├── .gitignore ├── constlightcurve.dat ├── doc ├── det_000.png ├── obsplot.png ├── obs_events.fits_detect.png ├── obs_events.fits_detect2.png ├── obs_events.fits_detect_bkg.png ├── obs_events.fits_detect_bkg2.png ├── obs_events.fits_detect_in.png ├── obs_events.fits_detect_in2.png └── detect.rst ├── extbkg.xcm ├── CTsphere.xcm ├── README.rst ├── NOTES.rst ├── simputshallowmerge.py ├── simputrepeat.py ├── flatimage.py ├── Makefile ├── createspectrum.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | *~ 2 | *.pi 3 | *.pdf 4 | -------------------------------------------------------------------------------- /constlightcurve.dat: -------------------------------------------------------------------------------- 1 | 0.0 1.0 2 | 1e10 1.0 3 | -------------------------------------------------------------------------------- /doc/det_000.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/det_000.png -------------------------------------------------------------------------------- /doc/obsplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obsplot.png -------------------------------------------------------------------------------- /doc/obs_events.fits_detect.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obs_events.fits_detect.png -------------------------------------------------------------------------------- /doc/obs_events.fits_detect2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obs_events.fits_detect2.png -------------------------------------------------------------------------------- /doc/obs_events.fits_detect_bkg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obs_events.fits_detect_bkg.png -------------------------------------------------------------------------------- /doc/obs_events.fits_detect_bkg2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obs_events.fits_detect_bkg2.png -------------------------------------------------------------------------------- /doc/obs_events.fits_detect_in.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obs_events.fits_detect_in.png -------------------------------------------------------------------------------- /doc/obs_events.fits_detect_in2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JohannesBuchner/athena-point-source-simulator/master/doc/obs_events.fits_detect_in2.png -------------------------------------------------------------------------------- /extbkg.xcm: -------------------------------------------------------------------------------- 1 | # Arne Rau's Athena WFI background model 2 | abund wilm 3 | xsect vern 4 | model apec+phabs*(apec+pow) 5 | 9.9E-02 6 | 1 7 | 0 8 | 1.7E-6 9 | 0.018 10 | 0.225 11 | 1 12 | 0 13 | 7.3E-7 14 | 1.45 15 | 2E-7 16 | 17 | -------------------------------------------------------------------------------- /CTsphere.xcm: -------------------------------------------------------------------------------- 1 | abund wilm 2 | xsect vern 3 | model atable{sphere0708.fits} 4 | 100 1 0.01 0.01 10000 10000 5 | 2 0.01 1 1.2 2.8 3 6 | 1 0.01 0.1 0.1 10 10 7 | 1 0.01 0.1 0.1 10 10 8 | 2 -0.001 0 0 5 5 9 | 1 0.01 0 0 1e+20 1e+24 10 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | Athena Point Source simulator 2 | ============================== 3 | 4 | This is a simulator pipeline intended first for the simulation of 5 | Compton-thick AGN observed with Athena. It can easily be adapted to other 6 | science cases and missions. 7 | 8 | Requirements 9 | ------------ 10 | SIXTE, SIMPUT and Xspec installed and loaded. 11 | 12 | Python modules: 13 | 14 | * astropy 15 | * numpy 16 | * matplotlib 17 | 18 | How to use 19 | ----------- 20 | 21 | $ make obsplot.pdf 22 | 23 | TODO 24 | ----------- 25 | 26 | * replace createspectrum.py with a athena pipeline tool. 27 | * attach xspec/BXA fitting as a task 28 | 29 | 30 | 31 | -------------------------------------------------------------------------------- /NOTES.rst: -------------------------------------------------------------------------------- 1 | ===================== 2 | Implementation notes 3 | ===================== 4 | 5 | Source model 6 | -------------- 7 | 8 | Murray Brightman's sphere model is used here to produce 1e24/cm² sources. 9 | 10 | Background model 11 | ----------------- 12 | 13 | The background spectrum has, when set up with the normalisations for /arcmin² instead of /cm²:: 14 | 15 | flux 0.5 2 16 | Model Flux 0.000901 photons (1.8037e-12 ergs/cm^2/s) range (0.50000 - 2.0000 keV) 17 | XSPEC12>flux 0.5 10 18 | Model Flux 1.6371e-06 photons (2.9029e-15 ergs/cm^2/s) range (0.50000 - 10.000 keV) 19 | 20 | The per-square degree flux in the 2-10keV band is therefore (multiplying by 60x60) 1e-11 erg/deg²/s 21 | The per-0.1 square degree flux is (multiplying by 6x6) 1e-13 erg/(0.1deg)²/s 22 | 23 | 24 | -------------------------------------------------------------------------------- /simputshallowmerge.py: -------------------------------------------------------------------------------- 1 | import astropy.io.fits as pyfits 2 | import sys 3 | import numpy 4 | 5 | a = pyfits.open(sys.argv[1]) 6 | b = pyfits.open(sys.argv[2]) 7 | 8 | e = 'SRC_CAT' 9 | # SRC_ID, SPECTRUM, IMAGE, TIMING need to be re-indexed 10 | # for b-sources 11 | b[e].data['SRC_ID'] += a[e].data['SRC_ID'].max() + 1 12 | for k in 'SPECTRUM', 'IMAGE', 'TIMING': 13 | # modify the indexes in b to refer to its file 14 | b[e].data[k] = ['%s%s' % (sys.argv[2], v) if v.startswith('[') else v for v in b[e].data[k]] 15 | 16 | # when updating data, some header information is dropped by pyfits 17 | orig_header = dict(a[e].header) 18 | a[e].data = numpy.hstack((a[e].data, b[e].data)) 19 | for k, v in orig_header.iteritems(): 20 | if k not in a[e].header and not k.startswith('NAXIS'): 21 | a[e].header[k] = v 22 | 23 | # save 24 | a.writeto(sys.argv[3], clobber=True) 25 | 26 | -------------------------------------------------------------------------------- /simputrepeat.py: -------------------------------------------------------------------------------- 1 | import astropy.io.fits as pyfits 2 | import sys 3 | import numpy 4 | 5 | a = pyfits.open(sys.argv[1]) 6 | 7 | e = 'SRC_CAT' 8 | # SRC_ID, SPECTRUM, IMAGE, TIMING need to be re-indexed 9 | # for b-sources 10 | center = a[e].data 11 | b = 0.1759 12 | tmax = 6 * 2 * numpy.pi 13 | tmin = numpy.pi * 3 / 4 14 | rmax = 0.2 15 | 16 | alldata = [center] 17 | for i, t in enumerate(numpy.linspace(0, tmax, 6*4*4+1)): 18 | if t < tmin: continue 19 | next = center.copy() 20 | next['SRC_ID'] += i 21 | next['RA'] += rmax * numpy.cos(t) * t/tmax 22 | next['DEC'] += rmax * numpy.sin(t) * t/tmax 23 | alldata.append(next) 24 | 25 | # when updating data, some header information is dropped by pyfits 26 | orig_header = dict(a[e].header) 27 | a[e].data = numpy.hstack(tuple(alldata)) 28 | for k, v in orig_header.iteritems(): 29 | if k not in a[e].header and not k.startswith('NAXIS'): 30 | a[e].header[k] = v 31 | 32 | # save 33 | a.writeto(sys.argv[2], clobber=True) 34 | 35 | -------------------------------------------------------------------------------- /flatimage.py: -------------------------------------------------------------------------------- 1 | import numpy 2 | import astropy.io.fits as pyfits 3 | 4 | npix = 9 5 | image = numpy.ones((npix,npix)) 6 | image[0,0] = 10 7 | image[-1,0] = 10 8 | image[0,-1] = 10 9 | image[-1,-1] = 10 10 | hdu = pyfits.PrimaryHDU(data = image) 11 | import datetime, time 12 | now = datetime.datetime.fromtimestamp(time.time()) 13 | nowstr = now.isoformat() 14 | nowstr = nowstr[:nowstr.rfind('.')] 15 | hdu.header['CREATOR'] = """Johannes Buchner """ 16 | hdu.header['DATE'] = nowstr 17 | hdu.header['HDUCLASS'] = 'HEASARC/SIMPUT' 18 | hdu.header['HDUCLAS1'] = 'IMAGE' 19 | hdu.header['HDUVERS'] = '1.1.0' 20 | hdu.header['CTYPE1'] = 'RA---TAN' 21 | hdu.header['CTYPE2'] = 'DEC--TAN' 22 | hdu.header['CRVAL1'] = 40.2 23 | hdu.header['CRVAL2'] = 12.8 24 | hdu.header['CRPIX1'] = 3 25 | hdu.header['CRPIX2'] = 3 26 | #hdu.header['CD1_1'] = 1 27 | #hdu.header['CD1_2'] = 0 28 | #hdu.header['CD2_1'] = 1 29 | #hdu.header['CD2_2'] = 0 30 | hdu.header['CUNIT1'] = 'deg' 31 | hdu.header['CUNIT2'] = 'deg' 32 | # wfi is 40x40arcmin 33 | # we do 50% overlap 34 | # 40*1.5/60 / npix ~= 0.1 degrees 35 | hdu.header['CDELT1'] = 0.1 36 | hdu.header['CDELT2'] = 0.1 37 | hdu.writeto('flatimage.fits', clobber=True) 38 | 39 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | # init SIXTE 2 | # init XSPEC 3 | 4 | XMLFILE := ${SIXTE}/share/sixte/instruments/athena/1190mm_wfi_wo_filter/depfet_b_1l_ff_large.xml 5 | 6 | all: obs.pi 7 | 8 | sphere0708.fits: 9 | wget http://www.mpe.mpg.de/~mbright/data/sphere0708.fits 10 | 11 | obs.simput: CTsphere.xcm constlightcurve.dat sphere0708.fits 12 | simputfile RA=40.2 Dec=12.8 XSPECFile="CTsphere.xcm" LCFile=constlightcurve.dat MJDREF=50800.0 Emin=2 Emax=10.0 srcFlux=1e-12 Simput="obs.simput" clobber=yes 13 | 14 | obs_repeated.simput: obs.simput 15 | python simputrepeat.py obs.simput obs_repeated.simput 16 | 17 | flatimage.fits: 18 | python flatimage.py 19 | 20 | extbkg.simput: extbkg.xcm constlightcurve.dat flatimage.fits 21 | # srcFlux is total, with the image size being (0.1 deg)^2 22 | # I computed the source spectrum flux in 0.5-10keV and set it here 23 | simputfile RA=40.2 Dec=12.8 XSPECFile="extbkg.xcm" LCFile=constlightcurve.dat MJDREF=50800.0 Emin=0.5 Emax=10 srcFlux=1e-13 Simput="extbkg.simput" clobber=yes IMAGE=flatimage.fits 24 | 25 | obs+extbkg.simput: obs_repeated.simput extbkg.simput 26 | python simputshallowmerge.py obs_repeated.simput extbkg.simput obs+extbkg.simput 27 | 28 | obs_events.fits: obs+extbkg.simput 29 | time runsixt EventList="obs_events.fits" PatternList="obs_pattern.fits" \ 30 | Mission="ATHENA" Instrument="WFI" Mode="1190mm_wfi_wo_filter" XMLFile=${XMLFILE} \ 31 | Simput="obs+extbkg.simput" Exposure=10000. RA=40.2 Dec=12.8 MJDREF=50814.0 clobber=yes 32 | 33 | detected.txt: obs_events.fits 34 | python createspectrum.py obs_events.fits ${SIXTE}/share/sixte/instruments/athena/1190mm_wfi_wo_filter/depfet_b_1l_ff_large.xml 35 | 36 | #obsplot.ps: obs.pi 37 | # xspec < test.xspec 38 | 39 | #obsplot.pdf: obsplot.ps 40 | # ps2pdf obsplot.ps 41 | 42 | clean: 43 | rm -f obs.simput obs_events.fits 44 | rm -f obs.pi obs_bkg.pi 45 | rm -f obsplot.ps obsplot.pdf 46 | 47 | -------------------------------------------------------------------------------- /doc/detect.rst: -------------------------------------------------------------------------------- 1 | ===================== 2 | Detection algorithm 3 | ===================== 4 | 5 | The detection algorithm consists of two stages. In the first stage a 6 | list of candidate sources is generated, in the second stage this list 7 | is vetted for its significance. Both stages rely on poisson statistics. 8 | 9 | Stage 1 10 | ---------- 11 | 12 | * The detected counts are smoothed with a convolution with a 7x7 pixel box. 13 | * A background map is created by combining for each pixel the neighboring boxes. 14 | A 5x5 grid of boxes is considered, for each of those the number of counts 15 | computed. The center box is ignored. A biased estimator is used (cutting 16 | the boxes with most counts) to compute a typical background count rate. 17 | * The significance of each pixel is computed using the poisson distribution. 18 | Pixels with p<1e-3 are marked as candidate sources. 19 | 20 | 21 | .. figure:: obs_events.fits_detect_in.png 22 | 23 | Image of counts 24 | 25 | .. figure:: obs_events.fits_detect_in2.png 26 | 27 | Box-smoothed image 28 | 29 | .. figure:: obs_events.fits_detect_bkg.png 30 | 31 | Background map 32 | 33 | .. figure:: obs_events.fits_detect.png 34 | 35 | Significance map 36 | 37 | 38 | 39 | 40 | 41 | Stage 2 42 | ---------- 43 | 44 | * The candidate pixels are masked and a new background map is computed, same 45 | as above. 46 | * Each candidate pixel is considered in turn, with higher significance pixels 47 | first. A box of 9x9 pixels centered there is considered. The centroid is 48 | computed and the position updated, if necessary, up to 3 times. 49 | Then the poisson probability, considering the new background map is computed. 50 | Sources with p<1e5 are considered detected. This provides a low fraction of false sources, 51 | but remains sensitive down to few (6) counts. 52 | The counts from detected sources are removed from the image, and not 53 | considered when handling the next candidate pixel. 54 | 55 | .. figure:: obs_events.fits_detect_bkg2.png 56 | 57 | Updated background map 58 | 59 | .. figure:: obs_events.fits_detect2.png 60 | 61 | Image of counts, with source detections marked. 62 | 63 | Source extraction 64 | ------------------ 65 | 66 | Finally at each position a source is extracted. Annuli of radius 0-4, 8-16 67 | are used to extract source and background spectra into .pi files. 68 | 69 | .. figure:: det_000.png 70 | 71 | Spectrum in Xspec. 72 | 73 | 74 | -------------------------------------------------------------------------------- /createspectrum.py: -------------------------------------------------------------------------------- 1 | import astropy.io.fits as pyfits 2 | import sys, os 3 | import numpy 4 | import matplotlib.pyplot as plt 5 | import scipy.stats 6 | 7 | import joblib 8 | mem = joblib.Memory('.') 9 | 10 | # open event file 11 | f = pyfits.open(sys.argv[1]) 12 | evtheader = f[0].header 13 | arf_filename = os.path.join(os.path.dirname(sys.argv[2]), evtheader['ANCRFILE']) 14 | rmf_filename = os.path.join(os.path.dirname(sys.argv[2]), evtheader['RESPFILE']) 15 | arf = pyfits.open(arf_filename) 16 | rmf = pyfits.open(rmf_filename) 17 | telescope = rmf['MATRIX'].header['TELESCOP'] 18 | instrument = rmf['MATRIX'].header['INSTRUME'] 19 | 20 | def create_pi(events, filename, **header): 21 | #bins = rmf['EBOUNDS'].data['E_MIN'].tolist() + [rmf['EBOUNDS'].data['E_MAX'][-1]] 22 | hist, bins = numpy.histogram(events, bins=numpy.arange(len(rmf['EBOUNDS'].data)+1)) 23 | hdus = [] 24 | hdu = pyfits.PrimaryHDU() 25 | import datetime, time 26 | now = datetime.datetime.fromtimestamp(time.time()) 27 | nowstr = now.isoformat() 28 | nowstr = nowstr[:nowstr.rfind('.')] 29 | hdu.header['CREATOR'] = """Johannes Buchner """ 30 | hdu.header['DATE'] = nowstr 31 | hdus.append(hdu) 32 | 33 | counts = numpy.array(list(enumerate(hist)), dtype=[('CHANNEL', '>i2'), ('COUNTS', '>i4')]) 34 | print counts 35 | hdu = pyfits.BinTableHDU(data=counts) 36 | hdu.header['DATE'] = nowstr 37 | hdu.header['EXTNAME'] = 'SPECTRUM' 38 | hdu.header['CORRFILE'] = 'none' 39 | hdu.header['CORRSCAL'] = 1.0 40 | hdu.header['HDUCLASS'] = 'OGIP' 41 | hdu.header['HDUVERS'] = '1.2.0' 42 | hdu.header['LONGSTRN'] = 'OGIP 1.0' 43 | hdu.header['GROUPING'] = 0 44 | hdu.header['FILTER'] = 0 45 | hdu.header['POISSERR'] = True 46 | hdu.header['SYS_ERR'] = 0 47 | hdu.header['QUALITY'] = 0 48 | hdu.header['DETCHANS'] = len(rmf['EBOUNDS'].data) 49 | hdu.header['HDUCLAS1'] = 'SPECTRUM' 50 | for k, v in header.iteritems(): 51 | hdu.header[k] = v 52 | hdu.header['CHANTYPE'] = 'PI' 53 | hdu.header['HDUCLAS2'] = 'TOTAL' 54 | hdu.header['HDUCLAS3'] = 'COUNT' 55 | hdus.append(hdu) 56 | hdus = pyfits.HDUList(hdus) 57 | hdus.writeto(filename, clobber=True) 58 | 59 | # plot events 60 | data = f['EVENTS'].data 61 | x = data['RAWX'] 62 | y = data['RAWY'] 63 | xlo, xhi = x.min(), x.max() 64 | ylo, yhi = y.min(), y.max() 65 | hist, xbins, ybins = numpy.histogram2d(x, y, bins=(range(xlo, xhi+1), range(ylo, yhi+1))) 66 | plt.imshow((hist > 0).transpose(), extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal') 67 | plt.savefig(sys.argv[1] + '.pdf', bbox_inches='tight') 68 | plt.close() 69 | 70 | @mem.cache 71 | def compute_sig(hist, w=5, h=5): 72 | bkg = numpy.zeros(hist.shape) 73 | print 'sliding...' 74 | results = numpy.zeros(hist.shape, dtype=int) 75 | for i in range(h, hist.shape[0]-h): 76 | for j in range(w, hist.shape[1]-w): 77 | k = hist[i-h:i+h+1,j-w:j+w+1].sum() 78 | results[i,j] = k 79 | 80 | print 'testing...' 81 | for i in range(h, hist.shape[0]-h): 82 | for j in range(w, hist.shape[1]-w): 83 | k = results[i,j] 84 | if not k > 0: continue 85 | nearbys = numpy.array([results[i+di, j+dj] for di in [-2*h, -h, 0, h, 2*h] for dj in [-2*w, -w, 0, w, 2*w] if i+di > 0 and j+dj > 0 and i+di= 0 and not (di == 0 and dj == 0)]) 86 | nearbys.sort() 87 | mid = scipy.stats.mstats.mquantiles(nearbys, 0.25)[0] 88 | nearbys2 = nearbys[nearbys <= 4*(mid+1)] 89 | if mid > 5: 90 | kmean = numpy.nanmedian(nearbys2[:-2]) 91 | else: 92 | kmean = numpy.nanmean(nearbys2[:-2]) 93 | if numpy.isnan(kmean) or kmean > 5: 94 | print nearbys 95 | bkg[i,j] = kmean 96 | return bkg, results 97 | 98 | 99 | @mem.cache 100 | def compute_bkg(hist, w=5, h=5): 101 | print 'sliding...' 102 | results = numpy.zeros(hist.shape) 103 | for i in range(h, hist.shape[0]-h): 104 | for j in range(w, hist.shape[1]-w): 105 | v = hist[i-h:i+h+1,j-w:j+w+1] 106 | if numpy.isnan(v).all(): 107 | results[i,j] = numpy.nan 108 | else: 109 | results[i,j] = numpy.nanmean(v) 110 | 111 | print 'testing...' 112 | results2 = numpy.zeros(hist.shape) - 1 113 | for i in range(h, hist.shape[0]-h): 114 | for j in range(w, hist.shape[1]-w): 115 | nearbys = numpy.array([results[i+di, j+dj] for di in [-2*h, -h, 0, h, 2*h] for dj in [-2*w, -w, 0, w, 2*w] if i+di > 0 and j+dj > 0 and i+di= 0 and not (di == 0 and dj == 0)]) 116 | nearbys.sort() 117 | mid = scipy.stats.mstats.mquantiles(nearbys, 0.25)[0] 118 | nearbys2 = nearbys[nearbys <= 4*(mid+1/(w*h))] 119 | kmean = numpy.nanmean(nearbys2[:-2]) 120 | if numpy.isnan(kmean) or kmean > 5./(w*h): 121 | print nearbys 122 | results2[i,j] = kmean 123 | return results2, results 124 | 125 | def detect_sources(x, y): 126 | """ 127 | Sliding box detection 128 | ---------------------- 129 | the counts in the box are compared to the background, 130 | which is from a 5x5 grid of boxes (without the middle, source box). 131 | The background rate is estimated using the median. 132 | Finally, the poisson survival rate is computed (1-cdf), 133 | and sources with >1e-6 accepted. 134 | """ 135 | print 'detection Stage 1...' 136 | hist, xbins, ybins = numpy.histogram2d(x, y, bins=(range(xlo, xhi+1), range(ylo, yhi+1))) 137 | plt.imshow(hist.transpose(), extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal') 138 | plt.savefig(sys.argv[1] + '_detect_in.pdf', bbox_inches='tight') 139 | plt.close() 140 | w=7 141 | h=7 142 | print ' computing background...' 143 | bkg, blurred = compute_sig(hist, w=w, h=h) 144 | plt.imshow(bkg, extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal', origin='lower') 145 | plt.savefig(sys.argv[1] + '_detect_bkg.pdf', bbox_inches='tight') 146 | plt.close() 147 | 148 | plt.imshow(blurred, extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal', origin='lower') 149 | plt.savefig(sys.argv[1] + '_detect_in2.pdf', bbox_inches='tight') 150 | plt.close() 151 | 152 | print ' computing significance...' 153 | bkg[bkg < 0.1] = 0.1 154 | sig = scipy.stats.poisson.sf(blurred, bkg) 155 | #print sig.max(), sig.min() 156 | sig[sig < 1e-300] = 1e-300 157 | sig = -numpy.log10(sig) 158 | gray = sig.copy() 159 | gray[gray < 3] = 3 160 | gray[gray > 6] = 6 161 | plt.figure() 162 | plt.imshow(gray, extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal', origin='lower') 163 | plt.savefig(sys.argv[1] + '_detect.pdf', bbox_inches='tight') 164 | plt.close() 165 | 166 | print 'detection Stage 2...' 167 | # go through pixels, ordered by significance 168 | hist_with_holes = hist.copy() 169 | hist_with_holes[sig > 3] = numpy.nan 170 | print ' computing background...' 171 | _, bkgmap = compute_bkg(hist_with_holes, h=11, w=11) 172 | hist_with_holes = hist.copy() 173 | #print 'bkgmap zero: %.2f%%' % (bkgmap == 0).mean() 174 | bkgmap[bkgmap == 0] = 0.1/(h*w) 175 | plt.imshow(bkgmap, extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal', origin='lower', vmin=0) 176 | plt.savefig(sys.argv[1] + '_detect_bkg2.pdf', bbox_inches='tight') 177 | plt.close() 178 | Y, X = numpy.meshgrid(numpy.arange(hist.shape[1]), numpy.arange(hist.shape[0])) 179 | w, h = 4, 4 180 | xweights = X[:2*w+1,:2*h+1] - w 181 | yweights = Y[:2*w+1,:2*h+1] - h 182 | positions = [] 183 | 184 | print ' finding individual sources...' 185 | for s, x, y in sorted(zip(sig[sig > 3], X[sig > 3], Y[sig > 3]), reverse=True): 186 | s = sig[x,y] 187 | if s < 3: 188 | continue 189 | for i in range(3): 190 | # take all counts within radius 5, 191 | counts = hist_with_holes[x-w:x+w+1,y-h:y+h+1] 192 | if counts.sum() == 0: 193 | break 194 | if counts.shape != xweights.shape: 195 | # source at border 196 | break 197 | # compute centroid 198 | xnext = int(round((xweights * counts).sum() / counts.sum())) 199 | ynext = int(round((yweights * counts).sum() / counts.sum())) 200 | if xnext == 0 and ynext == 0: 201 | break 202 | #print ' updating centroid', (xnext, ynext), (x+xnext, y+ynext) 203 | x += xnext 204 | y += ynext 205 | # compute significance 206 | nctsbkg = bkgmap[x,y] 207 | if nctsbkg == -1: 208 | print ' can not test here' 209 | continue 210 | ncts = counts.sum() 211 | s = scipy.stats.poisson.sf(ncts, nctsbkg * counts.size) 212 | #print ' significance', s, ncts, nctsbkg * counts.size 213 | # if significant: 214 | if s < 1e-5: 215 | # * store x,y, and extract spectrum 216 | # * set number of counts to zero in that region 217 | hist_with_holes[x-w:x+w+1,y-h:y+h+1] = 0 218 | # * set significance in that region to zero 219 | sig[x-h:x+h+1,y-h:y+h+1] = 0 220 | # 221 | plt.plot(y, x, 'x', color='r', alpha=0.5) 222 | plt.text(y, x, ' %d' % ncts, color='r', alpha=0.2) 223 | positions.append((x, y)) 224 | plt.imshow(hist, extent=[ylo, yhi, xlo, xhi], cmap='gray_r', interpolation='none', aspect='equal', origin='lower') 225 | plt.savefig(sys.argv[1] + '_detect2.pdf', bbox_inches='tight') 226 | plt.close() 227 | return positions 228 | 229 | def extract(xcenter, ycenter, file_prefix, **extra_header): 230 | radius1 = 4 231 | radius2 = 8 232 | radius3 = 16 233 | x = data['RAWX'] 234 | y = data['RAWY'] 235 | r = ((x - xcenter)**2 + (y - ycenter)**2 + 0.0)**0.5 236 | mask_src = r <= radius1 237 | mask_bkg2 = r >= radius2 238 | mask_bkg3 = r <= radius3 239 | mask_bkg = mask_bkg2 * mask_bkg3 240 | print mask_src.sum(), mask_bkg.sum() 241 | header = dict( 242 | ANCRFILE=arf_filename, 243 | RESPFILE=rmf_filename, 244 | TELESCOP=telescope, 245 | INSTRUME=instrument, 246 | EXPOSURE=evtheader['TSTOP'] - evtheader['TSTART'], 247 | AREASCAL=radius1**2 * 1., 248 | BACKSCAL=(radius3**2 - radius2**2) * 1. / radius1**2, 249 | **extra_header) 250 | 251 | bkg = create_pi(data['PI'][mask_bkg], file_prefix+'_bkg.pi', 252 | **header) 253 | src = create_pi(data['PI'][mask_src], file_prefix+'.pi', 254 | BACKFILE='obs_bkg.pi', 255 | **header) 256 | 257 | mask = numpy.logical_and(data['PI'] > 0.5, data['PI'] < 10) 258 | positions = detect_sources(x[mask], y[mask]) 259 | f = open('detected.txt', 'w') 260 | print 'Extracting sources...' 261 | for i, (x, y) in enumerate(positions): 262 | extract(x, y, file_prefix = 'det_%03d' % i, DETX=x, DETY=y) 263 | f.write('%d %d %s\n' % (x, y, 'det_%03d' % i)) 264 | 265 | 266 | 267 | 268 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. By contrast, 16 | our General Public Licenses are intended to guarantee your freedom to 17 | share and change all versions of a program--to make sure it remains free 18 | software for all its users. 19 | 20 | When we speak of free software, we are referring to freedom, not 21 | price. 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Remote Network Interaction; Use with the GNU General Public License. 541 | 542 | Notwithstanding any other provision of this License, if you modify the 543 | Program, your modified version must prominently offer all users 544 | interacting with it remotely through a computer network (if your version 545 | supports such interaction) an opportunity to receive the Corresponding 546 | Source of your version by providing access to the Corresponding Source 547 | from a network server at no charge, through some standard or customary 548 | means of facilitating copying of software. This Corresponding Source 549 | shall include the Corresponding Source for any work covered by version 3 550 | of the GNU General Public License that is incorporated pursuant to the 551 | following paragraph. 552 | 553 | Notwithstanding any other provision of this License, you have 554 | permission to link or combine any covered work with a work licensed 555 | under version 3 of the GNU General Public License into a single 556 | combined work, and to convey the resulting work. The terms of this 557 | License will continue to apply to the part which is the covered work, 558 | but the work with which it is combined will remain governed by version 559 | 3 of the GNU General Public License. 560 | 561 | 14. Revised Versions of this License. 562 | 563 | The Free Software Foundation may publish revised and/or new versions of 564 | the GNU Affero General Public License from time to time. Such new versions 565 | will be similar in spirit to the present version, but may differ in detail to 566 | address new problems or concerns. 567 | 568 | Each version is given a distinguishing version number. If the 569 | Program specifies that a certain numbered version of the GNU Affero General 570 | Public License "or any later version" applies to it, you have the 571 | option of following the terms and conditions either of that numbered 572 | version or of any later version published by the Free Software 573 | Foundation. If the Program does not specify a version number of the 574 | GNU Affero General Public License, you may choose any version ever published 575 | by the Free Software Foundation. 576 | 577 | If the Program specifies that a proxy can decide which future 578 | versions of the GNU Affero General Public License can be used, that proxy's 579 | public statement of acceptance of a version permanently authorizes you 580 | to choose that version for the Program. 581 | 582 | Later license versions may give you additional or different 583 | permissions. However, no additional obligations are imposed on any 584 | author or copyright holder as a result of your choosing to follow a 585 | later version. 586 | 587 | 15. Disclaimer of Warranty. 588 | 589 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 590 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 591 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 592 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 593 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 594 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 595 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 596 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 597 | 598 | 16. Limitation of Liability. 599 | 600 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 601 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 602 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 603 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 604 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 605 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 606 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 607 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 608 | SUCH DAMAGES. 609 | 610 | 17. Interpretation of Sections 15 and 16. 611 | 612 | If the disclaimer of warranty and limitation of liability provided 613 | above cannot be given local legal effect according to their terms, 614 | reviewing courts shall apply local law that most closely approximates 615 | an absolute waiver of all civil liability in connection with the 616 | Program, unless a warranty or assumption of liability accompanies a 617 | copy of the Program in return for a fee. 618 | 619 | END OF TERMS AND CONDITIONS 620 | 621 | How to Apply These Terms to Your New Programs 622 | 623 | If you develop a new program, and you want it to be of the greatest 624 | possible use to the public, the best way to achieve this is to make it 625 | free software which everyone can redistribute and change under these terms. 626 | 627 | To do so, attach the following notices to the program. It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published 637 | by the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | 663 | --------------------------------------------------------------------------------