def setup_class(cls):
from ..simulator import Simulator
dt = 0.01
cls.time_lag = 5.
data = np.load(os.path.join(datadir, "sample_variable_lc.npy"))
flux = data
times = np.arange(data.size) * dt
maxfreq = 0.25 / cls.time_lag
roll_amount = int(cls.time_lag // dt)
good = slice(roll_amount, roll_amount + int(200 // dt))
rolled_flux = np.array(np.roll(flux, roll_amount))
times, flux, rolled_flux = times[good], flux[good], rolled_flux[good]
length = times[-1] - times[0]
test_lc1 = Lightcurve(times,
flux,
err_dist="gauss",
dt=dt,
skip_checks=True)
test_lc2 = Lightcurve(test_lc1.time,
rolled_flux,
err_dist=test_lc1.err_dist,
dt=dt)
test_ev1, test_ev2 = EventList(), EventList()
test_ev1.simulate_times(test_lc1)
test_ev2.simulate_times(test_lc2)
test_ev1.energy = np.random.uniform(0.3, 9, len(test_ev1.time))
test_ev2.energy = np.random.uniform(9, 12, len(test_ev2.time))
cls.lag = LagEnergySpectrum(
test_ev1,
freq_interval=[0, maxfreq],
energy_spec=(0.3, 9, 1, "lin"),
ref_band=[9, 12],
bin_time=dt / 2,
segment_size=length,
events2=test_ev2,
)
# Make single event list
test_ev = test_ev1.join(test_ev2)
cls.lag_same = LagEnergySpectrum(
test_ev,
freq_interval=[0, maxfreq],
energy_spec=(0.3, 9, 1, "lin"),
ref_band=[9, 12],
bin_time=dt / 2,
segment_size=length,
)
def test_lag_invalid_evlist_warns(self):
ev = EventList(time=[], energy=[], gti=self.lag.events1.gti)
with pytest.warns(UserWarning) as record:
lag = LagEnergySpectrum(ev, [0., 0.5], (0.3, 9, 4, "lin"), [9, 12],
bin_time=0.1,
segment_size=30,
events2=self.lag.events2)
assert np.allclose(lag.spectrum, 0)
assert np.allclose(lag.spectrum_error, 0)
def setup_class(cls):
from ..simulator.simulator import Simulator
dt = 0.1
simulator = Simulator(dt, 1000, rms=0.4, mean=200)
test_lc1 = simulator.simulate(2)
test_lc2 = Lightcurve(test_lc1.time,
np.array(np.roll(test_lc1.counts, 2)),
err_dist=test_lc1.err_dist,
dt=dt)
test_ev1, test_ev2 = EventList(), EventList()
test_ev1.simulate_times(test_lc1)
test_ev2.simulate_times(test_lc2)
test_ev1.energy = np.random.uniform(0.3, 9, len(test_ev1.time))
test_ev2.energy = np.random.uniform(9, 12, len(test_ev2.time))
cls.lag = LagEnergySpectrum(test_ev1, [0., 0.5], (0.3, 9, 4, "lin"),
[9, 12],
bin_time=0.1,
segment_size=30,
events2=test_ev2)
def main(args=None):
import argparse
description = ('Calculates variability-energy spectra')
parser = argparse.ArgumentParser(description=description)
parser.add_argument("files", help="List of files", nargs='+')
parser.add_argument('-f',
"--freq-interval",
nargs=2,
type=float,
default=[0., 100],
help="Frequence interval")
parser.add_argument("--energy-values",
nargs=4,
type=str,
default="0.3 12 5 lin".split(" "),
help="Choose Emin, Emax, number of intervals,"
"interval spacing, lin or log")
parser.add_argument("--segment-size",
type=float,
default=512,
help="Length of the light curve intervals to be "
"averaged")
parser.add_argument("-b",
"--bin-time",
type=float,
default=1,
help="Bin time for the light curve")
parser.add_argument("--ref-band",
nargs=2,
type=float,
default=None,
help="Reference band when relevant")
parser.add_argument("--rms",
default=False,
action='store_true',
help="Calculate rms")
parser.add_argument("--covariance",
default=False,
action='store_true',
help="Calculate covariance spectrum")
parser.add_argument("--use-pi",
default=False,
action='store_true',
help="Energy intervals are specified as PI channels")
parser.add_argument("--cross-instr",
default=False,
action='store_true',
help="Use data files in pairs, for example with the"
"reference band from one and the subbands from the"
" other (useful in NuSTAR and multiple-detector "
"missions)")
parser.add_argument("--lag",
default=False,
action='store_true',
help="Calculate lag-energy")
parser.add_argument("--loglevel",
help=("use given logging level (one between INFO, "
"WARNING, ERROR, CRITICAL, DEBUG; "
"default:WARNING)"),
default='WARNING',
type=str)
parser.add_argument("--debug",
help="use DEBUG logging level",
default=False,
action='store_true')
args = parser.parse_args(args)
if args.debug:
args.loglevel = 'DEBUG'
numeric_level = getattr(logging, args.loglevel.upper(), None)
logging.basicConfig(filename='HENvarenergy.log',
level=numeric_level,
filemode='w')
filelist = []
energy_spec = (float(args.energy_values[0]), float(args.energy_values[1]),
int(args.energy_values[2]), args.energy_values[3])
from .io import sort_files
if args.cross_instr:
logging.info('Sorting file list')
sorted_files = sort_files(args.files)
logging.warning('Beware! For cpds and derivatives, I assume that the '
'files are from only two instruments and in pairs '
'(even in random order)')
instrs = list(sorted_files.keys())
files1 = sorted_files[instrs[0]]
files2 = sorted_files[instrs[1]]
else:
files1 = args.files
files2 = args.files
for fnames in zip(files1, files2):
fname = fnames[0]
fname2 = fnames[1]
events = load_events(fname)
events2 = load_events(fname2)
if not args.use_pi and \
(events.energy is None or events2.energy is None):
raise ValueError(
"If --use-pi is not specified, event lists must be"
" calibrated! Please use HENcalibrate.")
if args.rms:
rms = RmsEnergySpectrum(events,
args.freq_interval,
energy_spec,
segment_size=args.segment_size,
bin_time=args.bin_time,
events2=events2,
use_pi=args.use_pi)
out1 = hen_root(fname) + "_rms" + '.qdp'
start_energy = np.asarray(rms.energy_intervals)[:, 0]
stop_energy = np.asarray(rms.energy_intervals)[:, 1]
save_as_qdp([start_energy, stop_energy, rms.spectrum],
[None, None, rms.spectrum_error],
filename=out1)
filelist.append(out1)
if args.lag:
lag = LagEnergySpectrum(events,
args.freq_interval,
energy_spec,
args.ref_band,
segment_size=args.segment_size,
bin_time=args.bin_time,
events2=events2,
use_pi=args.use_pi)
start_energy = np.asarray(lag.energy_intervals)[:, 0]
stop_energy = np.asarray(lag.energy_intervals)[:, 1]
out2 = hen_root(fname) + "_lag" + '.qdp'
save_as_qdp([start_energy, stop_energy, lag.spectrum],
[None, None, lag.spectrum_error],
filename=out2)
filelist.append(out2)
if args.covariance:
try:
from stingray.varenergyspectrum import CovarianceEnergySpectrum
except:
logging.warning('This version of Stingray does not implement '
'the correct version of Covariance Spectrum.')
continue
cov = CovarianceEnergySpectrum(events,
args.freq_interval,
energy_spec,
args.ref_band,
segment_size=args.segment_size,
bin_time=args.bin_time,
events2=events2,
use_pi=args.use_pi)
start_energy = np.asarray(cov.energy_intervals)[:, 0]
stop_energy = np.asarray(cov.energy_intervals)[:, 1]
out2 = hen_root(fname) + "_cov" + '.qdp'
save_as_qdp([start_energy, stop_energy, cov.spectrum],
[None, None, cov.spectrum_error],
filename=out2)
filelist.append(out2)
return filelist
