def __init__(self,
emin,
emax,
nchan,
apec_root=None,
apec_vers="2.0.2",
thermal_broad=False):
if apec_root is None:
self.cocofile = check_file_location(
"apec_v%s_coco.fits" % apec_vers, "spectral_files")
self.linefile = check_file_location(
"apec_v%s_line.fits" % apec_vers, "spectral_files")
else:
self.cocofile = os.path.join(apec_root,
"apec_v%s_coco.fits" % apec_vers)
self.linefile = os.path.join(apec_root,
"apec_v%s_line.fits" % apec_vers)
if not os.path.exists(self.cocofile) or not os.path.exists(
self.linefile):
raise IOError("Cannot find the APEC files!\n %s\n, %s" %
(self.cocofile, self.linefile))
super(TableApecModel, self).__init__(emin, emax, nchan)
self.wvbins = hc / self.ebins[::-1].d
# H, He, and trace elements
self.cosmic_elem = [
1, 2, 3, 4, 5, 9, 11, 15, 17, 19, 21, 22, 23, 24, 25, 27, 29, 30
]
# Non-trace metals
self.metal_elem = [6, 7, 8, 10, 12, 13, 14, 16, 18, 20, 26, 28]
self.thermal_broad = thermal_broad
self.A = np.array([
0.0, 1.00794, 4.00262, 6.941, 9.012182, 10.811, 12.0107, 14.0067,
15.9994, 18.9984, 20.1797, 22.9898, 24.3050, 26.9815, 28.0855,
30.9738, 32.0650, 35.4530, 39.9480, 39.0983, 40.0780, 44.9559,
47.8670, 50.9415, 51.9961, 54.9380, 55.8450, 58.9332, 58.6934,
63.5460, 65.3800
])
try:
self.line_handle = _astropy.pyfits.open(self.linefile)
except IOError:
mylog.error("LINE file %s does not exist" % self.linefile)
raise IOError("LINE file %s does not exist" % self.linefile)
try:
self.coco_handle = _astropy.pyfits.open(self.cocofile)
except IOError:
mylog.error("COCO file %s does not exist" % self.cocofile)
raise IOError("COCO file %s does not exist" % self.cocofile)
self.Tvals = self.line_handle[1].data.field("kT")
self.nT = len(self.Tvals)
self.dTvals = np.diff(self.Tvals)
self.minlam = self.wvbins.min()
self.maxlam = self.wvbins.max()
def from_data_source(cls,
data_source,
redshift,
area,
exp_time,
source_model,
point_sources=False,
parameters=None,
center=None,
dist=None,
cosmology=None,
velocity_fields=None):
r"""
Initialize a :class:`~pyxsim.photon_list.PhotonList` from a yt data
source. The redshift, collecting area, exposure time, and cosmology
are stored in the *parameters* dictionary which is passed to the
*source_model* function.
Parameters
----------
data_source : :class:`~yt.data_objects.data_containers.YTSelectionContainer`
The data source from which the photons will be generated.
redshift : float
The cosmological redshift for the photons.
area : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The collecting area to determine the number of photons. If units are
not specified, it is assumed to be in cm^2.
exp_time : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The exposure time to determine the number of photons. If units are
not specified, it is assumed to be in seconds.
source_model : :class:`~pyxsim.source_models.SourceModel`
A source model used to generate the photons.
point_sources : boolean, optional
If True, the photons will be assumed to be generated from the exact
positions of the cells or particles and not smeared around within
a volume. Default: False
parameters : dict, optional
A dictionary of parameters to be passed for the source model to use,
if necessary.
center : string or array_like, optional
The origin of the photon spatial coordinates. Accepts "c", "max", or
a coordinate. If not specified, pyxsim attempts to use the "center"
field parameter of the data_source.
dist : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The angular diameter distance, used for nearby sources. This may be
optionally supplied instead of it being determined from the
*redshift* and given *cosmology*. If units are not specified, it is
assumed to be in kpc. To use this, the redshift must be set to zero.
cosmology : :class:`~yt.utilities.cosmology.Cosmology`, optional
Cosmological information. If not supplied, we try to get the
cosmology from the dataset. Otherwise, LCDM with the default yt
parameters is assumed.
velocity_fields : list of fields
The yt fields to use for the velocity. If not specified, the
following will be assumed:
['velocity_x', 'velocity_y', 'velocity_z'] for grid datasets
['particle_velocity_x', 'particle_velocity_y', 'particle_velocity_z'] for particle datasets
Examples
--------
>>> thermal_model = ThermalSourceModel(apec_model, Zmet=0.3)
>>> redshift = 0.05
>>> area = 6000.0 # assumed here in cm**2
>>> time = 2.0e5 # assumed here in seconds
>>> sp = ds.sphere("c", (500., "kpc"))
>>> my_photons = PhotonList.from_data_source(sp, redshift, area,
... time, thermal_model)
"""
ds = data_source.ds
if parameters is None:
parameters = {}
if cosmology is None:
if hasattr(ds, 'cosmology'):
cosmo = ds.cosmology
else:
cosmo = Cosmology()
else:
cosmo = cosmology
if dist is None:
if redshift <= 0.0:
msg = "If redshift <= 0.0, you must specify a distance to the " \
"source using the 'dist' argument!"
mylog.error(msg)
raise ValueError(msg)
D_A = cosmo.angular_diameter_distance(0.0,
redshift).in_units("Mpc")
else:
D_A = parse_value(dist, "kpc")
if redshift > 0.0:
mylog.warning("Redshift must be zero for nearby sources. "
"Resetting redshift to 0.0.")
redshift = 0.0
if isinstance(center, string_types):
if center == "center" or center == "c":
parameters["center"] = ds.domain_center
elif center == "max" or center == "m":
parameters["center"] = ds.find_max("density")[-1]
elif iterable(center):
if isinstance(center, YTArray):
parameters["center"] = center.in_units("code_length")
elif isinstance(center, tuple):
if center[0] == "min":
parameters["center"] = ds.find_min(center[1])[-1]
elif center[0] == "max":
parameters["center"] = ds.find_max(center[1])[-1]
else:
raise RuntimeError
else:
parameters["center"] = ds.arr(center, "code_length")
elif center is None:
if hasattr(data_source, "left_edge"):
parameters["center"] = 0.5 * (data_source.left_edge +
data_source.right_edge)
else:
parameters["center"] = data_source.get_field_parameter(
"center")
parameters["fid_exp_time"] = parse_value(exp_time, "s")
parameters["fid_area"] = parse_value(area, "cm**2")
parameters["fid_redshift"] = redshift
parameters["fid_d_a"] = D_A
parameters["hubble"] = cosmo.hubble_constant
parameters["omega_matter"] = cosmo.omega_matter
parameters["omega_lambda"] = cosmo.omega_lambda
if redshift > 0.0:
mylog.info(
"Cosmology: h = %g, omega_matter = %g, omega_lambda = %g" %
(cosmo.hubble_constant, cosmo.omega_matter,
cosmo.omega_lambda))
else:
mylog.info("Observing local source at distance %s." % D_A)
D_A = parameters["fid_d_a"].in_cgs()
dist_fac = 1.0 / (4. * np.pi * D_A.value * D_A.value *
(1. + redshift)**2)
spectral_norm = parameters["fid_area"].v * parameters[
"fid_exp_time"].v * dist_fac
source_model.setup_model(data_source, redshift, spectral_norm)
p_fields, v_fields, w_field = determine_fields(
ds, source_model.source_type, point_sources)
if velocity_fields is not None:
v_fields = velocity_fields
if p_fields[0] == ("index", "x"):
parameters["data_type"] = "cells"
else:
parameters["data_type"] = "particles"
citer = data_source.chunks([], "io")
photons = defaultdict(list)
for chunk in parallel_objects(citer):
chunk_data = source_model(chunk)
if chunk_data is not None:
ncells, number_of_photons, idxs, energies = chunk_data
photons["num_photons"].append(number_of_photons)
photons["energy"].append(energies)
photons["pos"].append(
np.array([
chunk[p_fields[0]].d[idxs], chunk[p_fields[1]].d[idxs],
chunk[p_fields[2]].d[idxs]
]))
photons["vel"].append(
np.array([
chunk[v_fields[0]].d[idxs], chunk[v_fields[1]].d[idxs],
chunk[v_fields[2]].d[idxs]
]))
if w_field is None:
photons["dx"].append(np.zeros(ncells))
else:
photons["dx"].append(chunk[w_field].d[idxs])
source_model.cleanup_model()
photon_units = {
"pos": ds.field_info[p_fields[0]].units,
"vel": ds.field_info[v_fields[0]].units,
"energy": "keV"
}
if w_field is None:
photon_units["dx"] = "kpc"
else:
photon_units["dx"] = ds.field_info[w_field].units
concatenate_photons(ds, photons, photon_units)
c = parameters["center"].to("kpc")
if sum(ds.periodicity) > 0:
# Fix photon coordinates for regions crossing a periodic boundary
dw = ds.domain_width.to("kpc")
le, re = find_object_bounds(data_source)
for i in range(3):
if ds.periodicity[i] and photons["pos"].shape[0] > 0:
tfl = photons["pos"][:, i] < le[i]
tfr = photons["pos"][:, i] > re[i]
photons["pos"][tfl, i] += dw[i]
photons["pos"][tfr, i] -= dw[i]
# Re-center all coordinates
if photons["pos"].shape[0] > 0:
photons["pos"] -= c
mylog.info("Finished generating photons.")
mylog.info("Number of photons generated: %d" %
int(np.sum(photons["num_photons"])))
mylog.info("Number of cells with photons: %d" % photons["dx"].size)
return cls(photons, parameters, cosmo)
def write_simput_file(self, prefix, clobber=False, emin=None, emax=None):
r"""
Write events to a SIMPUT file that may be read by the SIMX instrument
simulator.
Parameters
----------
prefix : string
The filename prefix.
clobber : boolean, optional
Set to True to overwrite previous files.
e_min : float, optional
The minimum energy of the photons to save in keV.
e_max : float, optional
The maximum energy of the photons to save in keV.
"""
pyfits = _astropy.pyfits
if isinstance(self.parameters["Area"], string_types):
mylog.error("Writing SIMPUT files is only supported if you didn't convolve with responses.")
raise TypeError("Writing SIMPUT files is only supported if you didn't convolve with responses.")
if emin is None:
emin = self["eobs"].min().value
if emax is None:
emax = self["eobs"].max().value
idxs = np.logical_and(self["eobs"].d >= emin, self["eobs"].d <= emax)
flux = np.sum(self["eobs"][idxs].in_units("erg")) / \
self.parameters["ExposureTime"]/self.parameters["Area"]
col1 = pyfits.Column(name='ENERGY', format='E', array=self["eobs"][idxs].d)
col2 = pyfits.Column(name='RA', format='D', array=self["xsky"][idxs].d)
col3 = pyfits.Column(name='DEC', format='D', array=self["ysky"][idxs].d)
coldefs = pyfits.ColDefs([col1, col2, col3])
tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
tbhdu.name = "PHLIST"
tbhdu.header["HDUCLASS"] = "HEASARC/SIMPUT"
tbhdu.header["HDUCLAS1"] = "PHOTONS"
tbhdu.header["HDUVERS"] = "1.1.0"
tbhdu.header["EXTVER"] = 1
tbhdu.header["REFRA"] = 0.0
tbhdu.header["REFDEC"] = 0.0
tbhdu.header["TUNIT1"] = "keV"
tbhdu.header["TUNIT2"] = "deg"
tbhdu.header["TUNIT3"] = "deg"
phfile = prefix+"_phlist.fits"
tbhdu.writeto(phfile, clobber=clobber)
col1 = pyfits.Column(name='SRC_ID', format='J', array=np.array([1]).astype("int32"))
col2 = pyfits.Column(name='RA', format='D', array=np.array([0.0]))
col3 = pyfits.Column(name='DEC', format='D', array=np.array([0.0]))
col4 = pyfits.Column(name='E_MIN', format='D', array=np.array([float(emin)]))
col5 = pyfits.Column(name='E_MAX', format='D', array=np.array([float(emax)]))
col6 = pyfits.Column(name='FLUX', format='D', array=np.array([flux.value]))
col7 = pyfits.Column(name='SPECTRUM', format='80A', array=np.array([phfile+"[PHLIST,1]"]))
col8 = pyfits.Column(name='IMAGE', format='80A', array=np.array([phfile+"[PHLIST,1]"]))
col9 = pyfits.Column(name='SRC_NAME', format='80A', array=np.array(["yt_src"]))
coldefs = pyfits.ColDefs([col1, col2, col3, col4, col5, col6, col7, col8, col9])
wrhdu = pyfits.BinTableHDU.from_columns(coldefs)
wrhdu.name = "SRC_CAT"
wrhdu.header["HDUCLASS"] = "HEASARC"
wrhdu.header["HDUCLAS1"] = "SIMPUT"
wrhdu.header["HDUCLAS2"] = "SRC_CAT"
wrhdu.header["HDUVERS"] = "1.1.0"
wrhdu.header["RADECSYS"] = "FK5"
wrhdu.header["EQUINOX"] = 2000.0
wrhdu.header["TUNIT2"] = "deg"
wrhdu.header["TUNIT3"] = "deg"
wrhdu.header["TUNIT4"] = "keV"
wrhdu.header["TUNIT5"] = "keV"
wrhdu.header["TUNIT6"] = "erg/s/cm**2"
simputfile = prefix+"_simput.fits"
wrhdu.writeto(simputfile, clobber=clobber)
def from_data_source(cls, data_source, redshift, area,
exp_time, source_model, point_sources=False,
parameters=None, center=None, dist=None,
cosmology=None, velocity_fields=None):
r"""
Initialize a :class:`~pyxsim.photon_list.PhotonList` from a yt data
source. The redshift, collecting area, exposure time, and cosmology
are stored in the *parameters* dictionary which is passed to the
*source_model* function.
Parameters
----------
data_source : :class:`~yt.data_objects.data_containers.YTSelectionContainer`
The data source from which the photons will be generated.
redshift : float
The cosmological redshift for the photons.
area : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The collecting area to determine the number of photons. If units are
not specified, it is assumed to be in cm^2.
exp_time : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The exposure time to determine the number of photons. If units are
not specified, it is assumed to be in seconds.
source_model : :class:`~pyxsim.source_models.SourceModel`
A source model used to generate the photons.
point_sources : boolean, optional
If True, the photons will be assumed to be generated from the exact
positions of the cells or particles and not smeared around within
a volume. Default: False
parameters : dict, optional
A dictionary of parameters to be passed for the source model to use,
if necessary.
center : string or array_like, optional
The origin of the photon spatial coordinates. Accepts "c", "max", or
a coordinate. If not specified, pyxsim attempts to use the "center"
field parameter of the data_source.
dist : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The angular diameter distance, used for nearby sources. This may be
optionally supplied instead of it being determined from the
*redshift* and given *cosmology*. If units are not specified, it is
assumed to be in kpc. To use this, the redshift must be set to zero.
cosmology : :class:`~yt.utilities.cosmology.Cosmology`, optional
Cosmological information. If not supplied, we try to get the
cosmology from the dataset. Otherwise, LCDM with the default yt
parameters is assumed.
velocity_fields : list of fields
The yt fields to use for the velocity. If not specified, the
following will be assumed:
['velocity_x', 'velocity_y', 'velocity_z'] for grid datasets
['particle_velocity_x', 'particle_velocity_y', 'particle_velocity_z'] for particle datasets
Examples
--------
>>> thermal_model = ThermalSourceModel(apec_model, Zmet=0.3)
>>> redshift = 0.05
>>> area = 6000.0 # assumed here in cm**2
>>> time = 2.0e5 # assumed here in seconds
>>> sp = ds.sphere("c", (500., "kpc"))
>>> my_photons = PhotonList.from_data_source(sp, redshift, area,
... time, thermal_model)
"""
ds = data_source.ds
if parameters is None:
parameters = {}
if cosmology is None:
if hasattr(ds, 'cosmology'):
cosmo = ds.cosmology
else:
cosmo = Cosmology()
else:
cosmo = cosmology
if dist is None:
if redshift <= 0.0:
msg = "If redshift <= 0.0, you must specify a distance to the " \
"source using the 'dist' argument!"
mylog.error(msg)
raise ValueError(msg)
D_A = cosmo.angular_diameter_distance(0.0, redshift).in_units("Mpc")
else:
D_A = parse_value(dist, "kpc")
if redshift > 0.0:
mylog.warning("Redshift must be zero for nearby sources. "
"Resetting redshift to 0.0.")
redshift = 0.0
if isinstance(center, string_types):
if center == "center" or center == "c":
parameters["center"] = ds.domain_center
elif center == "max" or center == "m":
parameters["center"] = ds.find_max("density")[-1]
elif iterable(center):
if isinstance(center, YTArray):
parameters["center"] = center.in_units("code_length")
elif isinstance(center, tuple):
if center[0] == "min":
parameters["center"] = ds.find_min(center[1])[-1]
elif center[0] == "max":
parameters["center"] = ds.find_max(center[1])[-1]
else:
raise RuntimeError
else:
parameters["center"] = ds.arr(center, "code_length")
elif center is None:
if hasattr(data_source, "left_edge"):
parameters["center"] = 0.5*(data_source.left_edge+data_source.right_edge)
else:
parameters["center"] = data_source.get_field_parameter("center")
parameters["fid_exp_time"] = parse_value(exp_time, "s")
parameters["fid_area"] = parse_value(area, "cm**2")
parameters["fid_redshift"] = redshift
parameters["fid_d_a"] = D_A
parameters["hubble"] = cosmo.hubble_constant
parameters["omega_matter"] = cosmo.omega_matter
parameters["omega_lambda"] = cosmo.omega_lambda
if redshift > 0.0:
mylog.info("Cosmology: h = %g, omega_matter = %g, omega_lambda = %g" %
(cosmo.hubble_constant, cosmo.omega_matter, cosmo.omega_lambda))
else:
mylog.info("Observing local source at distance %s." % D_A)
D_A = parameters["fid_d_a"].in_cgs()
dist_fac = 1.0/(4.*np.pi*D_A.value*D_A.value*(1.+redshift)**2)
spectral_norm = parameters["fid_area"].v*parameters["fid_exp_time"].v*dist_fac
source_model.setup_model(data_source, redshift, spectral_norm)
p_fields, v_fields, w_field = determine_fields(ds,
source_model.source_type,
point_sources)
if velocity_fields is not None:
v_fields = velocity_fields
if p_fields[0] == ("index", "x"):
parameters["data_type"] = "cells"
else:
parameters["data_type"] = "particles"
citer = data_source.chunks([], "io")
photons = defaultdict(list)
for chunk in parallel_objects(citer):
chunk_data = source_model(chunk)
if chunk_data is not None:
ncells, number_of_photons, idxs, energies = chunk_data
photons["num_photons"].append(number_of_photons)
photons["energy"].append(energies)
photons["pos"].append(np.array([chunk[p_fields[0]].d[idxs],
chunk[p_fields[1]].d[idxs],
chunk[p_fields[2]].d[idxs]]))
photons["vel"].append(np.array([chunk[v_fields[0]].d[idxs],
chunk[v_fields[1]].d[idxs],
chunk[v_fields[2]].d[idxs]]))
if w_field is None:
photons["dx"].append(np.zeros(ncells))
else:
photons["dx"].append(chunk[w_field].d[idxs])
source_model.cleanup_model()
photon_units = {"pos": ds.field_info[p_fields[0]].units,
"vel": ds.field_info[v_fields[0]].units,
"energy": "keV"}
if w_field is None:
photon_units["dx"] = "kpc"
else:
photon_units["dx"] = ds.field_info[w_field].units
concatenate_photons(ds, photons, photon_units)
c = parameters["center"].to("kpc")
if sum(ds.periodicity) > 0:
# Fix photon coordinates for regions crossing a periodic boundary
dw = ds.domain_width.to("kpc")
le, re = find_object_bounds(data_source)
for i in range(3):
if ds.periodicity[i] and photons["pos"].shape[0] > 0:
tfl = photons["pos"][:,i] < le[i]
tfr = photons["pos"][:,i] > re[i]
photons["pos"][tfl,i] += dw[i]
photons["pos"][tfr,i] -= dw[i]
# Re-center all coordinates
if photons["pos"].shape[0] > 0:
photons["pos"] -= c
mylog.info("Finished generating photons.")
mylog.info("Number of photons generated: %d" % int(np.sum(photons["num_photons"])))
mylog.info("Number of cells with photons: %d" % photons["dx"].size)
return cls(photons, parameters, cosmo)
def project_photons(self,
normal,
area_new=None,
exp_time_new=None,
redshift_new=None,
dist_new=None,
absorb_model=None,
sky_center=None,
no_shifting=False,
north_vector=None,
prng=None):
r"""
Projects photons onto an image plane given a line of sight.
Returns a new :class:`~pyxsim.event_list.EventList`.
Parameters
----------
normal : character or array-like
Normal vector to the plane of projection. If "x", "y", or "z", will
assume to be along that axis (and will probably be faster). Otherwise,
should be an off-axis normal vector, e.g [1.0, 2.0, -3.0]
area_new : float, (value, unit) tuple, or :class:`~yt.units.yt_array.YTQuantity`, optional
New value for the (constant) collecting area of the detector. If
units are not specified, is assumed to be in cm**2.
exp_time_new : float, (value, unit) tuple, or :class:`~yt.units.yt_array.YTQuantity`, optional
The new value for the exposure time. If units are not specified
it is assumed to be in seconds.
redshift_new : float, optional
The new value for the cosmological redshift.
dist_new : float, (value, unit) tuple, or :class:`~yt.units.yt_array.YTQuantity`, optional
The new value for the angular diameter distance, used for nearby sources.
This may be optionally supplied instead of it being determined from the
cosmology. If units are not specified, it is assumed to be in Mpc. To use this, the
redshift must be zero.
absorb_model : :class:`~pyxsim.spectral_models.AbsorptionModel`
A model for foreground galactic absorption.
sky_center : array-like, optional
Center RA, Dec of the events in degrees.
no_shifting : boolean, optional
If set, the photon energies will not be Doppler shifted.
north_vector : a sequence of floats
A vector defining the "up" direction. This option sets the orientation of
the plane of projection. If not set, an arbitrary grid-aligned north_vector
is chosen. Ignored in the case where a particular axis (e.g., "x", "y", or
"z") is explicitly specified.
prng : :class:`~numpy.random.RandomState` object or :mod:`~numpy.random`, optional
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 the :mod:`numpy.random` module.
Examples
--------
>>> L = np.array([0.1,-0.2,0.3])
>>> events = my_photons.project_photons(L, area_new=10000.,
... redshift_new=0.05)
"""
if prng is None:
prng = np.random
if redshift_new is not None and dist_new is not None:
mylog.error("You may specify a new redshift or distance, " +
"but not both!")
if sky_center is None:
sky_center = YTArray([30., 45.], "degree")
else:
sky_center = YTArray(sky_center, "degree")
dx = self.photons["dx"].d
if isinstance(normal, string_types):
# if on-axis, just use the maximum width of the plane perpendicular
# to that axis
w = self.parameters["Width"].copy()
w["xyz".index(normal)] = 0.0
ax_idx = np.argmax(w)
else:
# if off-axis, just use the largest width to make sure we get everything
ax_idx = np.argmax(self.parameters["Width"])
nx = self.parameters["Dimension"][ax_idx]
dx_min = (self.parameters["Width"] /
self.parameters["Dimension"])[ax_idx]
if not isinstance(normal, string_types):
L = np.array(normal)
orient = Orientation(L, north_vector=north_vector)
x_hat = orient.unit_vectors[0]
y_hat = orient.unit_vectors[1]
z_hat = orient.unit_vectors[2]
n_ph = self.photons["NumberOfPhotons"]
n_ph_tot = n_ph.sum()
parameters = {}
zobs0 = self.parameters["FiducialRedshift"]
D_A0 = self.parameters["FiducialAngularDiameterDistance"]
scale_factor = 1.0
if (exp_time_new is None and area_new is None and redshift_new is None
and dist_new is None):
my_n_obs = n_ph_tot
zobs = zobs0
D_A = D_A0
else:
if exp_time_new is None:
Tratio = 1.
else:
exp_time_new = parse_value(exp_time_new, "s")
Tratio = exp_time_new / self.parameters["FiducialExposureTime"]
if area_new is None:
Aratio = 1.
else:
area_new = parse_value(area_new, "cm**2")
Aratio = area_new / self.parameters["FiducialArea"]
if redshift_new is None and dist_new is None:
Dratio = 1.
zobs = zobs0
D_A = D_A0
else:
if dist_new is not None:
if redshift_new is not None and redshift_new > 0.0:
mylog.warning(
"Redshift must be zero for nearby sources. Resetting redshift to 0.0."
)
zobs = 0.0
D_A = parse_value(dist_new, "Mpc")
else:
zobs = redshift_new
D_A = self.cosmo.angular_diameter_distance(
0.0, zobs).in_units("Mpc")
scale_factor = (1. + zobs0) / (1. + zobs)
Dratio = D_A0*D_A0*(1.+zobs0)**3 / \
(D_A*D_A*(1.+zobs)**3)
fak = Aratio * Tratio * Dratio
if fak > 1:
raise ValueError(
"This combination of requested parameters results in "
"%g%% more photons collected than are " % (100. *
(fak - 1.)) +
"available in the sample. Please reduce the collecting "
"area, exposure time, or increase the distance/redshift "
"of the object. Alternatively, generate a larger sample "
"of photons.")
my_n_obs = np.int64(n_ph_tot * fak)
Nn = 4294967294
if my_n_obs == n_ph_tot:
if my_n_obs <= Nn:
idxs = np.arange(my_n_obs, dtype='uint32')
else:
idxs = np.arange(my_n_obs, dtype='uint64')
else:
if n_ph_tot <= Nn:
idxs = np.arange(n_ph_tot, dtype='uint32')
prng.shuffle(idxs)
idxs = idxs[:my_n_obs]
else:
Nc = np.int32(n_ph_tot / Nn)
idxs = np.zeros(my_n_obs, dtype=np.uint64)
Nup = np.uint32(my_n_obs / Nc)
for i in range(Nc + 1):
if (i + 1) * Nc < n_ph_tot:
idtm = np.arange(i * Nc, (i + 1) * Nc, dtype='uint64')
Nupt = Nup
else:
idtm = np.arange(i * Nc, n_ph_tot, dtype='uint64')
Nupt = my_n_obs - i * Nup
prng.shuffle(idtm)
idxs[i * Nup, i * Nup + Nupt] = idtm[:Nupt]
del (idtm)
# idxs = prng.permutation(n_ph_tot)[:my_n_obs].astype("int64")
obs_cells = np.searchsorted(self.p_bins, idxs, side='right') - 1
delta = dx[obs_cells]
if isinstance(normal, string_types):
if self.parameters["DataType"] == "cells":
xsky = prng.uniform(low=-0.5, high=0.5, size=my_n_obs)
ysky = prng.uniform(low=-0.5, high=0.5, size=my_n_obs)
elif self.parameters["DataType"] == "particles":
xsky = prng.normal(loc=0.0, scale=1.0, size=my_n_obs)
ysky = prng.normal(loc=0.0, scale=1.0, size=my_n_obs)
xsky *= delta
ysky *= delta
xsky += self.photons[axes_lookup[normal][0]].d[obs_cells]
ysky += self.photons[axes_lookup[normal][1]].d[obs_cells]
if not no_shifting:
vz = self.photons["v%s" % normal]
else:
if self.parameters["DataType"] == "cells":
x = prng.uniform(low=-0.5, high=0.5, size=my_n_obs)
y = prng.uniform(low=-0.5, high=0.5, size=my_n_obs)
z = prng.uniform(low=-0.5, high=0.5, size=my_n_obs)
elif self.parameters["DataType"] == "particles":
x = prng.normal(loc=0.0, scale=1.0, size=my_n_obs)
y = prng.normal(loc=0.0, scale=1.0, size=my_n_obs)
z = prng.normal(loc=0.0, scale=1.0, size=my_n_obs)
if not no_shifting:
vz = self.photons["vx"]*z_hat[0] + \
self.photons["vy"]*z_hat[1] + \
self.photons["vz"]*z_hat[2]
x *= delta
y *= delta
z *= delta
x += self.photons["x"].d[obs_cells]
y += self.photons["y"].d[obs_cells]
z += self.photons["z"].d[obs_cells]
xsky = x * x_hat[0] + y * x_hat[1] + z * x_hat[2]
ysky = x * y_hat[0] + y * y_hat[1] + z * y_hat[2]
del (delta)
if no_shifting:
eobs = self.photons["Energy"][idxs]
else:
# shift = -vz.in_cgs()/clight
# shift = np.sqrt((1.-shift)/(1.+shift))
# eobs = self.photons["Energy"][idxs]*shift[obs_cells]
shift = -vz[obs_cells].in_cgs() / clight
shift = np.sqrt((1. - shift) / (1. + shift))
eobs = self.photons["Energy"][idxs]
eobs *= shift
del (shift)
eobs *= scale_factor
if absorb_model is None:
detected = np.ones(eobs.shape, dtype='bool')
else:
detected = absorb_model.absorb_photons(eobs, prng=prng)
events = {}
dtheta = YTQuantity(np.rad2deg(dx_min / D_A), "degree")
events["xpix"] = xsky[detected] / dx_min.v + 0.5 * (nx + 1)
events["ypix"] = ysky[detected] / dx_min.v + 0.5 * (nx + 1)
events["eobs"] = eobs[detected]
events = comm.par_combine_object(events, datatype="dict", op="cat")
num_events = len(events["xpix"])
if comm.rank == 0:
mylog.info("Total number of observed photons: %d" % num_events)
if exp_time_new is None:
parameters["ExposureTime"] = self.parameters[
"FiducialExposureTime"]
else:
parameters["ExposureTime"] = exp_time_new
if area_new is None:
parameters["Area"] = self.parameters["FiducialArea"]
else:
parameters["Area"] = area_new
parameters["Redshift"] = zobs
parameters["AngularDiameterDistance"] = D_A.in_units("Mpc")
parameters["sky_center"] = sky_center
parameters["pix_center"] = np.array([0.5 * (nx + 1)] * 2)
parameters["dtheta"] = dtheta
return EventList(events, parameters)
def from_data_source(cls,
data_source,
redshift,
area,
exp_time,
source_model,
parameters=None,
center=None,
dist=None,
cosmology=None,
velocity_fields=None):
r"""
Initialize a :class:`~pyxsim.photon_list.PhotonList` from a yt data source.
The redshift, collecting area, exposure time, and cosmology are stored in the
*parameters* dictionary which is passed to the *source_model* function.
Parameters
----------
data_source : :class:`~yt.data_objects.data_containers.YTSelectionContainer`
The data source from which the photons will be generated.
redshift : float
The cosmological redshift for the photons.
area : float, (value, unit) tuple, or :class:`~yt.units.yt_array.YTQuantity`.
The collecting area to determine the number of photons. If units are
not specified, it is assumed to be in cm^2.
exp_time : float, (value, unit) tuple, or :class:`~yt.units.yt_array.YTQuantity`.
The exposure time to determine the number of photons. If units are
not specified, it is assumed to be in seconds.
source_model : :class:`~pyxsim.source_models.SourceModel`
A source model used to generate the photons.
parameters : dict, optional
A dictionary of parameters to be passed for the source model to use, if necessary.
center : string or array_like, optional
The origin of the photon spatial coordinates. Accepts "c", "max", or a coordinate.
If not specified, pyxsim attempts to use the "center" field parameter of the data_source.
dist : float, (value, unit) tuple, or :class:`~yt.units.yt_array.YTQuantity`, optional
The angular diameter distance, used for nearby sources. This may be
optionally supplied instead of it being determined from the *redshift*
and given *cosmology*. If units are not specified, it is assumed to be
in Mpc. To use this, the redshift must be set to zero.
cosmology : :class:`~yt.utilities.cosmology.Cosmology`, optional
Cosmological information. If not supplied, we try to get
the cosmology from the dataset. Otherwise, LCDM with
the default yt parameters is assumed.
velocity_fields : list of fields
The yt fields to use for the velocity. If not specified, the following will
be assumed:
['velocity_x', 'velocity_y', 'velocity_z'] for grid datasets
['particle_velocity_x', 'particle_velocity_y', 'particle_velocity_z'] for particle datasets
Examples
--------
>>> thermal_model = ThermalSourceModel(apec_model, Zmet=0.3)
>>> redshift = 0.05
>>> area = 6000.0 # assumed here in cm**2
>>> time = 2.0e5 # assumed here in seconds
>>> sp = ds.sphere("c", (500., "kpc"))
>>> my_photons = PhotonList.from_data_source(sp, redshift, area,
... time, thermal_model)
"""
ds = data_source.ds
if parameters is None:
parameters = {}
if cosmology is None:
if hasattr(ds, 'cosmology'):
cosmo = ds.cosmology
else:
cosmo = Cosmology()
else:
cosmo = cosmology
mylog.info(
"Cosmology: h = %g, omega_matter = %g, omega_lambda = %g" %
(cosmo.hubble_constant, cosmo.omega_matter, cosmo.omega_lambda))
if dist is None:
if redshift <= 0.0:
msg = "If redshift <= 0.0, you must specify a distance to the source using the 'dist' argument!"
mylog.error(msg)
raise ValueError(msg)
D_A = cosmo.angular_diameter_distance(0.0,
redshift).in_units("Mpc")
else:
D_A = parse_value(dist, "Mpc")
if redshift > 0.0:
mylog.warning(
"Redshift must be zero for nearby sources. Resetting redshift to 0.0."
)
redshift = 0.0
if center == "center" or center == "c":
parameters["center"] = ds.domain_center
elif center == "max" or center == "m":
parameters["center"] = ds.find_max("density")[-1]
elif iterable(center):
if isinstance(center, YTArray):
parameters["center"] = center.in_units("code_length")
elif isinstance(center, tuple):
if center[0] == "min":
parameters["center"] = ds.find_min(center[1])[-1]
elif center[0] == "max":
parameters["center"] = ds.find_max(center[1])[-1]
else:
raise RuntimeError
else:
parameters["center"] = ds.arr(center, "code_length")
elif center is None:
parameters["center"] = data_source.get_field_parameter("center")
parameters["FiducialExposureTime"] = parse_value(exp_time, "s")
parameters["FiducialArea"] = parse_value(area, "cm**2")
parameters["FiducialRedshift"] = redshift
parameters["FiducialAngularDiameterDistance"] = D_A
parameters["HubbleConstant"] = cosmo.hubble_constant
parameters["OmegaMatter"] = cosmo.omega_matter
parameters["OmegaLambda"] = cosmo.omega_lambda
D_A = parameters["FiducialAngularDiameterDistance"].in_cgs()
dist_fac = 1.0 / (4. * np.pi * D_A.value * D_A.value *
(1. + redshift)**2)
spectral_norm = parameters["FiducialArea"].v * parameters[
"FiducialExposureTime"].v * dist_fac
source_model.setup_model(data_source, redshift, spectral_norm)
p_fields, v_fields, w_field = determine_fields(
ds, source_model.source_type)
if velocity_fields is not None:
v_fields = velocity_fields
if p_fields[0] == ("index", "x"):
parameters["DataType"] = "cells"
else:
parameters["DataType"] = "particles"
if hasattr(data_source, "left_edge"):
# Region or grid
le = data_source.left_edge
re = data_source.right_edge
elif hasattr(data_source,
"radius") and not hasattr(data_source, "height"):
# Sphere
le = -data_source.radius + data_source.center
re = data_source.radius + data_source.center
else:
# Compute rough boundaries of the object
# DOES NOT WORK for objects straddling periodic
# boundaries yet
if sum(ds.periodicity) > 0:
mylog.warning("You are using a region that is not currently "
"supported for straddling periodic boundaries. "
"Check to make sure that this is not the case.")
le = ds.arr(np.zeros(3), "code_length")
re = ds.arr(np.zeros(3), "code_length")
for i, ax in enumerate(p_fields):
le[i], re[i] = data_source.quantities.extrema(ax)
dds_min = get_smallest_dds(ds, parameters["DataType"])
le = np.rint((le - ds.domain_left_edge) /
dds_min) * dds_min + ds.domain_left_edge
re = ds.domain_right_edge - np.rint(
(ds.domain_right_edge - re) / dds_min) * dds_min
width = re - le
parameters["Dimension"] = np.rint(width / dds_min).astype("int")
parameters["Width"] = parameters["Dimension"] * dds_min.in_units("kpc")
citer = data_source.chunks([], "io")
photons = defaultdict(list)
for chunk in parallel_objects(citer):
chunk_data = source_model(chunk)
if chunk_data is not None:
number_of_photons, idxs, energies = chunk_data
photons["NumberOfPhotons"].append(number_of_photons)
photons["Energy"].append(ds.arr(energies, "keV"))
photons["x"].append(chunk[p_fields[0]][idxs].in_units("kpc"))
photons["y"].append(chunk[p_fields[1]][idxs].in_units("kpc"))
photons["z"].append(chunk[p_fields[2]][idxs].in_units("kpc"))
photons["vx"].append(chunk[v_fields[0]][idxs].in_units("km/s"))
photons["vy"].append(chunk[v_fields[1]][idxs].in_units("km/s"))
photons["vz"].append(chunk[v_fields[2]][idxs].in_units("km/s"))
if w_field is None:
photons["dx"].append(ds.arr(np.zeros(len(idxs)), "kpc"))
else:
photons["dx"].append(chunk[w_field][idxs].in_units("kpc"))
source_model.cleanup_model()
concatenate_photons(photons)
# Translate photon coordinates to the source center
# Fix photon coordinates for regions crossing a periodic boundary
dw = ds.domain_width.to("kpc")
for i, ax in enumerate("xyz"):
if ds.periodicity[i] and len(photons[ax]) > 0:
tfl = photons[ax] < le[i].to('kpc')
tfr = photons[ax] > re[i].to('kpc')
photons[ax][tfl] += dw[i]
photons[ax][tfr] -= dw[i]
photons[ax] -= parameters["center"][i].in_units("kpc")
mylog.info("Finished generating photons.")
mylog.info("Number of photons generated: %d" %
int(np.sum(photons["NumberOfPhotons"])))
mylog.info("Number of cells with photons: %d" % len(photons["x"]))
return cls(photons, parameters, cosmo)