#freq_av_binned = freq_av.reshape(-1,4).sum(-1)
sn = (freq_av - freq_av.mean()) / freq_av.std()
peaks = np.argwhere(sn > 6)
with open('giant_pulses.txt', 'a') as f:
f.writelines(
['{0} {1}\n'.format(tsr[peak], sn[peak]) for peak in peaks])
for peak in peaks:
np.save(
'GP%s' % (tsr[peak]),
power[max(peak - buff, 0):min(peak + buff, power.shape[0]), :, :])
return power
if __name__ == '__main__':
args = CL_parser()
args.verbose = 0 if args.verbose is None else sum(args.verbose)
if args.fref is None:
args.fref = _fref
if args.rfi_filter_raw:
args.rfi_filter_raw = rfi_filter_raw
else:
args.rfi_filter_raw = None
if args.rfi_filter_power:
args.rfi_filter_power = rfi_filter_power
else:
args.rfi_filter_power = None
if args.reduction_defaults == 'gmrt':
sn = (profile - profile.mean()) / profile.std()
peaks = np.argwhere(sn > 6) #peak bins in binned profile
peaks = peaks*bscrunch #uncrunch peaks
#take only peaks corresponding to main/inter pulse - phase currently hardcoded
peaks = peaks[((phase[peaks]>0.06)&(phase[peaks]<0.08))|((phase[peaks]>0.46)&(phase[peaks]<0.50))]
#write information
with open('giant_pulses.txt', 'a') as f:
f.writelines(['{0} {1} {2} {3}\n'.format(tsr[peak], sn[int(peak/bscrunch)], peak, phase[peak]) for peak in peaks])
for peak in peaks:
np.save('GP%s' % (tsr[peak]), power[max(peak-buff,0):min(peak+buff,power.shape[0]),:,:] )
#np.save('pulse%s' % (tsr[peak]), sn[max(int(peak/bscrunch)-buff,0):min(int(peak/bscrunch)+buff,sn.shape[0])])
return power
if __name__ == '__main__':
args = CL_parser()
args.verbose = 0 if args.verbose is None else sum(args.verbose)
if args.fref is None:
args.fref = _fref
if args.rfi_filter_raw:
args.rfi_filter_raw = rfi_filter_raw
else:
args.rfi_filter_raw = None
if args.rfi_filter_power:
args.rfi_filter_power = rfi_filter_power
else:
args.rfi_filter_power = None
if args.reduction_defaults == 'gmrt':
def rfi_filter_raw(raw, nchan):
# note this should accomodate all data (including's lofar raw = complex)
rawbins = raw.reshape(-1, 2**11*nchan) # note, this is view!
std = rawbins.std(-1, keepdims=True)
ok = std < MAX_RMS
rawbins *= ok
return raw, ok
def rfi_filter_power(power):
return np.clip(power, 0., MAX_RMS**2 * power.shape[-1])
if __name__ == '__main__':
args = CL_parser()
args.verbose = 0 if args.verbose is None else sum(args.verbose)
if args.fref is None:
args.fref = _fref
if args.rfi_filter_raw:
args.rfi_filter_raw = rfi_filter_raw
else:
args.rfi_filter_raw = None
if args.reduction_defaults == 'lofar':
args.telescope = 'lofar'
# already channelized, determined from filehandle
# (previously args.nchan = 20)
args.ntw_min = 1020
args.waterfall = False