def fitsToPNG(fitsfile, pngfile, vmin=None, vmax=None, **kwargs):
#Display the TS map
#figure = plt.figure()
tsfig = aplpy.FITSFigure(fitsfile, convention='calabretta')
tsfig.set_tick_labels_font(size='small')
tsfig.set_axis_labels_font(size='small')
if (vmin is not None and vmax is not None):
tsfig.show_colorscale(cmap='gist_heat',
aspect='auto',
vmin=float(vmin),
vmax=float(vmax))
else:
#Get maximum of image
f = pyfits.open(fitsfile)
maximum = f[0].data.max()
f.close()
tsfig.show_colorscale(cmap='gist_heat',
aspect='auto',
vmin=0,
vmax=float(maximum))
# 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()
tsfig.add_colorbar()
if ('sources' in kwargs.keys()):
sources = kwargs['sources']
for src in sources:
tsfig.add_label(float(src[1]),
float(src[2]),
"%s" % src[0],
relative=False,
weight='bold',
color='green',
size='x-small',
verticalalignment='top',
horizontalalignment='left')
tsfig.show_markers([float(src[1])], [float(src[2])],
edgecolor='green',
marker='x')
pass
if ('ra' in kwargs.keys()):
ra = float(kwargs['ra'])
dec = float(kwargs['dec'])
tsfig.show_markers([ra], [dec],
edgecolor='cyan',
facecolor='cyan',
marker='x',
s=120,
alpha=0.5,
linewidth=3)
pass
#figure.canvas.draw()
tsfig.save(pngfile)
def displayImage(self):
self.image = aplpy.FITSFigure(self.skyimage,
convention='calabretta',
figure=self.figure,
subplot=[0.1, 0.10, 0.40, 0.7],
label='sky image')
imageFits = pyfits.open(self.skyimage)
img = imageFits[0].data
# Apply grayscale mapping of image
if (not self.empty):
skm = self.image.show_colorscale(cmap='gist_heat',
vmin=0.1,
vmax=max(img.flatten()),
stretch='log')
else:
skm = self.image.show_colorscale(cmap='gist_heat',
vmin=0,
vmax=0.1)
imageFits.close()
# Modify the tick labels for precision and format
self.image.tick_labels.set_xformat('ddd.dd')
self.image.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
try:
self.image.show_grid()
except:
#show_grid throw an exception if the grid was already there
pass
pass
#Try to plot a cross at the source position
if (self.obj_ra is not None):
self.image.show_markers([float(self.obj_ra)],
[float(self.obj_dec)],
edgecolor='cyan',
facecolor='cyan',
marker='x',
s=120,
alpha=0.5,
linewidth=2)
self.figure.canvas.draw()
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')
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