243 lines
10 KiB
Python
Executable File
243 lines
10 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,itertools
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import numpy as np
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from scipy import ndimage
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from optparse import OptionParser
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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 periodic_3Dpad(array, rimdim=(1,1,1)):
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rimdim = np.array(rimdim,'i')
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size = np.array(array.shape,'i')
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padded = np.empty(size+2*rimdim,array.dtype)
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padded[rimdim[0]:rimdim[0]+size[0],
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rimdim[1]:rimdim[1]+size[1],
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rimdim[2]:rimdim[2]+size[2]] = array
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p = np.zeros(3,'i')
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for side in range(3):
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for p[(side+2)%3] in range(padded.shape[(side+2)%3]):
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for p[(side+1)%3] in range(padded.shape[(side+1)%3]):
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for p[side%3] in range(rimdim[side%3]):
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spot = (p-rimdim)%size
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padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
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for p[side%3] in range(rimdim[side%3]+size[side%3],size[side%3]+2*rimdim[side%3]):
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spot = (p-rimdim)%size
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padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
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return padded
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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features = [
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{'aliens': 1, 'names': ['boundary','biplane'],},
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{'aliens': 2, 'names': ['tripleline',],},
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{'aliens': 3, 'names': ['quadruplepoint',],}
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]
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neighborhoods = {
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'neumann':np.array([
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[-1, 0, 0],
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[ 1, 0, 0],
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[ 0,-1, 0],
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[ 0, 1, 0],
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[ 0, 0,-1],
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[ 0, 0, 1],
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]),
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'moore':np.array([
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[-1,-1,-1],
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[ 0,-1,-1],
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[ 1,-1,-1],
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[-1, 0,-1],
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[ 0, 0,-1],
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[ 1, 0,-1],
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[-1, 1,-1],
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[ 0, 1,-1],
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[ 1, 1,-1],
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#
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[-1,-1, 0],
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[ 0,-1, 0],
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[ 1,-1, 0],
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[-1, 0, 0],
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#
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[ 1, 0, 0],
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[-1, 1, 0],
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[ 0, 1, 0],
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[ 1, 1, 0],
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#
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[-1,-1, 1],
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[ 0,-1, 1],
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[ 1,-1, 1],
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[-1, 0, 1],
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[ 0, 0, 1],
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[ 1, 0, 1],
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[-1, 1, 1],
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[ 0, 1, 1],
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[ 1, 1, 1],
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])
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}
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Add column(s) containing Euclidean distance to grain structural features: boundaries, triple lines, and quadruple points.
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""", version = scriptID)
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parser.add_option('-p',
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'--pos', '--position',
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dest = 'pos', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('-i',
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'--id', '--identifier',
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dest = 'id', metavar = 'string',
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help='label of grain identifier [%default]')
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parser.add_option('-t',
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'--type',
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dest = 'type', action = 'extend', metavar = '<string LIST>',
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help = 'feature type {{{}}} '.format(', '.join(map(lambda x:'/'.join(x['names']),features))) )
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parser.add_option('-n',
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'--neighborhood',
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dest = 'neighborhood', choices = neighborhoods.keys(), metavar = 'string',
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help = 'neighborhood type [neumann] {{{}}}'.format(', '.join(neighborhoods.keys())))
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parser.add_option('-s',
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'--scale',
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dest = 'scale', type = 'float', metavar = 'float',
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help = 'voxel size [%default]')
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parser.set_defaults(pos = 'pos',
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id = 'texture',
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neighborhood = 'neumann',
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scale = 1.0,
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)
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(options,filenames) = parser.parse_args()
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if options.type is None:
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parser.error('no feature type selected.')
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if not set(options.type).issubset(set(list(itertools.chain(*map(lambda x: x['names'],features))))):
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parser.error('type must be chosen from (%s).'%(', '.join(map(lambda x:'|'.join(x['names']),features))) )
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if 'biplane' in options.type and 'boundary' in options.type:
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parser.error('only one from aliases "biplane" and "boundary" possible.')
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feature_list = []
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for i,feature in enumerate(features):
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for name in feature['names']:
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for myType in options.type:
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if name.startswith(myType):
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feature_list.append(i) # remember valid features
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break
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# --- loop over input files -------------------------------------------------------------------------
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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, buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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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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column = {}
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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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else: coordCol = table.label_index(options.pos)
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if table.label_dimension(options.id) != 1: errors.append('grain identifier {} not found.'.format(options.id))
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else: idCol = table.label_index(options.id)
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if remarks != []:
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damask.util.croak(remarks)
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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 feature in feature_list:
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table.labels_append('ED_{}({})'.format(features[feature]['names'][0],options.id)) # 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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coords = [np.unique(table.data[:,coordCol+i]) for i in range(coordDim)]
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mincorner = np.array(map(min,coords))
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maxcorner = np.array(map(max,coords))
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grid = np.array(map(len,coords)+[1]*(3-len(coords)),'i')
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N = grid.prod()
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if N != len(table.data): errors.append('data count {} does not match grid {}.'.format(N,'x'.join(map(str,grid))))
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else: remarks.append('grid: {}x{}x{}'.format(*grid))
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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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# ------------------------------------------ process value field -----------------------------------
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stack = [table.data]
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neighborhood = neighborhoods[options.neighborhood]
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diffToNeighbor = np.empty(list(grid+2)+[len(neighborhood)],'i')
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microstructure = periodic_3Dpad(table.data[:,idCol].astype('i').reshape(grid,order='F'))
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for i,p in enumerate(neighborhood):
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stencil = np.zeros((3,3,3),'i')
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stencil[1,1,1] = -1
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stencil[p[0]+1,
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p[1]+1,
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p[2]+1] = 1
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diffToNeighbor[:,:,:,i] = ndimage.convolve(microstructure,stencil) # compare ID at each point...
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# ...to every one in the specified neighborhood
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# for same IDs at both locations ==> 0
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diffToNeighbor = np.sort(diffToNeighbor) # sort diff such that number of changes in diff (steps)...
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# ...reflects number of unique neighbors
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uniques = np.where(diffToNeighbor[1:-1,1:-1,1:-1,0] != 0, 1,0) # initialize unique value counter (exclude myself [= 0])
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for i in range(1,len(neighborhood)): # check remaining points in neighborhood
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uniques += np.where(np.logical_and(
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diffToNeighbor[1:-1,1:-1,1:-1,i] != 0, # not myself?
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diffToNeighbor[1:-1,1:-1,1:-1,i] != diffToNeighbor[1:-1,1:-1,1:-1,i-1],
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), # flip of ID difference detected?
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1,0) # count that flip
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distance = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d')
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for i,feature_id in enumerate(feature_list):
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distance[i,:,:,:] = np.where(uniques >= features[feature_id]['aliens'],0.0,1.0) # seed with 0.0 when enough unique neighbor IDs are present
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distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3
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distance = distance.reshape([len(feature_list),grid.prod(),1],order='F')
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for i in range(len(feature_list)):
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stack.append(distance[i,:])
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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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