#!/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 = scriptID.split()[1][:-3]
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.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.
""", version = scriptID)
accuracyChoices = ['2','4','6','8']
parser.add_option('--fdm', dest='accuracy', action='extend', metavar='<int LIST>',
help='degree of central difference accuracy (%s)'%(','.join(accuracyChoices)))
parser.add_option('--fft', dest='fft', action='store_true',
help='calculate divergence in Fourier space')
parser.add_option('-c','--coordinates', dest='coords', metavar = 'string',
help='column heading for coordinates [%default]')
parser.add_option('-v','--vector', dest='vector', action='extend', metavar='<string LIST>',
help='heading of columns containing vector field values')
parser.add_option('-t','--tensor', dest='tensor', action='extend', metavar='<string LIST>',
help='heading of columns containing tensor field values')
parser.set_defaults(coords = 'ip')
parser.set_defaults(accuracy = [])
parser.set_defaults(fft = False)
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...')
if not set(options.accuracy).issubset(set(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 = []
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'],True) # 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('%s.x'%options.coords) # columns containing location data
except ValueError:
file['croak'].write('no coordinate data (%s.x) found...\n'%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 ---------------------------------------------------
active = defaultdict(list)
column = defaultdict(dict)
values = defaultdict(dict)
divergence = defaultdict(dict)
for datatype,info in datainfo.items():
for label in info['label']:
key = '1_%s'%label
if key not in table.labels:
file['croak'].write('column %s not found...\n'%key)
else:
active[datatype].append(label)
column[datatype][label] = table.labels.index(key) # remember columns of requested data
values[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
reshape(list(grid)+[datainfo[datatype]['len']//3,3])
if label not in divergence[datatype]: divergence[datatype][label] = {}
for accuracy in options.accuracy:
divergence[datatype][label][accuracy] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len']//3)]).\
reshape(list(grid)+[datainfo[datatype]['len']//3])
# ------------------------------------------ assemble header ---------------------------------------
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels:
for accuracy in options.accuracy:
table.labels_append({True: ['%i_div%s(%s)'%(i+1,accuracy,label) for i in xrange(3)], # extend ASCII header with new labels
False:['div%s(%s)'%(accuracy,label)]} [datatype == 'tensor'])
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) = damask.util.gridLocation(idx,grid) # 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] = np.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.divergenceFFT(size,values[datatype][label])
else:
divergence[datatype][label][accuracy] =\
damask.core.math.divergenceFDM(size,eval(accuracy)//2-1,values[datatype][label])
# ------------------------------------------ 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
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)))
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