DAMASK_EICMD/processing/post/averageDown.py

153 lines
6.3 KiB
Python
Executable File

#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys
import numpy as np
import scipy.ndimage
from optparse import OptionParser
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 = """
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',
type = 'string', 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()
packing = np.array(options.packing,dtype = int)
shift = np.array(options.shift, dtype = int)
prefix = 'averagedDown{}x{}x{}_'.format(*packing)
if any(shift != 0): prefix += 'shift{:+}{:+}{:+}_'.format(*shift)
# --- loop over input files ------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
outname = os.path.join(os.path.dirname(name),
prefix+os.path.basename(name)) if name else name,
buffered = False)
except: continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
errors = []
remarks = []
colCoord = None
if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
else: colCoord = table.label_index(options.coords)
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ assemble header ---------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray()
if (any(options.grid) == 0 or any(options.size) == 0.0):
coords = [np.unique(table.data[:,colCoord+i]) for i in xrange(3)]
mincorner = np.array(map(min,coords))
maxcorner = np.array(map(max,coords))
grid = np.array(map(len,coords),'i')
size = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 equal to smallest among other spacings
delta = size/np.maximum(np.ones(3,'d'), grid)
origin = mincorner - 0.5*delta # shift from cell center to corner
else:
grid = np.array(options.grid,'i')
size = np.array(options.size,'d')
origin = np.zeros(3,'d')
packing = np.where(grid == 1,1,packing) # reset packing to 1 where grid==1
shift = np.where(grid == 1,0,shift) # reset shift to 0 where grid==1
packedGrid = np.maximum(np.ones(3,'i'),grid//packing)
averagedDown = scipy.ndimage.filters.uniform_filter( \
np.roll(
np.roll(
np.roll(table.data.reshape(list(grid)+[table.data.shape[1]],order = 'F'),
-shift[0],axis = 0),
-shift[1],axis = 1),
-shift[2],axis = 2),
size = list(packing) + [1],
mode = 'wrap',
origin = list(-(packing/2)) + [0])\
[::packing[0],::packing[1],::packing[2],:].reshape((packedGrid.prod(),table.data.shape[1]),order = 'F')
table.data = averagedDown
#--- generate grid --------------------------------------------------------------------------------
if colCoord:
x = (0.5 + shift[0] + np.arange(packedGrid[0],dtype=float))/packedGrid[0]*size[0] + origin[0]
y = (0.5 + shift[1] + np.arange(packedGrid[1],dtype=float))/packedGrid[1]*size[1] + origin[1]
z = (0.5 + shift[2] + np.arange(packedGrid[2],dtype=float))/packedGrid[2]*size[2] + origin[2]
xx = np.tile( x, packedGrid[1]* packedGrid[2])
yy = np.tile(np.repeat(y,packedGrid[0] ),packedGrid[2])
zz = np.repeat(z,packedGrid[0]*packedGrid[1])
table.data[:,colCoord:colCoord+3] = np.squeeze(np.dstack((xx,yy,zz)))
# ------------------------------------------ output result -----------------------------------------
table.data_writeArray()
# ------------------------------------------ output finalization -----------------------------------
table.close() # close ASCII tables