def xpobssim(**kwargs):
"""Run the ximpol fast simulator.
"""
assert (kwargs['configfile'].endswith('.py'))
if kwargs['outfile'] is None:
outfile = os.path.basename(kwargs['configfile']).replace(
'.py', '.fits')
mkdir(XIMPOL_DATA)
kwargs['outfile'] = os.path.join(XIMPOL_DATA, outfile)
logger.info('Setting output file path to %s...' % kwargs['outfile'])
if os.path.exists(kwargs['outfile']) and not kwargs['clobber']:
logger.info('Output file %s already exists.' % kwargs['outfile'])
logger.info('Remove the file or set "clobber = True" to overwite it.')
return kwargs['outfile']
chrono = xChrono()
logger.info('Setting the random seed to %d...' % kwargs['seed'])
numpy.random.seed(kwargs['seed'])
logger.info('Loading the instrument response functions...')
aeff, psf, modf, edisp = load_irfs(kwargs['irfname'])
logger.info('Done %s.' % chrono)
logger.info('Setting up the source model...')
module_name = os.path.basename(kwargs['configfile']).replace('.py', '')
ROI_MODEL = imp.load_source(module_name, kwargs['configfile']).ROI_MODEL
logger.info(ROI_MODEL)
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)
gti_list = [(kwargs['tstart'], tstop)]
kwargs['tstop'] = tstop
event_list = ROI_MODEL.rvs_event_list(aeff, psf, modf, edisp, **kwargs)
logger.info('Done %s.' % chrono)
simulation_info = xSimulationInfo()
simulation_info.gti_list = gti_list
simulation_info.roi_model = ROI_MODEL
simulation_info.irf_name = kwargs['irfname']
simulation_info.aeff = aeff
simulation_info.psf = psf
simulation_info.modf = modf
simulation_info.edisp = edisp
event_list.write_fits(kwargs['outfile'], simulation_info)
logger.info('All done %s!' % chrono)
return kwargs['outfile']
def xpobssim(**kwargs):
"""Run the ximpol fast simulator.
"""
assert(kwargs['configfile'].endswith('.py'))
if kwargs['outfile'] is None:
outfile = os.path.basename(kwargs['configfile']).replace('.py', '.fits')
mkdir(XIMPOL_DATA)
kwargs['outfile'] = os.path.join(XIMPOL_DATA, outfile)
logger.info('Setting output file path to %s...' % kwargs['outfile'])
if os.path.exists(kwargs['outfile']) and not kwargs['clobber']:
logger.info('Output file %s already exists.' % kwargs['outfile'])
logger.info('Remove the file or set "clobber = True" to overwite it.')
return kwargs['outfile']
chrono = xChrono()
logger.info('Setting the random seed to %d...' % kwargs['seed'])
numpy.random.seed(kwargs['seed'])
logger.info('Loading the instrument response functions...')
aeff, psf, modf, edisp = load_irfs(kwargs['irfname'])
logger.info('Done %s.' % chrono)
logger.info('Setting up the source model...')
module_name = os.path.basename(kwargs['configfile']).replace('.py', '')
ROI_MODEL = imp.load_source(module_name, kwargs['configfile']).ROI_MODEL
logger.info(ROI_MODEL)
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)
gti_list = [(kwargs['tstart'], tstop)]
kwargs['tstop'] = tstop
event_list = ROI_MODEL.rvs_event_list(aeff, psf, modf, edisp, **kwargs)
logger.info('Done %s.' % chrono)
simulation_info = xSimulationInfo()
simulation_info.gti_list = gti_list
simulation_info.roi_model = ROI_MODEL
simulation_info.irf_name = kwargs['irfname']
simulation_info.aeff = aeff
simulation_info.psf = psf
simulation_info.modf = modf
simulation_info.edisp = edisp
event_list.write_fits(kwargs['outfile'], simulation_info)
logger.info('All done %s!' % chrono)
return kwargs['outfile']
def chandra2ximpol(file_path, **kwargs):
"""Make the conversion from Chandra to ximpol.
"""
assert(file_path.endswith('.fits'))
if kwargs['outfile'] is None:
outfile = os.path.basename(file_path).replace('.fits','_xipe.fits')
mkdir(XIMPOL_DATA)
kwargs['outfile'] = os.path.join(XIMPOL_DATA, outfile)
logger.info('Setting output file path to %s...' % kwargs['outfile'])
if os.path.exists(kwargs['outfile']) and not kwargs['clobber']:
logger.info('Output file %s already exists.' % kwargs['outfile'])
logger.info('Remove the file or set "clobber = True" to overwite it.')
return kwargs['outfile']
chrono = xChrono()
logger.info('Setting the random seed to %d...' % kwargs['seed'])
numpy.random.seed(kwargs['seed'])
logger.info('Loading the instrument response functions...')
aeff, psf, modf, edisp = load_irfs(kwargs['irfname'])
c_aeff_name = 'chandra_acis_%s.arf' % kwargs['acis']
c_aeff_file = os.path.join(XIMPOL_IRF, 'fits', c_aeff_name)
logger.info('Reading Chandra effective area data from %s...' % c_aeff_file)
c_aeff = load_chandra_arf(c_aeff_file)
_x = aeff.x
_y = aeff.y/c_aeff(_x)
aeff_ratio = xInterpolatedUnivariateSplineLinear(_x, _y)
logger.info('Loading the input FITS event file...')
hdu_list = fits.open(file_path)
# If configuration file is not provided we assume a non-polarized source
if kwargs['configfile'] is None:
logger.info('Configuration file not provided.')
logger.info('Setting polarization angle and degree to zero...')
polarization_degree = constant(0.)
polarization_angle = constant(0.)
else:
logger.info('Setting up the polarization source model...')
module_name = os.path.basename(kwargs['configfile']).replace('.py', '')
polarization_degree = imp.load_source(module_name,
kwargs['configfile']).POLARIZATION_DEGREE
polarization_angle = imp.load_source(module_name,
kwargs['configfile']).POLARIZATION_ANGLE
logger.info('Done %s.' % chrono)
hdr = hdu_list[1].header
tbdata = hdu_list[1].data
tstart = hdr['TSTART']
tstop = hdr['TSTOP']
obs_time = tstop - tstart
logger.info('Chandra observation time: %i s.' %obs_time)
ra_pnt = hdr['RA_PNT']
dec_pnt = hdr['DEC_PNT']
ROI_MODEL = xROIModel(ra_pnt, dec_pnt)
logger.info('Reading Chandra data...')
col_mc_energy = tbdata['energy']*0.001 # eV -> keV
rnd_ratio = numpy.random.random(len(col_mc_energy))
# The condition col_mc_energy < 10. is needed to avoid to take the bunch of
# events with energy > 10 keV included into the Chandra photon list (we
# actually don't know the reason).
_mask = (rnd_ratio < aeff_ratio(col_mc_energy))*(col_mc_energy<10.)
# This is needed for over-sample the events in case of effective area ratio
# greater than 1.
_mask_ratio = (rnd_ratio < (aeff_ratio(col_mc_energy)-1.))*\
(col_mc_energy<10.)
col_mc_energy = numpy.append(col_mc_energy[_mask],
col_mc_energy[_mask_ratio])
col_time = numpy.append(tbdata['time'][_mask],tbdata['time'][_mask_ratio])
col_mc_ra, col_mc_dec = _get_radec(hdr, tbdata)
col_mc_ra = numpy.append(col_mc_ra[_mask], col_mc_ra[_mask_ratio])
col_mc_dec = numpy.append(col_mc_dec[_mask], col_mc_dec[_mask_ratio])
duration = kwargs['duration']
if not numpy.isnan(duration):
logger.info('Setting the observation time to %d s...' % duration)
scale = numpy.modf(duration/obs_time)
tstop = tstart + duration
logger.info('Scaling counts according to observation time...')
col_mc_energy, col_time, col_mc_ra, col_mc_dec = time_scaling(scale,
col_mc_energy, col_time, col_mc_ra, col_mc_dec)
logger.info('Converting from Chandra to ximpol...')
gti_list = [(tstart, tstop)]
event_list = xMonteCarloEventList()
event_list.set_column('TIME', col_time)
event_list.set_column('MC_ENERGY', col_mc_energy)
col_pha = edisp.matrix.rvs(col_mc_energy)
event_list.set_column('PHA', col_pha)
col_energy = edisp.ebounds(col_pha)
event_list.set_column('ENERGY',col_energy)
event_list.set_column('MC_RA', col_mc_ra)
event_list.set_column('MC_DEC', col_mc_dec)
col_ra, col_dec = psf.smear(col_mc_ra, col_mc_dec)
event_list.set_column('RA', col_ra)
event_list.set_column('DEC', col_dec)
pol_degree = polarization_degree(col_mc_energy, col_time, col_mc_ra,
col_mc_dec)
pol_angle = polarization_angle(col_mc_energy, col_time, col_mc_ra,
col_mc_dec)
col_pe_angle = modf.rvs_phi(col_mc_energy, pol_degree, pol_angle)
event_list.set_column('PE_ANGLE', col_pe_angle)
# Set the phase to rnd [0-1] for all non-periodic sources.
phase=numpy.random.uniform(0,1,len(col_dec))
event_list.set_column('PHASE', phase)
event_list.set_column('MC_SRC_ID', numpy.zeros(len(col_dec)))
logger.info('Done %s.' % chrono)
simulation_info = xSimulationInfo()
simulation_info.gti_list = gti_list
simulation_info.roi_model = ROI_MODEL
simulation_info.irf_name = kwargs['irfname']
simulation_info.aeff = aeff
simulation_info.psf = psf
simulation_info.modf = modf
simulation_info.edisp = edisp
event_list.sort()
event_list.write_fits(kwargs['outfile'], simulation_info)
logger.info('All done %s!' % chrono)
return kwargs['outfile']
def chandra2ximpol(file_path, **kwargs):
"""Make the conversion from Chandra to ximpol.
"""
assert file_path.endswith(".fits")
if kwargs["outfile"] is None:
outfile = os.path.basename(file_path).replace(".fits", "_xipe.fits")
mkdir(XIMPOL_DATA)
kwargs["outfile"] = os.path.join(XIMPOL_DATA, outfile)
logger.info("Setting output file path to %s..." % kwargs["outfile"])
if os.path.exists(kwargs["outfile"]) and not kwargs["clobber"]:
logger.info("Output file %s already exists." % kwargs["outfile"])
logger.info('Remove the file or set "clobber = True" to overwite it.')
return kwargs["outfile"]
chrono = xChrono()
logger.info("Setting the random seed to %d..." % kwargs["seed"])
numpy.random.seed(kwargs["seed"])
logger.info("Loading the instrument response functions...")
aeff, psf, modf, edisp = load_irfs(kwargs["irfname"])
c_aeff_name = "chandra_acis_%s.arf" % kwargs["acis"]
c_aeff_file = os.path.join(XIMPOL_IRF, "fits", c_aeff_name)
logger.info("Reading Chandra effective area data from %s..." % c_aeff_file)
c_aeff = _load_chandra_arf(c_aeff_file)
c_vign_name = "chandra_vignet.fits"
c_vign_file = os.path.join(XIMPOL_IRF, "fits", c_vign_name)
logger.info("Reading Chandra vignetiing data from %s..." % c_vign_file)
c_vign = _load_chandra_vign(c_vign_file)
aeff_ratio = _make_aeff_ratio(aeff, c_aeff, c_vign)
logger.info("Done %s." % chrono)
logger.info("Loading the input FITS event file...")
hdu_list = fits.open(file_path)
hdr = hdu_list[1].header
tbdata = hdu_list[1].data
tstart = hdr["TSTART"]
tstop = hdr["TSTOP"]
obs_time = tstop - tstart
logger.info("Chandra observation time: %i s." % obs_time)
ra_pnt = hdr["RA_PNT"]
dec_pnt = hdr["DEC_PNT"]
ROI_MODEL = xROIModel(ra_pnt, dec_pnt)
logger.info("Reading Chandra data...")
col_mc_energy = tbdata["energy"] * 0.001 # eV -> keV
col_mc_ra, col_mc_dec = _get_radec(hdr, tbdata)
ref_skyccord = SkyCoord(ra_pnt, dec_pnt, unit="deg")
evt_skycoord = SkyCoord(col_mc_ra, col_mc_dec, unit="deg")
separation = evt_skycoord.separation(ref_skyccord).arcmin
logger.info("Converting from Chandra to ximpol...")
rnd_ratio = numpy.random.random(len(col_mc_energy))
# The condition col_mc_energy < 10. is needed to avoid to take the bunch of
# events with energy > 10 keV included into the Chandra photon list (we
# actually don't know the reason).
_mask = (rnd_ratio < aeff_ratio(col_mc_energy, separation)) * (col_mc_energy < 10.0)
# This is needed for over-sample the events in case of effective area ratio
# greater than 1.
_mask_ratio = (rnd_ratio < (aeff_ratio(col_mc_energy, separation) - 1.0)) * (col_mc_energy < 10.0)
col_mc_energy = numpy.append(col_mc_energy[_mask], col_mc_energy[_mask_ratio])
col_mc_ra = numpy.append(col_mc_ra[_mask], col_mc_ra[_mask_ratio])
col_mc_dec = numpy.append(col_mc_dec[_mask], col_mc_dec[_mask_ratio])
col_time = numpy.append(tbdata["time"][_mask], tbdata["time"][_mask_ratio])
# If duration parameter is provided the counts are down- or oversampled.
duration = kwargs["duration"]
if not numpy.isnan(duration):
logger.info("Setting the observation time to %d s..." % duration)
scale = numpy.modf(duration / obs_time)
tstop = tstart + duration
logger.info("Scaling counts according to observation time...")
col_mc_energy, col_time, col_mc_ra, col_mc_dec = _time_scaling(
scale, col_mc_energy, col_time, col_mc_ra, col_mc_dec
)
# The Ra Dec coordinates are calculated here because they are needed in
# source id definition (in case of kwargs['chandra']==False)
col_ra, col_dec = psf.smear(col_mc_ra, col_mc_dec)
# The default source id in case of no regfile and for regions not selected
# is zero. For regions selected in the regfile the source id is determined
# by the order of definition (starting with 1).
logger.info("Defining source id...")
src_id = numpy.zeros(len(col_mc_dec))
n_reg = -1
if kwargs["regfile"] is not None:
regions = pyregion.open(kwargs["regfile"])
for n_reg, region in enumerate(regions):
if kwargs["mc"] is True:
mask = filter_region(region, col_mc_ra, col_mc_dec)
else:
mask = filter_region(region, col_ra, col_dec)
src_id[mask] = n_reg + 1
# If configuration file is not provided we assume a non-polarized source.
# In case of configfile with polarization model defined in different
# regions the photoelectron distribution is generated according to them.
if kwargs["configfile"] is None:
logger.info("Configuration file not provided.")
logger.info("Setting polarization angle and degree to zero...")
polarization_degree = constant(0.0)
polarization_angle = constant(0.0)
pol_dict = {0: [polarization_degree, polarization_angle]}
for k in range(1, n_reg + 2):
pol_dict[k] = [polarization_degree, polarization_angle]
else:
logger.info("Setting up the polarization source model...")
module_name = os.path.basename(kwargs["configfile"]).replace(".py", "")
pol_dict = imp.load_source(module_name, kwargs["configfile"]).POLARIZATION_DICT
logger.info("Generating photoelectron azimuthal distribution...")
col_pe_angle = numpy.empty(len(col_mc_dec))
for key, pol_list in pol_dict.items():
_mask_src = src_id == key
pol_degree = pol_list[0](
col_mc_energy[_mask_src], col_time[_mask_src], col_mc_ra[_mask_src], col_mc_dec[_mask_src]
)
pol_angle = pol_list[1](
col_mc_energy[_mask_src], col_time[_mask_src], col_mc_ra[_mask_src], col_mc_dec[_mask_src]
)
col_pe_angle[_mask_src] = modf.rvs_phi(col_mc_energy[_mask_src], pol_degree, pol_angle)
gti_list = [(tstart, tstop)]
event_list = xMonteCarloEventList()
event_list.set_column("TIME", col_time)
event_list.set_column("MC_ENERGY", col_mc_energy)
col_pha = edisp.matrix.rvs(col_mc_energy)
event_list.set_column("PHA", col_pha)
col_energy = edisp.ebounds(col_pha)
event_list.set_column("ENERGY", col_energy)
event_list.set_column("MC_RA", col_mc_ra)
event_list.set_column("MC_DEC", col_mc_dec)
event_list.set_column("RA", col_ra)
event_list.set_column("DEC", col_dec)
event_list.set_column("MC_SRC_ID", src_id)
event_list.set_column("PE_ANGLE", col_pe_angle)
# Set the phase to rnd [0-1] for all non-periodic sources.
phase = numpy.random.uniform(0, 1, len(col_dec))
event_list.set_column("PHASE", phase)
logger.info("Done %s." % chrono)
simulation_info = xSimulationInfo()
simulation_info.gti_list = gti_list
simulation_info.roi_model = ROI_MODEL
simulation_info.irf_name = kwargs["irfname"]
simulation_info.aeff = aeff
simulation_info.psf = psf
simulation_info.modf = modf
simulation_info.edisp = edisp
event_list.sort()
event_list.write_fits(kwargs["outfile"], simulation_info)
logger.info("All done %s!" % chrono)
return kwargs["outfile"]