def find_template(galaxy, set_range=None): params = set_params() params.gas = 0 params.reps = 0 params.set_range = set_range ## ----------========= Reading the spectrum ===============--------- dataCubeDirectory = get_dataCubeDirectory(galaxy) f = fits.open(dataCubeDirectory) ## write key parameters from header - can then be altered in future CRVAL_spec = f[1].header['CRVAL3'] CDELT_spec = f[1].header['CD3_3'] s = f[1].data.shape # Collapse to single spectrum gal_spec = np.zeros(s[0]) gal_noise = np.zeros(s[0]) for i in xrange(s[0]): gal_spec[i] = np.nansum( f[1].data[i, int(s[1] / 2.0 - 50):int(s[1] / 2.0 + 50), int(s[2] / 2.0 - 50):int(s[2] / 2.0 + 50)]) gal_noise[i] = np.sqrt( np.nansum(f[2].data[i, int(s[1] / 2.0 - 50):int(s[1] / 2.0 + 50), int(s[2] / 2.0 - 50):int(s[2] / 2.0 + 50)]**2)) del f ## ----------========= Calibrating the spectrum ===========--------- lam = np.arange(s[0]) * CDELT_spec + CRVAL_spec gal_spec, lam, cut = apply_range(gal_spec, window=201, repeats=0, lam=lam, return_cuts=True, set_range=params.set_range, n_sigma=2) lamRange = np.array([lam[0], lam[-1]]) gal_noise = gal_noise[cut] pp = run_ppxf(galaxy, gal_spec, gal_noise, lamRange, CDELT_spec, params, use_all_temp=True) pp.fig.savefig('%s/Data/muse/analysis/%s/find_temp.png' % (cc.base_dir, galaxy)) with open('%s/Data/muse/analysis/%s/templates.txt' % (cc.base_dir, galaxy), 'w') as f: for i in range(len(pp.component)): if pp.weights[i] != 0.0: f.write(str(i) + ' ' + str(pp.weights[i]) + '\n')
def mg_sigma(galaxy, aperture=1.0): ## ----------===============================================--------- ## ----------============= Input parameters ===============--------- ## ----------===============================================--------- params = set_params(reps=10, produce_plot=False, opt='pop', res=8.4, use_residuals=True) if cc.device == 'glamdring': dir = cc.base_dir else: dir = '%s/Data/muse' % (cc.base_dir) data_file = dir + "/analysis/galaxies.txt" # different data types need to be read separetly x_cent_gals, y_cent_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1, 2)) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] x_cent_pix = x_cent_gals[i_gal] y_cent_pix = y_cent_gals[i_gal] ## ----------===============================================--------- ## ----------=============== Run analysis =================--------- ## ----------===============================================--------- ## ----------========= Reading the spectrum ===============--------- f = fits.open(get_dataCubeDirectory(galaxy)) galaxy_data = f[1].data header = f[1].header galaxy_noise = f[2].data ## write key parameters from header - can then be altered in future CRVAL_spec = header['CRVAL3'] CDELT_spec = header['CD3_3'] s = galaxy_data.shape if aperture == 'R_e': ap = get_R_e(galaxy) / header['CDELT1'] else: ap = aperture ## ----------========== Spatially Integrating =============--------- frac_in_ap = in_aperture(x_cent_pix, y_cent_pix, ap, instrument='muse') galaxy_data = np.einsum('ijk,jk->ijk', galaxy_data, frac_in_ap) galaxy_noise = np.einsum('ijk,jk->ijk', galaxy_noise**2, frac_in_ap) bin_lin = np.nansum(galaxy_data, axis=(1, 2)) bin_lin_noise = np.sqrt(np.nansum(galaxy_noise, axis=(1, 2))) ## ----------========= Calibrating the spectrum ===========--------- lam = np.arange(s[0]) * CDELT_spec + CRVAL_spec bin_lin, lam, cut = apply_range(bin_lin, lam=lam, set_range=params.set_range, return_cuts=True) lamRange = np.array([lam[0], lam[-1]]) bin_lin_noise = bin_lin_noise[cut] pp = run_ppxf(galaxy, bin_lin, bin_lin_noise, lamRange, CDELT_spec, params) ## ----------=============== Find sigma_0 =================--------- sigma_0 = pp.sol[0][1] unc_sigma_0 = np.std(pp.MCstellar_kin[:, 1]) if aperture == 'R_e': area = np.sum(frac_in_ap) * header['CDELT1'] * header['CDELT2'] if area < 0.97 * np.pi * R_e**2: R = np.sqrt(area / np.pi) sigma_0 = sigma_0 * (R_e / R)**-0.066 unc_sigma_0 = np.sqrt(unc_sigma_0**2 + ( (R_e / R)**-0.066 * np.log(R_e / R) * 0.035)**2) # ## ----------============ Find dynamical mass ===============--------- # G = 4.302*10**-6 # kpc (km/s)^2 M_odot^-1 # M = 5.0 * R_e * sigma_0**2/G ## ----------============ Find dynamical mass ===============--------- mg, mg_uncert = get_absorption(['Mg_b'], pp=pp, instrument='muse', res=8.4) return mg['Mg_b'], mg_uncert['Mg_b'], sigma_0, unc_sigma_0
def compare_absortion(galaxy, R_sig=False, corr_lines='all'): f = fits.open(get_dataCubeDirectory(galaxy)) header = f[1].header lines = ['H_beta', 'Fe5015', 'Mg_b', 'Fe5270', 'Fe5335', 'Fe5406', 'Fe5709', 'Fe5782', 'NaD', 'TiO1', 'TiO2'] color = ['purple', 'k', 'orange', 'g', 'b', 'c', 'lightblue', 'grey', 'r', 'gold', 'pink'] R_e = get_R_e(galaxy) apertures = np.array([1.5, 2.5, 10, R_e/10, R_e/8, R_e/4, R_e/2]) # arcsec Ramp_sigma = {'ngc3557':[265, 247, 220], 'ic1459':[311, 269, 269], 'ic4296':[340, 310, 320]} R_sigma = interp1d([R_e/8, R_e/4, R_e/2], Ramp_sigma[galaxy], fill_value=(Ramp_sigma[galaxy][0], Ramp_sigma[galaxy][2]), bounds_error=False) data_file = "%s/Data/vimos/analysis/galaxies.txt" % (cc.base_dir) z_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1,), dtype=float) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0,),dtype=str) i_gal = np.where(galaxy_gals==galaxy)[0][0] z = z_gals[i_gal] data_file = "%s/Data/muse/analysis/galaxies.txt" % (cc.base_dir) x_cent_gals, y_cent_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1,2), dtype=int) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0,),dtype=str) i_gal = np.where(galaxy_gals==galaxy)[0][0] center = np.array([x_cent_gals[i_gal], y_cent_gals[i_gal]]) index = np.zeros((150,150,2)) for i in range(150): for j in range(150): index[i,j,:] = np.array([i,j]) - center fig, ax = plt.subplots() fig3, ax3 = plt.subplots() fig4, ax4 = plt.subplots() ax5 = ax4.twinx() my_values = {} my_errors = {} sigma = np.array([]) t=[] e=[] g=[] h=[] j=[] r=[] w=[] for a in apertures: params = set_params(reps=0, opt='pop', gas=1, lines=corr_lines, produce_plot=False) mask = np.sqrt(index[:,:,0]**2 + index[:,:,1]**2) * header['CD3_3'] < a spec = np.nansum(f[1].data[:,mask], axis=1) noise = np.sqrt(np.nansum(f[2].data[:,mask]**2, axis=1)) lam = np.arange(len(spec))*header['CD3_3'] + header['CRVAL3'] spec, lam, cut = apply_range(spec, lam=lam, set_range=params.set_range, return_cuts=True) lamRange = np.array([lam[0],lam[-1]]) noise = noise[cut] pp = run_ppxf(galaxy, spec, noise, lamRange, header['CD3_3'], params) plot = create_plot(pp) plot.lam = pp.lam*(1+pp.z)/(1+pp.z+(pp.sol[0][0]/c)) fig2, ax2, = plot.produce s = spectrum(lam=pp.lam, lamspec=pp.galaxy) for i, l in enumerate(lines): if l=='H_beta' or l=='Hbeta': l='hb' elif l=='Mg_b': l='mgb' elif l=='NaD': l='nad' elif l=='TiO1': l='tio1' elif l=='TiO2': l='tio2' elif l=='Fe5270': l='fe52' elif l=='Fe5335': l='fe53' ax2.axvspan(*getattr(s,l),color='b', alpha=0.5) lims = ax2.get_ylim() if i%2==0: ax2.text(np.mean(getattr(s,l)), lims[1] - 0.1*(lims[1]-lims[0]), l, size=8, ha='center') else: ax2.text(np.mean(getattr(s,l)), lims[1] - 0.15*(lims[1]-lims[0]), l, size=8, ha='center') ax2.axvspan(getattr(s,l+'cont')[0], getattr(s,l+'cont')[1], color='r', alpha=0.5) ax2.axvspan(getattr(s,l+'cont')[2], getattr(s,l+'cont')[3], color='r', alpha=0.5) fig2.savefig('/Data/lit_absorption/Rampazzo/%s_rad_%.2f_muse.png'%(galaxy, a)) plt.close(fig2) if R_sig: if isinstance(R_sig, bool): absorp, uncert = get_absorption(lines, pp=pp, instrument='muse', sigma=R_sigma(a)) sigma = np.append(sigma, R_sigma(a)) else: absorp, uncert = get_absorption(lines, pp=pp, instrument='muse', sigma=R_sigma(a)+R_sig*(pp.sol[0][1]-R_sigma(a))) sigma = np.append(sigma, R_sigma(a)+R_sig*(pp.sol[0][1]-R_sigma(a))) else: absorp, uncert = get_absorption(lines, pp=pp, instrument='muse')#, sigma=R_sigma(a)) sigma = np.append(sigma, pp.sol[0][1]) for l in lines: if a == min(apertures): my_values[l] = np.array([]) my_errors[l] = np.array([]) my_values[l] = np.append(my_values[l], absorp[l]) my_errors[l] = np.append(my_errors[l], uncert[l]) for i, l in enumerate(lines): ax.errorbar(a, absorp[l], yerr=uncert[l], color=color[i], fmt='x') for i, l in enumerate(lines): ax.errorbar(np.nan, np.nan, color=color[i], fmt='x', label=l) ax.legend(facecolor='w') Rampazzo_file = '%s/Data/lit_absorption/J_A+A_433_497_table9.txt' % (cc.base_dir) file_headings = np.loadtxt(Rampazzo_file, dtype=str)[0] for i, l in enumerate(lines): col = np.where(file_headings==l)[0][0] try: col2 = np.where(file_headings==l)[0][1] except: try: col2 = np.where(file_headings=='_'+l)[0][0] except: col2 = np.where(file_headings=='e_'+l)[0][0] R_obs, R_err = np.loadtxt(Rampazzo_file, unpack=True, skiprows=5, usecols=(col,col2)) R_galaxies = np.loadtxt(Rampazzo_file, unpack=True, skiprows=5, usecols=(0,), dtype=str) mask = R_galaxies==galaxy.upper() order = np.argsort(apertures) lit_value = Lick_to_LIS(l, R_obs[mask][order]) err = np.mean([np.abs(Lick_to_LIS(l, R_obs[mask][order] + R_err[mask][order]) - Lick_to_LIS(l, R_obs[mask][order])), np.abs(Lick_to_LIS(l, R_obs[mask][order] - R_err[mask][order]) - Lick_to_LIS(l, R_obs[mask][order]))], axis=0) ax.errorbar(apertures[order], lit_value, yerr=err, color=color[i]) if l=='H_beta' or l=='Hbeta': l2='hb' elif l=='Mg_b': l2='mgb' elif l=='NaD': l2='nad' elif l=='TiO1': l2='tio1' elif l=='TiO2': l2='tio2' elif l=='Fe5270': l2='fe52' elif l=='Fe5335': l2='fe53' else: l2=l ax3.scatter( np.abs(my_values[l][order] - lit_value)/my_values[l][order], np.abs(my_values[l][order] - lit_value)/np.sqrt(err**2 + my_errors[l][order]**2), color=color[i], s=4*apertures[order]**2, label=l) ax4.scatter(sigma[order], np.abs(my_values[l][order] - lit_value)/my_values[l][order], color=color[i], s=4*apertures[order]**2, label=l) ax5.scatter(sigma[order], np.abs(my_values[l][order] - lit_value)/np.sqrt(err**2 + my_errors[l][order]**2), marker='x', color=color[i], s=4*apertures[order]**2, label=l) t.extend(sigma[order]) g.extend(my_values[l][order]) h.extend(lit_value) j.extend(err) e.extend(my_errors[l][order]) r.extend([lines[i]]*len(sigma)) w.extend(4*apertures[order]**2) ax.set_ylabel(r'Index strength, $\AA$') ax.set_xlabel('Radius, arcsec') fig.savefig('%s/Data/lit_absorption/Rampazzo_aperture_%s_%i_muse.png' % ( cc.base_dir, galaxy, params.gas)) plt.close(fig) ax3.set_ylabel(r'Sigma difference ((Mine - Ramp)/Combined Uncert)') ax3.set_xlabel('Fractional difference ((Mine - Ramp)/Mine)') ax3.legend() fig3.savefig('%s/Data/lit_absorption/Rampazzo_aperture_%s_fractional_muse.png' % ( cc.base_dir, galaxy)) plt.close(fig3) ax4.set_xlabel('Vel dispersion') ax3.set_ylabel('Fractional difference ((Mine - Ramp)/Mine)') ax5.set_ylabel(r'Sigma difference ((Mine - Ramp)/Combined Uncert)') ax4.legend() fig4.savefig('%s/Data/lit_absorption/Rampazzo_aperture_%s_sigma_muse.png' % ( cc.base_dir, galaxy)) plt.close(fig4) return t, g, h, j, e, r, w
def compare_absortion(galaxy, O_sig=False, corr_lines='all'): f = fits.open(get_dataCubeDirectory(galaxy)) header = f[1].header # Load VIMOS values data_file = "%s/Data/vimos/analysis/galaxies.txt" % (cc.base_dir) z_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1, )) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] z = z_gals[i_gal] data_file = "%s/Data/muse/analysis/galaxies.txt" % (cc.base_dir) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] x_cent_gals, y_cent_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1, 2), dtype=int) center = np.array([x_cent_gals[i_gal], y_cent_gals[i_gal]]) data_file = "%s/Data/muse/analysis/galaxies2.txt" % (cc.base_dir) pa_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(3, )) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] pa = pa_gals[i_gal] lines = [ 'H_beta', 'Fe5015', 'Mg_b', 'Fe5270', 'Fe5335', 'Fe5406', 'Fe5709', 'NaD' ] e_lines = [ 'e_H_beta', 'e_Fe5015', 'e_Mg_b', 'e_Fe5270', 'e_Fe5335', 'e_Fe5406', 'e_Fe5709', 'e_NaD' ] # load Ogando values cols = Ogando_data = np.loadtxt('%s/Data/lit_absorption/Ogando.txt' % (cc.base_dir), dtype=str)[0] cols = [i for i, co in enumerate(cols) if co in lines or co in e_lines] Ogando_data = np.loadtxt('%s/Data/lit_absorption/Ogando.txt' % (cc.base_dir), unpack=True, skiprows=2, usecols=np.append([1, 2], cols)) galaxies = np.loadtxt('%s/Data/lit_absorption/Ogando.txt' % (cc.base_dir), unpack=True, skiprows=2, usecols=(0, ), dtype=str) i_gal = np.where(galaxies == galaxy)[0][0] O_sigma, O_sigma_err = 10**Ogando_data[0, i_gal], np.abs( 10**Ogando_data[0, i_gal] * Ogando_data[0, i_gal] * Ogando_data[1, i_gal] / 10) O_val = {} O_err = {} for i in range(0, 2 * len(lines), 2): O_val[lines[i / 2]] = Ogando_data[i, i_gal] O_err[lines[i / 2]] = Ogando_data[i + 1, i_gal] params = set_params(reps=0, opt='pop', gas=1, lines=corr_lines, produce_plot=False) mask = slitFoV(center, 4.1 / abs(header['CD1_1']) * 60**2, 2.5 / header['CD2_2'] * 60**2, pa, instrument='muse') ifu = np.array(f[1].data) ifu[np.isnan(ifu)] = 0 spec = np.einsum('ijk,jk->i', ifu, mask) ifu = np.array(f[2].data) ifu[np.isnan(ifu)] = 0 noise = np.sqrt(np.einsum('ijk,jk->i', ifu**2, mask)) lam = np.arange(len(spec)) * header['CD3_3'] + header['CRVAL3'] spec, lam, cut = apply_range(spec, lam=lam, set_range=params.set_range, return_cuts=True) lamRange = np.array([lam[0], lam[-1]]) noise = noise[cut] pp = run_ppxf(galaxy, spec, noise, lamRange, header['CD3_3'], params) if O_sig: if isinstance(O_sig, bool): absorp, uncert = get_absorption(lines, pp=pp, sigma=O_sigma(a), instrument='muse') sigma = O_sigma(a) else: absorp, uncert = get_absorption(lines, pp=pp, instrument='muse', sigma=O_sigma(a) + O_sig * (pp.sol[0][1] - O_sigma(a))) sigma = O_sigma(a) + O_sig * (pp.sol[0][1] - O_sigma(a)) else: absorp, uncert = get_absorption(lines, pp=pp, instrument='muse') sigma = pp.sol[0][1] my = [] e_my = [] og = [] e_og = [] sig = [] lin = [] for i, l in enumerate(lines): lin = np.append(lin, l) sig = np.append(sig, sigma) # Aperture correction: r_ab = 1.025 * np.sqrt(4.1 * 2.5 / np.pi) # arcsec r_ab = np.radians(r_ab / 60**2) * z * c / H * 1000 # kpc if l == 'H_beta' or l == 'Hbeta': l2 = 'hb' beta = 0.002 # from table 5 in Ogando '08 e_beta = 0.027 elif l == 'Fe5015': l2 = l beta = -0.012 e_beta = 0.027 elif l == 'Mg_b': l2 = 'mgb' beta = -0.031 e_beta = 0.034 elif l == 'NaD': l2 = 'nad' beta = -0.034 e_beta = 0.022 # elif l=='TiO1': # l2='tio1' # elif l=='TiO2': # l2='tio2' elif l == 'Fe5270': l2 = 'fe52' beta = -0.016 e_beta = 0.025 elif l == 'Fe5335': l2 = 'fe53' beta = -0.012 e_beta = 0.027 elif l == 'Fe5406': l2 = l beta = -0.015 e_beta = 0.029 elif l == 'Fe5702': l2 = l beta = 0 e_beta = 0.036 # Change to mag units s = spectrum(lam=pp.lam, lamspec=pp.galaxy) I = -2.5 * np.log10(1 - absorp[l] / np.diff(getattr(s, l2))[0]) e_I = np.abs(2.5 / np.log(10) * uncert[l] / (np.diff(getattr(s, l2))[0] - absorp[l])) I = I - beta * np.log10(r_ab / 1.19) # Choosen from Davies 1987 e_I = np.sqrt(e_I**2 + (e_beta**2 * np.log10(r_ab / 1.19))) absorp[l] = (1 - 10**(-I / 2.5)) * np.diff(getattr(s, l2))[0] # Back to A uncert[l] = np.abs(2.5 * absorp[l] * np.log(10) * e_I) lit_value = Ogando_data[i * 2 + 2, i_gal] e_lit_value = Ogando_data[i * 2 + 3, i_gal] e_lit_value = np.mean([ np.abs( Lick_to_LIS(l, lit_value + e_lit_value) - Lick_to_LIS(l, lit_value)), np.abs( Lick_to_LIS(l, lit_value - e_lit_value) - Lick_to_LIS(l, lit_value)) ]) lit_value = Lick_to_LIS(l, lit_value) my.append(absorp[l]) e_my.append(uncert[l]) og.append(lit_value) e_og.append(e_lit_value) return my, e_my, og, e_og, lin
def plot_stellar_pop(galaxy, method='median', opt='pop', D=None, overplot={}, gradient=True): print 'Plotting stellar population' if cc.device == 'glamdring': vin_dir = '%s/analysis_muse/%s/%s/pop' % (cc.base_dir, galaxy, opt) data_file = '%s/analysis_muse/galaxies.txt' % (cc.base_dir) else: vin_dir = '%s/Data/muse/analysis/%s/%s/pop' % (cc.base_dir, galaxy, opt) data_file = "%s/Data/muse/analysis/galaxies.txt" % (cc.base_dir) file_headings = np.loadtxt(data_file, dtype=str)[0] col = np.where(file_headings == 'SN_%s' % (opt))[0][0] x_cent_gals, y_cent_gals, SN_target_gals = np.loadtxt( data_file, unpack=True, skiprows=1, usecols=(1, 2, col), dtype='int,int,float') galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] SN_target = SN_target_gals[i_gal] center = (x_cent_gals[i_gal], y_cent_gals[i_gal]) data_file = "%s/Data/vimos/analysis/galaxies.txt" % (cc.base_dir) z_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1)) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] z = z_gals[i_gal] # Load pickle file from pickler.py out_dir = '%s/Data/muse/analysis' % (cc.base_dir) output = "%s/%s/%s" % (out_dir, galaxy, opt) out_plots = "%s/plots/population" % (output) if not os.path.exists(out_plots): os.makedirs(out_plots) if D is None and gradient != 'only': pickle_file = '%s/pickled' % (output) pickleFile = open("%s/dataObj.pkl" % (pickle_file), 'rb') D = pickle.load(pickleFile) pickleFile.close() f = fits.open(get_dataCubeDirectory(galaxy)) header = f[1].header if not gradient: f.close() if gradient != 'only': age = np.zeros(D.number_of_bins) met = np.zeros(D.number_of_bins) alp = np.zeros(D.number_of_bins) unc_age = np.zeros(D.number_of_bins) unc_met = np.zeros(D.number_of_bins) unc_alp = np.zeros(D.number_of_bins) if method == 'median': for i in xrange(D.number_of_bins): ag, me, al = np.loadtxt('%s/%i.dat' % (vin_dir, i), unpack=True) age[i] = ag[0] unc_age[i] = ag[1] met[i] = me[0] unc_met[i] = me[1] alp[i] = al[0] unc_alp[i] = al[1] title = '%s median' % (galaxy.upper()) u_title = '%s standard deviation' % (galaxy.upper()) elif method == 'mostlikely': # from peakdetect import peakdetect age1 = np.zeros(D.number_of_bins) met1 = np.zeros(D.number_of_bins) alp1 = np.zeros(D.number_of_bins) age2 = np.zeros(D.number_of_bins) met2 = np.zeros(D.number_of_bins) alp2 = np.zeros(D.number_of_bins) for i in xrange(D.number_of_bins): ag, me, al = np.loadtxt('%s/distribution/%i.dat' % (vin_dir, i), unpack=True) for plot, unc_plot, pop in zip([age, met, alp], [unc_age, unc_met, unc_alp], [ag, me, al]): hist = np.histogram(pop, bins=40) x = (hist[1][0:-1] + hist[1][1:]) / 2 hist = hist[0] # peaks = np.array(peakdetect(hist, x_axis=x, lookahead=4)[0]) # plot[i] = peaks[np.argmax(peaks[:,1]), 0] plot[i] = x[np.argmax(hist)] gt_fwhm = hist >= np.max(hist) / 2 unc_plot[i] = np.max(x[gt_fwhm]) - np.min(x[gt_fwhm]) title = '%s mostlikely' % (galaxy.upper()) u_title = '%s FWHM' % (galaxy.upper()) if gradient: figs = {} axs = {} rad = {} rad_err = {} for i in ['age', 'met', 'alp']: fig, ax = plt.subplots() figs[i] = fig axs[i] = ax rad[i] = [] rad_err[i] = [] if gradient != 'only': # Age ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, age, header, nodots=True, colorbar=True, label='Age (Gyrs)', vmin=0, vmax=15, title=title + ' Age', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center, redshift=z) if overplot: for o, color in overplot.iteritems(): add_(o, color, ax, galaxy) plt.gcf().savefig('%s/Age.png' % (out_plots)) plt.close() plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, unc_age, header, nodots=True, colorbar=True, label='Age (Gyrs)', vmin=0, vmax=15, title=u_title + ' Age', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, close=True, signal_noise_target=SN_target, center=center, save='%s/Age_uncert.png' % (out_plots)) # Metalicity ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, met, header, nodots=True, colorbar=True, label='Metalicity [Z/H]', vmin=-2.25, vmax=0.67, title=title + ' Metalicity', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center) if overplot: for o, color in overplot.iteritems(): add_(o, color, ax, galaxy) plt.gcf().savefig('%s/Metalicity.png' % (out_plots)) plt.close() plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, unc_met, header, nodots=True, colorbar=True, label='Metalicity', vmin=0, vmax=0.67 + 2.25, title=u_title + ' Metalicity [Z/H]', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center, save='%s/Metalicity_uncert.png' % (out_plots), close=True) # Alpha ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, alp, header, nodots=True, colorbar=True, label='Element Ratio [alpha/Fe]', vmin=-0.3, vmax=0.5, title=title + ' Alpha Enhancement', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center) if overplot: for o, color in overplot.iteritems(): add_(o, color, ax, galaxy) plt.gcf().savefig('%s/Alpha.png' % (out_plots)) plt.close() plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, unc_alp, header, nodots=True, colorbar=True, label='Element Ratio [alpha/Fe]', vmin=0, vmax=0.5 + 0.3, title=u_title + ' Alpha Enhancement', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center, save='%s/Alpha_uncert.png' % (out_plots), close=True) # Detailed (no clip on color axis) out_plots = "%s/plots/population_detail" % (output) if not os.path.exists(out_plots): os.makedirs(out_plots) # Age vmin, vmax = set_lims(age) ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, age, header, nodots=True, colorbar=True, label='Age (Gyrs)', title=title + ' Age', cmap='gnuplot2', vmin=vmin, vmax=vmax, flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center, redshift=z) if overplot: for o, color in overplot.iteritems(): add_(o, color, ax, galaxy) plt.gcf().savefig('%s/Age.png' % (out_plots)) plt.close() vmin, vmax = set_lims(unc_age) plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, unc_age, header, nodots=True, colorbar=True, label='Age (Gyrs)', title=u_title + ' Age', cmap='gnuplot2', vmin=vmin, vmax=vmax, flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, close=True, signal_noise_target=SN_target, center=center, save='%s/Age_uncert.png' % (out_plots)) # Metalicity vmin, vmax = set_lims(met) ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, met, header, nodots=True, colorbar=True, label='Metalicity [Z/H]', title=title + ' Metalicity', vmin=vmin, vmax=vmax, cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center) if overplot: for o, color in overplot.iteritems(): add_(o, color, ax, galaxy) plt.gcf().savefig('%s/Metalicity.png' % (out_plots)) plt.close() vmin, vmax = set_lims(unc_met) plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, unc_met, header, nodots=True, colorbar=True, label='Metalicity', vmin=vmin, vmax=vmax, title=u_title + ' Metalicity [Z/H]', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center, save='%s/Metalicity_uncert.png' % (out_plots), close=True) # Alpha vmin, vmax = set_lims(alp) ax = plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, alp, header, nodots=True, colorbar=True, label='Element Ratio [alpha/Fe]', vmin=vmin, vmax=vmax, title=title + ' Alpha Enhancement', cmap='gnuplot2', flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center) if overplot: for o, color in overplot.iteritems(): add_(o, color, ax, galaxy) plt.gcf().savefig('%s/Alpha.png' % (out_plots)) plt.close() vmin, vmax = set_lims(unc_alp) plot_velfield_nointerp(D.x, D.y, D.bin_num, D.xBar, D.yBar, unc_alp, header, nodots=True, colorbar=True, label='Element Ratio [alpha/Fe]', title=u_title + ' Alpha Enhancement', cmap='gnuplot2', vmin=vmin, vmax=vmax, flux_unbinned=D.unbinned_flux, signal_noise=D.SNRatio, signal_noise_target=SN_target, center=center, save='%s/Alpha_uncert.png' % (out_plots), close=True) if gradient: r = np.sqrt((D.xBar - center[0])**2 + (D.yBar - center[1])**2) for i in ['age', 'met', 'alp']: if i == 'age': y = np.log10(eval(i)) y_err = np.abs( eval('unc_' + i) / np.array(eval(i)) / np.log(10)) else: y = eval(i) y_err = eval('unc_' + i) axs[i].errorbar(r, y, yerr=y_err, fmt='.', c='k') params, cov = np.polyfit(r, y, 1, w=1 / y_err, cov=True) axs[i].plot(r, np.poly1d(params)(r), '--k') # params, residuals, _, _, _ = numpy.polyfit(r, y, 1, w=1/y_err, # full=True) # chi2 = residuals / (len(r) - 2) figs[i].text( 0.15, 0.84, r'grad: %.3f $\pm$ %.3f' % (params[0], np.sqrt(np.diag(cov))[0])) if gradient: out_plots = "%s/plots/population" % (output) index = np.zeros((150, 150, 2)) for i in range(index.shape[0]): for j in range(index.shape[1]): index[i, j, :] = np.array([i, j]) - center step_size = 12 annuli = np.arange(step_size, 100, step_size).astype(float) age_rad = np.zeros(len(annuli)) met_rad = np.zeros(len(annuli)) alp_rad = np.zeros(len(annuli)) age_err_rad = np.zeros(len(annuli)) met_err_rad = np.zeros(len(annuli)) alp_err_rad = np.zeros(len(annuli)) for i, a in enumerate(annuli): params = set_params(reps=0, opt='pop', gas=1, produce_plot=False) mask = (np.sqrt(index[:, :, 0]**2 + index[:, :, 1]**2) < a) * ( np.sqrt(index[:, :, 0]**2 + index[:, :, 1]**2) > a - step_size) spec = np.nansum(f[1].data[:, mask], axis=1) noise = np.sqrt(np.nansum(f[2].data[:, mask]**2, axis=1)) lam = np.arange(len(spec)) * header['CD3_3'] + header['CRVAL3'] spec, lam, cut = apply_range(spec, lam=lam, set_range=params.set_range, return_cuts=True) lamRange = np.array([lam[0], lam[-1]]) noise = noise[cut] pp = run_ppxf(galaxy, spec, noise, lamRange, header['CD3_3'], params) pop = population(pp=pp, instrument='muse', method=method) for i in ['age', 'met', 'alp']: if i == 'met': i2 = 'metallicity' elif i == 'alp': i2 = 'alpha' else: i2 = i rad[i].append(getattr(pop, i2)) rad_err[i].append(getattr(pop, 'unc_' + i)) annuli *= abs(header['CD1_1']) * (60**2) gradient_file = '%s/galaxies_pop_gradients.txt' % (out_dir) ageRe, ageG, e_ageG, metRe, metG, e_metG, alpRe, alpG, e_alpG = \ np.loadtxt(gradient_file, usecols=(1,2,3,4,5,6,7,8,9), unpack=True, skiprows=1) galaxy_gals = np.loadtxt(gradient_file, usecols=(0, ), unpack=True, skiprows=1, dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] R_e = get_R_e(galaxy) for i in ['age', 'met', 'alp']: axs[i].set_xlabel('Radius (arcsec)') if i == 'age': y = np.log10(rad[i]) y_err = np.abs( np.array(rad_err[i]) / np.array(rad[i]) / np.log(10)) else: y = np.array(rad[i]) y_err = np.array(rad_err[i]) axs[i].errorbar(annuli, y, yerr=y_err, fmt='x', c='r') params, cov = np.polyfit(annuli, y, 1, w=1 / y_err, cov=True) axs[i].plot(annuli, np.poly1d(params)(annuli), '-r') # params, residuals, _, _, _ = numpy.polyfit(annuli, y, 1, # w=1/y_err, full=True) # chi2 = residuals / (len(annuli) - 2) figs[i].text(0.15, 0.8, r'grad: %.3f $\pm$ %.3f' % (params[0], np.sqrt(np.diag(cov))[0]), color='r') if i == 'age': axs[i].set_ylabel('log(Age (Gyr))') ageG[i_gal], e_ageG[i_gal] = params[0], np.sqrt( np.diag(cov))[0] ageRe[i_gal] = np.poly1d(params)(R_e) elif i == 'met': axs[i].set_ylabel('Metalicity [Z/H]') metG[i_gal], e_metG[i_gal] = params[0], np.sqrt( np.diag(cov))[0] metRe[i_gal] = np.poly1d(params)(R_e) elif i == 'alp': axs[i].set_ylabel('Alpha Enhancement [alpha/Fe]') alpG[i_gal], e_alpG[i_gal] = params[0], np.sqrt( np.diag(cov))[0] alpRe[i_gal] = np.poly1d(params)(R_e) figs[i].savefig('%s/%s_grad.png' % (out_plots, i)) plt.close(i) temp = "{0:12}{1:7}{2:7}{3:7}{4:7}{5:7}{6:7}{7:7}{8:7}{9:7}\n" with open(gradient_file, 'w') as f: f.write( temp.format('Galaxy', 'ageRe', 'ageG', 'e_ageG', 'metRe', 'metG', 'e_metG', 'alpRe', 'alpG', 'e_alpG')) for i in range(len(galaxy_gals)): f.write( temp.format(galaxy_gals[i], str(round(ageRe[i], 1)), str(round(ageG[i], 3)), str(round(e_ageG[i], 3)), str(round(metRe[i], 1)), str(round(metG[i], 3)), str(round(e_metG[i], 3)), str(round(alpRe[i], 1)), str(round(alpG[i], 3)), str(round(e_alpG[i], 3)))) return D
def KDC_pop(galaxy): params = set_params(opt='pop') params.reps = 10 spec, noise, lam = get_specFromAperture(galaxy, app_size=1.0) CD = lam[1] - lam[0] spec, lam, cut = apply_range(spec, window=201, repeats=3, lam=lam, return_cuts=True, set_range=params.set_range, n_sigma=2) noise = noise[cut] lamRange = np.array([lam[0], lam[-1]]) pp = run_ppxf(galaxy, spec, noise, lamRange, CD, params, produce_plot=False) pop = population(pp=pp, galaxy=galaxy) pop.plot_probability_distribution(label=' of core region') data_file = "%s/Data/muse/analysis/galaxies_core.txt" % (cc.base_dir) age_gals, age_unc_gals, met_gals, met_unc_gals, alp_gals, alp_unc_gals, \ OIII_eqw_gals, OIII_eqw_uncer_gals, \ age_gals_outer, age_unc_gals_outer, met_gals_outer, met_unc_gals_outer, \ alp_gals_outer, alp_unc_gals_outer = np.loadtxt(data_file, unpack=True, skiprows=2, usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13,14)) galaxy_gals = np.loadtxt(data_file, skiprows=2, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] age_gals[i_gal] = pop.age age_unc_gals[i_gal] = pop.unc_age met_gals[i_gal] = pop.metallicity met_unc_gals[i_gal] = pop.unc_met alp_gals[i_gal] = pop.alpha alp_unc_gals[i_gal] = pop.unc_alp # Save plot from pop before clearing from memory f = pop.fig ax = pop.ax OIII_pos = np.argmin(np.abs(pp.lam - 5007)) peak_width = 20 OIII_pos += np.argmax(pop.e_line_spec[OIII_pos - peak_width:OIII_pos + peak_width]) - peak_width flux = np.trapz(pop.e_line_spec[OIII_pos - peak_width:OIII_pos + peak_width], x=pp.lam[OIII_pos - peak_width:OIII_pos + peak_width]) OIII_eqw_gals[i_gal] = flux / pop.continuum[OIII_pos] i_OIII = np.where( '[OIII]5007d' in [e for e in pp.templatesToUse if not e.isdigit()])[0][0] flux_uncert = trapz_uncert( pp.MCgas_uncert_spec[i_OIII, OIII_pos - peak_width:OIII_pos + peak_width], x=pp.lam[OIII_pos - peak_width:OIII_pos + peak_width]) cont_uncert = np.sqrt( np.sum((pp.noise**2 + pp.MCgas_uncert_spec**2)[i_OIII, OIII_pos])) OIII_eqw_uncer_gals[i_gal] = np.sqrt( OIII_eqw_gals[i_gal]**2 * ((flux_uncert / flux)**2 + (cont_uncert / pop.continuum[OIII_pos])**2)) del pop # Outside apperture spec, noise, lam = get_specFromAperture(galaxy, app_size=1.0, inside=False) CD = lam[1] - lam[0] spec, lam, cut = apply_range(spec, window=201, repeats=3, lam=lam, return_cuts=True, set_range=params.set_range, n_sigma=2) noise = noise[cut] lamRange = np.array([lam[0], lam[-1]]) pp_outside = run_ppxf(galaxy, spec, noise, lamRange, CD, params, produce_plot=False) pop_outside = population(pp=pp_outside, galaxy=galaxy) pop_outside.plot_probability_distribution(f=f, ax_array=ax, label=' of outer region') pop_outside.fig.suptitle( '%s Probability Distribution within inner 1 arcsec' % (galaxy.upper()), y=0.985) h, l = pop_outside.ax[0, 0].get_legend_handles_labels() pop_outside.ax[1, 1].legend(h, l, loc=1) pop_outside.fig.savefig('%s/Data/muse/analysis/%s/pop_1arcsec.png' % (cc.base_dir, galaxy)) age_gals_outer[i_gal] = pop_outside.age age_unc_gals_outer[i_gal] = pop_outside.unc_age met_gals_outer[i_gal] = pop_outside.metallicity met_unc_gals_outer[i_gal] = pop_outside.unc_met alp_gals_outer[i_gal] = pop_outside.alpha alp_unc_gals_outer[i_gal] = pop_outside.unc_alp del pop_outside temp = "{0:10}{1:6}{2:6}{3:6}{4:6}{5:6}{6:6}{7:9}{8:10}{9:6}{10:6}{11:6}{12:6}{13:6}{14:6}\n" with open(data_file, 'w') as f: f.write( ' Core (inner 1arcsec) Outer \n' ) f.write( temp.format('Galaxy', 'Age', 'error', 'Metal', 'error', 'Alpha', 'error', 'OIII_eqw', 'error', 'Age', 'error', 'Metal', 'error', 'Alpha', 'error')) for i in range(len(galaxy_gals)): f.write( temp.format(galaxy_gals[i], str(round(age_gals[i], 2)), str(round(age_unc_gals[i], 2)), str(round(met_gals[i], 2)), str(round(met_unc_gals[i], 2)), str(round(alp_gals[i], 2)), str(round(alp_unc_gals[i], 2)), str(round(OIII_eqw_gals[i], 4)), str(round(OIII_eqw_uncer_gals[i], 4)), str(round(age_gals_outer[i], 2)), str(round(age_unc_gals_outer[i], 2)), str(round(met_gals_outer[i], 2)), str(round(met_unc_gals_outer[i], 2)), str(round(alp_gals_outer[i], 2)), str(round(alp_unc_gals_outer[i], 2))))
def whole_image(galaxy, verbose=False): print galaxy max_reps = 100 if cc.device == 'glamdring': data_file = "%s/analysis/galaxies.txt" % (cc.base_dir) else: data_file = "%s/Data/vimos/analysis/galaxies.txt" % (cc.base_dir) galaxy_gals, z_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(0, 1), dtype=str) galaxy_gals = np.loadtxt(data_file, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] z = float(z_gals[i_gal]) D = z * c / H0 # Mpc data_file = "%s/Data/muse/analysis/galaxies.txt" % (cc.base_dir) x_gals, y_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(1, 2), dtype=int) galaxy_gals = np.loadtxt(data_file, unpack=True, skiprows=1, usecols=(0, ), dtype=str) i_gal = np.where(galaxy_gals == galaxy)[0][0] centre = (x_gals[i_gal], y_gals[i_gal]) limits_file = '%s/Data/muse/analysis/galaxies_gasMass.txt' % (cc.base_dir) galaxy_gals, mass, e_mass, bulmer, e_bulmer = np.loadtxt(limits_file, unpack=True, dtype=str, skiprows=1) i_gal = np.where(galaxy_gals == galaxy)[0][0] max_radius = 90 Mass_sav = 0 radius = float(max_radius) if 'ic' in galaxy: f = fits.open(get_dataCubeDirectory(galaxy)) elif 'ngc' in galaxy: f = fits.open(get_dataCubeDirectory(galaxy)[:-5] + '2.fits') while radius > 2: mask = in_aperture(centre[0], centre[1], radius, instrument='muse') spec = f[1].data noise = f[2].data spec[np.isnan(spec)] = 0 noise[np.isnan(noise)] = 0 spec = np.einsum('ijk,jk->i', spec, mask) #/np.sum(mask) noise = np.sqrt(np.einsum('ijk,jk->i', noise**2, mask)) #/np.sum(mask) if radius == max_radius: reps = max_reps params = set_params(opt='pop', reps=reps, temp_mismatch=True, produce_plot=False) lam = (np.arange(len(spec)) - (f[1].header['CRPIX3'] - 1)) * \ f[1].header['CD3_3'] + f[1].header['CRVAL3'] spec, lam, cut = apply_range(spec, lam=lam, return_cuts=True, set_range=params.set_range) lamRange = np.array([lam[0], lam[-1]]) noise = noise[cut] pp = run_ppxf(galaxy, spec, noise, lamRange, f[1].header['CD3_3'], params) # pp.ax.ax2.plot(pp.lam, pp.matrix[:, # pp.templatesToUse=='Hbeta'].flatten(), 'k') # pp.fig.savefig('%s.png'%(galaxy)) # pp.noise = np.min([pp.noise, np.abs(pp.galaxy-pp.bestfit)],axis=0) OIII_spec = pp.matrix[:, pp.templatesToUse == '[OIII]5007d'].flatten( ) * pp.weights[pp.templatesToUse == '[OIII]5007d'] Hb_spec = pp.matrix[:, pp.templatesToUse=='Hbeta'].flatten() * \ pp.weights[pp.templatesToUse=='Hbeta'] Hb_flux = np.trapz(Hb_spec, x=pp.lam) # Ha_flux = 2.86 * Hb_flux # print 'From Hbeta' # Mass = get_mass(Ha_flux, D, instrument='muse') # Solar masses # if max(OIII_spec)/np.median(pp.noise[ # (pp.lam < 5007./(1 + (pp.sol[1][0] - 300)/c)) * # (pp.lam > 5007./(1 + (pp.sol[1][0] + 300)/c))]) > 4: # if max(Hb_spec)/np.median(pp.noise[ # (pp.lam < 4861./(1 + (pp.sol[1][0] - 300)/c)) * # (pp.lam > 4861./(1 + (pp.sol[1][0] + 300)/c))]) > 2.5: # print ' %.2f log10(Solar Masses)' % (np.log10(Mass)) # else: # print ' <%.2f log10(Solar Masses)' % (np.log10(Mass)) # else: # print ' <%.2f log10(Solar Masses)' % (np.log10(Mass)) Ha_spec = pp.matrix[:, pp.templatesToUse=='Halpha'].flatten() * \ pp.weights[pp.templatesToUse=='Halpha'] Ha_flux = np.trapz(Ha_spec, x=pp.lam) Ha_spec2 = pp.matrix[:, pp.templatesToUse=='Halpha'].flatten() \ / np.max(pp.matrix[:, pp.templatesToUse=='Halpha']) \ * np.median(noise[(pp.lam < 6563./(1 + (pp.sol[1][0] - 300)/c)) * (pp.lam > 6563./(1 + (pp.sol[1][0] + 300)/c))]) Ha_flux2 = np.trapz(Ha_spec2, x=pp.lam) Mass2 = get_Mass(Ha_flux2, D, instrument='muse') if reps == max_reps: Hb_spec_uncert = pp.MCgas_uncert_spec[pp.templatesToUse[ pp.component != 0] == 'Hbeta', :].flatten() Hb_flux_uncert = trapz_uncert(Hb_spec_uncert, x=pp.lam) Ha_spec_uncert = pp.MCgas_uncert_spec[pp.templatesToUse[ pp.component != 0] == 'Halpha', :].flatten() Ha_flux_uncert = trapz_uncert(Ha_spec_uncert, x=pp.lam) Mass = get_Mass(Ha_flux, D, instrument='muse') e_Mass = get_Mass(Ha_flux_uncert, D, instrument='muse') if max(OIII_spec) / np.median(pp.noise[ (pp.lam < 5007. / (1 + (pp.sol[1][0] - 300) / c)) * (pp.lam > 5007. / (1 + (pp.sol[1][0] + 300) / c))]) > 4: if max(Ha_spec) / np.median(pp.noise[ (pp.lam < 6563. / (1 + (pp.sol[1][0] - 300) / c)) * (pp.lam > 6563. / (1 + (pp.sol[1][0] + 300) / c))]) > 2.5: if reps == max_reps: mass[i_gal] = str(round(np.log10(Mass), 4)) e_mass[i_gal] = str( round(np.abs(e_Mass / Mass / np.log(10)), 4)) if verbose: print '%s +/- %s log10(Solar Masses)' % (mass[i_gal], e_mass[i_gal]) # fig, ax = plt.subplots(2) # pp.ax = ax[0] # from ppxf import create_plot # fig, ax = create_plot(pp).produce # ax.set_xlim([4800, 4900]) # ax.legend() # pp.ax = ax[1] # from ppxf import create_plot # fig, ax = create_plot(pp).produce # ax.set_xlim([6500, 6600]) # fig.savefig('%s.png'%(galaxy)) radius = -1 else: # Repeat but calculate uncert reps = max_reps if max(Hb_spec) / np.median(pp.noise[ (pp.lam < 4861. / (1 + (pp.sol[1][0] - 300) / c)) * (pp.lam > 4861. / (1 + (pp.sol[1][0] + 300) / c))]) > 2.5: b = Ha_flux / Hb_flux e_bulmer[i_gal] = str( round( b * np.sqrt((Ha_flux_uncert / Ha_flux)**2 + (Hb_flux_uncert / Hb_flux)**2), 2)) bulmer[i_gal] = str(round(b, 2)) else: b = Ha_flux / Hb_flux e_bulmer[i_gal] = str( round( b * np.sqrt((Ha_flux_uncert / Ha_flux)**2 + (Hb_flux_uncert / Hb_flux)**2), 2)) bulmer[i_gal] = '<' + str(round(b, 2)) else: Mass_sav = max(Mass, Mass2, Mass_sav) if Mass_sav == Mass2: e_Mass = np.nan # if radius == max_radius: mass[i_gal] = '<' + str(round(np.log10(Mass_sav), 4)) e_mass[i_gal] = str( round(np.abs(e_Mass / Mass / np.log(10)), 4)) b = Ha_flux / Hb_flux e_bulmer[i_gal] = str( round( b * np.sqrt((Ha_flux_uncert / Ha_flux)**2 + (Hb_flux_uncert / Hb_flux)**2), 2)) bulmer[i_gal] = '<' + str(round(b, 2)) if verbose: print '%s +/- %s log10(Solar Masses)' % (mass[i_gal], e_mass[i_gal]) radius -= 5 reps = 0 else: Mass_sav = max(Mass, Mass2, Mass_sav) if Mass_sav == Mass2: e_Mass = np.nan # if radius == max_radius: mass[i_gal] = '<' + str(round(np.log10(Mass_sav), 4)) e_mass[i_gal] = str(round(np.abs(e_Mass / Mass / np.log(10)), 4)) b = Ha_flux / Hb_flux e_bulmer[i_gal] = str( round( b * np.sqrt((Ha_flux_uncert / Ha_flux)**2 + (Hb_flux_uncert / Hb_flux)**2), 2)) bulmer[i_gal] = '<' + str(round(b, 2)) if verbose: print '%s +/- %s log10(Solar Masses)' % (mass[i_gal], e_mass[i_gal]) radius -= 5 reps = 0 params = set_params(opt='pop', reps=reps, temp_mismatch=True, produce_plot=False) temp = "{0:12}{1:10}{2:10}{3:10}{4:10}\n" with open(limits_file, 'w') as l: l.write(temp.format('Galaxy', 'Mass', 'e_Mass', 'Bul_dec', 'e_Bul_dec')) for i in range(len(galaxy_gals)): l.write( temp.format(galaxy_gals[i], mass[i], e_mass[i], bulmer[i], e_bulmer[i]))