def xpmdp(**kwargs):
"""Calculate the MDP.
"""
logger.info('Loading the instrument response functions...')
aeff = load_arf(kwargs['irfname'])
modf = load_mrf(kwargs['irfname'])
module_name = os.path.basename(kwargs['configfile']).replace('.py', '')
ROI_MODEL = imp.load_source(module_name, kwargs['configfile']).ROI_MODEL
logger.info(ROI_MODEL)
# This is copied from xpobbsim and should probably be factored out.
# Actually, this should be a method of the ROI class. TBD
if kwargs['tstart'] < ROI_MODEL.min_validity_time():
kwargs['tstart'] = ROI_MODEL.min_validity_time()
logger.info('Simulation start time set to %s...' % kwargs['tstart'])
tstop = kwargs['tstart'] + kwargs['duration']
if tstop > ROI_MODEL.max_validity_time():
tstop = ROI_MODEL.max_validity_time()
logger.info('Simulation stop time set to %s...' % tstop)
kwargs['tstop'] = tstop
observation_time = kwargs['tstop'] - kwargs['tstart']
# This is copied from roi.py and should probably be factored out.
# Again, the ROI class should be able to sum the count spectra of all the
# component and expose the result.
sources = ROI_MODEL.values()
if len(sources) > 1:
abort('Multiple sources not implemented, yet.')
source = sources[0]
if isinstance(source, xPeriodicPointSource):
psamples = numpy.linspace(kwargs['phasemin'], kwargs['phasemax'], 100)
logger.info('Sampling phases: %s' % psamples)
count_spectrum = xCountSpectrum(source.energy_spectrum,
aeff,
psamples,
scale=observation_time)
time_integrated_spectrum = count_spectrum.build_time_integral()
else:
tsamples = source.sampling_time(kwargs['tstart'], kwargs['tstop'])
logger.info('Sampling times: %s' % tsamples)
count_spectrum = xCountSpectrum(source.energy_spectrum, aeff, tsamples)
time_integrated_spectrum = count_spectrum.build_time_integral()
# Thuis should be a callable method in the binning module.
ebinning = _make_binning(kwargs['ebinalg'], kwargs['emin'], kwargs['emax'],
kwargs['ebins'], kwargs['ebinning'])
# And this might be implemented in the irf.mrf module.
_x = time_integrated_spectrum.x
_y = time_integrated_spectrum.y * modf(_x)
mu_spectrum = xInterpolatedUnivariateSplineLinear(_x, _y)
for _emin, _emax in zip(ebinning[:-1], ebinning[1:]) +\
[(ebinning[0], ebinning[-1])]:
num_counts = count_spectrum.num_expected_counts(emin=_emin, emax=_emax)
mu_average = mu_spectrum.integral(_emin, _emax) / num_counts
mdp = 4.29 / mu_average / numpy.sqrt(num_counts)
logger.info('%.2f--%.2f keV: %d counts in %d s, mu %.3f, MDP %.2f%%' %\
(_emin, _emax, num_counts, observation_time, mu_average,
100*mdp))
def setUpClass(cls):
"""Setup.
"""
cls.aeff = load_arf(DEFAULT_IRF_NAME)
cls.modf = load_mrf(DEFAULT_IRF_NAME)
cls.generator = xAzimuthalResponseGenerator()
cls.emin = 1.
cls.emax = 10.
def xpmdp(**kwargs):
"""Calculate the MDP.
"""
logger.info('Loading the instrument response functions...')
aeff = load_arf(kwargs['irfname'])
modf = load_mrf(kwargs['irfname'])
module_name = os.path.basename(kwargs['configfile']).replace('.py', '')
ROI_MODEL = imp.load_source(module_name, kwargs['configfile']).ROI_MODEL
logger.info(ROI_MODEL)
# This is copied from xpobbsim and should probably be factored out.
# Actually, this should be a method of the ROI class. TBD
if kwargs['tstart'] < ROI_MODEL.min_validity_time():
kwargs['tstart'] = ROI_MODEL.min_validity_time()
logger.info('Simulation start time set to %s...' % kwargs['tstart'])
tstop = kwargs['tstart'] + kwargs['duration']
if tstop > ROI_MODEL.max_validity_time():
tstop = ROI_MODEL.max_validity_time()
logger.info('Simulation stop time set to %s...' % tstop)
kwargs['tstop'] = tstop
observation_time = kwargs['tstop'] - kwargs['tstart']
# This is copied from roi.py and should probably be factored out.
# Again, the ROI class should be able to sum the count spectra of all the
# component and expose the result.
sources = ROI_MODEL.values()
if len(sources) > 1:
abort('Multiple sources not implemented, yet.')
source = sources[0]
if isinstance(source, xPeriodicPointSource):
psamples = numpy.linspace(kwargs['phasemin'], kwargs['phasemax'], 100)
logger.info('Sampling phases: %s' % psamples)
count_spectrum = xCountSpectrum(source.energy_spectrum, aeff, psamples,
scale=observation_time)
time_integrated_spectrum = count_spectrum.build_time_integral()
else:
tsamples = source.sampling_time(kwargs['tstart'], kwargs['tstop'])
logger.info('Sampling times: %s' % tsamples)
count_spectrum = xCountSpectrum(source.energy_spectrum, aeff, tsamples)
time_integrated_spectrum = count_spectrum.build_time_integral()
# Thuis should be a callable method in the binning module.
ebinning =_make_binning(kwargs['ebinalg'], kwargs['emin'], kwargs['emax'],
kwargs['ebins'], kwargs['ebinning'])
# And this might be implemented in the irf.mrf module.
_x = time_integrated_spectrum.x
_y = time_integrated_spectrum.y*modf(_x)
mu_spectrum = xInterpolatedUnivariateSplineLinear(_x, _y)
for _emin, _emax in zip(ebinning[:-1], ebinning[1:]) +\
[(ebinning[0], ebinning[-1])]:
num_counts = count_spectrum.num_expected_counts(emin=_emin, emax=_emax)
mu_average = mu_spectrum.integral(_emin, _emax)/num_counts
mdp = 4.29/mu_average/numpy.sqrt(num_counts)
logger.info('%.2f--%.2f keV: %d counts in %d s, mu %.3f, MDP %.2f%%' %\
(_emin, _emax, num_counts, observation_time, mu_average,
100*mdp))
def setUpClass(cls):
"""Setup.
"""
cls.irf_name = "xipe_baseline"
cls.aeff = load_arf(cls.irf_name)
cls.modf = load_mrf(cls.irf_name)
cls.emin = 2.0
cls.emax = 8.0
cls.ebinning = numpy.linspace(cls.emin, cls.emax, 4)
cls.ism_model = xpeInterstellarAbsorptionModel()
def bin_(self):
"""Overloaded method.
"""
from ximpol.irf import load_mrf
from ximpol.irf.mrf import mdp99
modf = load_mrf(self.event_file.irf_name())
evt_header = self.event_file.hdu_list['PRIMARY'].header
if self.get('mc'):
energy = self.event_data['MC_ENERGY']
else:
energy = self.event_data['ENERGY']
phi = self.event_data['PE_ANGLE']
phi_hist, xedges, yedges = numpy.histogram2d(energy, phi,
bins=self.make_binning())
primary_hdu = self.build_primary_hdu()
emin, emax = xedges[:-1], xedges[1:]
emean = []
effmu = []
ncounts = []
mdp = []
# If there's more than one bin in energy, we're also interested in all
# the basic quantities in the entire energy range. This involves
# extending the emin and emax arrays and summing up all the
# phi histograms across the various energy slices.
if len(emin) > 1:
emin = numpy.append(emin, emin[0])
emax = numpy.append(emax, emax[-1])
_d1, _d2 = phi_hist.shape
phi_hist_sum = numpy.sum(phi_hist, axis=0).reshape((1, _d2))
phi_hist = numpy.append(phi_hist, phi_hist_sum, axis=0)
for _emin, _emax in zip(emin, emax):
_mask = (energy > _emin)*(energy < _emax)
_energy = energy[_mask]
_emean = numpy.mean(_energy)
_effmu = modf.weighted_average(_energy)
_ncounts = len(_energy)
_mdp = mdp99(_effmu, _ncounts)
emean.append(_emean)
effmu.append(_effmu)
ncounts.append(_ncounts)
mdp.append(_mdp)
data = [emin, emax, emean, effmu, ncounts, mdp, phi_hist]
xBinTableHDUMCUBE.set_phi_spec(self.get('phibins'))
mcube_hdu = xBinTableHDUMCUBE(data)
mcube_hdu.setup_header(self.event_file.primary_keywords())
gti_hdu = self.event_file.hdu_list['GTI']
hdu_list = fits.HDUList([primary_hdu, mcube_hdu, gti_hdu])
hdu_list.info()
logger.info('Writing binned MCUBE data to %s...' % self.get('outfile'))
hdu_list.writeto(self.get('outfile'), clobber=True)
logger.info('Done.')
def xpmdp(**kwargs):
"""Calculate the MDP.
"""
logger.info('Loading the instrument response functions...')
aeff = load_arf(kwargs['irfname'])
modf = load_mrf(kwargs['irfname'])
module_name = os.path.basename(kwargs['configfile']).replace('.py', '')
ROI_MODEL = imp.load_source(module_name, kwargs['configfile']).ROI_MODEL
logger.info(ROI_MODEL)
# This is copied from xpobbsim and should probably be factored out.
# Actually, this should be a method of the ROI class. TBD
if kwargs['tstart'] < ROI_MODEL.min_validity_time():
kwargs['tstart'] = ROI_MODEL.min_validity_time()
logger.info('Simulation start time set to %s...' % kwargs['tstart'])
tstop = kwargs['tstart'] + kwargs['duration']
if tstop > ROI_MODEL.max_validity_time():
tstop = ROI_MODEL.max_validity_time()
logger.info('Simulation stop time set to %s...' % tstop)
kwargs['tstop'] = tstop
# This is copied from roi.py and should probably be factored out.
# Again, the ROI class should be able to sum the count spectra of all the
# component and expose the result.
sources = ROI_MODEL.values()
if len(sources) > 1:
abort('Multiple sources not implemented, yet.')
source = sources[0]
if isinstance(source, xPeriodicPointSource):
observation_time = kwargs['tstop'] - kwargs['tstart']
psamples = numpy.linspace(kwargs['phasemin'], kwargs['phasemax'], 100)
logger.info('Sampling phases: %s' % psamples)
count_spectrum = xCountSpectrum(source.energy_spectrum, aeff, psamples,
source.column_density, source.redshift,
scale_factor=observation_time)
else:
tsamples = source.sampling_time(kwargs['tstart'], kwargs['tstop'])
logger.info('Sampling times: %s' % tsamples)
count_spectrum = xCountSpectrum(source.energy_spectrum, aeff, tsamples,
source.column_density, source.redshift)
# Do the actual work.
ebinning =_make_energy_binning(**kwargs)
mdp_table = count_spectrum.build_mdp_table(ebinning, modf)
logger.info(mdp_table)
file_path = kwargs['outfile']
if file_path is not None:
logger.info('Writing output file path %s...' % file_path)
open(file_path, 'w').write('%s\n\n%s' % (kwargs, mdp_table))
logger.info('Done.')
return mdp_table
def test_xipe_mrf(self):
"""Test the XIPE modulation factor.
This loads the modulation factor from the XIPE .mrf FITS file,
then the corresponding values from the text file that the response
functions are created from and makes sure that the two are close
enough.
"""
_x, _y = numpy.loadtxt(GPD_MODF_FILE_PATH, unpack=True)
modf = load_mrf(IRF_NAME)
_mask = _y > 0.
_x = _x[_mask]
_y = _y[_mask]
_delta = abs((_y - modf(_x))/_y)
self.assertTrue(_delta.max() < 5e-3, 'max. diff. %.9f' % _delta.max())
def test_xipe_mrf(self):
"""Test the XIPE modulation factor.
This loads the modulation factor from the XIPE .mrf FITS file,
then the corresponding values from the text file that the response
functions are created from and makes sure that the two are close
enough.
"""
_x, _y = numpy.loadtxt(GPD_MODF_FILE_PATH, unpack=True)
modf = load_mrf(IRF_NAME)
_mask = _y > 0.0
_x = _x[_mask]
_y = _y[_mask]
_delta = abs((_y - modf(_x)) / _y)
self.assertTrue(_delta.max() < 5e-3, "max. diff. %.9f" % _delta.max())
def __init__(self, file_path):
"""Constructor.
"""
xBinnedFileBase.__init__(self, file_path)
self.data = self.hdu_list['MODULATION'].data
self.emin = self.data['ENERGY_LO']
self.emax = self.data['ENERGY_HI']
self.emean = self.data['ENERGY_MEAN']
self.phi_y = self.data['PHI_HIST']
phibins = self.phi_y.shape[1]
self.phi_binning = numpy.linspace(0, 2*numpy.pi, phibins + 1)
self.phi_x = 0.5*(self.phi_binning[:-1] + self.phi_binning[1:])
from ximpol.irf import load_mrf
irf_name = self.hdu_list['PRIMARY'].header['IRFNAME']
self.modf = load_mrf(irf_name)
self.fit_results = []
def __init__(self, file_path):
"""Constructor.
"""
xBinnedFileBase.__init__(self, file_path)
self.data = self.hdu_list['MODULATION'].data
self.emin = self.data['ENERGY_LO']
self.emax = self.data['ENERGY_HI']
self.emean = self.data['ENERGY_MEAN']
self.phi_y = self.data['PHI_HIST']
phibins = self.phi_y.shape[1]
self.phi_binning = numpy.linspace(0, 2 * numpy.pi, phibins + 1)
self.phi_x = 0.5 * (self.phi_binning[:-1] + self.phi_binning[1:])
from ximpol.irf import load_mrf
irf_name = self.hdu_list['PRIMARY'].header['IRFNAME']
self.modf = load_mrf(irf_name)
self.fit_results = []
def bin_(self):
"""Overloaded method.
"""
from ximpol.irf import load_mrf
from ximpol.irf.mrf import mdp99
modf = load_mrf(self.event_file.irf_name())
evt_header = self.event_file.hdu_list['PRIMARY'].header
if self.get('mc'):
energy = self.event_data['MC_ENERGY']
else:
energy = self.event_data['ENERGY']
phi = self.event_data['PE_ANGLE']
phi_hist, xedges, yedges = numpy.histogram2d(energy,
phi,
bins=self.make_binning())
primary_hdu = self.build_primary_hdu()
emin, emax = xedges[:-1], xedges[1:]
emean = []
effmu = []
ncounts = []
mdp = []
for _emin, _emax in zip(emin, emax):
_mask = (energy > _emin) * (energy < _emax)
_energy = energy[_mask]
_emean = numpy.mean(_energy)
_effmu = modf.weighted_average(_energy)
_ncounts = len(_energy)
_mdp = mdp99(_effmu, _ncounts)
emean.append(_emean)
effmu.append(_effmu)
ncounts.append(_ncounts)
mdp.append(_mdp)
data = [emin, emax, emean, effmu, ncounts, mdp, phi_hist]
xBinTableHDUMCUBE.set_phi_spec(self.get('phibins'))
mcube_hdu = xBinTableHDUMCUBE(data)
mcube_hdu.setup_header(self.event_file.primary_keywords())
gti_hdu = self.event_file.hdu_list['GTI']
hdu_list = fits.HDUList([primary_hdu, mcube_hdu, gti_hdu])
hdu_list.info()
logger.info('Writing binned MCUBE data to %s...' % self.get('outfile'))
hdu_list.writeto(self.get('outfile'), clobber=True)
logger.info('Done.')
def bin_(self):
"""Overloaded method.
"""
from ximpol.irf import load_mrf
from ximpol.irf.mrf import mdp99
modf = load_mrf(self.event_file.irf_name())
evt_header = self.event_file.hdu_list['PRIMARY'].header
if self.get('mc'):
energy = self.event_data['MC_ENERGY']
else:
energy = self.event_data['ENERGY']
phi = self.event_data['PE_ANGLE']
phi_hist, xedges, yedges = numpy.histogram2d(energy, phi,
bins=self.make_binning())
primary_hdu = self.build_primary_hdu()
emin, emax = xedges[:-1], xedges[1:]
emean = []
effmu = []
ncounts = []
mdp = []
for _emin, _emax in zip(emin, emax):
_mask = (energy > _emin)*(energy < _emax)
_energy = energy[_mask]
_emean = numpy.mean(_energy)
_effmu = modf.weighted_average(_energy)
_ncounts = len(_energy)
_mdp = mdp99(_effmu, _ncounts)
emean.append(_emean)
effmu.append(_effmu)
ncounts.append(_ncounts)
mdp.append(_mdp)
data = [emin, emax, emean, effmu, ncounts, mdp, phi_hist]
xBinTableHDUMCUBE.set_phi_spec(self.get('phibins'))
mcube_hdu = xBinTableHDUMCUBE(data)
mcube_hdu.setup_header(self.event_file.primary_keywords())
gti_hdu = self.event_file.hdu_list['GTI']
hdu_list = fits.HDUList([primary_hdu, mcube_hdu, gti_hdu])
hdu_list.info()
logger.info('Writing binned MCUBE data to %s...' % self.get('outfile'))
hdu_list.writeto(self.get('outfile'), clobber=True)
logger.info('Done.')
def analyze():
"""
"""
if os.path.exists(ANALYSIS_FILE_PATH):
logger.info('%s exists, delete it if you want to recreate it.' %\
ANALYSIS_FILE_PATH)
return
modf = load_mrf('xipe_goal')
logger.info('Opening output file %s...' % ANALYSIS_FILE_PATH)
analysis_file = open(ANALYSIS_FILE_PATH, 'w')
for i, (_min, _max) in enumerate(PHASE_BINNING):
_evt_file = xEventFile(_sel_file_path(i))
_energy = _evt_file.event_data['ENERGY']
_phase = _evt_file.event_data['PHASE']
exp = (polarization_degree(_energy, _phase, 0, 0)*modf(_energy)).sum()/\
modf(_energy).sum()
_mcube = xBinnedModulationCube(_mcube_file_path(i))
_mcube.fit()
_fit_results = _mcube.fit_results[-1]
print _fit_results
print exp
print polarization_degree(_mcube.emean[-1], _phase, 0, 0)
raw_input()
_phase = 0.5*(_min + _max)
_phase_err = 0.5*(_max - _min)
_pol_deg = _fit_results.polarization_degree
_pol_deg_err = _fit_results.polarization_degree_error
_pol_angle = _fit_results.phase
_pol_angle_err = _fit_results.phase_error
_data = (_phase, _phase_err, _pol_deg, _pol_deg_err, _pol_angle,
_pol_angle_err)
_fmt = ('%.4e ' * len(_data)).strip()
_fmt = '%s\n' % _fmt
_line = _fmt % _data
analysis_file.write(_line)
analysis_file.close()
p_opt_min = 0.5
if p_opt_max > 0.5:
src = {
"name": name,
"flux_min": flux_min,
"flux_max": flux_max,
"p_opt_max": p_opt_max,
"p_opt_min": p_opt_min,
}
src_list.append(src)
pl_norm_ref = int_eflux2pl_norm(FLUX_REF, E_MIN, E_MAX, PL_INDEX)
logger.info("PL normalization @ %.3e erg cm^-2 s^-1: %.3e keV^-1 cm^-2 s^-1" % (FLUX_REF, pl_norm_ref))
aeff = load_arf(DEFAULT_IRF_NAME)
modf = load_mrf(DEFAULT_IRF_NAME)
tsamples = numpy.array([0, OBS_TIME_REF])
ebinning = numpy.array([E_MIN, E_MAX])
energy_spectrum = power_law(pl_norm_ref, PL_INDEX)
count_spectrum = xCountSpectrum(energy_spectrum, aeff, tsamples)
mdp_table = count_spectrum.build_mdp_table(ebinning, modf)
mdp_ref = mdp_table.mdp_values()[-1]
logger.info("Reference MDP for %s s: %.3f" % (OBS_TIME_REF, mdp_ref))
blazar_list = parse_blazar_list(PRIORITY_ONLY)
mirror_list = [1, 3, 9, 17, 18]
numpy.random.seed(10)
_color = numpy.random.random((3, len(blazar_list)))
# numpy.random.seed(1)
# _disp = numpy.random.uniform(0.7, 2., len(blazar_list))
def setUpClass(self):
"""Setup---here we essentially create the modulation factor.
"""
self.measx, self.measy = numpy.loadtxt(GPD_MODF_FILE_PATH, unpack=True)
self.modf = load_mrf(IRF_NAME)
self.interactive = sys.flags.interactive
def setUpClass(cls):
"""Setup---here we essentially create the modulation factor.
"""
cls.measx, cls.measy = numpy.loadtxt(GPD_MODF_FILE_PATH, unpack=True)
cls.modf = load_mrf(IRF_NAME)
cls.interactive = sys.flags.interactive
