Merge branch 'development' into fix-vtr-sanity-check
This commit is contained in:
Martin Diehl
commit
50599bc5c5
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@ -141,13 +141,6 @@ Pre_General:
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- release
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###################################################################################################
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Post_ASCIItable:
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stage: postprocessing
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script: ASCIItable/test.py
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except:
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- master
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- release
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Post_General:
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stage: postprocessing
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script: PostProcessing/test.py
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|
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2
PRIVATE
2
PRIVATE
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@ -1 +1 @@
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Subproject commit 92ca3e83b6093c1af277cfc06a504e4bb09fe8bc
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Subproject commit 65ec74c07052e77f35a4b5e80bf110aff1f5ae61
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@ -39,21 +39,21 @@ for filename in options.filenames:
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N_digits = 5 # hack to keep test intact
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for inc in damask.util.show_progress(results.iterate('increments'),len(results.increments)):
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table = damask.Table(np.ones(np.product(results.grid),dtype=int)*int(inc[3:]),{'inc':(1,)})
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table.add('pos',coords.reshape(-1,3))
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table = table.add('pos',coords.reshape(-1,3))
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results.pick('materialpoints',False)
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results.pick('constituents', True)
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for label in options.con:
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x = results.get_dataset_location(label)
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if len(x) != 0:
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table.add(label,results.read_dataset(x,0,plain=True).reshape(results.grid.prod(),-1))
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table = table.add(label,results.read_dataset(x,0,plain=True).reshape(results.grid.prod(),-1))
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results.pick('constituents', False)
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results.pick('materialpoints',True)
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for label in options.mat:
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x = results.get_dataset_location(label)
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if len(x) != 0:
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table.add(label,results.read_dataset(x,0,plain=True).reshape(results.grid.prod(),-1))
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table = table.add(label,results.read_dataset(x,0,plain=True).reshape(results.grid.prod(),-1))
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dirname = os.path.abspath(os.path.join(os.path.dirname(filename),options.dir))
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if not os.path.isdir(dirname):
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@ -181,14 +181,14 @@ for name in filenames:
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if options.shape:
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centers = damask.grid_filters.cell_coord(size,F)
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shapeMismatch = shapeMismatch(size,F,nodes,centers)
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table.add('shapeMismatch(({}))'.format(options.defgrad),
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shapeMismatch.reshape(-1,1,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table = table.add('shapeMismatch(({}))'.format(options.defgrad),
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shapeMismatch.reshape(-1,1,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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if options.volume:
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volumeMismatch = volumeMismatch(size,F,nodes)
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table.add('volMismatch(({}))'.format(options.defgrad),
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volumeMismatch.reshape(-1,1,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table = table.add('volMismatch(({}))'.format(options.defgrad),
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volumeMismatch.reshape(-1,1,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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@ -51,8 +51,8 @@ for name in filenames:
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shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
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field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
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curl = damask.grid_filters.curl(size,field)
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table.add('curlFFT({})'.format(label),
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curl.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape),order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table = table.add('curlFFT({})'.format(label),
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curl.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape),order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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|
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@ -67,8 +67,8 @@ for name in filenames:
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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for label in options.labels:
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table.add('d({})/d({})'.format(label,options.coordinates),
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derivative(table.get(options.coordinates),table.get(label)),
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scriptID+' '+' '.join(sys.argv[1:]))
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table = table.add('d({})/d({})'.format(label,options.coordinates),
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derivative(table.get(options.coordinates),table.get(label)),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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@ -53,19 +53,19 @@ for name in filenames:
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F = table.get(options.f).reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+(3,3))
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if options.nodal:
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table = damask.Table(damask.grid_filters.node_coord0(grid,size).reshape(-1,3,order='F'),
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{'pos':(3,)})
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table.add('avg({}).{}'.format(options.f,options.pos),
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damask.grid_filters.node_displacement_avg(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.add('fluct({}).{}'.format(options.f,options.pos),
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damask.grid_filters.node_displacement_fluct(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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{'pos':(3,)})\
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.add('avg({}).{}'.format(options.f,options.pos),
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damask.grid_filters.node_displacement_avg(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))\
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.add('fluct({}).{}'.format(options.f,options.pos),
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damask.grid_filters.node_displacement_fluct(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt')
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else:
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table.add('avg({}).{}'.format(options.f,options.pos),
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table = table.add('avg({}).{}'.format(options.f,options.pos),
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damask.grid_filters.cell_displacement_avg(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.add('fluct({}).{}'.format(options.f,options.pos),
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scriptID+' '+' '.join(sys.argv[1:]))\
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.add('fluct({}).{}'.format(options.f,options.pos),
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damask.grid_filters.cell_displacement_fluct(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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|
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@ -51,8 +51,8 @@ for name in filenames:
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shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
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field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
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div = damask.grid_filters.divergence(size,field)
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table.add('divFFT({})'.format(label),
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div.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)//3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table = table.add('divFFT({})'.format(label),
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div.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)//3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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|
|
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@ -180,8 +180,8 @@ for name in filenames:
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for i,feature in enumerate(feature_list):
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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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table = 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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table.to_file(sys.stdout if name is None else name)
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|
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@ -67,10 +67,10 @@ for name in filenames:
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damask.grid_filters.coord0_check(table.get(options.pos))
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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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table = 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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table.to_file(sys.stdout if name is None else name)
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|
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@ -51,8 +51,8 @@ for name in filenames:
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shape = (1,) if np.prod(field.shape)//np.prod(grid) == 1 else (3,) # scalar or vector
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field = field.reshape(tuple(grid)+(-1,),order='F')
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grad = damask.grid_filters.gradient(size,field)
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table.add('gradFFT({})'.format(label),
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grad.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)*3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table = table.add('gradFFT({})'.format(label),
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grad.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)*3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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@ -1,64 +0,0 @@
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#!/usr/bin/env python3
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import os
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import sys
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from io import StringIO
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from optparse import OptionParser
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import numpy as np
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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 options [ASCIItable(s)]', description = """
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Add data in column(s) of mapped ASCIItable selected from the row indexed by the value in a mapping column.
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Row numbers start at 1.
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""", version = scriptID)
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parser.add_option('--index',
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dest = 'index',
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type = 'string', metavar = 'string',
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help = 'column label containing row index')
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parser.add_option('-o','--offset',
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dest = 'offset',
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type = 'int', metavar = 'int',
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help = 'constant offset for index column value [%default]')
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parser.add_option('-l','--label',
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dest = 'label',
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action = 'extend', metavar = '<string LIST>',
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help = 'column label(s) to be appended')
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parser.add_option('-a','--asciitable',
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dest = 'asciitable',
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type = 'string', metavar = 'string',
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help = 'indexed ASCIItable')
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parser.set_defaults(offset = 0,
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)
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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if options.label is None:
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parser.error('no data columns specified.')
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if options.index is None:
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parser.error('no index column given.')
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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indexedTable = damask.Table.from_ASCII(options.asciitable)
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idx = np.reshape(table.get(options.index).astype(int) + options.offset,(-1))-1
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for data in options.label:
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table.add(data+'_addIndexed',indexedTable.get(data)[idx],scriptID+' '+' '.join(sys.argv[1:]))
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table.to_file(sys.stdout if name is None else name)
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|
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@ -1,118 +0,0 @@
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#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import sys
|
||||
from optparse import OptionParser
|
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|
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import numpy as np
|
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|
||||
import damask
|
||||
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
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|
||||
# --------------------------------------------------------------------
|
||||
# MAIN
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [ASCIItable(s)]', description = """
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Add data of selected column(s) from (first) row of linked ASCIItable that shares the linking column value.
|
||||
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('--link',
|
||||
dest = 'link', nargs = 2,
|
||||
type = 'string', metavar = 'string string',
|
||||
help = 'column labels of table and linked table containing linking values')
|
||||
parser.add_option('-l','--label',
|
||||
dest = 'label',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'column label(s) to add from linked ASCIItable')
|
||||
parser.add_option('-a','--asciitable',
|
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dest = 'asciitable',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'linked ASCIItable')
|
||||
|
||||
parser.set_defaults()
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
if options.label is None:
|
||||
parser.error('no data columns specified.')
|
||||
if options.link is None:
|
||||
parser.error('no linking columns given.')
|
||||
|
||||
# -------------------------------------- process linked ASCIItable --------------------------------
|
||||
|
||||
if options.asciitable is not None and os.path.isfile(options.asciitable):
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||||
|
||||
linkedTable = damask.ASCIItable(name = options.asciitable, readonly = True)
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||||
linkedTable.head_read() # read ASCII header info of linked table
|
||||
linkDim = linkedTable.label_dimension(options.link[1]) # dimension of linking column
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||||
|
||||
missing_labels = linkedTable.data_readArray([options.link[1]]+options.label) # try reading linked ASCII table
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||||
linkedTable.close() # close linked ASCII table
|
||||
|
||||
if len(missing_labels) > 0:
|
||||
damask.util.croak('column{} {} not found...'.format('s' if len(missing_labels) > 1 else '',', '.join(missing_labels)))
|
||||
if len(missing_labels) >= len(options.label):
|
||||
damask.util.croak('aborting...')
|
||||
sys.exit()
|
||||
|
||||
index = linkedTable.data[:,:linkDim]
|
||||
data = linkedTable.data[:,linkDim:]
|
||||
else:
|
||||
parser.error('no linked ASCIItable given.')
|
||||
|
||||
# --- loop over input files -----------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name)
|
||||
except IOError:
|
||||
continue
|
||||
damask.util.report(scriptName,"{} {} <== {} {}".format(name,damask.util.deemph('@ '+options.link[0]),
|
||||
options.asciitable,damask.util.deemph('@ '+options.link[1])))
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
|
||||
table.head_read()
|
||||
|
||||
# ------------------------------------------ sanity checks ----------------------------------------
|
||||
|
||||
errors = []
|
||||
|
||||
myLink = table.label_index (options.link[0])
|
||||
myLinkDim = table.label_dimension(options.link[0])
|
||||
if myLink < 0: errors.append('linking column {} not found.'.format(options.link[0]))
|
||||
if myLinkDim != linkDim: errors.append('dimension mismatch for column {}.'.format(options.link[0]))
|
||||
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# ------------------------------------------ assemble header --------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.labels_append(linkedTable.labels(raw = True)[linkDim:]) # extend with new labels (except for linked column)
|
||||
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data ------------------------------------------
|
||||
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
try:
|
||||
table.data_append(data[np.argwhere(np.all((list(map(float,table.data[myLink:myLink+myLinkDim])) - index)==0,
|
||||
axis=1))[0]]) # add data of first matching line
|
||||
except IndexError:
|
||||
table.data_append(np.nan*np.ones_like(data[0])) # or add NaNs
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
# ------------------------------------------ output finalization -----------------------------------
|
||||
|
||||
table.close() # close ASCII tables
|
||||
|
|
@ -137,14 +137,14 @@ for name in filenames:
|
|||
|
||||
|
||||
if 'rodrigues' in options.output:
|
||||
table.add('ro({})'.format(label),o.as_Rodrigues(), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.add('ro({})'.format(label),o.as_Rodrigues(), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
if 'eulers' in options.output:
|
||||
table.add('eu({})'.format(label),o.as_Eulers(options.degrees), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.add('eu({})'.format(label),o.as_Eulers(options.degrees), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
if 'quaternion' in options.output:
|
||||
table.add('qu({})'.format(label),o.as_quaternion(), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.add('qu({})'.format(label),o.as_quaternion(), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
if 'matrix' in options.output:
|
||||
table.add('om({})'.format(label),o.as_matrix(), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.add('om({})'.format(label),o.as_matrix(), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
if 'axisangle' in options.output:
|
||||
table.add('om({})'.format(label),o.as_axisangle(options.degrees), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.add('om({})'.format(label),o.as_axisangle(options.degrees), scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.to_file(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -187,6 +187,6 @@ for name in filenames:
|
|||
np.einsum('ijk,ik->ij',slip_normal, (o@normal)))
|
||||
|
||||
for i,label in enumerate(labels):
|
||||
table.add(label,S[:,i],scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.add(label,S[:,i],scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.to_file(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -1,142 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import sys
|
||||
from optparse import OptionParser, OptionGroup
|
||||
import math # noqa
|
||||
|
||||
import numpy as np
|
||||
|
||||
import damask
|
||||
|
||||
|
||||
def periodicAverage(coords, limits):
|
||||
"""Centroid in periodic domain, see https://en.wikipedia.org/wiki/Center_of_mass#Systems_with_periodic_boundary_conditions."""
|
||||
theta = 2.0*np.pi * (coords - limits[0])/(limits[1] - limits[0])
|
||||
theta_avg = np.pi + np.arctan2(-np.sin(theta).mean(axis=0), -np.cos(theta).mean(axis=0))
|
||||
return limits[0] + theta_avg * (limits[1] - limits[0])/2.0/np.pi
|
||||
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# MAIN
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [ASCIItable(s)]', description = """
|
||||
Apply a user-specified function to condense into a single row all those rows for which columns 'label' have identical values.
|
||||
Output table will contain as many rows as there are different (unique) values in the grouping column(s).
|
||||
Periodic domain averaging of coordinate values is supported.
|
||||
|
||||
Examples:
|
||||
For grain averaged values, replace all rows of particular 'texture' with a single row containing their average.
|
||||
{name} --label texture --function np.average data.txt
|
||||
""".format(name = scriptName), version = scriptID)
|
||||
|
||||
parser.add_option('-l','--label',
|
||||
dest = 'label',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'column label(s) for grouping rows')
|
||||
parser.add_option('-f','--function',
|
||||
dest = 'function',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'mapping function [%default]')
|
||||
parser.add_option('-a','--all',
|
||||
dest = 'all',
|
||||
action = 'store_true',
|
||||
help = 'apply mapping function also to grouping column(s)')
|
||||
|
||||
group = OptionGroup(parser, "periodic averaging", "")
|
||||
|
||||
group.add_option ('-p','--periodic',
|
||||
dest = 'periodic',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'coordinate label(s) to average across periodic domain')
|
||||
group.add_option ('--limits',
|
||||
dest = 'boundary',
|
||||
type = 'float', metavar = 'float float', nargs = 2,
|
||||
help = 'min and max of periodic domain %default')
|
||||
|
||||
parser.add_option_group(group)
|
||||
|
||||
parser.set_defaults(function = 'np.average',
|
||||
all = False,
|
||||
label = [],
|
||||
boundary = [0.0, 1.0])
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
funcModule,funcName = options.function.split('.')
|
||||
|
||||
try:
|
||||
mapFunction = getattr(locals().get(funcModule) or
|
||||
globals().get(funcModule) or
|
||||
__import__(funcModule),
|
||||
funcName)
|
||||
except Exception:
|
||||
mapFunction = None
|
||||
|
||||
if options.label is []:
|
||||
parser.error('no grouping column specified.')
|
||||
if not hasattr(mapFunction,'__call__'):
|
||||
parser.error('function "{}" is not callable.'.format(options.function))
|
||||
|
||||
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name)
|
||||
except IOError:
|
||||
continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ sanity checks ---------------------------------------
|
||||
|
||||
remarks = []
|
||||
errors = []
|
||||
|
||||
table.head_read()
|
||||
grpColumns = table.label_index(options.label)[::-1]
|
||||
grpColumns = grpColumns[np.where(grpColumns>=0)]
|
||||
|
||||
if len(grpColumns) == 0: errors.append('no valid grouping column present.')
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss=True)
|
||||
continue
|
||||
|
||||
# ------------------------------------------ assemble info ---------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data --------------------------------
|
||||
|
||||
table.data_readArray()
|
||||
indexrange = table.label_indexrange(options.periodic) if options.periodic is not None else []
|
||||
rows,cols = table.data.shape
|
||||
|
||||
table.data = table.data[np.lexsort(table.data[:,grpColumns].T)] # sort data by grpColumn(s)
|
||||
values,index = np.unique(table.data[:,grpColumns], axis=0, return_index=True) # unique grpColumn values and their positions
|
||||
index = sorted(np.append(index,rows)) # add termination position
|
||||
grpTable = np.empty((len(values), cols)) # initialize output
|
||||
|
||||
for i in range(len(values)): # iterate over groups (unique values in grpColumn)
|
||||
grpTable[i] = np.apply_along_axis(mapFunction,0,table.data[index[i]:index[i+1]]) # apply (general) mapping function
|
||||
grpTable[i,indexrange] = \
|
||||
periodicAverage(table.data[index[i]:index[i+1],indexrange],options.boundary) # apply periodicAverage mapping function
|
||||
|
||||
if not options.all: grpTable[i,grpColumns] = table.data[index[i],grpColumns] # restore grouping column value
|
||||
|
||||
table.data = grpTable
|
||||
|
||||
# ------------------------------------------ output result -------------------------------
|
||||
|
||||
table.data_writeArray()
|
||||
table.close() # close ASCII table
|
||||
|
|
@ -56,6 +56,6 @@ for name in filenames:
|
|||
data = table.get(label)
|
||||
uniques,inverse = np.unique(data,return_inverse=True,axis=0) if options.unique else (data,np.arange(len(data)))
|
||||
rng.shuffle(uniques)
|
||||
table.set(label,uniques[inverse], scriptID+' '+' '.join(sys.argv[1:]))
|
||||
table = table.set(label,uniques[inverse], scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.to_file(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -64,6 +64,5 @@ for name in filenames:
|
|||
'homogenization\t{}'.format(geom.homogenization)]
|
||||
|
||||
table = damask.Table(seeds[mask],{'pos':(3,)},comments)
|
||||
table.add('microstructure',microstructure[mask])
|
||||
table.to_file(sys.stdout if name is None else \
|
||||
os.path.splitext(name)[0]+'.seeds')
|
||||
table = table.add('microstructure',microstructure[mask])
|
||||
table.to_file(sys.stdout if name is None else os.path.splitext(name)[0]+'.seeds')
|
||||
|
|
|
|||
|
|
@ -155,11 +155,11 @@ for name in filenames:
|
|||
]
|
||||
|
||||
table = damask.Table(np.hstack((seeds,eulers)),{'pos':(3,),'euler':(3,)},comments)
|
||||
table.add('microstructure',np.arange(options.microstructure,options.microstructure + options.N,dtype=int))
|
||||
table = table.add('microstructure',np.arange(options.microstructure,options.microstructure + options.N,dtype=int))
|
||||
|
||||
if options.weights:
|
||||
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 = table.add('weight',weights)
|
||||
|
||||
table.to_file(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import re
|
||||
import copy
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
|
@ -29,6 +30,15 @@ class Table:
|
|||
self._label_condensed()
|
||||
|
||||
|
||||
def __copy__(self):
|
||||
"""Copy Table."""
|
||||
return copy.deepcopy(self)
|
||||
|
||||
def copy(self):
|
||||
"""Copy Table."""
|
||||
return self.__copy__()
|
||||
|
||||
|
||||
def _label_flat(self):
|
||||
"""Label data individually, e.g. v v v ==> 1_v 2_v 3_v."""
|
||||
labels = []
|
||||
|
|
@ -191,15 +201,16 @@ class Table:
|
|||
Human-readable information about the new data.
|
||||
|
||||
"""
|
||||
self._add_comment(label,data.shape[1:],info)
|
||||
dup = self.copy()
|
||||
dup._add_comment(label,data.shape[1:],info)
|
||||
|
||||
if re.match(r'[0-9]*?_',label):
|
||||
idx,key = label.split('_',1)
|
||||
iloc = self.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
|
||||
self.data.iloc[:,iloc] = data
|
||||
iloc = dup.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
|
||||
dup.data.iloc[:,iloc] = data
|
||||
else:
|
||||
self.data[label] = data.reshape(self.data[label].shape)
|
||||
|
||||
dup.data[label] = data.reshape(dup.data[label].shape)
|
||||
return dup
|
||||
|
||||
def add(self,label,data,info=None):
|
||||
"""
|
||||
|
|
@ -215,15 +226,17 @@ class Table:
|
|||
Human-readable information about the modified data.
|
||||
|
||||
"""
|
||||
self._add_comment(label,data.shape[1:],info)
|
||||
dup = self.copy()
|
||||
dup._add_comment(label,data.shape[1:],info)
|
||||
|
||||
self.shapes[label] = data.shape[1:] if len(data.shape) > 1 else (1,)
|
||||
dup.shapes[label] = data.shape[1:] if len(data.shape) > 1 else (1,)
|
||||
size = np.prod(data.shape[1:],dtype=int)
|
||||
new = pd.DataFrame(data=data.reshape(-1,size),
|
||||
columns=[label]*size,
|
||||
)
|
||||
new.index = self.data.index
|
||||
self.data = pd.concat([self.data,new],axis=1)
|
||||
new.index = dup.data.index
|
||||
dup.data = pd.concat([dup.data,new],axis=1)
|
||||
return dup
|
||||
|
||||
|
||||
def delete(self,label):
|
||||
|
|
@ -236,25 +249,31 @@ class Table:
|
|||
Column label.
|
||||
|
||||
"""
|
||||
self.data.drop(columns=label,inplace=True)
|
||||
del self.shapes[label]
|
||||
dup = self.copy()
|
||||
dup.data.drop(columns=label,inplace=True)
|
||||
del dup.shapes[label]
|
||||
return dup
|
||||
|
||||
|
||||
def rename(self,label_old,label_new,info=None):
|
||||
def rename(self,old,new,info=None):
|
||||
"""
|
||||
Rename column data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
label_old : str
|
||||
Old column label.
|
||||
label_new : str
|
||||
New column label.
|
||||
label_old : str or iterable of str
|
||||
Old column label(s).
|
||||
label_new : str or iterable of str
|
||||
New column label(s).
|
||||
|
||||
"""
|
||||
self.data.rename(columns={label_old:label_new},inplace=True)
|
||||
self.comments.append(f'{label_old} => {label_new}'+('' if info is None else f': {info}'))
|
||||
self.shapes = {(label if label != label_old else label_new):self.shapes[label] for label in self.shapes}
|
||||
dup = self.copy()
|
||||
columns = dict(zip([old] if isinstance(old,str) else old,
|
||||
[new] if isinstance(new,str) else new))
|
||||
dup.data.rename(columns=columns,inplace=True)
|
||||
dup.comments.append(f'{old} => {new}'+('' if info is None else f': {info}'))
|
||||
dup.shapes = {(label if label not in columns else columns[label]):dup.shapes[label] for label in dup.shapes}
|
||||
return dup
|
||||
|
||||
|
||||
def sort_by(self,labels,ascending=True):
|
||||
|
|
@ -269,10 +288,12 @@ class Table:
|
|||
Set sort order.
|
||||
|
||||
"""
|
||||
self._label_flat()
|
||||
self.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
|
||||
self._label_condensed()
|
||||
self.comments.append(f'sorted by [{", ".join(labels)}]')
|
||||
dup = self.copy()
|
||||
dup._label_flat()
|
||||
dup.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
|
||||
dup._label_condensed()
|
||||
dup.comments.append(f'sorted {"ascending" if ascending else "descending"} by {labels}')
|
||||
return dup
|
||||
|
||||
|
||||
def append(self,other):
|
||||
|
|
@ -290,7 +311,9 @@ class Table:
|
|||
if self.shapes != other.shapes or not self.data.columns.equals(other.data.columns):
|
||||
raise KeyError('Labels or shapes or order do not match')
|
||||
else:
|
||||
self.data = self.data.append(other.data,ignore_index=True)
|
||||
dup = self.copy()
|
||||
dup.data = dup.data.append(other.data,ignore_index=True)
|
||||
return dup
|
||||
|
||||
|
||||
def join(self,other):
|
||||
|
|
@ -308,9 +331,11 @@ class Table:
|
|||
if set(self.shapes) & set(other.shapes) or self.data.shape[0] != other.data.shape[0]:
|
||||
raise KeyError('Dublicated keys or row count mismatch')
|
||||
else:
|
||||
self.data = self.data.join(other.data)
|
||||
dup = self.copy()
|
||||
dup.data = dup.data.join(other.data)
|
||||
for key in other.shapes:
|
||||
self.shapes[key] = other.shapes[key]
|
||||
dup.shapes[key] = other.shapes[key]
|
||||
return dup
|
||||
|
||||
|
||||
def to_file(self,fname,format='ASCII',new_style=False):
|
||||
|
|
|
|||
|
|
@ -105,7 +105,7 @@ class TestOrientation:
|
|||
if update:
|
||||
coords = np.array([(1,i+1) for i,x in enumerate(eu)])
|
||||
table = Table(eu,{'Eulers':(3,)})
|
||||
table.add('pos',coords)
|
||||
table = table.add('pos',coords)
|
||||
table.to_ASCII(reference)
|
||||
assert np.allclose(eu,Table.from_ASCII(reference).get('Eulers'))
|
||||
|
||||
|
|
|
|||
|
|
@ -81,13 +81,11 @@ class TestTable:
|
|||
Table.from_ASCII(f)
|
||||
|
||||
def test_set(self,default):
|
||||
default.set('F',np.zeros((5,3,3)),'set to zero')
|
||||
d=default.get('F')
|
||||
d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
|
||||
assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
|
||||
|
||||
def test_set_component(self,default):
|
||||
default.set('1_F',np.zeros((5)),'set to zero')
|
||||
d=default.get('F')
|
||||
d = default.set('1_F',np.zeros((5)),'set to zero').get('F')
|
||||
assert np.allclose(d[...,0,0],0.0) and d.shape[1:] == (3,3)
|
||||
|
||||
def test_labels(self,default):
|
||||
|
|
@ -95,36 +93,34 @@ class TestTable:
|
|||
|
||||
def test_add(self,default):
|
||||
d = np.random.random((5,9))
|
||||
default.add('nine',d,'random data')
|
||||
assert np.allclose(d,default.get('nine'))
|
||||
assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
|
||||
|
||||
def test_rename_equivalent(self):
|
||||
x = np.random.random((5,13))
|
||||
t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
|
||||
s = t.get('s')
|
||||
t.rename('s','u')
|
||||
u = t.get('u')
|
||||
u = t.rename('s','u').get('u')
|
||||
assert np.all(s == u)
|
||||
|
||||
def test_rename_gone(self,default):
|
||||
default.rename('v','V')
|
||||
assert 'v' not in default.shapes and 'v' not in default.data.columns
|
||||
gone = default.rename('v','V')
|
||||
assert 'v' not in gone.shapes and 'v' not in gone.data.columns
|
||||
with pytest.raises(KeyError):
|
||||
default.get('v')
|
||||
gone.get('v')
|
||||
|
||||
def test_delete(self,default):
|
||||
default.delete('v')
|
||||
assert 'v' not in default.shapes and 'v' not in default.data.columns
|
||||
delete = default.delete('v')
|
||||
assert 'v' not in delete.shapes and 'v' not in delete.data.columns
|
||||
with pytest.raises(KeyError):
|
||||
default.get('v')
|
||||
delete.get('v')
|
||||
|
||||
def test_join(self):
|
||||
x = np.random.random((5,13))
|
||||
a = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
|
||||
y = np.random.random((5,3))
|
||||
b = Table(y,{'u':(3,)},['random test data'])
|
||||
a.join(b)
|
||||
assert np.array_equal(a.get('u'), b.get('u'))
|
||||
c = a.join(b)
|
||||
assert np.array_equal(c.get('u'), b.get('u'))
|
||||
|
||||
def test_join_invalid(self):
|
||||
x = np.random.random((5,13))
|
||||
|
|
@ -135,8 +131,8 @@ class TestTable:
|
|||
def test_append(self):
|
||||
x = np.random.random((5,13))
|
||||
a = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
|
||||
a.append(a)
|
||||
assert np.array_equal(a.data[:5].to_numpy(),a.data[5:].to_numpy())
|
||||
b = a.append(a)
|
||||
assert np.array_equal(b.data[:5].to_numpy(),b.data[5:].to_numpy())
|
||||
|
||||
def test_append_invalid(self):
|
||||
x = np.random.random((5,13))
|
||||
|
|
@ -163,29 +159,26 @@ class TestTable:
|
|||
x = np.random.random((5,13))
|
||||
t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
|
||||
unsort = t.get('s')
|
||||
t.sort_by('s')
|
||||
sort = t.get('s')
|
||||
sort = t.sort_by('s').get('s')
|
||||
assert np.all(np.sort(unsort,0)==sort)
|
||||
|
||||
def test_sort_component(self):
|
||||
x = np.random.random((5,12))
|
||||
t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
|
||||
unsort = t.get('4_F')
|
||||
t.sort_by('4_F')
|
||||
sort = t.get('4_F')
|
||||
sort = t.sort_by('4_F').get('4_F')
|
||||
assert np.all(np.sort(unsort,0)==sort)
|
||||
|
||||
def test_sort_revert(self):
|
||||
x = np.random.random((5,12))
|
||||
t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
|
||||
t.sort_by('4_F',ascending=False)
|
||||
sort = t.get('4_F')
|
||||
sort = t.sort_by('4_F',ascending=False).get('4_F')
|
||||
assert np.all(np.sort(sort,0)==sort[::-1,:])
|
||||
|
||||
def test_sort(self):
|
||||
t = Table(np.array([[0,1,],[2,1,]]),
|
||||
{'v':(2,)},
|
||||
['test data'])
|
||||
t.add('s',np.array(['b','a']))
|
||||
t.sort_by('s')
|
||||
['test data'])\
|
||||
.add('s',np.array(['b','a']))\
|
||||
.sort_by('s')
|
||||
assert np.all(t.get('1_v') == np.array([2,0]).reshape(2,1))
|
||||
|
|
|
|||
|
|
@ -255,7 +255,7 @@ subroutine crystallite_init
|
|||
do e = FEsolving_execElem(1),FEsolving_execElem(2)
|
||||
myNcomponents = homogenization_Ngrains(material_homogenizationAt(e))
|
||||
do i = FEsolving_execIP(1), FEsolving_execIP(2); do c = 1, myNcomponents
|
||||
crystallite_Fp0(1:3,1:3,c,i,e) = material_orientation0(c,i,e)%asMatrix() ! plastic def gradient reflects init orientation
|
||||
crystallite_Fp0(1:3,1:3,c,i,e) = material_orientation0(c,i,e)%asMatrix() ! Fp reflects initial orientation (see 10.1016/j.actamat.2006.01.005)
|
||||
crystallite_Fp0(1:3,1:3,c,i,e) = crystallite_Fp0(1:3,1:3,c,i,e) &
|
||||
/ math_det33(crystallite_Fp0(1:3,1:3,c,i,e))**(1.0_pReal/3.0_pReal)
|
||||
crystallite_Fi0(1:3,1:3,c,i,e) = constitutive_initialFi(c,i,e)
|
||||
|
|
|
|||
Loading…
Reference in New Issue