much improved algorithm to speed up grain identification.
This commit is contained in:
Philip Eisenlohr
parent
1994b5a4c1
commit
170d377092
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@ -5,7 +5,6 @@ import numpy as np
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import damask
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from optparse import OptionParser
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from scipy import spatial
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from collections import defaultdict
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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@ -23,7 +22,7 @@ parser.add_option('-r', '--radius',
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parser.add_option('-d', '--disorientation',
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dest = 'disorientation',
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type = 'float', metavar = 'float',
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help = 'disorientation threshold per grain [%default] (degrees)')
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help = 'disorientation threshold in degrees [%default]')
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parser.add_option('-s', '--symmetry',
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dest = 'symmetry',
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type = 'string', metavar = 'string',
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@ -61,7 +60,8 @@ parser.add_option('-p', '--position',
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type = 'string', metavar = 'string',
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help = 'spatial position of voxel [%default]')
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parser.set_defaults(symmetry = 'cubic',
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parser.set_defaults(disorientation = 5,
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symmetry = 'cubic',
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coords = 'pos',
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degrees = False,
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)
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@ -86,17 +86,16 @@ if np.sum(input) != 1: parser.error('needs exactly one input format.')
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(options.matrix,9,'matrix'),
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(options.quaternion,4,'quaternion'),
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][np.where(input)[0][0]] # select input label that was requested
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toRadians = np.pi/180.0 if options.degrees else 1.0 # rescale degrees to radians
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cos_disorientation = np.cos(options.disorientation/2.*toRadians)
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toRadians = np.pi/180.0 if options.degrees else 1.0 # rescale degrees to radians
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cos_disorientation = np.cos(np.radians(options.disorientation/2.)) # cos of half the disorientation angle
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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buffered = False)
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try: table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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@ -109,8 +108,10 @@ for name in filenames:
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errors = []
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remarks = []
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if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
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if not np.all(table.label_dimension(label) == dim): errors.append('input {} does not have dimension {}.'.format(label,dim))
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if not 3 >= table.label_dimension(options.coords) >= 1:
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errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.coords))
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if not np.all(table.label_dimension(label) == dim):
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errors.append('input {} does not have dimension {}.'.format(label,dim))
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else: column = table.label_index(label)
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if remarks != []: damask.util.croak(remarks)
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@ -122,8 +123,10 @@ for name in filenames:
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.labels_append('grainID_{}@{}'.format(label,
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options.disorientation if options.degrees else np.degrees(options.disorientation))) # report orientation source and disorientation in degrees
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table.labels_append('grainID_{}@{:g}'.format('+'.join(label)
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if isinstance(label, (list,tuple))
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else label,
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options.disorientation)) # report orientation source and disorientation
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table.head_write()
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# ------------------------------------------ process data ------------------------------------------
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@ -162,7 +165,7 @@ for name in filenames:
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time_delta = (time.clock()-tick) * (len(grainID) - p) / p
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bg.set_message('(%02i:%02i:%02i) processing point %i of %i (grain count %i)...'\
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%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),len(orientations)))
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%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),np.count_nonzero(memberCounts)))
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if inputtype == 'eulers':
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o = damask.Orientation(Eulers = np.array(map(float,table.data[column:column+3]))*toRadians,
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@ -179,84 +182,51 @@ for name in filenames:
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o = damask.Orientation(quaternion = np.array(map(float,table.data[column:column+4])),
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symmetry = options.symmetry).reduced()
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matched = False
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matched = False
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alreadyChecked = {}
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candidates = []
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bestDisorientation = damask.Quaternion([0,0,0,1]) # initialize to 180 deg rotation as worst case
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# check against last matched needs to be really picky. best would be to exclude jumps across the poke (checking distance between last and me?)
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# when walking through neighborhood first check whether grainID of that point has already been tested, if yes, skip!
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if matchedID != -1: # has matched before?
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matched = (o.quaternion.conjugated() * orientations[matchedID].quaternion).w > cos_disorientation
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if not matched:
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alreadyChecked = {}
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bestDisorientation = damask.Quaternion([0,0,0,1]) # initialize to 180 deg rotation as worst case
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for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring points
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gID = grainID[i]
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if gID != -1 and gID not in alreadyChecked: # indexed point belonging to a grain not yet tested?
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alreadyChecked[gID] = True # remember not to check again
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disorientation = o.disorientation(orientations[gID],SST = False)[0] # compare against other orientation
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if disorientation.quaternion.w > cos_disorientation and \
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disorientation.quaternion.w >= bestDisorientation.w: # within threshold and betterthan current best?
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for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring points
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gID = grainID[i]
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if gID != -1 and gID not in alreadyChecked: # indexed point belonging to a grain not yet tested?
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alreadyChecked[gID] = True # remember not to check again
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disorientation = o.disorientation(orientations[gID],SST = False)[0] # compare against other orientation
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if disorientation.quaternion.w > cos_disorientation: # within threshold ...
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candidates.append(gID) # remember as potential candidate
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if disorientation.quaternion.w >= bestDisorientation.w: # ... and better than current best?
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matched = True
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matchedID = gID # remember that grain
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bestDisorientation = disorientation.quaternion
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if not matched: # no match -> new grain found
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memberCounts += [1] # start new membership counter
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if matched: # did match existing grain
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memberCounts[matchedID] += 1
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if len(candidates) > 1: # ambiguity in grain identification?
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largestGrain = sorted(candidates,key=lambda x:memberCounts[x])[-1] # find largest among potential candidate grains
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matchedID = largestGrain
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for c in [c for c in candidates if c != largestGrain]: # loop over smaller candidates
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memberCounts[largestGrain] += memberCounts[c] # reassign member count of smaller to largest
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memberCounts[c] = 0
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grainID = np.where(np.in1d(grainID,candidates), largestGrain, grainID) # relabel grid points of smaller candidates as largest one
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else: # no match -> new grain found
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orientations += [o] # initialize with current orientation
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memberCounts += [1] # start new membership counter
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matchedID = g
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g += 1 # increment grain counter
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else: # did match existing grain
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memberCounts[matchedID] += 1
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grainID[p] = matchedID # remember grain index assigned to point
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p += 1 # increment point
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bg.set_message('identifying similar orientations among {} grains...'.format(len(orientations)))
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memberCounts = np.array(memberCounts)
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similarOrientations = [[] for i in xrange(len(orientations))]
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for i,orientation in enumerate(orientations[:-1]): # compare each identified orientation...
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for j in xrange(i+1,len(orientations)): # ...against all others that were defined afterwards
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if orientation.disorientation(orientations[j],SST = False)[0].quaternion.w > cos_disorientation: # similar orientations in both grainIDs?
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similarOrientations[i].append(j) # remember in upper triangle...
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similarOrientations[j].append(i) # ...and lower triangle of matrix
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if similarOrientations[i] != []:
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bg.set_message('grainID {} is as: {}'.format(i,' '.join(map(str,similarOrientations[i]))))
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stillShifting = True
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while stillShifting:
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stillShifting = False
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tick = time.clock()
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for p,gID in enumerate(grainID): # walk through all points
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if p > 0 and p % 1000 == 0:
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time_delta = (time.clock()-tick) * (len(grainID) - p) / p
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bg.set_message('(%02i:%02i:%02i) shifting ID of point %i out of %i (grain count %i)...'
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%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),len(orientations)))
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if similarOrientations[gID] != []: # orientation of my grainID is similar to someone else?
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similarNeighbors = defaultdict(int) # frequency of neighboring grainIDs sharing my orientation
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for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring point
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if grainID[i] in similarOrientations[gID]: # neighboring point shares my orientation?
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similarNeighbors[grainID[i]] += 1 # remember its grainID
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if similarNeighbors != {}: # found similar orientation(s) in neighborhood
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candidates = np.array([gID]+similarNeighbors.keys()) # possible replacement grainIDs for me
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grainID[p] = candidates[np.argsort(memberCounts[candidates])[-1]] # adopt ID that is most frequent in overall dataset
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memberCounts[gID] -= 1 # my former ID loses one fellow
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memberCounts[grainID[p]] += 1 # my new ID gains one fellow
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bg.set_message('{}:{} --> {}'.format(p,gID,grainID[p])) # report switch of grainID
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stillShifting = True
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grainIDs = np.where(np.array(memberCounts) > 0)[0] # identify "live" grain identifiers
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packingMap = dict(zip(list(grainIDs),range(len(grainIDs)))) # map to condense into consecutive IDs
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table.data_rewind()
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outputAlive = True
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p = 0
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while outputAlive and table.data_read(): # read next data line of ASCII table
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table.data_append(1+grainID[p]) # add grain ID
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table.data_append(1+packingMap[grainID[p]]) # add (condensed) grain ID
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outputAlive = table.data_write() # output processed line
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p += 1
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