Re-add option to integrate over or take extremum of peak

xgm.py

@@ -814,7 +814,8 @@ def matchXgmTimPulseId(data, use_apd=True, intstart=None, intstop=None,
 
 
 # Fast ADC
-def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop, period=None, npulses=None):
+def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop, 
+                 period=None, npulses=None, usePeakValue=False, peakType='pos'):
     ''' Computes peak integration from raw FastADC traces.
     
         Inputs:
@@ -831,6 +832,8 @@ def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop, period=Non
                 two bunches @ 4.5 MHz. 
             npulses: number of pulses. If None, takes the maximum number of
                 pulses according to the bunch patter (field 'npulses_sase3')
+            usePeakValue: bool, if True takes the peak value of the signal, 
+                          otherwise integrates over integration region.
             
         Output:
             results: DataArray with dims trainId x max(sase3 pulses) 
@@ -859,11 +862,18 @@ def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop, period=Non
         bkga = bkgstart + period*i
         bkgb = bkgstop + period*i
         bg = np.outer(np.median(data[keyraw][:,bkga:bkgb], axis=1), np.ones(b-a))
-        integ = np.trapz(data[keyraw][:,a:b] - bg, axis=1)
-        results[:,i] = integ
+        if usePeakValue:
+            if peakType=='pos':
+                val = np.max(data[keyraw][:,a:b] - bg, axis=1)
+            if peakType=='neg':
+                val = np.min(data[keyraw][:,a:b] - bg, axis=1)
+        else:
+            val = np.trapz(data[keyraw][:,a:b] - bg, axis=1)
+        results[:,i] = val
     return results
 
-def autoFindFastAdcPeaks(data, channel=5, window='small', display=False, plot=False):
+def autoFindFastAdcPeaks(data, channel=5, window='small', usePeakValue=False, 
+                         display=False, plot=False):
     ''' Automatically finds peaks in channel of Fast ADC trace, a minimum width of 4 
         samples. The find_peaks function and determination of the peak integration 
         region and baseline subtraction is optimized for typical photodiode signals
@@ -873,6 +883,8 @@ def autoFindFastAdcPeaks(data, channel=5, window='small', display=False, plot=Fa
             key: data key of the array of traces
             window: 'small' or 'large': defines the width of the integration region
                 centered on the peak.
+            usePeakValue: bool, if True takes the peak value of the signal, 
+                          otherwise integrates over integration region.
             display: bool, displays info on the pulses found
             plot: bool, plots regions of integration of the first pulse in the trace
         Output:
@@ -916,7 +928,8 @@ def autoFindFastAdcPeaks(data, channel=5, window='small', display=False, plot=Fa
         print(f'Found {npulses} {posNeg} pulses, avg. width={w}, period={period} samples, ' +
               f'rep. rate={1e6/(9.230769*period):.3f} kHz')
     fAdcPeaks = fastAdcPeaks(data, channel=channel, intstart=intstart, intstop=intstop,
-                         bkgstart=bkgstart, bkgstop=bkgstop, period=period, npulses=npulses)
+                         bkgstart=bkgstart, bkgstop=bkgstop, period=period, npulses=npulses,
+                         usePeakValue=usePeakValue, peakType=posNeg[:3])
     if plot:
         plt.figure()
         plt.plot(trace_plot, 'o-', ms=3)