283 lines
13 KiB
Python
Executable File
283 lines
13 KiB
Python
Executable File
#!/usr/bin/python
|
|
|
|
import os,re,sys,math,string,numpy,damask
|
|
from optparse import OptionParser, Option
|
|
|
|
# -----------------------------
|
|
class extendableOption(Option):
|
|
# -----------------------------
|
|
# used for definition of new option parser action 'extend', which enables to take multiple option arguments
|
|
# taken from online tutorial http://docs.python.org/library/optparse.html
|
|
|
|
ACTIONS = Option.ACTIONS + ("extend",)
|
|
STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
|
|
TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
|
|
ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
|
|
|
|
def take_action(self, action, dest, opt, value, values, parser):
|
|
if action == "extend":
|
|
lvalue = value.split(",")
|
|
values.ensure_value(dest, []).extend(lvalue)
|
|
else:
|
|
Option.take_action(self, action, dest, opt, value, values, parser)
|
|
|
|
def location(idx,res):
|
|
return ( idx % res[0], \
|
|
(idx // res[0]) % res[1], \
|
|
(idx // res[0] // res[1]) % res[2] )
|
|
|
|
def index(location,res):
|
|
return ( location[0] % res[0] + \
|
|
(location[1] % res[1]) * res[0] + \
|
|
(location[2] % res[2]) * res[0] * res[1] )
|
|
|
|
def prefixMultiply(what,len):
|
|
return {True: ['%i_%s'%(i+1,what) for i in range(len)],
|
|
False:[what]}[len>1]
|
|
|
|
|
|
# --------------------------------------------------------------------
|
|
# MAIN
|
|
# --------------------------------------------------------------------
|
|
|
|
FDcoefficients = [ \
|
|
[1.0/2.0, 0.0, 0.0, 0.0],\
|
|
[2.0/3.0,-1.0/12.0, 0.0, 0.0],\
|
|
[3.0/4.0,-3.0/20.0,1.0/ 60.0, 0.0],\
|
|
[4.0/5.0,-1.0/ 5.0,4.0/105.0,-1.0/280.0],\
|
|
]
|
|
parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
|
|
Add column(s) containing divergence of requested column(s).
|
|
Operates on periodic ordered three-dimensional data sets.
|
|
Deals with both vector- and tensor-valued fields.
|
|
|
|
""" + string.replace('$Id$','\n','\\n')
|
|
)
|
|
|
|
|
|
parser.add_option('--fdm', dest='accuracy', action='extend', type='string', \
|
|
help='degree of central difference accuracy')
|
|
parser.add_option('--fft', dest='fft', action='store_true', \
|
|
help='calculate divergence in Fourier space [%default]')
|
|
parser.add_option('-m','--memory', dest='memory', action='store_true', \
|
|
help='memory efficient calculation (not possible for FFT based divergency [%default]')
|
|
parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \
|
|
help='heading of columns containing vector field values')
|
|
parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \
|
|
help='heading of columns containing tensor field values')
|
|
parser.add_option('-d','--dimension', dest='dim', type='float', nargs=3, \
|
|
help='physical dimension of data set in x (fast) y z (slow) [%default]')
|
|
parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \
|
|
help='resolution of data set in x (fast) y z (slow)')
|
|
parser.add_option('-s','--skip', dest='skip', type='int', nargs=3, \
|
|
help='items skipped due to periodicity in x (fast) y z (slow)')
|
|
|
|
parser.set_defaults(accuracy = [])
|
|
parser.set_defaults(memory = False)
|
|
parser.set_defaults(fft = False)
|
|
parser.set_defaults(vector = [])
|
|
parser.set_defaults(tensor = [])
|
|
parser.set_defaults(dim = [])
|
|
parser.set_defaults(skip = [0,0,0])
|
|
accuracyChoices = [2,4,6,8]
|
|
|
|
(options,filenames) = parser.parse_args()
|
|
|
|
if len(options.vector) + len(options.tensor) == 0:
|
|
parser.error('no data column specified...')
|
|
if len(options.dim) < 3:
|
|
parser.error('improper dimension specification...')
|
|
if not options.res or len(options.res) < 3:
|
|
parser.error('improper resolution specification...')
|
|
for choice in options.accuracy:
|
|
if int(choice) not in accuracyChoices:
|
|
parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices)))
|
|
if options.fft: options.accuracy.append('fft')
|
|
|
|
resSkip = map(lambda (a,b): a+b,zip(options.res,options.skip))
|
|
datainfo = { # list of requested labels per datatype
|
|
'vector': {'len':3,
|
|
'label':[]},
|
|
'tensor': {'len':9,
|
|
'label':[]},
|
|
}
|
|
|
|
if options.vector != None: datainfo['vector']['label'] += options.vector
|
|
if options.tensor != None: datainfo['tensor']['label'] += options.tensor
|
|
|
|
# ------------------------------------------ setup file handles ---------------------------------------
|
|
|
|
files = []
|
|
if filenames == []:
|
|
files.append({'name':'STDIN', 'handle':sys.stdin})
|
|
else:
|
|
for name in filenames:
|
|
if os.path.exists(name):
|
|
files.append({'name':name, 'handle':open(name)})
|
|
|
|
# ------------------------------------------ loop over input files ---------------------------------------
|
|
|
|
for file in files:
|
|
print file['name']
|
|
|
|
content = file['handle'].readlines()
|
|
file['handle'].close()
|
|
|
|
# get labels by either read the first row, or - if keyword header is present - the last line of the header
|
|
|
|
headerlines = 1
|
|
m = re.search('(\d+)\s*head', content[0].lower())
|
|
if m:
|
|
headerlines = int(m.group(1))
|
|
passOn = content[1:headerlines]
|
|
headers = content[headerlines].split()
|
|
data = content[headerlines+1:]
|
|
|
|
regexp = re.compile('1_\d+_')
|
|
for i,l in enumerate(headers):
|
|
if regexp.match(l):
|
|
headers[i] = l[2:]
|
|
|
|
active = {}
|
|
column = {}
|
|
values = {}
|
|
div_field ={}
|
|
head = []
|
|
|
|
for datatype,info in datainfo.items():
|
|
for label in info['label']:
|
|
key = {True :'1_%s',
|
|
False:'%s' }[info['len']>1]%label
|
|
if key not in headers:
|
|
print 'column %s not found...'%key
|
|
else:
|
|
if datatype not in active: active[datatype] = []
|
|
if datatype not in column: column[datatype] = {}
|
|
if datatype not in values: values[datatype] = {}
|
|
if datatype not in div_field: div_field[datatype] = {}
|
|
active[datatype].append(label)
|
|
column[datatype][label] = headers.index(key)
|
|
values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\
|
|
options.res[0]*options.res[1]*options.res[2])]).\
|
|
reshape((options.res[0],options.res[1],options.res[2],\
|
|
3,datainfo[datatype]['len']//3))
|
|
|
|
for what in options.accuracy: # loop over all requested degrees of accuracy (plus potentially fft)
|
|
if not options.memory or what != 'fft': # FFT divergence excluded in memory saving mode
|
|
head += prefixMultiply('div%s(%s)'%(what,label),datainfo[datatype]['len']//3)
|
|
|
|
# ------------------------------------------ assemble header ---------------------------------------
|
|
|
|
output = '%i\theader'%(headerlines+1) + '\n' + \
|
|
''.join(passOn) + \
|
|
string.replace('$Id$','\n','\\n')+ '\t' + \
|
|
' '.join(sys.argv[1:]) + '\n' + \
|
|
'\t'.join(headers + head) + '\n' # build extended header
|
|
|
|
# ------------------------------------------ read value field ---------------------------------------
|
|
|
|
idx = 0
|
|
for line in data:
|
|
items = line.split()[:len(headers)]
|
|
if len(items) < len(headers): # skip too short lines (probably comments or invalid)
|
|
continue
|
|
locSkip = location(idx,resSkip)
|
|
if ( locSkip[0] < options.res[0]
|
|
and locSkip[1] < options.res[1]
|
|
and locSkip[2] < options.res[2] ): # only take values that are not periodic images
|
|
for datatype,labels in active.items():
|
|
for label in labels:
|
|
values[datatype][label][locSkip[0]][locSkip[1]][locSkip[2]]\
|
|
= numpy.reshape(items[column[datatype][label]:
|
|
column[datatype][label]+datainfo[datatype]['len']],(3,datainfo[datatype]['len']//3))
|
|
idx += 1
|
|
# ------------------------------------------ read file ---------------------------------------
|
|
if options.memory:
|
|
idx = 0
|
|
for line in data:
|
|
items = line.split()[:len(headers)]
|
|
if len(items) < len(headers):
|
|
continue
|
|
|
|
output += '\t'.join(items)
|
|
(x,y,z) = location(idx,options.res)
|
|
|
|
for datatype,labels in active.items():
|
|
for label in labels:
|
|
for accuracy in options.accuracy:
|
|
if accuracy == 'fft': continue
|
|
for k in range(datainfo[datatype]['len']/3): # formulas from Mikhail Itskov: Tensor Algebra and Tensor Analysis for Engineers, Springer 2009, p 52
|
|
theDiv = 0.0
|
|
for a in range(int(accuracy)//2):
|
|
|
|
theDiv += FDcoefficients[int(accuracy)//2-1][a] * \
|
|
( \
|
|
(values[datatype][label][location(index([x+1+a,y,z],options.res),options.res)[0]] \
|
|
[location(index([x+1+a,y,z],options.res),options.res)[1]] \
|
|
[location(index([x+1+a,y,z],options.res),options.res)[2]][k][0] - \
|
|
values[datatype][label][location(index([x-1-a,y,z],options.res),options.res)[0]] \
|
|
[location(index([x-1-a,y,z],options.res),options.res)[1]] \
|
|
[location(index([x-1-a,y,z],options.res),options.res)[2]][k][0]) * options.res[0] / options.dim[0] + \
|
|
(values[datatype][label][location(index([x,y+1+a,z],options.res),options.res)[0]] \
|
|
[location(index([x,y+1+a,z],options.res),options.res)[1]] \
|
|
[location(index([x,y+1+a,z],options.res),options.res)[2]][k][1] - \
|
|
values[datatype][label][location(index([x,y-1-a,z],options.res),options.res)[0]] \
|
|
[location(index([x,y-1-a,z],options.res),options.res)[1]] \
|
|
[location(index([x,y-1-a,z],options.res),options.res)[2]][k][1]) * options.res[1] / options.dim[1] + \
|
|
(values[datatype][label][location(index([x,y,z+1+a],options.res),options.res)[0]] \
|
|
[location(index([x,y,z+1+a],options.res),options.res)[1]] \
|
|
[location(index([x,y,z+1+a],options.res),options.res)[2]][k][2]- \
|
|
values[datatype][label][location(index([x,y,z-1-a],options.res),options.res)[0]] \
|
|
[location(index([x,y,z-1-a],options.res),options.res)[1]] \
|
|
[location(index([x,y,z-1-a],options.res),options.res)[2]][k][2]) * options.res[2] / options.dim[2] \
|
|
)
|
|
output += '\t%f'%theDiv
|
|
|
|
output += '\n'
|
|
idx += 1
|
|
|
|
else:
|
|
for datatype,labels in active.items():
|
|
for label in labels:
|
|
if label not in div_field[datatype]: div_field[datatype][label] = {}
|
|
for accuracy in options.accuracy:
|
|
div_field[datatype][label][accuracy] = numpy.array([0.0 for i in range((datainfo[datatype]['len'])//3*\
|
|
options.res[0]*options.res[1]*options.res[2])]).\
|
|
reshape((options.res[0],options.res[1],options.res[2],\
|
|
datainfo[datatype]['len']//3))
|
|
if accuracy == 'fft':
|
|
div_field[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
|
|
else:
|
|
div_field[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
|
|
|
|
idx = 0
|
|
for line in data:
|
|
items = line.split()[:len(headers)]
|
|
if len(items) < len(headers):
|
|
continue
|
|
|
|
output += '\t'.join(items)
|
|
|
|
|
|
for datatype,labels in active.items():
|
|
for label in labels:
|
|
for accuracy in options.accuracy:
|
|
for i in range(datainfo[datatype]['len']/3):
|
|
output += '\t%f'%div_field[datatype][label][accuracy][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]][i]
|
|
output += '\n'
|
|
idx += 1
|
|
|
|
|
|
# ------------------------------------------ output result ---------------------------------------
|
|
|
|
if file['name'] == 'STDIN':
|
|
print output
|
|
else:
|
|
file['handle'] = open(file['name']+'_tmp','w')
|
|
try:
|
|
file['handle'].write(output)
|
|
file['handle'].close()
|
|
os.rename(file['name']+'_tmp',file['name'])
|
|
except:
|
|
print 'error during writing',file['name']+'_tmp'
|