[skip sc] first draft
This commit is contained in:
Martin Diehl
parent
1c10459a5a
commit
fb286af354
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@ -1,11 +1,8 @@
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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 optparse import OptionParser
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import numpy as np
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import damask
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@ -37,53 +34,9 @@ parser.set_defaults(defgrad = 'f',
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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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# ------------------------------------------ read header ------------------------------------------
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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 = damask.Table(name)
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table.add_array('Cauchy',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)
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table.to_ASCII()
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@ -9,6 +9,7 @@ name = 'damask'
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# classes
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from .environment import Environment # noqa
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from .asciitable import ASCIItable # noqa
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from .table import Table # noqa
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from .config import Material # noqa
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from .colormaps import Colormap, Color # noqa
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@ -0,0 +1,77 @@
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import re
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import pandas as pd
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import numpy as np
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class Table():
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"""Read and write to ASCII tables"""
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def __init__(self,name):
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self.name = name
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with open(self.name) as f:
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header,keyword = f.readline().split()
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if keyword == 'header':
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header = int(header)
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else:
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raise Exception
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self.comments = [f.readline()[:-1] for i in range(header-1)]
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labels_raw = f.readline().split()
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self.data = pd.read_csv(f,delim_whitespace=True,header=None)
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labels_repeated = [l.split('_',1)[1] if '_' in l else l for l in labels_raw]
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self.data.rename(columns=dict(zip([l for l in self.data.columns],labels_repeated)),inplace=True)
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self.shape = {}
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for l in labels_raw:
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tensor_column = re.search(':.*?_',l)
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if tensor_column:
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my_shape = tensor_column.group()[1:-1].split('x')
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self.shape[l.split('_',1)[1]] = tuple([int(d) for d in my_shape])
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else:
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vector_column = re.match('.*?_',l)
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if vector_column:
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self.shape[l.split('_',1)[1]] = (int(l.split('_',1)[0]),)
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else:
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self.shape[l]=(1,)
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self.labels = list(dict.fromkeys(labels_repeated))
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def get_array(self,label):
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return self.data[label].to_numpy().reshape((-1,)+self.shape[label])
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def add_array(self,label,array,info):
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if np.product(array.shape[1:],dtype=int) == 1:
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self.comments.append('{}: {}'.format(label,info))
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else:
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self.comments.append('{} {}: {}'.format(label,array.shape[1:],info))
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self.shape[label] = array.shape[1:]
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self.labels.append(label)
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size = np.product(array.shape[1:])
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new_data = pd.DataFrame(data=array.reshape(-1,size),
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columns=[label for l in range(size)])
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self.data = pd.concat([self.data,new_data],axis=1)
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def to_ASCII(self,name=None):
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labels = []
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for l in self.labels:
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if(self.shape[l] == (1,)):
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labels.append('{}'.format(l))
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elif(len(self.shape[l]) == 1):
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labels+=['{}_{}'.format(i+1,l)\
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for i in range(self.shape[l][0])]
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else:
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labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.shape[l]]),l)\
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for i in range(np.product(self.shape[l]))]
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header = ['{} header'.format(len(self.comments)+1)]\
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+ self.comments\
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+ [' '.join(labels)]
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with open(name if name is not None else self.name,'w') as f:
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for line in header: f.write(line+'\n')
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self.data.to_csv(f,sep=' ',index=False,header=False)
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