def __init__(self, spectrum, arf):
"""
Generate a convolved spectrum by convolving a spectrum with an
ARF.
Parameters
----------
spectrum : :class:`~soxs.spectra.Spectrum` object
The input spectrum to convolve with.
arf : string or :class:`~soxs.instrument.AuxiliaryResponseFile`
The ARF to use in the convolution.
"""
if not isinstance(arf, AuxiliaryResponseFile):
arf = AuxiliaryResponseFile(arf)
self.arf = arf
earea = arf.interpolate_area(spectrum.emid)
rate = spectrum.flux * earea
super(ConvolvedSpectrum, self).__init__(spectrum.ebins, rate)
def __init__(self, spectrum, arf):
"""
Generate a convolved spectrum by convolving a spectrum with an
ARF.
Parameters
----------
spectrum : :class:`~soxs.spectra.Spectrum` object
The input spectrum to convolve with.
arf : string or :class:`~soxs.instrument.AuxiliaryResponseFile`
The ARF to use in the convolution.
"""
if not isinstance(arf, AuxiliaryResponseFile):
arf = AuxiliaryResponseFile(arf)
self.arf = arf
earea = arf.interpolate_area(spectrum.emid)
rate = spectrum.flux * earea
super(ConvolvedSpectrum, self).__init__(spectrum.ebins, rate)
def test_convolved_spectra():
arf = AuxiliaryResponseFile("xrs_hdxi_3x10.arf")
spec1 = Spectrum.from_powerlaw(2.0, 0.01, 1.0, 0.1, 10.0, 1000)
cspec1 = ConvolvedSpectrum(spec1, arf)
cspec2 = spec1*arf
spec2 = cspec1.deconvolve()
assert_array_equal(cspec1.ebins.value, cspec2.ebins.value)
assert_array_equal(spec1.ebins.value, spec2.ebins.value)
assert_array_equal(cspec1.flux.value, cspec2.flux.value)
assert_allclose(spec1.flux.value, spec2.flux.value)
def test_uniform_bkgnd_scale():
hdxi_arf = AuxiliaryResponseFile("xrs_hdxi_3x10.arf")
events, event_params = make_background((50, "ks"),
"hdxi", [30., 45.],
foreground=True,
instr_bkgnd=True,
ptsrc_bkgnd=False,
prng=prng)
ncts = np.logical_and(events["energy"] >= 0.7,
events["energy"] <= 2.0).sum()
t_exp = event_params["exposure_time"]
fov = (event_params["fov"] * 60.0)**2
S = ncts / t_exp / fov
dS = np.sqrt(ncts) / t_exp / fov
foreground = ConvolvedBackgroundSpectrum(hm_astro_bkgnd, hdxi_arf)
f_sum = foreground.get_flux_in_band(0.7, 2.0)[0]
i_sum = acisi_particle_bkgnd.get_flux_in_band(0.7, 2.0)[0]
b_sum = (f_sum + i_sum).to("ph/(arcsec**2*s)").value
assert np.abs(S - b_sum) < 1.645 * dS
def test_simulate_bkgnd_spectrum():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
prng = RandomState(29)
hdxi_arf = AuxiliaryResponseFile("xrs_hdxi_3x10.arf")
hdxi_rmf = RedistributionMatrixFile("xrs_hdxi.rmf")
exp_time = 50000.0
fov = 3600.0
simulate_spectrum(None,
"hdxi",
exp_time,
"test_bkgnd.pha",
instr_bkgnd=True,
foreground=True,
prng=prng,
overwrite=True,
bkgnd_area=(fov, "arcsec**2"))
ch_min = hdxi_rmf.e_to_ch(0.7) - hdxi_rmf.cmin
ch_max = hdxi_rmf.e_to_ch(2.0) - hdxi_rmf.cmin
f = pyfits.open("test_bkgnd.pha")
ncts = f["SPECTRUM"].data["COUNTS"][ch_min:ch_max].sum()
f.close()
S = ncts / exp_time / fov
dS = np.sqrt(ncts) / exp_time / fov
foreground = ConvolvedBackgroundSpectrum(hm_astro_bkgnd, hdxi_arf)
f_sum = foreground.get_flux_in_band(0.7, 2.0)[0]
i_sum = acisi_particle_bkgnd.get_flux_in_band(0.7, 2.0)[0]
b_sum = (f_sum + i_sum).to("ph/(arcsec**2*s)").value
assert np.abs(S - b_sum) < 1.645 * dS
os.chdir(curdir)
shutil.rmtree(tmpdir)
def generate_events(input_events, exp_time, instrument, sky_center,
no_dither=False, dither_params=None,
roll_angle=0.0, subpixel_res=False, prng=None):
"""
Take unconvolved events and convolve them with instrumental responses. This
function does the following:
1. Determines which events are observed using the ARF
2. Pixelizes the events, applying PSF effects and dithering
3. Determines energy channels using the RMF
This function is not meant to be called by the end-user but is used by
the :func:`~soxs.instrument.instrument_simulator` function.
Parameters
----------
input_events : string, dict, or None
The unconvolved events to be used as input. Can be one of the
following:
1. The name of a SIMPUT catalog file.
2. A Python dictionary containing the following items:
"ra": A NumPy array of right ascension values in degrees.
"dec": A NumPy array of declination values in degrees.
"energy": A NumPy array of energy values in keV.
"flux": The flux of the entire source, in units of erg/cm**2/s.
out_file : string
The name of the event file to be written.
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time to use, in seconds.
instrument : string
The name of the instrument to use, which picks an instrument
specification from the instrument registry.
sky_center : array, tuple, or list
The center RA, Dec coordinates of the observation, in degrees.
no_dither : boolean, optional
If True, turn off dithering entirely. Default: False
dither_params : array-like of floats, optional
The parameters to use to control the size and period of the dither
pattern. The first two numbers are the dither amplitude in x and y
detector coordinates in arcseconds, and the second two numbers are
the dither period in x and y detector coordinates in seconds.
Default: [8.0, 8.0, 1000.0, 707.0].
roll_angle : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The roll angle of the observation in degrees. Default: 0.0
subpixel_res: boolean, optional
If True, event positions are not randomized within the pixels
within which they are detected. Default: False
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.
"""
import pyregion._region_filter as rfilter
exp_time = parse_value(exp_time, "s")
roll_angle = parse_value(roll_angle, "deg")
prng = parse_prng(prng)
if isinstance(input_events, dict):
parameters = {}
for key in ["flux", "emin", "emax", "sources"]:
parameters[key] = input_events[key]
event_list = []
for i in range(len(parameters["flux"])):
edict = {}
for key in ["ra", "dec", "energy"]:
edict[key] = input_events[key][i]
event_list.append(edict)
elif isinstance(input_events, string_types):
# Assume this is a SIMPUT catalog
event_list, parameters = read_simput_catalog(input_events)
try:
instrument_spec = instrument_registry[instrument]
except KeyError:
raise KeyError("Instrument %s is not in the instrument registry!" % instrument)
if not instrument_spec["imaging"]:
raise RuntimeError("Instrument '%s' is not " % instrument_spec["name"] +
"designed for imaging observations!")
arf_file = get_response_path(instrument_spec["arf"])
rmf_file = get_response_path(instrument_spec["rmf"])
arf = AuxiliaryResponseFile(arf_file)
rmf = RedistributionMatrixFile(rmf_file)
nx = instrument_spec["num_pixels"]
plate_scale = instrument_spec["fov"]/nx/60. # arcmin to deg
plate_scale_arcsec = plate_scale * 3600.0
if not instrument_spec["dither"]:
dither_on = False
else:
dither_on = not no_dither
if dither_params is None:
dither_params = [8.0, 8.0, 1000.0, 707.0]
dither_dict = {"x_amp": dither_params[0],
"y_amp": dither_params[1],
"x_period": dither_params[2],
"y_period": dither_params[3],
"dither_on": dither_on,
"plate_scale": plate_scale_arcsec}
event_params = {}
event_params["exposure_time"] = exp_time
event_params["arf"] = arf.filename
event_params["sky_center"] = sky_center
event_params["pix_center"] = np.array([0.5*(2*nx+1)]*2)
event_params["num_pixels"] = nx
event_params["plate_scale"] = plate_scale
event_params["rmf"] = rmf.filename
event_params["channel_type"] = rmf.header["CHANTYPE"]
event_params["telescope"] = rmf.header["TELESCOP"]
event_params["instrument"] = instrument_spec['name']
event_params["mission"] = rmf.header.get("MISSION", "")
event_params["nchan"] = rmf.n_ch
event_params["roll_angle"] = roll_angle
event_params["fov"] = instrument_spec["fov"]
event_params["chan_lim"] = [rmf.cmin, rmf.cmax]
event_params["chips"] = instrument_spec["chips"]
event_params["dither_params"] = dither_dict
event_params["aimpt_coords"] = instrument_spec["aimpt_coords"]
w = pywcs.WCS(naxis=2)
w.wcs.crval = event_params["sky_center"]
w.wcs.crpix = event_params["pix_center"]
w.wcs.cdelt = [-plate_scale, plate_scale]
w.wcs.ctype = ["RA---TAN","DEC--TAN"]
w.wcs.cunit = ["deg"]*2
rot_mat = get_rot_mat(roll_angle)
all_events = defaultdict(list)
for i, evts in enumerate(event_list):
mylog.info("Detecting events from source %s." % parameters["sources"][i])
# Step 1: Use ARF to determine which photons are observed
mylog.info("Applying energy-dependent effective area from %s." % os.path.split(arf.filename)[-1])
refband = [parameters["emin"][i], parameters["emax"][i]]
events = arf.detect_events(evts, exp_time, parameters["flux"][i], refband, prng=prng)
n_evt = events["energy"].size
if n_evt == 0:
mylog.warning("No events were observed for this source!!!")
else:
# Step 2: Assign pixel coordinates to events. Apply dithering and
# PSF. Clip events that don't fall within the detection region.
mylog.info("Pixeling events.")
# Convert RA, Dec to pixel coordinates
xpix, ypix = w.wcs_world2pix(events["ra"], events["dec"], 1)
xpix -= event_params["pix_center"][0]
ypix -= event_params["pix_center"][1]
events.pop("ra")
events.pop("dec")
n_evt = xpix.size
# Rotate physical coordinates to detector coordinates
det = np.dot(rot_mat, np.array([xpix, ypix]))
detx = det[0,:] + event_params["aimpt_coords"][0]
dety = det[1,:] + event_params["aimpt_coords"][1]
# Add times to events
events['time'] = prng.uniform(size=n_evt, low=0.0,
high=event_params["exposure_time"])
# Apply dithering
x_offset, y_offset = perform_dither(events["time"], dither_dict)
detx -= x_offset
dety -= y_offset
# PSF scattering of detector coordinates
if instrument_spec["psf"] is not None:
psf_type, psf_spec = instrument_spec["psf"]
if psf_type == "gaussian":
sigma = psf_spec/sigma_to_fwhm/plate_scale_arcsec
detx += prng.normal(loc=0.0, scale=sigma, size=n_evt)
dety += prng.normal(loc=0.0, scale=sigma, size=n_evt)
else:
raise NotImplementedError("PSF type %s not implemented!" % psf_type)
# Convert detector coordinates to chip coordinates.
# Throw out events that don't fall on any chip.
cx = np.trunc(detx)+0.5*np.sign(detx)
cy = np.trunc(dety)+0.5*np.sign(dety)
if event_params["chips"] is None:
events["chip_id"] = np.zeros(n_evt, dtype='int')
keepx = np.logical_and(cx >= -0.5*nx, cx <= 0.5*nx)
keepy = np.logical_and(cy >= -0.5*nx, cy <= 0.5*nx)
keep = np.logical_and(keepx, keepy)
else:
events["chip_id"] = -np.ones(n_evt, dtype='int')
for i, chip in enumerate(event_params["chips"]):
thisc = np.ones(n_evt, dtype='bool')
rtype = chip[0]
args = chip[1:]
r = getattr(rfilter, rtype)(*args)
inside = r.inside(cx, cy)
thisc = np.logical_and(thisc, inside)
events["chip_id"][thisc] = i
keep = events["chip_id"] > -1
mylog.info("%d events were rejected because " % (n_evt-keep.sum()) +
"they do not fall on any CCD.")
n_evt = keep.sum()
if n_evt == 0:
mylog.warning("No events are within the field of view for this source!!!")
else:
# Keep only those events which fall on a chip
for key in events:
events[key] = events[key][keep]
# Convert chip coordinates back to detector coordinates, unless the
# user has specified that they want subpixel resolution
if subpixel_res:
events["detx"] = detx[keep]
events["dety"] = dety[keep]
else:
events["detx"] = cx[keep] + prng.uniform(low=-0.5, high=0.5, size=n_evt)
events["dety"] = cy[keep] + prng.uniform(low=-0.5, high=0.5, size=n_evt)
# Convert detector coordinates back to pixel coordinates by
# adding the dither offsets back in and applying the rotation
# matrix again
det = np.array([events["detx"] + x_offset[keep] - event_params["aimpt_coords"][0],
events["dety"] + y_offset[keep] - event_params["aimpt_coords"][1]])
pix = np.dot(rot_mat.T, det)
events["xpix"] = pix[0,:] + event_params['pix_center'][0]
events["ypix"] = pix[1,:] + event_params['pix_center'][1]
if n_evt > 0:
for key in events:
all_events[key] = np.concatenate([all_events[key], events[key]])
if len(all_events["energy"]) == 0:
mylog.warning("No events from any of the sources in the catalog were detected!")
for key in ["xpix", "ypix", "detx", "dety", "time", "chip_id", event_params["channel_type"]]:
all_events[key] = np.array([])
else:
# Step 4: Scatter energies with RMF
mylog.info("Scattering energies with RMF %s." % os.path.split(rmf.filename)[-1])
all_events = rmf.scatter_energies(all_events, prng=prng)
return all_events, event_params
from soxs.events import write_spectrum
from soxs.instrument_registry import get_instrument_from_registry
ckms = clight.in_units("km/s").v
def setup():
from yt.config import ytcfg
ytcfg["yt", "__withintesting"] = "True"
try:
mucal_spec = get_instrument_from_registry("mucal")
except KeyError:
pass
rmf = RedistributionMatrixFile(mucal_spec["rmf"])
arf = AuxiliaryResponseFile(mucal_spec['arf'])
fit_model = TableApecModel(rmf.elo[0], rmf.ehi[-1], rmf.n_e)
agen_var = TableApecModel(rmf.elo[0], rmf.ehi[-1], rmf.n_e,
var_elem=["O", "Ca"], thermal_broad=True)
def mymodel(pars, x, xhi=None):
dx = x[1]-x[0]
tm = TBabsModel(pars[0])
tbabs = tm.get_absorb(x+0.5*dx)
bapec = fit_model.return_spectrum(pars[1], pars[2], pars[3], pars[4], velocity=pars[5])
eidxs = np.logical_and(rmf.elo >= x[0]-0.5*dx, rmf.elo <= x[-1]+0.5*dx)
return tbabs*bapec[eidxs]
def mymodel_var(pars, x, xhi=None):
def plaw_fit(alpha_sim):
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
nH_sim = 0.02
norm_sim = 1.0e-4
redshift = 0.01
exp_time = 5.0e4
area = 40000.0
inst_name = "hdxi"
spec = Spectrum.from_powerlaw(alpha_sim, redshift, norm_sim)
spec.apply_foreground_absorption(nH_sim)
e = spec.generate_energies(exp_time, area)
pt_src = PointSourceModel(30.0, 45.0, e.size)
write_photon_list("plaw_model",
"plaw_model",
e.flux,
pt_src.ra,
pt_src.dec,
e,
clobber=True)
instrument_simulator("plaw_model_simput.fits",
"plaw_model_evt.fits",
exp_time,
inst_name, [30.0, 45.0],
astro_bkgnd=None,
instr_bkgnd_scale=0.0)
inst = get_instrument_from_registry(inst_name)
arf = AuxiliaryResponseFile(inst["arf"])
rmf = RedistributionMatrixFile(inst["rmf"])
os.system("cp %s ." % arf.filename)
os.system("cp %s ." % rmf.filename)
write_spectrum("plaw_model_evt.fits", "plaw_model_evt.pha", clobber=True)
load_user_model(mymodel, "wplaw")
add_user_pars("wplaw", ["nH", "norm", "redshift", "alpha"],
[0.01, norm_sim * 0.8, redshift, 0.9],
parmins=[0.0, 0.0, 0.0, 0.1],
parmaxs=[10.0, 1.0e9, 10.0, 10.0],
parfrozen=[False, False, True, False])
load_pha("plaw_model_evt.pha")
set_stat("cstat")
set_method("simplex")
ignore(":0.5, 9.0:")
set_model("wplaw")
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - nH_sim) < res.parmaxes[0]
assert np.abs(res.parvals[1] - norm_sim) < res.parmaxes[1]
assert np.abs(res.parvals[2] - alpha_sim) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def make_exposure_map(event_file, expmap_file, energy, weights=None,
asol_file=None, normalize=True, overwrite=False,
reblock=1, nhistx=16, nhisty=16, order=1):
"""
Make an exposure map for a SOXS event file, and optionally write
an aspect solution file. The exposure map will be created by
binning an aspect histogram over the range of the aspect solution.
Parameters
----------
event_file : string
The path to the event file to use for making the exposure map.
expmap_file : string
The path to write the exposure map file to.
energy : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, or NumPy array
The energy in keV to use when computing the exposure map, or
a set of energies to be used with the *weights* parameter. If
providing a set, it must be in keV.
weights : array-like, optional
The weights to use with a set of energies given in the
*energy* parameter. Used to create a more accurate exposure
map weighted by a range of energies. Default: None
asol_file : string, optional
The path to write the aspect solution file to, if desired.
Default: None
normalize : boolean, optional
If True, the exposure map will be divided by the exposure time
so that the map's units are cm**2. Default: True
overwrite : boolean, optional
Whether or not to overwrite an existing file. Default: False
reblock : integer, optional
Supply an integer power of 2 here to make an exposure map
with a different binning. Default: 1
nhistx : integer, optional
The number of bins in the aspect histogram in the DETX
direction. Default: 16
nhisty : integer, optional
The number of bins in the aspect histogram in the DETY
direction. Default: 16
order : integer, optional
The interpolation order to use when making the exposure map.
Default: 1
"""
import pyregion._region_filter as rfilter
from scipy.ndimage.interpolation import rotate, shift
from soxs.instrument import AuxiliaryResponseFile, perform_dither
if isinstance(energy, np.ndarray) and weights is None:
raise RuntimeError("Must supply a single value for the energy if "
"you do not supply weights!")
if not isinstance(energy, np.ndarray):
energy = parse_value(energy, "keV")
f_evt = pyfits.open(event_file)
hdu = f_evt["EVENTS"]
arf = AuxiliaryResponseFile(hdu.header["ANCRFILE"])
exp_time = hdu.header["EXPOSURE"]
nx = int(hdu.header["TLMAX2"]-0.5)//2
ny = int(hdu.header["TLMAX3"]-0.5)//2
ra0 = hdu.header["TCRVL2"]
dec0 = hdu.header["TCRVL3"]
xdel = hdu.header["TCDLT2"]
ydel = hdu.header["TCDLT3"]
x0 = hdu.header["TCRPX2"]
y0 = hdu.header["TCRPX3"]
xdet0 = 0.5*(2*nx+1)
ydet0 = 0.5*(2*ny+1)
xaim = hdu.header.get("AIMPT_X", 0.0)
yaim = hdu.header.get("AIMPT_Y", 0.0)
roll = hdu.header["ROLL_PNT"]
instr = instrument_registry[hdu.header["INSTRUME"].lower()]
dither_params = {}
if "DITHXAMP" in hdu.header:
dither_params["x_amp"] = hdu.header["DITHXAMP"]
dither_params["y_amp"] = hdu.header["DITHYAMP"]
dither_params["x_period"] = hdu.header["DITHXPER"]
dither_params["y_period"] = hdu.header["DITHYPER"]
dither_params["plate_scale"] = ydel*3600.0
dither_params["dither_on"] = True
else:
dither_params["dither_on"] = False
f_evt.close()
# Create time array for aspect solution
dt = 1.0 # Seconds
t = np.arange(0.0, exp_time+dt, dt)
# Construct WCS
w = pywcs.WCS(naxis=2)
w.wcs.crval = [ra0, dec0]
w.wcs.crpix = [x0, y0]
w.wcs.cdelt = [xdel, ydel]
w.wcs.ctype = ["RA---TAN","DEC--TAN"]
w.wcs.cunit = ["deg"]*2
# Create aspect solution if we had dithering.
# otherwise just set the offsets to zero
if dither_params["dither_on"]:
x_off, y_off = perform_dither(t, dither_params)
# Make the aspect histogram
x_amp = dither_params["x_amp"]/dither_params["plate_scale"]
y_amp = dither_params["y_amp"]/dither_params["plate_scale"]
x_edges = np.linspace(-x_amp, x_amp, nhistx+1, endpoint=True)
y_edges = np.linspace(-y_amp, y_amp, nhisty+1, endpoint=True)
asphist = np.histogram2d(x_off, y_off, (x_edges, y_edges))[0]
asphist *= dt
x_mid = 0.5*(x_edges[1:]+x_edges[:-1])/reblock
y_mid = 0.5*(y_edges[1:]+y_edges[:-1])/reblock
# Determine the effective area
eff_area = arf.interpolate_area(energy).value
if weights is not None:
eff_area = np.average(eff_area, weights=weights)
if instr["chips"] is None:
rtypes = ["Box"]
args = [[0.0, 0.0, instr["num_pixels"], instr["num_pixels"]]]
else:
rtypes = []
args = []
for i, chip in enumerate(instr["chips"]):
rtypes.append(chip[0])
args.append(np.array(chip[1:]))
tmpmap = np.zeros((2*nx, 2*ny))
for rtype, arg in zip(rtypes, args):
rfunc = getattr(rfilter, rtype)
new_args = parse_region_args(rtype, arg, xdet0-xaim-1.0, ydet0-yaim-1.0)
r = rfunc(*new_args)
tmpmap += r.mask(tmpmap).astype("float64")
tmpmap = downsample(tmpmap, reblock)
if dither_params["dither_on"]:
expmap = np.zeros(tmpmap.shape)
niter = nhistx*nhisty
pbar = tqdm(leave=True, total=niter, desc="Creating exposure map ")
for i in range(nhistx):
for j in range(nhisty):
expmap += shift(tmpmap, (x_mid[i], y_mid[j]), order=order)*asphist[i, j]
pbar.update(nhisty)
pbar.close()
else:
expmap = tmpmap*exp_time
expmap *= eff_area
if normalize:
expmap /= exp_time
if roll != 0.0:
rotate(expmap, roll, output=expmap, reshape=False)
expmap[expmap < 0.0] = 0.0
map_header = {"EXPOSURE": exp_time,
"MTYPE1": "EQPOS",
"MFORM1": "RA,DEC",
"CTYPE1": "RA---TAN",
"CTYPE2": "DEC--TAN",
"CRVAL1": ra0,
"CRVAL2": dec0,
"CUNIT1": "deg",
"CUNIT2": "deg",
"CDELT1": xdel*reblock,
"CDELT2": ydel*reblock,
"CRPIX1": 0.5*(2.0*nx//reblock+1),
"CRPIX2": 0.5*(2.0*ny//reblock+1)}
map_hdu = pyfits.ImageHDU(expmap, header=pyfits.Header(map_header))
map_hdu.name = "EXPMAP"
map_hdu.writeto(expmap_file, overwrite=overwrite)
if asol_file is not None:
if dither_params["dither_on"]:
det = np.array([x_off, y_off])
pix = np.dot(get_rot_mat(roll).T, det)
ra, dec = w.wcs_pix2world(pix[0,:]+x0, pix[1,:]+y0, 1)
col_t = pyfits.Column(name='time', format='D', unit='s', array=t)
col_ra = pyfits.Column(name='ra', format='D', unit='deg', array=ra)
col_dec = pyfits.Column(name='dec', format='D', unit='deg', array=dec)
coldefs = pyfits.ColDefs([col_t, col_ra, col_dec])
tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
tbhdu.name = "ASPSOL"
tbhdu.header["EXPOSURE"] = exp_time
hdulist = [pyfits.PrimaryHDU(), tbhdu]
pyfits.HDUList(hdulist).writeto(asol_file, overwrite=overwrite)
else:
mylog.warning("Refusing to write an aspect solution file because "
"there was no dithering.")
def simulate_spectrum(spec, instrument, exp_time, out_file,
instr_bkgnd=False, foreground=False,
ptsrc_bkgnd=False, bkgnd_area=None,
absorb_model="wabs", nH=0.05,
overwrite=False, prng=None):
"""
Generate a PI or PHA spectrum from a :class:`~soxs.spectra.Spectrum`
by convolving it with responses. To be used if one wants to
create a spectrum without worrying about spatial response. Similar
to XSPEC's "fakeit".
Parameters
----------
spec : :class:`~soxs.spectra.Spectrum`
The spectrum to be convolved. If None is supplied, only backgrounds
will be simulated (if they are turned on).
instrument : string
The name of the instrument to use, which picks an instrument
specification from the instrument registry.
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time in seconds.
out_file : string
The file to write the spectrum to.
instr_bkgnd : boolean, optional
Whether or not to include the instrumental/particle background.
Default: False
foreground : boolean, optional
Whether or not to include the local foreground.
Default: False
ptsrc_bkgnd : boolean, optional
Whether or not to include the unresolved point-source background.
Default: False
bkgnd_area : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The area on the sky for the background components, in square arcminutes.
Default: None, necessary to specify if any of the background components
are turned on.
absorb_model : string, optional
The absorption model to use, "wabs" or "tbabs". Default: "wabs"
nH : float, optional
The hydrogen column in units of 10**22 atoms/cm**2.
Default: 0.05
overwrite : boolean, optional
Whether or not to overwrite an existing file. Default: False
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.
Examples
--------
>>> spec = soxs.Spectrum.from_file("my_spectrum.txt")
>>> soxs.simulate_spectrum(spec, "lynx_lxm", 100000.0,
... "my_spec.pi", overwrite=True)
"""
from soxs.events import _write_spectrum
from soxs.instrument import RedistributionMatrixFile, \
AuxiliaryResponseFile
from soxs.spectra import ConvolvedSpectrum
from soxs.background.foreground import hm_astro_bkgnd
from soxs.background.instrument import instrument_backgrounds
from soxs.background.spectra import BackgroundSpectrum
prng = parse_prng(prng)
exp_time = parse_value(exp_time, "s")
try:
instrument_spec = instrument_registry[instrument]
except KeyError:
raise KeyError("Instrument %s is not in the instrument registry!" % instrument)
if foreground or instr_bkgnd or ptsrc_bkgnd:
if instrument_spec["grating"]:
raise NotImplementedError("Backgrounds cannot be included in simulations "
"of gratings spectra at this time!")
if bkgnd_area is None:
raise RuntimeError("The 'bkgnd_area' argument must be set if one wants "
"to simulate backgrounds! Specify a value in square "
"arcminutes.")
bkgnd_area = np.sqrt(parse_value(bkgnd_area, "arcmin**2"))
elif spec is None:
raise RuntimeError("You have specified no source spectrum and no backgrounds!")
arf_file = get_response_path(instrument_spec["arf"])
rmf_file = get_response_path(instrument_spec["rmf"])
arf = AuxiliaryResponseFile(arf_file)
rmf = RedistributionMatrixFile(rmf_file)
event_params = {}
event_params["RESPFILE"] = os.path.split(rmf.filename)[-1]
event_params["ANCRFILE"] = os.path.split(arf.filename)[-1]
event_params["TELESCOP"] = rmf.header["TELESCOP"]
event_params["INSTRUME"] = rmf.header["INSTRUME"]
event_params["MISSION"] = rmf.header.get("MISSION", "")
out_spec = np.zeros(rmf.n_ch)
if spec is not None:
cspec = ConvolvedSpectrum(spec, arf)
out_spec += rmf.convolve_spectrum(cspec, exp_time, prng=prng)
fov = None if bkgnd_area is None else np.sqrt(bkgnd_area)
if foreground:
mylog.info("Adding in astrophysical foreground.")
cspec_frgnd = ConvolvedSpectrum(hm_astro_bkgnd.to_spectrum(fov), arf)
out_spec += rmf.convolve_spectrum(cspec_frgnd, exp_time, prng=prng)
if instr_bkgnd and instrument_spec["bkgnd"] is not None:
mylog.info("Adding in instrumental background.")
instr_spec = instrument_backgrounds[instrument_spec["bkgnd"]]
cspec_instr = instr_spec.to_scaled_spectrum(fov,
instrument_spec["focal_length"])
out_spec += rmf.convolve_spectrum(cspec_instr, exp_time, prng=prng)
if ptsrc_bkgnd:
mylog.info("Adding in background from unresolved point-sources.")
spec_plaw = BackgroundSpectrum.from_powerlaw(1.45, 0.0, 2.0e-7, emin=0.01,
emax=10.0, nbins=300000)
spec_plaw.apply_foreground_absorption(nH, model=absorb_model)
cspec_plaw = ConvolvedSpectrum(spec_plaw.to_spectrum(fov), arf)
out_spec += rmf.convolve_spectrum(cspec_plaw, exp_time, prng=prng)
bins = (np.arange(rmf.n_ch)+rmf.cmin).astype("int32")
_write_spectrum(bins, out_spec, exp_time, rmf.header["CHANTYPE"],
event_params, out_file, overwrite=overwrite)
def make_background(exp_time, instrument, sky_center, foreground=True,
ptsrc_bkgnd=True, instr_bkgnd=True, no_dither=False,
dither_params=None, roll_angle=0.0, subpixel_res=False,
input_sources=None, absorb_model="wabs", nH=0.05, prng=None):
"""
Make background events.
Parameters
----------
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time to use, in seconds.
instrument : string
The name of the instrument to use, which picks an instrument
specification from the instrument registry.
sky_center : array, tuple, or list
The center RA, Dec coordinates of the observation, in degrees.
foreground : boolean, optional
Whether or not to include the Galactic foreground. Default: True
instr_bkgnd : boolean, optional
Whether or not to include the instrumental background. Default: True
no_dither : boolean, optional
If True, turn off dithering entirely. Default: False
dither_params : array-like of floats, optional
The parameters to use to control the size and period of the dither
pattern. The first two numbers are the dither amplitude in x and y
detector coordinates in arcseconds, and the second two numbers are
the dither period in x and y detector coordinates in seconds.
Default: [8.0, 8.0, 1000.0, 707.0].
ptsrc_bkgnd : boolean, optional
Whether or not to include the point-source background. Default: True
Default: 0.05
roll_angle : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The roll angle of the observation in degrees. Default: 0.0
subpixel_res: boolean, optional
If True, event positions are not randomized within the pixels
within which they are detected. Default: False
input_sources : string, optional
If set to a filename, input the point source positions, fluxes,
and spectral indices from an ASCII table instead of generating
them. Default: None
absorb_model : string, optional
The absorption model to use, "wabs" or "tbabs". Default: "wabs"
nH : float, optional
The hydrogen column in units of 10**22 atoms/cm**2.
Default: 0.05
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.
"""
from soxs.background import make_instrument_background, \
make_foreground, make_ptsrc_background
prng = parse_prng(prng)
exp_time = parse_value(exp_time, "s")
roll_angle = parse_value(roll_angle, "deg")
try:
instrument_spec = instrument_registry[instrument]
except KeyError:
raise KeyError("Instrument %s is not in the instrument registry!" % instrument)
if not instrument_spec["imaging"]:
raise RuntimeError("Instrument '%s' is not " % instrument_spec["name"] +
"designed for imaging observations!")
fov = instrument_spec["fov"]
input_events = defaultdict(list)
arf_file = get_response_path(instrument_spec["arf"])
arf = AuxiliaryResponseFile(arf_file)
rmf_file = get_response_path(instrument_spec["rmf"])
rmf = RedistributionMatrixFile(rmf_file)
if ptsrc_bkgnd:
mylog.info("Adding in point-source background.")
ptsrc_events = make_ptsrc_background(exp_time, fov, sky_center,
area=1.2*arf.max_area,
input_sources=input_sources,
absorb_model=absorb_model,
nH=nH, prng=prng)
for key in ["ra", "dec", "energy"]:
input_events[key].append(ptsrc_events[key])
input_events["flux"].append(ptsrc_events["flux"])
input_events["emin"].append(ptsrc_events["energy"].min())
input_events["emax"].append(ptsrc_events["energy"].max())
input_events["sources"].append("ptsrc_bkgnd")
events, event_params = generate_events(input_events, exp_time,
instrument, sky_center,
no_dither=no_dither,
dither_params=dither_params,
roll_angle=roll_angle,
subpixel_res=subpixel_res,
prng=prng)
mylog.info("Generated %d photons from the point-source background." % len(events["energy"]))
else:
nx = instrument_spec["num_pixels"]
events = defaultdict(list)
if not instrument_spec["dither"]:
dither_on = False
else:
dither_on = not no_dither
if dither_params is None:
dither_params = [8.0, 8.0, 1000.0, 707.0]
dither_dict = {"x_amp": dither_params[0],
"y_amp": dither_params[1],
"x_period": dither_params[2],
"y_period": dither_params[3],
"dither_on": dither_on,
"plate_scale": instrument_spec["fov"]/nx*60.0}
event_params = {"exposure_time": exp_time,
"fov": instrument_spec["fov"],
"num_pixels": nx,
"pix_center": np.array([0.5*(2*nx+1)]*2),
"channel_type": rmf.header["CHANTYPE"],
"sky_center": sky_center,
"dither_params": dither_dict,
"plate_scale": instrument_spec["fov"]/nx/60.0,
"chan_lim": [rmf.cmin, rmf.cmax],
"rmf": rmf_file, "arf": arf_file,
"telescope": rmf.header["TELESCOP"],
"instrument": instrument_spec['name'],
"mission": rmf.header.get("MISSION", ""),
"nchan": rmf.n_ch,
"roll_angle": roll_angle,
"aimpt_coords": instrument_spec["aimpt_coords"]}
if "chips" not in event_params:
event_params["chips"] = instrument_spec["chips"]
if foreground:
mylog.info("Adding in astrophysical foreground.")
bkg_events = make_foreground(event_params, arf, rmf, prng=prng)
for key in bkg_events:
events[key] = np.concatenate([events[key], bkg_events[key]])
if instr_bkgnd and instrument_spec["bkgnd"] is not None:
mylog.info("Adding in instrumental background.")
bkg_events = make_instrument_background(instrument_spec["bkgnd"],
event_params, rmf, prng=prng)
for key in bkg_events:
events[key] = np.concatenate([events[key], bkg_events[key]])
return events, event_params
def generate_events(input_events, exp_time, instrument, sky_center,
no_dither=False, dither_params=None,
roll_angle=0.0, subpixel_res=False, prng=None):
"""
Take unconvolved events and convolve them with instrumental responses. This
function does the following:
1. Determines which events are observed using the ARF
2. Pixelizes the events, applying PSF effects and dithering
3. Determines energy channels using the RMF
This function is not meant to be called by the end-user but is used by
the :func:`~soxs.instrument.instrument_simulator` function.
Parameters
----------
input_events : string, dict, or None
The unconvolved events to be used as input. Can be one of the
following:
1. The name of a SIMPUT catalog file.
2. A Python dictionary containing the following items:
"ra": A NumPy array of right ascension values in degrees.
"dec": A NumPy array of declination values in degrees.
"energy": A NumPy array of energy values in keV.
"flux": The flux of the entire source, in units of erg/cm**2/s.
out_file : string
The name of the event file to be written.
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time to use, in seconds.
instrument : string
The name of the instrument to use, which picks an instrument
specification from the instrument registry.
sky_center : array, tuple, or list
The center RA, Dec coordinates of the observation, in degrees.
no_dither : boolean, optional
If True, turn off dithering entirely. Default: False
dither_params : array-like of floats, optional
The parameters to use to control the size and period of the dither
pattern. The first two numbers are the dither amplitude in x and y
detector coordinates in arcseconds, and the second two numbers are
the dither period in x and y detector coordinates in seconds.
Default: [8.0, 8.0, 1000.0, 707.0].
roll_angle : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, optional
The roll angle of the observation in degrees. Default: 0.0
subpixel_res: boolean, optional
If True, event positions are not randomized within the pixels
within which they are detected. Default: False
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.
"""
import pyregion._region_filter as rfilter
exp_time = parse_value(exp_time, "s")
roll_angle = parse_value(roll_angle, "deg")
prng = parse_prng(prng)
if isinstance(input_events, dict):
parameters = {}
for key in ["flux", "emin", "emax", "sources"]:
parameters[key] = input_events[key]
event_list = []
for i in range(len(parameters["flux"])):
edict = {}
for key in ["ra", "dec", "energy"]:
edict[key] = input_events[key][i]
event_list.append(edict)
elif isinstance(input_events, string_types):
# Assume this is a SIMPUT catalog
event_list, parameters = read_simput_catalog(input_events)
try:
instrument_spec = instrument_registry[instrument]
except KeyError:
raise KeyError("Instrument %s is not in the instrument registry!" % instrument)
if not instrument_spec["imaging"]:
raise RuntimeError("Instrument '%s' is not " % instrument_spec["name"] +
"designed for imaging observations!")
arf_file = get_response_path(instrument_spec["arf"])
rmf_file = get_response_path(instrument_spec["rmf"])
arf = AuxiliaryResponseFile(arf_file)
rmf = RedistributionMatrixFile(rmf_file)
nx = instrument_spec["num_pixels"]
plate_scale = instrument_spec["fov"]/nx/60. # arcmin to deg
plate_scale_arcsec = plate_scale * 3600.0
if not instrument_spec["dither"]:
dither_on = False
else:
dither_on = not no_dither
if dither_params is None:
dither_params = [8.0, 8.0, 1000.0, 707.0]
dither_dict = {"x_amp": dither_params[0],
"y_amp": dither_params[1],
"x_period": dither_params[2],
"y_period": dither_params[3],
"dither_on": dither_on,
"plate_scale": plate_scale_arcsec}
event_params = {}
event_params["exposure_time"] = exp_time
event_params["arf"] = arf.filename
event_params["sky_center"] = sky_center
event_params["pix_center"] = np.array([0.5*(2*nx+1)]*2)
event_params["num_pixels"] = nx
event_params["plate_scale"] = plate_scale
event_params["rmf"] = rmf.filename
event_params["channel_type"] = rmf.header["CHANTYPE"]
event_params["telescope"] = rmf.header["TELESCOP"]
event_params["instrument"] = instrument_spec['name']
event_params["mission"] = rmf.header.get("MISSION", "")
event_params["nchan"] = rmf.n_ch
event_params["roll_angle"] = roll_angle
event_params["fov"] = instrument_spec["fov"]
event_params["chan_lim"] = [rmf.cmin, rmf.cmax]
event_params["chips"] = instrument_spec["chips"]
event_params["dither_params"] = dither_dict
event_params["aimpt_coords"] = instrument_spec["aimpt_coords"]
w = pywcs.WCS(naxis=2)
w.wcs.crval = event_params["sky_center"]
w.wcs.crpix = event_params["pix_center"]
w.wcs.cdelt = [-plate_scale, plate_scale]
w.wcs.ctype = ["RA---TAN","DEC--TAN"]
w.wcs.cunit = ["deg"]*2
rot_mat = get_rot_mat(roll_angle)
all_events = defaultdict(list)
for i, evts in enumerate(event_list):
mylog.info("Detecting events from source %s." % parameters["sources"][i])
# Step 1: Use ARF to determine which photons are observed
mylog.info("Applying energy-dependent effective area from %s." % os.path.split(arf.filename)[-1])
refband = [parameters["emin"][i], parameters["emax"][i]]
events = arf.detect_events(evts, exp_time, parameters["flux"][i], refband, prng=prng)
n_evt = events["energy"].size
if n_evt == 0:
mylog.warning("No events were observed for this source!!!")
else:
# Step 2: Assign pixel coordinates to events. Apply dithering and
# PSF. Clip events that don't fall within the detection region.
mylog.info("Pixeling events.")
# Convert RA, Dec to pixel coordinates
xpix, ypix = w.wcs_world2pix(events["ra"], events["dec"], 1)
xpix -= event_params["pix_center"][0]
ypix -= event_params["pix_center"][1]
events.pop("ra")
events.pop("dec")
n_evt = xpix.size
# Rotate physical coordinates to detector coordinates
det = np.dot(rot_mat, np.array([xpix, ypix]))
detx = det[0,:] + event_params["aimpt_coords"][0]
dety = det[1,:] + event_params["aimpt_coords"][1]
# Add times to events
events['time'] = prng.uniform(size=n_evt, low=0.0,
high=event_params["exposure_time"])
# Apply dithering
x_offset, y_offset = perform_dither(events["time"], dither_dict)
detx -= x_offset
dety -= y_offset
# PSF scattering of detector coordinates
if instrument_spec["psf"] is not None:
psf_type, psf_spec = instrument_spec["psf"]
if psf_type == "gaussian":
sigma = psf_spec/sigma_to_fwhm/plate_scale_arcsec
detx += prng.normal(loc=0.0, scale=sigma, size=n_evt)
dety += prng.normal(loc=0.0, scale=sigma, size=n_evt)
else:
raise NotImplementedError("PSF type %s not implemented!" % psf_type)
# Convert detector coordinates to chip coordinates.
# Throw out events that don't fall on any chip.
cx = np.trunc(detx)+0.5*np.sign(detx)
cy = np.trunc(dety)+0.5*np.sign(dety)
if event_params["chips"] is None:
events["chip_id"] = np.zeros(n_evt, dtype='int')
keepx = np.logical_and(cx >= -0.5*nx, cx <= 0.5*nx)
keepy = np.logical_and(cy >= -0.5*nx, cy <= 0.5*nx)
keep = np.logical_and(keepx, keepy)
else:
events["chip_id"] = -np.ones(n_evt, dtype='int')
for i, chip in enumerate(event_params["chips"]):
thisc = np.ones(n_evt, dtype='bool')
rtype = chip[0]
args = chip[1:]
r = getattr(rfilter, rtype)(*args)
inside = r.inside(cx, cy)
thisc = np.logical_and(thisc, inside)
events["chip_id"][thisc] = i
keep = events["chip_id"] > -1
mylog.info("%d events were rejected because " % (n_evt-keep.sum()) +
"they do not fall on any CCD.")
n_evt = keep.sum()
if n_evt == 0:
mylog.warning("No events are within the field of view for this source!!!")
else:
# Keep only those events which fall on a chip
for key in events:
events[key] = events[key][keep]
# Convert chip coordinates back to detector coordinates, unless the
# user has specified that they want subpixel resolution
if subpixel_res:
events["detx"] = detx[keep]
events["dety"] = dety[keep]
else:
events["detx"] = cx[keep] + prng.uniform(low=-0.5, high=0.5, size=n_evt)
events["dety"] = cy[keep] + prng.uniform(low=-0.5, high=0.5, size=n_evt)
# Convert detector coordinates back to pixel coordinates by
# adding the dither offsets back in and applying the rotation
# matrix again
det = np.array([events["detx"] + x_offset[keep] - event_params["aimpt_coords"][0],
events["dety"] + y_offset[keep] - event_params["aimpt_coords"][1]])
pix = np.dot(rot_mat.T, det)
events["xpix"] = pix[0,:] + event_params['pix_center'][0]
events["ypix"] = pix[1,:] + event_params['pix_center'][1]
if n_evt > 0:
for key in events:
all_events[key] = np.concatenate([all_events[key], events[key]])
if len(all_events["energy"]) == 0:
mylog.warning("No events from any of the sources in the catalog were detected!")
for key in ["xpix", "ypix", "detx", "dety", "time", "chip_id", event_params["channel_type"]]:
all_events[key] = np.array([])
else:
# Step 4: Scatter energies with RMF
mylog.info("Scattering energies with RMF %s." % os.path.split(rmf.filename)[-1])
all_events = rmf.scatter_energies(all_events, prng=prng)
return all_events, event_params
def test_annulus():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_in = 10.0
r_out = 30.0
e = spec.generate_energies(exp_time, area, prng=prng)
ann_src = AnnulusModel(ra0, dec0, r_in, r_out, e.size, prng=prng)
write_photon_list("ann",
"ann",
e.flux,
ann_src.ra,
ann_src.dec,
e,
overwrite=True)
instrument_simulator("ann_simput.fits",
"ann_evt.fits",
exp_time,
"hdxi", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
prng=prng)
inst = get_instrument_from_registry("hdxi")
arf = AuxiliaryResponseFile(inst["arf"])
cspec = ConvolvedSpectrum(spec, arf)
ph_flux = cspec.get_flux_in_band(0.5, 7.0)[0].value
S0 = ph_flux / (np.pi * (r_out**2 - r_in**2))
write_radial_profile("ann_evt.fits",
"ann_evt_profile.fits", [ra0, dec0],
1.1 * r_in,
0.9 * r_out,
100,
ctr_type="celestial",
emin=0.5,
emax=7.0,
overwrite=True)
load_data(1, "ann_evt_profile.fits", 3, ["RMID", "SUR_BRI", "SUR_BRI_ERR"])
set_stat("chi2")
set_method("levmar")
set_source("const1d.src")
src.c0 = 0.8 * S0
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - S0) < res.parmaxes[0]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_beta_model():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
r_c = 20.0
beta = 1.0
exp_time = Quantity(500.0, "ks")
e = spec.generate_energies(exp_time, area, prng=prng)
beta_src = BetaModel(ra0, dec0, r_c, beta, e.size, prng=prng)
write_photon_list("beta",
"beta",
e.flux,
beta_src.ra,
beta_src.dec,
e,
overwrite=True)
instrument_simulator("beta_simput.fits",
"beta_evt.fits",
exp_time,
"hdxi", [ra0, dec0],
ptsrc_bkgnd=False,
instr_bkgnd=False,
foreground=False,
prng=prng)
inst = get_instrument_from_registry("hdxi")
arf = AuxiliaryResponseFile(inst["arf"])
cspec = ConvolvedSpectrum(spec, arf)
ph_flux = cspec.get_flux_in_band(0.5, 7.0)[0].value
S0 = 3.0 * ph_flux / (2.0 * np.pi * r_c * r_c)
write_radial_profile("beta_evt.fits",
"beta_evt_profile.fits", [ra0, dec0],
0.0,
100.0,
200,
ctr_type="celestial",
emin=0.5,
emax=7.0,
overwrite=True)
load_data(1, "beta_evt_profile.fits", 3,
["RMID", "SUR_BRI", "SUR_BRI_ERR"])
set_stat("chi2")
set_method("levmar")
set_source("beta1d.src")
src.beta = 1.0
src.r0 = 10.0
src.ampl = 0.8 * S0
freeze(src.xpos)
fit()
set_covar_opt("sigma", 1.645)
covar()
res = get_covar_results()
assert np.abs(res.parvals[0] - r_c) < res.parmaxes[0]
assert np.abs(res.parvals[1] - beta) < res.parmaxes[1]
assert np.abs(res.parvals[2] - S0) < res.parmaxes[2]
os.chdir(curdir)
shutil.rmtree(tmpdir)
def arf(self):
if self._arf is None:
self._arf = AuxiliaryResponseFile(self.arf_file)
return self._arf
def make_exposure_map(event_file,
expmap_file,
energy,
weights=None,
asol_file=None,
normalize=True,
overwrite=False,
nhistx=16,
nhisty=16):
"""
Make an exposure map for a SOXS event file, and optionally write
an aspect solution file. The exposure map will be created by
binning an aspect histogram over the range of the aspect solution.
Parameters
----------
event_file : string
The path to the event file to use for making the exposure map.
expmap_file : string
The path to write the exposure map file to.
energy : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`, or NumPy array
The energy in keV to use when computing the exposure map, or
a set of energies to be used with the *weights* parameter. If
providing a set, it must be in keV.
weights : array-like, optional
The weights to use with a set of energies given in the
*energy* parameter. Used to create a more accurate exposure
map weighted by a range of energies. Default: None
asol_file : string, optional
The path to write the aspect solution file to, if desired.
Default: None
normalize : boolean, optional
If True, the exposure map will be divided by the exposure time
so that the map's units are cm**2. Default: True
overwrite : boolean, optional
Whether or not to overwrite an existing file. Default: False
nhistx : integer, optional
The number of bins in the aspect histogram in the DETX
direction. Default: 16
nhisty : integer, optional
The number of bins in the aspect histogram in the DETY
direction. Default: 16
"""
import pyregion._region_filter as rfilter
from scipy.ndimage.interpolation import rotate, shift
from soxs.instrument import AuxiliaryResponseFile, perform_dither
if isinstance(energy, np.ndarray) and weights is None:
raise RuntimeError("Must supply a single value for the energy if "
"you do not supply weights!")
if not isinstance(energy, np.ndarray):
energy = parse_value(energy, "keV")
f_evt = pyfits.open(event_file)
hdu = f_evt["EVENTS"]
arf = AuxiliaryResponseFile(hdu.header["ANCRFILE"])
exp_time = hdu.header["EXPOSURE"]
nx = int(hdu.header["TLMAX2"] - 0.5) // 2
ny = int(hdu.header["TLMAX3"] - 0.5) // 2
ra0 = hdu.header["TCRVL2"]
dec0 = hdu.header["TCRVL3"]
xdel = hdu.header["TCDLT2"]
ydel = hdu.header["TCDLT3"]
x0 = hdu.header["TCRPX2"]
y0 = hdu.header["TCRPX3"]
xdet0 = 0.5 * (2 * nx + 1)
ydet0 = 0.5 * (2 * ny + 1)
xaim = hdu.header.get("AIMPT_X", 0.0)
yaim = hdu.header.get("AIMPT_Y", 0.0)
roll = hdu.header["ROLL_PNT"]
instr = instrument_registry[hdu.header["INSTRUME"].lower()]
dither_params = {}
if "DITHXAMP" in hdu.header:
dither_params["x_amp"] = hdu.header["DITHXAMP"]
dither_params["y_amp"] = hdu.header["DITHYAMP"]
dither_params["x_period"] = hdu.header["DITHXPER"]
dither_params["y_period"] = hdu.header["DITHYPER"]
dither_params["plate_scale"] = ydel * 3600.0
dither_params["dither_on"] = True
else:
dither_params["dither_on"] = False
f_evt.close()
# Create time array for aspect solution
dt = 1.0 # Seconds
t = np.arange(0.0, exp_time + dt, dt)
# Construct WCS
w = pywcs.WCS(naxis=2)
w.wcs.crval = [ra0, dec0]
w.wcs.crpix = [x0, y0]
w.wcs.cdelt = [xdel, ydel]
w.wcs.ctype = ["RA---TAN", "DEC--TAN"]
w.wcs.cunit = ["deg"] * 2
# Create aspect solution if we had dithering.
# otherwise just set the offsets to zero
if dither_params["dither_on"]:
x_off, y_off = perform_dither(t, dither_params)
# Make the aspect histogram
x_amp = dither_params["x_amp"] / dither_params["plate_scale"]
y_amp = dither_params["y_amp"] / dither_params["plate_scale"]
x_edges = np.linspace(-x_amp, x_amp, nhistx + 1, endpoint=True)
y_edges = np.linspace(-y_amp, y_amp, nhisty + 1, endpoint=True)
asphist = np.histogram2d(x_off, y_off, (x_edges, y_edges))[0]
asphist *= dt
x_mid = 0.5 * (x_edges[1:] + x_edges[:-1])
y_mid = 0.5 * (y_edges[1:] + y_edges[:-1])
# Determine the effective area
eff_area = arf.interpolate_area(energy).value
if weights is not None:
eff_area = np.average(eff_area, weights=weights)
if instr["chips"] is None:
rtypes = ["Box"]
args = [[0.0, 0.0, instr["num_pixels"], instr["num_pixels"]]]
else:
rtypes = []
args = []
for i, chip in enumerate(instr["chips"]):
rtypes.append(chip[0])
args.append(np.array(chip[1:]))
tmpmap = np.zeros((2 * nx, 2 * ny))
for rtype, arg in zip(rtypes, args):
rfunc = getattr(rfilter, rtype)
new_args = parse_region_args(rtype, arg, xdet0 - xaim - 1.0,
ydet0 - yaim - 1.0)
r = rfunc(*new_args)
tmpmap += r.mask(tmpmap).astype("float64")
if dither_params["dither_on"]:
expmap = np.zeros((2 * nx, 2 * ny))
niter = nhistx * nhisty
pbar = tqdm(leave=True, total=niter, desc="Creating exposure map ")
for i in range(nhistx):
for j in range(nhisty):
expmap += shift(tmpmap,
(x_mid[i], y_mid[j]), order=0) * asphist[i, j]
pbar.update(nhisty)
pbar.close()
else:
expmap = tmpmap * exp_time
expmap *= eff_area
if normalize:
expmap /= exp_time
if roll != 0.0:
rotate(expmap, roll, output=expmap, reshape=False)
map_header = {
"EXPOSURE": exp_time,
"MTYPE1": "EQPOS",
"MFORM1": "RA,DEC",
"CTYPE1": "RA---TAN",
"CTYPE2": "DEC--TAN",
"CRVAL1": ra0,
"CRVAL2": dec0,
"CUNIT1": "deg",
"CUNIT2": "deg",
"CDELT1": xdel,
"CDELT2": ydel,
"CRPIX1": x0,
"CRPIX2": y0
}
map_hdu = pyfits.ImageHDU(expmap, header=pyfits.Header(map_header))
map_hdu.name = "EXPMAP"
map_hdu.writeto(expmap_file, overwrite=overwrite)
if asol_file is not None:
if dither_params["dither_on"]:
det = np.array([x_off, y_off])
pix = np.dot(get_rot_mat(roll).T, det)
ra, dec = w.wcs_pix2world(pix[0, :] + x0, pix[1, :] + y0, 1)
col_t = pyfits.Column(name='time', format='D', unit='s', array=t)
col_ra = pyfits.Column(name='ra', format='D', unit='deg', array=ra)
col_dec = pyfits.Column(name='dec',
format='D',
unit='deg',
array=dec)
coldefs = pyfits.ColDefs([col_t, col_ra, col_dec])
tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
tbhdu.name = "ASPSOL"
tbhdu.header["EXPOSURE"] = exp_time
hdulist = [pyfits.PrimaryHDU(), tbhdu]
pyfits.HDUList(hdulist).writeto(asol_file, overwrite=overwrite)
else:
mylog.warning("Refusing to write an aspect solution file because "
"there was no dithering.")
def setup():
from yt.config import ytcfg
ytcfg["yt", "__withintesting"] = "True"
try:
make_simple_instrument("acisi_cy19", "sq_acisi_cy19", 20.0, 2400)
except KeyError:
pass
acis_spec = get_instrument_from_registry("sq_acisi_cy19")
rmf = RedistributionMatrixFile(acis_spec["rmf"])
arf = AuxiliaryResponseFile(acis_spec['arf'])
def mymodel(pars, x, xhi=None):
dx = x[1] - x[0]
xmid = x + 0.5 * dx
wm = WabsModel(pars[0])
wabs = wm.get_absorb(xmid)
plaw = pars[1] * dx * (xmid * (1.0 + pars[2]))**(-pars[3])
return wabs * plaw
@requires_module("sherpa")
def test_power_law():
plaw_fit(1.1, prng=29)
plaw_fit(0.8)
def simulate_spectrum(spec,
instrument,
exp_time,
out_file,
overwrite=False,
prng=None):
"""
Generate a PI or PHA spectrum from a :class:`~soxs.spectra.Spectrum`
by convolving it with responses. To be used if one wants to
create a spectrum without worrying about spatial response. Similar
to XSPEC's "fakeit".
Parameters
----------
spec : :class:`~soxs.spectra.Spectrum`
The spectrum to be convolved.
instrument : string
The name of the instrument to use, which picks an instrument
specification from the instrument registry.
exp_time : float, (value, unit) tuple, or :class:`~astropy.units.Quantity`
The exposure time in seconds.
out_file : string
The file to write the spectrum to.
overwrite : boolean, optional
Whether or not to overwrite an existing file. Default: False
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.
Examples
--------
>>> spec = soxs.Spectrum.from_file("my_spectrum.txt")
>>> soxs.simulate_spectrum(spec, "mucal", 100000.0,
... "my_spec.pi", overwrite=True)
"""
from soxs.events import write_spectrum
from soxs.instrument import RedistributionMatrixFile, \
AuxiliaryResponseFile
from soxs.spectra import ConvolvedSpectrum
prng = parse_prng(prng)
exp_time = parse_value(exp_time, "s")
try:
instrument_spec = instrument_registry[instrument]
except KeyError:
raise KeyError("Instrument %s is not in the instrument registry!" %
instrument)
arf_file = check_file_location(instrument_spec["arf"], "files")
rmf_file = check_file_location(instrument_spec["rmf"], "files")
arf = AuxiliaryResponseFile(arf_file)
rmf = RedistributionMatrixFile(rmf_file)
cspec = ConvolvedSpectrum(spec, arf)
events = {}
events["energy"] = cspec.generate_energies(exp_time, prng=prng).value
events = rmf.scatter_energies(events, prng=prng)
events["arf"] = arf.filename
events["rmf"] = rmf.filename
events["exposure_time"] = exp_time
events["channel_type"] = rmf.header["CHANTYPE"]
events["telescope"] = rmf.header["TELESCOP"]
events["instrument"] = rmf.header["INSTRUME"]
events["mission"] = rmf.header.get("MISSION", "")
write_spectrum(events, out_file, overwrite=overwrite)