fix_jumps

sofia_redux.scan.custom.hawc_plus.integration.hawc_integration_numba_functions.fix_jumps(frame_valid, frame_data, frame_weights, modeling_frames, frame_parms, sample_flags, exclude_sample_flag, channel_indices, channel_parms, min_jump_level_frames, jump_flag, fix_each, fix_subarray, has_jumps, subarray, jump_counter, start_frame=None, end_frame=None, flag_before=0, flag_after=0)

Detect and fix jumps in the frame data for each channel.

If a given channel has jumps and settings imply that jumps may be fixed, the average frame data level between the jump is subtracted (frame and channel dependents are also updated). A change in the jump level occurs when the jump_counter value for a given channel changes to a new value. a block is defined as all frames having the same jump value before a jump change.

If the number of frames in a block is less than min_jump_level_frames, those frames will be flagged with the jump_flag sample flag. Otherwise, the frame data will be levelled as described above.

Parameters:

frame_validnumpy.ndarray (bool)

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

frame_datanumpy.ndarray (float)

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

frame_weightsnumpy.ndarray (float)

The relative frame weights of shape (n_frames,).

modeling_framesnumpy.ndarray (bool)

A boolean mask of shape (n_frames,) where True marks a frame as a modeling frame. Modeling frames will still be levelled, but will not be included when updating the frame or channel dependents.

frame_parmsnumpy.ndarray (float)

The frame dependents. Will be updated in-place if levelling occurs.

sample_flagsnumpy.ndarray (int)

The frame data sample flags. Typically non-zero samples will be excluded from processing. However, those samples not flagged with only exclude_sample_flag will be included.

exclude_sample_flagint

The sample flag to explicitly exclude from processing.

channel_indicesnumpy.ndarray (int)

The channel indices of shape (n_channels,) indicating which channels to process and mapping n_channels onto all_channels.

channel_parmsnumpy.ndarray (float)

The channel dependents of shape (all_channels,). Will be updated in-place when levelling occurs.

min_jump_level_framesint

The minimum number of frames in a jump block required for levelling. If this value is not reached, all samples in the block are flagged with jump_flag instead.

jump_flagint

The integer flag identifier with which to flag samples if the jump block length to which they belong is less than min_jump_level_frames and cannot be levelled.

fix_eachbool

If False, do not fix any channel with jumps.

fix_subarraynumpy.ndarray (bool)

An array of shape (n_subarrays,) where True indicates that any channel belonging to that subarray should have jumps corrected. Ignored if fix_each is False.

has_jumpsnumpy.ndarray (bool)

A boolean mask of shape (n_channels,) where True indicates that a channel has jumps that may be corrected.

subarraynumpy.ndarray (int)

An array of shape (n_channels,) containing the subarray number for each channel.

jump_counternumpy.ndarray (int)

The channel jumps of shape (n_frames, n_channels).

start_frameint, optional

The start frame from which to begin correction. The default is the first frame (0).

end_frameint, optional

The last frame at which to conclude correction (non-inclusive). The default is the total number of frames (n_frames).

flag_beforeint, optional

The number of frames to flag in the sample flags prior to a jump detection with jump_flag.

flag_afterint, optional

The number of frames to flag in the sample flags following a jump detection with jump_flag.

Returns:

no_jumpsnumpy.ndarray (bool)

A boolean mask of shape (n_channels,) where True indicates that a channel has no jumps.