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DAMASK_EICMD
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scripts now figure out dimension and resolution on their own...

This commit is contained in:
Philip Eisenlohr 2012-02-08 11:18:15 +00:00
parent 9c35e4e148
commit 282e4a0360
2 changed files with 82 additions and 47 deletions
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63
processing/post/addCurl.py
View File

@ -46,30 +46,22 @@ Deals with both vector- and tensor-valued fields.
) )
parser.add_option('-c','--coordinates', dest='coords', type='string',\
help='column heading for coordinates [%default]')
parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \ parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \
help='heading of columns containing vector field values') help='heading of columns containing vector field values')
parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \ parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \
help='heading of columns containing tensor field values') help='heading of columns containing tensor field values')
parser.add_option('-d','--dimension', dest='dim', type='float', nargs=3, \
help='physical dimension of data set in x (fast) y z (slow) [%default]')
parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \
help='resolution of data set in x (fast) y z (slow)')
parser.set_defaults(coords = 'ip')
parser.set_defaults(vector = []) parser.set_defaults(vector = [])
parser.set_defaults(tensor = []) parser.set_defaults(tensor = [])
parser.set_defaults(dim = [])
parser.set_defaults(skip = [0,0,0])
(options,filenames) = parser.parse_args() (options,filenames) = parser.parse_args()
if len(options.vector) + len(options.tensor) == 0: if len(options.vector) + len(options.tensor) == 0:
parser.error('no data column specified...') parser.error('no data column specified...')
if len(options.dim) < 3:
parser.error('improper dimension specification...')
if not options.res or len(options.res) < 3:
parser.error('improper resolution specification...')
resSkip = map(lambda (a,b): a+b,zip(options.res,options.skip))
datainfo = { # list of requested labels per datatype datainfo = { # list of requested labels per datatype
'vector': {'len':3, 'vector': {'len':3,
'label':[]}, 'label':[]},
@ -94,13 +86,40 @@ else:
# ------------------------------------------ loop over input files --------------------------------------- # ------------------------------------------ loop over input files ---------------------------------------
for file in files: for file in files:
if file['name'] != 'STDIN': print file['name'] if file['name'] != 'STDIN': print file['name'],
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info table.head_read() # read ASCII header info
table.info_append(string.replace('$Id$','\n','\\n') + \ table.info_append(string.replace('$Id$','\n','\\n') + \
'\t' + ' '.join(sys.argv[1:])) '\t' + ' '.join(sys.argv[1:]))
# --------------- figure out dimension and resolution
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
except ValueError:
print 'no coordinate data found...'
continue
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
if resolution[2] == 1:
dimension[2] = min(dimension[:2]/resolution[:2])
N = resolution.prod()
print '\t%s @ %s'%(dimension,resolution)
# --------------- figure out columns to process
active = {} active = {}
column = {} column = {}
values = {} values = {}
@ -121,14 +140,10 @@ for file in files:
if datatype not in curl: curl[datatype] = {} if datatype not in curl: curl[datatype] = {}
active[datatype].append(label) active[datatype].append(label)
column[datatype][label] = table.labels.index(key) # remember columns of requested data column[datatype][label] = table.labels.index(key) # remember columns of requested data
values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\ values[datatype][label] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
options.res[0]*options.res[1]*options.res[2])]).\ reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
reshape((options.res[0],options.res[1],options.res[2],\ curl[datatype][label] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
datainfo[datatype]['len']//3,3)) reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
curl[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\
options.res[0]*options.res[1]*options.res[2])]).\
reshape((options.res[0],options.res[1],options.res[2],\
datainfo[datatype]['len']//3,3))
table.labels_append(['%i_curlFFT(%s)'%(i+1,label) table.labels_append(['%i_curlFFT(%s)'%(i+1,label)
for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels
@ -139,9 +154,11 @@ for file in files:
# ------------------------------------------ read value field --------------------------------------- # ------------------------------------------ read value field ---------------------------------------
table.data_rewind()
idx = 0 idx = 0
while table.data_read(): # read next data line of ASCII table while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count (x,y,z) = location(idx,resolution) # figure out (x,y,z) position from line count
idx += 1 idx += 1
for datatype,labels in active.items(): # loop over vector,tensor for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested curls for label in labels: # loop over all requested curls
@ -153,14 +170,14 @@ for file in files:
for datatype,labels in active.items(): # loop over vector,tensor for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested curls for label in labels: # loop over all requested curls
curl[datatype][label] = damask.core.math.curl_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label]) curl[datatype][label] = damask.core.math.curl_fft(resolution,dimension,datainfo[datatype]['len']//3,values[datatype][label])
# ------------------------------------------ process data --------------------------------------- # ------------------------------------------ process data ---------------------------------------
table.data_rewind() table.data_rewind()
idx = 0 idx = 0
while table.data_read(): # read next data line of ASCII table while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count (x,y,z) = location(idx,resolution) # figure out (x,y,z) position from line count
idx += 1 idx += 1
for datatype,labels in active.items(): # loop over vector,tensor for datatype,labels in active.items(): # loop over vector,tensor

66
processing/post/addDivergence.py
View File

@ -49,32 +49,25 @@ Deals with both vector- and tensor-valued fields.
parser.add_option('--fdm', dest='accuracy', action='extend', type='string', \ parser.add_option('--fdm', dest='accuracy', action='extend', type='string', \
help='degree of central difference accuracy') help='degree of central difference accuracy')
parser.add_option('--fft', dest='fft', action='store_true', \ parser.add_option('--fft', dest='fft', action='store_true', \
help='calculate divergence in Fourier space [%default]') help='calculate divergence in Fourier space')
parser.add_option('-c','--coordinates', dest='coords', type='string',\
help='column heading for coordinates [%default]')
parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \ parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \
help='heading of columns containing vector field values') help='heading of columns containing vector field values')
parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \ parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \
help='heading of columns containing tensor field values') help='heading of columns containing tensor field values')
parser.add_option('-d','--dimension', dest='dim', type='float', nargs=3, \
help='physical dimension of data set in x (fast) y z (slow) [%default]')
parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \
help='resolution of data set in x (fast) y z (slow)')
parser.set_defaults(coords = 'ip')
parser.set_defaults(accuracy = []) parser.set_defaults(accuracy = [])
parser.set_defaults(fft = False) parser.set_defaults(fft = False)
parser.set_defaults(vector = []) parser.set_defaults(vector = [])
parser.set_defaults(tensor = []) parser.set_defaults(tensor = [])
parser.set_defaults(dim = [])
accuracyChoices = [2,4,6,8] accuracyChoices = [2,4,6,8]
(options,filenames) = parser.parse_args() (options,filenames) = parser.parse_args()
if len(options.vector) + len(options.tensor) == 0: if len(options.vector) + len(options.tensor) == 0:
parser.error('no data column specified...') parser.error('no data column specified...')
if len(options.dim) < 3:
parser.error('improper dimension specification...')
if not options.res or len(options.res) < 3:
parser.error('improper resolution specification...')
for choice in options.accuracy: for choice in options.accuracy:
if int(choice) not in accuracyChoices: if int(choice) not in accuracyChoices:
@ -107,13 +100,40 @@ else:
# ------------------------------------------ loop over input files --------------------------------------- # ------------------------------------------ loop over input files ---------------------------------------
for file in files: for file in files:
if file['name'] != 'STDIN': print file['name'] if file['name'] != 'STDIN': print file['name'],
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info table.head_read() # read ASCII header info
table.info_append(string.replace('$Id$','\n','\\n') + \ table.info_append(string.replace('$Id$','\n','\\n') + \
'\t' + ' '.join(sys.argv[1:])) '\t' + ' '.join(sys.argv[1:]))
# --------------- figure out dimension and resolution
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
except ValueError:
print 'no coordinate data found...'
continue
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
if resolution[2] == 1:
dimension[2] = min(dimension[:2]/resolution[:2])
N = resolution.prod()
print '\t%s @ %s'%(dimension,resolution)
# --------------- figure out columns to process
active = {} active = {}
column = {} column = {}
values = {} values = {}
@ -135,15 +155,11 @@ for file in files:
if label not in divergence[datatype]: divergence[datatype][label] = {} if label not in divergence[datatype]: divergence[datatype][label] = {}
active[datatype].append(label) active[datatype].append(label)
column[datatype][label] = table.labels.index(key) # remember columns of requested data column[datatype][label] = table.labels.index(key) # remember columns of requested data
values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\ values[datatype][label] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
options.res[0]*options.res[1]*options.res[2])]).\ reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
reshape((options.res[0],options.res[1],options.res[2],\
datainfo[datatype]['len']//3,3))
for accuracy in options.accuracy: for accuracy in options.accuracy:
divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']//3*\ divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len']//3)]).\
options.res[0]*options.res[1]*options.res[2])]).\ reshape(list(resolution)+[datainfo[datatype]['len']//3])
reshape((options.res[0],options.res[1],options.res[2],\
datainfo[datatype]['len']//3))
table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label) table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label)
for i in xrange(datainfo[datatype]['len']//3)]) # extend ASCII header with new labels for i in xrange(datainfo[datatype]['len']//3)]) # extend ASCII header with new labels
@ -154,9 +170,11 @@ for file in files:
# ------------------------------------------ read value field --------------------------------------- # ------------------------------------------ read value field ---------------------------------------
table.data_rewind()
idx = 0 idx = 0
while table.data_read(): # read next data line of ASCII table while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count (x,y,z) = location(idx,resolution) # figure out (x,y,z) position from line count
idx += 1 idx += 1
for datatype,labels in active.items(): # loop over vector,tensor for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested curls for label in labels: # loop over all requested curls
@ -169,15 +187,15 @@ for file in files:
for label in labels: # loop over all requested divergencies for label in labels: # loop over all requested divergencies
for accuracy in options.accuracy: for accuracy in options.accuracy:
if accuracy == 'FFT': if accuracy == 'FFT':
divergence[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label]) divergence[datatype][label][accuracy] = damask.core.math.divergence_fft(resolution,dimension,datainfo[datatype]['len']//3,values[datatype][label])
else: else:
divergence[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label]) divergence[datatype][label][accuracy] = damask.core.math.divergence_fdm(resolution,dimension,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
# ------------------------------------------ process data --------------------------------------- # ------------------------------------------ process data ---------------------------------------
table.data_rewind() table.data_rewind()
idx = 0 idx = 0
while table.data_read(): # read next data line of ASCII table while table.data_read(): # read next data line of ASCII table
(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count (x,y,z) = location(idx,resolution) # figure out (x,y,z) position from line count
idx += 1 idx += 1
for datatype,labels in active.items(): # loop over vector,tensor for datatype,labels in active.items(): # loop over vector,tensor