127 lines
6.6 KiB
Python
Executable File
127 lines
6.6 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# -*- coding: UTF-8 no BOM -*-
|
|
|
|
import os,sys,string
|
|
import numpy as np
|
|
from collections import defaultdict
|
|
from optparse import OptionParser
|
|
import damask
|
|
|
|
scriptID = string.replace('$Id$','\n','\\n')
|
|
scriptName = os.path.splitext(scriptID.split()[1])[0]
|
|
|
|
# --------------------------------------------------------------------
|
|
# MAIN
|
|
# --------------------------------------------------------------------
|
|
|
|
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options file[s]', description = """
|
|
Add column(s) containing deformed configuration of requested column(s).
|
|
Operates on periodic ordered three-dimensional data sets.
|
|
|
|
""", version = scriptID)
|
|
|
|
parser.add_option('-c','--coordinates', dest='coords', metavar='string',
|
|
help='column heading for coordinates [%default]')
|
|
parser.add_option('-f','--defgrad', dest='defgrad', metavar='string',
|
|
help='heading of columns containing tensor field values [%default]')
|
|
parser.set_defaults(coords = 'ipinitialcoord')
|
|
parser.set_defaults(defgrad = 'f' )
|
|
|
|
(options,filenames) = parser.parse_args()
|
|
|
|
datainfo = { # list of requested labels per datatype
|
|
'defgrad': {'len':9,
|
|
'label':[]},
|
|
}
|
|
|
|
datainfo['defgrad']['label'].append(options.defgrad)
|
|
|
|
# ------------------------------------------ setup file handles ------------------------------------
|
|
files = []
|
|
for name in filenames:
|
|
if os.path.exists(name):
|
|
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
|
|
|
|
#--- loop over input files -------------------------------------------------------------------------
|
|
for file in files:
|
|
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
|
|
|
|
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
|
|
table.head_read() # read ASCII header info
|
|
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
|
|
|
# --------------- figure out size and grid ---------------------------------------------------------
|
|
try:
|
|
locationCol = table.labels.index('1_%s'%options.coords) # columns containing location data
|
|
except ValueError:
|
|
try:
|
|
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data (legacy naming scheme)
|
|
except ValueError:
|
|
file['croak'].write('no coordinate data (1_%s/%s.x) found...\n'%(options.coords,options.coords))
|
|
continue
|
|
|
|
coords = [{},{},{}]
|
|
while table.data_read(): # read next data line of ASCII table
|
|
for j in xrange(3):
|
|
coords[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
|
|
grid = np.array([len(coords[0]),\
|
|
len(coords[1]),\
|
|
len(coords[2]),],'i') # grid is number of distinct coordinates found
|
|
size = grid/np.maximum(np.ones(3,'d'),grid-1.0)* \
|
|
np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
|
|
max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
|
|
max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
|
|
],'d') # size from bounding box, corrected for cell-centeredness
|
|
|
|
for i, points in enumerate(grid):
|
|
if points == 1:
|
|
options.packing[i] = 1
|
|
options.shift[i] = 0
|
|
mask = np.ones(3,dtype=bool)
|
|
mask[i]=0
|
|
size[i] = min(size[mask]/grid[mask]) # third spacing equal to smaller of other spacing
|
|
|
|
N = grid.prod()
|
|
|
|
# --------------- figure out columns to process ---------------------------------------------------
|
|
key = '1_%s'%datainfo['defgrad']['label'][0]
|
|
if key not in table.labels:
|
|
file['croak'].write('column %s not found...\n'%key)
|
|
continue
|
|
else:
|
|
column = table.labels.index(key) # remember columns of requested data
|
|
|
|
# ------------------------------------------ assemble header ---------------------------------------
|
|
table.labels_append(['%s_coords'%(coord+1) for coord in xrange(3)]) # extend ASCII header with new labels
|
|
table.head_write()
|
|
|
|
# ------------------------------------------ read deformation gradient field -----------------------
|
|
table.data_rewind()
|
|
F = np.array([0.0 for i in xrange(N*9)]).reshape([3,3]+list(grid))
|
|
idx = 0
|
|
while table.data_read():
|
|
(x,y,z) = damask.util.gridLocation(idx,grid) # figure out (x,y,z) position from line count
|
|
idx += 1
|
|
F[0:3,0:3,x,y,z] = np.array(map(float,table.data[column:column+9]),'d').reshape(3,3)
|
|
|
|
# ------------------------------------------ calculate coordinates ---------------------------------
|
|
Favg = damask.core.math.tensorAvg(F)
|
|
centroids = damask.core.mesh.deformedCoordsFFT(size,F,Favg)
|
|
|
|
# ------------------------------------------ process data ------------------------------------------
|
|
table.data_rewind()
|
|
idx = 0
|
|
outputAlive = True
|
|
while outputAlive and table.data_read(): # read next data line of ASCII table
|
|
(x,y,z) = damask.util.gridLocation(idx,grid) # figure out (x,y,z) position from line count
|
|
idx += 1
|
|
table.data_append(list(centroids[:,x,y,z]))
|
|
outputAlive = table.data_write() # output processed line
|
|
|
|
# ------------------------------------------ output result -----------------------------------------
|
|
outputAlive and table.output_flush() # just in case of buffered ASCII table
|
|
|
|
table.input_close() # close input ASCII table
|
|
table.output_close() # close output ASCII table
|
|
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
|