def create(self):
"""Overloaded method.
"""
self.xref = self.get('xref')
self.yref = self.get('yref')
self.nxpix = self.get('nxpix')
self.binsz = self.get('binsz') # Dimension of the pimap (degrees)
self.proj = self.get('proj')
sidex = self.nxpix * self.binsz
logger.info('Output image dimensions are %.1f x %.1f arcmin.' %\
(sidex*60, sidex*60))
logger.info('Center of the image is in R.A.=%.3f Dec.=%.3f' %
(self.xref, self.yref))
# Build the WCS object
self.w = wcs.WCS(naxis=2)
self.w.wcs.crpix = [(self.nxpix + 1) / 2, (self.nxpix + 1) / 2]
self.w.wcs.cdelt = [-self.binsz, self.binsz]
self.w.wcs.crval = [self.xref, self.yref]
self.w.wcs.ctype = ['RA---%s' % self.proj, 'DEC--%s' % self.proj]
self.w.wcs.equinox = 2000.0
#w.wcs.radesys = 'ICRS'
self.header = self.w.to_header()
self.pol_x = numpy.zeros((self.nxpix, self.nxpix))
self.pol_y = numpy.zeros((self.nxpix, self.nxpix))
pass
def __init__(self, file_path):
"""Constructor.
"""
assert (file_path.endswith('.fits'))
logger.info('Opening input binned file %s...' % file_path)
self.hdu_list = fits.open(file_path)
self.hdu_list.info()
def __init__(self, psf_file_path):
"""Constructor.
"""
logger.info("Reading PSF data from %s..." % psf_file_path)
self.hdu_list = fits.open(psf_file_path)
self.hdu_list.info()
_data = self.hdu_list["PSF"].data
W = _data["W"]
sigma = _data["SIGMA"]
N = _data["N"]
r_c = _data["R_C"]
eta = _data["ETA"]
self.__params = (W, sigma, N, r_c, eta)
# Tabulate the actual PSF values.
_r = numpy.linspace(0, self.MAX_RADIUS, 250)
_y = gauss_king(_r, *self.__params)
fmt = dict(xname="r", xunits="arcsec", yname="PSF", yunits="sr$^{-1}$")
xInterpolatedUnivariateSpline.__init__(self, _r, _y, k=2, **fmt)
# Include the solid angle for the actual underlying random generator.
_y *= 2 * numpy.pi * _r
fmt = dict(rvname="r", rvunits="arcsec", pdfname="$2 \\pi r \\times$ PSF", pdfunits="")
self.generator = xUnivariateGenerator(_r, _y, k=1, **fmt)
# Finally, calculate the
self.eef, self.hew = self.build_eef()
logger.info(self)
def add_circle(self, ra, dec, radius, pmax, type='radial'):
logger.info('add a circle at ra=%f, dec=%f, radius=%f. PMAX=%f' %
(ra, dec, rad, pmax))
for i in range(self.nxpix):
for j in range(self.nxpix):
# radial:
#posx = nxpix/2.-i
#posy = -nxpix/2.+j
# circular:
#p_x = i #-nxpix+j
#p_y = j #-nxpix+i
world = self.w.wcs_pix2world([[i, j]], 0)
w_ra = world[0][0]
w_dec = world[0][1]
dx = (w_ra - ra) * numpy.cos(
numpy.deg2rad(dec)) # Effect of the projection
dy = w_dec - dec
p_x = -dy
p_y = +dx
dist = numpy.sqrt(dx * dx + dy * dy)
#print w_ra, w_dec, ra, dec, dist, radius
if (dist > radius):
p_x = 0
p_y = 0
pass
self.pol_x[i, j] = p_x
self.pol_y[i, j] = p_y
pass
pass
pol_deg = numpy.sqrt(self.pol_x * self.pol_x + self.pol_y * self.pol_y)
self.pol_x *= pmax / pol_deg.max()
self.pol_y *= pmax / pol_deg.max()
pass
def bin_(self):
"""Overloaded method.
"""
evt_header = self.event_file.hdu_list['PRIMARY'].header
num_chans = evt_header['DETCHANS']
total_time = self.event_file.total_good_time()
binning = numpy.linspace(-0.5, num_chans -0.5, num_chans+1)
n, bins = numpy.histogram(self.event_data['PHA'], bins=binning)
primary_hdu = self.build_primary_hdu()
data = [numpy.arange(num_chans),
n/total_time,
numpy.sqrt(n)/total_time
]
spec_hdu = xBinTableHDUPHA1(data)
spec_hdu.setup_header(self.event_file.primary_keywords())
irf_name = evt_header['IRFNAME']
keywords = [('EXPOSURE', total_time, 'exposure time'),
('RESPFILE', irf_file_path(irf_name, 'rmf')),
('ANCRFILE', irf_file_path(irf_name, 'arf'))]
spec_hdu.setup_header(keywords)
hdu_list = fits.HDUList([primary_hdu, spec_hdu])
hdu_list.info()
logger.info('Writing binned PHA1 data to %s...' % self.get('outfile'))
hdu_list.writeto(self.get('outfile'), clobber=True)
logger.info('Done.')
def analyze():
"""Analyze the data.
"""
logger.info('Opening output file %s...' % ANALYSIS_FILE_PATH)
analysis_file = open(ANALYSIS_FILE_PATH, 'w')
for i, (_min, _max) in enumerate(zip(TIME_BINNING[:-1], TIME_BINNING[1:])):
_mcube = xBinnedModulationCube(_mcube_file_path(i))
_mcube.fit()
_fit_results = _mcube.fit_results[0]
_sel_file = xEventFile(_sel_file_path(i))
_time = numpy.average(_sel_file.event_data['TIME'])
_sel_file.close()
_time_errp = _max - _time
_time_errm = _time - _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
_spec_fitter = PIPELINE.xpxspec(_pha1_file_path(i), plot=False)
(_index, _index_err), (_norm,
_norm_err) = _spec_fitter.fit_parameters()
# The division by the phase interval is a workaround and we should
# keep track of that in xpselect.
_norm /= (_max - _min)
_norm_err /= (_max - _min)
_data = (_time, _time_errp, _time_errm, _pol_deg, _pol_deg_err,
_pol_angle, _pol_angle_err, _index, _index_err, _norm,
_norm_err)
_fmt = ('%.4e ' * len(_data)).strip()
_fmt = '%s\n' % _fmt
_line = _fmt % _data
analysis_file.write(_line)
analysis_file.close()
def add_circle(self,ra,dec,radius,pmax,ptype='circular',angle=0.0):
''' This implement the case of a circular shape'''
logger.info('add a circle at ra=%f, dec=%f, radius=%f. PMAX=%f'%(ra,dec,rad,pmax))
for i in range(self.nxpix):
for j in range(self.nxpix):
world = self.w.wcs_pix2world([[i,j]], 0)
w_ra = world[0][0]
w_dec = world[0][1]
dx = (w_ra-ra)*numpy.cos(numpy.deg2rad(dec)) # Effect of the projection
dy = w_dec-dec
if ptype is 'linear':
p_x = numpy.cos(numpy.deg2rad(angle))
p_y = numpy.sin(numpy.deg2rad(angle))
elif ptype is 'circular':
p_x = -dy
p_y = +dx
elif ptype is 'radial':
p_x = dx
p_y = dy
pass
dist = numpy.sqrt(dx*dx+dy*dy)
#print w_ra, w_dec, ra, dec, dist, radius
if (dist>radius):
p_x=0
p_y=0
pass
self.pol_x[i,j]=self.pol_x[i,j]+p_x
self.pol_y[i,j]=self.pol_y[i,j]+p_y
pass
pass
pol_deg=numpy.sqrt(self.pol_x*self.pol_x+self.pol_y*self.pol_y)
self.pol_x*=pmax/pol_deg.max()
self.pol_y*=pmax/pol_deg.max()
pass
def plot_swift_lc(grb_list,show=True):
"""Plots Swift GRB light curves.
"""
plt.figure(figsize=(10, 8), dpi=80)
plt.title('Swift XRT light curves')
num_grb = 0
for grb_name in grb_list:
flux_outfile = download_swift_grb_lc_file(grb_name, min_obs_time=21600)
if flux_outfile is not None:
integral_flux_spline = parse_light_curve(flux_outfile)
if integral_flux_spline is not None:
if grb_name == 'GRB 130427A':
integral_flux_spline.plot(num_points=1000,logx=True,\
logy=True,show=False,\
color="red",linewidth=1.0)
num_grb += 1
else:
c = random.uniform(0.4,0.8)
integral_flux_spline.plot(num_points=1000,logx=True,\
logy=True,show=False,\
color='%f'%c,linewidth=1.0)
num_grb += 1
else:
continue
logger.info('%i GRBs included in the plot.'%num_grb)
if show:
plt.show()
def parse_blazar_list(PRIORITY_ONLY):
"""
"""
logger.info("Parsing input file %s..." % BLAZAR_LIST_PATH)
src_list = []
input_file = open(BLAZAR_LIST_PATH)
for i in range(6):
input_file.next()
for line in input_file:
if line.startswith("-"):
if PRIORITY_ONLY is False:
for i in range(3):
line = input_file.next()
PRIORITY_ONLY = True
else:
return src_list
line = line.replace("BL Lac", "BL_Lac")
name, line, notes = line[:12].strip(), line[12:95], line[95:]
ra, dec, opt_class, sed_class, flux_max, flux_min, p_opt_max, p_opt_min = line.split()
flux_max = float(flux_max)
flux_min = float(flux_min)
p_opt_max = float(p_opt_max)
p_opt_min = float(p_opt_min)
if p_opt_min < 0.5:
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)
def __init__(self, file_path):
"""Constructor.
"""
assert(file_path.endswith('.fits'))
logger.info('Opening input binned file %s...' % file_path)
self.hdu_list = fits.open(file_path)
self.hdu_list.info()
def analyze():
"""Analyze the data.
"""
logger.info('Opening output file %s...' % ANALYSIS_FILE_PATH)
analysis_file = open(ANALYSIS_FILE_PATH, 'w')
for i, (_min, _max) in enumerate(zip(TIME_BINNING[:-1],
TIME_BINNING[1:])):
_mcube = xBinnedModulationCube(_mcube_file_path(i))
_mcube.fit()
_fit_results = _mcube.fit_results[0]
_sel_file = xEventFile(_sel_file_path(i))
_time = numpy.average(_sel_file.event_data['TIME'])
_sel_file.close()
_time_errp = _max - _time
_time_errm = _time - _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
_spec_fitter = PIPELINE.xpxspec(_pha1_file_path(i), plot=False)
(_index, _index_err), (_norm, _norm_err) = _spec_fitter.fit_parameters()
# The division by the phase interval is a workaround and we should
# keep track of that in xpselect.
_norm /= (_max - _min)
_norm_err /= (_max - _min)
_data = (_time, _time_errp, _time_errm, _pol_deg, _pol_deg_err,
_pol_angle, _pol_angle_err, _index, _index_err, _norm,
_norm_err)
_fmt = ('%.4e ' * len(_data)).strip()
_fmt = '%s\n' % _fmt
_line = _fmt % _data
analysis_file.write(_line)
analysis_file.close()
def create(self):
"""Overloaded method.
"""
self.xref = self.get('xref')
self.yref = self.get('yref')
self.nxpix = self.get('nxpix')
self.binsz = self.get('binsz') # Dimension of the pimap (degrees)
self.proj = self.get('proj')
sidex = self.nxpix*self.binsz
logger.info('Output image dimensions are %.1f x %.1f arcmin.' %\
(sidex*60, sidex*60))
logger.info('Center of the image is in R.A.=%.3f Dec.=%.3f' % (self.xref,self.yref))
# Build the WCS object
self.w = wcs.WCS(naxis=2)
self.w.wcs.crpix = [(self.nxpix+1)/2,(self.nxpix+1)/2]
self.w.wcs.cdelt = [-self.binsz, self.binsz]
self.w.wcs.crval = [self.xref, self.yref]
self.w.wcs.ctype = ['RA---%s' % self.proj, 'DEC--%s' % self.proj]
self.w.wcs.equinox = 2000.0
#w.wcs.radesys = 'ICRS'
self.header = self.w.to_header()
self.pol_x=numpy.zeros((self.nxpix,self.nxpix))
self.pol_y=numpy.zeros((self.nxpix,self.nxpix))
pass
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
logger.info('Opening output file %s...' % ANALYSIS_FILE_PATH)
analysis_file = open(ANALYSIS_FILE_PATH, 'w')
for i, (_min, _max) in enumerate(PHASE_BINNING):
_mcube = xBinnedModulationCube(_mcube_file_path(i))
_mcube.fit()
_fit_results = _mcube.fit_results[-1]
print _fit_results
_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()
def add_circle(self,ra,dec,radius,pmax,type='radial'):
logger.info('add a circle at ra=%f, dec=%f, radius=%f. PMAX=%f'%(ra,dec,rad,pmax))
for i in range(self.nxpix):
for j in range(self.nxpix):
# radial:
#posx = nxpix/2.-i
#posy = -nxpix/2.+j
# circular:
#p_x = i #-nxpix+j
#p_y = j #-nxpix+i
world = self.w.wcs_pix2world([[i,j]], 0)
w_ra = world[0][0]
w_dec = world[0][1]
dx = (w_ra-ra)*numpy.cos(numpy.deg2rad(dec)) # Effect of the projection
dy = w_dec-dec
p_x = -dy
p_y = +dx
dist = numpy.sqrt(dx*dx+dy*dy)
#print w_ra, w_dec, ra, dec, dist, radius
if (dist>radius):
p_x=0
p_y=0
pass
self.pol_x[i,j]=p_x
self.pol_y[i,j]=p_y
pass
pass
pol_deg=numpy.sqrt(self.pol_x*self.pol_x+self.pol_y*self.pol_y)
self.pol_x*=pmax/pol_deg.max()
self.pol_y*=pmax/pol_deg.max()
pass
def rvs_event_list(self, aeff, psf, modf, edisp, **kwargs):
"""Extract an event list for the full ROI.
Arguments
---------
aeff : :py:class:`ximpol.irf.arf.xEffectiveArea` object.
The effective area to be used.
psf : :py:class:`ximpol.irf.psf.xPointSpreadFunction` object.
The PSF to be used.
modf : :py:class:`ximpol.irf.mrf.xModulationFactor` object.
The modulation factor to the used.
edisp : :py:class:`ximpol.irf.rmf.xEnergyDispersion` object.
The energy dispersion to be used.
sampling_time : array
The array to sample the source light curve.
Warning
-------
The sampling_time should not be the same for all sources, and each
source should be able to decide its own in a sensible way.
(See issue #44.)
"""
event_list = xMonteCarloEventList()
for source in self.values():
logger.info('Generating event list for source "%s"...' %\
source.name)
event_list += source.rvs_event_list(aeff, psf, modf, edisp,
**kwargs)
event_list.apply_vignetting(aeff, self.ra, self.dec)
event_list.sort()
return event_list
def bin_(self):
"""Overloaded method.
"""
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']
counts, xedges, yedges = numpy.histogram2d(energy, phi,
bins=self.make_binning())
primary_hdu = self.build_primary_hdu()
emin, emax = xedges[:-1], xedges[1:]
emean = []
for _emin, _emax in zip(emin, emax):
emean.append(numpy.mean(energy[(energy > _emin)*(energy < _emax)]))
data = [emin, emax, emean, counts]
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 rvs_event_list(self, aeff, psf, modf, edisp, **kwargs):
"""Extract a random event list for the model component.
TODO: here we should pass the sampling phase, instead?
TODO: properly take into account the derivatives in the ephemeris.
"""
# Create the event list and the count spectrum.
event_list = xMonteCarloEventList()
# Mind the count spectrum is made in phase!
sampling_phase = numpy.linspace(0., 1., 100)
count_spectrum = xCountSpectrum(self.energy_spectrum, aeff,
sampling_phase, self.column_density,
self.redshift)
# All this is not properly taking into account the ephemeris.
min_time = kwargs['tstart']
max_time = kwargs['tstop']
#min_time=sampling_time[0]
#max_time = sampling_time[-1]
delta_time = (max_time - min_time)
period = self.ephemeris.period(min_time)
# This is not accurate, as we are effectively discarding the last
# fractional period. Need to think about it.
num_periods = int(delta_time/period)
num_expected_events = delta_time*count_spectrum.light_curve.norm()
# Extract the number of events to be generated based on the integral
# of the light curve over the simulation time.
num_events = numpy.random.poisson(num_expected_events)
logger.info('About to generate %d events...' % num_events)
# Extract the event phases and sort them.
col_phase = count_spectrum.light_curve.rvs(num_events)
event_list.set_column('PHASE', col_phase)
col_period = numpy.random.randint(0, num_periods, num_events)
col_time = (col_period + col_phase)*period
event_list.set_column('TIME', col_time)
# Extract the MC energies and smear them with the energy dispersion.
col_mc_energy = count_spectrum.rvs(col_phase)
event_list.set_column('MC_ENERGY', col_mc_energy)
col_pha = edisp.matrix.rvs(col_mc_energy)
event_list.set_column('PHA', col_pha)
event_list.set_column('ENERGY', edisp.ebounds(col_pha))
# Extract the MC sky positions and smear them with the PSF.
col_mc_ra, col_mc_dec = self.rvs_sky_coordinates(num_events)
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)
# Extract the photoelectron emission directions.
pol_degree = self.polarization_degree(col_mc_energy, col_phase,
col_mc_ra, col_mc_dec)
pol_angle = self.polarization_angle(col_mc_energy, col_phase,
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 source ID.
event_list.set_column('MC_SRC_ID', self.identifier)
event_list.sort()
return event_list
def xpsrccoords(source_name):
"""
"""
logger.info('Querying CDS name resolver for "%s"...' % source_name)
coords = SkyCoord.from_name(source_name)
print(coords.icrs)
print(coords.galactic)
logger.info('Done, bye!')
def parse(file_path, emin=1., emax=15., flux_scale=6.0067e-2):
"""Parse the input file with the complete spectral and polarization model.
"""
logger.info('Parsing input file %s...' % file_path)
energy, flux, angle, degree = numpy.loadtxt(file_path, unpack=True)
flux *= flux_scale
_mask = (energy >= emin) * (energy <= emax)
return energy[_mask], flux[_mask], angle[_mask], degree[_mask]
def __init__(self, mrf_file_path):
"""Constructor.
"""
logger.info('Reading energy dispersion data from %s...' % mrf_file_path)
self.hdu_list = fits.open(mrf_file_path)
self.hdu_list.info()
self.matrix = xEnergyDispersionMatrix(self.hdu_list['MATRIX'])
self.ebounds = xEnergyDispersionBounds(self.hdu_list['EBOUNDS'])
def build_grb_fits_file(data,outfile):
primary_hdu = xPrimaryHDU()
grb_info_hdu = xBinTableGRBmain(data)
hdu_list = fits.HDUList([primary_hdu, grb_info_hdu])
hdu_list.info()
logger.info('Writing GRB main infos table to %s...' % outfile)
hdu_list.writeto(outfile, clobber=True)
logger.info('Done.')
def parse(file_path, emin=1., emax=15., flux_scale=4.3576):
"""Parse the input file with the complete spectral and polarization model.
"""
logger.info('Parsing input file %s...' % file_path)
energy, flux, angle, degree = numpy.loadtxt(file_path, unpack=True)
flux *= flux_scale
_mask = (energy >= emin)*(energy <= emax)
return energy[_mask], flux[_mask], angle[_mask], degree[_mask]
def __init__(self, mrf_file_path):
"""Constructor.
"""
logger.info('Reading energy dispersion data from %s...' %
mrf_file_path)
self.hdu_list = fits.open(mrf_file_path)
self.hdu_list.info()
self.matrix = xEnergyDispersionMatrix(self.hdu_list['MATRIX'])
self.ebounds = xEnergyDispersionBounds(self.hdu_list['EBOUNDS'])
def __init__(self, file_path):
"""Constructor.
"""
assert (file_path.endswith('.fits'))
logger.info('Opening input event file %s...' % file_path)
self.hdu_list = fits.open(file_path)
self.hdu_list.info()
self.event_data = self.hdu_list['EVENTS'].data
self.roi_table = self.build_roi_table()
def parse(file_path, emin=1., emax=10.):
"""Parse the input file with the complete spectral and polarization model.
"""
logger.info('Parsing input file %s...' % file_path)
energy, flux, degree, angle = numpy.loadtxt(file_path, unpack=True,
usecols = (0,1,5,6))
angle += 90.
_mask = (energy >= emin)*(energy <= emax)
return energy[_mask], flux[_mask], degree[_mask], angle[_mask]
def __init__(self, file_path):
"""Constructor.
"""
assert(file_path.endswith('.fits'))
logger.info('Opening input event file %s...' % file_path)
self.hdu_list = fits.open(file_path)
self.hdu_list.info()
self.event_data = self.hdu_list['EVENTS'].data
self.roi_table = self.build_roi_table()
def fit_bin(self, i):
"""Fit the azimuthal distribution for the i-th energy slice.
"""
hist = (self.phi_y[i], self.phi_binning, None)
_fit_results = xAzimuthalResponseGenerator.fit_histogram(hist)
_fit_results.set_polarization(self.modf(self.emean[i]))
logger.info(_fit_results)
self.fit_results.append(_fit_results)
return _fit_results
def fit_bin(self, i):
"""Fit the azimuthal distribution for the i-th energy slice.
"""
hist = (self.phi_y[i], self.phi_binning, None)
_fit_results = xAzimuthalResponseGenerator.fit_histogram(hist)
_fit_results.set_polarization(self.modf(self.emean[i]))
logger.info(_fit_results)
self.fit_results.append(_fit_results)
return _fit_results
def cleanup_dist():
"""Cleanup the distribution folder.
"""
if os.path.exists(XIMPOL_DIST):
logger.info('Removing %s altogether...' % XIMPOL_DIST)
shutil.rmtree(XIMPOL_DIST)
filePath = os.path.join(XIMPOL_ROOT, 'MANIFEST')
if os.path.exists(filePath):
logger.info('Removing %s...' % filePath)
os.remove(filePath)
def cleanup(folder_path, patterns = ['*~', '*.pyc', '*.pyo']):
"""Cleanup a folder.
"""
logger.info('Cleaning up folder %s...' % folder_path)
fileList = []
for pattern in patterns:
fileList += glob.glob(os.path.join(folder_path, pattern))
for filePath in fileList:
logger.info('Removing %s...' % filePath)
os.remove(filePath)
def run():
PIPELINE.xpobssim(configfile=CFG_FILE, duration=SIM_DURATION,
outfile=EVT_FILE_PATH)
pha1_file_path = PIPELINE.xpbin(EVT_FILE_PATH, algorithm='PHA1')
spec_fitter = PIPELINE.xpxspec(pha1_file_path)
(index, index_err), (norm, norm_err) = spec_fitter.fit_parameters()
logger.info('Fitted PL norm = %.4f +- %4f (input = %.4f)' %\
(norm, norm_err, PL_NORM))
logger.info('Fitted PL index = %.4f +- %4f (input = %.4f)' %\
(index, index_err, PL_INDEX))
def run(save_plots=False):
"""Run all the tasks.
"""
if os.path.exists(ANALYSIS_FILE_PATH):
logger.info('%s exists, delete it if you want to recreate it.' %\
ANALYSIS_FILE_PATH)
else:
generate()
prepare()
analyze()
plot(save_plots)
def rmdir(dir_path):
""" Remove an entire (empty or non empty) folder.
"""
logger.info('About to remove folder %s...' % dir_path)
try:
shutil.rmtree(dir_path)
logger.info('Folder succesfully removed.')
status = 0
except Exception as e:
logger.error('Could not remove folder (%s)' % e)
status = 1
return status
def run(save_plots=False):
"""Run all the tasks.
"""
if os.path.exists(ANALYSIS_FILE_PATH):
logger.info('%s exists, delete it if you want to recreate it.' %\
ANALYSIS_FILE_PATH)
else:
calculate_mdp()
generate()
prepare()
analyze()
plot(save_plots)
def __init__(self, file_path, build_cdf=True):
"""Constructor.
"""
logger.info('Reading FITS image from %s...' % file_path)
self.hdu_list = fits.open(file_path)
self.hdu_list.info()
self.wcs = wcs.WCS(self.hdu_list['PRIMARY'].header)
self.data = self.hdu_list['PRIMARY'].data.transpose()
self.vmin = None
self.vmax = None
if build_cdf:
self.cdf = self.build_cdf()
def __init__(self, file_path, build_cdf=True):
"""Constructor.
"""
logger.info('Reading FITS image from %s...' % file_path)
self.hdu_list = fits.open(file_path)
self.hdu_list.info()
self.wcs = wcs.WCS(self.hdu_list['PRIMARY'].header)
self.data = self.hdu_list['PRIMARY'].data.transpose()
self.vmin = None
self.vmax = None
if build_cdf:
self.build_cdf()
def rmdir(dir_path):
""" Remove an entire (empty or non empty) folder.
"""
logger.info('About to remove folder %s...' % dir_path)
try:
shutil.rmtree(dir_path)
logger.info('Folder succesfully removed.')
status = 0
except Exception as e:
logger.error('Could not remove folder (%s)' % e)
status = 1
return status
def save_plot(self, outfile, arg_list=[], device='/cps', title=None,
xaxis='keV', logx=True):
"""Save the plot to the outfile directory. The default format is '.ps'.
"""
xspec.Plot.device = outfile + device
xspec.Plot.xAxis = xaxis
xspec.Plot.xLog = logx
if title is not None:
xspec.Plot.addCommand('label top '+ title)
args = ', '.join(x for x in arg_list)
logger.info('Saving the plot to %s...' %outfile)
xspec.Plot(args)
def parse_spectral_model(file_name, emin=0.9, emax=11.):
"""Parse the input file with the spectral point.
"""
file_path = os.path.join(XIMPOL_CONFIG, 'ascii', file_name)
logger.info('Parsing input file %s...' % file_path)
_energy, _flux, _fluxerr = numpy.loadtxt(file_path, unpack=True)
_mask = (_energy >= emin)*(_energy <= emax)
_energy = _energy[_mask]
_flux = _flux[_mask]
fmt = dict(xname='Energy', xunits='keV', yname='Flux',
yunits='cm$^{-2}$ s$^{-1}$ keV$^{-1}$')
return xInterpolatedUnivariateSplineLinear(_energy, _flux, **fmt)
def select_and_bin(radius = RADIUS):
for i,ra in enumerate(_ra):
for j,dec in enumerate(_dec):
logger.info('Analyzing region at ra = %s, dec = %s' % (ra, dec))
sel_file_path = get_sel_file_path(i,j)
mcube_file_path = get_mcube_file_path(i,j)
logger.info('Going to use %s and %s for the outputfiles..'%(sel_file_path,mcube_file_path))
pipeline.xpselect(evt_file_path, ra=ra, dec=dec, rad=radius,
outfile=sel_file_path)
pipeline.xpbin(sel_file_path, algorithm='MCUBE', ebinalg='LIST',
ebinning=E_BINNING, outfile = mcube_file_path)
def __init__(self, mrf_file_path):
"""Constructor.
"""
logger.info('Reading modulation factor data from %s...' % mrf_file_path)
self.hdu_list = fits.open(mrf_file_path)
self.hdu_list.info()
_data = self.hdu_list['MODFRESP'].data
_x = 0.5*(_data.field('ENERG_LO') + _data.field('ENERG_HI'))
_y = _data.field('MODFRESP')
fmt = dict(xname='Energy', xunits='keV', yname='Modulation factor',
optimize=True, tolerance=1e-4)
xInterpolatedUnivariateSplineLinear.__init__(self, _x, _y, **fmt)
self.generator = xAzimuthalResponseGenerator()
def run(save_plots=False):
"""Run all the tasks.
"""
if os.path.exists(ANALYSIS_FILE_PATH):
logger.info('%s exists, delete it if you want to recreate it.' %\
ANALYSIS_FILE_PATH)
else:
generate()
global PHASE_BINNING
PHASE_BINNING = _phase_binning()
prepare()
analyze()
plot(save_plots)
def parse_spectral_model(file_name, emin=0.5, emax=15.):
"""Parse the input file with the spectral point.
"""
file_path = os.path.join(XIMPOL_CONFIG, 'ascii', file_name)
logger.info('Parsing input file %s...' % file_path)
_energy, _flux = numpy.loadtxt(file_path, delimiter=',', unpack=True)
_mask = (_energy >= emin)*(_energy <= emax)
_energy = _energy[_mask]
_flux = _flux[_mask]
_flux /= _energy**2.
fmt = dict(xname='Energy', xunits='keV', yname='Flux',
yunits='cm$^{-2}$ s$^{-1}$ keV$^{-1}$')
return xInterpolatedUnivariateSplineLinear(_energy, _flux, **fmt)
def mv(source, dest):
"""Move a file.
Return 0 upon succesfull operation, 1 otherwise.
"""
logger.info('About to move %s to %s...' % (source, dest))
try:
shutil.move(source, dest)
logger.info('File succesfully copied.')
status = 0
except Exception as e:
logger.error('Could not move file (%s)' % e)
status = 1
return status
def select_and_bin():
"""
"""
logger.info('Creating the mapcube for the entire source...')
pipeline.xpbin(evt_file_path,
algorithm='MCUBE',
ebinalg='LIST',
ebinning=E_BINNING)
logger.info('Opening region file %s...' % reg_file_path)
regions = pyregion.open(reg_file_path)
logger.info('Found %d regions...' % len(regions))
for i, region in enumerate(regions):
ra, dec, rad = region.coord_list
rad *= 60.
logger.info('Analyzing region at ra = %s, dec = %s' % (ra, dec))
sel_file_path = get_sel_file_path(i)
mcube_file_path = get_mcube_file_path(i)
pipeline.xpselect(evt_file_path,
ra=ra,
dec=dec,
rad=rad,
outfile=sel_file_path)
pipeline.xpbin(sel_file_path,
algorithm='MCUBE',
ebinalg='LIST',
ebinning=E_BINNING,
outfile=mcube_file_path)
def optimize_grid_linear(x, y, tolerance=1e-4):
"""Optimize a pair of (x, y) arrays for the corresponding spline
definition.
This loops over the input arrays and removes unnecessary data points
to minimize the length of the arrays necessary to the spline definition.
Args
----
x : array
The input x-array.
y : array
The input y-array.
tolerance : float
The maximum relative difference between the generic yi value and the\
estrapolation of the two previous optimized data points for the point\
i to be removed.
"""
assert (len(x) == len(y))
logger.info('Optimizing grid with %d starting points...' % len(x))
# Start a new series with the first two points of the input arrays.
_x = [x[0], x[1]]
_y = [y[0], y[1]]
# Loop over the points 3 ... (N - 1).
for i, (_xi, _yi) in enumerate(zip(x, y)[2:-1]):
# Extrapolate the last two points of the new series to xi and
# see how far we are from the actual yi.
delta = interpolate(_x[-2], _y[-2], _x[-1], _y[-1], _xi) - _yi
if abs(delta / _yi) > tolerance:
# If the difference is larger than the tolerance, add a point.
# (This has the drawback that we tend to add pairs of point at
# each change of slope.)
_x.append(_xi)
_y.append(_yi)
# Interpolate the points last and (last - 2) to (last - 1).
delta = interpolate(_x[-3], _y[-3], _x[-1], _y[-1],
_x[-2]) - _y[-2]
if abs(delta / _y[-2]) < tolerance:
# If the penultimate point was not necessary, remove it.
_x.remove(_x[-2])
_y.remove(_y[-2])
# Append the last point of the original array to the list.
_x.append(x[-1])
_y.append(y[-1])
_x, _y = numpy.array(_x), numpy.array(_y)
logger.info('Done, %d points remaining.' % len(_x))
return _x, _y
def mkdir(dir_path):
"""Create a directory (unless it already exists).
Return 0 upon succesfull operation, 1 otherwise.
"""
if not os.path.exists(dir_path):
logger.info('About to create folder %s...' % dir_path)
try:
os.makedirs(dir_path)
logger.info('Folder succesfully created.')
status = 0
except Exception as e:
logger.error('Could not create folder (%s)' % e)
status = 1
return status
def xpselect(file_path, **kwargs):
"""Application for data subselection.
We want to (loosely) model this on
http://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtselect.txt
"""
assert(file_path.endswith('.fits'))
event_select = xEventSelect(file_path, **kwargs)
outfile = event_select.get('outfile')
if os.path.exists(outfile) and not event_select.get('clobber'):
logger.info('Output file %s already exists.' % outfile)
logger.info('Remove the file or set "clobber = True" to overwite it.')
else:
event_select.select()
return outfile
def distsrc():
""" Create a plain source distribution.
"""
tag, buildDate = version_info()
logger.info('Creating plain source distribution...')
distDir = os.path.join(XIMPOL_DIST, 'src')
srcLogFilePath = 'src.log'
# Create the distribution.
cmd('python setup.py sdist --dist-dir=%s --prune' % distDir,
verbose=False,
logFilePath=srcLogFilePath)
# Cleanup.
rm(srcLogFilePath)
rm(os.path.join(XIMPOL_ROOT, 'MANIFEST'))
logger.info('Done.')
def xpbin(file_path, **kwargs):
"""Application to bin the data.
We want to (loosely) model this on
http://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtbin.txt
"""
assert (file_path.endswith('.fits'))
event_binning = BIN_ALG_DICT[kwargs['algorithm']](file_path, **kwargs)
outfile = event_binning.get('outfile')
if os.path.exists(outfile) and not event_binning.get('clobber'):
logger.info('Output file %s already exists.' % outfile)
logger.info('Remove the file or set "clobber = True" to overwite it.')
else:
event_binning.bin_()
return outfile
def write_fits(self, file_path, simulation_info):
"""Write the event list and associated ancillary information to file.
Arguments
---------
file_path : str
The path to the output file.
simulation_info :
A generic container with all the relevant information about the
simulation.
Warning
-------
The information about the detector and telescope should be in the
primary header of the IRF tables, and that's where we should be
retrieving it from. (See issue #49.)
"""
primary_hdu = xPrimaryHDU()
roi_model = simulation_info.roi_model
irf_name = simulation_info.irf_name
ebounds_header = simulation_info.edisp.hdu_list['EBOUNDS'].header
gti_list = simulation_info.gti_list
keywords = [('ROIRA', roi_model.ra,
'right ascension of the ROI center'),
('ROIDEC', roi_model.dec, 'declination of the ROI center'),
('EQUINOX', 2000., 'equinox for RA and DEC'),
('IRFNAME', irf_name, 'name of the IRFs used for the MC'),
('TELESCOP', ebounds_header['TELESCOP']),
('INSTRUME', ebounds_header['INSTRUME']),
('DETNAM', ebounds_header['DETNAM']),
('DETCHANS', ebounds_header['DETCHANS'])]
primary_hdu.setup_header(keywords)
data = [self[name] for name in\
xBinTableHDUMonteCarloEvents.spec_names()]
event_hdu = xBinTableHDUMonteCarloEvents(data)
_start = numpy.array([gti[0] for gti in gti_list])
_stop = numpy.array([gti[1] for gti in gti_list])
gti_hdu = xBinTableHDUGTI([_start, _stop])
_src_id = numpy.array([src.identifier for src in roi_model.values()])
_src_name = numpy.array([src.name for src in roi_model.values()])
roi_hdu = xBinTableHDURoiTable([_src_id, _src_name])
hdu_list = fits.HDUList([primary_hdu, event_hdu, gti_hdu, roi_hdu])
hdu_list.info()
hdu_list.writeto(file_path, clobber=True)
logger.info('Event list written to %s...' % file_path)
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.
"""
evt_header = self.event_file.hdu_list['PRIMARY'].header
counts, edges = numpy.histogram(self.event_data['PHASE'],
bins=self.make_binning())
primary_hdu = self.build_primary_hdu()
data = [
self.bin_centers(edges),
self.bin_widths(edges), counts,
numpy.sqrt(counts)
]
rate_hdu = xBinTableHDUPHASG(data)
rate_hdu.setup_header(self.event_file.primary_keywords())
gti_hdu = self.event_file.hdu_list['GTI']
hdu_list = fits.HDUList([primary_hdu, rate_hdu, gti_hdu])
hdu_list.info()
logger.info('Writing binned PHASG data to %s...' % self.get('outfile'))
hdu_list.writeto(self.get('outfile'), clobber=True)
logger.info('Done.')
def __init__(self,
x,
y,
xname=None,
xunits=None,
yname=None,
yunits=None,
optimize=False,
tolerance=1e-4):
""" Constructor.
"""
if optimize:
oldx, oldy = x, y
x, y = optimize_grid_linear(x, y, tolerance)
xInterpolatedUnivariateSpline.__init__(self, x, y, None, [None, None],
1, xname, xunits, yname, yunits)
if optimize:
dist = self.dist(oldx, oldy)
logger.info('Relative (max/ave) dist. to original array: %e/%e' %\
(dist.max(), dist.sum()/len(dist)))
def cp(source, dest, create_tree=False):
"""Copy a file.
Return 0 upon succesfull operation, 1 otherwise.
"""
logger.info('About to copy %s to %s...' % (source, dest))
destFolder = os.path.dirname(dest)
if not os.path.exists(destFolder) and createTree:
mkdir(destFolder)
try:
if os.path.isdir(source):
shutil.copytree(source, dest)
else:
shutil.copy(source, dest)
logger.info('File succesfully copied.')
status = 0
except Exception as e:
logger.error('Could not copy file (%s)' % e)
status = 1
return status
def make_psf(irf_name):
"""Write the XIPE PSF parameters.
"""
logger.info('Creating XIPE effective area fits file...')
output_file_name = '%s.psf' % irf_name
output_file_path = os.path.join(XIMPOL_IRF, 'fits', output_file_name)
if os.path.exists(output_file_path):
rm(output_file_path)
logger.info('Creating PRIMARY HDU...')
primary_hdu = xPrimaryHDU('ximpol', XIPE_KEYWORDS, XIPE_COMMENTS)
print(repr(primary_hdu.header))
logger.info('Creating PSF HDU...')
data = PSF_PARAMETERS
psf_hdu = xBinTableHDUPSF(data, [], XIPE_COMMENTS)
print(repr(psf_hdu.header))
logger.info('Writing output file %s...' % output_file_path)
hdulist = fits.HDUList([primary_hdu, psf_hdu])
hdulist.info()
hdulist.writeto(output_file_path)
logger.info('Done.')
def save_current_figure(file_name,
folder=XIMPOL_DOC_FIGURES,
clear=True,
show=False):
"""Save the current matplotlib figure in `XIMPOL_DOC_FIGURES`.
Arguments
---------
file_name : string
The name of the output file.
clear : bool
If `True`, the current image is cleared after the fact.
"""
file_path = os.path.join(folder, file_name)
logger.info('Saving current figure to %s...' % file_path)
pyplot.savefig(file_path, transparent=True)
if show:
pyplot.show()
if clear:
pyplot.clf()
