def _write_source(self,
filename,
extver,
img_extver=None,
overwrite=False):
coldefs, header = self._get_source_hdu()
tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
tbhdu.name = self.src_type.upper()
if self.src_type == "phlist":
hduclas1 = "PHOTONS"
else:
hduclas1 = self.src_type.upper()
tbhdu.header["HDUCLASS"] = "HEASARC/SIMPUT"
tbhdu.header["HDUCLAS1"] = hduclas1
tbhdu.header["HDUVERS"] = "1.1.0"
tbhdu.header.update(header)
tbhdu.header["EXTVER"] = extver
if self.imhdu is not None:
self.imhdu.header["EXTVER"] = img_extver
if os.path.exists(filename) and not overwrite:
mylog.info(f"Appending this source to {filename}.")
with pyfits.open(filename, mode='append') as f:
f.append(tbhdu)
if self.imhdu is not None:
f.append(self.imhdu)
f.flush()
else:
if os.path.exists(filename):
mylog.warning(f"Overwriting {filename} with this source.")
else:
mylog.info(f"Writing source to {filename}.")
f = [pyfits.PrimaryHDU(), tbhdu]
if self.imhdu is not None:
f.append(self.imhdu)
pyfits.HDUList(f).writeto(filename, overwrite=overwrite)
if self.imhdu is not None:
self.imhdu.header["EXTVER"] = 1
def generate_events(source,
exp_time,
instrument,
sky_center,
no_dither=False,
dither_params=None,
roll_angle=0.0,
subpixel_res=False,
aimpt_shift=None,
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
aimpt_shift : array-like, optional
A two-float array-like object which shifts the aimpoint on the
detector from the nominal position. Units are in arcseconds.
Default: None, which results in no shift from the nominal aimpoint.
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.
"""
exp_time = parse_value(exp_time, "s")
roll_angle = parse_value(roll_angle, "deg")
prng = parse_prng(prng)
if source is None:
source_list = []
elif isinstance(source, dict):
parameters = {}
for key in ["flux", "emin", "emax", "src_names"]:
parameters[key] = source[key]
source_list = []
for i in range(len(parameters["flux"])):
phlist = SimputPhotonList(source["ra"][i], source["dec"][i],
source["energy"][i],
parameters['flux'][i],
parameters['src_names'][i])
source_list.append(phlist)
elif isinstance(source, str):
# Assume this is a SIMPUT catalog
source_list, parameters = read_simput_catalog(source)
try:
instrument_spec = instrument_registry[instrument]
except KeyError:
raise KeyError(
f"Instrument {instrument} is not in the instrument registry!")
if not instrument_spec["imaging"]:
raise RuntimeError(f"Instrument '{instrument_spec['name']}' is not "
f"designed for imaging observations!")
arf_file = get_data_file(instrument_spec["arf"])
rmf_file = get_data_file(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 aimpt_shift is None:
aimpt_shift = np.zeros(2)
aimpt_shift = ensure_numpy_array(aimpt_shift).astype('float64')
aimpt_shift /= plate_scale_arcsec
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 = {
"exposure_time": exp_time,
"arf": arf.filename,
"sky_center": sky_center,
"pix_center": np.array([0.5 * (2 * nx + 1)] * 2),
"num_pixels": nx,
"plate_scale": plate_scale,
"rmf": rmf.filename,
"channel_type": rmf.chan_type,
"telescope": rmf.header["TELESCOP"],
"instrument": instrument_spec['name'],
"mission": rmf.header.get("MISSION", ""),
"nchan": rmf.n_ch,
"roll_angle": roll_angle,
"fov": instrument_spec["fov"],
"chan_lim": [rmf.cmin, rmf.cmax],
"chips": instrument_spec["chips"],
"dither_params": dither_dict,
"aimpt_coords": instrument_spec["aimpt_coords"],
"aimpt_shift": aimpt_shift
}
# Set up WCS
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
# Determine rotation matrix
rot_mat = get_rot_mat(roll_angle)
# Set up PSF
psf_type = instrument_spec["psf"][0]
psf_class = psf_model_registry[psf_type]
psf = psf_class(instrument_spec, prng=prng)
all_events = defaultdict(list)
for i, src in enumerate(source_list):
mylog.info(
f"Detecting events from source {parameters['src_names'][i]}")
# Step 1: Use ARF to determine which photons are observed
mylog.info(f"Applying energy-dependent effective area from "
f"{os.path.split(arf.filename)[-1]}.")
refband = [parameters["emin"][i], parameters["emax"][i]]
if src.src_type == "phlist":
events = arf.detect_events_phlist(src.events.copy(),
exp_time,
parameters["flux"][i],
refband,
prng=prng)
elif src.src_type.endswith("spectrum"):
events = arf.detect_events_spec(src, exp_time, 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] + aimpt_shift[0]
dety = det[1, :] + event_params["aimpt_coords"][1] + aimpt_shift[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
mylog.info(f"Scattering events with a {psf}-based PSF.")
detx, dety = psf.scatter(detx, dety, events["energy"])
# 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)
events["chip_id"] = -np.ones(n_evt, dtype='int')
for i, chip in enumerate(event_params["chips"]):
rtype = chip[0]
args = chip[1:]
r, _ = create_region(rtype, args, 0.0, 0.0)
inside = r.contains(PixCoord(cx, cy))
events["chip_id"][inside] = i
keep = events["chip_id"] > -1
mylog.info(f"{n_evt-keep.sum()} events were rejected because "
f"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] - aimpt_shift[0],
events["dety"] + y_offset[keep] -
event_params["aimpt_coords"][1] - aimpt_shift[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(f"Scattering energies with "
f"RMF {os.path.split(rmf.filename)[-1]}.")
all_events = rmf.scatter_energies(all_events, prng=prng)
return all_events, event_params
def from_xspec(cls, model_string, params, emin=0.01, emax=50.0,
nbins=10000):
mylog.warning("The 'from_xspec' method has been deprecated: "
"use 'from_xspec_model' instead.")
cls.from_xspec_model(model_string, params, emin=emin, emax=emax, nbins=nbins)
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 from_xspec(cls, model_string, params, emin, emax, nbins):
mylog.warning("The 'from_xspec' method has been deprecated: "
"use 'from_xspec_model' instead.")
cls.from_xspec_model(model_string, params, emin, emax, nbins)
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 instrument_simulator(simput_file,
out_file,
exp_time,
instrument,
sky_center,
clobber=False,
dither_shape="square",
dither_size=16.0,
roll_angle=0.0,
astro_bkgnd=True,
instr_bkgnd=True,
prng=np.random):
"""
Take unconvolved events in a SIMPUT catalog and create an event
file from them. This function does the following:
1. Determines which events are observed using the ARF
2. Pixelizes the events, applying PSF effects and dithering
3. Adds instrumental and astrophysical background events
4. Determines energy channels using the RMF
5. Writes the events to a file
Parameters
----------
simput_file : string
The SIMPUT catalog file to be used as input.
out_file : string
The name of the event file to be written.
exp_time : float
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. Can also be a JSON
file with a new instrument specification. If this is the case,
it will be loaded into the instrument registry.
sky_center : array, tuple, or list
The center RA, Dec coordinates of the observation, in degrees.
clobber : boolean, optional
Whether or not to clobber an existing file with the same name.
Default: False
dither_shape : string
The shape of the dither. Currently "circle" or "square"
Default: "square"
dither_size : float
The size of the dither in arcseconds. Width of square or radius
of circle. Default: 16.0
roll_angle : float
The roll angle of the observation in degrees. Default: 0.0
astro_bkgnd : boolean, optional
Whether or not to include astrophysical background. Default: True
instr_bkgnd : boolean, optional
Whether or not to include instrumental/particle background. Default: True
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
--------
>>> instrument_simulator("sloshing_simput.fits", "sloshing_evt.fits", "hdxi_3x10",
... [30., 45.], clobber=True)
"""
event_list, parameters = read_simput_catalog(simput_file)
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)
nx = instrument_spec["num_pixels"]
plate_scale = instrument_spec["fov"] / nx / 60. # arcmin to deg
plate_scale_arcsec = plate_scale * 3600.0
dsize = dither_size / plate_scale_arcsec
event_params = {}
event_params["exposure_time"] = exp_time
event_params["arf"] = os.path.split(arf.filename)[-1]
event_params["sky_center"] = sky_center
event_params["pix_center"] = np.array([0.5 * (nx + 1)] * 2)
event_params["num_pixels"] = nx
event_params["plate_scale"] = plate_scale
event_params["rmf"] = os.path.split(rmf.filename)[-1]
event_params["channel_type"] = rmf.header["CHANTYPE"]
event_params["telescope"] = rmf.header["TELESCOP"]
event_params["instrument"] = rmf.header["INSTRUME"]
event_params["mission"] = rmf.header.get("MISSION", "")
event_params["nchan"] = rmf.ebounds_header["DETCHANS"]
event_params["roll_angle"] = roll_angle
event_params["fov"] = instrument_spec["fov"]
num = 0
for i in range(1, rmf.num_mat_columns + 1):
if rmf.header["TTYPE%d" % i] == "F_CHAN":
num = i
break
event_params["chan_lim"] = [
rmf.header["TLMIN%d" % num], rmf.header["TLMAX%d" % num]
]
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
roll_angle = np.deg2rad(roll_angle)
rot_mat = np.array([[np.sin(roll_angle), -np.cos(roll_angle)],
[-np.cos(roll_angle), -np.sin(roll_angle)]])
all_events = {}
first = True
for i, evts in enumerate(event_list):
mylog.info("Detecting events from source %d" % (i + 1))
# Step 1: Use ARF to determine which photons are observed
mylog.info("Applying energy-dependent effective area from %s." %
event_params["arf"])
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
# Dither pixel coordinates
x_offset = np.zeros(n_evt)
y_offset = np.zeros(n_evt)
if dither_shape == "circle":
r = dsize * np.sqrt(prng.uniform(size=n_evt))
theta = 2. * np.pi * prng.uniform(size=n_evt)
x_offset = r * np.cos(theta)
y_offset = r * np.sin(theta)
elif dither_shape == "square":
x_offset = dsize * prng.uniform(low=-0.5, high=0.5, size=n_evt)
y_offset = dsize * prng.uniform(low=-0.5, high=0.5, size=n_evt)
xpix -= x_offset
ypix -= y_offset
# Rotate physical coordinates to detector coordinates
det = np.dot(rot_mat, np.array([xpix, ypix]))
detx = det[0, :]
dety = det[1, :]
# PSF scattering of detector coordinates
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
events["chipx"] = np.round(detx + event_params['pix_center'][0])
events["chipy"] = np.round(dety + event_params['pix_center'][1])
# Throw out events that don't fall on the chip
keepx = np.logical_and(events["chipx"] >= 1.0,
events["chipx"] <= nx)
keepy = np.logical_and(events["chipy"] >= 1.0,
events["chipy"] <= nx)
keep = np.logical_and(keepx, keepy)
mylog.info("%d events were rejected because " %
(n_evt - keep.sum()) +
"they fall outside the field of view.")
n_evt = keep.sum()
if n_evt == 0:
mylog.warning(
"No events are within the field of view for this source!!!"
)
else:
for key in events:
events[key] = events[key][keep]
# Convert chip coordinates back to detector coordinates
events["detx"] = np.round(
events["chipx"] - event_params['pix_center'][0] +
prng.uniform(low=-0.5, high=0.5, size=n_evt))
events["dety"] = np.round(
events["chipy"] - event_params['pix_center'][1] +
prng.uniform(low=-0.5, high=0.5, size=n_evt))
# Convert detector coordinates back to pixel coordinates
pix = np.dot(rot_mat,
np.array([events["detx"], events["dety"]]))
events["xpix"] = pix[
0, :] + event_params['pix_center'][0] + x_offset[keep]
events["ypix"] = pix[
1, :] + event_params['pix_center'][1] + y_offset[keep]
if n_evt > 0:
for key in events:
if first:
all_events[key] = events[key]
else:
all_events[key] = np.concatenate(
[all_events[key], events[key]])
first = False
if all_events["energy"].size == 0:
mylog.warning(
"No events from any of the sources in the catalog were detected!")
# Step 3: Add astrophysical background
if astro_bkgnd:
mylog.info("Adding in astrophysical background.")
bkg_events = add_background(astro_bkgnd,
event_params,
rot_mat,
prng=prng)
for key in all_events:
all_events[key] = np.concatenate(
[all_events[key], bkg_events[key]])
# Step 4: Add particle background
if instr_bkgnd:
mylog.info("Adding in instrumental background.")
bkg_events = add_background(
instrument_spec["bkgnd"],
event_params,
rot_mat,
prng=prng,
focal_length=instrument_spec["focal_length"])
for key in all_events:
all_events[key] = np.concatenate(
[all_events[key], bkg_events[key]])
if all_events["energy"].size == 0:
raise RuntimeError("No events were detected!!!")
# Step 5: Scatter energies with RMF
if all_events["energy"].size > 0:
mylog.info("Scattering energies with RMF %s." % event_params['rmf'])
all_events = rmf.scatter_energies(all_events, prng=prng)
# Step 6: Add times to events
all_events['time'] = np.random.uniform(size=all_events["energy"].size,
low=0.0,
high=event_params["exposure_time"])
write_event_file(all_events, event_params, out_file, clobber=clobber)
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 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_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.")
