"""
Library of SPAM functions for reading and writing TSV files.
Copyright (C) 2020 SPAM Contributors
This program is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your option)
any later version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy
import os
[docs]
def writeRegistrationTSV(fileName, regCentre, regReturns):
'''
This function writes a correctly formatted TSV file from the result of a single `register()` call, allowing it to be used as an initial registration.
Parameters
----------
fileName : string
The file name for output, suggestion: it should probably end with ".tsv"
regCentre : 3-component list
A list containing the point at which `Phi` has been measured.
This is typically the middle of the image, and can be obtained as follows:
(numpy.array( im.shape )-1)/2.0
The conversion to a numpy array is necessary, since tuples cannot be divided by a number directly.
regReturns : dictionary
This should be the return dictionary from `register`.
From this dictionary will be extracted: 'Phi', 'error', 'iterations', 'returnStatus', 'deltaPhiNorm'
'''
try:
regPhi = regReturns['Phi']
except:
print("spam.helpers.tsvio.writeRegistrationTSV(): Attempting to read old format")
regPhi = regReturns['PhiCentre']
# catch 2D images
if len(regCentre) == 2:
regCentre = [0, regCentre[0], regCentre[1]]
# Make one big array for writing:
header = "NodeNumber\tZpos\tYpos\tXpos\tFzz\tFzy\tFzx\tZdisp\tFyz\tFyy\tFyx\tYdisp\tFxz\tFxy\tFxx\tXdisp\terror\titerations\treturnStatus\tdeltaPhiNorm"
try:
outMatrix = numpy.array([[1],
[regCentre[0]],
[regCentre[1]],
[regCentre[2]],
[regPhi[0, 0]],
[regPhi[0, 1]],
[regPhi[0, 2]],
[regPhi[0, 3]],
[regPhi[1, 0]],
[regPhi[1, 1]],
[regPhi[1, 2]],
[regPhi[1, 3]],
[regPhi[2, 0]],
[regPhi[2, 1]],
[regPhi[2, 2]],
[regPhi[2, 3]],
[regReturns['error']],
[regReturns['iterations']],
[regReturns['returnStatus']],
[regReturns['deltaPhiNorm']]])
except:
print("spam.helpers.tsvio.writeRegistrationTSV(): Attempting to read old format")
outMatrix = numpy.array([[1],
[regCentre[0]],
[regCentre[1]],
[regCentre[2]],
[regPhi[0, 0]],
[regPhi[0, 1]],
[regPhi[0, 2]],
[regPhi[0, 3]],
[regPhi[1, 0]],
[regPhi[1, 1]],
[regPhi[1, 2]],
[regPhi[1, 3]],
[regPhi[2, 0]],
[regPhi[2, 1]],
[regPhi[2, 2]],
[regPhi[2, 3]],
[regReturns['error']],
[regReturns['iterationNumber']],
[regReturns['returnStatus']],
[regReturns['deltaPhiNorm']]])
numpy.savetxt(fileName,
outMatrix.T,
fmt='%.7f',
delimiter='\t',
newline='\n',
comments='',
header=header)
[docs]
def writeStrainTSV(fileName, points, decomposedFfield, firstColumn="StrainPointNumber", startRow=0):
"""
This function writes strains to a TSV file, hiding the complexity of counting and naming columns
Parameters
----------
fileName : string
fileName including full path and .tsv at the end to write
points : Nx3 numpy array
Points at which the strain is defined
decomposedFfield : dictionary
Dictionary containing strain components as per output from spam.deformation.FfieldRegularQ8, FfieldRegularGeers or FfieldBagi
firstColumn : string, optional
How to name the first column (series number) of the TSV
Default = "StrainPointNumber"
startRow : int, optional
Are your points and strains offset from zero? Offset TSV by adding blank lines, don't use this if you don't know what you're doing
Default = 0
Returns
-------
None
"""
# This is the minimum header for everyone
header = "{}\tZpos\tYpos\tXpos".format(firstColumn)
# Allocate minimum output array
outMatrix = numpy.array([numpy.arange(points.shape[0]),
points[:, 0],
points[:, 1],
points[:, 2]]).T
nCols = 4
for component in decomposedFfield.keys():
if component == 'vol' or component == 'dev' or component == 'volss' or component == 'devss':
header = header + "\t{}".format(component)
outMatrix = numpy.hstack([outMatrix, numpy.array([decomposedFfield[component].ravel()]).T])
nCols += 1
if component == 'r' or component == 'z':
for n, di in enumerate(['z', 'y', 'x']):
header = header + "\t{}{}".format(component, di)
outMatrix = numpy.hstack([outMatrix, numpy.array([decomposedFfield[component].reshape(-1,3)[:,n].ravel()]).T])
nCols += 1
if component == 'e' or component == 'U':
for n, di in enumerate(['z', 'y', 'x']):
for m, dj in enumerate(['z', 'y', 'x']):
if m>=n:
header = header + "\t{}{}{}".format(component, di, dj)
outMatrix = numpy.hstack([outMatrix, numpy.array([decomposedFfield[component].reshape(-1,3,3)[:,n,m].ravel()]).T])
nCols += 1
# This is mostly for discrete Strains, where we can avoid or not the zero-numbered grain
if startRow > 0:
for i in range(startRow):
header = header+'\n0.0'
for j in range(1,nCols):
header = header+'\t0.0'
numpy.savetxt(fileName,
outMatrix,
delimiter='\t',
fmt='%.7f',
newline='\n',
comments='',
header=header)
[docs]
def readCorrelationTSV(fileName, fieldBinRatio=1.0, readOnlyDisplacements=False, readConvergence=True, readPixelSearchCC=False, readError=False, readLabelDilate=False):
"""
This function reads a TSV file containing a field of deformation functions **Phi** at one or a number of points.
This is typically the output of the spam-ldic and spam-ddic scripts,
or anything written by `writeRegistrationTSV`.
Parameters
----------
fileName : string
Name of the file
fieldBinRatio : int, optional
if the input field is refer to a binned version of the image
`e.g.`, if ``fieldBinRatio = 2`` the field_name values have been calculated
for an image half the size of what the returned PhiField is referring to
Default = 1.0
readOnlyDisplacements : bool, optional
Read "zDisp", "yDisp", "xDisp", displacements from the TSV file, and not the rest of the Phi matrix?
Default = False
readConvergence : bool, optional
Read "returnStatus", "deltaPhiNorm", "iterations", from file
Default = True
readPixelSearchCC : bool, optional
Read "pixelSearchCC" from file
Default = False
readError : bool, optional
Read '"error"from file
Default = False
readLabelDilate : bool, optional
Read "LabelDilate" from file
Default = False
Returns
-------
Dictionary containing:
fieldDims: 1x3 array of the field dimensions (ZYX) (for a regular grid DIC result)
numberOfLabels: number of labels (for a discrete DIC result)
fieldCoords: nx3 array of n points coordinates (ZYX)
PhiField: nx4x4 array of n points transformation operators
returnStatus: nx1 array of n points returnStatus from the correlation
deltaPhiNorm: nx1 array of n points deltaPhiNorm from the correlation
iterations: nx1 array of n points iterations from the correlation
pixelSearchCC: nx1 array of n points pixelSearchCC from the correlation
error: nx1 array of n points error from the correlation
labelDilate: nx1 array of n points labelDilate from the correlation
"""
if not os.path.isfile(fileName):
print("\n\tspam.tsvio.readCorrelationTSV(): {} is not a file. Exiting.".format(fileName))
return
f = numpy.genfromtxt(fileName, delimiter="\t", names=True)
# RS = []
# deltaPhiNorm = []
# pixelSearchCC = []
# If this is a one-line TSV file (an initial registration for example)
if f.size == 1:
#print("\tspam.tsvio.readCorrelationTSV(): {} seems only to have one line.".format(fileName))
nPoints = 1
numberOfLabels = 1
fieldDims = [1, 1, 1]
# Sort out the field coordinates
fieldCoords = numpy.zeros((nPoints, 3))
fieldCoords[:, 0] = f['Zpos'] * fieldBinRatio
fieldCoords[:, 1] = f['Ypos'] * fieldBinRatio
fieldCoords[:, 2] = f['Xpos'] * fieldBinRatio
# Sort out the components of Phi
PhiField = numpy.zeros((nPoints, 4, 4))
PhiField[0] = numpy.eye(4)
# Fill in displacements
try:
PhiField[0, 0, 3] = f['Zdisp'] * fieldBinRatio
PhiField[0, 1, 3] = f['Ydisp'] * fieldBinRatio
PhiField[0, 2, 3] = f['Xdisp'] * fieldBinRatio
except ValueError:
PhiField[0, 0, 3] = f['F14'] * fieldBinRatio
PhiField[0, 1, 3] = f['F24'] * fieldBinRatio
PhiField[0, 2, 3] = f['F34'] * fieldBinRatio
if not readOnlyDisplacements:
try:
# Get non-displacement components
PhiField[0, 0, 0] = f['Fzz']
PhiField[0, 0, 1] = f['Fzy']
PhiField[0, 0, 2] = f['Fzx']
PhiField[0, 1, 0] = f['Fyz']
PhiField[0, 1, 1] = f['Fyy']
PhiField[0, 1, 2] = f['Fyx']
PhiField[0, 2, 0] = f['Fxz']
PhiField[0, 2, 1] = f['Fxy']
PhiField[0, 2, 2] = f['Fxx']
except:
print("spam.helpers.tsvio.readCorrelationTSV(): Attempting to read old format, please update your TSV file (F11 should be Fzz and so on)")
# Get non-displacement components
PhiField[0, 0, 0] = f['F11']
PhiField[0, 0, 1] = f['F12']
PhiField[0, 0, 2] = f['F13']
PhiField[0, 1, 0] = f['F21']
PhiField[0, 1, 1] = f['F22']
PhiField[0, 1, 2] = f['F23']
PhiField[0, 2, 0] = f['F31']
PhiField[0, 2, 1] = f['F32']
PhiField[0, 2, 2] = f['F33']
if readConvergence:
try:
# Return ReturnStatus, SubPixelDeltaFnorm, SubPixelIterations
RS = f['returnStatus']
deltaPhiNorm = f['deltaPhiNorm']
iterations = f['iterations']
except:
print("spam.helpers.tsvio.readCorrelationTSV(): Attempting to read old format, please update your TSV file (SubPix should be )")
# Return ReturnStatus, SubPixelDeltaFnorm, SubPixelIterations
RS = f['SubPixReturnStat']
deltaPhiNorm = f['SubPixDeltaPhiNorm']
iterations = f['SubPixIterations']
if readError:
try:
error = f['error']
except ValueError:
pass
# Return pixelSearchCC
if readPixelSearchCC:
pixelSearchCC = numpy.zeros(nPoints)
try:
pixelSearchCC = f['pixelSearchCC']
except ValueError:
pass
# Return error
if readError:
error = numpy.zeros(nPoints)
try:
error = f['error']
except ValueError:
pass
# Return labelDilate
if readLabelDilate:
labelDilate = numpy.zeros(nPoints)
try:
labelDilate = f['LabelDilate']
except ValueError:
pass
pixelSearchCC = 0
# there is more than one line in the TSV file -- a field -- typical case
else:
nPoints = f.size
# check if it is a ddic or ldic result
try:
f["NodeNumber"]
# it's a local DIC result with grid points regularly spaced
DICgrid = True
DICdiscrete = False
except ValueError:
# it's a discrete DIC result with values in each label's centre of mass
DICdiscrete = True
DICgrid = False
# Sort out the field coordinates
fieldCoords = numpy.zeros((nPoints, 3))
fieldCoords[:, 0] = f['Zpos'] * fieldBinRatio
fieldCoords[:, 1] = f['Ypos'] * fieldBinRatio
fieldCoords[:, 2] = f['Xpos'] * fieldBinRatio
if DICgrid:
fieldDims = numpy.array([len(numpy.unique(f['Zpos'])), len(numpy.unique(f['Ypos'])), len(numpy.unique(f['Xpos']))])
numberOfLabels = 0
print("\tspam.tsvio.readCorrelationTSV(): Field dimensions: {}".format(fieldDims))
elif DICdiscrete:
numberOfLabels = len(f["Label"])
fieldDims = [0, 0, 0]
print("\tspam.tsvio.readCorrelationTSV(): Number of labels: {}".format(numberOfLabels))
# create ReturnStatus and deltaPhiNorm matrices if asked
if readConvergence:
try:
RS = numpy.zeros(nPoints)
RS[:] = f[:]['returnStatus']
deltaPhiNorm = numpy.zeros(nPoints)
deltaPhiNorm = f[:]['deltaPhiNorm']
iterations = numpy.zeros(nPoints)
iterations = f[:]['iterations']
except:
print("spam.helpers.tsvio.readCorrelationTSV(): Attempting to read old format, please update your TSV file")
RS = numpy.zeros(nPoints)
RS[:] = f[:]['SubPixReturnStat']
deltaPhiNorm = numpy.zeros(nPoints)
deltaPhiNorm = f[:]['SubPixDeltaPhiNorm']
iterations = numpy.zeros(nPoints)
iterations = f[:]['SubPixIterations']
# Return pixelSearchCC
if readPixelSearchCC:
pixelSearchCC = numpy.zeros(nPoints)
try:
pixelSearchCC = f[:]['pixelSearchCC']
except ValueError:
pass
# Return error
if readError:
error = numpy.zeros(nPoints)
try:
error = f[:]['error']
except ValueError:
pass
# Return labelDilate
if readLabelDilate:
labelDilate = numpy.zeros(nPoints)
try:
labelDilate = f[:]['LabelDilate']
except ValueError:
pass
# Sort out the components of Phi
PhiField = numpy.zeros((nPoints, 4, 4))
for n in range(nPoints):
# Initialise with Identity matrix
PhiField[n] = numpy.eye(4)
# Fill in displacements
try:
PhiField[n, 0, 3] = f[n]['Zdisp'] * fieldBinRatio
PhiField[n, 1, 3] = f[n]['Ydisp'] * fieldBinRatio
PhiField[n, 2, 3] = f[n]['Xdisp'] * fieldBinRatio
except ValueError:
PhiField[n, 0, 3] = f[n]['F14'] * fieldBinRatio
PhiField[n, 1, 3] = f[n]['F24'] * fieldBinRatio
PhiField[n, 2, 3] = f[n]['F34'] * fieldBinRatio
if not readOnlyDisplacements:
try:
# Get non-displacement components
PhiField[n, 0, 0] = f[n]['Fzz']
PhiField[n, 0, 1] = f[n]['Fzy']
PhiField[n, 0, 2] = f[n]['Fzx']
PhiField[n, 1, 0] = f[n]['Fyz']
PhiField[n, 1, 1] = f[n]['Fyy']
PhiField[n, 1, 2] = f[n]['Fyx']
PhiField[n, 2, 0] = f[n]['Fxz']
PhiField[n, 2, 1] = f[n]['Fxy']
PhiField[n, 2, 2] = f[n]['Fxx']
except:
print("spam.helpers.tsvio.readCorrelationTSV(): Attempting to read old format, please update your TSV file (F11 should be Fzz and so on)")
# Get non-displacement components
PhiField[n, 0, 0] = f[n]['F11']
PhiField[n, 0, 1] = f[n]['F12']
PhiField[n, 0, 2] = f[n]['F13']
PhiField[n, 1, 0] = f[n]['F21']
PhiField[n, 1, 1] = f[n]['F22']
PhiField[n, 1, 2] = f[n]['F23']
PhiField[n, 2, 0] = f[n]['F31']
PhiField[n, 2, 1] = f[n]['F32']
PhiField[n, 2, 2] = f[n]['F33']
output = {"fieldDims": fieldDims,
"numberOfLabels": numberOfLabels,
"fieldCoords": fieldCoords}
if readConvergence:
output.update({"returnStatus": RS,
"deltaPhiNorm": deltaPhiNorm,
"iterations": iterations})
if readError:
output.update({"error": error})
if readPixelSearchCC:
output.update({"pixelSearchCC": pixelSearchCC})
if readLabelDilate:
output.update({"LabelDilate": labelDilate})
if readOnlyDisplacements:
output.update({"displacements": PhiField[:, 0:3, -1]})
else:
output.update({"PhiField": PhiField})
return output
[docs]
def readStrainTSV(fileName):
"""
This function reads a strain TSV file written by `spam-discreteStrain` or `spam-regularStrain`
Parameters
----------
fileName : string
Name of the file
Returns
-------
Dictionary containing:
fieldDims: 1x3 array of the field dimensions (ZYX)
fieldCoords : nx3 array of the field coordinates (ZYX)
numberOfLabels: number of labels (for a discrete strain result)
vol: nx1 array of n points with volumetric strain computed under the hypotesis of large strains (if computed)
dev: nx1 array of n points with deviatoric strain computed under the hypotesis of large strains (if computed)
volss: nx1 array of n points with volumetric strain computed under the hypotesis of small strains (if computed)
devss: nx1 array of n points with deviatoric strain computed under the hypotesis of small strains (if computed)
r : nx3 array of n points with the components of the rotation vector (if computed)
z : nx3 array of n points with the components of the zoom vector (if computed)
U : nx3x3 array of n points with the components of the right-hand stretch tensor (if computed)
e : nx3x3 array of n points with the components of the strain tensor in small strains (if computed)
"""
if not os.path.isfile(fileName):
print("\n\tspam.tsvio.readStrainTSV(): {} is not a file. Exiting.".format(fileName))
return
#Read the TSV
f = numpy.genfromtxt(fileName, delimiter="\t", names=True)
#Number of points
nPoints = f.size
#Get keys from file
keys = f.dtype.names
#Create empyt dictionary to be filled
output = {}
#Read and add the label coordinates
fieldCoords = numpy.zeros((nPoints, 3))
fieldCoords[:, 0] = f['Zpos']
fieldCoords[:, 1] = f['Ypos']
fieldCoords[:, 2] = f['Xpos']
output['fieldCoords'] = fieldCoords
#Check if we are working with a regular grid or discrete
grid = False
discrete = False
if numpy.abs(fieldCoords[2,0] - fieldCoords[3,0]) == 0:
grid = True
else:
discrete = True
if grid:
fieldDims = numpy.array([len(numpy.unique(f['Zpos'])), len(numpy.unique(f['Ypos'])), len(numpy.unique(f['Xpos']))])
output['fieldDims'] = fieldDims
output['numberOfLabels'] = 0
else:
output['fieldDims'] = [0, 0, 0]
output['numberOfLabels'] = nPoints
#Check for all the possible keys
if 'vol' in keys:
volStrain = numpy.zeros((nPoints, 1))
volStrain[:, 0] = f['vol']
output['vol'] = volStrain
if 'dev' in keys:
devStrain = numpy.zeros((nPoints, 1))
devStrain[:, 0] = f['dev']
output['dev'] = devStrain
if 'volss' in keys:
volss = numpy.zeros((nPoints, 1))
volss[:, 0] = f['volss']
output['volss'] = volss
if 'devss' in keys:
devss = numpy.zeros((nPoints, 1))
devss[:, 0] = f['devss']
output['devss'] = devss
if 'rz' in keys:
r = numpy.zeros((nPoints, 3))
r[:, 0] = f['rz']
r[:, 1] = f['ry']
r[:, 2] = f['rx']
output['r'] = r
if 'zz' in keys:
# Zooms, these are very badly named like this
z = numpy.zeros((nPoints, 3))
z[:, 0] = f['zz']
z[:, 1] = f['zy']
z[:, 2] = f['zx']
output['z'] = z
if 'Uzz' in keys:
# Symmetric, so fill in both sides
U = numpy.zeros((nPoints, 3, 3))
U[:, 0, 0] = f['Uzz']
U[:, 1, 1] = f['Uyy']
U[:, 2, 2] = f['Uxx']
U[:, 0, 1] = f['Uzy']
U[:, 1, 0] = f['Uzy']
U[:, 0, 2] = f['Uzx']
U[:, 2, 0] = f['Uzx']
U[:, 1, 2] = f['Uyx']
U[:, 2, 1] = f['Uyx']
output['U'] = U
if 'ezz' in keys:
# Symmetric, so fill in both sides
e = numpy.zeros((nPoints, 3, 3))
e[:, 0, 0] = f['ezz']
e[:, 1, 1] = f['eyy']
e[:, 2, 2] = f['exx']
e[:, 0, 1] = f['ezy']
e[:, 1, 0] = f['ezy']
e[:, 0, 2] = f['ezx']
e[:, 2, 0] = f['ezx']
e[:, 1, 2] = f['eyx']
e[:, 2, 1] = f['eyx']
output['e'] = e
return output
[docs]
def TSVtoTIFF(fileName, fieldBinRatio=1.0, lab=None, returnRS=False, outDir=None, prefix=None):
'''
This function converts a TSV file (typically the output of spam-ldic and spam-ddic scripts)
to a tiff file for visualising the deformation field.
Parameters
----------
fileName : string
Name of the file
fieldBinRatio : int, optional
if the input field is refer to a binned version of the image
`e.g.`, if ``fieldBinRatio = 2`` the field_name values have been calculated
for an image half the size of what the returned PhiField is referring to
Default = 1.0
lab : 3D numpy array, optional
The labelled image of the reference state. Highly recommended argument in case of a discrete correlation result.
Default = None
returnRS : bool, optional
if True: will return the returnStatus of the correlation as a tiff file
Default = False
outDir : string, optional
Output directory
Default is directory of the input field file
prefix : string, optional
Prefix for output files
Default is the basename of the input field file (without extension)
'''
import tifffile
# use the helper function to read the TSV file
fi = readCorrelationTSV(fileName, fieldBinRatio=fieldBinRatio, readOnlyDisplacements=True, readConvergence=returnRS)
displacements = fi["displacements"]
PhiComponents = [['Zdisp', 0],
['Ydisp', 1],
['Xdisp', 2]]
# set output directory if none
if outDir is None:
if os.path.dirname(fileName) == "":
outDir = "./"
else:
outDir = os.path.dirname(fileName)
else:
os.makedirs(outDir)
# output file name prefix
if prefix is None:
prefix = os.path.splitext(os.path.basename(fileName))[0]
# check if it is a ddic result
if fi["numberOfLabels"] != 0:
if lab:
labelled = tifffile.imread(lab)
import spam.label
for component in PhiComponents:
tifffile.imwrite("{}/{}-{}.tif".format(outDir, prefix, component[0]),
spam.label.convertLabelToFloat(labelled, displacements[:, component[1]]).astype('<f4'))
if returnRS:
tifffile.imwrite("{}/{}-RS.tif".format(outDir, prefix),
spam.label.convertLabelToFloat(labelled, fi["returnStatus"]).astype('<f4'))
else:
print("\tspam.tsvio.TSVtoTIFF(): The labelled image of the reference state is needed as input. Exiting.")
return
# if not, is a ldic result
else:
dims = fi["fieldDims"]
for component in PhiComponents:
tifffile.imwrite("{}/{}-{}.tif".format(outDir, prefix, component[0]),
displacements[:, component[1]].reshape(dims).astype('<f4'))
if returnRS:
tifffile.imwrite("{}/{}-RS.tif".format(outDir, prefix),
fi["returnStatus"].reshape(dims).astype('<f4'))
[docs]
def TSVtoVTK(fileName, fieldBinRatio=1.0, pixelSize=1.0, returnRS=False, outDir=None, prefix=None):
'''
This function converts a TSV file (typically the output of the ldic and ddic scripts)
to a VTK file for visualising the deformation field.
Parameters
----------
fileName : string
Name of the file
fieldBinRatio : int, optional
if the input field is refer to a binned version of the image
`e.g.`, if ``fieldBinRatio = 2`` the field values have been calculated
for an image half the size of what the returned PhiField is referring to
Default = 1.0
pixelSize: float
physical size of a pixel (i.e. 1mm/px)
Default = 1.0
returnRS : bool, optional
if True: will return the SubPixelReturnStatus of the correlation
Default = False
outDir : string
Output directory
Default is directory of the input field file
prefix : string
Prefix for output files
Default is the basename of the input field file (without extension)
'''
import spam.helpers
# use the helper function to read the TSV file
fi = readCorrelationTSV(fileName, fieldBinRatio=fieldBinRatio)
PhiField = fi["PhiField"]
# set output directory if none
if outDir is None:
if os.path.dirname(fileName) == "":
outDir = "./"
else:
outDir = os.path.dirname(fileName)
else:
os.makedirs(outDir)
# output file name prefix
if prefix is None:
prefix = os.path.splitext(os.path.basename(fileName))[0]
# check if it is a ddic result
if fi["numberOfLabels"] != 0:
coords = fi["fieldCoords"][1:] * pixelSize
if not returnRS:
pointData = {"displacements": PhiField[1:, :-1, 3] * pixelSize}
else:
pointData = {"displacements": PhiField[1:, :-1, 3] * pixelSize,
"returnStatus": fi["returnStatus"][1:]}
spam.helpers.writeGlyphsVTK(coords, pointData, fileName="{}/{}.vtk".format(outDir, prefix))
# if not, is a ldic result
else:
dims = fi["fieldDims"]
coords = fi["fieldCoords"] * pixelSize
aspectRatio = numpy.array([numpy.unique(coords[:, i])[1] - numpy.unique(coords[:, i])[0] if len(numpy.unique(coords[:, i])) > 1 else numpy.unique(coords[:, i])[0] for i in range(3)])
origin = coords[0] - aspectRatio/2.0
if not returnRS:
cellData = {"displacements": (PhiField[:, :-1, 3] * pixelSize).reshape((dims[0], dims[1], dims[2], 3))}
else:
cellData = {"displacements": (PhiField[:, :-1, 3] * pixelSize).reshape((dims[0], dims[1], dims[2], 3)),
"returnStatus": fi["returnStatus"].reshape(dims[0], dims[1], dims[2])}
spam.helpers.writeStructuredVTK(aspectRatio=aspectRatio, origin = origin, cellData=cellData, fileName="{}/{}.vtk".format(outDir, prefix))
