = ZSFMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "zsfgas_mstar_z.pdf") ################ will plot non-star-forming gas metallicity vs. stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm Z_{\\rm non-SF,gas})$" xmin, xmax, ymin, ymax = 7, 12, -5, -1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (znsf > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(znsf[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(ZNSFMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = ZNSFMstar[idx, 0, ind] errdn = ZNSFMstar[idx, 1, ind] errup = ZNSFMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "znsfgas_mstar_z.pdf") ################ will plot stellar ages vs stellar mass xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm age_{\\star}/Gyr)$" xmin, xmax, ymin, ymax = 7, 12, 0, 1.1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #from SUBFIND ind = np.where((mstot > 0) & (sn == s) & (age > 0)) rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(age[ind])) ind = np.where(rplot[0, :] != 0.) xplot = xmf[ind] yplot = rplot[0, ind] errdn = rplot[1, ind] errup = rplot[2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='EAGLE L25') if idx > 0: ax.plot(xplot, yplot[0], 'b', linestyle='dashed') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='b', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='b', alpha=0.2, interpolate=True) #VR ind = np.where(AgeSMstar[idx, 0, :] != 0.) xplot = xmf[ind] yplot = AgeSMstar[idx, 0, ind] errdn = AgeSMstar[idx, 1, ind] errup = AgeSMstar[idx, 2, ind] if idx == 0: ax.plot(xplot, yplot[0], 'r', label='VR') if idx > 0: ax.plot(xplot, yplot[0], 'r') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='r', alpha=0.2, interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='r', alpha=0.2, interpolate=True) if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "starage_mstar_z.pdf")
def plot_bmf_resolve(plt, outdir, obsdir, hist_bmf, hist_bmf_sat): load_resolve_mf_obs = functools.partial(_load_resolve_mf_obs, obsdir) fig = plt.figure(figsize=(9.5, 10.5)) xtit = "$\\rm log_{10} (\\rm M^{\\prime}_{\\rm bar}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm dn/dM / Mpc^{-3} dex^{-1})$" xmin, xmax, ymin, ymax = 9, 12, -6, -1 xleg = xmax - 0.3 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) # all halos ################################## ax = fig.add_subplot(321) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=ytit) ax.tick_params(labelsize=13) ax.text(xleg, yleg, '$\\rm all\, halos$') #Predicted SMHM ind = np.where(hist_bmf[0, :] != 0) xplot = xmf[ind] yplot = hist_bmf[0, ind] ax.errorbar(xplot, yplot[0], color='k') ind = np.where(hist_bmf_sat[0, :] != 0) xplot = xmf[ind] yplot = hist_bmf_sat[0, ind] ax.errorbar(xplot, yplot[0], color='k', linestyle="dashed") #RESOLVE observations M, No, Nodn, Noup = load_resolve_mf_obs('bmassfunction_resolve.txt', [0, 1, 2, 3]) resolve_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'grey', 'o', yerrdn_val=No) M, No, Nodn, Noup = load_resolve_mf_obs('bmassfunction_eco.txt', [0, 1, 2, 3]) eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'grey', 's', yerrdn_val=No) # low mass halos ################################## ax = fig.add_subplot(322) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=None) xleg = xmax - 0.63 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) ax.text(xleg, yleg, '$11<\\rm log_{10}(M_{\\rm halo}/M_{\odot})<11.4$') #Predicted SMHM ind = np.where(hist_bmf[1, :] != 0) xplot = xmf[ind] yplot = hist_bmf[1, ind] ax.errorbar(xplot, yplot[0], color='b') ind = np.where(hist_bmf_sat[1, :] != 0) xplot = xmf[ind] yplot = hist_bmf_sat[1, ind] ax.errorbar(xplot, yplot[0], color='b', linestyle="dashed") #RESOLVE observations M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionlowmasshalos_resolve.txt', [0, 1, 2, 3, 7, 8, 9]) resolve_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'b', 'o', yerrdn_val=0.1) resolve_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'b', 'o', yerrdn_val=0.1, fillstyle='full', markersize=3) M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionlowmasshalos_eco.txt', [0, 1, 2, 3, 7, 8, 9]) eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'b', 's', yerrdn_val=0.1) eco_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'b', 's', fillstyle='full', markersize=3) # medium mass halos ################################## ax = fig.add_subplot(323) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=ytit) ax.text(xleg, yleg, '$11.4<\\rm log_{10}(M_{\\rm halo}/M_{\odot})<12$') #Predicted SMHM ind = np.where(hist_bmf[2, :] != 0) xplot = xmf[ind] yplot = hist_bmf[2, ind] ax.errorbar(xplot, yplot[0], color='g') ind = np.where(hist_bmf_sat[2, :] != 0) xplot = xmf[ind] yplot = hist_bmf_sat[2, ind] ax.errorbar(xplot, yplot[0], color='g', linestyle="dashed") #RESOLVE observations M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionmedmasshalos_resolve.txt', [0, 1, 2, 3, 7, 8, 9]) resolve_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'g', 'o', yerrdn_val=0.1) resolve_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'g', 'o', fillstyle='full', markersize=3) M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionmedmasshalos_eco.txt', [0, 1, 2, 3, 7, 8, 9]) eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'g', 's', yerrdn_val=0.1) eco_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'g', 's', fillstyle='full', markersize=3) # medium high mass halos ################################## ax = fig.add_subplot(324) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=xtit, ytit=None) ax.text(xleg, yleg, '$12<\\rm log_{10}(M_{\\rm halo}/M_{\odot})<13.5$') #Predicted SMHM ind = np.where(hist_bmf[3, :] != 0) xplot = xmf[ind] yplot = hist_bmf[3, ind] ax.errorbar(xplot, yplot[0], color='r', label="all galaxies") ind = np.where(hist_bmf_sat[3, :] != 0) xplot = xmf[ind] yplot = hist_bmf_sat[3, ind] ax.errorbar(xplot, yplot[0], color='r', linestyle="dashed", label="satellites") #RESOLVE observations M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionhighmasshalos_resolve.txt', [0, 1, 2, 3, 7, 8, 9]) resolve_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'r', 'o', yerrdn_val=0.1, label="RESOLVE all") resolve_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'r', 'o', fillstyle='full', markersize=3, label="RESOLVE satellites") M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionhighmasshalos_eco.txt', [0, 1, 2, 3, 7, 8, 9]) eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'r', 's', yerrdn_val=No, label="ECO all") eco_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'r', 's', fillstyle='full', markersize=3, label="ECO satellites") common.prepare_legend(ax, ['k', 'k', 'k', 'k', 'k', 'k'], loc=2, bbox_to_anchor=(0.1, -0.4)) # high mass halos ################################## ax = fig.add_subplot(325) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit) ax.text(xleg, yleg, '$\\rm log_{10}(M_{\\rm halo}/M_{\odot})>13.5$') #Predicted SMHM ind = np.where(hist_bmf[4, :] != 0) xplot = xmf[ind] yplot = hist_bmf[4, ind] ax.errorbar(xplot, yplot[0], color='orange') ind = np.where(hist_bmf_sat[4, :] != 0) xplot = xmf[ind] yplot = hist_bmf_sat[4, ind] ax.errorbar(xplot, yplot[0], color='orange', linestyle="dashed") M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs( 'bmassfunctionclusterhalos_eco.txt', [0, 1, 2, 3, 7, 8, 9]) eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'orange', 's', yerrdn_val=No) eco_mf_obs_as_errorbar(ax, M, Ns, Nsdn, Nsup, 'orange', 's', fillstyle='full', markersize=3, label="ECO satellites") common.savefig(outdir, fig, 'bmf_resolve.pdf')
def plot_mHI_mhalo_resolve(plt, outdir, obsdir, mHImhalo, mHImhalo_true): resolve_obs_as_errorbars = functools.partial(_resolve_obs_as_errorbars, obsdir) # Plots gas metallicity vs. stellar mass fig = plt.figure(figsize=(5, 9)) xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$" ytit = "$\\rm log_{10}(\\rm M_{\\rm HI}/M_{\odot})$" xmin, xmax, ymin, ymax = 10.5, 15.2, 7, 12 xleg = xmin + 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) # centrals ax = fig.add_subplot(211) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'centrals') #Predicted relation ind = np.where(mHImhalo[1, 0, :] != 0) yplot = (mHImhalo[1, 0, ind]) errdn = (mHImhalo[1, 1, ind]) errup = (mHImhalo[1, 2, ind]) xplot = xmf[ind] ax.plot(xplot, yplot[0], color='k', label="G/S $>0.05$") ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='grey', interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='grey', interpolate=True) ind = np.where(mHImhalo_true[1, 0, :] != 0) yplot = (mHImhalo_true[1, 0, ind]) xplot = xmf[ind] ax.errorbar(xplot, yplot[0], color='b', linestyle="dashed", label="true $M_{\\rm HI}$") #RESOLVE observations resolve_obs_as_errorbars(ax, 'himassvgroupmasscentral_resolve.txt', [0, 4, 2, 6], 'orange', 'o', label='RESOLVE') resolve_obs_as_errorbars(ax, 'himassvgroupmasscentral_eco.txt', [0, 4, 2, 6], 'orange', 'o', label='ECO') common.prepare_legend(ax, ['k', 'b', 'orange', 'orange'], loc=4) # satellites ax = fig.add_subplot(212) common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'satellites') #Predicted relation ind = np.where(mHImhalo[2, 0, :] != 0) yplot = (mHImhalo[2, 0, ind]) errdn = (mHImhalo[2, 1, ind]) errup = (mHImhalo[2, 2, ind]) xplot = xmf[ind] ax.plot(xplot, yplot[0], color='k') ax.fill_between(xplot, yplot[0], yplot[0] - errdn[0], facecolor='grey', interpolate=True) ax.fill_between(xplot, yplot[0], yplot[0] + errup[0], facecolor='grey', interpolate=True) ind = np.where(mHImhalo_true[2, 0, :] != 0) yplot = (mHImhalo_true[2, 0, ind]) xplot = xmf[ind] ax.errorbar(xplot, yplot[0], color='b', linestyle="dashed") #RESOLVE observations resolve_obs_as_errorbars(ax, 'himassvgroupmasssatellite_resolve.txt', [0, 4, 2, 6], 'orange', 'o') resolve_obs_as_errorbars(ax, 'himassvgroupmasssatellite_eco.txt', [0, 4, 2, 6], 'orange', 's') #Save figure common.savefig(outdir, fig, 'mHI-mhalo_resolve.pdf')
def plot_mf_z(plt, outdir, snap, vol_eagle, histmtot, histm30, histmgas, histmall): ########################### total stellar mass function xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 7, 12, -6, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] sn, subgn, mstot, ms30, mgas30, mdm30, mbh30 = common.load_observation( '/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 29, 22, 23, 31, 32]) mall = ms30 + mgas30 + mdm30 + mbh30 for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #SMF from SUBFIND ind = np.where((mstot > 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(mstot[ind]), bins=np.append(mbins, mupp)) histsfof = H y = histsfof[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b', label='EAGLE L25') if idx > 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b') #Predicted HMF y = histmtot[idx, :] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "smf_tot_z.pdf") ############################# stellar mass function (30kpc aperture) xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 7, 12, -6, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #SMF from SUBFIND ind = np.where((ms30 > 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(ms30[ind]), bins=np.append(mbins, mupp)) histsfof = H y = histsfof[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b', label='EAGLE L25') if idx > 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b') #Predicted HMF y = histm30[idx, :] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "smf_30kpc_z.pdf") ############################# gas mass function (30kpc aperture) xtit = "$\\rm log_{10} (\\rm M_{\\rm gas, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm gas}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 7, 12, -6, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #SMF from SUBFIND ind = np.where((mgas30 > 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(mgas30[ind]), bins=np.append(mbins, mupp)) histsfof = H y = histsfof[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b', label='EAGLE L25') if idx > 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b') #Predicted HMF y = histmgas[idx, :] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "gasmf_30kpc_z.pdf") ##################################### total mass function (30kpc aperture) xtit = "$\\rm log_{10} (\\rm M_{\\rm tot, 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm ror}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 7, 14, -6, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s in zip(subplots, idx, zins, snap): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) #SMF from SUBFIND ind = np.where((mall > 0) & (sn == s)) H, bins_edges = np.histogram(np.log10(mall[ind]), bins=np.append(mbins, mupp)) histsfof = H y = histsfof[:] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b', label='EAGLE L25') if idx > 0: ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b') #Predicted HMF y = histmall[idx, :] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed') if idx == 0: common.prepare_legend(ax, ['b', 'r']) common.savefig(outdir, fig, "allmassmf_30kpc_z.pdf")
def plot_mf_z(plt, outdir, obsdir, snap, vol_eagle, histmtot, histm30): #construct relevant observational datasets for SMF z0obs = [] lm, p, dpdn, dpup = common.load_observation(obsdir, 'mf/SMF/GAMAII_BBD_GSMFs.dat', [0, 1, 2, 3]) xobs = lm indx = np.where(p > 0) yobs = np.log10(p[indx]) ydn = yobs - np.log10(p[indx] - dpdn[indx]) yup = np.log10(p[indx] + dpup[indx]) - yobs z0obs.append((observation("Wright+2017", xobs[indx], yobs, ydn, yup, err_absolute=False), 'o')) # Moustakas (Chabrier IMF), ['Moustakas+2013, several redshifts'] zdnM13, lmM13, pM13, dp_dn_M13, dp_up_M13 = common.load_observation( obsdir, 'mf/SMF/SMF_Moustakas2013.dat', [0, 3, 5, 6, 7]) xobsM13 = lmM13 yobsM13 = np.full(xobsM13.shape, -999.) lerrM13 = np.full(xobsM13.shape, -999.) herrM13 = np.full(xobsM13.shape, 999.) indx = np.where(pM13 < 1) yobsM13[indx] = (pM13[indx]) indx = np.where(dp_dn_M13 > 0) lerrM13[indx] = dp_dn_M13[indx] indx = np.where(dp_up_M13 > 0) herrM13[indx] = dp_up_M13[indx] # Muzzin (Kroupa IMF), ['Moustakas+2013, several redshifts'] zdnMu13, zupMu13, lmMu13, pMu13, dp_dn_Mu13, dp_up_Mu13 = common.load_observation( obsdir, 'mf/SMF/SMF_Muzzin2013.dat', [0, 1, 2, 4, 5, 5]) # -0.09 corresponds to the IMF correction xobsMu13 = lmMu13 - 0.09 yobsMu13 = np.full(xobsMu13.shape, -999.) lerrMu13 = np.full(xobsMu13.shape, -999.) herrMu13 = np.full(xobsMu13.shape, 999.) indx = np.where(pMu13 < 1) yobsMu13[indx] = (pMu13[indx]) indx = np.where(dp_dn_Mu13 > 0) lerrMu13[indx] = dp_dn_Mu13[indx] indx = np.where(dp_up_Mu13 > 0) herrMu13[indx] = dp_up_Mu13[indx] # Santini 2012 (Salpeter IMF) zdnS12, lmS12, pS12, dp_dn_S12, dp_up_S12 = common.load_observation( obsdir, 'mf/SMF/SMF_Santini2012.dat', [0, 2, 3, 4, 5]) hobs = 0.7 # factor 0.24 corresponds to the IMF correction. xobsS12 = lmS12 - 0.24 + np.log10(hobs / h0) yobsS12 = np.full(xobsS12.shape, -999.) lerrS12 = np.full(xobsS12.shape, -999.) herrS12 = np.full(xobsS12.shape, 999.) indx = np.where(pS12 < 1) yobsS12[indx] = (pS12[indx]) + np.log10(pow(h0 / hobs, 3.0)) indx = np.where(dp_dn_S12 > 0) lerrS12[indx] = dp_dn_S12[indx] indx = np.where(dp_up_S12 > 0) herrS12[indx] = dp_up_S12[indx] # Wright et al. (2018, several reshifts). Assumes Chabrier IMF. zD17, lmD17, pD17, dp_dn_D17, dp_up_D17 = common.load_observation( obsdir, 'mf/SMF/Wright18_CombinedSMF.dat', [0, 1, 2, 3, 4]) hobs = 0.7 pD17 = pD17 - 3.0 * np.log10(hobs) lmD17 = lmD17 - np.log10(hobs) # z0.5 obs z05obs = [] in_redshift = np.where(zdnM13 == 0.4) z05obs.append((observation("Moustakas+2013", xobsM13[in_redshift], yobsM13[in_redshift], lerrM13[in_redshift], herrM13[in_redshift], err_absolute=False), 'o')) in_redshift = np.where(zdnMu13 == 0.5) z05obs.append((observation("Muzzin+2013", xobsMu13[in_redshift], yobsMu13[in_redshift], lerrMu13[in_redshift], herrMu13[in_redshift], err_absolute=False), '+')) in_redshift = np.where(zD17 == 0.5) z05obs.append((observation("Wright+2018", lmD17[in_redshift], pD17[in_redshift], dp_dn_D17[in_redshift], dp_up_D17[in_redshift], err_absolute=False), 'D')) # z1 obs z1obs = [] in_redshift = np.where(zdnM13 == 0.8) z1obs.append((observation("Moustakas+2013", xobsM13[in_redshift], yobsM13[in_redshift], lerrM13[in_redshift], herrM13[in_redshift], err_absolute=False), 'o')) in_redshift = np.where(zdnMu13 == 1) z1obs.append((observation("Muzzin+2013", xobsMu13[in_redshift], yobsMu13[in_redshift], lerrMu13[in_redshift], herrMu13[in_redshift], err_absolute=False), '+')) in_redshift = np.where(zD17 == 1) z1obs.append((observation("Wright+2018", lmD17[in_redshift], pD17[in_redshift], dp_dn_D17[in_redshift], dp_up_D17[in_redshift], err_absolute=False), 'D')) #z2 obs z2obs = [] in_redshift = np.where(zupMu13 == 2.5) z2obs.append((observation("Muzzin+2013", xobsMu13[in_redshift], yobsMu13[in_redshift], lerrMu13[in_redshift], herrMu13[in_redshift], err_absolute=False), '+')) in_redshift = np.where(zdnS12 == 1.8) z2obs.append((observation("Santini+2012", xobsS12[in_redshift], yobsS12[in_redshift], lerrS12[in_redshift], herrS12[in_redshift], err_absolute=False), 'o')) in_redshift = np.where(zD17 == 2) z2obs.append((observation("Wright+2018", lmD17[in_redshift], pD17[in_redshift], dp_dn_D17[in_redshift], dp_up_D17[in_redshift], err_absolute=False), 'D')) # z3 obs z3obs = [] in_redshift = np.where(zupMu13 == 3.0) z3obs.append((observation("Muzzin+2013", xobsMu13[in_redshift], yobsMu13[in_redshift], lerrMu13[in_redshift], herrMu13[in_redshift], err_absolute=False), '+')) in_redshift = np.where(zdnS12 == 2.5) z3obs.append((observation("Santini+2012", xobsS12[in_redshift], yobsS12[in_redshift], lerrS12[in_redshift], herrS12[in_redshift], err_absolute=False), 'o')) in_redshift = np.where(zD17 == 3) z3obs.append((observation("Wright+2018", lmD17[in_redshift], pD17[in_redshift], dp_dn_D17[in_redshift], dp_up_D17[in_redshift], err_absolute=False), 'D')) # z4 obs z4obs = [] in_redshift = np.where(zupMu13 == 4.0) z4obs.append((observation("Muzzin+2013", xobsMu13[in_redshift], yobsMu13[in_redshift], lerrMu13[in_redshift], herrMu13[in_redshift], err_absolute=False), '+')) in_redshift = np.where(zdnS12 == 3.5) z4obs.append((observation("Santini+2012", xobsS12[in_redshift], yobsS12[in_redshift], lerrS12[in_redshift], herrS12[in_redshift], err_absolute=False), 'o')) in_redshift = np.where(zD17 == 4) z4obs.append((observation("Wright+2018", lmD17[in_redshift], pD17[in_redshift], dp_dn_D17[in_redshift], dp_up_D17[in_redshift], err_absolute=False), 'D')) ########################### total stellar mass function xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm tot}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 7, 12, -6, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] observations = (z0obs, z1obs, z2obs) for subplot, idx, z, s, obs_and_markers in zip(subplots, idx, zins, snap, observations): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) # Observations for obs, marker in obs_and_markers: common.errorbars(ax, obs.x, obs.y, obs.yerrdn, obs.yerrup, 'grey', marker, err_absolute=obs.err_absolute, label=obs.label) #Predicted HMF y = histmtot[idx, :] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='solid', label='VR') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='solid') if idx == 0: cols = ['r'] + ['grey', 'grey', 'grey'] common.prepare_legend(ax, cols) common.savefig(outdir, fig, "smf_tot_z_comp_obs.pdf") ############################# stellar mass function (30kpc aperture) xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm 30kpc}/M_{\odot})$" ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$" xmin, xmax, ymin, ymax = 7, 12, -6, 1 xleg = xmax - 0.2 * (xmax - xmin) yleg = ymax - 0.1 * (ymax - ymin) fig = plt.figure(figsize=(5, 10)) idx = [0, 1, 2] zins = [0, 1, 2] subplots = [311, 312, 313] for subplot, idx, z, s, obs_and_markers in zip(subplots, idx, zins, snap, observations): ax = fig.add_subplot(subplot) if (idx == 2): xtitplot = xtit else: xtitplot = ' ' common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1)) ax.text(xleg, yleg, 'z=%s' % (str(z))) # Observations for obs, marker in obs_and_markers: common.errorbars(ax, obs.x, obs.y, obs.yerrdn, obs.yerrup, 'grey', marker, err_absolute=obs.err_absolute, label=obs.label) #Predicted HMF y = histm30[idx, :] ind = np.where(y != 0.) if idx == 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='solid', label='VR') if idx > 0: ax.plot(xmf[ind], y[ind], 'r', linestyle='solid') if idx == 0: cols = ['r'] + ['grey', 'grey', 'grey'] common.prepare_legend(ax, cols) else: cols = ['grey', 'grey', 'grey'] common.prepare_legend(ax, cols) common.savefig(outdir, fig, "smf_30kpc_z_comp_obs.pdf")