get_robust_correlated

sofia_redux.scan.signal.signal_numba_functions.get_robust_correlated(frame_data, frame_weights, frame_valid, channel_indices, channel_g, channel_wg2, sample_flags, resolution, max_dependence=0.25)

Derive the robust gain increment and weights.

The robust correlated gain increments and associated weights are derived in a similar way to the maximum-likelihood increments calculated using get_ml_correlated() but with a median calculation instead of a mean (please see numba_functions.smart_median() for further details). The input weights and values passed into the median (which returns the increment values and weights) are given by:

values = d / g weight = fw * cw * g^2

where d is the frame_data, g are the channel gains, fw are the frame relative weights, and cw are the channel weights. The median is performed over all channels and frames within a given block (see get_ml_correlated()). Any invalid results are returned as zero values increments and increment weights.

Parameters:

frame_datanumpy.ndarray (float)

The frame data of shape (n_frames, all_channels).

frame_weightsnumpy.ndarray (float)

The array of frame relative weights of shape (n_frames,).

frame_validnumpy.ndarray (bool)

A boolean mask of shape (n_frames,) where False excludes a frame from any calculations or updates. Should include both invalid frames and modeling flags.

channel_indicesnumpy.ndarray (int)

An array of shape (n_channels,) mapping n_channels to all_channels.

channel_gnumpy.ndarray (float)

An array of shape (n_channels,) containing the channel gains.

channel_wg2numpy.ndarray (float)

An array of shape (n_channels,) containing the channel gains^2 multiplied by the channel weights.

sample_flagsnumpy.ndarray (int)

An array of sample flags of shape (n_frames, all_channels) where any non-zero value excludes a data sample from being included in calculations.

resolutionint

The signal resolution (number of frames).

max_dependencefloat, optional

The maximum dependence of a single datum before switching to weighted mean. Please see numba_functions.smart_median_1d() for further details.

Returns:

mean, mean_weightfloat, float

The mean as described above and weight (denominator).