DAMASK_EICMD/processing/post/averageDown.py

#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-

import os,sys,string
import numpy as np
from optparse import OptionParser
import damask

scriptID   = string.replace('$Id$','\n','\\n')
scriptName = os.path.splitext(scriptID.split()[1])[0]

# --------------------------------------------------------------------
#                                MAIN
# --------------------------------------------------------------------

parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Average each data block of size 'packing' into single values thus reducing the former grid to grid/packing.

""", version = scriptID)

parser.add_option('-c','--coordinates',
                  dest='coords',
                  metavar='string',
                  help='column heading for coordinates [%default]')
parser.add_option('-p','--packing',
                  dest='packing',
                  type='int', nargs=3,
                  metavar='int int int',
                  help='size of packed group [%default]')
parser.add_option('--shift',
                  dest='shift',
                  type='int', nargs=3,
                  metavar='int int int',
                  help='shift vector of packing stencil [%default]')
parser.add_option('-g', '--grid',
                  dest='grid',
                  type='int', nargs=3,
                  metavar='int int int',
                  help='grid in x,y,z [autodetect]')
parser.add_option('-s', '--size',       dest='size', type='float', nargs=3, metavar='float float float',
                  help='size in x,y,z [autodetect]')
parser.set_defaults(coords  = 'ipinitialcoord',
                    packing = (2,2,2),
                    shift   = (0,0,0),
                    grid    = (0,0,0),
                    size    = (0.0,0.0,0.0))

(options,filenames) = parser.parse_args()

options.packing = np.array(options.packing)
options.shift = np.array(options.shift)

prefix = 'averagedDown%ix%ix%i_'%(options.packing[0],options.packing[1],options.packing[2])
if np.any(options.shift != 0):
  prefix += 'shift%+i%+i%+i_'%(options.shift[0],options.shift[1],options.shift[2])

# --- loop over input files -------------------------------------------------------------------------

if filenames == []: filenames = ['STDIN']

for name in filenames:
  if not (name == 'STDIN' or os.path.exists(name)): continue
  table = damask.ASCIItable(name = name, outname = prefix+name,
                            buffered = False)
  table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))

# ------------------------------------------ read header -------------------------------------------  

  table.head_read()

# --------------- figure out size and grid ---------------------------------------------------------
  try:
    elemCol = table.labels.index('elem')
    locationCol = table.labels.index('1_%s'%options.coords)                                         # columns containing location data
  except ValueError:
    try:
      locationCol = table.labels.index('%s.x'%options.coords)                                       # columns containing location data (legacy naming scheme)
    except ValueError:
      table.croak('no coordinate (1_%s/%s.x) and/or elem data found...\n'%(options.coords,options.coords))
      continue

  if (any(options.grid)==0 or any(options.size)==0.0):
    coords = [{},{},{}]
    while table.data_read():                                                                        # read next data line of ASCII table
      for j in xrange(3):
        coords[j][str(table.data[locationCol+j])] = True                                            # remember coordinate along x,y,z
    grid = np.array([len(coords[0]),\
                     len(coords[1]),\
                     len(coords[2]),],'i')                                                          # resolution is number of distinct coordinates found
    size = grid/np.maximum(np.ones(3,'d'),grid-1.0)* \
              np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
                        max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
                        max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
                        ],'d')                                                                      # size from bounding box, corrected for cell-centeredness
    origin = np.array([min(map(float,coords[0].keys())),\
                       min(map(float,coords[1].keys())),\
                       min(map(float,coords[2].keys())),\
                      ],'d') - 0.5 * size / grid
  else:
    grid = np.array(options.grid,'i')
    size  = np.array(options.size,'d')
    origin     = np.zeros(3,'d')

  for i, res in enumerate(grid):
    if res == 1:
      options.packing[i] = 1
      options.shift[i]   = 0
      mask = np.ones(3,dtype=bool)
      mask[i]=0
      size[i] = min(size[mask]/grid[mask])                                                          # third spacing equal to smaller of other spacing
  
  packing   = np.array(options.packing,'i')
  shift     = np.array(options.shift,'i')
  downSized = np.maximum(np.ones(3,'i'),grid//packing)
  outSize   = np.ceil(np.array(grid,'d')/np.array(packing,'d'))
    
# ------------------------------------------ assemble header ---------------------------------------
  table.head_write()

# ------------------------------------------ process data ------------------------------------------
  table.data_rewind()
  data = np.zeros(outSize.tolist()+[len(table.labels)])
  p = np.zeros(3,'i')
  
  for p[2] in xrange(grid[2]):
    for p[1] in xrange(grid[1]):
      for p[0] in xrange(grid[0]):
        d = ((p-shift)%grid)//packing
        table.data_read()
        data[d[0],d[1],d[2],:] += np.array(table.data_asFloat(),'d')                                # convert to np array
   
  data /= packing.prod()

  elementSize = size/grid*packing
  posOffset = (shift+[0.5,0.5,0.5])*elementSize
  elem = 1
  for c in xrange(downSized[2]):
    for b in xrange(downSized[1]):
      for a in xrange(downSized[0]):
        for i,x in enumerate([a,b,c]):
          data[a,b,c,locationCol+i] = posOffset[i] + x*elementSize[i] + origin[i]
        data[a,b,c,elemCol] = elem
        table.data = data[a,b,c,:].tolist()
        outputAlive = table.data_write()                                                            # output processed line
        elem += 1

# ------------------------------------------ output finalization -----------------------------------  

  table.close()                                                                                     # close ASCII tables