#!/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: imageDataDeformed.py 4188 2015-05-20 23:21:35Z p.eisenlohr $','\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