def mc_goodness_fit(par, drop, niter, zlo=-1.): # Determine goodness of fit given the parameters. # Draw the same number of points from the model as observed, then calculate the # loglikelihood of the drawn points. Calculate the probability that a simulated # observation has lower loglikelihood than the observed points. if drop == 'b': cat1 = 'bdrops_gf_v2.cat' cat2 = 'bdrops_udf_gf_v2.cat' kgrid1 = mlutil.readkgrid('kernel_I.p') kgrid2 = mlutil.readkgrid('kernel_I_udf.p') zdf1 = 'zdgrid_bdrops.p' zdf2 = 'zdgrid_bdrops_udf.p' mcfile = 'M1500_to_i.txt' mc = bl.mconvert(mcfile) mk = mc(4.0) chisqnulim = [0.4, 5.0] elif drop == 'v': cat1 = 'vdrops_gf_v2.cat' cat2 = 'vdrops_udf_gf_v2.cat' kgrid1 = mlutil.readkgrid('kernel_Z.p') kgrid2 = mlutil.readkgrid('kernel_Z_udf.p') zdf1 = 'zdgrid_vdrops.p' zdf2 = 'zdgrid_vdrops_udf.p' mcfile = 'M1500_to_z.txt' mc = bl.mconvert(mcfile) mk = mc(5.0) chisqnulim = [0.5, 5.0] cullflags = [0, 1, 2, 3, 4, 12, 13, 14, 18] limits1 = array([[21.0, 26.5], [-2.0, 3.0]]) limits2 = array([[23.0, 28.5], [-2.0, 3.0]]) #limits1 = bl.limits1 #limits2 = bl.limits2 pixdx = array([0.02, 0.02]) mag1, re1, crit1 = fl.cleandata(cat1, chisqnulim=chisqnulim[0], magautolim=26.5, cullflags=cullflags, limits=limits1, zlo=zlo, drop=drop) mag2, re2, crit2 = fl.cleandata(cat2, chisqnulim=chisqnulim[1], magautolim=28.5, cullflags=cullflags, limits=limits2, zlo=zlo, drop=drop) data1 = array([mag1, log10(re1)]) data2 = array([mag2, log10(re2)]) N1 = len(mag1) N2 = len(mag2) print N1, N2, N1 + N2 model1 = bl.bivariate_lf(par, limits1, pixdx, drop, 'goods', kgrid=kgrid1, zdgridfile=zdf1,\ mcfile=mcfile, meankcorr=mk, add_interloper=True, norm=-1.) model2 = bl.bivariate_lf(par, limits2, pixdx, drop, 'udf', kgrid=kgrid2, zdgridfile=zdf2,\ mcfile=mcfile, meankcorr=mk, add_interloper=True, norm=-1.) sum1 = sum(model1.model.ravel()) * pixdx[0] * pixdx[1] sum2 = sum(model2.model.ravel()) * pixdx[0] * pixdx[1] phistar_mod = float(N1 + N2) / (sum1 + sum2) print phistar_mod model1.model = phistar_mod * model1.model model2.model = phistar_mod * model2.model logl_ref = bf.loglikelihood(data1, model1, floor=0.) + bf.loglikelihood( data2, model2, floor=0.) #logl_ref = bf.mlfunc(par, data1, data2, limits1, limits2, pixdx, kgrid1, kgrid2, # 1.0, 1.0, -21.0, zdf1, zdf2, mcfile, 1, mk, 0, -1., 'phistar', drop) print "logl_ref", logl_ref simlogl_arr = zeros(niter) # actually -1*logL... print "Start drawing simulated observations..." t1 = time.time() for i in range(niter): if i % 1000 == 0: print i simdata1 = mlutil.draw_from_pdf(N1, model1, model1.limits) simdata2 = mlutil.draw_from_pdf(N2, model2, model2.limits) simlogl1 = bf.loglikelihood(simdata1, model1) simlogl2 = bf.loglikelihood(simdata2, model2) simlogl = simlogl1 + simlogl2 simlogl_arr[i] = simlogl t2 = time.time() dt = t2 - t1 dtmin = int(floor(dt)) / 60 dtsec = dt % 60 n_worse = sum(simlogl_arr > logl_ref) print "%d iterations took %d min %.1f sec" % (niter, dtmin, dtsec) print "Percentage of simulated observations with lower log-likelihood: %.2f %%" % ( 100. * float(n_worse) / float(niter)) return logl_ref, simlogl_arr
def reduced_chi2(par, drop, mbins1, rbins1, mbins2, rbins2, chisqnulim=(0.4, 5.0), zlo=-1): """ Calculate the reduced chi2 of the best-fit model mbins, rbins should include the upper limits. """ if drop == 'b': cat1 = 'bdrops_gf_v2.cat' cat2 = 'bdrops_udf_gf_v2.cat' kgrid1 = mlutil.readkgrid('kernel_I.p') kgrid2 = mlutil.readkgrid('kernel_I_udf.p') zdf1 = 'zdgrid_bdrops.p' zdf2 = 'zdgrid_bdrops_udf.p' mcfile = 'M1500_to_i.txt' mc = bl.mconvert(mcfile) mk = mc(4.0) elif drop == 'v': cat1 = 'vdrops_gf_v2.cat' cat2 = 'vdrops_udf_gf_v2.cat' kgrid1 = mlutil.readkgrid('kernel_Z.p') kgrid2 = mlutil.readkgrid('kernel_Z_udf.p') zdf1 = 'zdgrid_vdrops.p' zdf2 = 'zdgrid_vdrops_udf.p' mcfile = 'M1500_to_z.txt' mc = bl.mconvert(mcfile) mk = mc(5.0) cullflags = [0, 1, 2, 3, 4, 12, 13, 14, 18, 19] limits1 = array([[21.0, 26.5], [-2.0, 3.0]]) limits2 = array([[23.0, 28.5], [-2.0, 3.0]]) pixdx = array([0.02, 0.02]) modshape1 = (limits1[:, 1] - limits1[:, 0]) / pixdx modshape1 = around(modshape1).astype('int') modshape2 = (limits2[:, 1] - limits2[:, 0]) / pixdx modshape2 = around(modshape2).astype('int') # bin the points & tally the counts mag1, re1, crit1 = cleandata(cat1, chisqnulim=chisqnulim[0], magautolim=26.5, cullflags=cullflags, limits=limits1, zlo=zlo) mag2, re2, crit2 = cleandata(cat2, chisqnulim=chisqnulim[1], magautolim=28.5, cullflags=cullflags, limits=limits2, zlo=zlo) bincounts1 = histogram2d(mag1, log10(re1), bins=[mbins1, rbins1])[0].astype('float') bincounts2 = histogram2d(mag2, log10(re2), bins=[mbins2, rbins2])[0].astype('float') #print bincounts1, bincounts2 # calculate the best-fit models model1 = bl.bivariate_lf(par, limits1, pixdx, kgrid=kgrid1, zdgridfile=zdf1, \ mcfile=mcfile, drop=drop, field='goods', meankcorr=mk, add_interloper=True) model2 = bl.bivariate_lf(par, limits2, pixdx, kgrid=kgrid2, zdgridfile=zdf2, \ mcfile=mcfile, drop=drop, field='udf', meankcorr=mk, add_interloper=True) phistar_mod = phistar(par, drop, zlo=zlo) model1.model = phistar_mod * model1.model model2.model = phistar_mod * model2.model #model1.model = ones(modshape1)/(modshape1[0]*modshape1[1]*pixdx[0]*pixdx[1])*len(mag1) #model2.model = ones(modshape2)/(modshape2[0]*modshape2[1]*pixdx[0]*pixdx[1])*len(mag2) print sum(model1.model.ravel()) * pixdx[0] * pixdx[1] chi2tot = 0. nbins = 0 mindex1 = (mbins1 - 21.0) / 0.02 mindex1 = around(mindex1).astype('int') rindex1 = (rbins1 - (-2.0)) / 0.02 rindex1 = around(rindex1).astype('int') mindex2 = (mbins2 - 23.0) / 0.02 mindex2 = around(mindex2).astype('int') rindex2 = (rbins2 - (-2.0)) / 0.02 rindex2 = around(rindex2).astype('int') num_exp1 = [] # number of expected num_exp2 = [] num_obs1 = bincounts1.ravel()[bincounts1.ravel() >= 5] num_obs2 = bincounts2.ravel()[bincounts2.ravel() >= 5] # iterate through all bins and calculate the chi2 for i in range(len(mbins1) - 1): for j in range(len(rbins1) - 1): if bincounts1[i, j] >= 5: num_mod = sum(model1.model[mindex1[i]:mindex1[i + 1], rindex1[j]:rindex1[j + 1]].ravel()) num_mod = num_mod * pixdx[0] * pixdx[1] num_exp1 += [num_mod] #chi2 = (bincounts1[i,j] - num_mod)**2 / num_mod #print bincounts1[i,j], num_mod #chi2tot += chi2 #nbins += 1 #if bincounts1[i,j] < nmin: nmin = bincounts1[i,j] for i in range(len(mbins2) - 1): for j in range(len(rbins2) - 1): if bincounts2[i, j] >= 5: num_mod = sum(model2.model[mindex2[i]:mindex2[i + 1], rindex2[j]:rindex2[j + 1]].ravel()) num_mod = num_mod * pixdx[0] * pixdx[1] num_exp2 += [num_mod] #chi2 = (bincounts2[i,j] - num_mod)**2 / num_mod #chi2tot += chi2 #nbins += 1 print "nbins", nbins # Run chi-square test num_exp = concatenate([num_exp1, num_exp2]) num_obs = concatenate([num_obs1, num_obs2]) ndeg = len( num_exp) - 1 # degree of freedom = num. of contributing bins - 1 print "ndeg", ndeg chi2, pval = stats.mstats.chisquare(num_obs, f_exp=num_exp) print chi2, pval #print num_exp, num_obs #chi2nu = chi2tot / float(ndeg) return chi2, pval, num_exp, num_obs
from numpy import * import bivariate_lf as bl import bivariate_fit as bf import fit_lbg as fl 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)
#!/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