DAMASK_EICMD/processing/post/addDivergence.py

199 lines
9.6 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[1] * res[0] )
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
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('-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.set_defaults(accuracy = [])
parser.set_defaults(fft = False)
parser.set_defaults(vector = [])
parser.set_defaults(tensor = [])
parser.set_defaults(dim = [])
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')
if not options.accuracy:
parser.error('no accuracy selected')
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', 'input':sys.stdin, 'output':sys.stdout})
else:
for name in filenames:
if os.path.exists(name):
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')})
# ------------------------------------------ loop over input files ---------------------------------------
for file in files:
if file['name'] != 'STDIN': print file['name']
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
table.info_append(string.replace('$Id$','\n','\\n') + \
'\t' + ' '.join(sys.argv[1:]))
active = {}
column = {}
values = {}
divergence = {}
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 table.labels:
sys.stderr.write('column %s not found...\n'%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 divergence: divergence[datatype] = {}
if label not in divergence[datatype]: divergence[datatype][label] = {}
active[datatype].append(label)
column[datatype][label] = table.labels.index(key) # remember columns of requested data
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],\
datainfo[datatype]['len']//3,3))
for accuracy in options.accuracy:
divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(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))
table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label)
for i in xrange(datainfo[datatype]['len']//3)]) # extend ASCII header with new labels
# ------------------------------------------ assemble header ---------------------------------------
table.head_write()
# ------------------------------------------ read value field ---------------------------------------
idx = 0
while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count
idx += 1
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested curls
values[datatype][label][x,y,z] = numpy.array(
map(float,table.data[column[datatype][label]:
column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(datainfo[datatype]['len']//3,3)
# ------------------------------------------ process value field ---------------------------------------
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested divergencies
for accuracy in options.accuracy:
if accuracy == 'FFT':
divergence[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
else:
divergence[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
# ------------------------------------------ process data ---------------------------------------
table.data_rewind()
idx = 0
while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count
idx += 1
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested
for accuracy in options.accuracy:
table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
table.data_write() # output processed line
# ------------------------------------------ output result ---------------------------------------
table.output_flush() # just in case of buffered ASCII table
file['input'].close() # close input ASCII table
if file['name'] != 'STDIN':
file['output'].close # close output ASCII table
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new