From db7c4bba45d253e867ea6ee4468ef0c3d726e600 Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Sun, 24 Apr 2016 14:18:29 -0500 Subject: [PATCH] same functionality can be accomplished with existing scripts. addEuclideanDistance + geom_fromTable (reLabel) + specify grid and size --- processing/pre/geom_fromEuclideanDistance.py | 219 ------------------- processing/pre/seeds_fromTable.py | 138 ------------ 2 files changed, 357 deletions(-) delete mode 100755 processing/pre/geom_fromEuclideanDistance.py delete mode 100755 processing/pre/seeds_fromTable.py diff --git a/processing/pre/geom_fromEuclideanDistance.py b/processing/pre/geom_fromEuclideanDistance.py deleted file mode 100755 index a932583c2..000000000 --- a/processing/pre/geom_fromEuclideanDistance.py +++ /dev/null @@ -1,219 +0,0 @@ -#!/usr/bin/env python -# -*- coding: UTF-8 no BOM -*- - -import os,sys,math,itertools -import numpy as np -from scipy import ndimage -from optparse import OptionParser -import damask - -scriptName = os.path.splitext(os.path.basename(__file__))[0] -scriptID = ' '.join([scriptName,damask.version]) - -def periodic_3Dpad(array, rimdim=(1,1,1)): - - rimdim = np.array(rimdim,'i') - size = np.array(array.shape,'i') - padded = np.empty(size+2*rimdim,array.dtype) - padded[rimdim[0]:rimdim[0]+size[0], - rimdim[1]:rimdim[1]+size[1], - rimdim[2]:rimdim[2]+size[2]] = array - - p = np.zeros(3,'i') - for side in xrange(3): - for p[(side+2)%3] in xrange(padded.shape[(side+2)%3]): - for p[(side+1)%3] in xrange(padded.shape[(side+1)%3]): - for p[side%3] in xrange(rimdim[side%3]): - spot = (p-rimdim)%size - padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]] - for p[side%3] in xrange(rimdim[side%3]+size[side%3],size[side%3]+2*rimdim[side%3]): - spot = (p-rimdim)%size - padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]] - return padded - -#-------------------------------------------------------------------------------------------------- -# MAIN -#-------------------------------------------------------------------------------------------------- - -features = [ - {'aliens': 1, 'alias': ['boundary','biplane'],}, - {'aliens': 2, 'alias': ['tripleline',],}, - {'aliens': 3, 'alias': ['quadruplepoint',],} - ] - -neighborhoods = { - 'neumann':np.array([ - [-1, 0, 0], - [ 1, 0, 0], - [ 0,-1, 0], - [ 0, 1, 0], - [ 0, 0,-1], - [ 0, 0, 1], - ]), - 'moore':np.array([ - [-1,-1,-1], - [ 0,-1,-1], - [ 1,-1,-1], - [-1, 0,-1], - [ 0, 0,-1], - [ 1, 0,-1], - [-1, 1,-1], - [ 0, 1,-1], - [ 1, 1,-1], -# - [-1,-1, 0], - [ 0,-1, 0], - [ 1,-1, 0], - [-1, 0, 0], -# - [ 1, 0, 0], - [-1, 1, 0], - [ 0, 1, 0], - [ 1, 1, 0], -# - [-1,-1, 1], - [ 0,-1, 1], - [ 1,-1, 1], - [-1, 0, 1], - [ 0, 0, 1], - [ 1, 0, 1], - [-1, 1, 1], - [ 0, 1, 1], - [ 1, 1, 1], - ]) - } - -parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ -Produce geom files containing Euclidean distance to grain structural features: -boundaries, triple lines, and quadruple points. - -""", version = scriptID) - -parser.add_option('-t','--type', - dest = 'type', - action = 'extend', metavar = '', - help = 'feature type {%s} '%(', '.join(map(lambda x:'|'.join(x['alias']),features))) ) -parser.add_option('-n','--neighborhood', - dest = 'neighborhood', - choices = neighborhoods.keys(), metavar = 'string', - help = 'type of neighborhood {%s} [neumann]'%(', '.join(neighborhoods.keys()))) -parser.add_option('-s', '--scale', - dest = 'scale', - type = 'float', metavar = 'float', - help = 'voxel size [%default]') - -parser.set_defaults(type = [], - neighborhood = 'neumann', - scale = 1.0, - ) - -(options,filenames) = parser.parse_args() - -if len(options.type) == 0 or \ - not set(options.type).issubset(set(list(itertools.chain(*map(lambda x: x['alias'],features))))): - parser.error('sleect feature type from (%s).'%(', '.join(map(lambda x:'|'.join(x['alias']),features))) ) -if 'biplane' in options.type and 'boundary' in options.type: - parser.error("only one alias out 'biplane' and 'boundary' required") - -feature_list = [] -for i,feature in enumerate(features): - for name in feature['alias']: - for myType in options.type: - if name.startswith(myType): - feature_list.append(i) # remember selected features - break - -# --- loop over input files ------------------------------------------------------------------------- - -if filenames == []: filenames = [None] - -for name in filenames: - try: - table = damask.ASCIItable(name = name, - buffered = False, labeled = False, readonly = True) - 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 - - table.close() - - neighborhood = neighborhoods[options.neighborhood] - convoluted = np.empty([len(neighborhood)]+list(info['grid']+2),'i') - structure = periodic_3Dpad(microstructure) - - for i,p in enumerate(neighborhood): - stencil = np.zeros((3,3,3),'i') - stencil[1,1,1] = -1 - stencil[p[0]+1, - p[1]+1, - p[2]+1] = 1 - convoluted[i,:,:,:] = ndimage.convolve(structure,stencil) - - convoluted = np.sort(convoluted,axis = 0) - uniques = np.where(convoluted[0,1:-1,1:-1,1:-1] != 0, 1,0) # initialize unique value counter (exclude myself [= 0]) - - for i in xrange(1,len(neighborhood)): # check remaining points in neighborhood - uniques += np.where(np.logical_and( - convoluted[i,1:-1,1:-1,1:-1] != convoluted[i-1,1:-1,1:-1,1:-1], # flip of ID difference detected? - convoluted[i,1:-1,1:-1,1:-1] != 0), # not myself? - 1,0) # count flip - - for feature in feature_list: - try: - table = damask.ASCIItable(outname = features[feature]['alias'][0]+'_'+name if name else name, - buffered = False, labeled = False) - except: continue - - damask.util.croak(features[feature]['alias'][0]) - - distance = np.where(uniques >= features[feature]['aliens'],0.0,1.0) # seed with 0.0 when enough unique neighbor IDs are present - distance = ndimage.morphology.distance_transform_edt(distance)*[options.scale]*3 - - info['microstructures'] = int(math.ceil(distance.max())) - -#--- write header --------------------------------------------------------------------------------- - - table.info_clear() - table.info_append(extra_header+[ - scriptID + ' ' + ' '.join(sys.argv[1:]), - "grid\ta {grid[0]}\tb {grid[1]}\tc {grid[2]}".format(grid=info['grid']), - "size\tx {size[0]}\ty {size[1]}\tz {size[2]}".format(size=info['size']), - "origin\tx {origin[0]}\ty {origin[1]}\tz {origin[2]}".format(origin=info['origin']), - "homogenization\t{homog}".format(homog=info['homogenization']), - "microstructures\t{microstructures}".format(microstructures=info['microstructures']), - ]) - table.labels_clear() - table.head_write() - -# --- write microstructure information ------------------------------------------------------------ - - formatwidth = int(math.floor(math.log10(distance.max())+1)) - table.data = distance.reshape((info['grid'][0],info['grid'][1]*info['grid'][2]),order='F').transpose() - table.data_writeArray('%%%ii'%(formatwidth),delimiter=' ') - -#--- output finalization -------------------------------------------------------------------------- - - table.close() diff --git a/processing/pre/seeds_fromTable.py b/processing/pre/seeds_fromTable.py deleted file mode 100755 index fd45a954b..000000000 --- a/processing/pre/seeds_fromTable.py +++ /dev/null @@ -1,138 +0,0 @@ -#!/usr/bin/env python -# -*- coding: UTF-8 no BOM -*- - -import os,itertools -import numpy as np -from optparse import OptionParser -import damask - -scriptName = os.path.splitext(os.path.basename(__file__))[0] -scriptID = ' '.join([scriptName,damask.version]) - -#-------------------------------------------------------------------------------------------------- -# MAIN -#-------------------------------------------------------------------------------------------------- - -parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ -Create seed file by taking microstructure indices from given ASCIItable column. -White and black-listing of microstructure indices is possible. - -Examples: ---white 1,2,5 --index grainID isolates grainID entries of value 1, 2, and 5; ---black 1 --index grainID takes all grainID entries except for value 1. - -""", version = scriptID) - -parser.add_option('-p', - '--pos', '--seedposition', - dest = 'pos', - type = 'string', metavar = 'string', - help = 'label of coordinates [%default]') -parser.add_option('--boundingbox', - dest = 'box', - type = 'float', nargs = 6, metavar = ' '.join(['float']*6), - help = 'min (x,y,z) and max (x,y,z) coordinates of bounding box [tight]') -parser.add_option('-m', - '--microstructure', - dest = 'microstructure', - type = 'string', metavar = 'string', - help = 'label of microstructures [%default]') -parser.add_option('--weight', - dest = 'weight', - type = 'string', metavar = 'string', - help = 'label of weights [%default]') -parser.add_option('-w', - '--white', - dest = 'whitelist', - action = 'extend', metavar = '', - help = 'whitelist of microstructure indices') -parser.add_option('-b', - '--black', - dest = 'blacklist', - action = 'extend', metavar = '', - help = 'blacklist of microstructure indices') - -parser.set_defaults(pos = 'pos', - microstructure = 'microstructure', - weight = None, - ) - -(options,filenames) = parser.parse_args() - -if options.whitelist is not None: options.whitelist = map(int,options.whitelist) -if options.blacklist is not None: options.blacklist = map(int,options.blacklist) - -# --- loop over input files ------------------------------------------------------------------------- - -if filenames == []: filenames = [None] - -for name in filenames: - try: table = damask.ASCIItable(name = name, - outname = os.path.splitext(name)[0]+'.seeds' if name else name, - buffered = False) - except: continue - damask.util.report(scriptName,name) - - table.head_read() # read ASCII header info - -# ------------------------------------------ sanity checks --------------------------------------- - - missing_labels = table.data_readArray([options.pos,options.microstructure] + - ([options.weight] if options.weight else [])) - - errors = [] - if len(missing_labels) > 0: - errors.append('column{} {} not found'.format('s' if len(missing_labels) > 1 else '', - ', '.join(missing_labels))) - input = {options.pos: 3, - options.microstructure: 1,} - if options.weight: input.update({options.weight: 1}) - for label, dim in input.iteritems(): - if table.label_dimension(label) != dim: - errors.append('column {} has wrong dimension'.format(label)) - - if errors != []: - damask.util.croak(errors) - table.close(dismiss = True) # close ASCII table file handles and delete output file - continue - -# ------------------------------------------ process data ------------------------------------------ - -# --- finding bounding box ------------------------------------------------------------------------- - - boundingBox = np.array((np.amin(table.data[:,0:3],axis = 0),np.amax(table.data[:,0:3],axis = 0))) - if options.box: - boundingBox[0,:] = np.minimum(options.box[0:3],boundingBox[0,:]) - boundingBox[1,:] = np.maximum(options.box[3:6],boundingBox[1,:]) - -# --- rescaling coordinates ------------------------------------------------------------------------ - - table.data[:,0:3] -= boundingBox[0,:] - table.data[:,0:3] /= boundingBox[1,:]-boundingBox[0,:] - -# --- filtering of grain voxels -------------------------------------------------------------------- - - mask = np.logical_and( - np.ones_like(table.data[:,3],bool) if options.whitelist is None \ - else np.in1d(table.data[:,3].ravel(), options.whitelist).reshape(table.data[:,3].shape), - np.ones_like(table.data[:,3],bool) if options.blacklist is None \ - else np.invert(np.in1d(table.data[:,3].ravel(), options.blacklist).reshape(table.data[:,3].shape)) - ) - table.data = table.data[mask] - -# ------------------------------------------ assemble header --------------------------------------- - - table.info = [ - scriptID, - 'size {}'.format(' '.join(list(itertools.chain.from_iterable(zip(['x','y','z'], - map(str,boundingBox[1,:]-boundingBox[0,:])))))), - ] - table.labels_clear() - table.labels_append(['1_pos','2_pos','3_pos','microstructure'] + - ['weight'] if options.weight else []) # implicitly switching label processing/writing on - table.head_write() - -# ------------------------------------------ output result --------------------------------------- - - table.data_writeArray() - table.close() # close ASCII tables