def damask_run(geomfile, loadfile):
# simulation
check = subprocess.call(
shlex.split("DAMASK_spectral --geometry %s --load %s" %
(geomfile, loadfile)))
jobname = geomfile.split('.')[0] + '_' + loadfile.split('.')[0]
if check == 0:
subprocess.call(
shlex.split("postResults --cr f,p %s.spectralOut" %
(jobname))) #--co totalshear
postfn = jobname + ".txt"
subprocess.call(shlex.split("addCauchy ./postProc/%s" % (postfn)))
subprocess.call(
shlex.split("addStrainTensors -0 -v ./postProc/%s" % (postfn)))
subprocess.call(
shlex.split("addMises -s Cauchy -e ln'('V')' ./postProc/%s" %
(postfn)))
table = damask.ASCIItable("./postProc/%s" % (postfn), readonly=True)
table.head_read()
table.data_readArray(["4_ln(V)",
"4_Cauchy"]) #(["Mises(ln(V))","Mises(Cauchy)"])
# or (["totalshear","Mises(Cauchy)"])
sim_flow = FlowCurve(data=table.data, Unit='Pa')
sim_data_n = len(sim_flow.data)
sim_data_acc = [[sim_flow.acc_strain[i], sim_flow.von_stress[i]]
for i in range(sim_data_n)]
np.savetxt("./postProc/%s_acc.dat" % (jobname), sim_data_acc)
sim_data_plastic = [[sim_flow.strain[i], sim_flow.stress[i]]
for i in range(sim_data_n)]
np.savetxt("./postProc/%s_plastic.dat" % (jobname), sim_data_plastic)
#return sim_data_acc
else:
print("damask failure")
def compare_TablesStatistically(
self,
files=[None, None], # list of file names
columns=[None], # list of list of column labels (per file)
meanTol=1.0e-4,
stdTol=1.0e-6,
preFilter=1.0e-9):
"""
calculate statistics of tables
threshold can be used to ignore small values (a negative number disables this feature)
"""
if not (isinstance(files, Iterable) and not isinstance(files, str)
): # check whether list of files is requested
files = [str(files)]
tables = [
damask.ASCIItable(name=filename, readonly=True)
for filename in files
]
for table in tables:
table.head_read()
columns += [columns[0]] * (len(files) - len(columns)
) # extend to same length as files
columns = columns[:len(files)] # truncate to same length as files
for i, column in enumerate(columns):
if column is None:
columns[i] = tables[i].labels(
raw=True) # if no column is given, read all
logging.info('comparing ASCIItables statistically')
for i in xrange(len(columns)):
columns[i] = columns[0] if not columns[i] else \
([columns[i]] if not (isinstance(columns[i], Iterable) and not isinstance(columns[i], str)) else \
columns[i]
)
logging.info(files[i] + ':' + ','.join(columns[i]))
if len(files) < 2:
return True # single table is always close to itself...
data = []
for table, labels in zip(tables, columns):
table.data_readArray(labels)
data.append(table.data)
table.close()
for i in xrange(1, len(data)):
delta = data[i] - data[i - 1]
normBy = (np.abs(data[i]) + np.abs(data[i - 1])) * 0.5
normedDelta = np.where(normBy > preFilter, delta / normBy, 0.0)
mean = np.amax(np.abs(np.mean(normedDelta, 0)))
std = np.amax(np.std(normedDelta, 0))
logging.info('mean: {:f}'.format(mean))
logging.info('std: {:f}'.format(std))
return (mean < meanTol) & (std < stdTol)
def compare_Array(self, File1, File2):
import numpy as np
logging.info('\n '.join(['comparing', File1, File2]))
table1 = damask.ASCIItable(name=File1, readonly=True)
table1.head_read()
len1 = len(table1.info) + 2
table2 = damask.ASCIItable(name=File2, readonly=True)
table2.head_read()
len2 = len(table2.info) + 2
refArray = np.nan_to_num(
np.genfromtxt(File1,
missing_values='n/a',
skip_header=len1,
autostrip=True))
curArray = np.nan_to_num(
np.genfromtxt(File2,
missing_values='n/a',
skip_header=len2,
autostrip=True))
if len(curArray) == len(refArray):
refArrayNonZero = refArray[refArray.nonzero()]
curArray = curArray[refArray.nonzero()]
max_err = np.max(
abs(refArrayNonZero[curArray.nonzero()] /
curArray[curArray.nonzero()] - 1.))
max_loc = np.argmax(
abs(refArrayNonZero[curArray.nonzero()] /
curArray[curArray.nonzero()] - 1.))
refArrayNonZero = refArrayNonZero[curArray.nonzero()]
curArray = curArray[curArray.nonzero()]
print(
' ********\n * maximum relative error {} between {} and {}\n ********'
.format(max_err, refArrayNonZero[max_loc], curArray[max_loc]))
return max_err
else:
raise Exception('mismatch in array size to compare')
defgrad='f',
pos='pos',
nodal=False,
)
(options, filenames) = parser.parse_args()
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
outname = (os.path.splitext(name)[0] + '_nodal' +
os.path.splitext(name)[1]) if (options.nodal and name) else None
try:
table = damask.ASCIItable(name=name, outname=outname, buffered=False)
except:
continue
damask.util.report(
scriptName,
'{}{}'.format(name, ' --> {}'.format(outname) if outname else ''))
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
errors = []
remarks = []
(options,filenames) = parser.parse_args()
options.pix_size = eval(options.pix_size)
options.pix_size *= 1e6
# --- loop over input files ------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
out = name.split('.000')[0]
ext = name.split('.')[1]
table = damask.ASCIItable(name = name,
outname = out+"_geom."+ext,
labeled = False,
buffered = False,
readonly = True)
except: continue
damask.util.report(scriptName,name)
table.head_read()
table.data_readArray()
table.labels_clear()
table.info_clear()
table.info_append([
scriptID + ' ' + ' '.join(sys.argv[1:]),
"grid\ta {}\tb {}\tc 1".format(table.data.shape[0], table.data.shape[1]),
"size\tx {}\ty {}\tz 1".format(options.pix_size * (table.data.shape[0] - 1), options.pix_size * (table.data.shape[1] - 1)),
"origin\tx 0.0\ty 0.0\tz 0.0",
"homogenization\t1",
])
from scipy import signal
from mpl_toolkits.mplot3d import Axes3D
import math
""" Does a cross correlation between simulated spheroconical indenter tip and tip impression from AFM data """
scriptID = string.replace(
'$Id: Correlation_AFM.py 157 2015-11-06 14:37:59Z chakra34 $', '\n', '\\n')
AFM_filename = sys.argv[1] # AFM file name
filename = sys.argv[2] # simulated tip file name
tip_data = string(sys.argv[3]) if len(sys.argv) > 3 else "3_displacement"
N = int(sys.argv[4]) if len(sys.argv) > 4 else 11 # patch dimension in pixel
#------------------- Reading the simulated tip from file -----------------
table1 = damask.ASCIItable(name=filename, buffered=False)
table1.head_read()
table1.data_readArray()
file = open(filename)
index = table1.label_index(tip_data)
tip = np.array(table1.data[:, index]).reshape(N, N)
#------------------- Reading AFM Data ------------------------------------
table = damask.ASCIItable(name=AFM_filename,
buffered=False,
labeled=False,
readonly=True)
table.head_read()
table.data_readArray()
parser = OptionParser(option_class=damask.extendableOption, usage='%prog [geomfile(s)]', description = """
Translate geom description into ASCIItable containing position and microstructure.
""", version = scriptID)
(options, filenames) = parser.parse_args()
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try: table = damask.ASCIItable(name = name,
outname = os.path.splitext(name)[0]+'.txt' if name else name,
buffered = False,
labeled = False,
)
except: continue
damask.util.report(scriptName,name)
# --- interpret header ----------------------------------------------------------------------------
table.head_read()
info,extra_header = table.head_getGeom()
damask.util.croak(['grid a b c: %s'%(' x '.join(map(str,info['grid']))),
'size x y z: %s'%(' x '.join(map(str,info['size']))),
'origin x y z: %s'%(' : '.join(map(str,info['origin']))),
'homogenization: %i'%info['homogenization'],
'microstructures: %i'%info['microstructures'],
if options.float_pix:
options.pix_size = (options.float_pix)
else:
options.pix_size = eval(options.pix_size)
options.alpha = np.radians(options.alpha)
grid_y, grid_x = np.meshgrid(
options.pix_size * np.linspace(-0.5 * options.dimension[0],
0.5 * options.dimension[0],
num=options.dimension[0],
endpoint=True),
options.pix_size * np.linspace(-0.5 * options.dimension[0],
0.5 * options.dimension[0],
num=options.dimension[0],
endpoint=True))
table = damask.ASCIItable()
#--------------------------- append header and info ----------------------------------------
table.info_append(repr(options.pix_size))
table.info_append((options.dimension[0], options.dimension[1]))
table.labels_append(['{}_pos'.format(1+i) for i in range(3)]+ \
['{}_displacement'.format(1+i) for i in range(3)])
table.head_write()
#---------------------------- output result ------------------------------------------------
tip = np.zeros(np.shape(grid_x), dtype=float)
for j in xrange(len(grid_y)):
for i in xrange(len(grid_x)):
parser.set_defaults(rmin = 0,
afm = False,
positive = False,
)
(options,filenames) = parser.parse_args()
if not options.new_d:
options.new_d = [options.rmax,options.rmax]
#------------------- loop over input files ------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
outname = os.path.join(os.path.dirname(name),"subset_"+os.path.basename(name)),
readonly = True,
)
except: continue
damask.util.report(scriptName,name)
table.head_read()
table.data_readArray(options.label)
if options.afm == False :
if options.dimension[0] * options.dimension[1] != table.data.shape[0] :
if options.dimension[0] * options.dimension[1] != table.data.shape[1]:
print("dimensions do not match")
sys.exit()
else :
options.dimension = np.array([table.data.shape[1], table.data.shape[0]])
if options.center == None:
flipud=False,
)
(options, filenames) = parser.parse_args()
if options.multiplier == None:
print("There has to be a multiplier!!!, aborting")
sys.exit()
if not options.sim_file or not os.path.exists(options.sim_file):
parser.error('No file selected for comparison')
#------------------- Reading the simulated tip from file -----------------
table1 = damask.ASCIItable(name=options.sim_file,
buffered=False,
readonly=True)
table1.head_read()
table1.data_readArray()
if options.flipud == True: table1.data = np.flipud(table1.data)
#------------------- loop over input files ------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(
name=name,
readonly=True,
)
except:
def damask_sim(params,geomfile,loadfile,materialfile,sections):
material = MaterialFile(materialfile) # read from previous file
# plastic_phenopowerlaw
# slip
params_write = {}
j = 0 # params count
#s0 = 0 # params section count start
for section in sections:
phenopowerlaw_slip = material.data["phase"][section.lower()]
if "phenopowerlaw" not in phenopowerlaw_slip["plasticity"]:
print("section %s requires plasticty phenopowerlaw")
exit
nslip_a = [ i for i,n in enumerate(phenopowerlaw_slip["nslip"]) if n!=0] # active slip systems
params_index =[["gdot0_slip",[0]],
["n_slip",[0]],
["tau0_slip",nslip_a],
["tausat_slip",nslip_a],
["a_slip",[0]],
["h0_slipslip",[0]],
["taub_c",[0]],
["taub_d",[0]],]
#s1 = s0 + sum([len(v[1]) for v in params_index])
params_check = {}
params_write[section] = phenopowerlaw_slip
for p in params_index:
k = p[0]
v = p[1]
for i in v:
params_check.setdefault(k,[]).append(params[j])
params_write[section][k][i] = params[j]
j += 1
# check
#try:
# assert(params_check["gdot0_slip"]>0)
# assert(params_check["n_slip"]>0)
# for i in range(len(nslip_a)):
# assert(params_check["tau0_slip"][i]>0)
# assert(params_check["tausat_slip"][i]>params_check["tau0_slip"][i])
# assert(params_check["a_slip"]>0)
# assert(params_check["taub_c"]>0)
# assert(params_check["taub_d"]>0)
#except AssertionError, e:
# raise Exception(e.args)
material.change_params(params_write)
# simulation
check = subprocess.call(shlex.split("DAMASK_spectral --geometry %s --load %s" %(geomfile,loadfile)))
jobname = geomfile.split('.')[0] + '_' + loadfile.split('.')[0]
if check == 0:
subprocess.call(shlex.split("postResults --cr f,p %s.spectralOut" %(jobname)))
postfn = jobname+".txt"
subprocess.call(shlex.split("addCauchy ./postProc/%s" %(postfn)))
subprocess.call(shlex.split("addStrainTensors -0 -v ./postProc/%s" %(postfn)))
subprocess.call(shlex.split("addMises -s Cauchy -e ln'('V')' ./postProc/%s" %(postfn)))
table = damask.ASCIItable("./postProc/%s" %(postfn),readonly=True)
table.head_read()
table.data_readArray(["Mises(ln(V))","Mises(Cauchy)"])
sim_flow = FlowCurve(data=table.data,Unit='Pa')
sim_data_n = len(sim_flow.data)
sim_data_acc = [[sim_flow.acc_strain[i],sim_flow.von_stress[i]] for i in range(sim_data_n)]
np.savetxt("./postProc/%s_acc.dat"%(jobname),sim_data_acc)
sim_data_plastic =[[sim_flow.strain[i],sim_flow.stress[i]] for i in range(sim_data_n)]
np.savetxt("./postProc/%s_plastic.dat"%(jobname),sim_data_plastic)
return sim_data_plastic
else:
print("damask failure")
return [[float("inf"),float("inf")]]
def compare_Table(self,
headings0,
file0,
headings1,
file1,
normHeadings='',
normType=None,
absoluteTolerance=False,
perLine=False,
skipLines=[]):
import numpy as np
logging.info('\n '.join(['comparing ASCII Tables', file0, file1]))
if normHeadings == '': normHeadings = headings0
# check if comparison is possible and determine lenght of columns
if len(headings0) == len(headings1) == len(normHeadings):
dataLength = len(headings0)
length = [1 for i in xrange(dataLength)]
shape = [[] for i in xrange(dataLength)]
data = [[] for i in xrange(dataLength)]
maxError = [0.0 for i in xrange(dataLength)]
absTol = [absoluteTolerance for i in xrange(dataLength)]
column = [[1 for i in xrange(dataLength)] for j in xrange(2)]
norm = [[] for i in xrange(dataLength)]
normLength = [1 for i in xrange(dataLength)]
normShape = [[] for i in xrange(dataLength)]
normColumn = [1 for i in xrange(dataLength)]
for i in xrange(dataLength):
if headings0[i]['shape'] != headings1[i]['shape']:
raise Exception('shape mismatch between {} and {} '.format(
headings0[i]['label'], headings1[i]['label']))
shape[i] = headings0[i]['shape']
for j in xrange(np.shape(shape[i])[0]):
length[i] *= shape[i][j]
normShape[i] = normHeadings[i]['shape']
for j in xrange(np.shape(normShape[i])[0]):
normLength[i] *= normShape[i][j]
else:
raise Exception(
'trying to compare {} with {} normed by {} data sets'.format(
len(headings0), len(headings1), len(normHeadings)))
table0 = damask.ASCIItable(name=file0, readonly=True)
table0.head_read()
table1 = damask.ASCIItable(name=file1, readonly=True)
table1.head_read()
for i in xrange(dataLength):
key0 = ('1_' if length[i] > 1 else '') + headings0[i]['label']
key1 = ('1_' if length[i] > 1 else '') + headings1[i]['label']
normKey = ('1_'
if normLength[i] > 1 else '') + normHeadings[i]['label']
if key0 not in table0.labels(raw=True):
raise Exception(
'column {} not found in 1. table...\n'.format(key0))
elif key1 not in table1.labels(raw=True):
raise Exception(
'column {} not found in 2. table...\n'.format(key1))
elif normKey not in table0.labels(raw=True):
raise Exception(
'column {} not found in 1. table...\n'.format(normKey))
else:
column[0][i] = table0.label_index(key0)
column[1][i] = table1.label_index(key1)
normColumn[i] = table0.label_index(normKey)
line0 = 0
while table0.data_read(): # read next data line of ASCII table
if line0 not in skipLines:
for i in xrange(dataLength):
myData = np.array(map(float,table0.data[column[0][i]:\
column[0][i]+length[i]]),'d')
normData = np.array(map(float,table0.data[normColumn[i]:\
normColumn[i]+normLength[i]]),'d')
data[i] = np.append(data[i], np.reshape(myData, shape[i]))
if normType == 'pInf':
norm[i] = np.append(norm[i], np.max(np.abs(normData)))
else:
norm[i] = np.append(
norm[i],
np.linalg.norm(np.reshape(normData, normShape[i]),
normType))
line0 += 1
for i in xrange(dataLength):
if not perLine:
norm[i] = [
np.max(norm[i]) for j in xrange(line0 - len(skipLines))
]
data[i] = np.reshape(data[i], [line0 - len(skipLines), length[i]])
if any(norm[i]) == 0.0 or absTol[i]:
norm[i] = [1.0 for j in xrange(line0 - len(skipLines))]
absTol[i] = True
if perLine:
logging.warning(
'At least one norm of {} in 1. table is 0.0, using absolute tolerance'
.format(headings0[i]['label']))
else:
logging.warning(
'Maximum norm of {} in 1. table is 0.0, using absolute tolerance'
.format(headings0[i]['label']))
line1 = 0
while table1.data_read(): # read next data line of ASCII table
if line1 not in skipLines:
for i in xrange(dataLength):
myData = np.array(map(float,table1.data[column[1][i]:\
column[1][i]+length[i]]),'d')
maxError[i] = max(
maxError[i],
np.linalg.norm(
np.reshape(
myData - data[i][line1 - len(skipLines), :],
shape[i])) / norm[i][line1 - len(skipLines)])
line1 += 1
if (line0 != line1):
raise Exception(
'found {} lines in 1. table but {} in 2. table'.format(
line0, line1))
logging.info(' ********')
for i in xrange(dataLength):
if absTol[i]:
logging.info(
' * maximum absolute error {} between {} and {}'.format(
maxError[i], headings0[i]['label'],
headings1[i]['label']))
else:
logging.info(
' * maximum relative error {} between {} and {}'.format(
maxError[i], headings0[i]['label'],
headings1[i]['label']))
logging.info(' ********')
return maxError
metavar='int int int',
help='direction of elastic modulus [%default]')
parser.set_defaults(hkl=(1, 1, 1), )
(options, filenames) = parser.parse_args()
if options.stiffness is None:
parser.error('no data column specified...')
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name=name)
except IOError:
continue
damask.util.report(scriptName, name)
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
remarks = []
columns = []
for i, column in enumerate(table.label_index(options.stiffness)):
if column < 0:
'output': sys.stdout,
'croak': sys.stderr
}
file['croak'].write('\033[1m' + scriptName + '\033[0m\n')
else:
if not os.path.exists(name): continue
file = {
'name': name,
'input': open(name),
'output': open(name + '_tmp', 'w'),
'croak': sys.stderr
}
file['croak'].write('\033[1m' + scriptName + '\033[0m: ' +
file['name'] + '\n')
table = damask.ASCIItable(file['input'], file['output'],
buffered=False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
# --------------- figure out columns to process ---------------------------------------------------
active = []
column = {}
columnMissing = False
for label in datainfo['label']:
key = label
if key in table.labels(raw=True):
active.append(label)
column[label] = table.labels.index(
key) # remember columns of requested data
else:
grid = (512,512),
homogenization = 1,
crystallite = 1)
(options,filename) = parser.parse_args()
if np.any(options.grid < 2):
parser('invalid grid a b c.')
if np.any(options.size <= 0.0):
parser('invalid size x y z.')
# --- open input files ----------------------------------------------------------------------------
if filename == []: filename = [None]
table = damask.ASCIItable(outname = filename[0],
buffered = False)
damask.util.report(scriptName,filename[0])
options.omega *= math.pi/180.0 # rescale ro radians
rotation = np.array([[ math.cos(options.omega),math.sin(options.omega),],
[-math.sin(options.omega),math.cos(options.omega),]],'d')
box = np.dot(np.array([[options.canal,0.],[0.,options.aspect*options.canal]]).transpose(),rotation)
info = {
'grid': np.ones(3,'i'),
'size': np.ones(3,'d'),
'origin': np.zeros(3,'d'),
'microstructures': 3,
if options.z > 0: options.z -= 1 # adjust to 0-based indexing
# --- color palette ---------------------------------------------------------------------------------
theMap = damask.Colormap(predefined=options.color)
if options.invert: theMap = theMap.invert()
theColors = np.uint8(np.array(theMap.export(format='list',steps=256))*255)
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
buffered = False,
labeled = options.label is not None,
readonly = True)
except: continue
table.report_name(scriptName,name)
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# --------------- figure out columns to process ---------------------------------------------------
errors = []
if table.label_dimension(options.label) != 1:
errors.append('no scalar data ({}) found.'.format(options.label))
if table.label_dimension(options.defgrad) != 9:
errors.append('no deformation gradient tensor (1..9_{}) found.'.format(options.defgrad))
parser.set_defaults(pix_size='20.0e-6/255', )
(options, filenames) = parser.parse_args()
options.pix_size = eval(options.pix_size)
#------------------- loop over input files ------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
out_file = "out_" + name
try:
table = damask.ASCIItable(
name=name,
outname="out_" + name,
buffered=False,
)
except:
continue
table.croak(damask.util.emph(scriptName) + (': ' + name if name else ''))
#-------------------------- reading head and generating grid -----------------------------
table.head_read()
table.data_readArray()
grid = np.array([table.data.shape[1], table.data.shape[0]], dtype=int)
#-------------------------------process and output result --------------------------------
table.info_clear()
'--add',
'--table',
dest='table',
action='extend',
metavar='<string LIST>',
help='tables to add')
(options, filenames) = parser.parse_args()
# --- 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)
tables = []
for addTable in options.table:
try:
tables.append(
damask.ASCIItable(name=addTable, buffered=False,
readonly=True))
except:
continue
# ------------------------------------------ read headers ------------------------------------------
parser.set_defaults(d = 1)
parser.set_defaults(N = 1)
parser.set_defaults(renumber = False)
parser.set_defaults(immutable = [])
(options, filenames) = parser.parse_args()
options.immutable = map(int,options.immutable)
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
buffered = False, labeled = False)
except: continue
damask.util.report(scriptName,name)
# --- interpret header ----------------------------------------------------------------------------
table.head_read()
info,extra_header = table.head_getGeom()
damask.util.croak(['grid a b c: %s'%(' x '.join(map(str,info['grid']))),
'size x y z: %s'%(' x '.join(map(str,info['size']))),
'origin x y z: %s'%(' : '.join(map(str,info['origin']))),
'homogenization: %i'%info['homogenization'],
'microstructures: %i'%info['microstructures'],
])
(options,filenames) = parser.parse_args()
for i in options.axes:
if i.lower() not in ['x','+x','-x','y','+y','-y','z','+z','-z']:
parser.error('invalid axes %s %s %s' %(options.axes[0],options.axes[1],options.axes[2]))
# --- loop over input files -------------------------------------------------------------------------
if filenames == []:
print 'missing input files'
exit()
for filename in filenames:
try:
table = damask.ASCIItable(name = filename,
outname = os.path.splitext(filename)[-2]+'.geom',
buffered = False, labeled = False)
except: continue
damask.util.report(scriptName,filename)
# open file and read data
fopen = open(filename, 'r')
if os.path.splitext(filename)[-1] == '.ang':
eulerangles, coords, phase, geomHeader = readAngFile(fopen, options.phase, options.column, options.threshold)
info['grid'][:] = ebsdInfo['ncols_odd'], ebsdInfo['nrows'], 1
info['size'][:] = ebsdInfo['ncols_odd']*ebsdInfo['xstep'], ebsdInfo['nrows']*ebsdInfo['ystep'], min(ebsdInfo['xstep'], ebsdInfo['ystep'])
elif os.path.splitext(filename)[-1] == '.ctf':
eulerangles, coords, phase, geomHeader = readCtfFile(fopen)
info['grid'][:] = ebsdInfo['xcells'], ebsdInfo['ycells'], 1
parser.error("Either dimension or size has to be specified if pixel size is given.")
else:
if options.size and options.grid:
options.pix_size = options.size/options.grid
else:
parser.error("Both dimension and size has to be specified if pixel size is not given.")
# --------------------------------------- loop over input files -------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
out_file = "out_"+os.path.basename(name)
try:
table = damask.ASCIItable(name = name,
outname = os.path.join(os.path.dirname(name),out_file),
buffered = False)
except: continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header and data ------------------------------------------
table.head_read()
table.data_readArray([options.coords])
if len(table.data[0]) != 3:
continue
#-------------------------------------------- process and store output ---------------------------------------
if options.tilt != 0.0: #tilt before coordinates rotation
ang_tilt = np.radians(options.tilt)
tilt = np.zeros((len(table.data),2),dtype=float)
tilt[:,0] = table.data[:,0]*np.cos(ang_tilt) + table.data[:,2]*np.sin(ang_tilt)
def compare_Tables(
self,
files=[None, None], # list of file names
columns=[None], # list of list of column labels (per file)
rtol=1e-5,
atol=1e-8,
preFilter=-1.0,
postFilter=-1.0,
debug=False):
"""
compare tables with np.allclose
threshold can be used to ignore small values (a negative number disables this feature)
"""
if not (isinstance(files, Iterable) and not isinstance(files, str)
): # check whether list of files is requested
files = [str(files)]
tables = [
damask.ASCIItable(name=filename, readonly=True)
for filename in files
]
for table in tables:
table.head_read()
columns += [columns[0]] * (len(files) - len(columns)
) # extend to same length as files
columns = columns[:len(files)] # truncate to same length as files
for i, column in enumerate(columns):
if column is None:
columns[i] = tables[i].labels(
raw=True) # if no column is given, read all
logging.info('comparing ASCIItables')
for i in xrange(len(columns)):
columns[i] = columns[0] if not columns[i] else \
([columns[i]] if not (isinstance(columns[i], Iterable) and not isinstance(columns[i], str)) else \
columns[i]
)
logging.info(files[i] + ':' + ','.join(columns[i]))
if len(files) < 2:
return True # single table is always close to itself...
maximum = np.zeros(len(columns[0]), dtype='f')
data = []
for table, labels in zip(tables, columns):
table.data_readArray(labels)
data.append(
np.where(
np.abs(table.data) < preFilter, np.zeros_like(table.data),
table.data))
maximum += np.abs(table.data).max(axis=0)
table.close()
maximum /= len(tables)
maximum = np.where(maximum > 0.0, maximum,
1) # avoid div by zero for empty columns
for i in xrange(len(data)):
data[i] /= maximum
mask = np.zeros_like(table.data, dtype='bool')
for table in data:
mask |= np.where(np.abs(table) < postFilter, True,
False) # mask out (all) tiny values
allclose = True # start optimistic
for i in xrange(1, len(data)):
if debug:
t0 = np.where(mask, 0.0, data[i - 1])
t1 = np.where(mask, 0.0, data[i])
j = np.argmin(np.abs(t1) * rtol + atol - np.abs(t0 - t1))
logging.info('{:f}'.format(
np.amax(np.abs(t0 - t1) / (np.abs(t1) * rtol + atol))))
logging.info('{:f} {:f}'.format((t0 * maximum).flatten()[j],
(t1 * maximum).flatten()[j]))
allclose &= np.allclose(np.where(mask, 0.0, data[i - 1]),
np.where(mask, 0.0, data[i]), rtol,
atol) # accumulate "pessimism"
return allclose
parser.set_defaults(
mode='cell',
pos='pos',
)
(options, filenames) = parser.parse_args()
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(
name=name,
buffered=False,
labeled=True,
readonly=True,
)
except:
continue
damask.util.report(scriptName, name)
# --- interpret header ----------------------------------------------------------------------------
table.head_read()
remarks = []
errors = []
coordDim = table.label_dimension(options.pos)
if not 3 >= coordDim >= 1:
errors.append(
damask.util.report(scriptName, options.seedFile)
if options.randomSeed is None:
options.randomSeed = int(os.urandom(4).encode('hex'), 16)
damask.util.croak(options.randomSeed)
delta = (options.scale / options.grid[0], options.scale / options.grid[1],
options.scale / options.grid[2])
baseFile = os.path.splitext(os.path.basename(options.seedFile))[0]
points = float(reduce(mul, options.grid))
# ----------- calculate target distribution and bin edges
targetGeomFile = os.path.splitext(os.path.basename(
options.target))[0] + '.geom'
targetGeomTable = damask.ASCIItable(targetGeomFile,
None,
labeled=False,
readonly=True)
targetGeomTable.head_read()
info, devNull = targetGeomTable.head_getGeom()
nMicrostructures = info['microstructures']
targetVolFrac = np.bincount(targetGeomTable.microstructure_read(info['grid']))[1:nMicrostructures+1]/\
float(info['grid'].prod())
target = []
for i in xrange(1, nMicrostructures + 1):
targetHist, targetBins = np.histogram(targetVolFrac,
bins=i) #bin boundaries
target.append({'histogram': targetHist, 'bins': targetBins})
# ----------- create initial seed file or open existing one
bestSeedsVFile = StringIO()
if os.path.isfile(os.path.splitext(options.seedFile)[0] + '.seeds'):
dest='simfile',
type='string',
metavar='string',
help='simulated file to interpolate')
parser.add_option('--show',
action="store_true",
dest="show",
help="showing the interpolated result [False]")
parser.set_defaults(show=False, )
(options, filenames) = parser.parse_args()
table_exp = damask.ASCIItable(name=options.expfile,
buffered=False,
readonly=True)
table_exp.head_read()
table_exp.data_readArray((options.expX, options.expY))
x_exp_index = table_exp.label_index(options.expX)
y_exp_index = table_exp.label_index(options.expY)
x_exp_data = table_exp.data[:, x_exp_index]
y_exp_data = table_exp.data[:, y_exp_index]
table_sim = damask.ASCIItable(name=options.simfile,
buffered=False,
readonly=True)
table_sim.head_read()
table_sim.data_readArray((options.simX, options.simY))
x_sim_index = table_sim.label_index(options.simX)
y_sim_index = table_sim.label_index(options.simY)
def run(self):
global bestSeedsUpdate
global bestSeedsVFile
global nMicrostructures
global delta
global points
global target
global match
global baseFile
global maxSeeds
s.acquire()
bestMatch = match
s.release()
random.seed(options.randomSeed +
self.threadID) # initializes to given seeds
knownSeedsUpdate = bestSeedsUpdate - 1.0 # trigger update of local best seeds
randReset = True # aquire new direction
myBestSeedsVFile = StringIO() # store local copy of best seeds file
perturbedSeedsVFile = StringIO() # perturbed best seeds file
perturbedGeomVFile = StringIO() # tessellated geom file
#--- still not matching desired bin class ----------------------------------------------------------
while bestMatch < options.threshold:
s.acquire() # ensure only one thread acces global data
if bestSeedsUpdate > knownSeedsUpdate: # write best fit to virtual file
knownSeedsUpdate = bestSeedsUpdate
bestSeedsVFile.reset()
myBestSeedsVFile.close()
myBestSeedsVFile = StringIO()
i = 0
for line in bestSeedsVFile:
myBestSeedsVFile.write(line)
s.release()
if randReset: # new direction because current one led to worse fit
randReset = False
NmoveGrains = random.randrange(1, maxSeeds)
selectedMs = []
direction = []
for i in xrange(NmoveGrains):
selectedMs.append(random.randrange(1, nMicrostructures))
direction.append(
np.array(((random.random() - 0.5) * delta[0],
(random.random() - 0.5) * delta[1],
(random.random() - 0.5) * delta[2])))
perturbedSeedsVFile.close() # reset virtual file
perturbedSeedsVFile = StringIO()
myBestSeedsVFile.reset()
perturbedSeedsTable = damask.ASCIItable(
myBestSeedsVFile, perturbedSeedsVFile,
labeled=True) # write best fit to perturbed seed file
perturbedSeedsTable.head_read()
perturbedSeedsTable.head_write()
outputAlive = True
ms = 1
i = 0
while outputAlive and perturbedSeedsTable.data_read(
): # perturbe selected microstructure
if ms in selectedMs:
newCoords = np.array(
tuple(map(float, perturbedSeedsTable.data[0:3])) +
direction[i])
newCoords = np.where(
newCoords >= 1.0, newCoords - 1.0,
newCoords) # ensure that the seeds remain in the box
newCoords = np.where(newCoords < 0.0, newCoords + 1.0,
newCoords)
perturbedSeedsTable.data[0:3] = [
format(f, '8.6f') for f in newCoords
]
direction[i] *= 2.
i += 1
ms += 1
perturbedSeedsTable.data_write()
#--- do tesselation with perturbed seed file ----------------------------------------------------------
perturbedGeomVFile.close()
perturbedGeomVFile = StringIO()
perturbedSeedsVFile.reset()
perturbedGeomVFile.write(
damask.util.execute('geom_fromVoronoiTessellation ' + ' -g ' +
' '.join(map(str, options.grid)),
streamIn=perturbedSeedsVFile)[0])
perturbedGeomVFile.reset()
#--- evaluate current seeds file ----------------------------------------------------------------------
perturbedGeomTable = damask.ASCIItable(perturbedGeomVFile,
None,
labeled=False,
readonly=True)
perturbedGeomTable.head_read()
for i in perturbedGeomTable.info:
if i.startswith('microstructures'):
myNmicrostructures = int(i.split('\t')[1])
perturbedGeomTable.data_readArray()
perturbedGeomTable.output_flush()
currentData = np.bincount(
perturbedGeomTable.data.astype(int).ravel())[1:] / points
currentError = []
currentHist = []
for i in xrange(
nMicrostructures
): # calculate the deviation in all bins per histogram
currentHist.append(
np.histogram(currentData, bins=target[i]['bins'])[0])
currentError.append(
np.sqrt(
np.square(
np.array(target[i]['histogram'] -
currentHist[i])).sum()))
# as long as not all grains are within the range of the target, use the deviation to left and right as error
if currentError[0] > 0.0:
currentError[0] *= (
(target[0]['bins'][0] - np.min(currentData))**2.0 +
(target[0]['bins'][1] - np.max(currentData))**2.0
)**0.5 # norm of deviations by number of usual bin deviation
s.acquire() # do the evaluation serially
bestMatch = match
#--- count bin classes with no mismatch ----------------------------------------------------------------------
myMatch = 0
for i in xrange(nMicrostructures):
if currentError[i] > 0.0: break
myMatch = i + 1
if myNmicrostructures == nMicrostructures:
for i in xrange(min(nMicrostructures, myMatch + options.bins)):
if currentError[i] > target[i][
'error']: # worse fitting, next try
randReset = True
break
elif currentError[i] < target[i]['error']: # better fit
bestSeedsUpdate = time.time(
) # save time of better fit
damask.util.croak('Thread {:d}: Better match ({:d} bins, {:6.4f} --> {:6.4f})'\
.format(self.threadID,i+1,target[i]['error'],currentError[i]))
damask.util.croak(' target: ' +
np.array_str(target[i]['histogram']))
damask.util.croak(' best: ' +
np.array_str(currentHist[i]))
currentSeedsName = baseFile + '_' + str(
bestSeedsUpdate
).replace(
'.', '-'
) # name of new seed file (use time as unique identifier)
perturbedSeedsVFile.reset()
bestSeedsVFile.close()
bestSeedsVFile = StringIO()
sys.stdout.flush()
with open(
currentSeedsName + '.seeds',
'w') as currentSeedsFile: # write to new file
for line in perturbedSeedsVFile:
currentSeedsFile.write(line)
bestSeedsVFile.write(line)
for j in xrange(nMicrostructures
): # save new errors for all bins
target[j]['error'] = currentError[j]
if myMatch > match: # one or more new bins have no deviation
damask.util.croak(
'Stage {:d} cleared'.format(myMatch))
match = myMatch
sys.stdout.flush()
break
if i == min(
nMicrostructures, myMatch + options.bins
) - 1: # same quality as before: take it to keep on moving
bestSeedsUpdate = time.time()
perturbedSeedsVFile.reset()
bestSeedsVFile.close()
bestSeedsVFile = StringIO()
for line in perturbedSeedsVFile:
bestSeedsVFile.write(line)
for j in xrange(nMicrostructures):
target[j]['error'] = currentError[j]
randReset = True
else: #--- not all grains are tessellated
damask.util.croak('Thread {:d}: Microstructure mismatch ({:d} microstructures mapped)'\
.format(self.threadID,myNmicrostructures))
randReset = True
s.release()
parser.error('only one from aliases "biplane" and "boundary" possible.')
feature_list = []
for i,feature in enumerate(features):
for name in feature['names']:
for myType in options.type:
if name.startswith(myType):
feature_list.append(i) # remember valid features
break
# --- 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 = []
column = {}
coordDim = table.label_dimension(options.pos)
if not 3 >= coordDim >= 1:
help='column label for grouping rows')
(options, filenames) = parser.parse_args()
if options.label is None:
parser.error('no grouping column specified.')
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(
name=name,
outname=os.path.join(
os.path.split(name)[0], options.label + '_averaged_' +
os.path.split(name)[1]) if name else name,
buffered=False)
except:
continue
damask.util.report(scriptName, name)
# ------------------------------------------ sanity checks ---------------------------------------
table.head_read()
if table.label_dimension(options.label) != 1:
damask.util.croak('column {} is not of scalar dimension.'.format(
options.label))
table.close(dismiss=True) # close ASCIItable and remove empty file
continue
parser.set_defaults(homogenization=1)
parser.set_defaults(phase=[1, 2])
parser.set_defaults(crystallite=1)
parser.set_defaults(config=False)
parser.set_defaults(compress=False)
parser.set_defaults(precision='2')
(options, filenames) = parser.parse_args()
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name=name,
outname=os.path.splitext(name)[-2] +
'.geom' if name else name,
buffered=False,
labeled=False)
except:
continue
damask.util.report(scriptName, name)
info = {
'grid': np.zeros(3, 'i'),
'size': np.zeros(3, 'd'),
'origin': np.zeros(3, 'd'),
'microstructures': 0,
'homogenization': options.homogenization
}
coords = [{}, {}, {}]
help='linked ASCIItable')
parser.set_defaults()
(options, filenames) = parser.parse_args()
if options.label is None:
parser.error('no data columns specified.')
if options.link is None:
parser.error('no linking columns given.')
# -------------------------------------- process linked ASCIItable --------------------------------
if options.asciitable is not None and os.path.isfile(options.asciitable):
linkedTable = damask.ASCIItable(name=options.asciitable, readonly=True)
linkedTable.head_read() # read ASCII header info of linked table
linkDim = linkedTable.label_dimension(
options.link[1]) # dimension of linking column
missing_labels = linkedTable.data_readArray(
[options.link[1]] + options.label) # try reading linked ASCII table
linkedTable.close() # close linked ASCII table
if len(missing_labels) > 0:
damask.util.croak('column{} {} not found...'.format(
's' if len(missing_labels) > 1 else '', ', '.join(missing_labels)))
if len(missing_labels) >= len(options.label):
damask.util.croak('aborting...')
sys.exit()