DAMASK_EICMD/processing/post/imageDataDeformed.py

189 lines
9.1 KiB
Python
Executable File

#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,string
import numpy as np
from optparse import OptionParser
from PIL import Image, ImageDraw
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 = """
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)')
parser.add_option('-r','--range', dest='range', type='float', nargs=2, metavar='float float',
help='data range (min max) [auto]')
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('-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 displacment fluctuation [%default]')
parser.add_option('-z','--layer', dest='z', type='int', metavar='int',
help='index of z plane to plot [%default]')
parser.add_option('--fliplr', dest='flipLR', action='store_true',
help='flip around vertical axis')
parser.add_option('--flipud', dest='flipUD', action='store_true',
help='flip around horizontal axis')
parser.add_option('--crop', dest='crop', type='int', nargs=4, metavar=' '.join(['int']*3),
help='pixels cropped on left, right, top, bottom')
parser.add_option('--show', dest='show', action='store_true',
help='show resulting image')
parser.add_option('-N','--pixelsize', dest='pixelsize', type='int', metavar='int',
help='pixels per cell edge')
parser.set_defaults(label = None,
range = [0.0,0.0],
dimension = [],
size = [],
z = 1,
abs = False,
log = False,
defgrad = 'f',
scaling = [1.,1.,1.],
flipLR = False,
flipUD = False,
color = "gray",
invert = False,
crop = [0,0,0,0],
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 = ['STDIN']
for name in filenames:
if name == 'STDIN':
file = {'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr}
file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
else:
if not os.path.exists(name): continue
file = {'name':name,
'input':open(name),
'output':open(os.path.splitext(name)[0]+ \
('' if options.label == None else '_'+options.label)+ \
'.png','w'),
'croak':sys.stderr}
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
table = damask.ASCIItable(file['input'],file['output'],
buffered = False, # make unbuffered ASCII_table
labels = options.label != None) # no labels when taking 2D dataset
table.head_read() # read ASCII header info
# --------------- figure out columns to process ---------------------------------------------------
errors = []
if table.label_dimension(options.label) != 1:
errors.append('no scalar data (%s) found...'%options.label)
if table.label_dimension(options.defgrad) != 9:
errors.append('no deformation gradient tensor (1..9_%s) found...'%options.defgrad)
if errors != []:
file['croak'].write('\n'.join(errors)+'\n')
table.close(dismiss = True)
continue
table.data_readArray([options.label,options.defgrad])
F = table.data[:,1:10].transpose().reshape([3,3]+list(options.dimension),order='F')
data = table.data[:,0 ].transpose().reshape( 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])*options.dimension\
/options.size)[:2].astype('i') # determine image size from number of cells in overall bounding box
im = Image.new('RGBA',imagesize)
draw = ImageDraw.Draw(im)
for y in xrange(options.dimension[1]):
for x in xrange(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])]),
outline = None)
# if options.flipLR: table.data = np.fliplr(table.data)
# if options.flipUD: table.data = np.flipud(table.data)
# (height,width,bands) = table.data.shape
# im = Image.fromarray(table.data.astype('uint8'), 'RGB').\
# crop(( options.crop[0],
# options.crop[2],
# width -options.crop[1],
# height-options.crop[3]))
# ------------------------------------------ output result -----------------------------------------
im.save(file['output'],format = "PNG")
if options.show: im.show()
table.close() # close ASCII table file handles