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event_list = np.column_stack((time_vals, events_table.columns["E"].values))
band_width = energy_range[1] - energy_range[0]
band_step = band_width / n_bands
from_val = energy_range[0]
band_interest = []
for i in range(n_bands):
band_interest.extend([[energy_range[0] + (i * band_step), energy_range[0] + ((i + 1) * band_step)]])
energy_arr.extend([(energy_range[0] + (i * band_step) + energy_range[0] + ((i + 1) * band_step))/2])
if std < 0:
std = None
# Calculates the Covariance Spectrum
cs = Covariancespectrum(event_list, dt, band_interest=band_interest, ref_band_interest=ref_band_interest, std=std)
covariance_arr = nan_and_inf_to_num(cs.covar)
covariance_err_arr = nan_and_inf_to_num(cs.covar_error)
else:
logging.warn('get_covariance_spectrum: Lc duration must be greater than bin size!')
return common_error("LC duration must be greater than bin size")
else:
logging.warn('get_covariance_spectrum: E column not found!')
return common_error("E column not found")
else:
logging.warn('get_covariance_spectrum: No events data!')
return common_error('No events data')
else:
logging.warn('get_covariance_spectrum: Wrong dataset type!')
return common_error("Wrong dataset type")