Merge branch 'new-ASCII' into grid-filters
This commit is contained in:
Martin Diehl
commit
98d5738fe6
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@ -2,10 +2,9 @@
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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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@ -36,54 +35,15 @@ parser.set_defaults(defgrad = 'f',
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)
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(options,filenames) = parser.parse_args()
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name, buffered = False)
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except:
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continue
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damask.util.report(scriptName,name)
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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table.add_array('Cauchy',
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damask.mechanics.Cauchy(table.get_array(options.defgrad).reshape(-1,3,3),
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table.get_array(options.stress).reshape(-1,3,3)).reshape(-1,9),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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column = {}
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for tensor in [options.defgrad,options.stress]:
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dim = table.label_dimension(tensor)
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if dim < 0: errors.append('column {} not found.'.format(tensor))
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elif dim != 9: errors.append('column {} is not a tensor.'.format(tensor))
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else:
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column[tensor] = table.label_index(tensor)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.labels_append(['{}_Cauchy'.format(i+1) for i in range(9)]) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ process data ------------------------------------------
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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F = np.array(list(map(float,table.data[column[options.defgrad]:column[options.defgrad]+9])),'d').reshape(3,3)
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P = np.array(list(map(float,table.data[column[options.stress ]:column[options.stress ]+9])),'d').reshape(3,3)
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table.data_append(list(1.0/np.linalg.det(F)*np.dot(P,F.T).reshape(9))) # [Cauchy] = (1/det(F)) * [P].[F_transpose]
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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table.to_ASCII(sys.stdout if name is None else name)
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@ -2,22 +2,16 @@
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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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def determinant(m):
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return +m[0]*m[4]*m[8] \
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+m[1]*m[5]*m[6] \
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+m[2]*m[3]*m[7] \
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-m[2]*m[4]*m[6] \
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-m[1]*m[3]*m[8] \
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-m[0]*m[5]*m[7]
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# --------------------------------------------------------------------
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# MAIN
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@ -34,61 +28,18 @@ parser.add_option('-t','--tensor',
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help = 'heading of columns containing tensor field values')
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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if options.tensor is None:
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parser.error('no data column specified.')
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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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 tensor in options.tensor:
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table.add_array('det({})'.format(tensor),
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np.linalg.det(table.get_array(tensor).reshape(-1,3,3)),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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items = {
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'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.tensor, 'column': []},
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}
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errors = []
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remarks = []
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for type, data in items.items():
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for what in data['labels']:
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dim = table.label_dimension(what)
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if dim != data['dim']: remarks.append('column {} is not a {}...'.format(what,type))
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else:
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items[type]['column'].append(table.label_index(what))
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table.labels_append('det({})'.format(what)) # extend ASCII header with new labels
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.head_write()
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# ------------------------------------------ process data ------------------------------------------
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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for type, data in items.items():
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for column in data['column']:
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table.data_append(determinant(list(map(float,table.data[column: column+data['dim']]))))
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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table.to_ASCII(sys.stdout if name is None else name)
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@ -2,6 +2,7 @@
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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 damask
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@ -9,17 +10,6 @@ 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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oneThird = 1.0/3.0
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def deviator(m,spherical = False): # Careful, do not change the value of m, its intent(inout)!
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sph = oneThird*(m[0]+m[4]+m[8])
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dev = [
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m[0]-sph, m[1], m[2],
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m[3], m[4]-sph, m[5],
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m[6], m[7], m[8]-sph,
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]
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return dev,sph if spherical else dev
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# --------------------------------------------------------------------
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# MAIN
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@ -40,67 +30,22 @@ parser.add_option('-s','--spherical',
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help = 'report spherical part of tensor (hydrostatic component, pressure)')
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(options,filenames) = parser.parse_args()
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if options.tensor is None:
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parser.error('no data column specified...')
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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if options.tensor is None:
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parser.error('no data column specified...')
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name, buffered = False)
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except:
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continue
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damask.util.report(scriptName,name)
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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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for tensor in options.tensor:
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table.add_array('dev({})'.format(tensor),
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damask.mechanics.deviatoric_part(table.get_array(tensor).reshape(-1,3,3)).reshape((-1,9)),
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scriptID+' '+' '.join(sys.argv[1:]))
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if options.spherical:
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table.add_array('sph({})'.format(tensor),
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damask.mechanics.spherical_part(table.get_array(tensor).reshape(-1,3,3)),
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scriptID+' '+' '.join(sys.argv[1:]))
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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items = {
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'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.tensor, 'active':[], 'column': []},
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}
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errors = []
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remarks = []
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column = {}
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for type, data in items.items():
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for what in data['labels']:
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dim = table.label_dimension(what)
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if dim != data['dim']: remarks.append('column {} is not a {}.'.format(what,type))
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else:
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items[type]['active'].append(what)
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items[type]['column'].append(table.label_index(what))
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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for type, data in items.items():
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for label in data['active']:
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table.labels_append(['{}_dev({})'.format(i+1,label) for i in range(data['dim'])] + \
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(['sph({})'.format(label)] if options.spherical else [])) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ process data ------------------------------------------
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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for type, data in items.items():
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for column in data['column']:
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table.data_append(deviator(list(map(float,table.data[column:
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column+data['dim']])),options.spherical))
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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table.to_ASCII(sys.stdout if name is None else name)
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@ -2,10 +2,8 @@
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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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from collections import OrderedDict
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import numpy as np
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import damask
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@ -13,15 +11,6 @@ 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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def Mises(what,tensor):
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dev = tensor - np.trace(tensor)/3.0*np.eye(3)
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symdev = 0.5*(dev+dev.T)
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return np.sqrt(np.sum(symdev*symdev.T)*
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{
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'stress': 3.0/2.0,
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'strain': 2.0/3.0,
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}[what.lower()])
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# --------------------------------------------------------------------
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# MAIN
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|
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@ -47,62 +36,21 @@ parser.set_defaults(strain = [],
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(options,filenames) = parser.parse_args()
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if options.stress is [] and options.strain is []:
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parser.error('no data column specified...')
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# --- loop over input files -------------------------------------------------------------------------
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parser.error('no data column specified...')
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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items = OrderedDict([
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('strain', {'dim': 9, 'shape': [3,3], 'labels':options.strain, 'active':[], 'column': []}),
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('stress', {'dim': 9, 'shape': [3,3], 'labels':options.stress, 'active':[], 'column': []})
|
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])
|
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errors = []
|
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remarks = []
|
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|
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for type, data in items.items():
|
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for what in data['labels']:
|
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dim = table.label_dimension(what)
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if dim != data['dim']: remarks.append('column {} is not a {}...'.format(what,type))
|
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else:
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items[type]['active'].append(what)
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items[type]['column'].append(table.label_index(what))
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table.labels_append('Mises({})'.format(what)) # extend ASCII header with new labels
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
|
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|
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# ------------------------------------------ assemble header --------------------------------------
|
||||
|
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
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table.head_write()
|
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|
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# ------------------------------------------ process data ------------------------------------------
|
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|
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outputAlive = True
|
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while outputAlive and table.data_read(): # read next data line of ASCII table
|
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for type, data in items.items():
|
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for column in data['column']:
|
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table.data_append(Mises(type,
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np.array(table.data[column:column+data['dim']],'d').reshape(data['shape'])))
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outputAlive = table.data_write() # output processed line
|
||||
|
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# ------------------------------------------ output finalization -----------------------------------
|
||||
|
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table.close() # close input ASCII table (works for stdin)
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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 strain in options.strain:
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table.add_array('Mises({})'.format(strain),
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damask.mechanics.Mises_strain(damask.mechanics.symmetric(table.get_array(strain).reshape(-1,3,3))),
|
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scriptID+' '+' '.join(sys.argv[1:]))
|
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for stress in options.stress:
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table.add_array('Mises({})'.format(stress),
|
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damask.mechanics.Mises_stress(damask.mechanics.symmetric(table.get_array(stress).reshape(-1,3,3))),
|
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scriptID+' '+' '.join(sys.argv[1:]))
|
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|
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table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -2,10 +2,9 @@
|
|||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
||||
import damask
|
||||
|
||||
|
||||
|
|
@ -36,53 +35,16 @@ parser.set_defaults(defgrad = 'f',
|
|||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
|
||||
table.head_read()
|
||||
table.add_array('S',
|
||||
damask.mechanics.PK2(table.get_array(options.defgrad).reshape(-1,3,3),
|
||||
table.get_array(options.stress).reshape(-1,3,3)).reshape(-1,9),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
# ------------------------------------------ sanity checks ----------------------------------------
|
||||
|
||||
errors = []
|
||||
column = {}
|
||||
|
||||
for tensor in [options.defgrad,options.stress]:
|
||||
dim = table.label_dimension(tensor)
|
||||
if dim < 0: errors.append('column {} not found.'.format(tensor))
|
||||
elif dim != 9: errors.append('column {} is not a tensor.'.format(tensor))
|
||||
else:
|
||||
column[tensor] = table.label_index(tensor)
|
||||
|
||||
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(['{}_S'.format(i+1) for i in range(9)]) # extend ASCII header with new labels
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data ------------------------------------------
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
F = np.array(list(map(float,table.data[column[options.defgrad]:column[options.defgrad]+9])),'d').reshape(3,3)
|
||||
P = np.array(list(map(float,table.data[column[options.stress ]:column[options.stress ]+9])),'d').reshape(3,3)
|
||||
table.data_append(list(np.dot(np.linalg.inv(F),P).reshape(9))) # [S] =[P].[F-1]
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
# ------------------------------------------ output finalization -----------------------------------
|
||||
|
||||
table.close() # close input ASCII table (works for stdin)
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -2,10 +2,9 @@
|
|||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
||||
import damask
|
||||
|
||||
|
||||
|
|
@ -35,58 +34,17 @@ parser.set_defaults(label = [],
|
|||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
if len(options.label) != len(options.factor):
|
||||
parser.error('number of column labels and factors do not match.')
|
||||
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
if len(options.label) != len(options.factor):
|
||||
parser.error('number of column labels and factors do not match.')
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
for i,label in enumerate(options.label):
|
||||
table.set_array(label,table.get_array(label)*float(options.factor[i]),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.head_read()
|
||||
|
||||
errors = []
|
||||
remarks = []
|
||||
columns = []
|
||||
dims = []
|
||||
factors = []
|
||||
|
||||
for what,factor in zip(options.label,options.factor):
|
||||
col = table.label_index(what)
|
||||
if col < 0: remarks.append('column {} not found...'.format(what,type))
|
||||
else:
|
||||
columns.append(col)
|
||||
factors.append(float(factor))
|
||||
dims.append(table.label_dimension(what))
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data ------------------------------------------
|
||||
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
for col,dim,factor in zip(columns,dims,factors): # loop over items
|
||||
table.data[col:col+dim] = factor * np.array(table.data[col:col+dim],'d')
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
# ------------------------------------------ output finalization -----------------------------------
|
||||
|
||||
table.close() # close ASCII tables
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -2,10 +2,9 @@
|
|||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import numpy as np
|
||||
|
||||
import damask
|
||||
|
||||
|
||||
|
|
@ -35,58 +34,17 @@ parser.set_defaults(label = [],
|
|||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
if len(options.label) != len(options.offset):
|
||||
parser.error('number of column labels and offsets do not match.')
|
||||
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
if len(options.label) != len(options.offset):
|
||||
parser.error('number of column labels and offsets do not match.')
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
for i,label in enumerate(options.label):
|
||||
table.set_array(label,table.get_array(label)+float(options.offset[i]),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))
|
||||
|
||||
table.head_read()
|
||||
|
||||
errors = []
|
||||
remarks = []
|
||||
columns = []
|
||||
dims = []
|
||||
offsets = []
|
||||
|
||||
for what,offset in zip(options.label,options.offset):
|
||||
col = table.label_index(what)
|
||||
if col < 0: remarks.append('column {} not found...'.format(what,type))
|
||||
else:
|
||||
columns.append(col)
|
||||
offsets.append(float(offset))
|
||||
dims.append(table.label_dimension(what))
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data ------------------------------------------
|
||||
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
for col,dim,offset in zip(columns,dims,offsets): # loop over items
|
||||
table.data[col:col+dim] = offset + np.array(table.data[col:col+dim],'d')
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
# ------------------------------------------ output finalization -----------------------------------
|
||||
|
||||
table.close() # close ASCII tables
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ name = 'damask'
|
|||
# classes
|
||||
from .environment import Environment # noqa
|
||||
from .asciitable import ASCIItable # noqa
|
||||
from .table import Table # noqa
|
||||
|
||||
from .config import Material # noqa
|
||||
from .colormaps import Colormap, Color # noqa
|
||||
|
|
|
|||
|
|
@ -18,17 +18,17 @@ class DADF5():
|
|||
"""
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
def __init__(self,filename):
|
||||
def __init__(self,fname):
|
||||
"""
|
||||
Opens an existing DADF5 file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
filename : str
|
||||
fname : str
|
||||
name of the DADF5 file to be openend.
|
||||
|
||||
"""
|
||||
with h5py.File(filename,'r') as f:
|
||||
with h5py.File(fname,'r') as f:
|
||||
|
||||
if f.attrs['DADF5-major'] != 0 or not 2 <= f.attrs['DADF5-minor'] <= 3:
|
||||
raise TypeError('Unsupported DADF5 version {} '.format(f.attrs['DADF5-version']))
|
||||
|
|
@ -64,7 +64,7 @@ class DADF5():
|
|||
'con_physics': self.con_physics,
|
||||
'mat_physics': self.mat_physics}
|
||||
|
||||
self.filename = filename
|
||||
self.fname = fname
|
||||
|
||||
|
||||
def __manage_visible(self,datasets,what,action):
|
||||
|
|
@ -298,7 +298,7 @@ class DADF5():
|
|||
|
||||
groups = []
|
||||
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for i in self.iter_visible('increments'):
|
||||
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
|
||||
for oo in self.iter_visible(o):
|
||||
|
|
@ -315,7 +315,7 @@ class DADF5():
|
|||
def list_data(self):
|
||||
"""Return information on all active datasets in the file."""
|
||||
message = ''
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for s,i in enumerate(self.iter_visible('increments')):
|
||||
message+='\n{} ({}s)\n'.format(i,self.times[s])
|
||||
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
|
||||
|
|
@ -336,7 +336,7 @@ class DADF5():
|
|||
def get_dataset_location(self,label):
|
||||
"""Return the location of all active datasets with given label."""
|
||||
path = []
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for i in self.iter_visible('increments'):
|
||||
k = '/'.join([i,'geometry',label])
|
||||
try:
|
||||
|
|
@ -358,14 +358,14 @@ class DADF5():
|
|||
|
||||
def get_constituent_ID(self,c=0):
|
||||
"""Pointwise constituent ID."""
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
names = f['/mapping/cellResults/constituent']['Name'][:,c].astype('str')
|
||||
return np.array([int(n.split('_')[0]) for n in names.tolist()],dtype=np.int32)
|
||||
|
||||
|
||||
def get_crystal_structure(self): # ToDo: extension to multi constituents/phase
|
||||
"""Info about the crystal structure."""
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
return f[self.get_dataset_location('orientation')[0]].attrs['Lattice'].astype('str') # np.bytes_ to string
|
||||
|
||||
|
||||
|
|
@ -375,7 +375,7 @@ class DADF5():
|
|||
|
||||
If more than one path is given, the dataset is composed of the individual contributions.
|
||||
"""
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
shape = (self.Nmaterialpoints,) + np.shape(f[path[0]])[1:]
|
||||
if len(shape) == 1: shape = shape +(1,)
|
||||
dataset = np.full(shape,np.nan,dtype=np.dtype(f[path[0]]))
|
||||
|
|
@ -418,7 +418,7 @@ class DADF5():
|
|||
)
|
||||
return np.concatenate((x[:,:,:,None],y[:,:,:,None],y[:,:,:,None]),axis = 3).reshape([np.product(self.grid),3])
|
||||
else:
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
return f['geometry/x_c'][()]
|
||||
|
||||
|
||||
|
|
@ -798,7 +798,7 @@ class DADF5():
|
|||
todo = []
|
||||
# ToDo: It would be more memory efficient to read only from file when required, i.e. do to it in pool.add_task
|
||||
for group in self.groups_with_datasets([d['label'] for d in datasets_requested]):
|
||||
with h5py.File(self.filename,'r') as f:
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
datasets_in = {}
|
||||
for d in datasets_requested:
|
||||
loc = f[group+'/'+d['label']]
|
||||
|
|
@ -813,7 +813,7 @@ class DADF5():
|
|||
N_not_calculated = len(todo)
|
||||
while N_not_calculated > 0:
|
||||
result = results.get()
|
||||
with h5py.File(self.filename,'a') as f: # write to file
|
||||
with h5py.File(self.fname,'a') as f: # write to file
|
||||
dataset_out = f[result['group']].create_dataset(result['label'],data=result['data'])
|
||||
for k in result['meta'].keys():
|
||||
dataset_out.attrs[k] = result['meta'][k].encode()
|
||||
|
|
|
|||
|
|
@ -239,8 +239,8 @@ class Geom():
|
|||
header.append('homogenization {}'.format(self.get_homogenization()))
|
||||
return header
|
||||
|
||||
@classmethod
|
||||
def from_file(cls,fname):
|
||||
@staticmethod
|
||||
def from_file(fname):
|
||||
"""
|
||||
Reads a geom file.
|
||||
|
||||
|
|
@ -300,7 +300,7 @@ class Geom():
|
|||
if not np.any(np.mod(microstructure.flatten(),1) != 0.0): # no float present
|
||||
microstructure = microstructure.astype('int')
|
||||
|
||||
return cls(microstructure.reshape(grid),size,origin,homogenization,comments)
|
||||
return Geom(microstructure.reshape(grid),size,origin,homogenization,comments)
|
||||
|
||||
|
||||
def to_file(self,fname,pack=None):
|
||||
|
|
|
|||
|
|
@ -19,7 +19,26 @@ def Cauchy(F,P):
|
|||
else:
|
||||
sigma = np.einsum('i,ijk,ilk->ijl',1.0/np.linalg.det(F),P,F)
|
||||
return symmetric(sigma)
|
||||
|
||||
|
||||
|
||||
def PK2(F,P):
|
||||
"""
|
||||
Return 2. Piola-Kirchhoff stress calculated from 1. Piola-Kirchhoff stress and deformation gradient.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
F : numpy.array of shape (:,3,3) or (3,3)
|
||||
Deformation gradient.
|
||||
P : numpy.array of shape (:,3,3) or (3,3)
|
||||
1. Piola-Kirchhoff stress.
|
||||
|
||||
"""
|
||||
if np.shape(F) == np.shape(P) == (3,3):
|
||||
S = np.dot(np.linalg.inv(F),P)
|
||||
else:
|
||||
S = np.einsum('ijk,ikl->ijl',np.linalg.inv(F),P)
|
||||
return S
|
||||
|
||||
|
||||
def strain_tensor(F,t,m):
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -0,0 +1,138 @@
|
|||
import re
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
class Table():
|
||||
"""Store spreadsheet-like data."""
|
||||
|
||||
def __init__(self,array,headings,comments=None):
|
||||
"""
|
||||
New spreadsheet data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
array : numpy.ndarray
|
||||
Data.
|
||||
headings : dict
|
||||
Column headings. Labels as keys and shape as tuple. Example 'F':(3,3) for a deformation gradient.
|
||||
comments : iterable of str, optional
|
||||
Additional, human-readable information
|
||||
|
||||
"""
|
||||
self.data = pd.DataFrame(data=array)
|
||||
|
||||
d = {}
|
||||
i = 0
|
||||
for label in headings:
|
||||
for components in range(np.prod(headings[label])):
|
||||
d[i] = label
|
||||
i+=1
|
||||
|
||||
self.data.rename(columns=d,inplace=True)
|
||||
|
||||
if comments is None:
|
||||
self.comments = []
|
||||
else:
|
||||
self.comments = [c for c in comments]
|
||||
|
||||
self.headings = headings
|
||||
|
||||
@staticmethod
|
||||
def from_ASCII(fname):
|
||||
"""
|
||||
Create table from ASCII file.
|
||||
|
||||
The first line needs to indicate the number of subsequent header lines as 'n header'.
|
||||
Vector data labels are indicated by '1_x, 2_x, ..., n_x'.
|
||||
Tensor data labels are indicated by '3x3:1_x, 3x3:2_x, ..., 3x3:9_x'.
|
||||
"""
|
||||
try:
|
||||
f = open(fname)
|
||||
except TypeError:
|
||||
f = fname
|
||||
|
||||
header,keyword = f.readline().split()
|
||||
if keyword == 'header':
|
||||
header = int(header)
|
||||
else:
|
||||
raise Exception
|
||||
comments = [f.readline()[:-1] for i in range(header-1)]
|
||||
labels = f.readline().split()
|
||||
|
||||
headings = {}
|
||||
for label in labels:
|
||||
tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label)
|
||||
if tensor_column:
|
||||
my_shape = tensor_column.group().split(':',1)[0].split('x')
|
||||
headings[label.split('_',1)[1]] = tuple([int(d) for d in my_shape])
|
||||
else:
|
||||
vector_column = re.match(r'[0-9]*?_',label)
|
||||
if vector_column:
|
||||
headings[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
|
||||
else:
|
||||
headings[label]=(1,)
|
||||
|
||||
return Table(np.loadtxt(f),headings,comments)
|
||||
|
||||
def get_array(self,label):
|
||||
"""Return data as array."""
|
||||
if re.match(r'[0-9]*?_',label):
|
||||
idx,key = label.split('_',1)
|
||||
return self.data[key].to_numpy()[:,int(idx)-1]
|
||||
else:
|
||||
return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
|
||||
|
||||
def set_array(self,label,array,info):
|
||||
"""Set data."""
|
||||
if np.prod(array.shape[1:],dtype=int) == 1:
|
||||
self.comments.append('{}: {}'.format(label,info))
|
||||
else:
|
||||
self.comments.append('{} {}: {}'.format(label,array.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] = array
|
||||
else:
|
||||
self.data[label] = array.reshape(self.data[label].shape)
|
||||
|
||||
|
||||
def get_labels(self):
|
||||
"""Return the labels of all columns."""
|
||||
return [label for label in self.headings]
|
||||
|
||||
def add_array(self,label,array,info):
|
||||
if np.prod(array.shape[1:],dtype=int) == 1:
|
||||
self.comments.append('{}: {}'.format(label,info))
|
||||
else:
|
||||
self.comments.append('{} {}: {}'.format(label,array.shape[1:],info))
|
||||
|
||||
self.headings[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
|
||||
size = np.prod(array.shape[1:],dtype=int)
|
||||
new_data = pd.DataFrame(data=array.reshape(-1,size),
|
||||
columns=[label for l in range(size)])
|
||||
self.data = pd.concat([self.data,new_data],axis=1)
|
||||
|
||||
def to_ASCII(self,fname):
|
||||
labels = []
|
||||
for l in self.headings:
|
||||
if(self.headings[l] == (1,)):
|
||||
labels.append('{}'.format(l))
|
||||
elif(len(self.headings[l]) == 1):
|
||||
labels+=['{}_{}'.format(i+1,l)\
|
||||
for i in range(self.headings[l][0])]
|
||||
else:
|
||||
labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.headings[l]]),l)\
|
||||
for i in range(np.prod(self.headings[l],dtype=int))]
|
||||
|
||||
header = ['{} header'.format(len(self.comments)+1)]\
|
||||
+ self.comments\
|
||||
+ [' '.join(labels)]
|
||||
|
||||
try:
|
||||
f = open(fname,'w')
|
||||
except TypeError:
|
||||
f = fname
|
||||
for line in header: f.write(line+'\n')
|
||||
self.data.to_csv(f,sep=' ',index=False,header=False)
|
||||
Loading…
Reference in New Issue