DAMASK_EICMD/processing/post/imageDataDeformed.py

#!/usr/bin/env python2.7

import os
import sys
from optparse import OptionParser

import numpy as np
from PIL import Image, ImageDraw

import damask


scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID   = ' '.join([scriptName,damask.version])


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

parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Generate PNG image from scalar data on grid deformed by (periodic) deformation gradient.

""", version = scriptID)

parser.add_option('-l','--label',
                  dest = 'label',
                  type = 'string', metavar = 'string',
                  help = 'column containing data [all]')
parser.add_option('-r','--range',
                  dest = 'range',
                  type = 'float', nargs = 2, metavar = 'float float',
                  help = 'data range (min max) [auto]')
parser.add_option('--gap', '--transparent',
                  dest = 'gap',
                  type = 'float', metavar = 'float',
                  help = 'value to treat as transparent [%default]')
parser.add_option('-d','--dimension',
                  dest = 'dimension',
                  type = 'int', nargs = 3, metavar = ' '.join(['int']*3),
                  help = 'data dimension (x/y/z)')
parser.add_option('-s','--size',
                  dest = 'size',
                  type = 'float', nargs = 3, metavar = ' '.join(['float']*3),
                  help = 'box size (x/y/z)')
parser.add_option('-f','--defgrad',
                  dest = 'defgrad', metavar = 'string',
                  help = 'column label of deformation gradient [%default]')
parser.add_option('--scaling',
                  dest = 'scaling',
                  type = 'float', nargs = 3, metavar = ' '.join(['float']*3),
                  help = 'x/y/z scaling of displacement fluctuation [%default]')
parser.add_option('-z','--layer',
                  dest = 'z',
                  type = 'int', metavar = 'int',
                  help = 'index of z plane to plot [%default]')
parser.add_option('--color',
                  dest = 'color',
                  type = 'string', metavar = 'string',
                  help = 'color scheme')
parser.add_option('--invert',
                  dest = 'invert',
                  action = 'store_true',
                  help = 'invert color scheme')
parser.add_option('--abs',
                  dest = 'abs',
                  action = 'store_true',
                  help = 'magnitude of values')
parser.add_option('--log',
                  dest = 'log',
                  action = 'store_true',
                  help = 'log10 of values')
parser.add_option('-N','--pixelsize',
                  dest = 'pixelsize',
                  type = 'int', metavar = 'int',
                  help = 'pixels per cell edge')
parser.add_option('--show',
                  dest = 'show',
                  action = 'store_true',
                  help = 'show resulting image')

parser.set_defaults(label = None,
                    range = [0.0,0.0],
                    dimension = [],
                    size = [],
                    z = 1,
                    abs = False,
                    log = False,
                    defgrad = 'f',
                    scaling = [1.,1.,1.],
                    color = "gray",
                    invert = False,
                    pixelsize = 1,
                    show = False,
                   )

(options,filenames) = parser.parse_args()

options.size      = np.array(options.size)
options.dimension = np.array(options.dimension)
options.range     = np.array(options.range)

if options.z > 0: options.z -= 1                                                                    # adjust to 0-based indexing

# --- color palette ---------------------------------------------------------------------------------

theMap = damask.Colormap(predefined=options.color)
if options.invert: theMap = theMap.invert()
theColors = np.uint8(np.array(theMap.export(format='list',steps=256))*255)

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

if filenames == []: filenames = [None]

for name in filenames:
  try:
    table = damask.ASCIItable(name = name,
                              buffered = False,
                              labeled = options.label is not None,
                              readonly = True)
  except: continue
  table.report_name(scriptName,name)

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

  table.head_read()

# --------------- figure out columns to process  ---------------------------------------------------
  
  errors = []
  if table.label_dimension(options.label) != 1:
    errors.append('no scalar data ({}) found.'.format(options.label))
  if table.label_dimension(options.defgrad) != 9:
    errors.append('no deformation gradient tensor (1..9_{}) found.'.format(options.defgrad))
  
  if errors != []:
    table.croak('\n'.join(errors)+'\n')
    table.close(dismiss = True)
    continue
  
  table.data_readArray([options.label,options.defgrad])
  
  data = table.data[:,0   ].transpose().reshape(      list(options.dimension),order='F')
  F    = table.data[:,1:10].transpose().reshape([3,3]+list(options.dimension),order='F')

  if options.abs:  data = np.abs(data)
  if options.log:  data = np.log10(data)
  if np.all(options.range == 0.0): options.range = np.array([data.min(),data.max()])
  elif options.log:                options.range = np.log10(options.range)
  
  data = (       data         - options.range.min()) / \
         (options.range.max() - options.range.min())                                                  # data scaled to fraction of range
  
  data = np.clip(data,0.0,1.0)                                                                        # cut off outliers (should be none)
  
# ---------------- calculate coordinates -----------------------------------------------------------
  
  Favg      = damask.core.math.tensorAvg(F)
  centroids = damask.core.mesh.deformedCoordsFFT(options.size,F,Favg,options.scaling)
  nodes     = damask.core.mesh.nodesAroundCentres(options.size,Favg,centroids)
  
  boundingBox = np.array([ \
                 [np.amin(nodes[0,:,:,options.z]),np.amin(nodes[1,:,:,options.z]),np.amin(nodes[2,:,:,options.z])],
                 [np.amax(nodes[0,:,:,options.z]),np.amax(nodes[1,:,:,options.z]),np.amax(nodes[2,:,:,options.z])],
                ])                                                                                  # find x-y bounding box for given z layer
  
  nodes -= boundingBox[0].repeat(np.prod(options.dimension+1)).reshape([3]+list(options.dimension+1))
  nodes *= (options.pixelsize*options.dimension/options.size).repeat(np.prod(options.dimension+1)).\
                                                                reshape([3]+list(options.dimension+1))
  imagesize = (options.pixelsize*(boundingBox[1]-boundingBox[0])*                                   # determine image size from number of 
                                           options.dimension/options.size)[:2].astype('i')          # cells in overall bounding box
  im = Image.new('RGBA',imagesize)
  draw = ImageDraw.Draw(im)
  
  for y in range(options.dimension[1]):
    for x in range(options.dimension[0]):
      draw.polygon([nodes[0,x  ,y  ,options.z],
                    nodes[1,x  ,y  ,options.z],
                    nodes[0,x+1,y  ,options.z],
                    nodes[1,x+1,y  ,options.z],
                    nodes[0,x+1,y+1,options.z],
                    nodes[1,x+1,y+1,options.z],
                    nodes[0,x  ,y+1,options.z],
                    nodes[1,x  ,y+1,options.z],
                   ],
                   fill =    tuple(theColors[int(255*data[x,y,options.z])],
                                   0 if data[x,y,options.z] == options.gap else 255),
                   outline = None)
  
# ------------------------------------------ output result -----------------------------------------

  im.save(os.path.splitext(name)[0]+ \
          ('_'+options.label if options.label else '')+ \
          '.png' if name else sys.stdout,
          format = "PNG")

  table.close()                                                                                     # close ASCII table
  if options.show: im.show()