def getSourcesInTheROI(ra, dec, roi, met):
roi = float(roi)
#config = fermiatSUlib.Configuration()
# xml = readXml.SourceModel(config['FGL_XML_CATALOG'])
# fglSources = {k: v for k, v in xml.srcList.iteritems() if k.find("2FGL")>=0}
#Open the FITS file, keeping only certain classes of sources
f = pyfits.open(os.environ['FERMISOURCECATALOG'])
fglFITS = f['LAT_Point_Source_Catalog'].data
f.close()
classesToKeep = FGL_CLASSES
def filtering(x):
if (x.field('CLASS1') in classesToKeep):
return True
else:
return False
pass
pass
fglSources = filter(filtering, fglFITS)
sources = []
for src in fglSources:
try:
thisRA = float(src.field("RAJ2000"))
thisDec = float(src.field("DEJ2000"))
thisDistance = getAngularDistance(float(ra), float(dec), thisRA,
thisDec)
except:
#Extended sources
continue
if (thisDistance <= roi):
if (src.field('ASSOC1') != ""):
k = "%s\n(%s)" % (src.field("ASSOC1"), src.field("CLASS1"))
else:
k = "%s\n(%s)" % (src.field("Source_Name"),
src.field("CLASS1"))
sources.append([k, thisRA, thisDec, thisDistance])
pass
#Now add the Sun, if it is inside the ROI
sundir = sunpos.getSunPosition(met)
ra_sun, dec_sun = (sundir.ra(), sundir.dec())
sunDistance = getAngularDistance(float(ra), float(dec), ra_sun, dec_sun)
if (sunDistance <= roi):
k = "Sun pos. at\ntrigger time"
sources.append([k, ra_sun, dec_sun, sunDistance])
pass
return sources
def get_coverage(ft2file, ligo_map_file, met_t1, met_t2, theta_cut,
zenith_cut):
ft2data = pyfits.getdata(ft2file)
ligo_map = hp.read_map(ligo_map_file)
ligo_ra, ligo_dec = pix_to_sky(range(ligo_map.shape[0]), 512)
# Filter out pixels with too low probability to gain speed
interesting_idx = (ligo_map > 1e-7)
print("Kept %s pixels for considerations in LIGO map" %
np.sum(interesting_idx))
# Get entries from the FT2 file every 10 s (cadence)
start, ra_scz, dec_scz, ra_zenith, dec_zenith = _gtmktime(ft2data,
met_t1,
met_t2,
cadence=30)
print("\nCoverage will be computed from %.1f to %.1f\n" %
(start.min(), start.max()))
coverage = np.zeros_like(start)
for i, (t, rz, dz, rz2, dz2) in enumerate(
zip(start, ra_scz, dec_scz, ra_zenith, dec_zenith)):
# Get the angular distance between the current pointing and
# all the LIGO pixels
d = getAngularDistance(rz, dz, ligo_ra[interesting_idx],
ligo_dec[interesting_idx])
zenith = getAngularDistance(rz2, dz2, ligo_ra[interesting_idx],
ligo_dec[interesting_idx])
# Select the LIGO pixels within the LAT FOV
idx = (d < theta_cut) & (zenith < zenith_cut)
coverage[i] = np.sum(ligo_map[interesting_idx][idx])
sys.stdout.write("\r%.1f percent completed" %
((i + 1) / float(coverage.shape[0]) * 100.0))
sys.stdout.write("\n")
return start, coverage
else:
n_entries = int(n_entries)
stop_time = args.met_stop
log.info(
"Computing angular distance between pointing in the pointing history and "
"desired position (%s, %s)..." % (args.ra, args.dec))
# Compute angular distance between desired (RA, Dec) and the pointing of the LAT
ra_scz, dec_scz = f['SC_DATA'].data.field(
"RA_SCZ"), f['SC_DATA'].data.field("DEC_SCZ")
ang_dis = getAngularDistance(args.ra, args.dec, ra_scz, dec_scz)
# Find the time where the distance was at its minimum (keeping some margin to later select the data)
min_idx = ang_dis[:-n_entries].argmin()
time_of_min_distance = f['SC_DATA'].data.field("START")[min_idx]
log.info("At MET = %.3f the distance between the pointing and the "
"input position was %.3f deg" %
(time_of_min_distance, ang_dis[min_idx]))
# Now create a new FT2 file with the pointing fixed at the desired position
new_sc_table = pyfits.BinTableHDU(f['SC_DATA'].data[min_idx:min_idx +
n_entries])
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
xmlmodel = thisCommand.getParValue('xmlmodel')
tsltcube = thisCommand.getParValue('tsltcube')
tsexpomap = thisCommand.getParValue('tsexpomap')
tsmap = thisCommand.getParValue('tsmap')
step = float(thisCommand.getParValue('step'))
side = thisCommand.getParValue('side')
if(side=='auto'):
side = None
# showmodelimage = thisCommand.getParValue('showmodelimage')
# optimize = thisCommand.getParValue('optimizeposition')
# tsmin = float(thisCommand.getParValue('tsmin'))
# skymap = thisCommand.getParValue('skymap')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
origra = float(dataHandling._getParamFromXML(xmlmodel,'RA'))
origdec = float(dataHandling._getParamFromXML(xmlmodel,'DEC'))
sourceName = dataHandling._getParamFromXML(xmlmodel,'OBJECT')
#Verify that TS map, if provided, is compatible with the position in the XML
if(tsexpomap is not None and tsexpomap!=''):
if(os.path.exists(tsexpomap)):
header = pyfits.getheader(tsexpomap)
ra,dec = (float(header.get('CRVAL1')),float(header.get('CRVAL2')))
angdist = getAngularDistance(origra,origdec,ra,dec)
if(angdist > 0.1):
print("Provided exposure map has a different center. Will compute it again.")
tsexpomap = None
else:
print("Provided exposure map does not exist. Will compute it again.")
tsexpomap = None
LATdata = dataHandling.LATData(eventfile,rspfile,ft2file)
tsmap = LATdata.makeTSmap(xmlmodel,sourceName,step,side,tsmap,tsltcube,tsexpomap)
tsltcube = LATdata.livetimeCube
tsexpomap = LATdata.exposureMap
ra,dec,tsmax = dataHandling.findMaximumTSmap(tsmap,tsexpomap)
print("\nCoordinates of the maximum of the TS map in the allowed region (TS = %.1f):" %(tsmax))
print("(R.A., Dec.) = (%6.3f, %6.3f)\n" %(ra,dec))
print("Distance from ROI center = %6.3f\n\n" %(getAngularDistance(origra,origdec,ra,dec)))
if(figure is not None):
from GtBurst import aplpy
#Display the TS map
figure.clear()
tsfig = aplpy.FITSFigure(tsmap,convention='calabretta',figure=figure)
tsfig.set_tick_labels_font(size='small')
tsfig.set_axis_labels_font(size='small')
tsfig.show_colorscale(cmap='gist_heat',aspect='auto')
tsfig.show_markers([ra], [dec], edgecolor='green', facecolor='none', marker='o', s=10, alpha=0.5)
# Modify the tick labels for precision and format
tsfig.tick_labels.set_xformat('ddd.dd')
tsfig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
tsfig.show_grid()
figure.canvas.draw()
pass
return 'tsmap', tsmap, 'tsmap_ra', ra, 'tsmap_dec', dec, 'tsmap_maxTS', tsmax, 'tsltcube', tsltcube, 'tsexpomap', tsexpomap
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
xmlmodel = thisCommand.getParValue('xmlmodel')
tsltcube = thisCommand.getParValue('tsltcube')
tsexpomap = thisCommand.getParValue('tsexpomap')
tsmap = thisCommand.getParValue('tsmap')
step = float(thisCommand.getParValue('step'))
side = thisCommand.getParValue('side')
if (side == 'auto'):
side = None
# showmodelimage = thisCommand.getParValue('showmodelimage')
# optimize = thisCommand.getParValue('optimizeposition')
# tsmin = float(thisCommand.getParValue('tsmin'))
# skymap = thisCommand.getParValue('skymap')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
origra = float(dataHandling._getParamFromXML(xmlmodel, 'RA'))
origdec = float(dataHandling._getParamFromXML(xmlmodel, 'DEC'))
sourceName = dataHandling._getParamFromXML(xmlmodel, 'OBJECT')
#Verify that TS map, if provided, is compatible with the position in the XML
if (tsexpomap is not None and tsexpomap != ''):
if (os.path.exists(tsexpomap)):
header = pyfits.getheader(tsexpomap)
ra, dec = (float(header.get('CRVAL1')),
float(header.get('CRVAL2')))
angdist = getAngularDistance(origra, origdec, ra, dec)
if (angdist > 0.1):
print(
"Provided exposure map has a different center. Will compute it again."
)
tsexpomap = None
else:
print(
"Provided exposure map does not exist. Will compute it again.")
tsexpomap = None
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
tsmap = LATdata.makeTSmap(xmlmodel, sourceName, step, side, tsmap,
tsltcube, tsexpomap)
tsltcube = LATdata.livetimeCube
tsexpomap = LATdata.exposureMap
ra, dec, tsmax = dataHandling.findMaximumTSmap(tsmap, tsexpomap)
print(
"\nCoordinates of the maximum of the TS map in the allowed region (TS = %.1f):"
% (tsmax))
print("(R.A., Dec.) = (%6.3f, %6.3f)\n" % (ra, dec))
print("Distance from ROI center = %6.3f\n\n" %
(getAngularDistance(origra, origdec, ra, dec)))
if (figure is not None):
from GtBurst import aplpy
#Display the TS map
figure.clear()
tsfig = aplpy.FITSFigure(tsmap, convention='calabretta', figure=figure)
tsfig.set_tick_labels_font(size='small')
tsfig.set_axis_labels_font(size='small')
tsfig.show_colorscale(cmap='gist_heat', aspect='auto')
tsfig.show_markers([ra], [dec],
edgecolor='green',
facecolor='none',
marker='o',
s=10,
alpha=0.5)
# Modify the tick labels for precision and format
tsfig.tick_labels.set_xformat('ddd.dd')
tsfig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
tsfig.show_grid()
figure.canvas.draw()
pass
return 'tsmap', tsmap, 'tsmap_ra', ra, 'tsmap_dec', dec, 'tsmap_maxTS', tsmax, 'tsltcube', tsltcube, 'tsexpomap', tsexpomap
def cutout(filename, ra_or_l, dec_or_b, coordsys, radius, outfile, clobber=True):
"""
Inputs:
file - .fits filename or pyfits HDUList. If HDU is 3d (data-cube), the 3rd dimension
(the one which is not a sky coordinate) will be kept untouched. Dimensions > 3
are not supported
ra_or_l,dec_or_b - Longitude and latitude of the center of the new image. The longitude
coordinate (R.A. or L) goes from 0 to 360, while the latitude coordinate
goes from -90 to 90.
coordsys - Coordinate system for the center of the new image ('galactic' or 'equatorial')
radius - radius of the region of interest (deg)
outfile - output file
clobber - overwrite the file if existing? (True or False)
"""
if coordsys not in ['equatorial','galactic']:
raise ValueError("Unknown coordinate system '%s'" %(coordsys))
if(ra_or_l < 0 or ra_or_l > 360):
raise RuntimeError("The longitude coordinate must be 0 < longitude < 360")
if(dec_or_b < -90 or dec_or_b > 90):
raise RuntimeError("The latitude coordinate must be -90 < latitude < 90")
with pyfits.open(filename) as f:
if(f[0].data.ndim==3):
isCube = True
elif(f[0].data.ndim==2):
isCube = False
else:
raise RuntimeError("Do not know how to handle a cube with %s dimensions" %(f[0].data.shape[0]))
pass
head = f[0].header.copy()
cd1 = head.get('CDELT1') if head.get('CDELT1') else head.get('CD1_1')
cd2 = head.get('CDELT2') if head.get('CDELT2') else head.get('CD2_2')
if cd1 is None or cd2 is None:
raise Exception("Missing CD or CDELT keywords in header")
wcs = pywcs.WCS(head)
#Ensure that the center is expressed in the same coordinate system as the original
#image
if coordsys=='equatorial' and wcs.wcs.lngtyp=='GLON':
#Convert RA,Dec in Galactic coordinates
sdir = SkyDir(ra_or_l,dec_or_b,SkyDir.EQUATORIAL)
xc,yc = (sdir.l(),sdir.b())
elif coordsys=='galactic' and wcs.wcs.lngtyp=='RA':
#Convert L,B in Equatorial coordinates
sdir = SkyDir(ra_or_l,dec_or_b,SkyDir.EQUATORIAL)
xc,yc = (sdir.ra(),sdir.dec())
else:
#Image and input are in the same system already
xc,yc = (ra_or_l,dec_or_b)
pass
#Find the pixel corresponding to the center
if(isCube):
#Data cube
coord = numpy.array([[xc],[yc],[1]]).T
xx,yy,z = wcs.wcs_sky2pix(coord,0)[0]
shapez,shapey,shapex = f[0].data.shape
#Compute the sky coordinates of all pixels
#(will use them later for the angular distance)
#The code below is much faster, but does the same thing as this
#one here:
#coord = numpy.zeros((shapex*shapey,3))
#h = 0
#for i in range(shapex):
# for j in range(shapey):
# coord[h] = [i+1,j+1,1]
# h += 1
coord = numpy.ones((shapex*shapey,3))
firstColBase = numpy.arange(shapex)+1
firstColumn = numpy.repeat(firstColBase,shapey)
secondColumn = numpy.array(range(shapey)*shapex)+1
coord[:,0] = firstColumn
coord[:,1] = secondColumn
#Note that pix2sky always return the latitude from 0 to 360 deg
res = wcs.wcs_pix2sky(coord,1)
ras = res[:,0]
decs = res[:,1]
else:
#Normal image
coord = numpy.array([[xc],[yc]]).T
xx,yy = wcs.wcs_sky2pix(coord,0)[0]
shapey,shapex = f[0].data.shape
#Compute the sky coordinates of all pixels
#(will use them later for the angular distance)
coord = numpy.ones((shapex*shapey,2))
firstColBase = numpy.arange(shapex)+1
firstColumn = numpy.repeat(firstColBase,shapey)
secondColumn = numpy.array(range(shapey)*shapex)+1
coord[:,0] = firstColumn
coord[:,1] = secondColumn
#Note that pix2sky always return the latitude from 0 to 360 deg
res = wcs.wcs_pix2sky(coord,1)
ras = res[:,0]
decs = res[:,1]
pass
print("Center is (%s,%s) pixel, (%s,%s) sky" %(xx,yy,xc,yc))
#Cannot deal with fractional pixel
if(xx - int(xx) >= 1e-4):
print("Approximating the X pixel: %s -> %s" %(xx,int(xx)))
xx = int(xx)
if(yy - int(yy) >= 1e-4):
print("Approximating the Y pixel: %s -> %s" %(yy,int(yy)))
yy = int(yy)
pass
#Now select the pixels to keep
#Pre-select according to a bounding box
#(huge gain of speed in the computation of distances)
ra_min_,ra_max_,dec_min_,dec_max_,pole = getBoundingCoordinates(xc,yc,radius)
if(pole):
#Nothing we can really do, except masking out (zeroing) all the useless parts
print("\nOne of the poles is within the region. Cannot cut the image. I will zero-out useless parts")
img = f[0].data
#Find the pixel corresponding to (xc,yc-radius)
coord = numpy.array([[xc],[yc-radius],[1]]).T
res = wcs.wcs_sky2pix(coord,0)[0]
mask_ymax = int(numpy.ceil(res[1]))
#Now find the pixel corresponding to (xc,yc+radius)
coord = numpy.array([[xc],[yc+radius-180.0],[1]]).T
res = wcs.wcs_sky2pix(coord,0)[0]
mask_ymin = int(numpy.floor(res[1]))
img[:,mask_ymin:mask_ymax,:] = img[:,mask_ymin:mask_ymax,:]*0.0
else:
if(ra_min_ > ra_max_):
#Circular inversion (ex: ra_min_ = 340.0 and ra_max_ = 20.0)
idx = (ra_min_ <= ras) | (ras <= ra_max_)
else:
idx = (ra_min_ <= ras) & (ras <= ra_max_)
pass
if(dec_min_ > dec_max_):
#Circular inversion (ex: ra_min_ = 340.0 and ra_max_ = 20.0)
idx = ((dec_min_ <= decs) | (decs <= dec_max_)) & idx
else:
idx = ((dec_min_ <= decs) & (decs <= dec_max_)) & idx
pass
ras = ras[idx]
decs = decs[idx]
#Compute all angular distances of remaining pixels
distances = getAngularDistance(xc,yc,ras,decs)
#Select all pixels within the provided radius
idx = (distances <= radius)
selected_ras = ras[idx]
selected_decs = decs[idx]
#Now transform back into pixels values
if(isCube):
coord = numpy.vstack([selected_ras,selected_decs,[1]*selected_ras.shape[0]]).T
else:
coord = numpy.vstack([selected_ras,selected_decs]).T
pass
res = wcs.wcs_sky2pix(coord,0)
#Now check if the range of x is not continuous (i.e., we are
#wrapping around the borders)
uniquex = numpy.unique(res[:,0])
deltas = uniquex[1:]-uniquex[:-1]
if(deltas.max()>1):
#We are wrapping around the borders
# |-------x1 x2-------|
#We want to express x2 as a negative index starting
#from the right border, and set it as xmin.
#Then we set x1 as xmax.
#This way the .take method below will start accumulating
#the image from x2 to the right border |, then from the left
#border | to x1, in this order
#Find x2
x2id = deltas.argmax()+1
x2 = int(uniquex[x2id])
x1 = int(uniquex[x2id-1])
xmin = x2-shapex
xmax = x1
ymin,ymax = (int(res[:,1].min()),int(res[:,1].max()))
else:
xmin,xmax,ymin,ymax = (int(res[:,0].min()),int(res[:,0].max()),int(res[:,1].min()),int(res[:,1].max()))
pass
print("X range -> %s - %s" %(xmin,xmax))
print("Y range -> %s - %s" %(ymin,ymax))
print("Input image shape is ([z],y,x) = %s" %(str(f[0].shape)))
#Using the mode='wrap' option we wrap around the edges of the image,
#if ymin is negative
if(isCube):
img = f[0].data.take(range(ymin,ymax),mode='wrap', axis=1).take(range(xmin,xmax),mode='wrap',axis=2)
else:
img = f[0].data.take(range(ymin,ymax),mode='wrap', axis=0).take(range(xmin,xmax),mode='wrap',axis=1)
pass
#Put the origin of the projection in the right place
#in the new image
head['CRPIX1'] -= xmin
head['CRPIX2'] -= ymin
#Update the length of the axis
head['NAXIS1'] = int(xmax-xmin)
head['NAXIS2'] = int(ymax-ymin)
if head.get('NAXIS1') == 0 or head.get('NAXIS2') == 0:
raise ValueError("Map has a 0 dimension: %i,%i." % (head.get('NAXIS1'),head.get('NAXIS2')))
pass
newfile = pyfits.PrimaryHDU(data=img,header=head)
newfile.writeto(outfile,clobber=clobber)
#Append the other extension, if present
for i in range(1,len(f)):
pyfits.append(outfile,f[i].data,header=f[i].header)
pass #Close the input file
#Now re-open the output file and fix the wcs
#by moving the reference pixel to the 1,1 pixel
#This guarantee that no pixel will be at a distance larger than 180 deg
#from the reference pixel, which would confuse downstream software
if(not pole):
with pyfits.open(outfile,'update') as outf:
head = outf[0].header
#Get the
newwcs = pywcs.WCS(head)
#Find the sky coordinates of the 1,1 pixel
if(isCube):
#Data cube
coord = numpy.array([[1],[1],[1]]).T
sx,sy,z = newwcs.wcs_pix2sky(coord,1)[0]
else:
#Normal image
coord = numpy.array([[1],[1]]).T
sx,sy = newwcs.wcs_pix2sky(coord,1)[0]
pass
head['CRPIX1'] = 1
head['CRVAL1'] = sx
pass
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
showmodelimage = thisCommand.getParValue('showmodelimage')
optimize = thisCommand.getParValue('optimizeposition')
spectralfiles = thisCommand.getParValue('spectralfiles')
tsmin = float(thisCommand.getParValue('tsmin'))
skymap = thisCommand.getParValue('skymap')
liketype = thisCommand.getParValue('liketype')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
try:
if (liketype == 'unbinned'):
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(
xmlmodel, tsmin, expomap=expomap, ltcube=ltcube)
else:
#Generation of spectral files and optimization of the position is
#not supported yet for binned analysis
if (spectralfiles == 'yes'):
print(
"\nWARNING: you specified spectralfiles=yes, but the generation of spectral files is not supported for binned analysis\n"
)
spectralfiles = 'no'
if (optimize == 'yes'):
print(
"\nWARNING: you specified optimize=yes, but position optimization is not supported for binned analysis\n"
)
optimize = 'no'
outfilelike, sources = LATdata.doBinnedLikelihoodAnalysis(
xmlmodel, tsmin, expomap=expomap, ltcube=ltcube)
except GtBurstException as gt:
raise gt
except:
raise
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel, 'IRF')
ra = dataHandling._getParamFromXML(xmlmodel, 'RA')
dec = dataHandling._getParamFromXML(xmlmodel, 'DEC')
name = dataHandling._getParamFromXML(xmlmodel, 'OBJECT')
try:
grb = filter(lambda x: x.name.lower().find(name.lower()) >= 0,
sources)[0]
grb_TS = grb.TS
except:
#A model without GRB
print("\nWarning: no GRB in the model!")
grb_TS = -1
pass
if (irf == None):
print(
"\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing..."
)
else:
dataHandling._writeParamIntoXML(outfilelike,
IRF=irf,
OBJECT=name,
RA=ra,
DEC=dec)
pass
if (spectralfiles == 'yes'):
phafile, rspfile, bakfile = LATdata.doSpectralFiles(outfilelike)
pass
localizationMessage = ''
bestra = ''
bestdec = ''
poserr = ''
distance = ''
if (optimize == 'yes'):
sourceName = name
#If the TS for the source is above tsmin, optimize its position
#grb = filter(lambda x:x.name.lower().find(sourceName.lower())>=0,sources)[0]
if (math.ceil(grb_TS) >= tsmin):
try:
bestra, bestdec, poserr = LATdata.optimizeSourcePosition(
outfilelike, sourceName)
except:
raise GtBurstException(
207,
"gtfindsrc execution failed. Were the source detected in the likelihood step?"
)
else:
localizationMessage += "\nNew localization from gtfindsrc:\n\n"
localizationMessage += "(R.A., Dec) = (%6.3f, %6.3f)\n" % (
bestra, bestdec)
localizationMessage += "68 %s containment radius = %6.3f\n" % (
'%', poserr)
localizationMessage += "90 %s containment radius = %6.3f\n" % (
'%', 1.41 * poserr)
distance = getAngularDistance(float(ra), float(dec),
float(bestra), float(bestdec))
localizationMessage += "Distance from initial position = %6.3f\n\n" % (
distance)
localizationMessage += "NOTE: this new localization WILL NOT be used by default. If you judge"
localizationMessage += " it is a better localization than the one you started with, update the"
localizationMessage += " coordinates yourself and re-run the likelihood\n"
pass
pass
if (figure != None and skymap != None and showmodelimage == 'yes'):
#Now produce the binned exposure map (needed in order to display the fitted model as an image)
modelmapfile = LATdata.makeModelSkyMap(outfilelike)
#Display point sources in the image, and report in the table
#all 2FGL sources with TS > 9 + always the GRB, independently of its TS
detectedSources = []
grbFlux = 1e-13
for src in sources:
weight = 'bold'
if (src.type == 'PointSource'):
if (src.TS > 4):
detectedSources.append(src)
if (src.name.find('2FGL') < 0):
#GRB
grbFlux = src.flux
pass
pass
pass
#Display the counts map
from GtBurst import aplpy
import matplotlib.pyplot as plt
figure.clear()
orig = aplpy.FITSFigure(skymap,
convention='calabretta',
figure=figure,
subplot=[0.1, 0.1, 0.45, 0.7])
vmax = max(pyfits.open(skymap)[0].data.flatten())
nEvents = numpy.sum(pyfits.open(skymap)[0].data)
telapsed = pyfits.open(eventfile)[0].header['TSTOP'] - pyfits.open(
eventfile)[0].header['TSTART']
orig.set_tick_labels_font(size='small')
orig.set_axis_labels_font(size='small')
orig.show_colorscale(cmap='gist_heat',
vmin=0.1,
vmax=max(vmax, 0.11),
stretch='log',
aspect='auto')
# Modify the tick labels for precision and format
orig.tick_labels.set_xformat('ddd.dd')
orig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
orig.show_grid()
#Renormalize the modelmapfile to the flux of the grb
f = pyfits.open(modelmapfile, 'update')
f[0].data = f[0].data / numpy.max(f[0].data) * nEvents / telapsed
print(numpy.max(f[0].data))
f.close()
img = aplpy.FITSFigure(modelmapfile,
convention='calabretta',
figure=figure,
subplot=[0.55, 0.1, 0.4, 0.7])
img.set_tick_labels_font(size='small')
img.set_axis_labels_font(size='small')
#vmax = max(pyfits.open(modelmapfile)[0].data.flatten())
img.show_colorscale(cmap='gist_heat', aspect='auto', stretch='log')
for src in detectedSources:
img.add_label(float(src.ra),
float(src.dec),
"%s\n(ts = %i)" % (src.name, int(math.ceil(src.TS))),
relative=False,
weight=weight,
color='green',
size='small')
pass
# Modify the tick labels for precision and format
img.tick_labels.set_xformat('ddd.dd')
img.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
img.show_grid()
#ax = figure.add_axes([0.1,0.72,0.85,0.25],frame_on=False)
#ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
#col_labels =['Source Name','TS','Energy flux','Photon index']
#table_vals = map(lambda x:[x.name,"%i" %(int(math.ceil(x.TS))),
# "%s +/- %s" % (x.flux,x.fluxError),
# "%s +/- %s" % (x.photonIndex,x.photonIndexError)],detectedSources)
#
#if(len(table_vals)>0):
# the_table = ax.table(cellText=table_vals,
# colLabels=col_labels,
# loc='upper center')
figure.canvas.draw()
figure.savefig("likelihood_results.png")
pass
if (figure != None):
#Assume we have an X server running
#Now display the results
likemsg = "Log(likelihood) = %s" % (LATdata.logL)
displayResults(
figure.canvas._tkcanvas, LATdata.resultsStrings + "\n" + likemsg +
"\n" + localizationMessage)
print(localizationMessage)
return 'likexmlresults', outfilelike, 'TS', grb_TS, 'bestra', bestra, 'bestdec', bestdec, 'poserr', poserr, 'distance', distance, 'sources', sources
def cutout(filename,
ra_or_l,
dec_or_b,
coordsys,
radius,
outfile,
clobber=True):
"""
Inputs:
file - .fits filename or pyfits HDUList. If HDU is 3d (data-cube), the 3rd dimension
(the one which is not a sky coordinate) will be kept untouched. Dimensions > 3
are not supported
ra_or_l,dec_or_b - Longitude and latitude of the center of the new image. The longitude
coordinate (R.A. or L) goes from 0 to 360, while the latitude coordinate
goes from -90 to 90.
coordsys - Coordinate system for the center of the new image ('galactic' or 'equatorial')
radius - radius of the region of interest (deg)
outfile - output file
clobber - overwrite the file if existing? (True or False)
"""
if coordsys not in ['equatorial', 'galactic']:
raise ValueError("Unknown coordinate system '%s'" % (coordsys))
if (ra_or_l < 0 or ra_or_l > 360):
raise RuntimeError(
"The longitude coordinate must be 0 < longitude < 360")
if (dec_or_b < -90 or dec_or_b > 90):
raise RuntimeError(
"The latitude coordinate must be -90 < latitude < 90")
with pyfits.open(filename) as f:
if (f[0].data.ndim == 3):
isCube = True
elif (f[0].data.ndim == 2):
isCube = False
else:
raise RuntimeError(
"Do not know how to handle a cube with %s dimensions" %
(f[0].data.shape[0]))
pass
head = f[0].header.copy()
cd1 = head.get('CDELT1') if head.get('CDELT1') else head.get(
'CD1_1')
cd2 = head.get('CDELT2') if head.get('CDELT2') else head.get(
'CD2_2')
if cd1 is None or cd2 is None:
raise Exception("Missing CD or CDELT keywords in header")
wcs = pywcs.WCS(head)
#Ensure that the center is expressed in the same coordinate system as the original
#image
if coordsys == 'equatorial' and wcs.wcs.lngtyp == 'GLON':
#Convert RA,Dec in Galactic coordinates
sdir = SkyDir(ra_or_l, dec_or_b, SkyDir.EQUATORIAL)
xc, yc = (sdir.l(), sdir.b())
elif coordsys == 'galactic' and wcs.wcs.lngtyp == 'RA':
#Convert L,B in Equatorial coordinates
sdir = SkyDir(ra_or_l, dec_or_b, SkyDir.EQUATORIAL)
xc, yc = (sdir.ra(), sdir.dec())
else:
#Image and input are in the same system already
xc, yc = (ra_or_l, dec_or_b)
pass
#Find the pixel corresponding to the center
if (isCube):
#Data cube
coord = numpy.array([[xc], [yc], [1]]).T
xx, yy, z = wcs.wcs_sky2pix(coord, 0)[0]
shapez, shapey, shapex = f[0].data.shape
#Compute the sky coordinates of all pixels
#(will use them later for the angular distance)
#The code below is much faster, but does the same thing as this
#one here:
#coord = numpy.zeros((shapex*shapey,3))
#h = 0
#for i in range(shapex):
# for j in range(shapey):
# coord[h] = [i+1,j+1,1]
# h += 1
coord = numpy.ones((shapex * shapey, 3))
firstColBase = numpy.arange(shapex) + 1
firstColumn = numpy.repeat(firstColBase, shapey)
secondColumn = numpy.array(range(shapey) * shapex) + 1
coord[:, 0] = firstColumn
coord[:, 1] = secondColumn
#Note that pix2sky always return the latitude from 0 to 360 deg
res = wcs.wcs_pix2sky(coord, 1)
ras = res[:, 0]
decs = res[:, 1]
else:
#Normal image
coord = numpy.array([[xc], [yc]]).T
xx, yy = wcs.wcs_sky2pix(coord, 0)[0]
shapey, shapex = f[0].data.shape
#Compute the sky coordinates of all pixels
#(will use them later for the angular distance)
coord = numpy.ones((shapex * shapey, 2))
firstColBase = numpy.arange(shapex) + 1
firstColumn = numpy.repeat(firstColBase, shapey)
secondColumn = numpy.array(range(shapey) * shapex) + 1
coord[:, 0] = firstColumn
coord[:, 1] = secondColumn
#Note that pix2sky always return the latitude from 0 to 360 deg
res = wcs.wcs_pix2sky(coord, 1)
ras = res[:, 0]
decs = res[:, 1]
pass
print("Center is (%s,%s) pixel, (%s,%s) sky" % (xx, yy, xc, yc))
#Cannot deal with fractional pixel
if (xx - int(xx) >= 1e-4):
print("Approximating the X pixel: %s -> %s" % (xx, int(xx)))
xx = int(xx)
if (yy - int(yy) >= 1e-4):
print("Approximating the Y pixel: %s -> %s" % (yy, int(yy)))
yy = int(yy)
pass
#Now select the pixels to keep
#Pre-select according to a bounding box
#(huge gain of speed in the computation of distances)
ra_min_, ra_max_, dec_min_, dec_max_, pole = getBoundingCoordinates(
xc, yc, radius)
if (pole):
#Nothing we can really do, except masking out (zeroing) all the useless parts
print(
"\nOne of the poles is within the region. Cannot cut the image. I will zero-out useless parts"
)
img = f[0].data
#Find the pixel corresponding to (xc,yc-radius)
coord = numpy.array([[xc], [yc - radius], [1]]).T
res = wcs.wcs_sky2pix(coord, 0)[0]
mask_ymax = int(numpy.ceil(res[1]))
#Now find the pixel corresponding to (xc,yc+radius)
coord = numpy.array([[xc], [yc + radius - 180.0], [1]]).T
res = wcs.wcs_sky2pix(coord, 0)[0]
mask_ymin = int(numpy.floor(res[1]))
img[:,
mask_ymin:mask_ymax, :] = img[:,
mask_ymin:mask_ymax, :] * 0.0
else:
if (ra_min_ > ra_max_):
#Circular inversion (ex: ra_min_ = 340.0 and ra_max_ = 20.0)
idx = (ra_min_ <= ras) | (ras <= ra_max_)
else:
idx = (ra_min_ <= ras) & (ras <= ra_max_)
pass
if (dec_min_ > dec_max_):
#Circular inversion (ex: ra_min_ = 340.0 and ra_max_ = 20.0)
idx = ((dec_min_ <= decs) | (decs <= dec_max_)) & idx
else:
idx = ((dec_min_ <= decs) & (decs <= dec_max_)) & idx
pass
ras = ras[idx]
decs = decs[idx]
#Compute all angular distances of remaining pixels
distances = getAngularDistance(xc, yc, ras, decs)
#Select all pixels within the provided radius
idx = (distances <= radius)
selected_ras = ras[idx]
selected_decs = decs[idx]
#Now transform back into pixels values
if (isCube):
coord = numpy.vstack([
selected_ras, selected_decs,
[1] * selected_ras.shape[0]
]).T
else:
coord = numpy.vstack([selected_ras, selected_decs]).T
pass
res = wcs.wcs_sky2pix(coord, 0)
#Now check if the range of x is not continuous (i.e., we are
#wrapping around the borders)
uniquex = numpy.unique(res[:, 0])
deltas = uniquex[1:] - uniquex[:-1]
if (deltas.max() > 1):
#We are wrapping around the borders
# |-------x1 x2-------|
#We want to express x2 as a negative index starting
#from the right border, and set it as xmin.
#Then we set x1 as xmax.
#This way the .take method below will start accumulating
#the image from x2 to the right border |, then from the left
#border | to x1, in this order
#Find x2
x2id = deltas.argmax() + 1
x2 = int(uniquex[x2id])
x1 = int(uniquex[x2id - 1])
xmin = x2 - shapex
xmax = x1
ymin, ymax = (int(res[:, 1].min()), int(res[:, 1].max()))
else:
xmin, xmax, ymin, ymax = (int(res[:, 0].min()),
int(res[:, 0].max()),
int(res[:, 1].min()),
int(res[:, 1].max()))
pass
print("X range -> %s - %s" % (xmin, xmax))
print("Y range -> %s - %s" % (ymin, ymax))
print("Input image shape is ([z],y,x) = %s" %
(str(f[0].shape)))
#Using the mode='wrap' option we wrap around the edges of the image,
#if ymin is negative
if (isCube):
img = f[0].data.take(range(ymin, ymax),
mode='wrap',
axis=1).take(range(xmin, xmax),
mode='wrap',
axis=2)
else:
img = f[0].data.take(range(ymin, ymax),
mode='wrap',
axis=0).take(range(xmin, xmax),
mode='wrap',
axis=1)
pass
#Put the origin of the projection in the right place
#in the new image
head['CRPIX1'] -= xmin
head['CRPIX2'] -= ymin
#Update the length of the axis
head['NAXIS1'] = int(xmax - xmin)
head['NAXIS2'] = int(ymax - ymin)
if head.get('NAXIS1') == 0 or head.get('NAXIS2') == 0:
raise ValueError("Map has a 0 dimension: %i,%i." %
(head.get('NAXIS1'), head.get('NAXIS2')))
pass
newfile = pyfits.PrimaryHDU(data=img, header=head)
newfile.writeto(outfile, clobber=clobber)
#Append the other extension, if present
for i in range(1, len(f)):
pyfits.append(outfile, f[i].data, header=f[i].header)
pass #Close the input file
#Now re-open the output file and fix the wcs
#by moving the reference pixel to the 1,1 pixel
#This guarantee that no pixel will be at a distance larger than 180 deg
#from the reference pixel, which would confuse downstream software
if (not pole):
with pyfits.open(outfile, 'update') as outf:
head = outf[0].header
#Get the
newwcs = pywcs.WCS(head)
#Find the sky coordinates of the 1,1 pixel
if (isCube):
#Data cube
coord = numpy.array([[1], [1], [1]]).T
sx, sy, z = newwcs.wcs_pix2sky(coord, 1)[0]
else:
#Normal image
coord = numpy.array([[1], [1]]).T
sx, sy = newwcs.wcs_pix2sky(coord, 1)[0]
pass
head['CRPIX1'] = 1
head['CRVAL1'] = sx
pass
def getXmlForSourcesInTheROI(self, ra, dec, rad, output, exposure):
# Loop the tree and remove all sources more than rad away from the center
# of the ROI
root = self.tree.getroot()
srcs = 0
for source in root.findall('source'):
spatialModel = source.findall('spatialModel')[0]
# Remove point sources which cannot contribute any photon given the exposure
aeff = 0.6e4 # cm2
if (source.get('type') == 'PointSource'):
try:
npred = float(source.get('Flux1000')) * exposure * aeff
except TypeError:
# This is not a 3FGL source, so it has no Flux1000 attribute. Keep it
continue
if npred < 0.1:
root.remove(source)
continue
if (source.get('type') == 'PointSource'):
# Get coordinates of this point source
coords = {}
for p in spatialModel.iter('parameter'):
coords[p.get('name').lower()] = float(p.get('value'))
pass
thisRa = coords['ra']
thisDec = coords['dec']
thisDist = getAngularDistance(ra, dec, thisRa, thisDec)
# Remove the source if the distance is above the requested radius,
# or if the distance is below 0.01 (i.e., the source under analysis
# is the same as the current one)
if (float(thisDist) >= float(rad)):
# Remove this source
root.remove(source)
elif (float(thisDist) <= 0.01):
print("\nWARNING: Auto-removed source %s\n" %
(source.get('name')))
root.remove(source)
else:
print("Keeping point source %s (%4.2f deg away)..." %
(source.get('name'), float(thisDist)))
# Fix all parameters
specModel = source.findall('spectrum')[0]
for p in specModel.findall('parameter'):
p.set('free', '0')
pass
srcs += 1
elif (source.get('type') == 'DiffuseSource'):
# Get coordinates of the center of this diffuse source
try:
thisRa = float(source.get('RA'))
thisDec = float(source.get('DEC'))
except TypeError:
# This is not a 3FGL source, keep it and continue
continue
thisDist = getAngularDistance(ra, dec, thisRa, thisDec)
# Remove source if its center is more than twice its size out of the ROI
if (float(thisDist) >=
float(rad) + 2 * float(source.get('Extension'))):
root.remove(source)
else:
# Fix all parameters
specModel = source.findall('spectrum')[0]
for p in specModel.findall('parameter'):
p.set('free', '0')
pass
# Now correct the name of the FITS template so that the likelihood
# will find it
spatialModel = source.findall('spatialModel')[0]
filename = spatialModel.get('file')
templatesPath = os.path.join(
os.path.join(getDataPath(), 'templates'))
newFilename = os.path.abspath(
os.path.join(templatesPath, filename))
spatialModel.set('file', '%s' % newFilename)
srcs += 1
print(
"Keeping diffuse source %s (%4.2f deg away) using template %s..."
% (source.get('name'), float(thisDist), newFilename))
else:
print("WTF")
raise
pass
print("Kept %s point sources from the FGL catalog" % (srcs))
self.tree.write(output)
def getXmlForSourcesInTheROI(self, ra, dec, rad, output, exposure):
# Loop the tree and remove all sources more than rad away from the center
# of the ROI
root = self.tree.getroot()
srcs = 0
for source in root.findall('source'):
spatialModel = source.findall('spatialModel')[0]
# Remove point sources which cannot contribute any photon given the exposure
aeff = 0.6e4 # cm2
if (source.get('type') == 'PointSource'):
try:
npred = float(source.get('Flux1000')) * exposure * aeff
except TypeError:
# This is not a 3FGL source, so it has no Flux1000 attribute. Keep it
continue
if npred < 0.1:
root.remove(source)
continue
if (source.get('type') == 'PointSource'):
# Get coordinates of this point source
coords = {}
for p in spatialModel.iter('parameter'):
coords[p.get('name').lower()] = float(p.get('value'))
pass
thisRa = coords['ra']
thisDec = coords['dec']
thisDist = getAngularDistance(ra, dec, thisRa, thisDec)
# Remove the source if the distance is above the requested radius,
# or if the distance is below 0.01 (i.e., the source under analysis
# is the same as the current one)
if (float(thisDist) >= float(rad)):
# Remove this source
root.remove(source)
elif (float(thisDist) <= 0.01):
print("\nWARNING: Auto-removed source %s\n" % (source.get('name')))
root.remove(source)
else:
print("Keeping point source %s (%4.2f deg away)..." % (source.get('name'), float(thisDist)))
# Fix all parameters
specModel = source.findall('spectrum')[0]
for p in specModel.findall('parameter'):
p.set('free', '0')
pass
srcs += 1
elif (source.get('type') == 'DiffuseSource'):
# Get coordinates of the center of this diffuse source
try:
thisRa = float(source.get('RA'))
thisDec = float(source.get('DEC'))
except TypeError:
# This is not a 3FGL source, keep it and continue
continue
thisDist = getAngularDistance(ra, dec, thisRa, thisDec)
# Remove source if its center is more than twice its size out of the ROI
if (float(thisDist) >= float(rad) + 2 * float(source.get('Extension'))):
root.remove(source)
else:
# Fix all parameters
specModel = source.findall('spectrum')[0]
for p in specModel.findall('parameter'):
p.set('free', '0')
pass
# Now correct the name of the FITS template so that the likelihood
# will find it
spatialModel = source.findall('spatialModel')[0]
filename = spatialModel.get('file')
templatesPath = os.path.join(os.path.join(getDataPath(), 'templates'))
newFilename = os.path.abspath(os.path.join(templatesPath, filename))
spatialModel.set('file', '%s' % newFilename)
srcs += 1
print("Keeping diffuse source %s (%4.2f deg away) using template %s..." % (
source.get('name'), float(thisDist), newFilename))
else:
print("WTF")
raise
pass
print("Kept %s point sources from the FGL catalog" % (srcs))
self.tree.write(output)
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
showmodelimage = thisCommand.getParValue('showmodelimage')
optimize = thisCommand.getParValue('optimizeposition')
spectralfiles = thisCommand.getParValue('spectralfiles')
tsmin = float(thisCommand.getParValue('tsmin'))
skymap = thisCommand.getParValue('skymap')
liketype = thisCommand.getParValue('liketype')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
flemin = thisCommand.getParValue('flemin')
flemax = thisCommand.getParValue('flemax')
clul = float(thisCommand.getParValue('clul'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
assert clul < 1.0, "The confidence level for the upper limit (clul) must be < 1"
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile,rspfile,ft2file)
try:
if(liketype=='unbinned'):
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(xmlmodel,tsmin,expomap=expomap,ltcube=ltcube,emin=flemin,emax=flemax, clul=clul)
else:
#Generation of spectral files and optimization of the position is
#not supported yet for binned analysis
if(spectralfiles=='yes'):
print("\nWARNING: you specified spectralfiles=yes, but the generation of spectral files is not supported for binned analysis\n")
spectralfiles = 'no'
if(optimize=='yes'):
print("\nWARNING: you specified optimize=yes, but position optimization is not supported for binned analysis\n")
optimize = 'no'
outfilelike, sources = LATdata.doBinnedLikelihoodAnalysis(xmlmodel,tsmin,expomap=expomap,ltcube=ltcube,emin=flemin,emax=flemax, clul=clul)
except GtBurstException as gt:
raise gt
except:
raise
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel,'IRF')
ra = dataHandling._getParamFromXML(xmlmodel,'RA')
dec = dataHandling._getParamFromXML(xmlmodel,'DEC')
name = dataHandling._getParamFromXML(xmlmodel,'OBJECT')
try:
grb = filter(lambda x:x.name.lower().find(name.lower())>=0,sources)[0]
grb_TS = grb.TS
except:
#A model without GRB
print("\nWarning: no GRB in the model!")
grb_TS = -1
pass
if(irf is None):
print("\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing...")
else:
dataHandling._writeParamIntoXML(outfilelike,IRF=irf,OBJECT=name,RA=ra,DEC=dec)
pass
if(spectralfiles=='yes'):
phafile,rspfile,bakfile = LATdata.doSpectralFiles(outfilelike)
pass
localizationMessage = ''
bestra = ''
bestdec = ''
poserr = ''
distance = ''
if(optimize=='yes'):
sourceName = name
#If the TS for the source is above tsmin, optimize its position
#grb = filter(lambda x:x.name.lower().find(sourceName.lower())>=0,sources)[0]
if(math.ceil(grb_TS) >= tsmin):
try:
bestra,bestdec,poserr = LATdata.optimizeSourcePosition(outfilelike,sourceName)
except:
raise GtBurstException(207,"gtfindsrc execution failed. Were the source detected in the likelihood step?")
else:
localizationMessage += "\nNew localization from gtfindsrc:\n\n"
localizationMessage += "(R.A., Dec) = (%6.3f, %6.3f)\n" %(bestra,bestdec)
localizationMessage += "68 %s containment radius = %6.3f\n" %('%',poserr)
localizationMessage += "90 %s containment radius = %6.3f\n" %('%',1.41*poserr)
distance = getAngularDistance(float(ra),float(dec),float(bestra),float(bestdec))
localizationMessage += "Distance from initial position = %6.3f\n\n" %(distance)
localizationMessage += "NOTE: this new localization WILL NOT be used by default. If you judge"
localizationMessage += " it is a better localization than the one you started with, update the"
localizationMessage += " coordinates yourself and re-run the likelihood\n"
pass
pass
if(figure is not None and skymap is not None and showmodelimage=='yes'):
#Now produce the binned exposure map (needed in order to display the fitted model as an image)
modelmapfile = LATdata.makeModelSkyMap(outfilelike)
#Display point sources in the image, and report in the table
#all 3FGL sources with TS > 9 + always the GRB, independently of its TS
detectedSources = []
grbFlux = 1e-13
for src in sources:
weight = 'bold'
if(src.type=='PointSource'):
if(src.TS > 4):
detectedSources.append(src)
if(src.name.find('3FGL')<0):
#GRB
grbFlux = src.flux
pass
pass
pass
#Display the counts map
from GtBurst import aplpy
import matplotlib.pyplot as plt
figure.clear()
orig = aplpy.FITSFigure(skymap,convention='calabretta',
figure=figure,subplot=[0.1,0.1,0.45,0.7])
vmax = max(pyfits.open(skymap)[0].data.flatten())
nEvents = numpy.sum(pyfits.open(skymap)[0].data)
telapsed = pyfits.open(eventfile)[0].header['TSTOP']-pyfits.open(eventfile)[0].header['TSTART']
orig.set_tick_labels_font(size='small')
orig.set_axis_labels_font(size='small')
orig.show_colorscale(cmap='gist_heat',vmin=0.1,vmax=max(vmax,0.11),stretch='log',aspect='auto')
# Modify the tick labels for precision and format
orig.tick_labels.set_xformat('ddd.dd')
orig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
orig.show_grid()
#Renormalize the modelmapfile to the flux of the grb
f = pyfits.open(modelmapfile,'update')
f[0].data = f[0].data/numpy.max(f[0].data)*nEvents/telapsed
print(numpy.max(f[0].data))
f.close()
img = aplpy.FITSFigure(modelmapfile,convention='calabretta',
figure=figure,subplot=[0.55,0.1,0.4,0.7])
img.set_tick_labels_font(size='small')
img.set_axis_labels_font(size='small')
#vmax = max(pyfits.open(modelmapfile)[0].data.flatten())
img.show_colorscale(cmap='gist_heat',aspect='auto',stretch='log')
for src in detectedSources:
img.add_label(float(src.ra),float(src.dec),
"%s\n(ts = %i)" %(src.name,int(math.ceil(src.TS))),
relative=False,weight=weight,
color='green', size='small')
pass
# Modify the tick labels for precision and format
img.tick_labels.set_xformat('ddd.dd')
img.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
img.show_grid()
#ax = figure.add_axes([0.1,0.72,0.85,0.25],frame_on=False)
#ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
#col_labels =['Source Name','TS','Energy flux','Photon index']
#table_vals = map(lambda x:[x.name,"%i" %(int(math.ceil(x.TS))),
# "%s +/- %s" % (x.flux,x.fluxError),
# "%s +/- %s" % (x.photonIndex,x.photonIndexError)],detectedSources)
#
#if(len(table_vals)>0):
# the_table = ax.table(cellText=table_vals,
# colLabels=col_labels,
# loc='upper center')
figure.canvas.draw()
figure.savefig("likelihood_results.png")
pass
if(figure is not None):
#Assume we have an X server running
#Now display the results
likemsg = "Log(likelihood) = %s" %(LATdata.logL)
displayResults(figure.canvas._tkcanvas, LATdata.resultsStrings + "\n" + likemsg + "\n" + localizationMessage)
print(localizationMessage)
return 'likexmlresults', outfilelike, 'TS', grb_TS, 'bestra', bestra, 'bestdec', bestdec, 'poserr', poserr, 'distance', distance,'sources', sources