changed from core module fftw to fftwpack from latex, tolerances need to be less strict
This commit is contained in:
Martin Diehl
parent
17a5fd1963
commit
895421e677
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@ -1,7 +1,7 @@
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#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string
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import os,sys,string,math,operator
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import numpy as np
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from collections import defaultdict
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from optparse import OptionParser
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@ -10,6 +10,58 @@ import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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#--------------------------------------------------------------------------------------------------
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#> @brief calculates curl field using differentation in Fourier space
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#> @todo enable odd resolution
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#--------------------------------------------------------------------------------------------------
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def curlFFT(geomdim,field):
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grid = np.array(np.shape(field)[0:3])
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wgt = 1.0/np.array(grid).prod()
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if len(np.shape(field)) == 4:
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dataType = 'vector'
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elif len(np.shape(field)) == 5:
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dataType = 'tensor'
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field_fourier=np.fft.fftpack.rfftn(field,axes=(0,1,2))
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curl_fourier=np.zeros(field_fourier.shape,'c8')
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# differentiation in Fourier space
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k_s=np.zeros([3],'i')
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TWOPIIMG = (0.0+2.0j*math.pi)
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for i in xrange(grid[0]):
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k_s[0] = i
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if(i > grid[0]/2 ): k_s[0] = k_s[0] - grid[0]
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for j in xrange(grid[1]):
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k_s[1] = j
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if(j > grid[1]/2 ): k_s[1] = k_s[1] - grid[1]
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for k in xrange(grid[2]/2+1):
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k_s[2] = k
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if(k > grid[2]/2 ): k_s[2] = k_s[2] - grid[2]
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xi=np.array([k_s[2]/geomdim[2]+0.0j,k_s[1]/geomdim[1]+0.j,k_s[0]/geomdim[0]+0.j],'c8')
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if dataType == 'tensor':
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for l in xrange(3):
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curl_fourier[i,j,k,0,l] = ( field_fourier[i,j,k,l,2]*xi[1]\
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-field_fourier[i,j,k,l,1]*xi[2]) *TWOPIIMG
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curl_fourier[i,j,k,1,l] = (-field_fourier[i,j,k,l,2]*xi[0]\
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+field_fourier[i,j,k,l,0]*xi[2]) *TWOPIIMG
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curl_fourier[i,j,k,2,l] = ( field_fourier[i,j,k,l,1]*xi[0]\
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-field_fourier[i,j,k,l,0]*xi[1]) *TWOPIIMG
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elif dataType == 'vector':
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curl_fourier[i,j,k,0] = ( field_fourier[i,j,k,2]*xi[1]\
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-field_fourier[i,j,k,1]*xi[2]) *TWOPIIMG
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curl_fourier[i,j,k,1] = (-field_fourier[i,j,k,2]*xi[0]\
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+field_fourier[i,j,k,0]*xi[2]) *TWOPIIMG
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curl_fourier[i,j,k,2] = ( field_fourier[i,j,k,1]*xi[0]\
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-field_fourier[i,j,k,0]*xi[1]) *TWOPIIMG
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curl=np.fft.fftpack.irfftn(curl_fourier,axes=(0,1,2))
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if dataType == 'tensor':
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return curl.reshape([grid.prod(),9])
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if dataType == 'vector':
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return curl.reshape([grid.prod(),3])
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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@ -37,14 +89,16 @@ if len(options.vector) + len(options.tensor) == 0:
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parser.error('no data column specified...')
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datainfo = { # list of requested labels per datatype
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'vector': {'len':3,
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'vector': {'shape':[3],
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'len':3,
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'label':[]},
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'tensor': {'len':9,
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'tensor': {'shape':[3,3],
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'len':9,
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'label':[]},
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}
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if options.vector != None: datainfo['vector']['label'] += options.vector
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if options.tensor != None: datainfo['tensor']['label'] += options.tensor
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if options.vector != None: datainfo['vector']['label'] = options.vector
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if options.tensor != None: datainfo['tensor']['label'] = options.tensor
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# ------------------------------------------ setup file handles ------------------------------------
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files = []
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@ -60,20 +114,48 @@ for file in files:
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table.head_read() # read ASCII header info
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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# --------------- figure out size and grid ---------------------------------------------------------
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# --------------- figure out columns for coordinates and vector/tensor fields to process ---------
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column = defaultdict(dict)
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pos = 0 # when reading in the table via data_readArray, the first key is at colum 0
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try:
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locationCol = table.labels.index('1_%s'%options.coords) # columns containing location data
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column['coords'] = pos
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pos+=3 # advance by data len (columns) for next key
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keys=['%i_%s'%(i+1,options.coords) for i in xrange(3)] # store labels for column keys
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except ValueError:
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try:
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locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data (legacy naming scheme)
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column['coords'] = pos
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pos+=3 # advance by data len (columns) for next key
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directions = ['x','y','z']
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keys=['%s.%s'%(options.coords,directions[i]) for i in xrange(3)] # store labels for column keys
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except ValueError:
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file['croak'].write('no coordinate data (1_%s/%s.x) found...\n'%(options.coords,options.coords))
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file['croak'].write('no coordinate data (1_%s) found...\n'%options.coords)
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continue
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active = defaultdict(list)
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for datatype,info in datainfo.items():
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for label in info['label']:
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key = '1_%s'%label
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if key not in table.labels:
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file['croak'].write('column %s not found...\n'%key)
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else:
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active[datatype].append(label)
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column[label] = pos
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pos+=datainfo[datatype]['len']
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keys+=['%i_%s'%(i+1,label) for i in xrange(datainfo[datatype]['len'])] # extend ASCII header with new labels
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table.data_readArray(keys)
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# --------------- assemble new header (columns containing curl) -----------------------------------
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels:
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table.labels_append(['%i_curlFFT(%s)'%(i+1,label) for i in xrange(datainfo[datatype]['len'])])# extend ASCII header with new labels
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table.head_write()
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# --------------- figure out size and grid ---------------------------------------------------------
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coords = [{},{},{}]
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while table.data_read(): # read next data line of ASCII table
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for i in xrange(table.data.shape[0]):
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for j in xrange(3):
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coords[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
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coords[j][str(table.data[i,j])] = True # remember coordinate along x,y,z
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grid = np.array([len(coords[0]),\
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len(coords[1]),\
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len(coords[2]),],'i') # grid is number of distinct coordinates found
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@ -82,69 +164,29 @@ for file in files:
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max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
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max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
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],'d') # size from bounding box, corrected for cell-centeredness
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for i, points in enumerate(grid):
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if points == 1:
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mask = np.ones(3,dtype=bool)
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mask[i]=0
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size[i] = min(size[mask]/grid[mask]) # third spacing equal to smaller of other spacing
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N = grid.prod()
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# --------------- figure out columns to process ---------------------------------------------------
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active = defaultdict(list)
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column = defaultdict(dict)
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values = defaultdict(dict)
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curl = defaultdict(dict)
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for datatype,info in datainfo.items():
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for label in info['label']:
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key = '1_%s'%label
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if key not in table.labels:
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file['croak'].write('column %s not found...\n'%key)
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else:
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active[datatype].append(label)
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column[datatype][label] = table.labels.index(key) # remember columns of requested data
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values[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
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reshape(list(grid)+[datainfo[datatype]['len']//3,3])
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curl[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
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reshape(list(grid)+[datainfo[datatype]['len']//3,3])
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# ------------------------------------------ assemble header ---------------------------------------
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels:
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table.labels_append(['%i_curlFFT(%s)'%(i+1,label)
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for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ read value field --------------------------------------
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table.data_rewind()
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idx = 0
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while table.data_read(): # read next data line of ASCII table
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(x,y,z) = damask.util.gridLocation(idx,grid) # figure out (x,y,z) position from line count
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idx += 1
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested curls
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values[datatype][label][x,y,z] = np.array(
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map(float,table.data[column[datatype][label]:
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column[datatype][label]+datainfo[datatype]['len']]),'d') \
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.reshape(datainfo[datatype]['len']//3,3)
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# ------------------------------------------ process value field -----------------------------------
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested curls
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curl[datatype][label] = damask.core.math.curlFFT(size,values[datatype][label])
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curl = defaultdict(dict)
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested curls
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curl[datatype][label] = curlFFT(size[::-1], # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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table.data[:,column[label]:column[label]+datainfo[datatype]['len']].\
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reshape([grid[2],grid[1],grid[0]]+datainfo[datatype]['shape']))
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# ------------------------------------------ process data ------------------------------------------
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table.data_rewind()
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idx = 0
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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(x,y,z) = damask.util.gridLocation(idx,grid) # figure out (x,y,z) position from line count
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idx += 1
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested norms
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table.data_append(list(curl[datatype][label][x,y,z].reshape(datainfo[datatype]['len'])))
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table.data_append(list(curl[datatype][label][idx,:]))
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idx+=1
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output result -----------------------------------------
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@ -1,7 +1,7 @@
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#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string
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import os,sys,string,math,operator
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import numpy as np
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from collections import defaultdict
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from optparse import OptionParser
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@ -10,6 +10,51 @@ import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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#--------------------------------------------------------------------------------------------------
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#> @brief calculates curl field using differentation in Fourier space
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#> @todo enable odd resolution
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#--------------------------------------------------------------------------------------------------
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def divFFT(geomdim,field):
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grid = np.array(np.shape(field)[0:3])
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wgt = 1.0/np.array(grid).prod()
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field_fourier=np.fft.fftpack.rfftn(field,axes=(0,1,2))
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if len(np.shape(field)) == 4:
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dataType = 'vector'
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div_fourier=np.zeros(field_fourier.shape[0:3],'c8') # div is a scalar
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elif len(np.shape(field)) == 5:
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dataType = 'tensor'
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div_fourier=np.zeros(field_fourier.shape[0:4],'c8') # div is a vector
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# differentiation in Fourier space
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k_s=np.zeros([3],'i')
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TWOPIIMG = (0.0+2.0j*math.pi)
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for i in xrange(grid[0]):
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k_s[0] = i
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if(i > grid[0]/2 ): k_s[0] = k_s[0] - grid[0]
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for j in xrange(grid[1]):
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k_s[1] = j
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if(j > grid[1]/2 ): k_s[1] = k_s[1] - grid[1]
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for k in xrange(grid[2]/2+1):
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k_s[2] = k
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if(k > grid[2]/2 ): k_s[2] = k_s[2] - grid[2]
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xi=np.array([k_s[2]/geomdim[2]+0.0j,k_s[1]/geomdim[1]+0.j,k_s[0]/geomdim[0]+0.j],'c8')
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if dataType == 'tensor':
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for l in xrange(3):
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div_fourier[i,j,k,l] = sum(field_fourier[i,j,k,l,0:3]*xi) *TWOPIIMG
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elif dataType == 'vector':
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div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
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div=np.fft.fftpack.irfftn(div_fourier,axes=(0,1,2))
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print div.shape
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if dataType == 'tensor':
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return div.reshape([grid.prod(),3])
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if dataType == 'vector':
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return div.reshape([grid.prod(),1])
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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@ -21,20 +66,13 @@ Deals with both vector- and tensor-valued fields.
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""", version = scriptID)
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accuracyChoices = ['2','4','6','8']
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parser.add_option('--fdm', dest='accuracy', action='extend', metavar='<int LIST>',
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help='degree of central difference accuracy (%s)'%(','.join(accuracyChoices)))
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parser.add_option('--fft', dest='fft', action='store_true',
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help='calculate divergence in Fourier space')
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parser.add_option('-c','--coordinates', dest='coords', metavar = 'string',
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parser.add_option('-c','--coordinates', dest='coords', metavar='string',
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help='column heading for coordinates [%default]')
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parser.add_option('-v','--vector', dest='vector', action='extend', metavar='<string LIST>',
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help='heading of columns containing vector field values')
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parser.add_option('-t','--tensor', dest='tensor', action='extend', metavar='<string LIST>',
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help='heading of columns containing tensor field values')
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parser.set_defaults(coords = 'ipinitialcoord')
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parser.set_defaults(accuracy = [])
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parser.set_defaults(fft = False)
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parser.set_defaults(vector = [])
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parser.set_defaults(tensor = [])
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@ -42,22 +80,18 @@ parser.set_defaults(tensor = [])
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if len(options.vector) + len(options.tensor) == 0:
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parser.error('no data column specified...')
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if not set(options.accuracy).issubset(set(accuracyChoices)):
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parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices)))
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if options.fft: options.accuracy.append('FFT')
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if not options.accuracy:
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parser.error('no accuracy selected')
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datainfo = { # list of requested labels per datatype
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'vector': {'len':3,
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datainfo = { # list of requested labels per datatype
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'vector': {'shape':[3],
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'len':3,
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'label':[]},
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'tensor': {'len':9,
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'tensor': {'shape':[3,3],
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'len':9,
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'label':[]},
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}
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if options.vector != None: datainfo['vector']['label'] += options.vector
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if options.tensor != None: datainfo['tensor']['label'] += options.tensor
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if options.vector != None: datainfo['vector']['label'] = options.vector
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if options.tensor != None: datainfo['tensor']['label'] = options.tensor
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# ------------------------------------------ setup file handles ------------------------------------
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files = []
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@ -73,20 +107,49 @@ for file in files:
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table.head_read() # read ASCII header info
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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# --------------- figure out size and grid ---------------------------------------------------------
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# --------------- figure out columns for coordinates and vector/tensor fields to process ---------
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column = defaultdict(dict)
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pos = 0 # when reading in the table via data_readArray, the first key is at colum 0
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try:
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locationCol = table.labels.index('1_%s'%options.coords) # columns containing location data
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column['coords'] = pos
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pos+=3 # advance by data len (columns) for next key
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keys=['%i_%s'%(i+1,options.coords) for i in xrange(3)] # store labels for column keys
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except ValueError:
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try:
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locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data (legacy naming scheme)
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column['coords'] = pos
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pos+=3 # advance by data len (columns) for next key
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directions = ['x','y','z']
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keys=['%s.%s'%(options.coords,directions[i]) for i in xrange(3)] # store labels for column keys
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except ValueError:
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file['croak'].write('no coordinate data (1_%s/%s.x) found...\n'%(options.coords,options.coords))
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file['croak'].write('no coordinate data (1_%s) found...\n'%options.coords)
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continue
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active = defaultdict(list)
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for datatype,info in datainfo.items():
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for label in info['label']:
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key = '1_%s'%label
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if key not in table.labels:
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file['croak'].write('column %s not found...\n'%key)
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else:
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active[datatype].append(label)
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column[label] = pos
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pos+=datainfo[datatype]['len']
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keys+=['%i_%s'%(i+1,label) for i in xrange(datainfo[datatype]['len'])] # extend ASCII header with new labels
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table.data_readArray(keys)
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# --------------- assemble new header (columns containing curl) -----------------------------------
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels:
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table.labels_append(['divFFT(%s)'%(label) if datatype == 'vector' else
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||||
'%i_divFFT(%s)'%(i+1,label) for i in xrange(datainfo[datatype]['len']//3)])# extend ASCII header with new labels
|
||||
table.head_write()
|
||||
|
||||
# --------------- figure out size and grid ---------------------------------------------------------
|
||||
coords = [{},{},{}]
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
for i in xrange(table.data.shape[0]):
|
||||
for j in xrange(3):
|
||||
coords[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
|
||||
coords[j][str(table.data[i,j])] = True # remember coordinate along x,y,z
|
||||
grid = np.array([len(coords[0]),\
|
||||
len(coords[1]),\
|
||||
len(coords[2]),],'i') # grid is number of distinct coordinates found
|
||||
|
|
@ -95,82 +158,32 @@ for file in files:
|
|||
max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
|
||||
max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
|
||||
],'d') # size from bounding box, corrected for cell-centeredness
|
||||
|
||||
for i, points in enumerate(grid):
|
||||
if points == 1:
|
||||
mask = np.ones(3,dtype=bool)
|
||||
mask[i]=0
|
||||
size[i] = min(size[mask]/grid[mask]) # third spacing equal to smaller of other spacing
|
||||
|
||||
N = grid.prod()
|
||||
|
||||
# --------------- figure out columns to process ---------------------------------------------------
|
||||
active = defaultdict(list)
|
||||
column = defaultdict(dict)
|
||||
values = defaultdict(dict)
|
||||
divergence = defaultdict(dict)
|
||||
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
key = '1_%s'%label
|
||||
if key not in table.labels:
|
||||
file['croak'].write('column %s not found...\n'%key)
|
||||
else:
|
||||
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(grid)+[datainfo[datatype]['len']//3,3])
|
||||
if label not in divergence[datatype]: divergence[datatype][label] = {}
|
||||
for accuracy in options.accuracy:
|
||||
divergence[datatype][label][accuracy] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len']//3)]).\
|
||||
reshape(list(grid)+[datainfo[datatype]['len']//3])
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
for datatype,labels in active.items(): # loop over vector,tensor
|
||||
for label in labels:
|
||||
for accuracy in options.accuracy:
|
||||
table.labels_append({True: ['%i_div%s(%s)'%(i+1,accuracy,label) for i in xrange(3)], # extend ASCII header with new labels
|
||||
False:['div%s(%s)'%(accuracy,label)]} [datatype == 'tensor'])
|
||||
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.util.gridLocation(idx,grid) # 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] = np.array(
|
||||
map(float,table.data[column[datatype][label]:
|
||||
column[datatype][label]+datainfo[datatype]['len']]),'d') \
|
||||
.reshape(datainfo[datatype]['len']//3,3)
|
||||
|
||||
# ------------------------------------------ process value field -----------------------------------
|
||||
div = defaultdict(dict)
|
||||
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(size,values[datatype][label])
|
||||
else:
|
||||
divergence[datatype][label][accuracy] =\
|
||||
damask.core.math.divergenceFDM(size,eval(accuracy)//2-1,values[datatype][label])
|
||||
|
||||
for label in labels: # loop over all requested curls
|
||||
div[datatype][label] = divFFT(size[::-1], # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
|
||||
table.data[:,column[label]:column[label]+datainfo[datatype]['len']].\
|
||||
reshape([grid[2],grid[1],grid[0]]+datainfo[datatype]['shape']))
|
||||
# ------------------------------------------ process data ------------------------------------------
|
||||
table.data_rewind()
|
||||
idx = 0
|
||||
outputAlive = True
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
(x,y,z) = damask.util.gridLocation(idx,grid) # 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:
|
||||
table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
|
||||
for label in labels: # loop over all requested norms
|
||||
table.data_append(list(div[datatype][label][idx,:]))
|
||||
idx+=1
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
# ------------------------------------------ output result -----------------------------------------
|
||||
# ------------------------------------------ output result -----------------------------------------
|
||||
outputAlive and table.output_flush() # just in case of buffered ASCII table
|
||||
|
||||
table.input_close() # close input ASCII table
|
||||
|
|
|
|||
Loading…
Reference in New Issue