new class, less code
This commit is contained in:
Martin Diehl
parent
b65c3959f1
commit
49caa77bbd
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@ -1,17 +1,20 @@
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#!/usr/bin/env python3
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,math,random
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import numpy as np
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import damask
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import os
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import sys
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import random
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from io import StringIO
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from optparse import OptionParser,OptionGroup
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import numpy as np
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from scipy import spatial
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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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# ------------------------------------------ aux functions ---------------------------------
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def kdtree_search(cloud, queryPoints):
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"""Find distances to nearest neighbor among cloud (N,d) for each of the queryPoints (n,d)."""
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@ -116,32 +119,26 @@ parser.set_defaults(randomSeed = None,
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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options.fraction = np.array(options.fraction)
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options.grid = np.array(options.grid)
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gridSize = options.grid.prod()
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fraction = np.array(options.fraction)
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grid = np.array(options.grid)
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if options.randomSeed is None: options.randomSeed = int(os.urandom(4).hex(), 16)
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np.random.seed(options.randomSeed) # init random generators
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random.seed(options.randomSeed)
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for name in filenames:
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try:
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table = damask.ASCIItable(outname = name)
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except IOError:
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continue
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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 gridSize == 0:
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errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(options.grid == 0)[0]])))
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if options.N > gridSize/10.:
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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.*math.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./math.pi/(options.distance/2.)**3)))
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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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@ -149,24 +146,23 @@ for name in filenames:
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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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if not options.selective:
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n = np.maximum(np.ones(3),np.array(options.grid*options.fraction),
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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/options.fraction)).T # pick options.N of those, rattle position,
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(n/fraction)).T # 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)*options.grid/max(options.grid)
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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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@ -194,26 +190,12 @@ for name in filenames:
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)))
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comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
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'grid\ta {}\tb {}\tc {}'.format(*options.grid),
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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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# ------------------------------------------ assemble header ---------------------------------------
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table.info_clear()
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table.info_append(comments)
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table.labels_clear()
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table.labels_append( ['{dim}_{label}'.format(dim = 1+k,label = 'pos') for k in range(3)] +
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['{dim}_{label}'.format(dim = 1+k,label = 'euler') for k in range(3)] +
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['microstructure'] +
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(['weight'] if options.weights else []))
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table.head_write()
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table.output_flush()
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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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# --- write seeds information ------------------------------------------------------------
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table.data = data
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table.data_writeArray()
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# --- output finalization --------------------------------------------------------------------------
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table.close() # close ASCII table
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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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