changed library function name table.labels_index() to table.label_index()

lib/damask/asciitable.py

@@ -164,8 +164,8 @@ class ASCIItable():
     self.__IO__['labels'] = False
 
 # ------------------------------------------------------------------
-  def labels_index(self,
+  def label_index(self,
-                   labels):
+                  labels):
     '''
        tell index of column label(s).
        return numpy array if asked for list of labels.
@@ -193,6 +193,36 @@ class ASCIItable():
 
     return np.array(idx) if isinstance(idx,list) else idx
 
+# ------------------------------------------------------------------
+  def labels_dimension(self,
+                       labels):
+    '''
+       tell dimension (length) of column label(s).
+       return numpy array if asked for list of labels.
+       transparently deals with label positions implicitly given as numbers or their headings given as strings.
+    '''
+    if isinstance(labels,list):                                           # check whether list of labels is requested
+      dim = []
+      for label in labels:
+        if label != None:
+          try:
+            idx.append(int(label))                                        # column given as integer number?
+          except ValueError:
+            try:
+              idx.append(self.labels.index(label))                        # locate string in label list
+            except ValueError:
+             idx.append(-1)                                               # not found...
+    else:
+      try:
+        idx = int(labels)
+      except ValueError:
+        try:
+          idx = self.labels.index(labels)
+        except(ValueError):
+          idx = None if labels == None else -1
+
+    return np.array(idx) if isinstance(idx,list) else idx
+
 # ------------------------------------------------------------------
   def info_append(self,
                   what):
@@ -273,7 +303,7 @@ class ASCIItable():
       use = np.arange(self.__IO__['validReadSize'])                                             # use all columns (and keep labels intact)
       labels_missing = []
     else:
-      indices = self.labels_index(labels)                                                           # check requested labels
+      indices = self.label_index(labels)                                                           # check requested labels
       present  = np.where(indices >= 0)[0]                                                          # positions in request list of labels that are present ...
       missing  = np.where(indices <  0)[0]                                                          # ... and missing in table
       labels_missing    =      np.array(     labels)        [missing]                               # corresponding labels ...

processing/post/averageTable.py

@@ -58,9 +58,9 @@ for file in files:
 
   rows, cols = table.data_readArray()
 
-  table.data = table.data[np.lexsort([table.data[:,table.labels_index(options.key)]])]
+  table.data = table.data[np.lexsort([table.data[:,table.label_index(options.key)]])]
   
-  values, index = np.unique(table.data[:,table.labels_index(options.key)], return_index=True)
+  values, index = np.unique(table.data[:,table.label_index(options.key)], return_index=True)
   index = np.append(index,rows)
   avgTable = np.empty((len(values), cols))
   

processing/post/sortTable.py

@@ -65,7 +65,7 @@ for file in files:
 
   table.data_readArray()
   cols = []
-  for column in table.labels_index(options.keys):
+  for column in table.label_index(options.keys):
     cols += [table.data[:,column]]
 
   ind = np.lexsort(cols)

processing/pre/geom_fromVoronoiTessellation.py

@@ -153,32 +153,32 @@ for file in files:
   table.head_read()
 
   labels = []
-  if np.all(table.labels_index(['1_coords','2_coords','3_coords']) != -1):
+  if np.all(table.label_index(['1_coords','2_coords','3_coords']) != -1):
     coords = ['1_coords','2_coords','3_coords']
-  elif np.all(table.labels_index(['x','y','z']) != -1):
+  elif np.all(table.label_index(['x','y','z']) != -1):
     coords = ['x','y','z']
   else:
     file['croak'].write('no coordinate data (1/2/3_coords | x/y/z) found ...')
     continue
 
   labels += coords
-  hasEulers = np.all(table.labels_index(['phi1','Phi','phi2']) != -1)
+  hasEulers = np.all(table.label_index(['phi1','Phi','phi2']) != -1)
   if hasEulers:
     labels += ['phi1','Phi','phi2']
     
-  hasGrains = table.labels_index('microstructure') != -1
+  hasGrains = table.label_index('microstructure') != -1
   if hasGrains:
     labels += ['microstructure']
     
-  hasWeight = table.labels_index('weight') != -1
+  hasWeight = table.label_index('weight') != -1
   if hasWeight:
     labels += ['weight']
       
   table.data_readArray(labels)
-  coords = table.data[:,table.labels_index(coords)]
+  coords = table.data[:,table.label_index(coords)]
-  eulers = table.data[:,table.labels_index(['phi1','Phi','phi2'])] if hasEulers else np.zeros(3*len(coords))
+  eulers = table.data[:,table.label_index(['phi1','Phi','phi2'])] if hasEulers else np.zeros(3*len(coords))
-  grain = table.data[:,table.labels_index('microstructure')]       if hasGrains else 1+np.arange(len(coords))
+  grain = table.data[:,table.label_index('microstructure')]       if hasGrains else 1+np.arange(len(coords))
-  weights = table.data[:,table.labels_index('weight')]             if hasWeight else np.zeros(len(coords))
+  weights = table.data[:,table.label_index('weight')]             if hasWeight else np.zeros(len(coords))
   grainIDs = np.unique(grain).astype('i')