266 lines
14 KiB
Python
Executable File
266 lines
14 KiB
Python
Executable File
#!/usr/bin/env python
|
|
|
|
import os,sys,time,copy
|
|
import numpy as np
|
|
import damask
|
|
from optparse import OptionParser
|
|
from scipy import spatial
|
|
from collections import defaultdict
|
|
|
|
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
|
scriptID = ' '.join([scriptName,damask.version])
|
|
|
|
|
|
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
|
|
Add grain index based on similiarity of crystal lattice orientation.
|
|
|
|
""", version = scriptID)
|
|
|
|
parser.add_option('-r', '--radius',
|
|
dest = 'radius',
|
|
type = 'float', metavar = 'float',
|
|
help = 'search radius')
|
|
parser.add_option('-d', '--disorientation',
|
|
dest = 'disorientation',
|
|
type = 'float', metavar = 'float',
|
|
help = 'disorientation threshold per grain [%default] (degrees)')
|
|
parser.add_option('-s', '--symmetry',
|
|
dest = 'symmetry',
|
|
type = 'string', metavar = 'string',
|
|
help = 'crystal symmetry [%default]')
|
|
parser.add_option('-e', '--eulers',
|
|
dest = 'eulers',
|
|
type = 'string', metavar = 'string',
|
|
help = 'Euler angles')
|
|
parser.add_option( '--degrees',
|
|
dest = 'degrees',
|
|
action = 'store_true',
|
|
help = 'Euler angles are given in degrees [%default]')
|
|
parser.add_option('-m', '--matrix',
|
|
dest = 'matrix',
|
|
type = 'string', metavar = 'string',
|
|
help = 'orientation matrix')
|
|
parser.add_option('-a',
|
|
dest = 'a',
|
|
type = 'string', metavar = 'string',
|
|
help = 'crystal frame a vector')
|
|
parser.add_option('-b',
|
|
dest = 'b',
|
|
type = 'string', metavar = 'string',
|
|
help = 'crystal frame b vector')
|
|
parser.add_option('-c',
|
|
dest = 'c',
|
|
type = 'string', metavar = 'string',
|
|
help = 'crystal frame c vector')
|
|
parser.add_option('-q', '--quaternion',
|
|
dest = 'quaternion',
|
|
type = 'string', metavar = 'string',
|
|
help = 'quaternion')
|
|
parser.add_option('-p', '--position',
|
|
dest = 'coords',
|
|
type = 'string', metavar = 'string',
|
|
help = 'spatial position of voxel [%default]')
|
|
|
|
parser.set_defaults(symmetry = 'cubic',
|
|
coords = 'pos',
|
|
degrees = False,
|
|
)
|
|
|
|
(options, filenames) = parser.parse_args()
|
|
|
|
if options.radius == None:
|
|
parser.error('no radius specified.')
|
|
|
|
input = [options.eulers != None,
|
|
options.a != None and \
|
|
options.b != None and \
|
|
options.c != None,
|
|
options.matrix != None,
|
|
options.quaternion != None,
|
|
]
|
|
|
|
if np.sum(input) != 1: parser.error('needs exactly one input format.')
|
|
|
|
(label,dim,inputtype) = [(options.eulers,3,'eulers'),
|
|
([options.a,options.b,options.c],[3,3,3],'frame'),
|
|
(options.matrix,9,'matrix'),
|
|
(options.quaternion,4,'quaternion'),
|
|
][np.where(input)[0][0]] # select input label that was requested
|
|
toRadians = np.pi/180.0 if options.degrees else 1.0 # rescale degrees to radians
|
|
cos_disorientation = np.cos(options.disorientation/2.*toRadians)
|
|
|
|
# --- loop over input files -------------------------------------------------------------------------
|
|
|
|
if filenames == []: filenames = [None]
|
|
|
|
for name in filenames:
|
|
try:
|
|
table = damask.ASCIItable(name = name,
|
|
buffered = False)
|
|
except: continue
|
|
damask.util.report(scriptName,name)
|
|
|
|
# ------------------------------------------ read header -------------------------------------------
|
|
|
|
table.head_read()
|
|
|
|
# ------------------------------------------ sanity checks -----------------------------------------
|
|
|
|
errors = []
|
|
remarks = []
|
|
|
|
if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
|
|
if not np.all(table.label_dimension(label) == dim): errors.append('input {} has wrong dimension {}.'.format(label,dim))
|
|
else: column = table.label_index(label)
|
|
|
|
if remarks != []: damask.util.croak(remarks)
|
|
if errors != []:
|
|
damask.util.croak(errors)
|
|
table.close(dismiss = True)
|
|
continue
|
|
|
|
# ------------------------------------------ assemble header ---------------------------------------
|
|
|
|
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
|
table.labels_append('grainID_{}@{}'.format(label,
|
|
options.disorientation if options.degrees else np.degrees(options.disorientation))) # report orientation source and disorientation in degrees
|
|
table.head_write()
|
|
|
|
# ------------------------------------------ process data ------------------------------------------
|
|
|
|
# ------------------------------------------ build KD tree -----------------------------------------
|
|
|
|
# --- start background messaging
|
|
|
|
bg = damask.util.backgroundMessage()
|
|
bg.start()
|
|
|
|
bg.set_message('reading positions...')
|
|
|
|
table.data_readArray(options.coords) # read position vectors
|
|
grainID = -np.ones(len(table.data),dtype=int)
|
|
|
|
start = tick = time.clock()
|
|
bg.set_message('building KD tree...')
|
|
kdtree = spatial.KDTree(copy.deepcopy(table.data))
|
|
|
|
# ------------------------------------------ assign grain IDs --------------------------------------
|
|
|
|
tick = time.clock()
|
|
|
|
orientations = [] # quaternions found for grain
|
|
memberCounts = [] # number of voxels in grain
|
|
p = 0 # point counter
|
|
g = 0 # grain counter
|
|
matchedID = -1
|
|
lastDistance = np.dot(kdtree.data[-1]-kdtree.data[0],kdtree.data[-1]-kdtree.data[0]) # (arbitrarily) use diagonal of cloud
|
|
|
|
table.data_rewind()
|
|
while table.data_read(): # read next data line of ASCII table
|
|
|
|
if p > 0 and p % 1000 == 0:
|
|
|
|
time_delta = (time.clock()-tick) * (len(grainID) - p) / p
|
|
bg.set_message('(%02i:%02i:%02i) processing point %i of %i (grain count %i)...'%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),len(orientations)))
|
|
|
|
if inputtype == 'eulers':
|
|
o = damask.Orientation(Eulers = np.array(map(float,table.data[column:column+3]))*toRadians,
|
|
symmetry = options.symmetry).reduced()
|
|
elif inputtype == 'matrix':
|
|
o = damask.Orientation(matrix = np.array(map(float,table.data[column:column+9])).reshape(3,3).transpose(),
|
|
symmetry = options.symmetry).reduced()
|
|
elif inputtype == 'frame':
|
|
o = damask.Orientation(matrix = np.array(map(float,table.data[column[0]:column[0]+3] + \
|
|
table.data[column[1]:column[1]+3] + \
|
|
table.data[column[2]:column[2]+3])).reshape(3,3),
|
|
symmetry = options.symmetry).reduced()
|
|
elif inputtype == 'quaternion':
|
|
o = damask.Orientation(quaternion = np.array(map(float,table.data[column:column+4])),
|
|
symmetry = options.symmetry).reduced()
|
|
|
|
matched = False
|
|
|
|
# check against last matched needs to be really picky. best would be to exclude jumps across the poke (checking distance between last and me?)
|
|
# when walking through neighborhood first check whether grainID of that point has already been tested, if yes, skip!
|
|
|
|
if matchedID != -1: # has matched before?
|
|
matched = (o.quaternion.conjugated() * orientations[matchedID].quaternion).w > cos_disorientation
|
|
|
|
if not matched:
|
|
alreadyChecked = {}
|
|
bestDisorientation = damask.Quaternion([0,0,0,1]) # initialize to 180 deg rotation as worst case
|
|
for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring points
|
|
gID = grainID[i]
|
|
if gID != -1 and gID not in alreadyChecked: # an already indexed point belonging to a grain not yet tested?
|
|
alreadyChecked[gID] = True # remember not to check again
|
|
disorientation = o.disorientation(orientations[gID],SST = False)[0] # compare against that grain's orientation (and skip requirement of axis within SST)
|
|
if disorientation.quaternion.w > cos_disorientation and \
|
|
disorientation.quaternion.w >= bestDisorientation.w: # within disorientation threshold and better than current best?
|
|
matched = True
|
|
matchedID = gID # remember that grain
|
|
bestDisorientation = disorientation.quaternion
|
|
|
|
if not matched: # no match -> new grain found
|
|
memberCounts += [1] # start new membership counter
|
|
orientations += [o] # initialize with current orientation
|
|
matchedID = g
|
|
g += 1 # increment grain counter
|
|
|
|
else: # did match existing grain
|
|
memberCounts[matchedID] += 1
|
|
|
|
grainID[p] = matchedID # remember grain index assigned to point
|
|
p += 1 # increment point
|
|
|
|
bg.set_message('identifying similar orientations among {} grains...'.format(len(orientations)))
|
|
|
|
memberCounts = np.array(memberCounts)
|
|
similarOrientations = [[] for i in xrange(len(orientations))]
|
|
|
|
for i,orientation in enumerate(orientations[:-1]): # compare each identified orientation...
|
|
for j in xrange(i+1,len(orientations)): # ...against all others that were defined afterwards
|
|
if orientation.disorientation(orientations[j],SST = False)[0].quaternion.w > cos_disorientation: # similar orientations in both grainIDs?
|
|
similarOrientations[i].append(j) # remember in upper triangle...
|
|
similarOrientations[j].append(i) # ...and lower triangle of matrix
|
|
|
|
if similarOrientations[i] != []:
|
|
bg.set_message('grainID {} is as: {}'.format(i,' '.join(map(str,similarOrientations[i]))))
|
|
|
|
stillShifting = True
|
|
while stillShifting:
|
|
stillShifting = False
|
|
tick = time.clock()
|
|
|
|
for p,gID in enumerate(grainID): # walk through all points
|
|
if p > 0 and p % 1000 == 0:
|
|
|
|
time_delta = (time.clock()-tick) * (len(grainID) - p) / p
|
|
bg.set_message('(%02i:%02i:%02i) shifting ID of point %i out of %i (grain count %i)...'%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),len(orientations)))
|
|
if similarOrientations[gID] != []: # orientation of my grainID is similar to someone else?
|
|
similarNeighbors = defaultdict(int) # dict holding frequency of neighboring grainIDs that share my orientation (freq info not used...)
|
|
for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring points
|
|
if grainID[i] in similarOrientations[gID]: # neighboring point shares my orientation?
|
|
similarNeighbors[grainID[i]] += 1 # remember its grainID
|
|
if similarNeighbors != {}: # found similar orientation(s) in neighborhood
|
|
candidates = np.array([gID]+similarNeighbors.keys()) # possible replacement grainIDs for me
|
|
grainID[p] = candidates[np.argsort(memberCounts[candidates])[-1]] # adopt ID that is most frequent in overall dataset
|
|
memberCounts[gID] -= 1 # my former ID loses one fellow
|
|
memberCounts[grainID[p]] += 1 # my new ID gains one fellow
|
|
bg.set_message('{}:{} --> {}'.format(p,gID,grainID[p])) # report switch of grainID
|
|
stillShifting = True
|
|
|
|
table.data_rewind()
|
|
|
|
outputAlive = True
|
|
p = 0
|
|
while outputAlive and table.data_read(): # read next data line of ASCII table
|
|
table.data_append(1+grainID[p]) # add grain ID
|
|
outputAlive = table.data_write() # output processed line
|
|
p += 1
|
|
|
|
bg.set_message('done after {} seconds'.format(time.clock()-start))
|
|
|
|
# ------------------------------------------ output finalization -----------------------------------
|
|
|
|
table.close() # close ASCII tables
|