168 lines
7.4 KiB
Python
Executable File
168 lines
7.4 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# -*- coding: UTF-8 no BOM -*-
|
|
|
|
import os,sys,string
|
|
import numpy as np
|
|
import damask
|
|
from optparse import OptionParser
|
|
|
|
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
|
scriptID = ' '.join([scriptName,damask.version])
|
|
|
|
# --------------------------------------------------------------------
|
|
# MAIN
|
|
# --------------------------------------------------------------------
|
|
|
|
parser = OptionParser(option_class=damask.extendableOption, usage='%prog [options] datafile[s]', description = """
|
|
Calculates the standard deviation of data in blocks of size 'packing' thus reducing the former resolution
|
|
to resolution/packing.
|
|
|
|
""", version = scriptID)
|
|
|
|
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 = 'ipinitialcoord')
|
|
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 = np.array(options.packing)
|
|
options.shift = np.array(options.shift)
|
|
|
|
prefix = 'stddevDown%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])
|
|
|
|
# ------------------------------------------ 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('$Id$','\n','\\n') + \
|
|
'\t' + ' '.join(sys.argv[1:]))
|
|
|
|
|
|
# --------------- figure out size and grid ---------------------------------------------------------
|
|
try:
|
|
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:
|
|
file['croak'].write('no coordinate data (1_%s/%s.x) found...\n'%(options.coords,options.coords))
|
|
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 = np.array([len(grid[0]),\
|
|
len(grid[1]),\
|
|
len(grid[2]),],'i') # resolution is number of distinct coordinates found
|
|
dimension = resolution/np.maximum(np.ones(3,'d'),resolution-1.0)* \
|
|
np.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 = np.array(options.resolution,'i')
|
|
dimension = np.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 = np.array(options.packing,'i')
|
|
shift = np.array(options.shift,'i')
|
|
downSized = np.maximum(np.ones(3,'i'),resolution//packing)
|
|
outSize = np.ceil(np.array(resolution,'d')/np.array(packing,'d'))
|
|
|
|
print '\t%s @ %s --> %s'%(dimension,resolution,downSized)
|
|
|
|
# ------------------------------------------ assemble header ---------------------------------------
|
|
|
|
table.head_write()
|
|
|
|
# ------------------------------------------ process data ---------------------------------------
|
|
|
|
dataavg = np.zeros(outSize.tolist()+[len(table.labels)])
|
|
datavar = np.zeros(outSize.tolist()+[len(table.labels)])
|
|
p = np.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],:] += np.array(table.data_asFloat(),'d') # convert to np 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],:] += (np.array(table.data_asFloat(),'d') - dataavg[d[0],d[1],d[2],:])**2
|
|
|
|
datavar = np.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:
|
|
table.input_close() # close input ASCII table
|
|
if file['name'] != 'STDIN':
|
|
table.output_close() # close output ASCII table
|