def runLikelihood(subdir, tpl_file):
'''This runction runs the likelihood code on a set of pixels in a
subdirectory. It takes as input the subdirectory to work on and a
template counts map. It reads it's configuration from a pickle
file (par.pck) that should be located in the subdirectory and the
pixel locations from another pickle file (pixel.pck). It then
creats an overall likelihood object, does a quick global fit and
then loops over the pixels. At each pixel, it creats a test
source, fits that source, calculates the TS of the source and
writes the results to an output file in the subdirectory called
ts_results.dat.'''
parfile = open("par.pck", "r")
pars = pickle.load(parfile)
pixelfile = open("pixel.pck", "r")
pixels = pickle.load(pixelfile)
pixel_coords = PixelCoords(tpl_file)
obs = UnbinnedObs(resolve_fits_files(pars['evfile']),
resolve_fits_files(pars['scfile']),
expMap='../'+pars['expmap'],
expCube='../'+pars['expcube'],
irfs=pars['irfs'])
like = UnbinnedAnalysis(obs, '../'+pars['srcmdl'], pars['optimizer'])
like.setFitTolType(pars['toltype'])
like.optimize(0)
loglike0 = like()
test_src = getPointSource(like)
target_name = 'testSource'
test_src.setName(target_name)
outfile = 'ts_results.dat'
finished_pixels = []
if os.path.isfile(outfile):
input = open(outfile, 'r')
for line in input:
tokens = line.strip().split()
ij = int(tokens[0]), int(tokens[1])
finished_pixels.append(ij)
input.close()
output = open(outfile, 'a')
for indx, i, j in pixels:
if (i, j) in finished_pixels:
continue
ra, dec = pixel_coords(i, j)
test_src.setDir(ra, dec, True, False)
like.addSource(test_src)
like.optimize(0)
ts = -2*(like() - loglike0)
output.write("%3i %3i %.3f %.3f %.5f\n" % (i, j, ra, dec, ts))
output.flush()
like.deleteSource(target_name)
output.close()
def runLikelihood(subdir, tpl_file):
'''This runction runs the likelihood code on a set of pixels in a
subdirectory. It takes as input the subdirectory to work on and a
template counts map. It reads it's configuration from a pickle
file (par.pck) that should be located in the subdirectory and the
pixel locations from another pickle file (pixel.pck). It then
creats an overall likelihood object, does a quick global fit and
then loops over the pixels. At each pixel, it creats a test
source, fits that source, calculates the TS of the source and
writes the results to an output file in the subdirectory called
ts_results.dat.'''
parfile = open("par.pck", "r")
pars = pickle.load(parfile)
pixelfile = open("pixel.pck", "r")
pixels = pickle.load(pixelfile)
pixel_coords = PixelCoords(tpl_file)
obs = UnbinnedObs(resolve_fits_files(pars['evfile']),
resolve_fits_files(pars['scfile']),
expMap='../' + pars['expmap'],
expCube='../' + pars['expcube'],
irfs=pars['irfs'])
like = UnbinnedAnalysis(obs, '../' + pars['srcmdl'], pars['optimizer'])
like.setFitTolType(pars['toltype'])
like.optimize(0)
loglike0 = like()
test_src = getPointSource(like)
target_name = 'testSource'
test_src.setName(target_name)
outfile = 'ts_results.dat'
finished_pixels = []
if os.path.isfile(outfile):
input = open(outfile, 'r')
for line in input:
tokens = line.strip().split()
ij = int(tokens[0]), int(tokens[1])
finished_pixels.append(ij)
input.close()
output = open(outfile, 'a')
for indx, i, j in pixels:
if (i, j) in finished_pixels:
continue
ra, dec = pixel_coords(i, j)
test_src.setDir(ra, dec, True, False)
like.addSource(test_src)
like.optimize(0)
ts = -2 * (like() - loglike0)
output.write("%3i %3i %.3f %.3f %.5f\n" % (i, j, ra, dec, ts))
output.flush()
like.deleteSource(target_name)
output.close()
