using central functionality
- Table class for table data - grid_filters for grid related functions
This commit is contained in:
Martin Diehl
parent
b293498864
commit
6989679d3b
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@ -121,13 +121,14 @@ parser.set_defaults(pos = 'pos',
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)
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(options,filenames) = parser.parse_args()
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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if options.type is None:
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if options.type is None:
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parser.error('no feature type selected.')
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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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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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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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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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parser.error('only one from aliases "biplane" and "boundary" possible.')
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feature_list = []
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feature_list = []
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for i,feature in enumerate(features):
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for i,feature in enumerate(features):
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@ -137,104 +138,49 @@ for i,feature in enumerate(features):
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feature_list.append(i) # remember valid features
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feature_list.append(i) # remember valid features
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break
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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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for name in filenames:
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try: table = damask.ASCIItable(name = name, buffered = False)
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damask.util.report(scriptName,name)
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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 = 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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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.get(options.id).astype('i').reshape(grid,order='F'))
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# ------------------------------------------ sanity checks ----------------------------------------
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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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errors = []
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stencil[1,1,1] = -1
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remarks = []
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stencil[p[0]+1,
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p[1]+1,
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if not 3 >= table.label_dimension(options.pos) >= 1:
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p[2]+1] = 1
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errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.pos))
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diffToNeighbor[:,:,:,i] = ndimage.convolve(microstructure,stencil) # compare ID at each point...
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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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grid,size = damask.util.coordGridAndSize(table.data[:,table.label_indexrange(options.pos)])
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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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# ...to every one in the specified neighborhood
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# for same IDs at both locations ==> 0
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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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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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# ...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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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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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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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] != 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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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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), # flip of ID difference detected?
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1,0) # count that flip
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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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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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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,:,:,:] = 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[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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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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for i,feature in enumerate(feature_list):
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table.data_writeArray('%.12g')
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table.add('ED_{}({})'.format(features[feature]['names'][0],options.id),
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distance[i,:],
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scriptID+' '+' '.join(sys.argv[1:]))
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# ------------------------------------------ output finalization -----------------------------------
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table.to_ASCII(sys.stdout if name is None else name)
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table.close() # close input ASCII table (works for stdin)
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@ -30,7 +30,7 @@ parser.add_option('-p','--pos','--periodiccellcenter',
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type = 'string', metavar = 'string',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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help = 'label of coordinates [%default]')
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parser.add_option('-s','--scalar',
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parser.add_option('-s','--scalar',
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dest = 'scalar',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of scalar field values')
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help = 'label(s) of scalar field values')
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parser.add_option('-o','--order',
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parser.add_option('-o','--order',
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@ -56,78 +56,21 @@ parser.set_defaults(pos = 'pos',
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)
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(options,filenames) = parser.parse_args()
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(options,filenames) = parser.parse_args()
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if options.scalar is None:
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parser.error('no data column specified.')
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# --- loop over input files ------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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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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for name in filenames:
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try: table = damask.ASCIItable(name = name,buffered = False)
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damask.util.report(scriptName,name)
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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 = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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damask.grid_filters.coord0_check(table.get(options.pos))
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table.head_read()
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for label in options.labels:
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table.add('Gauss{}({})'.format(options.sigma,label),
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ndimage.filters.gaussian_filter(table.get(label).reshape((-1)),
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options.sigma,options.order,
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mode = 'wrap' if options.periodic else 'nearest'),
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scriptID+' '+' '.join(sys.argv[1:]))
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# ------------------------------------------ sanity checks ----------------------------------------
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table.to_ASCII(sys.stdout if name is None else name)
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items = {
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'scalar': {'dim': 1, 'shape': [1], 'labels':options.scalar, 'active':[], 'column': []},
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}
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errors = []
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remarks = []
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column = {}
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if table.label_dimension(options.pos) != 3: errors.append('coordinates {} are not a vector.'.format(options.pos))
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else: colCoord = table.label_index(options.pos)
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for type, data in items.items():
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for what in (data['labels'] if data['labels'] is not None else []):
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dim = table.label_dimension(what)
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if dim != data['dim']: remarks.append('column {} is not a {}.'.format(what,type))
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else:
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items[type]['active'].append(what)
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items[type]['column'].append(table.label_index(what))
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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 type, data in items.items():
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for label in data['active']:
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table.labels_append(['Gauss{}({})'.format(options.sigma,label)]) # 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 type, data in items.items():
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for i,label in enumerate(data['active']):
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stack.append(ndimage.filters.gaussian_filter(table.data[:,data['column'][i]],
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options.sigma,options.order,
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mode = 'wrap' if options.periodic else 'nearest'
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).reshape([table.data.shape[0],1])
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)
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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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@ -217,6 +217,19 @@ def cell_coord0_2_DNA(coord0,ordered=True):
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return (grid,size,origin)
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return (grid,size,origin)
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def coord0_check(coord0):
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"""
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Check whether coordinates lie on a regular grid
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Parameters
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----------
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coord0 : numpy.ndarray
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array of undeformed cell coordinates.
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"""
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cell_coord0_2_DNA(coord0,ordered=True)
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def node_coord0(grid,size,origin=np.zeros(3)):
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def node_coord0(grid,size,origin=np.zeros(3)):
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"""
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"""
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