DAMASK_EICMD/processing/post/addCurl.py

198 lines
9.1 KiB
Python
Executable File

#!/usr/bin/env 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 curl 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('-c','--coordinates', dest='coords', type='string',\
help='column heading for coordinates [%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.set_defaults(coords = 'ip')
parser.set_defaults(vector = [])
parser.set_defaults(tensor = [])
(options,filenames) = parser.parse_args()
if len(options.vector) + len(options.tensor) == 0:
parser.error('no data column specified...')
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:]))
# --------------- figure out dimension and resolution
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
except ValueError:
print 'no coordinate data found...'
continue
grid = [{},{},{}]
while table.data_read(): # read next data line of ASCII table
for j in xrange(3):
grid[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
resolution = numpy.array([len(grid[0]),\
len(grid[1]),\
len(grid[2]),],'i') # resolution is number of distinct coordinates found
dimension = resolution/numpy.maximum(numpy.ones(3,'d'),resolution-1.0)* \
numpy.array([max(map(float,grid[0].keys()))-min(map(float,grid[0].keys())),\
max(map(float,grid[1].keys()))-min(map(float,grid[1].keys())),\
max(map(float,grid[2].keys()))-min(map(float,grid[2].keys())),\
],'d') # dimension from bounding box, corrected for cell-centeredness
if resolution[2] == 1:
dimension[2] = min(dimension[:2]/resolution[:2])
N = resolution.prod()
print '\t%s @ %s'%(dimension,resolution)
# --------------- figure out columns to process
active = {}
column = {}
values = {}
curl = {}
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 curl: curl[datatype] = {}
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(N*datainfo[datatype]['len'])]).\
reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
curl[datatype][label] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
table.labels_append(['%i_curlFFT(%s)'%(i+1,label)
for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels
# ------------------------------------------ assemble header ---------------------------------------
table.head_write()
# ------------------------------------------ read value field ---------------------------------------
table.data_rewind()
idx = 0
while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,resolution) # 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 curls
curl[datatype][label] = damask.core.math.curl_fft(resolution,dimension,datainfo[datatype]['len']//3,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,resolution) # 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 norms
table.data_append(list(curl[datatype][label][x,y,z].reshape(datainfo[datatype]['len'])))
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