def make_diffuse_background(bkg_events, event_params, rmf, prng=None): from soxs.instrument import perform_dither n_e = bkg_events["energy"].size bkg_events['time'] = prng.uniform(size=n_e, low=0.0, high=event_params["exposure_time"]) x_offset, y_offset = perform_dither(bkg_events["time"], event_params["dither_params"]) rot_mat = get_rot_mat(event_params["roll_angle"]) det = np.array([ bkg_events["detx"] + x_offset - event_params["aimpt_coords"][0], bkg_events["dety"] + y_offset - event_params["aimpt_coords"][1] ]) pix = np.dot(rot_mat.T, det) bkg_events["xpix"] = pix[0, :] + event_params['pix_center'][0] bkg_events["ypix"] = pix[1, :] + event_params['pix_center'][1] mylog.info("Scattering energies with RMF %s." % os.path.split(rmf.filename)[-1]) bkg_events = rmf.scatter_energies(bkg_events, prng=prng) return bkg_events
def make_diffuse_background(bkg_events, event_params, rmf, prng=None): from soxs.instrument import perform_dither n_e = bkg_events["energy"].size bkg_events['time'] = prng.uniform(size=n_e, low=0.0, high=event_params["exposure_time"]) x_offset, y_offset = perform_dither(bkg_events["time"], event_params["dither_params"]) rot_mat = get_rot_mat(event_params["roll_angle"]) det = np.array([ bkg_events["detx"] + x_offset - event_params["aimpt_coords"][0] - event_params["aimpt_shift"][0], bkg_events["dety"] + y_offset - event_params["aimpt_coords"][1] - event_params["aimpt_shift"][1] ]) pix = np.dot(rot_mat.T, det) bkg_events["xpix"] = pix[0, :] + event_params['pix_center'][0] bkg_events["ypix"] = pix[1, :] + event_params['pix_center'][1] return bkg_events
def add_background_from_file(events, event_params, bkg_file): f = pyfits.open(bkg_file) hdu = f["EVENTS"] sexp = event_params["exposure_time"] bexp = hdu.header["EXPOSURE"] if event_params["exposure_time"] > hdu.header["EXPOSURE"]: raise RuntimeError( "The background file does not have sufficient exposure! Source " "exposure time %g, background exposure time %g." % (sexp, bexp)) for k1, k2 in key_map.items(): if event_params[k1] != hdu.header[k2]: raise RuntimeError("'%s' keyword does not match! %s vs. %s" % (k1, event_params[k1], hdu.header[k2])) rmf1 = os.path.split(event_params["rmf"])[-1] rmf2 = hdu.header["RESPFILE"] arf1 = os.path.split(event_params["arf"])[-1] arf2 = hdu.header["ANCRFILE"] if rmf1 != rmf2: raise RuntimeError("RMFs do not match! %s vs. %s" % (rmf1, rmf2)) if arf1 != arf2: raise RuntimeError("ARFs do not match! %s vs. %s" % (arf1, arf2)) idxs = hdu.data["TIME"] < sexp mylog.info("Adding %d background events from %s." % (idxs.sum(), bkg_file)) if event_params["roll_angle"] == hdu.header["ROLL_PNT"]: xpix = hdu.data["X"][idxs] ypix = hdu.data["Y"][idxs] else: rot_mat = get_rot_mat(event_params["roll_angle"]) xpix, ypix = np.dot( rot_mat.T, np.array([hdu.data["DETX"][idxs], hdu.data["DETY"][idxs]])) xpix += hdu.header["TCRPX2"] ypix += hdu.header["TCRPX3"] all_events = {} for key in [ "detx", "dety", "time", "ccd_id", event_params["channel_type"] ]: all_events[key] = np.concatenate( [events[key], hdu.data[key.upper()][idxs]]) all_events["xpix"] = np.concatenate([events["xpix"], xpix]) all_events["ypix"] = np.concatenate([events["ypix"], ypix]) all_events["energy"] = np.concatenate( [events["energy"], hdu.data["ENERGY"][idxs] / 1000.0]) f.close() return all_events
def make_diffuse_background(bkg_events, event_params, rmf, prng=None): from soxs.instrument import perform_dither n_e = bkg_events["energy"].size bkg_events['time'] = prng.uniform(size=n_e, low=0.0, high=event_params["exposure_time"]) x_offset, y_offset = perform_dither(bkg_events["time"], event_params["dither_params"]) rot_mat = get_rot_mat(event_params["roll_angle"]) det = np.array([bkg_events["detx"] + x_offset - event_params["aimpt_coords"][0], bkg_events["dety"] + y_offset - event_params["aimpt_coords"][1]]) pix = np.dot(rot_mat.T, det) bkg_events["xpix"] = pix[0, :] + event_params['pix_center'][0] bkg_events["ypix"] = pix[1, :] + event_params['pix_center'][1] mylog.info("Scattering energies with RMF %s." % os.path.split(rmf.filename)[-1]) bkg_events = rmf.scatter_energies(bkg_events, prng=prng) return bkg_events
def rotate_xy(theta, x, y): coords = np.dot(get_rot_mat(theta), np.array([x, y])) return coords
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 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_background_from_file(events, event_params, bkg_file): from soxs.instrument import perform_dither f = pyfits.open(bkg_file) hdu = f["EVENTS"] 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"] = hdu.header["TCDLT3"] * 3600.0 dither_params["dither_on"] = True else: dither_params["dither_on"] = False sexp = event_params["exposure_time"] bexp = hdu.header["EXPOSURE"] if event_params["exposure_time"] > hdu.header["EXPOSURE"]: raise RuntimeError( "The background file does not have sufficient exposure! Source " "exposure time %g, background exposure time %g." % (sexp, bexp)) for k1, k2 in key_map.items(): if event_params[k1] != hdu.header[k2]: raise RuntimeError("'%s' keyword does not match! %s vs. %s" % (k1, event_params[k1], hdu.header[k2])) rmf1 = os.path.split(event_params["rmf"])[-1] rmf2 = hdu.header["RESPFILE"] arf1 = os.path.split(event_params["arf"])[-1] arf2 = hdu.header["ANCRFILE"] if rmf1 != rmf2: raise RuntimeError("RMFs do not match! %s vs. %s" % (rmf1, rmf2)) if arf1 != arf2: raise RuntimeError("ARFs do not match! %s vs. %s" % (arf1, arf2)) idxs = hdu.data["TIME"] < sexp mylog.info("Adding %d background events from %s." % (idxs.sum(), bkg_file)) if event_params["roll_angle"] == hdu.header["ROLL_PNT"]: xpix = hdu.data["X"][idxs] ypix = hdu.data["Y"][idxs] else: rot_mat = get_rot_mat(event_params["roll_angle"]) if dither_params["dither_on"]: t = hdu.data["TIME"][idxs] x_off, y_off = perform_dither(t, dither_params) else: x_off = 0.0 y_off = 0.0 det = np.array([ hdu.data["DETX"][idxs] + x_off - event_params["aimpt_coords"][0], hdu.data["DETY"][idxs] + y_off - event_params["aimpt_coords"][1] ]) xpix, ypix = np.dot(rot_mat.T, det) xpix += hdu.header["TCRPX2"] ypix += hdu.header["TCRPX3"] all_events = {} for key in [ "detx", "dety", "time", "ccd_id", event_params["channel_type"] ]: all_events[key] = np.concatenate( [events[key], hdu.data[key.upper()][idxs]]) all_events["xpix"] = np.concatenate([events["xpix"], xpix]) all_events["ypix"] = np.concatenate([events["ypix"], ypix]) all_events["energy"] = np.concatenate( [events["energy"], hdu.data["ENERGY"][idxs] / 1000.0]) f.close() return all_events
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 add_background_from_file(events, event_params, bkg_file): from soxs.instrument import perform_dither f = pyfits.open(bkg_file) hdu = f["EVENTS"] 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"] = hdu.header["TCDLT3"]*3600.0 dither_params["dither_on"] = True else: dither_params["dither_on"] = False sexp = event_params["exposure_time"] bexp = hdu.header["EXPOSURE"] if event_params["exposure_time"] > hdu.header["EXPOSURE"]: raise RuntimeError("The background file does not have sufficient exposure! Source " "exposure time %g, background exposure time %g." % (sexp, bexp)) for k1, k2 in key_map.items(): if event_params[k1] != hdu.header[k2]: raise RuntimeError("'%s' keyword does not match! %s vs. %s" % (k1, event_params[k1], hdu.header[k2])) rmf1 = os.path.split(event_params["rmf"])[-1] rmf2 = hdu.header["RESPFILE"] arf1 = os.path.split(event_params["arf"])[-1] arf2 = hdu.header["ANCRFILE"] if rmf1 != rmf2: raise RuntimeError("RMFs do not match! %s vs. %s" % (rmf1, rmf2)) if arf1 != arf2: raise RuntimeError("ARFs do not match! %s vs. %s" % (arf1, arf2)) idxs = hdu.data["TIME"] < sexp mylog.info("Adding %d background events from %s." % (idxs.sum(), bkg_file)) if event_params["roll_angle"] == hdu.header["ROLL_PNT"]: xpix = hdu.data["X"][idxs] ypix = hdu.data["Y"][idxs] else: rot_mat = get_rot_mat(event_params["roll_angle"]) if dither_params["dither_on"]: t = hdu.data["TIME"][idxs] x_off, y_off = perform_dither(t, dither_params) else: x_off = 0.0 y_off = 0.0 det = np.array([hdu.data["DETX"][idxs] + x_off - event_params["aimpt_coords"][0], hdu.data["DETY"][idxs] + y_off - event_params["aimpt_coords"][1]]) xpix, ypix = np.dot(rot_mat.T, det) xpix += hdu.header["TCRPX2"] ypix += hdu.header["TCRPX3"] all_events = {} for key in ["detx", "dety", "time", "ccd_id", event_params["channel_type"]]: all_events[key] = np.concatenate([events[key], hdu.data[key.upper()][idxs]]) all_events["xpix"] = np.concatenate([events["xpix"], xpix]) all_events["ypix"] = np.concatenate([events["ypix"], ypix]) all_events["energy"] = np.concatenate([events["energy"], hdu.data["ENERGY"][idxs]/1000.0]) f.close() return all_events
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 make_uniform_background(energy, event_params, rmf, prng=None): from soxs.instrument import perform_dither import pyregion._region_filter as rfilter prng = parse_prng(prng) bkg_events = {} n_events = energy.size nx = event_params["num_pixels"] bkg_events["detx"] = prng.uniform(low=-0.5 * nx, high=0.5 * nx, size=n_events) bkg_events["dety"] = prng.uniform(low=-0.5 * nx, high=0.5 * nx, size=n_events) bkg_events["energy"] = energy if event_params["chips"] is None: bkg_events["chip_id"] = np.zeros(n_events, dtype='int') else: bkg_events["chip_id"] = -np.ones(n_events, dtype='int') for i, chip in enumerate(event_params["chips"]): thisc = np.ones(n_events, dtype='bool') rtype = chip[0] args = chip[1:] r = getattr(rfilter, rtype)(*args) inside = r.inside(bkg_events["detx"], bkg_events["dety"]) thisc = np.logical_and(thisc, inside) bkg_events["chip_id"][thisc] = i keep = bkg_events["chip_id"] > -1 for key in bkg_events: bkg_events[key] = bkg_events[key][keep] n_e = bkg_events["energy"].size bkg_events['time'] = prng.uniform(size=n_e, low=0.0, high=event_params["exposure_time"]) x_offset, y_offset = perform_dither(bkg_events["time"], event_params["dither_params"]) rot_mat = get_rot_mat(event_params["roll_angle"]) det = np.array([ bkg_events["detx"] + x_offset - event_params["aimpt_coords"][0], bkg_events["dety"] + y_offset - event_params["aimpt_coords"][1] ]) pix = np.dot(rot_mat.T, det) bkg_events["xpix"] = pix[0, :] + event_params['pix_center'][0] bkg_events["ypix"] = pix[1, :] + event_params['pix_center'][1] mylog.info("Scattering energies with RMF %s." % os.path.split(rmf.filename)[-1]) bkg_events = rmf.scatter_energies(bkg_events, prng=prng) return bkg_events
def rotate_xy(theta, x, y, inverse=False): rot_mat = get_rot_mat(theta) if inverse: rot_mat = np.linalg.inv(rot_mat) coords = np.dot(rot_mat, np.array([x, y])) return coords