def _integrate(data, masks, frequencies, time_axes, ctx):
"""
Integrate over time and frequency on the TOD plane.
:param data: TOD
:param masks: mask
:param frequencies: frequency axis after integration
:param time_axes: time axis after integration
:param ctx: context
:return: TOD, time axis, frequency axis after integration
"""
integration_time = ctx.params.integration_time
integration_frequency = ctx.params.integration_frequency
frequencies = smooth(np.atleast_2d(frequencies), integration_frequency, axis=1)[0]
time_axes = smooth(np.atleast_2d(time_axes), integration_time, axis=1)[0]
data = smooth(data, integration_time, axis=1)
data = smooth(data, integration_frequency, axis=0)
if masks is not None:
# 'real' mask
masks = masks
masks = smooth(masks, integration_time, axis=1)
masks = (smooth(masks, integration_frequency, axis=0) > 0)
else:
masks=get_empty_mask(data.shape)
tod_vx = ma.array(data, mask=masks)
tod_vy = ma.array(data, mask=masks)
return tod_vx, tod_vy, frequencies, time_axes
def _integrate(data, masks, frequencies, time_axes, ctx):
"""
Integrate over time and frequency on the TOD plane.
:param data: TOD
:param masks: mask
:param frequencies: frequency axis after integration
:param time_axes: time axis after integration
:param ctx: context
:return: TOD, time axis, frequency axis after integration
"""
integration_time = ctx.params.integration_time
integration_frequency = ctx.params.integration_frequency
frequencies = smooth(np.atleast_2d(frequencies),
integration_frequency,
axis=1)[0]
time_axes = smooth(np.atleast_2d(time_axes), integration_time, axis=1)[0]
data = smooth(data, integration_time, axis=1)
data = smooth(data, integration_frequency, axis=0)
if masks is not None:
# 'real' mask
masks = masks
masks = smooth(masks, integration_time, axis=1)
masks = (smooth(masks, integration_frequency, axis=0) > 0)
else:
masks = get_empty_mask(data.shape)
tod_vx = ma.array(data, mask=masks)
tod_vy = ma.array(data, mask=masks)
return tod_vx, tod_vy, frequencies, time_axes
def get_rfi_mask(tod,
mask=None,
chi_1=35000,
eta_i=[0.5, 0.55, 0.62, 0.75, 1],
normalize_standing_waves=True,
suppress_dilation=False,
plotting=True,
sm_kwargs=None,
di_kwargs=None):
"""
Computes a mask to cover the RFI in a data set.
:param data: array containing the signal and RFI
:param mask: the initial mask
:param chi_1: First threshold
:param eta_i: List of sensitivities
:param normalize_standing_waves: whether to normalize standing waves
:param suppress_dilation: if true, mask dilation is suppressed
:param plotting: True if statistics plot should be displayed
:param sm_kwargs: smoothing key words
:param di_kwargs: dilation key words
:return mask: the mask covering the identified RFI
"""
data = tod.data
if mask is None:
mask = get_empty_mask(data.shape)
if sm_kwargs is None: sm_kwargs = get_sm_kwargs()
if plotting: sum_threshold_utils.plot_moments(data)
if normalize_standing_waves:
data = normalize(data, mask)
if plotting: sum_threshold_utils.plot_moments(data)
p = 1.5
m = np.arange(1, MAX_PIXELS)
M = 2**(m - 1)
chi_i = chi_1 / p**np.log2(m)
st_mask = mask
for eta in eta_i:
st_mask = _run_sumthreshold(data, st_mask, eta, M, chi_i, sm_kwargs,
plotting)
dilated_mask = st_mask
if not suppress_dilation:
if di_kwargs is None: di_kwargs = get_di_kwrags()
dilated_mask = binary_mask_dilation(dilated_mask - mask, **di_kwargs)
if plotting:
sum_threshold_utils.plot_dilation(st_mask, mask, dilated_mask)
return dilated_mask + mask