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| # | ||
| # ICRAR - International Centre for Radio Astronomy Research | ||
| # (c) UWA - The University of Western Australia, 2018 | ||
| # Copyright by UWA (in the framework of the ICRAR) | ||
| # | ||
| # This program is free software: you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public License as published by | ||
| # the Free Software Foundation, either version 3 of the License, or | ||
| # (at your option) any later version. | ||
| # | ||
| # This program is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public License | ||
| # along with this program. If not, see <https://www.gnu.org/licenses/>. | ||
| # | ||
| """Size plots""" | ||
|
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| import functools | ||
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| import numpy as np | ||
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| import os | ||
| import common | ||
| import utilities_statistics as us | ||
|
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| # Initialize arguments | ||
| zlist = [0] | ||
|
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| ################################## | ||
| #Constants | ||
| RExp = 1.67 | ||
| MpcToKpc = 1e3 | ||
| G = 4.299e-9 #Gravity constant in units of (km/s)^2 * Mpc/Msun | ||
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| mlow = 6.5 | ||
| mupp = 12.5 | ||
| dm = 0.2 | ||
| mbins = np.arange(mlow,mupp,dm) | ||
| xmf = mbins + dm/2.0 | ||
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| def prepare_data(hdf5_data, age_bins, sfh, delta_t, LBT, index, disk_size, disk_size_age): | ||
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| (h0, _, mdisk, mbulge, rdisk, typeg, age) = hdf5_data | ||
| (bulge_diskins_hist, bulge_mergers_hist, disk_hist) = sfh | ||
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| bin_it = functools.partial(us.wmedians, xbins=xmf) | ||
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| BTthresh = 0.5 | ||
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| mstar = mdisk + mbulge | ||
| ind = np.where(mstar > 0) | ||
| mdisk = mdisk[ind] | ||
| mbulge = mbulge[ind] | ||
| rdisk = rdisk[ind] | ||
| typeg = typeg[ind] | ||
| age = age[ind] | ||
| mstar = mstar[ind] | ||
|
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| age_disk = np.zeros(shape = len(mdisk)) | ||
| for j in range(0,len(mdisk)): | ||
| sfrtimesage = np.sum(disk_hist[j,:] * delta_t[:] * LBT[:]) | ||
| massformed = np.sum(disk_hist[j,:] * delta_t[:]) | ||
| age_disk[j] = sfrtimesage / massformed | ||
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| ind = np.where((mdisk > 0) & (mdisk / mstar >= BTthresh)) | ||
| disk_size[index,:] = bin_it(x=np.log10(mdisk[ind]) - np.log10(float(h0)), | ||
| y=np.log10(rdisk[ind]*MpcToKpc) - np.log10(float(h0))) | ||
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| BtoT = mbulge / mstar | ||
| ind = np.where((mdisk > 0)) | ||
| print("#log10(mdisk/Msun) log10(rdisk/kpc) age_disk age_gal B/T") | ||
| for a,b,c,d,e in zip(np.log10(mdisk[ind]) - np.log10(float(h0)), np.log10(rdisk[ind]*MpcToKpc) - np.log10(float(h0)), age_disk[ind], 13.7969 - age[ind], BtoT[ind]): | ||
| print(a,b,c,d,e) | ||
|
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| age_disk = 13.7969 - age | ||
| age_meds = np.zeros(shape = len(age_bins)-1) | ||
| for j in range(0,len(age_bins)-1): | ||
| ind = np.where((mdisk > 0) & (age_disk >= age_bins[j]) & (age_disk < age_bins[j+1]) & (mdisk / mstar >= BTthresh)) | ||
| disk_size_age[index,j,:] = bin_it(x=np.log10(mdisk[ind]) - np.log10(float(h0)), | ||
| y=np.log10(rdisk[ind]*MpcToKpc) - np.log10(float(h0))) | ||
| age_meds[j] = np.mean(age_disk[ind]) | ||
|
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| ind = np.where((mdisk >= 5e7) & (mdisk / mstar >= BTthresh)) | ||
| return(np.log10(mdisk[ind]) - np.log10(float(h0)), np.log10(rdisk[ind]*MpcToKpc) - np.log10(float(h0)), age_disk[ind], age_meds) | ||
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| def plot_age_disk(plt, outdir, obsdir, mdisk_z0, rdisk_z0, age_z0, disk_size, disk_size_age, age_bins, age_meds): | ||
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| fig = plt.figure(figsize=(5,4.5)) | ||
| xtit = "$\\rm log_{10} (\\rm M_{\\rm disk}/M_{\odot})$" | ||
| ytit = "$\\rm log_{10} (\\rm r_{\\star,disk}/kpc)$" | ||
| xmin, xmax, ymin, ymax = 8, 12, -0.5, 2 | ||
|
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| # LTG ################################## | ||
| ax = fig.add_subplot(111) | ||
| plt.subplots_adjust(bottom=0.15, left=0.15) | ||
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| common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) | ||
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| im = ax.hexbin(mdisk_z0, rdisk_z0, age_z0, xscale='linear', yscale='linear', gridsize=(20,20), cmap='magma', mincnt=30) | ||
| #cbar_ax = fig.add_axes([0.86, 0.15, 0.025, 0.7]) | ||
| cbar = fig.colorbar(im)#, cax=cbar_ax) | ||
| cbar.ax.set_ylabel('mean stellar age') | ||
|
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||
| #Predicted size-mass for disks in disk=dominated galaxies | ||
| ind = np.where(disk_size[0,0,:] != 0) | ||
| xplot = xmf[ind] | ||
| yplot = disk_size[0,0,ind] | ||
| errdn = disk_size[0,1,ind] | ||
| errup = disk_size[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") | ||
|
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| def plot_robotham_size_relation_use_bins(ax, age_meds): | ||
| for age in age_meds: | ||
| y1 = 0.35 * (xmf - 10) - 0.06 * (age) + 1.01 | ||
| ax.plot(xmf, y1, linestyle='dotted', color='k') | ||
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| def plot_lange_relation(ax): | ||
| #Lange et al. (2016) | ||
| a = 5.56 | ||
| aerr = 1.745 | ||
| b = 0.274 | ||
| ind = np.where(xmf < 10.3) | ||
| rL16 = np.log10(a*pow((pow(10.0,xmf)/1e10),b)) | ||
| ax.plot(xmf[ind],rL16[ind],'b', linestyle='solid', label ='L16 disks') | ||
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| 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="50th, 68th, 90th") | ||
| ax.plot(m[38:83], r[38:83], linestyle='dotted',color='b') | ||
| ax.plot(m[85:128], r[85:129], linestyle='dotted',color='b') | ||
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| plot_robotham_size_relation_use_bins(ax, np.array([3, 6, 9, 12])) | ||
| plot_lange_relation(ax) | ||
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| common.prepare_legend(ax, ['b','b','k','r'], loc=2) | ||
| common.savefig(outdir, fig, 'age_disks.pdf') | ||
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| fig = plt.figure(figsize=(5,4.5)) | ||
| xtit = "$\\rm log_{10} (\\rm M_{\\rm disk}/M_{\odot})$" | ||
| ytit = "$\\rm log_{10} (\\rm r_{\\star,disk}/kpc)$" | ||
| xmin, xmax, ymin, ymax = 8, 12, -0.5, 2 | ||
|
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| # LTG ################################## | ||
| ax = fig.add_subplot(111) | ||
| plt.subplots_adjust(bottom=0.15, left=0.15) | ||
|
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| common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1)) | ||
|
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| for j in range(0,len(age_bins)-1): | ||
| ind = np.where(disk_size_age[0,j,0,:] != 0) | ||
| xplot = xmf[ind] | ||
| yplot = disk_size_age[0,j,0,ind] | ||
| errdn = disk_size_age[0,j,1,ind] | ||
| errup = disk_size_age[0,j,2,ind] | ||
| ax.errorbar(xplot,yplot[0],yerr=[errdn[0],errup[0]], ls='None', mfc='None', marker='o', label='age=[%s,%s]' % (str(age_bins[j]), str(age_bins[j+1]))) | ||
|
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| plot_robotham_size_relation_use_bins(ax, age_meds) | ||
| common.prepare_legend(ax, ['k','k','k','k','k','k','k'], loc=2) | ||
| common.savefig(outdir, fig, 'age_bins_disks.pdf') | ||
|
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| def main(modeldir, outdir, redshift_table, subvols, obsdir): | ||
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| plt = common.load_matplotlib() | ||
| fields = {'galaxies': ('mstars_disk', 'mstars_bulge', 'rstar_disk', 'type', | ||
| 'mean_stellar_age')} | ||
|
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| sfh_fields = {'bulges_diskins': ('star_formation_rate_histories'), | ||
| 'bulges_mergers': ('star_formation_rate_histories'), | ||
| 'disks': ('star_formation_rate_histories')} | ||
|
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| # Loop over redshift and subvolumes | ||
|
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| age_bins=[0,3,6,10,14] | ||
| disk_size = np.zeros(shape = (len(zlist), 3, len(xmf))) | ||
| disk_size_age = np.zeros(shape = (len(zlist), len(age_bins)-1, 3, len(xmf))) | ||
|
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| for index, snapshot in enumerate(redshift_table[zlist]): | ||
| hdf5_data = common.read_data(modeldir, snapshot, fields, subvols) | ||
| sfh, delta_t, LBT = common.read_sfh(modeldir, 199, sfh_fields, subvols) | ||
|
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| (mdisk, rdisk, age, age_meds) = prepare_data(hdf5_data, age_bins, sfh, delta_t, LBT, index, disk_size, disk_size_age) | ||
| if(index == 0): | ||
| mdisk_z0 = mdisk | ||
| rdisk_z0 = rdisk | ||
| age_z0 = age | ||
| age_meds_z0 = age_meds | ||
|
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| print("Will produce plots") | ||
| plot_age_disk(plt, outdir, obsdir, mdisk_z0, rdisk_z0, age_z0, disk_size, disk_size_age, age_bins, age_meds) | ||
|
|
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| if __name__ == '__main__': | ||
| main(*common.parse_args()) |
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