DAMASK_EICMD/processing/post/addGradient.py

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#!/usr/bin/env python3
import os
import sys
from optparse import OptionParser
import numpy as np
import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
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def merge_dicts(*dict_args):
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"""Given any number of dicts, shallow copy and merge into a new dict, with precedence going to key value pairs in latter dicts."""
result = {}
for dictionary in dict_args:
result.update(dictionary)
return result
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def gradFFT(geomdim,field):
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"""Calculate gradient of a vector or scalar field by transforming into Fourier space."""
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
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
TWOPIIMG = 2.0j*np.pi
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einsums = {
1:'ijkl,ijkm->ijkm', # scalar, 1 -> 3
3:'ijkl,ijkm->ijklm', # vector, 3 -> 3x3
}
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 # Nyquist freq=0 for even grid (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 # Nyquist freq=0 for even grid (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')
grad_fourier = np.einsum(einsums[n],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 options [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)
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parser.add_option('-p','--pos','--periodiccellcenter',
dest = 'pos',
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type = 'string', metavar = 'string',
help = 'label of coordinates [%default]')
parser.add_option('-l','--label',
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.')
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# --- define possible data types -------------------------------------------------------------------
datatypes = {
1: {'name': 'scalar',
'shape': [1],
},
3: {'name': 'vector',
'shape': [3],
},
}
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# --- 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 = []
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active = []
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)
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for me in options.data:
dim = table.label_dimension(me)
if dim in datatypes:
active.append(merge_dicts({'label':me},datatypes[dim]))
remarks.append('differentiating {} "{}"...'.format(datatypes[dim]['name'],me))
else:
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remarks.append('skipping "{}" of dimension {}...'.format(me,dim) if dim != -1 else \
'"{}" not found...'.format(me) )
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:]))
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for data in active:
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table.labels_append(['{}_gradFFT({})'.format(i+1,data['label'])
for i in range(coordDim*np.prod(np.array(data['shape'])))]) # extend ASCII header with new labels
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray()
grid,size = damask.util.coordGridAndSize(table.data[:,table.label_indexrange(options.pos)])
# ------------------------------------------ process value field -----------------------------------
stack = [table.data]
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for data in active:
# 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(data['label'])].
reshape(grid[::-1].tolist()+data['shape'])))
# ------------------------------------------ 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)