DAMASK_EICMD/processing/post/addGradient.py

152 lines
6.3 KiB
Python
Executable File

#!/usr/bin/env python2.7
# -*- coding: UTF-8 no BOM -*-
import os,sys,math
import numpy as np
from optparse import OptionParser
from collections import defaultdict
import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
def gradFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1])
N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector'
elif n == 1: dataType = 'scalar'
field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
grad_fourier = np.empty(field_fourier.shape+(3,),'c16')
# differentiation in Fourier space
# Question: why are grid[0,1,2] normalized by geomdim[2,1,0]??
TWOPIIMG = 2.0j*math.pi
k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_si = np.arange(grid[0]//2+1)/geomdim[2]
kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
if dataType == 'vector': # vector, 3 -> 3x3
grad_fourier = np.einsum('ijkl,ijkm->ijklm',field_fourier,k_s)*TWOPIIMG
elif dataType == 'scalar': # scalar, 1 -> 3
grad_fourier = np.einsum('ijkl,ijkm->ijkm',field_fourier,k_s)*TWOPIIMG
return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n])
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [ASCIItable(s)]', description = """
Add column(s) containing gradient of requested column(s).
Operates on periodic ordered three-dimensional data sets
of vector and scalar fields.
""", version = scriptID)
parser.add_option('-p','--pos','--periodiccellcenter',
dest = 'pos',
type = 'string', metavar = 'string',
help = 'label of coordinates [%default]')
parser.add_option('-d','--data',
dest = 'data',
action = 'extend', metavar = '<string LIST>',
help = 'label(s) of field values')
parser.set_defaults(pos = 'pos',
)
(options,filenames) = parser.parse_args()
if options.data is None: parser.error('no data column specified.')
# --- loop over input files ------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try: table = damask.ASCIItable(name = name,buffered = False)
except: continue
damask.util.report(scriptName,name)
# --- interpret header ----------------------------------------------------------------------------
table.head_read()
remarks = []
errors = []
active = defaultdict(list)
coordDim = table.label_dimension(options.pos)
if coordDim != 3:
errors.append('coordinates "{}" must be three-dimensional.'.format(options.pos))
else: coordCol = table.label_index(options.pos)
for i,dim in enumerate(table.label_dimension(options.data)):
me = options.data[i]
if dim == -1:
remarks.append('"{}" not found...'.format(me))
elif dim == 1:
active['scalar'].append(me)
remarks.append('differentiating scalar "{}"...'.format(me))
elif dim == 3:
active['vector'].append(me)
remarks.append('differentiating vector "{}"...'.format(me))
else:
remarks.append('skipping "{}" of dimension {}...'.format(me,dim))
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ assemble header --------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for type, data in active.iteritems():
for label in data:
table.labels_append(['{}_gradFFT({})'.format(i+1,label) for i in range(3*table.label_dimension(label))]) # extend ASCII header with new labels
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray()
coords = [np.unique(table.data[:,coordCol+i]) for i in range(3)]
mincorner = np.array(map(min,coords))
maxcorner = np.array(map(max,coords))
grid = np.array(map(len,coords),'i')
size = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 equal to smallest among other ones
# ------------------------------------------ process value field -----------------------------------
stack = [table.data]
for type, data in active.iteritems():
for i,label in enumerate(data):
# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
stack.append(gradFFT(size[::-1],
table.data[:,table.label_indexrange(label)].
reshape(grid[::-1].tolist()+[table.label_dimension(label)])))
# ------------------------------------------ output result -----------------------------------------
if len(stack) > 1: table.data = np.hstack(tuple(stack))
table.data_writeArray('%.12g')
# ------------------------------------------ output finalization -----------------------------------
table.close() # close input ASCII table (works for stdin)