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 = scriptID.split()[1][:-3]

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

# ------------------------------------------ setup file handles ------------------------------------
files = []
for name in filenames:
  if os.path.exists(name):
    files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})

#--- loop over input files -------------------------------------------------------------------------
for file in files:
  file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')

  table = damask.ASCIItable(file['input'],file['output'],False)                                     # make unbuffered ASCII_table
  table.head_read()                                                                                 # read ASCII header info
  table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))

# --------------- figure out size and grid ---------------------------------------------------------
  try:
    locationCol = table.labels.index('%s.x'%options.coords)                                         # columns containing location data
    elemCol = table.labels.index('elem')
  except ValueError:
    file['croak'].write('no coordinate (%s.x) and/or elem data found...\n'%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 result -----------------------------------------  
  outputAlive and table.output_flush()                                                              # just in case of buffered ASCII table

  file['input'].close()                                                                             # close input ASCII table
  file['output'].close()                                                                            # close output ASCII table
  os.rename(file['name']+'_tmp',\
            os.path.join(os.path.dirname(file['name']),prefix+os.path.basename(file['name'])))