def get_noise_factor(wavelet, level, dec, beam=None):
# n = (250000)
n = (200, 200)
background = nputils.gaussian_noise(n, 0, 1)
if beam is not None:
background = beam.convolve(background)
return get_noise_factor_from_background(wavelet, level, dec, background)
def get_noise_factor(wavelet, level, dec, beam=None):
# n = (250000)
n = (200, 200)
background = nputils.gaussian_noise(n, 0, 1)
if beam is not None:
background = beam.convolve(background)
return get_noise_factor_from_background(wavelet, level, dec, background)
def dec_noise_factor(dec, bg, beam=None, **kargs):
if not isinstance(bg, np.ndarray):
n = (200, 200)
bg = nputils.gaussian_noise(n, 0, bg)
if beam is not None:
bg = beam.convolve(bg)
scales = dec(bg, **kargs)
return [scale.std() for scale in scales[:-1]]
def dec_noise_factor(dec, bg, beam=None, **kargs):
if not isinstance(bg, np.ndarray):
n = (200, 200)
bg = nputils.gaussian_noise(n, 0, bg)
if beam is not None:
bg = beam.convolve(bg)
scales = dec(bg, **kargs)
return [scale.std() for scale in scales[:-1]]
