def _spectrum_function(self):
spec = np.zeros(len(self.energy_intervals))
spec_err = np.zeros_like(spec)
for i, eint in enumerate(self.energy_intervals):
lc = self._construct_lightcurves(eint, exclude=False,
only_base=True)
spec[i], spec_err[i] = excess_variance(lc, self.normalization)
return spec, spec_err
def _spectrum_function(self):
spec = np.zeros(len(self.energy_intervals))
spec_err = np.zeros_like(spec)
for i, eint in enumerate(self.energy_intervals):
lc = self._construct_lightcurves(eint, exclude=False,
only_base=True)
spec[i], spec_err[i] = excess_variance(lc, self.normalization)
return spec, spec_err
def evar_fun(lc):
from stingray.utils import excess_variance
return excess_variance(lc, normalization='none')
def evar_fun(lc):
from stingray.utils import excess_variance
return excess_variance(lc, normalization='none')
def excvar_norm(lc):
return excess_variance(lc, normalization='norm_xs')
def fvar(lc):
return excess_variance(lc, normalization='fvar')
def excvar(lc):
from stingray.utils import excess_variance
return excess_variance(lc, normalization='fvar')
def excvar(lc):
from stingray.utils import excess_variance
return excess_variance(lc, normalization='fvar')
