diff --git a/processing/post/addCauchy.py b/processing/post/addCauchy.py index 18c4ec215..9037567f8 100755 --- a/processing/post/addCauchy.py +++ b/processing/post/addCauchy.py @@ -1,11 +1,8 @@ #!/usr/bin/env python3 import os -import sys from optparse import OptionParser -import numpy as np - import damask @@ -37,53 +34,9 @@ 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) - -# ------------------------------------------ read header ------------------------------------------ - - table.head_read() - -# ------------------------------------------ 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(['{}_Cauchy'.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(1.0/np.linalg.det(F)*np.dot(P,F.T).reshape(9))) # [Cauchy] = (1/det(F)) * [P].[F_transpose] - outputAlive = table.data_write() # output processed line - -# ------------------------------------------ output finalization ----------------------------------- - - table.close() # close input ASCII table (works for stdin) + table = damask.Table(name) + table.add_array('Cauchy',damask.mechanics.Cauchy(table.get_array(options.defgrad).reshape(-1,3,3), + table.get_array(options.stress).reshape(-1,3,3)).reshape(-1,9), + scriptID) + table.to_ASCII() diff --git a/python/damask/__init__.py b/python/damask/__init__.py index f876d1417..da699be29 100644 --- a/python/damask/__init__.py +++ b/python/damask/__init__.py @@ -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 diff --git a/python/damask/table.py b/python/damask/table.py new file mode 100644 index 000000000..81901c252 --- /dev/null +++ b/python/damask/table.py @@ -0,0 +1,77 @@ +import re + +import pandas as pd +import numpy as np + +class Table(): + """Read and write to ASCII tables""" + + def __init__(self,name): + self.name = name + with open(self.name) as f: + header,keyword = f.readline().split() + if keyword == 'header': + header = int(header) + else: + raise Exception + self.comments = [f.readline()[:-1] for i in range(header-1)] + labels_raw = f.readline().split() + self.data = pd.read_csv(f,delim_whitespace=True,header=None) + + labels_repeated = [l.split('_',1)[1] if '_' in l else l for l in labels_raw] + self.data.rename(columns=dict(zip([l for l in self.data.columns],labels_repeated)),inplace=True) + + self.shape = {} + for l in labels_raw: + tensor_column = re.search(':.*?_',l) + if tensor_column: + my_shape = tensor_column.group()[1:-1].split('x') + self.shape[l.split('_',1)[1]] = tuple([int(d) for d in my_shape]) + else: + vector_column = re.match('.*?_',l) + if vector_column: + self.shape[l.split('_',1)[1]] = (int(l.split('_',1)[0]),) + else: + self.shape[l]=(1,) + + self.labels = list(dict.fromkeys(labels_repeated)) + + + def get_array(self,label): + return self.data[label].to_numpy().reshape((-1,)+self.shape[label]) + + + def add_array(self,label,array,info): + if np.product(array.shape[1:],dtype=int) == 1: + self.comments.append('{}: {}'.format(label,info)) + + else: + self.comments.append('{} {}: {}'.format(label,array.shape[1:],info)) + + self.shape[label] = array.shape[1:] + self.labels.append(label) + size = np.product(array.shape[1:]) + 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,name=None): + labels = [] + for l in self.labels: + if(self.shape[l] == (1,)): + labels.append('{}'.format(l)) + elif(len(self.shape[l]) == 1): + labels+=['{}_{}'.format(i+1,l)\ + for i in range(self.shape[l][0])] + else: + labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.shape[l]]),l)\ + for i in range(np.product(self.shape[l]))] + + header = ['{} header'.format(len(self.comments)+1)]\ + + self.comments\ + + [' '.join(labels)] + + with open(name if name is not None else self.name,'w') as f: + for line in header: f.write(line+'\n') + self.data.to_csv(f,sep=' ',index=False,header=False)