source: fact/tools/pyscripts/pyfact/cleaners.py@ 13151

#!/usr/bin/python -tti
#
# Dominik Neise
# TU Dortmund
# March 2012
import numpy as np
import random
from coor import Coordinator # class which prepares next neighbor dictionary

# just a dummy callback function
def _dummy( data, core_c, core, surv ):
    pass

class AmplitudeCleaner( object ):
    """ Image Cleaning based on signal strength
    
    signal strength is a very general term here
    it could be:
        * max amplitude
        * integral
        * max of sliding sum
        * ...
    The Cleaning procedure or algorith is based on the 
    3 step precedute on the diss of M.Gauk called 'absolute cleaning'
    """
    
    def __init__(self, coreTHR, edgeTHR=None):
        """ initialize object
        
        set the two needed thresholds
        in case only one is given:
            edgeTHR is assumen to be coreTHR/2.
        """
        self.coreTHR = coreTHR
        if edgeTHR==None:
            self.edgeTHR = coreTHR/2.
        else:
            self.edgeTHR = edgeTHR

        self.return_bool_mask = True # default value!
        
        # init coordinator
        self.coordinator = Coordinator()
        # retrieve next neighbor dict
        self.nn = self.coordinator.nn
        
    def __call__( self, data, return_bool_mask=None , callback=_dummy ):
        """ compute cleaned image
        
        the return value might be:
            np.array of same shape as data  (dtype=bool)
        or 
            an np.array (dtype=int), which lengths is the number of 
            pixel which survived the cleaning, and which contains the CHIDs 
            of these survivors
            
        the default is to return the bool array
        but if you set it once, differently, eg like this:
            myAmplitudeCleaner.return_bool_mask = False
        or like
        myAmplitudeCleaner( mydata, False)
        
        it will be stored, until you change it again...
        """
        #shortcuts
        coreTHR = self.coreTHR
        edgeTHR = self.edgeTHR
        nn = self.nn
        
        # once set, never forget :-)
        if return_bool_mask != None:
            self.return_bool_mask = return_bool_mask
        return_bool_mask = self.return_bool_mask
        
        # these will hold the outcome of..
        core_c = np.zeros( len(data), dtype=bool ) # ... step 1
        core = np.zeros( len(data), dtype=bool ) # ... step 2
        surv = np.zeros( len(data), dtype=bool ) # ... step 3
        # It could be done in one variable, but for debugging and simplicity, 
        # I use more ... 

        # this is Gauks step 1
        core_c = data > coreTHR
        # loop over all candidates and check if it has a next neighbor core pixel

        for c in np.where(core_c)[0]:
            # loop over all n'eighbors of c'andidate
            for n in nn[c]:
                # if c has a neighbor, beeing also a candidate
                # then c is definitely a core.
                    # Note: DN 13.03.12
                    # actually the neighbor is also now found to be core pixel,
                    # and still this knowledge is thrown away and later this
                    # neighbor itself is treated again as a c'andidate.
                    # this should be improved.
                if core_c[n]:
                    core[c]=True
                    break
        # at this moment step 2 is done
        
        # start of step 3.
        # every core pixel is automaticaly a survivor, --> copy it
        surv = core.copy()
        for c in np.where(core)[0]:
            for n in nn[c]:
                # if neighbor is a core pixel, then do nothing
                if core[n]:
                    pass
                # if neighbor is over edgeTHR, it is lucky and survived.
                elif data[n] > edgeTHR:
                    surv[n] = True
                    
        
        callback( data, core_c, core, surv) 

        if return_bool_mask:
            return surv
        else:
            return np.where(surv)[0]

            
    def info(self):
        """ print Cleaner Informatio
        
        """
        print 'coreTHR:  ', self.coreTHR
        print 'edgeTHR: ', self.edgeTHR
        print 'return_bool_mask:', self.return_bool_mask

def _test_callback( data, core_c, core, surv ):
    """ test callback functionality of AmplitudeCleaner"""
    print 'core_c', np.where(core_c)[0], '<--', core_c.sum()
    print 'core', np.where(core)[0], '<--', core.sum()
    print 'surv', np.where(surv)[0], '<--', surv.sum()
    print 'data', '*'*60
    print data
    

def _test_cleaner():
    """ test for class AmplitudeCleaner"""
    from plotters import CamPlotter
    NPIX = 1440
    SIGMA = 1

    CORE_THR = 45
    EDGE_THR = 18
    
    harvey_keitel = AmplitudeCleaner( CORE_THR, EDGE_THR)
    harvey_keitel.info()
    # if you wonder why the cleaner object is called is it is:
    # http://www.youtube.com/watch?v=pf-Amvro2SY

    nn = Coordinator().nn

    testdata = np.zeros( NPIX )
    #add some noise
    testdata += 3

    # 'make' 3  doubleflowers
    cores = []
    for i in range(3):
        cores.append( random.randint(0, NPIX-1) )
        nene = nn[ cores[-1] ]                  # shortcut
        luckynn = random.sample( nene, 1)[0]    # shortcut
        #print nene
        #print luckynn
        cores.append( luckynn )
    edges = []
    for c in cores:
        for n in nn[c]:
            if n not in cores:
                edges.append(n)
    
    # add those doubleflowers to the testdata
    for c in cores:
        testdata[c] += 1.2*CORE_THR
    for e in edges:
        testdata[e] += 1.2*EDGE_THR
    
    
    #cleaning_mask = harvey_keitel(testdata, callback=_test_callback)
    cleaning_mask = harvey_keitel(testdata)
    
    plotall = CamPlotter('all')
    plotclean = CamPlotter('cleaned')
    
    plotall(testdata)
    plotclean(testdata, cleaning_mask)



if __name__ == '__main__':
    """ tests  """

    _test_cleaner()