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DAMASK_EICMD
mirror of https://github.com/achalhp/DAMASK_EICMD.git
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Merge remote branch 'origin/development' into cmake

This commit is contained in:
zhangc43 2016-05-02 09:51:58 -04:00
parent c8f85453d2 c0b4bb6b44
commit 6c9ca73534
4 changed files with 34 additions and 30 deletions
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1
.gitattributes vendored
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@ -7,3 +7,4 @@
*.png binary
*.jpg binary
*.cae binary
installation/mods_Abaqus/abaqus_v6_windows.env -crlf

2
VERSION
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@ -1 +1 @@
v2.0.0-201-gd497503
v2.0.0-214-g6b57eec

34
processing/post/addEuclideanDistance.py
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@ -159,7 +159,9 @@ for name in filenames:
if table.label_dimension(options.id) != 1: errors.append('grain identifier {} not found.'.format(options.id))
else: idCol = table.label_index(options.id)
if remarks != []: damask.util.croak(remarks)
if remarks != []:
damask.util.croak(remarks)
remarks = []
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
@ -184,6 +186,8 @@ for name in filenames:
N = grid.prod()
if N != len(table.data): errors.append('data count {} does not match grid {}.'.format(N,'x'.join(map(str,grid))))
else: remarks.append('grid: {}x{}x{}'.format(*grid))
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
@ -194,33 +198,37 @@ for name in filenames:
stack = [table.data]
neighborhood = neighborhoods[options.neighborhood]
convoluted = np.empty([len(neighborhood)]+list(grid+2),'i')
microstructure = periodic_3Dpad(np.array(table.data[:,idCol].reshape(grid),'i'))
diffToNeighbor = np.empty(list(grid+2)+[len(neighborhood)],'i')
microstructure = periodic_3Dpad(table.data[:,idCol].astype('i').reshape(grid,order='F'))
for i,p in enumerate(neighborhood):
stencil = np.zeros((3,3,3),'i')
stencil[1,1,1] = -1
stencil[p[0]+1,
p[1]+1,
p[2]+1] = 1
convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil)
diffToNeighbor[:,:,:,i] = ndimage.convolve(microstructure,stencil) # compare ID at each point...
# ...to every one in the specified neighborhood
# for same IDs at both locations ==> 0
distance = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d')
convoluted = np.sort(convoluted,axis = 0)
uniques = np.where(convoluted[0,1:-1,1:-1,1:-1] != 0, 1,0) # initialize unique value counter (exclude myself [= 0])
diffToNeighbor = np.sort(diffToNeighbor) # sort diff such that number of changes in diff (steps)...
# ...reflects number of unique neighbors
uniques = np.where(diffToNeighbor[1:-1,1:-1,1:-1,0] != 0, 1,0) # initialize unique value counter (exclude myself [= 0])
for i in xrange(1,len(neighborhood)): # check remaining points in neighborhood
uniques += np.where(np.logical_and(
convoluted[i,1:-1,1:-1,1:-1] != convoluted[i-1,1:-1,1:-1,1:-1], # flip of ID difference detected?
convoluted[i,1:-1,1:-1,1:-1] != 0), # not myself?
1,0) # count flip
diffToNeighbor[1:-1,1:-1,1:-1,i] != 0, # not myself?
diffToNeighbor[1:-1,1:-1,1:-1,i] != diffToNeighbor[1:-1,1:-1,1:-1,i-1],
), # flip of ID difference detected?
1,0) # count that flip
distance = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d')
for i,feature_id in enumerate(feature_list):
distance[i,:,:,:] = np.where(uniques >= features[feature_id]['aliens'],0.0,1.0) # seed with 0.0 when enough unique neighbor IDs are present
distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3
distance.shape = ([len(feature_list),grid.prod(),1])
distance = distance.reshape([len(feature_list),grid.prod(),1],order='F')
for i in xrange(len(feature_list)):
stack.append(distance[i,:])

27
processing/post/sortTable.py
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@ -14,7 +14,7 @@ scriptID = ' '.join([scriptName,damask.version])
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Sort rows by given column label(s).
Sort rows by given (or all) column label(s).
Examples:
With coordinates in columns "x", "y", and "z"; sorting with x slowest and z fastest varying index: --label x,y,z.
@ -30,25 +30,19 @@ parser.add_option('-r','--reverse',
action = 'store_true',
help = 'sort in reverse')
parser.set_defaults(key = [],
reverse = False,
parser.set_defaults(reverse = False,
)
(options,filenames) = parser.parse_args()
if options.keys is None:
parser.error('No sorting column(s) specified.')
options.keys.reverse() # numpy sorts with most significant column as last
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
buffered = False)
try: table = damask.ASCIItable(name = name,
buffered = False)
except: continue
damask.util.report(scriptName,name)
@ -61,15 +55,16 @@ for name in filenames:
# ------------------------------------------ process data ---------------------------------------
table.data_readArray()
keys = table.labels[::-1] if options.keys is None else options.keys[::-1] # numpy sorts with most significant column as last
cols = []
remarks = []
for i,column in enumerate(table.label_index(options.keys)):
if column < 0:
remarks.append("label {0} not present.".format(options.keys[i]))
else:
cols += [table.data[:,column]]
for i,column in enumerate(table.label_index(keys)):
if column < 0: remarks.append('label "{}" not present...'.format(keys[i]))
else: cols += [table.data[:,column]]
if remarks != []: damask.util.croak(remarks)
ind = np.lexsort(cols) if cols != [] else np.arange(table.data.shape[0])
if options.reverse: ind = ind[::-1]