示例#1
0
def main():

    settingsf = param_file.split('.')[-2]
    expt = countModel(modelFamily,
                      nlaws,
                      settingsf, [dataset],
                      floatNoise,
                      doRedshiftSlices=doRedshiftSlices)

    # Set up MPI
    world = MPI.COMM_WORLD
    rank = world.rank
    size = world.size
    master = rank == 0

    if master:
        set_module = importlib.import_module(settingsf)
        globals().update(set_module.__dict__)

    note = 'MPI processors checked in: rank/size = (%i/%i)' % (rank, size)
    print note

    if master:
        try:
            os.mkdir(outdir)
            # Fix permissions
            os.chmod(outdir, 0755)
        except OSError:
            pass

        logf = os.path.join(outdir, logfile)
        if master and os.path.exists(logf): os.remove(logf)
        log = open(logf, 'w')
        remark(log, note)

    # Wait here after check-in...
    world.Barrier()
    if master: print 'All %i processors checked in...' % size

    # Broadcast global settings variables
    if master:
        set_dict = set_module.__dict__
    else:
        set_dict = None
    set_dict = world.bcast(set_dict, root=0)

    if not master:
        globals().update(set_dict)
        #print globals()

    # Wait here after broadcast...
    world.Barrier()
    if master: print 'All %i processors received OK...\n' % size

    # Write settings variables to file
    if master:
        variablesf = os.path.join(outdir, variablesfile)
        dump_variable_values(set_module, variablesf, verbose=False)

        startTime = time.strftime('%X %x %Z')
        shutil.copy(param_file, os.path.join(outdir, 'bayestack_settings.py'))
        if doRedshiftSlices:
            for datafile in datafiles:
                shutil.copy(datafile, outdir)
        else:
            shutil.copy(datafile, outdir)
        shutil.copy('bayestackClasses.py', outdir)
        shutil.copy('bayestack.py', outdir)
        shutil.copy('lumfuncUtils.py', outdir)

        notes=['Time now is %s' % startTime,\
               'Settings file: %s' % param_file,\
               'Data file: %s' % datafile]
        remarks(log, notes)

        # This is to allow import of settings from outdir
        # i.e. from outdir import * [or whatever]
        init_file = '__init__.py'
        initf = os.path.join(outdir, init_file)
        touch(initf)

        notes=['Bins taken from %s' % datafile,\
               '# Bin occupancies [i uJy uJy field^-1]:']
        remarks(log, notes)
        if doRedshiftSlices:
            print 'Multi data'
            for df in expt.survey.datafiles[0]:
                print df
                for ibin in xrange(expt.fnbins[df]):
                    try:
                        line = '%i %f %f %f' % (ibin + 1, expt.fbins[df][ibin],
                                                expt.fbins[df][ibin + 1],
                                                expt.fdata[df][ibin])
                    except IndexError:
                        print "Probably your binstyle doesn't match the datafile bins"
                        sys.exit(0)
                    remark(log, line)

        else:
            for ibin in xrange(expt.nbins):
                try:
                    line = '%i %f %f %f' % (ibin + 1, expt.bins[ibin],
                                            expt.bins[ibin + 1],
                                            expt.data[ibin])
                except IndexError:
                    print "Probably your binstyle doesn't match the datafile bins"
                    sys.exit(0)
                remark(log, line)

    # Run MultiNest
    if master:
        t0 = time.time()
        print outdir
    try:
        pymultinest.run(expt.loglike,expt.logprior,expt.nparams,\
                    resume=RESUME,verbose=True,\
                    multimodal=multimodal,max_modes=max_modes,write_output=True,\
                    n_live_points=n_live_points,\
                    evidence_tolerance=evidence_tolerance,\
                    # mode_tolerance=-1e90 bugfix for earlier versions
                    # of PyMultiNest


                    mode_tolerance=-1e90,seed=SEED_SAMP,max_iter=max_iter,\
                    importance_nested_sampling=do_INS,\
      sampling_efficiency=sampling_efficiency,\
                    outputfiles_basename=os.path.join(outdir,outstem),\
        # NB MPI is already init'ed by mpi4py (crashes otherwise)

                    init_MPI=False)
    except:
        return 1

    if master:
        stopTime = time.strftime('%X %x %Z')
        t1 = time.time()
        dt = t1 - t0

        # Touch the output dir so Dropbox picks it up
        touch(outdir)

        notes=['Time then was %s' % startTime,\
               'Time now is %s' % stopTime,\
               'Execution took %6.4f sec (~ %i hours %i min) with %i cores' % \
                                         (dt,divmod(dt,3600)[0],int(divmod(dt,3600)[1]/60),size),\
               'Arguments:  %s' % ' '.join(sys.argv),\
               'Model used: %s'%modelFamily,\
               'INS   = %s' % do_INS,\
               'nlive = %i' % n_live_points,\
               'Run comment: %s' % comment,\
               'Now execute:',\
               '\n./plot.py %s' % outdir,\
               'and\n./reconstruct.py %s' % outdir]

        remarks(log, notes)
        log.close()

        print 'Parameters were:', expt.parameters

        # Copy the stats file so it's legible on my iPhone, Google, email etc.
        stats_dotdat = '%(od)s/%(os)sstats.dat' % {'od': outdir, 'os': outstem}
        stats_dottxt = '%(od)s/%(os)sstats.txt' % {'od': outdir, 'os': outstem}
        shutil.copy(stats_dotdat, stats_dottxt)

        # Now make all the files world readable
        globlist = glob.glob(os.path.join(outdir, '*'))
        [os.chmod(f, 0644) for f in globlist]
示例#2
0
def main():

    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module=importlib.import_module(setf)
    globals().update(set_module.__dict__)

    expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
    recon_expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
    #print expt.data
    #print expt.bins

    # Get MAP parameters
    print '\n'*5
    print len(expt.parameters)
    ncols = len(expt.parameters)
    summf=os.path.join(outdir,'1-summary.txt')
    summary=numpy.genfromtxt(summf)[-1,:]
    drawmap=summary[-(ncols+2):-2] 
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap=ana.get_best_fit()['parameters']
    
    if dataset=='sdss':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf)==20:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][4:]))
        print datafiles[0][4:]
    elif dataset=='cosmos':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf)==24:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][8:]))
        print datafiles[0][8:]

    elif 'sim' in dataset:
        d=numpy.genfromtxt(os.path.join(outdir,'sim.txt'))
    
    #bins2 = numpy.arange(21.4,29.2,0.4)
    bins2 = numpy.arange(18.0,29.2,0.4)
    bins3 = numpy.arange(18.,29.2,0.1)
    print len(expt.parameters)
    print 'this is 1st num ', num
    #sys.exit()
    params = drawmap# [0]
    #for i in drawmap:
    #	params.append(i)
    print params
    print expt.parameters
    #novak 2017
    L_n   =[21.77,22.15,22.46,22.77,23.09,23.34]
    rho_n =[-2.85,-2.88,-3.12,-3.55,-4.05,-4.63]
    L_ner_u =[0.23,0.18,0.19, 0.2, 0.21, 0.28]
    L_ner_d =[1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
    rho_ner=[0.09, 0.03,0.0355,0.059,0.1,0.234]
    mcalpine=openlit('mcalpine-2013-tot.txt')
    padovani=openlit('Padovani_2015_RL_AGN')[1:]
    mcalpine_panels=[0,1,2,3,4,4,5,6,6]
    padovani_panels=[0,1,0,2,2,3,3,5,6,6]
    #print padovani
    print padovani[0][:,0],padovani[0][:,1],padovani[0][:,2],padovani[0][:,3]
    #sys.exit()
    
            

     #novak 2018
    L_n2   =[[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56],\
             [22.30,22.61,22.96,23.38,23.80,24.10, 24.55,25.14],\
             [22.61,22.86,23.14,23.45,23.82,24.14, 24.40,24.71],\
             [22.85,23.16,23.69,24.24,24.80,25.31, 25.96,26.69],\
             [23.10,23.38,23.86,24.36,24.86,25.35, 25.94,26.36],\
             [23.32,23.57,23.94,24.32,24.67,25.06, 25.47,25.96],\
             [23.54,23.75,24.13,24.45,24.90,25.27, 25.74,26.10],\
             [23.73,23.99,24.57,25.10,25.68,26.18, 26.83,27.51],\
             [24.01,24.26,24.76,25.26,25.91,26.19, 27.45],\
             [24.30,24.56,24.80,25.13,25.37,25.80, 25.97,26.49]]
             
 
    L_ner_u2  =[[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03],\
                [0.11,0.20, 0.26, 0.25, 0.24, 0.35, 0.31, 0.15],\
                [0.08,0.15, 0.20, 0.22, 0.17, 0.18, 0.24, 0.28],\
                [0.072,0.33,0.38, 0.40, 0.48, 0.42, 0.41, 0.28],\
                [0.080,0.30,0.32, 0.32, 0.32, 0.33, 0.24, 0.34],\
                [0.068,0.22,0.24, 0.25, 0.29, 0.30, 0.28, 0.21],\
                [0.067,0.22,0.21, 0.26, 0.18, 0.17,0.075, 0.11],\
                [0.093,0.36,0.31, 0.30, 0.25, 0.28, 0.16,0.028],\
                [0.076,0.34,0.36, 0.38, 0.24, 0.47, 0.27],\
                [0.097,0.14,0.20, 0.16, 0.23, 0.092,0.22,0.026]]
                
    
    L_ner_d2  =[[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50],\
               [0.29, 0.21, 0.15, 0.16, 0.17, 0.06, 0.099,0.29],\
               [0.24, 0.17, 0.12, 0.10, 0.15, 0.15, 0.08,0.074],\
               [0.16, 0.24, 0.19, 0.17, 0.15, 0.09, 0.16, 0.33],\
               [0.30, 0.19, 0.18, 0.18, 0.18, 0.17, 0.26, 0.18],\
               [0.14, 0.18, 0.15, 0.14, 0.10, 0.095,0.11, 0.21],\
               [0.19, 0.14, 0.15, 0.11, 0.19, 0.19, 0.29, 0.28],\
               [0.40, 0.17, 0.21, 0.23, 0.27, 0.25, 0.37, 0.53],\
               [0.20, 0.17, 0.16, 0.14, 0.27, 0.042, 0.27],\
               [0.22, 0.16, 0.10, 0.14,0.072, 0.21, 0.08, 0.30]]

    rho_n2 =[[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07],\
             [-2.95,-3.17,-3.46,-4.24,-4.76,-5.41, -5.23,-5.44],\
             [-2.95,-3.11,-3.44,-3.81,-4.37,-4.48, -4.90,-5.14],\
             [-2.99,-3.24,-3.89,-4.54,-5.27,-5.33, -5.69,-5.89],\
             [-3.32,-3.51,-4.06,-4.74,-5.28,-5.43, -6.08,-5.70],\
             [-3.29,-3.54,-4.02,-4.45,-5.11,-5.56, -5.37,-6.07],\
             [-3.43,-3.61,-4.19,-4.56,-5.09,-5.34, -5.70,-5.75],\
             [-3.88,-3.98,-4.64,-5.33,-5.73,-6.27, -6.14,-6.77],\
             [-3.85,-4.28,-5.05,-5.60,-5.68,-6.44, -6.59],\
             [-4.43,-4.91,-5.46,-5.58,-5.89,-6.23, -6.26,-6.91]]
       
    rho_ner_u2=[[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34],\
                [0.048,0.027,0.036,0.089,0.17,0.45, 0.34, 0.45],\
                [0.062,0.024,0.032,0.048,0.096, 0.11,0.18,0.25],\
                [0.042,0.018,0.035,0.073,0.180, 0.20,0.34,0.45],\
                [0.045,0.019,0.033,0.072,0.15,  0.17,0.45,0.25],\
                [0.040,0.022,0.033,0.055,0.13,  0.22,0.17,0.45],\
                [0.061,0.020,0.035,0.055,0.099, 0.14,0.22,0.25],\
                [0.044,0.022,0.044,0.11, 0.16, 0.34, 0.28,0.76],\
                [0.048,0.025,0.058,0.13, 0.24, 0.34, 0.45],\
                [0.084,0.058,0.11 ,0.24, 0.20, 0.28, 0.28,0.76]]
                
    rho_ner_d2=[[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30],\
                [0.043,0.026,0.033,0.074,0.16,0.37, 0.30, 0.37],\
                [0.054,0.023,0.030,0.043,0.079,0.086,0.17,0.22],\
                [0.038,0.017,0.032,0.062,0.17,0.19, 0.30, 0.37],\
                [0.041,0.019,0.031,0.062,0.11,0.16, 0.37, 0.22],\
                [0.037,0.021,0.031,0.049,0.098,0.20,0.16, 0.37],\
                [0.054,0.019,0.032,0.049,0.081,0.11,0.20, 0.22],\
                [0.040,0.021,0.040,0.085,0.15, 0.30, 0.25,0.52],\
                [0.043,0.024,0.051,0.10, 0.15, 0.30, 0.37],\
                [0.070,0.051,0.087,0.16,0.19, 0.25, 0.25, 0.52]]

    
    fig = plt.figure()
    pre_chain='12'
    #pre_chain='01'
    chains=['12a','12b','12c','12d','12e','12f','12g','12h','12i']#chains_191062
    chains=['22a','22b','22c','22d','22e','22f','22g','22h','22i']
    chains=['32a','32b','32c','32d','32e','32f','32g','32h','32i']
    chains=['42a','42b','42c','42d','42e','42f','42g','42h','42i']
    chains=['62a_2','62b_2','62c_2','62d_2','62e_2','62f_2','62g_2','62h_2','62i_2'] #chains_191062
    chains=['01a_3','01b_3','01c_3','01d_3','01e','01f_3','01g_3','01h_3','01i_3']#chains_191101
    chains=['01a_4','01b_4','01c_4','01d_4','01e_4','01f_4','01g_4','01h_4','01i_4_2']#chains_191101 7x7 grid
    chains=['02a_4','02b_4','02c_4','02d_4','02e_4','02f_4','02g_5','02h_4','02i_4']#chains_191101 7x7 grid lognorm
    #chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5']#chains_191101 3x3 grid scaled
    #chains=['01a_6','01b_6_3','01c_6','01d_6_2','01e_6_2','01f_6','01g_6_2','01h_6_2','01i_6_2']#chains_191101 11x11 grid
    #chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i'] #chains_1912 #includeing maskregion
    chains=['01a_2_1','01b_2_1','01c_2_1','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2'] #1912 exclding peterinclde match
    #chains=['01a_2','01b_2','01c_2_1','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2'] #chains_1912 excluding peter
    #chains=['01a_3','01b_3','01c_3','01d_3','01e_3','01f_3_1','01g','01h','01i'] #chains_1912 including peter
    #chains=['01a_4_1','01b_4_1','01c_4','01d_4','01e_4','01f_4_1','01g_4','01h_4','01i_4'] #chains_1912 no mass selection
    chains=['02a','02b_2','02c','02d','02e','02f','02g','02h','02i'] #lgnorm #chains_1912 excluding peter
    chains=['01a_1','01b','01c','01d','01e','01f_1','01g','01h','01i'] #chains_2001 DR4
    chains4=['01a_4','01b_2','01c_2','01d_2','01e_2','01f_2','01g','01h_2','01i_2'] #chains_2001 DR4 remove phi2
    #chains=['01a_9','01b_9','01c_9','01d_9','01e_9','01f_9','01g_9','01h_9','01i_9','01j_9'] #chains_2001 DR4 dpl_dpl no mass selection
    chains=['01a_7','01b_7','01c_7','01d_7','01e_7','01f_7','01g_7','01h_7','01i_7','01j_7'] #chains_2001 DR4 dpl_dpl
    chains4=['01a_6','01b_6','01c_6','01d_6','01e_6_3','01f_6','01g_6','01h_6','01i_6','01j_6'] #chains_2001 DR4 dpl_dpl Lmin=15
    #chains=['01a_6_3','01b_6_3','01c_6_3','01d_6_2','01e_6_2','01f_6_2','01g_6_2','01h_6_3','01i_6_3','01j_6_3'] #chains_2001 DR4 dpl_dpl Lmin=21
    chains2=['02a_z_6x','02b_z_6x','02c_z_6x','02d_z_6x','02e_z_6x','02f_z_6x','02g_z_6x','02h_z_6x','02i_z_6x','02j_z_6x']#Novak individual model
    chains2=['02a_z_6x_1','02a_z_6x_2','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x']#Novak individual model newz
    #chains=['02a_4','02b_3','02c_4','02d_4','02e_4','02f_4','02g_4','02h_4','02i_4','02j_3'] ##chains_2001 DR4 logn_dpl Lmin=19
    #chains=['01a_9_2','01b_9_2','01c_9_2','01d_9_2','01e_9_2_1','01f_9_2','01g_9_2_2','01h_9_2','01i_9_2_1','01j_9_2_1'] #chains_2001 DR4 dpl_dpl no mass selection fixed zmean
    
    #chains=['01a_6_1','01a_8_2','01b_6_3','01c_6_3','01d_6_2','01e_6_2','01f_6_2','01g_6_2','01h_6_3','01i_6_3','01j_6_3'] #chains_2001 DR4 dpl_dpl Lmin=19 newz
    #chains=['02a_4','02b_4','02c_4','02d_4','02e_4','02f_4','02g_4','02h_4','02i_4','02j_3'] ##chains_2001 DR4 logn_dpl Lmin=21 newz  
    #chains=['02a_z_6x','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x']#Novak individual model newz
    
    chains=['01a_1','01b_1','01c_1','01d_1','01e_1','01f_1','01g_1','01h_1','01i_1','01j_1'] #95% mass sample
    chains=['02a_z_9x','02b_z_9x','02c_z_9x','02d_z_9x','02e_z_9x','02f_z_9x','02g_z_9x','02h_z_9x','02i_z_9x','02j_z_9x']#95 mass limit
    
    chains=['01a_2','01b_2','01c_2','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2','01j_2'] #50% mass sample
    chains=['02a_z_9x_1','02b_z_9x_1','02c_z_9x_1','02d_z_9x_1','02e_z_9x_1','02f_z_9x_1','02g_z_9x_1','02h_z_9x_1','02i_z_9x_1','02j_z_9x_1']#50 mass limi1
    
    chains2=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5','01j_5'] #stellar mass>10.2 dpl
    chains2=['02a_5','02b_5','02c_5','02d_5','02e_5','02f_5','02g_5','02h_5','02i_5','02j_5'] #stellar mass>10.2 dpl
    chains=['02a_z_7x','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x'] #stellar mass>10.2
    
    chains=['01a_8','01b_8','01c_8','01d_8','01e_8','01f_8','01g_8','01h_8','01i_8','01j'] #stellar mass<10. dpl
    chains4=['02a_3','02b_3','02c_3','02d_3','02e_3','02f_3','02g_3','02h_3','02i_3','02j_3'] #stellar mass<10. logn
    chains3=['02a_z_5x','02b_z_5x','02c_z_5x','02d_z_5x','02e_z_5x','02f_z_5x','02g_z_5x','02h_z_5x','02i_z_5x','02j_z_5x']#stellar mass limit mass<10
     #chains3=['02a_z_5f','02b_z_5f','02c_z_5f','02d_z_5f','02e_z_5f','02f_z_5f','02g_z_5f','02h_z_5f','02i_z_5f','02j_z_8x']#stellar mass limit mass<10
    
    #chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i','01j'] #dpl #chains_2001 DR4 newz new_area
    chains=['02a','02b','02c','02d','02e','02f','02g','02h','02i','02j'] #lgnorm #chains_2001 DR4 newz new_area
    chains=['02a_z_8x','02b_z_8x','02c_z_8x','02d_z_8x','02e_z_8x','02f_z_8x','02g_z_8x','02h_z_8x','02i_z_8x','02j_z_8x']#fixed model newz area
    chains=['02a_z_8x_6','02b_z_8x_6','02c_z_8x_6','02d_z_8x_6','02e_z_8x_6','02f_z_8x_6','02g_z_8x_6','02h_z_8x_6','02i_z_8x_6','02j_z_8x_6']#fixed cut data more bins _el lmin lose
    chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_3']#fixed cut data more bins _el lmin lose
    
    #chains3='02z_31_3' #Novak evolution fit
    #chains3='02z_31_5' #Fixed evolution newz, area
    #chains3='02z_31_6' #Fixed evolution newz, area, 10.2 mass lim
    #z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
    #z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
    print expt.parameters
    n,m=3,3
        
    #print xrecon
    #print yrecon
    #sys.exit()
    
    z_new=True
    Replot=False
    z_nov = [0.31, 0.5, 0.69, 0.9, 1.16, 1.44, 1.81, 2.18, 2.81, 3.71, 4.83]
    if z_new:
        z =[0.1,0.3,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]
        z=[0.1,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]#oldest ;)
        #chains=['11a','11b','11c_3','11d_3','11e_3','11f_3','11g_3','11h','11i','11j', '11k', '11l' ,'11m', '11n']#chains_201111
    else:
        z=[0.1,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]#oldest ;)
    
    
    #ana3=pymultinest.analyse.Analyzer(ncols-1,\
    #    outputfiles_basename=os.path.join('chains_2002%s'%chains3,outstem))
    #drawmap3=ana3.get_best_fit()['parameters']
    #params3 = drawmap3
    
    #parameters2= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']
    #parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN', 'LMAX']
    #parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN_1', 'LMIN_2', 'LMIN_3', 'LMIN_4', 'LMIN_5', 'LMIN_6', 'LMIN_7', 'LMIN_8', 'LMIN_9', 'LMIN', 'LMAX']
    #parameters4=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
    parameters2= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']
    parameters3= ['noise', 'LMIN', 'LMAX', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
    parameters4= ['noise', 'LMIN', 'LMAX', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSIGMA'] #lognorm
    parameters2=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
    parameters3= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']#fixed
    parameters2=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSIGMA']
        
    pass1=False
    for num in range(1,11):
    #num = 1.
     print num
     if num==2:pass1=False
     print 'new num ',num
     z_min,z_max, z_m = get_z(num)
     dl = get_dl(z_m)
     Vmax = get_Vmax(z_min,z_max)
     dsdl = get_dsdl(z_m,dl)

     #z_m = (z_min + z_max)/2                       
     dl = get_dl(z_m)
    
     print z_min,z_max
     print expt.kind
     area = SURVEY_AREA*sqDeg2sr 
     area1 = SURVEY_AREA*sqDeg2sr
     print SURVEY_AREA
     ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains[num-1]),outstem))
     drawmap=ana.get_best_fit()['parameters']
     drawmid=ana.get_stats()['marginals']
     params = drawmap
     
     ana2=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_20%s%s'%('11',chains2[num-1]),outstem))
     drawmap2=ana2.get_best_fit()['parameters']
     params2 = drawmap2
     
     ana3=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains3[num-1]),outstem))
     drawmap3=ana3.get_best_fit()['parameters']
     params3 = drawmap3
     
     ana4=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_20%s%s'%('11',chains4[num-1]),outstem))
     drawmap4=ana4.get_best_fit()['parameters']
     params4 = drawmap4

  
     
     settingf='chains_20%s%s/bayestack_settings.py'%(pre_chain,chains[num-1])
     f = open(settingf, 'r')
     mod = f.readlines()[31].split()[0]
     print mod
     if 'logn' in mod:
        if 'dpl' in mod:
            model='LFlognorm_dpl'
        elif '_el' in mod:
            model='LFevol_logn_el' 
        elif 'pl_' in mod:
            model='LFpl_lognorm'
        elif 'phi' in mod:
            model='LFevol_phi_logn_mat'
        elif 'mat' in mod:
            model='LFevol_logn_mat'
        else:
            model='LFlognorm'
     elif 'LFdpl' in mod or 'LFpl' in mod:
        if 'LFdpl_dpl' in mod:
            model='LFdpl_dpl'
        elif '_pl' in mod:
            model='LFdpl_pl'
        elif 'LFpl_' in mod:
            model='LFpl_dpl'
        elif 'LFpl' in mod:
            model='LFpl'
        else:
            model = 'LFdpl'
     
     with open('chains_20%s%s/params.tex'%(pre_chain,chains[num -1])) as f:
        parameters=[line.split(None,1)[0] for line in f]
    #model='LFdpl_pl'
     print parameters
     print model
     print 'LSIGMA' in parameters
     '''
     if model in ['LFsch','LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl','LFpl_lognorm']:
        Lnorm=params[parameters.index('LNORM')]
        Lstar=params[parameters.index('LSTAR')]
        Lslope=params[parameters.index('LSLOPE')]
        #Lzevol=params[parameters.index('LZEVOL')]
     if model in ['LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl']:
        Lslope2=params[parameters.index('LSLOPE2')]
     if model in ['LFlognorm','LFpl', 'LFdpl', 'LFdpl_pl','LFpl_dpl', 'LFlognorm_dpl','LFpl_lognorm','LFdpl_dpl']:
        Lnorm_2=params[parameters.index('LNORM_2')]
        Lstar_2=params[parameters.index('LSTAR_2')]
        Lslope_2=params[parameters.index('LSLOPE_2')]

     if model in ['LFdpl_dpl','LFdpl']:
        Lslope2_2=params[parameters.index('LSLOPE2_2')]
    
     if model in ['LFlognorm','LFlognorm_dpl','LFpl_lognorm']:
        Lsigma = params[parameters.index('LSIGMA')]
     '''
     Lmin=params[parameters.index('LMIN')]
     Lmax=params[parameters.index('LMAX')]
     
     #Lmin=params[expt.parameters.index('LMIN')]
     #Lmax=params[expt.parameters.index('LMAX')]
     

     SMIN  = get_sbins(numpy.power(10,Lmin),z_m,dl)*1e6
     SMAX  = get_sbins(numpy.power(10,Lmax),z_m,dl)*1e6
     sigma,fsigma,Lmin = numpy.log10(get_Lbins([SURVEY_NOISE,18.9, Lmin],z_m,dl,'muJy')*(1.4/3)**(-.7))
     sigma2,fsigma2 = numpy.log10(get_Lbins([450,2400],z_m,dl,'muJy'))
     print z_m,dl, sigma,fsigma
     print SURVEY_NOISE,SURVEY_NOISE*5,SURVEY_AREA
     
     s=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%(pre_chain,chains[num -1]))
     s2=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%('11',chains2[num -1]))
     s3=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%(pre_chain,chains3[num -1]))
     s4=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%('11',chains4[num -1]))
     xrecon=s[:-1,0]; yrecon=s[:-1,1]
     yrecon_d=s[:-1,2]; yrecon_u=s[:-1,3]
     yrecon_rms = s[:-1,4]
     yrecon_avr = s[:-1,5]
     yrecon_rms_down = yrecon_rms
    
     xreco = get_Lbins(xrecon,z_m,dl,'muJy')#*(1.4/3)**(-.7)
     yreco = yrecon
     lin_yrecon = numpy.log10(yreco)
     lin_xrecon = numpy.log10(xreco)
     
     xrecon2=s2[:-1,0]; yrecon2=s2[:-1,1]
     yrecon_d2=s2[:-1,2]; yrecon_u2=s2[:-1,3]
     lin_xrecon2 = numpy.log10(get_Lbins(xrecon2,z_m,dl,'muJy'))
     
     xrecon3=s3[:-1,0]; yrecon3=s3[:-1,1]
     yrecon_d3=s3[:-1,2]; yrecon_u3=s3[:-1,3]
     lin_xrecon3 = numpy.log10(get_Lbins(xrecon3,z_m,dl,'muJy'))
     
     xrecon4=s4[:-1,0]; yrecon4=s4[:-1,1]
     yrecon_d4=s4[:-1,2]; yrecon_u4=s4[:-1,3]
     lin_xrecon4 = numpy.log10(get_Lbins(xrecon4,z_m,dl,'muJy'))
    
    
     bins2 = numpy.arange(15.,25.,0.4)
     bins  = numpy.arange(fsigma-0.4,29.2,0.4)#bin +0.2
     if not os.path.isfile('cos_data/cos_s%s_LF.el'%num) and z_new or not os.path.isfile('cos_data/cos_s%s_LF.el'%num) and not z_new or Replot:
      print 'doing all calculations'
      #l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
      #l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
      if z_new:
        L,z_L = open_anytype('cos_data/cos_s%s.txt'%num,(2,3), F='uJy',L=True,getz=True,band='S')
        print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
      else:
        L,z_L = open_anytype('cos_data/cos_s%s.txt'%num,(2,3), F='uJy',L=True,getz=True,band='S')
        print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
      L_c,z_c=open_anytype('cosmos_counter_parts_II.txt',(3,4),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
      z_c2,S_c2,L_c2 =numpy.loadtxt('cosmos_counter_parts_II.txt',unpack=True, usecols=(3,4,5))
      L_c2,z_c2=open_anytype('cosmos_comp_vla.el',(2,3),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
      L_c3,z_c3=open_anytype('cosmos_vla.el',(2,3),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
      L_c4,z_c4=open_anytype('/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',(-4,-1),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
      L_c5,z_c5=open_anytype('/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',(3,-1),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
      #L_c2=L_c2[(z_c2<z_max)&(z_c2 >z_min)]
      #S_c2=S_c2[(z_c2<z_max)&(z_c2 >z_min)]
      #z_c2=z_c2[(z_c2<z_max)&(z_c2 >z_min)]
    
    
    #rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
    #rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
      rho_3,er3,b3,n3 = calc_Vmax(calcu_zeff(z_L,L,1.15e-31),L,bins,z_max,z_min,area,table=True)
    #rho_4,er4 = calc_Vmax(99.,L,bins2,z_max,z_min,area)
      print 'COS15 VLA'
      rho_5,er5,b5,n5 = calc_Vmax(calcu_zeff(z_c,L_c,1.15e-31),L_c,bins,z_max,z_min,1.77*sqDeg2sr,table=True)
      print 'mass lim DR4-vla'
      rho_6,er6,b6,n6 = calc_Vmax(calcu_zeff(z_c2,L_c2,1.15e-31),L_c2,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
      print 'DR4-vla'
      rho_7,er7,b7,n7 = calc_Vmax(calcu_zeff(z_c3,L_c3,1.15e-31),L_c3,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
      print 'cos-DR4-vla DR4 photo-z'
      rho_8,er8,b8,n8 = calc_Vmax(calcu_zeff(z_c4,L_c4,1.15e-31),L_c4,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
      print 'cos-DR4-vla COS15 photo-z'
      rho_9,er9,b9,n9 = calc_Vmax(calcu_zeff(z_c5,L_c5,1.15e-31),L_c5,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
      for i in range(len(b5)):
        print '%5.2f %7d %10d %13d %17d %11d'%(b5[i],n3[i],n7[i],n8[i],n9[i],n5[i])
      if z_new:
        f = open('cos_data/cos_s%s_LF_4.el'%num,'w') #I used LF_2 for the final results, _5 for stellar-mass 10.2
        print 'saving data for future use in cos_data/cos_s%s_LF_4.el'%num
      else:
        f = open('cos_data/cos_s%s_LF_old.el'%num,'w')
        print 'saving data for future use in cos_data/cos_s%s_LF.el'%num
      for i in range(len(bins)):
        f.write('%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n'%(bins[i], rho_3[i],er3[i],rho_5[i],er5[i],rho_6[i],er6[i],rho_7[i],er7[i],rho_8[i],er8[i],rho_9[i],er9[i]) )
      f.close()
    
      dl_c=get_dl(z_c2)
      L_c3=numpy.log10(get_Lbins(S_c2,z_c2,numpy.array(dl_c),'muJy')*(1.4/3.)**(-.7))
     else:
        if z_new:
            bins,rho_3,er3,rho_5,er5,rho_6,er6 ,rho_7,er7 ,rho_8,er8 ,rho_9,er9 = numpy.loadtxt('cos_data/cos_s%s_LF_2.el'%num, unpack=True)
            print 'Using stored RLF for the new z (The one with more z bins). If you want to calculate the plot the set "Replot=True"'
        else:
            bins,rho_3,er3,rho_5,er5,rho_6,er6 = numpy.loadtxt('cos_data/cos_s%s_LF_old.el'%num, unpack=True)  
            print 'Using stored RLF for the is old z bins. If you want to calculate the plot the set "Replot=True"'       
     #ssd = numpy.loadtxt('%s/data_cos_s%s.txt'%(outdir,num))
     ssd = numpy.loadtxt('cos_data/cos_s%s.txt'%(num), unpack=True, usecols=(-1,2))
     ssbin2 = ssd[:,0] #2 
     ssbin1 = ssd[:,2] #0
     Nbin2  =  ssd[:,3] 
     zbin3  = ssd[:,-1]
     dl1 = get_dl(z_min)
     dl2 = get_dl(z_max)
    #dl3 = get_dl(zbin3)
    
     Ncount = sum(Nbin2[fsigma>ssbin1])    
     Llmin = numpy.log10(get_Lbins([fsigma/numpy.sqrt(Ncount)],z_min,get_dl(z_min),'muJy'))[0]
     Llmin*=(1.4/3.)**(-.7)
     print Llmin
    #sys.exit()
       
     if z_new:
        
        try:pos=get_changed_multiplot(n,m,num)
        except:print 'changed pos doesnt work for ',num
        if num==4:
            pos = 10
        elif num==8:
            pos=11
        elif num==10:
            pos=11
        elif num==11:
            pos=11
        if num>4 and num!=8:
            n_s = 1
            if num>8:
                n_s+=1
            pos=get_changed_multiplot(n,m,num-n_s)
        ax=fig.add_subplot(n+1,m,pos)
        print 'plot change', n, m ,pos  
     else:
        ax=fig.add_subplot(n,m,get_changed_multiplot(n,m,num))
    
     plt.rc('lines', linewidth=2)
     
     #plt.errorbar(bins,rho_1 ,yerr=er1,fmt='*r',label='detected extracted',markersize=11) #bin
     
     
     '''
     if model in ['LFdpl','LFdpl_dpl']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lslope2_2,Lnorm_2)
     elif model in ['LFpl','LFdpl_pl']:
        faint =lumfuncUtils.powerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lnorm_2)
     elif model in ['LFpl_dpl']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
     
     elif model =='LFevol_logn_mat':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=parameters,\
               family='LFevol_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3] 
     else:
        faint = lumfuncUtils.lognormpl(numpy.power(10,bins3), Lstar_2,Lslope_2,Lsigma,Lnorm_2)
     #phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)
    '''
    #phi_dpl
    #dpl  = 
     dm =0.4#05
     hm=0.
     #if num==1:hm=-0.25 
     ##if num>1:num-=1
     L_1 = numpy.power(10,bins3)/(1 + z_m)**(2.86-z_m*0.7)
     L_2 = numpy.power(10,bins3)/(1 + z_m)**(2.95-z_m*0.29)
     print z_m, z_nov[num-1]
     Novak17=lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3/2.5))
     Novak17_2=lumfuncUtils.lognormpl(numpy.power(10,bins3)/(1 + z_nov[num-1])**(3.16-z_nov[num-1]*0.32),\
        numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3/2.5))
     phi_agn_n=lumfuncUtils.doublepowerlaw(L_1,24.59,1.27,0.49,numpy.log10(10**(-5.5)))
     phi_tot_n = phi_agn_n+Novak17
     phi_tot_n2 = phi_agn_n+Novak17_2
     #phi_2 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params2,paramsList=parameters2,\
     #   family='LFdpl_dpl',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
     #phi_3 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params3,paramsList=parameters3,\
     #   family='LFlognorm',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
     phi = [lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=parameters,\
        family=model,inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
     #phi_2 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params2,paramsList=parameters2,\
     #   family='LFlognorm_dpl',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
     phi_3 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params3,paramsList=parameters3,\
        family='LFevol_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
     #faint_3 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params3,paramsList=parameters3,\
     #   family='LFevol_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]   
     phi_4 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params4,paramsList=parameters4,\
        family='LFlognorm',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0) for lL in bins3]
     #sys.exit()
     
     ##plt.plot(bins3+hm,numpy.log10(faint)-dm,'--k',linewidth=4 ,label='SF MAP Indiv. Novak 2018 fit')
     #plt.plot(numpy.log10(10**bins3),numpy.log10(phi_dpl)-dm,'--c', label='agn')
     #plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7))+hm,numpy.log10(faint)-dm,'--b' ,label='faint-end')   
     #plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP No mass-limit',linewidth=1.8)
     
     plt.errorbar(bins[2:],rho_3[2:] ,yerr=er3[2:],fmt='hb', fillstyle= 'none', markeredgewidth=3 ,label=r'$\rm{1/V_{max}}$',markersize=12) #bin
     #plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP Model A $M/M_\odot > 10$',linewidth=1.8)
     #plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP $\rm{Model}$ $\rm{C}$ Individual',linewidth=1.8)
     #plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP $\rm{Model}$ $\rm{C}$ 50% mass limit',linewidth=1.8)
     ##plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-k', label='MAP Fixed Individual fit',linewidth=2.)
     ##plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-k', label=r'$\rm{Total}$ $\rm{MAP}$ $\rm{Model}$ $\rm{C}$',linewidth=3)
     #plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi) -0.4 ,fmt='-k', label=r'$\rm{Total}$ $\rm{MAP}$ $\rm{Model}$ $\rm{C}$',linewidth=3)
     #plt.plot(bins3+hm,numpy.log10(faint)-dm,'-.b' ,label='SF MAP $\rm{Model}$ $\rm{C}$ Individual $M > 10 M/M_\odot$')
     #plt.plot(bins3+hm,numpy.log10(faint_3)-dm,'-g' ,label='SF MAP $\rm{Model}$ $\rm{C}$ PLE $M > 10 M/M_\odot$')
     #plt.plot(bins3+hm,numpy.log10(faint)-dm,'-.b' ,label='SF MAP $\rm{Model}$ $\rm{B}$')
     
     #if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_4) -0.4,fmt='--',color='orange',linewidth=3,label=r'$8.5<\log_{10}(M/M_\odot) <10$ $\rm{MAP}$  $\rm{Model}$ $\rm{B}$ ')
     #if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_3) -0.4,fmt='.',color='purple',linewidth=3,label=r'$8.5<\log_{10}(M/M_\odot) <10$ $\rm{MAP}$  $\rm{Model}$ $\rm{C}$ ')
     #plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_2) -0.4,fmt='-.r',linewidth=5.,label=r'$\log_{10}(M/M_\odot) > 10$ $\rm{Model}$ $\rm{B}$')
     #phi_5=numpy.array(phi_2)+numpy.array(phi_3)
     #plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_5) -0.4,fmt='-.b',linewidth=3.,label=r'$\rm{Total}$ $\rm{Model C+B}$')
     
     
     #if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_4) -0.4,fmt='--',color='orange',linewidth=3,label=r'$10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{MAP}$  $\rm{Model}$ $\rm{B}$ ')
     #if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_3) -0.4,fmt='.',color='purple',linewidth=3,label=r'$10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{MAP}$  $\rm{Model}$ $\rm{C}$ ')
     #plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_2) -0.4,fmt='-.r',linewidth=5.,label=r'$M > 10^{10} M_\odot$ $\rm{Model}$ $\rm{B}$')
     
     #plt.errorbar(lin_xrecon+hm,numpy.log10(yrecon_avr)-dm,fmt='-c', label = 'Average')
     #plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_4) -0.4,fmt='--k',linewidth=3,label='MAP with mass limit')
     ##plt.errorbar(bins,rho_5 ,yerr=er5,fmt='^k',label='VLA-COSMOS2015',markersize=10) #bin
     ##plt.errorbar(bins,rho_8 ,yerr=er8,fmt='sc',label='VLA-COSMOS2015 in DR4 using DR4 z',markersize=10) #bin
     ##plt.errorbar(bins,rho_9 ,yerr=er9,fmt='*m',label='VLA-COSMOS2015 in DR4 using COS15z',markersize=14) #bin
     ##plt.errorbar(bins,rho_6 ,yerr=er6,fmt='ok',label='DR4 detected',fillstyle='none',markersize=14) #bin
     ##plt.errorbar(bins,rho_7 ,yerr=er6,fmt='dg',label='DR4 detected - Full NIR sample',markersize=12) #bin
     #if not pass1:plt.errorbar(numpy.array(L_n2[num-1]),numpy.array(rho_n2[num-1]) -0.4,xerr=[L_ner_d2[num-1],L_ner_u2[num-1]],yerr=[rho_ner_d2[num-1],rho_ner_u2[num-1]], fmt='sr',fillstyle='none',ecolor='k',markeredgewidth=1.5, label='Total Novak+2018')
     #plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_tot_n),fmt='--k',linewidth=3,label='Total Novak+2018')
     #plt.errorbar(bins3, numpy.log10(Novak17_2),fmt='.r',linewidth=0.4,label='SF Novak + 2017')
     #if num <9:
        #plt.errorbar(mcalpine[mcalpine_panels[num] -1][:,0] ,mcalpine[mcalpine_panels[num] -1][:,1]-0.4 ,yerr=mcalpine[mcalpine_panels[num] -1][:,2],fmt='>c', fillstyle= 'none', markeredgewidth=2 ,label='Total McAlpine+2013',markersize=12)
     #print padovani[num-1]
     #if num <9 and num!=2:
        #plt.errorbar(padovani[padovani_panels[num] -1][:,0],padovani[padovani_panels[num] -1][:,1] -(9-numpy.log10(10)),fmt='dk', fillstyle= 'none', markeredgewidth=1 ,label='padovani+2015 RL AGN',markersize=13) 
     #plt.errorbar(bins3, numpy.log10(Novak17)-0.4,fmt='--b',label='SF galaxies Novak + 2017')
     
     #plt.plot(bins3,numpy.log10(phi_agn_n),'--g', label='local agn nov')
     ##if num>1:num+=1
     
     #plt.plot(L_s, numpy.log10(rho_1), '*m',label='bins LF',markersize=8)
     #plt.errorbar(bins,rho_5 ,yerr=er5,fmt='*r',label='Total',markersize=10) #bin
     #plt.errorbar(bins,rho_6 ,yerr=er6,fmt='>g',label='Total LF',markersize=10) #bin
     #plt.fill_between(lin_xrecon+hm,numpy.log10((yrecon_d))-dm,numpy.log10(yrecon_u) -dm, color='k',label='95% Model C Individaul' ,alpha=0.2)
     #plt.fill_between(lin_xrecon+hm,numpy.log10((yrecon_d))-dm,numpy.log10(yrecon_u) -dm, color='k',label='95% Model A $M/M_\odot > 10$' ,alpha=0.2)
     plt.fill_between(lin_xrecon+hm,numpy.log10((yrecon_d))-dm,numpy.log10(yrecon_u) -dm, color='k',label=r'$\rm{95%}$  $\rm{Total}$ $\rm{Model}$ $\rm{C}$' ,alpha=0.5)
     plt.fill_between(lin_xrecon2+hm,numpy.log10((yrecon_d2))-dm,numpy.log10(yrecon_u2) -dm, color='r',label=r'$\rm{95%}$  $\log_{10}(M/M_\odot) > 10$ $\rm{Model}$ $\rm{B}$' ,alpha=0.5)
     plt.fill_between(lin_xrecon4+hm,numpy.log10((yrecon_d4))-dm,numpy.log10(yrecon_u4) -dm, color='orange',label=r'$\rm{95%}$  $8.5<\log_{10}(M/M_\odot) <10$ $\rm{Model}$ $\rm{B}$ ' ,alpha=0.7)
     plt.fill_between(lin_xrecon3+hm,numpy.log10((yrecon_d3))-dm,numpy.log10(yrecon_u3) -dm, color='purple',label=r'$\rm{95%}$  $8.5<\log_{10}(M/M_\odot) <10$ $\rm{Model}$ $\rm{C}$ ' ,alpha=0.7)
     #plt.errorbar(bins[1:],rho_3[1:] ,yerr=er3[1:],fmt='h', fillstyle= 'none', markeredgewidth=3 ,label='extracted fluxes (No mass limit)',markersize=12) #bin
    
    
     #plt.fill_between(lin_xrecon2+hm,numpy.log10((yrecon_d2))-dm,numpy.log10(yrecon_u2) -dm, color='r',label=r'$\rm{95%}$  $M > 10^{10} M_\odot$ $\rm{Model}$ $\rm{B}$' ,alpha=0.5)
     #if num!=10:plt.fill_between(lin_xrecon4+hm,numpy.log10((yrecon_d4))-dm,numpy.log10(yrecon_u4) -dm, color='orange',label=r'$\rm{95%}$  $10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{Model}$ $\rm{B}$ ' ,alpha=0.7)
     #if num!=10:plt.fill_between(lin_xrecon3+hm,numpy.log10((yrecon_d3))-dm,numpy.log10(yrecon_u3) -dm, color='purple',label=r'$\rm{95%}$  $10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{Model}$ $\rm{C}$ ' ,alpha=0.7)
    
     
     plt.ylim(-8.,-1.6)
     plt.axvline(fsigma,color='g',linestyle='dashed',label=r'$\rm{5\sigma}$')
     #plt.axvline(sigma,color='g')
     #plt.axvline(Lmin,color='r',linewidth=2)
     ##z_new=True

     if num<2 or num<6 and num>3 or num>6 and num<9:
        plt.xticks( visible=False)
        plt.ylim(-8.,-1.6)
     
     if num<5:
        plt.xlim(18.6,25.7)
        plt.text(18.8,-7.,r'$\rm{%.1f < z < %.1f}$ %s$\rm{z_{MED} = %.2f}$'%(z_min,z_max,'\n',z_m),fontsize = 28 ) # \n $M_i$ < -22
     elif num<9:
        plt.xlim(20.,27.2)
        plt.text(20.2,-7.,r'$\rm{%.1f < z < %.1f}$ %s$\rm{z_{MED} = %.2f}$'%(z_min,z_max,'\n',z_m),fontsize = 28 ) # \n $M_i$ < -22
     else:
        plt.xlim(20.6,27.8)   
        plt.text(21.,-7.,r'$\rm{%.1f < z < %.1f}$ %s$\rm{z_{MED} = %.2f}$'%(z_min,z_max,'\n',z_m),fontsize = 28 ) # \n $M_i$ < -22
        
     
     plt.tick_params(axis='both',which = 'minor',width=1)
     plt.tick_params(axis='both',which = 'major', width=1.5)
     ax.xaxis.set_minor_locator(AutoMinorLocator())
     #ax.yaxis.set_minor_locator(AutoMinorLocator())
     #plt.axes().xaxis.set_minor_locator(AutoMinorLocator())
     #plt.axes().yaxis.set_minor_locator(AutoMinorLocator())
     #plt.minorticks_on()
    
     if num==4:
    	plt.xticks([19,20,21,22,23,24,25],fontsize=30)
    	plt.xticks( visible=True)
    	handles,labels=plt.gca().get_legend_handles_labels()
    	print labels
    	print len(labels)
    	#order=[0, 5, 6,1, 2 ,3 ,4, 7, 8, 9]#model_C
    	order=[0,5,1,4,2,3]
    	#plt.legend(frameon=False).draggable()
    	plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],frameon=False).draggable()
     if num==8:
       plt.xticks([21,22,23,24,25,26,27],fontsize=30)
       plt.xticks( visible=True)
     if num == 10:
    	plt.xticks([21,22,23,24,25,26,27],fontsize=30)
    	plt.xticks( visible=True)
    	
     if num==1 or num ==2 or num==3 or num==4:
         plt.yticks([-2.,-3,-4,-5, -6, -7], fontsize=30)
         if num==4:
            plt.yticks([-2.,-3,-4,-5, -6, -7, -8], fontsize=30)
         continue
     plt.yticks(visible=False) 
     '''#z_new=False
          print 'Is this still num ',num
     if num<2 or num<6 and num>3 or num>6 and num<9:
        plt.xticks( visible=False)
        plt.ylim(-7.6,-1.6)
     
     if num<4:
        plt.xlim(19,25.8)
        plt.text(19.2,-7.,r'$\rm{%.1f < z < %.1f}$'%(z_min,z_max),fontsize = 32 ) # \n $M_i$ < -22
     elif num<7:
        plt.xlim(20.8,27.2)
        plt.text(21.,-7.,r'$\rm{%.1f < z < %.1f}$'%(z_min,z_max),fontsize = 33 ) # \n $M_i$ < -22
     else:
        plt.xlim(21.3,27.8)   
        plt.text(21.5,-7.,r'$\rm{%.1f < z < %.1f}$'%(z_min,z_max),fontsize = 32 ) # \n $M_i$ < -22
        
    
     if num==3:
    	plt.xticks([19,20,21,22,23,24,25],fontsize=22)
     if num==6:
       plt.xticks([21,22,23,24,25,26,27],fontsize=22)
     if num == 9:
    	plt.xticks([22,23,24,25,26,27],fontsize=22)
    	plt.legend(frameon=False).draggable()
     if num==1 or num ==2 or num==3 or num==10:
         plt.yticks([-2.,-3,-4,-5, -6], fontsize=22)
         if num==3:
            plt.yticks([-2.,-3,-4,-5, -6, -7], fontsize=22)
         continue
     plt.yticks(visible=False) 
     '''
     
    
    fig.text(0.06,0.5,r'$\rm{log_{10}[\Phi /(Mpc^{-3}mag^{-1})]}$',fontsize=32, ha='center',va='center', rotation='vertical')
    fig.text(0.5,0.04,r'$\rm{log_{10}[L_{1.4}/(W}$ $\rm{Hz^{-1})]}$',fontsize = 32, ha='center',va='center')    
    #plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    #plt.tick_params(axis='both',which = 'major', labelsize=20,width =2)
    #plt.tick_params(axis='both',which = 'minor', labelsize=10, width=2)
    plt.subplots_adjust(hspace=0,wspace=0)
    #plt.xtick(fontsize=15)
    #plt.ytick(fontsize=15)
    
    #plt.ylim(-10.2,-5.8)
    #plt.ylim(-11,-5.5)
     #ax.xaxis.set_minor_locator(AutoMinorLocator())
     #ax.yaxis.set_minor_locator(AutoMinorLocator())
    #print truth['LMIN']
    
    plotf='%s/LF_recon_s1.pdf' % (outdir)
    plt.show()
    
	
    return 0
示例#3
0
def main():
    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module = importlib.import_module(setf)
    globals().update(set_module.__dict__)

    # Load prior ranges etc.
    expt = countModel(modelFamily, nlaws, setf, dataset, floatNoise)
    # Insert hacks here
    #plotRanges['C']=[0,200]
    print modelFamily
    if modelFamily == 'LFsch':
        labelDict= {'noise': '$\sigma$','LMIN':r'$\log_{10}[L_\rm{min}]$','LMAX':r'$\log_{10}[L_\rm{max}]$',\
           'LNORM':'$\log_{10}[\Phi_1^*]$','LSTAR': '$\log_{10}[L_1^*]$','LSLOPE':r'$\alpha$'}#,'LZEVOL':'$Z_{evol}$'}

    elif modelFamily == 'LFpl':
        print 'this..'
        labelDict= {'LMIN':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
           'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_1$','LSLOPE2_2':r'$\alpha_1$'}

    elif modelFamily == 'LFdpl':
        print 'this..'
        labelDict= {'LMIN':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
           'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_1$','LSLOPE2_2':r'$\beta_1$','LZEVOL':'$Z_{evol}$'}

    elif modelFamily == 'LFlognorm':
        labelDict= {'LMIN':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
                                'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_1$','LSIGMA':r'$\sigma_{LF}$','LZEVOL':'$Z_{evol}$'}

    elif modelFamily == 'LFlognorm_dpl':
        print 'this is the right model'
        labelDict= {'LMIN':r'$\log_{10}[L_{\rm{min}_1}]$','LMAX2':r'$\log_{10}[L_{\rm{max}_1}]$','LMIN2':r'$\log_{10}[L_{\rm{min}_2}]$','LMAX':r'$\log_{10}[L_{\rm{max}_2}]$',\
                    'LNORM':'$\log_{10}[\Phi_1^*]$','LSTAR': '$\log_{10}[L_1^*]$','LSLOPE':r'$\alpha_1$',\
                    'LSLOPE2': r'$\beta_1$', 'LNORM_2':'$\log_{10}[\Phi_2^*]$','LSTAR_2': '$\log_{10}[L_2^*]$','LSLOPE_2':r'$\alpha_2$','LSIGMA':r'$\sigma_{LF}$'}
        print labelDict

    elif modelFamily == 'LFdpl_pl':
        labelDict= {'noise': '$\sigma$','LMIN':r'$\log_{10}[L_{\rm{min}_1}]$','LMAX2': r'$\log_{10}[L_{\rm{max}_1}]$','LMIN2':r'$\log_{10}[L_{\rm{min}_2}]$','LMAX':r'$\log_{10}[L_{\rm{max}_2]$',\
                    'LNORM':'$\log_{10}[\Phi_1^*]$','LSTAR': '$\log_{10}[L_1^*]$','LSLOPE':r'$\alpha_1$',\
                    'LSLOPE2': r'$\beta_1$', 'LNORM_2':'$\log_{10}[\Phi_2^*]$','LSTAR_2': '$\log_{10}[L_2^*]$','LSLOPE_2':r'$\alpha_2$','LSLOPE2_2':r'$\beta_2$'}

    elif modelFamily == 'LFdpl_dpl':
        labelDict= {'noise': '$\sigma$','LMIN': r'$L_{\rm{min}_1}$','LMAX2': r'$L_{\rm{max}_1}$','LMIN2':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
                    'LNORM':'$\Phi_1^*$','LSTAR': '$L_1^*$','LSLOPE':r'$\alpha_1$',\
                    'LSLOPE2': r'$\beta_1$', 'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_2$','LSLOPE2_2':r'$\beta_2$'}

    elif modelFamily == 'LFevol_logn_L':
        labelDict= {'noise': '$\sigma$','LMIN':'$L_{min}$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
                    'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$', 'LMIN_1':'$L_{min1}$', 'LMIN_2':'$L_{min2}$' ,'LMIN_3':'$L_{min3}$' ,'LMIN_4':'$L_{min4}$' ,'LMIN_5':'$L_{min5}$' ,'LMIN_6':'$L_{min6}$' ,'LMIN_7':'$L_{min7}$' ,'LMIN_8':'$L_{min8}$' ,'LMIN_9':'$L_{min9}$'}

    elif modelFamily == 'LFevol':
        labelDict= {'noise': '$\sigma$','LMIN':'$L_{min}$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
                    'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
    elif modelFamily == 'LFevol_dpl':
        labelDict= {'noise': '$\sigma$','LMIN':'$S_{min} (\mu Jy)$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
                    'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
    elif modelFamily == 'LFevol_logn':
        labelDict= {'noise': '$\sigma$','LMIN':'$S_{min} (\mu Jy)$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
                    'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
    elif modelFamily == 'LFevol_phi_logn':
        labelDict= {'noise': '$\sigma$','LMIN':'$S_{min} (\mu Jy)$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$','A_B':r'$\alpha^D_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
                    'B_SF':r'$\beta_{SF}$','B_D':r'$\beta^D_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
    elif modelFamily == 'LFevol_logn_mat' or modelFamily == 'LFevol_logn_el':
        labelDict = {
            'noise': '$\sigma$',
            'LMIN': r'$\log_{10}[L_{\rm{min}}]$',
            'LMAX': r'$\log_{10}[L_{\rm{max}}]$',
            'A_agn': r'$\alpha_{AGN}$',
            'A_SF': r'$\alpha_{SF}$'
        }

    elif modelFamily == 'LFevol_dpl_s':
        labelDict = {
            'noise': '$\sigma$',
            'LMIN': '$S_{min} (\mu Jy)$',
            'LMAX': '$L_{max}$',
            'A_SF': r'$\alpha_{SF}$',
            'B_SF': r'$\beta_{SF}$'
        }

    elif modelFamily == 'LFevol_logn_s':
        labelDict = {
            'noise': '$\sigma$',
            'LMIN': '$S_{min} (\mu Jy)$',
            'LMAX': '$L_{max}$',
            'A_SF': r'$\alpha_{SF}$',
            'B_SF': r'$\beta_{SF}$'
        }
    elif modelFamily == 'LFevol_logn_mat':
        labelDict = {
            'noise': '$\sigma$',
            'LMIN': '$L_{min}$',
            'LMAX': '$L_{max}$',
            'A_agn': r'$L_{agn}$',
            'A_SF': r'$\L_{SF}$'
        }

    else:
        labelDict = dict((name, lables) for name in expt.parameters)

    plotTruth = dict((name, -99.0) for name in expt.parameters)
    if modelFamily in ['LFsch', 'LFdpl_pl', 'LFdpl_dpl', 'LFlognorm_dpl']:
        Lnorm = expt.parameters[expt.parameters.index('LNORM')]
        Lstar = expt.parameters[expt.parameters.index('LSTAR')]
        Lslope = expt.parameters[expt.parameters.index('LSLOPE')]
        #Lzevol=expt.parameters[expt.parameters.index('LZEVOL')]
    if modelFamily in ['LFdpl_dpl', 'LFlognorm_dpl']:
        Lslope2 = expt.parameters[expt.parameters.index('LSLOPE2')]
    if modelFamily in [
            'LFlognorm', 'LFdpl', 'LFpl', 'LFdpl_pl', 'LFlognorm_dpl',
            'LFdpl_dpl'
    ]:
        Lnorm = expt.parameters[expt.parameters.index('LNORM_2')]
        Lstar = expt.parameters[expt.parameters.index('LSTAR_2')]
        Lslope = expt.parameters[expt.parameters.index('LSLOPE_2')]
    if modelFamily in ['LFdpl_dpl']:
        Lslope2_2 = expt.parameters[expt.parameters.index('LSLOPE2_2')]

    noise = expt.parameters[expt.parameters.index('noise')]
    plotRanges = dict((k, v[-2:]) for (k, v) in expt.priorsDict.items())
    '''
    furniture={'TRUNCATE_phi':False,'TRUNCATE_phi_limit':2.0e7,\
               'Lstar':Lnorm ,'FONTSIZE':4,\
               'ROTATION':60.0,'FIGSIZE':(8.27,11.69), 'DPI':400,\
               'AXIS_LABEL_OFFSET':-0.3,'LOG_BINS':Lnorm,\
               'PADDING':0.05}
    '''
    furniture={'TRUNCATE_Lstar':False,'TRUNCATE_Lstar_limit':2.0e7,\
               'FONTSIZE':4,\
               'ROTATION':60.0,'FIGSIZE':(8.27,11.69), 'DPI':400,\
               'AXIS_LABEL_OFFSET':-0.3}

    #furniture={}
    # Load the data
    chain = numpy.loadtxt('%s/1-post_equal_weights.dat' % outdir)  #
    stats = fetchStats(outdir, expt.parameters, plotTruth)

    print 'start'
    #print chain[0][1:]
    chain = numpy.array([c[1:] for c in chain])
    #chain=numpy.delete(chain,-1,1)
    print expt.parameters
    #print expt.parameters.keys()
    remove = ['noise']  #,'LMAX2', 'LMAX']
    #parameters= [ele for ele in expt.parameters if ele not in remove]
    #chain =numpy.delete(chain,[1],1)
    #print chain
    #print parameters
    #print chains
    #print chain2
    #sys.exit()
    '''
    print chain[0]
    #sys.exit()
    #chain=pylab.loadtxt('%s/1-ev.dat'%outdir) 
    print expt.parameters,'oka'
    print modelFamily
    
    labels=[labelDict[param] for param in expt.parameters[1:] ]
    rc('xtick',labelsize=24)
    rc('ytick',labelsize=24)
    
    if corner_tri:
        figure= corner.corner(chains,bins=50,labels=labels,color='#0057f6',label_kwargs={'fontsize':32},plot_contours=True,fill_contours=True,contour_kwargs={'linewidth':100})#contour_kwargs={'levels',0.6})
    show()
    sys.exit()
    
    
    plotRanges={'noise': [148., 160],'LMIN': [20, 26],'LMAX':[23,30] ,'LMIN2': [20, 28],'LMAX2':[23,30],\
                'LNORM':[-10,2],'LSTAR': [24,30],'LSLOPE':[-5,5],'LSLOPE2':[-5,5], 'LNORM_2':[-10,2],\
                'LSTAR_2': [20,26],'LSLOPE_2':[-2,2],'LSLOPE2_2':[-5,1]}
    # Local plot
    line=False
    print labelDict
    autoscale=True
    print 'this is noise',noise
    title=''#r'$\rm{%s \mu Jy}$'%noise#%s - %s'%(outdir,dataset)
    
    truth=plotTruth
    bundle=contour_plot.contourTri(chain,\
                            line=line,\
                            labels=expt.parameters[1:],\
                            ranges=plotRanges,truth=truth,\
                            autoscale=autoscale,title=title,\
                            X_LABEL_OFFSET=-0.8,Y_LABEL_OFFSET=-0.7 ,\
                            labelDict=labelDict\
                            ,furniture=furniture)
    '''
    # Plot for publication
    print 'latex stuff'
    printLaTeX(expt.parameters, stats, dump=outdir)
    line = False
    autoscale = True
    title = ''
    truth = {
        'noise': 150,
        'LMIN': 21.7,
        'LMAX': 24.6,
        'LNORM_2': numpy.log10(1e-7),
        'LSTAR_2': 23.1,
        'LSLOPE_2': 1.,
        'LSLOPE2_2': 1.
    }

    truth = {par: stats[par][0] for par in expt.parameters}
    print 'This is the truth\n \n'
    print truth

    #plotRanges={'noise': [148., 160],'LMIN': [20, 26],'LMAX':[26,30] ,'LMIN2': [20, 30],'LMAX2':[20,30],\
    #            'LNORM':[-10,-2],'LSTAR': [24,30],'LSLOPE':[-5,5],'LSLOPE2':[-5,5], 'LNORM_2':[-10,-2],\
    #            'LSTAR_2': [20,26],'LSLOPE_2':[-5,5],'LSLOPE2_2':[-5,5]}
    extn = 'png'
    plotRanges = {}

    bundle=contour_plot.contourTri(chain,\
                            line=line,\
                            outfile='%s/triangle_%s.%s'%(outdir,outdir[-3:],extn),\
                            labels=expt.parameters[1:],\
                            ranges=plotRanges,truth=truth,\
                            autoscale=autoscale,title=title,\
                            X_LABEL_OFFSET=-0.55,Y_LABEL_OFFSET=-0.45,FONTSIZE=24.,\
                            binsize=50,labelDict=labelDict)
    #X_LABEL_OFFSET=-0.45,Y_LABEL_OFFSET=-0.35,FONTSIZE=15.,\ dpl_dpl
    #X_LABEL_OFFSET=-0.45,Y_LABEL_OFFSET=-0.35,FONTSIZE=20.,\ model C

    stats = fetchStats(outdir, expt.parameters, plotTruth)
    print stats
    printLaTeX(expt.parameters, stats, dump=outdir)

    return 0
示例#4
0
def main():
    """
    """

    # Import the settings variables
    print 'Settings file is %s' % param_file

    # Import the settings variables
    set_module = importlib.import_module(settingsf)
    globals().update(set_module.__dict__)

    # Set up the experiment
    #bins=[12.679592, 31.84969509, 80.00281696, 200.9579904, 504.78364938, 1010, 3267.95919972]# 0.4 bins
    bins = numpy.logspace(0, 3.01, 10)
    bins = list(bins)
    bins.append(2e3)
    bins1 = numpy.arange(17.2, 28.2, 0.1)
    print 'run expt'
    nbins = len(bins)

    z_m = redshifts[0]
    dl = get_dl(z_m)
    sbin1 = get_sbins(10**bins1, z_m, dl) * 1e6
    expt = countModel(modelFamily,
                      nlaws,
                      settingsf, [dataset],
                      floatNoise,
                      doRedshiftSlices=True,
                      mybins=sbin1)

    #expt=countModel(modelFamily,nlaws,settingsf,dataset,floatNoise,\
    #               doRedshiftSlices=True)#numpy.logspace(0,3.0,20))

    #print 'these are my beens', expt.bins
    #sys.exit()

    f = '%spost_equal_weights.dat' % outstem
    #f='%sev.dat'% outstem
    f = os.path.join(outdir, f)
    # Fetch best-fit parameters and calculate best-fit line
    plotTruth = dict((name, -99.0) for name in expt.parameters)

    # Load equally-weighted posterior samples
    x = numpy.genfromtxt(f)
    nsamp = x.shape[0]
    ncols = x.shape[1]  # The fifth [seventh] column is the posterior value
    # There must be a better way, but:
    #ncols = 14
    z = numpy.zeros((nsamp, ncols - 1 + expt.nbins))
    z[:, :-(expt.nbins - 1)] = x
    # Shift posterior values to end
    z[:, -1] = z[:, ncols - 1]  # Copy...
    z[:, ncols - 1] = 0.0  # ...and blank

    # Fetch best-fit parameters and calculate best-fit line
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap = ana.get_best_fit()['parameters']
    #print 'These is MAP',drawml
    #sys.exit()

    #summf=os.path.join(outdir,'1-summary.txt')
    #summary=numpy.genfromtxt(summf)[-1,:]
    #drawmap=summary[-(ncols+1):-2]
    print 'Now this is MAP', drawmap
    #drawmap = [  1.76792100e+02,   5.24202324e+18,   7.67284256e+24,   2.52157587e+1, 9.63136831e+22,   3.30233162e+00]

    if True:
        print '--> Calculating *ML* reconstruction'
        drawmap = drawmap

    # Convert drawmap into correct units etc.
    power = 2.5
    #drawmap[6]-=0.6
    #ymap=expt.evaluate(expt.convertPosterior(drawmap,power))
    ymap = expt.evaluate(drawmap)[0]  #,expt.binsMedian)
    #ymap=expt.realise(drawmap)
    #print 'ymap'
    #print ymap
    #print 'yy',['%e'%y for y in ymap]
    #sys.exit(0)

    for isamp in xrange(nsamp):
        #z[isamp,ncols-1:]=expt.evaluate(expt.convertPosterior(z[isamp,:],power))
        #z[isamp,:][6]-=0.6
        z[isamp,
          ncols - 1:] = expt.evaluate(z[isamp, :])[0]  #,expt.binsMedian)
        #print z[isamp,:]
        #sys.exit()

    # Blanking, 0.0 -> NaN
    z[numpy.where(z == 0.0)] = 'NaN'
    #sys.exit()

    # Save the raw reconstructions
    reconf = 'recon_raw.txt'
    reconf = os.path.join(outdir, reconf)
    recons = z[:, ncols - 1:]
    #numpy.savetxt(reconf,recons)

    # Generate stats here...
    s = numpy.zeros((len(expt.binsMedian), 8))
    s[:, 0] = expt.binsMedian

    #print '# ibin flux fit low high dlower dupper skew kurtosis'
    #print '%6s %12s %12s %12s %12s %12s %12s %12s %12s'%('bin', 'lower phi_rms','lower phi', 'l_err','ymap','u_err', 'upper phi','u_phi_rms', '2*rms')
    for ibin in xrange(len(expt.binsMedian)):
        x = recons[:, ibin]
        # Remove NaNs from stats vectors
        # http://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
        x = x[~numpy.isnan(x)]
        #ss=stats.bayes_mvs(x,alpha=0.68)[0]
        #x*=numpy.power(s[ibin,0]/1.0e6,2.5)
        #print bins1[ibin]
        #plt.hist(numpy.log10(x),bins=15)
        #plt.xlabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$',fontsize = 20)
        #plt.show()
        #tt=peak_confidence(x,bins=10)

        try:
            ss = numpy.zeros(3)
            ss[0], dlow, dhigh, ss[1], ss[2] = calculate_confidence(
                x, alpha=0.95, ret_all=True)
            #ss[0],dlow,dhigh,ss[1],ss[2]=calculate_confidence2(x,alpha=0.68,ret_all=True,\
            #                                                   value_central=ymap[ibin],\
            #                                                   truncate_edges=True)
        except:
            ss = numpy.nan * numpy.ones(3)
            print "didn't work for ", x
            continue
        #sys.exit()
        #tt=peak_confidence(x,bins=10)
        #ss*=numpy.power(s[ibin,0]/1.0e6,2.5)
        #print ss[0],tt
#        s[ibin,1]=ss[0]  # median
#        s[ibin,1]=tt     # peak
        s[ibin, 1] = ymap[ibin]  # MAP
        #print ymap
        s[ibin, 2] = ss[1]  # lower
        s[ibin, 3] = ss[2]  # upper
        s[ibin, 4] = 2 * numpy.std(
            x)  # (s[ibin,3]- s[ibin,2])/(2*numpy.std(x))# skewness
        s[ibin, 5] = ss[0]

        #print '%6.1f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f'%(bins1[ibin], numpy.log10((ymap[ibin] - s[ibin,4])), numpy.log10(ss[1]),numpy.log10(s[ibin,2]),numpy.log10(ymap[ibin]),numpy.log10(s[ibin,3]) ,numpy.log10(ss[2]), numpy.log10(s[ibin,4]+ ymap[ibin]), numpy.log10(s[ibin,4]) )
        #print ibin,s[ibin,0],s[ibin,1],dlow,dhigh,ss[1],ss[2],s[ibin,4],s[ibin,5]#,stats.skewtest(x)
        #sys.exit()

    # ...and output to file
    #sys.exit()
    rstatsf = 'recon_stats.txt'
    rstatsf = os.path.join(outdir, rstatsf)
    hdr = '# median_flux_uJy dnds_2p5_Jy1p5srm1 delta_dnds_2p5_lower_Jy1p5srm1 delta_dnds_2p5_upper_Jy1p5srm1 skewness kurtosis'
    fid = open(rstatsf, 'w')
    #print hdr
    #print s

    print '-> Writing stats (i.e. dnds2p5+/- v. S/uJy) to %s' % rstatsf
    fid.write('%s\n' % hdr)
    numpy.savetxt(fid, s)
    fid.close()
    print 'Finished.'

    return 0
示例#5
0
def main():
    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module = importlib.import_module(setf)
    globals().update(set_module.__dict__)

    expt = countModel(modelFamily,
                      nlaws,
                      setf, [dataset],
                      floatNoise,
                      doRedshiftSlices=True)
    #recon_expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
    #print expt.data
    #print expt.bins

    # Get MAP parameters
    print '\n' * 5
    print len(expt.parameters)
    ncols = len(expt.parameters)
    summf = os.path.join(outdir, '1-summary.txt')
    summary = numpy.genfromtxt(summf)[-1, :]
    drawmap = summary[-(ncols + 2):-2]
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap = ana.get_best_fit()['parameters']

    if dataset == 'sdss':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 20:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
        print datafiles[0][4:]
    elif dataset == 'cosmos':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 24:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
        print datafiles[0][8:]

    elif 'sim' in dataset:
        d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))

    #bins2 = numpy.arange(21.4,29.2,0.4)
    bins2 = numpy.arange(18.0, 29.2, 0.4)
    bins3 = numpy.arange(18., 29.2, 0.01)

    print len(expt.parameters)
    print 'this is 1st num ', num
    #sys.exit()
    params = drawmap  # [0]
    #for i in drawmap:
    #	params.append(i)
    print params
    print expt.parameters
    #novak 2017
    L_n = [21.77, 22.15, 22.46, 22.77, 23.09, 23.34]
    rho_n = [-2.85, -2.88, -3.12, -3.55, -4.05, -4.63]
    L_ner_u = [0.23, 0.18, 0.19, 0.2, 0.21, 0.28]
    L_ner_d = [1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
    rho_ner = [0.09, 0.03, 0.0355, 0.059, 0.1, 0.234]

    #novak 2018
    L_n2   =[[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56],\
             [22.30,22.61,22.96,23.38,23.80,24.10, 24.55,25.14],\
             [22.61,22.86,23.14,23.45,23.82,24.14, 24.40,24.71],\
             [22.85,23.16,23.69,24.24,24.80,25.31, 25.96,26.69],\
             [23.10,23.38,23.86,24.36,24.86,25.35, 25.94,26.36],\
             [23.32,23.57,23.94,24.32,24.67,25.06, 25.47,25.96],\
             [23.54,23.75,24.13,24.45,24.90,25.27, 25.74,26.10],\
             [23.73,23.99,24.57,25.10,25.68,26.18, 26.83,27.51],\
             [24.01,24.26,24.76,25.26,25.91,26.19, 27.45],\
             [24.30,24.56,24.80,25.13,25.37,25.80, 25.97,26.49]]


    L_ner_u2  =[[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03],\
                [0.11,0.20, 0.26, 0.25, 0.24, 0.35, 0.31, 0.15],\
                [0.08,0.15, 0.20, 0.22, 0.17, 0.18, 0.24, 0.28],\
                [0.072,0.33,0.38, 0.40, 0.48, 0.42, 0.41, 0.28],\
                [0.080,0.30,0.32, 0.32, 0.32, 0.33, 0.24, 0.34],\
                [0.068,0.22,0.24, 0.25, 0.29, 0.30, 0.28, 0.21],\
                [0.067,0.22,0.21, 0.26, 0.18, 0.17,0.075, 0.11],\
                [0.093,0.36,0.31, 0.30, 0.25, 0.28, 0.16,0.028],\
                [0.076,0.34,0.36, 0.38, 0.24, 0.47, 0.27],\
                [0.097,0.14,0.20, 0.16, 0.23, 0.092,0.22,0.026]]


    L_ner_d2  =[[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50],\
               [0.29, 0.21, 0.15, 0.16, 0.17, 0.06, 0.099,0.29],\
               [0.24, 0.17, 0.12, 0.10, 0.15, 0.15, 0.08,0.074],\
               [0.16, 0.24, 0.19, 0.17, 0.15, 0.09, 0.16, 0.33],\
               [0.30, 0.19, 0.18, 0.18, 0.18, 0.17, 0.26, 0.18],\
               [0.14, 0.18, 0.15, 0.14, 0.10, 0.095,0.11, 0.21],\
               [0.19, 0.14, 0.15, 0.11, 0.19, 0.19, 0.29, 0.28],\
               [0.40, 0.17, 0.21, 0.23, 0.27, 0.25, 0.37, 0.53],\
               [0.20, 0.17, 0.16, 0.14, 0.27, 0.042, 0.27],\
               [0.22, 0.16, 0.10, 0.14,0.072, 0.21, 0.08, 0.30]]

    rho_n2 =[[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07],\
             [-2.95,-3.17,-3.46,-4.24,-4.76,-5.41, -5.23,-5.44],\
             [-2.95,-3.11,-3.44,-3.81,-4.37,-4.48, -4.90,-5.14],\
             [-2.99,-3.24,-3.89,-4.54,-5.27,-5.33, -5.69,-5.89],\
             [-3.32,-3.51,-4.06,-4.74,-5.28,-5.43, -6.08,-5.70],\
             [-3.29,-3.54,-4.02,-4.45,-5.11,-5.56, -5.37,-6.07],\
             [-3.43,-3.61,-4.19,-4.56,-5.09,-5.34, -5.70,-5.75],\
             [-3.88,-3.98,-4.64,-5.33,-5.73,-6.27, -6.14,-6.77],\
             [-3.85,-4.28,-5.05,-5.60,-5.68,-6.44, -6.59],\
             [-4.43,-4.91,-5.46,-5.58,-5.89,-6.23, -6.26,-6.91]]

    rho_ner_u2=[[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34],\
                [0.048,0.027,0.036,0.089,0.17,0.45, 0.34, 0.45],\
                [0.062,0.024,0.032,0.048,0.096, 0.11,0.18,0.25],\
                [0.042,0.018,0.035,0.073,0.180, 0.20,0.34,0.45],\
                [0.045,0.019,0.033,0.072,0.15,  0.17,0.45,0.25],\
                [0.040,0.022,0.033,0.055,0.13,  0.22,0.17,0.45],\
                [0.061,0.020,0.035,0.055,0.099, 0.14,0.22,0.25],\
                [0.044,0.022,0.044,0.11, 0.16, 0.34, 0.28,0.76],\
                [0.048,0.025,0.058,0.13, 0.24, 0.34, 0.45],\
                [0.084,0.058,0.11 ,0.24, 0.20, 0.28, 0.28,0.76]]

    rho_ner_d2=[[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30],\
                [0.043,0.026,0.033,0.074,0.16,0.37, 0.30, 0.37],\
                [0.054,0.023,0.030,0.043,0.079,0.086,0.17,0.22],\
                [0.038,0.017,0.032,0.062,0.17,0.19, 0.30, 0.37],\
                [0.041,0.019,0.031,0.062,0.11,0.16, 0.37, 0.22],\
                [0.037,0.021,0.031,0.049,0.098,0.20,0.16, 0.37],\
                [0.054,0.019,0.032,0.049,0.081,0.11,0.20, 0.22],\
                [0.040,0.021,0.040,0.085,0.15, 0.30, 0.25,0.52],\
                [0.043,0.024,0.051,0.10, 0.15, 0.30, 0.37],\
                [0.070,0.051,0.087,0.16,0.19, 0.25, 0.25, 0.52]]

    fig = plt.figure()
    pre_chain = 12
    pre_chain = '02'

    chains = [
        '01a_1', '01b', '01c', '01d', '01e', '01f_1', '01g', '01h', '01i'
    ]  #chains_2001 DR4
    chains = [
        '01a_4', '01b_2', '01c_2', '01d_2', '01e_2', '01f_2', '01g', '01h_2',
        '01i_2'
    ]  #chains_2001 DR4 remove phi2
    chains = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']
    #z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
    #z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
    print expt.parameters
    n, m = 3, 3

    z_new = True
    Replot = False
    z_nov = [0.31, 0.5, 0.69, 0.9, 1.16, 1.44, 1.81, 2.18, 2.81, 3.71, 4.83]
    if z_new:
        z = [0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
        z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
        #chains=['11a','11b','11c_3','11d_3','11e_3','11f_3','11g_3','11h','11i','11j', '11k', '11l' ,'11m', '11n']#chains_201111
    else:
        z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]  #oldest ;)

    model = modelFamily
    if model in [
            'LFsch', 'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z',
            'LFlognorm_dpl', 'LFpl_lognorm'
    ]:
        Lnorm = params[expt.parameters.index('LNORM')]
        Lstar = params[expt.parameters.index('LSTAR')]
        Lslope = params[expt.parameters.index('LSLOPE')]
        #Lzevol=params[expt.parameters.index('LZEVOL')]
    if model in [
            'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFlognorm_dpl'
    ]:
        Lslope2 = params[expt.parameters.index('LSLOPE2')]
    if model in [
            'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFpl_dpl',
            'LFlognorm_dpl', 'LFpl_lognorm', 'LFdpl_dpl', 'LFdpl_dpl_z'
    ]:
        Lnorm_2 = params[expt.parameters.index('LNORM_2')]
        Lstar_2 = params[expt.parameters.index('LSTAR_2')]
        Lslope_2 = params[expt.parameters.index('LSLOPE_2')]

    if model in ['LFdpl_dpl', 'LFdpl', 'LFdpl_dpl_z']:
        Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]

    if model in ['LFlognorm', 'LFlognorm_dpl', 'LFpl_lognorm']:
        Lsigma = params[expt.parameters.index('LSIGMA')]

    if modelFamily in [
            'LFdpl_dpl_z', 'LFevol', 'LFevol_dpl', 'LFevol_logn',
            'LFevol_logn_L'
    ]:
        alpha_agn = params[expt.parameters.index('A_agn')]
        alpha_SF = params[expt.parameters.index('A_SF')]
        beta_agn = params[expt.parameters.index('B_agn')]
        beta_SF = params[expt.parameters.index('B_SF')]

    if modelFamily in ['LFevol_dpl_s', 'LFevol_logn_s']:
        alpha_SF = params[expt.parameters.index('A_SF')]
        beta_SF = params[expt.parameters.index('B_SF')]

    if modelFamily in ['LFevol_dpl_a', 'LFevol_logn_a']:
        alpha_agn = params[expt.parameters.index('A_agn')]
        beta_agn = params[expt.parameters.index('B_agn')]

    Lmin = params[expt.parameters.index('LMIN')]
    LMIN = params[expt.parameters.index('LMIN')]
    Lmax = params[expt.parameters.index('LMAX')]

    loglike = 0.
    loglike_nov = 0.
    print model

    bins3 = numpy.arange(15., 30.2, 0.2)
    #xbins  = numpy.arange(fsigma,26.2,0.4)#bin +0.2
    sbin4 = get_sbins(10**bins3, z[0], get_dl(z[0])) * 1e6
    expt2 = countModel(modelFamily,
                       nlaws,
                       setf, [dataset],
                       floatNoise,
                       doRedshiftSlices=True,
                       mybins=sbin4)
    sbin5 = medianArray(sbin4)
    phi_tot3 = expt2.evaluate(params)
    #print phi_tot3
    #print len(phi_tot3[0]), len(bins3)

    for num in range(1, 10):
        #num = 1.
        print num
        print 'new num ', num
        z_min, z_max, z_m = get_z(num, False)
        dl = get_dl(z_m)
        Vmax = get_Vmax(z_min, z_max)
        dsdl = get_dsdl(z_m, dl)

        #z_m = (z_min + z_max)/2
        dl = get_dl(z_m)

        print z_min, z_max, 'lmin ',

        SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
        SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
        sigma, fsigma, Lmin = numpy.log10(
            get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5, LMIN], z_m, dl, 'muJy')
            * (1.4 / 3)**(-.7))
        sigma2, fsigma2 = numpy.log10(get_Lbins([450, 2400], z_m, dl, 'muJy'))
        print Lmin
        print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA

        print num, '%s/recon_stats_%s.txt' % (outdir, chains[num - 1])
        s = numpy.loadtxt('%s/recon_stats_%s.txt' % (outdir, chains[num - 1]))
        print chains[num - 1]
        xrecon = s[:-1, 0]
        yrecon = s[:-1, 1]
        yrecon_d = s[:-1, 2]
        yrecon_u = s[:-1, 3]
        yrecon_rms = s[:-1, 4]
        yrecon_avr = s[:-1, 5]
        yrecon_rms_down = yrecon_rms

        #area = 6672*sqDeg2sr #143165.15  49996.49 59876.8861135
        sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
        #sbin4 = get_sbins(10**bins3,z_m,dl)*1e6

        xreco = get_Lbins(xrecon, z_m, dl, 'muJy')  #*(1.4/3)**(-.7)
        yreco = yrecon

        print SMIN, SMAX
        #sys.exit()
        lin_yrecon = numpy.log10(yreco)
        #print xrecon
        lin_xrecon = numpy.log10(xreco)

        #bins3 = numpy.arange(15.,30.,0.4)
        bins3 = numpy.log10(get_Lbins(sbin4, z_m, dl,
                                      'muJy'))  #*(1.4/3)**(-.7))
        #bins  = numpy.arange(fsigma,26.2,0.4)#bin +0.2
        #sbin4 = get_sbins(10**bins3,z_m,dl)*1e6
        if not os.path.isfile('cos_data/cos_s%s_LF.el' %
                              num) and z_new or not os.path.isfile(
                                  'cos_data/cos_s%s_LF_old.el' %
                                  num) and not z_new or Replot:
            print 'doing all calculations'
            #l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
            #l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
            if z_new:
                L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
                                      F='uJy',
                                      L=True,
                                      getz=True,
                                      band='S')
                print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
            else:
                L, z_L = open_anytype('cos_data/cos_s%s_old.txt' % num, (2, 3),
                                      F='uJy',
                                      L=True,
                                      getz=True,
                                      band='S')
                print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
            L_c, z_c = open_anytype('cosmos_counter_parts_II.txt', (3, 4),
                                    [z_min, z_max],
                                    F='uJy',
                                    L=True,
                                    getz=True,
                                    band='S')
            z_c2, S_c2, L_c2 = numpy.loadtxt('cosmos_counter_parts_II.txt',
                                             unpack=True,
                                             usecols=(3, 4, 5))
            L_c2 = L_c2[(z_c2 < z_max) & (z_c2 > z_min)]
            S_c2 = S_c2[(z_c2 < z_max) & (z_c2 > z_min)]
            z_c2 = z_c2[(z_c2 < z_max) & (z_c2 > z_min)]

            #rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
            #rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
            rho_3, er3 = calc_Vmax(calcu_zeff(z_L, L, 1.15e-31), L, bins,
                                   z_max, z_min, area)
            #rho_4,er4 = calc_Vmax(99.,L,bins2,z_max,z_min,area)
            rho_5, er5 = calc_Vmax(calcu_zeff(z_c, L_c, 1.15e-31), L_c, bins,
                                   z_max, z_min, area)
            rho_6, er6 = calc_Vmax(calcu_zeff(z_c2, 10**L_c2, 1.15e-31),
                                   10**L_c2, bins, z_max, z_min, area)
            if z_new:
                f = open('cos_data/cos_s%s_LF.el' % num, 'w')
            else:
                f = open('cos_data/cos_s%s_LF_old.el' % num, 'w')
            for i in range(len(bins)):
                f.write('%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n' %
                        (bins[i], rho_3[i], er3[i], rho_5[i], er5[i], rho_6[i],
                         er6[i]))
            f.close()

            dl_c = get_dl(z_c2)
            L_c3 = numpy.log10(
                get_Lbins(S_c2, z_c2, numpy.array(dl_c), 'muJy') *
                (1.4 / 3.)**(-.7))
        else:
            if z_new:
                #bins,rho_3,er3,rho_5,er5,rho_6,er6 = numpy.loadtxt('cos_data/cos_s%s_LF.el'%num, unpack=True)
                bins, rho_3, er3, rho_5, er5, rho_6, er6, rho_7, er7, rho_8, er8, rho_9, er9 = numpy.loadtxt(
                    'cos_data/cos_s%s_LF_2.el' % num, unpack=True)
                print 'Using stored RLF for the new z (The one with more z bins). If you want to calculate the plot the set "Replot=True"'
            else:
                bins, rho_3, er3, rho_5, er5, rho_6, er6 = numpy.loadtxt(
                    'cos_data/cos_s%s_LF_old.el' % num, unpack=True)
                print 'Using stored RLF for the is old z bins. If you want to calculate the plot the set "Replot=True"'
        ssd = numpy.loadtxt('%s/data_cos_s%s.txt' % (outdir, num))
        #ssd = numpy.loadtxt('cos_data/data_cos_s%s.txt'%(num))
        ssbin2 = ssd[:, 0]  #2
        ssbin1 = ssd[:, 2]  #0
        Nbin2 = ssd[:, 3]
        zbin3 = ssd[:, -1]
        dl1 = get_dl(z_min)
        dl2 = get_dl(z_max)
        #dl3 = get_dl(zbin3)

        Ncount = sum(Nbin2[fsigma > ssbin1])
        Llmin = numpy.log10(
            get_Lbins([fsigma / numpy.sqrt(Ncount)], z_min, get_dl(z_min),
                      'muJy'))[0]
        Llmin *= (1.4 / 3.)**(-.7)
        print Llmin
        dn = expt.realise(params)
        nov_real = expt.realise([2.3, 2.95, 2.86, -0.29, -0.7, 19.5, 26.0])
        #sys.exit()

        fig.add_subplot(n, m, get_changed_multiplot(n, m, num))

        plt.rc('lines', linewidth=2)

        #plt.errorbar(bins,rho_1 ,yerr=er1,fmt='*r',label='detected extracted',markersize=11) #bin
        plt.errorbar(bins[1:],
                     rho_3[1:],
                     yerr=er3[1:],
                     fmt='ob',
                     label='extracted',
                     markersize=8)  #bin
        '''
     if model in ['LFdpl','LFdpl_dpl']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lslope2_2,Lnorm_2)
     elif model in ['LFpl','LFdpl_pl']:
        faint =lumfuncUtils.powerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lnorm_2)
     elif model in ['LFpl_dpl']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
     elif model in ['LFdpl_dpl_z']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
     else:
        faint = lumfuncUtils.lognormpl(numpy.power(10,bins3), Lstar_2,Lslope_2,Lsigma,Lnorm_2)
        
    '''
        #phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)

        #phi_dpl
        #dpl  =
        dm = 0.4  #05
        hm = 0.

        Novak17 = lumfuncUtils.lognormpl(
            numpy.power(10, bins3) /
            (1 + z_nov[num - 1])**(3.16 - z_nov[num - 1] * 0.32),
            numpy.log10(1.85e21), 1.22, 0.63, numpy.log10(3.55e-3))
        Novak18=[lumfuncUtils.LF(S,z_m,0,0,dl,[2.3,2.86, 2.95, -0.70, -0.29,0.01,28.],\
                ['noise','A_agn','A_SF','B_agn','B_SF','LMIN','LMAX'],area=SURVEY_AREA,\
                family='LFevol_logn') for S in sbin4/1.0e6]

        phi_tot2 = [lumfuncUtils.LF(S,z_m,0,0,dl,params,expt.parameters,area=expt.survey.SURVEY_AREA,\
                                  family=modelFamily) for S in sbin5/1.0e6]

        L_1 = numpy.power(10, bins3) / (1 + z_m)**(2.86 - z_m * 0.7)
        L_2 = numpy.power(10, bins3) / (1 + z_m)**(2.95 - z_m * 0.29)
        L_4 = numpy.power(10, bins3) / (1 + z_m)**(alpha_SF + z_m * beta_SF)
        #L_3 = numpy.power(10,bins3)/(1 + z_m)**(alpha_agn+z_m*beta_agn)
        print z_m
        #print L_4
        #print L_3
        #sys.exit()
        phi_agn_n = lumfuncUtils.doublepowerlaw(L_1, 24.59, 1.27, 0.49,
                                                numpy.log10(10**(-5.5)))
        phi_sf_n = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22,
                                          0.63, numpy.log10(3.55e-3))
        phi_exp =[lumfuncUtils.LF(S,z_m,0,0,dl,[2.3,3, 5.58, -0.9, -1.99,0.01,28.],\
                ['noise','A_agn','A_SF','B_agn','B_SF','LMIN','LMAX'],area=SURVEY_AREA,\
                family='LFevol_logn') for S in sbin4/1.0e6]
        #Novak18= phi_agn_n+phi_sf_n
        #print 'this is Lminz', numpy.log10(10**18/(1 + z_m)**(alpha_SF+z_m*beta_SF))
        #phi_agn=lumfuncUtils.doublepowerlaw(L_3,24.59,1.27,0.49,numpy.log10(2.5*10**(-5.5)))
        phi_agn = 0
        phi_sf = lumfuncUtils.doublepowerlaw(L_4, 22.41, 2.4, 0.43,
                                             -3.50)  #0.33 2.97
        phi_sf = lumfuncUtils.lognormpl(L_4, numpy.log10(1.85e21), 1.22, 0.63,
                                        numpy.log10(3.55e-3))
        phi_tot = phi_agn + phi_sf
        #for k in range(len(phi_sf)):
        #   print bins3[k],numpy.power(10,bins3[k]), numpy.log10(L_4[k]), numpy.log10(L_3[k]), numpy.log10(phi_sf[k]), numpy.log10(phi_agn[k]),numpy.log10(phi_tot[k])
        #sys.ek
        #phi_sf =lumfuncUtils.lognormpl(L_4,numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3))

        #plt.plot(bins3+hm,numpy.log10(faint)-dm,'--b' ,label='faint-end')
        #plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7))+hm,numpy.log10(faint)-dm,'--b' ,label='faint-end')
        #plt.plot(numpy.log10(10**bins3),numpy.log10(phi_sf)-dm,'--c', label=' SF')
        #plt.plot(bins3,numpy.log10(phi_agn)-dm,'--r', label='AGN')
        #plt.plot(numpy.log10(10**bins3),numpy.log10(phi_tot)-dm,'--g',linewidth=3, label='Tot')
        #plt.plot(numpy.log10(10**bins3[:-1]),numpy.log10(phi_tot2)-dm,'-k',linewidth=4, label='Tot2')
        #plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7)),numpy.log10(phi_exp)-dm,'-k',linewidth=4, label='Expected')
        plt.errorbar(lin_xrecon + hm,
                     lin_yrecon - 0.4,
                     fmt='-b',
                     label='MAP',
                     linewidth=3)
        #plt.plot(numpy.log10(10**bins3[:-1] *(1.4/3)**(-.7)),numpy.log10(phi_tot3[num-1])-dm,'-r',linewidth=3, label='Tot3')
        plt.errorbar(lin_xrecon + hm,
                     numpy.log10(yrecon_avr) - dm,
                     fmt='-c',
                     label='Average')
        plt.errorbar(numpy.array(L_n2[num - 1]),
                     numpy.array(rho_n2[num - 1]) - 0.4,
                     xerr=[L_ner_d2[num - 1], L_ner_u2[num - 1]],
                     yerr=[rho_ner_d2[num - 1], rho_ner_u2[num - 1]],
                     fmt='sk',
                     label='Total RLF Novak et al 2018')
        #plt.errorbar(bins3, numpy.log10(Novak17)-0.4,fmt='--b',label='Local SF Novak et al 2017')
        #plt.plot(bins3,numpy.log10(phi_agn_n),'--g', label='Local AGN Smolcic et al. 2017')
        plt.errorbar(numpy.log10(10**bins3),
                     numpy.log10(Novak18) - 0.4,
                     fmt='--k',
                     label='Total RLF Novak et al 2018')
        #plt.plot(L_s, numpy.log10(rho_1), '*m',label='bins LF',markersize=8)
        #plt.errorbar(bins,rho_5 ,yerr=er5,fmt='*r',label='Total',markersize=10) #bin
        #plt.errorbar(bins,rho_6 ,yerr=er6,fmt='>g',label='Total LF',markersize=10) #bin
        plt.fill_between(lin_xrecon + hm,
                         numpy.log10((yrecon_d)) - dm,
                         numpy.log10(yrecon_u) - dm,
                         color='k',
                         alpha=0.2)
        #plt.axvline(Lmin,color='r')
        print 'This is the dn'
        print z_m
        #print dn.keys()
        '''
     for key in dn.keys():
        if round(key,2)==round(z_m,2):z_m=key
     
     dn=dn[z_m]
     nov_real=nov_real[z_m]
     #print nov_real
     #dn=dn[round(z_m,2)]
     for i in range(len(ssbin1)):
            loglike_i= Nbin2[i]*numpy.log(dn[i]) + Nbin2[i] - (Nbin2[i] + 0.5)*numpy.log(Nbin2[i]) - 0.5*numpy.log(2*numpy.pi)  - dn[i]
            loglike_nov_i= Nbin2[i]*numpy.log(nov_real[i]) + Nbin2[i] - (Nbin2[i] + 0.5)*numpy.log(Nbin2[i]) - 0.5*numpy.log(2*numpy.pi)  - nov_real[i]
            if nov_real[i]>0:loglike_nov+=loglike_nov_i
            if dn[i]>0:loglike+=loglike_i
            print '%10.5f %8.2d %8.2f %8.2f %8.2f %8.2f %8.2f %8.2f'%(ssbin1[i], Nbin2[i], dn[i],nov_real[i],loglike_i,loglike_nov_i,loglike, loglike_nov)
    
    
     '''
        plt.ylim(-7.6, -1.6)
        plt.axvline(fsigma, color='k', linestyle='dashed')
        plt.axvline(sigma, color='g')
        #plt.axvline(Llmin,color='r',linewidth=5)
        print 'Is this still num ', num
        if num < 2 or num < 6 and num > 3 or num > 6 and num < 9:
            plt.xticks(visible=False)
            plt.ylim(-7.6, -1.6)

        if num < 4:
            plt.xlim(19, 25.8)
            plt.text(19.2,
                     -7.,
                     '%.1f < z < %.1f' % (z_min, z_max),
                     fontsize=19)  # \n $M_i$ < -22
        elif num < 7:
            plt.xlim(20.8, 27.2)
            plt.text(21., -7., '%.1f < z < %.1f' % (z_min, z_max),
                     fontsize=19)  # \n $M_i$ < -22
        else:
            plt.xlim(21.3, 27.8)
            plt.text(21.5,
                     -7.,
                     '%.1f < z < %.1f' % (z_min, z_max),
                     fontsize=19)  # \n $M_i$ < -22

        if num == 3:
            plt.xticks([19, 20, 21, 22, 23, 24, 25], fontsize=22)
        if num == 6:
            plt.xticks([21, 22, 23, 24, 25, 26, 27], fontsize=22)
        if num == 9:
            plt.xticks([22, 23, 24, 25, 26, 27], fontsize=22)
            plt.legend(frameon=False).draggable()
        if num == 1 or num == 2 or num == 3 or num == 10:
            plt.yticks([-2., -3, -4, -5, -6], fontsize=22)
            if num == 3:
                plt.yticks([-2., -3, -4, -5, -6, -7], fontsize=22)
            continue
        plt.yticks(visible=False)

    fig.text(0.06,
             0.5,
             r'$\rm{log_{10}[\Phi /(Mpc^{-3}mag^{-1})]}$',
             fontsize=30,
             ha='center',
             va='center',
             rotation='vertical')
    fig.text(0.5,
             0.04,
             r'$\rm{log_{10}[L_{1.4}/(W}$ $\rm{Hz^{-1})]}$',
             fontsize=30,
             ha='center',
             va='center')
    #plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    plt.tick_params(axis='both', which='major', labelsize=20, width=3)
    plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
    plt.subplots_adjust(hspace=0, wspace=0)
    #plt.xtick(fontsize=15)
    #plt.ytick(fontsize=15)

    #plt.ylim(-10.2,-5.8)
    #plt.ylim(-11,-5.5)
    #ax.xaxis.set_minor_locator(AutoMinorLocator())
    #ax.yaxis.set_minor_locator(AutoMinorLocator())
    #print truth['LMIN']

    plotf = '%s/LF_recon_s1.pdf' % (outdir)
    plt.show()

    return 0
示例#6
0
def main():
    """
    """

    # Import the settings variables
    print 'Settings file is %s' % param_file

    # Import the settings variables
    set_module = importlib.import_module(settingsf)
    globals().update(set_module.__dict__)

    # Set up the experiment
    #bins=[12.679592, 31.84969509, 80.00281696, 200.9579904, 504.78364938, 1010, 3267.95919972]# 0.4 bins
    chains = [
        '01a_3', '01b_3', '01c_3', '01d_3', '01e', '01f', '01g', '01h', '01i',
        '01j'
    ]  #chains_1912 including peter
    chains = [
        '01a_3', '01b_2', '01c_2', '01d_2', '01e_2', '01f_2', '01g', '01h_2',
        '01i_2', '01j_3'
    ]  #chains_2001 DR4 remove phi2
    chains = [
        '01a_8', '01b_8', '01c_8', '01d_8', '01e_8', '01f_8', '01g_7', '01h_8',
        '01i_8', '01j_7'
    ]  #chains_2001 DR4 dpl_dpl
    chains = [
        '01a_9', '01b_9', '01c_9', '01d_9', '01e_9', '01f_9', '01g_9', '01h_9',
        '01i_9', '01j_9'
    ]  #chains_2001 DR4 dpl_dpl no mass selection
    chains = [
        '02a_4_1', '02a_4_2', '02b_4', '02c_4', '02d_4', '02e_4', '02f_4',
        '02g_4', '02h_4', '02i_4', '02j_3'
    ]
    chains = [
        '02a_z_6x_1', '02a_z_6x_2', '02b_z_7x', '02c_z_7x', '02d_z_7x',
        '02e_z_7x', '02f_z_7x', '02g_z_7x', '02h_z_7x', '02i_z_7x', '02j_z_7x'
    ]
    chains = [
        '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3',
        '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3'
    ]
    chains = [
        '02a_z_8x', '02b_z_8x', '02c_z_8x', '02d_z_8x', '02e_z_8x', '02f_z_8x',
        '02g_z_8x', '02h_z_8x', '02i_z_8x', '02j_z_8x'
    ]  #fixed model newz area
    chains = [
        '02a', '02b', '02c', '02d', '02e', '02f', '02g', '02h', '02i', '02j'
    ]  #lgnorm #chains_2001 DR4 newz new_area
    #chains=['02a_3','02b_3','02c_3','02d_3','02e_3','02f_3','02g_3','02h_3','02i_3','02j_3'] #mass <10
    #chains=['02a_z_6e','02b_z_6e','02c_z_6e','02d_z_6e','02e_z_6e','02e_z_6e','02g_z_6e','02h_z_6e','02i_z_6e','02j_z_6e']# New fixed 6e (using a)
    #chains=['02a_z_6f','02b_z_6f','02c_z_6f','02d_z_6f','02e_z_6f','02e_z_6f','02g_z_6f','02h_z_6f','02i_z_6f','02j_z_6f']# New fixed 6f (using a)
    #chains=['02a_z_6g','02b_z_6g','02c_z_6g','02d_z_6g','02e_z_6g','02e_z_6g','02g_z_6g','02h_z_6g','02i_z_6g','02j_z_6g']# New fixed 6f (using a)
    #chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5','01j_5'] #stellar mass>10.2
    chains = [
        '02a_5', '02b_5', '02c_5', '02d_5', '02e_5', '02f_5', '02g_5', '02h_5',
        '02i_5', '02j'
    ]  #stellar mass>10
    chains = [
        '02a_3', '02b_3', '02c_3', '02d_3', '02e_3', '02f_3', '02g_3', '02h_3',
        '02i_3', '02j_3'
    ]  #stellar mass<10. logn
    #chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i','01j'] #dpl #chains_2001 DR4 newz new_area
    #chains=['02a_z_5x','02b_z_5x','02c_z_5x','02d_z_5x','02e_z_5x','02f_z_5x','02g_z_5x','02h_z_5x','02i_z_5x','02j_z_5x']#stellar mass limit mass<10
    #chains=['02a_z_5f','02b_z_5f','02c_z_5f','02d_z_5f','02e_z_5f','02f_z_5f','02g_z_5f','02h_z_5f','02i_z_5f','02j_z_5x']#stellar mass limit mass<10
    #chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_10','02i_z_8x_6_10','02j_z_8x_6_10']#fixed cut data more bins _el lmin lose
    #chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_1','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_4']
    #chains='02z_31_5_15'
    pre_chain = '11'
    bins = numpy.logspace(0, 3.01, 10)
    bins = list(bins)
    bins.append(2e3)
    bins1 = numpy.arange(15.2, 30.2, 0.1)
    nbins = len(bins)
    z_m = redshifts[0]
    dl = get_dl(z_m)
    sbin1 = get_sbins(10**bins1, z_m, dl) * 1e6
    expt = countModel(modelFamily,
                      nlaws,
                      settingsf, [dataset],
                      floatNoise,
                      doRedshiftSlices=True,
                      mybins=sbin1)

    Lmin = 21  #21.5# 19. 21.5
    Lmax = 26.
    z_med = []
    SFRD, SFRD_l, SFRD_h = [], [], []
    sfrd_map = []
    nov_z, nov_z_u, nov_z_l, nov_sfrd, nov_sfrd_u, nov_sfrd_l, Lower, lw_up, lw_lw = numpy.loadtxt(
        'novak_2017.txt', usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8), unpack=True)
    print modelFamily
    evaluations = []
    evaluate = True
    for num in range(1, 11):
        print num

        z_min, z_max, z_m = get_z(num, z_new=False)
        z_med.append(z_m)
        print z_m

        #f='%sev.dat'% outstem
        f = '1-post_equal_weights.dat'
        f = os.path.join('chains_20%s%s' % (pre_chain, chains[num - 1]), f)
        #f=os.path.join('chains_20%s%s'%(pre_chain,chains),f)
        print 'os.pathf', f

        #sfrd_map
        # Load equally-weighted posterior samples
        x = numpy.genfromtxt(f)
        nsamp = x.shape[0]
        ncols = x.shape[1]  # The fifth [seventh] column is the posterior value
        # There must be a better way, but:
        #ncols = 14
        z = numpy.zeros((nsamp, ncols - 1 + expt.nbins))
        z[:, :-(expt.nbins - 1)] = x
        # Shift posterior values to end
        z[:, -1] = z[:, ncols - 1]  # Copy...
        z[:, ncols - 1] = 0.0  # ...and blank

        print 'outstem', outstem

        # Fetch best-fit parameters and calculate best-fit line
        ana=pymultinest.analyse.Analyzer(ncols-1,\
           outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains[num-1]),outstem))
        drawmap = ana.get_best_fit()['parameters']

        #ana=pymultinest.analyse.Analyzer(ncols-1,\
        #   outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains),outstem))
        #drawmap=ana.get_best_fit()['parameters']

        if True:
            print '--> Calculating *ML* reconstruction'
            drawmap = drawmap

        #ymap=expt.evaluate(drawmap)[0]#,expt.binsMedian)
        q_l, q, q_h = get_q(z_m)
        yrecon, lin_xrecon2, str_q_l = 0, 0, 0
        sfrd = []
        sfrd_map.append(
            get_sfrd_z(yrecon, lin_xrecon2, str_q_l, drawmap, expt.parameters,
                       modelFamily, q_h, 26., Lmin, z_m)[0])
        print drawmap
        for isamp in xrange(nsamp):
            #z[isamp,ncols-1:]=expt.evaluate(z[isamp,:])[0]#,expt.binsMedian)
            #Lmin =z[isamp,:][expt.parameters.index('LMIN')]

            Lmax = z[isamp, :][expt.parameters.index('LMAX')]
            #sfrd_z_l=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,z[isamp,:],expt.parameters,modelFamily,q_l ,Lmax,Lmin,z_m)[0] #lower limit
            #sfrd_z_h=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,z[isamp,:],expt.parameters,modelFamily,q_h ,Lmax,Lmin,z_m)[0] #upper limit
            sfrd_z = get_sfrd_z(yrecon, lin_xrecon2, str_q_l, z[isamp, :],
                                expt.parameters, modelFamily, q, Lmax, Lmin,
                                z_m)[0]  #26, 21.5
            #plt.plot(z_m,numpy.log10(sfrd_z),'.', alpha=0.02)
            #plt.plot(z_m,numpy.log10(sfrd_z_l),'.', alpha=0.01)
            #plt.plot(z_m,numpy.log10(sfrd_z_h),'.', alpha=0.01)
            #sfrd.append(sfrd_z_l)
            sfrd.append(sfrd_z)
            #sfrd.append(sfrd_z_h)
        #plt.show()

    # Blanking, 0.0 -> NaN
    #sys.exit()

        s_med, dlow, dhigh, s_low, s_high = calculate_confidence(sfrd,
                                                                 alpha=0.95,
                                                                 ret_all=True)
        SFRD.append(s_med)
        SFRD_l.append(s_low)
        SFRD_h.append(s_high)
        '''
     plt.hist(numpy.log10(sfrd),20)
     plt.axvline(numpy.log10(s_med),color='r')
     plt.axvline(numpy.log10(s_low),color='k')
     plt.axvline(numpy.log10(s_high), color='k')
     plt.xlabel(r'$\rm{\log10(SFRD[M_\odot yr^{-1} Mpc^{-3}])}$',fontsize=20)
     plt.show()
     
    '''
    sfrdf = 'sfrd_chains_20%s%s_Lmin_%s' % (pre_chain, chains[0], '21_5')
    #sfrdf='sfrd_chains_20%s%s_Lmin_%s'%(pre_chain,chains,'21_5')
    fils = open(sfrdf, 'w')
    hdr = '# median redshift  sfrd_median  sfrd_low sfrd_high'
    fils.write('%s\n' % hdr)
    for i in range(len(SFRD)):
        fils.write('%f %f %f %f \n' %
                   (z_med[i], SFRD[i], SFRD_l[i], SFRD_h[i]))
    fils.close()

    z_b = numpy.arange(0, 60) / 10.
    Ilbert_h = sfrd_Behroozi(z_b,
                             z0=0.95 - 0.410,
                             A=-1.,
                             B=0.194 - 0.082,
                             C=0.111 + 0.04)
    Ilbert_l = sfrd_Behroozi(z_b,
                             z0=0.95 + 0.343,
                             A=-1.,
                             B=0.194 + 0.128,
                             C=0.111 - 0.029)
    G_x, Gruppioni = numpy.loadtxt('Gruppioni_2013',
                                   unpack=True,
                                   delimiter=',')
    G_x1, Gruppioni_yup = numpy.loadtxt('Gruppioni_2013_y_up',
                                        unpack=True,
                                        delimiter=',')
    G_x2, Gruppioni_ydown = numpy.loadtxt('Gruppioni_2013_y_down',
                                          unpack=True,
                                          delimiter=',')
    Burg_z, Burg_FIR, Burg_FIR_er, Burg_tot, Burg_tot_er = numpy.loadtxt(
        'Burgarella_2013', unpack=True)
    plt.fill_between(z_med,
                     numpy.log10((SFRD_l)),
                     numpy.log10(SFRD_h),
                     color='b',
                     alpha=0.2)
    plt.errorbar(z_med,
                 numpy.log10(SFRD),
                 fmt='*',
                 markersize=14,
                 label='Faint LF function')
    #plt.errorbar(z_med,numpy.log10(sfrd_map),fmt='pr',markersize=14, label='Faint LF function')
    plt.errorbar(z_b,
                 numpy.log10(sfrd_Behroozi(z_b)),
                 fmt='--',
                 markersize=12,
                 label='Behroozi et al. 2013')
    plt.errorbar(z_b,
                 numpy.log10(
                     sfrd_Behroozi(z_b, z0=0.95, A=-1., B=0.194, C=0.111)),
                 fmt=':',
                 markersize=12,
                 label='IIlbert et al. 2013 (UltraVista DR1)')
    plt.errorbar(z_b,
                 numpy.log10(sfrd_Madau(z_b)),
                 fmt='.-',
                 markersize=12,
                 label='Madau&Dickinson 2014')
    try:
        plt.errorbar(numpy.array(nov_z),
                     numpy.array(nov_sfrd),
                     xerr=[nov_z_l, nov_z_u],
                     yerr=[nov_sfrd_l, nov_sfrd_u],
                     fmt='sk',
                     label='Total SFRD Novak et al 2017')
    except:
        print ''
    plt.ylabel(r'$\rm{\log10(SFRD[M_\odot yr^{-1} Mpc^{-3}])}$', fontsize=30)
    plt.xlabel(r'$\rm{z} $', fontsize=30)
    #plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    plt.tick_params(axis='both', which='major', labelsize=20, width=3)
    plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
    plt.show()

    return 0
示例#7
0
def main():

    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module=importlib.import_module(setf)
    globals().update(set_module.__dict__)

    expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
    recon_expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
    #print expt.data
    #print expt.bins

    # Get MAP parameters
    print '\n'*5
    print len(expt.parameters)
    ncols = len(expt.parameters)
    summf=os.path.join(outdir,'1-summary.txt')
    summary=numpy.genfromtxt(summf)[-1,:]
    drawmap=summary[-(ncols+2):-2] 
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap=ana.get_best_fit()['parameters']
    
    if dataset=='sdss':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf)==20:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][4:]))
        print datafiles[0][4:]
    elif dataset=='cosmos':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf)==24:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][8:]))
        print datafiles[0][8:]

    elif 'sim' in dataset:
        d=numpy.genfromtxt(os.path.join(outdir,'sim.txt'))
    
    #bins2 = numpy.arange(21.4,29.2,0.4)
    bins2 = numpy.arange(18.0,29.2,0.4)
    bins3 = numpy.arange(18.,29.2,0.01)
    print len(expt.parameters)
    print 'this is 1st num ', num
    #sys.exit()
    params = drawmap# [0]
    #for i in drawmap:
    #	params.append(i)
    print params
    print expt.parameters
    #novak 2017
    L_n   =[21.77,22.15,22.46,22.77,23.09,23.34]
    rho_n =[-2.85,-2.88,-3.12,-3.55,-4.05,-4.63]
    L_ner_u =[0.23,0.18,0.19, 0.2, 0.21, 0.28]
    L_ner_d =[1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
    rho_ner=[0.09, 0.03,0.0355,0.059,0.1,0.234]

     #novak 2018

    nov_z,nov_z_u, nov_z_l,  nov_sfrd, nov_sfrd_u, nov_sfrd_l,  Lower, lw_up, lw_lw, nov_phi_L, nov_phi_L2 = numpy.loadtxt('novak_2017.txt',usecols=(0,1,2, 3,4,5, 6,7,8,-2,-1),unpack=True)
    
    #fig,ax = subplots()
    pre_chain='12'
    chains=['01a_4','01b_4','01c_4','01d_4','01e_4','01f_4','01g_4','01h_4','01i_4_2', '01j_4_2']#chains_191101 7x7 grid
    chains=['02a_4','02b_4','02c_4','02d_4','02e_4','02f_4','02g_5','02h_4','02i_4','02j_4']#chains_191101 7x7 grid lognorm
    #chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5']#chains_191101 3x3 grid scaled
    #chains=['01a_6','01b_6','01c_6','01d_6','01e_6','01f_6','01g_6','01h_6','01i_6']#chains_191101 11x11 grid
    #chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i'] #chains_1912 #includeing maskregion
    chains=['01a_3','01b_3','01c_3','01d_3','01e','01f','01g','01h','01i','01j'] #chains_1912 including peter
    #chains=['01a_2_1','01b_2_1','01c_2_1','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2','01j_2']
    #chains=['01a_4_1','01b_4_1','01c_4','01d_4','01e_4','01f_4_1','01g_4','01h_4','01i_4','01j_4'] #chains_1912 no mass selection
    #chains=['01a_3','01b_2','01c_2','01d_2','01e_2','01f_2','01g','01h_2','01i_2','01j_3'] #chains_2001 DR4 remove phi2
    chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5','01j_5'] #chains_2001 DR4 remove phi2 new changes
    chains4=['01a_8','01b_8','01c_8','01d_8','01e_8','01f_8','01g_7','01h_8','01i_8','01j_7'] #chains_2001 DR4 dpl_dpl
    chains=['01a_9','01b_9','01c_9','01d_9','01e_9','01f_9','01g_9','01h_9','01i_9','01j_9'] #chains_2001 DR4 dpl_dpl no mass selection
    #chains=['02a_z_9_6','02b_z_9_6','02c_z_9_6','02d_z_9_6','02e_z_9_6','02f_z_9_6','02g_z_9_6','02h_z_9_6','02i_z_9_6','02j_z_9_6']
    chains=['01a_6_1','01a_8_2','01b_6_3','01c_6_3','01d_6_2','01e_6_2','01f_6_2','01g_6_2','01h_6_3','01i_6_3','01j_6_3'] #chains_2001 DR4 dpl_dpl Lmin=19 newz
    chains=['02a_4_1','02a_4_2','02b_4','02c_4','02d_4','02e_4','02f_4','02g_4','02h_4','02i_4','02j_3']
    chains2=['02a_z_5x','02b_z_5x','02c_z_5x','02d_z_5x','02e_z_5x','02f_z_5x','02g_z_5x','02h_z_5x','02i_z_5x_2','02j_z_5x']
    chains2=['02a_z_6x_1','02a_z_6x_2','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x']
    
    #chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i','01j'] #dpl #chains_2001 DR4 newz new_area
    chains4=['02a','02b','02c','02d','02e','02f','02g','02h','02i','02j'] #lgnorm #chains_2001 DR4 newz new_area
    chains2=['02a_z_8x','02b_z_8x','02c_z_8x','02d_z_8x','02e_z_8x','02f_z_8x','02g_z_8x','02h_z_8x','02i_z_8x','02j_z_8x']#fixed model newz area
    chains=['02a_z_8x_4','02b_z_8x_4','02c_z_8x_4','02d_z_8x_4','02e_z_8x_4','02f_z_8x_4','02g_z_8x_4','02h_z_8x_4','02i_z_8x_4','02j_z_8x_4']#fixed model newz area
    #chains=['02a_z_8x_6','02b_z_8x_6','02c_z_8x_6','02d_z_8x_6','02e_z_8x_6','02f_z_8x_6','02g_z_8x_6','02h_z_8x_6','02i_z_8x_6','02j_z_8x_6']#fixed cut data more bins wrong noise
    chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_3']#fixed cut data more bins _el lmin lose
    chains2=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_3']#fixed cut data more bins _el lmin lose
    chains3='02z_31_5_15'
    print expt.parameters
    
    z_new=True
    Replot=False
    z_nov = [0.31, 0.5, 0.69, 0.9, 1.16, 1.44, 1.81, 2.18, 2.81, 3.71, 4.83]
    if z_new:
        z =[0.1,0.3,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]
        #chains=['11a','11b','11c_3','11d_3','11e_3','11f_3','11g_3','11h','11i','11j', '11k', '11l' ,'11m', '11n']#chains_191111
    else:
        z=[0.1,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]#oldest ;)
        
    sfrd, sfrd_l,sfrd_h,z_med = numpy.zeros(len(z)+1),numpy.zeros(len(z)+1) ,\
         numpy.zeros(len(z)+1) ,numpy.zeros(len(z)+1) 
    sfrd_nov, sfrd_nov_l,sfrd_nov_h, sfrd_2, sfrd_3, sfrd_4, sfrd_2_l, sfrd_3_l, sfrd_2_h, sfrd_3_h  = numpy.zeros(len(z)+1),\
         numpy.zeros(len(z)+1), numpy.zeros(len(z)+1) ,numpy.zeros(len(z)+1),numpy.zeros(len(z)+1),\
         numpy.zeros(len(z)+1), numpy.zeros(len(z)+1) ,numpy.zeros(len(z)+1),numpy.zeros(len(z)+1) ,\
         numpy.zeros(len(z)+1)
    
    
    ana3=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_2012%s'%chains3,outstem))
    drawmap3=ana3.get_best_fit()['parameters']
    params3 = drawmap3
    print 'chains_2002%s'%chains3
    print params3
    parameters2= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']
    parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN', 'LMAX']
    parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN_1', 'LMIN_2', 'LMIN_3', 'LMIN_4', 'LMIN_5', 'LMIN_6', 'LMIN_7', 'LMIN_8', 'LMIN_9', 'LMIN', 'LMAX']
    parameters4=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
    for num in range(1,11):
    #num = 1.
     print num
     z_min,z_max, z_m = get_z(num,False)
     dl = get_dl(z_m)
     Vmax = get_Vmax(z_min,z_max)
     dsdl = get_dsdl(z_m,dl)

     dl = get_dl(z_m)
    
     print z_min,z_m,z_max
     print expt.kind
     area = SURVEY_AREA*sqDeg2sr 
     area1 = SURVEY_AREA*sqDeg2sr
     print SURVEY_AREA
     ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains[num-1]),outstem))
     drawmap=ana.get_best_fit()['parameters']
     drawmid=ana.get_stats()['marginals']
     params = drawmap
     
     print params
     
     ana2=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join('chains_2012%s'%(chains2[num-1]),outstem))
     drawmap2=ana2.get_best_fit()['parameters']
     params2 = drawmap2
     
     #ana4=pymultinest.analyse.Analyzer(ncols-1,\
     #   outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains4[num-1]),outstem))
     #drawmap4=ana4.get_best_fit()['parameters']
     #params4 = drawmap4
     
     print 'parameter 1 ', params
     print 'parameters 2 ', params2
     print 'parameters 3 ', params3
     
     settingf='chains_20%s%s/bayestack_settings.py'%(pre_chain,chains[num-1])
     f = open(settingf, 'r')
     mod = f.readlines()[31].split()[0]
     print mod
     if 'logn' in mod:
        if 'dpl' in mod:
            model='LFlognorm_dpl'
        elif '_el' in mod:
            model='LFevol_logn_el' 
        elif 'pl_' in mod:
            model='LFpl_lognorm'
        else:
            model='LFlognorm'
     elif 'LFdpl' in mod or 'LFpl' in mod:
        if 'LFdpl_dpl' in mod:
            model='LFdpl_dpl'
        elif '_pl' in mod:
            model='LFdpl_pl'
        elif 'LFpl_' in mod:
            model='LFpl_dpl'
        elif 'LFpl' in mod:
            model='LFpl'
        else:
            model = 'LFdpl'    
     
     with open('chains_20%s%s/params.tex'%(pre_chain,chains[num -1])) as f:
        parameters=[line.split(None,1)[0] for line in f]
    #model='LFdpl_pl'
     print parameters
     print model
     print 'LSIGMA' in parameters
     
     if model in ['LFsch','LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl']:
        Lnorm=params[parameters.index('LNORM')]
        Lstar=params[parameters.index('LSTAR')]
        Lslope=params[parameters.index('LSLOPE')]
        #Lzevol=params[parameters.index('LZEVOL')]
     if model in ['LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl']:
        Lslope2=params[parameters.index('LSLOPE2')]
     if model in ['LFlognorm','LFpl', 'LFdpl', 'LFdpl_pl','LFpl_dpl', 'LFlognorm_dpl','LFdpl_dpl']:
        Lnorm_2=params[parameters.index('LNORM_2')]
        Lstar_2=params[parameters.index('LSTAR_2')]
        Lslope_2=params[parameters.index('LSLOPE_2')]

     if model in ['LFdpl_dpl','LFdpl']:
        Lslope2_2=params[parameters.index('LSLOPE2_2')]
    
     if model in ['LFlognorm','LFlognorm_dpl']:
        Lsigma = params[parameters.index('LSIGMA')]

     Lmin=params[parameters.index('LMIN')]
     Lmax=params[parameters.index('LMAX')]
     #Lmax2=params[parameters.index('LMAX2')]
     Lmax2=Lmax

    
     SMIN  = get_sbins(numpy.power(10,Lmin),z_m,dl)*1e6
     SMAX  = get_sbins(numpy.power(10,Lmax),z_m,dl)*1e6
     sigma,fsigma = numpy.log10(get_Lbins([SURVEY_NOISE,SURVEY_NOISE*5],z_m,dl,'muJy')*(1.4/3)**(-.7))
     sigma2,fsigma2 = numpy.log10(get_Lbins([450,2400],z_m,dl,'muJy'))
     print z_m,dl, sigma,fsigma
     print SURVEY_NOISE,SURVEY_NOISE*5,SURVEY_AREA
     
     s=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%(pre_chain,chains[num -1]))
     xrecon=s[:-1,0]; yrecon=s[:-1,1]
     yrecon_d=s[:-1,2]; yrecon_u=s[:-1,3]
     yrecon_rms = s[:-1,4]
     yrecon_avr = s[:-1,5]
     yrecon_rms_down = yrecon_rms
     
     yrec_u = yrecon_u - yrecon
     yrec_d = yrecon - yrecon_d
     

     #print yrecon_d
     #print yrecon
     #print yrecon_u
     #sys.exit()
     
     #y_err_u = sqrt(yrec)     
     
     xreco = get_Lbins(xrecon,z_m,dl,'muJy')#*(1.4/3)**(-.7)
     xreco2 = get_Lbins(xrecon,z_m,dl,'muJy')
     yreco = yrecon
     lin_yrecon = numpy.log10(yreco)
     lin_xrecon = numpy.log10(xreco)
     lin_xrecon2 = numpy.log10(xreco2)
     q_l,q,q_h = get_q(z_m)
     
     #print z_nov[num -1]
     try:
        yrecon_nov = lumfuncUtils.lognormpl(get_Lbins(xrecon,z_m,dl,'muJy')/(1 + z_nov[num-1])**(3.16-z_nov[num-1]*0.32), numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3))
        
     except:
        print ''
     
     str_q_l = get_sfr_q(1.,q_l,xreco2)
     str_q_h = get_sfr_q(1.,q_h,xreco2)
     str_q   = get_sfr_q(1.,q,xreco2)
     
     
     #sys.exit()
     
     lmin = 21.5#. 21.5
     Lmax=26.
     sfrd_z_l=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,params,parameters,model,q_l ,Lmax2,lmin,z_m)[0] #lower limit
     sfrd_z_h=get_sfrd_z(yrecon,lin_xrecon2,str_q_h,params,parameters,model,q_h ,Lmax2,lmin,z_m)[0] #upper limit
     sfrd_z  =get_sfrd_z(yrecon,lin_xrecon2,str_q  ,params,parameters,model,q ,Lmax2,lmin,z_m)[0]  #26, 21.5
     
     sfrd_2_z =get_sfrd_z(yrecon,lin_xrecon2,str_q  ,params2,parameters2,'LFevol_logn_mat',q ,Lmax2,lmin,z_m)[0]
     sfrd_3_z =get_sfrd_z(yrecon,lin_xrecon2,str_q  ,params3,parameters3,'LFevol_logn_L',q ,Lmax2,lmin,z_m)[0]
     
     #sfrd_4_z =get_sfrd_z(yrecon,lin_xrecon2,str_q  ,params4,parameters4,'LFdpl_dpl',q ,Lmax2,lmin,z_m)[0]
     
     sfrd_2_z_l =get_sfrd_z(yrecon,lin_xrecon2,str_q_l  ,params2,parameters2,'LFevol_logn_mat',q_l ,Lmax2,lmin,z_m)[0]
     sfrd_3_z_l =get_sfrd_z(yrecon,lin_xrecon2,str_q_l  ,params3,parameters3,'LFevol_logn_L',q_l ,Lmax2,lmin,z_m)[0]
     sfrd_2_z_h =get_sfrd_z(yrecon,lin_xrecon2,str_q_h  ,params2,parameters2,'LFevol_logn_mat',q_h ,Lmax2,lmin,z_m)[0]
     sfrd_3_z_h =get_sfrd_z(yrecon,lin_xrecon2,str_q_h  ,params3,parameters3,'LFevol_logn_L',q_h ,Lmax2,lmin,z_m)[0]
     
     #sfrd_z_l=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,params,parameters,model,q_l ,Lmax2,Lmin)[0] #lower limit
     #sfrd_z_h=get_sfrd_z(yrecon,lin_xrecon2,str_q_h,params,parameters,model,q_h ,Lmax2,Lmin)[0] #upper limit
     #sfrd_z  =get_sfrd_z(yrecon,lin_xrecon2,str_q  ,params,parameters,model,q ,Lmax2,Lmin)[0]  #26, 21.5
     #sys.exit()
     
     #sfrd_nov_z =get_sfrd_z(yrecon_nov,lin_xrecon,str_q ,params,parameters,'novak',get_q(z_nov[num-1])[1] ,26,lmin, z=z_nov[num-1])[0] 
     #print 'This is nov z ', z_nov[num-1]
     #sys.exit()
     #sfrd_z  =get_sfrd_z(yrecon,lin_xrecon,str_q  ,params,parameters,model,q ,Lmax2,Lmin)[0]
     
     
     #sfrd_z,sfrd_zerr= get_sfrd_z(yrecon,lin_xrecon,str_q,numpy.inf,0.)
     
     
     sfrd[num-1]   = sfrd_z
     sfrd_l[num-1] = sfrd_z_l
     sfrd_h[num-1] = sfrd_z_h
     z_med[num-1]    = z_m
     #sfrd_nov[num-1] =sfrd_nov_z
     sfrd_2[num-1] = sfrd_2_z
     sfrd_3[num-1] = sfrd_3_z
     #sfrd_4[num-1] = sfrd_4_z
     sfrd_2_l[num-1] = sfrd_2_z_l
     sfrd_3_l[num-1] = sfrd_3_z_l
     sfrd_2_h[num-1] = sfrd_2_z_h
     sfrd_3_h[num-1] = sfrd_3_z_h
     
     #plt.plot(lin_xrecon,numpy.log10(str_q),'*',markersize=8)

    try:
        zrecon, srecon, srecon_l, srecon_h =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains[0],'21_5'),unpack=True)
    except:
        print 'Please note that the 95 percent region has to be run for chains_2001%s ... python reconstruct_lf_plot.py'%chains[0]
        print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
        zrecon, srecon, srecon_l, srecon_h=0,0,0,0
    try:
        zrecon, srecon2, srecon_l2, srecon_h2 =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains[0],'19'),unpack=True)
    except:
        print 'Please note that the 95 percent region has to be run for chains_2001%s ... python reconstruct_lf_plot.py'%chains[0]
        print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
        zrecon2, srecon2, srecon_l2, srecon_h2=0,0,0,0
    

    try:
        zrecon_2, srecon_2, srecon_l_2, srecon_h_2 =numpy.loadtxt('sfrd_chains_2011%s_Lmin_%s'%(chains4[0],'21_5'),unpack=True)
    except:
        print 'Please note that the 95 percent region has to be run for chains_2002%s ... python reconstruct_lf_plot.py'%(chains2[0])
        print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
        zrecon_2, srecon_2, srecon_l_2, srecon_h_2=0,0,0,0
    
    try:
        zrecon_3, srecon_3, srecon_l_3, srecon_h_3 =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains3,'21_5'),unpack=True)
    except:
        print 'Please note that the 95 percent region has to be run for chains_2002%s ... python reconstruct_lf_plot.py'%chains3[0]
        print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
        zrecon_3, srecon_3, srecon_l_3, srecon_h_3=0,0,0,0
        
    try:
        zrecon_3_2, srecon_3_2, srecon_l_3_2, srecon_h_3_2 =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains3,'21_5'),unpack=True)
    except:
        print 'Please note that the 95 percent region has to be run for chains_2002%s ... python reconstruct_lf_plot.py'%chains3[0]
        print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
        zrecon_3_2, srecon_3_2, srecon_l_3_2, srecon_h_3_2=0,0,0,0
    #plt.rc('lines', linewidth=2)
    #plt.xlabel(r'$\rm{log_{10}[L_{1.4}/(W}$ $\rm{Hz^{-1})]}$',fontsize = 30)
    #plt.ylabel(r'$\rm{log_{10}[SFR_{IR}(M_\odot yr^{-1})]}$',fontsize = 30)
    #plt.tick_params(axis='both',which = 'major', labelsize=20,width =3)
    #plt.tick_params(axis='both',which = 'minor', labelsize=10, width=2)
    
    #plt.show()
    #fig = plt.figure()
    #print z_med
    #print sfrd_l
    #print sfrd
    #print sfrd_h
    #print len(z),len(sfrd)
    #Salpeter to Chabrier * 0.63
    
    
    print zrecon_2,srecon_2, srecon_l_2, srecon_h_2
    srecon_2_err=2*0.434*(srecon_h_2)/numpy.log10(srecon_2)
    z_b=numpy.arange(0,60)/10.
    Ilbert_h = sfrd_Behroozi(z_b,z0=0.95-0.410,A=-1.,B=0.194-0.082,C=0.111+0.04)
    Ilbert_l = sfrd_Behroozi(z_b,z0=0.95+0.343,A=-1.,B=0.194+0.128,C=0.111-0.029)
    parasa   = parsa_z(z_b,a=0.11,b=1.48,c=2.75)
    Koprowski= parsa_z(z_b,a=0.18,b=1.04,c=1.77)

    #plt.errorbar(z_med,numpy.log10(sfrd),fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{C}$')
    #plt.errorbar(z_med,numpy.log10(sfrd_3),fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{C}$ $\rm{PLE}$')
    plt.errorbar(zrecon_2,numpy.log10(srecon_2),yerr=srecon_2_err,fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{B}$')
    #plt.errorbar(z_med,numpy.log10(sfrd),fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{C}$')
    #plt.errorbar(z_med,numpy.log10(sfrd_2),fmt='h',fillstyle='none',markeredgewidth=3,markersize=15, label=r'$\rm{Model}$ $\rm{C}$')
    #plt.errorbar(z_med,numpy.log10(sfrd_3),fmt='-k',linewidth=5, label=r'$\rm{Model}$ $\rm{C}$ $\rm{PLE}$')
    
    #plt.errorbar(zrecon_3_2,numpy.log10(srecon_3_2),fmt='-m',linewidth=5, label=r'$\rm{Model}$ $\rm{C}$ $\rm{PLE}$ $\rm{q(z=0)}$')
    #plt.errorbar(zrecon_2,numpy.log10(srecon_2),fmt='.-k',linewidth=5, label=r'$\rm{Model}$ $\rm{B}$')

    G_x , Gruppioni       = numpy.loadtxt('Gruppioni_2013',unpack=True,delimiter=',')
    G_x1, Gruppioni_yup   = numpy.loadtxt('Gruppioni_2013_y_up',unpack=True,delimiter=',')
    G_x2, Gruppioni_ydown = numpy.loadtxt('Gruppioni_2013_y_down',unpack=True,delimiter=',')
    Burg_z, Burg_FIR, Burg_FIR_er, Burg_tot, Burg_tot_er = numpy.loadtxt('Burgarella_2013',unpack=True)
    Lzl,Lzh,Lphi_tot,Lphi_ir,Ler = numpy.loadtxt('Liu_2018',unpack=True)
    print Lzl,Lzh
    Lz = numpy.mean(numpy.array([Lzl,Lzh]),axis=0)
    print Lz

    #plt.errorbar(z_med,numpy.log10(sfrd_4),fmt='--k',linewidth=7, label='No mass-limit')
    plt.errorbar(Lz,Lphi_tot,yerr=Ler,fmt='*g',markeredgewidth=1.8,markersize=16, fillstyle='none', label=r'$\rm{Liu+2018}$ $\rm{FIR+UV}$')
    try:
        plt.errorbar(numpy.array(nov_z),numpy.array(nov_sfrd),xerr=[nov_z_l,nov_z_u],yerr=[nov_sfrd_l,nov_sfrd_u], fmt='sr',fillstyle='none',ecolor='k',markeredgewidth=1.5, markersize=10, label=r'$\rm{Novak+2017}$ $\rm{PLE}$ $\rm{radio}$')
    except:
        print ''
    
    plt.errorbar(z_b,numpy.log10(Koprowski),fmt='--k',linewidth=3, label=r'$\rm{Koprowski+2017}$ $\rm{IR}$')
    #plt.errorbar(z_b,numpy.log10(parasa),fmt='.g',linewidth=3, label=r'$\rm{Parsa}$ + $\rm{2016}$ $\rm{UV}$')
    plt.errorbar(z_b,numpy.log10(sfrd_Madau(z_b)),fmt='o-c',linewidth=3,fillstyle='none', label=r'$\rm{Madau&Dickinson}$ $\rm{2014}$')
    #plt.errorbar(z_med,numpy.log10(sfrd_l)+0.4,fmt='--',markersize=8,label='low')
    #plt.errorbar(z_med,numpy.log10(sfrd_h)+0.4,fmt='--',markersize=8,label='high')
   
    #plt.errorbar(numpy.array(nov_z),numpy.array(Lower),xerr=[nov_z_l,nov_z_u],yerr=[lw_up, lw_lw], fmt='*g', label='lower limit SFRD Novak et al 2017')
    #plt.yscale('log')
    #plt.fill_between(z_med,numpy.log10((sfrd_l)),numpy.log10(sfrd_h), color='b',alpha=0.5)
    #plt.fill_between(z_med,numpy.log10((sfrd_2_l)),numpy.log10(sfrd_2_h), color='c',alpha=0.7)
    #plt.fill_between(z_med,numpy.log10((sfrd_3_l)),numpy.log10(sfrd_3_h), color='g',alpha=0.5)
    #plt.fill_between(zrecon,numpy.log10((srecon_l)),numpy.log10(srecon_h), label='95% Free Model',color='b',alpha=0.3)
    #plt.fill_between(z_b,numpy.log10((Ilbert_l)),numpy.log10(Ilbert_h), color='orange',alpha=0.2)
    #plt.fill_between(zrecon,numpy.log10((srecon_l_2)),numpy.log10(srecon_h_2), label='95% Model B',color='g',alpha=0.6)
    #plt.fill_between(zrecon,numpy.log10(srecon_l_3_2),numpy.log10(srecon_h_3_2),edgecolor="k",linewidth=5, label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{PLE}$ $\rm{q_{TIR}(z=0)}$',color='m',alpha=0.6)
    plt.fill_between(zrecon,numpy.log10((srecon_l_3)),numpy.log10(srecon_h_3), label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{PLE}$',color='m',alpha=0.6)
    plt.fill_between(zrecon,numpy.log10((srecon_l)),numpy.log10(srecon_h), label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{Individual}$',color='b',alpha=0.4)
    plt.fill_between(zrecon,numpy.log10((srecon_l2)),numpy.log10(srecon_h2), edgecolor="k", linewidth=4, label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{Individual}$ $\rm{q_{TIR}(z=0)}$',color='b',alpha=0.4)
    plt.fill_between(G_x,numpy.log10(Gruppioni_ydown),numpy.log10(Gruppioni_yup), color='k',alpha=0.3,label=r'$\rm{Gruppioni+2013}$ $\rm{IR}$')
    #plt.fill_between(nov_z,nov_phi_L, nov_phi_L2, color='r',alpha=0.4,label='Novak+2017 $\Phi$ and L Evolution')
    #plt.fill_between(Burg_z,numpy.log10(0.63*0.01*(Burg_FIR - Burg_FIR_er)),numpy.log10(0.63*0.01*(Burg_FIR + Burg_FIR_er)), color='r',alpha=0.2,label='Burgarella-2013 FIR')
    #plt.fill_between(Burg_z,numpy.log10(0.63*0.01*(Burg_tot - Burg_tot_er)),numpy.log10(0.63*0.01*(Burg_tot + Burg_tot_er)), color='g',alpha=0.2,label='Burgarella-2013 Total')
    plt.ylabel(r'$\rm{\log10(SFRD[M_\odot yr^{-1} Mpc^{-3}])}$',fontsize=32)
    plt.xlabel(r'$\rm{z} $',fontsize = 35)    
    #plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    plt.tick_params(axis='both',which = 'major', labelsize=20,width =1)
    plt.tick_params(axis='both',which = 'minor', labelsize=10, width=1)
    plt.subplots_adjust(hspace=0,wspace=0)
    #plt.xtick(fontsize=15)
    #plt.ytick(fontsize=15)
    
    #plt.ylim(-10.2,-5.8)
    ##plt.ylim(-2.7,0)
    plt.ylim(-2.2,-0.5)
    plt.xlim(0,3.8)
    #plt.ylim(-2.3,-0.3)
    #ax.xaxis.set_minor_locator(AutoMinorLocator())
    #ax.yaxis.set_minor_locator(AutoMinorLocator())
    #print truth['LMIN']
    
    plotf='%s/LF_recon_s1.pdf' % (outdir)
    handles,labels=plt.gca().get_legend_handles_labels()
    print labels
    print len(labels)
    order=[4, 1,2, 0,3,5, 6, 7,8]
    plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],frameon=False,ncol=2, prop={'size':24}).draggable()
    plt.show()
    
	
    return 0
示例#8
0
def main():
    """
    """

    # Import the settings variables
    print 'Settings file is %s' % param_file

    # Import the settings variables
    set_module = importlib.import_module(settingsf)
    globals().update(set_module.__dict__)

    # Set up the experiment
    #bins=[12.679592, 31.84969509, 80.00281696, 200.9579904, 504.78364938, 1010, 3267.95919972]# 0.4 bins

    bins = numpy.logspace(0, 3.01, 10)
    bins = list(bins)
    bins.append(2e3)
    bins2 = numpy.arange(18., 27., 0.2)
    bins1 = numpy.arange(18.2, 27.2, 0.2)
    print redshifts
    nbins = len(bins)
    z_m = redshifts[0]
    dl = get_dl(z_m)
    sbin1 = get_sbins(10**bins1, z_m, dl) * 1e6
    sbin2 = get_sbins(10**bins2, z_m, dl) * 1e6
    #L = 10**bins1*(1.4/3.)**(.7)
    #L =get_Lbins(sbin1,z_m,dl)*(1.4/3.)**(-.7)
    expt = countModel(modelFamily,
                      nlaws,
                      settingsf, [dataset],
                      floatNoise,
                      doRedshiftSlices=True,
                      mybins=sbin2)
    f = '%spost_equal_weights.dat' % outstem
    f = os.path.join(outdir, f)
    print 'os.pathf', f
    chain = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']
    # Load equally-weighted posterior samples
    x = numpy.genfromtxt(f)
    nsamp = x.shape[0]
    ncols = x.shape[1]  # The fifth [seventh] column is the posterior value
    # There must be a better way, but:
    #ncols = 14
    z = numpy.zeros((nsamp, ncols - 1 + expt.nbins))
    z[:, :-(expt.nbins - 1)] = x
    # Shift posterior values to end
    z[:, -1] = z[:, ncols - 1]  # Copy...
    z[:, ncols - 1] = 0.0  # ...and blank

    print 'outstem', outstem

    # Fetch best-fit parameters and calculate best-fit line
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap = ana.get_best_fit()['parameters']

    if True:
        print '--> Calculating *ML* reconstruction'
        drawmap = drawmap

    ymap = expt.evaluate(drawmap)  #,expt.binsMedian)
    #print ymap

    evaluations = []
    evaluate = True
    for num in range(1, 12):
        print num

        z_min, z_max, z_m = get_z(num, False)

        # for k in range(len(phi_sf)):
        #    print sbin1[k],bins1[k],numpy.power(10,bins1[k]),numpy.log10(L_1[k]),numpy.log10(L_2[k]), numpy.log10(phi_sf[k]), numpy.log10(phi_agn[k]),numpy.log10(ymap[k]),numpy.log10(ymap2[k]),numpy.log10(ymap_ev[num-1][k])
        #sys.ek
        #print ymap

        for isamp in xrange(nsamp):
            if evaluate:
                evaluations.append(expt.evaluate(z[isamp, :]))

            z[isamp, ncols - 1:] = evaluations[isamp][num - 1]
            #[LF(S,z_m,0,0,dl,z[isamp,:],\
            #expt.parameters,area=expt.survey.SURVEY_AREA,\
            #family=modelFamily) for S in expt.binsMedian/1.0e6]
            #print             [LF(S,z_m,0,0,dl,z[isamp,:],\
            #                  expt.parameters,area=expt.survey.SURVEY_AREA,\
            #                  family=modelFamily) for S in expt.binsMedian/1.0e6]
            #sys.exit()

        #sys.exit()
        evaluate = False
        z[numpy.where(z == 0.0)] = 'NaN'

        s = numpy.zeros((len(expt.binsMedian), 8))
        s[:, 0] = expt.binsMedian
        recons = z[:, ncols - 1:]

        #print '# ibin flux fit low high dlower dupper skew kurtosis'
        print '%6s %12s %12s %12s %12s %12s %12s %12s %12s' % (
            'bin', 'lower phi_rms', 'lower phi', 'l_err', 'ymap', 'u_err',
            'upper phi', 'u_phi_rms', '2*rms')
        for ibin in xrange(len(expt.binsMedian)):
            x = recons[:, ibin]
            # Remove NaNs from stats vectors
            # http://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
            x = x[~numpy.isnan(x)]
            #ss=stats.bayes_mvs(x,alpha=0.68)[0]
            #x*=numpy.power(s[ibin,0]/1.0e6,2.5)
            #print bins1[ibin]
            #plt.hist(numpy.log10(x),bins=15)
            #plt.xlabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$',fontsize = 20)
            #plt.show()
            tt = peak_confidence(x, bins=10)

            try:
                ss = numpy.zeros(3)
                ss[0], dlow, dhigh, ss[1], ss[2] = calculate_confidence(
                    x, alpha=0.95, ret_all=True)
                #ss[0],dlow,dhigh,ss[1],ss[2]=calculate_confidence2(x,alpha=0.68,ret_all=True,\
                #                                                   value_central=ymap[ibin],\
                #                                                   truncate_edges=True)
            except:
                ss = numpy.nan * numpy.ones(3)
                print "didn't work for ", x
                continue
            #sys.exit()
            #tt=peak_confidence(x,bins=10)
            #ss*=numpy.power(s[ibin,0]/1.0e6,2.5)
            #print ss[0],tt
#        s[ibin,1]=ss[0]  # median
#        s[ibin,1]=tt     # peak
            s[ibin, 1] = ymap[num - 1][ibin]  # MAP
            #print ymap
            s[ibin, 2] = ss[1]  # lower
            s[ibin, 3] = ss[2]  # upper
            s[ibin, 4] = 2 * numpy.std(
                x)  # (s[ibin,3]- s[ibin,2])/(2*numpy.std(x))# skewness
            s[ibin, 5] = ss[0]

            #print '%6.1f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f'%(bins1[ibin], numpy.log10((ymap[ibin] - s[ibin,4])), numpy.log10(ss[1]),numpy.log10(s[ibin,2]),numpy.log10(ymap[ibin]),numpy.log10(s[ibin,3]) ,numpy.log10(ss[2]), numpy.log10(s[ibin,4]+ ymap[ibin]), numpy.log10(s[ibin,4]) )
            #print ibin,s[ibin,0],s[ibin,1],dlow,dhigh,ss[1],ss[2],s[ibin,4],s[ibin,5]#,stats.skewtest(x)
            #sys.exit()

    # ...and output to file
    #sys.exit()
        rstatsf = 'recon_stats_%s.txt' % (chain[num - 1])
        rstatsf = os.path.join(outdir, rstatsf)
        hdr = '# median_flux_uJy dnds_2p5_Jy1p5srm1 delta_dnds_2p5_lower_Jy1p5srm1 delta_dnds_2p5_upper_Jy1p5srm1 skewness kurtosis'
        fid = open(rstatsf, 'w')
        print hdr
        print s

        print '-> Writing stats (i.e. dnds2p5+/- v. S/uJy) to %s' % rstatsf
        fid.write('%s\n' % hdr)
        numpy.savetxt(fid, s)
        fid.close()

        print 'Finished.'

    return 0
示例#9
0
def main():
    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module = importlib.import_module(setf)
    globals().update(set_module.__dict__)

    expt = countModel(modelFamily,
                      nlaws,
                      setf, [dataset],
                      floatNoise,
                      doRedshiftSlices=True)
    recon_expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
    #print expt.data
    #print expt.bins

    # Get MAP parameters
    print '\n' * 5
    print len(expt.parameters)
    ncols = len(expt.parameters)
    summf = os.path.join(outdir, '1-summary.txt')
    summary = numpy.genfromtxt(summf)[-1, :]
    drawmap = summary[-(ncols + 2):-2]
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap = ana.get_best_fit()['parameters']

    if dataset == 'sdss':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 20:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
        print datafiles[0][4:]
    elif dataset == 'cosmos':
        #print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 24:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
        #print '%s%s'%(outdir,datafiles[0][8:])
        print datafiles[0][8:]

    elif 'sim' in dataset:
        d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))

    ssbin = d[:, 2]
    Nbin = d[:, 3]
    dnds = d[:, 4]
    sbin2 = ssbin
    dnds2 = dnds
    #bins2 = numpy.arange(21.4,29.2,0.4)
    bins2 = numpy.arange(18.0, 29.2, 0.4)
    bins3 = numpy.arange(18., 29.2, 0.01)
    print len(expt.parameters)
    print 'this is 1st num ', num
    #sys.exit()
    params = drawmap  # [0]
    #for i in drawmap:
    #	params.append(i)
    print params
    print expt.parameters

    if modelFamily in [
            'LFsch', 'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z',
            'LFlognorm_dpl', 'LFpl_lognorm'
    ]:
        Lnorm = params[expt.parameters.index('LNORM')]
        Lstar = params[expt.parameters.index('LSTAR')]
        Lslope = params[expt.parameters.index('LSLOPE')]
        #Lzevol=params[expt.parameters.index('LZEVOL')]
    if modelFamily in ['LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFlognorm_dpl']:
        Lslope2 = params[expt.parameters.index('LSLOPE2')]
    if modelFamily in [
            'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFpl_dpl',
            'LFlognorm_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFpl_lognorm'
    ]:
        Lnorm_2 = params[expt.parameters.index('LNORM_2')]
        Lstar_2 = params[expt.parameters.index('LSTAR_2')]
        Lslope_2 = params[expt.parameters.index('LSLOPE_2')]

    if modelFamily in ['LFdpl_dpl', 'LFdpl', 'LFdpl_dpl_z']:
        Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]

    if modelFamily in [
            'LFlognorm', 'LFlognorm_dpl', 'LFpl_lognorm', 'LFevol_logn_sigma'
    ]:
        Lsigma = params[expt.parameters.index('LSIGMA')]
    if modelFamily in [
            'LFdpl_dpl_z', 'LFevol_dpl', 'LFevol_logn', 'LFevol_logn_all',
            'LFevol_logn_all_L'
    ]:
        alpha_agn = params[expt.parameters.index('A_agn')]
        alpha_SF = params[expt.parameters.index('A_SF')]
        beta_agn = params[expt.parameters.index('B_agn')]
        beta_SF = params[expt.parameters.index('B_SF')]
    if modelFamily in ['LFevol_dpl_s', 'LFevol_logn_s']:
        alpha_SF = params[expt.parameters.index('A_SF')]
        beta_SF = params[expt.parameters.index('B_SF')]
    if modelFamily in [
            'LFevol_logn_lnorm', 'LFevol_logn_all', 'LFevol_logn_all_L'
    ]:
        Lnorm = params[expt.parameters.index('LNORM')]
        Lstar = params[expt.parameters.index('LSTAR')]

    if modelFamily in [
            'LFevol_logn_mat', 'LFevol_phi_logn_mat', 'LFevol_logn_el',
            'LFevol_logn_sigma', 'LFevol_logn_lmin', 'LFevol_logn_lnorm'
    ]:
        alpha_SF = params[expt.parameters.index('A_SF')]
        alpha_agn = params[expt.parameters.index('A_agn')]
    if modelFamily in [
            'LFevol_logn_slope', 'LFevol_logn_all', 'LFevol_logn_all_L'
    ]:
        alpha_SF = params[expt.parameters.index('A_SF')]
        alpha_agn = params[expt.parameters.index('A_agn')]
        Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]

    Lmin = params[expt.parameters.index('LMIN')]
    Lmax = params[expt.parameters.index('LMAX')]
    truth = {
        'noise': 150,
        'LMIN': 22.,
        'LMAX': 24.5,
        'LNORM': numpy.log10(1e-7),
        'LSTAR': 23.3,
        'LSLOPE': 3.,
        'LSLOPE2': 1.
    }
    #z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
    #z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
    print expt.parameters
    s = pylab.loadtxt('%s/recon_stats.txt' % outdir)
    xrecon = s[:-1, 0]
    yrecon = s[:-1, 1]
    yrecon_d = s[:-1, 2]
    yrecon_u = s[:-1, 3]
    yrecon_rms = s[:-1, 4]
    yrecon_avr = s[:-1, 5]
    yrecon_rms_down = yrecon_rms

    #novak 2018
    L_n2   =[[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56],\
             [22.30,22.61,22.96,23.38,23.80,24.10, 24.55,25.14],\
             [22.61,22.86,23.14,23.45,23.82,24.14, 24.40,24.71],\
             [22.85,23.16,23.69,24.24,24.80,25.31, 25.96,26.69],\
             [23.10,23.38,23.86,24.36,24.86,25.35, 25.94,26.36],\
             [23.32,23.57,23.94,24.32,24.67,25.06, 25.47,25.96],\
             [23.54,23.75,24.13,24.45,24.90,25.27, 25.74,26.10],\
             [23.73,23.99,24.57,25.10,25.68,26.18, 26.83,27.51],\
             [24.01,24.26,24.76,25.26,25.91,26.19, 27.45],\
             [24.30,24.56,24.80,25.13,25.37,25.80, 25.97,26.49]]


    L_ner_u2  =[[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03],\
                [0.11,0.20, 0.26, 0.25, 0.24, 0.35, 0.31, 0.15],\
                [0.08,0.15, 0.20, 0.22, 0.17, 0.18, 0.24, 0.28],\
                [0.072,0.33,0.38, 0.40, 0.48, 0.42, 0.41, 0.28],\
                [0.080,0.30,0.32, 0.32, 0.32, 0.33, 0.24, 0.34],\
                [0.068,0.22,0.24, 0.25, 0.29, 0.30, 0.28, 0.21],\
                [0.067,0.22,0.21, 0.26, 0.18, 0.17,0.075, 0.11],\
                [0.093,0.36,0.31, 0.30, 0.25, 0.28, 0.16,0.028],\
                [0.076,0.34,0.36, 0.38, 0.24, 0.47, 0.27],\
                [0.097,0.14,0.20, 0.16, 0.23, 0.092,0.22,0.026]]


    L_ner_d2  =[[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50],\
               [0.29, 0.21, 0.15, 0.16, 0.17, 0.06, 0.099,0.29],\
               [0.24, 0.17, 0.12, 0.10, 0.15, 0.15, 0.08,0.074],\
               [0.16, 0.24, 0.19, 0.17, 0.15, 0.09, 0.16, 0.33],\
               [0.30, 0.19, 0.18, 0.18, 0.18, 0.17, 0.26, 0.18],\
               [0.14, 0.18, 0.15, 0.14, 0.10, 0.095,0.11, 0.21],\
               [0.19, 0.14, 0.15, 0.11, 0.19, 0.19, 0.29, 0.28],\
               [0.40, 0.17, 0.21, 0.23, 0.27, 0.25, 0.37, 0.53],\
               [0.20, 0.17, 0.16, 0.14, 0.27, 0.042, 0.27],\
               [0.22, 0.16, 0.10, 0.14,0.072, 0.21, 0.08, 0.30]]

    rho_n2 =[[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07],\
             [-2.95,-3.17,-3.46,-4.24,-4.76,-5.41, -5.23,-5.44],\
             [-2.95,-3.11,-3.44,-3.81,-4.37,-4.48, -4.90,-5.14],\
             [-2.99,-3.24,-3.89,-4.54,-5.27,-5.33, -5.69,-5.89],\
             [-3.32,-3.51,-4.06,-4.74,-5.28,-5.43, -6.08,-5.70],\
             [-3.29,-3.54,-4.02,-4.45,-5.11,-5.56, -5.37,-6.07],\
             [-3.43,-3.61,-4.19,-4.56,-5.09,-5.34, -5.70,-5.75],\
             [-3.88,-3.98,-4.64,-5.33,-5.73,-6.27, -6.14,-6.77],\
             [-3.85,-4.28,-5.05,-5.60,-5.68,-6.44, -6.59],\
             [-4.43,-4.91,-5.46,-5.58,-5.89,-6.23, -6.26,-6.91]]

    rho_ner_u2=[[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34],\
                [0.048,0.027,0.036,0.089,0.17,0.45, 0.34, 0.45],\
                [0.062,0.024,0.032,0.048,0.096, 0.11,0.18,0.25],\
                [0.042,0.018,0.035,0.073,0.180, 0.20,0.34,0.45],\
                [0.045,0.019,0.033,0.072,0.15,  0.17,0.45,0.25],\
                [0.040,0.022,0.033,0.055,0.13,  0.22,0.17,0.45],\
                [0.061,0.020,0.035,0.055,0.099, 0.14,0.22,0.25],\
                [0.044,0.022,0.044,0.11, 0.16, 0.34, 0.28,0.76],\
                [0.048,0.025,0.058,0.13, 0.24, 0.34, 0.45],\
                [0.084,0.058,0.11 ,0.24, 0.20, 0.28, 0.28,0.76]]

    rho_ner_d2=[[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30],\
                [0.043,0.026,0.033,0.074,0.16,0.37, 0.30, 0.37],\
                [0.054,0.023,0.030,0.043,0.079,0.086,0.17,0.22],\
                [0.038,0.017,0.032,0.062,0.17,0.19, 0.30, 0.37],\
                [0.041,0.019,0.031,0.062,0.11,0.16, 0.37, 0.22],\
                [0.037,0.021,0.031,0.049,0.098,0.20,0.16, 0.37],\
                [0.054,0.019,0.032,0.049,0.081,0.11,0.20, 0.22],\
                [0.040,0.021,0.040,0.085,0.15, 0.30, 0.25,0.52],\
                [0.043,0.024,0.051,0.10, 0.15, 0.30, 0.37],\
                [0.070,0.051,0.087,0.16,0.19, 0.25, 0.25, 0.52]]
    #print xrecon
    #print yrecon
    #sys.exit()
    z_new = True
    Replot = False
    loglike = 0.
    loglike_nov = 0.
    lmin3 = [19.2, 19.8, 20.5, 20.8, 21.6, 22.2, 22.5, 23., 22.9, 23.]
    lmin3 = [19.7, 20.3, 21., 21.3, 22.1, 22.7, 23., 23.5, 23.4, 23.5]
    lmin3 = [20.2, 20.8, 21.5, 21.8, 22.6, 23.2, 23.5, 24., 23.9, 24.]
    lmin3 = [20.7, 21.3, 22., 22.3, 23.1, 23.7, 24., 24.5, 24.4, 24.5]
    lmin3 = [21.7, 22.3, 23., 23.3, 24.1, 24.7, 25., 25.5, 25.4, 25.5]
    lmin3 = [21.2, 21.8, 22.5, 22.8, 23.6, 24.2, 24.5, 25., 24.9, 25.]

    sm = [
        0.05162624, 0.0651364, 0.17144366, 0.19570955, 0.73934002, 1.58958387
    ]
    sm = [
        0.16325651, 0.20597938, 0.54215247, 0.61888793, 2.33799844, 5.02670556
    ]
    sm = [
        0.51626243, 0.651364, 1.71443663, 1.95709547, 7.39340024, 15.89583871
    ]
    sm = [
        1.63256513, 2.05979383, 5.42152466, 6.18887928, 23.37998442,
        50.26705564
    ]
    sm = [
        16.32565134, 20.5979383, 54.21524659, 61.88879277, 233.79984425,
        502.67055637
    ]
    sm = [5.16, 6.51, 17.14, 19.57, 73.93, 158.95, 206.55]

    if not z_new:
        z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
    else:
        z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]  #oldest ;)
    num = int(num)
    #num = 1.
    print num
    z_min, z_max, z_m = get_z(num, z_new=False)
    dl = get_dl(z_m)
    Vmax = get_Vmax(z_min, z_max)
    dsdl = get_dsdl(z_m, dl)

    #z_m = (z_min + z_max)/2
    dl = get_dl(z_m)

    print z_min, z_max
    print expt.kind
    area = SURVEY_AREA * sqDeg2sr
    area1 = SURVEY_AREA * sqDeg2sr
    print SURVEY_AREA

    SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
    SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
    sigma, fsigma = numpy.log10(
        get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5], z_m, dl, 'muJy') *
        (1.4 / 3)**(-.7))
    print z_m, dl, sigma, fsigma
    print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA

    #area = 6672*sqDeg2sr #143165.15  49996.49 59876.8861135
    sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
    sbin4 = get_sbins(10**bins3, z_m, dl) * 1e6
    #L_s   = numpy.log10(get_Lbins(ssbin,z_m,dl,'muJy'))
    #print expt.binsMedian
    #sys.exit()

    xreco = get_Lbins(xrecon, z_m, dl, 'muJy')  #*(1.4/3)**(-.7)
    yreco = yrecon

    print SMIN, SMAX
    #sys.exit()
    lin_yrecon = numpy.log10(yreco)
    lin_xrecon = numpy.log10(xreco)

    bins2 = numpy.arange(15., 25., 0.4)
    bins = numpy.arange(fsigma - 0.2, 29.2, 0.4)  #bin +0.2
    #bins =[23.6,23.9,24.1,24.3,24.9,25.6,26.,26.7,28] #2< z<3.2
    #bins =[23.6, 23.9,24.1,24.4,24.6,24.9,25.2,25.6,26.,26.4,27.,27.5,28] #3.2< z<4
    bins = numpy.array(bins)
    if not os.path.isfile(
            'cos_data/cos_s%s_LF.el' % num) and z_new or not os.path.isfile(
                'cos_data/cos_s%s_LF_old.el' % num) and not z_new or Replot:
        print 'Reploting the RLF and storing them for next time.'
        #l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
        #l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
        if z_new:
            L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
                                  F='uJy',
                                  L=True,
                                  getz=True,
                                  band='S')
            print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
        else:
            L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
                                  F='uJy',
                                  L=True,
                                  getz=True,
                                  band='S')
            print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
        L_c, z_c = open_anytype('cosmos_counter_parts_II.txt', (3, 4),
                                [z_min, z_max],
                                F='uJy',
                                L=True,
                                getz=True,
                                band='S')
        L_c2, z_c2 = open_anytype('cosmos_comp_vla.el', (2, 3), [z_min, z_max],
                                  F='uJy',
                                  L=True,
                                  getz=True,
                                  band='S')
        L_c3, z_c3 = open_anytype('cosmos_vla.el', (2, 3), [z_min, z_max],
                                  F='uJy',
                                  L=True,
                                  getz=True,
                                  band='S')
        L_c4, z_c4 = open_anytype(
            '/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',
            (-4, -1), [z_min, z_max],
            F='uJy',
            L=True,
            getz=True,
            band='S')
        L_c5, z_c5 = open_anytype(
            '/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',
            (3, -1), [z_min, z_max],
            F='uJy',
            L=True,
            getz=True,
            band='S')
        #z_c2,S_c2,L_c2 =numpy.loadtxt('cosmos_counter_parts_II.txt',unpack=True, usecols=(3,4,5))
        #L_c2=L_c2[(z_c2<z_max)&(z_c2 >z_min)]
        #S_c2=S_c2[(z_c2<z_max)&(z_c2 >z_min)]
        #z_c2=z_c2[(z_c2<z_max)&(z_c2 >z_min)]

        #rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
        #rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
        #print numpy.log10(L)
        #print (1.4/3)**(-.7)
        #print numpy.log10(L*(1.4/3)**(-.7))
        #sys.exit()
        rho_3, er3, b3, n3 = calc_Vmax(calcu_zeff(z_L, L, 1.15e-31),
                                       L,
                                       bins,
                                       z_max,
                                       z_min,
                                       area,
                                       table=True)
        #rho_4,er4 = calc_Vmax(99.,L,bins,z_max,z_min,area)
        print 'COS15 VLA'
        rho_5, er5, b5, n5 = calc_Vmax(calcu_zeff(z_c, L_c, 1.15e-31),
                                       L_c,
                                       bins,
                                       z_max,
                                       z_min,
                                       1.77 * sqDeg2sr,
                                       table=True)
        print 'mass lim DR4-vla'
        rho_6, er6, b6, n6 = calc_Vmax(calcu_zeff(z_c2, L_c2, 1.15e-31),
                                       L_c2,
                                       bins,
                                       z_max,
                                       z_min,
                                       1.5 * sqDeg2sr,
                                       table=True)
        print 'DR4-vla'
        rho_7, er7, b7, n7 = calc_Vmax(calcu_zeff(z_c3, L_c3, 1.15e-31),
                                       L_c3,
                                       bins,
                                       z_max,
                                       z_min,
                                       1.5 * sqDeg2sr,
                                       table=True)
        print 'cos-DR4-vla DR4 photo-z'
        rho_8, er8, b8, n8 = calc_Vmax(calcu_zeff(z_c4, L_c4, 1.15e-31),
                                       L_c4,
                                       bins,
                                       z_max,
                                       z_min,
                                       1.5 * sqDeg2sr,
                                       table=True)
        print 'cos-DR4-vla COS15 photo-z'
        rho_9, er9, b9, n9 = calc_Vmax(calcu_zeff(z_c5, L_c5, 1.15e-31),
                                       L_c5,
                                       bins,
                                       z_max,
                                       z_min,
                                       1.5 * sqDeg2sr,
                                       table=True)
        for i in range(len(b3)):
            print '%5.2f %7d %10d %13d %17d %11d' % (b3[i], n3[i], n7[i],
                                                     n8[i], n9[i], n5[i])
        if z_new:
            f = open('cos_data/cos_s%s_LF_5.el' % num, 'w')
            #_2 paper _5 stellar mass 10.2
            print 'saving data for future use in cos_data/cos_s%s_LF_5.el' % num
        else:
            f = open('cos_data/cos_s%s_LF_old.el' % num, 'w')
            print 'saving data for future use in cos_data/cos_s%s_LF_old.el' % num
        for i in range(len(bins)):
            f.write(
                '%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n'
                %
                (bins[i], rho_3[i], er3[i], rho_5[i], er5[i], rho_6[i], er6[i],
                 rho_7[i], er7[i], rho_8[i], er8[i], rho_9[i], er9[i]))
        f.close()

    else:
        if z_new:
            bins, rho_3, er3, rho_5, er5, rho_6, er6, rho_7, er7, rho_8, er8, rho_9, er9 = numpy.loadtxt(
                'cos_data/cos_s%s_LF_2.el' % num, unpack=True)
            print 'Using stored RLF for the new z (The one with more z bins ) from cos_data/cos_s%s_LF.el. If you want to calculate the plot the set "Replot=True"' % num
        else:
            bins, rho_3, er3, rho_5, er5, rho_6, er6, rho_7, er7, rho_8, er8, rho_9, er9 = numpy.loadtxt(
                'cos_data/cos_s%s_LF_old.el' % num, unpack=True)
            print 'Using stored RLF for the is old z bins in fr cos_data/cos_s%s_LF.el. If you want to calculate the plot the set "Replot=True"' % num

    #novak 2017
    '''
    L_n   =[21.77,22.15,22.46,22.77,23.09,23.34]
    rho_n =[-2.85,-2.88,-3.12,-3.55,-4.05,-4.63]
    L_ner_u =[0.23,0.18,0.19, 0.2, 0.21, 0.28]
    L_ner_d =[1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
    rho_ner=[0.09, 0.03,0.0355,0.059,0.1,0.234]
    
    #novak 2018
    L_n2   =[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56]
    rho_n2 =[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07]	    
    L_ner_u2  =[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03]
    L_ner_d2  =[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50]
    rho_ner_u2=[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34]
    rho_ner_d2=[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30]
    #l*= (1.4/3)**(-.7)
    #l2*= (1.4/3)**(-.7)
    #L*= (1.4/3)**(-.7) 
    '''

    #Lbins2 = numpy.log10(lf.get_Lbins(sbin2[sbin2>0],z_m,dl))
    print z_min, z_max

    #bins  = numpy.arange(fsigma -4.2,30,0.4) #bin
    #s,s_z = numpy.loadtxt('sdss/dr12s_s1_vol.txt', unpack=True, usecols=(-1,2))
    s, s_z = numpy.loadtxt('cos_data/cos_s%s.txt' % (num),
                           unpack=True,
                           usecols=(-1, 2))
    #s=s*1e6
    print fsigma

    L_s = (get_Lbins(ssbin, z_m, dl, 'muJy')) * (1.4 / 3.)**(-.7)
    L_s = numpy.log10(L_s)
    rho_1 = lumfuncs(
        Nbin, L_s,
        calc_Vmax(calcu_zeff(z_m, 10**L_s),
                  L_s,
                  L_s,
                  z_max,
                  z_min,
                  area,
                  getLF=False))
    #L_s = numpy.log10(10**L_s*(1.4/3.)**(-.7))
    #sys.exit()

    rho_11 = dntoLF(L_s, Nbin, dsdl, Vmax)
    #dn     = expt.realise(params)
    #nov_real=expt.realise([2.3,2.95,2.86,-0.29,-0.7,19.5,26.0])

    fig, ax = plt.subplots()

    plt.rc('lines', linewidth=2)

    if modelFamily in ['LFdpl', 'LFdpl_dpl', 'LFdpl_dpl_z']:
        faint = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), Lstar_2,
                                            Lslope_2, Lslope2_2, Lnorm_2)
    elif modelFamily in ['LFpl', 'LFdpl_pl']:
        faint = lumfuncUtils.powerlaw(numpy.power(10, bins3), Lstar_2,
                                      Lslope_2, Lnorm_2)
    elif modelFamily in ['LFpl_dpl', 'LFdpl_dpl_z']:
        faint = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), Lstar,
                                            Lslope, Lslope2, Lnorm)
    elif modelFamily == 'LFevol_logn_mat':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
    elif modelFamily == 'LFevol_phi_logn_mat':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_phi_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
    elif modelFamily == 'LFevol_logn_el':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_el',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
    elif modelFamily == 'LFevol_phi_logn':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_phi_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]
    elif modelFamily == 'LFevol_logn':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]

    elif modelFamily == 'LFevol_logn_slope':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_slope',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]
    elif modelFamily == 'LFevol_logn_sigma':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_sigma',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]
    elif modelFamily == 'LFevol_logn_lstar':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_lstar',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]
    elif modelFamily == 'LFevol_logn_lmin':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_lmin',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]
    elif modelFamily == 'LFevol_logn_lnorm':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_lnorm',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]
    elif modelFamily == 'LFevol_logn_all':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_all',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]

    elif modelFamily == 'LFevol_logn_all_L':
        faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
               family='LFevol_logn_all_L',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for  lL in bins3]

    else:
        faint = lumfuncUtils.lognormpl(numpy.power(10, bins3), Lstar_2,
                                       Lslope_2, Lsigma, Lnorm_2)

    #phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)

    L_1 = numpy.power(10, bins3) / (1 + z_m)**(2.86 - z_m * 0.7)
    L_2 = numpy.power(10, bins3) / (1 + z_m)**(2.95 - z_m * 0.29)
    #L_4 = numpy.power(10,bins3)/(1 + z_m)**(alpha_SF)
    #L_3 = numpy.power(10,bins3)/(1 + z_m)**(alpha_agn)
    #L_4 = numpy.power(10,bins3)/(10)**(alpha_SF)
    #L_3 = numpy.power(10,bins3)/(10)**(1)#alpha_agn)
    #for i in range(len(L_3)):
    #print bins3[i],numpy.log10(L_3[i])
    #L_4 = numpy.power(10,bins3)/(1 + z_m)**(alpha_SF+z_m*beta_SF)
    #L_3 = numpy.power(10,bins3)/(1 + z_m)**(alpha_agn+z_m*beta_agn)
    phi_agn_n = lumfuncUtils.doublepowerlaw(L_1, 24.59, 1.27, 0.49,
                                            numpy.log10(10**(-5.5)))
    phi_sf_n = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22, 0.63,
                                      numpy.log10(3.55e-3 / 2.5))  #0.33 2.97

    #phi_sf =lumfuncUtils.lognormpl(L_4, 20.56,1.22,0.63,-2.26)#numpy.log10(3.55e-3/2.5))#0.33 2.97
    phi_sf2 = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22, 0.63,
                                     numpy.log10(3.55e-3 / 2.5))  #0.33 2.97

    #phi_sf =lumfuncUtils.lognormpl(L_4, Lstar,1.22,0.63,Lnorm)#numpy.log10(3.55e-3/2.5))#0.33 2.97
    #phi_sf =lumfuncUtils.lognormpl(L_4, numpy.log10(1.85e21),1.22,0.63,numpy.log10(3.55e-3/2.5))#0.33 2.97

    nov_Tot_n = phi_agn_n + phi_sf_n
    #phi_agn=lumfuncUtils.doublepowerlaw(L_3,24.59,1.27,0.49,numpy.log10(10**(-5.5)))
    #print 'this is Lminz', numpy.log10(10**18/(1 + z_m)**(alpha_SF+z_m*beta_SF))
    #phi_agn=lumfuncUtils.doublepowerlaw(L_3,24.59,1.27,0.49,numpy.log10(10**(-5.5)))
    phi_1 = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), 23.68, 3.68,
                                        0.8, -3.36)  #0.33 2.97
    phi_2 = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), 24.5, 1.37,
                                        0.5, -4.)
    #tot = phi_1+phi_2
    dm = 0.4  #02
    hm = 0.
    #Map =expt.evaluate(params)[0]
    bins_map = get_Lbins(expt.binsMedian, z_m, dl, 'muJy')
    bins1_4 = numpy.log10(10**bins * (1.4 / 3.)**(-.7))
    #fix=[lumfuncUtils.LF(L=numpy.power(10,lL),params=[3.75,2.55,4.74,0,0,20.3,26.65],paramsList=expt.parameters,\
    #           family='LFevol_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for  lL in bins3]

    #plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7)),numpy.log10(faint)-dm,'--b' ,label='faint-end')

    #plt.plot(numpy.log10(bins_map),numpy.log10(Map), label='my MAP')
    #plt.plot(bins3,numpy.log10(fix)-dm,'--c', label='Fix')
    #plt.plot(bins3,numpy.log10(phi_agn),'-.c', label='local agn')

    #plt.plot(bins3,numpy.log10(phi_sf),'--r', label='local sf')
    #plt.plot(bins3,numpy.log10(phi_sf2),'--c', label='local sf2')
    #plt.plot(numpy.log10(10**bins3),numpy.log10(phi_dpl)-dm,'--c', label='agn')
    #plt.plot(numpy.log10(10**bins3*(1.4/3.)**(.7)),numpy.log10(tot)-0.4,'-.g',linewidth=5, label='Tot')

    #plt.plot(bins3,numpy.log10(phi_agn),'--r', label='local agn nov')
    plt.errorbar(lin_xrecon + hm,
                 lin_yrecon - dm,
                 fmt='-b',
                 label='This work Total',
                 linewidth=2)
    plt.plot(numpy.log10(10**bins3),
             numpy.log10(faint) - dm,
             '--b',
             label='This work SFG')
    #plt.errorbar(lin_xrecon+hm,numpy.log10(yrecon_avr)-dm,fmt='-g', label = 'Average')
    #plt.plot(L_s2+.2, numpy.log10(rho_10), '*c',label='average LF',markersize=13)#bin + 0.2
    plt.errorbar(bins,
                 rho_3,
                 yerr=er3,
                 fmt='ob',
                 label='This work 1/Vmax',
                 markersize=8)  #bin
    #plt.errorbar(bins2[17:],rho_3[17:] ,yerr=er3[17:],fmt='ob',label='extracted',markersize=8) #bin
    #plt.errorbar(bins,rho_5 ,yerr=er5,fmt='^k',label='VLA-COSMOS2015',markersize=10) #bin
    #plt.errorbar(bins,rho_8 ,yerr=er8,fmt='sc',label='VLA-COSMOS2015 in DR4 using DR4 z',markersize=10) #bin
    #plt.errorbar(bins,rho_9 ,yerr=er9,fmt='*m',label='VLA-COSMOS2015 in DR4 using COS15z',markersize=14) #bin
    #plt.errorbar(bins,rho_6 ,yerr=er6,fmt='ok',label='DR4 detected',fillstyle='none',markersize=14) #bin
    #plt.errorbar(bins,rho_7 ,yerr=er6,fmt='dg',label='DR4 detected - Full NIR sample',markersize=12) #bin
    #plt.plot(L_s, numpy.log10(rho_1), '*m',label='low LF',markersize=10)
    #plt.plot(L_s, numpy.log10(rho_11), 'sc',label='reconstruct LF',markersize=10)
    #plt.errorbar(numpy.array(L_n2[num-1]),numpy.array(rho_n2[num-1]) -0.4,xerr=[L_ner_d2[num-1],L_ner_u2[num-1]],yerr=[rho_ner_d2[num-1],rho_ner_u2[num-1]], fmt='sr',fillstyle='none',ecolor='r',markeredgewidth=1.5, label='Total RLF Novak+2018')
    plt.errorbar(numpy.log10(10**bins3),
                 numpy.log10(nov_Tot_n),
                 fmt='-r',
                 label='Total Novak + 2018')
    plt.plot(bins3, numpy.log10(phi_sf_n), '-.r', label='SFG Novak + 2017')
    '''
    print 'This is the dn'
    
    print dn
    
    for key in dn.keys():
        if round(key,2)==round(z_m,2):z_m=key

    dn=dn[z_m]
    nov_real=nov_real[z_m]
    for i in range(len(L_s)):
            loglike_i= Nbin[i]*numpy.log(dn[i]) + Nbin[i] - (Nbin[i] + 0.5)*numpy.log(Nbin[i]) - 0.5*numpy.log(2*numpy.pi)  - dn[i]
            loglike_nov_i= Nbin[i]*numpy.log(nov_real[i]) + Nbin[i] - (Nbin[i] + 0.5)*numpy.log(Nbin[i]) - 0.5*numpy.log(2*numpy.pi)  - nov_real[i]
            if nov_real[i]>0:loglike_nov+=loglike_nov_i
            if dn[i]>0:loglike+=loglike_i
            print '%10.5f %8.2d %8.2f %8.2f %8.2f %8.2f %8.2f %8.2f'%(ssbin[i], Nbin[i], dn[i],nov_real[i],loglike_i,loglike_nov_i,loglike, loglike_nov)
    '''
    #plt.errorbar(bins[1:],rho_2[1:] ,yerr=er2[1:],fmt='ok',label='dectected catalog',markersize=10) #bin
    #plt.errorbar(bins2[1:]-0.2,rho_3[1:],yerr=er3[1:],fmt='sr',label='noisy',markersize=10) #bin
    #plt.errorbar(bins2[1:]-0.2,rho_4[1:],yerr=er4[1:],fmt='vm',label='noisy lim',markersize=10) #bin
    #plt.errorbar(numpy.array(L_n),numpy.array(rho_n) -0.4,xerr=[L_ner_d,L_ner_u],yerr=rho_ner, fmt='^c', label='Novak et al 2017')
    #plt.errorbar(numpy.array(L_n2),numpy.array(rho_n2) -0.4,xerr=[L_ner_d2,L_ner_u2],yerr=[rho_ner_d2,rho_ner_u2], fmt='sk', label='Total RLF Novak et al 2018')

    #plt.plot(bins3+0.4,numpy.log10(logn)-0.4 ,'--k', label='faint-end')

    #plt.plot(bins3,numpy.log10(logn) ,'--', label='lognorm')

    #plt.plot(bins3,numpy.log10(logn2)-dm,'--r', label='lognorm2')
    #

    #plt.plot(bins3,numpy.log10(phi_dpl2), label='dpl bright-end2')
    #plt.plot(Lbins4[(Lbins4<23.6) &(Lbins4>22.12)],numpy.log10(phi_dpl)[(Lbins4<23.6) &(Lbins4>22.12)], '-c',linewidth=5 ,label = r'$\rm{ MAP dpl < 5\sigma}$')
    #plt.plot(bins2,numpy.log10(logn_2),label='starforming_2')
    #plt.plot(bins2,numpy.log10(logn_3),label='starforming_3')
    #plt.plot(bins2,numpy.log10(logn_4),label='starforming_4')

    #plt.fill_between(xrecon,numpy.log10(yrecon - yrecon_down2 ) ,numpy.log10(yrecon+yrecon_up2), color='c',alpha=0.2)

    #plt.errorbar(lin_xrecon+0.4,lin_yrecon -0.4,fmt='-b', label='MAP',linewidth=2)
    #plt.errorbar(lin_xrecon+0.4,lin_yrecon- 0.4,yerr=numpy.log10(yrecon_rms),fmt='r',label='rms')
    #plt.plot(L_s, numpy.log10(rho_1), '*c',label='average LF',markersize=10)
    #plt.plot(L_s5, numpy.log10(rho_14)-0.2, '*r',label='average LF',markersize=13)
    #plt.plot(L_s6+0.0, numpy.log10(rho_16)-0.2, '*b',label='average LF(bin)',markersize=13)
    #plt.plot(bins2[:-1], numpy.log10(rho_15), '*g',label='LF average',markersize=13)
    #print len(rho_11),len(rho_10),len(rho_12)
    #plt.fill_betweenx(numpy.log10(rho_10) ,L_s3,L_s4, color='red',alpha=0.2)

    #plt.plot(L_s6, numpy.log10(rho_13), '*b',label='average LF(bin+0.2)',markersize=13)#bin + 0.2
    #plt.plot(L_s1, numpy.log10(rho_1), 'oc',label='average LF(bin)',markersize=13)#bin

    #plt.plot(Lbins4,lin_phi ,'--b',label='MAP')

    #plt.fill_between(lin_xrecon+.0,numpy.log10((yrecon_avr -yrecon_rms))-dm,numpy.log10(yrecon_avr +yrecon_rms)-dm , color='r',alpha=0.3)

    #plt.fill_between(lin_xrecon[1:-1]+0.2,numpy.log10(yreco[1:-1] - yrecon_down[1:-1] )-dm,numpy.log10(yreco[1:-1]+yrecon_up[1:-1])-dm , color='b',alpha=0.2)
    plt.fill_between(lin_xrecon + hm,
                     numpy.log10((yrecon_d)) - dm,
                     numpy.log10(yrecon_u) - dm,
                     color='k',
                     alpha=0.2)

    #plt.errorbar(bins[2:],rho_1[2:],yerr=er1[2:],fmt = 'ob',label='Detected')
    #plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='ob',label='Detected') #working one
    #plt.plot(Lbins2,rho,'ob',label='simulation')
    #plt.plot(Lbins3,rho_,label='Noisy fluxes')
    #rho_2
    #plt.errorbar(bins,rho_2 -0.2,yerr=er2,fmt='ok',label='data',markersize=10) #bin +0.2

    #plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='vm',label='Noisy NVSS')
    #plt.errorbar(bins2,rho_7-0.2,yerr=er6,fmt='xc',label='NVSS Detected')
    #for i in range(len(bins)):
    # print bins[i], rho_2[i]
    #sys.exit()

    #plt.errorbar(Lbins2,rho, err,fmt = 'ob',label='simulation')
    #plt.errorbar(Lbins2,rho_1,err_1,fmt = 'or',label='simulation_noisy')
    #plt.errorbar(L2,rho2,color='g',label='Analytic')
    #plt.errorbar(L3,rho3,color='g',label='Analytic II')
    #plt.axvspan(22.55 ,23.51,color='r',alpha=0.2)
    #plt.axvline(Lmin,color='r',linestyle='dashed')
    plt.axvline(fsigma, color='k', linestyle='dashed')
    #plt.axvline(sigma,color='g')
    #plt.axvline(Llmin,color='r',linewidth=5)
    #plt.axvline(sigma2,color='m')
    #plt.axvline(fsigma2,color='m')
    #plt.axvline(23.89,color='b')
    #plt.axvline(23.71,color='c')
    #plt.axvline(23.52,color='r')
    #plt.axvline(truth['LMIN'],color='r', linestyle='dashed', label = 'True params')
    #plt.axvline(truth['LMAX'],color='r', linestyle='dashed')
    #plt.axvline(truth['LSTAR'],color='b')
    #plt.text(truth['LSTAR']-0.2,-9., '$L_*$',fontsize=18)
    #plt.axvline(Lmax,color='r', label= 'MAP params')
    #plt.axvline(Lmin,color='b')
    #plt.axvline(Lmax, color='r',linestyle='dashed')
    #plt.text(21,-1, '$L_*$',fontsize=18)
    #plt.text(fsigma+0.1,-6.7, '$5\sigma$',fontsize=25)
    #plt.text(sigma+0.1,-6.7, '$\sigma$',fontsize=25)
    plt.xlabel(r'$\rm{log_{10}[L_{1.4 GHz}/(W}$ $\rm{Hz^{-1})]}$', fontsize=30)
    plt.ylabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$', fontsize=30)
    lmin3_14 = 10**lmin3[num - 1] * (1.4 / 3.)**(-.7)
    #plt.text(22,-7.7,'%.1f < z < %.1f %s Lmin=%6.2f %s Lmin_cut=%6.2f %s Smin_cut=%6.2f $\mu$Jy'%(z_min,z_max,'\n',Lmin,'\n',numpy.log10(lmin3_14),'\n',sm[num-1]),fontsize = 30,alpha=0.6 ) # \n $M_i$ < -22
    #plt.text(22,-7.7,'%.1f < z < %.1f %s Lmin=%6.2f'%(z_min,z_max,'\n',Lmin),fontsize = 30,alpha=0.6 ) # \n $M_i$ < -22
    plt.text(22,
             -7.7,
             '%.1f < z < %.1f' % (z_min, z_max),
             fontsize=30,
             alpha=0.6)  # \n $M_i$ < -22
    #plt.text(22.9,-8.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    plt.tick_params(axis='both', which='major', labelsize=20, width=3)
    plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
    #plt.tight_layout()
    #plt.xtick(fontsize=15)
    #plt.ytick(fontsize=15)
    plt.xlim(20, 28.)
    plt.ylim(-9, -2)
    #plt.ylim(-11,-5.5)
    ax.xaxis.set_minor_locator(AutoMinorLocator())
    ax.yaxis.set_minor_locator(AutoMinorLocator())
    #print truth['LMIN']
    plt.legend(frameon=False).draggable()
    plotf = '%s/LF_recon_s1.pdf' % (outdir)
    plt.show()

    return 0
示例#10
0
def main():
    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module = importlib.import_module(setf)
    globals().update(set_module.__dict__)

    expt = countModel(modelFamily,
                      nlaws,
                      setf, [dataset],
                      floatNoise,
                      doRedshiftSlices=True)
    recon_expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
    #print expt.data
    #print expt.bins

    # Get MAP parameters
    print '\n' * 5
    print len(expt.parameters)
    ncols = len(expt.parameters)
    summf = os.path.join(outdir, '1-summary.txt')
    summary = numpy.genfromtxt(summf)[-1, :]
    drawmap = summary[-(ncols + 2):-2]
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap = ana.get_best_fit()['parameters']
    chains = ['a', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
    if dataset == 'sdss':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 20:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
        print datafiles[0][4:]
    elif dataset == 'cosmos':
        #print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 24:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
        #print '%s%s'%(outdir,datafiles[0][8:])
        print datafiles[0][8:]

    elif 'sim' in dataset:
        d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))

    ssbin = d[:, 2]
    Nbin = d[:, 3]
    dnds = d[:, 4]
    sbin2 = ssbin
    dnds2 = dnds
    #bins2 = numpy.arange(21.4,29.2,0.4)
    bins2 = numpy.arange(18.0, 29.2, 0.4)
    bins3 = numpy.arange(18., 29.2, 0.01)
    print len(expt.parameters)
    num = 5
    print 'this is 1st num ', num
    #sys.exit()
    params = drawmap  # [0]
    #for i in drawmap:
    #	params.append(i)
    print params
    print expt.parameters

    if modelFamily in [
            'LFsch', 'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z',
            'LFlognorm_dpl', 'LFpl_lognorm'
    ]:
        Lnorm = params[expt.parameters.index('LNORM')]
        Lstar = params[expt.parameters.index('LSTAR')]
        Lslope = params[expt.parameters.index('LSLOPE')]
        #Lzevol=params[expt.parameters.index('LZEVOL')]
    if modelFamily in [
            'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFlognorm_dpl'
    ]:
        Lslope2 = params[expt.parameters.index('LSLOPE2')]
    if modelFamily in [
            'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFpl_dpl',
            'LFlognorm_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFpl_lognorm'
    ]:
        Lnorm_2 = params[expt.parameters.index('LNORM_2')]
        Lstar_2 = params[expt.parameters.index('LSTAR_2')]
        Lslope_2 = params[expt.parameters.index('LSLOPE_2')]

    if modelFamily in ['LFdpl_dpl', 'LFdpl_dpl_z', 'LFdpl']:
        Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]

    if modelFamily in ['LFlognorm', 'LFlognorm_dpl', 'LFpl_lognorm']:
        Lsigma = params[expt.parameters.index('LSIGMA')]

    if modelFamily in ['LFdpl_dpl_z', 'LFevol']:
        alpha_agn = params[expt.parameters.index('A_agn')]
        alpha_SF = params[expt.parameters.index('A_SF')]
        beta_agn = params[expt.parameters.index('B_agn')]
        beta_SF = params[expt.parameters.index('B_SF')]
    if modelFamily in ['LFevol_dpl_s', 'LFevol_logn_s']:
        alpha_SF = params[expt.parameters.index('A_SF')]
        beta_SF = params[expt.parameters.index('B_SF')]

    Lmin = params[expt.parameters.index('LMIN')]
    Lmax = params[expt.parameters.index('LMAX')]
    truth = {
        'noise': 150,
        'LMIN': 22.,
        'LMAX': 24.5,
        'LNORM': numpy.log10(1e-7),
        'LSTAR': 23.3,
        'LSLOPE': 3.,
        'LSLOPE2': 1.
    }
    #z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
    #z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
    print expt.parameters
    s = pylab.loadtxt('%s/recon_stats_%s.txt' % (outdir, chains[num - 1]))
    xrecon = s[:-1, 0]
    yrecon = s[:-1, 1]
    yrecon_d = s[:-1, 2]
    yrecon_u = s[:-1, 3]
    yrecon_rms = s[:-1, 4]
    yrecon_avr = s[:-1, 5]
    yrecon_rms_down = yrecon_rms

    for i in range(len(yrecon_rms)):
        if yrecon_avr[i] < yrecon_rms[i]:
            yrecon_rms_down[i] = 10**(numpy.log10(yrecon_avr[i]) - 0.01)

    #print xrecon
    #print yrecon
    #sys.exit()
    z_new = False
    Replot = False

    if z_new:
        z = [
            0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 1.9, 2.2, 2.5, 2.8, 3.2,
            3.6, 4.0
        ]
    else:
        z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]  #oldest ;)
    num = int(num)
    #num = 1.
    print num
    z_min, z_max, z_m = get_z(num, z_new=z_new)
    dl = get_dl(z_m)
    Vmax = get_Vmax(z_min, z_max)
    dsdl = get_dsdl(z_m, dl)

    z_m = (z_min + z_max) / 2
    dl = get_dl(z_m)

    print z_min, z_max
    print expt.kind
    area = SURVEY_AREA * sqDeg2sr
    area1 = SURVEY_AREA * sqDeg2sr
    print SURVEY_AREA

    SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
    SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
    sigma, fsigma = numpy.log10(
        get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5], z_m, dl, 'muJy') *
        (1.4 / 3)**(-.7))
    print z_m, dl, sigma, fsigma
    print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA

    #area = 6672*sqDeg2sr #143165.15  49996.49 59876.8861135
    sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
    sbin4 = get_sbins(10**bins3, z_m, dl) * 1e6
    #L_s   = numpy.log10(get_Lbins(ssbin,z_m,dl,'muJy'))
    #print expt.binsMedian
    #sys.exit()

    xreco = get_Lbins(xrecon, z_m, dl, 'muJy')  #*(1.4/3)**(-.7)
    yreco = yrecon

    print SMIN, SMAX
    #sys.exit()
    lin_yrecon = numpy.log10(yreco)
    lin_xrecon = numpy.log10(xreco)

    bins2 = numpy.arange(15., 25., 0.4)
    bins = numpy.arange(fsigma, 26.2, 0.4)  #bin +0.2
    if not os.path.isfile(
            'cos_data/cos_s%s_LF.el' % num) and z_new or not os.path.isfile(
                'cos_data/cos_s%s_LF_old.el' % num) and not z_new or Replot:
        print 'Reploting the RLF and storing them for next time.'
        #l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
        #l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
        if z_new:
            L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
                                  F='uJy',
                                  L=True,
                                  getz=True,
                                  band='S')
            print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
        else:
            L, z_L = open_anytype('cos_data/cos_s%s_old.txt' % num, (2, 3),
                                  F='uJy',
                                  L=True,
                                  getz=True,
                                  band='S')
            print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
        L_c, z_c = open_anytype('cosmos_counter_parts_II.txt', (3, 4),
                                [z_min, z_max],
                                F='uJy',
                                L=True,
                                getz=True,
                                band='S')
        z_c2, S_c2, L_c2 = numpy.loadtxt('cosmos_counter_parts_II.txt',
                                         unpack=True,
                                         usecols=(3, 4, 5))
        L_c2 = L_c2[(z_c2 < z_max) & (z_c2 > z_min)]
        S_c2 = S_c2[(z_c2 < z_max) & (z_c2 > z_min)]
        z_c2 = z_c2[(z_c2 < z_max) & (z_c2 > z_min)]

        #rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
        #rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
        #print numpy.log10(L)
        #print (1.4/3)**(-.7)
        #print numpy.log10(L*(1.4/3)**(-.7))
        #sys.exit()
        rho_3, er3 = calc_Vmax(calcu_zeff(z_L, L, 1.15e-31), L, bins, z_max,
                               z_min, area)
        rho_4, er4 = calc_Vmax(99., L, bins, z_max, z_min, area)
        rho_5, er5 = calc_Vmax(calcu_zeff(z_c, L_c, 1.15e-31), L_c, bins,
                               z_max, z_min, area)
        rho_6, er6 = calc_Vmax(calcu_zeff(z_c2, 10**L_c2, 1.15e-31), 10**L_c2,
                               bins, z_max, z_min, area)
        if z_new:
            f = open('cos_data/cos_s%s_LF.el' % num, 'w')
        else:
            f = open('cos_data/cos_s%s_LF_old.el' % num, 'w')
        for i in range(len(bins)):
            f.write('%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n' %
                    (bins[i], rho_3[i], er3[i], rho_5[i], er5[i], rho_6[i],
                     er6[i]))
        f.close()

        dl_c = get_dl(z_c2)
        L_c3 = numpy.log10(
            get_Lbins(S_c2, z_c2, numpy.array(dl_c), 'muJy') *
            (1.4 / 3.)**(-.7))
    else:
        if z_new:
            bins, rho_3, er3, rho_5, er5, rho_6, er6 = numpy.loadtxt(
                'cos_data/cos_s%s_LF.el' % num, unpack=True)
            print 'Using stored RLF for the new z (The one with more z bins). If you want to calculate the plot the set "Replot=True"'
        else:
            bins, rho_3, er3, rho_5, er5, rho_6, er6 = numpy.loadtxt(
                'cos_data/cos_s%s_LF_old.el' % num, unpack=True)
            print 'Using stored RLF for the is old z bins. If you want to calculate the plot the set "Replot=True"'

    #novak 2017
    L_n = [21.77, 22.15, 22.46, 22.77, 23.09, 23.34]
    rho_n = [-2.85, -2.88, -3.12, -3.55, -4.05, -4.63]
    L_ner_u = [0.23, 0.18, 0.19, 0.2, 0.21, 0.28]
    L_ner_d = [1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
    rho_ner = [0.09, 0.03, 0.0355, 0.059, 0.1, 0.234]

    #novak 2018
    L_n2 = [21.77, 22.24, 22.68, 23.16, 23.69, 24.34, 24.74, 25.56]
    rho_n2 = [-2.84, -2.90, -3.34, -4.00, -4.92, -4.92, -5.22, -5.07]
    L_ner_u2 = [0.23, 0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03]
    L_ner_d2 = [1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50]
    rho_ner_u2 = [0.08, 0.024, 0.038, 0.083, 0.28, 0.28, 0.45, 0.34]
    rho_ner_d2 = [0.07, 0.023, 0.035, 0.070, 0.25, 0.25, 0.37, 0.30]
    #l*= (1.4/3)**(-.7)
    #l2*= (1.4/3)**(-.7)
    #L*= (1.4/3)**(-.7)

    #Lbins2 = numpy.log10(lf.get_Lbins(sbin2[sbin2>0],z_m,dl))
    print z_min, z_max

    #bins  = numpy.arange(fsigma -4.2,30,0.4) #bin
    #s,s_z = numpy.loadtxt('sdss/dr12s_s1_vol.txt', unpack=True, usecols=(-1,2))
    s, s_z = numpy.loadtxt('cos_data/cos_s%s.txt' % (num),
                           unpack=True,
                           usecols=(-1, 2))
    #s=s*1e6
    print fsigma

    #sys.exit()

    Ncount = sum(Nbin[fsigma > ssbin])
    Llmin = numpy.log10(
        get_Lbins([fsigma / numpy.sqrt(Ncount)], z_min, get_dl(z_min),
                  'muJy'))[0]
    Llmin *= (1.4 / 3.)**(-.7)
    print Llmin

    L_s = (get_Lbins(ssbin, z_m, dl, 'muJy'))
    L_s *= (1.4 / 3.)**(-.7)
    L_s = numpy.log10(L_s)
    rho_1 = lumfuncs(
        Nbin, L_s,
        calc_Vmax(calcu_zeff(z_m, 10**L_s),
                  L_s,
                  L_s,
                  z_max,
                  z_min,
                  area,
                  getLF=False))
    #sys.exit()

    fig, ax = plt.subplots()

    plt.rc('lines', linewidth=2)
    '''
    if modelFamily in ['LFdpl','LFdpl_dpl']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lslope2_2,Lnorm_2)
    elif modelFamily in ['LFpl','LFdpl_pl']:
        faint =lumfuncUtils.powerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lnorm_2)
    elif modelFamily in ['LFpl_dpl','LFdpl_dpl_z']:
        faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
    else:
        faint = lumfuncUtils.lognormpl(numpy.power(10,bins3), Lstar_2,Lslope_2,Lsigma,Lnorm_2)
    '''

    #phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)
    #L_2 = numpy.power(10,bins3)/(1 + z_m)**(alpha_SF+z_m*beta_SF)
    L_2 = numpy.power(
        10, bins3) * (1.4 / 3.)**(-.7) / (1 + z_m)**(2.95 - z_m * 0.29)
    L_1 = numpy.power(
        10, bins3) * (1.4 / 3.)**(-.7) / (1 + z_m)**(2.86 - z_m * 0.7)
    phi_agn = lumfuncUtils.doublepowerlaw(L_1, 24.29, 1.27, 0.49,
                                          numpy.log10(2.5 * 10**(-5.5)))
    phi_sf = lumfuncUtils.doublepowerlaw(L_2, 22.11, 2.4, 0.43,
                                         -3.10)  #0.33 2.97
    phi_logn = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22, 0.63,
                                      numpy.log10(3.55e-3))
    #phi_pl =lumfuncUtils.powerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lnorm)
    dm = 0.  #05
    hm = 0.
    Map = expt.evaluate(params)[0]
    bins_map = get_Lbins(expt.binsMedian, z_m, dl, 'muJy')

    #plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7)),numpy.log10(faint)-dm,'--b' ,label='faint-end')
    plt.plot(bins3, numpy.log10(phi_sf) - dm, '--c', label='local sf')
    plt.plot(bins3, numpy.log10(phi_agn) - dm, '--c', label='local agn')
    plt.plot(bins3, numpy.log10(phi_logn) - dm, '--r', label='local lognorm')
    #plt.plot(numpy.log10(bins_map),numpy.log10(Map), label='my MAP')
    plt.errorbar(lin_xrecon + hm,
                 lin_yrecon - dm,
                 fmt='-k',
                 label='MAP',
                 linewidth=2)
    plt.errorbar(lin_xrecon + hm,
                 numpy.log10(yrecon_avr) - dm,
                 fmt='-g',
                 label='Average')
    #plt.plot(L_s2+.2, numpy.log10(rho_10), '*c',label='average LF',markersize=13)#bin + 0.2
    plt.errorbar(bins,
                 rho_3,
                 yerr=er3,
                 fmt='ob',
                 label='extracted',
                 markersize=8)  #bin
    #plt.errorbar(bins2[17:],rho_3[17:] ,yerr=er3[17:],fmt='ob',label='extracted',markersize=8) #bin
    #plt.errorbar(bins,rho_5 ,yerr=er5,fmt='*r',label='Total',markersize=8) #bin
    #plt.errorbar(bins,rho_6 ,yerr=er6,fmt='>g',label='Total LF',markersize=10) #bin
    plt.plot(L_s, numpy.log10(rho_1), '*m', label='low LF', markersize=10)
    #plt.errorbar(bins[1:],rho_2[1:] ,yerr=er2[1:],fmt='ok',label='dectected catalog',markersize=10) #bin
    #plt.errorbar(bins2[1:]-0.2,rho_3[1:],yerr=er3[1:],fmt='sr',label='noisy',markersize=10) #bin
    #plt.errorbar(bins2[1:]-0.2,rho_4[1:],yerr=er4[1:],fmt='vm',label='noisy lim',markersize=10) #bin
    #plt.errorbar(numpy.array(L_n),numpy.array(rho_n) -0.4,xerr=[L_ner_d,L_ner_u],yerr=rho_ner, fmt='^c', label='Novak et al 2017')
    #plt.errorbar(numpy.array(L_n2),numpy.array(rho_n2) -0.4,xerr=[L_ner_d2,L_ner_u2],yerr=[rho_ner_d2,rho_ner_u2], fmt='sk', label='Total RLF Novak et al 2018')

    #plt.plot(bins3+0.4,numpy.log10(logn)-0.4 ,'--k', label='faint-end')

    #plt.plot(bins3,numpy.log10(logn) ,'--', label='lognorm')

    #plt.plot(bins3,numpy.log10(logn2)-dm,'--r', label='lognorm2')
    #

    #plt.plot(bins3,numpy.log10(phi_dpl2), label='dpl bright-end2')
    #plt.plot(Lbins4[(Lbins4<23.6) &(Lbins4>22.12)],numpy.log10(phi_dpl)[(Lbins4<23.6) &(Lbins4>22.12)], '-c',linewidth=5 ,label = r'$\rm{ MAP dpl < 5\sigma}$')
    #plt.plot(bins2,numpy.log10(logn_2),label='starforming_2')
    #plt.plot(bins2,numpy.log10(logn_3),label='starforming_3')
    #plt.plot(bins2,numpy.log10(logn_4),label='starforming_4')

    #plt.fill_between(xrecon,numpy.log10(yrecon - yrecon_down2 ) ,numpy.log10(yrecon+yrecon_up2), color='c',alpha=0.2)

    #plt.errorbar(lin_xrecon+0.4,lin_yrecon -0.4,fmt='-b', label='MAP',linewidth=2)
    #plt.errorbar(lin_xrecon+0.4,lin_yrecon- 0.4,yerr=numpy.log10(yrecon_rms),fmt='r',label='rms')
    #plt.plot(L_s, numpy.log10(rho_1), '*c',label='average LF',markersize=10)
    #plt.plot(L_s5, numpy.log10(rho_14)-0.2, '*r',label='average LF',markersize=13)
    #plt.plot(L_s6+0.0, numpy.log10(rho_16)-0.2, '*b',label='average LF(bin)',markersize=13)
    #plt.plot(bins2[:-1], numpy.log10(rho_15), '*g',label='LF average',markersize=13)
    #print len(rho_11),len(rho_10),len(rho_12)
    #plt.fill_betweenx(numpy.log10(rho_10) ,L_s3,L_s4, color='red',alpha=0.2)

    #plt.plot(L_s6, numpy.log10(rho_13), '*b',label='average LF(bin+0.2)',markersize=13)#bin + 0.2
    #plt.plot(L_s1, numpy.log10(rho_1), 'oc',label='average LF(bin)',markersize=13)#bin

    #plt.plot(Lbins4,lin_phi ,'--b',label='MAP')

    #plt.fill_between(lin_xrecon+.0,numpy.log10((yrecon_avr -yrecon_rms))-dm,numpy.log10(yrecon_avr +yrecon_rms)-dm , color='r',alpha=0.3)

    #plt.fill_between(lin_xrecon[1:-1]+0.2,numpy.log10(yreco[1:-1] - yrecon_down[1:-1] )-dm,numpy.log10(yreco[1:-1]+yrecon_up[1:-1])-dm , color='b',alpha=0.2)
    plt.fill_between(lin_xrecon + hm,
                     numpy.log10((yrecon_d)),
                     numpy.log10(yrecon_u) - dm,
                     color='k',
                     alpha=0.2)

    #plt.errorbar(bins[2:],rho_1[2:],yerr=er1[2:],fmt = 'ob',label='Detected')
    #plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='ob',label='Detected') #working one
    #plt.plot(Lbins2,rho,'ob',label='simulation')
    #plt.plot(Lbins3,rho_,label='Noisy fluxes')
    #rho_2
    #plt.errorbar(bins,rho_2 -0.2,yerr=er2,fmt='ok',label='data',markersize=10) #bin +0.2

    #plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='vm',label='Noisy NVSS')
    #plt.errorbar(bins2,rho_7-0.2,yerr=er6,fmt='xc',label='NVSS Detected')
    #for i in range(len(bins)):
    # print bins[i], rho_2[i]
    #sys.exit()

    #plt.errorbar(Lbins2,rho, err,fmt = 'ob',label='simulation')
    #plt.errorbar(Lbins2,rho_1,err_1,fmt = 'or',label='simulation_noisy')
    #plt.errorbar(L2,rho2,color='g',label='Analytic')
    #plt.errorbar(L3,rho3,color='g',label='Analytic II')
    #plt.axvspan(22.55 ,23.51,color='r',alpha=0.2)
    #plt.axvline(Lmin,color='r',linestyle='dashed')
    plt.axvline(fsigma, color='k', linestyle='dashed')
    #plt.axvline(sigma,color='g')
    plt.axvline(Llmin, color='r', linewidth=5)
    #plt.axvline(sigma2,color='m')
    #plt.axvline(fsigma2,color='m')
    #plt.axvline(23.89,color='b')
    #plt.axvline(23.71,color='c')
    #plt.axvline(23.52,color='r')
    #plt.axvline(truth['LMIN'],color='r', linestyle='dashed', label = 'True params')
    #plt.axvline(truth['LMAX'],color='r', linestyle='dashed')
    #plt.axvline(truth['LSTAR'],color='b')
    #plt.text(truth['LSTAR']-0.2,-9., '$L_*$',fontsize=18)
    #plt.axvline(Lmax,color='r', label= 'MAP params')
    #plt.axvline(Lstar,color='b',linestyle='dashed')
    #plt.axvline(Lmax, color='r',linestyle='dashed')
    #plt.text(Lstar-0.12,-10.5, '$L_*$',fontsize=18)
    #plt.text(fsigma+0.1,-6.7, '$5\sigma$',fontsize=25)
    #plt.text(sigma+0.1,-6.7, '$\sigma$',fontsize=25)
    plt.xlabel(r'$\rm{log_{10}[L_{1.4 GHz}/(W}$ $\rm{Hz^{-1})]}$', fontsize=30)
    plt.ylabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$', fontsize=30)
    plt.text(19.9,
             -7.7,
             '%.1f < z < %.1f' % (z_min, z_max),
             fontsize=30,
             alpha=0.6)  # \n $M_i$ < -22
    #plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    plt.tick_params(axis='both', which='major', labelsize=20, width=3)
    plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
    #plt.tight_layout()
    #plt.xtick(fontsize=15)
    #plt.ytick(fontsize=15)
    plt.xlim(18, 27.)
    plt.ylim(-11.2, 0.)
    #plt.ylim(-11,-5.5)
    ax.xaxis.set_minor_locator(AutoMinorLocator())
    ax.yaxis.set_minor_locator(AutoMinorLocator())
    #print truth['LMIN']
    plt.legend(frameon=False).draggable()
    plotf = '%s/LF_recon_s1.pdf' % (outdir)
    plt.show()

    return 0
示例#11
0
def main():
    """
    """

    print 'Settings file is %s' % setf

    # Import the settings variables
    set_module = importlib.import_module(setf)
    globals().update(set_module.__dict__)

    expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
    recon_expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
    #print expt.data
    #print expt.bins

    # Get MAP parameters
    print '\n' * 5
    print len(expt.parameters)
    ncols = len(expt.parameters)
    summf = os.path.join(outdir, '1-summary.txt')
    summary = numpy.genfromtxt(summf)[-1, :]
    drawmap = summary[-(ncols + 2):-2]
    ana=pymultinest.analyse.Analyzer(ncols-1,\
        outputfiles_basename=os.path.join(outdir,outstem))
    drawmap = ana.get_best_fit()['parameters']

    if dataset == 'sdss':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 20:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
        print datafiles[0][4:]
    elif dataset == 'cosmos':
        print datafiles, len(datafiles[0])
        dataf = datafiles[0]
        if len(dataf) == 24:
            num = dataf[-5]
            print num
        else:
            num = dataf[-6:-4]
        d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
        print datafiles[0][8:]

    elif 'sim' in dataset:
        d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))

    ssbin = d[:, 2]
    Nbin = d[:, 3]
    dnds = d[:, 4]
    sbin2 = ssbin
    dnds2 = dnds
    #bins2 = numpy.arange(21.4,29.2,0.4)
    bins2 = numpy.arange(18.0, 29.2, 0.4)
    bins3 = numpy.arange(18., 29.2, 0.01)
    print len(expt.parameters)
    print 'this is 1st num ', num
    #sys.exit()
    params = drawmap  # [0]
    #for i in drawmap:
    #	params.append(i)
    print params
    print expt.parameters

    if modelFamily in ['LFsch', 'LFdpl_pl', 'LFdpl_dpl', 'LFlognorm_dpl']:
        Lnorm = params[expt.parameters.index('LNORM')]
        Lstar = params[expt.parameters.index('LSTAR')]
        Lslope = params[expt.parameters.index('LSLOPE')]
        #Lzevol=params[expt.parameters.index('LZEVOL')]
    if modelFamily in ['LFdpl_pl', 'LFdpl_dpl', 'LFlognorm_dpl']:
        Lslope2 = params[expt.parameters.index('LSLOPE2')]
    if modelFamily in [
            'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFlognorm_dpl',
            'LFdpl_dpl'
    ]:
        Lnorm_2 = params[expt.parameters.index('LNORM_2')]
        Lstar_2 = params[expt.parameters.index('LSTAR_2')]
        Lslope_2 = params[expt.parameters.index('LSLOPE_2')]

    if modelFamily in ['LFdpl_dpl', 'LFdpl']:
        Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]

    if modelFamily in ['LFlognorm', 'LFlognorm_dpl']:
        Lsigma = params[expt.parameters.index('LSIGMA')]

    Lmin = params[expt.parameters.index('LMIN')]
    Lmax = params[expt.parameters.index('LMAX')]
    truth = {
        'noise': 150,
        'LMIN': 22.,
        'LMAX': 24.5,
        'LNORM': numpy.log10(1e-7),
        'LSTAR': 23.3,
        'LSLOPE': 3.,
        'LSLOPE2': 1.
    }
    #z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
    #z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
    print expt.parameters
    s = pylab.loadtxt('%s/recon_stats.txt' % outdir)
    xrecon = s[:-1, 0]
    yrecon = s[:-1, 1]
    yrecon_d = s[:-1, 2]
    yrecon_u = s[:-1, 3]
    yrecon_rms = s[:-1, 4]
    yrecon_avr = s[:-1, 5]
    yrecon_rms_down = yrecon_rms

    for i in range(len(yrecon_rms)):
        if yrecon_avr[i] < yrecon_rms[i]:
            yrecon_rms_down[i] = 10**(numpy.log10(yrecon_avr[i]) - 0.01)

    #print xrecon
    #print yrecon
    #sys.exit()

    z = [
        0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.15, 2.35, 2.55, 2.85, 3.15,
        3.5
    ]  #oldest ;)
    num = int(num)
    #num = 1.
    print num
    z_min, z_max, z_m = get_z(num)
    dl = get_dl(z_m)
    Vmax = get_Vmax(z_min, z_max)
    dsdl = get_dsdl(z_m, dl)

    z_m = (z_min + z_max) / 2
    dl = get_dl(z_m)

    print z_min, z_max
    print expt.kind
    area = SURVEY_AREA * sqDeg2sr
    area1 = SURVEY_AREA * sqDeg2sr
    print SURVEY_AREA

    SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
    SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
    sigma, fsigma = numpy.log10(
        get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5], z_m, dl,
                  'muJy'))  #*(1.4/3)**(-.7))
    sigma2, fsigma2 = numpy.log10(get_Lbins([450, 2400], z_m, dl, 'muJy'))
    print z_m, dl, sigma, fsigma
    print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA

    #area = 6672*sqDeg2sr #143165.15  49996.49 59876.8861135
    sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
    sbin4 = get_sbins(10**bins3, z_m, dl) * 1e6
    L_s = numpy.log10(get_Lbins(ssbin, z_m, dl, 'muJy'))
    #print expt.binsMedian
    #sys.exit()

    xreco = get_Lbins(xrecon, z_m, dl, 'muJy')  #*(1.4/3)**(-.7)
    yreco = yrecon

    print SMIN, SMAX
    #sys.exit()
    lin_yrecon = numpy.log10(yreco)
    print xrecon
    lin_xrecon = numpy.log10(xreco)

    bins2 = numpy.arange(15., 25., 0.4)

    bins5, rho_5, er5, rho_6, er6 = numpy.loadtxt('cos_data/skads_s1_3_LF.el',
                                                  unpack=True)

    #novak 2017
    L_n = [21.77, 22.15, 22.46, 22.77, 23.09, 23.34]
    rho_n = [-2.85, -2.88, -3.12, -3.55, -4.05, -4.63]
    L_ner_u = [0.23, 0.18, 0.19, 0.2, 0.21, 0.28]
    L_ner_d = [1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
    rho_ner = [0.09, 0.03, 0.0355, 0.059, 0.1, 0.234]

    #novak 2018
    L_n2 = [21.77, 22.24, 22.68, 23.16, 23.69, 24.34, 24.74, 25.56]
    rho_n2 = [-2.84, -2.90, -3.34, -4.00, -4.92, -4.92, -5.22, -5.07]
    L_ner_u2 = [0.23, 0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03]
    L_ner_d2 = [1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50]
    rho_ner_u2 = [0.08, 0.024, 0.038, 0.083, 0.28, 0.28, 0.45, 0.34]
    rho_ner_d2 = [0.07, 0.023, 0.035, 0.070, 0.25, 0.25, 0.37, 0.30]
    #l*= (1.4/3)**(-.7)
    #l2*= (1.4/3)**(-.7)
    #L*= (1.4/3)**(-.7)

    #Lbins2 = numpy.log10(lf.get_Lbins(sbin2[sbin2>0],z_m,dl))
    print z_min, z_max

    bins = numpy.arange(fsigma - 0., 26.2, 0.4)  #bin +0.2
    #bins  = numpy.arange(fsigma -4.2,30,0.4) #bin
    #s,s_z = numpy.loadtxt('sdss/dr12s_s1_vol.txt', unpack=True, usecols=(-1,2))

    fig, ax = plt.subplots()

    plt.rc('lines', linewidth=2)

    if modelFamily in ['LFdpl', 'LFdpl_dpl']:
        faint = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), Lstar_2,
                                            Lslope_2, Lslope2_2, Lnorm_2)
    elif modelFamily in ['LFpl', 'LFdpl_pl']:
        faint = lumfuncUtils.powerlaw(numpy.power(10, bins3), Lstar_2,
                                      Lslope_2, Lnorm_2)
    else:
        faint = lumfuncUtils.lognormpl(numpy.power(10, bins3), Lstar_2,
                                       Lslope_2, Lsigma, Lnorm_2)

    dm = 0.  #05
    hm = 0.

    plt.errorbar(bins5,
                 rho_6,
                 yerr=er6,
                 fmt='sr',
                 label=r'$\rm{25 >\log_{10}(L) > %s}$' % (20.8),
                 markersize=10)
    plt.errorbar(bins5,
                 rho_5,
                 yerr=er5,
                 fmt='*b',
                 label=r'$\rm{25 >\log_{10}(L) > %s}$' % (20.8),
                 markersize=10)
    plt.plot(bins3 + hm, numpy.log10(faint) - dm, '--b', label='faint-end')
    plt.errorbar(lin_xrecon + hm,
                 lin_yrecon - dm,
                 fmt='-k',
                 label='MAP',
                 linewidth=2)
    plt.errorbar(lin_xrecon + hm,
                 numpy.log10(yrecon_avr) - dm,
                 fmt='-g',
                 label='Average')
    #plt.errorbar(numpy.array(L_n),numpy.array(rho_n) -0.4,xerr=[L_ner_d,L_ner_u],yerr=rho_ner, fmt='^c', label='Novak et al 2017')
    #plt.errorbar(numpy.array(L_n2),numpy.array(rho_n2) -0.4,xerr=[L_ner_d2,L_ner_u2],yerr=[rho_ner_d2,rho_ner_u2], fmt='sk', label='Total RLF Novak et al 2018')

    plt.fill_between(lin_xrecon + hm,
                     numpy.log10((yrecon_d)),
                     numpy.log10(yrecon_u) - dm,
                     color='k',
                     alpha=0.2)

    plt.axvline(fsigma, color='k', linestyle='dashed')
    #plt.axvline(sigma,color='g')
    #plt.axvline(Llmin,color='r',linewidth=5)

    plt.xlabel(r'$\rm{log_{10}[L_{3 GHz}/(W}$ $\rm{Hz^{-1})]}$', fontsize=30)
    plt.ylabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$', fontsize=30)
    plt.text(19.9,
             -7.7,
             '%.1f < z < %.1f' % (0.1, 0.4),
             fontsize=30,
             alpha=0.6)  # \n $M_i$ < -22
    #plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
    plt.tick_params(axis='both', which='major', labelsize=20, width=3)
    plt.tick_params(axis='both', which='minor', labelsize=10, width=2)

    plt.xlim(18, 26.)
    plt.ylim(-7, -1)
    ax.xaxis.set_minor_locator(AutoMinorLocator())
    ax.yaxis.set_minor_locator(AutoMinorLocator())
    #print truth['LMIN']
    plt.legend(frameon=False).draggable()
    plotf = '%s/LF_recon_s1.pdf' % (outdir)
    plt.show()

    return 0