// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
int32_t MergeColonies::getSeed(int32_t newFid)
{
setErrorCondition(0);
int32_t numfeatures = static_cast<int32_t>(m_FeaturePhasesPtr.lock()->getNumberOfTuples());
SIMPL_RANDOMNG_NEW()
int32_t seed = -1;
int32_t randfeature = 0;
// Precalculate some constants
int32_t totalFMinus1 = numfeatures - 1;
size_t counter = 0;
randfeature = int32_t(float(rg.genrand_res53()) * float(totalFMinus1));
while (seed == -1 && counter < numfeatures)
{
if (randfeature > totalFMinus1) { randfeature = randfeature - numfeatures; }
if (m_FeatureParentIds[randfeature] == -1) { seed = randfeature; }
randfeature++;
counter++;
}
if (seed >= 0)
{
m_FeatureParentIds[seed] = newFid;
QVector<size_t> tDims(1, newFid + 1);
getDataContainerArray()->getDataContainer(m_FeatureIdsArrayPath.getDataContainerName())->getAttributeMatrix(getNewCellFeatureAttributeMatrixName())->resizeAttributeArrays(tDims);
updateFeatureInstancePointers();
}
return seed;
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void PrimaryRolledPreset::initializeCOverATableModel(StatsGenPlotWidget* plot, QVector<float> binNumbers)
{
// Make sure the distribution is set correctly
plot->setDistributionType(SIMPL::DistributionType::Beta, false);
// This line basically makes sure we have the distribution type we are looking for
SGBetaTableModel* model = qobject_cast<SGBetaTableModel*> (plot->tableModel());
if (NULL == model)
{
return;
}
qint32 count = binNumbers.count();
// Remove all the current rows in the table model
model->removeRows(0, model->rowCount());
float alpha, beta;
SIMPL_RANDOMNG_NEW()
QVector<float> alphas;
QVector<float> betas;
QVector<QColor> colors = GenerateColors(count, 160, 255);
for (qint32 i = 0; i < count; ++i)
{
alpha = (0 * i) + (1.1 + (28.9 * (1.0 / m_AspectRatio2))) + (rg.genrand_res53());
beta = (0 * i) + (30 - (28.9 * (1.0 / m_AspectRatio2))) + (rg.genrand_res53());
alphas.push_back(alpha);
betas.push_back(beta);
}
QVector<QVector<float> > data;
data.push_back(alphas);
data.push_back(betas);
model->setTableData(binNumbers, data, colors);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
int32_t GroupMicroTextureRegions::getSeed(int32_t newFid)
{
setErrorCondition(0);
int32_t numfeatures = static_cast<int32_t>(m_FeaturePhasesPtr.lock()->getNumberOfTuples());
float c1[3] = { 0.0f, 0.0f, 0.0f };
uint32_t phase1 = 0;
QuatF* avgQuats = reinterpret_cast<QuatF*>(m_AvgQuats);
float caxis[3] = { 0.0f, 0.0f, 1.0f };
QuatF q1 = QuaternionMathF::New();
float g1[3][3] = { { 0.0f, 0.0f, 0.0f }, { 0.0f, 0.0f, 0.0f } };
float g1t[3][3] = { { 0.0f, 0.0f, 0.0f }, { 0.0f, 0.0f, 0.0f } };
SIMPL_RANDOMNG_NEW()
int32_t seed = -1;
int32_t randfeature = 0;
// Precalculate some constants
int32_t totalFMinus1 = numfeatures - 1;
size_t counter = 0;
randfeature = int32_t(float(rg.genrand_res53()) * float(totalFMinus1));
while (seed == -1 && counter < numfeatures)
{
if (randfeature > totalFMinus1) { randfeature = randfeature - numfeatures; }
if (m_FeatureParentIds[randfeature] == -1) { seed = randfeature; }
randfeature++;
counter++;
}
if (seed >= 0)
{
m_FeatureParentIds[seed] = newFid;
QVector<size_t> tDims(1, newFid + 1);
getDataContainerArray()->getDataContainer(m_FeatureIdsArrayPath.getDataContainerName())->getAttributeMatrix(getNewCellFeatureAttributeMatrixName())->resizeAttributeArrays(tDims);
updateFeatureInstancePointers();
if (m_UseRunningAverage == true)
{
QuaternionMathF::Copy(avgQuats[seed], q1);
phase1 = m_CrystalStructures[m_FeaturePhases[seed]];
FOrientArrayType om(9);
FOrientTransformsType::qu2om(FOrientArrayType(q1), om);
om.toGMatrix(g1);
// transpose the g matrix so when caxis is multiplied by it
// it will give the sample direction that the caxis is along
MatrixMath::Transpose3x3(g1, g1t);
MatrixMath::Multiply3x3with3x1(g1t, caxis, c1);
// normalize so that the dot product can be taken below without
// dividing by the magnitudes (they would be 1)
MatrixMath::Normalize3x1(c1);
MatrixMath::Copy3x1(c1, avgCaxes);
MatrixMath::Multiply3x1withConstant(avgCaxes, m_Volumes[seed]);
}
}
return seed;
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void RegularGridSampleSurfaceMesh::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
SIMPL_RANDOMNG_NEW()
DataContainer::Pointer m = getDataContainerArray()->getDataContainer(getDataContainerName());
m->getGeometryAs<ImageGeom>()->setDimensions(m_XPoints, m_YPoints, m_ZPoints);
m->getGeometryAs<ImageGeom>()->setOrigin(m_Origin.x, m_Origin.y, m_Origin.z);
m->getGeometryAs<ImageGeom>()->setResolution(m_Resolution.x, m_Resolution.y, m_Resolution.z);
SampleSurfaceMesh::execute();
notifyStatusMessage(getHumanLabel(), "Complete");
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void PrimaryEquiaxedPreset::initializeNeighborTableModel(StatsGenPlotWidget* plot, QVector<float> binNumbers)
{
// Make sure the distribution is set correctly
plot->setDistributionType(DREAM3D::DistributionType::LogNormal, false);
// This line basically makes sure we have the distribution type we are looking for
SGLogNormalTableModel* model = qobject_cast<SGLogNormalTableModel*> (plot->tableModel());
if (NULL == model)
{
return;
}
qint32 count = binNumbers.count();
// Remove all the current rows in the table model
model->removeRows(0, model->rowCount());
float mu, sigma;
SIMPL_RANDOMNG_NEW()
QVector<float> mus;
QVector<float> sigmas;
QVector<QString> colors;
QStringList colorNames = QColor::colorNames();
qint32 colorOffset = 21;
int middlebin = count / 2;
for (qint32 i = 0; i < count; ++i)
{
mu = log(14.0 + (2.0 * float(i - middlebin)));
sigma = 0.3 + (float(middlebin - i) / float(middlebin * 10));
mus.push_back(mu);
sigmas.push_back(sigma);
colors.push_back(colorNames[colorOffset++]);
if (colorOffset == colorNames.size())
{
colorOffset = 21;
}
}
QVector<QVector<float> > data;
data.push_back(mus);
data.push_back(sigmas);
model->setTableData(binNumbers, data, colors);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void PrimaryEquiaxedPreset::initializeOmega3TableModel(StatsGenPlotWidget* plot, QVector<float> binNumbers)
{
// Make sure the distribution is set correctly
plot->setDistributionType(DREAM3D::DistributionType::Beta, false);
// This line basically makes sure we have the distribution type we are looking for
SGBetaTableModel* model = qobject_cast<SGBetaTableModel*>(plot->tableModel());
if (NULL == model)
{
return;
}
qint32 count = binNumbers.count();
// Remove all the current rows in the table model
// model->removeRows(0, model->rowCount());
float alpha, beta;
SIMPL_RANDOMNG_NEW()
QVector<float> alphas;
QVector<float> betas;
QVector<QString> colors;
QStringList colorNames = QColor::colorNames();
qint32 colorOffset = 21;
for (qint32 i = 0; i < count; ++i)
{
alpha = (0 * i) + 10.0 + rg.genrand_res53();
beta = (0 * i) + 1.5 + (0.5 * rg.genrand_res53());
alphas.push_back(alpha);
betas.push_back(beta);
colors.push_back(colorNames[colorOffset++]);
if (colorOffset == colorNames.size())
{
colorOffset = 21;
}
}
QVector<QVector<float> > data;
data.push_back(alphas);
data.push_back(betas);
model->setTableData(binNumbers, data, colors);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void PrimaryRolledPreset::initializeNeighborTableModel(StatsGenPlotWidget* plot, QVector<float> binNumbers)
{
// Make sure the distribution is set correctly
plot->setDistributionType(SIMPL::DistributionType::LogNormal, false);
// This line basically makes sure we have the distribution type we are looking for
SGLogNormalTableModel* model = qobject_cast<SGLogNormalTableModel*> (plot->tableModel());
if (NULL == model)
{
return;
}
qint32 count = binNumbers.count();
// Remove all the current rows in the table model
model->removeRows(0, model->rowCount());
float mu, sigma;
SIMPL_RANDOMNG_NEW()
QVector<float> mus;
QVector<float> sigmas;
QVector<QColor> colors = GenerateColors(count, 160, 255);
int middlebin = count / 2;
for (qint32 i = 0; i < count; ++i)
{
mu = log(8.0 + (1.0 * float(i - middlebin)));
sigma = 0.3 + (float(middlebin - i) / float(middlebin * 10));
mus.push_back(mu);
sigmas.push_back(sigma);
}
QVector<QVector<float> > data;
data.push_back(mus);
data.push_back(sigmas);
model->setTableData(binNumbers, data, colors);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void InsertAtoms::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
// Validate that the selected AvgQuats array has tuples equal to the largest
// Feature Id; the filter would not crash otherwise, but the user should
// be notified of unanticipated behavior ; this cannot be done in the dataCheck since
// we don't have acces to the data yet
int32_t numFeaturesIn = static_cast<int32_t>(m_AvgQuatsPtr.lock()->getNumberOfTuples());
bool mismatchedFeatures = true;
int32_t largestFeature = 0;
size_t numTuples = m_SurfaceMeshFaceLabelsPtr.lock()->getNumberOfTuples();
for (size_t i = 0; i < numTuples; i++)
{
if (m_SurfaceMeshFaceLabels[2 * i] > largestFeature)
{
largestFeature = m_SurfaceMeshFaceLabels[2 * i];
if (largestFeature >= numFeaturesIn)
{
mismatchedFeatures = true;
break;
}
}
else if (m_SurfaceMeshFaceLabels[2 * i + 1] > largestFeature)
{
largestFeature = m_SurfaceMeshFaceLabels[2 * i + 1];
if (largestFeature >= numFeaturesIn)
{
mismatchedFeatures = true;
break;
}
}
}
if (mismatchedFeatures == true)
{
QString ss = QObject::tr("The number of Features in the AvgQuats array (%1) is larger than the largest Feature Id in the SurfaceMeshFaceLabels array").arg(numFeaturesIn);
setErrorCondition(-5555);
notifyErrorMessage(getHumanLabel(), ss, getErrorCondition());
return;
}
if (largestFeature != (numFeaturesIn - 1))
{
QString ss = QObject::tr("The number of Features in the AvgQuats array (%1) does not match the largest Feature Id in the SurfaceMeshFaceLabels array").arg(numFeaturesIn);
setErrorCondition(-5555);
notifyErrorMessage(getHumanLabel(), ss, getErrorCondition());
return;
}
FloatVec3_t latticeConstants;
latticeConstants.x = m_LatticeConstants.x / 10000.0;
latticeConstants.y = m_LatticeConstants.y / 10000.0;
latticeConstants.z = m_LatticeConstants.z / 10000.0;
DataContainer::Pointer sm = getDataContainerArray()->getDataContainer(getSurfaceMeshFaceLabelsArrayPath().getDataContainerName());
SIMPL_RANDOMNG_NEW()
#ifdef SIMPLib_USE_PARALLEL_ALGORITHMS
tbb::task_scheduler_init init;
bool doParallel = true;
#endif
// pull down faces
TriangleGeom::Pointer triangleGeom = sm->getGeometryAs<TriangleGeom>();
int64_t numFaces = m_SurfaceMeshFaceLabelsPtr.lock()->getNumberOfTuples();
// create array to hold bounding vertices for each face
FloatArrayType::Pointer llPtr = FloatArrayType::CreateArray(3, "Lower_Left_Internal_Use_Only");
FloatArrayType::Pointer urPtr = FloatArrayType::CreateArray(3, "Upper_Right_Internal_Use_Only");
float* ll = llPtr->getPointer(0);
float* ur = urPtr->getPointer(0);
VertexGeom::Pointer faceBBs = VertexGeom::CreateGeometry(2 * numFaces, "faceBBs");
// walk through faces to see how many features there are
int32_t g1 = 0, g2 = 0;
int32_t maxFeatureId = 0;
for (int64_t i = 0; i < numFaces; i++)
{
g1 = m_SurfaceMeshFaceLabels[2 * i];
g2 = m_SurfaceMeshFaceLabels[2 * i + 1];
if (g1 > maxFeatureId) { maxFeatureId = g1; }
if (g2 > maxFeatureId) { maxFeatureId = g2; }
}
// add one to account for feature 0
int32_t numFeatures = maxFeatureId + 1;
// create a dynamic list array to hold face lists
Int32Int32DynamicListArray::Pointer faceLists = Int32Int32DynamicListArray::New();
QVector<int32_t> linkCount(numFeatures, 0);
// fill out lists with number of references to cells
typedef boost::shared_array<int32_t> SharedInt32Array_t;
SharedInt32Array_t linkLocPtr(new int32_t[numFaces]);
int32_t* linkLoc = linkLocPtr.get();
::memset(linkLoc, 0, numFaces * sizeof(int32_t));
// traverse data to determine number of faces belonging to each feature
for (int64_t i = 0; i < numFaces; i++)
{
g1 = m_SurfaceMeshFaceLabels[2 * i];
g2 = m_SurfaceMeshFaceLabels[2 * i + 1];
if (g1 > 0) { linkCount[g1]++; }
if (g2 > 0) { linkCount[g2]++; }
}
// now allocate storage for the faces
faceLists->allocateLists(linkCount);
// traverse data again to get the faces belonging to each feature
for (int64_t i = 0; i < numFaces; i++)
{
g1 = m_SurfaceMeshFaceLabels[2 * i];
g2 = m_SurfaceMeshFaceLabels[2 * i + 1];
if (g1 > 0) { faceLists->insertCellReference(g1, (linkLoc[g1])++, i); }
if (g2 > 0) { faceLists->insertCellReference(g2, (linkLoc[g2])++, i); }
// find bounding box for each face
GeometryMath::FindBoundingBoxOfFace(triangleGeom, i, ll, ur);
faceBBs->setCoords(2 * i, ll);
faceBBs->setCoords(2 * i + 1, ur);
}
// generate the list of sampling points fom subclass
QVector<VertexGeom::Pointer> points(numFeatures);
QVector<BoolArrayType::Pointer> inFeature(numFeatures);
for (int32_t i = 0; i < numFeatures; i++)
{
points[i] = VertexGeom::CreateGeometry(0, "_INTERNAL_USE_ONLY_points");
inFeature[i] = BoolArrayType::CreateArray(0, "_INTERNAL_USE_ONLY_inside");
}
QuatF* avgQuats = reinterpret_cast<QuatF*>(m_AvgQuats);
#ifdef SIMPLib_USE_PARALLEL_ALGORITHMS
if (doParallel == true)
{
tbb::parallel_for(tbb::blocked_range<size_t>(0, numFeatures),
InsertAtomsImpl(triangleGeom, faceLists, faceBBs, avgQuats, latticeConstants, m_Basis, points, inFeature), tbb::auto_partitioner());
}
else
#endif
{
InsertAtomsImpl serial(triangleGeom, faceLists, faceBBs, avgQuats, latticeConstants, m_Basis, points, inFeature);
serial.checkPoints(0, numFeatures);
}
assign_points(points, inFeature);
notifyStatusMessage(getHumanLabel(), "Complete");
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
QString InitializeSyntheticVolume::estimateNumFeatures(IntVec3_t dims, FloatVec3_t res)
{
float totalvol = 0.0f;
int32_t phase = 0;
totalvol = (dims.x * res.x) * (dims.y * res.y) * (dims.z * res.z);
if (totalvol == 0.0)
{
return "-1";
}
DataContainerArray::Pointer dca = getDataContainerArray();
// Get the PhaseTypes - Remember there is a Dummy PhaseType in the first slot of the array
QVector<size_t> cDims(1, 1); // This states that we are looking for an array with a single component
UInt32ArrayType::Pointer phaseType = dca->getPrereqArrayFromPath<UInt32ArrayType, AbstractFilter>(NULL, getInputPhaseTypesArrayPath(), cDims);
if (phaseType.get() == NULL)
{
QString ss = QObject::tr("Phase types array could not be downcast using std::dynamic_pointer_cast<T> when estimating the number of grains. The path is %1").arg(getInputPhaseTypesArrayPath().serialize());
setErrorCondition(-11002);
notifyErrorMessage(getHumanLabel(), ss, getErrorCondition());
return "0";
}
QVector<size_t> statsDims(1, 1);
StatsDataArray::Pointer statsPtr = dca->getPrereqArrayFromPath<StatsDataArray, AbstractFilter>(this, getInputStatsArrayPath(), statsDims);
if (statsPtr.get() == NULL)
{
QString ss = QObject::tr("Statistics array could not be downcast using std::dynamic_pointer_cast<T> when estimating the number of grains. The path is %1").arg(getInputStatsArrayPath().serialize());
notifyErrorMessage(getHumanLabel(), ss, -11001);
return "0";
}
if (!phaseType->isAllocated())
{
if (getInputStatsFile().isEmpty())
{
QString ss = QObject::tr("Phase types array has not been allocated and the input statistics file is empty");
setErrorCondition(-1000);
notifyWarningMessage(getHumanLabel(), ss, getErrorCondition());
return "-1";
}
QFileInfo fi(getInputStatsFile());
if (fi.exists() == false)
{
QString ss = QObject::tr("Phase types array has not been allocated and the input statistics file does not exist at '%1'").arg(fi.absoluteFilePath());
setErrorCondition(-1001);
notifyWarningMessage(getHumanLabel(), ss, getErrorCondition());
return "-1";
}
hid_t fileId = -1;
herr_t err = 0;
// open the file
fileId = H5Utilities::openFile(getInputStatsFile().toLatin1().constData(), true);
// This will make sure if we return early from this method that the HDF5 File is properly closed.
HDF5ScopedFileSentinel scopedFileSentinel(&fileId, true);
DataArrayPath dap = getInputPhaseTypesArrayPath();
// Generate the path to the AttributeMatrix
QString hPath = DREAM3D::StringConstants::DataContainerGroupName + "/" + dap.getDataContainerName() + "/" + dap.getAttributeMatrixName();
// Open the AttributeMatrix Group
hid_t amGid = H5Gopen(fileId, hPath.toLatin1().data(), H5P_DEFAULT );
scopedFileSentinel.addGroupId(&amGid);
err = phaseType->readH5Data(amGid);
if (err < 0)
{
QString ss = QObject::tr("Error reading phase type data");
setErrorCondition(-1003);
notifyWarningMessage(getHumanLabel(), ss, getErrorCondition());
return "-1";
}
if (!phaseType->isAllocated())
{
QString ss = QObject::tr("Phase types Array was not allocated due to an error reading the data from the statistics file %1").arg(fi.absoluteFilePath());
setErrorCondition(-1002);
notifyWarningMessage(getHumanLabel(), ss, getErrorCondition());
return "-1";
}
}
// Create a Reference Variable so we can use the [] syntax
StatsDataArray& statsDataArray = *(statsPtr.get());
std::vector<int32_t> primaryphases;
std::vector<double> primaryphasefractions;
double totalprimaryfractions = 0.0;
// find which phases are primary phases
for (size_t i = 1; i < phaseType->getNumberOfTuples(); ++i)
{
if (phaseType->getValue(i) == DREAM3D::PhaseType::PrimaryPhase)
{
PrimaryStatsData* pp = PrimaryStatsData::SafePointerDownCast(statsDataArray[i].get());
primaryphases.push_back(i);
primaryphasefractions.push_back(pp->getPhaseFraction());
totalprimaryfractions = totalprimaryfractions + pp->getPhaseFraction();
}
}
// scale the primary phase fractions to total to 1
for (int32_t i = 0; i < primaryphasefractions.size(); i++)
{
primaryphasefractions[i] = primaryphasefractions[i] / totalprimaryfractions;
}
SIMPL_RANDOMNG_NEW()
// generate the Features
int32_t gid = 1;
float currentvol = 0.0f;
float vol = 0.0f;
float diam = 0.0f;
bool volgood = false;
for (int32_t j = 0; j < primaryphases.size(); ++j)
{
float curphasetotalvol = totalvol * primaryphasefractions[j];
while (currentvol < curphasetotalvol)
{
volgood = false;
phase = primaryphases[j];
PrimaryStatsData* pp = PrimaryStatsData::SafePointerDownCast(statsDataArray[phase].get());
if (NULL == pp)
{
QString ss = QObject::tr("Tried to cast a statsDataArray[%1].get() to a PrimaryStatsData* "
"pointer but this resulted in a NULL pointer.\n")
.arg(phase).arg(phase);
setErrorCondition(-666);
notifyErrorMessage(getHumanLabel(), ss, getErrorCondition());
return "-1";
}
while (volgood == false)
{
volgood = true;
if (pp->getFeatureSize_DistType() == DREAM3D::DistributionType::LogNormal)
{
float avgdiam = pp->getFeatureSizeDistribution().at(0)->getValue(0);
float sddiam = pp->getFeatureSizeDistribution().at(1)->getValue(0);
diam = rg.genrand_norm(avgdiam, sddiam);
diam = expf(diam);
if (diam >= pp->getMaxFeatureDiameter()) { volgood = false; }
if (diam < pp->getMinFeatureDiameter()) { volgood = false; }
vol = (4.0f / 3.0f) * (M_PI) * ((diam * 0.5f) * (diam * 0.5f) * (diam * 0.5f));
}
}
currentvol = currentvol + vol;
gid++;
}
}
return QString::number(gid);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void AdaptiveAlignmentMutualInformation::form_features_sections()
{
SIMPL_RANDOMNG_NEW()
DataContainer::Pointer m = getDataContainerArray()->getDataContainer(getDataContainerName());
size_t udims[3] = { 0, 0, 0 };
m->getGeometryAs<ImageGeom>()->getDimensions(udims);
int64_t dims[3] =
{
static_cast<int64_t>(udims[0]),
static_cast<int64_t>(udims[1]),
static_cast<int64_t>(udims[2]),
};
int64_t point = 0;
int64_t seed = 0;
bool noseeds = false;
int32_t featurecount = 1;
int64_t neighbor = 0;
QuatF q1 = QuaternionMathF::New();
QuatF q2 = QuaternionMathF::New();
QuatF* quats = reinterpret_cast<QuatF*>(m_Quats);
float w = 0.0f;
float n1 = 0.0f;
float n2 = 0.0f;
float n3 = 0.0f;
int64_t randx = 0;
int64_t randy = 0;
bool good = false;
int64_t x = 0, y = 0, z = 0;
int64_t col = 0, row = 0;
size_t size = 0;
size_t initialVoxelsListSize = 1000;
m_FeatureCounts->resize(dims[2]);
featurecounts = m_FeatureCounts->getPointer(0);
int32_t* miFeatureIds = m_MIFeaturesPtr->getPointer(0);
std::vector<int64_t> voxelslist(initialVoxelsListSize, -1);
int64_t neighpoints[4] = { 0, 0, 0, 0 };
neighpoints[0] = -dims[0];
neighpoints[1] = -1;
neighpoints[2] = 1;
neighpoints[3] = dims[0];
uint32_t phase1 = 0, phase2 = 0;
for (int64_t slice = 0; slice < dims[2]; slice++)
{
float prog = ((float)slice / dims[2]) * 100;
QString ss = QObject::tr("Aligning Sections || Identifying Features on Sections || %1% Complete").arg(QString::number(prog, 'f', 0));
notifyStatusMessage(getMessagePrefix(), getHumanLabel(), ss);
featurecount = 1;
noseeds = false;
while (noseeds == false)
{
seed = -1;
randx = int64_t(float(rg.genrand_res53()) * float(dims[0]));
randy = int64_t(float(rg.genrand_res53()) * float(dims[1]));
for (int64_t j = 0; j < dims[1]; ++j)
{
for (int64_t i = 0; i < dims[0]; ++i)
{
x = randx + i;
y = randy + j;
z = slice;
if (x > dims[0] - 1) { x = x - dims[0]; }
if (y > dims[1] - 1) { y = y - dims[1]; }
point = (z * dims[0] * dims[1]) + (y * dims[0]) + x;
if ((m_UseGoodVoxels == false || m_GoodVoxels[point] == true) && miFeatureIds[point] == 0 && m_CellPhases[point] > 0)
{
seed = point;
}
if (seed > -1) { break; }
}
if (seed > -1) { break; }
}
if (seed == -1) { noseeds = true; }
if (seed >= 0)
{
size = 0;
miFeatureIds[seed] = featurecount;
voxelslist[size] = seed;
size++;
for (size_t j = 0; j < size; ++j)
{
int64_t currentpoint = voxelslist[j];
col = currentpoint % dims[0];
row = (currentpoint / dims[0]) % dims[1];
QuaternionMathF::Copy(quats[currentpoint], q1);
phase1 = m_CrystalStructures[m_CellPhases[currentpoint]];
for (int32_t i = 0; i < 4; i++)
{
good = true;
neighbor = currentpoint + neighpoints[i];
if ((i == 0) && row == 0) { good = false; }
if ((i == 3) && row == (dims[1] - 1)) { good = false; }
if ((i == 1) && col == 0) { good = false; }
if ((i == 2) && col == (dims[0] - 1)) { good = false; }
if (good == true && miFeatureIds[neighbor] <= 0 && m_CellPhases[neighbor] > 0)
{
w = std::numeric_limits<float>::max();
QuaternionMathF::Copy(quats[neighbor], q2);
phase2 = m_CrystalStructures[m_CellPhases[neighbor]];
if (phase1 == phase2)
{
w = m_OrientationOps[phase1]->getMisoQuat(q1, q2, n1, n2, n3);
}
if (w < m_MisorientationTolerance)
{
miFeatureIds[neighbor] = featurecount;
voxelslist[size] = neighbor;
size++;
if (std::vector<int64_t>::size_type(size) >= voxelslist.size())
{
size = voxelslist.size();
voxelslist.resize(size + initialVoxelsListSize);
for (std::vector<int64_t>::size_type v = size; v < voxelslist.size(); ++v) { voxelslist[v] = -1; }
}
}
}
}
}
voxelslist.erase(std::remove(voxelslist.begin(), voxelslist.end(), -1), voxelslist.end());
featurecount++;
voxelslist.assign(initialVoxelsListSize, -1);
}
}
featurecounts[slice] = featurecount;
}
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void EstablishMatrixPhase::establish_matrix()
{
notifyStatusMessage(getHumanLabel(), "Establishing Matrix");
SIMPL_RANDOMNG_NEW()
DataContainer::Pointer m = getDataContainerArray()->getDataContainer(getOutputCellAttributeMatrixPath().getDataContainerName());
StatsDataArray& statsDataArray = *(m_StatsDataArray.lock());
size_t udims[3] =
{ 0, 0, 0 };
m->getGeometryAs<ImageGeom>()->getDimensions(udims);
#if (CMP_SIZEOF_SIZE_T == 4)
typedef int32_t DimType;
#else
typedef int64_t DimType;
#endif
DimType dims[3] =
{
static_cast<DimType>(udims[0]),
static_cast<DimType>(udims[1]),
static_cast<DimType>(udims[2]),
};
sizex = dims[0] * m->getGeometryAs<ImageGeom>()->getXRes();
sizey = dims[1] * m->getGeometryAs<ImageGeom>()->getYRes();
sizez = dims[2] * m->getGeometryAs<ImageGeom>()->getZRes();
totalvol = sizex * sizey * sizez;
size_t totalPoints = m_FeatureIdsPtr.lock()->getNumberOfTuples();
size_t currentnumfeatures = m_FeaturePhasesPtr.lock()->getNumberOfTuples();
size_t numensembles = m_PhaseTypesPtr.lock()->getNumberOfTuples();
QVector<size_t> tDims(1, 1);
if (currentnumfeatures == 0)
{
m->getAttributeMatrix(m_OutputCellFeatureAttributeMatrixName)->resizeAttributeArrays(tDims);
updateFeatureInstancePointers();
currentnumfeatures = 1;
}
firstMatrixFeature = currentnumfeatures;
float random = 0.0f;
float totalmatrixfractions = 0.0f;
for (size_t i = 1; i < numensembles; ++i)
{
if (m_PhaseTypes[i] == DREAM3D::PhaseType::MatrixPhase)
{
MatrixStatsData* mp = MatrixStatsData::SafePointerDownCast(statsDataArray[i].get());
if (NULL == mp)
{
QString ss = QObject::tr("Tried to cast a statsDataArray[%1].get() to a MatrixStatsData* "
"pointer but this resulted in a NULL pointer. The value at m_PhaseTypes[%2] = %3 does not match up "
"with the type of pointer stored in the StatsDataArray (MatrixStatsData)\n")
.arg(i).arg(i).arg(m_PhaseTypes[i]);
notifyErrorMessage(getHumanLabel(), ss, -666);
setErrorCondition(-666);
return;
}
matrixphases.push_back(i);
matrixphasefractions.push_back(mp->getPhaseFraction());
totalmatrixfractions = totalmatrixfractions + mp->getPhaseFraction();
}
}
for (int32_t i = 0; i < matrixphases.size(); i++)
{
matrixphasefractions[i] = matrixphasefractions[i] / totalmatrixfractions;
if (i > 0) { matrixphasefractions[i] = matrixphasefractions[i] + matrixphasefractions[i - 1]; }
}
size_t j = 0;
for (size_t i = 0; i < totalPoints; ++i)
{
if ((m_UseMask == false && m_FeatureIds[i] <= 0) || (m_UseMask == true && m_Mask[i] == true && m_FeatureIds[i] <= 0))
{
random = static_cast<float>( rg.genrand_res53() );
j = 0;
while (random > matrixphasefractions[j])
{
j++;
}
if (m->getAttributeMatrix(m_OutputCellFeatureAttributeMatrixName)->getNumTuples() <= (firstMatrixFeature + j))
{
tDims[0] = (firstMatrixFeature + j) + 1;
m->getAttributeMatrix(m_OutputCellFeatureAttributeMatrixName)->resizeAttributeArrays(tDims);
updateFeatureInstancePointers();
m_NumFeatures[j] = 1;
}
m_FeatureIds[i] = (firstMatrixFeature + j);
m_CellPhases[i] = matrixphases[j];
m_FeaturePhases[(firstMatrixFeature + j)] = matrixphases[j];
}
else if (m_UseMask == true && m_Mask[i] == false)
{
m_FeatureIds[i] = 0;
m_CellPhases[i] = 0;
}
}
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
std::vector<float> RadialDistributionFunction::GenerateRandomDistribution(float minDistance, float maxDistance, int numBins, std::vector<float> boxdims, std::vector<float> boxres)
{
std::vector<float> freq(numBins, 0);
std::vector<float> randomCentroids;
std::vector<std::vector<float> > distancelist;
int32_t largeNumber = 1000;
int32_t numDistances = largeNumber * (largeNumber - 1);
// boxdims are the dimensions of the box in microns
// boxres is the resoultion of the box in microns
int32_t xpoints = boxdims[0] / boxres[0];
int32_t ypoints = boxdims[1] / boxres[1];
int32_t zpoints = boxdims[2] / boxres[2];
int32_t bin;
int32_t totalpoints = xpoints * ypoints * zpoints;
float x, y, z;
float xn, yn, zn;
float xc, yc, zc;
float r;
size_t featureOwnerIdx = 0;
size_t column, row, plane;
float stepsize = (maxDistance - minDistance) / numBins;
float maxBoxDistance = sqrtf((boxdims[0] * boxdims[0]) + (boxdims[1] * boxdims[1]) + (boxdims[2] * boxdims[2]));
int32_t current_num_bins = ceil((maxBoxDistance - minDistance) / (stepsize));
freq.resize(current_num_bins + 1);
SIMPL_RANDOMNG_NEW();
randomCentroids.resize(largeNumber * 3);
//Generating all of the random points and storing their coordinates in randomCentroids
for (int32_t i = 0; i < largeNumber; i++)
{
featureOwnerIdx = static_cast<size_t>(rg.genrand_res53() * totalpoints);
column = featureOwnerIdx % xpoints;
row = int(featureOwnerIdx / xpoints) % ypoints;
plane = featureOwnerIdx / (xpoints * ypoints);
xc = static_cast<float>(column * boxres[0]) ;
yc = static_cast<float>(row * boxres[1]);
zc = static_cast<float>(plane * boxres[2]);
randomCentroids[3 * i] = xc;
randomCentroids[3 * i + 1] = yc;
randomCentroids[3 * i + 2] = zc;
}
distancelist.resize(largeNumber);
//Calculating all of the distances and storing them in the distnace list
for (int32_t i = 1; i < largeNumber; i++)
{
x = randomCentroids[3 * i];
y = randomCentroids[3 * i + 1];
z = randomCentroids[3 * i + 2];
for (int32_t j = i + 1; j < largeNumber; j++)
{
xn = randomCentroids[3 * j];
yn = randomCentroids[3 * j + 1];
zn = randomCentroids[3 * j + 2];
r = sqrtf((x - xn) * (x - xn) + (y - yn) * (y - yn) + (z - zn) * (z - zn));
distancelist[i].push_back(r);
distancelist[j].push_back(r);
}
}
//bin up the distance list
for (int32_t i = 0; i < largeNumber; i++)
{
for (size_t j = 0; j < distancelist[i].size(); j++)
{
bin = (distancelist[i][j] - minDistance) / stepsize;
if (distancelist[i][j] < minDistance)
{
bin = -1;
}
freq[bin + 1]++;
}
}
for (int32_t i = 0; i < current_num_bins + 1; i++)
{
freq[i] = freq[i] / (numDistances);
}
return freq;
}
static void CalculateCubicODFData(T* e1s, T* e2s, T* e3s,
T* weights, T* sigmas,
bool normalize, T* odf, size_t numEntries)
{
SIMPL_RANDOMNG_NEW()
CubicOps ops;
Int32ArrayType::Pointer textureBins = Int32ArrayType::CreateArray(numEntries, "TextureBins");
int32_t* TextureBins = textureBins->getPointer(0);
float addweight = 0;
float totaladdweight = 0;
float totalweight = float(ops.getODFSize());
int bin, addbin;
int bin1, bin2, bin3;
int addbin1, addbin2, addbin3;
float dist, fraction;
for (size_t i = 0; i < numEntries; i++)
{
FOrientArrayType eu(e1s[i], e2s[i], e3s[i]);
FOrientArrayType rod(4);
OrientationTransforms<FOrientArrayType, float>::eu2ro(eu, rod);
rod = ops.getODFFZRod(rod);
bin = ops.getOdfBin(rod);
TextureBins[i] = static_cast<int>(bin);
}
for (int i = 0; i < ops.getODFSize(); i++)
{
odf[i] = 0;
}
for (size_t i = 0; i < numEntries; i++)
{
bin = TextureBins[i];
bin1 = bin % 18;
bin2 = (bin / 18) % 18;
bin3 = bin / (18 * 18);
for (int j = -sigmas[i]; j <= sigmas[i]; j++)
{
int jsqrd = j * j;
for (int k = -sigmas[i]; k <= sigmas[i]; k++)
{
int ksqrd = k * k;
for (int l = -sigmas[i]; l <= sigmas[i]; l++)
{
int lsqrd = l * l;
addbin1 = bin1 + int(j);
addbin2 = bin2 + int(k);
addbin3 = bin3 + int(l);
//if(addbin1 < 0) addbin1 = addbin1 + 18;
//if(addbin1 >= 18) addbin1 = addbin1 - 18;
//if(addbin2 < 0) addbin2 = addbin2 + 18;
//if(addbin2 >= 18) addbin2 = addbin2 - 18;
//if(addbin3 < 0) addbin3 = addbin3 + 18;
//if(addbin3 >= 18) addbin3 = addbin3 - 18;
int good = 1;
if(addbin1 < 0) { good = 0; }
if(addbin1 >= 18) { good = 0; }
if(addbin2 < 0) { good = 0; }
if(addbin2 >= 18) { good = 0; }
if(addbin3 < 0) { good = 0; }
if(addbin3 >= 18) { good = 0; }
addbin = (addbin3 * 18 * 18) + (addbin2 * 18) + (addbin1);
dist = powf((jsqrd + ksqrd + lsqrd), 0.5);
fraction = 1.0 - (double(dist / int(sigmas[i])) * double(dist / int(sigmas[i])));
if(dist <= int(sigmas[i]) && good == 1)
{
addweight = (weights[i] * fraction);
if(sigmas[i] == 0.0) { addweight = weights[i]; }
odf[addbin] = odf[addbin] + addweight;
totaladdweight = totaladdweight + addweight;
}
}
}
}
}
if(totaladdweight > totalweight)
{
float scale = (totaladdweight / totalweight);
for (int i = 0; i < ops.getODFSize(); i++)
{
odf[i] = odf[i] / scale;
}
}
else
{
float remainingweight = totalweight - totaladdweight;
float background = remainingweight / static_cast<float>(ops.getODFSize());
for (int i = 0; i < ops.getODFSize(); i++)
{
odf[i] += background;
}
}
if (normalize == true)
{
// Normalize the odf
for (int i = 0; i < ops.getODFSize(); i++)
{
odf[i] = odf[i] / totalweight;
}
}
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void SampleSurfaceMesh::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
DataContainer::Pointer sm = getDataContainerArray()->getDataContainer(m_SurfaceMeshFaceLabelsArrayPath.getDataContainerName());
SIMPL_RANDOMNG_NEW()
#ifdef SIMPLib_USE_PARALLEL_ALGORITHMS
tbb::task_scheduler_init init;
bool doParallel = true;
#endif
TriangleGeom::Pointer triangleGeom = sm->getGeometryAs<TriangleGeom>();
// pull down faces
int64_t numFaces = m_SurfaceMeshFaceLabelsPtr.lock()->getNumberOfTuples();
// create array to hold bounding vertices for each face
FloatArrayType::Pointer llPtr = FloatArrayType::CreateArray(3, "_INTERNAL_USE_ONLY_Lower_Left");
FloatArrayType::Pointer urPtr = FloatArrayType::CreateArray(3, "_INTERNAL_USE_ONLY_Upper_Right");
float* ll = llPtr->getPointer(0);
float* ur = urPtr->getPointer(0);
VertexGeom::Pointer faceBBs = VertexGeom::CreateGeometry(2 * numFaces, "_INTERNAL_USE_ONLY_faceBBs");
// walk through faces to see how many features there are
int32_t g1 = 0, g2 = 0;
int32_t maxFeatureId = 0;
for (int64_t i = 0; i < numFaces; i++)
{
g1 = m_SurfaceMeshFaceLabels[2 * i];
g2 = m_SurfaceMeshFaceLabels[2 * i + 1];
if (g1 > maxFeatureId) { maxFeatureId = g1; }
if (g2 > maxFeatureId) { maxFeatureId = g2; }
}
// add one to account for feature 0
int32_t numFeatures = maxFeatureId + 1;
// create a dynamic list array to hold face lists
Int32Int32DynamicListArray::Pointer faceLists = Int32Int32DynamicListArray::New();
std::vector<int32_t> linkCount(numFeatures, 0);
// fill out lists with number of references to cells
typedef boost::shared_array<int32_t> SharedInt32Array_t;
SharedInt32Array_t linkLocPtr(new int32_t[numFaces]);
int32_t* linkLoc = linkLocPtr.get();
::memset(linkLoc, 0, numFaces * sizeof(int32_t));
// traverse data to determine number of faces belonging to each feature
for (int64_t i = 0; i < numFaces; i++)
{
g1 = m_SurfaceMeshFaceLabels[2 * i];
g2 = m_SurfaceMeshFaceLabels[2 * i + 1];
if (g1 > 0) { linkCount[g1]++; }
if (g2 > 0) { linkCount[g2]++; }
}
// now allocate storage for the faces
faceLists->allocateLists(linkCount);
// traverse data again to get the faces belonging to each feature
for (int64_t i = 0; i < numFaces; i++)
{
g1 = m_SurfaceMeshFaceLabels[2 * i];
g2 = m_SurfaceMeshFaceLabels[2 * i + 1];
if (g1 > 0) { faceLists->insertCellReference(g1, (linkLoc[g1])++, i); }
if (g2 > 0) { faceLists->insertCellReference(g2, (linkLoc[g2])++, i); }
// find bounding box for each face
GeometryMath::FindBoundingBoxOfFace(triangleGeom, i, ll, ur);
faceBBs->setCoords(2 * i, ll);
faceBBs->setCoords(2 * i + 1, ur);
}
// generate the list of sampling points from subclass
VertexGeom::Pointer points = generate_points();
if(getErrorCondition() < 0 || NULL == points.get()) { return; }
int64_t numPoints = points->getNumberOfVertices();
// create array to hold which polyhedron (feature) each point falls in
Int32ArrayType::Pointer iArray = Int32ArrayType::NullPointer();
iArray = Int32ArrayType::CreateArray(numPoints, "_INTERNAL_USE_ONLY_polyhedronIds");
iArray->initializeWithZeros();
int32_t* polyIds = iArray->getPointer(0);
#ifdef SIMPLib_USE_PARALLEL_ALGORITHMS
if (doParallel == true)
{
tbb::parallel_for(tbb::blocked_range<size_t>(0, numFeatures),
SampleSurfaceMeshImpl(triangleGeom, faceLists, faceBBs, points, polyIds), tbb::auto_partitioner());
}
else
#endif
{
SampleSurfaceMeshImpl serial(triangleGeom, faceLists, faceBBs, points, polyIds);
serial.checkPoints(0, numFeatures);
}
assign_points(iArray);
notifyStatusMessage(getHumanLabel(), "Complete");
}
