DAMASK_EICMD/processing/post/averageDown.py

150 lines
6.5 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
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 = """
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',
metavar='string',
help='column heading for coordinates [%default]')
parser.add_option('-p','--packing',
dest='packing',
type='int', nargs=3,
metavar='int int int',
help='size of packed group [%default]')
parser.add_option('--shift',
dest='shift',
type='int', nargs=3,
metavar='int int int',
help='shift vector of packing stencil [%default]')
parser.add_option('-g', '--grid',
dest='grid',
type='int', nargs=3,
metavar='int int int',
help='grid in x,y,z [autodetect]')
parser.add_option('-s', '--size', dest='size', type='float', nargs=3, metavar='float float float',
help='size in x,y,z [autodetect]')
parser.set_defaults(coords = 'ipinitialcoord',
packing = (2,2,2),
shift = (0,0,0),
grid = (0,0,0),
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])
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = ['STDIN']
for name in filenames:
if not (name == 'STDIN' or os.path.exists(name)): continue
table = damask.ASCIItable(name = name, outname = prefix+name,
buffered = False)
table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))
# ------------------------------------------ read header -------------------------------------------
table.head_read()
# --------------- figure out size and grid ---------------------------------------------------------
try:
elemCol = table.labels.index('elem')
locationCol = table.labels.index('1_%s'%options.coords) # columns containing location data
except ValueError:
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data (legacy naming scheme)
except ValueError:
table.croak('no coordinate (1_%s/%s.x) and/or elem data found...\n'%(options.coords,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 finalization -----------------------------------
table.close() # close ASCII tables