Esempio n. 1
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def main():
    options,args = parser.parse_args(sys.argv[1:])

    if len(args) < 1:
        parser.print_help()
        sys.exit(45)

    run=args[0]

    c = shapesim.read_config(run)

    run         = c['run']
    sim_config  = c['sim_config']
    mcmc_config = c['mcmc_config']
    s2n_vals    = c['s2n_vals']

    ns2n = len(s2n_vals)

    d = shapesim.get_wq_dir(run, bytrial=True, fs='local')
    if not os.path.exists(d):
        os.makedirs(d)
    d = shapesim.get_output_dir(run, sub='bytrial')
    if not os.path.exists(d):
        os.makedirs(d)


    groups=''
    if options.groups is not None:
        groups = 'group: [%s]' % options.groups

    smax=numpy.iinfo('i8').max
    for is2n in xrange(ns2n):

        s2n = s2n_vals[is2n]

        npair, nsplit = get_npair_nsplit(c, is2n)

        for isplit in xrange(nsplit):
            job_name='%s-%03i-%03i' % (run,is2n,isplit)

            # note the wq logs are local
            wqurl = shapesim.get_wq_url(run,0,is2n,itrial=isplit,fs='local')
            output = shapesim.get_output_url(run, 0, is2n, itrial=isplit)

            seed = numpy.random.randint(smax)

            wlog("writing wq script:",wqurl)
            with open(wqurl,'w') as fobj:
                d={'job_name':job_name,
                   'version':options.version,
                   'groups':groups,
                   'pri':options.priority,
                   'sim_config':sim_config,
                   'mcmc_config':mcmc_config,
                   's2n':s2n,
                   'npair':npair,
                   'seed':seed,
                   'output':output}
                wqscript=_wqtemplate % d
                fobj.write(wqscript)
Esempio n. 2
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def main():
    options, args = parser.parse_args(sys.argv[1:])

    if len(args) < 1:
        parser.print_help()
        sys.exit(45)

    simname = args[0]
    ss = shapesim.ShapeSim(simname)

    orient = ss.get("orient", "rand")
    if orient == "ring":
        numper_str = ""
    else:
        if len(args) < 2:
            parser.print_help()
            sys.exit(45)
        numper_str = args[1]

    if ss.fs != "hdfs":
        raise ValueError("This only works for HDFS right now " "would need to worry about making dirs")

    c = shapesim.read_config(simname)

    groups = options.groups
    if groups is None:
        groups = ""
    else:
        groups = "group: [%s]" % groups

    wqd = shapesim.get_cache_wq_dir(simname)
    if not os.path.exists(wqd):
        os.makedirs(wqd)

    extra = ""
    if options.bynode:
        extra = "mode: bynode\nN: 1"
    elif options.ncores is not None:
        ncores = int(options.ncores)
        extra = "mode: bycore1\nN: %d" % ncores

    for is2 in xrange(c["nums2"]):
        for ie in xrange(c["nume"]):
            job_name = "%s-%i-%i" % (simname, is2, ie)

            wqurl = shapesim.get_cache_wq_url(simname, is2, ie)

            wlog("writing wq script:", wqurl)
            with open(wqurl, "w") as fobj:
                wqscript = _wqtemplate % {
                    "job_name": job_name,
                    "simname": simname,
                    "is2": is2,
                    "ie": ie,
                    "numper": numper_str,
                    "groups": groups,
                    "extra": extra,
                    "pri": options.priority,
                }
                fobj.write(wqscript)
Esempio n. 3
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def main():
    options,args = parser.parse_args(sys.argv[1:])

    if len(args) < 1:
        parser.print_help()
        sys.exit(45)

    run=args[0]

    c = shapesim.read_config(run)
    cs = shapesim.read_config(c['sim'])

    group=options.group
    if group is not None:
        group = 'group: ['+group+']'
    else:
        group=''

    mode=''
    if options.bynode:
        mode='mode: bynode'

    extra=''
    if options.extra is not None:
        extra='\n'.join( options.extra.split(';') )

    wqd = shapesim.get_wq_dir(run, combine=True)
    if not os.path.exists(wqd):
        os.makedirs(wqd)

    if run[0:8] == 'gmix-fit':
        rstr=run.replace('gmix-fit','gmix')
    else:
        rstr=run
 
    n1 = cs['nums2']

    runtype = c['runtype']
    if runtype == 'byellip':
        n2 = cs['nume']
    else:
        n2 = shapesim.get_nums2n(c)

    for i1 in xrange(n1):
        for i2 in xrange(n2):
            job_name='%s-combine-%03i-%03i' % (rstr,i1,i2)

            wqurl = shapesim.get_wq_url(run,i1,i2,combine=True)

            wlog("writing wq script:",wqurl)
            with open(wqurl,'w') as fobj:
                wqscript=_wqtemplate % {'job_name':job_name,
                                        'run':run, 
                                        'i1':i1,
                                        'i2':i2,
                                        'group':group,
                                        'mode':mode,
                                        'extra':extra,
                                        'pri':options.priority}
                fobj.write(wqscript)
Esempio n. 4
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def create_sim_wq_byellip(run, groups=None):
    import pbs

    c = read_config(run)
    objmodel = c['objmodel']
    psfmodel = c['psfmodel']

    if groups is not None:
        groups = 'groups: [%s]' % groups 
    else:
        groups=''

    s2vals=sample_s2(c['mins2'],c['maxs2'],c['ns2'])
    evals=numpy.linspace(c['mine'],c['maxe'],c['nume'])
    for s2 in s2vals:
        for ellip in evals:
            extra = '%0.3f-%0.3f' % (s2,ellip)
            job_name='%s-%s' % (run,extra)

            wqurl = get_wq_url(run,objmodel,psfmodel,extra)
            opts = '--s2 %(s2)0.3f -e %(ellip)0.3f' % {'s2':s2,'ellip':ellip}

            eu.ostools.makedirs_fromfile(wqurl,verbose=True)
            wlog("writing wq script:",wqurl)
            with open(wqurl,'w') as fobj:
                wqscript=_wqtemplate % {'run':run, 
                                        'job_name':job_name,
                                        'opts':opts,
                                        'groups':groups}
                fobj.write(wqscript)
Esempio n. 5
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    def outfile(self, ellip, type='res'):
        f = get_simfile(self['run'], 
                        self['objmodel'], self['psfmodel'], 
                        self['s2'], ellip, self['psf_ellip'],
                        type=type)

        dir=os.path.dirname(f)
        if not os.path.exists(dir):
            wlog("Making output dir:",dir)
            try:
                os.makedirs(dir)
            except:
                # probably a race condition
                pass
        return f
Esempio n. 6
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    def process(self):
        c=self['filternum']
        self.set_admom()

        ntrials = len(self['psf']['trials'])

        chi2arr=zeros(ntrials) + 1.e9
        gmlist=[]

        im=self.psf.copy()
        im_min = im.min()
        if im_min <= 0:
            im -= im_min
            sky=0.001*im.max()
            im += sky
        else:
            sky = im_min


        for i,trial in enumerate(self['psf']['trials']):
            #prior,width = self.get_prior_turb(trial)
            guess = self.get_em_guess(trial)

            if self['verbose']:
                wlog('guess')
                gmix_print(guess,title='guess:')

            gm = gmix_image.GMixEM(im,guess,
                                   sky=sky,
                                   maxiter=4000,
                                   tol=1.e-6,
                                   coellip=False,
                                   cocenter=False) # true required for deconv



            chi2arr[i] = gm.get_fdiff()
            if self['verbose']:
                gmix_print(gm.pars,title='pars:')
                wlog("chi2/pdeg:",chi2arr[i])

            gmlist.append(gm)

        w=chi2arr.argmin()
        self.gm = gmlist[w]

        flags = gm.get_flags()
        if flags != 0:
            printflags('em',flags)
            raise ValueError("error")
        if self['verbose']:
            gmix_print(gm.pars,title='popt:')

            wlog("\n")

            wlog("numiter gmix:",gm.numiter)
Esempio n. 7
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    def process(self):
        c=self['filternum']
        self.set_admom()

        ntrials = len(self['psf']['trials'])

        chi2arr=zeros(ntrials) + 1.e9
        gmlist=[]

        for i,trial in enumerate(self['psf']['trials']):
            ngauss=trial['ngauss']
            if ngauss==3:
                prior,width = self.get_prior_turb(trial)
            elif ngauss==2:
                prior,width = self.get_prior_2generic(trial)
            else:
                raise ValueError("only have ngauss in [2,3] now")
            #prior,width = self.get_prior_2psfield(trial)
            #prior,width = self.get_prior_test(trial)

            if self['verbose']:
                print_pars(prior,front="guess: ")
            gm = gmix_image.GMixFitCoellip(self.psf, self['skysig'],
                                           prior,width, verbose=False)
            #gm = gmix_image.gmix_fit.GMixFitCoellipNoPrior(self.psf, self['skysig'],
            #                                               prior, verbose=False)

            if gm.flags != 0:
                gmix_image.printflags("flags:",gm.flags)
                raise ValueError("error")
            chi2arr[i] = gm.get_chi2per(gm.popt)
            if self['verbose']:
                print_pars(gm.popt,front="pars:  ")
                print_pars(gm.perr,front="perr:  ")
                wlog("chi2/pdeg:",chi2arr[i])

            gmlist.append(gm)

        w=chi2arr.argmin()
        self.gm = gmlist[w]
        wlog('w:',w)

        if self['verbose']:
            print_pars(chi2arr,front='chi2/deg: ')

            wlog("\n")

            print_pars(gm.popt,front='popt: ')
            print_pars(gm.perr,front='perr: ')
            #wlog("s2n:",s2n)
            wlog("numiter gmix:",gm.numiter)
            ngauss=(len(gm.popt)-4)/2
Esempio n. 8
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    def do_regauss(self, ci, verbose_local=True):
        """
        ci is a convolved image
        """
        rgkeys = self.rgkeys

        rgkeys['guess'] = (ci['cov_admom'][0] + ci['cov_admom'][2])/2
        rgkeys['guess_psf'] = (ci['cov_psf_admom'][0] + ci['cov_psf_admom'][2])/2
        if 's2n' in self:
            rgkeys['sigsky'] = self['sigsky']
        
        if verbose_local:
            wlog("running regauss")
        rgkeys['verbose'] = verbose_local

        rg = admom.ReGauss(ci.image, ci['cen'][0], ci['cen'][1], ci.psf, **rgkeys)
        rg.do_all()
        self.add_unweighted_truth(rg, ci.image0)
        if self['verbose']:
            wlog("uwcorrstats")
            wlog(rg['uwcorrstats'])


        if rg['rgstats'] == None or rg['rgcorrstats'] == None:
            raise RuntimeError("Failed to run regauss")
        if rg['rgstats']['whyflag'] != 0:
            raise RuntimeError("regauss failed: '%s'" % rg['rgstats']['whystr'])

        # copy out the data
        output = self.copy_output(ci, rg)
        return output
Esempio n. 9
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    def write_images(self, ellip, ci):
        f=self.outfile(ellip, 'image')
        wlog("writing image file:",f)
        eu.io.write(f, ci.image, clobber=True)

        f=self.outfile(ellip, 'image0')
        wlog("writing image0 file:",f)
        eu.io.write(f, ci.image0, clobber=True)

        f=self.outfile(ellip, 'psf')
        wlog("writing psf file:",f)
        eu.io.write(f, ci.psf, clobber=True)
Esempio n. 10
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    def cache_window(self):
        import sdsspy
        w=sdsspy.window.Window()
        if self.verbose:
            wlog("Cacheing window flist")
        flist = w.read('flist')
        
        if self.verbose:
            wlog("Extracting matching fields")
        rfwhm = flist['psf_fwhm'][ :,self['filternum'] ] 
        w,=numpy.where(  (rfwhm > self['min_seeing']) 
                       & (rfwhm < self['max_seeing']) 
                       & (flist['score'] > 0.1) 
                       & (flist['rerun'] == '301') )
        if w.size == 0:
            raise ValueError("No runs found with seeing in [%0.3f,%0.3f]" % (min_seeing,max_seeing))

        if self.verbose:
            wlog("  Found:",w.size)
        self.flist = flist[w]
Esempio n. 11
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    def run_ellip(self, ellip):

        wlog("ellip:",ellip)
        outfile = self.outfile(ellip)
        wlog("outfile:",outfile)

        # get a n ew RandomConvolvedImage with this ellip
        wlog("getting convolved image")

        #if 's2n' in self:
        if True:
            trials_file = self.outfile(ellip,type='trials')
            #output, trials = self.do_regauss_trials(ci)
            output, trials = self.do_regauss_trials(ellip)
            eu.io.write(trials_file, trials, clobber=True)
        else:
            ci = self.new_convolved_image(ellip)
            output = self.do_regauss(ci)

        eu.io.write(outfile, output, clobber=True)

        if self['debug']:
            self.write_images(ellip, ci)
Esempio n. 12
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    def get_prior_2psfield(self, trial):
        """
        Take two of the guesses from the psfield sigma1,sigma2
        """
        ngauss=trial['ngauss']
        eguess=trial['eguess']
        uniform_p=trial['uniform_p']
        randomize=trial['randomize']

        if ngauss != 2:
            raise ValueError("ngauss==2 only for now")

        npars=2*ngauss+4
        prior=zeros(npars)
        width=zeros(npars) + 1.e20
        
        prior[0] = self.amres['row']
        prior[1] = self.amres['col']

        if eguess is not None:
            prior[2],prior[3] = eguess
        else:
            prior[2] = self.amres['e1']
            prior[3] = self.amres['e2']

        T = self.amres['Irr'] + self.amres['Icc']

        c = self['filternum']
        T1 = 2*self.psfield['psf_sigma1'][0,c]**2
        T2 = 2*self.psfield['psf_sigma2'][0,c]**2

        Tmax = T2
        Tfrac1 = T1/T2
        prior[4] = Tmax
        prior[5] = Tfrac1

        if uniform_p:
            wlog("    uniform p")
            prior[6] = self['counts']/ngauss
            prior[7] = self['counts']/ngauss
        else:
            # psf_b is p2/p1
            pvals = array([self.psfield['psf_b'][0,c], 1.])
            pvals /= self['counts']*pvals.sum()
            prior[6] = pvals[0]
            prior[7] = pvals[1]


        if randomize:
            wlog("    randomizing")
            e1start=prior[2]
            e2start=prior[3]
            prior[2],prior[3] = randomize_e1e2(e1start,e2start)

            prior[4] += prior[4]*0.05*(randu()-0.5)
            prior[5] += prior[5]*0.05*(randu()-0.5)
            prior[6] += prior[6]*0.05*(randu()-0.5)
            prior[7] += prior[7]*0.05*(randu()-0.5)

            pvals = prior[ [6,7] ].copy()
            pvals *= self['counts']/pvals.sum()
            prior[6] = pvals[0]
            prior[7] = pvals[1]


        return prior, width
Esempio n. 13
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    def process_nlsolver(self):
        wlog("Using jarvis solver")
        c=self['filternum']
        self.set_admom()

        ntrials = len(self['psf']['trials'])

        chi2arr=zeros(ntrials) + 1.e9
        gmlist=[]

        for i,trial in enumerate(self['psf']['trials']):
            ngauss=trial['ngauss']
            if ngauss==3:
                prior,width = self.get_prior_turb(trial)
            elif ngauss==2:
                prior,width = self.get_prior_2generic(trial)
            else:
                raise ValueError("only have ngauss in [2,3] now")

            #prior,width = self.get_prior_2psfield(trial)
            #prior,width = self.get_prior_test(trial)
            #prior,width = self.get_prior_2generic(trial)

            if self['verbose']:
                print_pars(prior,front="guess: ")
            # kludge using skysig=1 for now
            maxiter=2000
            psf=None
            gm=gmix_image.gmix_nlsolve.GMixCoellipSolver(self.psf, prior, 1., maxiter, psf, False)

            success=gm.get_success()
            if not success:
                raise ValueError("error")

            chi2per=gm.get_chi2per()

            # kludge
            chi2per /= self['skysig']**2

            chi2arr[i] = chi2per
            if self['verbose']:
                popt = gm.get_pars()
                cov=gm.get_pcov()
                perr=sqrt(diag(cov))

                print_pars(popt,front="pars:  ")
                # kludge
                perr *= self['skysig']
                print_pars(perr,front="perr:  ")
                wlog("chi2/pdeg:",chi2arr[i])

            gmlist.append(gm)

        w=chi2arr.argmin()
        self.gm = gmlist[w]
        wlog('w:',w)

        if self['verbose']:
            print_pars(chi2arr,front='chi2/deg: ')

            wlog("\n")

            print_pars(gm.get_pars(),front='popt: ')
            cov=gm.get_pcov()
            perr=sqrt(diag(cov))
            # kludge
            perr *= self['skysig']
            print_pars(perr,front='perr: ')
Esempio n. 14
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 def run_many_ellip(self):
     for ellip in self.ellipvals():
         self.run_ellip(ellip)
     wlog("Done many_ellip")
Esempio n. 15
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def psfield_compare_model(rsp=None, generator='filter', next=False):
    """
    compare psf reconstructions with the best fit models in the 6th header.
    """

    import images
    import biggles
    from scipy.ndimage.filters import gaussian_filter
    import fimage

    filter='r'
    fnum=2
    if rsp is None:
        rsp=RandomSDSSPSF(1.3,1.5,filter,verbose=True)
        image,meta = rsp.next(meta=True)
    else:
        if rsp.psf is None:
            image,meta = rsp.current(meta=True)
        else:
            if next:
                image,meta = rsp.next(meta=True)
            else:
                image,meta = rsp.current(meta=True)

    cen = [(image.shape[0]-1)/2, (image.shape[1]-1)/2]

    extra='_2G'
    a = 1.0
    b = meta['psf_b'+extra][0,fnum]
    s1 = meta['psf_sigma1'+extra][0,fnum]
    s2 = meta['psf_sigma2'+extra][0,fnum]

    if generator == 'fimage':
        fake1 = fimage.makeimage('gauss',image.shape,cen,s1**2,0,s1**2,counts=1)
        fake2 = fimage.makeimage('gauss',image.shape,cen,s2**2,0,s2**2,counts=1)

        a /= (s1**2 + b*s2**2)
        fake = a*( s1**2*fake1 + b*s2**2*fake2 )
    elif generator == 'imsim':
        import imsim
        fake1 = imsim.mom2disk('gauss',s1**2,0,s1**2,image.shape,cen=cen,counts=1)
        fake2 = imsim.mom2disk('gauss',s2**2,0,s2**2,image.shape,cen=cen,counts=1)

        a /= (s1**2 + b*s2**2)
        fake = a*( s1**2*fake1 + b*s2**2*fake2 )

    elif generator == 'filter':
        a /= (s1**2 + b*s2**2)
        fake1 = numpy.zeros_like(image)
        # convolve delta function with gaussians
        fake1[cen[0],cen[1]] = 1
        fake = a * (s1**2 * gaussian_filter(fake1, (s1,s1)) + b*s2**2*gaussian_filter(fake1, (s2,s2)))
    else:
        raise ValueError("unknown generator type: '%s'" % generator)

    wlog("image counts:",image.sum(),"model counts:",fake.sum())

    resid = fake-image

    wlog("summed residuals:",resid.sum())

    maxval = max( image.max(), fake.max() )
    minval = 0.0

    levels=7
    tab=biggles.Table(2,3)
    #tab=biggles.Table(3,2)
    implt=images.view(image, levels=levels, show=False, min=minval, max=maxval)
    fakeplt=images.view(fake, levels=levels, show=False, min=minval, max=maxval)
    residplt=images.view(resid, show=False, min=minval, max=maxval)

    #sigma = numpy.sqrt((res['Irr']+res['Icc'])/2.0)
    #lab = biggles.PlotLabel(0.1,0.9,r'$\sigma$: %0.3f' % sigma, fontsize=4, halign='left')
    #fakeplt.add(lab)

    implt.title='original'
    fakeplt.title='gaussian '+generator
    residplt.title='residuals'


    # cross-sections
    imrows = image[:,cen[1]]
    imcols = image[cen[0],:]
    fakerows = fake[:,cen[1]]
    fakecols = fake[cen[0],:]
    resrows = resid[:,cen[1]]
    rescols = resid[cen[0],:]

    himrows = biggles.Histogram(imrows, color='blue')
    himcols = biggles.Histogram(imcols, color='blue')
    hfakerows = biggles.Histogram(fakerows, color='orange')
    hfakecols = biggles.Histogram(fakecols, color='orange')
    hresrows = biggles.Histogram(resrows, color='red')
    hrescols = biggles.Histogram(rescols, color='red')

    himrows.label = 'image'
    hfakerows.label = 'model'
    hresrows.label = 'resid'
    key = biggles.PlotKey(0.1,0.9,[himrows,hfakerows,hresrows]) 
    rplt=biggles.FramedPlot()
    rplt.add( himrows, hfakerows, hresrows,key )

    cplt=biggles.FramedPlot()
    cplt.add( himcols, hfakecols, hrescols )

    rplt.aspect_ratio=1
    cplt.aspect_ratio=1


    tab[0,0] = implt
    tab[0,1] = fakeplt
    tab[0,2] = residplt
    tab[1,0] = rplt
    tab[1,1] = cplt

    #tab[0,0] = implt
    #tab[0,1] = fakeplt
    #tab[1,0] = residplt
    #tab[1,1] = rplt
    #tab[2,0] = cplt


    tab.show()


    return rsp
Esempio n. 16
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    def get_prior_2generic(self, trial):
        """
        generic guesses
        """

        wlog("using generic guesses")
        ngauss=trial['ngauss']
        eguess=trial['eguess']
        uniform_p=trial['uniform_p']
        randomize=trial['randomize']

        if ngauss != 2:
            raise ValueError("ngauss==2 only for now")

        npars=2*ngauss+4
        prior=zeros(npars)
        width=zeros(npars) + 1.e20
        
        prior[0] = self.amres['row']
        prior[1] = self.amres['col']

        if eguess is not None:
            prior[2],prior[3] = eguess
        else:
            prior[2] = self.amres['e1']
            prior[3] = self.amres['e2']

        T = self.amres['Irr'] + self.amres['Icc']

        Tmax = T*3
        T1 = T*0.5
        Tfrac1 = T1/Tmax

        prior[4] = Tmax
        prior[5] = Tfrac1

        if uniform_p:
            wlog("    uniform p")
            prior[6] = self['counts']/ngauss
            prior[7] = self['counts']/ngauss
        else:
            prior[6] = self['counts']*0.2
            prior[7] = self['counts']*0.8


        if randomize:
            wlog("    randomizing")
            e1start=prior[2]
            e2start=prior[3]
            prior[2],prior[3] = randomize_e1e2(e1start,e2start)

            prior[4] += prior[4]*0.05*(randu()-0.5)
            prior[5] += prior[5]*0.05*(randu()-0.5)
            prior[6] += prior[6]*0.05*(randu()-0.5)
            prior[7] += prior[7]*0.05*(randu()-0.5)

            pvals = prior[ [6,7] ].copy()
            pvals *= self['counts']/pvals.sum()
            prior[6] = pvals[0]
            prior[7] = pvals[1]


        return prior, width
Esempio n. 17
0
    def get_prior_turb(self, trial):
        ngauss=trial['ngauss']
        eguess=trial['eguess']
        uniform_p=trial['uniform_p']
        randomize=trial['randomize']

        if ngauss != 3:
            raise ValueError("ngauss==3 only for now")

        npars=2*ngauss+4
        prior=zeros(npars)
        width=zeros(npars)
        
        prior[0] = self.amres['row']
        prior[1] = self.amres['col']
        width[0] = 1000
        width[1] = 1000

        if eguess is not None:
            prior[2],prior[3] = eguess
        else:
            prior[2] = self.amres['e1']
            prior[3] = self.amres['e2']

        T = self.amres['Irr'] + self.amres['Icc']

        # turbulent psf guess
        Tmax = T*8.3
        Tfrac1 = 1.7/8.3
        Tfrac2 = 0.8/8.3
        prior[4] = Tmax
        prior[5] = Tfrac1
        prior[6] = Tfrac2

        if uniform_p:
            wlog("    uniform p")
            prior[7] = self['counts']/ngauss
            prior[8] = self['counts']/ngauss
            prior[9] = self['counts']/ngauss
        else:
            prior[7] = self['counts']*0.08
            prior[8] = self['counts']*0.38
            prior[9] = self['counts']*0.53

        # uninformative priors
        width[2] = 1000
        width[3] = 1000
        width[4] = 1000
        width[5:] = 1000

        if randomize:
            wlog("    randomizing")
            e1start=prior[2]
            e2start=prior[3]
            prior[2],prior[3] = randomize_e1e2(e1start,e2start)

            prior[4] += prior[4]*0.05*(randu()-0.5)
            prior[5] += prior[5]*0.05*(randu()-0.5)
            prior[6] += prior[6]*0.05*(randu()-0.5)
            prior[7] += prior[7]*0.05*(randu()-0.5)
            prior[8] += prior[8]*0.05*(randu()-0.5)
            prior[9] += prior[9]*0.05*(randu()-0.5)

        return prior, width
Esempio n. 18
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def compare_s2n(amrun, serun, matches=None):
    import biggles
    amc = collate.open_columns(amrun)
    sec = collate.open_columns(serun)

    if matches is None:
        wlog('reading am rid')
        arid = amc['rid'][:]
        wlog('  read:  ',arid.size)
        wlog('  unique:',numpy.unique(arid).size)

        wlog('reading se rid')
        srid = sec['rid'][:]
        wlog('  read:  ',srid.size)
        wlog('  unique:',numpy.unique(srid).size)

        wlog('matching rids')
        ma, ms = eu.numpy_util.match(arid, srid)

        if ma.size != arid.size:
            raise RuntimeError("matched only %d/%d" % (ma.size, arid.size))

    else:
        ma = matches['ma']
        ms = matches['ms']

    wlog("Reading am s2n, whyflag, Irr, Icc")
    am_s2n = amc['s2n'][:]
    am_irr = amc['Irr'][:]
    am_icc = amc['Icc'][:]
    #whyflag = amc['whyflag'][:]

    wlog("Reading se s2n,shear_flags")
    se_s2n = sec['shear_s2n'][:]
    seflags = sec['shear_flags'][:]

    #w=where1( (whyflag[ma] == 0) & (seflags[ms] == 0) )
    """
    w=where1(  (am_s2n[ma] > 0) 
             & (am_irr[ma] > 0) 
             & (am_icc[ma] > 0) 
             & (seflags[ms] == 0) )
    """
    w=where1(  (am_s2n[ma] > 0) & (seflags[ms] == 0) )

    msk = ms[w]
    mak = ma[w]

    wlog("min 'good' am s2n: ",am_s2n[mak].min())

    nperbin=100000

    wlog("binning nperbin:",nperbin)

    bs = eu.stat.Binner(se_s2n[msk], am_s2n[mak])
    bs.dohist(nperbin=nperbin, min=20.0, max=1000.0)
    bs.calc_stats()

    wlog("plotting")
    plt=eu.plotting.bscatter(bs['xmean'],bs['ymean'],yerr=bs['yerr'],
                             show=False,
                             xlabel=r'$s2n_{SH}$',ylabel=r'$s2n_{AM}$')


    coeff = numpy.polyfit(bs['xmean'], bs['ymean'], 1)
    poly=numpy.poly1d(coeff)

    flabt='m: %0.2f b: %0.3f' % (coeff[0],coeff[1])
    flab=biggles.PlotLabel(0.1,0.9,flabt,halign='left')
    plt.add(flab)


    ps = biggles.Curve(bs['xmean'], poly(bs['xmean']), color='blue')
    plt.add(ps)
    plt.show()

    plt.write_eps('/direct/astro+u/esheldon/tmp/compare-s2n.eps')

    wlog("binning sigma")
    sig=sqrt((am_irr[mak] + am_icc[mak])/2.)
    bs2 = eu.stat.Binner(sig, am_s2n[mak]/se_s2n[msk])
    #bs2.dohist(nperbin=nperbin)
    bs2.dohist(binsize=0.1, min=1, max=20)
    bs2.calc_stats()

    wlog("plotting")
    wp=where1(bs2['xmean'] > 0)
    plt2=eu.plotting.bscatter(bs2['xmean'][wp],
                              bs2['ymean'][wp],
                              yerr=bs2['yerr'][wp],
                              show=False,
                              xlabel=r'$\sigma_{AM} [pixels]$', 
                              ylabel=r'$s2n_{AM}/s2n_{SH}$')

    flatv = ones(bs2['xmean'][wp].size)*coeff[0]
    flat = biggles.Curve(bs2['xmean'][wp], flatv, color='blue')
    plt2.add(flat)
    plt2.show()
    plt2.write_eps('/direct/astro+u/esheldon/tmp/compare-s2n-vs-sigma.eps')

    return {'ma':ma, 'ms':ms}
Esempio n. 19
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def main():
    options,args = parser.parse_args(sys.argv[1:])

    if len(args) < 1:
        parser.print_help()
        sys.exit(45)

    run=args[0]

    c = shapesim.read_config(run)
    cs = shapesim.read_config(c['sim'])


    if options.bytrial:
        if 'stack-' in run:
            ntrial=c['nsplit']
        else:
            orient=cs.get('orient','rand')
            if orient == 'ring':
                ntrial = cs['nsplit']
            else:
                ntrial = cs['ntrial']


    wqd = shapesim.get_wq_dir(run, bytrial=options.bytrial)
    if not os.path.exists(wqd):
        os.makedirs(wqd)

    extra=''
    if options.bynode:
        extra='mode: bynode\nN: 1'
    elif options.ncores is not None:
        ncores=int(options.ncores)
        extra='mode: bycore1\nN: %d' % ncores

    if run[0:8] == 'gmix-fit':
        rstr=run.replace('gmix-fit','gmix')
    else:
        rstr=run

    n1 = shapesim.get_numT(cs)

    runtype = c['runtype']
    if runtype == 'byellip':
        n2 = cs['nume']
    else:
        n2 = shapesim.get_nums2n(c)



    for i1 in xrange(n1):
        for i2 in xrange(n2):
            groups=''
            if options.i2new is not None and i2 <= int(options.i2new):
                groups='group: [new,new2]'
            elif options.i2new1 is not None and i2 <= int(options.i2new1):
                groups='group: [new]'
            elif options.i2new2 is not None and i2 <= int(options.i2new2):
                groups='group: [new2]'
            elif options.groups is not None:
                groups = 'group: [%s]' % options.groups

            if options.bytrial:
                for itrial in xrange(ntrial):
                    job_name='%s-%03i-%03i-%02i' % (rstr,i1,i2,itrial)
                    wqurl = shapesim.get_wq_url(run,i1,i2,itrial=itrial)
                    wlog("writing wq script:",wqurl)
                    with open(wqurl,'w') as fobj:
                        d={'job_name':job_name,
                           'run':run, 
                           'i1':i1,
                           'i2':i2,
                           'itrial':itrial,
                           'groups':groups,
                           'extra':extra,
                           'pri':options.priority}
                        wqscript=_wqtemplate_bytrial % d
                        fobj.write(wqscript)


            else:
                job_name='%s-%03i-%03i' % (rstr,i1,i2)
                wqurl = shapesim.get_wq_url(run,i1,i2)

                wlog("writing wq script:",wqurl)
                with open(wqurl,'w') as fobj:
                    wqscript=_wqtemplate % {'job_name':job_name,
                                            'run':run, 
                                            'i1':i1,
                                            'i2':i2,
                                            'groups':groups,
                                            'extra':extra,
                                            'pri':options.priority}
                    fobj.write(wqscript)
Esempio n. 20
0
    def run_ellip(self, ellip):
        """

        Do nrand realizations for each ellipticity value

        If convergence fails, retry ntrial times 
        """

        wlog("ellip:",ellip)
        outfile = self.outfile(ellip)
        wlog("outfile:",outfile)

        dir=os.path.dirname(outfile)
        if not os.path.exists(dir):
            wlog("Making output dir:",dir)
            try:
                os.makedirs(dir)
            except:
                # probably race condition
                pass


        robj=None

        # get a n ew RandomConvolvedImage with this ellip
        rci = self.new_random_convolved_image(ellip)


        # do all the randoms, allowing for a certain number of failures to
        # retry

        randi=0
        trial = 0
        nrand = self['nrand']
        ntrial = self['ntrial']
        while randi < nrand and trial < ntrial:

            # moments of the object pre-convolution
            Tguess0 = rci.objpars['Irr_meas'] + rci.objpars['Icc_meas']

            # moments after convolution
            Tguess=rci['Irr'] + rci['Icc']

            # moments of the psf
            Tguess_psf = rci['Irr_psf'] + rci['Icc_psf']

            # get moments before convolution
            amtrue = admom.admom(rci.image0, rci['cen'][0], rci['cen'][1],
                                 Tguess=Tguess0)
            if amtrue['whyflag'] == 0:
                # do regauss here
                rg = admom.ReGauss(rci.image, rci['cen'][0], rci['cen'][1],
                                   rci.psf, Tguess=Tguess,Tguess_psf=Tguess_psf)
                rg.do_all()

                if rg['rgstats'] != None and rg['rgcorrstats'] != None:
                    if rg['rgstats']['whyflag'] == 0:
                        # copy out the data
                        output = self.copy_output(amtrue, rg, rci['theta'])
                        if robj is None:
                            robj = eu.sfile.Open(outfile, 'w')
                        robj.write(output)

                        # only now do we increment randi
                        randi += 1

            trial += 1
            if randi < nrand and trial < ntrial:
                rci = self.new_random_convolved_image(ellip)

        if robj is not None:
            robj.close()

        wlog("ntrial:",trial," nfail:",trial-nrand)
        wlog("randi/nrand: %s/%s" % (randi,nrand))
        if randi != nrand:
            wlog("Exceeded max trials, failed to get all",nrand," realizations")