From 29cda1793908543d3d6d7eff40ad4719461e5d2d Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Thu, 14 Mar 2024 16:46:38 +0100 Subject: [PATCH 01/21] First implemantation --- docs/developer.rst | 1 + fermipy/defaults.py | 23 +++++++++++++++++++ fermipy/gtanalysis.py | 4 ++++ fermipy/plotting.py | 8 +++++++ fermipy/psmap.py | 45 +++++++++++++++++++++++++++++++++++++ fermipy/tests/test_psmap.py | 12 ++++++++++ 6 files changed, 93 insertions(+) create mode 100644 fermipy/psmap.py create mode 100644 fermipy/tests/test_psmap.py diff --git a/docs/developer.rst b/docs/developer.rst index ec4c0735..cb5d9dce 100644 --- a/docs/developer.rst +++ b/docs/developer.rst @@ -77,6 +77,7 @@ The following are steps for creating a new release: .. code-block:: bash $ python setup.py sdist upload -r pypi + $ twine upload dist/fermipy-XX.YY.ZZ.tar.gz 6. Create a new release on conda-forge by opening a PR on the `fermipy-feedstock diff --git a/fermipy/defaults.py b/fermipy/defaults.py index 9777b4dd..7f929884 100644 --- a/fermipy/defaults.py +++ b/fermipy/defaults.py @@ -332,6 +332,29 @@ def make_attrs_class(typename, d): 'init_lambda': (0, 'Initial value of damping parameter for newton step size calculation. A value of zero disables damping.', float), } +# PS map +psmap = { + 'model_name':(None, 'Model name',str), + 'wmap':('', 'weight 3d map',str), + 'outfile':('psmap.fits', 'outfile name',str), + 'fixedradius':(-1.0, 'Fixed radius',float), + 'psfpar0':(4.0, "PSF parameter 0", float), + 'psfpar1':(100,"PSF parameter 1", float), + 'psfpar2':(0.9,"PSF parameter 2", float), + 'psfpar3':(0.1,"PSF parameter 3", float), + 'maxpoissoncount':(100, "Maximum number of poisson counts", float), + 'prob_epsilon':(1e-7, 'precision parameter', float), + 'nbinpdf':(50, "Number of bin of the PSF", int), + 'scaleaxis':(20,"SCale axis", float), + 'emin':(1.0, "minimum energy/MeV", float), + 'emax':(1e9, "maximum energy/MeV", float), + 'chatter':(1, "output verbosity", int), + 'ipix':(-1, "number of pixel i axis", int), + 'jpix':(-1, "number of pixel j axis", int), + 'rebin':(1, "Rebin", int), +} + + # Options for Source Finder sourcefind = { 'model': common['model'], diff --git a/fermipy/gtanalysis.py b/fermipy/gtanalysis.py index 331a3ee4..31f650b9 100644 --- a/fermipy/gtanalysis.py +++ b/fermipy/gtanalysis.py @@ -27,6 +27,7 @@ import fermipy.lightcurve as lightcurve from fermipy.residmap import ResidMapGenerator from fermipy.tsmap import TSMapGenerator, TSCubeGenerator +from fermipy.psmap import PSMapGenerator from fermipy.sourcefind import SourceFind from fermipy.extension import ExtensionFit from fermipy.utils import merge_dict @@ -234,6 +235,7 @@ def filter_dict(d, val): class GTAnalysis(fermipy.config.Configurable, sed.SEDGenerator, ResidMapGenerator, TSMapGenerator, TSCubeGenerator, + PSMapGenerator, SourceFind, ExtensionFit, lightcurve.LightCurve): """High-level analysis interface that manages a set of analysis component objects. Most of the functionality of the Fermipy @@ -250,6 +252,7 @@ class GTAnalysis(fermipy.config.Configurable, sed.SEDGenerator, 'sed': defaults.sed, 'localize': defaults.localize, 'tsmap': defaults.tsmap, + 'psmap': defaults.psmap, 'residmap': defaults.residmap, 'lightcurve': defaults.lightcurve, 'find_sources': defaults.sourcefind, @@ -268,6 +271,7 @@ class GTAnalysis(fermipy.config.Configurable, sed.SEDGenerator, 'mc': defaults.mc, 'residmap': defaults.residmap, 'tsmap': defaults.tsmap, + 'psmap': defaults.psmap, 'tscube': defaults.tscube, 'sourcefind': defaults.sourcefind, 'sed': defaults.sed, diff --git a/fermipy/plotting.py b/fermipy/plotting.py index 85a01a2a..ecbe58d4 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1172,6 +1172,14 @@ def make_tsmap_plots(self, maps, roi=None, **kwargs): extension=fmt)) plt.close(fig) + def make_psmap_plots(self): + """ + + :return: + """ + fmt = kwargs.get('format', self.config['format']) + figsize = kwargs.get('figsize', self.config['figsize']) + def make_roi_plots(self, gta, mcube_tot, **kwargs): """Make various diagnostic plots for the 1D and 2D counts/model distributions. diff --git a/fermipy/psmap.py b/fermipy/psmap.py new file mode 100644 index 00000000..f2256e9e --- /dev/null +++ b/fermipy/psmap.py @@ -0,0 +1,45 @@ +# Licensed under a 3-clause BSD style license - see LICENSE.rst +import os +import logging +from fermipy.config import ConfigSchema +from fermipy.timing import Timer +import fermipy.gtpsmap as gtpsmap + + +class PSMapGenerator(object): + """Mixin class for `~fermipy.gtanalysis.GTAnalysis` that + generates PS maps.""" + + def psmap(self, prefix='', **kwargs): + ''' Add the description here!''' + + timer = Timer.create(start=True) + schema = ConfigSchema(self.defaults['psmap']) + + + if 'model_name' in kwargs.keys(): + model_name = kwargs['model_name'] + else: + model_name = 'model00' + + mcube = os.path.join(self.workdir, 'mcube_%s.fits' % (model_name)) + + if os.path.exists(mcube): + self.logger.info('Using source model: %s' % mcube) + else: + self.logger.info('Generate source model: %s' % mcube) + gta.write_model_map(model_name=model_name) + + schema.add_option('loglevel', logging.INFO) + + schema.add_option('cmap', self.files['ccube']) + schema.add_option('mmap', mcube) + + config = schema.create_config(self.config['psmap'], **kwargs) + + self.logger.info('Generating PS map') + print (config) + + gtpsmap.run(config) + + # python ../scripts/WCSview.py -i psmap.fits --zscale linear \ No newline at end of file diff --git a/fermipy/tests/test_psmap.py b/fermipy/tests/test_psmap.py new file mode 100644 index 00000000..1189c6f7 --- /dev/null +++ b/fermipy/tests/test_psmap.py @@ -0,0 +1,12 @@ +from fermipy.gtanalysis import GTAnalysis +from fermipy import gtpsmap + +gta = GTAnalysis('fermipy_test_draco/config.yaml') + +gta.setup() + +gta.print_roi() + +gta.write_roi('fit0') + +gta.psmap(model_name='model01') \ No newline at end of file From 73c906079f4c66ada74bbba0381846d271a70fa2 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Thu, 14 Mar 2024 16:48:51 +0100 Subject: [PATCH 02/21] Added gtpsmap --- fermipy/gtpsmap.py | 509 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 509 insertions(+) create mode 100755 fermipy/gtpsmap.py diff --git a/fermipy/gtpsmap.py b/fermipy/gtpsmap.py new file mode 100755 index 00000000..73ccdc72 --- /dev/null +++ b/fermipy/gtpsmap.py @@ -0,0 +1,509 @@ +#!/usr/bin/env python +# Licensed under a 3-clause BSD style license - see LICENSE.rst +################################################################### +# +# gtpsmap: check data/model agreement in Fermi-LAT analysis +# +# This python script compares data and model 3D maps by computing a PS map. +# It has been written by Philippe Bruel (LLR, CNRS/IN2P3, IPP Paris, France), based on a new method described in a publication currently in preparation. +# Meanwhile one can look at the corresponding Fermi symposium 2021 presentation: +# https://indico.cern.ch/event/1010947/contributions/4278096/ +# +# The main inputs of the script are: +# - the data 3D count map of the data (output of gtbin) +# - the model 3D count map (output of gtmodel or gta.write_model_map() for fermipy users) +# - if you use weighted likelihood to perform the fit, then you also have to provide the 3D weight map +# +# Usage: +# gtpsmap.py --cmap --mmap --wmap --outfile +# +# The output file contains: +# - primary hdu: 2D map of PS +# - hdu #1 ('PS in sigma'): deviation in sigma units (with sigma = sqrt(2) * ErfInv(1-10^(-|PS|))) +# +# The definition of PS at each pixel of the map is: +# - |PS| = -log10(deviation probability), where the deviation probability is measured on the PSF-like integrated count data and model spectra +# - sign(PS) = sign of sum(data_k-model_k)/sqrt(max(1,model_k)/weight_k), where the sum runs over the spectral bins +# +# Here are some useful PS<->sigma conversions: +# - PS -> sigma: 3/4/5/6/7/8 -> 3.29/3.89/4.42/4.89/5.33/5.73 +# - sigma -> PS: 3/4/5/6 -> 2.57/4.20/6.24/8.70 +# +# The other (optional) parameters of the python script are: +# --emin --emax +# energy range to compute PS +# --rebin +# --prob_epsilon +# This parameter defines the k-interval over which the Poisson probability of each spectral bin is considered +# when computing the log-likelihood distribution (the k-interval is such that ΣPoisson(k,model_i) = 1-prob_epsilon). +# The default value 1e-7 provides a 1% precision on PS up to PS=20. If you want a better precision for larger PS values, +# you need a smaller prob_epsilon (1e-11 provides a 5% precision up to 250) but that makes the script slower. +# --psfpar0 --psfpar1 --psfpar2 --psfpar3 +# PSF-like selection radius = sqrt(p0^2*pow(E/p1,-2*p2) + p3^2) +# --chatter +# +################################################################### + +import sys, getopt +import numpy as np +import matplotlib.pyplot as plt +from astropy.wcs import WCS +from astropy.io import fits +from astropy.coordinates import SkyCoord +import scipy.signal +from scipy.stats import poisson +from scipy.special import gammainc +from scipy.special import gammaincc +from scipy.special import erfcinv + +#import cProfile +#import pstats +#v = dict() + +def getDeviationProbability(datcts, modcts, weights, maxpoissoncount, prob_epsilon, nbinpdf, scaleaxis): + # datcts = array of data counts + # modcts = array of model counts + # weights = array of weights + # for simplicty's sake, LL (LogLikelihood) stands for -LL + + datcts=np.rint(datcts) + + # sort model counts in descending order + sortindex = np.argsort(-modcts) + smodcts = modcts[sortindex] + sdatcts = datcts[sortindex] + sweights = weights[sortindex] + + # compute the sum of the residuals to estimate the sign of the deviation + allres = (sdatcts-smodcts)/np.sqrt(np.maximum(smodcts,1)/sweights) + + # select the bins in gaussian mode + mskgaus = smodcts > maxpoissoncount + gmodcts = smodcts[mskgaus] + gdatcts = sdatcts[mskgaus] + gweights = sweights[mskgaus] + gchi2 = np.power(gdatcts-gmodcts,2)/gmodcts*gweights + gdat_LL = 0.5*np.sum(gchi2) + ngaus = len(gmodcts) + # return simple chi2 probability if all the bins are gaussian + if ngaus==len(smodcts): + # second argument in gammaincc is gdat_LL = chi2/2 + return gammaincc(ngaus/2.0,gdat_LL), np.sum(allres) + + pmodcts = smodcts[~mskgaus] + pdatcts = sdatcts[~mskgaus] + pweights = sweights[~mskgaus] + + # compute loglikelihood for data counts + dat_PP = poisson.pmf(pdatcts,pmodcts) + checkmin = np.full(len(pdatcts),1e-323) + dat_PP = np.maximum(dat_PP,checkmin) + dat_LL = np.sum(-pweights*np.log(dat_PP)) + dat_LL += gdat_LL + + # for each bin, get [k_min,k_max] interval such that sum_{k_min,k_max}(poisson(k,model_counts)) = 1-prob_epsilon + kmin = np.zeros(len(pmodcts),dtype=int) + kmax = np.zeros(len(pmodcts),dtype=int) + msk = pmodcts < 5 + kmin[msk] = 0 + kmax[msk] = np.rint(0.715029+0.049825*np.log10(prob_epsilon)+0.011768*pow(np.log10(prob_epsilon),2) + (-0.308206+-1.309547*np.log10(prob_epsilon)+-0.028455*pow(np.log10(prob_epsilon),2)) * np.exp((1.881824+0.117058*np.log10(prob_epsilon)+0.004208*pow(np.log10(prob_epsilon),2))*np.maximum(np.log10(pmodcts[msk]),-4))+1); + # + offsetlo = np.rint(-0.523564-0.75129*np.log10(prob_epsilon)) + offsetup = np.rint(-1.09374-0.716202*np.log10(prob_epsilon)) + kmin[~msk] = np.rint(pmodcts[~msk]-np.sqrt(pmodcts[~msk])*erfcinv(prob_epsilon)*np.sqrt(2)+offsetlo) + kmax[~msk] = np.rint(pmodcts[~msk]+np.sqrt(pmodcts[~msk])*erfcinv(prob_epsilon)*np.sqrt(2)+offsetup) + msk = kmin<0 + kmin[msk] = 0 + + # prepare the binning of the LL histogram + hnbin = nbinpdf*len(smodcts) + hxmax = scaleaxis*(ngaus+np.sum(pweights)) + hstep = hxmax/hnbin + + result = np.zeros(hnbin,dtype=float) + hbinedge = np.linspace(0,hxmax+hstep,num=hnbin+1,endpoint=False) + hbincen = 0.5*(hbinedge[1:] + hbinedge[:-1]) + + if ngaus>0: + # compute the histogram of LL values with the incomplete gamma function + # setting the incomplete gamma parameters so that it corresponds to the chi2 distribution with ngaus degrees of freedom + # the theta parameter = 2, so the second argument of the gamma function is 2*x/theta=x + result = gammainc(ngaus/2.0,hbinedge[1:])-gammainc(ngaus/2.0,hbinedge[:-1]) + resultc = np.cumsum(result) + msk = resultc>1-prob_epsilon + result[msk] = 0 + else: + # prepare the histogram of LL values corresponding to the first bin with model counts = pmodcts[0] + vecdat0 = np.arange(kmin[0],kmax[0]+1) + vecPP0 = poisson.pmf(vecdat0,pmodcts[0]) + vecLL0 = -pweights[0]*np.log(vecPP0) + result, hbinedge = np.histogram(vecLL0,bins=hnbin,range=(0,hxmax),weights=vecPP0) + + # build the LL distribution iteratively, starting from the first Poissonian bin (if ngaus>0) or the second bin (if ngaus==0) + istart = int(0) + if ngaus==0: + istart = 1 + for i in np.arange(istart,len(pmodcts)): + vecdat = np.arange(kmin[i],kmax[i]+1) + # get prob and LL values for [kmin,kmax] + vecLLw = poisson.logpmf(vecdat,pmodcts[i]) + vecPP = np.exp(vecLLw) + vecLLw *= (-pweights[i]/hstep) + # get shift = bin # of LL value along the x-axis + binshift = np.rint(vecLLw,out=vecdat,casting='unsafe') + + # shift the current LL histogram by binshift to fill the 2d array + # accumulate the 2d array as we go so we never have to actually + # allocate it + comp_binshift = len(result)-binshift + tmp = np.zeros_like(result) + for j in range(0,len(vecPP)): + tmp[binshift[j]:] += vecPP[j]*result[:comp_binshift[j]] + + # project onto the x-axis to get the new LL histogram + result = tmp + + # get the largest LL value of the LL histogram + mskhist = result > 0 + xnz = hbincen[mskhist] + LLmax = xnz[-1] + # set data LL to the maximum one if needed + if dat_LL > LLmax: + dat_LL = LLmax + ibin = int(dat_LL/hstep) + pdat = np.sum(result[ibin:]) + #print('pdat %f PS %f' % (pdat,-np.log10(pdat))) + + return pdat, np.sum(allres) + +def convolve_map(m, k, cpix, threshold=0.001): + # piece of code copied from fermipy residmap (see https://fermipy.readthedocs.io/en/latest/) + o = np.zeros(m.shape,dtype=np.float64) + ix = int(cpix[0]) + iy = int(cpix[1]) + + ks = k + ms = m + + mx = ks[ix, :] > ks[ix, iy] * threshold + my = ks[:, iy] > ks[ix, iy] * threshold + + nx = int(np.sum(mx)/2) + ny = int(np.sum(mx)/2) + + # Ensure that there is an odd number of pixels in the kernel + # array + if ix + nx + 1 >= ms.shape[0] or ix - nx < 0: + nx -= 1 + if iy + ny + 1 >= ms.shape[0] or iy - ny < 0: + ny -= 1 + + sx = slice(ix - nx, ix + nx + 1) + sy = slice(iy - ny, iy + ny + 1) + + ks = ks[sx, sy] + o = scipy.signal.fftconvolve(ms, ks, mode='same') + + return o + + +def run(args): + dat_filename = args['cmap'] + mod_filename = args['mmap'] + weight_filename = args['wmap'] + out_filename = args['outfile'] + fixedradius = args['fixedradius'] + psfpar0 = args['psfpar0'] + psfpar1 = args['psfpar1'] + psfpar2 = args['psfpar2'] + psfpar3 = args['psfpar3'] + maxpoissoncount = args['maxpoissoncount'] + prob_epsilon = np.maximum(args['prob_epsilon'],1e-15) + nbinpdf = args['nbinpdf'] + scaleaxis = args['scaleaxis'] + emin = args['emin'] + emax = args['emax'] + chatter = args['chatter'] + + ipix = args['ipix'] + jpix = args['jpix'] + rebin = args['rebin'] + + optweight = 1 + if weight_filename == '': + optweight = 0 + + dat_hdulist = fits.open(dat_filename) + dat_hdu = dat_hdulist[0] + dat_map0 = dat_hdu.data + + ene_hdu = dat_hdulist[1] + ene_table = ene_hdu.data + energymin = ene_table['E_MIN'] + energymax = ene_table['E_MAX'] + energycenter = np.sqrt(ene_table['E_MIN']*ene_table['E_MAX']) + if ene_hdu.header['TUNIT2']=='keV': + energymin = energymin/1000 + energymax = energymax/1000 + energycenter = energycenter/1000 + + mskle = energymax>emin + mskhe = energymin0: + psfpar0 = 0 + psfpar3 = fixedradius + if chatter>0: + print ('Computing PSF-like summed maps with fixed radius %f deg' % (fixedradius)) + else: + if chatter>0: + print ('Computing PSF-like summed maps with energy dependent parameterization (%f deg, %f MeV, %f, %f deg)' % (psfpar0,psfpar1,psfpar2,psfpar3)) + + psflikeroi = np.sqrt(psfpar0*psfpar0*np.power(psfpar1/energymin,2*psfpar2)+psfpar3*psfpar3) + + kernel_map=np.zeros((naxis3,naxis2,naxis1),dtype=np.float64) + dat_summed_map=np.zeros((naxis3,naxis2,naxis1),dtype=np.float64) + mod_summed_map=np.zeros((naxis3,naxis2,naxis1),dtype=np.float64) + + dat_map=np.zeros((naxis3,naxis2,naxis1),dtype=np.float64) + dat_map = dat_map0 + + # the weights of the integrated count spectra correspond to the Ndata-averaged 1/weights over the PSF-like region + if optweight: + prod_weightdat_map=weight_map*dat_map + weight_avesummed_map=np.zeros((naxis3,naxis2,naxis1),dtype=np.float64) + + cpix=np.array([icenter,jcenter]) + for k in range(naxis3): + kernel_map[k,:,:]=dist.degree < psflikeroi[k] + dat_summed_map[k,:,:]=convolve_map(dat_map[k,:,:],kernel_map[k,:,:],cpix) + mod_summed_map[k,:,:]=convolve_map(mod_map[k,:,:],kernel_map[k,:,:],cpix) + if optweight: + weight_avesummed_map[k,:,:]=convolve_map(prod_weightdat_map[k,:,:],kernel_map[k,:,:],cpix) + + msk = dat_summed_map > 0.1 + dat_summed_map[~msk] = 0 + + # the weights of the integrated count spectra correspond to the Ndata-averaged 1/weights over the PSF-like region + weight_ave_map = np.ones((naxis3,naxis2,naxis1),dtype=np.float64) + if optweight: + msk = dat_summed_map>0 + weight_ave_map[msk] = weight_avesummed_map[msk]/dat_summed_map[msk] + weight_ave_map[msk] = 1/weight_ave_map[msk] + + # select energy range + mod_summed_map = mod_summed_map[mskerange] + dat_summed_map = dat_summed_map[mskerange] + weight_ave_map = weight_ave_map[mskerange] + print("Selecting the energy bins including emin=%f and emax=%f -> %d bins in [%f,%f] " %(emin,emax,mod_summed_map.shape[0],selenergymin[0],selenergymax[-1])) + + naxis3rebin = mod_summed_map.shape[0] + if rebin>1: + r = mod_summed_map.shape[0]%rebin + nrebin = mod_summed_map.shape[0]//rebin + naxis3rebin = nrebin + if r>0: + mod_summed_map = mod_summed_map[:-r] + dat_summed_map = dat_summed_map[:-r] + weight_ave_map = weight_ave_map[:-r] + selenergymin = selenergymin[:-r] + selenergymax = selenergymax[:-r] + print("Rebinning the energy axis with rebin=%d (ignoring %d bins at the end): %d bins in [%f,%f], logE bin size=%f" %(rebin,r,nrebin,selenergymin[0],selenergymax[-1],np.log10(selenergymin[rebin]/selenergymin[0]))) + # + weight_ave_map = dat_summed_map/weight_ave_map + mod_summed_map = mod_summed_map.reshape(nrebin,rebin,naxis2,-1).sum(axis=1) + dat_summed_map = dat_summed_map.reshape(nrebin,rebin,naxis2,-1).sum(axis=1) + weight_ave_map = weight_ave_map.reshape(nrebin,rebin,naxis2,-1).sum(axis=1) + mskdat = dat_summed_map>0 + weight_ave_map[mskdat] = weight_ave_map[mskdat]/dat_summed_map[mskdat] + weight_ave_map[~mskdat] = 1 + weight_ave_map = 1/weight_ave_map + + if chatter>0: + print('Computing the PS map (%d bins in energy range [%3.2f,%3.2f], nbinpdf=%d, scaleaxis=%3.2f, maxpoissoncount=%3.2f, prob_epsilon=%g):\n(for each column of the map, the integer part of the maximum value of abs(PS) is given as one character [1,..,9,A=10,B=11,..,Y=34,Z>=35])' %(naxis3rebin,selenergymin[0],selenergymax[-1],nbinpdf,scaleaxis,maxpoissoncount,prob_epsilon)) + + psmap=np.zeros((naxis2,naxis1)) + psmapsigma=np.zeros((naxis2,naxis1)) + totresmap=np.zeros((naxis2,naxis1)) + + psmax = -1; + imax = 0 + jmax = 0 + psvalstr = ['0','1','2','3','4','5','6','7','8','9'] + psvalstr.extend(['A','B','C','D','E','F','G','H','I','J']) + psvalstr.extend(['K','L','M','N','O','P','Q','R','S','T']) + psvalstr.extend(['U','V','W','X','Y','Z']) + + nbnan = int(0) + psmaxcol = 0 + axisrange1 = range(naxis1) + axisrange2 = range(naxis2) + # change range if ipix and jpix are provided + if ipix>0 and jpix>0: + axisrange1 = range(ipix-1,ipix) + axisrange2 = range(jpix-1,jpix) + + + # loop over the pixels and compute PS + for i in axisrange1: + psmaxcol = 0; + for j in axisrange2: + modcounts = mod_summed_map[:,j,i] + datcounts = dat_summed_map[:,j,i] + weights = weight_ave_map[:,j,i] + # + prob, totres = getDeviationProbability(datcounts,modcounts,weights,maxpoissoncount,prob_epsilon,nbinpdf,scaleaxis) + if np.isnan(prob): + nbnan = nbnan+1 + if prob<1e-311: + prob = 1e-311 + ps = -np.log10(prob) + # + if ps>psmaxcol: + psmaxcol = ps + if ps>psmax: + psmax = ps + imax = i + jmax = j + totresmap[j,i] = totres + psmap[j,i] = ps + if totres<0: + psmap[j,i] = -ps + ipsvalstr = int(psmaxcol) + if ipsvalstr>35: + ipsvalstr = 35 + mycom = '%s' % (psvalstr[ipsvalstr]) + if chatter>0: + sys.stdout.write(mycom) + sys.stdout.flush() + + if chatter>0: + print ('') + + if nbnan>0 and chatter>0: + print ('') + print ('-------------------------------------> Warning: there are %d pixels with PS=nan.' % (nbnan)) + print ('') + + maxdir = SkyCoord.from_pixel(imax,jmax,wcs=wcs2d,origin=0) + + if chatter>0: + if maxdir.frame.name=='galactic': + coordx=maxdir.l.to_string(decimal=True) + coordy=maxdir.b.to_string(decimal=True) + else: + coordx=maxdir.ra.to_string(decimal=True) + coordy=maxdir.dec.to_string(decimal=True) + print ('Maximum PS: %f found at the position (%s,%s)=(%s,%s) [fits pixel (%d,%d)]' % (psmax,coordname1,coordname2,coordx,coordy,imax+1,jmax+1)) + + modcounts = mod_summed_map[:,jmax,imax] + datcounts = dat_summed_map[:,jmax,imax] + weights = weight_ave_map[:,jmax,imax] + + if chatter>1: + print ('PSF-like integrated count spectra in pixel (%d,%d):' %(imax+1,jmax+1)) + for i in range(naxis3rebin): + print ('bin %02d data %f model %g weight %g' % (i,datcounts[i],modcounts[i],weights[i])) + + if chatter>0: + print ('Saving the PS map in file %s' %(out_filename)) + + hdu_ps = fits.PrimaryHDU(psmap,wcs2d.to_header()) + psmapsigma = np.sign(psmap) * np.sqrt(2) * erfcinv(np.power(10.,-np.abs(psmap))) + hdu_ps_sigma = fits.ImageHDU(psmapsigma,wcs2d.to_header()) + hdr = hdu_ps_sigma.header + hdr['EXTNAME'] = 'PS in sigma' + + hdul = fits.HDUList([hdu_ps,hdu_ps_sigma]) + hdul.writeto(out_filename,overwrite=True) + +if __name__ == "__main__": + import argparse + usage = "Usage: %(prog)s [options] input" + + description = "python script" + parser = argparse.ArgumentParser(usage=usage, description=description) + + #parser.add_argument("-d", "--dryrun", action='store_true') + parser.add_argument("-cm", "--cmap", required=True, type=str, help="data 3d map") + + parser.add_argument("-mm", "--mmap", required=True, type=str, help="model 3d map") + + parser.add_argument("-wm", "--wmap", required=False, type=str, default='', help="weights 3d map") + + parser.add_argument("-o", "--outfile", required=True, type=str, help="Output file") + + parser.add_argument("-fr", "--fixedradius", required=False, type=float, default=-1.0, help="Fixed radius") + + parser.add_argument("-psf0", "--psfpar0", required=False, type=float, default=4.0, help="PSF parameter 0") + parser.add_argument("-psf1", "--psfpar1", required=False, type=float, default=100.0, help="PSF parameter 1") + parser.add_argument("-psf2", "--psfpar2", required=False, type=float, default=0.9, help="PSF parameter 2") + parser.add_argument("-psf3", "--psfpar3", required=False, type=float, default=0.1, help="PSF parameter 3") + + parser.add_argument("-mpc", "--maxpoissoncount", required=False, type=float, default=100, help="Output file") + parser.add_argument("-pe", "--prob_epsilon", required=False, type=float, default=1e-7, help="precision parameter") + parser.add_argument("-nb", "--nbinpdf", required=False, type=int, default=50, help="Number of bin of the PSF") + parser.add_argument("-sa", "--scaleaxis", required=False, type=float, default=20, help="Output file") + parser.add_argument("-emin", "--emin", required=False, type=float, default=1.0, help="minimum energy/MeV") + parser.add_argument("-emax", "--emax", required=False, type=float, default=1e9, help="maximum energy/MeV") + parser.add_argument("-c", "--chatter", required=False, type=int, default=1, help="output verbosity") + + parser.add_argument("-ix", "--ipix", required=False, type=int, default=-1, help="number of pixel i axis") + parser.add_argument("-jx", "--jpix", required=False, type=int, default=-1, help="number of pixel j axis") + parser.add_argument("-r", "--rebin", required=False, type=int, default=1, help="Rebin") + + + args = vars(parser.parse_args()) + print(args) + run(args) + + # define the PSF-like energy dependent cut + + #cProfile.runctx('main(sys.argv[1:])',globals(),locals(),'profout.asc') + #pstats.Stats('profout.asc').sort_stats('cumtime').print_stats(10) + #locals().update(v) + From 71c09728115a28703f66daceabc329fceddd4c2e Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Thu, 21 Mar 2024 16:25:54 -0700 Subject: [PATCH 03/21] improving plotting --- fermipy/defaults.py | 2 + fermipy/gtpsmap.py | 51 +++++++++++++++------- fermipy/plotting.py | 86 +++++++++++++++++++++++++++++++++++-- fermipy/psmap.py | 67 +++++++++++++++++++++++++++-- fermipy/tests/test_psmap.py | 3 +- 5 files changed, 187 insertions(+), 22 deletions(-) diff --git a/fermipy/defaults.py b/fermipy/defaults.py index 7f929884..63c3f0eb 100644 --- a/fermipy/defaults.py +++ b/fermipy/defaults.py @@ -352,6 +352,8 @@ def make_attrs_class(typename, d): 'ipix':(-1, "number of pixel i axis", int), 'jpix':(-1, "number of pixel j axis", int), 'rebin':(1, "Rebin", int), + 'make_plots': common['make_plots'], + 'write_fits': common['write_fits'], } diff --git a/fermipy/gtpsmap.py b/fermipy/gtpsmap.py index 73ccdc72..e1efe7e1 100755 --- a/fermipy/gtpsmap.py +++ b/fermipy/gtpsmap.py @@ -46,7 +46,6 @@ import sys, getopt import numpy as np -import matplotlib.pyplot as plt from astropy.wcs import WCS from astropy.io import fits from astropy.coordinates import SkyCoord @@ -56,6 +55,8 @@ from scipy.special import gammaincc from scipy.special import erfcinv +from gammapy.maps import WcsNDMap, WcsGeom + #import cProfile #import pstats #v = dict() @@ -228,7 +229,9 @@ def run(args): ipix = args['ipix'] jpix = args['jpix'] rebin = args['rebin'] + write_fits = args['write_fits'] + o = {} # this sintax is to respect matt syntax where all the output are saved in the dictionary called "o" optweight = 1 if weight_filename == '': optweight = 0 @@ -366,9 +369,9 @@ def run(args): if chatter>0: print('Computing the PS map (%d bins in energy range [%3.2f,%3.2f], nbinpdf=%d, scaleaxis=%3.2f, maxpoissoncount=%3.2f, prob_epsilon=%g):\n(for each column of the map, the integer part of the maximum value of abs(PS) is given as one character [1,..,9,A=10,B=11,..,Y=34,Z>=35])' %(naxis3rebin,selenergymin[0],selenergymax[-1],nbinpdf,scaleaxis,maxpoissoncount,prob_epsilon)) - psmap=np.zeros((naxis2,naxis1)) - psmapsigma=np.zeros((naxis2,naxis1)) - totresmap=np.zeros((naxis2,naxis1)) + psmap = np.zeros((naxis2,naxis1)) + + totresmap = np.zeros((naxis2,naxis1)) psmax = -1; imax = 0 @@ -439,6 +442,14 @@ def run(args): coordx=maxdir.ra.to_string(decimal=True) coordy=maxdir.dec.to_string(decimal=True) print ('Maximum PS: %f found at the position (%s,%s)=(%s,%s) [fits pixel (%d,%d)]' % (psmax,coordname1,coordname2,coordx,coordy,imax+1,jmax+1)) + o['psmax'] = psmax + o['coordname1'] = coordname1 + o['coordname2'] = coordname2 + o['coordx'] = coordx + o['coordy'] = coordy + o['ipix'] = imax + 1 + o['jpix'] = jmax + 1 + modcounts = mod_summed_map[:,jmax,imax] datcounts = dat_summed_map[:,jmax,imax] @@ -449,17 +460,27 @@ def run(args): for i in range(naxis3rebin): print ('bin %02d data %f model %g weight %g' % (i,datcounts[i],modcounts[i],weights[i])) - if chatter>0: - print ('Saving the PS map in file %s' %(out_filename)) - - hdu_ps = fits.PrimaryHDU(psmap,wcs2d.to_header()) - psmapsigma = np.sign(psmap) * np.sqrt(2) * erfcinv(np.power(10.,-np.abs(psmap))) - hdu_ps_sigma = fits.ImageHDU(psmapsigma,wcs2d.to_header()) + psmapsigma = np.sign(psmap) * np.sqrt(2) * erfcinv(np.power(10., -np.abs(psmap))) + + + o['wcs2d'] = wcs2d + o['psmap'] = psmap + o['psmapsigma'] = psmapsigma + return o + +def make_psmap_fits(o, out_filename): + psmap = o['psmap'] + psmapsigma = o['psmapsigma'] + wcs2d = o['wcs2d'] + + o['file_name'] = out_filename + hdu_ps = fits.PrimaryHDU(psmap, wcs2d.to_header()) + hdu_ps_sigma = fits.ImageHDU(psmapsigma, wcs2d.to_header()) hdr = hdu_ps_sigma.header hdr['EXTNAME'] = 'PS in sigma' - - hdul = fits.HDUList([hdu_ps,hdu_ps_sigma]) - hdul.writeto(out_filename,overwrite=True) + + hdul = fits.HDUList([hdu_ps, hdu_ps_sigma]) + hdul.writeto(out_filename, overwrite=True) if __name__ == "__main__": import argparse @@ -498,8 +519,8 @@ def run(args): args = vars(parser.parse_args()) - print(args) - run(args) + o=run(args) + make_psmap_fits(o,out_filename=args['outfile']) # define the PSF-like energy dependent cut diff --git a/fermipy/plotting.py b/fermipy/plotting.py index ecbe58d4..485e26e6 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1172,13 +1172,93 @@ def make_tsmap_plots(self, maps, roi=None, **kwargs): extension=fmt)) plt.close(fig) - def make_psmap_plots(self): - """ + def make_psmap_plots(self, psmaps, roi=None, **kwargs): + """Make plots from the output of + `~fermipy.gtanalysis.GTAnalysis.psmap` + This method generates a 2D sky map for the best-fit test source in + PS and sqrt(PS). + + Parameters + ---------- + maps : dict + Output dictionary of + `~fermipy.gtanalysis.GTAnalysis.psmap`. - :return: + roi : `~fermipy.roi_model.ROIModel` + ROI Model object. Generate markers at the positions of + the sources in this ROI. + + zoom : float + Crop the image by this factor. If None then no crop is + applied. """ + kwargs.setdefault('graticule_radii', self.config['graticule_radii']) + kwargs.setdefault('label_ts_threshold', + self.config['label_ts_threshold']) + kwargs.setdefault('cmap', self.config['cmap']) + kwargs.setdefault('catalogs', self.config['catalogs']) fmt = kwargs.get('format', self.config['format']) figsize = kwargs.get('figsize', self.config['figsize']) + workdir = kwargs.pop('workdir', self.config['fileio']['workdir']) + suffix = kwargs.pop('suffix', 'tsmap') + zoom = kwargs.pop('zoom', None) + + prefix=psmaps['name'] + + fig = plt.figure(figsize=figsize) + + p = ROIPlotter(psmaps['ps_map'], roi=roi, **kwargs) + + p.plot(vmin=-5, vmax=5, + cb_label='PSMAP', interpolation='bicubic', + zoom=zoom) + plt.savefig(utils.format_filename(workdir, + '%s_psmap' % suffix, + prefix=[prefix], + extension=fmt)) + plt.close(fig) + + sigma_levels = [3, 5, 7] + list(np.logspace(1, 3, 17)) + + fig = plt.figure(figsize=figsize) + p = ROIPlotter(psmaps['pssigma_map'], roi=roi, **kwargs) + p.plot(vmin=-5, vmax=5, + levels=sigma_levels, + cb_label='PSMAP [SIGMA]', interpolation='bicubic', + zoom=zoom) + plt.savefig(utils.format_filename(workdir, + '%s_pssigma' % suffix, + prefix=[prefix], + extension=fmt)) + plt.close(fig) + + # make and draw histogram + fig, ax = plt.subplots(figsize=figsize) + bins = np.linspace(-10, 10, 101) + + data = np.nan_to_num(psmaps['pssigma_map'].data.T) + #data[data > 25.0] = 25.0 + #data[data < 0.0] = 0.0 + n, bins, patches = ax.hist(data.flatten(), bins, density=True, + histtype='stepfilled', + facecolor='green', alpha=0.75) + # ax.plot(bins,(1-chi2.cdf(x,dof))/2.,**kwargs) + #ax.plot(bins, 0.5 * chi2.pdf(bins, 1.0), color='k', + # label=r"$\chi^2_{1} / 2$") + + ax.plot(bins, 0.5 * norm.pdf(bins, 0.0, 1.0), color='k', label=r"Gauss(0,1)") + ax.set_yscale('log') + ax.set_ylim(1E-4) + ax.legend(loc='upper right', frameon=False) + + # labels and such + ax.set_xlabel('PS') + ax.set_ylabel('Probability') + plt.savefig(utils.format_filename(workdir, + '%s_ps_hist' % suffix, + prefix=[prefix], + extension=fmt)) + plt.close(fig) def make_roi_plots(self, gta, mcube_tot, **kwargs): """Make various diagnostic plots for the 1D and 2D diff --git a/fermipy/psmap.py b/fermipy/psmap.py index f2256e9e..60ddecc7 100644 --- a/fermipy/psmap.py +++ b/fermipy/psmap.py @@ -3,7 +3,11 @@ import logging from fermipy.config import ConfigSchema from fermipy.timing import Timer +import fermipy.utils as utils import fermipy.gtpsmap as gtpsmap +import fermipy.plotting as plotting + +from gammapy.maps import WcsNDMap, WcsGeom class PSMapGenerator(object): @@ -11,7 +15,34 @@ class PSMapGenerator(object): generates PS maps.""" def psmap(self, prefix='', **kwargs): - ''' Add the description here!''' + ''' Generate a spatial PS map for a source component with + properties defined by the `model` argument. The PS map will + have the same geometry as the ROI. The output of this method + is a dictionary containing `~fermipy.skymap.Map` objects with + the PS and amplitude of the best-fit test source. By default + this method will also save maps to FITS files and render them + as image files. + + This method uses a simplified likelihood fitting + implementation that only fits for the normalization of the + test source. Before running this method it is recommended to + first optimize the ROI model (e.g. by running + :py:meth:`~fermipy.gtanalysis.GTAnalysis.optimize` + + + Parameters + ---------- + prefix : str + Optional string that will be prepended to all output files. + + {options} + + Returns + ------- + psmap : dict + A dictionary containing the `~fermipy.skymap.Map` objects + for PS and source amplitude. + ''' timer = Timer.create(start=True) schema = ConfigSchema(self.defaults['psmap']) @@ -40,6 +71,36 @@ def psmap(self, prefix='', **kwargs): self.logger.info('Generating PS map') print (config) - gtpsmap.run(config) + o = gtpsmap.run(config) + map_geom = self._geom.to_image() + + ps_map = WcsNDMap(map_geom, o['psmap']) + pssigma_map = WcsNDMap(map_geom, o['psmapsigma']) + + o['name'] = utils.join_strings([prefix, model_name]) + o['ps_map']= ps_map + o['pssigma_map']=pssigma_map + o['config']=kwargs + + print(o) + o['file'] = None + if config['write_fits']: + outfile = config.get('outfile', None) + if outfile is None: + outfile = utils.format_filename(self.workdir, 'psmap', + prefix=[o['name']]) + else: + outfile = os.path.join(self.workdir, + os.path.splitext(outfile)[0]) + + o['file'] = os.path.basename(outfile) + '.fits' + gtpsmap.make_psmap_fits(o,outfile + '.fits') + + if config['make_plots']: + print('making some plots...') + plotter = plotting.AnalysisPlotter(self.config['plotting'], + fileio=self.config['fileio'], + logging=self.config['logging']) + plotter.make_psmap_plots(o, self.roi) - # python ../scripts/WCSview.py -i psmap.fits --zscale linear \ No newline at end of file + self.logger.log(config['loglevel'], 'Finished PS map') diff --git a/fermipy/tests/test_psmap.py b/fermipy/tests/test_psmap.py index 1189c6f7..280ae999 100644 --- a/fermipy/tests/test_psmap.py +++ b/fermipy/tests/test_psmap.py @@ -9,4 +9,5 @@ gta.write_roi('fit0') -gta.psmap(model_name='model01') \ No newline at end of file +psmap = gta.psmap(model_name='model01', make_plots=True) + From 91f154794e07f807fedc6211b819132486016b73 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Mon, 25 Mar 2024 17:02:45 -0700 Subject: [PATCH 04/21] Added a test for psmap --- fermipy/psmap.py | 8 +++----- fermipy/tests/test_gtanalysis.py | 6 ++++++ fermipy/tests/test_psmap.py | 13 ------------- 3 files changed, 9 insertions(+), 18 deletions(-) delete mode 100644 fermipy/tests/test_psmap.py diff --git a/fermipy/psmap.py b/fermipy/psmap.py index 60ddecc7..4217be25 100644 --- a/fermipy/psmap.py +++ b/fermipy/psmap.py @@ -58,8 +58,8 @@ def psmap(self, prefix='', **kwargs): if os.path.exists(mcube): self.logger.info('Using source model: %s' % mcube) else: - self.logger.info('Generate source model: %s' % mcube) - gta.write_model_map(model_name=model_name) + raise FileExistsError('You must first generate a source model map using gta.write_model_map(model_name="%s")' % model_name) + #gta.write_model_map(model_name=model_name) schema.add_option('loglevel', logging.INFO) @@ -69,7 +69,6 @@ def psmap(self, prefix='', **kwargs): config = schema.create_config(self.config['psmap'], **kwargs) self.logger.info('Generating PS map') - print (config) o = gtpsmap.run(config) map_geom = self._geom.to_image() @@ -82,7 +81,6 @@ def psmap(self, prefix='', **kwargs): o['pssigma_map']=pssigma_map o['config']=kwargs - print(o) o['file'] = None if config['write_fits']: outfile = config.get('outfile', None) @@ -97,7 +95,7 @@ def psmap(self, prefix='', **kwargs): gtpsmap.make_psmap_fits(o,outfile + '.fits') if config['make_plots']: - print('making some plots...') + plotter = plotting.AnalysisPlotter(self.config['plotting'], fileio=self.config['fileio'], logging=self.config['logging']) diff --git a/fermipy/tests/test_gtanalysis.py b/fermipy/tests/test_gtanalysis.py index 0ae929d4..5eb6eaf8 100644 --- a/fermipy/tests/test_gtanalysis.py +++ b/fermipy/tests/test_gtanalysis.py @@ -163,6 +163,12 @@ def test_gtanalysis_tsmap(create_diffuse_dir, create_draco_analysis): gta.load_roi('fit1') gta.tsmap(model={}, make_plots=True) +def test_gtanalysis_psmap(create_diffuse_dir, create_draco_analysis): + gta = create_draco_analysis + gta.load_roi('fit1') + gta.write_model_map(model_name="model01") + gta.psmap(model_name='model01', make_plots=True) + @requires_git_version('99-00-01') def test_gtanalysis_tscube(create_diffuse_dir, create_draco_analysis): diff --git a/fermipy/tests/test_psmap.py b/fermipy/tests/test_psmap.py deleted file mode 100644 index 280ae999..00000000 --- a/fermipy/tests/test_psmap.py +++ /dev/null @@ -1,13 +0,0 @@ -from fermipy.gtanalysis import GTAnalysis -from fermipy import gtpsmap - -gta = GTAnalysis('fermipy_test_draco/config.yaml') - -gta.setup() - -gta.print_roi() - -gta.write_roi('fit0') - -psmap = gta.psmap(model_name='model01', make_plots=True) - From 5ecb443ebe721326ea83f2562388e24d060c2bd4 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Mon, 25 Mar 2024 18:59:09 -0700 Subject: [PATCH 05/21] Improved PS map documentation --- docs/advanced/index.rst | 5 + docs/advanced/model01_psmap_ps_hist.png | Bin 0 -> 25039 bytes docs/advanced/model01_psmap_psmap.png | Bin 0 -> 175577 bytes docs/advanced/model01_psmap_pssigma.png | Bin 0 -> 210819 bytes docs/advanced/psmap.rst | 149 ++++++++++++++++++++++++ docs/config/psmap.csv | 20 ++++ docs/index.rst | 2 +- fermipy/gtpsmap.py | 4 +- fermipy/plotting.py | 2 +- 9 files changed, 178 insertions(+), 4 deletions(-) create mode 100644 docs/advanced/model01_psmap_ps_hist.png create mode 100644 docs/advanced/model01_psmap_psmap.png create mode 100644 docs/advanced/model01_psmap_pssigma.png create mode 100644 docs/advanced/psmap.rst create mode 100644 docs/config/psmap.csv diff --git a/docs/advanced/index.rst b/docs/advanced/index.rst index 7a0a05bf..7e708602 100644 --- a/docs/advanced/index.rst +++ b/docs/advanced/index.rst @@ -7,6 +7,10 @@ Advanced Analysis Methods This page documents some of the more advanced methods and features available in Fermipy: +* :ref:`psmap`: Generate a PS map for a new source + centered at each spatial bin in the ROI using the algorithm described in + Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). + * :ref:`tsmap`: Generate a test statistic (TS) map for a new source centered at each spatial bin in the ROI. @@ -52,6 +56,7 @@ available in Fermipy: curvature lightcurve extension + psmap tsmap tscube residmap diff --git a/docs/advanced/model01_psmap_ps_hist.png b/docs/advanced/model01_psmap_ps_hist.png new file mode 100644 index 0000000000000000000000000000000000000000..f1b609944352c45d531b6133056cabea7fcd81e4 GIT binary patch literal 25039 zcmeFZXH->P)-`xQ5DXv&K#-(>fg~!3AmNH4NkGXNk(?zX@u~=l2uf6toHLSx1VKbV zB#VIL43cyH*7m8Yuj=ip?)UBf(WA%c8cz-IUd}oD?7j91bI!G2+?5q4J4kyF!!R;Q ziCgj*wvP$J2vte;aCITN5kG zD_qyOE^{y$+u1#|6}WiO;vav&Wo2V@k!fr9Gu-6BLkTro3_GHW{!frBl4OEmx`L9o zZYVmvpXq(%s<=|Nx3H#v_D)EhC{;){3kTr=vlgZyrO(ePHGR&rt(M%^ptRK}F5?mt zYwmtvrQ&?bqJ}IDA1m*{mi%0z<6w^BX(B}mLKaWf9(`|?@ip=HEfR!kXUD%SItw!_ zIlaDO^xf6)THX6KSH5uFlU^_)tddAqnHv4VWJiHRKi_&`gz%%1hlm3GGx0PY{op1e zAc7xw6H+Gh&pS6U0{C&CmXH+vv+e)C^#AMAM5j!Rx8k{aH8nf??xjnYa=OfwL-)ag 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given by + +.. math:: + + \mathrm{TS} = 2 \sum_{k} \ln L(\mu,\theta|n_{k}) - \ln L(0,\theta|n_{k}) + +where the summation index *k* runs over both spatial and energy bins, +μ is the test source normalization parameter, and θ represents the +parameters of the background model. The likelihood fitting +implementation used by :py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` +only fits the test source normalization (μ). Shape parameters of the +test source and parameters of background components are fixed to their +current values. + +Examples +-------- + +The spatial and spectral properties of the convolution kernel are +defined with the ``model`` dictionary argument. The ``model`` +dictionary format is the same as accepted by +:py:meth:`~fermipy.gtanalysis.GTAnalysis.add_source`. + +.. code-block:: python + + # Generate TS map for a power-law point source with Index=2.0 + model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'} + maps = gta.tsmap('fit1',model=model) + + # Generate TS map for a power-law point source with Index=2.0 and + # restricting the analysis to E > 3.16 GeV + model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'} + maps = gta.tsmap('fit1_emin35',model=model,erange=[3.5,None]) + + # Generate TS maps for a power-law point source with Index=1.5, 2.0, and 2.5 + model={'SpatialModel' : 'PointSource'} + maps = [] + for index in [1.5,2.0,2.5]: + model['Index'] = index + maps += [gta.tsmap('fit1',model=model)] + +The ``multithread`` option can be enabled to split the calculation +across all available cores: + +.. code-block:: python + + maps = gta.tsmap('fit1',model=model,multithread=True) + +Note that care should be taken when using this option in an +environment where the number of cores per process is restricted such +as a batch farm. + +:py:meth:`~fermipy.gtanalysis.GTAnalysis.tsmap` returns a ``maps`` +dictionary containing `~fermipy.skymap.Map` representations of the TS +and predicted counts (NPred) of the best-fit test source at each position. + +.. code-block:: python + + model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'} + maps = gta.tsmap('fit1',model=model) + print('TS at Pixel (50,50): ',maps['ts'].counts[50,50]) + +The contents of the output dictionary are given in the following table. + +============= ====================== ================================================================= +Key Type Description +============= ====================== ================================================================= +amplitude `~fermipy.skymap.Map` Best-fit test source amplitude + expressed in terms of the spectral prefactor. +npred `~fermipy.skymap.Map` Best-fit test source amplitude + expressed in terms of the total model counts (Npred). +ts `~fermipy.skymap.Map` Test source TS (twice the logLike difference between null and + alternate hypothese). +sqrt_ts `~fermipy.skymap.Map` Square-root of the test source TS. +file str Path to a FITS file containing the maps (TS, etc.) generated by + this method. +src_dict dict Dictionary defining the properties of the test source. +============= ====================== ================================================================= + +The ``write_fits`` and ``write_npy`` options can used to write the +output to a FITS or numpy file. All output files are prepended with +the `prefix` argument. + +Diagnostic plots can be generated by setting ``make_plots=True`` or by +passing the output dictionary to +`~fermipy.plotting.AnalysisPlotter.make_residmap_plots`: + +.. code-block:: python + + maps = gta.tsmap('fit1',model=model, make_plots=True) + gta.plotter.make_tsmap_plots(maps, roi=gta.roi) + +This will generate the following plots: + +* ``tsmap_sqrt_ts`` : Map of sqrt(TS) values. The color map is truncated at + 5 sigma with isocontours at 2 sigma intervals indicating values + above this threshold. + +* ``tsmap_npred`` : Map of best-fit source amplitude in counts. + +* ``tsmap_ts_hist`` : Histogram of TS values for all points in the + map. Overplotted is the reference distribution for chi-squared with + one degree of freedom (expectation from Chernoff's theorem). + +.. |image_psmap| image:: model01_psmap_psmap.png + :width: 100% + +.. |image_pssigma| image:: model01_psmap_pssigma.png + :width: 100% + +.. |image_ps_hist| image:: model01_psmap_ps_hist.png + :width: 100% + +.. csv-table:: + :header: PS Map, Sigma (PS) Map, PS Histogram + :widths: 33, 33, 33 + + |image_psmap|, |image_pssigma|, |image_ps_hist| + + +Configuration +------------- + +The default configuration of the method is controlled with the +:ref:`config_tsmap` section of the configuration file. The default +configuration can be overriden by passing the option as a *kwargs* +argument to the method. + +.. csv-table:: *psmap* Options + :header: Option, Default, Description + :file: ../config/psmap.csv + :delim: , + :widths: 10,10,80 + +Reference/API +------------- + +.. automethod:: fermipy.gtanalysis.GTAnalysis.psmap + :noindex: + + diff --git a/docs/config/psmap.csv b/docs/config/psmap.csv new file mode 100644 index 00000000..63834dd3 --- /dev/null +++ b/docs/config/psmap.csv @@ -0,0 +1,20 @@ +``model_name``, None, Name of the model file +``wmap``, None, weights 3d map +``outfile``, psmap.fits, Name of the output fits file +``fixedradius``, -1.0, Fixed radius +``psfpar0``, 4.0, PSF parameter 0 +``psfpar1``, 100.0, PSF parameter 1 +``psfpar2``, 0.9, PSF parameter 2 +``psfpar3``, 0.1, PSF parameter 3 +``maxpoissoncount``, 100.0, Maximum number of counts +``prob_epsilon``, 1e-07, precision parameter +``nbinpdf``, 50, Number of bin of the PSF +``scaleaxis``, 20.0, Scale axis +``emin``, 1.0, minimum energy/MeV +``emax``, 1000000000.0, maximum energy/MeV +``chatter``, 1, output verbosity +``ipix``, -1, number of pixel i axis +``jpix``, -1, number of pixel j axis +``rebin``, 1, Rebin +``make_plots``, False, Make Plots +``write_fits``, True, Write fits file in output diff --git a/docs/index.rst b/docs/index.rst index 71590a98..d5b3b614 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,7 +27,7 @@ performing common analysis tasks: * Extracting a spectral energy distribution (SED) of a source. -* Generating TS and residual maps for a region of interest. +* Generating PS/TS and residual maps for a region of interest. * Finding new source candidates. diff --git a/fermipy/gtpsmap.py b/fermipy/gtpsmap.py index e1efe7e1..34e0d419 100755 --- a/fermipy/gtpsmap.py +++ b/fermipy/gtpsmap.py @@ -505,10 +505,10 @@ def make_psmap_fits(o, out_filename): parser.add_argument("-psf2", "--psfpar2", required=False, type=float, default=0.9, help="PSF parameter 2") parser.add_argument("-psf3", "--psfpar3", required=False, type=float, default=0.1, help="PSF parameter 3") - parser.add_argument("-mpc", "--maxpoissoncount", required=False, type=float, default=100, help="Output file") + parser.add_argument("-mpc", "--maxpoissoncount", required=False, type=float, default=100, help="Maximum number of counts") parser.add_argument("-pe", "--prob_epsilon", required=False, type=float, default=1e-7, help="precision parameter") parser.add_argument("-nb", "--nbinpdf", required=False, type=int, default=50, help="Number of bin of the PSF") - parser.add_argument("-sa", "--scaleaxis", required=False, type=float, default=20, help="Output file") + parser.add_argument("-sa", "--scaleaxis", required=False, type=float, default=20, help="Scale axis") parser.add_argument("-emin", "--emin", required=False, type=float, default=1.0, help="minimum energy/MeV") parser.add_argument("-emax", "--emax", required=False, type=float, default=1e9, help="maximum energy/MeV") parser.add_argument("-c", "--chatter", required=False, type=int, default=1, help="output verbosity") diff --git a/fermipy/plotting.py b/fermipy/plotting.py index 485e26e6..c455034f 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1200,7 +1200,7 @@ def make_psmap_plots(self, psmaps, roi=None, **kwargs): fmt = kwargs.get('format', self.config['format']) figsize = kwargs.get('figsize', self.config['figsize']) workdir = kwargs.pop('workdir', self.config['fileio']['workdir']) - suffix = kwargs.pop('suffix', 'tsmap') + suffix = kwargs.pop('suffix', 'psmap') zoom = kwargs.pop('zoom', None) prefix=psmaps['name'] From 0a7940d95463f1368c3f31956d8de3c82a96a894 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Tue, 26 Mar 2024 13:47:15 -0700 Subject: [PATCH 06/21] Remove few deprecation warning. Try too fix two tests that randomly fail --- fermipy/plotting.py | 32 ++++++++++++++++++-------------- fermipy/roi_model.py | 2 +- fermipy/tests/test_gtanalysis.py | 6 ++++-- 3 files changed, 23 insertions(+), 17 deletions(-) diff --git a/fermipy/plotting.py b/fermipy/plotting.py index c455034f..9042d44f 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -721,7 +721,10 @@ def plot_lnlscan(sed, **kwargs): logli = np.interp(fluxCenters, flux, logl) llhMatrix[i, :] = logli - cmap = copy.deepcopy(plt.cm.get_cmap(cmap)) + #cmap = copy.deepcopy(plt.cm.get_cmap(cmap)) + cmap = copy.deepcopy(matplotlib.colormaps.get_cmap(cmap)) + + # cmap.set_under('w') if cmap_trunc_lo is not None or cmap_trunc_hi is not None: @@ -919,7 +922,7 @@ def plot(self, iaxis): loge_bounds=self._loge_bounds) p._data += p1.data p.plot_projection(iaxis, color=matplotlib.cm.Reds(cf), - noerror=True, label='%.4f$^\circ$' % w) + noerror=True, label=r'%.4f$^\circ$' % w) class AnalysisPlotter(fermipy.config.Configurable): @@ -1012,7 +1015,7 @@ def make_residmap_plots(self, maps, roi=None, **kwargs): fig = plt.figure(figsize=figsize) p = ROIPlotter(maps['sigma'], roi=roi, **kwargs) p.plot(vmin=-5, vmax=5, levels=sigma_levels, - cb_label='Significance [$\sigma$]', interpolation='bicubic', + cb_label=r'Significance [$\sigma$]', interpolation='bicubic', cmap=cmap_resid, zoom=zoom) plt.savefig(utils.format_filename(workdir, 'residmap_sigma', @@ -1128,7 +1131,7 @@ def make_tsmap_plots(self, maps, roi=None, **kwargs): fig = plt.figure(figsize=figsize) p = ROIPlotter(maps['sqrt_ts'], roi=roi, **kwargs) p.plot(vmin=0, vmax=5, levels=sigma_levels, - cb_label='Sqrt(TS) [$\sigma$]', interpolation='bicubic', + cb_label=r'Sqrt(TS) [$\sigma$]', interpolation='bicubic', zoom=zoom) plt.savefig(utils.format_filename(workdir, '%s_sqrt_ts' % suffix, @@ -1409,7 +1412,7 @@ def make_localization_plots(self, loc, roi=None, **kwargs): p.plot(levels=[-200, -100, -50, -20, -9.21, -5.99, -2.3, -1.0], cmap=cmap, vmin=vmin, colors=['k'], - interpolation='bicubic', cb_label='2$\\times\Delta\ln$L') + interpolation='bicubic', cb_label=r'2$\times\Delta\ln$L') cdelt0 = np.abs(tsmap.geom.wcs.wcs.cdelt[0]) cdelt1 = np.abs(tsmap.geom.wcs.wcs.cdelt[1]) @@ -1484,7 +1487,7 @@ def make_localization_plots(self, loc, roi=None, **kwargs): p.plot(levels=[-200, -100, -50, -20, -9.21, -5.99, -2.3, -1.0], cmap=cmap, vmin=vmin, colors=['k'], - interpolation='bicubic', cb_label='2$\\times\Delta\ln$L') + interpolation='bicubic', cb_label=r'2$\times\Delta\ln$L') cdelt0 = np.abs(tsmap.geom.wcs.wcs.cdelt[0]) cdelt1 = np.abs(tsmap.geom.wcs.wcs.cdelt[1]) @@ -1503,16 +1506,16 @@ def make_localization_plots(self, loc, roi=None, **kwargs): label='New Position') plot_error_ellipse(loc, scan_pix, cdelt, edgecolor='w', - color='w', colname='pos_r68', label='68% Uncertainty') + color='w', colname='pos_r68', label=r'68% Uncertainty') plot_error_ellipse(loc, scan_pix, cdelt, edgecolor='w', - color='w', colname='pos_r99', label='99% Uncertainty', + color='w', colname='pos_r99', label=r'99% Uncertainty', linestyle='--') handles, labels = plt.gca().get_legend_handles_labels() h0 = Line2D([], [], color='w', marker='None', - label='68% Uncertainty', linewidth=1.0) + label=r'68% Uncertainty', linewidth=1.0) h1 = Line2D([], [], color='w', marker='None', - label='99% Uncertainty', linewidth=1.0, + label=r'99% Uncertainty', linewidth=1.0, linestyle='--') plt.legend(handles=handles + [h0, h1]) @@ -1625,7 +1628,8 @@ def _plot_extension_ebin(self, ext, roi=None, **kwargs): logli = np.interp(widthCenters, width, logl) llhMatrix[i, :] = logli - cmap = copy.deepcopy(plt.cm.get_cmap(cmap)) + #cmap = copy.deepcopy(plt.cm.get_cmap(cmap)) + cmap = copy.deepcopy(matplotlib.colormaps.get_cmap(cmap)) # cmap.set_under('w') if cmap_trunc_lo is not None or cmap_trunc_hi is not None: @@ -1672,14 +1676,14 @@ def _plot_extension_tsmap(self, ext, roi=None, **kwargs): if ext['ts_ext'] > 9.0: p.draw_circle(ext['ext'], skydir=c, edgecolor='lime', linestyle='-', - linewidth=1.0, label='R$_{68}$', path_effects=[path_effect]) + linewidth=1.0, label=r'R$_{68}$', path_effects=[path_effect]) p.draw_circle(ext['ext'] + ext['ext_err'], skydir=c, edgecolor='lime', linestyle='--', - linewidth=1.0, label='R$_{68}$ $\pm 1 \sigma$', path_effects=[path_effect]) + linewidth=1.0, label=r'R$_{68}$ $\pm 1 \sigma$', path_effects=[path_effect]) p.draw_circle(ext['ext'] - ext['ext_err'], skydir=c, edgecolor='lime', linestyle='--', linewidth=1.0, path_effects=[path_effect]) else: p.draw_circle(ext['ext_ul95'], skydir=c, edgecolor='lime', linestyle='--', - linewidth=1.0, label='R$_{68}$ 95% UL', + linewidth=1.0, label=r'R$_{68}$ 95% UL', path_effects=[path_effect]) leg = plt.gca().legend(frameon=False, loc='upper left') diff --git a/fermipy/roi_model.py b/fermipy/roi_model.py index 8aa45917..62706391 100644 --- a/fermipy/roi_model.py +++ b/fermipy/roi_model.py @@ -944,7 +944,7 @@ def set_roi_geom(self, geom): return self['offset_roi_edge'] = float( - wcs_utils.distance_to_edge(geom, self.skydir)) + wcs_utils.distance_to_edge(geom, self.skydir)[0]) def set_spatial_model(self, spatial_model, spatial_pars): diff --git a/fermipy/tests/test_gtanalysis.py b/fermipy/tests/test_gtanalysis.py index 5eb6eaf8..60fb0695 100644 --- a/fermipy/tests/test_gtanalysis.py +++ b/fermipy/tests/test_gtanalysis.py @@ -129,7 +129,8 @@ def test_gtanalysis_load_roi(create_diffuse_dir, create_draco_analysis): def test_gtanalysis_optimize(create_diffuse_dir, create_draco_analysis): gta = create_draco_analysis gta.load_roi('fit0') - gta.optimize() + try: gta.optimize() + except ValueError: gta.optimize() def test_gtanalysis_fit(create_diffuse_dir, create_draco_analysis): @@ -260,7 +261,8 @@ def test_gtanalysis_sed(create_diffuse_dir, create_draco_analysis): 'Prefactor': prefactor}) gta.free_source('draco') - gta.fit() + try: gta.fit() + except ValueError: gta.fit() o = gta.sed('draco', make_plots=True) From 74336a8b1b002dffdb99e425706d1db0204b18c1 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Tue, 26 Mar 2024 15:54:02 -0700 Subject: [PATCH 07/21] Improved documentation for psmap --- docs/advanced/psmap.rst | 162 +++++++++++++++++++++++----------------- docs/conf.py | 2 +- docs/config.rst | 15 ++++ docs/config/psmap.csv | 12 +-- fermipy/plotting.py | 5 +- fermipy/psmap.py | 1 + 6 files changed, 120 insertions(+), 77 deletions(-) diff --git a/docs/advanced/psmap.rst b/docs/advanced/psmap.rst index 6d7d8630..518fbf82 100644 --- a/docs/advanced/psmap.rst +++ b/docs/advanced/psmap.rst @@ -4,22 +4,64 @@ PS Map ====== :py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` generates a PS map for an additional source component centered at each -spatial bin in the ROI. The PSmap algorithm is described in detail in Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). +spatial bin in the ROI. The PSmap algorithm is described in detail in +Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). For each spatial bin the method calculates the maximum likelihood test statistic given by .. math:: - \mathrm{TS} = 2 \sum_{k} \ln L(\mu,\theta|n_{k}) - \ln L(0,\theta|n_{k}) + \mathrm{L} = - \sum_{k}\log P(x_{k} m_{k}) + +where P is the Poisson probability and :math:`x_{k}` are independent random Poisson +variables of mean :math:`m_{k}`, the spatially integrated number of model counts in the spectral bin :math:`k`. +The p-value is the integral of the probability distribution function (pdf) of :math:`L` above +:math:`L_{data} = − \sum_{k} log P(n_{k} , m_{k})`, the value obtained with the data integrated count spectra :math:`n_{k}`. +In other words, the p-value is the :math:`L` complementary cumulative distribution function (CCDF) at Ldata. +For the spectral bins with Gaussian statistics, the Poisson probability of parameter :math:`m_{k}` can be replaced with +a Gaussian with mean and variance equal to :math:`m_{k}`. Ignoring the constant term, we have: + +.. math:: + L_{Gaus} = \frac{1}{2} \sum_{k} \frac{(x_{k}-m_{k})^{2}}{m_{k}} + +As a consequence, the :math:`L_{Gaus}` pdf can be simply derived from the :math:`\chi^2` distribution with a number of +degrees of freedom equal to the number of spectral bins with Gaussian statistics. In order to compute the :math:`L` pdf, +we thus start by sorting the counts in decreasing order, then compute the :math:`L_{Gaus}` pdf corresponding to all the +bins with a number of counts greater than ``maxpoissoncount=100`` and finally perform the iterative computation over the +remaining bins. We note that this p-value computation provides a simple extension of the :math:`\chi^2`-test to +histograms with counts in the Poisson regime. +The PS data/model deviation estimator is defined as: + +.. math:: + \mathrm{|PS|} = - \log_{10} \mathrm{(p-value)} + +and give it the sign of the sum of the residuals in sigma units: + +.. math:: + \mathrm{sign(PS)} = \mathrm{sign}\left(\sum_{k} \frac{(x_{k}-m_{k})}{\mathrm{max}(1,\sqrt{m_{k}})}\right) + +which allows us to estimate whether the deviation is positive (data>model) or negative (data 3.16 GeV - model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'} - maps = gta.tsmap('fit1_emin35',model=model,erange=[3.5,None]) - - # Generate TS maps for a power-law point source with Index=1.5, 2.0, and 2.5 - model={'SpatialModel' : 'PointSource'} - maps = [] - for index in [1.5,2.0,2.5]: - model['Index'] = index - maps += [gta.tsmap('fit1',model=model)] - -The ``multithread`` option can be enabled to split the calculation -across all available cores: - -.. code-block:: python - - maps = gta.tsmap('fit1',model=model,multithread=True) - -Note that care should be taken when using this option in an -environment where the number of cores per process is restricted such -as a batch farm. + # Write the source model map (after performing the fit) + gta.write_model_map(model_name="model01") -:py:meth:`~fermipy.gtanalysis.GTAnalysis.tsmap` returns a ``maps`` -dictionary containing `~fermipy.skymap.Map` representations of the TS -and predicted counts (NPred) of the best-fit test source at each position. + # Generate the PS map + psmap = gta.psmap(model_name='model01', make_plots=True) -.. code-block:: python - - model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'} - maps = gta.tsmap('fit1',model=model) - print('TS at Pixel (50,50): ',maps['ts'].counts[50,50]) - -The contents of the output dictionary are given in the following table. +:py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` returns a dictionary containing the following variables: ============= ====================== ================================================================= Key Type Description ============= ====================== ================================================================= -amplitude `~fermipy.skymap.Map` Best-fit test source amplitude - expressed in terms of the spectral prefactor. -npred `~fermipy.skymap.Map` Best-fit test source amplitude - expressed in terms of the total model counts (Npred). -ts `~fermipy.skymap.Map` Test source TS (twice the logLike difference between null and - alternate hypothese). -sqrt_ts `~fermipy.skymap.Map` Square-root of the test source TS. -file str Path to a FITS file containing the maps (TS, etc.) generated by - this method. -src_dict dict Dictionary defining the properties of the test source. +psmax float maximum of the ps map +coordname1 str Name of the coordinate of the ps maximum +coordname2 str Name of the coordinaste of the ps maximum +coordx float Value of the X coordinate of the ps maximum +coordy float Value of the Y coordinate of the ps maximum +ipix int Value of the i pixel of the of the ps maximum +jpix int Value of the j pixel of the of the ps maximum +wcs2d WCS Keywords WCS of the ps map +psmap np.array PSMAP +psmapsigma np.array PSMAP in sigma units +name str NAmke of the model +ps_map `~fermipy.skymap.Map` WcsNDMap PSMAP +pssigma_map `~fermipy.skymap.Map` WcsNDMap PSMAP in sigma units +config dict Dictionary of the input configuration +file str Name of the output file +file_name str Full path of the output file ============= ====================== ================================================================= -The ``write_fits`` and ``write_npy`` options can used to write the -output to a FITS or numpy file. All output files are prepended with -the `prefix` argument. +The ``write_fits`` option can used to write the output to a FITS or numpy file. + +.. code-block:: python + + print('PS maximum value=%.2f, at %s=%.2f, %s=%.2f' %(psmap['psmax'], + psmap['coordname1'],float(psmap['coordx']), + psmap['coordname2'],float(psmap['coordy']))) + + PS maximum value=3.85, at GLON-AIT=86.75, GLAT-AIT=38.62 Diagnostic plots can be generated by setting ``make_plots=True`` or by -passing the output dictionary to -`~fermipy.plotting.AnalysisPlotter.make_residmap_plots`: +passing the output dictionary to `~fermipy.plotting.AnalysisPlotter.make_psmap_plots`: .. code-block:: python - maps = gta.tsmap('fit1',model=model, make_plots=True) - gta.plotter.make_tsmap_plots(maps, roi=gta.roi) + psmap = gta.psmap(model_name='model01', make_plots=True) + //equivalent to: + gta.plotter.make_tsmap_plots(psmap, roi=gta.roi) This will generate the following plots: -* ``tsmap_sqrt_ts`` : Map of sqrt(TS) values. The color map is truncated at - 5 sigma with isocontours at 2 sigma intervals indicating values +* ``image_psmap`` : Map of PS values. The color map is truncated at + 5 sigma with isocontours at 3,4,5 PS intervals indicating values above this threshold. -* ``tsmap_npred`` : Map of best-fit source amplitude in counts. +* ``image_pssigma`` : Map of PS values converted in sigma. The color map is truncated at + 5 sigma with isocontours at 3,4,5 PS intervals indicating values + above this threshold. -* ``tsmap_ts_hist`` : Histogram of TS values for all points in the - map. Overplotted is the reference distribution for chi-squared with - one degree of freedom (expectation from Chernoff's theorem). +* ``image_ps_hist`` : Histogram of PS values for all points in the + map. Overplotted is the reference distribution for a gaussian with mean 0 and sigma=1. .. |image_psmap| image:: model01_psmap_psmap.png :width: 100% @@ -130,7 +154,7 @@ Configuration ------------- The default configuration of the method is controlled with the -:ref:`config_tsmap` section of the configuration file. The default +:ref:`config_psmap` section of the configuration file. The default configuration can be overriden by passing the option as a *kwargs* argument to the method. diff --git a/docs/conf.py b/docs/conf.py index 594eb4fb..036a894b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -113,7 +113,7 @@ def __getattr__(cls, name): # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'en' # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: diff --git a/docs/config.rst b/docs/config.rst index de72d570..7fb7baf7 100644 --- a/docs/config.rst +++ b/docs/config.rst @@ -433,6 +433,21 @@ about using this method see the :ref:`findsources` page. :delim: tab :widths: 10,10,80 +.. _config_psmap: + +psmap +----- + +The options in *psmap* control the default behavior of the +`~fermipy.gtanalysis.GTAnalysis.psmap` method. For more information +about using this method see the :ref:`psmap` page. + +.. csv-table:: *psmap* Options + :header: Option, Default, Description + :file: config/psmap.csv + :delim: , + :widths: 10,10,80 + .. _config_tsmap: tsmap diff --git a/docs/config/psmap.csv b/docs/config/psmap.csv index 63834dd3..c3a7b5a5 100644 --- a/docs/config/psmap.csv +++ b/docs/config/psmap.csv @@ -2,12 +2,12 @@ ``wmap``, None, weights 3d map ``outfile``, psmap.fits, Name of the output fits file ``fixedradius``, -1.0, Fixed radius -``psfpar0``, 4.0, PSF parameter 0 -``psfpar1``, 100.0, PSF parameter 1 -``psfpar2``, 0.9, PSF parameter 2 -``psfpar3``, 0.1, PSF parameter 3 +``psfpar0``, 4.0, PSF parameter p0 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) +``psfpar1``, 100.0, PSF parameter p1 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) +``psfpar2``, 0.9, PSF parameter p2 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) +``psfpar3``, 0.1, PSF parameter p3 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) ``maxpoissoncount``, 100.0, Maximum number of counts -``prob_epsilon``, 1e-07, precision parameter +``prob_epsilon``, 1e-07, This parameter defines the k-interval over which the Poisson probability of each spectral bin is considered when computing the log-likelihood distribution. The default value 1e-7 provides a 1% precision on PS up to PS=20. prob_epsilon=1e-11 provides a 5% precision up to 250 but that makes the script slower. ``nbinpdf``, 50, Number of bin of the PSF ``scaleaxis``, 20.0, Scale axis ``emin``, 1.0, minimum energy/MeV @@ -15,6 +15,6 @@ ``chatter``, 1, output verbosity ``ipix``, -1, number of pixel i axis ``jpix``, -1, number of pixel j axis -``rebin``, 1, Rebin +``rebin``, 1, nb of bins merged when rebinning the energy axis ``make_plots``, False, Make Plots ``write_fits``, True, Write fits file in output diff --git a/fermipy/plotting.py b/fermipy/plotting.py index 9042d44f..2af60aeb 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1212,7 +1212,10 @@ def make_psmap_plots(self, psmaps, roi=None, **kwargs): p = ROIPlotter(psmaps['ps_map'], roi=roi, **kwargs) + ps_levels = [ 2.57, 4.20, 6.24] + p.plot(vmin=-5, vmax=5, + levels=ps_levels, cb_label='PSMAP', interpolation='bicubic', zoom=zoom) plt.savefig(utils.format_filename(workdir, @@ -1221,7 +1224,7 @@ def make_psmap_plots(self, psmaps, roi=None, **kwargs): extension=fmt)) plt.close(fig) - sigma_levels = [3, 5, 7] + list(np.logspace(1, 3, 17)) + sigma_levels = [3, 4, 5] fig = plt.figure(figsize=figsize) p = ROIPlotter(psmaps['pssigma_map'], roi=roi, **kwargs) diff --git a/fermipy/psmap.py b/fermipy/psmap.py index 4217be25..36a31bc3 100644 --- a/fermipy/psmap.py +++ b/fermipy/psmap.py @@ -102,3 +102,4 @@ def psmap(self, prefix='', **kwargs): plotter.make_psmap_plots(o, self.roi) self.logger.log(config['loglevel'], 'Finished PS map') + return o \ No newline at end of file From bbdb6272435e8cf550254043d1c7b71982e06b50 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Tue, 26 Mar 2024 16:25:38 -0700 Subject: [PATCH 08/21] Improve psmap doc --- docs/advanced/model01_psmap_psmap.png | Bin 175577 -> 175936 bytes docs/advanced/psmap.rst | 12 ++++++------ docs/changelog.rst | 13 ++++++++++++- fermipy/psmap.py | 19 ++++++++----------- 4 files changed, 26 insertions(+), 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+------------------ * Small bug fixes. * pinned astropy<6 diff --git a/fermipy/psmap.py b/fermipy/psmap.py index 36a31bc3..46159b02 100644 --- a/fermipy/psmap.py +++ b/fermipy/psmap.py @@ -15,25 +15,22 @@ class PSMapGenerator(object): generates PS maps.""" def psmap(self, prefix='', **kwargs): - ''' Generate a spatial PS map for a source component with - properties defined by the `model` argument. The PS map will - have the same geometry as the ROI. The output of this method + ''' Generate a spatial PS map for evaluate the data/model comparison + The PS map will have the same geometry as the ROI. The output of this method is a dictionary containing `~fermipy.skymap.Map` objects with - the PS and amplitude of the best-fit test source. By default + the PS amplitude and sigma equivalent. By default this method will also save maps to FITS files and render them as image files. - - This method uses a simplified likelihood fitting - implementation that only fits for the normalization of the - test source. Before running this method it is recommended to - first optimize the ROI model (e.g. by running - :py:meth:`~fermipy.gtanalysis.GTAnalysis.optimize` - + psmap requirtes a model map to be compute, therefore the user must run + gta.write_model_map(model_name="model01") before running + psmap = gta.psmap(model_name='model01') Parameters ---------- prefix : str Optional string that will be prepended to all output files. + kwargs : Any + these will override the psmap configuration file {options} From 8fa210b5cee86cfe23597ade86fc1d0537c815e6 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Wed, 27 Mar 2024 09:36:42 -0700 Subject: [PATCH 09/21] Changed the label of the x-axis in the histogram (it is the distribution of ps in sigma) --- fermipy/plotting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fermipy/plotting.py b/fermipy/plotting.py index 2af60aeb..0e78a2b7 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1258,7 +1258,7 @@ def make_psmap_plots(self, psmaps, roi=None, **kwargs): ax.legend(loc='upper right', frameon=False) # labels and such - ax.set_xlabel('PS') + ax.set_xlabel('PSMAP [SIGMA]) ax.set_ylabel('Probability') plt.savefig(utils.format_filename(workdir, '%s_ps_hist' % suffix, From 28bd0774b7cc739fd2a10d831fb3f8fabd293e1a Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Wed, 27 Mar 2024 09:45:03 -0700 Subject: [PATCH 10/21] Stupid me --- fermipy/plotting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fermipy/plotting.py b/fermipy/plotting.py index 0e78a2b7..d98f78d0 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1258,7 +1258,7 @@ def make_psmap_plots(self, psmaps, roi=None, **kwargs): ax.legend(loc='upper right', frameon=False) # labels and such - ax.set_xlabel('PSMAP [SIGMA]) + ax.set_xlabel('PSMAP [SIGMA]') ax.set_ylabel('Probability') plt.savefig(utils.format_filename(workdir, '%s_ps_hist' % suffix, From f688e05ec62d7799ebd43cc86b74b602164337a4 Mon Sep 17 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z?45IR637Az2)r0_uo>jMEP}B_wM240#5LBZw>G3Z3wGLL_?|6E`Kwq$p_rm=M4^VE z%S9c&|Hv5lgFRq;Y7y6H)SE>}xpDaBA z5YdOm+ITJ6YN%PU)7@zN7!v7+zIs3~g>nTQ849iPoGjdpfkd3|(7%$9aLMqlJYI1Y z=kIyQ0CHy&t0OQ~;qMiES>NqSKBjU}=#83~nhrts zFF*se$y}du*oZ(BvnQdzUQ3G2_a!mLu7`((Tn3?vOAvdq;PXmPB+^}^lYj| zejSjZNhwt$)OHGGUCROxXzqnqPwWTwVh~B3;L_qXphuLWbFYx5CIC!v^K-S#7rrpcO(GK+ zU=UL21b*@mfHO!B7`Z|8*&e_{`BedGl+|`nL{ab(TC1wy?f(JwKdmgSUsfT1b^HT4 OakuU6&Lvw9pZ^cCOEB&L From d81f840ebf21b73b3682b8e7cebfb8b060665f24 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Thu, 28 Mar 2024 17:31:44 -0700 Subject: [PATCH 12/21] This is try to eliminate the problem with NaN See issue (566) --- fermipy/gtanalysis.py | 22 ++++++++++++++-------- fermipy/utils.py | 2 +- 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/fermipy/gtanalysis.py b/fermipy/gtanalysis.py index 31f650b9..9fe1388a 100644 --- a/fermipy/gtanalysis.py +++ b/fermipy/gtanalysis.py @@ -2411,7 +2411,6 @@ def profile_norm(self, name, logemin=None, logemax=None, reoptimize=False, in the profile likelihood scan. """ - self.logger.debug('Profiling %s', name) if savestate: @@ -2430,7 +2429,6 @@ def profile_norm(self, name, logemin=None, logemax=None, reoptimize=False, loge_bounds = self.loge_bounds if logemin is not None or logemax is not None: self.set_energy_range(logemin, logemax) - # Find a sequence of values for the normalization scan if xvals is None: if reoptimize: @@ -2438,6 +2436,7 @@ def profile_norm(self, name, logemin=None, logemax=None, reoptimize=False, **kwargs) else: xvals = self._find_scan_pts(name, npts=9) + lnlp = self.profile(name, parName, reoptimize=False, xvals=xvals) lims = utils.get_parameter_limits(lnlp['xvals'], @@ -2452,26 +2451,31 @@ def profile_norm(self, name, logemin=None, logemax=None, reoptimize=False, 'Refitting normalization.') self.like.optimize(0) xvals = self._find_scan_pts(name, npts=npts) + lnlp = self.profile(name, parName, reoptimize=False, xvals=xvals) + + lims = utils.get_parameter_limits(lnlp['xvals'], lnlp['dloglike'], cl_limit=0.99) + self.logger.debug(' ======> %s %s %s %s' %(xvals[0], lims['ll'], lims['x0'], lims['ul'])) if np.isfinite(lims['ll']): xhi = np.linspace(lims['x0'], lims['ul'], npts - npts // 2) xlo = np.linspace(lims['ll'], lims['x0'], npts // 2) xvals = np.concatenate((xlo[:-1], xhi)) - xvals = np.insert(xvals, 0, 0.0) + #NO xvals = np.insert(xvals, 0, 0.0) elif np.abs(lnlp['dloglike'][0] - lims['lnlmax']) > 0.1: - lims['ll'] = 0.0 + lims['ll'] = lims['x0']/npts #NO 0.0 xhi = np.linspace(lims['x0'], lims['ul'], (npts + 1) - (npts + 1) // 2) xlo = np.linspace(lims['ll'], lims['x0'], (npts + 1) // 2) xvals = np.concatenate((xlo[:-1], xhi)) else: - xvals = np.linspace(0, lims['ul'], npts) + lims['ll'] = lims['ul'] / npts # This was 0 + xvals = np.linspace(lims['ll'], lims['ul'], npts) o = self.profile(name, parName, reoptimize=reoptimize, xvals=xvals, @@ -2518,8 +2522,9 @@ def _find_scan_pts(self, name, logemin=None, logemax=None, npts=20): if npred < 10: val *= 1. / max(1e-16, npred) - xvals = val * 10 ** np.linspace(-1.0, 3.0, npts - 1) - xvals = np.insert(xvals, 0, 0.0) + xvals = val * 10 ** np.linspace(-1.0, 3.0, npts) + #xvals = val * 10 ** np.linspace(-1.0, 3.0, npts - 1) + # NO xvals = np.insert(xvals, 0, 0.0) else: npts_lo = npts // 2 @@ -2527,7 +2532,7 @@ def _find_scan_pts(self, name, logemin=None, logemax=None, npts=20): xhi = np.linspace(0, 1, npts_hi) xlo = np.linspace(-1, 0, npts_lo) xvals = val * 10 ** np.concatenate((xlo[:-1], xhi)) - xvals = np.insert(xvals, 0, 0.0) + #xvals = np.insert(xvals, 0, 0.0) return xvals @@ -2560,6 +2565,7 @@ def _find_scan_pts_reopt(self, name, logemin=None, logemax=None, npts=20, lims0['x0'] + lims0['err_hi'], lims0['ul']]) xvals = xvals[np.isfinite(xvals)] + # Generate likelihood profile w/ free nuisance pars if np.isnan(xvals).any(): raise ValueError( diff --git a/fermipy/utils.py b/fermipy/utils.py index 355ba302..3ee721e2 100644 --- a/fermipy/utils.py +++ b/fermipy/utils.py @@ -698,7 +698,7 @@ def find_function_root(fn, x0, xb, delta=0.0, bounds=None): try: return brentq(lambda t: fn(t) + delta, x0, xb, xtol=xtol) - except RuntimeError: + except (RuntimeError, ValueError): raise RuntimeError("Brentq failed ", x0, xb, fn(x0)+delta, fn(xb)+delta, xtol, delta) From 0ca9d7c416c4a621336d1c04bbf77dc701e334ec Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Fri, 29 Mar 2024 11:13:58 -0700 Subject: [PATCH 13/21] Removed the try except in the test --- fermipy/tests/test_gtanalysis.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/fermipy/tests/test_gtanalysis.py b/fermipy/tests/test_gtanalysis.py index 60fb0695..5eb6eaf8 100644 --- a/fermipy/tests/test_gtanalysis.py +++ b/fermipy/tests/test_gtanalysis.py @@ -129,8 +129,7 @@ def test_gtanalysis_load_roi(create_diffuse_dir, create_draco_analysis): def test_gtanalysis_optimize(create_diffuse_dir, create_draco_analysis): gta = create_draco_analysis gta.load_roi('fit0') - try: gta.optimize() - except ValueError: gta.optimize() + gta.optimize() def test_gtanalysis_fit(create_diffuse_dir, create_draco_analysis): @@ -261,8 +260,7 @@ def test_gtanalysis_sed(create_diffuse_dir, create_draco_analysis): 'Prefactor': prefactor}) gta.free_source('draco') - try: gta.fit() - except ValueError: gta.fit() + gta.fit() o = gta.sed('draco', make_plots=True) From 2ea350b003241ae0eaf8f7f9982970dcc907c228 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Fri, 29 Mar 2024 17:03:36 -0700 Subject: [PATCH 14/21] Fixing the documentation --- docs/conf.py | 35 +++++++++++++++++------------------ docs/requirements.txt | 1 + fermipy/gtutils.py | 10 +++++++--- 3 files changed, 25 insertions(+), 21 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 036a894b..91c7eafd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,22 +17,21 @@ #import sphinx_bootstrap_theme if sys.version_info.major == 2: - import mock - from mock import Mock as MagicMock + from mock import Mock else: - from unittest import mock - from unittest.mock import Mock as MagicMock + from unittest.mock import Mock + +try: + import GtApp # has fermitools, no mock needed + MOCK_MODULES = []##'pyLikelihood','pyIrfLoader'] +except: + # Mock modules needed + MOCK_MODULES = ['pyLikelihood','GtApp', + 'BinnedAnalysis','SrcModel','AnalysisBase', + 'LikelihoodState','pyIrfLoader', + 'SummedLikelihood','FluxDensity'] -class Mock(MagicMock): - @classmethod - def __getattr__(cls, name): - return Mock() -#MOCK_MODULES = ['pyLikelihood','pyIrfLoader', -# 'BinnedAnalysis','UnbinnedAnalysis','SrcModel','AnalysisBase', -# 'SummedLikelihood','FluxDensity','LikelihoodState', -# 'GtApp'] -MOCK_MODULES = ['pyLikelihood','pyIrfLoader'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) @@ -97,7 +96,7 @@ def __getattr__(cls, name): # General information about the project. project = u'Fermipy' author = u'Fermipy Developers' -copyright = u'2016-2022, ' + author +copyright = u'2016-2024, ' + author # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -156,10 +155,10 @@ def __getattr__(cls, name): on_rtd = os.environ.get('READTHEDOCS', None) == 'True' -if not on_rtd: # only import and set the theme if we're building docs locally - import sphinx_rtd_theme - html_theme = 'sphinx_rtd_theme' - html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] +#if not on_rtd: # only import and set the theme if we're building docs locally +import sphinx_rtd_theme +html_theme = 'sphinx_rtd_theme' +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. diff --git a/docs/requirements.txt b/docs/requirements.txt index 31914324..25117a01 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,3 +5,4 @@ astropy pyyaml healpy gammapy +sphinx_rtd_theme \ No newline at end of file diff --git a/fermipy/gtutils.py b/fermipy/gtutils.py index f9783c2d..b23f3fae 100644 --- a/fermipy/gtutils.py +++ b/fermipy/gtutils.py @@ -9,12 +9,16 @@ from LikelihoodState import LikelihoodState import pyIrfLoader +import SummedLikelihood as sl +import BinnedAnalysis as ba + + pyIrfLoader.Loader_go() _funcFactory = pyLike.SourceFactory_funcFactory() -import BinnedAnalysis as ba -import SummedLikelihood + + from fermipy import utils from fermipy import model_utils @@ -485,7 +489,7 @@ def restore(self): self._srcmaps[name][i]) -class SummedLikelihood(SummedLikelihood.SummedLikelihood): +class SummedLikelihood(sl.SummedLikelihood): def nFreeParams(self): """Count the number of free parameters in the active model.""" From 494edcf5bf8dc02fb898dc2afbd3977fd0aa5a25 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Fri, 29 Mar 2024 18:22:25 -0700 Subject: [PATCH 15/21] Improve documentation, add ts_threshold option to plot SED --- docs/config.rst | 2 +- docs/config/psmap.csv | 40 ++++++++++++++++++++-------------------- docs/config/sed.csv | 1 + fermipy/defaults.py | 3 +++ fermipy/plotting.py | 2 +- 5 files changed, 26 insertions(+), 22 deletions(-) diff --git a/docs/config.rst b/docs/config.rst index 7fb7baf7..ee27475f 100644 --- a/docs/config.rst +++ b/docs/config.rst @@ -445,7 +445,7 @@ about using this method see the :ref:`psmap` page. .. csv-table:: *psmap* Options :header: Option, Default, Description :file: config/psmap.csv - :delim: , + :delim: tab :widths: 10,10,80 .. _config_tsmap: diff --git a/docs/config/psmap.csv b/docs/config/psmap.csv index c3a7b5a5..507ba760 100644 --- a/docs/config/psmap.csv +++ b/docs/config/psmap.csv @@ -1,20 +1,20 @@ -``model_name``, None, Name of the model file -``wmap``, None, weights 3d map -``outfile``, psmap.fits, Name of the output fits file -``fixedradius``, -1.0, Fixed radius -``psfpar0``, 4.0, PSF parameter p0 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) -``psfpar1``, 100.0, PSF parameter p1 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) -``psfpar2``, 0.9, PSF parameter p2 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) -``psfpar3``, 0.1, PSF parameter p3 (PSF-like selection radius = sqrt(p0^2*(E/p1)^(-2*p2) + p3^2)) -``maxpoissoncount``, 100.0, Maximum number of counts -``prob_epsilon``, 1e-07, This parameter defines the k-interval over which the Poisson probability of each spectral bin is considered when computing the log-likelihood distribution. The default value 1e-7 provides a 1% precision on PS up to PS=20. prob_epsilon=1e-11 provides a 5% precision up to 250 but that makes the script slower. -``nbinpdf``, 50, Number of bin of the PSF -``scaleaxis``, 20.0, Scale axis -``emin``, 1.0, minimum energy/MeV -``emax``, 1000000000.0, maximum energy/MeV -``chatter``, 1, output verbosity -``ipix``, -1, number of pixel i axis -``jpix``, -1, number of pixel j axis -``rebin``, 1, nb of bins merged when rebinning the energy axis -``make_plots``, False, Make Plots -``write_fits``, True, Write fits file in output +``chatter`` 1 output verbosity +``emax`` 1000000000.0 maximum energy/MeV +``emin`` 1.0 minimum energy/MeV +``fixedradius`` -1.0 Fixed radius +``ipix`` -1 number of pixel i axis +``jpix`` -1 number of pixel j axis +``make_plots`` False Generate diagnostic plots. +``maxpoissoncount`` 100 Maximum number of poisson counts +``model_name`` None Model name +``nbinpdf`` 50 Number of bin of the PSF +``outfile`` psmap.fits outfile name +``prob_epsilon`` 1e-07 precision parameter +``psfpar0`` 4.0 PSF parameter 0 +``psfpar1`` 100 PSF parameter 1 +``psfpar2`` 0.9 PSF parameter 2 +``psfpar3`` 0.1 PSF parameter 3 +``rebin`` 1 Rebin +``scaleaxis`` 20 SCale axis +``wmap`` weight 3d map +``write_fits`` True Write the output to a FITS file. diff --git a/docs/config/sed.csv b/docs/config/sed.csv index 45c0c99a..dab753f8 100644 --- a/docs/config/sed.csv +++ b/docs/config/sed.csv @@ -5,6 +5,7 @@ ``free_radius`` None Free normalizations of background sources within this angular distance in degrees from the source of interest. If None then no sources will be freed. ``make_plots`` False Generate diagnostic plots. ``ul_confidence`` 0.95 Confidence level for flux upper limit. +``ul_ts_threshold`` 4 Minimum threshold of TS for displaying flux point below this vlaue an Upper Limit is calculated. ``use_local_index`` False Use a power-law approximation to the shape of the global spectrum in each bin. If this is false then a constant index set to `bin_index` will be used. ``write_fits`` True Write the output to a FITS file. ``write_npy`` True Write the output dictionary to a numpy file. diff --git a/fermipy/defaults.py b/fermipy/defaults.py index 63c3f0eb..380658d4 100644 --- a/fermipy/defaults.py +++ b/fermipy/defaults.py @@ -437,6 +437,9 @@ def make_attrs_class(typename, d): 'free_radius': common['free_radius'], 'free_pars': (None, 'Set the parameters of the source of interest that will be freed when performing ' 'the global fit. By default all parameters will be freed.', list), + 'ul_ts_threshold': (4, 'Minimum threshold of TS for displaying flux point ' + 'below this vlaue an Upper Limit is calculated.', float), + 'ul_confidence': (0.95, 'Confidence level for flux upper limit.', float), 'cov_scale': (3.0, 'Scale factor that sets the strength of the prior on nuisance ' diff --git a/fermipy/plotting.py b/fermipy/plotting.py index d98f78d0..2b08b744 100644 --- a/fermipy/plotting.py +++ b/fermipy/plotting.py @@ -1368,7 +1368,7 @@ def make_sed_plots(self, sed, **kwargs): figsize = kwargs.get('figsize', self.config['figsize']) p = SEDPlotter(sed) fig = plt.figure(figsize=figsize) - p.plot() + p.plot(**kwargs) outfile = utils.format_filename(self.config['fileio']['workdir'], 'sed', prefix=[prefix, name], From ebbc1e185e525e3e84d5ee43554fa87e12b7aca6 Mon Sep 17 00:00:00 2001 From: Nicola Omodei Date: Tue, 2 Apr 2024 08:37:21 -0700 Subject: [PATCH 16/21] Update psmap.rst From 6df7bb4df86e6e8090b12d57019bd32de71b1422 Mon Sep 17 00:00:00 2001 From: phbruel <39769998+phbruel@users.noreply.github.com> Date: Tue, 2 Apr 2024 19:11:39 +0200 Subject: [PATCH 17/21] Update psmap.rst (Philippe's version) --- docs/advanced/psmap.rst | 165 ++++++++++++++++++++++------------------ 1 file changed, 92 insertions(+), 73 deletions(-) diff --git a/docs/advanced/psmap.rst b/docs/advanced/psmap.rst index e1dc3584..4f39c428 100644 --- a/docs/advanced/psmap.rst +++ b/docs/advanced/psmap.rst @@ -3,70 +3,16 @@ PS Map ====== -:py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` generates a PS map for an additional source component centered at each -spatial bin in the ROI. The PSmap algorithm is described in detail in -Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). +:py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` quantifies the 3d data/model agreement by computing the PS at each spatial bin in the ROI according to the algorithm described in +Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). For each spatial bin, the algorithm first computes the data and model count spectra integrated over an energy dependent region (following the PSF energy dependence) and then computes the p-value that the data count spectrum is compatible with the model count spectrum (using the likelihood statistics). The absolute value of PS is -log10(p-value) and its sign corresponds to the sign of the sum over the spectrum of the residuals in sigma. The algorithm also provides a PS map in sigma units. -For each spatial bin the method calculates the maximum likelihood test -statistic given by - -.. math:: - - \mathrm{L} = - \sum_{k}\log P(x_{k} m_{k}) - -where P is the Poisson probability and :math:`x_{k}` are independent random Poisson -variables of mean :math:`m_{k}`, the spatially integrated number of model counts in the spectral bin :math:`k`. -The p-value is the integral of the probability distribution function (pdf) of :math:`L` above -:math:`L_{data} = − \sum_{k} log P(n_{k} , m_{k})`, the value obtained with the data integrated count spectra :math:`n_{k}`. -In other words, the p-value is the :math:`L` complementary cumulative distribution function (CCDF) at Ldata. -For the spectral bins with Gaussian statistics, the Poisson probability of parameter :math:`m_{k}` can be replaced with -a Gaussian with mean and variance equal to :math:`m_{k}`. Ignoring the constant term, we have: - -.. math:: - L_{Gaus} = \frac{1}{2} \sum_{k} \frac{(x_{k}-m_{k})^{2}}{m_{k}} - -As a consequence, the :math:`L_{Gaus}` pdf can be simply derived from the :math:`\chi^2` distribution with a number of -degrees of freedom equal to the number of spectral bins with Gaussian statistics. In order to compute the :math:`L` pdf, -we thus start by sorting the counts in decreasing order, then compute the :math:`L_{Gaus}` pdf corresponding to all the -bins with a number of counts greater than ``maxpoissoncount=100`` and finally perform the iterative computation over the -remaining bins. We note that this p-value computation provides a simple extension of the :math:`\chi^2`-test to -histograms with counts in the Poisson regime. -The PS data/model deviation estimator is defined as: - -.. math:: - \mathrm{|PS|} = - \log_{10} \mathrm{(p-value)} - -and give it the sign of the sum of the residuals in sigma units: - -.. math:: - \mathrm{sign(PS)} = \mathrm{sign}\left(\sum_{k} \frac{(x_{k}-m_{k})}{\mathrm{max}(1,\sqrt{m_{k}})}\right) - -which allows us to estimate whether the deviation is positive (data>model) or negative (datamodel) or negative (data Date: Tue, 2 Apr 2024 19:18:16 +0200 Subject: [PATCH 18/21] Update psmap.rst (Philippe breaking lines) --- docs/advanced/psmap.rst | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/docs/advanced/psmap.rst b/docs/advanced/psmap.rst index 4f39c428..15e49f0a 100644 --- a/docs/advanced/psmap.rst +++ b/docs/advanced/psmap.rst @@ -3,8 +3,13 @@ PS Map ====== -:py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` quantifies the 3d data/model agreement by computing the PS at each spatial bin in the ROI according to the algorithm described in -Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). For each spatial bin, the algorithm first computes the data and model count spectra integrated over an energy dependent region (following the PSF energy dependence) and then computes the p-value that the data count spectrum is compatible with the model count spectrum (using the likelihood statistics). The absolute value of PS is -log10(p-value) and its sign corresponds to the sign of the sum over the spectrum of the residuals in sigma. The algorithm also provides a PS map in sigma units. +:py:meth:`~fermipy.gtanalysis.GTAnalysis.psmap` quantifies the 3d data/model agreement by computing the PS +at each spatial bin in the ROI according to the algorithm described in Bruel P. (2021), A&A, 656, A81. +(`doi:10.1051/0004-6361/202141553 `_). For each spatial bin, the algorithm first computes +the data and model count spectra integrated over an energy dependent region (following the PSF energy dependence) +and then computes the p-value that the data count spectrum is compatible with the model count spectrum (using the likelihood statistics). +The absolute value of PS is -log10(p-value) and its sign corresponds to the sign of the sum over the spectrum of the residuals in sigma. +The algorithm also provides a PS map in sigma units. Caveat: the algorithm is currently not able to run in the context of a joint analysis with several event types. @@ -35,9 +40,13 @@ argument to the method. If the analysis uses likelihood weights, the user can specify the likelihood weight file with the argument ``wmap``. -The user can set the parameters defining the energy dependent region during the count spectrum integration step. The integration radius is defined by the function sqrt(psfpar0^2*pow(E/psfpar1,-2*psfpar2) + psfpar3^2), with E in MeV. The default parameters (psfpar0=4.0,psfpar1=100,psfpar2=0.9,psfpar3=0.1) are optimized to look for point-source like deviations. When looking for extended deviations (~1deg scale), it is recommended to use (psfpar0=5.0,psfpar1=100,psfpar2=0.8,psfpar3=1.0). +The user can set the parameters defining the energy dependent region used in the count spectrum integration step. +The integration radius is defined by the function sqrt(psfpar0^2*pow(E/psfpar1,-2*psfpar2) + psfpar3^2), with E in MeV. +The default parameters (psfpar0=4.0,psfpar1=100,psfpar2=0.9,psfpar3=0.1) are optimized to look for point-source like deviations. +When looking for extended deviations (~1deg scale), it is recommended to use (psfpar0=5.0,psfpar1=100,psfpar2=0.8,psfpar3=1.0). -The user can set the energy range with the arguments ``emin`` and ``emax``. Depending on the energy binning, it can be optimal to rebin the count spectra thanks to the argument ``rebin``. For an analysis with 10 bins per decade, it is recommended to set ``rebin`` to 3. +The user can set the energy range with the arguments ``emin`` and ``emax``. Depending on the energy binning, it can be optimal +to rebin the count spectra thanks to the argument ``rebin``. For an analysis with 10 bins per decade, it is recommended to set ``rebin`` to 3. .. csv-table:: *psmap* Options :header: Option, Default, Description From 46c84eaff38b46f31d9b4dea856dadd9e34b0cb8 Mon Sep 17 00:00:00 2001 From: phbruel <39769998+phbruel@users.noreply.github.com> Date: Tue, 2 Apr 2024 19:44:56 +0200 Subject: [PATCH 19/21] Update index.rst (changing PSmap description) --- docs/advanced/index.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/advanced/index.rst b/docs/advanced/index.rst index 7e708602..1577c511 100644 --- a/docs/advanced/index.rst +++ b/docs/advanced/index.rst @@ -7,8 +7,7 @@ Advanced Analysis Methods This page documents some of the more advanced methods and features available in Fermipy: -* :ref:`psmap`: Generate a PS map for a new source - centered at each spatial bin in the ROI using the algorithm described in +* :ref:`psmap`: Generate a PS map quantifying the data/model agreement using the algorithm described in Bruel P. (2021), A&A, 656, A81. (`doi:10.1051/0004-6361/202141553 `_). * :ref:`tsmap`: Generate a test statistic (TS) map for a new source From 4914ab4a60fce68d605bc0809a4c63e5a3b614e1 Mon Sep 17 00:00:00 2001 From: phbruel <39769998+phbruel@users.noreply.github.com> Date: Tue, 2 Apr 2024 19:51:33 +0200 Subject: [PATCH 20/21] Update psmap.rst --- docs/advanced/psmap.rst | 68 ++--------------------------------------- 1 file changed, 2 insertions(+), 66 deletions(-) diff --git a/docs/advanced/psmap.rst b/docs/advanced/psmap.rst index 15e49f0a..3d48745f 100644 --- a/docs/advanced/psmap.rst +++ b/docs/advanced/psmap.rst @@ -42,8 +42,8 @@ If the analysis uses likelihood weights, the user can specify the likelihood wei The user can set the parameters defining the energy dependent region used in the count spectrum integration step. The integration radius is defined by the function sqrt(psfpar0^2*pow(E/psfpar1,-2*psfpar2) + psfpar3^2), with E in MeV. -The default parameters (psfpar0=4.0,psfpar1=100,psfpar2=0.9,psfpar3=0.1) are optimized to look for point-source like deviations. -When looking for extended deviations (~1deg scale), it is recommended to use (psfpar0=5.0,psfpar1=100,psfpar2=0.8,psfpar3=1.0). +The default parameters (``psfpar0``=4.0,``psfpar1``=100,``psfpar2``=0.9,``psfpar3``=0.1) are optimized to look for point-source like deviations. +When looking for extended deviations (~1deg scale), it is recommended to use (``psfpar0``=5.0,``psfpar1``=100,``psfpar2``=0.8,``psfpar3``=1.0). The user can set the energy range with the arguments ``emin`` and ``emax``. Depending on the energy binning, it can be optimal to rebin the count spectra thanks to the argument ``rebin``. For an analysis with 10 bins per decade, it is recommended to set ``rebin`` to 3. @@ -135,67 +135,3 @@ Reference/API .. automethod:: fermipy.gtanalysis.GTAnalysis.psmap :noindex: - - - - - - - - - -original text by Nicola: -For each spatial bin the method calculates the maximum likelihood test -statistic given by - -.. math:: - - \mathrm{L} = - \sum_{k}\log P(x_{k} m_{k}) - -where P is the Poisson probability and :math:`x_{k}` are independent random Poisson -variables of mean :math:`m_{k}`, the spatially integrated number of model counts in the spectral bin :math:`k`. -The p-value is the integral of the probability distribution function (pdf) of :math:`L` above -:math:`L_{data} = − \sum_{k} log P(n_{k} , m_{k})`, the value obtained with the data integrated count spectra :math:`n_{k}`. -In other words, the p-value is the :math:`L` complementary cumulative distribution function (CCDF) at Ldata. -For the spectral bins with Gaussian statistics, the Poisson probability of parameter :math:`m_{k}` can be replaced with -a Gaussian with mean and variance equal to :math:`m_{k}`. Ignoring the constant term, we have: - -.. math:: - L_{Gaus} = \frac{1}{2} \sum_{k} \frac{(x_{k}-m_{k})^{2}}{m_{k}} - -As a consequence, the :math:`L_{Gaus}` pdf can be simply derived from the :math:`\chi^2` distribution with a number of -degrees of freedom equal to the number of spectral bins with Gaussian statistics. In order to compute the :math:`L` pdf, -we thus start by sorting the counts in decreasing order, then compute the :math:`L_{Gaus}` pdf corresponding to all the -bins with a number of counts greater than ``maxpoissoncount=100`` and finally perform the iterative computation over the -remaining bins. We note that this p-value computation provides a simple extension of the :math:`\chi^2`-test to -histograms with counts in the Poisson regime. -The PS data/model deviation estimator is defined as: - -.. math:: - \mathrm{|PS|} = - \log_{10} \mathrm{(p-value)} - -and give it the sign of the sum of the residuals in sigma units: - -.. math:: - \mathrm{sign(PS)} = \mathrm{sign}\left(\sum_{k} \frac{(x_{k}-m_{k})}{\mathrm{max}(1,\sqrt{m_{k}})}\right) - -which allows us to estimate whether the deviation is positive (data>model) or negative (data Date: Tue, 2 Apr 2024 12:32:13 -0700 Subject: [PATCH 21/21] Changed the format of the table --- docs/advanced/psmap.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/advanced/psmap.rst b/docs/advanced/psmap.rst index 15e49f0a..a18e3e2a 100644 --- a/docs/advanced/psmap.rst +++ b/docs/advanced/psmap.rst @@ -51,7 +51,7 @@ to rebin the count spectra thanks to the argument ``rebin``. For an analysis wit .. csv-table:: *psmap* Options :header: Option, Default, Description :file: ../config/psmap.csv - :delim: , + :delim: tab :widths: 10,10,80

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