changed numpy.readtxt logic from "data_asArray" returning a copy to inplace "data_readArray" now directly storing into self.data

lib/damask/asciitable.py

@@ -198,8 +198,7 @@ class ASCIItable():
     line = self.__IO__['in'].readline()                                               # get next data row
     if self.__IO__['labels']:
       items = line.split()[:self.__IO__['validReadSize']]                             # use up to valid size (label count)
-      self.data = {False:   [],
-                    True: items}[len(items) == self.__IO__['validReadSize']]          # take if correct number of entries
+      self.data = items if len(items) == self.__IO__['validReadSize'] else []         # take if correct number of entries
     else:
       self.data = line.split()                                                        # take all
       
@@ -212,6 +211,23 @@ class ASCIItable():
       self.__IO__['in'].readline()
     self.data_read()
 
+# ------------------------------------------------------------------
+  def data_readArray(self,
+                     labels = []):
+    import numpy
+    '''
+       read whole data of all (given) labels as numpy array
+    '''
+
+    if labels == []: indices = range(self.__IO__['validReadSize'])              # use all columns
+    else: indices = self.labels_index(labels)                                   # use specified columns
+
+    self.data_rewind()
+    self.data = numpy.loadtxt(self.__IO__['in'], usecols=indices)
+    if len(self.data.shape) < 2:                                                 # single column
+      self.data = self.data.reshape(self.data.shape[0],1)
+    return self.data.shape
+    
 # ------------------------------------------------------------------
   def data_write(self):
     if len(self.data) == 0: return
@@ -261,20 +277,3 @@ class ASCIItable():
 # ------------------------------------------------------------------
   def data_asFloat(self):
     return map(self._transliterateToFloat,self.data)
-
-# ------------------------------------------------------------------
-  def data_asArray(self,
-                   labels = []):
-    import numpy
-    '''
-       read whole data of all (given) labels as numpy array
-    '''
-
-    if labels == []: indices = range(self.__IO__['validReadSize'])              # use all columns
-    else: indices = self.labels_index(labels)                                   # use specified columns
-
-    self.data_rewind()
-    theArray = numpy.loadtxt(self.__IO__['in'], usecols=indices)
-    if len(theArray.shape) < 2:                                                 # single column
-      theArray = theArray.reshape(theArray.shape[0],1)
-    return theArray

processing/post/addEuclideanDistance.py

@@ -161,12 +161,12 @@ for file in files:
 
 # ------------------------------------------ process data ---------------------------------------   
   
-  structure = table.data_asArray(['ip.x','ip.y','ip.z',options.id])
+  table.data_readArray(['ip.x','ip.y','ip.z',options.id])
 
   grid = [{},{},{}]
-  for i in xrange(len(structure)):
+  for i in xrange(len(table.data)):
     for j in xrange(3):
-      grid[j][str(structure[i,j])] = True
+      grid[j][str(table.data[i,j])] = True
 
   resolution = numpy.array(map(len,grid),'i')
   unitlength = 0.0
@@ -175,7 +175,7 @@ for file in files:
 
   neighborhood = neighborhoods[options.neighborhood]
   convoluted = numpy.empty([len(neighborhood)]+list(resolution+2),'i')
-  microstructure = periodic_3Dpad(numpy.array(structure[:,3].reshape(resolution),'i'))
+  microstructure = periodic_3Dpad(numpy.array(table.data[:,3].reshape(resolution),'i'))
   
   for i,p in enumerate(neighborhood):
     stencil = numpy.zeros((3,3,3),'i')

processing/post/permuteData.py

@@ -97,9 +97,9 @@ for file in files:
 # ------------------------------------------ process data ---------------------------------------  
 
   permutation = {}
-  theColumns = table.data_asArray([column['scalar'][label] for label in active['scalar']])
+  table.data_readArray([column['scalar'][label] for label in active['scalar']])
   for i,label in enumerate(active['scalar']):
-    unique = list(set(theColumns[:,i]))
+    unique = list(set(table.data[:,i]))
     permutated = numpy.random.permutation(unique)
     permutation[label] = dict(zip(unique,permutated))