Merge branch 'grid-filters' into MiscImprovements
This commit is contained in:
Martin Diehl
commit
0d1ff72c45
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@ -4,7 +4,7 @@ import os
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import sys
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from optparse import OptionParser
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import re
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import collections
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from collections.abc import Iterable
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import math # noqa
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import scipy # noqa
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@ -18,7 +18,7 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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def listify(x):
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return x if isinstance(x, collections.Iterable) else [x]
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return x if isinstance(x, Iterable) else [x]
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# --------------------------------------------------------------------
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@ -65,9 +65,10 @@ for i in range(len(options.formulas)):
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if filenames == []: filenames = [None]
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for name in filenames:
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try: table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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try:
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table = damask.ASCIItable(name = name, buffered = False)
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except IOError:
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continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header -------------------------------------------
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@ -41,9 +41,9 @@ if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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buffered = False)
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except IOError: continue
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table = damask.ASCIItable(name = name, buffered = False)
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except IOError:
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continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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@ -2,6 +2,7 @@
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import os
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import sys
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from io import StringIO
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from optparse import OptionParser
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import numpy as np
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@ -12,48 +13,6 @@ import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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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."""
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result = {}
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for dictionary in dict_args:
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result.update(dictionary)
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return result
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def curlFFT(geomdim,field):
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"""Calculate curl of a vector or tensor field by transforming into Fourier space."""
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shapeFFT = np.array(np.shape(field))[0:3]
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grid = np.array(np.shape(field)[2::-1])
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N = grid.prod() # field size
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n = np.array(np.shape(field)[3:]).prod() # data size
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field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
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curl_fourier = np.empty(field_fourier.shape,'c16')
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# differentiation in Fourier space
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TWOPIIMG = 2.0j*np.pi
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einsums = {
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3:'slm,ijkl,ijkm->ijks', # vector, 3 -> 3
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9:'slm,ijkl,ijknm->ijksn', # tensor, 3x3 -> 3x3
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}
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k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
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if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
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if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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k_si = np.arange(grid[0]//2+1)/geomdim[2]
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kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
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k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
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e = np.zeros((3, 3, 3))
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e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = 1.0 # Levi-Civita symbols
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e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0
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curl_fourier = np.einsum(einsums[n],e,k_s,field_fourier)*TWOPIIMG
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return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n])
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# --------------------------------------------------------------------
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# MAIN
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@ -61,8 +20,7 @@ def curlFFT(geomdim,field):
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [ASCIItable(s)]', description = """
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Add column(s) containing curl of requested column(s).
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Operates on periodic ordered three-dimensional data sets
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of vector and tensor fields.
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Operates on periodic ordered three-dimensional data sets of vector and tensor fields.
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""", version = scriptID)
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parser.add_option('-p','--pos','--periodiccellcenter',
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@ -70,93 +28,30 @@ parser.add_option('-p','--pos','--periodiccellcenter',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('-l','--label',
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dest = 'data',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of field values')
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parser.set_defaults(pos = 'pos',
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)
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(options,filenames) = parser.parse_args()
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if options.data is None: parser.error('no data column specified.')
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# --- define possible data types -------------------------------------------------------------------
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datatypes = {
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3: {'name': 'vector',
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'shape': [3],
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},
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9: {'name': 'tensor',
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'shape': [3,3],
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},
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}
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# --- loop over input files ------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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if options.labels is None: parser.error('no data column specified.')
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for name in filenames:
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try: table = damask.ASCIItable(name = name,buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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damask.util.report(scriptName,name)
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# --- interpret header ----------------------------------------------------------------------------
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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grid,size,origin = damask.grid_filters.cell_coord0_2_DNA(table.get(options.pos))
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table.head_read()
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remarks = []
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errors = []
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active = []
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coordDim = table.label_dimension(options.pos)
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if coordDim != 3:
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errors.append('coordinates "{}" must be three-dimensional.'.format(options.pos))
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else: coordCol = table.label_index(options.pos)
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for me in options.data:
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dim = table.label_dimension(me)
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if dim in datatypes:
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active.append(merge_dicts({'label':me},datatypes[dim]))
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remarks.append('differentiating {} "{}"...'.format(datatypes[dim]['name'],me))
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else:
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remarks.append('skipping "{}" of dimension {}...'.format(me,dim) if dim != -1 else \
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'"{}" not found...'.format(me) )
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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for data in active:
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table.labels_append(['{}_curlFFT({})'.format(i+1,data['label'])
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for i in range(np.prod(np.array(data['shape'])))]) # extend ASCII header with new labels
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table.head_write()
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# --------------- figure out size and grid ---------------------------------------------------------
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table.data_readArray()
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grid,size = damask.util.coordGridAndSize(table.data[:,table.label_indexrange(options.pos)])
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# ------------------------------------------ process value field -----------------------------------
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stack = [table.data]
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for data in active:
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# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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stack.append(curlFFT(size[::-1],
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table.data[:,table.label_indexrange(data['label'])].
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reshape(grid[::-1].tolist()+data['shape'])))
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# ------------------------------------------ output result -----------------------------------------
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if len(stack) > 1: table.data = np.hstack(tuple(stack))
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table.data_writeArray('%.12g')
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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for label in options.labels:
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field = table.get(label)
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shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
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field = field.reshape(np.append(grid[::-1],shape))
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table.add('curlFFT({})'.format(label),
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damask.grid_filters.curl(size[::-1],field).reshape((-1,np.prod(shape))),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else name)
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@ -2,10 +2,10 @@
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import os
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import sys
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from io import StringIO
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from optparse import OptionParser
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import numpy as np
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import scipy.ndimage
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import damask
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@ -14,79 +14,6 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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#--------------------------------------------------------------------------------------------------
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def cell2node(cellData,grid):
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nodeData = 0.0
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datalen = np.array(cellData.shape[3:]).prod()
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for i in range(datalen):
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node = scipy.ndimage.convolve(cellData.reshape(tuple(grid[::-1])+(datalen,))[...,i],
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np.ones((2,2,2))/8., # 2x2x2 neighborhood of cells
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mode = 'wrap',
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origin = -1, # offset to have cell origin as center
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) # now averaged at cell origins
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node = np.append(node,node[np.newaxis,0,:,:,...],axis=0) # wrap along z
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node = np.append(node,node[:,0,np.newaxis,:,...],axis=1) # wrap along y
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node = np.append(node,node[:,:,0,np.newaxis,...],axis=2) # wrap along x
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nodeData = node[...,np.newaxis] if i==0 else np.concatenate((nodeData,node[...,np.newaxis]),axis=-1)
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return nodeData
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#--------------------------------------------------------------------------------------------------
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def displacementAvgFFT(F,grid,size,nodal=False,transformed=False):
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"""Calculate average cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
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if nodal:
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x, y, z = np.meshgrid(np.linspace(0,size[2],1+grid[2]),
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np.linspace(0,size[1],1+grid[1]),
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np.linspace(0,size[0],1+grid[0]),
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indexing = 'ij')
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else:
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delta = size/grid*0.5
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x, y, z = np.meshgrid(np.linspace(delta[2],size[2]-delta[2],grid[2]),
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np.linspace(delta[1],size[1]-delta[1],grid[1]),
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np.linspace(delta[0],size[0]-delta[0],grid[0]),
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indexing = 'ij')
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origCoords = np.concatenate((z[:,:,:,None],y[:,:,:,None],x[:,:,:,None]),axis = 3)
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F_fourier = F if transformed else np.fft.rfftn(F,axes=(0,1,2)) # transform or use provided data
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Favg = np.real(F_fourier[0,0,0,:,:])/grid.prod() # take zero freq for average
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avgDisplacement = np.einsum('ml,ijkl->ijkm',Favg-np.eye(3),origCoords) # dX = Favg.X
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return avgDisplacement
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#--------------------------------------------------------------------------------------------------
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def displacementFluctFFT(F,grid,size,nodal=False,transformed=False):
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"""Calculate cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
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integrator = 0.5j * size / np.pi
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kk, kj, ki = np.meshgrid(np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2])),
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np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1])),
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np.arange(grid[0]//2+1),
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indexing = 'ij')
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k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
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k_sSquared = np.einsum('...l,...l',k_s,k_s)
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k_sSquared[0,0,0] = 1.0 # ignore global average frequency
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#--------------------------------------------------------------------------------------------------
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# integration in Fourier space
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displacement_fourier = -np.einsum('ijkml,ijkl,l->ijkm',
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F if transformed else np.fft.rfftn(F,axes=(0,1,2)),
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k_s,
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integrator,
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) / k_sSquared[...,np.newaxis]
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#--------------------------------------------------------------------------------------------------
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# backtransformation to real space
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displacement = np.fft.irfftn(displacement_fourier,grid[::-1],axes=(0,1,2))
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return cell2node(displacement,grid) if nodal else displacement
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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@ -100,7 +27,7 @@ Outputs at cell centers or cell nodes (into separate file).
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parser.add_option('-f',
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'--defgrad',
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dest = 'defgrad',
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dest = 'f',
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metavar = 'string',
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help = 'label of deformation gradient [%default]')
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parser.add_option('-p',
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@ -113,108 +40,34 @@ parser.add_option('--nodal',
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action = 'store_true',
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help = 'output nodal (instead of cell-centered) displacements')
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parser.set_defaults(defgrad = 'f',
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pos = 'pos',
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parser.set_defaults(f = 'f',
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pos = 'pos',
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)
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(options,filenames) = parser.parse_args()
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# --- loop over input files -------------------------------------------------------------------------
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|
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if filenames == []: filenames = [None]
|
||||
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for name in filenames:
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outname = (os.path.splitext(name)[0] +
|
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'_nodal' +
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os.path.splitext(name)[1]) if (options.nodal and name) else None
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||||
try: table = damask.ASCIItable(name = name,
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outname = outname,
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buffered = False)
|
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except: continue
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damask.util.report(scriptName,'{}{}'.format(name if name else '',
|
||||
' --> {}'.format(outname) if outname else ''))
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damask.util.report(scriptName,name)
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|
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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remarks = []
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if table.label_dimension(options.defgrad) != 9:
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errors.append('deformation gradient "{}" is not a 3x3 tensor.'.format(options.defgrad))
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||||
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coordDim = table.label_dimension(options.pos)
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if not 3 >= coordDim >= 1:
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errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.pos))
|
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elif coordDim < 3:
|
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remarks.append('appending {} dimension{} to coordinates "{}"...'.format(3-coordDim,
|
||||
's' if coordDim < 2 else '',
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options.pos))
|
||||
|
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if remarks != []: damask.util.croak(remarks)
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||||
if errors != []:
|
||||
damask.util.croak(errors)
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table.close(dismiss=True)
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continue
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# --------------- figure out size and grid ---------------------------------------------------------
|
||||
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table.data_readArray([options.defgrad,options.pos])
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table.data_rewind()
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||||
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||||
if len(table.data.shape) < 2: table.data.shape += (1,) # expand to 2D shape
|
||||
if table.data[:,9:].shape[1] < 3:
|
||||
table.data = np.hstack((table.data,
|
||||
np.zeros((table.data.shape[0],
|
||||
3-table.data[:,9:].shape[1]),dtype='f'))) # fill coords up to 3D with zeros
|
||||
|
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grid,size = damask.util.coordGridAndSize(table.data[:,9:12])
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N = grid.prod()
|
||||
|
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if N != len(table.data): errors.append('data count {} does not match grid {}x{}x{}.'.format(N,*grid))
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ process data ------------------------------------------
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F_fourier = np.fft.rfftn(table.data[:,:9].reshape(grid[2],grid[1],grid[0],3,3),axes=(0,1,2)) # perform transform only once...
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||||
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fluctDisplacement = displacementFluctFFT(F_fourier,grid,size,options.nodal,transformed=True)
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avgDisplacement = displacementAvgFFT (F_fourier,grid,size,options.nodal,transformed=True)
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# ------------------------------------------ assemble header ---------------------------------------
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||||
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if options.nodal:
|
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table.info_clear()
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||||
table.labels_clear()
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||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.labels_append((['{}_pos' .format(i+1) for i in range(3)] if options.nodal else []) +
|
||||
['{}_avg({}).{}' .format(i+1,options.defgrad,options.pos) for i in range(3)] +
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||||
['{}_fluct({}).{}'.format(i+1,options.defgrad,options.pos) for i in range(3)] )
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||||
table.head_write()
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||||
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||||
# ------------------------------------------ output data -------------------------------------------
|
||||
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||||
Zrange = np.linspace(0,size[2],1+grid[2]) if options.nodal else range(grid[2])
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||||
Yrange = np.linspace(0,size[1],1+grid[1]) if options.nodal else range(grid[1])
|
||||
Xrange = np.linspace(0,size[0],1+grid[0]) if options.nodal else range(grid[0])
|
||||
|
||||
for i,z in enumerate(Zrange):
|
||||
for j,y in enumerate(Yrange):
|
||||
for k,x in enumerate(Xrange):
|
||||
if options.nodal: table.data_clear()
|
||||
else: table.data_read()
|
||||
table.data_append([x,y,z] if options.nodal else [])
|
||||
table.data_append(list( avgDisplacement[i,j,k,:]))
|
||||
table.data_append(list(fluctDisplacement[i,j,k,:]))
|
||||
table.data_write()
|
||||
|
||||
# ------------------------------------------ output finalization -----------------------------------
|
||||
|
||||
table.close() # close ASCII tables
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
grid,size,origin = damask.grid_filters.cell_coord0_2_DNA(table.get(options.pos))
|
||||
|
||||
F = table.get(options.f).reshape(np.append(grid[::-1],(3,3)))
|
||||
if options.nodal:
|
||||
table = damask.Table(damask.grid_filters.node_coord0(grid[::-1],size[::-1]).reshape((-1,3)),
|
||||
{'pos':(3,)})
|
||||
table.add('avg({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.node_displacement_avg(size[::-1],F).reshape((-1,3)),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table.add('fluct({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.node_displacement_fluct(size[::-1],F).reshape((-1,3)),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table.to_ASCII(sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt')
|
||||
else:
|
||||
table.add('avg({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.cell_displacement_avg(size[::-1],F).reshape((-1,3)),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table.add('fluct({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.cell_displacement_fluct(size[::-1],F).reshape((-1,3)),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
|
@ -12,53 +13,14 @@ import damask
|
|||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
||||
def merge_dicts(*dict_args):
|
||||
"""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
|
||||
|
||||
def divFFT(geomdim,field):
|
||||
"""Calculate divergence of a vector or tensor 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)
|
||||
div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16')
|
||||
|
||||
# differentiation in Fourier space
|
||||
TWOPIIMG = 2.0j*np.pi
|
||||
einsums = {
|
||||
3:'ijkl,ijkl->ijk', # vector, 3 -> 1
|
||||
9:'ijkm,ijklm->ijkl', # tensor, 3x3 -> 3
|
||||
}
|
||||
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')
|
||||
|
||||
div_fourier = np.einsum(einsums[n],k_s,field_fourier)*TWOPIIMG
|
||||
|
||||
return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n//3])
|
||||
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# MAIN
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [ASCIItable(s)]', description = """
|
||||
Add column(s) containing curl of requested column(s).
|
||||
Operates on periodic ordered three-dimensional data sets
|
||||
of vector and tensor fields.
|
||||
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [ASCIItable(s)]', description = """
|
||||
Add column(s) containing divergence of requested column(s).
|
||||
Operates on periodic ordered three-dimensional data sets of vector and tensor fields.
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-p','--pos','--periodiccellcenter',
|
||||
|
|
@ -66,95 +28,30 @@ parser.add_option('-p','--pos','--periodiccellcenter',
|
|||
type = 'string', metavar = 'string',
|
||||
help = 'label of coordinates [%default]')
|
||||
parser.add_option('-l','--label',
|
||||
dest = 'data',
|
||||
dest = 'labels',
|
||||
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.')
|
||||
|
||||
# --- define possible data types -------------------------------------------------------------------
|
||||
|
||||
datatypes = {
|
||||
3: {'name': 'vector',
|
||||
'shape': [3],
|
||||
},
|
||||
9: {'name': 'tensor',
|
||||
'shape': [3,3],
|
||||
},
|
||||
}
|
||||
|
||||
# --- loop over input files ------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
if options.labels is None: parser.error('no data column specified.')
|
||||
|
||||
for name in filenames:
|
||||
try: table = damask.ASCIItable(name = name,buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# --- interpret header ----------------------------------------------------------------------------
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
grid,size,origin = damask.grid_filters.cell_coord0_2_DNA(table.get(options.pos))
|
||||
|
||||
table.head_read()
|
||||
|
||||
remarks = []
|
||||
errors = []
|
||||
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)
|
||||
|
||||
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:
|
||||
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:]))
|
||||
for data in active:
|
||||
table.labels_append(['divFFT({})'.format(data['label']) if data['shape'] == [3] \
|
||||
else '{}_divFFT({})'.format(i+1,data['label'])
|
||||
for i in range(np.prod(np.array(data['shape']))//3)]) # 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]
|
||||
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(divFFT(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)
|
||||
for label in options.labels:
|
||||
field = table.get(label)
|
||||
shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
|
||||
field = field.reshape(np.append(grid[::-1],shape))
|
||||
table.add('divFFT({})'.format(label),
|
||||
damask.grid_filters.divergence(size[::-1],field).reshape((-1,np.prod(shape)//3)),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
|
@ -12,44 +13,6 @@ import damask
|
|||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
||||
def merge_dicts(*dict_args):
|
||||
"""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
|
||||
|
||||
def gradFFT(geomdim,field):
|
||||
"""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
|
||||
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
|
||||
|
|
@ -57,9 +20,7 @@ def gradFFT(geomdim,field):
|
|||
|
||||
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.
|
||||
|
||||
Operates on periodic ordered three-dimensional data sets of scalar and vector fields.
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-p','--pos','--periodiccellcenter',
|
||||
|
|
@ -67,7 +28,7 @@ parser.add_option('-p','--pos','--periodiccellcenter',
|
|||
type = 'string', metavar = 'string',
|
||||
help = 'label of coordinates [%default]')
|
||||
parser.add_option('-l','--label',
|
||||
dest = 'data',
|
||||
dest = 'labels',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'label(s) of field values')
|
||||
|
||||
|
|
@ -75,85 +36,22 @@ parser.set_defaults(pos = 'pos',
|
|||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
if options.data is None: parser.error('no data column specified.')
|
||||
|
||||
# --- define possible data types -------------------------------------------------------------------
|
||||
|
||||
datatypes = {
|
||||
1: {'name': 'scalar',
|
||||
'shape': [1],
|
||||
},
|
||||
3: {'name': 'vector',
|
||||
'shape': [3],
|
||||
},
|
||||
}
|
||||
|
||||
# --- loop over input files ------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
if options.labels is None: parser.error('no data column specified.')
|
||||
|
||||
for name in filenames:
|
||||
try: table = damask.ASCIItable(name = name,buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# --- interpret header ----------------------------------------------------------------------------
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
grid,size,origin = damask.grid_filters.cell_coord0_2_DNA(table.get(options.pos))
|
||||
|
||||
table.head_read()
|
||||
|
||||
remarks = []
|
||||
errors = []
|
||||
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)
|
||||
|
||||
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:
|
||||
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:]))
|
||||
for data in active:
|
||||
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]
|
||||
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)
|
||||
for label in options.labels:
|
||||
field = table.get(label)
|
||||
shape = (1,) if np.prod(field.shape)//np.prod(grid) == 1 else (3,) # scalar or vector
|
||||
field = field.reshape(np.append(grid[::-1],shape))
|
||||
table.add('gradFFT({})'.format(label),
|
||||
damask.grid_filters.gradient(size[::-1],field).reshape((-1,np.prod(shape)*3)),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -125,9 +125,10 @@ R = damask.Rotation.fromAxisAngle(np.array(options.labrotation),options.degrees,
|
|||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
buffered = False)
|
||||
except Exception: continue
|
||||
try:
|
||||
table = damask.ASCIItable(name = name, buffered = False)
|
||||
except IOError:
|
||||
continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
|
|
|
|||
|
|
@ -78,36 +78,15 @@ for name in filenames:
|
|||
table = damask.ASCIItable(name = name,readonly=True)
|
||||
table.head_read() # read ASCII header info
|
||||
|
||||
# ------------------------------------------ sanity checks ---------------------------------------
|
||||
|
||||
coordDim = table.label_dimension(options.pos)
|
||||
|
||||
errors = []
|
||||
if not 3 >= coordDim >= 2:
|
||||
errors.append('coordinates "{}" need to have two or three dimensions.'.format(options.pos))
|
||||
if not np.all(table.label_dimension(label) == dim):
|
||||
errors.append('input "{}" needs to have dimension {}.'.format(label,dim))
|
||||
if options.phase and table.label_dimension(options.phase) != 1:
|
||||
errors.append('phase column "{}" is not scalar.'.format(options.phase))
|
||||
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
continue
|
||||
|
||||
table.data_readArray([options.pos] \
|
||||
+ (label if isinstance(label, list) else [label]) \
|
||||
+ ([options.phase] if options.phase else []))
|
||||
|
||||
if coordDim == 2:
|
||||
table.data = np.insert(table.data,2,np.zeros(len(table.data)),axis=1) # add zero z coordinate for two-dimensional input
|
||||
if options.phase is None:
|
||||
table.data = np.column_stack((table.data,np.ones(len(table.data)))) # add single phase if no phase column given
|
||||
|
||||
grid,size = damask.util.coordGridAndSize(table.data[:,0:3])
|
||||
coords = [np.unique(table.data[:,i]) for i in range(3)]
|
||||
mincorner = np.array(list(map(min,coords)))
|
||||
origin = mincorner - 0.5*size/grid # shift from cell center to corner
|
||||
|
||||
grid,size,origin = damask.grid_filters.cell_coord0_2_DNA(table.data[:,0:3])
|
||||
|
||||
indices = np.lexsort((table.data[:,0],table.data[:,1],table.data[:,2])) # indices of position when sorting x fast, z slow
|
||||
microstructure = np.empty(grid,dtype = int) # initialize empty microstructure
|
||||
|
|
@ -142,7 +121,6 @@ for name in filenames:
|
|||
config_header += ['<microstructure>']
|
||||
for i,data in enumerate(unique):
|
||||
config_header += ['[Grain{}]'.format(i+1),
|
||||
'crystallite 1',
|
||||
'(constituent)\tphase {}\ttexture {}\tfraction 1.0'.format(int(data[4]),i+1),
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -2,10 +2,8 @@
|
|||
|
||||
import os
|
||||
import sys
|
||||
from optparse import OptionParser
|
||||
from io import StringIO
|
||||
|
||||
import numpy as np
|
||||
from optparse import OptionParser
|
||||
|
||||
import damask
|
||||
|
||||
|
|
@ -24,38 +22,25 @@ Translate geom description into ASCIItable containing position and microstructur
|
|||
""", version = scriptID)
|
||||
|
||||
(options, filenames) = parser.parse_args()
|
||||
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
geom = damask.Geom.from_file(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
geom = damask.Geom.from_file(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
damask.util.croak(geom)
|
||||
|
||||
damask.util.croak(geom)
|
||||
coord0 = damask.grid_filters.cell_coord0(geom.grid,geom.size,geom.origin).reshape((-1,3),order='F')
|
||||
|
||||
# --- generate grid --------------------------------------------------------------------------------
|
||||
comments = geom.comments \
|
||||
+ [scriptID + ' ' + ' '.join(sys.argv[1:]),
|
||||
"grid\ta {}\tb {}\tc {}".format(*geom.grid),
|
||||
"size\tx {}\ty {}\tz {}".format(*geom.size),
|
||||
"origin\tx {}\ty {}\tz {}".format(*geom.origin),
|
||||
"homogenization\t{}".format(geom.homogenization)]
|
||||
|
||||
grid = geom.get_grid()
|
||||
size = geom.get_size()
|
||||
origin = geom.get_origin()
|
||||
table = damask.Table(coord0,{'pos':(3,)},comments)
|
||||
table.add('microstructure',geom.microstructure.reshape((-1,1)))
|
||||
|
||||
x = (0.5 + np.arange(grid[0],dtype=float))/grid[0]*size[0]+origin[0]
|
||||
y = (0.5 + np.arange(grid[1],dtype=float))/grid[1]*size[1]+origin[1]
|
||||
z = (0.5 + np.arange(grid[2],dtype=float))/grid[2]*size[2]+origin[2]
|
||||
|
||||
xx = np.tile( x, grid[1]* grid[2])
|
||||
yy = np.tile(np.repeat(y,grid[0] ),grid[2])
|
||||
zz = np.repeat(z,grid[0]*grid[1])
|
||||
|
||||
# --- create ASCII table --------------------------------------------------------------------------
|
||||
|
||||
table = damask.ASCIItable(outname = os.path.splitext(name)[0]+'.txt' if name else name)
|
||||
table.info_append(geom.get_comments() + [scriptID + '\t' + ' '.join(sys.argv[1:])])
|
||||
table.labels_append(['{}_{}'.format(1+i,'pos') for i in range(3)]+['microstructure'])
|
||||
table.head_write()
|
||||
table.output_flush()
|
||||
table.data = np.squeeze(np.dstack((xx,yy,zz,geom.microstructure.flatten('F'))),axis=0)
|
||||
table.data_writeArray()
|
||||
table.close()
|
||||
table.to_ASCII(sys.stdout if name is None else \
|
||||
os.path.splitext(name)[0]+'.txt')
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: UTF-8 no BOM -*-
|
||||
|
||||
import os,sys
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import damask
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
|
|
@ -191,78 +192,45 @@ parser.add_option('-p', '--port',
|
|||
dest = 'port',
|
||||
type = 'int', metavar = 'int',
|
||||
help = 'Mentat connection port [%default]')
|
||||
parser.add_option('--homogenization',
|
||||
dest = 'homogenization',
|
||||
type = 'int', metavar = 'int',
|
||||
help = 'homogenization index to be used [auto]')
|
||||
|
||||
parser.set_defaults(port = None,
|
||||
homogenization = None,
|
||||
)
|
||||
parser.set_defaults(port = None,
|
||||
)
|
||||
|
||||
(options, filenames) = parser.parse_args()
|
||||
|
||||
if options.port:
|
||||
try:
|
||||
import py_mentat
|
||||
except:
|
||||
parser.error('no valid Mentat release found.')
|
||||
if options.port is not None:
|
||||
try:
|
||||
import py_mentat
|
||||
except ImportError:
|
||||
parser.error('no valid Mentat release found.')
|
||||
|
||||
# --- loop over input files ------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
outname = os.path.splitext(name)[0]+'.proc' if name else name,
|
||||
buffered = False, labeled = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# --- interpret header ----------------------------------------------------------------------------
|
||||
|
||||
table.head_read()
|
||||
info,extra_header = table.head_getGeom()
|
||||
if options.homogenization: info['homogenization'] = options.homogenization
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
damask.util.croak(['grid a b c: %s'%(' x '.join(map(str,info['grid']))),
|
||||
'size x y z: %s'%(' x '.join(map(str,info['size']))),
|
||||
'origin x y z: %s'%(' : '.join(map(str,info['origin']))),
|
||||
'homogenization: %i'%info['homogenization'],
|
||||
'microstructures: %i'%info['microstructures'],
|
||||
])
|
||||
geom = damask.Geom.from_file(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
microstructure = geom.get_microstructure().flatten(order='F')
|
||||
|
||||
errors = []
|
||||
if np.any(info['grid'] < 1): errors.append('invalid grid a b c.')
|
||||
if np.any(info['size'] <= 0.0): errors.append('invalid size x y z.')
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# --- read data ------------------------------------------------------------------------------------
|
||||
|
||||
microstructure = table.microstructure_read(info['grid']).reshape(info['grid'].prod(),order='F') # read microstructure
|
||||
|
||||
cmds = [\
|
||||
init(),
|
||||
mesh(info['grid'],info['size']),
|
||||
material(),
|
||||
geometry(),
|
||||
initial_conditions(info['homogenization'],microstructure),
|
||||
'*identify_sets',
|
||||
'*show_model',
|
||||
'*redraw',
|
||||
'*draw_automatic',
|
||||
]
|
||||
|
||||
outputLocals = {}
|
||||
if options.port:
|
||||
py_mentat.py_connect('',options.port)
|
||||
output(cmds,outputLocals,'Mentat')
|
||||
py_mentat.py_disconnect()
|
||||
else:
|
||||
output(cmds,outputLocals,table.__IO__['out']) # bad hack into internals of table class...
|
||||
|
||||
table.close()
|
||||
cmds = [\
|
||||
init(),
|
||||
mesh(geom.grid,geom.size),
|
||||
material(),
|
||||
geometry(),
|
||||
initial_conditions(geom.homogenization,microstructure),
|
||||
'*identify_sets',
|
||||
'*show_model',
|
||||
'*redraw',
|
||||
'*draw_automatic',
|
||||
]
|
||||
|
||||
outputLocals = {}
|
||||
if options.port:
|
||||
py_mentat.py_connect('',options.port)
|
||||
output(cmds,outputLocals,'Mentat')
|
||||
py_mentat.py_disconnect()
|
||||
else:
|
||||
with sys.stdout if name is None else open(os.path.splitext(name)[0]+'.proc','w') as f:
|
||||
output(cmds,outputLocals,f)
|
||||
|
|
|
|||
|
|
@ -1,9 +1,12 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: UTF-8 no BOM -*-
|
||||
|
||||
import os,sys
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
||||
import damask
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
|
|
@ -29,88 +32,39 @@ parser.add_option('-b',
|
|||
action = 'extend', metavar = '<int LIST>',
|
||||
dest = 'blacklist',
|
||||
help = 'blacklist of grain IDs')
|
||||
parser.add_option('-p',
|
||||
'--pos', '--seedposition',
|
||||
dest = 'pos',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'label of coordinates [%default]')
|
||||
|
||||
parser.set_defaults(whitelist = [],
|
||||
blacklist = [],
|
||||
pos = 'pos',
|
||||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
options.whitelist = list(map(int,options.whitelist))
|
||||
options.blacklist = list(map(int,options.blacklist))
|
||||
|
||||
# --- loop over output files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
options.whitelist = [int(i) for i in options.whitelist]
|
||||
options.blacklist = [int(i) for i in options.blacklist]
|
||||
|
||||
for name in filenames:
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
outname = os.path.splitext(name)[0]+'.seeds' if name else name,
|
||||
buffered = False,
|
||||
labeled = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
geom = damask.Geom.from_file(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
microstructure = geom.get_microstructure().reshape((-1,1),order='F')
|
||||
|
||||
# --- interpret header ----------------------------------------------------------------------------
|
||||
mask = np.logical_and(np.in1d(microstructure,options.whitelist,invert=False) if options.whitelist else \
|
||||
np.full(geom.grid.prod(),True,dtype=bool),
|
||||
np.in1d(microstructure,options.blacklist,invert=True) if options.blacklist else \
|
||||
np.full(geom.grid.prod(),True,dtype=bool))
|
||||
|
||||
seeds = np.concatenate((damask.grid_filters.cell_coord0(geom.grid,geom.size).reshape((-1,3)),
|
||||
microstructure),
|
||||
axis=1)[mask]
|
||||
|
||||
comments = geom.comments \
|
||||
+ [scriptID + ' ' + ' '.join(sys.argv[1:]),
|
||||
"grid\ta {}\tb {}\tc {}".format(*geom.grid),
|
||||
"size\tx {}\ty {}\tz {}".format(*geom.size),
|
||||
"origin\tx {}\ty {}\tz {}".format(*geom.origin),
|
||||
"homogenization\t{}".format(geom.homogenization)]
|
||||
|
||||
table.head_read()
|
||||
info,extra_header = table.head_getGeom()
|
||||
damask.util.report_geom(info)
|
||||
|
||||
errors = []
|
||||
if np.any(info['grid'] < 1): errors.append('invalid grid a b c.')
|
||||
if np.any(info['size'] <= 0.0): errors.append('invalid size x y z.')
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# --- read data ------------------------------------------------------------------------------------
|
||||
|
||||
microstructure = table.microstructure_read(info['grid']) # read (linear) microstructure
|
||||
|
||||
# --- generate grid --------------------------------------------------------------------------------
|
||||
|
||||
x = (0.5 + np.arange(info['grid'][0],dtype=float))/info['grid'][0]*info['size'][0]+info['origin'][0]
|
||||
y = (0.5 + np.arange(info['grid'][1],dtype=float))/info['grid'][1]*info['size'][1]+info['origin'][1]
|
||||
z = (0.5 + np.arange(info['grid'][2],dtype=float))/info['grid'][2]*info['size'][2]+info['origin'][2]
|
||||
|
||||
xx = np.tile( x, info['grid'][1]* info['grid'][2])
|
||||
yy = np.tile(np.repeat(y,info['grid'][0] ),info['grid'][2])
|
||||
zz = np.repeat(z,info['grid'][0]*info['grid'][1])
|
||||
|
||||
mask = np.logical_and(np.in1d(microstructure,options.whitelist,invert=False) if options.whitelist != []
|
||||
else np.full_like(microstructure,True,dtype=bool),
|
||||
np.in1d(microstructure,options.blacklist,invert=True ) if options.blacklist != []
|
||||
else np.full_like(microstructure,True,dtype=bool))
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
table.info_clear()
|
||||
table.info_append(extra_header+[
|
||||
scriptID + ' ' + ' '.join(sys.argv[1:]),
|
||||
"grid\ta {}\tb {}\tc {}".format(*info['grid']),
|
||||
"size\tx {}\ty {}\tz {}".format(*info['size']),
|
||||
"origin\tx {}\ty {}\tz {}".format(*info['origin']),
|
||||
"homogenization\t{}".format(info['homogenization']),
|
||||
"microstructures\t{}".format(info['microstructures']),
|
||||
])
|
||||
table.labels_clear()
|
||||
table.labels_append(['{dim}_{label}'.format(dim = 1+i,label = options.pos) for i in range(3)]+['microstructure'])
|
||||
table.head_write()
|
||||
table.output_flush()
|
||||
|
||||
# --- write seeds information ------------------------------------------------------------
|
||||
|
||||
table.data = np.squeeze(np.dstack((xx,yy,zz,microstructure)))[mask]
|
||||
table.data_writeArray()
|
||||
|
||||
# ------------------------------------------ finalize output ---------------------------------------
|
||||
|
||||
table.close()
|
||||
table = damask.Table(seeds,{'pos':(3,),'microstructure':(1,)},comments)
|
||||
table.to_ASCII(sys.stdout if name is None else \
|
||||
os.path.splitext(name)[0]+'.seeds')
|
||||
|
|
|
|||
|
|
@ -1,11 +1,14 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: UTF-8 no BOM -*-
|
||||
|
||||
import os,math,sys
|
||||
import numpy as np
|
||||
import damask
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
||||
import damask
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
||||
|
|
@ -35,117 +38,58 @@ parser.add_option('-y',
|
|||
action = 'store_true',
|
||||
dest = 'y',
|
||||
help = 'poke 45 deg along y')
|
||||
parser.add_option('-p','--position',
|
||||
dest = 'position',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'column label for coordinates [%default]')
|
||||
|
||||
parser.set_defaults(x = False,
|
||||
y = False,
|
||||
box = [0.0,1.0,0.0,1.0,0.0,1.0],
|
||||
N = 16,
|
||||
position = 'pos',
|
||||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
options.box = np.array(options.box).reshape(3,2)
|
||||
|
||||
# --- loop over output files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
outname = os.path.splitext(name)[-2]+'_poked_{}.seeds'.format(options.N) if name else name,
|
||||
buffered = False, labeled = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# --- interpret header ----------------------------------------------------------------------------
|
||||
|
||||
table.head_read()
|
||||
info,extra_header = table.head_getGeom()
|
||||
|
||||
damask.util.croak(['grid a b c: %s'%(' x '.join(map(str,info['grid']))),
|
||||
'size x y z: %s'%(' x '.join(map(str,info['size']))),
|
||||
'origin x y z: %s'%(' : '.join(map(str,info['origin']))),
|
||||
'homogenization: %i'%info['homogenization'],
|
||||
'microstructures: %i'%info['microstructures'],
|
||||
])
|
||||
|
||||
errors = []
|
||||
if np.any(info['grid'] < 1): errors.append('invalid grid a b c.')
|
||||
if np.any(info['size'] <= 0.0): errors.append('invalid size x y z.')
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# --- read data ------------------------------------------------------------------------------------
|
||||
|
||||
microstructure = table.microstructure_read(info['grid']).reshape(info['grid'],order='F') # read microstructure
|
||||
|
||||
# --- do work ------------------------------------------------------------------------------------
|
||||
|
||||
newInfo = {
|
||||
'microstructures': 0,
|
||||
}
|
||||
offset = (np.amin(options.box, axis=1)*info['grid']/info['size']).astype(int)
|
||||
box = np.amax(options.box, axis=1) - np.amin(options.box, axis=1)
|
||||
|
||||
Nx = int(options.N/math.sqrt(options.N*info['size'][1]*box[1]/info['size'][0]/box[0]))
|
||||
Ny = int(options.N/math.sqrt(options.N*info['size'][0]*box[0]/info['size'][1]/box[1]))
|
||||
Nz = int(box[2]*info['grid'][2])
|
||||
|
||||
damask.util.croak('poking {} x {} x {} in box {} {} {}...'.format(Nx,Ny,Nz,*box))
|
||||
|
||||
seeds = np.zeros((Nx*Ny*Nz,4),'d')
|
||||
grid = np.zeros(3,'i')
|
||||
|
||||
n = 0
|
||||
for i in range(Nx):
|
||||
for j in range(Ny):
|
||||
grid[0] = round((i+0.5)*box[0]*info['grid'][0]/Nx-0.5)+offset[0]
|
||||
grid[1] = round((j+0.5)*box[1]*info['grid'][1]/Ny-0.5)+offset[1]
|
||||
for k in range(Nz):
|
||||
grid[2] = k + offset[2]
|
||||
grid %= info['grid']
|
||||
seeds[n,0:3] = (0.5+grid)/info['grid'] # normalize coordinates to box
|
||||
seeds[n, 3] = microstructure[grid[0],grid[1],grid[2]]
|
||||
if options.x: grid[0] += 1
|
||||
if options.y: grid[1] += 1
|
||||
n += 1
|
||||
|
||||
newInfo['microstructures'] = len(np.unique(seeds[:,3]))
|
||||
|
||||
# --- report ---------------------------------------------------------------------------------------
|
||||
if (newInfo['microstructures'] != info['microstructures']):
|
||||
damask.util.croak('--> microstructures: %i'%newInfo['microstructures'])
|
||||
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
table.info_clear()
|
||||
table.info_append(extra_header+[
|
||||
scriptID + ' ' + ' '.join(sys.argv[1:]),
|
||||
"poking\ta {}\tb {}\tc {}".format(Nx,Ny,Nz),
|
||||
"grid\ta {}\tb {}\tc {}".format(*info['grid']),
|
||||
"size\tx {}\ty {}\tz {}".format(*info['size']),
|
||||
"origin\tx {}\ty {}\tz {}".format(*info['origin']),
|
||||
"homogenization\t{}".format(info['homogenization']),
|
||||
"microstructures\t{}".format(newInfo['microstructures']),
|
||||
])
|
||||
table.labels_clear()
|
||||
table.labels_append(['{dim}_{label}'.format(dim = 1+i,label = options.position) for i in range(3)]+['microstructure'])
|
||||
table.head_write()
|
||||
table.output_flush()
|
||||
|
||||
# --- write seeds information ------------------------------------------------------------
|
||||
|
||||
table.data = seeds
|
||||
table.data_writeArray()
|
||||
damask.util.report(scriptName,name)
|
||||
geom = damask.Geom.from_file(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
|
||||
# --- output finalization --------------------------------------------------------------------------
|
||||
|
||||
table.close() # close ASCII table
|
||||
offset =(np.amin(options.box, axis=1)*geom.grid/geom.size).astype(int)
|
||||
box = np.amax(options.box, axis=1) \
|
||||
- np.amin(options.box, axis=1)
|
||||
|
||||
Nx = int(options.N/np.sqrt(options.N*geom.size[1]*box[1]/geom.size[0]/box[0]))
|
||||
Ny = int(options.N/np.sqrt(options.N*geom.size[0]*box[0]/geom.size[1]/box[1]))
|
||||
Nz = int(box[2]*geom.grid[2])
|
||||
|
||||
damask.util.croak('poking {} x {} x {} in box {} {} {}...'.format(Nx,Ny,Nz,*box))
|
||||
|
||||
seeds = np.zeros((Nx*Ny*Nz,4),'d')
|
||||
g = np.zeros(3,'i')
|
||||
|
||||
n = 0
|
||||
for i in range(Nx):
|
||||
for j in range(Ny):
|
||||
g[0] = round((i+0.5)*box[0]*geom.grid[0]/Nx-0.5)+offset[0]
|
||||
g[1] = round((j+0.5)*box[1]*geom.grid[1]/Ny-0.5)+offset[1]
|
||||
for k in range(Nz):
|
||||
g[2] = k + offset[2]
|
||||
g %= geom.grid
|
||||
seeds[n,0:3] = (g+0.5)/geom.grid # normalize coordinates to box
|
||||
seeds[n, 3] = geom.microstructure[g[0],g[1],g[2]]
|
||||
if options.x: g[0] += 1
|
||||
if options.y: g[1] += 1
|
||||
n += 1
|
||||
|
||||
|
||||
comments = geom.comments \
|
||||
+ [scriptID + ' ' + ' '.join(sys.argv[1:]),
|
||||
"poking\ta {}\tb {}\tc {}".format(Nx,Ny,Nz),
|
||||
"grid\ta {}\tb {}\tc {}".format(*geom.grid),
|
||||
"size\tx {}\ty {}\tz {}".format(*geom.size),
|
||||
"origin\tx {}\ty {}\tz {}".format(*geom.origin),
|
||||
"homogenization\t{}".format(geom.homogenization)]
|
||||
|
||||
table = damask.Table(seeds,{'pos':(3,),'microstructure':(1,)},comments)
|
||||
table.to_ASCII(sys.stdout if name is None else \
|
||||
os.path.splitext(name)[0]+'_poked_{}.seeds'.format(options.N))
|
||||
|
|
|
|||
|
|
@ -23,4 +23,5 @@ from .util import extendableOption # noqa
|
|||
|
||||
# functions in modules
|
||||
from . import mechanics # noqa
|
||||
from . import grid_filters # noqa
|
||||
|
||||
|
|
|
|||
|
|
@ -205,6 +205,9 @@ class Geom():
|
|||
else:
|
||||
self.homogenization = homogenization
|
||||
|
||||
@property
|
||||
def grid(self):
|
||||
return self.get_grid()
|
||||
|
||||
def get_microstructure(self):
|
||||
"""Return the microstructure representation."""
|
||||
|
|
|
|||
|
|
@ -0,0 +1,299 @@
|
|||
from scipy import spatial
|
||||
import numpy as np
|
||||
|
||||
def __ks(size,grid,first_order=False):
|
||||
"""
|
||||
Get wave numbers operator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
|
||||
"""
|
||||
k_sk = np.where(np.arange(grid[0])>grid[0]//2,np.arange(grid[0])-grid[0],np.arange(grid[0]))/size[0]
|
||||
if grid[0]%2 == 0 and first_order: k_sk[grid[0]//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]))/size[1]
|
||||
if grid[1]%2 == 0 and first_order: k_sj[grid[1]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
|
||||
|
||||
k_si = np.arange(grid[2]//2+1)/size[2]
|
||||
|
||||
kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
|
||||
return np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
|
||||
|
||||
|
||||
def curl(size,field):
|
||||
"""
|
||||
Calculate curl of a vector or tensor field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
|
||||
"""
|
||||
n = np.prod(field.shape[3:])
|
||||
k_s = __ks(size,field.shape[:3],True)
|
||||
|
||||
e = np.zeros((3, 3, 3))
|
||||
e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = +1.0 # Levi-Civita symbol
|
||||
e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0
|
||||
|
||||
field_fourier = np.fft.rfftn(field,axes=(0,1,2))
|
||||
curl = (np.einsum('slm,ijkl,ijkm ->ijks', e,k_s,field_fourier)*2.0j*np.pi if n == 3 else # vector, 3 -> 3
|
||||
np.einsum('slm,ijkl,ijknm->ijksn',e,k_s,field_fourier)*2.0j*np.pi) # tensor, 3x3 -> 3x3
|
||||
|
||||
return np.fft.irfftn(curl,axes=(0,1,2),s=field.shape[:3])
|
||||
|
||||
|
||||
def divergence(size,field):
|
||||
"""
|
||||
Calculate divergence of a vector or tensor field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
|
||||
"""
|
||||
n = np.prod(field.shape[3:])
|
||||
k_s = __ks(size,field.shape[:3],True)
|
||||
|
||||
field_fourier = np.fft.rfftn(field,axes=(0,1,2))
|
||||
divergence = (np.einsum('ijkl,ijkl ->ijk', k_s,field_fourier)*2.0j*np.pi if n == 3 else # vector, 3 -> 1
|
||||
np.einsum('ijkm,ijklm->ijkl',k_s,field_fourier)*2.0j*np.pi) # tensor, 3x3 -> 3
|
||||
|
||||
return np.fft.irfftn(divergence,axes=(0,1,2),s=field.shape[:3])
|
||||
|
||||
|
||||
def gradient(size,field):
|
||||
"""
|
||||
Calculate gradient of a vector or scalar field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
|
||||
"""
|
||||
n = np.prod(field.shape[3:])
|
||||
k_s = __ks(size,field.shape[:3],True)
|
||||
|
||||
field_fourier = np.fft.rfftn(field,axes=(0,1,2))
|
||||
gradient = (np.einsum('ijkl,ijkm->ijkm', field_fourier,k_s)*2.0j*np.pi if n == 1 else # scalar, 1 -> 3
|
||||
np.einsum('ijkl,ijkm->ijklm',field_fourier,k_s)*2.0j*np.pi) # vector, 3 -> 3x3
|
||||
|
||||
return np.fft.irfftn(gradient,axes=(0,1,2),s=field.shape[:3])
|
||||
|
||||
|
||||
def cell_coord0(grid,size,origin=np.zeros(3)):
|
||||
"""
|
||||
Cell center positions (undeformed).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
grid : numpy.ndarray
|
||||
number of grid points.
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
|
||||
"""
|
||||
start = origin + size/grid*.5
|
||||
end = origin - size/grid*.5 + size
|
||||
x, y, z = np.meshgrid(np.linspace(start[2],end[2],grid[2]),
|
||||
np.linspace(start[1],end[1],grid[1]),
|
||||
np.linspace(start[0],end[0],grid[0]),
|
||||
indexing = 'ij')
|
||||
|
||||
return np.concatenate((z[:,:,:,None],y[:,:,:,None],x[:,:,:,None]),axis = 3)
|
||||
|
||||
def cell_displacement_fluct(size,F):
|
||||
"""
|
||||
Cell center displacement field from fluctuation part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
deformation gradient field.
|
||||
|
||||
"""
|
||||
integrator = 0.5j*size/np.pi
|
||||
|
||||
k_s = __ks(size,F.shape[:3],False)
|
||||
k_s_squared = np.einsum('...l,...l',k_s,k_s)
|
||||
k_s_squared[0,0,0] = 1.0
|
||||
|
||||
displacement = -np.einsum('ijkml,ijkl,l->ijkm',
|
||||
np.fft.rfftn(F,axes=(0,1,2)),
|
||||
k_s,
|
||||
integrator,
|
||||
) / k_s_squared[...,np.newaxis]
|
||||
|
||||
return np.fft.irfftn(displacement,axes=(0,1,2),s=F.shape[:3])
|
||||
|
||||
def cell_displacement_avg(size,F):
|
||||
"""
|
||||
Cell center displacement field from average part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
deformation gradient field.
|
||||
|
||||
"""
|
||||
F_avg = np.average(F,axis=(0,1,2))
|
||||
return np.einsum('ml,ijkl->ijkm',F_avg-np.eye(3),cell_coord0(F.shape[:3],size))
|
||||
|
||||
def cell_coord0_2_DNA(coord0,ordered=True):
|
||||
"""
|
||||
Return grid 'DNA', i.e. grid, size, and origin from array of cell positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
coord0 : numpy.ndarray
|
||||
array of undeformed cell coordinates.
|
||||
ordered : bool, optional
|
||||
expect coord0 data to be ordered (x fast, z slow).
|
||||
|
||||
"""
|
||||
coords = [np.unique(coord0[:,i]) for i in range(3)]
|
||||
mincorner = np.array(list(map(min,coords)))
|
||||
maxcorner = np.array(list(map(max,coords)))
|
||||
grid = np.array(list(map(len,coords)),'i')
|
||||
size = grid/np.maximum(grid-1,1) * (maxcorner-mincorner)
|
||||
delta = size/grid
|
||||
origin = mincorner - delta*.5
|
||||
|
||||
if grid.prod() != len(coord0):
|
||||
raise ValueError('Data count {} does not match grid {}.'.format(len(coord0),grid))
|
||||
|
||||
start = origin + delta*.5
|
||||
end = origin - delta*.5 + size
|
||||
|
||||
if not np.allclose(coords[0],np.linspace(start[0],end[0],grid[0])) and \
|
||||
np.allclose(coords[1],np.linspace(start[1],end[1],grid[1])) and \
|
||||
np.allclose(coords[2],np.linspace(start[2],end[2],grid[2])):
|
||||
raise ValueError('Regular grid spacing violated.')
|
||||
|
||||
if ordered and not np.allclose(coord0.reshape(tuple(grid[::-1])+(3,)),cell_coord0(grid,size,origin)):
|
||||
raise ValueError('Input data is not a regular grid.')
|
||||
|
||||
return (grid,size,origin)
|
||||
|
||||
|
||||
def node_coord0(grid,size,origin=np.zeros(3)):
|
||||
"""
|
||||
Nodal positions (undeformed).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
grid : numpy.ndarray
|
||||
number of grid points.
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
|
||||
"""
|
||||
x, y, z = np.meshgrid(np.linspace(origin[2],size[2]+origin[2],1+grid[2]),
|
||||
np.linspace(origin[1],size[1]+origin[1],1+grid[1]),
|
||||
np.linspace(origin[0],size[0]+origin[0],1+grid[0]),
|
||||
indexing = 'ij')
|
||||
|
||||
return np.concatenate((z[:,:,:,None],y[:,:,:,None],x[:,:,:,None]),axis = 3)
|
||||
|
||||
def node_displacement_fluct(size,F):
|
||||
"""
|
||||
Nodal displacement field from fluctuation part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
deformation gradient field.
|
||||
|
||||
"""
|
||||
return cell_2_node(cell_displacement_fluct(size,F))
|
||||
|
||||
def node_displacement_avg(size,F):
|
||||
"""
|
||||
Nodal displacement field from average part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray
|
||||
physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
deformation gradient field.
|
||||
|
||||
"""
|
||||
F_avg = np.average(F,axis=(0,1,2))
|
||||
return np.einsum('ml,ijkl->ijkm',F_avg-np.eye(3),node_coord0(F.shape[:3],size))
|
||||
|
||||
|
||||
def cell_2_node(cell_data):
|
||||
"""Interpolate cell data to nodal data."""
|
||||
n = ( cell_data + np.roll(cell_data,1,(0,1,2))
|
||||
+ np.roll(cell_data,1,(0,)) + np.roll(cell_data,1,(1,)) + np.roll(cell_data,1,(2,))
|
||||
+ np.roll(cell_data,1,(0,1)) + np.roll(cell_data,1,(1,2)) + np.roll(cell_data,1,(2,0)))*0.125
|
||||
|
||||
return np.pad(n,((0,1),(0,1),(0,1))+((0,0),)*len(cell_data.shape[3:]),mode='wrap')
|
||||
|
||||
def node_2_cell(node_data):
|
||||
"""Interpolate nodal data to cell data."""
|
||||
c = ( node_data + np.roll(node_data,1,(0,1,2))
|
||||
+ np.roll(node_data,1,(0,)) + np.roll(node_data,1,(1,)) + np.roll(node_data,1,(2,))
|
||||
+ np.roll(node_data,1,(0,1)) + np.roll(node_data,1,(1,2)) + np.roll(node_data,1,(2,0)))*0.125
|
||||
|
||||
return c[:-1,:-1,:-1]
|
||||
|
||||
def node_coord0_2_DNA(coord0,ordered=False):
|
||||
"""
|
||||
Return grid 'DNA', i.e. grid, size, and origin from array of nodal positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
coord0 : numpy.ndarray
|
||||
array of undeformed nodal coordinates
|
||||
ordered : bool, optional
|
||||
expect coord0 data to be ordered (x fast, z slow).
|
||||
|
||||
"""
|
||||
coords = [np.unique(coord0[:,i]) for i in range(3)]
|
||||
mincorner = np.array(list(map(min,coords)))
|
||||
maxcorner = np.array(list(map(max,coords)))
|
||||
grid = np.array(list(map(len,coords)),'i') - 1
|
||||
size = maxcorner-mincorner
|
||||
origin = mincorner
|
||||
|
||||
if (grid+1).prod() != len(coord0):
|
||||
raise ValueError('Data count {} does not match grid {}.'.format(len(coord0),grid))
|
||||
|
||||
if not np.allclose(coords[0],np.linspace(mincorner[0],maxcorner[0],grid[0]+1)) and \
|
||||
np.allclose(coords[1],np.linspace(mincorner[1],maxcorner[1],grid[1]+1)) and \
|
||||
np.allclose(coords[2],np.linspace(mincorner[2],maxcorner[2],grid[2]+1)):
|
||||
raise ValueError('Regular grid spacing violated.')
|
||||
|
||||
if ordered and not np.allclose(coord0.reshape(tuple((grid+1)[::-1])+(3,)),node_coord0(grid,size,origin)):
|
||||
raise ValueError('Input data is not a regular grid.')
|
||||
|
||||
return (grid,size,origin)
|
||||
|
||||
|
||||
def regrid(size,F,new_grid):
|
||||
"""tbd."""
|
||||
c = cell_coord0(F.shape[:3][::-1],size) \
|
||||
+ cell_displacement_avg(size,F) \
|
||||
+ cell_displacement_fluct(size,F)
|
||||
|
||||
outer = np.dot(np.average(F,axis=(0,1,2)),size)
|
||||
for d in range(3):
|
||||
c[np.where(c[:,:,:,d]<0)] += outer[d]
|
||||
c[np.where(c[:,:,:,d]>outer[d])] -= outer[d]
|
||||
|
||||
tree = spatial.cKDTree(c.reshape((-1,3)),boxsize=outer)
|
||||
return tree.query(cell_coord0(new_grid,outer))[1]
|
||||
|
|
@ -97,7 +97,6 @@ class Table():
|
|||
|
||||
@property
|
||||
def labels(self):
|
||||
"""Return the labels of all columns."""
|
||||
return list(self.shapes.keys())
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,54 @@
|
|||
import pytest
|
||||
import numpy as np
|
||||
|
||||
from damask import grid_filters
|
||||
|
||||
class TestGridFilters:
|
||||
|
||||
def test_cell_coord0(self):
|
||||
size = np.random.random(3)
|
||||
grid = np.random.randint(8,32,(3))
|
||||
coord = grid_filters.cell_coord0(grid,size)
|
||||
assert np.allclose(coord[0,0,0],size/grid*.5) and coord.shape == tuple(grid[::-1]) + (3,)
|
||||
|
||||
def test_node_coord0(self):
|
||||
size = np.random.random(3)
|
||||
grid = np.random.randint(8,32,(3))
|
||||
coord = grid_filters.node_coord0(grid,size)
|
||||
assert np.allclose(coord[-1,-1,-1],size) and coord.shape == tuple(grid[::-1]+1) + (3,)
|
||||
|
||||
def test_coord0(self):
|
||||
size = np.random.random(3)
|
||||
grid = np.random.randint(8,32,(3))
|
||||
c = grid_filters.cell_coord0(grid+1,size+size/grid)
|
||||
n = grid_filters.node_coord0(grid,size) + size/grid*.5
|
||||
assert np.allclose(c,n)
|
||||
|
||||
@pytest.mark.parametrize('mode',[('cell'),('node')])
|
||||
def test_grid_DNA(self,mode):
|
||||
"""Ensure that xx_coord0_2_DNA is the inverse of xx_coord0."""
|
||||
grid = np.random.randint(8,32,(3))
|
||||
size = np.random.random(3)
|
||||
origin = np.random.random(3)
|
||||
|
||||
coord0 = eval('grid_filters.{}_coord0(grid,size,origin)'.format(mode)) # noqa
|
||||
_grid,_size,_origin = eval('grid_filters.{}_coord0_2_DNA(coord0.reshape((-1,3)))'.format(mode))
|
||||
assert np.allclose(grid,_grid) and np.allclose(size,_size) and np.allclose(origin,_origin)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('mode',[('cell'),('node')])
|
||||
def test_displacement_avg_vanishes(self,mode):
|
||||
"""Ensure that random fluctuations in F do not result in average displacement."""
|
||||
size = np.random.random(3) # noqa
|
||||
grid = np.random.randint(8,32,(3))
|
||||
F = np.random.random(tuple(grid)+(3,3))
|
||||
F += np.eye(3) - np.average(F,axis=(0,1,2))
|
||||
assert np.allclose(eval('grid_filters.{}_displacement_avg(size,F)'.format(mode)),0.0)
|
||||
|
||||
@pytest.mark.parametrize('mode',[('cell'),('node')])
|
||||
def test_displacement_fluct_vanishes(self,mode):
|
||||
"""Ensure that constant F does not result in fluctuating displacement."""
|
||||
size = np.random.random(3) # noqa
|
||||
grid = np.random.randint(8,32,(3))
|
||||
F = np.broadcast_to(np.random.random((3,3)), tuple(grid)+(3,3)) # noqa
|
||||
assert np.allclose(eval('grid_filters.{}_displacement_fluct(size,F)'.format(mode)),0.0)
|
||||
Loading…
Reference in New Issue