multipactor_detectors module

Define functions to detect where multipactor happens.

quantity_is_above_threshold(quantity, threshold, consecutive_criterion=0, minimum_number_of_points=1, **kwargs)

Detect where quantity is above a given threshold.

Parameters:

• quantity (ndarray[tuple[Any, ...], dtype[double]]) – Array of measured multipactor quantity.

• threshold (float) – Quantity value above which multipactor is detected.

• consecutive_criterion (int, default: 0) – If provided, we gather multipactor zones that were separated by consecutive_criterion measure points or less.

• minimum_number_of_points (int, default: 1) – If provided, the multipactor must happen on at least minimum_number_of_points consecutive points, otherwise we consider that it was a measurement flaw.

• kwargs (Any)

Return type:

ndarray[tuple[Any, ...], dtype[bool]]

Returns:

True where multipactor was detected.

quantity_is_above_local_average(quantity, baseline_window=300, threshold_factor=3.0, consecutive_criterion=0, minimum_number_of_points=1, **kwargs)

Detect where quantity is above the local average.

Procedure is the following:

1. Compute running mean (slow trend).

2. Compute array of residuals between actual data and running mean.

3. Average array of residuals to get mean difference level.

4. Multipactor happens where residuals are above the noise level, scaled by threshold_factor.

Parameters:

• quantity (ndarray[tuple[Any, ...], dtype[double]]) – Array of measured multipactor quantity.

• baseline_window (int, default: 300) – Window size (in samples) for baseline estimation. Set it to two power cycles for a good first estimation.

• threshold_factor (float, default: 3.0) – Multiplier for noise level above baseline. This can be negative!

• consecutive_criterion (int, default: 0) – If provided, we gather multipactor zones that were separated by consecutive_criterion measurement points or less.

• minimum_number_of_points (int, default: 1) – If provided, the multipactor must happen on at least minimum_number_of_points consecutive points, otherwise we consider that it was a measurement flaw.

Return type:

ndarray[tuple[Any, ...], dtype[bool]]

Returns:

True where multipactor was detected.

quantity_is_above_lower_envelope(quantity, envelope_window=150, threshold_factor=3.0, consecutive_criterion=0, minimum_number_of_points=1)

Detect where quantity is above the local average.

Procedure is the following:

1. Compute lower envelope (slow trend without local bumps).

2. Compute array of residuals between actual data and lower envelope.

3. Average array of residuals to get mean difference level.

4. Multipactor happens wmin_widthhere residuals are above the noise level, scaled by threshold_factor.

Parameters:

• quantity (ndarray[tuple[Any, ...], dtype[double]]) – Array of measured multipactor quantity.

• envelope_window (int, default: 150) – Window size (in samples) for lower envelope calculation. Set it to one power cycle for a good first estimation.

• threshold_factor (float, default: 3.0) – Multiplier for noise level above lower envelope. This can be negative!

• consecutive_criterion (int, default: 0) – If provided, we gather multipactor zones that were separated by consecutive_criterion measurement points or less.

• minimum_number_of_points (int, default: 1) – If provided, the multipactor must happen on at least minimum_number_of_points consecutive points, otherwise we consider that it was a measurement flaw.

Return type:

ndarray[tuple[Any, ...], dtype[bool]]

Returns:

True where multipactor was detected.

residual_threshold(data, baseline, factor=1.0)

Compute residuals and a robust noise-based detection threshold.

The baseline is assumed to represent typical non-multipactor behavior. Residuals are defined as positive excursions of data above this baseline. A robust estimate of the residual noise amplitude is obtained from the median absolute deviation and scaled to define a detection threshold.

Notes

Noise-based limit above which residuals are classified as multipactor.

Parameters:

• data (ndarray[tuple[Any, ...], dtype[double]]) – Instrument data.

• baseline (ndarray[tuple[Any, ...], dtype[double]]) – Estimate of the non-multipactor baseline. Typically obtained with running_mean() or lower_envelope().

• factor (float, default: 1.0) – Multiplicative factor applied to the noise estimate to set the detection strictness. Higher values reduce false positives.

Return type:

tuple[ndarray[tuple[Any, ...], dtype[double]], float]

Returns:

• NDArray[np.float64] – Difference data - baseline.

• float – Noise-based limit above which residuals are classified as multipactor.

start_and_end_of_contiguous_true_zones(multipactor)

Get indexes of the entry and exit of contiguous multipactor zones.

Parameters:

multipactor (ndarray[tuple[Any, ...], dtype[bool]]) – Iterable where True means there is multipactor, False no multipactor, and np.nan undetermined.

Return type:

list[tuple[int, int]]

Returns:

List of first and last index of every multipactor band (multipactor contiguous zone).