cait.filter

cait.filter.box_car_smoothing(event, length=50)

Calculates a moving average on an event array and returns the smoothed event

Parameters

• event – 1D array, the event to calcualte the MA

• length – the length of the moving average

Returns

1D array the smoothed array

cait.filter.filter_event(event, transfer_function, window=False)

this function filters a single event

Parameters

• event – 1D array of the one event that should be filtered, size N

• transfer_function – the filter in fourier space, size N/2 +1 complex numpy array

• window – bool, if activated the array is multiplied with a window function befor filtering

Returns

1D array length N, the filtered event

cait.filter.get_amplitudes(events_array, stdevent, nps, hard_restrict=False, down=1, window=False, peakpos=None, return_peakpos=False, flexibility=20)

This function determines the amplitudes of several events with optimal sig-noise-ratio.

Parameters

• events_array – 2D array (nmbr_events, rec_length), the events to determine ph

• stdevent – 1D array, the standardevent

• nps – 1D array, length N/2 + 1, the noise power spectrum

• hard_restrict – bool, The maximum search is restricted to 20-30% of the record window.

• down – int, a factor by which the events and filter is downsampled before application

• window – bool, if activated the array is multiplied with a window function befor filtering

• peakpos – array of length nmbr_events,

• return_peakpos – bool, if true a second array is returned, namely the peak positions within the arrays

• flexibility – int, in case a peak position is provided, the maximum search can still deviate by this amount of samples

Returns

1D array size (nmbr_events), the phs after of filtering; if return_peakpos is true, this is instead a 2-tuple of the of_ph and the maximum positions

cait.filter.normalization_constant(stdevent, nps)

this function is needed as utility for the function optimal_transition_function and calculates the normalization constant s.t. the Variance of an event is preserved Remark: We do not use this function, as we want to preserve the height of a peak and not the RMS.

Parameters

• stdevent – 1D array of the standardevent with length N

• nps – 1D array of the noise power spectrum with length N/2 + 1

Returns

integer, the normalization constant

cait.filter.optimal_transfer_function(stdevent, nps, window=True)

This function calculates the transition function for an optimal filter.

Parameters

• stdevent – 1D array, pulse shape standard event with length N

• nps – 1D array, the NPS of a baseline, with length N/2 + 1

• window – bool, include a window function to the standard event

Returns

1D complex numpy array of length N/2 + 1, the optimal transfer function