def action(self, Data) :
params = self.params
# Keep track of how many pre existing flags there are for feedback
# purposes.
already_flagged = ma.count_masked(Data.data)
if params["rotate"]:
if (tuple(Data.field['CRVAL4']) == (1, 2, 3, 4)):
rotate_pol.rotate(Data, (-5,-7,-8,-6))
Data.add_history('Rotated to XX,XY,YX,YY')
# Few operations to be performed before flagging.
if params["perform_hanning"] :
hanning.hanning_smooth(Data)
Data.add_history('Hanning smoothed.')
if params["cal_scale"] :
cal_scale.scale_by_cal(Data, True, False, False)
Data.add_history('Converted to units of noise cal temperture.')
# Flag the data.
apply_cuts(Data, sigma_thres=params['sigma_thres'],
badness_thres=params['badness_thres'],
time_cut=params['time_cut'])
Data.add_history('Flagged Bad Data.', ('Sigma threshold: '
+ str(self.params['sigma_thres']), 'Badness threshold: '
+ str(self.params['badness_thres']), 'Time mask size: '
+ str(self.params['time_cut'])))
# Report the number of new flags.
new_flags = ma.count_masked(Data.data) - already_flagged
self.block_feedback = str(new_flags) + ', '
return Data
def action(self, Data):
'''Prepares Data and flags RFI.
Parameters
----------
Data : DataBlock
Contains information in a usable format direct from GBT.
Returns
-------
Data : DataBlock
The input `Data` with RFI flagged. Will also be cal scaled and
rotated to XX,YY... if so chosen.
'''
params = self.params
# Keep track of how many pre existing flags there are for feedback
# purposes.
already_flagged = ma.count_masked(Data.data)
if params["rotate"]:
if (tuple(Data.field['CRVAL4']) == (1, 2, 3, 4)):
rotate_pol.rotate(Data, (-5, -7, -8, -6))
Data.add_history('Rotated to XX,XY,YX,YY')
# Few operations to be performed before flagging.
if params["perform_hanning"]:
hanning.hanning_smooth(Data)
Data.add_history('Hanning smoothed.')
if params["cal_scale"] or params["cal_phase"]:
cal_scale.scale_by_cal(Data,
params['cal_scale'],
False,
False,
False,
rotate=params['cal_phase'])
Data.add_history('Converted to units of noise cal temperture.')
# Flag the data.
apply_cuts(Data,
sigma_thres=params['sigma_thres'],
badness_thres=params['badness_thres'],
time_cut=params['time_cut'])
Data.add_history(
'Flagged Bad Data.',
('Sigma threshold: ' + str(self.params['sigma_thres']),
'Badness threshold: ' + str(self.params['badness_thres']),
'Time mask size: ' + str(self.params['time_cut'])))
# Report the number of new flags.
new_flags = ma.count_masked(Data.data) - already_flagged
percent = float(new_flags) / Data.data.size * 100
self.block_feedback = '%d (%f%%), ' % (new_flags, percent)
return Data
def action(self, Data):
'''Prepares Data and flags RFI.
Parameters
----------
Data : DataBlock
Contains information in a usable format direct from GBT.
Returns
-------
Data : DataBlock
The input `Data` with RFI flagged. Will also be cal scaled and
rotated to XX,YY... if so chosen.
'''
params = self.params
# Keep track of how many pre existing flags there are for feedback
# purposes.
already_flagged = ma.count_masked(Data.data)
if params["rotate"]:
if (tuple(Data.field['CRVAL4']) == (1, 2, 3, 4)):
rotate_pol.rotate(Data, (-5,-7,-8,-6))
Data.add_history('Rotated to XX,XY,YX,YY')
# Few operations to be performed before flagging.
if params["perform_hanning"] :
hanning.hanning_smooth(Data)
Data.add_history('Hanning smoothed.')
if params["cal_scale"] or params["cal_phase"]:
cal_scale.scale_by_cal(Data, params['cal_scale'], False, False,
False, rotate=params['cal_phase'])
Data.add_history('Converted to units of noise cal temperture.')
# Flag the data.
apply_cuts(Data, sigma_thres=params['sigma_thres'],
badness_thres=params['badness_thres'],
time_cut=params['time_cut'])
Data.add_history('Flagged Bad Data.', ('Sigma threshold: '
+ str(self.params['sigma_thres']), 'Badness threshold: '
+ str(self.params['badness_thres']), 'Time mask size: '
+ str(self.params['time_cut'])))
# Report the number of new flags.
new_flags = ma.count_masked(Data.data) - already_flagged
percent = float(new_flags) / Data.data.size * 100
self.block_feedback = '%d (%f%%), ' % (new_flags, percent)
return Data