improved file handling etc. to follow newest style
This commit is contained in:
Martin Diehl
parent
11a4b59c69
commit
2f0ecdf7e8
|
|
@ -3,6 +3,7 @@
|
|||
|
||||
import os,re,sys,math,string
|
||||
import numpy as np
|
||||
from collections import defaultdict
|
||||
from optparse import OptionParser
|
||||
import damask
|
||||
|
||||
|
|
@ -18,10 +19,9 @@ Add column(s) containing curl of requested column(s).
|
|||
Operates on periodic ordered three-dimensional data sets.
|
||||
Deals with both vector- and tensor-valued fields.
|
||||
|
||||
""", version=string.replace('$Id$','\n','\\n')
|
||||
""", version = string.replace(scriptID,'\n','\\n')
|
||||
)
|
||||
|
||||
|
||||
parser.add_option('-c','--coordinates', dest='coords', type='string', metavar='string', \
|
||||
help='column heading for coordinates [%default]')
|
||||
parser.add_option('-v','--vector', dest='vector', action='extend', type='string', metavar='<string LIST>', \
|
||||
|
|
@ -49,19 +49,15 @@ if options.tensor != None: datainfo['tensor']['label'] += options.tensor
|
|||
|
||||
# ------------------------------------------ setup file handles ------------------------------------
|
||||
files = []
|
||||
if filenames == []:
|
||||
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
|
||||
else:
|
||||
for name in filenames:
|
||||
if os.path.exists(name):
|
||||
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
|
||||
for name in filenames:
|
||||
if os.path.exists(name):
|
||||
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
|
||||
|
||||
#--- loop over input files ------------------------------------------------------------------------
|
||||
for file in files:
|
||||
if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
|
||||
else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
|
||||
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
|
||||
|
||||
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
|
||||
table = damask.ASCIItable(file['input'],file['output'],True) # make unbuffered ASCII_table
|
||||
table.head_read() # read ASCII header info
|
||||
table.info_append(string.replace(scriptID,'\n','\\n') + '\t' + ' '.join(sys.argv[1:]))
|
||||
|
||||
|
|
@ -73,29 +69,27 @@ for file in files:
|
|||
continue
|
||||
|
||||
grid = [{},{},{}]
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
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
|
||||
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
|
||||
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
|
||||
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()
|
||||
|
||||
# --------------- figure out columns to process
|
||||
active = {}
|
||||
column = {}
|
||||
values = {}
|
||||
curl = {}
|
||||
|
||||
head = []
|
||||
# --------------- figure out columns to process --------------------------------------------------
|
||||
active = defaultdict(list)
|
||||
column = defaultdict(dict)
|
||||
values = defaultdict(dict)
|
||||
curl = defaultdict(dict)
|
||||
missingColumns = False
|
||||
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
|
|
@ -104,28 +98,28 @@ for file in files:
|
|||
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 curl: curl[datatype] = {}
|
||||
active[datatype].append(label)
|
||||
column[datatype][label] = table.labels.index(key) # remember columns of requested data
|
||||
values[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
|
||||
reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
|
||||
curl[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
|
||||
reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
|
||||
table.labels_append(['%i_curlFFT(%s)'%(i+1,label)
|
||||
for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels
|
||||
|
||||
if missingColumns:
|
||||
continue
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
table.labels_append(['%i_curlFFT(%s)'%(i+1,label)
|
||||
for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ read value field --------------------------------------
|
||||
table.data_rewind()
|
||||
idx = 0
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = damask.gridLocation(idx,resolution) # figure out (x,y,z) position from line count
|
||||
(x,y,z) = damask.util.gridLocation(idx,resolution) # 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 curls
|
||||
|
|
@ -141,10 +135,10 @@ for file in files:
|
|||
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
table.data_rewind()
|
||||
outputAlive = True
|
||||
idx = 0
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = damask.gridLocation(idx,resolution) # figure out (x,y,z) position from line count
|
||||
(x,y,z) = damask.util.gridLocation(idx,resolution) # 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 norms
|
||||
|
|
@ -152,11 +146,9 @@ for file in files:
|
|||
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
|
||||
# ------------------------------------------ output result ---------------------------------------
|
||||
outputAlive and table.output_flush() # just in case of buffered ASCII table
|
||||
|
||||
file['input'].close() # close input ASCII table (works for stdin)
|
||||
file['output'].close() # close output ASCII table (works for stdout)
|
||||
if file['name'] != 'STDIN':
|
||||
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
|
||||
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
|
||||
|
|
|
|||
|
|
@ -1,64 +1,38 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: UTF-8 no BOM -*-
|
||||
|
||||
import os,re,sys,math,string,numpy,damask
|
||||
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 ( 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[1] * res[0] )
|
||||
|
||||
import os,re,sys,math,string
|
||||
import numpy as np
|
||||
from collections import defaultdict
|
||||
from optparse import OptionParser
|
||||
import damask
|
||||
|
||||
scriptID = '$Id$'
|
||||
scriptName = scriptID.split()[1]
|
||||
accuracyChoices = ['2','4','6','8']
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# MAIN
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
|
||||
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
|
||||
Add column(s) containing divergence of requested column(s).
|
||||
Operates on periodic ordered three-dimensional data sets.
|
||||
Deals with both vector- and tensor-valued fields.
|
||||
|
||||
""" + string.replace('$Id$','\n','\\n')
|
||||
""", version = string.replace(scriptID,'\n','\\n')
|
||||
)
|
||||
|
||||
accuracyChoices = ['2','4','6','8']
|
||||
|
||||
parser.add_option('--fdm', dest='accuracy', action='extend', type='string', \
|
||||
parser.add_option('--fdm', dest='accuracy', action='extend', type='string', metavar='<int LIST>', \
|
||||
help='degree of central difference accuracy (%s)'%(','.join(accuracyChoices)))
|
||||
parser.add_option('--fft', dest='fft', action='store_true', \
|
||||
help='calculate divergence in Fourier space')
|
||||
parser.add_option('-c','--coordinates', dest='coords', type='string',\
|
||||
parser.add_option('-c','--coordinates', dest='coords', type='string', metavar = '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', metavar='<string LIST>', \
|
||||
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', metavar='<string LIST>', \
|
||||
help='heading of columns containing tensor field values')
|
||||
|
||||
parser.set_defaults(coords = 'ip')
|
||||
parser.set_defaults(accuracy = [])
|
||||
parser.set_defaults(fft = False)
|
||||
|
|
@ -87,60 +61,49 @@ datainfo = { # lis
|
|||
if options.vector != None: datainfo['vector']['label'] += options.vector
|
||||
if options.tensor != None: datainfo['tensor']['label'] += options.tensor
|
||||
|
||||
# ------------------------------------------ setup file handles ---------------------------------------
|
||||
|
||||
# ------------------------------------------ setup file handles ------------------------------------
|
||||
files = []
|
||||
if filenames == []:
|
||||
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout})
|
||||
else:
|
||||
for name in filenames:
|
||||
if os.path.exists(name):
|
||||
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')})
|
||||
|
||||
|
||||
# ------------------------------------------ loop over input files ---------------------------------------
|
||||
for name in filenames:
|
||||
if os.path.exists(name):
|
||||
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
|
||||
|
||||
#--- loop over input files ------------------------------------------------------------------------
|
||||
for file in files:
|
||||
if file['name'] != 'STDIN': print file['name'],
|
||||
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
|
||||
|
||||
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:]))
|
||||
table = damask.ASCIItable(file['input'],file['output'],True) # make unbuffered ASCII_table
|
||||
table.head_read() # read ASCII header info
|
||||
table.info_append(string.replace(scriptID,'\n','\\n') + '\t' + ' '.join(sys.argv[1:]))
|
||||
|
||||
# --------------- figure out dimension and resolution
|
||||
# --------------- figure out dimension and resolution ----------------------------------------------
|
||||
try:
|
||||
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
|
||||
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
|
||||
except ValueError:
|
||||
print 'no coordinate data found...'
|
||||
file['croak'].write('no coordinate data found...\n'%key)
|
||||
continue
|
||||
|
||||
grid = [{},{},{}]
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
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
|
||||
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
|
||||
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 = {}
|
||||
column = {}
|
||||
values = {}
|
||||
divergence = {}
|
||||
|
||||
head = []
|
||||
# --------------- figure out columns to process --------------------------------------------------
|
||||
active = defaultdict(list)
|
||||
column = defaultdict(dict)
|
||||
values = defaultdict(dict)
|
||||
divergence = defaultdict(dict)
|
||||
missingColumns = False
|
||||
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
|
|
@ -149,71 +112,67 @@ for file in files:
|
|||
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 divergence: divergence[datatype] = {}
|
||||
if label not in divergence[datatype]: divergence[datatype][label] = {}
|
||||
active[datatype].append(label)
|
||||
column[datatype][label] = table.labels.index(key) # remember columns of requested data
|
||||
values[datatype][label] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
|
||||
column[datatype][label] = table.labels.index(key) # remember columns of requested data
|
||||
values[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
|
||||
reshape(list(resolution)+[datainfo[datatype]['len']//3,3])
|
||||
if label not in divergence[datatype]: divergence[datatype][label] = {}
|
||||
for accuracy in options.accuracy:
|
||||
divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(N*datainfo[datatype]['len']//3)]).\
|
||||
divergence[datatype][label][accuracy] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len']//3)]).\
|
||||
reshape(list(resolution)+[datainfo[datatype]['len']//3])
|
||||
if datatype == 'vector': # extend ASCII header with new labels
|
||||
table.labels_append(['div%s(%s)'%(accuracy,label)])
|
||||
if datatype == 'tensor':
|
||||
table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label) for i in xrange(3)])
|
||||
|
||||
if missingColumns:
|
||||
continue
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
for accuracy in options.accuracy:
|
||||
if datatype == 'vector': # extend ASCII header with new labels
|
||||
table.labels_append(['div%s(%s)'%(accuracy,label)])
|
||||
if datatype == 'tensor':
|
||||
table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label) for i in xrange(3)])
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ read value field ---------------------------------------
|
||||
|
||||
# ------------------------------------------ read value field --------------------------------------
|
||||
table.data_rewind()
|
||||
|
||||
idx = 0
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = location(idx,resolution) # figure out (x,y,z) position from line count
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = damask.util.gridLocation(idx,resolution) # 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 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 ---------------------------------------
|
||||
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] = np.array(
|
||||
map(float,table.data[column[datatype][label]:
|
||||
column[datatype][label]+datainfo[datatype]['len']]),'d') \
|
||||
.reshape(datainfo[datatype]['len']//3,3)
|
||||
|
||||
for datatype,labels in active.items(): # loop over vector,tensor
|
||||
for label in labels: # loop over all requested divergencies
|
||||
# ------------------------------------------ process value field -----------------------------------
|
||||
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.divergenceFFT(dimension,values[datatype][label])
|
||||
else:
|
||||
divergence[datatype][label][accuracy] = damask.core.math.divergenceFDM(dimension,eval(accuracy)//2-1,values[datatype][label])
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
table.data_rewind()
|
||||
idx = 0
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = location(idx,resolution) # figure out (x,y,z) position from line count
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = damask.util.gridLocation(idx,resolution) # 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 datatype,labels in active.items(): # loop over vector,tensor
|
||||
for label in labels: # loop over all requested
|
||||
for accuracy in options.accuracy:
|
||||
table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
|
||||
|
||||
table.data_write() # output processed line
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
|
||||
# ------------------------------------------ output result ---------------------------------------
|
||||
outputAlive and table.output_flush() # just in case of buffered ASCII table
|
||||
|
||||
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
|
||||
file['input'].close() # close input ASCII table (works for stdin)
|
||||
file['output'].close() # close output ASCII table (works for stdout)
|
||||
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
|
||||
|
|
|
|||
Loading…
Reference in New Issue