Merge branch 'addGradient' into 'development'

Add gradient

FFT-based gradient calculation similar to addCurl and addDivergence. Polished other two while on it anyway.

See merge request !2

processing/post/addCurl.py

@@ -10,40 +10,35 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
 scriptID   = ' '.join([scriptName,damask.version])
 
 def curlFFT(geomdim,field):
- N = grid.prod()                                                                                    # field size
+ grid = np.array(np.shape(field)[2::-1])
- n = np.array(np.shape(field)[3:]).prod()                                                           # data size
+ N = grid.prod()                                                                          # field size
+ n = np.array(np.shape(field)[3:]).prod()                                                 # data size
 
- if   n == 3:
+ if   n == 3:   dataType = 'vector'
-   dataType = 'vector'
+ elif n == 9:   dataType = 'tensor'
- elif n == 9:
-   dataType = 'tensor'
 
  field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2))
  curl_fourier  = np.zeros(field_fourier.shape,'c16')
 
 # differentiation in Fourier space
  k_s = np.zeros([3],'i')
- TWOPIIMG = (0.0+2.0j*math.pi)
+ TWOPIIMG = 2.0j*math.pi
  for i in xrange(grid[2]):
    k_s[0] = i
-   if(grid[2]%2==0 and i == grid[2]//2):                                                            # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+   if grid[2]%2 == 0 and i == grid[2]//2:  k_s[0] = 0                                     # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
-     k_s[0]=0
+   elif i > grid[2]//2:                    k_s[0] -= grid[2]
-   elif (i > grid[2]//2): 
-     k_s[0] = k_s[0] - grid[2]
 
    for j in xrange(grid[1]):
      k_s[1] = j
-     if(grid[1]%2==0 and j == grid[1]//2):                                                          # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+     if grid[1]%2 == 0 and j == grid[1]//2: k_s[1] = 0                                    # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
-       k_s[1]=0
+     elif j > grid[1]//2:                   k_s[1] -= grid[1]
-     elif (j > grid[1]//2): 
-       k_s[1] = k_s[1] - grid[1]
 
      for k in xrange(grid[0]//2+1):
        k_s[2] = k
-       if(grid[0]%2==0 and k == grid[0]//2):                                                        # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+       if grid[0]%2 == 0 and k == grid[0]//2: k_s[2] = 0                                  # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
-         k_s[2]=0
+
+       xi = (k_s/geomdim)[2::-1].astype('c16')                                            # reversing the field input order
 
-       xi = np.array([k_s[2]/geomdim[2]+0.0j,k_s[1]/geomdim[1]+0.j,k_s[0]/geomdim[0]+0.j],'c16')
        if dataType == 'tensor': 
          for l in xrange(3):
            curl_fourier[i,j,k,0,l] = ( field_fourier[i,j,k,l,2]*xi[1]\
@@ -100,10 +95,8 @@ if options.vector is None and options.tensor is None:
 if filenames == []: filenames = [None]
 
 for name in filenames:
-  try:
+  try:    table = damask.ASCIItable(name = name,buffered = False)
-    table = damask.ASCIItable(name = name,buffered = False)
+  except: continue
-  except:
-    continue
   damask.util.report(scriptName,name)
 
 # ------------------------------------------ read header ------------------------------------------
@@ -161,9 +154,10 @@ for name in filenames:
   stack = [table.data]
   for type, data in items.iteritems():
     for i,label in enumerate(data['active']):
-      stack.append(curlFFT(size[::-1],                                                              # we need to reverse order here, because x 
+      # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
-                           table.data[:,data['column'][i]:data['column'][i]+data['dim']].           # is fastest,ie rightmost, but leftmost in 
+      stack.append(curlFFT(size[::-1],
-                           reshape([grid[2],grid[1],grid[0]]+data['shape'])))                       # our x,y,z notation
+                           table.data[:,data['column'][i]:data['column'][i]+data['dim']].
+                           reshape([grid[2],grid[1],grid[0]]+data['shape'])))
 
 # ------------------------------------------ output result -----------------------------------------
 

processing/post/addDivergence.py

@@ -10,39 +10,35 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
 scriptID   = ' '.join([scriptName,damask.version])
 
 def divFFT(geomdim,field):
- N = grid.prod()                                                                                    # field size
+ grid = np.array(np.shape(field)[2::-1])
- n = np.array(np.shape(field)[3:]).prod()                                                           # data size
+ N = grid.prod()                                                                          # field size
+ n = np.array(np.shape(field)[3:]).prod()                                                 # data size
 
  field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2))
- div_fourier   = np.zeros(field_fourier.shape[0:len(np.shape(field))-1],'c16')                      # size depents on whether tensor or vector
+ div_fourier   = np.zeros(field_fourier.shape[0:len(np.shape(field))-1],'c16')            # size depents on whether tensor or vector
 
 # differentiation in Fourier space
  k_s=np.zeros([3],'i')
- TWOPIIMG = (0.0+2.0j*math.pi)
+ TWOPIIMG = 2.0j*math.pi
  for i in xrange(grid[2]):
    k_s[0] = i
-   if(grid[2]%2==0 and i == grid[2]//2):                                                            # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+   if grid[2]%2 == 0 and i == grid[2]//2:  k_s[0] = 0                                     # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
-     k_s[0]=0
+   elif i > grid[2]//2:                    k_s[0] -= grid[2]
-   elif (i > grid[2]//2): 
-     k_s[0] = k_s[0] - grid[2]
 
    for j in xrange(grid[1]):
      k_s[1] = j
-     if(grid[1]%2==0 and j == grid[1]//2):                                                          # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+     if grid[1]%2 == 0 and j == grid[1]//2: k_s[1] = 0                                    # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
-       k_s[1]=0
+     elif j > grid[1]//2:                   k_s[1] -= grid[1]
-     elif (j > grid[1]//2): 
-       k_s[1] = k_s[1] - grid[1]
 
      for k in xrange(grid[0]//2+1):
        k_s[2] = k
-       if(grid[0]%2==0 and k == grid[0]//2):                                                        # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+       if grid[0]%2 == 0 and k == grid[0]//2: k_s[2] = 0                                  # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
-         k_s[2]=0
 
-       xi=np.array([k_s[2]/geomdim[2]+0.0j,k_s[1]/geomdim[1]+0.j,k_s[0]/geomdim[0]+0.j],'c16')
+       xi = (k_s/geomdim)[2::-1].astype('c16')                                            # reversing the field input order
-       if n == 9:                                                                                   # tensor, 3x3 -> 3
+       if n == 9:                                                                         # tensor, 3x3 -> 3
          for l in xrange(3):
            div_fourier[i,j,k,l] = sum(field_fourier[i,j,k,l,0:3]*xi) *TWOPIIMG
-       elif n == 3:                                                                                 # vector, 3 -> 1
+       elif n == 3:                                                                       # vector, 3 -> 1
          div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
 
  return np.fft.fftpack.irfftn(div_fourier,axes=(0,1,2)).reshape([N,n/3])
@@ -80,15 +76,13 @@ parser.set_defaults(coords = 'ipinitialcoord',
 if options.vector is None and options.tensor is None:
   parser.error('no data column specified.')
 
-# --- loop over input files -------------------------------------------------------------------------
+# --- loop over input files ------------------------------------------------------------------------
 
 if filenames == []: filenames = [None]
 
 for name in filenames:
-  try:
+  try:    table = damask.ASCIItable(name = name,buffered = False)
-    table = damask.ASCIItable(name = name,buffered = False)
+  except: continue
-  except:
-    continue
   damask.util.report(scriptName,name)
 
 # ------------------------------------------ read header ------------------------------------------
@@ -140,16 +134,17 @@ for name in filenames:
   maxcorner = np.array(map(max,coords))
   grid   = np.array(map(len,coords),'i')
   size   = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner)                        # size from edge to edge = dim * n/(n-1) 
-  size   = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1]))                             # spacing for grid==1 set to smallest among other spacings
+  size   = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1]))                             # spacing for grid==1 equal to smallest among other ones
 
 # ------------------------------------------ process value field -----------------------------------
 
   stack = [table.data]
   for type, data in items.iteritems():
     for i,label in enumerate(data['active']):
-      stack.append(divFFT(size[::-1],                                                               # we need to reverse order here, because x
+      # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
-                          table.data[:,data['column'][i]:data['column'][i]+data['dim']].            # is fastest,ie rightmost, but leftmost in 
+      stack.append(divFFT(size[::-1],
-                          reshape([grid[2],grid[1],grid[0]]+data['shape'])))                        # our x,y,z notation
+                          table.data[:,data['column'][i]:data['column'][i]+data['dim']].
+                          reshape([grid[2],grid[1],grid[0]]+data['shape'])))
 
 # ------------------------------------------ output result -----------------------------------------
 

processing/post/addGradient.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 no BOM -*-
+
+import os,sys,math
+import numpy as np
+from optparse import OptionParser
+import damask
+
+scriptName = os.path.splitext(os.path.basename(__file__))[0]
+scriptID   = ' '.join([scriptName,damask.version])
+
+def gradFFT(geomdim,field):
+ grid = np.array(np.shape(field)[2::-1])
+ N = grid.prod()                                                                          # field size
+ n = np.array(np.shape(field)[3:]).prod()                                                 # data size
+ if   n == 3:   dataType = 'vector'
+ elif n == 1:   dataType = 'scalar'
+
+ field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2))
+ grad_fourier   = np.zeros(field_fourier.shape+(3,),'c16')
+
+# differentiation in Fourier space
+ k_s = np.zeros([3],'i')
+ TWOPIIMG = 2.0j*math.pi
+ for i in xrange(grid[2]):
+   k_s[0] = i
+   if grid[2]%2 == 0 and i == grid[2]//2:  k_s[0] = 0                                     # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+   elif i > grid[2]//2:                    k_s[0] -= grid[2]
+
+   for j in xrange(grid[1]):
+     k_s[1] = j
+     if grid[1]%2 == 0 and j == grid[1]//2: k_s[1] = 0                                    # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+     elif j > grid[1]//2:                   k_s[1] -= grid[1]
+
+     for k in xrange(grid[0]//2+1):
+       k_s[2] = k
+       if grid[0]%2 == 0 and k == grid[0]//2: k_s[2] = 0                                  # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
+
+       xi = (k_s/geomdim)[2::-1].astype('c16')                                            # reversing the field order
+       
+       grad_fourier[i,j,k,0,:] = field_fourier[i,j,k,0]*xi *TWOPIIMG                      # vector field from scalar data
+
+       if dataType == 'vector':
+         grad_fourier[i,j,k,1,:] = field_fourier[i,j,k,1]*xi *TWOPIIMG                    # tensor field from vector data
+         grad_fourier[i,j,k,2,:] = field_fourier[i,j,k,2]*xi *TWOPIIMG
+
+ return np.fft.fftpack.irfftn(grad_fourier,axes=(0,1,2)).reshape([N,3*n])
+
+
+# --------------------------------------------------------------------
+#                                MAIN
+# --------------------------------------------------------------------
+
+parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
+Add column(s) containing gradient of requested column(s).
+Operates on periodic ordered three-dimensional data sets.
+Deals with both vector- and scalar fields.
+
+""", version = scriptID)
+
+parser.add_option('-c','--coordinates',
+                  dest = 'coords',
+                  type = 'string', metavar='string',
+                  help = 'column heading for coordinates [%default]')
+parser.add_option('-v','--vector',
+                  dest = 'vector',
+                  action = 'extend', metavar = '<string LIST>',
+                  help = 'heading of columns containing vector field values')
+parser.add_option('-s','--scalar',
+                  dest = 'scalar',
+                  action = 'extend', metavar = '<string LIST>',
+                  help = 'heading of columns containing scalar field values')
+
+parser.set_defaults(coords = 'ipinitialcoord',
+                   )
+
+(options,filenames) = parser.parse_args()
+
+if options.vector is None and options.scalar is None:
+  parser.error('no data column specified.')
+
+# --- 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 ----------------------------------------
+
+  items = {
+            'scalar': {'dim': 1, 'shape': [1], 'labels':options.scalar, 'active':[], 'column': []},
+            'vector': {'dim': 3, 'shape': [3],   'labels':options.vector, 'active':[], 'column': []},
+          }
+  errors  = []
+  remarks = []
+  column = {}
+  
+  if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
+  else: colCoord = table.label_index(options.coords)
+
+  for type, data in items.iteritems():
+    for what in (data['labels'] if data['labels'] is not None else []):
+      dim = table.label_dimension(what)
+      if dim != data['dim']: remarks.append('column {} is not a {}.'.format(what,type))
+      else:
+        items[type]['active'].append(what)
+        items[type]['column'].append(table.label_index(what))
+
+  if remarks != []: damask.util.croak(remarks)
+  if errors  != []:
+    damask.util.croak(errors)
+    table.close(dismiss = True)
+    continue
+
+# ------------------------------------------ assemble header --------------------------------------
+
+  table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
+  for type, data in items.iteritems():
+    for label in data['active']:
+      table.labels_append(['{}_gradFFT({})'.format(i+1,label) for i in xrange(3 * data['dim'])])        # extend ASCII header with new labels
+  table.head_write()
+
+# --------------- figure out size and grid ---------------------------------------------------------
+
+  table.data_readArray()
+
+  coords = [np.unique(table.data[:,colCoord+i]) for i in xrange(3)]
+  mincorner = np.array(map(min,coords))
+  maxcorner = np.array(map(max,coords))
+  grid   = np.array(map(len,coords),'i')
+  size   = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner)                        # size from edge to edge = dim * n/(n-1) 
+  size   = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1]))     
+
+# ------------------------------------------ process value field -----------------------------------
+
+  stack = [table.data]
+  for type, data in items.iteritems():
+    for i,label in enumerate(data['active']):
+      # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
+      stack.append(gradFFT(size[::-1],
+                           table.data[:,data['column'][i]:data['column'][i]+data['dim']].
+                           reshape([grid[2],grid[1],grid[0]]+data['shape'])))
+
+# ------------------------------------------ output result -----------------------------------------
+
+  if len(stack) > 1: table.data = np.hstack(tuple(stack))
+  table.data_writeArray('%.12g')
+
+# ------------------------------------------ output finalization -----------------------------------
+
+  table.close()                                                                                     # close input ASCII table (works for stdin)