xgm.py

# -*- coding: utf-8 -*-
""" Toolbox for SCS.

    Various utilities function to quickly process data measured at the SCS instruments.

    Copyright (2019) SCS Team.
"""
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr


def pulsePatternInfo(data, plot=False):
    ''' display general information on the pulse patterns operated by SASE1 and SASE3.
        This is useful to track changes of number of pulses or mode of operation of
        SASE1 and SASE3. It also determines which SASE comes first in the train and
        the minimum separation between the two SASE sub-trains.
        
        Inputs:
            data: xarray Dataset containing pulse pattern info from the bunch decoder MDL: 
            {'sase1, sase3', 'npulses_sase1', 'npulses_sase3'}
            plot: bool enabling/disabling the plotting of the pulse patterns
            
        Outputs:
            print of pulse pattern info. If plot==True, plot of the pulse pattern.
    '''
    #Which SASE comes first?
    npulses_sa3 = data['npulses_sase3']       
    npulses_sa1 = data['npulses_sase1']  
    dedicated = False
    if np.all(npulses_sa1.where(npulses_sa3 !=0, drop=True) == 0):
        dedicated = True
        print('No SASE 1 pulses during SASE 3 operation')
    if np.all(npulses_sa3.where(npulses_sa1 !=0, drop=True) == 0):
        dedicated = True
        print('No SASE 3 pulses during SASE 1 operation')
    if dedicated==False:
        pulseIdmin_sa1 = data['sase1'].where(npulses_sa1 != 0).where(data['sase1']>1).min().values
        pulseIdmax_sa1 = data['sase1'].where(npulses_sa1 != 0).where(data['sase1']>1).max().values
        pulseIdmin_sa3 = data['sase3'].where(npulses_sa3 != 0).where(data['sase3']>1).min().values
        pulseIdmax_sa3 = data['sase3'].where(npulses_sa3 != 0).where(data['sase3']>1).max().values
        #print(pulseIdmin_sa1, pulseIdmax_sa1, pulseIdmin_sa3, pulseIdmax_sa3)
        if pulseIdmin_sa1 > pulseIdmax_sa3:
            t = 0.220*(pulseIdmin_sa1 - pulseIdmax_sa3 + 1)
            print('SASE 3 pulses come before SASE 1 pulses (minimum separation %.1f µs)'%t)
        elif pulseIdmin_sa3 > pulseIdmax_sa1:
            t = 0.220*(pulseIdmin_sa3 - pulseIdmax_sa1 + 1)
            print('SASE 1 pulses come before SASE 3 pulses (minimum separation %.1f µs)'%t)
        else:
            print('Interleaved mode')
    
    #What is the pulse pattern of each SASE?
    for key in['sase3', 'sase1']:
        print('\n*** %s pulse pattern: ***'%key.upper())
        npulses = data['npulses_%s'%key]
        sase = data[key]
        if not np.all(npulses == npulses[0]):
            print('Warning: number of pulses per train changed during the run!')
        #take the derivative along the trainId to track changes in pulse number:
        diff = npulses.diff(dim='trainId')
        #only keep trainIds where a change occured:
        diff = diff.where(diff !=0, drop=True)
        #get a list of indices where a change occured:
        idx_change = np.argwhere(np.isin(npulses.trainId.values,
                                         diff.trainId.values, assume_unique=True))[:,0]
        #add index 0 to get the initial pulse number per train:
        idx_change = np.insert(idx_change, 0, 0)
        print('npulses\tindex From\tindex To\ttrainId From\ttrainId To\trep. rate [kHz]')
        for i,idx in enumerate(idx_change):
            n = npulses[idx]
            idxFrom = idx
            trainIdFrom = npulses.trainId[idx]
            if i < len(idx_change)-1:
                idxTo = idx_change[i+1]-1
            else:
                idxTo = npulses.shape[0]-1
            trainIdTo = npulses.trainId[idxTo]
            if n <= 1:
                print('%i\t%i\t\t%i\t\t%i\t%i'%(n, idxFrom, idxTo, trainIdFrom, trainIdTo))
            else:
                f = 1/((sase[idxFrom,1] - sase[idxFrom,0])*222e-6)
                print('%i\t%i\t\t%i\t\t%i\t%i\t%.0f'%(n, idxFrom, idxTo, trainIdFrom, trainIdTo, f))
    print('\n')
    if plot:
        plt.figure(figsize=(6,3))
        plt.plot(data['npulses_sase3'].trainId, data['npulses_sase3'], 'o-', ms=3, label='SASE 3')
        plt.xlabel('trainId')
        plt.ylabel('pulses per train')
        plt.plot(data['npulses_sase1'].trainId, data['npulses_sase1'], '^-', ms=3, color='C2', label='SASE 1')
        plt.legend()
        plt.tight_layout()
        
        
def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM'):
    ''' Extract SASE1- or SASE3-only XGM data.
        There are various cases depending on i) the mode of operation (10 Hz
        with fresh bunch, dedicated trains to one SASE, pulse on demand), 
        ii) the potential change of number of pulses per train in each SASE
        and iii) the order (SASE1 first, SASE3 first, interleaved mode).
        
        Inputs:
            data: xarray Dataset containing xgm data
            sase: key of sase to select: {'sase1', 'sase3'}
            xgm: key of xgm to select: {'SA3_XGM', 'SCS_XGM'}
            
        Output:
            DataArray that has all trainIds that contain a lasing
            train in sase, with dimension equal to the maximum number of pulses of 
            that sase in the run. The missing values, in case of change of number of pulses,
            are filled with NaNs.
    '''
    result = None
    npulses_sa3 = data['npulses_sase3']       
    npulses_sa1 = data['npulses_sase1']
    dedicated = 0
    if np.all(npulses_sa1.where(npulses_sa3 !=0, drop=True) == 0):
        dedicated += 1
        print('No SASE 1 pulses during SASE 3 operation')
    if np.all(npulses_sa3.where(npulses_sa1 !=0, drop=True) == 0):
        dedicated += 1
        print('No SASE 3 pulses during SASE 1 operation')
    #Alternating pattern with dedicated pulses in SASE1 and SASE3:
    if dedicated==2:
        if sase=='sase1':
            result = data[xgm].where(npulses_sa1>0, drop=True)[:,:npulses_sa1.max().values]
        else:
            result = data[xgm].where(npulses_sa3>0, drop=True)[:,:npulses_sa3.max().values]
        result = result.where(result != 1.0)
        return result
    # SASE1 and SASE3 bunches in a same train: find minimum indices of first and
    # maximum indices of last pulse per train
    else:
        pulseIdmin_sa1 = data['sase1'].where(npulses_sa1 != 0).where(data['sase1']>1).min().values
        pulseIdmax_sa1 = data['sase1'].where(npulses_sa1 != 0).where(data['sase1']>1).max().values
        pulseIdmin_sa3 = data['sase3'].where(npulses_sa3 != 0).where(data['sase3']>1).min().values
        pulseIdmax_sa3 = data['sase3'].where(npulses_sa3 != 0).where(data['sase3']>1).max().values
        if pulseIdmin_sa1 > pulseIdmax_sa3:
            sa3First = True
        elif pulseIdmin_sa3 > pulseIdmax_sa1:
            sa3First = False
        else:
            print('Interleaved mode')

    #take the derivative along the trainId to track changes in pulse number:
    diff = npulses_sa3.diff(dim='trainId')
    #only keep trainIds where a change occured:
    diff = diff.where(diff != 0, drop=True)
    #get a list of indices where a change occured:
    idx_change_sa3 = np.argwhere(np.isin(npulses_sa3.trainId.values,
                                     diff.trainId.values, assume_unique=True))[:,0]

    #Same for SASE 1:
    diff = npulses_sa1.diff(dim='trainId')
    diff = diff.where(diff !=0, drop=True)
    idx_change_sa1 = np.argwhere(np.isin(npulses_sa1.trainId.values,
                                     diff.trainId.values, assume_unique=True))[:,0]

    #create index that locates all changes of pulse number in both SASE1 and 3:
    #add index 0 to get the initial pulse number per train:
    idx_change = np.unique(np.concatenate(([0], idx_change_sa3, idx_change_sa1))).astype(int)
    if sase=='sase1':
        npulses = npulses_sa1
        maxpulses = int(npulses_sa1.max().values)
    else:
        npulses = npulses_sa3
        maxpulses = int(npulses_sa3.max().values)
    for i,k in enumerate(idx_change):    
        #skip if no pulses after the change:
        if npulses[idx_change[i]]==0:
            continue
        #calculate indices
        if sa3First:
            a = 0
            b = int(npulses_sa3[k].values)
            c = b
            d = int(c + npulses_sa1[k].values)
        else:
            a = int(npulses_sa1[k].values)
            b = int(a + npulses_sa3[k].values)
            c = 0
            d = a
        if sase=='sase1':
            a = c
            b = d
        if i==len(idx_change)-1:
            l = None
        else:
            l = idx_change[i+1]
        temp = data[xgm][k:l,a:a+maxpulses].copy()
        temp[:,b:] = np.NaN
        if result is None:
            result = temp
        else:
            result = xr.concat([result, temp], dim='trainId')
    return result


def saseContribution(data, sase='sase1', xgm='SA3_XGM'):
    ''' Calculate the relative contribution of SASE 1 or SASE 3 pulses 
        for each train in the run. Supports fresh bunch, dedicated trains
        and pulse on demand modes.
        
        Inputs:
            data: xarray Dataset containing xgm data
            sase: key of sase for which the contribution is computed: {'sase1', 'sase3'}
            xgm: key of xgm to select: {'SA3_XGM', 'SCS_XGM'}
            
        Output:
            1D DataArray equal to sum(sase)/sum(sase1+sase3)

    '''
    xgm_sa1 = selectSASEinXGM(data, 'sase1', xgm=xgm)
    xgm_sa3 = selectSASEinXGM(data, 'sase3', xgm=xgm)
    #Fill missing train ids with 0
    r = xr.align(*[xgm_sa1, xgm_sa3], join='outer', exclude=['XGMbunchId'])
    xgm_sa1 = r[0].fillna(0)
    xgm_sa3 = r[1].fillna(0)

    contrib = xgm_sa1.sum(axis=1)/(xgm_sa1.sum(axis=1) + xgm_sa3.sum(axis=1))
    if sase=='sase1':
        return contrib
    else:
        return 1 - contrib


def filterOnTrains(data, key='sase3'):
    ''' Removes train ids for which there was no pulse in sase='sase1' or 'sase3' branch
        
        Inputs:
            data: xarray Dataset
            sase: SASE onwhich to filter: {'sase1', 'sase3'}
            
        Output:
            filtered xarray Dataset
    '''
    key = 'npulses_' + key
    res = data.where(data[key]>0, drop=True)
    return res


def calibrateXGMs(data, rollingWindow=200, plot=False):
    ''' Calibrate the fast (pulse-resolved) signals of the XTD10 and SCS XGM 
        (read in intensityTD property) to the respective slow ion signal 
        (photocurrent read by Keithley, channel 'pulseEnergy.photonFlux.value').
        One has to take into account the possible signal created by SASE1 pulses. In the
        tunnel, this signal is usually large enough to be read by the XGM and the relative
        contribution C of SASE3 pulses to the overall signal is computed.
        In the tunnel, the calibration F is defined as:
            F = E_slow / E_fast_avg, where
        E_fast_avg is the rolling average (with window rollingWindow) of the fast signal.
        In SCS XGM, the signal from SASE1 is usually in the noise, so we calculate the 
        average over the pulse-resolved signal of SASE3 pulses only and calibrate it to the 
        slow signal modulated by the SASE3 contribution:
            F = (N1+N3) * E_avg * C/(N3 * E_fast_avg_sase3), where N1 and N3 are the number 
        of pulses in SASE1 and SASE3, E_fast_avg_sase3 is the rolling average (with window
        rollingWindow) of the SASE3-only fast signal.
        
        Inputs:
            data: xarray Dataset
            rollingWindow: length of running average to calculate E_fast_avg
            plot: boolean, plot the calibration output
            
        Output:
            factors: numpy ndarray of shape 1 x 2 containing 
                     [XTD10 calibration factor, SCS calibration factor]
    '''
    noSCS = noSA3 = False
    sa3_calib_factor = None
    scs_calib_factor = None
    if 'SCS_XGM' not in data:
        print('no SCS XGM data. Skipping calibration for SCS XGM')
        noSCS = True
    if 'SA3_XGM' not in data:
        print('no SASE3 XGM data. Skipping calibration for SASE3 XGM')
        noSA3 = True
    if noSCS and noSA3:
        return np.array([None, None])
    start = 0
    stop = None
    npulses = data['npulses_sase3']
    ntrains = npulses.shape[0]
    # First, in case of change in number of pulses, locate a region where
    # the number of pulses is maximum.
    if not np.all(npulses == npulses[0]):
        print('Warning: Number of pulses per train changed during the run!')
        start = np.argmax(npulses.values)
        stop = ntrains + np.argmax(npulses.values[::-1]) - 1
        if stop - start < rollingWindow:
            print('not enough consecutive data points with the largest number of pulses per train')
        start += rollingWindow
        stop = np.min((ntrains, stop+rollingWindow))
    # Calibrate SASE3 XGM with all signal from SASE1 and SASE3
    if not noSA3:
        xgm_avg = data['SA3_XGM'].where(data['SA3_XGM'] != 1.0).mean(axis=1)
        rolling_sa3_xgm = xgm_avg.rolling(trainId=rollingWindow).mean()
        ratio = data['SA3_XGM_SLOW']/rolling_sa3_xgm
        sa3_calib_factor = ratio[start:stop].mean().values
        print('calibration factor SA3 XGM: %f'%sa3_calib_factor)

    # Calibrate SCS XGM with SASE3-only contribution
    sa3contrib = saseContribution(data, 'sase3', 'SA3_XGM')
    if not noSCS:
        scs_sase3_fast = selectSASEinXGM(data, 'sase3', 'SCS_XGM').mean(axis=1)
        meanFast = scs_sase3_fast.rolling(trainId=rollingWindow).mean()
        ratio = ((data['npulses_sase3']+data['npulses_sase1']) *
                 data['SCS_XGM_SLOW'] * sa3contrib) / (meanFast * data['npulses_sase3'])
        scs_calib_factor = ratio[start:stop].median().values
        print('calibration factor SCS XGM: %f'%scs_calib_factor)
        
    if plot:
        plt.figure(figsize=(8,8))
        plt.subplot(211)
        plt.title('E[uJ] = %.2f x IntensityTD' %(sa3_calib_factor))
        plt.plot(data['SA3_XGM_SLOW'], label='SA3 slow', color='C1')
        plt.plot(rolling_sa3_xgm*sa3_calib_factor,
                 label='SA3 fast signal rolling avg', color='C4')
        plt.ylabel('Energy [uJ]')
        plt.xlabel('train in run')
        plt.legend(loc='upper left', fontsize=10)
        plt.twinx()
        plt.plot(xgm_avg*sa3_calib_factor, label='SA3 fast signal train avg', alpha=0.2, color='C4')
        plt.ylabel('Calibrated SA3 fast signal [uJ]')
        plt.legend(loc='lower right', fontsize=10)

        plt.subplot(212)
        plt.title('E[uJ] = %.2f x HAMP' %scs_calib_factor)
        plt.plot(data['SCS_XGM_SLOW'], label='SCS slow (all SASE)', color='C0')
        slow_avg_sase3 = data['SCS_XGM_SLOW']*(data['npulses_sase1']
                                                    +data['npulses_sase3'])*sa3contrib/data['npulses_sase3']
        plt.plot(slow_avg_sase3, label='SCS slow (SASE3 only)', color='C1')
        plt.plot(meanFast*scs_calib_factor, label='SCS HAMP rolling avg', color='C2')
        plt.ylabel('Energy [uJ]')
        plt.xlabel('train in run')
        plt.legend(loc='upper left', fontsize=10)
        plt.twinx()
        plt.plot(scs_sase3_fast*scs_calib_factor, label='SCS HAMP train avg', alpha=0.2, color='C2')
        plt.ylabel('Calibrated HAMP signal [uJ]')
        plt.legend(loc='lower right', fontsize=10)
        plt.tight_layout()

    return np.array([sa3_calib_factor, scs_calib_factor])


def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset=1760, mcp=1, npulses=None):
    ''' Computes peak integration from raw MCP traces.
    
        Inputs:
            data: xarray Dataset containing MCP raw traces (e.g. 'MCP1raw')
            intstart: trace index of integration start
            intstop: trace index of integration stop
            bkgstart: trace index of background start
            bkgstop: trace index of background stop
            t_offset: index separation between two pulses
            mcp: MCP channel number
            npulses: number of pulses
            
        Output:
            results: DataArray with dims trainId x max(sase3 pulses)*1MHz/intra-train rep.rate 
            
    '''
    keyraw = 'MCP{}raw'.format(mcp)
    if keyraw not in data:
        raise ValueError("Source not found: {}!".format(keyraw))
    if npulses is None:
        npulses = int((data['sase3'].max().values + 1)/4)
    sa3 = data['sase3'].where(data['sase3']>1)/4
    sa3 -= sa3[:,0]
    results = xr.DataArray(np.empty((sa3.shape[0], npulses)), coords=sa3.coords,
                           dims=['trainId', 'MCP{}fromRaw'.format(mcp)])
    for i in range(npulses):
        a = intstart + t_offset*i
        b = intstop + t_offset*i
        bkga = bkgstart + t_offset*i
        bkgb = bkgstop + t_offset*i
        bg = np.outer(np.median(data[keyraw][:,bkga:bkgb], axis=1), np.ones(b-a))
        results[:,i] = np.trapz(data[keyraw][:,a:b] - bg, axis=1)
    return results


def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
              bkgstart=None, bkgstop=None, t_offset=1760, npulses=None):
    ''' Extract peak-integrated data from TIM where pulses are from SASE3 only.
        If use_apd is False it calculates integration from raw traces. 
        The missing values, in case of change of number of pulses, are filled
        with NaNs.
        
            data: xarray Dataset containing MCP raw traces (e.g. 'MCP1raw')
            intstart: trace index of integration start
            intstop: trace index of integration stop
            bkgstart: trace index of background start
            bkgstop: trace index of background stop
            t_offset: index separation between two pulses
            mcp: MCP channel number
            npulses: number of pulses to compute

        Output:
            tim: DataArray of shape trainId only for SASE3 pulses x N 
                 with N=max(number of pulses per train)
    '''
    key = 'MCP{}apd'.format(mcp)
    if use_apd:
        apd = data[key]
    else:
        apd = mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset, mcp, npulses)
    npulses_sa3 = data['npulses_sase3']
    sa3 = data['sase3'].where(data['sase3']>1, drop=True)/4
    sa3 -= sa3[:,0]
    sa3 = sa3.astype(int)
    if np.all(npulses_sa3 == npulses_sa3[0]):
        tim = apd[:, sa3[0].values]
        return tim
    maxpulses = int(npulses_sa3.max().values)
    diff = npulses_sa3.diff(dim='trainId')
    #only keep trainIds where a change occured:
    diff = diff.where(diff != 0, drop=True)
    #get a list of indices where a change occured:
    idx_change = np.argwhere(np.isin(npulses_sa3.trainId.values,
                                     diff.trainId.values, assume_unique=True))[:,0]
    #add index 0 to get the initial pulse number per train:
    idx_change = np.insert(idx_change, 0, 0)
    tim = None
    for i,idx in enumerate(idx_change):
        if npulses_sa3[idx]==0:
            continue
        if i==len(idx_change)-1:
            l = None
        else:
            l = idx_change[i+1]
        b = npulses_sa3[idx].values
        temp = apd[idx:l,:maxpulses].copy()
        temp[:,b:] = np.NaN
        if tim is None:
            tim = temp
        else:
            tim = xr.concat([tim, temp], dim='trainId')
    return tim


def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intstart=None,
                 intstop=None, bkgstart=None, bkgstop=None, t_offset=1760, npulses_apd=None):
    ''' Calibrate TIM signal (Peak-integrated signal) to the slow ion signal of SCS_XGM
        (photocurrent read by Keithley, channel 'pulseEnergy.photonFlux.value').
        The aim is to find F so that E_tim_peak[uJ] = F x TIM_peak. For this, we want to
        match the SASE3-only average TIM pulse peak per train (TIM_avg) to the slow XGM 
        signal E_slow.
        Since E_slow is the average energy per pulse over all SASE1 and SASE3 
        pulses (N1 and N3), we first extract the relative contribution C of the SASE3 pulses
        by looking at the pulse-resolved signals of the SA3_XGM in the tunnel.
        There, the signal of SASE1 is usually strong enough to be above noise level.
        Let TIM_avg be the average of the TIM pulses (SASE3 only).
        The calibration factor is then defined as: F = E_slow * C * (N1+N3) / ( N3 * TIM_avg ).
        If N3 changes during the run, we locate the indices for which N3 is maximum and define
        a window where to apply calibration (indices start/stop).
        
        Warning: the calibration does not include the transmission by the KB mirrors!
        
        Inputs:
            data: xarray Dataset
            rollingWindow: length of running average to calculate TIM_avg
            mcp: MCP channel
            plot: boolean. If True, plot calibration results.
            use_apd: boolean. If False, the TIM pulse peaks are extract from raw traces using
                     getTIMapd
            intstart: trace index of integration start
            intstop: trace index of integration stop
            bkgstart: trace index of background start
            bkgstop: trace index of background stop
            t_offset: index separation between two pulses
            mcp: MCP channel number
            npulses_apd: number of pulses
            
        Output:
            F: float, TIM calibration factor.
        
    '''
    start = 0
    stop = None
    npulses = data['npulses_sase3']
    ntrains = npulses.shape[0]
    if not np.all(npulses == npulses[0]):
        start = np.argmax(npulses.values)
        stop = ntrains + np.argmax(npulses.values[::-1]) - 1
        if stop - start < rollingWindow:
            print('not enough consecutive data points with the largest number of pulses per train')
        start += rollingWindow
        stop = np.min((ntrains, stop+rollingWindow))
    filteredTIM = getTIMapd(data, mcp, use_apd, intstart, intstop, bkgstart, bkgstop, t_offset, npulses_apd)
    sa3contrib = saseContribution(data, 'sase3', 'SA3_XGM')
    avgFast = filteredTIM.mean(axis=1).rolling(trainId=rollingWindow).mean()
    ratio = ((data['npulses_sase3']+data['npulses_sase1']) *
             data['SCS_XGM_SLOW'] * sa3contrib) / (avgFast*data['npulses_sase3'])
    F = float(ratio[start:stop].median().values)

    if plot:
        fig = plt.figure(figsize=(8,5))
        ax = plt.subplot(211)
        ax.set_title('E[uJ] = {:2e} x TIM (MCP{})'.format(F, mcp))
        ax.plot(data['SCS_XGM_SLOW'], label='SCS XGM slow (all SASE)', color='C0')
        slow_avg_sase3 = data['SCS_XGM_SLOW']*(data['npulses_sase1']
                                                    +data['npulses_sase3'])*sa3contrib/data['npulses_sase3']
        ax.plot(slow_avg_sase3, label='SCS XGM slow (SASE3 only)', color='C1')
        ax.plot(avgFast*F, label='Calibrated TIM rolling avg', color='C2')
        ax.legend(loc='upper left', fontsize=8)
        ax.set_ylabel('Energy [$\mu$J]', size=10)
        ax.plot(filteredTIM.mean(axis=1)*F, label='Calibrated TIM train avg', alpha=0.2, color='C9')
        ax.legend(loc='best', fontsize=8, ncol=2)
        plt.xlabel('train in run')
        
        ax = plt.subplot(234)
        xgm_fast = selectSASEinXGM(data)
        ax.scatter(filteredTIM, xgm_fast, s=5, alpha=0.1)
        fit, cov = np.polyfit(filteredTIM.values.flatten(),xgm_fast.values.flatten(),1, cov=True)
        y=np.poly1d(fit)
        x=np.linspace(filteredTIM.min(), filteredTIM.max(), 10)
        ax.plot(x, y(x), lw=2, color='r')
        ax.set_ylabel('Raw HAMP [$\mu$J]', size=10)
        ax.set_xlabel('TIM (MCP{}) signal'.format(mcp), size=10)
        ax.annotate(s='y(x) = F x + A\n'+
                    'F = %.3e\n$\Delta$F/F = %.2e\n'%(fit[0],np.abs(np.sqrt(cov[0,0])/fit[0]))+
                    'A = %.3e'%fit[1],
                    xy=(0.5,0.6), xycoords='axes fraction', fontsize=10, color='r')
        print('TIM calibration factor: %e'%(F))
        
        ax = plt.subplot(235)
        ax.hist(filteredTIM.values.flatten()*F, bins=50, rwidth=0.8)
        ax.set_ylabel('number of pulses', size=10)
        ax.set_xlabel('Pulse energy MCP{} [uJ]'.format(mcp), size=10)
        ax.set_yscale('log')
        
        ax = plt.subplot(236)
        if not use_apd:
            pulseStart = intstart
            pulseStop = intstop
        else:
            pulseStart = data.attrs['run'].get_array(
                'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.pulseStart.value')[0].values
            pulseStop = data.attrs['run'].get_array(
                'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.pulseStop.value')[0].values
            
        if 'MCP{}raw'.format(mcp) not in data:
            tid, data = data.attrs['run'].train_from_index(0)
            trace = data['SCS_UTC1_ADQ/ADC/1:network']['digitizers.channel_1_D.raw.samples']
            print('no raw data for MCP{}. Loading trace from MCP1'.format(mcp))
            label_trace='MCP1 Voltage [V]'
        else:
            trace = data['MCP{}raw'.format(mcp)][0]
            label_trace='MCP{} Voltage [V]'.format(mcp)
        ax.plot(trace[:pulseStop+25], 'o-', ms=2, label='trace')
        ax.axvspan(pulseStart, pulseStop, color='C2', alpha=0.2, label='APD region')
        ax.axvline(pulseStart, color='gray', ls='--')
        ax.axvline(pulseStop, color='gray', ls='--')
        ax.set_xlim(pulseStart - 25, pulseStop + 25)
        ax.set_ylabel(label_trace, size=10)
        ax.set_xlabel('sample #', size=10)
        ax.legend(fontsize=8)
        plt.tight_layout()

    return F

''' TIM calibration table
    Dict with key= photon energy and value= array of polynomial coefficients for each MCP (1,2,3).
    The polynomials correspond to a fit of the logarithm of the calibration factor as a function
    of MCP voltage. If P is a polynomial and V the MCP voltage, the calibration factor (in microjoule
    per APD signal) is given by -exp(P(V)).
    This table was generated from the calibration of March 2019, proposal 900074, semester 201930, 
    runs 69 - 111 (Ni edge):  https://in.xfel.eu/elog/SCS+Beamline/2323
    runs 113 - 153 (Co edge): https://in.xfel.eu/elog/SCS+Beamline/2334
    runs 163 - 208 (Fe edge): https://in.xfel.eu/elog/SCS+Beamline/2349
'''
tim_calibration_table = {
    705.5: np.array([
        [-6.85344690e-12,  5.00931986e-08, -1.27206912e-04, 1.15596821e-01, -3.15215367e+01],
        [ 1.25613942e-11, -5.41566381e-08,  8.28161004e-05, -7.27230153e-02,  3.10984925e+01],
        [ 1.14094964e-12,  7.72658935e-09, -4.27504907e-05, 4.07253378e-02, -7.00773062e+00]]),
    779: np.array([
        [ 4.57610777e-12, -2.33282497e-08,  4.65978738e-05, -6.43305156e-02,  3.73958623e+01],
        [ 2.96325102e-11, -1.61393276e-07,  3.32600044e-04, -3.28468195e-01,  1.28328844e+02],
        [ 1.14521506e-11, -5.81980336e-08,  1.12518434e-04, -1.19072484e-01,  5.37601559e+01]]),
    851: np.array([
        [ 3.15774215e-11, -1.71452934e-07,  3.50316512e-04, -3.40098861e-01,  1.31064501e+02],
        [5.36341958e-11, -2.92533156e-07,  6.00574534e-04, -5.71083140e-01,  2.10547161e+02],
        [ 3.69445588e-11, -1.97731342e-07,  3.98203522e-04, -3.78338599e-01,  1.41894119e+02]])
}

def timFactorFromTable(voltage, photonEnergy, mcp=1):
    ''' Returns an energy calibration factor for TIM integrated peak signal (APD)
        according to calibration from March 2019, proposal 900074, semester 201930, 
        runs 69 - 111 (Ni edge):  https://in.xfel.eu/elog/SCS+Beamline/2323
        runs 113 - 153 (Co edge): https://in.xfel.eu/elog/SCS+Beamline/2334
        runs 163 - 208 (Fe edge): https://in.xfel.eu/elog/SCS+Beamline/2349
        Uses the tim_calibration_table declared above.
        
        Inputs:
            voltage: MCP voltage in volts.
            photonEnergy: FEL photon energy in eV. Calibration factor is linearly
                interpolated between the known values from the calibration table. 
            mcp: MCP channel (1, 2, or 3).
            
        Output:
            f: calibration factor in microjoule per APD signal
    '''
    energies = np.sort([key for key in tim_calibration_table])
    if photonEnergy not in energies:
        if photonEnergy > energies.max():
            photonEnergy = energies.max()
        elif photonEnergy < energies.min():
            photonEnergy = energies.min()
        else:
            idx = np.searchsorted(energies, photonEnergy) - 1
            polyA = np.poly1d(tim_calibration_table[energies[idx]][mcp-1])
            polyB = np.poly1d(tim_calibration_table[energies[idx+1]][mcp-1])
            fA = -np.exp(polyA(voltage))
            fB = -np.exp(polyB(voltage))
            f = fA + (fB-fA)/(energies[idx+1]-energies[idx])*(photonEnergy - energies[idx])
            return f
    poly = np.poly1d(tim_calibration_table[photonEnergy][mcp-1])
    f = -np.exp(poly(voltage))
    return f