axes.set_xscale("log")
axes.set_yscale("log")
classification=np.empty_like(psrlist,dtype=object)
Edot=np.empty_like(psrlist,dtype=float)
luminosity=np.empty_like(psrlist,dtype=float)
luminosity_error_statistical=np.empty_like(psrlist,dtype=float)
luminosity_lower_error_systematic=np.empty_like(psrlist,dtype=float)
luminosity_upper_error_systematic=np.empty_like(psrlist,dtype=float)
luminosity_ul=np.empty_like(psrlist,dtype=float)
luminosity_significant=np.empty_like(psrlist,dtype=bool)
for i,psr in enumerate(psrlist):
classification[i]=cat.get_off_peak_classification(psr)
Edot[i]=cat.get_edot(psr)
y, y_err_stat, y_lower_err_sys, y_upper_err_sys, y_ul, significant = cat.get_luminosity(psr)
luminosity[i]=y
#luminosity_lower_error[i]=y_lower_err
#luminosity_upper_error[i]=y_upper_err
luminosity_error_statistical[i]=y_err_stat
luminosity_lower_error_systematic[i]=y_lower_err_sys
luminosity_upper_error_systematic[i]=y_upper_err_sys
luminosity_ul[i]=y_ul
luminosity_significant[i]=significant
def plot_stat(cut, **kwargs):
