calculate_coupling_increment

sofia_redux.scan.source_models.source_numba_functions.calculate_coupling_increment(map_indices, base_values, map_values, map_noise, sync_gains, source_gains, frame_data, frame_weight, frame_gains, frame_valid, sample_flags, channel_indices, min_s2n, max_s2n, exclude_flag)

Calculate and return the coupling increment factor.

The coupling increment factor may be used to increment current coupling values and weights for each channel by:

increment = sum_{frames}(fw * residual * expected) /
            sum_{frames}(fw * expected^2)

where fw are the frame weights, d are the frame data and:

expected = frame_gain * channel_source_gain * map_value
prior = frame_gain * channel_sync_gain * base_value
residual = d + prior - expected

The channel sync gains and base values are the channel source gains and map values from the previous iteration.

Parameters:

map_indicesnumpy.ndarray (int)

The indices of each frame/channel sample on the source map. Should be of shape (n_dimensions, n_frames, all_channels) with dimensions in (x, y) FITS order.

base_valuesnumpy.ndarray (float)

An image of arbitrary grid shape in n_dimensions. Contains the base map values (priors).

map_valuesnumpy.ndarray (float)

The current map image of arbitrary grid shape in n_dimensions.

map_noisenumpy.ndarray (float)

The current map noise values of arbitrary grid shape in n_dimensions.

sync_gainsnumpy.ndarray (float)

The previous channel source gains (sync gains) of shape (all_channels,).

source_gainsnumpy.ndarray (float)

The current channel source gains of shape (all_channels,).

frame_datanumpy.ndarray (float)

The integration frame data of shape (n_frames, all_channels,).

frame_weightnumpy.ndarray (float)

The relative frame weights of shape (n_frames,).

frame_gainsnumpy.ndarray (float)

The frame gains of shape (n_frames,).

frame_validnumpy.ndarray (bool)

A boolean mask of shape (n_frames,) where False excludes a frame from processing.

sample_flagsnumpy.ndarray (int)

The integration sample flags of shape (n_frames, all_channels) where any sample flagged with exclude_flag will be excluded from processing.

channel_indicesnumpy.ndarray (int)

The channel indices for which to calculate coupling increments of shape (n_channels,).

min_s2nfloat

The minimum signal-to-noise ratio on the map for which to calculate coupling increments.

max_s2nfloat

The maximum signal-to-noise ratio on the map for which to calculate coupling increments.

exclude_flagint

An integer flag used to exclude samples from processing.

Returns:

coupling_incrementnumpy.ndarray (float)

The coupling increment factors of shape (all_channels,).