diff --git a/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.cpp b/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.cpp
index 311da563c840d54338bde943d19ac26397e88dbe..ce4f3715f831a99b09d23fddf2b72cc956c47e44 100644
--- a/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.cpp
+++ b/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.cpp
@@ -57,7 +57,7 @@ std::size_t Partition::numberOfMeshItems(
OGS_FATAL("Mesh items other than nodes and cells are not supported.");
}
-std::ostream& Partition::writeNodesBinary(
+std::ostream& Partition::writeNodes(
std::ostream& os, std::vector<std::size_t> const& global_node_ids) const
{
std::vector<NodeStruct> nodes_buffer;
@@ -87,7 +87,7 @@ NodeWiseMeshPartitioner::IntegerType getNumberOfIntegerVariablesOfElements(
});
}
-std::ostream& Partition::writeConfigBinary(std::ostream& os) const
+std::ostream& Partition::writeConfig(std::ostream& os) const
{
long const data[] = {
static_cast<long>(nodes.size()),
@@ -763,16 +763,16 @@ void NodeWiseMeshPartitioner::renumberNodeIndices(
}
template <typename T>
-void writePropertyVectorValuesBinary(std::ostream& os,
- MeshLib::PropertyVector<T> const& pv)
+void writePropertyVectorValues(std::ostream& os,
+ MeshLib::PropertyVector<T> const& pv)
{
os.write(reinterpret_cast<const char*>(pv.data()), pv.size() * sizeof(T));
}
template <typename T>
-bool writePropertyVectorBinary(MeshLib::PropertyVector<T> const* const pv,
- MeshLib::MeshItemType const mesh_item_type,
- std::ostream& out_val, std::ostream& out_meta)
+bool writePropertyVector(MeshLib::PropertyVector<T> const* const pv,
+ MeshLib::MeshItemType const mesh_item_type,
+ std::ostream& out_val, std::ostream& out_meta)
{
if (pv == nullptr)
{
@@ -790,15 +790,15 @@ bool writePropertyVectorBinary(MeshLib::PropertyVector<T> const* const pv,
pvmd.fillPropertyVectorMetaDataTypeInfo<T>();
pvmd.number_of_components = pv->getNumberOfGlobalComponents();
pvmd.number_of_tuples = pv->getNumberOfTuples();
- writePropertyVectorValuesBinary(out_val, *pv);
- MeshLib::IO::writePropertyVectorMetaDataBinary(out_meta, pvmd);
+ writePropertyVectorValues(out_val, *pv);
+ MeshLib::IO::writePropertyVectorMetaData(out_meta, pvmd);
return true;
}
-void writePropertiesBinary(const std::string& file_name_base,
- MeshLib::Properties const& partitioned_properties,
- std::vector<Partition> const& partitions,
- MeshLib::MeshItemType const mesh_item_type)
+void writeProperties(const std::string& file_name_base,
+ MeshLib::Properties const& partitioned_properties,
+ std::vector<Partition> const& partitions,
+ MeshLib::MeshItemType const mesh_item_type)
{
auto const number_of_properties =
partitioned_properties.size(mesh_item_type);
@@ -831,7 +831,7 @@ void writePropertiesBinary(const std::string& file_name_base,
applyToPropertyVectors(
partitioned_properties, [&](auto type, auto const& property) {
- return writePropertyVectorBinary<decltype(type)>(
+ return writePropertyVector<decltype(type)>(
dynamic_cast<MeshLib::PropertyVector<decltype(type)> const*>(
property),
mesh_item_type, out_val, out);
@@ -858,10 +858,10 @@ struct ConfigOffsets
long element_rank_offset;
long ghost_element_rank_offset;
- std::ostream& writeConfigBinary(std::ostream& os) const;
+ std::ostream& writeConfig(std::ostream& os) const;
};
-std::ostream& ConfigOffsets::writeConfigBinary(std::ostream& os) const
+std::ostream& ConfigOffsets::writeConfig(std::ostream& os) const
{
os.write(reinterpret_cast<const char*>(this), sizeof(ConfigOffsets));
@@ -910,7 +910,7 @@ ConfigOffsets incrementConfigOffsets(ConfigOffsets const& oldConfig,
/// 1. The number of all non-ghost element integer variables for each
/// partition.
/// 2. The number of all ghost element integer variables for each partition.
-std::tuple<std::vector<long>, std::vector<long>> writeConfigDataBinary(
+std::tuple<std::vector<long>, std::vector<long>> writeConfigData(
const std::string& file_name_base, std::vector<Partition> const& partitions)
{
auto const file_name_cfg = file_name_base + "_partitioned_msh_cfg" +
@@ -929,9 +929,9 @@ std::tuple<std::vector<long>, std::vector<long>> writeConfigDataBinary(
ConfigOffsets config_offsets = {0, 0, 0}; // 0 for first partition.
for (const auto& partition : partitions)
{
- partition.writeConfigBinary(of_bin_cfg);
+ partition.writeConfig(of_bin_cfg);
- config_offsets.writeConfigBinary(of_bin_cfg);
+ config_offsets.writeConfig(of_bin_cfg);
auto const& new_offsets = computePartitionElementOffsets(partition);
config_offsets = incrementConfigOffsets(config_offsets, new_offsets);
@@ -989,10 +989,10 @@ std::unordered_map<std::size_t, long> enumerateLocalNodeIds(
/// \param num_elem_integers The numbers of all non-ghost element
/// integer variables of each partitions.
/// \param num_g_elem_integers The numbers of all ghost element
-void writeElementsBinary(std::string const& file_name_base,
- std::vector<Partition> const& partitions,
- std::vector<long> const& num_elem_integers,
- std::vector<long> const& num_g_elem_integers)
+void writeElements(std::string const& file_name_base,
+ std::vector<Partition> const& partitions,
+ std::vector<long> const& num_elem_integers,
+ std::vector<long> const& num_g_elem_integers)
{
const std::string npartitions_str = std::to_string(partitions.size());
@@ -1058,9 +1058,9 @@ void writeElementsBinary(std::string const& file_name_base,
/// \param file_name_base The prefix of the file name.
/// \param partitions the list of partitions
/// \param global_node_ids global numbering of nodes
-void writeNodesBinary(const std::string& file_name_base,
- std::vector<Partition> const& partitions,
- std::vector<std::size_t> const& global_node_ids)
+void writeNodes(const std::string& file_name_base,
+ std::vector<Partition> const& partitions,
+ std::vector<std::size_t> const& global_node_ids)
{
auto const file_name = file_name_base + "_partitioned_msh_nod" +
std::to_string(partitions.size()) + ".bin";
@@ -1072,28 +1072,27 @@ void writeNodesBinary(const std::string& file_name_base,
for (const auto& partition : partitions)
{
- partition.writeNodesBinary(os, global_node_ids);
+ partition.writeNodes(os, global_node_ids);
}
}
-void NodeWiseMeshPartitioner::writeBinary(const std::string& file_name_base)
+void NodeWiseMeshPartitioner::write(const std::string& file_name_base)
{
- writePropertiesBinary(file_name_base, _partitioned_properties, _partitions,
- MeshLib::MeshItemType::Node);
- writePropertiesBinary(file_name_base, _partitioned_properties, _partitions,
- MeshLib::MeshItemType::Cell);
+ writeProperties(file_name_base, _partitioned_properties, _partitions,
+ MeshLib::MeshItemType::Node);
+ writeProperties(file_name_base, _partitioned_properties, _partitions,
+ MeshLib::MeshItemType::Cell);
- const auto elem_integers =
- writeConfigDataBinary(file_name_base, _partitions);
+ const auto elem_integers = writeConfigData(file_name_base, _partitions);
const std::vector<IntegerType>& num_elem_integers =
std::get<0>(elem_integers);
const std::vector<IntegerType>& num_g_elem_integers =
std::get<1>(elem_integers);
- writeElementsBinary(file_name_base, _partitions, num_elem_integers,
- num_g_elem_integers);
+ writeElements(file_name_base, _partitions, num_elem_integers,
+ num_g_elem_integers);
- writeNodesBinary(file_name_base, _partitions, _nodes_global_ids);
+ writeNodes(file_name_base, _partitions, _nodes_global_ids);
}
void NodeWiseMeshPartitioner::writeOtherMesh(
@@ -1101,131 +1100,21 @@ void NodeWiseMeshPartitioner::writeOtherMesh(
std::vector<Partition> const& partitions,
MeshLib::Properties const& partitioned_properties) const
{
- writeNodesBinary(output_filename_base, partitions, _nodes_global_ids);
+ writeNodes(output_filename_base, partitions, _nodes_global_ids);
const auto elem_integers =
- writeConfigDataBinary(output_filename_base, partitions);
+ writeConfigData(output_filename_base, partitions);
const std::vector<IntegerType>& num_elem_integers =
std::get<0>(elem_integers);
const std::vector<IntegerType>& num_g_elem_integers =
std::get<1>(elem_integers);
- writeElementsBinary(output_filename_base, partitions, num_elem_integers,
- num_g_elem_integers);
-
- writePropertiesBinary(output_filename_base, partitioned_properties,
- partitions, MeshLib::MeshItemType::Node);
- writePropertiesBinary(output_filename_base, partitioned_properties,
- partitions, MeshLib::MeshItemType::Cell);
-}
+ writeElements(output_filename_base, partitions, num_elem_integers,
+ num_g_elem_integers);
-void NodeWiseMeshPartitioner::writeConfigDataASCII(
- const std::string& file_name_base)
-{
- const std::string fname = file_name_base + "_partitioned_cfg" +
- std::to_string(_npartitions) + ".msh";
- std::fstream os_subd_head(fname, std::ios::out | std::ios::trunc);
- const std::string mesh_info =
- "Subdomain mesh ("
- "Number of nodes; Number of base nodes;"
- " Number of regular elements; Number of ghost elements;"
- " Number of non-ghost base nodes; Number of non-ghost nodes"
- " Number of base nodes of the global mesh;"
- " Number of nodes of the global mesh;"
- " Number of integer variables to define non-ghost elements;"
- " Number of integer variables to define ghost elements.)";
- os_subd_head << mesh_info << "\n";
- os_subd_head << _npartitions << "\n";
-
- for (const auto& partition : _partitions)
- {
- os_subd_head << partition.nodes.size();
- os_subd_head << " " << partition.number_of_base_nodes;
- os_subd_head << " " << partition.regular_elements.size();
- os_subd_head << " " << partition.ghost_elements.size();
- os_subd_head << " " << partition.number_of_non_ghost_base_nodes;
- os_subd_head << " " << partition.number_of_non_ghost_nodes;
- os_subd_head << " " << _mesh->getNumberOfBaseNodes();
- os_subd_head << " " << _mesh->getNumberOfNodes();
- os_subd_head << " "
- << getNumberOfIntegerVariablesOfElements(
- partition.regular_elements);
- os_subd_head << " "
- << getNumberOfIntegerVariablesOfElements(
- partition.ghost_elements)
- << " 0\n";
- }
+ writeProperties(output_filename_base, partitioned_properties, partitions,
+ MeshLib::MeshItemType::Node);
+ writeProperties(output_filename_base, partitioned_properties, partitions,
+ MeshLib::MeshItemType::Cell);
}
-
-void NodeWiseMeshPartitioner::writeElementsASCII(
- const std::string& file_name_base)
-{
- const std::string fname = file_name_base + "_partitioned_elems_" +
- std::to_string(_npartitions) + ".msh";
- std::fstream os_subd(fname, std::ios::out | std::ios::trunc);
- for (const auto& partition : _partitions)
- {
- // Set the local node indices of the current partition.
- IntegerType node_local_id_offset = 0;
- std::vector<IntegerType> nodes_local_ids(_mesh->getNumberOfNodes(), -1);
- for (const auto* node : partition.nodes)
- {
- nodes_local_ids[node->getID()] = node_local_id_offset;
- ++node_local_id_offset;
- }
-
- for (const auto* elem : partition.regular_elements)
- {
- writeLocalElementNodeIndices(os_subd, *elem, nodes_local_ids);
- }
- for (const auto* elem : partition.ghost_elements)
- {
- writeLocalElementNodeIndices(os_subd, *elem, nodes_local_ids);
- }
- os_subd << std::endl;
- }
-}
-
-void NodeWiseMeshPartitioner::writeNodesASCII(const std::string& file_name_base)
-{
- const std::string fname = file_name_base + "_partitioned_nodes_" +
- std::to_string(_npartitions) + ".msh";
- std::fstream os_subd_node(fname, std::ios::out | std::ios::trunc);
- os_subd_node.precision(std::numeric_limits<double>::digits10);
- os_subd_node.setf(std::ios::scientific);
-
- for (const auto& partition : _partitions)
- {
- for (const auto* node : partition.nodes)
- {
- double const* coords = node->getCoords();
- os_subd_node << _nodes_global_ids[node->getID()] << " " << coords[0]
- << " " << coords[1] << " " << coords[2] << "\n";
- }
- os_subd_node << std::endl;
- }
-}
-
-void NodeWiseMeshPartitioner::writeASCII(const std::string& file_name_base)
-{
- writeConfigDataASCII(file_name_base);
- writeElementsASCII(file_name_base);
- writeNodesASCII(file_name_base);
-}
-
-void NodeWiseMeshPartitioner::writeLocalElementNodeIndices(
- std::ostream& os,
- const MeshLib::Element& elem,
- const std::vector<IntegerType>& local_node_ids)
-{
- unsigned mat_id = 0; // TODO: Material ID to be set from the mesh data
- os << mat_id << " " << static_cast<unsigned>(elem.getCellType()) << " "
- << elem.getNumberOfNodes() << " ";
- for (unsigned i = 0; i < elem.getNumberOfNodes(); i++)
- {
- os << " " << local_node_ids[elem.getNodeIndex(i)];
- }
- os << "\n";
-}
-
} // namespace ApplicationUtils
diff --git a/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.h b/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.h
index 5e2b086d82e4f94df556a7253840327b5a4392fd..b825209637348673fd88ec38cc7828941aafee60 100644
--- a/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.h
+++ b/Applications/Utils/ModelPreparation/PartitionMesh/NodeWiseMeshPartitioner.h
@@ -42,11 +42,11 @@ struct Partition
std::size_t numberOfMeshItems(MeshLib::MeshItemType const item_type) const;
- std::ostream& writeNodesBinary(
+ std::ostream& writeNodes(
std::ostream& os,
std::vector<std::size_t> const& global_node_ids) const;
- std::ostream& writeConfigBinary(std::ostream& os) const;
+ std::ostream& writeConfig(std::ostream& os) const;
};
/// Creates partitioned mesh properties for nodes and cells.
@@ -98,13 +98,9 @@ public:
MeshLib::PropertyVector<std::size_t>* const bulk_element_ids_pv,
std::vector<Partition> const& local_partitions) const;
- /// Write the partitions into ASCII files
- /// \param file_name_base The prefix of the file name.
- void writeASCII(const std::string& file_name_base);
-
/// Write the partitions into binary files
/// \param file_name_base The prefix of the file name.
- void writeBinary(const std::string& file_name_base);
+ void write(const std::string& file_name_base);
void writeOtherMesh(
std::string const& output_filename_base,
@@ -146,30 +142,6 @@ private:
void processPartition(std::size_t const part_id,
const bool is_mixed_high_order_linear_elems);
-
- /// Write the configuration data of the partition data in ASCII files.
- /// \param file_name_base The prefix of the file name.
- void writeConfigDataASCII(const std::string& file_name_base);
-
- /// Write the element integer variables of all partitions into
- /// ASCII files.
- /// \param file_name_base The prefix of the file name.
- void writeElementsASCII(const std::string& file_name_base);
-
- /// Write the nodes of all partitions into a ASCII file.
- /// \param file_name_base The prefix of the file name.
- void writeNodesASCII(const std::string& file_name_base);
-
- /*!
- \brief Write local indices of element nodes to a ASCII file
- \param os Output stream
- \param elem Element
- \param local_node_ids Local node indices of a partition
- */
- void writeLocalElementNodeIndices(
- std::ostream& os,
- const MeshLib::Element& elem,
- const std::vector<IntegerType>& local_node_ids);
};
} // namespace ApplicationUtils
diff --git a/Applications/Utils/ModelPreparation/PartitionMesh/PartitionMesh.cpp b/Applications/Utils/ModelPreparation/PartitionMesh/PartitionMesh.cpp
index d87ea7ba2569fa666b03035859874de28fc379bd..2700b194485d04fc2448da923ad6da4bbda138b1 100644
--- a/Applications/Utils/ModelPreparation/PartitionMesh/PartitionMesh.cpp
+++ b/Applications/Utils/ModelPreparation/PartitionMesh/PartitionMesh.cpp
@@ -72,9 +72,6 @@ int main(int argc, char* argv[])
"q", "lh_elements", "Mixed linear and high order elements.", false);
cmd.add(lh_elems_flag);
- TCLAP::SwitchArg ascii_flag("a", "ascii", "Enable ASCII output.", false);
- cmd.add(ascii_flag);
-
TCLAP::ValueArg<std::string> log_level_arg("l", "log-level",
"the verbosity of logging "
"messages: none, error, warn, "
@@ -216,16 +213,8 @@ int main(int argc, char* argv[])
partitioned_properties);
}
- if (ascii_flag.getValue())
- {
- INFO("Write the data of partitions into ASCII files ...");
- mesh_partitioner.writeASCII(output_file_name_wo_extension);
- }
- else
- {
- INFO("Write the data of partitions into binary files ...");
- mesh_partitioner.writeBinary(output_file_name_wo_extension);
- }
+ INFO("Write the data of partitions into binary files ...");
+ mesh_partitioner.write(output_file_name_wo_extension);
INFO("Total runtime: {:g} s.", run_timer.elapsed());
INFO("Total CPU time: {:g} s.", CPU_timer.elapsed());
diff --git a/Applications/Utils/Tests.cmake b/Applications/Utils/Tests.cmake
index 7ae12f78e11ea7d0206cf5f402eb8bda858dcae3..0712218c137876def27234418cccd32447a66b44 100644
--- a/Applications/Utils/Tests.cmake
+++ b/Applications/Utils/Tests.cmake
@@ -105,21 +105,6 @@ AddTest(
river_bc_prism.vtu prism_river_bc.vtu number_bulk_elements number_bulk_elements 0 0
)
-# Mac is producing slightly different partitioning, so the results are not
-# comparable.
-AddTest(
- NAME partmesh_2Dmesh_3partitions_ascii
- PATH NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII
- WORKING_DIRECTORY ${Data_SOURCE_DIR}/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII
- EXECUTABLE partmesh
- EXECUTABLE_ARGS -a -m -n 3 -i 2Dmesh.vtu -o ${Data_BINARY_DIR}/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII
- REQUIREMENTS NOT (OGS_USE_MPI OR APPLE)
- TESTER diff
- DIFF_DATA 2Dmesh_partitioned_elems_3.msh
- 2Dmesh_partitioned_cfg3.msh
- 2Dmesh_partitioned_nodes_3.msh
-)
-
# Mac is producing slightly different partitioning, so the results are not
# comparable.
AddTest(
diff --git a/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.cpp b/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.cpp
index 3bc763e00b0c079a76a80adfcbfd545d7772b2ec..725101c89b88f8cdcb5ddc0047b185fb72b230c0 100644
--- a/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.cpp
+++ b/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.cpp
@@ -83,18 +83,17 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::read(
std::string const fname_new = file_name_base + "_partitioned_msh_cfg" +
std::to_string(_mpi_comm_size) + ".bin";
- if(!BaseLib::IsFileExisting(fname_new)) // doesn't exist binary file.
+ if(!BaseLib::IsFileExisting(fname_new)) // binary file does not exist.
{
- INFO("Reading ASCII mesh file ...");
-
- mesh = readASCII(file_name_base);
+ OGS_FATAL(
+ "Required binary file {:s} does not exist.\n"
+ "Reading of ASCII mesh file is not supported since OGS version "
+ "6.3.3.",
+ fname_new);
}
- else
- {
- INFO("Reading binary mesh file ...");
- mesh = readBinary(file_name_base);
- }
+ INFO("Reading binary mesh file ...");
+ mesh = readMesh(file_name_base);
INFO("[time] Reading the mesh took {:f} s.", timer.elapsed());
@@ -105,7 +104,7 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::read(
template <typename DATA>
bool
-NodePartitionedMeshReader::readBinaryDataFromFile(std::string const& filename,
+NodePartitionedMeshReader::readDataFromFile(std::string const& filename,
MPI_Offset offset, MPI_Datatype type, DATA& data) const
{
// Check container size
@@ -140,7 +139,7 @@ NodePartitionedMeshReader::readBinaryDataFromFile(std::string const& filename,
return true;
}
-MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readBinary(
+MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readMesh(
const std::string &file_name_base)
{
//----------------------------------------------------------------------------------
@@ -148,7 +147,7 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readBinary(
const std::string fname_header = file_name_base + "_partitioned_msh_";
const std::string fname_num_p_ext = std::to_string(_mpi_comm_size) + ".bin";
- if (!readBinaryDataFromFile(fname_header + "cfg" + fname_num_p_ext,
+ if (!readDataFromFile(fname_header + "cfg" + fname_num_p_ext,
static_cast<MPI_Offset>(
static_cast<unsigned>(_mpi_rank) * sizeof(_mesh_info)),
MPI_LONG, _mesh_info))
@@ -158,7 +157,7 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readBinary(
// Read Nodes
std::vector<NodeData> nodes(_mesh_info.nodes);
- if (!readBinaryDataFromFile(fname_header + "nod" + fname_num_p_ext,
+ if (!readDataFromFile(fname_header + "nod" + fname_num_p_ext,
static_cast<MPI_Offset>(_mesh_info.offset[2]), _mpi_node_type, nodes))
return nullptr;
@@ -170,7 +169,7 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readBinary(
// Read non-ghost elements
std::vector<unsigned long> elem_data(
_mesh_info.regular_elements + _mesh_info.offset[0]);
- if (!readBinaryDataFromFile(fname_header + "ele" + fname_num_p_ext,
+ if (!readDataFromFile(fname_header + "ele" + fname_num_p_ext,
static_cast<MPI_Offset>(_mesh_info.offset[3]), MPI_LONG, elem_data))
return nullptr;
@@ -183,7 +182,7 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readBinary(
std::vector<unsigned long> ghost_elem_data(
_mesh_info.ghost_elements + _mesh_info.offset[1]);
- if (!readBinaryDataFromFile(fname_header + "ele_g" + fname_num_p_ext,
+ if (!readDataFromFile(fname_header + "ele_g" + fname_num_p_ext,
static_cast<MPI_Offset>(_mesh_info.offset[4]), MPI_LONG, ghost_elem_data))
return nullptr;
@@ -192,22 +191,22 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readBinary(
//----------------------------------------------------------------------------------
// read the properties
- MeshLib::Properties p(readPropertiesBinary(file_name_base));
+ MeshLib::Properties p(readProperties(file_name_base));
return newMesh(BaseLib::extractBaseName(file_name_base), mesh_nodes,
glb_node_ids, mesh_elems, p);
}
-MeshLib::Properties NodePartitionedMeshReader::readPropertiesBinary(
+MeshLib::Properties NodePartitionedMeshReader::readProperties(
const std::string& file_name_base) const
{
MeshLib::Properties p;
- readPropertiesBinary(file_name_base, MeshLib::MeshItemType::Node, p);
- readPropertiesBinary(file_name_base, MeshLib::MeshItemType::Cell, p);
+ readProperties(file_name_base, MeshLib::MeshItemType::Node, p);
+ readProperties(file_name_base, MeshLib::MeshItemType::Cell, p);
return p;
}
-void NodePartitionedMeshReader::readPropertiesBinary(
+void NodePartitionedMeshReader::readProperties(
const std::string& file_name_base, MeshLib::MeshItemType t,
MeshLib::Properties& p) const
{
@@ -236,7 +235,7 @@ void NodePartitionedMeshReader::readPropertiesBinary(
if (!vec_pvmd[i])
{
OGS_FATAL(
- "Error in NodePartitionedMeshReader::readPropertiesBinary: "
+ "Error in NodePartitionedMeshReader::readProperties: "
"Could not read the meta data for the PropertyVector {:d}",
i);
}
@@ -262,7 +261,7 @@ void NodePartitionedMeshReader::readPropertiesBinary(
if (!all_pvpmd_read_ok)
{
OGS_FATAL(
- "Error in NodePartitionedMeshReader::readPropertiesBinary: "
+ "Error in NodePartitionedMeshReader::readProperties: "
"Could not read the partition meta data for the mpi process {:d}",
_mpi_rank);
}
@@ -353,227 +352,6 @@ void NodePartitionedMeshReader::readDomainSpecificPartOfPropertyVectors(
}
}
-bool NodePartitionedMeshReader::openASCIIFiles(
- std::string const& file_name_base, std::ifstream& is_cfg,
- std::ifstream& is_node, std::ifstream& is_elem) const
-{
- const std::string fname_header = file_name_base + "_partitioned_";
- const std::string fname_num_p_ext = std::to_string(_mpi_comm_size) + ".msh";
-
- { // Configuration.
- std::string const filename = fname_header + "cfg" + fname_num_p_ext;
- is_cfg.open(filename);
-
- if( !is_cfg.good() )
- {
- ERR("Error opening file {:s} for input.", filename);
- return false;
- }
-
- std::string tmp_line;
- std::getline(is_cfg, tmp_line);
- int num_parts;
- is_cfg >> num_parts >> std::ws;
-
- if(num_parts != _mpi_comm_size)
- {
- ERR("Aborting computation because of number of cores"
- "/ subdomains mismatch.");
- return false;
- }
- }
-
- { // Nodes.
- std::string const filename = fname_header + "nodes_" + fname_num_p_ext;
- is_node.open(filename);
- if( !is_node.good() )
- {
- ERR("Error opening file {:s} for input.", filename);
- return false;
- }
- }
-
- { // Elements.
- std::string const filename = fname_header + "elems_" + fname_num_p_ext;
- is_elem.open(filename);
- if( !is_elem.good() )
- {
- ERR("Error opening file {:s} for input.", filename);
- return false;
- }
- }
-
- return true;
-}
-
-bool NodePartitionedMeshReader::readCastNodesASCII(std::ifstream& is_node,
- const int part_id, std::vector<MeshLib::Node*> &mesh_nodes,
- std::vector<unsigned long> &glb_node_ids) const
-{
- int const message_tag = 0;
-
- // MPI_Send/Recv can handle only int. Check for overflow.
- if (!is_safely_convertable<unsigned long, int>(_mesh_info.nodes))
- {
- ERR("Too large number of nodes to read.");
- return false;
- }
-
- std::vector<NodeData> nodes(_mesh_info.nodes);
-
- if(_mpi_rank == 0)
- {
- for(unsigned long k = 0; k < _mesh_info.nodes; k++)
- {
- NodeData &node = nodes[k];
- is_node >> node.index >> node.x >> node.y >> node.z >> std::ws;
- }
-
- if(part_id == 0)
- setNodes(nodes, mesh_nodes, glb_node_ids);
- else
- MPI_Send(nodes.data(), static_cast<int>(_mesh_info.nodes),
- _mpi_node_type, part_id, message_tag, _mpi_comm);
- }
- else if(_mpi_rank == part_id)
- {
- MPI_Recv(nodes.data(), static_cast<int>(_mesh_info.nodes),
- _mpi_node_type, 0, message_tag, _mpi_comm, MPI_STATUS_IGNORE);
- setNodes(nodes, mesh_nodes, glb_node_ids);
- }
-
- return true;
-}
-
-bool NodePartitionedMeshReader::readCastElemsASCII(std::ifstream& is_elem,
- const int part_id, const std::size_t data_size, const bool process_ghost,
- const std::vector<MeshLib::Node*> &mesh_nodes,
- std::vector<MeshLib::Element*> &mesh_elems) const
-{
- int const message_tag = 0;
-
- // MPI_Send/Recv can handle only int. Check for overflow.
- if (!is_safely_convertable<std::size_t, int>(data_size))
- {
- ERR("Too large number of elements to read.");
- return false;
- }
-
- std::vector<unsigned long> elem_data(data_size);
- if(_mpi_rank == 0)
- {
- readElementASCII(is_elem, elem_data, process_ghost);
-
- if(part_id == 0)
- {
- if(!process_ghost)
- mesh_elems.resize(
- _mesh_info.regular_elements + _mesh_info.ghost_elements);
- setElements(mesh_nodes, elem_data, mesh_elems, process_ghost);
- }
- else
- MPI_Send(elem_data.data(), static_cast<int>(data_size), MPI_LONG,
- part_id, message_tag, _mpi_comm);
- }
- else if(_mpi_rank == part_id)
- {
- MPI_Recv(elem_data.data(), static_cast<int>(data_size), MPI_LONG,
- 0, message_tag, _mpi_comm, MPI_STATUS_IGNORE);
-
- if(!process_ghost)
- mesh_elems.resize(
- _mesh_info.regular_elements + _mesh_info.ghost_elements);
- setElements(mesh_nodes, elem_data, mesh_elems, process_ghost);
- }
-
- return true;
-}
-
-MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::readASCII(
- const std::string &file_name_base)
-{
- std::ifstream is_cfg;
- std::ifstream is_node;
- std::ifstream is_elem;
-
- bool file_opened = false;
- if(_mpi_rank == 0)
- {
- file_opened = openASCIIFiles(file_name_base, is_cfg, is_node, is_elem);
-
- if(!file_opened)
- {
- is_cfg.close();
- is_node.close();
- is_elem.close();
- }
- }
-
- MPI_Bcast(&file_opened, 1, MPI_INT, 0, _mpi_comm);
-
- if(!file_opened)
- return nullptr;
-
- MeshLib::NodePartitionedMesh* np_mesh = nullptr;
- std::vector<MeshLib::Node*> mesh_nodes;
- std::vector<unsigned long> glb_node_ids;
- std::vector<MeshLib::Element*> mesh_elems;
-
- for(int i = 0; i < _mpi_comm_size; i++)
- {
- if(_mpi_rank == 0)
- {
- // Read first part into _mesh_info which is equal with the binary
- // structure.
- for(unsigned long j = 0; j < 10; ++j)
- is_cfg >> *(_mesh_info.data() + j);
- // The last positon is the extra_flag.
- is_cfg >> _mesh_info.extra_flag;
- is_cfg >> std::ws;
- }
-
- MPI_Bcast(_mesh_info.data(), static_cast<int>(_mesh_info.size()),
- MPI_LONG, 0, _mpi_comm);
-
- //---------------------------------------------------------------------
- // Read Nodes
- if (!readCastNodesASCII(is_node, i, mesh_nodes, glb_node_ids))
- break;
-
- //---------------------------------------------------------------------
- // Read elements
- if (!readCastElemsASCII(is_elem, i,
- _mesh_info.regular_elements + _mesh_info.offset[0],
- false, mesh_nodes, mesh_elems))
- break;
-
- //---------------------------------------------------------------------
- // Ghost elements
- if (!readCastElemsASCII(is_elem, i,
- _mesh_info.ghost_elements + _mesh_info.offset[1],
- true, mesh_nodes, mesh_elems))
- break;
-
- if(_mpi_rank == i) {
- // reading ascii properties is not implemented
- MeshLib::Properties properties;
- np_mesh = newMesh(BaseLib::extractBaseName(file_name_base),
- mesh_nodes, glb_node_ids, mesh_elems, properties);
- }
- }
-
- if(_mpi_rank == 0)
- {
- is_cfg.close();
- is_node.close();
- is_elem.close();
- }
-
- MPI_Barrier(_mpi_comm);
-
- return np_mesh;
-}
-
MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::newMesh(
std::string const& mesh_name,
std::vector<MeshLib::Node*> const& mesh_nodes,
@@ -588,25 +366,6 @@ MeshLib::NodePartitionedMesh* NodePartitionedMeshReader::newMesh(
_mesh_info.active_nodes);
}
-void NodePartitionedMeshReader::readElementASCII(std::ifstream &ins,
- std::vector<unsigned long>& elem_data, const bool ghost) const
-{
- // Set number of elements.
- unsigned long const ne =
- ghost ? _mesh_info.ghost_elements : _mesh_info.regular_elements;
- unsigned long id_offset_elem = ne;
- for(unsigned long j = 0; j < ne; j++)
- {
- elem_data[j] = id_offset_elem;
- ins >> elem_data[id_offset_elem++]; //mat. idx
- ins >> elem_data[id_offset_elem++]; //type
- ins >> elem_data[id_offset_elem]; //nnodes
- unsigned long const nn_e = elem_data[id_offset_elem++];
- for(unsigned long k = 0; k < nn_e; k++)
- ins >> elem_data[id_offset_elem++];
- }
-}
-
void NodePartitionedMeshReader::setNodes(const std::vector<NodeData> &node_data,
std::vector<MeshLib::Node*> &mesh_node,
std::vector<unsigned long> &glb_node_ids) const
diff --git a/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.h b/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.h
index e7fae45575d882cc350cad80d6560f8f4fa654ad..df5abcb92e74d4cbcdb922f5c5731bef4c17fe70 100644
--- a/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.h
+++ b/MeshLib/IO/MPI_IO/NodePartitionedMeshReader.h
@@ -31,7 +31,7 @@ class Properties;
namespace IO
{
-/// Class for parallel reading of ascii or binary partitioned mesh files into a
+/// Class for parallel reading of binary partitioned mesh files into a
/// NodePartitionedMesh via MPI.
class NodePartitionedMeshReader final
{
@@ -42,9 +42,8 @@ public:
~NodePartitionedMeshReader();
/*!
- \brief Create a NodePartitionedMesh object, read data to it,
- and return a pointer to it. Data files are either in
- ASCII format or binary format.
+ \brief Create a NodePartitionedMesh object, read data to it, and return
+ a pointer to it. Data files are in binary format.
\param file_name_base Name of file to be read, and it must be base name without name extension.
\return Pointer to Mesh object. If the creation of mesh object
fails, return a null pointer.
@@ -94,8 +93,7 @@ private:
/// element of global mesh,
unsigned long global_nodes; ///< 7: Number of all nodes of global mesh,
unsigned long offset[5]; ///< 8~12: Offsets of positions of partitions
- /// in the data arrays (only 8 and 9 are used
- /// for ascii input)
+ /// in the data arrays.
unsigned long extra_flag; ///< 13: Reserved for extra flag.
std::size_t size() const { return 14; }
@@ -120,13 +118,15 @@ private:
MeshLib::Properties const& properties) const;
/*!
- \brief Parallel reading of a binary file via MPI_File_read, and it is called by readBinary
- to read files of mesh data head, nodes, non-ghost elements and ghost elements, respectively.
- \note In case of failure during opening of the file, an
- error message is printed.
+ Parallel reading of a binary file via MPI_File_read, reading mesh data
+ head, nodes, non-ghost elements and ghost elements.
+
+ \note In case of failure during opening of the file, an error message is
+ printed.
\note If the number of elements in container is larger than
MPI_file_read() supports (maximum of current \c int type), an
error is printed.
+
\param filename File name containing data.
\param offset Displacement of the data accessible from the view.
see MPI_File_set_view() documentation.
@@ -134,10 +134,11 @@ private:
\param data A container to be filled with data. Its size is used
to determine how many values should be read.
\tparam DATA A homogeneous contaner type supporting data() and size().
+
\return True on success and false otherwise.
*/
template <typename DATA>
- bool readBinaryDataFromFile(std::string const& filename, MPI_Offset offset,
+ bool readDataFromFile(std::string const& filename, MPI_Offset offset,
MPI_Datatype type, DATA& data) const;
/*!
@@ -163,12 +164,12 @@ private:
path to the file and without file extension.
\return Pointer to Mesh object.
*/
- MeshLib::NodePartitionedMesh* readBinary(const std::string &file_name_base);
+ MeshLib::NodePartitionedMesh* readMesh(const std::string &file_name_base);
- MeshLib::Properties readPropertiesBinary(
+ MeshLib::Properties readProperties(
const std::string& file_name_base) const;
- void readPropertiesBinary(const std::string& file_name_base,
+ void readProperties(const std::string& file_name_base,
MeshLib::MeshItemType t,
MeshLib::Properties& p) const;
@@ -200,91 +201,11 @@ private:
pvmd.number_of_components;
if (!is.read(reinterpret_cast<char*>(pv->data()), number_of_bytes))
OGS_FATAL(
- "Error in NodePartitionedMeshReader::readPropertiesBinary: "
+ "Error in NodePartitionedMeshReader::readProperties: "
"Could not read part {:d} of the PropertyVector.",
_mpi_rank);
}
- /*!
- \brief Open ASCII files of node partitioned mesh data.
-
- \param file_name_base Name of file to be read, which must be a name
- with the
- path to the file and without file extension.
- \param is_cfg Input stream for the file contains
- configuration data.
- \param is_node Input stream for the file contains node data.
- \param is_elem Input stream for the file contains element
- data.
- \return Return true if all files are good.
- */
- bool openASCIIFiles(std::string const& file_name_base,
- std::ifstream& is_cfg, std::ifstream& is_node,
- std::ifstream& is_elem) const;
-
- /*!
- \brief Read mesh nodes from an ASCII file and cast to the corresponding rank.
-
- \param is_node Input stream for the file contains node data.
- \param part_id Partition ID.
- \param mesh_nodes Node vector to be filled.
- \param glb_node_ids Global Node IDs to be filled.
- */
- bool readCastNodesASCII(std::ifstream& is_node, const int part_id,
- std::vector<MeshLib::Node*> &mesh_nodes,
- std::vector<unsigned long> &glb_node_ids) const;
-
- /*!
- \brief Read mesh elements from an ASCII file and cast to the corresponding rank.
-
- \param is_elem Input stream for the file contains element data.
- \param part_id Partition ID.
- \param data_size Total size of the data to be read. This type is an
- int because of MPI_Send() implicit cast.
- \param process_ghost Flag to process ghost element.
- \param mesh_nodes Node vector to be filled.
- \param mesh_elems Element vector to be filled.
- */
- bool readCastElemsASCII(std::ifstream& is_elem, const int part_id,
- const std::size_t data_size, const bool process_ghost,
- const std::vector<MeshLib::Node*> &mesh_nodes,
- std::vector<MeshLib::Element*> &mesh_elems) const;
-
- /*!
- \brief Create a NodePartitionedMesh object, read ASCII mesh data,
- and return a pointer to it.
- Three ASCII files have to been read in this function named as:
- file_name_base+_partitioned_cfg[number of partitions].msh
- file_name_base+_partitioned_nodes[number of partitions].msh
- file_name_base+_partitioned_elems[number of partitions].msh
- in which, the first file contains an array of integers for the
- PartitionMeshInfo for all partitions
-
- the second file contains nodes information of global IDs and coordinates
- of all partitions.
-
- the third file contains element information of material ID, element type
- and node IDs of each element of all partitoions.
- \param file_name_base Name of file to be read, which must be a name with the
- path to the file and without file extension.
- \return Pointer to Mesh object.
- */
- MeshLib::NodePartitionedMesh* readASCII(const std::string &file_name_base);
-
- /*!
- \brief Read elements data from ASCII file.
- \param ins Input stream.
- \param elem_data Vector that contains element data, which to be filled.
- Note: Entries from 0 to ne-1, where ne is the number of elements,
- contain the ID of the starting entry of each element. While
- entries after ne-1 store element data including material ID,
- element type, and node IDs.
- \param ghost Flag to read ghost elements.
- */
- void readElementASCII(std::ifstream &ins,
- std::vector<unsigned long>& elem_data,
- const bool ghost = false) const;
-
/*!
\brief Set mesh nodes from a tempory array containing node data read from file.
\param node_data Vector containing node data read from file.
diff --git a/MeshLib/IO/MPI_IO/PropertyVectorMetaData.h b/MeshLib/IO/MPI_IO/PropertyVectorMetaData.h
index 3eb94a726674f2089415dca8ae4ad85a7688b579..47a704b8df10997768217375c952a69d9fc46cbb 100644
--- a/MeshLib/IO/MPI_IO/PropertyVectorMetaData.h
+++ b/MeshLib/IO/MPI_IO/PropertyVectorMetaData.h
@@ -40,7 +40,7 @@ struct PropertyVectorMetaData
}
};
-inline void writePropertyVectorMetaDataBinary(
+inline void writePropertyVectorMetaData(
std::ostream& os, PropertyVectorMetaData const& pvmd)
{
std::string::size_type s(pvmd.property_name.length());
diff --git a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_0.msh b/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_0.msh
deleted file mode 100644
index a9c0db2285c56433dc6567c8f44beabd603f6516..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_0.msh
+++ /dev/null
@@ -1,1060 +0,0 @@
-0 0.000000e+00 0.000000e+00 0.000000e+00
-1 1.000000e-01 0.000000e+00 0.000000e+00
-2 2.000000e-01 0.000000e+00 0.000000e+00
-3 0.000000e+00 1.000000e-01 0.000000e+00
-4 1.000000e-01 1.000000e-01 0.000000e+00
-5 2.000000e-01 1.000000e-01 0.000000e+00
-6 0.000000e+00 2.000000e-01 0.000000e+00
-7 1.000000e-01 2.000000e-01 0.000000e+00
-8 2.000000e-01 2.000000e-01 0.000000e+00
-9 0.000000e+00 3.000000e-01 0.000000e+00
-10 1.000000e-01 3.000000e-01 0.000000e+00
-11 2.000000e-01 3.000000e-01 0.000000e+00
-12 0.000000e+00 4.000000e-01 0.000000e+00
-13 1.000000e-01 4.000000e-01 0.000000e+00
-14 2.000000e-01 4.000000e-01 0.000000e+00
-15 3.000000e-01 4.000000e-01 0.000000e+00
-16 0.000000e+00 5.000000e-01 0.000000e+00
-17 1.000000e-01 5.000000e-01 0.000000e+00
-18 2.000000e-01 5.000000e-01 0.000000e+00
-19 3.000000e-01 5.000000e-01 0.000000e+00
-20 0.000000e+00 6.000000e-01 0.000000e+00
-21 1.000000e-01 6.000000e-01 0.000000e+00
-22 2.000000e-01 6.000000e-01 0.000000e+00
-23 3.000000e-01 6.000000e-01 0.000000e+00
-24 4.000000e-01 6.000000e-01 0.000000e+00
-25 0.000000e+00 7.000000e-01 0.000000e+00
-26 1.000000e-01 7.000000e-01 0.000000e+00
-27 2.000000e-01 7.000000e-01 0.000000e+00
-28 3.000000e-01 7.000000e-01 0.000000e+00
-29 4.000000e-01 7.000000e-01 0.000000e+00
-30 5.000000e-01 7.000000e-01 0.000000e+00
-31 0.000000e+00 8.000000e-01 0.000000e+00
-32 1.000000e-01 8.000000e-01 0.000000e+00
-33 2.000000e-01 8.000000e-01 0.000000e+00
-34 3.000000e-01 8.000000e-01 0.000000e+00
-35 4.000000e-01 8.000000e-01 0.000000e+00
-36 5.000000e-01 8.000000e-01 0.000000e+00
-37 0.000000e+00 9.000000e-01 0.000000e+00
-38 1.000000e-01 9.000000e-01 0.000000e+00
-39 2.000000e-01 9.000000e-01 0.000000e+00
-40 3.000000e-01 9.000000e-01 0.000000e+00
-41 4.000000e-01 9.000000e-01 0.000000e+00
-42 5.000000e-01 9.000000e-01 0.000000e+00
-43 0.000000e+00 1.000000e+00 0.000000e+00
-44 1.000000e-01 1.000000e+00 0.000000e+00
-45 2.000000e-01 1.000000e+00 0.000000e+00
-46 3.000000e-01 1.000000e+00 0.000000e+00
-47 4.000000e-01 1.000000e+00 0.000000e+00
-48 5.000000e-01 1.000000e+00 0.000000e+00
-49 0.000000e+00 0.000000e+00 1.000000e-01
-50 1.000000e-01 0.000000e+00 1.000000e-01
-51 2.000000e-01 0.000000e+00 1.000000e-01
-52 0.000000e+00 1.000000e-01 1.000000e-01
-53 1.000000e-01 1.000000e-01 1.000000e-01
-54 2.000000e-01 1.000000e-01 1.000000e-01
-55 0.000000e+00 2.000000e-01 1.000000e-01
-56 1.000000e-01 2.000000e-01 1.000000e-01
-57 2.000000e-01 2.000000e-01 1.000000e-01
-58 0.000000e+00 3.000000e-01 1.000000e-01
-59 1.000000e-01 3.000000e-01 1.000000e-01
-60 2.000000e-01 3.000000e-01 1.000000e-01
-61 0.000000e+00 4.000000e-01 1.000000e-01
-62 1.000000e-01 4.000000e-01 1.000000e-01
-63 2.000000e-01 4.000000e-01 1.000000e-01
-64 3.000000e-01 4.000000e-01 1.000000e-01
-65 0.000000e+00 5.000000e-01 1.000000e-01
-66 1.000000e-01 5.000000e-01 1.000000e-01
-67 2.000000e-01 5.000000e-01 1.000000e-01
-68 3.000000e-01 5.000000e-01 1.000000e-01
-69 0.000000e+00 6.000000e-01 1.000000e-01
-70 1.000000e-01 6.000000e-01 1.000000e-01
-71 2.000000e-01 6.000000e-01 1.000000e-01
-72 3.000000e-01 6.000000e-01 1.000000e-01
-73 4.000000e-01 6.000000e-01 1.000000e-01
-74 0.000000e+00 7.000000e-01 1.000000e-01
-75 1.000000e-01 7.000000e-01 1.000000e-01
-76 2.000000e-01 7.000000e-01 1.000000e-01
-77 3.000000e-01 7.000000e-01 1.000000e-01
-78 4.000000e-01 7.000000e-01 1.000000e-01
-79 5.000000e-01 7.000000e-01 1.000000e-01
-80 0.000000e+00 8.000000e-01 1.000000e-01
-81 1.000000e-01 8.000000e-01 1.000000e-01
-82 2.000000e-01 8.000000e-01 1.000000e-01
-83 3.000000e-01 8.000000e-01 1.000000e-01
-84 4.000000e-01 8.000000e-01 1.000000e-01
-85 5.000000e-01 8.000000e-01 1.000000e-01
-86 0.000000e+00 9.000000e-01 1.000000e-01
-87 1.000000e-01 9.000000e-01 1.000000e-01
-88 2.000000e-01 9.000000e-01 1.000000e-01
-89 3.000000e-01 9.000000e-01 1.000000e-01
-90 4.000000e-01 9.000000e-01 1.000000e-01
-91 5.000000e-01 9.000000e-01 1.000000e-01
-92 0.000000e+00 1.000000e+00 1.000000e-01
-93 1.000000e-01 1.000000e+00 1.000000e-01
-94 2.000000e-01 1.000000e+00 1.000000e-01
-95 3.000000e-01 1.000000e+00 1.000000e-01
-96 4.000000e-01 1.000000e+00 1.000000e-01
-97 5.000000e-01 1.000000e+00 1.000000e-01
-98 0.000000e+00 0.000000e+00 2.000000e-01
-99 1.000000e-01 0.000000e+00 2.000000e-01
-100 2.000000e-01 0.000000e+00 2.000000e-01
-101 0.000000e+00 1.000000e-01 2.000000e-01
-102 1.000000e-01 1.000000e-01 2.000000e-01
-103 2.000000e-01 1.000000e-01 2.000000e-01
-104 0.000000e+00 2.000000e-01 2.000000e-01
-105 1.000000e-01 2.000000e-01 2.000000e-01
-106 2.000000e-01 2.000000e-01 2.000000e-01
-107 0.000000e+00 3.000000e-01 2.000000e-01
-108 1.000000e-01 3.000000e-01 2.000000e-01
-109 2.000000e-01 3.000000e-01 2.000000e-01
-110 0.000000e+00 4.000000e-01 2.000000e-01
-111 1.000000e-01 4.000000e-01 2.000000e-01
-112 2.000000e-01 4.000000e-01 2.000000e-01
-113 3.000000e-01 4.000000e-01 2.000000e-01
-114 0.000000e+00 5.000000e-01 2.000000e-01
-115 1.000000e-01 5.000000e-01 2.000000e-01
-116 2.000000e-01 5.000000e-01 2.000000e-01
-117 3.000000e-01 5.000000e-01 2.000000e-01
-118 0.000000e+00 6.000000e-01 2.000000e-01
-119 1.000000e-01 6.000000e-01 2.000000e-01
-120 2.000000e-01 6.000000e-01 2.000000e-01
-121 3.000000e-01 6.000000e-01 2.000000e-01
-122 4.000000e-01 6.000000e-01 2.000000e-01
-123 0.000000e+00 7.000000e-01 2.000000e-01
-124 1.000000e-01 7.000000e-01 2.000000e-01
-125 2.000000e-01 7.000000e-01 2.000000e-01
-126 3.000000e-01 7.000000e-01 2.000000e-01
-127 4.000000e-01 7.000000e-01 2.000000e-01
-128 5.000000e-01 7.000000e-01 2.000000e-01
-129 0.000000e+00 8.000000e-01 2.000000e-01
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diff --git a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_1.msh b/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_1.msh
deleted file mode 100644
index f84f16e1603acd163f08fee4729fa54a8b08b3bd..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_1.msh
+++ /dev/null
@@ -1,1021 +0,0 @@
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diff --git a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_2.msh b/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_2.msh
deleted file mode 100644
index 679768ab2fcb3a972d537db4faaef1e4b72f02da..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partition_2.msh
+++ /dev/null
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diff --git a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_cfg3.msh b/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_cfg3.msh
deleted file mode 100644
index 738b927dacc9be7f0128819ba9d1c2dd466c5fe7..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_cfg3.msh
+++ /dev/null
@@ -1,5 +0,0 @@
-Subdomain mesh (Number of nodes; Number of base nodes; Number of regular elements; Number of ghost elements; Number of non-ghost base nodes; Number of non-ghost nodes Number of base nodes of the global mesh; Number of nodes of the global mesh; Number of integer variables to define non-ghost elements; Number of integer variables to define ghost elements.)
-3
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diff --git a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_elems_3.msh b/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_elems_3.msh
deleted file mode 100644
index f96ae7c2fdcb1346773d541a64f1acc60baa93c1..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_elems_3.msh
+++ /dev/null
@@ -1,1236 +0,0 @@
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-
diff --git a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_nodes_3.msh b/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_nodes_3.msh
deleted file mode 100644
index 7db55519567145772cb90bfa4768639afdc3cb07..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/ASCII/mesh_3d_partitioned_nodes_3.msh
+++ /dev/null
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diff --git a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh.mesh b/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh.mesh
deleted file mode 100644
index 729ff5e143a9ce27b9f2b0339f2b221542497cd7..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh.mesh
+++ /dev/null
@@ -1,1176 +0,0 @@
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diff --git a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh.vtu b/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh.vtu
deleted file mode 100644
index 132275f4f70d6be9d293dbb5463b512a58d2f2bd..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh.vtu
+++ /dev/null
@@ -1,54 +0,0 @@
-<?xml version="1.0"?>
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- <UnstructuredGrid>
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- <PointData>
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
- </DataArray>
- <DataArray type="Float64" Name="pressure" format="binary" RangeMin="0" RangeMax="11900000">
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
- </DataArray>
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diff --git a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_cfg3.msh b/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_cfg3.msh
deleted file mode 100644
index 724c04d035470070c335a968e77a1686e9c2dfe8..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_cfg3.msh
+++ /dev/null
@@ -1,5 +0,0 @@
-Subdomain mesh (Number of nodes; Number of base nodes; Number of regular elements; Number of ghost elements; Number of non-ghost base nodes; Number of non-ghost nodes Number of base nodes of the global mesh; Number of nodes of the global mesh; Number of integer variables to define non-ghost elements; Number of integer variables to define ghost elements.)
-3
-1264 1264 365 28 1192 1192 1222 3593 4015 308 0
-1283 1283 375 25 1206 1206 1222 3593 4125 275 0
-1317 1317 382 53 1195 1195 1222 3593 4077 583 0
diff --git a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_elems_3.msh b/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_elems_3.msh
deleted file mode 100644
index 0f2dbbf72834e22b41add44724a05be6eac464c1..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_elems_3.msh
+++ /dev/null
@@ -1,1231 +0,0 @@
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diff --git a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_nodes_3.msh b/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_nodes_3.msh
deleted file mode 100644
index 22eca3ec9e1c82ef9f37f9f1ee5f7876ba9496c9..0000000000000000000000000000000000000000
--- a/Tests/Data/NodePartitionedMesh/partmesh_2Dmesh_3partitions/ASCII/2Dmesh_partitioned_nodes_3.msh
+++ /dev/null
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