source: fact/tools/pyscripts/sandbox/dneise/timecal.py@ 13415

#!/usr/bin/python -tti
#
# calculation of DRS Time Calibration constants
# initial implementation by Remo Dietlicher and Werner Lustermann (ETH Zurich)
# based on C++ classes by Oliver Grimm (ETH Zurich)
#
# this python implementation was coded by
# D. Neise (TU Dortmund) April 2012
#
import numpy as np
from pyfact     import RawData
from drs_spikes import DRSSpikes
from extractor import ZeroXing
class CalculateDrsTimeCalibrationConstants( object ):
    """ basic analysis class for the calculation of DRS time aperture jitter calibration constants
    """
    
    def __init__(self, dpath, cpath):
        """
        *dpath*: data file path, file should contain a periodic signal in eahc 9th channel
        *cpath*: std DRS amplitude calibration path
        """
        #dfname = '/data00/fact-construction/raw/2011/11/24/20111124_113.fits.gz'
        #calfname = '/data00/fact-construction/raw/2011/11/24/20111124_111.drs.fits.gz'
        self.data_path = dpath
        self.calib_path = cpath
        self.run = RawData(dpath, cpath, return_dict = True)
        
        if self.run.nroi != 1024:
            raise TypeError('Time Calibration Data must have ROI=1024, ROI of '+dpath+' is: '+str(self.run.nroi))
        
        self.despike = DRSSpikes()
        self.zero_crossing_finder = ZeroXing(slope = -1) # -1 means falling edge
        self.fsampling = 2.         # sampling frequency
        self.freq = 250.            # testfrequency
        self.P_nom = 1000./self.freq     # nominal Period due to testfrequency
        self.DAMPING = 0.02         # Damping applied to correction
        
        # this ndarray will contain the best estimate of the true width of each slice after a __call__
        # but it is used to determine the current best estimate inside the __call__ as well,
        # since the algorithm is iteratively implemented
        #self.time_calib = np.linspace( 0. , 512, num=1024, endpoint=False).repeat(run.npix/9).reshape( (1024,run.npix/9) ).transpose()
        self.time_calib = np.ones( (self.run.npix/9, 1024) )/self.fsampling
    def __call__(self):
        """ the class-call method performs the actual calculation based on the input data
            returns: tuple of length 160 containing tuples of length 1024 containing the length of each cell in ns
            the class contains self.result after the call.
        """        
        # loop over events
        counter = 0
        for event in self.run:
            print 'counter', counter
            counter +=1
            # in the next() method, which is called during looping,
            # the data is automatically amplitude calibrated.
            # we just need every 9th pixel, so this is a place 
            # for optimization
            data = event['acal_data']
            
            # slice out every 9th channel
            niners_ids = range(8,self.run.npix,9) # [8, 17, 26, ..., 1430, 1439]
            data = data[niners_ids,:]
            start_cell = event['start_cells'][niners_ids,:]
            
            data = self.despike( data )
            
            # shift data down by the mean           # possible in one line
                # calculate mean
            mean = data.mean(1)
                # make mean in the same shape as data, for elementwise subtraction
            mean = mean.repeat(data.shape[1]).reshape(data.shape)
                # subtraction
            data = data - mean
            
            # find zero crossings with falling edge
            # the output is a list, of lists, of slices.
            # each slice, shows a zero crossing. in fact, is is the slice where
            # the data is still positive ... in the next slice it is negative, (or just zero).
            # This was called 'MeanXing' before
            all_crossings = self.zero_crossing_finder(data)
            
            # now we have to calculate the exact time of the crossings
            # but in units of nanoseconds therefor we use self.time_calib
            # rolled appropriately, and summed up in order to have the integral timing calibration
            #
            #the list, 'time_of_all_crossings' will contain sublists,
            # which in turn will contain the times, in ns, for each zero-crossing
            time_of_all_crossings = []
            # the list, 'float_slice_of_all_crossings'
            # is similar to the list above, but i just contains the position of the crossings
            # in the pipeline, but more precisely than 'all_crossings', the position is linear interpolated
            float_slice_of_all_crossings = []
            
            for chip_id,crossings in enumerate(all_crossings):
                time_of_all_crossings.append([])
                float_slice_of_all_crossings.append([])
                time = np.roll(self.time_calib[chip_id], -start_cell[chip_id]).cumsum()
                for crossing in crossings:
                    # performing linear interpolation of time of zero crossing
                    # what fraction of a slide is the interpolated crossing away from the integral slice id
                    fraction_of_slice = data[chip_id,crossing] / (data[chip_id,crossing]-data[chip_id,crossing+1])
                    float_slice_of_all_crossings[-1].append(fraction_of_slice + crossing)
                    slope = time[crossing+1] - time[crossing]
                    time_of_crossing = time[crossing] + slope * fraction_of_slice
                    time_of_all_crossings[-1].append(time_of_crossing)
            
            # Now one can use those two lists, which were just created
            # to calculate the number(float) of slices between two consecutive crossings
            # as well as the *measured* time between two crossings.
            # on the other hand, the period of the test signal is known to be self.P_nom
            # so in case the measured time is longer or shorter than the known Period
            # the associated time of all involved slices 
            # is assumed to be a bit longer or short than 1./self.fsampling
            
            # first we make the sublists of the lists to be numpy.arrays 
            for i in range(len(float_slice_of_all_crossings)): #both list have equal length
                float_slice_of_all_crossings[i] = np.array(float_slice_of_all_crossings[i])
                time_of_all_crossings[i] = np.array(time_of_all_crossings[i])
            
            # now we can easily calculate the measured periods using np.diff()
            all_measured_periods = []
            for chip_times in time_of_all_crossings:
                all_measured_periods.append(np.diff(chip_times))
            
            for chip,chip_periods in enumerate(all_measured_periods):
                
                for i,period in enumerate(chip_periods):
                    #shortcuts to the involved slices
                    start = all_crossings[chip][i]
                    end = all_crossings[chip][i+1]
                    interval = end-start
                    rest = 1024-interval 
                    
                    delta_period = (self.P_nom - period) * self.DAMPING
                    # nominal_number_of_slices 
                    nom_num_slices = period * self.fsampling
                    
                    # now the delta_period is distributed to all slices
                        # I do not exactly understand the +1 here ... error prone!!!
                    # the following explanation assumes delta_period is positive
                    # the slices between start and end get a positive correction value
                    # This correction value is given by delta_period / nom_num_slices
                    pos_fraction = delta_period / nom_num_slices
                    # the total positive correction sums up to
                    total_positive_correction = interval * pos_fraction
                    # the total negative correction must sum up to the sampe value
                    total_negative_correction = rest * (interval * (pos_fraction/rest))
                    # so we can call the product following 'rest' the 'neg_fraction
                    neg_fraction = -1 * interval/rest * pos_fraction
                    #print '1. should be zero', total_positive_correction - total_negative_correction
                    #assert total_positive_correction - total_negative_correction == 0
                    
                    correction = np.zeros(1024)
                    correction[start+1:end+1] = pos_fraction
                    correction[:start+1]      = neg_fraction
                    correction[end+1:]        = neg_fraction
                    #print '2.   should be zero', correction.sum()
                    #assert correction.sum() == 0
                    
                    # now we have to add these correction values to self.time_calib
                    self.time_calib[chip,:] += np.roll(correction, start_cell[chip])