239 lines
12 KiB
Python
Executable File
239 lines
12 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,math
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import numpy as np
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from optparse import OptionParser
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from scipy import ndimage
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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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#--------------------------------------------------------------------------------------------------
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# MAIN
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#--------------------------------------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog [option(s)] [geomfile(s)]', description = """
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Smoothens out interface roughness by simulated curvature flow.
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This is achieved by the diffusion of each initially sharply bounded grain volume within the periodic domain
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up to a given distance 'd' voxels.
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The final geometry is assembled by selecting at each voxel that grain index for which the concentration remains largest.
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""", version = scriptID)
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parser.add_option('-d', '--distance',
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dest = 'd',
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type = 'float', metavar = 'float',
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help = 'diffusion distance in voxels [%default]')
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parser.add_option('-N', '--iterations',
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dest = 'N',
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type = 'int', metavar = 'int',
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help = 'curvature flow iterations [%default]')
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parser.add_option('-i', '--immutable',
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action = 'extend', dest = 'immutable', metavar = '<int LIST>',
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help = 'list of immutable microstructure indices')
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parser.add_option('-r', '--renumber',
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dest = 'renumber', action='store_true',
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help = 'output consecutive microstructure indices')
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parser.add_option('--ndimage',
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dest = 'ndimage', action='store_true',
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help = 'use ndimage.gaussian_filter in lieu of explicit FFT')
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parser.set_defaults(d = 1,
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N = 1,
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immutable = [],
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renumber = False,
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ndimage = False,
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)
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(options, filenames) = parser.parse_args()
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options.immutable = map(int,options.immutable)
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getInterfaceEnergy = lambda A,B: np.float32((A*B != 0)*(A != B)*1.0) # 1.0 if A & B are distinct & nonzero, 0.0 otherwise
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struc = ndimage.generate_binary_structure(3,1) # 3D von Neumann neighborhood
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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,
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buffered = False,
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labeled = False)
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except: continue
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damask.util.report(scriptName,name)
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# --- interpret header ----------------------------------------------------------------------------
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table.head_read()
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info,extra_header = table.head_getGeom()
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damask.util.croak(['grid a b c: {}'.format(' x '.join(map(str,info['grid']))),
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'size x y z: {}'.format(' x '.join(map(str,info['size']))),
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'origin x y z: {}'.format(' : '.join(map(str,info['origin']))),
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'homogenization: {}'.format(info['homogenization']),
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'microstructures: {}'.format(info['microstructures']),
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])
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errors = []
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if np.any(info['grid'] < 1): errors.append('invalid grid a b c.')
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if np.any(info['size'] <= 0.0): errors.append('invalid size x y z.')
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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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# --- read data -----------------------------------------------------------------------------------
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microstructure = np.tile(table.microstructure_read(info['grid']).reshape(info['grid'],order='F'),
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np.where(info['grid'] == 1, 2,1)) # make one copy along dimensions with grid == 1
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grid = np.array(microstructure.shape)
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# --- initialize support data ---------------------------------------------------------------------
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# store a copy the initial microstructure to find locations of immutable indices
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microstructure_original = np.copy(microstructure)
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if not options.ndimage:
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X,Y,Z = np.mgrid[0:grid[0],0:grid[1],0:grid[2]]
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# Calculates gaussian weights for simulating 3d diffusion
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gauss = np.exp(-(X*X + Y*Y + Z*Z)/(2.0*options.d*options.d),dtype=np.float32) \
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/np.power(2.0*np.pi*options.d*options.d,(3.0 - np.count_nonzero(info['grid'] == 1))/2.,dtype=np.float32)
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gauss[:,:,:grid[2]/2:-1] = gauss[:,:,1:(grid[2]+1)/2] # trying to cope with uneven (odd) grid size
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gauss[:,:grid[1]/2:-1,:] = gauss[:,1:(grid[1]+1)/2,:]
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gauss[:grid[0]/2:-1,:,:] = gauss[1:(grid[0]+1)/2,:,:]
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gauss = np.fft.rfftn(gauss).astype(np.complex64)
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for smoothIter in range(options.N):
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interfaceEnergy = np.zeros(microstructure.shape,dtype=np.float32)
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for i in (-1,0,1):
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for j in (-1,0,1):
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for k in (-1,0,1):
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# assign interfacial energy to all voxels that have a differing neighbor (in Moore neighborhood)
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interfaceEnergy = np.maximum(interfaceEnergy,
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getInterfaceEnergy(microstructure,np.roll(np.roll(np.roll(
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microstructure,i,axis=0), j,axis=1), k,axis=2)))
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# periodically extend interfacial energy array by half a grid size in positive and negative directions
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periodic_interfaceEnergy = np.tile(interfaceEnergy,(3,3,3))[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2]
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# transform bulk volume (i.e. where interfacial energy remained zero), store index of closest boundary voxel
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index = ndimage.morphology.distance_transform_edt(periodic_interfaceEnergy == 0.,
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return_distances = False,
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return_indices = True)
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# want array index of nearest voxel on periodically extended boundary
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periodic_bulkEnergy = periodic_interfaceEnergy[index[0],
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index[1],
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index[2]].reshape(2*grid) # fill bulk with energy of nearest interface
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if options.ndimage:
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periodic_diffusedEnergy = ndimage.gaussian_filter(
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np.where(ndimage.morphology.binary_dilation(periodic_interfaceEnergy > 0.,
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structure = struc,
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iterations = int(round(options.d*2.))-1, # fat boundary
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),
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periodic_bulkEnergy, # ...and zero everywhere else
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0.),
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sigma = options.d)
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else:
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diffusedEnergy = np.fft.irfftn(np.fft.rfftn(
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np.where(
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ndimage.morphology.binary_dilation(interfaceEnergy > 0.,
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structure = struc,
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iterations = int(round(options.d*2.))-1),# fat boundary
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periodic_bulkEnergy[grid[0]/2:-grid[0]/2, # retain filled energy on fat boundary...
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2], # ...and zero everywhere else
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0.)).astype(np.complex64) *
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gauss).astype(np.float32)
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periodic_diffusedEnergy = np.tile(diffusedEnergy,(3,3,3))[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # periodically extend the smoothed bulk energy
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# transform voxels close to interface region
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index = ndimage.morphology.distance_transform_edt(periodic_diffusedEnergy >= 0.95*np.amax(periodic_diffusedEnergy),
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return_distances = False,
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return_indices = True) # want index of closest bulk grain
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periodic_microstructure = np.tile(microstructure,(3,3,3))[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # periodically extend the microstructure
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microstructure = periodic_microstructure[index[0],
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index[1],
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index[2]].reshape(2*grid)[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # extent grains into interface region
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# replace immutable microstructures with closest mutable ones
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index = ndimage.morphology.distance_transform_edt(np.in1d(microstructure,options.immutable).reshape(grid),
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return_distances = False,
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return_indices = True)
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microstructure = microstructure[index[0],
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index[1],
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index[2]]
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immutable = np.zeros(microstructure.shape, dtype=np.bool)
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# find locations where immutable microstructures have been in original structure
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for micro in options.immutable:
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immutable += microstructure_original == micro
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# undo any changes involving immutable microstructures
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microstructure = np.where(immutable, microstructure_original,microstructure)
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# --- renumber to sequence 1...Ngrains if requested -----------------------------------------------
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# http://stackoverflow.com/questions/10741346/np-frequency-counts-for-unique-values-in-an-array
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if options.renumber:
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newID = 0
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for microstructureID,count in enumerate(np.bincount(microstructure.flatten())):
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if count != 0:
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newID += 1
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microstructure = np.where(microstructure == microstructureID, newID, microstructure)
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newInfo = {'microstructures': 0,}
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newInfo['microstructures'] = microstructure.max()
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# --- report --------------------------------------------------------------------------------------
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remarks = []
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if newInfo['microstructures'] != info['microstructures']:
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remarks.append('--> microstructures: {}'.format(newInfo['microstructures']))
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if remarks != []: damask.util.croak(remarks)
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# --- write header --------------------------------------------------------------------------------
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table.labels_clear()
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table.info_clear()
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table.info_append(extra_header+[
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scriptID + ' ' + ' '.join(sys.argv[1:]),
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"grid\ta {grid[0]}\tb {grid[1]}\tc {grid[2]}".format(grid=info['grid']),
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"size\tx {size[0]}\ty {size[1]}\tz {size[2]}".format(size=info['size']),
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"origin\tx {origin[0]}\ty {origin[1]}\tz {origin[2]}".format(origin=info['origin']),
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"homogenization\t{homog}".format(homog=info['homogenization']),
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"microstructures\t{microstructures}".format(microstructures=newInfo['microstructures']),
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])
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table.head_write()
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# --- write microstructure information ------------------------------------------------------------
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formatwidth = int(math.floor(math.log10(microstructure.max())+1))
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table.data = microstructure[::1 if info['grid'][0]>1 else 2,
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::1 if info['grid'][1]>1 else 2,
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::1 if info['grid'][2]>1 else 2,].\
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reshape((info['grid'][0],info['grid'][1]*info['grid'][2]),order='F').transpose()
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table.data_writeArray('%{}i'.format(formatwidth),delimiter = ' ')
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# --- output finalization --------------------------------------------------------------------------
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table.close()
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