data_operations.find_sine_gaussian_transients#

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Summary#

Finds sine-gaussian transients in data

Signature#

def find_sine_gaussian_transients(strain_wt, qmask, valid_mask, dt, clipwidth, freqs_lines, mask_freqs, sine_gaussian_intervals = None, sine_gaussian_thresholds = None, edgesafety = 1, fftsize = params.DEF_FFTSIZE, zero_mask = True, nfire = params.NPERFILE, renorm_wt = True, mask_save = None, verbose = True)

Input variables#
Name	Type	Default	Description
`strain_wt`			Whitened strain data
`qmask`			Boolean mask with zeros at holes in unwhitened data
`valid_mask`			Boolean mask with zeros where we cannot trust whitened data
`dt`			Time interval between successive elements of data (s)
`clipwidth`			Half-width of window around outliers to zero (in s)
`freqs_lines`			Array with frequencies over which we detected lines
`mask_freqs`			Boolean mask on freqs with zeros at varying lines
`sine_gaussian_intervals`		None	Frequency bands within which we look for Sine-Gaussian noise [central frequency, df = (upper - lower frequency)] Hz
`sine_gaussian_thresholds`		None	Amplitude thresholds for sine gaussian transients computed from waveforms. If None, detection depends on params.NPERFILE
`edgesafety`		1	Safety margin at the edge where we cannot trust the nature of the whitened data (we haven’t applied this to the mask when we get here)
`fftsize`		params.DEF_FFTSIZE	FFTsize for overlap-save
`zero_mask`		True	Flag indicating whether to zero the mask at glitches. Turn off for vetoing
`nfire`		params.NPERFILE	Number of times glitch detector fires per perfect file
`renorm_wt`		True	Flag whether we scaled the whitened data to have unit variance after highpass
`mask_save`		None	If desired, boolean mask on strain_wt with zeros at data to avoid
`verbose`		True	If true, prints details about sine-Gaussian check

Output variables#

Return annotation	Docstring type	Description
`None`		Zeros qmask in place, and returns 1. List of indices that jumped 2. Masks with zero where we zeroed the data, for each interval 3. String with details of the test

Docstring#

Finds sine-gaussian transients in data
:param strain_wt: Whitened strain data
:param qmask: Boolean mask with zeros at holes in unwhitened data
:param valid_mask:
    Boolean mask with zeros where we cannot trust whitened data
:param dt: Time interval between successive elements of data (s)
:param clipwidth: Half-width of window around outliers to zero (in s)
:param freqs_lines: Array with frequencies over which we detected lines
:param mask_freqs: Boolean mask on freqs with zeros at varying lines
:param sine_gaussian_intervals:
    Frequency bands within which we look for Sine-Gaussian noise
    [central frequency, df = (upper - lower frequency)] Hz
:param sine_gaussian_thresholds:
    Amplitude thresholds for sine gaussian transients computed from
    waveforms. If None, detection depends on params.NPERFILE
:param edgesafety:
    Safety margin at the edge where we cannot trust the nature of the
    whitened data (we haven't applied this to the mask when we get here)
:param fftsize: FFTsize for overlap-save
:param zero_mask:
    Flag indicating whether to zero the mask at glitches. Turn off for
    vetoing
:param nfire: Number of times glitch detector fires per perfect file
:param renorm_wt:
    Flag whether we scaled the whitened data to have unit variance after
    highpass
:param mask_save:
    If desired, boolean mask on strain_wt with zeros at data to avoid
:param verbose: If true, prints details about sine-Gaussian check
:return: Zeros qmask in place, and returns
         1. List of indices that jumped
         2. Masks with zero where we zeroed the data, for each interval
         3. String with details of the test