[skip sc] first draft

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.Table(name)
-    table = damask.ASCIItable(name = name, buffered = False)
+    table.add_array('Cauchy',damask.mechanics.Cauchy(table.get_array(options.defgrad).reshape(-1,3,3),
-  except:
+                                                     table.get_array(options.stress).reshape(-1,3,3)).reshape(-1,9),
-    continue
+                    scriptID)
-  damask.util.report(scriptName,name)
+    table.to_ASCII()
-
-# ------------------------------------------ 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)

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

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)