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DAMASK_EICMD
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fixed bug in asciitable when updating labels for readArray, fixed checking for euler angels presence in tessellation, improved help

This commit is contained in:
Martin Diehl 2015-05-14 17:07:50 +00:00
parent 7ea350b90e
commit fa1368204d
4 changed files with 63 additions and 77 deletions
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14
lib/damask/asciitable.py
View File

@ -258,12 +258,14 @@ class ASCIItable():
read whole data of all (given) labels as numpy array read whole data of all (given) labels as numpy array
''' '''
if labels == []: indices = range(self.__IO__['validReadSize']) # use all columns if labels != []: # read only some labels
else: indices = self.labels_index(labels) # get indices to read
indices = self.labels_index(labels) # use specified columns tempDict = dict(zip(indices, labels)) # column <> label connections
dictionary = dict(zip(indices, labels)) self.labels = [tempDict[label] for label in sorted(tempDict)] # sort labels to reflect order from np.readtxt
self.labels_index = range(len(dictionary)) self.__IO__['validReadSize'] = len(labels)
self.labels = [dictionary[label] for label in sorted(dictionary)] else:
indices = range(self.__IO__['validReadSize']) # use all columns
try: try:
self.data_rewind() # try to wind back to start of data self.data_rewind() # try to wind back to start of data
except: except:

12
processing/pre/geom_check.py
View File

@ -40,19 +40,11 @@ parser.set_defaults(data = True)
#--- setup file handles -------------------------------------------------------------------------- #--- setup file handles --------------------------------------------------------------------------
files = [] files = []
if filenames == []: if filenames == []:
files.append({'name':'STDIN', files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr, })
'input':sys.stdin,
'output':sys.stdout,
'croak':sys.stderr,
})
else: else:
for name in filenames: for name in filenames:
if os.path.exists(name): if os.path.exists(name):
files.append({'name':name, files.append({'name':name, 'input':open(name), 'output':sys.stdout, 'croak':sys.stdout, })
'input':open(name),
'output':sys.stdout,
'croak':sys.stdout,
})
#--- loop over input files ------------------------------------------------------------------------ #--- loop over input files ------------------------------------------------------------------------
for file in files: for file in files:

82
processing/pre/geom_fromVoronoiTessellation.py
View File

@ -31,43 +31,43 @@ def meshgrid2(*arrs):
return tuple(ans) return tuple(ans)
def laguerreTessellation(undeformed, coords): def laguerreTessellation(undeformed, coords):
bestdist = np.ones(len(undeformed)) * np.finfo('d').max bestdist = np.ones(len(undeformed)) * np.finfo('d').max
bestseed = np.zeros(len(undeformed)) bestseed = np.zeros(len(undeformed))
for i,seed in enumerate(coords): for i,seed in enumerate(coords):
for copy in np.array([[1, 0, 0, ], for copy in np.array([[1, 0, 0, ],
[0, 1, 0, ], [0, 1, 0, ],
[0, 0, 1, ], [0, 0, 1, ],
[-1, 0, 0, ], [-1, 0, 0, ],
[0, -1, 0, ], [0, -1, 0, ],
[0, 0, -1, ], [0, 0, -1, ],
[1, 1, 0, ], [1, 1, 0, ],
[1, 0, 1, ], [1, 0, 1, ],
[0, 1, 1, ], [0, 1, 1, ],
[-1, 1, 0, ], [-1, 1, 0, ],
[-1, 0, 1, ], [-1, 0, 1, ],
[0, -1, 1, ], [0, -1, 1, ],
[-1, -1, 0, ], [-1, -1, 0, ],
[-1, 0, -1, ], [-1, 0, -1, ],
[0, -1, -1, ], [0, -1, -1, ],
[1, -1, 0, ], [1, -1, 0, ],
[1, 0, -1, ], [1, 0, -1, ],
[0, 1, -1, ], [0, 1, -1, ],
[1, 1, 1, ], [1, 1, 1, ],
[-1, 1, 1, ], [-1, 1, 1, ],
[1, -1, 1, ], [1, -1, 1, ],
[1, 1, -1, ], [1, 1, -1, ],
[-1, -1, -1, ], [-1, -1, -1, ],
[1, -1, -1, ], [1, -1, -1, ],
[-1, 1, -1, ], [-1, 1, -1, ],
[-1, -1, 1, ]]).astype(float): [-1, -1, 1, ]]).astype(float):
diff = undeformed - np.repeat((seed+info['size']*copy).reshape(3,1),len(undeformed),axis=1).T diff = undeformed - np.repeat((seed+info['size']*copy).reshape(3,1),len(undeformed),axis=1).T
dist = np.sum(diff*diff,axis=1) - weights[i] dist = np.sum(diff*diff,axis=1) - weights[i]
bestseed = np.where(dist < bestdist, np.ones(len(undeformed))*(i+1),bestseed) bestseed = np.where(dist < bestdist, np.ones(len(undeformed))*(i+1),bestseed)
bestdist = np.where(dist < bestdist, dist,bestdist) bestdist = np.where(dist < bestdist, dist,bestdist)
return bestseed return bestseed
# -------------------------------------------------------------------- # --------------------------------------------------------------------
@ -109,8 +109,6 @@ parser.add_option('--secondphase', type='float', dest='secondphase', metavar= 'f
help='volume fraction of randomly distribute second phase [%default]') help='volume fraction of randomly distribute second phase [%default]')
parser.add_option('--laguerre', dest='laguerre', action='store_true', parser.add_option('--laguerre', dest='laguerre', action='store_true',
help='for weighted voronoi (Laguerre) tessellation [%default]') help='for weighted voronoi (Laguerre) tessellation [%default]')
parser.set_defaults(grid = (0,0,0)) parser.set_defaults(grid = (0,0,0))
parser.set_defaults(size = (0.0,0.0,0.0)) parser.set_defaults(size = (0.0,0.0,0.0))
parser.set_defaults(origin = (0.0,0.0,0.0)) parser.set_defaults(origin = (0.0,0.0,0.0))
@ -159,7 +157,7 @@ for file in files:
file['croak'].write('no coordinate data (1_coords/x) found ...') file['croak'].write('no coordinate data (1_coords/x) found ...')
continue continue
hasEulers = np.any(np.asarray(table.labels_index(['phi1','Phi','phi2'])) == -1) hasEulers = np.all(np.asarray(table.labels_index(['phi1','Phi','phi2'])) != -1)
if hasEulers: if hasEulers:
labels += ['phi1','Phi','phi2'] labels += ['phi1','Phi','phi2']
@ -174,8 +172,8 @@ for file in files:
table.data_readArray(labels) table.data_readArray(labels)
coords = table.data[:,table.labels_index(coords)] coords = table.data[:,table.labels_index(coords)]
eulers = table.data[:,table.labels_index(['phi1','Phi','phi2'])] if hasEulers else np.zeros(3*len(coords)) eulers = table.data[:,table.labels_index(['phi1','Phi','phi2'])] if hasEulers else np.zeros(3*len(coords))
grain = table.data[:,table.labels_index('microstructure')] if hasGrains else 1+np.arange(len(coords)) grain = table.data[:,table.labels.index('microstructure')] if hasGrains else 1+np.arange(len(coords))
weights = table.data[:,table.labels_index('weight')] if hasWeight else np.zeros(len(coords)) weights = table.data[:,table.labels.index('weight')] if hasWeight else np.zeros(len(coords))
grainIDs = np.unique(grain).astype('i') grainIDs = np.unique(grain).astype('i')
@ -308,6 +306,4 @@ for file in files:
if file['name'] != 'STDIN': if file['name'] != 'STDIN':
table.input_close() table.input_close()
table.output_close() table.output_close()
os.rename(file['name']+'_tmp',os.path.splitext(file['name'])[0] + \ os.rename(file['name']+'_tmp',os.path.splitext(file['name'])[0] + '_material.config' if options.config else '.geom')
{True: '_material.config',
False:'.geom'}[options.config])

32
processing/pre/seeds_fromTable.py
View File

@ -36,22 +36,18 @@ Examples:
""", version = scriptID) """, version = scriptID)
parser.add_option('-p', '--positions', dest = 'pos', type = 'string', parser.add_option('-p', '--positions', dest = 'pos', metavar='string',
help = 'coordinate label') help = 'coordinate label')
parser.add_option('--boundingbox', dest = 'box', type = 'float', nargs = 6, parser.add_option('--boundingbox', dest = 'box', type = 'float', nargs = 6,
help = 'min (x,y,z) and max (x,y,z) to specify bounding box [auto]') help = 'min (x,y,z) and max (x,y,z) to specify bounding box [auto]')
parser.add_option('-i', '--index', dest = 'index', type = 'string', parser.add_option('-i', '--index', dest = 'index', type = 'string',
help = 'microstructure index label') help = 'microstructure index label')
parser.add_option('-w','--white', dest = 'whitelist', action = 'extend', type = 'string', \ parser.add_option('-w','--white', dest = 'whitelist', action = 'extend',\
help = 'white list of microstructure indices', metavar = '<LIST>') help = 'white list of microstructure indices', metavar = '<LIST>')
parser.add_option('-b','--black', dest = 'blacklist', action = 'extend', type = 'string', \ parser.add_option('-b','--black', dest = 'blacklist', action = 'extend',\
help = 'black list of microstructure indices', metavar = '<LIST>') help = 'black list of microstructure indices', metavar = '<LIST>')
parser.set_defaults(pos = 'pos') parser.set_defaults(pos = 'pos')
parser.set_defaults(index = 'microstructure') parser.set_defaults(index = 'microstructure')
parser.set_defaults(box = ())
parser.set_defaults(whitelist = [])
parser.set_defaults(blacklist = [])
(options,filenames) = parser.parse_args() (options,filenames) = parser.parse_args()
@ -62,10 +58,10 @@ datainfo = { # lis
'label':[]}, 'label':[]},
} }
if options.pos != None: datainfo['vector']['label'] += [options.pos] datainfo['vector']['label'] += [options.pos]
if options.index != None: datainfo['scalar']['label'] += [options.index] datainfo['scalar']['label'] += [options.index]
options.whitelist = map(int,options.whitelist) if options.whitelist != None: options.whitelist = map(int,options.whitelist)
options.blacklist = map(int,options.blacklist) if options.blacklist != None: options.blacklist = map(int,options.blacklist)
#--- setup file handles -------------------------------------------------------------------------- #--- setup file handles --------------------------------------------------------------------------
files = [] files = []
@ -126,10 +122,10 @@ for file in files:
#--- filtering of grain voxels ------------------------------------------------------------------------------------ #--- filtering of grain voxels ------------------------------------------------------------------------------------
mask = np.logical_and(\ mask = np.logical_and(\
np.ones_like(table.data[:,3],bool) \ np.ones_like(table.data[:,3],bool) \
if options.whitelist == [] \ if options.whitelist == None \
else np.in1d(table.data[:,3].ravel(), options.whitelist).reshape(table.data[:,3].shape), else np.in1d(table.data[:,3].ravel(), options.whitelist).reshape(table.data[:,3].shape),
np.ones_like(table.data[:,3],bool) \ np.ones_like(table.data[:,3],bool) \
if options.blacklist == [] \ if options.blacklist == None \
else np.invert(np.in1d(table.data[:,3].ravel(), options.blacklist).reshape(table.data[:,3].shape)) else np.invert(np.in1d(table.data[:,3].ravel(), options.blacklist).reshape(table.data[:,3].shape))
) )
table.data = table.data[mask] table.data = table.data[mask]