def plot_bdrops_vdrops_sizemag(ax, dcolor='black', lcolors=['brown','green','red','orange']): linestyles = ['-','--',':','-.'] mci = bl.mconvert('M1500_to_i.txt') mcz = bl.mconvert('M1500_to_z.txt') m0i = -21.0 + mci(4.0) # the i-band apparent mag at z=4 corresponding to M0=-21 at 1500A m0z = -21.0 + mcz(5.0) # the z-band apparent mag at z=5 corresponding to M0=-21 at 1500A logrpeak_b = zeros(4); logrpeak_v = zeros(4) mavg_b = zeros(4); mavg_v = zeros(4) yerrs_b = zeros((2,4)); yerrs_v = zeros((2,4)) logsigma_b = sizemag_rel_bdrops['sigma'] / log(10.) logsigma_v = sizemag_rel_vdrops['sigma'] / log(10.) for i in range(4): # B-dropouts mavg_b[i] = average(sizemag_rel_bdrops['mbins'][i]) logrpeak_b[i] = sizemag_rel_bdrops['logr0'][i] - 0.4 * param_bdrops['beta'] * \ (mavg_b[i] - m0i) yerrs_b[0][i] = 10.**logrpeak_b[i] * (1. - 10.**((-1.)*logsigma_b[i])) yerrs_b[1][i] = 10.**logrpeak_b[i] * (10.**logsigma_b[i] - 1.) # V-dropouts mavg_v[i] = average(sizemag_rel_vdrops['mbins'][i]) logrpeak_v[i] = sizemag_rel_vdrops['logr0'][i] - 0.4 * param_vdrops['beta'] * \ (mavg_v[i] - m0z) yerrs_v[0][i] = 10.**logrpeak_v[i] * (1. - 10.**((-1.)*logsigma_v[i])) yerrs_v[1][i] = 10.**logrpeak_v[i] * (10.**logsigma_v[i] - 1.) #fig = plt.figure(figsize=(10,8)) #ax = fig.add_subplot(111) ax.errorbar(mavg_b, 0.03 * 10.**logrpeak_b, yerr=0.03*yerrs_b, xerr=[0.5,0.5,0.5,0.75],\ fmt='o', ms=10, ecolor=dcolor, mec=dcolor, mfc=dcolor, label='B-dropouts') ax.errorbar(mavg_v+0.1, 0.03 * 10.**logrpeak_v, yerr=0.03*yerrs_v, xerr=[0.5,0.5,0.5,0.75],\ fmt='^', ms=10, ecolor=dcolor, mec=dcolor, mfc='none', label='V-dropouts', mew=1.8) ax.set_xlabel('apparent magnitude') ax.set_ylabel(r'$R_e$ [arcsec]') # plot various power-laws pindex = [0.1, 0.2, 0.3, 0.4] marr = arange(23., 29., 0.1) for i in range(len(pindex)): Larr = 10.**(marr / -2.5) rarr = Larr ** pindex[i] f = 0.03*10.**0.8396 / rarr[20] rarr = rarr * f ax.plot(marr, rarr, ls=linestyles[i], lw=2.0, label=r'$\bar{R} \propto L^{%.1f}$' % pindex[i],color=lcolors[i]) ax.set_xlim(23.,28.8) ax.legend(loc=1, numpoints=1) return ax
import mlutil import zdist import os, sys, time from multiprocessing import Queue, Process, Pool ## initialize necessary things here parv = array([-1.68527018, -20.50967054, 0.8757289, 0.85187255, 0.26594964]) dlimits1 = array([[24.0, 25.0], [-2.0, 3.0]]) dlimits2 = dlimits1.copy() mlimits1 = array([[21.0, 26.5], [-2.0, 3.0]]) mlimits2 = array([[23.0, 28.5], [-2.0, 3.0]]) kgrid1 = mlutil.readkgrid('kernel_Z.p') kgrid2 = mlutil.readkgrid('kernel_Z_udf.p') zdgrid1 = zdist.read_zdgrid('zdgrid_vdrops.p') zdgrid2 = zdist.read_zdgrid('zdgrid_vdrops_udf.p') mc = bl.mconvert('M1500_to_z.txt') logr0_arr = arange(0.7, 1.0, 0.005) sigma_arr = arange(1.0, 1.3, 0.005) logr0_grid, sigma_grid = meshgrid(logr0_arr, sigma_arr) logr0_grid, sigma_grid = map(ravel, [logr0_grid, sigma_grid]) #print logr0_grid nprocs = 3 #chunksize = float(niter) / nproc q_logl = Queue() q_pars = Queue() par = parv.copy() N = len(logr0_arr) * len(sigma_arr) logl = zeros(N) chunksize = float(N) / float(nprocs) procs = []
#!/usr/bin/env python from numpy import * from pygoods import * import bivariate_lf as bl import bivariate_fit as bf import fit_lbg as fl import mlutil parb = array([-1.61711035, -20.53430348, 0.81505052, 0.76553555, 0.20435599]) parv = array([-1.68527018, -20.50967054, 0.8757289, 0.85187255, 0.26594964]) kgrid1 = mlutil.readkgrid('kernel_I.p') kgrid2 = mlutil.readkgrid('kernel_I_udf.p') kgrid3 = mlutil.readkgrid('kernel_Z.p') kgrid4 = mlutil.readkgrid('kernel_Z_udf.p') mci = bl.mconvert('M1500_to_i.txt') mcz = bl.mconvert('M1500_to_z.txt') def logl_sigma_bdrops(sarr=arange(0.3, 1.0, 0.05)): mag1, re1, crit1 = fl.cleandata('bdrops_gf_v2.cat', chisqnulim=0.4, magautolim=26.5, limits=bl.limits1, drop='b') mag2, re2, crit2 = fl.cleandata('bdrops_udf_gf_v2.cat', chisqnulim=5.0, magautolim=28.5, limits=bl.limits2, drop='b') data1 = array([mag1, log10(re1)])
def plot_sizedist(parb, parv): mod_lograrr = arange(bl.limits1[1][0], bl.limits1[1][1], 0.02) magb1, reb1, critb1 = fl.cleandata('bdrops_gf_v2.cat', drop='b', limits=bl.limits1, zlo=3.0) magb2, reb2, critb2 = fl.cleandata('bdrops_udf_gf_v2.cat', chisqnulim=5.0, magautolim=28.5,\ limits=bl.limits2, drop='b', zlo=3.0) kgridb1 = mlutil.readkgrid('kernel_I.p') kgridb2 = mlutil.readkgrid('kernel_I_udf.p') mci = bl.mconvert('M1500_to_i.txt') reb = concatenate((reb1, reb2)) modelb1 = bl.bivariate_lf(parb, bl.limits1, bl.pixdx, 'b', 'goods', kgrid=kgridb1, zdgridfile='zdgrid_bdrops.p', mcfile='M1500_to_i.txt', meankcorr=mci(4.0)) modelb2 = bl.bivariate_lf(parb, bl.limits2, bl.pixdx, 'b', 'udf', kgrid=kgridb2, zdgridfile='zdgrid_bdrops_udf.p', mcfile='M1500_to_i.txt', meankcorr=mci(4.0)) sizedist_b = (modelb1.model.sum(axis=0) + modelb2.model.sum(axis=0)) / 10.**mod_lograrr magv1, rev1, critv1 = fl.cleandata('vdrops_gf_v2.cat', chisqnulim=0.5, drop='v', limits=bl.limits1, zlo=4.0) magv2, rev2, critv2 = fl.cleandata('vdrops_udf_gf_v2.cat', chisqnulim=5.0, magautolim=28.5,\ limits=bl.limits2, drop='v', zlo=4.0) rev = concatenate((rev1, rev2)) kgridv1 = mlutil.readkgrid('kernel_Z.p') kgridv2 = mlutil.readkgrid('kernel_Z_udf.p') mcz = bl.mconvert('M1500_to_z.txt') modelv1 = bl.bivariate_lf(parv, bl.limits1, bl.pixdx, 'v', 'goods', kgrid=kgridv1, zdgridfile='zdgrid_vdrops.p', mcfile='M1500_to_z.txt', meankcorr=mcz(5.0)) modelv2 = bl.bivariate_lf(parv, bl.limits2, bl.pixdx, 'v', 'udf', kgrid=kgridv2, zdgridfile='zdgrid_vdrops_udf.p', mcfile='M1500_to_z.txt', meankcorr=mcz(5.0)) sizedist_v = (modelv1.model.sum(axis=0) + modelb2.model.sum(axis=0)) / 10.**mod_lograrr # fit the uncorrected size distribution with lognormal function xout_b = fln.fit_lognormal(drop='b') print xout_b xout_v = fln.fit_lognormal(drop='v') print xout_v # plot fig = plt.figure(figsize=(8, 10)) ax1 = fig.add_subplot(211) rarr = arange(0.001, 41., 1.) pl_rarr = arange(0.001, 41., 0.01) h1 = ax1.hist(reb, rarr, color='gray', ec='none') fb = fln.lognormal(xout_b[0], xout_b[1], pl_rarr) ax1.plot(pl_rarr, fb * max(h1[0]) / max(fb), color='black', label=r'$\sigma=%.2f$' % xout_b[1]) ax1.plot(10.**mod_lograrr, sizedist_b * max(h1[0]) / max(sizedist_b), color='red', label=r'$\sigma=%.2f$; corrected' % parb[3]) ax2 = fig.add_subplot(212) h2 = ax2.hist(rev, rarr, color='gray', ec='none') fv = fln.lognormal(xout_v[0], xout_v[1], pl_rarr) ax2.plot(pl_rarr, fv * max(h2[0]) / max(fv), color='black', label=r'$\sigma=%.2f$' % xout_v[1]) ax2.plot(10.**mod_lograrr, sizedist_v * max(h2[0]) / max(sizedist_v), color='red', label=r'$\sigma=%.2f$; corrected' % parv[3]) ax1.set_xlim(0, 40) ax2.set_xlim(0, 40) ax1.set_xlabel('Re [0.03" / pixel]') ax2.set_xlabel('Re [0.03" / pixel]') ax1.legend(loc=1) ax2.legend(loc=1) ax1.set_title('B-dropouts (z~4)') ax2.set_title('V-dropouts (z~5)') return fig
def show_model(par, drop, field, newfig=True, axCent=None, fig1=None, lfbw=0.2, sdbw=0.2, colors=['blue', 'green', 'black', 'red']): if drop == 'b': zmean = 4.0 mc = bl.mconvert('M1500_to_i.txt') if field == 'goods': cat = 'bdrops_gf_v3.cat' zdgrid = zdist.read_zdgrid('zdgrid/zdgrid_bdrops_nolya.p') limits = limitsb1 magautolim = 26.5 chisqnulim = 0.4 reerrlim = 0.6 kgridfile = 'tfkernel/kernel_I.p' dataset = 'GOODS' elif field == 'udf': cat = 'bdrops_udf_gf_v3.cat' zdgrid = zdist.read_zdgrid('zdgrid/zdgrid_bdrops_udf_nolya.p') limits = limitsb2 magautolim = 28.5 chisqnulim = 5.0 reerrlim = 0.6 kgridfile = 'tfkernel/kernel_I_udf.p' dataset = 'HUDF' elif drop == 'v': zmean = 5.0 mc = bl.mconvert('M1500_to_z.txt') if field == 'goods': cat = 'vdrops_gf_v2.cat' zdgrid = zdist.read_zdgrid('zdgrid/zdgrid_vdrops_nolya_bston5.p') limits = limitsv1 magautolim = 26.5 chisqnulim = 0.5 reerrlim = 0.6 kgridfile = 'tfkernel/kernel_Z.p' dataset = 'GOODS' elif field == 'udf': cat = 'vdrops_udf_gf_v2.cat' zdgrid = zdist.read_zdgrid( 'zdgrid/zdgrid_vdrops_udf_nolya_bston5.p') limits = limitsv2 magautolim = 28.5 chisqnulim = 5.0 reerrlim = 0.6 kgridfile = 'tfkernel/kernel_Z_udf.p' dataset = 'HUDF' kgrid = mlutil.readkgrid(kgridfile) meankcorr = mc(zmean) fp = matplotlib.font_manager.FontProperties(size=9) if newfig: fig1 = plt.figure(figsize=(10, 15)) axCent = plt.subplot(111) divider = make_axes_locatable(axCent) restlim = array([[-26.5, -15.5], [-0.5, 2.0]]) pixdx = array([0.02, 0.02]) if drop == 'b': z0 = 3.0 else: z0 = 4.0 zd_flat = zdist.zdgrid(-25.0, -15.0, 0.5, -0.6, 1.8, 0.2, z0, 6.0, 0.1, drop, zdgrid.area) zd_flat.flat_zdgrid(zlo=zmean - 0.5, zhi=zmean + 0.5) f = open('zdgrid_flat.p', 'w') cPickle.dump(zd_flat, f, 2) f.close() model0 = bl.bivariate_lf(par, limits, pixdx, drop, field, mc=mc, meankcorr=meankcorr,\ zdgrid=None) V0 = zdgrid.dVdz[(zdgrid.zarr >= (zmean - 0.5)) & (zdgrid.zarr <= (zmean + 0.5))] model0.model = model0.model * sum(V0) model_ds = bl.bivariate_lf(par, limits, pixdx, drop, field, kgrid=None, meankcorr=meankcorr,\ zdgrid=zdgrid, mc=mc, norm=-1) model = bl.bivariate_lf(par, limits, pixdx, drop, field, kgrid=kgrid, meankcorr=meankcorr, M0=-21.0, zdgrid=zdgrid, mc=mc, norm=-1) # Show model + data points #axCent.imshow(model.model.swapaxes(0,1), origin = 'lower', vmin = vmin, vmax = vmax, # aspect = 'auto') mag, re, crit = fl.cleandata(cat, chisqnulim=chisqnulim, reerr_ratiolim=reerrlim, limits=limits, drop=drop) npts = len(mag) print npts axCent.scatter(mag, log10(re), s=4, color='black') axCent.contour(arange(limits[0][0],limits[0][1],pixdx[0]),\ arange(limits[1][0],limits[1][1],pixdx[1]),model.model.swapaxes(0,1),6,colors=colors[0]) yf = 0.5 axCent.set_yticks(arange(limits[1][0], limits[1][1], 0.5)) axCent.set_yticklabels(arange(limits[1][0], limits[1][1], 0.5)) if drop == 'b': axCent.set_xlabel(r'GALFIT MAG in $i_{775}$') axCent.set_ylabel(r'GALFIT $\log_{10}(R_e)$ in $i_{775}$ [pixel]') elif drop == 'v': axCent.set_xlabel(r'GALFIT MAG in $z_{850}$') axCent.set_ylabel(r'GALFIT $\log_{10}(R_e)$ in $z_{850}$ [pixel]') #plt.suptitle(r'$\vec{\mathbf{P}}=[%.2f, %.2f, %.2f$",$ %.2f, %.2f]$' % (par[0],par[1], # 10.**par[2]*0.03,par[3],par[4]),size=20) #axCent.text(0.1,0.1,"par = [%.2f,%.2f,%.2f,%.2f,%.2f]"%tuple(par),transform=axCent.transAxes, # color='black') # plot a straight line through the observed points ydata = findline(mag, log10(re)) xr = arange(limits[0][0], limits[0][1], 0.02) axCent.plot(xr, ydata[0] * xr + ydata[1], ':', c='black') # plot the straight line corresponding to the power-law relation with logR0 and beta m0 = -21.0 + mc(zmean) b = par[2] + 0.4 * par[4] * m0 axCent.plot(xr, -0.4 * par[4] * xr + b, '--', lw=2.5, c=colors[3]) axCent.text(0.1, 0.9, dataset, transform=axCent.transAxes, color='black', size=14) # Show LF axLF = divider.append_axes("top", size=1.2, pad=0.0, sharex=axCent) n, bins = histogram(mag, arange(limits[0, 0], limits[0, 1] + lfbw, lfbw)) nerr = [sqrt(n), sqrt(n)] for i in range(len(n)): if n[i] == 1: nerr[0][i] = 1. - 1.e-3 axLF.errorbar(bins[:-1]+lfbw/2., n, yerr=nerr, fmt='.', ms=14.,\ mfc='black', ls='None', mec='black', ecolor='black', capsize=6) LF = model.model.sum(axis=1) # LF here contains the volume already LFtot = sum(LF) * pixdx[0] / lfbw normfactor = npts / LFtot # normalize the LF to predict the total number of points LF = LF * normfactor LF0 = model0.model.sum(axis=1) LF0 = LF0 * normfactor LF_ds = model_ds.model.sum(axis=1) LF_ds = LF_ds * normfactor axLF.semilogy(arange(limits[0,0],limits[0,1],pixdx[0]), LF, color = colors[2],\ nonposy='mask', label='GALFIT TF') axLF.semilogy(arange(limits[0,0],limits[0,1],pixdx[0]), LF_ds, color = colors[1],\ ls=':',lw=2,nonposy='mask',label='w/ dropout sel. kernel') axLF.semilogy(arange(limits[0,0],limits[0,1],pixdx[0]), LF0, color=colors[0],\ ls='--',lw=2,nonposy='mask',label='Schechter') axLF.set_yticks([1.e-2, 1., 1.e2]) axLF.set_ylim(1.e-2, max(n) * 50.) xf = 1.0 axCent.set_xticks(arange(limits[0][0], limits[0][1] + 1., 1.)) axCent.set_xticklabels(arange(limits[0][0], limits[0][1] + 1., 1.)) axCent.set_xlim(limits[0][0], limits[0][1]) #axLF.legend(loc='upper left', prop=fp) # Show size distribution axSD = divider.append_axes("right", size="35%", pad=0.0, sharey=axCent) n, bins = histogram(log10(re), arange(limits[1, 0], limits[1, 1] + sdbw, sdbw)) nerr = [sqrt(n), sqrt(n)] for i in range(len(n)): if n[i] == 1: nerr[0][i] = 1. - 1.e-3 axSD.errorbar(n, bins[:-1]+sdbw/2., xerr=nerr, fmt='.', ms=14,\ mfc='black', ls='None', mec='black', ecolor='black', capsize=6) SD = model.model.sum(axis=0) SDtot = sum(SD) * pixdx[1] / sdbw normfactor = npts / SDtot SD = SD * normfactor sizer = arange(limits[1, 0], limits[1, 1], pixdx[1]) axSD.semilogx(SD, sizer, color=colors[2], label='GALFIT TF') SD0 = model0.model.sum(axis=0) SD0 = SD0 * normfactor SD_ds = model_ds.model.sum(axis=0) SD_ds = SD_ds * normfactor axSD.semilogx(SD_ds, sizer, color=colors[1], ls=':', lw=2, label='w/ dropout sel. kernel') axSD.semilogx(SD0, sizer, color=colors[0], ls='--', lw=2, label='lognormal') axSD.set_xticks([1., 10., 1.e2]) axSD.set_xlim(1.e-2, max(SD) * 5) #axSD.legend(loc='lower right', prop=fp) #axCent.set_ylim(limits[1][0], limits[1][1]) axCent.set_ylim(-0.6, 1.8) plt.draw() fig1.show() for tl in axLF.get_xticklabels(): tl.set_visible(False) for tl in axSD.get_yticklabels(): tl.set_visible(False) return model, axCent, axSD, axLF, fig1