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DAMASK_EICMD
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Polishing 

- keep microstructure as integer
- avoid constant reshape
- IMPORTANT: random order has changed!
This commit is contained in:
Martin Diehl 2020-03-20 00:36:48 +01:00
parent 0556827f29
commit 064dabe34b
2 changed files with 50 additions and 60 deletions
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2
PRIVATE

@ -1 +1 @@
Subproject commit 05ac971ce58fc399dd99be9151b7d61d049aec42 Subproject commit 1b08e028a6177d03a0d4202e5feed2ec29f91c19

108
processing/pre/seeds_fromRandom.py
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@ -126,75 +126,65 @@ np.random.seed(options.randomSeed)
random.seed(options.randomSeed) random.seed(options.randomSeed)
for name in filenames: for name in filenames:
damask.util.report(scriptName,name) damask.util.report(scriptName,name)
# --- sanity checks ------------------------------------------------------------------------- remarks = []
remarks = [] errors = []
errors = [] if any(grid==0):
if any(grid==0): errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]])))
errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]]))) if options.N > grid.prod()/10.:
if options.N > grid.prod()/10.: remarks.append('seed count exceeds 0.1 of grid points.')
remarks.append('seed count exceeds 0.1 of grid points.') if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5:
if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5: remarks.append('maximum recommended seed point count for given distance is {}'.
remarks.append('maximum recommended seed point count for given distance is {}'. format(int(3./8./np.pi/(options.distance/2.)**3)))
format(int(3./8./np.pi/(options.distance/2.)**3)))
if remarks != []: damask.util.croak(remarks) if remarks != []: damask.util.croak(remarks)
if errors != []: if errors != []:
damask.util.croak(errors) damask.util.croak(errors)
sys.exit() sys.exit()
# --- do work ------------------------------------------------------------------------------------ eulers = np.random.rand(options.N,3) # create random Euler triplets
grainEuler = np.random.rand(3,options.N) # create random Euler triplets eulers[:,0] *= 360.0 # phi_1 is uniformly distributed
grainEuler[0,:] *= 360.0 # phi_1 is uniformly distributed eulers[:,1] = np.degrees(np.arccos(2*eulers[:,1]-1)) # cos(Phi) is uniformly distributed
grainEuler[1,:] = np.degrees(np.arccos(2*grainEuler[1,:]-1)) # cos(Phi) is uniformly distributed eulers[:,2] *= 360.0 # phi_2 is uniformly distributed
grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed
if not options.selective: if not options.selective:
n = np.maximum(np.ones(3),np.array(grid*fraction), n = np.maximum(np.ones(3),np.array(grid*fraction),dtype=int,casting='unsafe') # find max grid indices within fraction
dtype=int,casting='unsafe') # find max grid indices within fraction meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction
meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(n.prod(),3) # assemble list of 3D coordinates
coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(3,n.prod()).T # assemble list of 3D coordinates seeds = (random.sample(coords.tolist(),options.N)+np.random.rand(options.N,3))\
seeds = ((random.sample(list(coords),options.N)+np.random.random(options.N*3).reshape(options.N,3))\ / \
/ \ (n/fraction) # pick options.N of those, rattle position,
(n/fraction)).T # pick options.N of those, rattle position,
# and rescale to fall within fraction # and rescale to fall within fraction
else: else:
seeds = np.zeros((options.N,3),dtype=float) # seed positions array seeds = np.zeros((options.N,3)) # seed positions array
seeds[0] = np.random.random(3)*grid/max(grid) seeds[0] = np.random.random(3)*grid/max(grid)
i = 1 # start out with one given point i = 1 # start out with one given point
if i%(options.N/100.) < 1: damask.util.croak('.',False)
while i < options.N:
candidates = np.random.random(options.numCandidates*3).reshape(options.numCandidates,3)
distances = kdtree_search(seeds[:i],candidates)
best = distances.argmax()
if distances[best] > options.distance: # require minimum separation
seeds[i] = candidates[best] # maximum separation to existing point cloud
i += 1
if i%(options.N/100.) < 1: damask.util.croak('.',False) if i%(options.N/100.) < 1: damask.util.croak('.',False)
damask.util.croak('') while i < options.N:
seeds = seeds.T # prepare shape for stacking candidates = np.random.rand(options.numCandidates,3)
distances = kdtree_search(seeds[:i],candidates)
best = distances.argmax()
if distances[best] > options.distance: # require minimum separation
seeds[i] = candidates[best] # maximum separation to existing point cloud
i += 1
if i%(options.N/100.) < 1: damask.util.croak('.',False)
if options.weights: damask.util.croak('')
weights = [np.random.uniform(low = 0, high = options.max, size = options.N)] if options.max > 0.0 \
else [np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)]
data = np.transpose(np.vstack(tuple([seeds, comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
grainEuler, 'grid\ta {}\tb {}\tc {}'.format(*grid),
np.arange(options.microstructure,options.microstructure + options.N), 'randomSeed\t{}'.format(options.randomSeed),
] + (weights if options.weights else []) ]
)))
comments = [scriptID + ' ' + ' '.join(sys.argv[1:]), table = damask.Table(np.hstack((seeds,eulers)),{'pos':(3,),'euler':(3,)},comments)
'grid\ta {}\tb {}\tc {}'.format(*grid), table.add('microstructure',np.arange(options.microstructure,options.microstructure + options.N,dtype=int))
'randomSeed\t{}'.format(options.randomSeed),
]
shapes = {'pos':(3,),'euler':(3,),'microstructure':(1,)} if options.weights:
if options.weights: shapes['weight'] = (1,) weights = np.random.uniform(low = 0, high = options.max, size = options.N) if options.max > 0.0 \
else np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)
table.add('weight',weights)
table = damask.Table(data,shapes,comments) table.to_ASCII(sys.stdout if name is None else name)
table.to_ASCII(sys.stdout if name is None else name)