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):
"""
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 _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 sample_data():
"""
Import data from .txt file and return a light curve object.
Returns
-------
sample: :class:`Lightcurve` object
The :class:`Lightcurve` object with the desired time stamps
and counts.
"""
lc_file = pkg_resources.resource_stream(__name__, "datasets/lc_sample.txt")
data = np.loadtxt(lc_file)
# Extract first and second columns to indicate dates and counts respectively
dates = data[0:len(data), 0]
counts = data[0:len(data), 1]
# Return class:`Lightcurve` object
return lightcurve.Lightcurve(dates, counts)
def sample_data():
"""
Import data from .txt file and return a light curve object.
Returns
-------
sample: :class:`Lightcurve` object
The :class:`Lightcurve` object with the desired time stamps
and counts.
"""
lc_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
"datasets", "lc_sample.txt")
data = np.loadtxt(lc_file)
# Extract first and second columns to indicate dates and counts respectively
dates = data[0:len(data), 0]
counts = data[0:len(data), 1]
# Return class:`Lightcurve` object
return lightcurve.Lightcurve(dates, counts)
def _make_segment_psd(self, lc, segment_size):
assert isinstance(lc, lightcurve.Lightcurve)
## number of bins per segment
nbins = int(segment_size / lc.dt)
start_ind = 0
end_ind = nbins
ps_all = []
nphots_all = []
while end_ind <= lc.counts.shape[0]:
time = lc.time[start_ind:end_ind]
counts = lc.counts[start_ind:end_ind]
lc_seg = lightcurve.Lightcurve(time, counts)
ps_seg = Powerspectrum(lc_seg, norm=self.norm)
ps_all.append(ps_seg)
nphots_all.append(np.sum(lc_seg.counts))
start_ind += nbins
end_ind += nbins
return ps_all, nphots_all
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError("Matplotlib required for plot()")
cont = plt.contourf(self.freq,
self.freq,
self.bispec_phase,
100,
cmap=plt.cm.Spectral_r)
plt.colorbar(cont)
plt.title('Bispectrum Phase')
plt.xlabel('freq 1')
plt.ylabel('freq 2')
if axis is not None:
plt.axis(axis)
# Save figure
if save:
if filename is None:
plt.savefig('bispec_phase.png')
else:
plt.savefig(filename)
return plt
lc = lightcurve.Lightcurve([1, 2, 3, 4, 5], [2, 3, 1, 1, 2])
bs = Bispectrum(lc)
print(bs.bispec_mag)
