def generate_fluxes(exp_time, area, fov, prng):
from soxs.data import cdf_fluxes, cdf_gal, cdf_agn
exp_time = parse_value(exp_time, "s")
area = parse_value(area, "cm**2")
fov = parse_value(fov, "arcmin")
logf = np.log10(cdf_fluxes)
n_gal = np.rint(cdf_gal[-1])
n_agn = np.rint(cdf_agn[-1])
F_gal = cdf_gal / cdf_gal[-1]
F_agn = cdf_agn / cdf_agn[-1]
f_gal = InterpolatedUnivariateSpline(F_gal, logf)
f_agn = InterpolatedUnivariateSpline(F_agn, logf)
eph_mean_erg = 1.0*erg_per_keV
S_min_obs = eph_mean_erg/(exp_time*area)
mylog.debug("Flux of %g erg/cm^2/s gives roughly "
"one photon during exposure." % S_min_obs)
fov_area = fov**2
n_gal = int(n_gal*fov_area/3600.0)
n_agn = int(n_agn*fov_area/3600.0)
mylog.debug("%d AGN, %d galaxies in the FOV." % (n_agn, n_gal))
randvec1 = prng.uniform(size=n_agn)
agn_fluxes = 10**f_agn(randvec1)
randvec2 = prng.uniform(size=n_gal)
gal_fluxes = 10**f_gal(randvec2)
return agn_fluxes, gal_fluxes
def generate_fluxes(fov, prng):
from soxs.data import cdf_fluxes, cdf_gal, cdf_agn
prng = parse_prng(prng)
fov = parse_value(fov, "arcmin")
logf = np.log10(cdf_fluxes)
n_gal = np.rint(cdf_gal[-1])
n_agn = np.rint(cdf_agn[-1])
F_gal = cdf_gal / cdf_gal[-1]
F_agn = cdf_agn / cdf_agn[-1]
f_gal = InterpolatedUnivariateSpline(F_gal, logf)
f_agn = InterpolatedUnivariateSpline(F_agn, logf)
fov_area = fov**2
n_gal = int(n_gal * fov_area / 3600.0)
n_agn = int(n_agn * fov_area / 3600.0)
mylog.debug(f"{n_agn} AGN, {n_gal} galaxies in the FOV.")
randvec1 = prng.uniform(size=n_agn)
agn_fluxes = 10**f_agn(randvec1)
randvec2 = prng.uniform(size=n_gal)
gal_fluxes = 10**f_gal(randvec2)
return agn_fluxes, gal_fluxes
def make_ptsrc_background(exp_time,
fov,
sky_center,
absorb_model="wabs",
nH=0.05,
area=40000.0,
input_sources=None,
output_sources=None,
prng=None):
r"""
Make a point-source background.
Parameters
----------
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time of the observation in seconds.
fov : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The field of view in arcminutes.
sky_center : array-like
The center RA, Dec of the field of view in degrees.
absorb_model : string, optional
The absorption model to use, "wabs" or "tbabs". Default: "wabs"
nH : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The hydrogen column in units of 10**22 atoms/cm**2.
Default: 0.05
area : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The effective area in cm**2. It must be large enough
so that a sufficiently large sample is drawn for the
ARF. Default: 40000.
input_sources : string, optional
If set to a filename, input the source positions, fluxes,
and spectral indices from an ASCII table instead of generating
them. Default: None
output_sources : string, optional
If set to a filename, output the properties of the sources
within the field of view to a file. Default: None
prng : :class:`~numpy.random.RandomState` object, integer, or None
A pseudo-random number generator. Typically will only
be specified if you have a reason to generate the same
set of random numbers, such as for a test. Default is None,
which sets the seed based on the system time.
"""
prng = parse_prng(prng)
exp_time = parse_value(exp_time, "s")
fov = parse_value(fov, "arcmin")
if nH is not None:
nH = parse_value(nH, "1.0e22*cm**-2")
area = parse_value(area, "cm**2")
if input_sources is None:
ra0, dec0, fluxes, ind = generate_sources(fov, sky_center, prng=prng)
num_sources = fluxes.size
else:
mylog.info(f"Reading in point-source properties from {input_sources}.")
t = ascii.read(input_sources)
ra0 = t["RA"].data
dec0 = t["Dec"].data
fluxes = t["flux_0.5_2.0_keV"].data
ind = t["index"].data
num_sources = fluxes.size
mylog.debug(f"Generating spectra from {num_sources} sources.")
# If requested, output the source properties to a file
if output_sources is not None:
t = Table([ra0, dec0, fluxes, ind],
names=('RA', 'Dec', 'flux_0.5_2.0_keV', 'index'))
t["RA"].unit = "deg"
t["Dec"].unit = "deg"
t["flux_0.5_2.0_keV"].unit = "erg/(cm**2*s)"
t["index"].unit = ""
t.write(output_sources, format='ascii.ecsv', overwrite=True)
# Pre-calculate for optimization
eratio = spec_emax / spec_emin
oma = 1.0 - ind
invoma = 1.0 / oma
invoma[oma == 0.0] = 1.0
fac1 = spec_emin**oma
fac2 = spec_emax**oma - fac1
fluxscale = get_flux_scale(ind, fb_emin, fb_emax, spec_emin, spec_emax)
# Using the energy flux, determine the photon flux by simple scaling
ref_ph_flux = fluxes * fluxscale * keV_per_erg
# Now determine the number of photons we will generate
n_photons = prng.poisson(ref_ph_flux * exp_time * area)
all_energies = []
all_ra = []
all_dec = []
for i, nph in enumerate(n_photons):
if nph > 0:
# Generate the energies in the source frame
u = prng.uniform(size=nph)
if ind[i] == 1.0:
energies = spec_emin * (eratio**u)
else:
energies = fac1[i] + u * fac2[i]
energies **= invoma[i]
# Assign positions for this source
ra = ra0[i] * np.ones(nph)
dec = dec0[i] * np.ones(nph)
all_energies.append(energies)
all_ra.append(ra)
all_dec.append(dec)
mylog.debug("Finished generating spectra.")
all_energies = np.concatenate(all_energies)
all_ra = np.concatenate(all_ra)
all_dec = np.concatenate(all_dec)
all_nph = all_energies.size
# Remove some of the photons due to Galactic foreground absorption.
# We will throw a lot of stuff away, but this is more general and still
# faster.
if nH is not None:
if absorb_model == "wabs":
absorb = get_wabs_absorb(all_energies, nH)
elif absorb_model == "tbabs":
absorb = get_tbabs_absorb(all_energies, nH)
randvec = prng.uniform(size=all_energies.size)
all_energies = all_energies[randvec < absorb]
all_ra = all_ra[randvec < absorb]
all_dec = all_dec[randvec < absorb]
all_nph = all_energies.size
mylog.debug(
f"{all_nph} photons remain after foreground galactic absorption.")
all_flux = np.sum(all_energies) * erg_per_keV / (exp_time * area)
output_events = {
"ra": all_ra,
"dec": all_dec,
"energy": all_energies,
"flux": all_flux
}
return output_events
def add_instrument_to_registry(inst_spec):
"""
Add an instrument specification to the registry, contained
in either a dictionary or a JSON file.
The *inst_spec* must have the structure as shown below.
The order is not important. If you use a JSON file, the
structure is the same, but the file cannot include comments,
and use "null" instead of "None", and "true" or "false"
instead of "True" or "False".
For the "chips" entry, "None" means no chips and the detector
field of view is a single square. If you want to have multiple
chips, they must be specified in a format described in the
online documentation.
>>> {
... "name": "lynx_hdxi", # The short name of the instrument
... "arf": "xrs_hdxi_3x10.arf", # The file containing the ARF
... "rmf": "xrs_hdxi.rmf", # The file containing the RMF
... "bkgnd": ["lynx_hdxi_particle_bkgnd.pha", 1.0], # The name of the particle background file and the area of extraction
... "fov": 20.0, # The field of view in arcminutes
... "focal_length": 10.0, # The focal length in meters
... "num_pixels": 4096, # The number of pixels on a side in the FOV
... "dither": True, # Whether or not to dither the instrument
... "psf": ["image", "chandra_psf.fits", 6], # The type of PSF and associated parameters
... "chips": [["Box", 0, 0, 4096, 4096]], # The specification for the chips
... "aimpt_coords": [0.0, 0.0], # The detector coordinates of the aimpoint
... "imaging": True # Whether or not this is a imaging instrument
... "grating": False # Whether or not this is a grating instrument
... }
"""
if isinstance(inst_spec, dict):
inst = inst_spec
elif os.path.exists(inst_spec):
with open(inst_spec, "r") as f:
inst = json.load(f)
name = inst["name"]
if name in instrument_registry:
raise KeyError(f"The instrument with name {name} is already in the "
f"registry! Assign a different name!")
# Catch older JSON files which don't distinguish between imagings
# and non-imagings
if "imaging" not in inst:
mylog.warning("Instrument specifications must now include an 'imaging' "
"item, which determines whether or not this instrument "
"specification supports imaging. Default is True.")
inst["imaging"] = True
if "grating" not in inst:
mylog.warning("Instrument specifications must now include an 'grating' "
"item, which determines whether or not this instrument "
"specification corresponds to a gratings instrument. "
"Default is False.")
inst["grating"] = False
if inst["grating"] and inst["imaging"]:
raise RuntimeError("Currently, gratings instrument specifications cannot "
"have 'imaging' == True!")
if inst['imaging']:
default_set = {"name", "arf", "rmf", "bkgnd", "fov", "chips",
"aimpt_coords", "focal_length", "num_pixels",
"dither", "psf", "imaging", "grating"}
else:
default_set = {"name", "arf", "rmf", "bkgnd", "focal_length",
"imaging", "grating"}
my_keys = set(inst.keys())
if my_keys != default_set:
missing = default_set.difference(my_keys)
raise RuntimeError(f"One or more items is missing from the instrument "
f"specification!\nItems needed: {missing}")
instrument_registry[name] = inst
mylog.debug(f"The {name} instrument specification has been added "
f"to the instrument registry.")
return name
def add_instrument_to_registry(inst_spec):
"""
Add an instrument specification to the registry, contained
in either a dictionary or a JSON file.
The *inst_spec* must have the structure as shown below.
The order is not important. If you use a JSON file, the
structure is the same, but the file cannot include comments,
and use "null" instead of "None", and "true" or "false"
instead of "True" or "False".
For the "chips" entry, "None" means no chips and the detector
field of view is a single square. If you want to have multiple
chips, they must be specified in a format described in the
online documentation.
>>> {
... "name": "lynx_hdxi", # The short name of the instrument
... "arf": "xrs_hdxi_3x10.arf", # The file containing the ARF
... "rmf": "xrs_hdxi.rmf", # The file containing the RMF
... "bkgnd": "acisi", # The name of the particle background
... "fov": 20.0, # The field of view in arcminutes
... "focal_length": 10.0, # The focal length in meters
... "num_pixels": 4096, # The number of pixels on a side in the FOV
... "dither": True, # Whether or not to dither the instrument
... "psf": ["gaussian", 0.5], # The type of PSF and its HPD
... "chips": None, # The specification for the chips
... "aimpt_coords": [0.0, 0.0], # The detector coordinates of the aimpoint
... "imaging": True # Whether or not this is a imaging instrument
... "grating": False # Whether or not this is a grating instrument
... }
"""
if isinstance(inst_spec, dict):
inst = inst_spec
elif os.path.exists(inst_spec):
f = open(inst_spec, "r")
inst = json.load(f)
f.close()
name = inst["name"]
if name in instrument_registry:
raise KeyError(
"The instrument with name %s is already in the registry! Assign a different name!"
% name)
# Catch older JSON files which don't distinguish between imagings and non-imagings
if "imaging" not in inst:
mylog.warning(
"Instrument specifications must now include an 'imaging' item, which "
"determines whether or not this instrument specification supports "
"imaging. Default is True.")
inst["imaging"] = True
if "grating" not in inst:
mylog.warning(
"Instrument specifications must now include an 'grating' item, which "
"determines whether or not this instrument specification corresponds "
"to a gratings instrument. Default is False.")
inst["grating"] = False
if inst["grating"] and inst["imaging"]:
raise RuntimeError(
"Currently, gratings instrument specifications cannot have "
"'imaging' == True!")
if inst['imaging']:
# Catch older JSON files without chip definitions
if "chips" not in inst:
mylog.warning(
"Instrument specifications must now include a 'chips' item, which details "
"the layout of the chips if there are more that one. Assuming None for "
"one chip that covers the entire field of view.")
inst["chips"] = None
# Catch older JSON files without aimpoint coordinates
if "aimpt_coords" not in inst:
mylog.warning(
"Instrument specifications must now include a 'aimpt_coords' item, which "
"details the position in detector coordinates of the nominal aimpoint. "
"Assuming [0.0, 0.0].")
inst["aimpt_coords"] = [0.0, 0.0]
default_set = {
"name", "arf", "rmf", "bkgnd", "fov", "chips", "aimpt_coords",
"focal_length", "num_pixels", "dither", "psf", "imaging", "grating"
}
else:
default_set = {
"name", "arf", "rmf", "bkgnd", "focal_length", "imaging", "grating"
}
my_keys = set(inst.keys())
# Don't check things we don't need
if "dep_name" in my_keys:
my_keys.remove("dep_name")
if my_keys != default_set:
missing = default_set.difference(my_keys)
raise RuntimeError(
"One or more items is missing from the instrument specification!\n"
"Items needed: %s" % missing)
instrument_registry[name] = inst
mylog.debug(
"The %s instrument specification has been added to the instrument registry."
% name)
return name
def add_instrument_to_registry(inst_spec):
"""
Add an instrument specification to the registry, contained
in either a dictionary or a JSON file.
The *inst_spec* must have the structure as shown below.
The order is not important. If you use a JSON file, the
structure is the same, but the file cannot include comments,
and use "null" instead of "None", and "true" or "false"
instead of "True" or "False".
For the "chips" entry, "None" means no chips and the detector
field of view is a single square. If you want to have multiple
chips, they must be specified in a format described in the
online documentation.
>>> {
... "name": "lynx_hdxi", # The short name of the instrument
... "arf": "xrs_hdxi_3x10.arf", # The file containing the ARF
... "rmf": "xrs_hdxi.rmf", # The file containing the RMF
... "bkgnd": "acisi", # The name of the particle background
... "fov": 20.0, # The field of view in arcminutes
... "focal_length": 10.0, # The focal length in meters
... "num_pixels": 4096, # The number of pixels on a side in the FOV
... "dither": True, # Whether or not to dither the instrument
... "psf": ["gaussian", 0.5], # The type of PSF and its HPD
... "chips": None, # The specification for the chips
... "aimpt_coords": [0.0, 0.0], # The detector coordinates of the aimpoint
... "imaging": True # Whether or not this is a imaging instrument
... "grating": False # Whether or not this is a grating instrument
... }
"""
if isinstance(inst_spec, dict):
inst = inst_spec
elif os.path.exists(inst_spec):
f = open(inst_spec, "r")
inst = json.load(f)
f.close()
name = inst["name"]
if name in instrument_registry:
raise KeyError("The instrument with name %s is already in the registry! Assign a different name!" % name)
# Catch older JSON files which don't distinguish between imagings and non-imagings
if "imaging" not in inst:
mylog.warning("Instrument specifications must now include an 'imaging' item, which "
"determines whether or not this instrument specification supports "
"imaging. Default is True.")
inst["imaging"] = True
if "grating" not in inst:
mylog.warning("Instrument specifications must now include an 'grating' item, which "
"determines whether or not this instrument specification corresponds "
"to a gratings instrument. Default is False.")
inst["grating"] = False
if inst["grating"] and inst["imaging"]:
raise RuntimeError("Currently, gratings instrument specifications cannot have "
"'imaging' == True!")
if inst['imaging']:
# Catch older JSON files without chip definitions
if "chips" not in inst:
mylog.warning("Instrument specifications must now include a 'chips' item, which details "
"the layout of the chips if there are more that one. Assuming None for "
"one chip that covers the entire field of view.")
inst["chips"] = None
# Catch older JSON files without aimpoint coordinates
if "aimpt_coords" not in inst:
mylog.warning("Instrument specifications must now include a 'aimpt_coords' item, which "
"details the position in detector coordinates of the nominal aimpoint. "
"Assuming [0.0, 0.0].")
inst["aimpt_coords"] = [0.0, 0.0]
default_set = {"name", "arf", "rmf", "bkgnd", "fov", "chips",
"aimpt_coords", "focal_length", "num_pixels",
"dither", "psf", "imaging", "grating"}
else:
default_set = {"name", "arf", "rmf", "bkgnd", "focal_length", "imaging", "grating"}
my_keys = set(inst.keys())
# Don't check things we don't need
my_keys.remove("dep_name")
if my_keys != default_set:
missing = default_set.difference(my_keys)
raise RuntimeError("One or more items is missing from the instrument specification!\n"
"Items needed: %s" % missing)
instrument_registry[name] = inst
mylog.debug("The %s instrument specification has been added to the instrument registry." % name)
return name
def make_ptsrc_background(exp_time, fov, sky_center, absorb_model="wabs",
nH=0.05, area=40000.0, input_sources=None,
output_sources=None, prng=None):
r"""
Make a point-source background.
Parameters
----------
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time of the observation in seconds.
fov : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The field of view in arcminutes.
sky_center : array-like
The center RA, Dec of the field of view in degrees.
absorb_model : string, optional
The absorption model to use, "wabs" or "tbabs". Default: "wabs"
nH : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The hydrogen column in units of 10**22 atoms/cm**2.
Default: 0.05
area : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The effective area in cm**2. It must be large enough
so that a sufficiently large sample is drawn for the
ARF. Default: 40000.
input_sources : string, optional
If set to a filename, input the source positions, fluxes,
and spectral indices from an ASCII table instead of generating
them. Default: None
output_sources : string, optional
If set to a filename, output the properties of the sources
within the field of view to a file. Default: None
prng : :class:`~numpy.random.RandomState` object, integer, or None
A pseudo-random number generator. Typically will only
be specified if you have a reason to generate the same
set of random numbers, such as for a test. Default is None,
which sets the seed based on the system time.
"""
prng = parse_prng(prng)
exp_time = parse_value(exp_time, "s")
fov = parse_value(fov, "arcmin")
if nH is not None:
nH = parse_value(nH, "1.0e22*cm**-2")
area = parse_value(area, "cm**2")
if input_sources is None:
ra0, dec0, fluxes, ind = generate_sources(exp_time, fov, sky_center,
area=area, prng=prng)
num_sources = fluxes.size
else:
mylog.info("Reading in point-source properties from %s." % input_sources)
t = ascii.read(input_sources)
ra0 = t["RA"].data
dec0 = t["Dec"].data
fluxes = t["flux_0.5_2.0_keV"].data
ind = t["index"].data
num_sources = fluxes.size
mylog.debug("Generating spectra from %d sources." % num_sources)
# If requested, output the source properties to a file
if output_sources is not None:
t = Table([ra0, dec0, fluxes, ind],
names=('RA', 'Dec', 'flux_0.5_2.0_keV', 'index'))
t["RA"].unit = "deg"
t["Dec"].unit = "deg"
t["flux_0.5_2.0_keV"].unit = "erg/(cm**2*s)"
t["index"].unit = ""
t.write(output_sources, format='ascii.ecsv', overwrite=True)
# Pre-calculate for optimization
eratio = spec_emax/spec_emin
oma = 1.0-ind
invoma = 1.0/oma
invoma[oma == 0.0] = 1.0
fac1 = spec_emin**oma
fac2 = spec_emax**oma-fac1
fluxscale = get_flux_scale(ind, fb_emin, fb_emax, spec_emin, spec_emax)
# Using the energy flux, determine the photon flux by simple scaling
ref_ph_flux = fluxes*fluxscale*keV_per_erg
# Now determine the number of photons we will generate
n_photons = prng.poisson(ref_ph_flux*exp_time*area)
all_energies = []
all_ra = []
all_dec = []
for i, nph in enumerate(n_photons):
if nph > 0:
# Generate the energies in the source frame
u = prng.uniform(size=nph)
if ind[i] == 1.0:
energies = spec_emin*(eratio**u)
else:
energies = fac1[i] + u*fac2[i]
energies **= invoma[i]
# Assign positions for this source
ra = ra0[i]*np.ones(nph)
dec = dec0[i]*np.ones(nph)
all_energies.append(energies)
all_ra.append(ra)
all_dec.append(dec)
mylog.debug("Finished generating spectra.")
all_energies = np.concatenate(all_energies)
all_ra = np.concatenate(all_ra)
all_dec = np.concatenate(all_dec)
all_nph = all_energies.size
# Remove some of the photons due to Galactic foreground absorption.
# We will throw a lot of stuff away, but this is more general and still
# faster.
if nH is not None:
if absorb_model == "wabs":
absorb = get_wabs_absorb(all_energies, nH)
elif absorb_model == "tbabs":
absorb = get_tbabs_absorb(all_energies, nH)
randvec = prng.uniform(size=all_energies.size)
all_energies = all_energies[randvec < absorb]
all_ra = all_ra[randvec < absorb]
all_dec = all_dec[randvec < absorb]
all_nph = all_energies.size
mylog.debug("%d photons remain after foreground galactic absorption." % all_nph)
all_flux = np.sum(all_energies)*erg_per_keV/(exp_time*area)
output_events = {"ra": all_ra, "dec": all_dec,
"energy": all_energies, "flux": all_flux}
return output_events