changed to new structure (using damask module)
This commit is contained in:
Martin Diehl
parent
5a658d1b82
commit
27a976c04f
|
|
@ -23,29 +23,20 @@ class extendableOption(Option):
|
|||
|
||||
def location(idx,res):
|
||||
return ( idx % res[0], \
|
||||
(idx // res[0]) % res[1], \
|
||||
(idx // res[0] // res[1]) % res[2] )
|
||||
( 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] )
|
||||
( location[1] % res[1]) * res[0] + \
|
||||
( location[2] % res[2]) * res[1] * res[0] )
|
||||
|
||||
def prefixMultiply(what,len):
|
||||
return {True: ['%i_%s'%(i+1,what) for i in range(len)],
|
||||
False:[what]}[len>1]
|
||||
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# MAIN
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
FDcoefficients = [ \
|
||||
[1.0/2.0, 0.0, 0.0, 0.0],\
|
||||
[2.0/3.0,-1.0/12.0, 0.0, 0.0],\
|
||||
[3.0/4.0,-3.0/20.0,1.0/ 60.0, 0.0],\
|
||||
[4.0/5.0,-1.0/ 5.0,4.0/105.0,-1.0/280.0],\
|
||||
]
|
||||
parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
|
||||
Add column(s) containing divergence of requested column(s).
|
||||
Operates on periodic ordered three-dimensional data sets.
|
||||
|
|
@ -59,8 +50,6 @@ parser.add_option('--fdm', dest='accuracy', action='extend', type='
|
|||
help='degree of central difference accuracy')
|
||||
parser.add_option('--fft', dest='fft', action='store_true', \
|
||||
help='calculate divergence in Fourier space [%default]')
|
||||
parser.add_option('-m','--memory', dest='memory', action='store_true', \
|
||||
help='memory efficient calculation (not possible for FFT based divergency [%default]')
|
||||
parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \
|
||||
help='heading of columns containing vector field values')
|
||||
parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \
|
||||
|
|
@ -69,16 +58,13 @@ 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.add_option('-s','--skip', dest='skip', type='int', nargs=3, \
|
||||
help='items skipped due to periodicity in x (fast) y z (slow)')
|
||||
|
||||
|
||||
parser.set_defaults(accuracy = [])
|
||||
parser.set_defaults(memory = False)
|
||||
parser.set_defaults(fft = False)
|
||||
parser.set_defaults(vector = [])
|
||||
parser.set_defaults(tensor = [])
|
||||
parser.set_defaults(dim = [])
|
||||
parser.set_defaults(skip = [0,0,0])
|
||||
accuracyChoices = [2,4,6,8]
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
|
@ -89,12 +75,14 @@ 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:
|
||||
if int(choice) not in accuracyChoices:
|
||||
parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices)))
|
||||
if options.fft: options.accuracy.append('fft')
|
||||
if options.fft: options.accuracy.append('FFT')
|
||||
if not options.accuracy:
|
||||
parser.error('no accuracy selected')
|
||||
|
||||
resSkip = map(lambda (a,b): a+b,zip(options.res,options.skip))
|
||||
datainfo = { # list of requested labels per datatype
|
||||
'vector': {'len':3,
|
||||
'label':[]},
|
||||
|
|
@ -109,174 +97,102 @@ if options.tensor != None: datainfo['tensor']['label'] += options.tensor
|
|||
|
||||
files = []
|
||||
if filenames == []:
|
||||
files.append({'name':'STDIN', 'handle':sys.stdin})
|
||||
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout})
|
||||
else:
|
||||
for name in filenames:
|
||||
if os.path.exists(name):
|
||||
files.append({'name':name, 'handle':open(name)})
|
||||
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')})
|
||||
|
||||
# ------------------------------------------ loop over input files ---------------------------------------
|
||||
|
||||
# ------------------------------------------ loop over input files ---------------------------------------
|
||||
|
||||
for file in files:
|
||||
print file['name']
|
||||
if file['name'] != 'STDIN': print file['name']
|
||||
|
||||
content = file['handle'].readlines()
|
||||
file['handle'].close()
|
||||
|
||||
# get labels by either read the first row, or - if keyword header is present - the last line of the header
|
||||
|
||||
headerlines = 1
|
||||
m = re.search('(\d+)\s*head', content[0].lower())
|
||||
if m:
|
||||
headerlines = int(m.group(1))
|
||||
passOn = content[1:headerlines]
|
||||
headers = content[headerlines].split()
|
||||
data = content[headerlines+1:]
|
||||
|
||||
regexp = re.compile('1_\d+_')
|
||||
for i,l in enumerate(headers):
|
||||
if regexp.match(l):
|
||||
headers[i] = l[2:]
|
||||
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:]))
|
||||
|
||||
active = {}
|
||||
column = {}
|
||||
values = {}
|
||||
div_field ={}
|
||||
divergence = {}
|
||||
|
||||
head = []
|
||||
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
key = {True :'1_%s',
|
||||
False:'%s' }[info['len']>1]%label
|
||||
if key not in headers:
|
||||
print 'column %s not found...'%key
|
||||
if key not in table.labels:
|
||||
sys.stderr.write('column %s not found...\n'%key)
|
||||
else:
|
||||
if datatype not in active: active[datatype] = []
|
||||
if datatype not in column: column[datatype] = {}
|
||||
if datatype not in values: values[datatype] = {}
|
||||
if datatype not in div_field: div_field[datatype] = {}
|
||||
if datatype not in active: active[datatype] = []
|
||||
if datatype not in column: column[datatype] = {}
|
||||
if datatype not in values: values[datatype] = {}
|
||||
if datatype not in divergence: divergence[datatype] = {}
|
||||
if label not in divergence[datatype]: divergence[datatype][label] = {}
|
||||
active[datatype].append(label)
|
||||
column[datatype][label] = headers.index(key)
|
||||
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']*\
|
||||
options.res[0]*options.res[1]*options.res[2])]).\
|
||||
reshape((options.res[0],options.res[1],options.res[2],\
|
||||
3,datainfo[datatype]['len']//3))
|
||||
options.res[0]*options.res[1]*options.res[2])]).\
|
||||
reshape((options.res[0],options.res[1],options.res[2],\
|
||||
datainfo[datatype]['len']//3,3))
|
||||
for accuracy in options.accuracy:
|
||||
divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']//3*\
|
||||
options.res[0]*options.res[1]*options.res[2])]).\
|
||||
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)
|
||||
for i in xrange(datainfo[datatype]['len']//3)]) # extend ASCII header with new labels
|
||||
|
||||
for what in options.accuracy: # loop over all requested degrees of accuracy (plus potentially fft)
|
||||
if not options.memory or what != 'fft': # FFT divergence excluded in memory saving mode
|
||||
head += prefixMultiply('div%s(%s)'%(what,label),datainfo[datatype]['len']//3)
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
output = '%i\theader'%(headerlines+1) + '\n' + \
|
||||
''.join(passOn) + \
|
||||
string.replace('$Id$','\n','\\n')+ '\t' + \
|
||||
' '.join(sys.argv[1:]) + '\n' + \
|
||||
'\t'.join(headers + head) + '\n' # build extended header
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ read value field ---------------------------------------
|
||||
|
||||
idx = 0
|
||||
for line in data:
|
||||
items = line.split()[:len(headers)]
|
||||
if len(items) < len(headers): # skip too short lines (probably comments or invalid)
|
||||
continue
|
||||
locSkip = location(idx,resSkip)
|
||||
if ( locSkip[0] < options.res[0]
|
||||
and locSkip[1] < options.res[1]
|
||||
and locSkip[2] < options.res[2] ): # only take values that are not periodic images
|
||||
for datatype,labels in active.items():
|
||||
for label in labels:
|
||||
values[datatype][label][locSkip[0]][locSkip[1]][locSkip[2]]\
|
||||
= numpy.reshape(items[column[datatype][label]:
|
||||
column[datatype][label]+datainfo[datatype]['len']],(3,datainfo[datatype]['len']//3))
|
||||
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
|
||||
idx += 1
|
||||
# ------------------------------------------ read file ---------------------------------------
|
||||
if options.memory:
|
||||
idx = 0
|
||||
for line in data:
|
||||
items = line.split()[:len(headers)]
|
||||
if len(items) < len(headers):
|
||||
continue
|
||||
|
||||
output += '\t'.join(items)
|
||||
(x,y,z) = location(idx,options.res)
|
||||
|
||||
for datatype,labels in active.items():
|
||||
for label in labels:
|
||||
for accuracy in options.accuracy:
|
||||
if accuracy == 'fft': continue
|
||||
for k in range(datainfo[datatype]['len']/3): # formulas from Mikhail Itskov: Tensor Algebra and Tensor Analysis for Engineers, Springer 2009, p 52
|
||||
theDiv = 0.0
|
||||
for a in range(int(accuracy)//2):
|
||||
for datatype,labels in active.items(): # loop over vector,tensor
|
||||
for label in labels: # loop over all requested curls
|
||||
values[datatype][label][x,y,z] = numpy.array(
|
||||
map(float,table.data[column[datatype][label]:
|
||||
column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(datainfo[datatype]['len']//3,3)
|
||||
# ------------------------------------------ process value field ---------------------------------------
|
||||
|
||||
theDiv += FDcoefficients[int(accuracy)//2-1][a] * \
|
||||
( \
|
||||
(values[datatype][label][location(index([x+1+a,y,z],options.res),options.res)[0]] \
|
||||
[location(index([x+1+a,y,z],options.res),options.res)[1]] \
|
||||
[location(index([x+1+a,y,z],options.res),options.res)[2]][k][0] - \
|
||||
values[datatype][label][location(index([x-1-a,y,z],options.res),options.res)[0]] \
|
||||
[location(index([x-1-a,y,z],options.res),options.res)[1]] \
|
||||
[location(index([x-1-a,y,z],options.res),options.res)[2]][k][0]) * options.res[0] / options.dim[0] + \
|
||||
(values[datatype][label][location(index([x,y+1+a,z],options.res),options.res)[0]] \
|
||||
[location(index([x,y+1+a,z],options.res),options.res)[1]] \
|
||||
[location(index([x,y+1+a,z],options.res),options.res)[2]][k][1] - \
|
||||
values[datatype][label][location(index([x,y-1-a,z],options.res),options.res)[0]] \
|
||||
[location(index([x,y-1-a,z],options.res),options.res)[1]] \
|
||||
[location(index([x,y-1-a,z],options.res),options.res)[2]][k][1]) * options.res[1] / options.dim[1] + \
|
||||
(values[datatype][label][location(index([x,y,z+1+a],options.res),options.res)[0]] \
|
||||
[location(index([x,y,z+1+a],options.res),options.res)[1]] \
|
||||
[location(index([x,y,z+1+a],options.res),options.res)[2]][k][2]- \
|
||||
values[datatype][label][location(index([x,y,z-1-a],options.res),options.res)[0]] \
|
||||
[location(index([x,y,z-1-a],options.res),options.res)[1]] \
|
||||
[location(index([x,y,z-1-a],options.res),options.res)[2]][k][2]) * options.res[2] / options.dim[2] \
|
||||
)
|
||||
output += '\t%f'%theDiv
|
||||
|
||||
output += '\n'
|
||||
idx += 1
|
||||
|
||||
else:
|
||||
for datatype,labels in active.items():
|
||||
for label in labels:
|
||||
if label not in div_field[datatype]: div_field[datatype][label] = {}
|
||||
for datatype,labels in active.items(): # loop over vector,tensor
|
||||
for label in labels: # loop over all requested divergencies
|
||||
for accuracy in options.accuracy:
|
||||
if accuracy == 'FFT':
|
||||
divergence[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
|
||||
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])
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
|
||||
table.data_rewind()
|
||||
idx = 0
|
||||
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
|
||||
idx += 1
|
||||
|
||||
for datatype,labels in active.items(): # loop over vector,tensor
|
||||
for label in labels: # loop over all requested
|
||||
for accuracy in options.accuracy:
|
||||
div_field[datatype][label][accuracy] = numpy.array([0.0 for i in range((datainfo[datatype]['len'])//3*\
|
||||
options.res[0]*options.res[1]*options.res[2])]).\
|
||||
reshape((options.res[0],options.res[1],options.res[2],\
|
||||
datainfo[datatype]['len']//3))
|
||||
if accuracy == 'fft':
|
||||
div_field[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
|
||||
else:
|
||||
div_field[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
|
||||
table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
|
||||
|
||||
table.data_write() # output processed line
|
||||
|
||||
idx = 0
|
||||
for line in data:
|
||||
items = line.split()[:len(headers)]
|
||||
if len(items) < len(headers):
|
||||
continue
|
||||
|
||||
output += '\t'.join(items)
|
||||
|
||||
|
||||
for datatype,labels in active.items():
|
||||
for label in labels:
|
||||
for accuracy in options.accuracy:
|
||||
for i in range(datainfo[datatype]['len']/3):
|
||||
output += '\t%f'%div_field[datatype][label][accuracy][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]][i]
|
||||
output += '\n'
|
||||
idx += 1
|
||||
|
||||
|
||||
# ------------------------------------------ output result ---------------------------------------
|
||||
|
||||
if file['name'] == 'STDIN':
|
||||
print output
|
||||
else:
|
||||
file['handle'] = open(file['name']+'_tmp','w')
|
||||
try:
|
||||
file['handle'].write(output)
|
||||
file['handle'].close()
|
||||
os.rename(file['name']+'_tmp',file['name'])
|
||||
except:
|
||||
print 'error during writing',file['name']+'_tmp'
|
||||
table.output_flush() # just in case of buffered ASCII table
|
||||
|
||||
file['input'].close() # close input ASCII table
|
||||
if file['name'] != 'STDIN':
|
||||
file['output'].close # close output ASCII table
|
||||
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
|
||||
|
|
|
|||
Loading…
Reference in New Issue