Polishing
- keep microstructure as integer - avoid constant reshape - IMPORTANT: random order has changed!
This commit is contained in:
Martin Diehl
parent
0556827f29
commit
064dabe34b
2
PRIVATE
2
PRIVATE
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@ -1 +1 @@
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Subproject commit 05ac971ce58fc399dd99be9151b7d61d049aec42
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Subproject commit 1b08e028a6177d03a0d4202e5feed2ec29f91c19
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@ -126,75 +126,65 @@ np.random.seed(options.randomSeed)
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random.seed(options.randomSeed)
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for name in filenames:
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damask.util.report(scriptName,name)
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damask.util.report(scriptName,name)
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# --- sanity checks -------------------------------------------------------------------------
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remarks = []
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errors = []
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if any(grid==0):
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errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]])))
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if options.N > grid.prod()/10.:
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remarks.append('seed count exceeds 0.1 of grid points.')
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if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5:
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remarks.append('maximum recommended seed point count for given distance is {}'.
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format(int(3./8./np.pi/(options.distance/2.)**3)))
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remarks = []
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errors = []
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if any(grid==0):
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errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]])))
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if options.N > grid.prod()/10.:
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remarks.append('seed count exceeds 0.1 of grid points.')
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if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5:
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remarks.append('maximum recommended seed point count for given distance is {}'.
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format(int(3./8./np.pi/(options.distance/2.)**3)))
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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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sys.exit()
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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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sys.exit()
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# --- do work ------------------------------------------------------------------------------------
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grainEuler = np.random.rand(3,options.N) # create random Euler triplets
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grainEuler[0,:] *= 360.0 # phi_1 is uniformly distributed
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grainEuler[1,:] = np.degrees(np.arccos(2*grainEuler[1,:]-1)) # cos(Phi) is uniformly distributed
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grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed
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eulers = np.random.rand(options.N,3) # create random Euler triplets
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eulers[:,0] *= 360.0 # phi_1 is uniformly distributed
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eulers[:,1] = np.degrees(np.arccos(2*eulers[:,1]-1)) # cos(Phi) is uniformly distributed
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eulers[:,2] *= 360.0 # phi_2 is uniformly distributed
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if not options.selective:
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n = np.maximum(np.ones(3),np.array(grid*fraction),
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dtype=int,casting='unsafe') # find max grid indices within fraction
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meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction
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coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(3,n.prod()).T # assemble list of 3D coordinates
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seeds = ((random.sample(list(coords),options.N)+np.random.random(options.N*3).reshape(options.N,3))\
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/ \
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(n/fraction)).T # pick options.N of those, rattle position,
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if not options.selective:
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n = np.maximum(np.ones(3),np.array(grid*fraction),dtype=int,casting='unsafe') # find max grid indices within fraction
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meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction
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coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(n.prod(),3) # assemble list of 3D coordinates
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seeds = (random.sample(coords.tolist(),options.N)+np.random.rand(options.N,3))\
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/ \
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(n/fraction) # pick options.N of those, rattle position,
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# and rescale to fall within fraction
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else:
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seeds = np.zeros((options.N,3),dtype=float) # seed positions array
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seeds[0] = np.random.random(3)*grid/max(grid)
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i = 1 # start out with one given point
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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while i < options.N:
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candidates = np.random.random(options.numCandidates*3).reshape(options.numCandidates,3)
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distances = kdtree_search(seeds[:i],candidates)
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best = distances.argmax()
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if distances[best] > options.distance: # require minimum separation
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seeds[i] = candidates[best] # maximum separation to existing point cloud
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i += 1
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else:
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seeds = np.zeros((options.N,3)) # seed positions array
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seeds[0] = np.random.random(3)*grid/max(grid)
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i = 1 # start out with one given point
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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damask.util.croak('')
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seeds = seeds.T # prepare shape for stacking
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while i < options.N:
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candidates = np.random.rand(options.numCandidates,3)
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distances = kdtree_search(seeds[:i],candidates)
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best = distances.argmax()
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if distances[best] > options.distance: # require minimum separation
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seeds[i] = candidates[best] # maximum separation to existing point cloud
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i += 1
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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if options.weights:
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weights = [np.random.uniform(low = 0, high = options.max, size = options.N)] if options.max > 0.0 \
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else [np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)]
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damask.util.croak('')
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data = np.transpose(np.vstack(tuple([seeds,
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grainEuler,
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np.arange(options.microstructure,options.microstructure + options.N),
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] + (weights if options.weights else [])
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)))
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comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
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'grid\ta {}\tb {}\tc {}'.format(*grid),
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'randomSeed\t{}'.format(options.randomSeed),
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]
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comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
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'grid\ta {}\tb {}\tc {}'.format(*grid),
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'randomSeed\t{}'.format(options.randomSeed),
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]
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table = damask.Table(np.hstack((seeds,eulers)),{'pos':(3,),'euler':(3,)},comments)
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table.add('microstructure',np.arange(options.microstructure,options.microstructure + options.N,dtype=int))
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shapes = {'pos':(3,),'euler':(3,),'microstructure':(1,)}
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if options.weights: shapes['weight'] = (1,)
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if options.weights:
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weights = np.random.uniform(low = 0, high = options.max, size = options.N) if options.max > 0.0 \
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else np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)
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table.add('weight',weights)
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table = damask.Table(data,shapes,comments)
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table.to_ASCII(sys.stdout if name is None else name)
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table.to_ASCII(sys.stdout if name is None else name)
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