DAMASK_EICMD/processing/post/stddevDown.py

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#!/usr/bin/env python
import os,re,sys,math,string,numpy,damask,time
from optparse import OptionParser, Option
# -----------------------------
class extendableOption(Option):
# -----------------------------
# used for definition of new option parser action 'extend', which enables to take multiple option arguments
# taken from online tutorial http://docs.python.org/library/optparse.html
ACTIONS = Option.ACTIONS + ("extend",)
STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
def take_action(self, action, dest, opt, value, values, parser):
if action == "extend":
lvalue = value.split(",")
values.ensure_value(dest, []).extend(lvalue)
else:
Option.take_action(self, action, dest, opt, value, values, parser)
def location(idx,res):
return numpy.array([ idx % res[0], \
(idx // res[0]) % res[1], \
(idx // res[0] // res[1]) % res[2] ])
def index(location,res):
return ( location[0] % res[0] + \
(location[1] % res[1]) * res[0] + \
(location[2] % res[2]) * res[0] * res[1] )
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=extendableOption, usage='%prog [options] [file[s]]', description = """
Calculates the standard deviation of data in blocks of size 'packing' thus reducing the former resolution
to resolution/packing. (Requires numpy.)
""" + string.replace('$Id: averageDown.py 1857 2012-10-31 10:06:11Z MPIE\m.diehl $','\n','\\n')
)
parser.add_option('-c','--coordinates', dest='coords', type='string',\
help='column heading for coordinates [%default]')
parser.add_option('-p','--packing', dest='packing', type='int', nargs=3, \
help='dimension of packed group %default')
parser.add_option('-s','--shift', dest='shift', type='int', nargs=3, \
help='shift vector of packing stencil %default')
parser.add_option('-r','--resolution', dest='resolution', type='int', nargs=3, \
help='resolution in x,y,z [autodetect]')
parser.add_option('-d','--dimension', dest='dimension', type='float', nargs=3, \
help='dimension in x,y,z [autodetect]')
parser.set_defaults(coords = 'ip')
parser.set_defaults(packing = [2,2,2])
parser.set_defaults(shift = [0,0,0])
parser.set_defaults(resolution = [0,0,0])
parser.set_defaults(dimension = [0.0,0.0,0.0])
(options,filenames) = parser.parse_args()
if len(options.packing) < 3:
parser.error('packing needs three parameters...')
if len(options.shift) < 3:
parser.error('shift needs three parameters...')
options.packing = numpy.array(options.packing)
options.shift = numpy.array(options.shift)
prefix = 'stddevDown%ix%ix%i_'%(options.packing[0],options.packing[1],options.packing[2])
if numpy.any(options.shift != 0):
prefix += 'shift%+i%+i%+i_'%(options.shift[0],options.shift[1],options.shift[2])
# ------------------------------------------ setup file handles ---------------------------------------
files = []
if filenames == []:
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout})
else:
for name in filenames:
name = os.path.relpath(name)
if os.path.exists(name):
files.append({'name':name, 'input':open(name),
'output':open(os.path.join(os.path.dirname(name),prefix+os.path.basename(name)),'w')})
# ------------------------------------------ loop over input files ---------------------------------------
for file in files:
if file['name'] != 'STDIN': print file['name'],
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
table.info_append(string.replace('$Id: averageDown.py 1857 2012-10-31 10:06:11Z MPIE\m.diehl $','\n','\\n') + \
'\t' + ' '.join(sys.argv[1:]))
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
elemCol = table.labels.index('elem') # columns containing location data
except ValueError:
print 'no coordinate data or element data found...'
continue
if (any(options.resolution)==0 or any(options.dimension)==0.0):
grid = [{},{},{}]
while table.data_read(): # read next data line of ASCII table
for j in xrange(3):
grid[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
resolution = numpy.array([len(grid[0]),\
len(grid[1]),\
len(grid[2]),],'i') # resolution is number of distinct coordinates found
dimension = resolution/numpy.maximum(numpy.ones(3,'d'),resolution-1.0)* \
numpy.array([max(map(float,grid[0].keys()))-min(map(float,grid[0].keys())),\
max(map(float,grid[1].keys()))-min(map(float,grid[1].keys())),\
max(map(float,grid[2].keys()))-min(map(float,grid[2].keys())),\
],'d') # dimension from bounding box, corrected for cell-centeredness
else:
resolution = numpy.array(options.resolution,'i')
dimension = numpy.array(options.dimension,'d')
if resolution[2] == 1:
options.packing[2] = 1
options.shift[2] = 0
dimension[2] = min(dimension[:2]/resolution[:2]) # z spacing equal to smaller of x or y spacing
packing = numpy.array(options.packing,'i')
shift = numpy.array(options.shift,'i')
downSized = numpy.maximum(numpy.ones(3,'i'),resolution//packing)
outSize = numpy.ceil(numpy.array(resolution,'d')/numpy.array(packing,'d'))
print '\t%s @ %s --> %s'%(dimension,resolution,downSized)
# ------------------------------------------ assemble header ---------------------------------------
table.head_write()
# ------------------------------------------ process data ---------------------------------------
dataavg = numpy.zeros(outSize.tolist()+[len(table.labels)])
datavar = numpy.zeros(outSize.tolist()+[len(table.labels)])
p = numpy.zeros(3,'i')
table.data_rewind()
for p[2] in xrange(resolution[2]):
for p[1] in xrange(resolution[1]):
for p[0] in xrange(resolution[0]):
d = ((p-shift)%resolution)//packing
table.data_read()
dataavg[d[0],d[1],d[2],:] += numpy.array(table.data_asFloat(),'d') # convert to numpy array
dataavg /= packing.prod()
table.data_rewind()
for p[2] in xrange(resolution[2]):
for p[1] in xrange(resolution[1]):
for p[0] in xrange(resolution[0]):
d = ((p-shift)%resolution)//packing
table.data_read()
datavar[d[0],d[1],d[2],:] += (numpy.array(table.data_asFloat(),'d') - dataavg[d[0],d[1],d[2],:])**2
datavar = numpy.sqrt(datavar/packing.prod())
posOffset = (shift+[0.5,0.5,0.5])*dimension/resolution
elementSize = dimension/resolution*packing
elem = 1
for c in xrange(downSized[2]):
for b in xrange(downSized[1]):
for a in xrange(downSized[0]):
datavar[a,b,c,locationCol:locationCol+3] = posOffset + [a,b,c]*elementSize
datavar[a,b,c,elemCol] = elem
table.data = datavar[a,b,c,:].tolist()
table.data_write() # output processed line
elem += 1
# ------------------------------------------ output result ---------------------------------------
table.output_flush() # just in case of buffered ASCII table
# ------------------------------------------ close file handles ---------------------------------------
for file in files:
file['input'].close() # close input ASCII table
if file['name'] != 'STDIN':
file['output'].close() # close output ASCII table