DAMASK_EICMD/processing/post/imageDataDeformed.py

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#!/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,
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labeled = options.label is not None,
readonly = True)
except: continue
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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 != []:
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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))
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nodes *= (options.pixelsize*options.dimension/options.size).repeat(np.prod(options.dimension+1)).\
reshape([3]+list(options.dimension+1))
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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)
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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()