def _make_segment_spectrum(self, lc, segment_size):
if not isinstance(lc, lightcurve.Lightcurve):
raise TypeError("lc must be a lightcurve.Lightcurve object")
if self.gti is None:
self.gti = lc.gti
check_gtis(self.gti)
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc.time)
power_all = []
nphots_all = []
for start_ind, end_ind in zip(start_inds, end_inds):
time = lc.time[start_ind:end_ind]
counts = lc.counts[start_ind:end_ind]
counts_err = lc.counts_err[start_ind:end_ind]
lc_seg = lightcurve.Lightcurve(time,
counts,
err=counts_err,
err_dist=lc.err_dist.lower())
power_seg = Powerspectrum(lc_seg, norm=self.norm)
power_all.append(power_seg)
nphots_all.append(np.sum(lc_seg.counts))
return power_all, nphots_all
def _make_segment_spectrum(self, lc, segment_size):
if not isinstance(lc, lightcurve.Lightcurve):
raise TypeError("lc must be a lightcurve.Lightcurve object")
if self.gti is None:
self.gti = lc.gti
check_gtis(self.gti)
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc.time)
power_all = []
nphots_all = []
for start_ind, end_ind in zip(start_inds, end_inds):
time = lc.time[start_ind:end_ind]
counts = lc.counts[start_ind:end_ind]
counts_err = lc.counts_err[start_ind: end_ind]
lc_seg = lightcurve.Lightcurve(time, counts, err=counts_err,
err_dist=lc.err_dist.lower())
power_seg = Powerspectrum(lc_seg, norm=self.norm)
power_all.append(power_seg)
nphots_all.append(np.sum(lc_seg.counts))
return power_all, nphots_all
def _make_segment_spectrum(self, lc1, lc2, segment_size):
# TODO: need to update this for making cross spectra.
assert isinstance(lc1, Lightcurve)
assert isinstance(lc2, Lightcurve)
if lc1.tseg != lc2.tseg:
raise ValueError("Lightcurves do not have same tseg.")
# If dt differs slightly, its propagated error must not be more than
# 1/100th of the bin
if not np.isclose(lc1.dt, lc2.dt, rtol=0.01 * lc1.dt / lc1.tseg):
raise ValueError("Light curves do not have same time binning dt.")
# In case a small difference exists, ignore it
lc1.dt = lc2.dt
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
lc1.gti = lc2.gti = self.gti
lc1._apply_gtis()
lc2._apply_gtis()
check_gtis(self.gti)
cs_all = []
nphots1_all = []
nphots2_all = []
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc1.time,
dt=lc1.dt)
for start_ind, end_ind in zip(start_inds, end_inds):
time_1 = lc1.time[start_ind:end_ind]
counts_1 = lc1.counts[start_ind:end_ind]
counts_1_err = lc1.counts_err[start_ind:end_ind]
time_2 = lc2.time[start_ind:end_ind]
counts_2 = lc2.counts[start_ind:end_ind]
counts_2_err = lc2.counts_err[start_ind:end_ind]
gti1 = np.array([[time_1[0] - lc1.dt / 2,
time_1[-1] + lc1.dt / 2]])
gti2 = np.array([[time_2[0] - lc2.dt / 2,
time_2[-1] + lc2.dt / 2]])
lc1_seg = Lightcurve(time_1, counts_1, err=counts_1_err,
err_dist=lc1.err_dist,
gti=gti1,
dt=lc1.dt)
lc2_seg = Lightcurve(time_2, counts_2, err=counts_2_err,
err_dist=lc2.err_dist,
gti=gti2,
dt=lc2.dt)
cs_seg = Crossspectrum(lc1_seg, lc2_seg, norm=self.norm)
cs_all.append(cs_seg)
nphots1_all.append(np.sum(lc1_seg.counts))
nphots2_all.append(np.sum(lc2_seg.counts))
return cs_all, nphots1_all, nphots2_all
def _make_segment_spectrum(self, lc1, lc2, segment_size):
# TODO: need to update this for making cross spectra.
assert isinstance(lc1, Lightcurve)
assert isinstance(lc2, Lightcurve)
if lc1.tseg != lc2.tseg:
raise ValueError("Lightcurves do not have same tseg.")
# If dt differs slightly, its propagated error must not be more than
# 1/100th of the bin
if not np.isclose(lc1.dt, lc2.dt, rtol=0.01 * lc1.dt / lc1.tseg):
raise ValueError("Light curves do not have same time binning dt.")
# In case a small difference exists, ignore it
lc1.dt = lc2.dt
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
lc1.gti = lc2.gti = self.gti
lc1._apply_gtis()
lc2._apply_gtis()
check_gtis(self.gti)
cs_all = []
nphots1_all = []
nphots2_all = []
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc1.time,
dt=lc1.dt)
for start_ind, end_ind in zip(start_inds, end_inds):
time_1 = lc1.time[start_ind:end_ind]
counts_1 = lc1.counts[start_ind:end_ind]
counts_1_err = lc1.counts_err[start_ind:end_ind]
time_2 = lc2.time[start_ind:end_ind]
counts_2 = lc2.counts[start_ind:end_ind]
counts_2_err = lc2.counts_err[start_ind:end_ind]
gti1 = np.array([[time_1[0] - lc1.dt / 2,
time_1[-1] + lc1.dt / 2]])
gti2 = np.array([[time_2[0] - lc2.dt / 2,
time_2[-1] + lc2.dt / 2]])
lc1_seg = Lightcurve(time_1, counts_1, err=counts_1_err,
err_dist=lc1.err_dist,
gti=gti1,
dt=lc1.dt)
lc2_seg = Lightcurve(time_2, counts_2, err=counts_2_err,
err_dist=lc2.err_dist,
gti=gti2,
dt=lc2.dt)
cs_seg = Crossspectrum(lc1_seg, lc2_seg, norm=self.norm)
cs_all.append(cs_seg)
nphots1_all.append(np.sum(lc1_seg.counts))
nphots2_all.append(np.sum(lc2_seg.counts))
return cs_all, nphots1_all, nphots2_all
def _make_crossspectrum(self, lc1, lc2):
# make sure the inputs work!
if not isinstance(lc1, lightcurve.Lightcurve):
raise TypeError("lc1 must be a lightcurve.Lightcurve object")
if not isinstance(lc2, lightcurve.Lightcurve):
raise TypeError("lc2 must be a lightcurve.Lightcurve object")
# Then check that GTIs make sense
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
if self.gti.shape[0] != 1:
raise TypeError("Non-averaged Cross Spectra need "
"a single Good Time Interval")
lc1 = lc1.split_by_gti()[0]
lc2 = lc2.split_by_gti()[0]
# total number of photons is the sum of the
# counts in the light curve
self.nphots1 = np.float64(np.sum(lc1.counts))
self.nphots2 = np.float64(np.sum(lc2.counts))
self.meancounts1 = np.mean(lc1.counts)
self.meancounts2 = np.mean(lc2.counts)
# the number of data points in the light curve
if lc1.n != lc2.n:
raise StingrayError("Light curves do not have same number "
"of time bins per segment.")
if lc1.dt != lc2.dt:
raise StingrayError("Light curves do not have "
"same time binning dt.")
self.n = lc1.n
# the frequency resolution
self.df = 1.0 / lc1.tseg
# the number of averaged periodograms in the final output
# This should *always* be 1 here
self.m = 1
# make the actual Fourier transform and compute cross spectrum
self.freq, self.unnorm_power = self._fourier_cross(lc1, lc2)
# If co-spectrum is desired, normalize here. Otherwise, get raw back
# with the imaginary part still intact.
self.power = self._normalize_crossspectrum(self.unnorm_power, lc1.tseg)
def _make_crossspectrum(self, lc1, lc2):
## make sure the inputs work!
if not isinstance(lc1, lightcurve.Lightcurve):
raise TypeError("lc1 must be a lightcurve.Lightcurve object")
if not isinstance(lc2, lightcurve.Lightcurve):
raise TypeError("lc2 must be a lightcurve.Lightcurve object")
# Then check that GTIs make sense
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
if self.gti.shape[0] != 1:
raise TypeError("Non-averaged Cross Spectra need "
"a single Good Time Interval")
lc1 = lc1.split_by_gti()[0]
lc2 = lc2.split_by_gti()[0]
## total number of photons is the sum of the
## counts in the light curve
self.nphots1 = np.float64(np.sum(lc1.counts))
self.nphots2 = np.float64(np.sum(lc2.counts))
self.meancounts1 = np.mean(lc1.counts)
self.meancounts2 = np.mean(lc2.counts)
## the number of data points in the light curve
if lc1.counts.shape[0] != lc2.counts.shape[0]:
raise StingrayError("Light curves do not have same number "
"of time bins per segment.")
if lc1.dt != lc2.dt:
raise StingrayError("Light curves do not have "
"same time binning dt.")
self.n = lc1.counts.shape[0]
## the frequency resolution
self.df = 1.0/lc1.tseg
## the number of averaged periodograms in the final output
## This should *always* be 1 here
self.m = 1
## make the actual Fourier transform and compute cross spectrum
self.freq, self.unnorm_power = self._fourier_cross(lc1, lc2)
## If co-spectrum is desired, normalize here. Otherwise, get raw back
## with the imaginary part still intact.
self.power = self._normalize_crossspectrum(self.unnorm_power, lc1.tseg)
def _make_segment_spectrum(self, lc, segment_size):
"""
Split the light curves into segments of size ``segment_size``, and calculate a power spectrum for
each.
Parameters
----------
lc : :class:`stingray.Lightcurve` objects\
The input light curve
segment_size : ``numpy.float``
Size of each light curve segment to use for averaging.
Returns
-------
power_all : list of :class:`Powerspectrum` objects
A list of power spectra calculated independently from each light curve segment
nphots_all : ``numpy.ndarray``
List containing the number of photons for all segments calculated from ``lc``
"""
if not isinstance(lc, lightcurve.Lightcurve):
raise TypeError("lc must be a lightcurve.Lightcurve object")
if self.gti is None:
self.gti = lc.gti
else:
if not np.all(lc.gti == self.gti):
self.gti = np.vstack([self.gti, lc.gti])
check_gtis(self.gti)
start_inds, end_inds = \
bin_intervals_from_gtis(lc.gti, segment_size, lc.time, dt=lc.dt)
power_all = []
nphots_all = []
for start_ind, end_ind in zip(start_inds, end_inds):
time = lc.time[start_ind:end_ind]
counts = lc.counts[start_ind:end_ind]
counts_err = lc.counts_err[start_ind:end_ind]
lc_seg = lightcurve.Lightcurve(time,
counts,
err=counts_err,
err_dist=lc.err_dist.lower(),
skip_checks=True,
dt=lc.dt)
power_seg = Powerspectrum(lc_seg, norm=self.norm)
power_all.append(power_seg)
nphots_all.append(np.sum(lc_seg.counts))
return power_all, nphots_all
def _apply_gtis(self):
"""Apply GTIs to a light curve after modification."""
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti)
self.time = self.time[good]
self.counts = self.counts[good]
self.counts_err = self.counts_err[good]
self.countrate = self.countrate[good]
self.countrate_err = self.countrate_err[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
def _make_segment_spectrum(self, lc1, lc2, segment_size):
# TODO: need to update this for making cross spectra.
assert isinstance(lc1, Lightcurve)
assert isinstance(lc2, Lightcurve)
if lc1.dt != lc2.dt:
raise ValueError("Light curves do not have same time binning dt.")
if lc1.tseg != lc2.tseg:
raise ValueError("Lightcurves do not have same tseg.")
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
cs_all = []
nphots1_all = []
nphots2_all = []
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc1.time)
for start_ind, end_ind in zip(start_inds, end_inds):
time_1 = lc1.time[start_ind:end_ind]
counts_1 = lc1.counts[start_ind:end_ind]
counts_1_err = lc1.counts_err[start_ind:end_ind]
time_2 = lc2.time[start_ind:end_ind]
counts_2 = lc2.counts[start_ind:end_ind]
counts_2_err = lc2.counts_err[start_ind:end_ind]
lc1_seg = Lightcurve(time_1,
counts_1,
err=counts_1_err,
err_dist=lc1.err_dist)
lc2_seg = Lightcurve(time_2,
counts_2,
err=counts_2_err,
err_dist=lc2.err_dist)
cs_seg = Crossspectrum(lc1_seg, lc2_seg, norm=self.norm)
cs_all.append(cs_seg)
nphots1_all.append(np.sum(lc1_seg.counts))
nphots2_all.append(np.sum(lc2_seg.counts))
return cs_all, nphots1_all, nphots2_all
def _make_segment_spectrum(self, lc, segment_size):
"""
Split the light curves into segments of size ``segment_size``, and calculate a power spectrum for
each.
Parameters
----------
lc : :class:`stingray.Lightcurve` objects\
The input light curve
segment_size : ``numpy.float``
Size of each light curve segment to use for averaging.
Returns
-------
power_all : list of :class:`Powerspectrum` objects
A list of power spectra calculated independently from each light curve segment
nphots_all : ``numpy.ndarray``
List containing the number of photons for all segments calculated from ``lc``
"""
if not isinstance(lc, lightcurve.Lightcurve):
raise TypeError("lc must be a lightcurve.Lightcurve object")
if self.gti is None:
self.gti = lc.gti
check_gtis(self.gti)
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc.time, dt=lc.dt)
power_all = []
nphots_all = []
for start_ind, end_ind in zip(start_inds, end_inds):
time = lc.time[start_ind:end_ind]
counts = lc.counts[start_ind:end_ind]
counts_err = lc.counts_err[start_ind: end_ind]
lc_seg = lightcurve.Lightcurve(time, counts, err=counts_err,
err_dist=lc.err_dist.lower())
power_seg = Powerspectrum(lc_seg, norm=self.norm)
power_all.append(power_seg)
nphots_all.append(np.sum(lc_seg.counts))
return power_all, nphots_all
def test_check_gtis_shape(self):
with pytest.raises(TypeError):
check_gtis([0, 1])
with pytest.raises(TypeError):
check_gtis([[0, 1], [0]])
with pytest.raises(TypeError):
check_gtis([[0, 1], [[0], [3]]])
with pytest.raises(TypeError):
check_gtis([[0, 1, 4], [0, 3, 4]])
def _apply_gtis(self):
"""
Apply GTIs to a light curve. Filters the ``time``, ``counts``, ``countrate``, ``counts_err`` and
``countrate_err`` arrays for all bins that fall into Good Time Intervals and recalculates mean
count(rate) and the number of bins.
"""
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
self.counts = self.counts[good]
self.counts_err = self.counts_err[good]
self.countrate = self.countrate[good]
self.countrate_err = self.countrate_err[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
def _apply_gtis(self):
"""
Apply GTIs to a light curve. Filters the ``time``, ``counts``, ``countrate``, ``counts_err`` and
``countrate_err`` arrays for all bins that fall into Good Time Intervals and recalculates mean
count(rate) and the number of bins.
"""
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
self.counts = self.counts[good]
self.counts_err = self.counts_err[good]
self.countrate = self.countrate[good]
self.countrate_err = self.countrate_err[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
def _make_segment_spectrum(self, lc1, lc2, segment_size):
# TODO: need to update this for making cross spectra.
assert isinstance(lc1, lightcurve.Lightcurve)
assert isinstance(lc2, lightcurve.Lightcurve)
if lc1.dt != lc2.dt:
raise ValueError("Light curves do not have same time binning dt.")
if lc1.tseg != lc2.tseg:
raise ValueError("Lightcurves do not have same tseg.")
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
cs_all = []
nphots1_all = []
nphots2_all = []
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc1.time)
for start_ind, end_ind in zip(start_inds, end_inds):
time_1 = lc1.time[start_ind:end_ind]
counts_1 = lc1.counts[start_ind:end_ind]
time_2 = lc2.time[start_ind:end_ind]
counts_2 = lc2.counts[start_ind:end_ind]
lc1_seg = lightcurve.Lightcurve(time_1, counts_1)
lc2_seg = lightcurve.Lightcurve(time_2, counts_2)
cs_seg = Crossspectrum(lc1_seg, lc2_seg, norm=self.norm)
cs_all.append(cs_seg)
nphots1_all.append(np.sum(lc1_seg.counts))
nphots2_all.append(np.sum(lc2_seg.counts))
return cs_all, nphots1_all, nphots2_all
def __init__(self, time, counts, err=None, input_counts=True,
gti=None, err_dist='poisson', mjdref=0, dt=None):
"""
Make a light curve object from an array of time stamps and an
array of counts.
Parameters
----------
time: iterable
A list or array of time stamps for a light curve
counts: iterable, optional, default None
A list or array of the counts in each bin corresponding to the
bins defined in `time` (note: use `input_counts=False` to
input the count range, i.e. counts/second, otherwise use
counts/bin).
err: iterable, optional, default None:
A list or array of the uncertainties in each bin corresponding to
the bins defined in `time` (note: use `input_counts=False` to
input the count rage, i.e. counts/second, otherwise use
counts/bin). If None, we assume the data is poisson distributed
and calculate the error from the average of the lower and upper
1-sigma confidence intervals for the Poissonian distribution with
mean equal to `counts`.
input_counts: bool, optional, default True
If True, the code assumes that the input data in 'counts'
is in units of counts/bin. If False, it assumes the data
in 'counts' is in counts/second.
gti: 2-d float array, default None
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They are *not* applied to the data by default.
They will be used by other methods to have an indication of the
"safe" time intervals to use during analysis.
err_dist: str, optional, default=None
Statistic of the Lightcurve, it is used to calculate the
uncertainties and other statistical values apropriately.
Default makes no assumptions and keep errors equal to zero.
mjdref: float
MJD reference (useful in most high-energy mission data)
Attributes
----------
time: numpy.ndarray
The array of midpoints of time bins.
bin_lo:
The array of lower time stamp of time bins.
bin_hi:
The array of higher time stamp of time bins.
counts: numpy.ndarray
The counts per bin corresponding to the bins in `time`.
counts_err: numpy.ndarray
The uncertainties corresponding to `counts`
countrate: numpy.ndarray
The counts per second in each of the bins defined in `time`.
countrate_err: numpy.ndarray
The uncertainties corresponding to `countrate`
meanrate: float
The mean count rate of the light curve.
meancounts: float
The mean counts of the light curve.
n: int
The number of data points in the light curve.
dt: float
The time resolution of the light curve.
mjdref: float
MJD reference date (tstart / 86400 gives the date in MJD at the
start of the observation)
tseg: float
The total duration of the light curve.
tstart: float
The start time of the light curve.
gti: 2-d float array
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They indicate the "safe" time intervals
to be used during the analysis of the light curve.
err_dist: string
Statistic of the Lightcurve, it is used to calculate the
uncertainties and other statistical values apropriately.
It propagates to Spectrum classes.
"""
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
if err is not None:
if not np.all(np.isfinite(err)):
raise ValueError("There are inf or NaN values in "
"your err array")
else:
if err_dist.lower() not in valid_statistics:
# err_dist set can be increased with other statistics
raise StingrayError("Statistic not recognized."
"Please select one of these: ",
"{}".format(valid_statistics))
if err_dist.lower() == 'poisson':
# Instead of the simple square root, we use confidence
# intervals (should be valid for low fluxes too)
err_low, err_high = poisson_conf_interval(np.asarray(counts),
interval='frequentist-confidence', sigma=1)
# calculate approximately symmetric uncertainties
err_low -= np.asarray(counts)
err_high -= np.asarray(counts)
err = (np.absolute(err_low) + np.absolute(err_high))/2.0
# other estimators can be implemented for other statistics
else:
simon("Stingray only uses poisson err_dist at the moment, "
"We are setting your errors to zero. "
"Sorry for the inconvenience.")
err = np.zeros_like(counts)
self.mjdref = mjdref
self.time = np.asarray(time)
if dt is None:
self.dt = np.median(self.time[1:] - self.time[:-1])
else:
self.dt = dt
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
self.err_dist = err_dist
self.tstart = self.time[0] - 0.5*self.dt
self.tseg = self.time[-1] - self.time[0] + self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti)
self.time = self.time[good]
if input_counts:
self.counts = np.asarray(counts)[good]
self.countrate = self.counts / self.dt
self.counts_err = np.asarray(err)[good]
self.countrate_err = np.asarray(err)[good] / self.dt
else:
self.countrate = np.asarray(counts)[good]
self.counts = self.countrate * self.dt
self.counts_err = np.asarray(err)[good] * self.dt
self.countrate_err = np.asarray(err)[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = np.diff(self.time)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
def __init__(self, time, counts, input_counts=True, gti=None):
"""
Make a light curve object from an array of time stamps and an
array of counts.
Parameters
----------
time: iterable
A list or array of time stamps for a light curve
counts: iterable, optional, default None
A list or array of the counts in each bin corresponding to the
bins defined in `time` (note: **not** the count rate, i.e.
counts/second, but the counts/bin).
input_counts: bool, optional, default True
If True, the code assumes that the input data in 'counts'
is in units of counts/bin. If False, it assumes the data
in 'counts' is in counts/second.
gti: 2-d float array, default None
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They are *not* applied to the data by default.
They will be used by other methods to have an indication of the
"safe" time intervals to use during analysis.
Attributes
----------
time: numpy.ndarray
The array of midpoints of time bins.
bin_lo:
The array of lower time stamp of time bins.
bin_hi:
The array of higher time stamp of time bins.
counts: numpy.ndarray
The counts per bin corresponding to the bins in `time`.
countrate: numpy.ndarray
The counts per second in each of the bins defined in `time`.
meanrate: float
The mean count rate of the light curve.
meancounts: float
The mean counts of the light curve.
n: int
The number of data points in the light curve.
dt: float
The time resolution of the light curve.
tseg: float
The total duration of the light curve.
tstart: float
The start time of the light curve.
gti: 2-d float array
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They indicate the "safe" time intervals
to be used during the analysis of the light curve.
"""
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
self.time = np.asarray(time)
self.dt = time[1] - time[0]
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
if input_counts:
self.counts = np.asarray(counts)
self.countrate = self.counts / self.dt
else:
self.countrate = np.asarray(counts)
self.counts = self.countrate * self.dt
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = np.diff(self.time)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
self.tseg = self.time[-1] - self.time[0] + self.dt
self.tstart = self.time[0] - 0.5 * self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
def _make_segment_spectrum(self, lc1, lc2, segment_size):
"""
Split the light curves into segments of size ``segment_size``, and calculate a cross spectrum for
each.
Parameters
----------
lc1, lc2 : :class:`stingray.Lightcurve` objects
Two light curves used for computing the cross spectrum.
segment_size : ``numpy.float``
Size of each light curve segment to use for averaging.
Returns
-------
cs_all : list of :class:`Crossspectrum`` objects
A list of cross spectra calculated independently from each light curve segment
nphots1_all, nphots2_all : ``numpy.ndarray` for each of ``lc1`` and ``lc2``
Two lists containing the number of photons for all segments calculated from ``lc1`` and ``lc2``.
"""
# TODO: need to update this for making cross spectra.
assert isinstance(lc1, Lightcurve)
assert isinstance(lc2, Lightcurve)
if lc1.tseg != lc2.tseg:
simon("Lightcurves do not have same tseg. This means that the data"
"from the two channels are not completely in sync. This "
"might or might not be an issue. Keep an eye on it.")
# If dt differs slightly, its propagated error must not be more than
# 1/100th of the bin
if not np.isclose(lc1.dt, lc2.dt, rtol=0.01 * lc1.dt / lc1.tseg):
raise ValueError("Light curves do not have same time binning dt.")
# In case a small difference exists, ignore it
lc1.dt = lc2.dt
gti = cross_two_gtis(lc1.gti, lc2.gti)
lc1.apply_gtis()
lc2.apply_gtis()
if self.gti is None:
self.gti = gti
else:
if not np.all(self.gti == gti):
self.gti = np.vstack([self.gti, gti])
check_gtis(self.gti)
cs_all = []
nphots1_all = []
nphots2_all = []
start_inds, end_inds = \
bin_intervals_from_gtis(gti, segment_size, lc1.time,
dt=lc1.dt)
simon("Errorbars on cross spectra are not thoroughly tested. "
"Please report any inconsistencies.")
for start_ind, end_ind in zip(start_inds, end_inds):
time_1 = lc1.time[start_ind:end_ind]
counts_1 = lc1.counts[start_ind:end_ind]
counts_1_err = lc1.counts_err[start_ind:end_ind]
time_2 = lc2.time[start_ind:end_ind]
counts_2 = lc2.counts[start_ind:end_ind]
counts_2_err = lc2.counts_err[start_ind:end_ind]
gti1 = np.array([[time_1[0] - lc1.dt / 2,
time_1[-1] + lc1.dt / 2]])
gti2 = np.array([[time_2[0] - lc2.dt / 2,
time_2[-1] + lc2.dt / 2]])
lc1_seg = Lightcurve(time_1, counts_1, err=counts_1_err,
err_dist=lc1.err_dist,
gti=gti1,
dt=lc1.dt, skip_checks=True)
lc2_seg = Lightcurve(time_2, counts_2, err=counts_2_err,
err_dist=lc2.err_dist,
gti=gti2,
dt=lc2.dt, skip_checks=True)
with warnings.catch_warnings(record=True) as w:
cs_seg = Crossspectrum(lc1_seg, lc2_seg, norm=self.norm, power_type=self.power_type)
cs_all.append(cs_seg)
nphots1_all.append(np.sum(lc1_seg.counts))
nphots2_all.append(np.sum(lc2_seg.counts))
return cs_all, nphots1_all, nphots2_all
def _make_crossspectrum(self, lc1, lc2):
"""
Auxiliary method computing the normalized cross spectrum from two
light curves. This includes checking for the presence of and
applying Good Time Intervals, computing the unnormalized Fourier
cross-amplitude, and then renormalizing using the required
normalization. Also computes an uncertainty estimate on the cross
spectral powers.
Parameters
----------
lc1, lc2 : :class:`stingray.Lightcurve` objects
Two light curves used for computing the cross spectrum.
"""
# make sure the inputs work!
if not isinstance(lc1, Lightcurve):
raise TypeError("lc1 must be a lightcurve.Lightcurve object")
if not isinstance(lc2, Lightcurve):
raise TypeError("lc2 must be a lightcurve.Lightcurve object")
if self.lc2.mjdref != self.lc1.mjdref:
raise ValueError("MJDref is different in the two light curves")
# Then check that GTIs make sense
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
if self.gti.shape[0] != 1:
raise TypeError("Non-averaged Cross Spectra need "
"a single Good Time Interval")
lc1 = lc1.split_by_gti()[0]
lc2 = lc2.split_by_gti()[0]
# total number of photons is the sum of the
# counts in the light curve
self.nphots1 = np.float64(np.sum(lc1.counts))
self.nphots2 = np.float64(np.sum(lc2.counts))
self.meancounts1 = lc1.meancounts
self.meancounts2 = lc2.meancounts
# the number of data points in the light curve
if lc1.n != lc2.n:
raise StingrayError("Light curves do not have same number "
"of time bins per segment.")
# If dt differs slightly, its propagated error must not be more than
# 1/100th of the bin
if not np.isclose(lc1.dt, lc2.dt, rtol=0.01 * lc1.dt / lc1.tseg):
raise StingrayError("Light curves do not have same time binning "
"dt.")
# In case a small difference exists, ignore it
lc1.dt = lc2.dt
self.n = lc1.n
# the frequency resolution
self.df = 1.0 / lc1.tseg
# the number of averaged periodograms in the final output
# This should *always* be 1 here
self.m = 1
# make the actual Fourier transform and compute cross spectrum
self.freq, self.unnorm_power = self._fourier_cross(lc1, lc2)
# If co-spectrum is desired, normalize here. Otherwise, get raw back
# with the imaginary part still intact.
self.power = self._normalize_crossspectrum(self.unnorm_power, lc1.tseg)
if lc1.err_dist.lower() != lc2.err_dist.lower():
simon("Your lightcurves have different statistics."
"The errors in the Crossspectrum will be incorrect.")
elif lc1.err_dist.lower() != "poisson":
simon("Looks like your lightcurve statistic is not poisson."
"The errors in the Powerspectrum will be incorrect.")
if self.__class__.__name__ in ['Powerspectrum',
'AveragedPowerspectrum']:
self.power_err = self.power / np.sqrt(self.m)
elif self.__class__.__name__ in ['Crossspectrum',
'AveragedCrossspectrum']:
# This is clearly a wild approximation.
simon("Errorbars on cross spectra are not thoroughly tested. "
"Please report any inconsistencies.")
unnorm_power_err = np.sqrt(2) / np.sqrt(self.m) # Leahy-like
unnorm_power_err /= (2 / np.sqrt(self.nphots1 * self.nphots2))
unnorm_power_err += np.zeros_like(self.power)
self.power_err = \
self._normalize_crossspectrum(unnorm_power_err, lc1.tseg)
else:
self.power_err = np.zeros(len(self.power))
def _make_segment_spectrum(self, lc, segment_size, silent=False):
"""
Split the light curves into segments of size ``segment_size``, and
calculate a power spectrum for each.
Parameters
----------
lc : :class:`stingray.Lightcurve` objects\
The input light curve
segment_size : ``numpy.float``
Size of each light curve segment to use for averaging.
Other parameters
----------------
silent : bool, default False
Suppress progress bars
Returns
-------
power_all : list of :class:`Powerspectrum` objects
A list of power spectra calculated independently from each light curve segment
nphots_all : ``numpy.ndarray``
List containing the number of photons for all segments calculated from ``lc``
"""
if not isinstance(lc, Lightcurve):
raise TypeError("lc must be a Lightcurve object")
current_gtis = lc.gti
if self.gti is None:
self.gti = lc.gti
else:
if not np.allclose(lc.gti, self.gti):
self.gti = np.vstack([self.gti, lc.gti])
check_gtis(self.gti)
start_inds, end_inds = \
bin_intervals_from_gtis(current_gtis, segment_size, lc.time, dt=lc.dt)
power_all = []
nphots_all = []
local_show_progress = show_progress
if not self.show_progress or silent:
def local_show_progress(a): return a
for start_ind, end_ind in \
local_show_progress(zip(start_inds, end_inds)):
time = lc.time[start_ind:end_ind]
counts = lc.counts[start_ind:end_ind]
counts_err = lc.counts_err[start_ind: end_ind]
if np.sum(counts) == 0:
warnings.warn(
"No counts in interval {}--{}s".format(time[0], time[-1]))
continue
lc_seg = Lightcurve(time, counts, err=counts_err,
err_dist=lc.err_dist.lower(),
skip_checks=True, dt=lc.dt)
power_seg = Powerspectrum(lc_seg, norm=self.norm)
power_all.append(power_seg)
nphots_all.append(np.sum(lc_seg.counts))
return power_all, nphots_all
def __init__(self, time, counts, err=None, input_counts=True,
gti=None, err_dist='poisson', mjdref=0, dt=None):
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
if err is not None:
if not np.all(np.isfinite(err)):
raise ValueError("There are inf or NaN values in "
"your err array")
else:
if err_dist.lower() not in valid_statistics:
# err_dist set can be increased with other statistics
raise StingrayError("Statistic not recognized."
"Please select one of these: ",
"{}".format(valid_statistics))
if err_dist.lower() == 'poisson':
# Instead of the simple square root, we use confidence
# intervals (should be valid for low fluxes too)
err = poisson_symmetrical_errors(counts)
else:
simon("Stingray only uses poisson err_dist at the moment, "
"We are setting your errors to zero. "
"Sorry for the inconvenience.")
err = np.zeros_like(counts)
self.mjdref = mjdref
self.time = np.asarray(time)
dt_array = np.diff(np.sort(self.time))
dt_array_unsorted = np.diff(self.time)
unsorted = np.any(dt_array_unsorted < 0)
if dt is None:
if unsorted:
logging.warning("The light curve is unordered! This may cause "
"unexpected behaviour in some methods! Use "
"sort() to order the light curve in time and "
"check that the time resolution `dt` is "
"calculated correctly!")
self.dt = np.median(dt_array)
else:
self.dt = dt
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
self.err_dist = err_dist
if unsorted:
self.tstart = np.min(self.time) - 0.5 * self.dt
self.tseg = np.max(self.time) - np.min(self.time) + self.dt
else:
self.tstart = self.time[0] - 0.5 * self.dt
self.tseg = self.time[-1] - self.time[0] + self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
if input_counts:
self.counts = np.asarray(counts)[good]
self.countrate = self.counts / self.dt
self.counts_err = np.asarray(err)[good]
self.countrate_err = np.asarray(err)[good] / self.dt
else:
self.countrate = np.asarray(counts)[good]
self.counts = self.countrate * self.dt
self.counts_err = np.asarray(err)[good] * self.dt
self.countrate_err = np.asarray(err)[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = []
for g in self.gti:
mask = create_gti_mask(self.time, [g], dt=self.dt)
t = self.time[mask]
dt_array.extend(np.diff(t))
dt_array = np.asarray(dt_array)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
def test_check_gti_fails_empty(self):
with pytest.raises(ValueError) as excinfo:
check_gtis([])
assert 'Empty' in str(excinfo.value)
def test_check_gtis_values(self):
with pytest.raises(ValueError):
check_gtis([[0, 2], [1, 3]])
with pytest.raises(ValueError):
check_gtis([[1, 0]])
def _make_crossspectrum(self, lc1, lc2):
# make sure the inputs work!
if not isinstance(lc1, Lightcurve):
raise TypeError("lc1 must be a lightcurve.Lightcurve object")
if not isinstance(lc2, Lightcurve):
raise TypeError("lc2 must be a lightcurve.Lightcurve object")
if self.lc2.mjdref != self.lc1.mjdref:
raise ValueError("MJDref is different in the two light curves")
# Then check that GTIs make sense
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
if self.gti.shape[0] != 1:
raise TypeError("Non-averaged Cross Spectra need "
"a single Good Time Interval")
lc1 = lc1.split_by_gti()[0]
lc2 = lc2.split_by_gti()[0]
# total number of photons is the sum of the
# counts in the light curve
self.nphots1 = np.float64(np.sum(lc1.counts))
self.nphots2 = np.float64(np.sum(lc2.counts))
self.meancounts1 = lc1.meancounts
self.meancounts2 = lc2.meancounts
# the number of data points in the light curve
if lc1.n != lc2.n:
raise StingrayError("Light curves do not have same number "
"of time bins per segment.")
# If dt differs slightly, its propagated error must not be more than
# 1/100th of the bin
if not np.isclose(lc1.dt, lc2.dt, rtol=0.01 * lc1.dt / lc1.tseg):
raise StingrayError("Light curves do not have same time binning "
"dt.")
# In case a small difference exists, ignore it
lc1.dt = lc2.dt
self.n = lc1.n
# the frequency resolution
self.df = 1.0 / lc1.tseg
# the number of averaged periodograms in the final output
# This should *always* be 1 here
self.m = 1
# make the actual Fourier transform and compute cross spectrum
self.freq, self.unnorm_power = self._fourier_cross(lc1, lc2)
# If co-spectrum is desired, normalize here. Otherwise, get raw back
# with the imaginary part still intact.
self.power = self._normalize_crossspectrum(self.unnorm_power, lc1.tseg)
if lc1.err_dist.lower() != lc2.err_dist.lower():
simon("Your lightcurves have different statistics."
"The errors in the Crossspectrum will be incorrect.")
elif lc1.err_dist.lower() != "poisson":
simon("Looks like your lightcurve statistic is not poisson."
"The errors in the Powerspectrum will be incorrect.")
if self.__class__.__name__ in ['Powerspectrum',
'AveragedPowerspectrum']:
self.power_err = self.power / np.sqrt(self.m)
elif self.__class__.__name__ in ['Crossspectrum',
'AveragedCrossspectrum']:
# This is clearly a wild approximation.
simon("Errorbars on cross spectra are not thoroughly tested. "
"Please report any inconsistencies.")
unnorm_power_err = np.sqrt(2) / np.sqrt(self.m) # Leahy-like
unnorm_power_err /= (2 / np.sqrt(self.nphots1 * self.nphots2))
unnorm_power_err += np.zeros_like(self.power)
self.power_err = \
self._normalize_crossspectrum(unnorm_power_err, lc1.tseg)
else:
self.power_err = np.zeros(len(self.power))
def __init__(self, time, counts, err=None, input_counts=True,
gti=None, err_dist='poisson', mjdref=0, dt=None):
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time and counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
if err is not None:
if not np.all(np.isfinite(err)):
raise ValueError("There are inf or NaN values in "
"your err array")
else:
if err_dist.lower() not in valid_statistics:
# err_dist set can be increased with other statistics
raise StingrayError("Statistic not recognized."
"Please select one of these: ",
"{}".format(valid_statistics))
if err_dist.lower() == 'poisson':
# Instead of the simple square root, we use confidence
# intervals (should be valid for low fluxes too)
err = poisson_symmetrical_errors(counts)
else:
simon("Stingray only uses poisson err_dist at the moment, "
"We are setting your errors to zero. "
"Sorry for the inconvenience.")
err = np.zeros_like(counts)
self.mjdref = mjdref
self.time = np.asarray(time)
dt_array = np.diff(np.sort(self.time))
dt_array_unsorted = np.diff(self.time)
unsorted = np.any(dt_array_unsorted < 0)
if dt is None:
if unsorted:
logging.warning("The light curve is unordered! This may cause "
"unexpected behaviour in some methods! Use "
"sort() to order the light curve in time and "
"check that the time resolution `dt` is "
"calculated correctly!")
self.dt = np.median(dt_array)
else:
self.dt = dt
self.bin_lo = self.time - 0.5 * self.dt
self.bin_hi = self.time + 0.5 * self.dt
self.err_dist = err_dist
if unsorted:
self.tstart = np.min(self.time) - 0.5 * self.dt
self.tseg = np.max(self.time) - np.min(self.time) + self.dt
else:
self.tstart = self.time[0] - 0.5 * self.dt
self.tseg = self.time[-1] - self.time[0] + self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)
good = create_gti_mask(self.time, self.gti, dt=self.dt)
self.time = self.time[good]
if input_counts:
self.counts = np.asarray(counts)[good]
self.countrate = self.counts / self.dt
self.counts_err = np.asarray(err)[good]
self.countrate_err = np.asarray(err)[good] / self.dt
else:
self.countrate = np.asarray(counts)[good]
self.counts = self.countrate * self.dt
self.counts_err = np.asarray(err)[good] * self.dt
self.countrate_err = np.asarray(err)[good]
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = []
for g in self.gti:
mask = create_gti_mask(self.time, [g], dt=self.dt)
t = self.time[mask]
dt_array.extend(np.diff(t))
dt_array = np.asarray(dt_array)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
def _make_multitaper_periodogram(self, lc, NW=4, adaptive=False,
jackknife=True, low_bias=True,
lombscargle=False):
"""Compute the normalized multitaper spectral estimate.
This includes checking for the presence of and applying Good Time Intervals,
computing the a nitime inspired normalized power spectrum, unnormalizing it,
and then renormalizing it using the required normalization.
Parameters
----------
lc : :class:`stingray.Lightcurve` objects
Two light curves used for computing the cross spectrum.
NW: float, optional, default ``4``
The normalized half-bandwidth of the data tapers, indicating a
multiple of the fundamental frequency of the DFT (Fs/N).
Common choices are n/2, for n >= 4.
adaptive: boolean, optional, default ``False``
Use an adaptive weighting routine to combine the PSD estimates of
different tapers.
jackknife: boolean, optional, default ``True``
Use the jackknife method to make an estimate of the PSD variance
at each point.
low_bias: boolean, optional, default ``True``
Rather than use 2NW tapers, only use the tapers that have better than
90% spectral concentration within the bandwidth (still using
a maximum of 2NW tapers)
"""
if not isinstance(lc, Lightcurve):
raise TypeError("lc must be a lightcurve.Lightcurve object")
if self.gti is None:
self.gti = cross_two_gtis(lc.gti, lc.gti)
check_gtis(self.gti)
if self.gti.shape[0] != 1:
raise TypeError("Non-averaged Spectra need "
"a single Good Time Interval")
lc = lc.split_by_gti()[0]
self.meancounts = lc.meancounts
self.nphots = np.float64(np.sum(lc.counts))
self.err_dist = 'poisson'
if lc.err_dist == 'poisson':
self.var = lc.meancounts
else:
self.var = np.mean(lc.counts_err) ** 2
self.err_dist = 'gauss'
self.dt = lc.dt
self.n = lc.n
# the frequency resolution
self.df = 1.0 / lc.tseg
# the number of averaged periodograms in the final output
# This should *always* be 1 here
self.m = 1
if lombscargle:
self.freq, self.multitaper_norm_power = \
self._fourier_multitaper_lomb_scargle(lc, NW=NW,
low_bias=low_bias)
self.unnorm_power = self.multitaper_norm_power * lc.n * 2
else:
self.freq, self.multitaper_norm_power = \
self._fourier_multitaper(lc, NW=NW, adaptive=adaptive,
jackknife=jackknife, low_bias=low_bias)
self.unnorm_power = self.multitaper_norm_power * lc.n / lc.dt
self.power = \
self._normalize_multitaper(self.unnorm_power, lc.tseg)
if lc.err_dist.lower() != "poisson":
simon("Looks like your lightcurve statistic is not poisson."
"The errors in the Powerspectrum will be incorrect.")
self.power_err = self.power / np.sqrt(self.m)
def _make_crossspectrum(self, lc1, lc2):
# make sure the inputs work!
if not isinstance(lc1, Lightcurve):
raise TypeError("lc1 must be a lightcurve.Lightcurve object")
if not isinstance(lc2, Lightcurve):
raise TypeError("lc2 must be a lightcurve.Lightcurve object")
# Then check that GTIs make sense
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
check_gtis(self.gti)
if self.gti.shape[0] != 1:
raise TypeError("Non-averaged Cross Spectra need "
"a single Good Time Interval")
lc1 = lc1.split_by_gti()[0]
lc2 = lc2.split_by_gti()[0]
# total number of photons is the sum of the
# counts in the light curve
self.nphots1 = np.float64(np.sum(lc1.counts))
self.nphots2 = np.float64(np.sum(lc2.counts))
self.meancounts1 = lc1.meancounts
self.meancounts2 = lc2.meancounts
# the number of data points in the light curve
if lc1.n != lc2.n:
raise StingrayError("Light curves do not have same number "
"of time bins per segment.")
if lc1.dt != lc2.dt:
raise StingrayError("Light curves do not have "
"same time binning dt.")
self.n = lc1.n
# the frequency resolution
self.df = 1.0 / lc1.tseg
# the number of averaged periodograms in the final output
# This should *always* be 1 here
self.m = 1
# make the actual Fourier transform and compute cross spectrum
self.freq, self.unnorm_power = self._fourier_cross(lc1, lc2)
# If co-spectrum is desired, normalize here. Otherwise, get raw back
# with the imaginary part still intact.
self.power = self._normalize_crossspectrum(self.unnorm_power, lc1.tseg)
if lc1.err_dist.lower() != lc2.err_dist.lower():
simon("Your lightcurves have different statistics."
"The errors in the Crossspectrum will be incorrect.")
elif lc1.err_dist.lower() != "poisson":
simon("Looks like your lightcurve statistic is not poisson."
"The errors in the Powerspectrum will be incorrect.")
if self.__class__.__name__ in [
'Powerspectrum', 'AveragedPowerspectrum'
]:
self.power_err = self.power / np.sqrt(self.m)
elif self.__class__.__name__ in [
'Crossspectrum', 'AveragedCrossspectrum'
]:
# This is clearly a wild approximation.
simon("Errorbars on cross spectra are not thoroughly tested. "
"Please report any inconsistencies.")
unnorm_power_err = np.sqrt(2) / np.sqrt(self.m) # Leahy-like
unnorm_power_err /= (2 / np.sqrt(self.nphots1 * self.nphots2))
unnorm_power_err += np.zeros_like(self.power)
self.power_err = \
self._normalize_crossspectrum(unnorm_power_err, lc1.tseg)
else:
self.power_err = np.zeros(len(self.power))
def _make_segment_spectrum(self, lc1, lc2, segment_size):
"""
Split the light curves into segments of size ``segment_size``, and calculate a cross spectrum for
each.
Parameters
----------
lc1, lc2 : :class:`stingray.Lightcurve` objects
Two light curves used for computing the cross spectrum.
segment_size : ``numpy.float``
Size of each light curve segment to use for averaging.
Returns
-------
cs_all : list of :class:`Crossspectrum`` objects
A list of cross spectra calculated independently from each light curve segment
nphots1_all, nphots2_all : ``numpy.ndarray` for each of ``lc1`` and ``lc2``
Two lists containing the number of photons for all segments calculated from ``lc1`` and ``lc2``.
"""
# TODO: need to update this for making cross spectra.
assert isinstance(lc1, Lightcurve)
assert isinstance(lc2, Lightcurve)
if lc1.tseg != lc2.tseg:
raise ValueError("Lightcurves do not have same tseg.")
# If dt differs slightly, its propagated error must not be more than
# 1/100th of the bin
if not np.isclose(lc1.dt, lc2.dt, rtol=0.01 * lc1.dt / lc1.tseg):
raise ValueError("Light curves do not have same time binning dt.")
# In case a small difference exists, ignore it
lc1.dt = lc2.dt
if self.gti is None:
self.gti = cross_two_gtis(lc1.gti, lc2.gti)
lc1.gti = lc2.gti = self.gti
lc1._apply_gtis()
lc2._apply_gtis()
check_gtis(self.gti)
cs_all = []
nphots1_all = []
nphots2_all = []
start_inds, end_inds = \
bin_intervals_from_gtis(self.gti, segment_size, lc1.time,
dt=lc1.dt)
for start_ind, end_ind in zip(start_inds, end_inds):
time_1 = lc1.time[start_ind:end_ind]
counts_1 = lc1.counts[start_ind:end_ind]
counts_1_err = lc1.counts_err[start_ind:end_ind]
time_2 = lc2.time[start_ind:end_ind]
counts_2 = lc2.counts[start_ind:end_ind]
counts_2_err = lc2.counts_err[start_ind:end_ind]
gti1 = np.array([[time_1[0] - lc1.dt / 2,
time_1[-1] + lc1.dt / 2]])
gti2 = np.array([[time_2[0] - lc2.dt / 2,
time_2[-1] + lc2.dt / 2]])
lc1_seg = Lightcurve(time_1, counts_1, err=counts_1_err,
err_dist=lc1.err_dist,
gti=gti1,
dt=lc1.dt)
lc2_seg = Lightcurve(time_2, counts_2, err=counts_2_err,
err_dist=lc2.err_dist,
gti=gti2,
dt=lc2.dt)
cs_seg = Crossspectrum(lc1_seg, lc2_seg, norm=self.norm, power_type=self.power_type)
cs_all.append(cs_seg)
nphots1_all.append(np.sum(lc1_seg.counts))
nphots2_all.append(np.sum(lc2_seg.counts))
return cs_all, nphots1_all, nphots2_all
def __init__(self, time, counts, input_counts=True, gti=None):
"""
Make a light curve object from an array of time stamps and an
array of counts.
Parameters
----------
time: iterable
A list or array of time stamps for a light curve
counts: iterable, optional, default None
A list or array of the counts in each bin corresponding to the
bins defined in `time` (note: **not** the count rate, i.e.
counts/second, but the counts/bin).
input_counts: bool, optional, default True
If True, the code assumes that the input data in 'counts'
is in units of counts/bin. If False, it assumes the data
in 'counts' is in counts/second.
gti: 2-d float array, default None
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They are *not* applied to the data by default.
They will be used by other methods to have an indication of the
"safe" time intervals to use during analysis.
Attributes
----------
time: numpy.ndarray
The array of midpoints of time bins.
counts: numpy.ndarray
The counts per bin corresponding to the bins in `time`.
countrate: numpy.ndarray
The counts per second in each of the bins defined in `time`.
meanrate: float
The mean count rate of the light curve.
meancounts: float
The mean counts of the light curve.
n: int
The number of data points in the light curve.
dt: float
The time resolution of the light curve.
tseg: float
The total duration of the light curve.
tstart: float
The start time of the light curve.
gti: 2-d float array
[[gti0_0, gti0_1], [gti1_0, gti1_1], ...]
Good Time Intervals. They indicate the "safe" time intervals
to be used during the analysis of the light curve.
"""
if not np.all(np.isfinite(time)):
raise ValueError("There are inf or NaN values in "
"your time array!")
if not np.all(np.isfinite(counts)):
raise ValueError("There are inf or NaN values in "
"your counts array!")
if len(time) != len(counts):
raise StingrayError("time are counts array are not "
"of the same length!")
if len(time) <= 1:
raise StingrayError("A single or no data points can not create "
"a lightcurve!")
self.time = np.asarray(time)
self.dt = time[1] - time[0]
if input_counts:
self.counts = np.asarray(counts)
self.countrate = self.counts / self.dt
else:
self.countrate = np.asarray(counts)
self.counts = self.countrate * self.dt
self.meanrate = np.mean(self.countrate)
self.meancounts = np.mean(self.counts)
self.n = self.counts.shape[0]
# Issue a warning if the input time iterable isn't regularly spaced,
# i.e. the bin sizes aren't equal throughout.
dt_array = np.diff(self.time)
if not (np.allclose(dt_array, np.repeat(self.dt, dt_array.shape[0]))):
simon("Bin sizes in input time array aren't equal throughout! "
"This could cause problems with Fourier transforms. "
"Please make the input time evenly sampled.")
self.tseg = self.time[-1] - self.time[0] + self.dt
self.tstart = self.time[0] - 0.5*self.dt
self.gti = \
np.asarray(assign_value_if_none(gti,
[[self.tstart,
self.tstart + self.tseg]]))
check_gtis(self.gti)