Merge branch 'Results.add_grid_filters' into 'development'
Result.add_xxx for functions on regular grids See merge request damask/DAMASK!394
This commit is contained in:
Francisco Jose Gallardo Basile
commit
0ef5825d35
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@ -1,58 +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 column(s) containing curl of requested column(s).
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Operates on periodic ordered three-dimensional data sets of vector and tensor fields.
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""", version = scriptID)
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parser.add_option('-p','--pos','--periodiccellcenter',
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dest = 'pos',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('-l','--label',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of field values')
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parser.set_defaults(pos = 'pos',
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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.labels is None: parser.error('no data column specified.')
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.Table.load(StringIO(''.join(sys.stdin.read())) if name is None else name)
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grid,size,origin = damask.grid_filters.cellsSizeOrigin_coordinates0_point(table.get(options.pos))
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for label in options.labels:
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field = table.get(label)
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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 = 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.save((sys.stdout if name is None else name))
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@ -1,58 +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 column(s) containing divergence of requested column(s).
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Operates on periodic ordered three-dimensional data sets of vector and tensor fields.
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""", version = scriptID)
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parser.add_option('-p','--pos','--periodiccellcenter',
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dest = 'pos',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('-l','--label',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of field values')
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parser.set_defaults(pos = 'pos',
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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.labels is None: parser.error('no data column specified.')
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.Table.load(StringIO(''.join(sys.stdin.read())) if name is None else name)
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grid,size,origin = damask.grid_filters.cellsSizeOrigin_coordinates0_point(table.get(options.pos))
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for label in options.labels:
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field = table.get(label)
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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 = 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.save((sys.stdout if name is None else name))
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@ -1,58 +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 column(s) containing gradient of requested column(s).
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Operates on periodic ordered three-dimensional data sets of scalar and vector fields.
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""", version = scriptID)
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parser.add_option('-p','--pos','--periodiccellcenter',
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dest = 'pos',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('-l','--label',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of field values')
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parser.set_defaults(pos = 'pos',
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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.labels is None: parser.error('no data column specified.')
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.Table.load(StringIO(''.join(sys.stdin.read())) if name is None else name)
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grid,size,origin = damask.grid_filters.cellsSizeOrigin_coordinates0_point(table.get(options.pos))
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for label in options.labels:
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field = table.get(label)
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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 = 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.save((sys.stdout if name is None else name))
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@ -163,11 +163,11 @@ class Config(dict):
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@abc.abstractmethod
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def is_complete(self):
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"""Check for completeness."""
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pass
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raise NotImplementedError
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@property
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@abc.abstractmethod
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def is_valid(self):
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"""Check for valid file layout."""
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pass
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raise NotImplementedError
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@ -26,6 +26,8 @@ from . import util
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h5py3 = h5py.__version__[0] == '3'
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chunk_size = 1024**2//8 # for compression in HDF5
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def _read(dataset):
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"""Read a dataset and its metadata into a numpy.ndarray."""
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@ -107,6 +109,8 @@ class Result:
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self.cells = f['geometry'].attrs['cells']
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self.size = f['geometry'].attrs['size']
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self.origin = f['geometry'].attrs['origin']
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else:
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self.add_curl = self.add_divergence = self.add_gradient = None
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r=re.compile('increment_[0-9]+')
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self.increments = sorted([i for i in f.keys() if r.match(i)],key=util.natural_sort)
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@ -187,12 +191,14 @@ class Result:
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choice = list(datasets).copy() if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
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[datasets]
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if what == 'increments':
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what_ = what if what.endswith('s') else what+'s'
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if what_ == 'increments':
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choice = [c if isinstance(c,str) and c.startswith('increment_') else
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self.increments[c] if isinstance(c,int) and c<0 else
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f'increment_{c}' for c in choice]
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elif what == 'times':
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what = 'increments'
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elif what_ == 'times':
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what_ = 'increments'
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if choice == ['*']:
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choice = self.increments
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else:
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@ -206,18 +212,18 @@ class Result:
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elif np.isclose(c,self.times[idx+1]):
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choice.append(self.increments[idx+1])
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valid = _match(choice,getattr(self,what))
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existing = set(self.visible[what])
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valid = _match(choice,getattr(self,what_))
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existing = set(self.visible[what_])
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dup = self.copy()
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if action == 'set':
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dup.visible[what] = sorted(set(valid), key=util.natural_sort)
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dup.visible[what_] = sorted(set(valid), key=util.natural_sort)
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elif action == 'add':
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add = existing.union(valid)
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dup.visible[what] = sorted(add, key=util.natural_sort)
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dup.visible[what_] = sorted(add, key=util.natural_sort)
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elif action == 'del':
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diff = existing.difference(valid)
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dup.visible[what] = sorted(diff, key=util.natural_sort)
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dup.visible[what_] = sorted(diff, key=util.natural_sort)
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return dup
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@ -1122,8 +1128,8 @@ class Result:
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'label': f"{t}({F['label']})",
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'meta': {
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'unit': F['meta']['unit'],
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'description': '{} stretch tensor of {} ({})'.format('left' if t.upper() == 'V' else 'right',
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F['label'],F['meta']['description']),
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'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
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+f"of {F['label']} ({F['meta']['description']})", # noqa
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'creator': 'add_stretch_tensor'
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}
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}
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@ -1143,7 +1149,148 @@ class Result:
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self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
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def _job(self,group,func,datasets,args,lock):
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@staticmethod
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def _add_curl(f,size):
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return {
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'data': grid_filters.curl(size,f['data']),
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'label': f"curl({f['label']})",
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'meta': {
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'unit': f['meta']['unit']+'/m',
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'description': f"curl of {f['label']} ({f['meta']['description']})",
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'creator': 'add_curl'
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}
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}
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def add_curl(self,f):
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"""
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Add curl of a field.
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Parameters
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----------
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f : str
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Name of vector or tensor field dataset.
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Notes
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-----
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This function is only available for structured grids,
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i.e. results from the grid solver.
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"""
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self._add_generic_grid(self._add_curl,{'f':f},{'size':self.size})
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@staticmethod
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def _add_divergence(f,size):
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return {
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'data': grid_filters.divergence(size,f['data']),
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'label': f"divergence({f['label']})",
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'meta': {
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'unit': f['meta']['unit']+'/m',
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'description': f"divergence of {f['label']} ({f['meta']['description']})",
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'creator': 'add_divergence'
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}
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}
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def add_divergence(self,f):
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"""
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Add divergence of a field.
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Parameters
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----------
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f : str
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Name of vector or tensor field dataset.
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Notes
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-----
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This function is only available for structured grids,
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i.e. results from the grid solver.
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"""
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self._add_generic_grid(self._add_divergence,{'f':f},{'size':self.size})
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@staticmethod
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def _add_gradient(f,size):
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return {
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'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
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f['data'].reshape(f['data'].shape+(1,))),
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'label': f"gradient({f['label']})",
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'meta': {
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'unit': f['meta']['unit']+'/m',
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'description': f"gradient of {f['label']} ({f['meta']['description']})",
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'creator': 'add_gradient'
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}
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}
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def add_gradient(self,f):
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"""
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Add gradient of a field.
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Parameters
|
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----------
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f : str
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Name of scalar or vector field dataset.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This function is only available for structured grids,
|
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i.e. results from the grid solver.
|
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"""
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self._add_generic_grid(self._add_gradient,{'f':f},{'size':self.size})
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def _add_generic_grid(self,func,datasets,args={},constituents=None):
|
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"""
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General function to add data on a regular grid.
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Parameters
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||||
----------
|
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func : function
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Callback function that calculates a new dataset from one or
|
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more datasets per HDF5 group.
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datasets : dictionary
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Details of the datasets to be used:
|
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{arg (name to which the data is passed in func): label (in HDF5 file)}.
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args : dictionary, optional
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Arguments parsed to func.
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"""
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if len(datasets) != 1 or self.N_constituents !=1:
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raise NotImplementedError
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at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
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with h5py.File(self.fname, 'a') as f:
|
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for increment in self.place(datasets.values(),False).items():
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for ty in increment[1].items():
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for field in ty[1].items():
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d = list(field[1].values())[0]
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if np.any(d.mask): continue
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||||
dataset = {'f':{'data':np.reshape(d.data,tuple(self.cells)+d.data.shape[1:]),
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'label':list(datasets.values())[0],
|
||||
'meta':d.data.dtype.metadata}}
|
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r = func(**dataset,**args)
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result = r['data'].reshape((-1,)+r['data'].shape[3:])
|
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for x in self.visible[ty[0]+'s']:
|
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if ty[0] == 'phase':
|
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result1 = result[at_cell_ph[0][x]]
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if ty[0] == 'homogenization':
|
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result1 = result[at_cell_ho[x]]
|
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|
||||
path = '/'.join(['/',increment[0],ty[0],x,field[0]])
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dataset = f[path].create_dataset(r['label'],data=result1)
|
||||
|
||||
now = datetime.datetime.now().astimezone()
|
||||
dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
|
||||
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
|
||||
|
||||
for l,v in r['meta'].items():
|
||||
dataset.attrs[l.lower()]=v if h5py3 else v.encode()
|
||||
creator = dataset.attrs['creator'] if h5py3 else \
|
||||
dataset.attrs['creator'].decode()
|
||||
dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \
|
||||
f'damask.Result.{creator} v{damask.version}'.encode()
|
||||
|
||||
|
||||
def _job_pointwise(self,group,func,datasets,args,lock):
|
||||
"""Execute job for _add_generic_pointwise."""
|
||||
try:
|
||||
datasets_in = {}
|
||||
|
|
@ -1159,8 +1306,7 @@ class Result:
|
|||
return [group,r]
|
||||
except Exception as err:
|
||||
print(f'Error during calculation: {err}.')
|
||||
return None
|
||||
|
||||
return [None,None]
|
||||
|
||||
def _add_generic_pointwise(self,func,datasets,args={}):
|
||||
"""
|
||||
|
|
@ -1178,7 +1324,6 @@ class Result:
|
|||
Arguments parsed to func.
|
||||
|
||||
"""
|
||||
chunk_size = 1024**2//8
|
||||
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4)))
|
||||
lock = mp.Manager().Lock()
|
||||
|
||||
|
|
@ -1195,34 +1340,34 @@ class Result:
|
|||
print('No matching dataset found, no data was added.')
|
||||
return
|
||||
|
||||
default_arg = partial(self._job,func=func,datasets=datasets,args=args,lock=lock)
|
||||
default_arg = partial(self._job_pointwise,func=func,datasets=datasets,args=args,lock=lock)
|
||||
|
||||
for result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
|
||||
for group,result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
|
||||
if not result:
|
||||
continue
|
||||
lock.acquire()
|
||||
with h5py.File(self.fname, 'a') as f:
|
||||
try:
|
||||
if self._allow_modification and result[0]+'/'+result[1]['label'] in f:
|
||||
dataset = f[result[0]+'/'+result[1]['label']]
|
||||
dataset[...] = result[1]['data']
|
||||
if self._allow_modification and '/'.join([group,result['label']]) in f:
|
||||
dataset = f['/'.join([group,result['label']])]
|
||||
dataset[...] = result['data']
|
||||
dataset.attrs['overwritten'] = True
|
||||
else:
|
||||
if result[1]['data'].size >= chunk_size*2:
|
||||
shape = result[1]['data'].shape
|
||||
if result['data'].size >= chunk_size*2:
|
||||
shape = result['data'].shape
|
||||
chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:]
|
||||
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'],
|
||||
maxshape=shape, chunks=chunks,
|
||||
compression='gzip', compression_opts=6,
|
||||
shuffle=True,fletcher32=True)
|
||||
dataset = f[group].create_dataset(result['label'],data=result['data'],
|
||||
maxshape=shape, chunks=chunks,
|
||||
compression='gzip', compression_opts=6,
|
||||
shuffle=True,fletcher32=True)
|
||||
else:
|
||||
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
|
||||
dataset = f[group].create_dataset(result['label'],data=result['data'])
|
||||
|
||||
now = datetime.datetime.now().astimezone()
|
||||
dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
|
||||
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
|
||||
|
||||
for l,v in result[1]['meta'].items():
|
||||
for l,v in result['meta'].items():
|
||||
dataset.attrs[l.lower()]=v if h5py3 else v.encode()
|
||||
creator = dataset.attrs['creator'] if h5py3 else \
|
||||
dataset.attrs['creator'].decode()
|
||||
|
|
|
|||
|
|
@ -49,10 +49,12 @@ class TestConfig:
|
|||
assert Config({'A':np.ones(3,'i')}).__repr__() == Config({'A':[1,1,1]}).__repr__()
|
||||
|
||||
def test_abstract_is_valid(self):
|
||||
assert Config().is_valid is None
|
||||
with pytest.raises(NotImplementedError):
|
||||
Config().is_valid
|
||||
|
||||
def test_abstract_is_complete(self):
|
||||
assert Config().is_complete is None
|
||||
with pytest.raises(NotImplementedError):
|
||||
Config().is_complete
|
||||
|
||||
@pytest.mark.parametrize('data',[Rotation.from_random(),Orientation.from_random(lattice='cI')])
|
||||
def test_rotation_orientation(self,data):
|
||||
|
|
|
|||
|
|
@ -269,6 +269,38 @@ class TestResult:
|
|||
with pytest.raises(TypeError):
|
||||
default.add_calculation('#invalid#*2')
|
||||
|
||||
|
||||
@pytest.mark.parametrize('shape',['vector','tensor'])
|
||||
def test_add_curl(self,default,shape):
|
||||
if shape == 'vector': default.add_calculation('#F#[:,:,0]','x','1','just a vector')
|
||||
if shape == 'tensor': default.add_calculation('#F#[:,:,:]','x','1','just a tensor')
|
||||
x = default.place('x')
|
||||
default.add_curl('x')
|
||||
in_file = default.place('curl(x)')
|
||||
in_memory = grid_filters.curl(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
|
||||
assert (in_file==in_memory).all()
|
||||
|
||||
@pytest.mark.parametrize('shape',['vector','tensor'])
|
||||
def test_add_divergence(self,default,shape):
|
||||
if shape == 'vector': default.add_calculation('#F#[:,:,0]','x','1','just a vector')
|
||||
if shape == 'tensor': default.add_calculation('#F#[:,:,:]','x','1','just a tensor')
|
||||
x = default.place('x')
|
||||
default.add_divergence('x')
|
||||
in_file = default.place('divergence(x)')
|
||||
in_memory = grid_filters.divergence(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
|
||||
assert (in_file==in_memory).all()
|
||||
|
||||
@pytest.mark.parametrize('shape',['scalar','pseudo_scalar','vector'])
|
||||
def test_add_gradient(self,default,shape):
|
||||
if shape == 'pseudo_scalar': default.add_calculation('#F#[:,0,0:1]','x','1','a pseudo scalar')
|
||||
if shape == 'scalar': default.add_calculation('#F#[:,0,0]','x','1','just a scalar')
|
||||
if shape == 'vector': default.add_calculation('#F#[:,:,1]','x','1','just a vector')
|
||||
x = default.place('x').reshape((np.product(default.cells),-1))
|
||||
default.add_gradient('x')
|
||||
in_file = default.place('gradient(x)')
|
||||
in_memory = grid_filters.gradient(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
|
||||
assert (in_file==in_memory).all()
|
||||
|
||||
@pytest.mark.parametrize('overwrite',['off','on'])
|
||||
def test_add_overwrite(self,default,overwrite):
|
||||
last = default.view('increments',-1)
|
||||
|
|
|
|||
Loading…
Reference in New Issue