void run ()
{
DWI::Tractography::Properties properties;
DWI::Tractography::Writer<> writer (argument.back(), properties);
for (size_t n = 0; n < argument.size()-1; n++) {
Math::Matrix<float> M;
try {
M.load (argument[n]);
if (M.columns() != 3)
throw Exception ("file \"" + argument[n] + "\" does not contain 3 columns - ignored");
DWI::Tractography::Streamline<float> tck (M.rows());
for (size_t i = 0; i < M.rows(); i++) {
tck[i].set (M (i,0), M (i,1), M (i,2));
}
writer (tck);
writer.total_count++;
}
catch (Exception) { }
}
}
DWI2QBI (const Math::Matrix<value_type>& FRT_SHT, Math::Matrix<value_type>& normalise_SHT, const DWI::Shells& shells) :
FRT_SHT (FRT_SHT),
normalise_SHT (normalise_SHT),
shells (shells),
dwi (FRT_SHT.columns()),
qbi (FRT_SHT.rows()),
amps (normalise ? normalise_SHT.rows() : 0) { }
void
TestMatrix::runSubTest18(double& res, double& expected, std::string& subTestName)
{
expected = 1;
subTestName = "simple_symmetric_invert";
#ifdef COSMO_LAPACK
Math::SymmetricMatrix<double> mat(2, 2);
mat(0, 0) = 2;
mat(1, 1) = 3;
mat(1, 0) = 1;
mat.writeIntoTextFile("test_files/matrix_test_18_original.txt");
Math::SymmetricMatrix<double> invMat = mat;
invMat.invert();
invMat.writeIntoTextFile("test_files/matrix_test_18_inverse.txt");
Math::Matrix<double> prod = mat;
prod *= invMat;
prod.writeIntoTextFile("test_files/matrix_test_18_product.txt");
res = 1;
for(int i = 0; i < prod.rows(); ++i)
{
for(int j = 0; j < prod.rows(); ++j)
{
if(i == j)
{
if(!Math::areEqual(prod(i, j), 1.0, 1e-5))
{
output_screen("FAIL! Diagonal element " << i << " must be 1 but it is " << prod(i, j) << std::endl);
res = 0;
}
}
else
{
if(!Math::areEqual(prod(i, j), 0.0, 1e-5))
{
output_screen("FAIL! Non-diagonal element " << i << " " << j << " must be 0 but it is " << prod(i, j) << std::endl);
res = 0;
}
}
}
}
#else
output_screen_clean("This test (below) is skipped because Cosmo++ has not been linked to lapack" << std::endl);
res = 1;
#endif
}
void writeAsciiMatrix(const std::string& fname, const Math::Matrix<T,P,S>& M,
const std::string& meta, const bool trans = false) {
Math::Range start(0,0);
Math::Range end(M.rows(), M.cols());
std::ofstream ofs(fname.c_str());
if (!ofs.is_open())
throw(std::runtime_error("Cannot open " + fname + " for writing."));
MatrixWriteImpl<T,P,S,internal::BasicMatrixFormatter<T> >::write(ofs, M, meta, start, end, trans);
}
void verify_matrix (Math::Matrix<float>& in, const node_t num_nodes)
{
if (in.rows() != in.columns())
throw Exception ("Connectome matrix is not square (" + str(in.rows()) + " x " + str(in.columns()) + ")");
if (in.rows() != num_nodes)
throw Exception ("Connectome matrix contains " + str(in.rows()) + " nodes; expected " + str(num_nodes));
for (node_t row = 0; row != num_nodes; ++row) {
for (node_t column = row+1; column != num_nodes; ++column) {
const float lower_value = in (column, row);
const float upper_value = in (row, column);
if (upper_value && lower_value && (upper_value != lower_value))
throw Exception ("Connectome matrix is not symmetrical");
if (!upper_value && lower_value)
in (row, column) = lower_value;
in (column, row) = 0.0f;
} }
}
void run ()
{
try {
Math::Matrix<value_type> directions = DWI::Directions::load_cartesian<value_type> (argument[0]);
report (str(argument[0]), directions);
}
catch (Exception& E) {
Math::Matrix<value_type> directions (str(argument[0]));
DWI::normalise_grad (directions);
if (directions.columns() < 3)
throw Exception ("unexpected matrix size for DW scheme \"" + str(argument[0]) + "\"");
print (str(argument[0]) + " [ " + str(directions.rows()) + " volumes ]\n");
DWI::Shells shells (directions);
for (size_t n = 0; n < shells.count(); ++n) {
Math::Matrix<value_type> subset (shells[n].count(), 3);
for (size_t i = 0; i < subset.rows(); ++i)
subset.row(i) = directions.row(shells[n].get_volumes()[i]).sub(0,3);
report ("\nb = " + str(shells[n].get_mean()), subset);
}
}
}
void run()
{
InputBufferType dwi_buffer (argument[0], Image::Stride::contiguous_along_axis (3));
Math::Matrix<cost_value_type> grad = DWI::get_valid_DW_scheme<cost_value_type> (dwi_buffer);
size_t dwi_axis = 3;
while (dwi_buffer.dim (dwi_axis) < 2) ++dwi_axis;
INFO ("assuming DW images are stored along axis " + str (dwi_axis));
Math::Matrix<cost_value_type> bmatrix;
DWI::grad2bmatrix (bmatrix, grad);
Math::Matrix<cost_value_type> binv (bmatrix.columns(), bmatrix.rows());
Math::pinv (binv, bmatrix);
int method = 1;
Options opt = get_options ("method");
if (opt.size()) method = opt[0][0];
opt = get_options ("regularisation");
cost_value_type regularisation = 5000.0;
if (opt.size()) regularisation = opt[0][0];
opt = get_options ("mask");
Ptr<MaskBufferType> mask_buffer;
Ptr<MaskBufferType::voxel_type> mask_vox;
if (opt.size()){
mask_buffer = new MaskBufferType (opt[0][0]);
Image::check_dimensions (*mask_buffer, dwi_buffer, 0, 3);
mask_vox = new MaskBufferType::voxel_type (*mask_buffer);
}
Image::Header dt_header (dwi_buffer);
dt_header.set_ndim (4);
dt_header.dim (3) = 6;
dt_header.datatype() = DataType::Float32;
dt_header.DW_scheme() = grad;
OutputBufferType dt_buffer (argument[1], dt_header);
InputBufferType::voxel_type dwi_vox (dwi_buffer);
OutputBufferType::voxel_type dt_vox (dt_buffer);
Image::ThreadedLoop loop ("estimating tensor components...", dwi_vox, 1, 0, 3);
Processor processor (dwi_vox, dt_vox, mask_vox, bmatrix, binv, method, regularisation, loop.inner_axes()[0], dwi_axis);
loop.run_outer (processor);
}
void save_bvecs_bvals (const Image::Header& header, const std::string& path)
{
std::string bvecs_path, bvals_path;
if (path.size() >= 5 && path.substr (path.size() - 5, path.size()) == "bvecs") {
bvecs_path = path;
bvals_path = path.substr (0, path.size() - 5) + "bvals";
} else if (path.size() >= 5 && path.substr (path.size() - 5, path.size()) == "bvals") {
bvecs_path = path.substr (0, path.size() - 5) + "bvecs";
bvals_path = path;
} else {
bvecs_path = path + "bvecs";
bvals_path = path + "bvals";
}
const Math::Matrix<float>& grad (header.DW_scheme());
Math::Matrix<float> G (grad.rows(), 3);
// rotate vectors from scanner space to image space
Math::Matrix<float> D (header.transform());
Math::Permutation p (4);
int signum;
Math::LU::decomp (D, p, signum);
Math::Matrix<float> image2scanner (4,4);
Math::LU::inv (image2scanner, D, p);
Math::Matrix<float> rotation = image2scanner.sub (0,3,0,3);
Math::Matrix<float> grad_G = grad.sub (0, grad.rows(), 0, 3);
Math::mult (G, float(0.0), float(1.0), CblasNoTrans, grad_G, CblasTrans, rotation);
// deal with FSL requiring gradient directions to coincide with data strides
// also transpose matrices in preparation for file output
std::vector<size_t> order = Image::Stride::order (header, 0, 3);
Math::Matrix<float> bvecs (3, grad.rows());
Math::Matrix<float> bvals (1, grad.rows());
for (size_t n = 0; n < G.rows(); ++n) {
bvecs(0,n) = header.stride(order[0]) > 0 ? G(n,order[0]) : -G(n,order[0]);
bvecs(1,n) = header.stride(order[1]) > 0 ? G(n,order[1]) : -G(n,order[1]);
bvecs(2,n) = header.stride(order[2]) > 0 ? G(n,order[2]) : -G(n,order[2]);
bvals(0,n) = grad(n,3);
}
bvecs.save (bvecs_path);
bvals.save (bvals_path);
}
void
AAKR::computeDistance(Math::Matrix query)
{
if (query.rows() != 1)
throw std::runtime_error("unable to compute distance: reference is not row vector.");
if ((unsigned)query.columns() != sampleSize())
throw std::runtime_error("unable to compute distance: sample size does not match.");
m_distances.fill(0.0);
// Fill distances vector.
for (unsigned i = 0; i < m_num_values; i++)
{
Math::Matrix q = query - m_norm.row(i);
m_distances(i) = std::sqrt(sum(q * transpose(q)));
};
}
void
AAKR::add(Math::Matrix v)
{
if (dataSize() == 0)
throw std::runtime_error("unable to add: data window size is undefined.");
if (v.rows() != 1)
throw std::runtime_error("unable to add: new sample is not a row vector.");
if (sampleSize() == 0)
m_data.resize(dataSize(), v.columns());
if ((unsigned)v.columns() != sampleSize())
throw std::runtime_error("unable to add: sample size does not match.");
// Write to the data set.
m_data.set(m_index, m_index, 0, sampleSize() - 1, v);
// Increment data set index.
increment();
}
void run ()
{
Math::Matrix<value_type> directions = DWI::Directions::load_cartesian<value_type> (argument[0]);
size_t num_permutations = 1e8;
Options opt = get_options ("permutations");
if (opt.size())
num_permutations = opt[0][0];
Shared eddy_shared (directions, num_permutations);
Thread::run (Thread::multi (Processor (eddy_shared)), "eval thread");
auto& signs = eddy_shared.get_best_signs();
for (size_t n = 0; n < directions.rows(); ++n)
if (signs[n] < 0)
directions.row(n) *= -1.0;
bool cartesian = get_options("cartesian").size();
DWI::Directions::save (directions, argument[1], cartesian);
}
void solve_nonlinear () {
for (size_t i = 0; i < signals.rows(); ++i) {
const Math::Vector<cost_value_type> signal (signals.row(i));
Math::Vector<cost_value_type> values (tensors.row(i));
cost.set_voxel (&signal, &values);
Math::Vector<cost_value_type> x (cost.size());
cost.init (x);
//Math::check_function_gradient (cost, x, 1e-10, true);
Math::GradientDescent<Cost> optim (cost);
try { optim.run (10000, 1e-8); }
catch (Exception& E) {
E.display();
}
//x = optim.state();
//Math::check_function_gradient (cost, x, 1e-10, true);
cost.get_values (values, optim.state());
}
}
Shared (const Math::Matrix<value_type>& directions, size_t target_num_permutations) :
directions (directions), target_num_permutations (target_num_permutations), num_permutations(0),
progress ("optimising directions for eddy-currents...", target_num_permutations),
best_signs (directions.rows(), 1), best_eddy (std::numeric_limits<value_type>::max()) { }
void report (const std::string& title, const Math::Matrix<value_type>& directions)
{
std::vector<value_type> NN_bipolar (directions.rows(), 0.0);
std::vector<value_type> NN_unipolar (directions.rows(), 0.0);
std::vector<value_type> E_bipolar (directions.rows(), 0.0);
std::vector<value_type> E_unipolar (directions.rows(), 0.0);
for (size_t i = 0; i < directions.rows()-1; ++i) {
for (size_t j = i+1; j < directions.rows(); ++j) {
value_type cos_angle = Math::dot (directions.row(i).sub(0,3), directions.row(j).sub(0,3));
NN_unipolar[i] = std::max (NN_unipolar[i], cos_angle);
NN_unipolar[j] = std::max (NN_unipolar[j], cos_angle);
cos_angle = std::abs(cos_angle);
NN_bipolar[i] = std::max (NN_bipolar[i], cos_angle);
NN_bipolar[j] = std::max (NN_bipolar[j], cos_angle);
value_type E =
Math::pow2 (directions(i,0) - directions(j,0)) +
Math::pow2 (directions(i,1) - directions(j,1)) +
Math::pow2 (directions(i,2) - directions(j,2));
E = value_type (1.0) / E;
E_unipolar[i] += E;
E_unipolar[j] += E;
value_type E2 =
Math::pow2 (directions(i,0) + directions(j,0)) +
Math::pow2 (directions(i,1) + directions(j,1)) +
Math::pow2 (directions(i,2) + directions(j,2));
E += value_type (1.0) / E2;
E_bipolar[i] += E;
E_bipolar[j] += E;
}
}
auto report_NN = [](const std::vector<value_type>& NN) {
value_type min = std::numeric_limits<value_type>::max();
value_type mean = 0.0;
value_type max = 0.0;
for (auto a : NN) {
a = (180.0/Math::pi) * std::acos (a);
mean += a;
min = std::min (min, a);
max = std::max (max, a);
}
mean /= NN.size();
print (" nearest-neighbour angles: mean = " + str(mean) + ", range [ " + str(min) + " - " + str(max) + " ]\n");
};
auto report_E = [](const std::vector<value_type>& E) {
value_type min = std::numeric_limits<value_type>::max();
value_type total = 0.0;
value_type max = 0.0;
for (auto e : E) {
total += e;
min = std::min (min, e);
max = std::max (max, e);
}
print (" energy: total = " + str(total) + ", mean = " + str(total/E.size()) + ", range [ " + str(min) + " - " + str(max) + " ]\n");
};
print (title + " [ " + str(directions.rows()) + " directions ]\n\n");
print (" Bipolar electrostatic repulsion model:\n");
report_NN (NN_bipolar);
report_E (E_bipolar);
print ("\n Unipolar electrostatic repulsion model:\n");
report_NN (NN_unipolar);
report_E (E_unipolar);
std::string lmax_results;
for (size_t lmax = 2; lmax <= Math::SH::LforN (directions.rows()); lmax += 2)
lmax_results += " " + str(DWI::condition_number_for_lmax (directions, lmax));
print ("\n Spherical Harmonic fit:\n condition numbers for lmax = " + str(2) + " -> "
+ str(Math::SH::LforN (directions.rows())) + ":" + lmax_results + "\n\n");
}
void run() {
Image::BufferPreload<float> data_in (argument[0], Image::Stride::contiguous_along_axis (3));
auto voxel_in = data_in.voxel();
Math::Matrix<value_type> grad (DWI::get_valid_DW_scheme<float> (data_in));
// Want to support non-shell-like data if it's just a straight extraction
// of all dwis or all bzeros i.e. don't initialise the Shells class
std::vector<size_t> volumes;
bool bzero = get_options ("bzero").size();
Options opt = get_options ("shell");
if (opt.size()) {
DWI::Shells shells (grad);
shells.select_shells (false, false);
for (size_t s = 0; s != shells.count(); ++s) {
DEBUG ("Including data from shell b=" + str(shells[s].get_mean()) + " +- " + str(shells[s].get_stdev()));
for (std::vector<size_t>::const_iterator v = shells[s].get_volumes().begin(); v != shells[s].get_volumes().end(); ++v)
volumes.push_back (*v);
}
// Remove DW information from header if b=0 is the only 'shell' selected
bzero = (shells.count() == 1 && shells[0].is_bzero());
} else {
const float bzero_threshold = File::Config::get_float ("BValueThreshold", 10.0);
for (size_t row = 0; row != grad.rows(); ++row) {
if ((bzero && (grad (row, 3) < bzero_threshold)) || (!bzero && (grad (row, 3) > bzero_threshold)))
volumes.push_back (row);
}
}
if (volumes.empty())
throw Exception ("No " + str(bzero ? "b=0" : "dwi") + " volumes present");
std::sort (volumes.begin(), volumes.end());
Image::Header header (data_in);
if (volumes.size() == 1)
header.set_ndim (3);
else
header.dim (3) = volumes.size();
Math::Matrix<value_type> new_grad (volumes.size(), grad.columns());
for (size_t i = 0; i < volumes.size(); i++)
new_grad.row (i) = grad.row (volumes[i]);
header.DW_scheme() = new_grad;
Image::Buffer<value_type> data_out (argument[1], header);
auto voxel_out = data_out.voxel();
Image::Loop outer ("extracting volumes...", 0, 3);
if (voxel_out.ndim() == 4) {
for (auto i = outer (voxel_out, voxel_in); i; ++i) {
for (size_t i = 0; i < volumes.size(); i++) {
voxel_in[3] = volumes[i];
voxel_out[3] = i;
voxel_out.value() = voxel_in.value();
}
}
} else {
const size_t volume = volumes[0];
for (auto i = outer (voxel_out, voxel_in); i; ++i) {
voxel_in[3] = volume;
voxel_out.value() = voxel_in.value();
}
}
}
