robust_channel_weights

sofia_redux.scan.integration.integration_numba_functions.robust_channel_weights(frame_data, relative_weights, sample_flags, valid_frames, channel_indices)

Derive robust channel variance and weights.

The returned weight and variance for a channel i is given as:

var = median(relative_weight * frame_data[:, i] ** 2) weight = sum(relative_weight)

taken over all valid frames for the channel i and zero flagged samples for the channel i.

Parameters:

frame_datanumpy.ndarray (float)

An (n_frame, n_channel) array of frame data values.

relative_weightsnumpy.ndarray (float)

The relative frame weights of shape (n_frames,).

sample_flagsnumpy.ndarray (int)

The frame data sample flags of shape (n_frames, n_channels). Only frames that are unflagged (flag=0) will be included in the derivation.

valid_framesnumpy.ndarray (bool)

An array indicating which frames are valid (True) and should be included in the derivation.

channel_indicesnumpy.ndarray (int)

The channel indices to include in the derivation of shape (n,) where n <= n_channels.

Returns:

variance_sum, variance_weightnumpy.ndarray, numpy.ndarray

The channel variance sum (variance * weight) and channel variance weights, both of shape (n,) and float type.