more general constructor for Table

reading from file is just one case (now handled by static method).
General constructor needs data and header information as dictionary.
Works only with python 3.7 where dict keeps the insertion order. Earlier
python versions/other implementations might fail.

processing/post/addCauchy.py

@@ -1,6 +1,8 @@
 #!/usr/bin/env python3
 
 import os
+import sys
+from io import StringIO
 from optparse import OptionParser
 
 import damask
@@ -34,9 +36,14 @@ parser.set_defaults(defgrad = 'f',
 
 (options,filenames) = parser.parse_args()
 
+if filenames == []: filenames = [None]
+
 for name in filenames:
-    table = damask.Table(name)
-    table.add_array('Cauchy',damask.mechanics.Cauchy(table.get_array(options.defgrad).reshape(-1,3,3),
+    damask.util.report(scriptName,name)
+
+    table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else 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(name)
+    table.to_ASCII(sys.stdout if name is None else name)

python/damask/table.py

@@ -4,9 +4,42 @@ import pandas as pd
 import numpy as np
 
 class Table():
-    """Read and write to ASCII tables"""
+    """Store spreadsheet-like data."""
   
-    def __init__(self,fname):
+    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):
         try:
             f = open(fname)
         except TypeError:
@@ -17,43 +50,38 @@ class Table():
             header = int(header)
         else:
             raise Exception
-        self.comments = [f.readline()[:-1] for i in range(header-1)]
+        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     = [l.split('_',1)[1] if '_' in l else l for l in labels_raw]
        
-        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 = {}
+        headings = {}
         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])
+                headings[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]),)
+                    headings[l.split('_',1)[1]] = (int(l.split('_',1)[0]),)
                 else:
-                    self.shape[l]=(1,)
+                    headings[l]=(1,)
         
-        self.labels = list(dict.fromkeys(labels_repeated))
+        return Table(np.loadtxt(f),headings,comments)
 
 
     def get_array(self,label):
-        return self.data[label].to_numpy().reshape((-1,)+self.shape[label])
+        return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
 
 
     def add_array(self,label,array,info):
-        if np.product(array.shape[1:],dtype=int) == 1: 
+        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.shape[label] = array.shape[1:]
-        self.labels.append(label)
-        size = np.product(array.shape[1:]) 
+        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)
@@ -61,15 +89,15 @@ class Table():
 
     def to_ASCII(self,fname):
         labels = []
-        for l in self.labels:
-            if(self.shape[l] == (1,)):
+        for l in self.headings:
+            if(self.headings[l] == (1,)):
                 labels.append('{}'.format(l))
-            elif(len(self.shape[l]) == 1):
+            elif(len(self.headings[l]) == 1):
                 labels+=['{}_{}'.format(i+1,l)\
-                         for i in range(self.shape[l][0])]
+                         for i in range(self.headings[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]))]
+                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\