From cf55d15a7d954e74df146c0bed3a29ac51ef457d Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 10 May 2019 11:20:42 -0500
Subject: [PATCH 001/139] Adding to mesh abstraction to support other
 operations external to the mesh source files.

---
 include/openmc/mesh.h |  7 +++-
 src/eigenvalue.cpp    |  1 +
 src/mesh.cpp          | 85 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 92 insertions(+), 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 7dfb4c5202..af02dc8cb9 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -37,6 +37,9 @@ extern std::vector<std::unique_ptr<Mesh>> meshes;
 
 } // namespace model
 
+//==============================================================================
+//! Tessellation of n-dimensional Euclidean space by congruent squares or cubes
+//==============================================================================
 
 class Mesh
 {
@@ -209,7 +212,6 @@ public:
   void to_hdf5(hid_t group) const override;
 
   // Data members
-
   double volume_frac_; //!< Volume fraction of each mesh element
   xt::xtensor<double, 1> width_; //!< Width of each mesh element
 };
@@ -307,6 +309,9 @@ public:
 
   std::string filename_; //!< Path to unstructured mesh file
 
+  bool intersects(Position& r0, Position r1, int* ijk);
+
+
 private:
 
   //! Find all intersections with faces of the mesh.
diff --git a/src/eigenvalue.cpp b/src/eigenvalue.cpp
index f5d8a9c807..a27009c00b 100644
--- a/src/eigenvalue.cpp
+++ b/src/eigenvalue.cpp
@@ -585,6 +585,7 @@ double ufs_get_weight(const Particle& p)
   if (simulation::source_frac(mesh_bin) != 0.0) {
     return simulation::ufs_mesh->volume_frac_
       / simulation::source_frac(mesh_bin);
+    // return m->get_volume_frac() / simulation::source_frac(mesh_bin);
   } else {
     return 1.0;
   }
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 7176f84854..ea1dc3eb7e 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -5,6 +5,7 @@
 #include <cmath>  // for ceil
 #include <memory> // for allocator
 #include <string>
+#include <sstream>
 
 #ifdef OPENMC_MPI
 #include "mpi.h"
@@ -874,6 +875,79 @@ void RegularMesh::to_hdf5(hid_t group) const
   close_group(mesh_group);
 }
 
+xt::xtensor<double, 1>
+RegularMesh::count_sites(const Particle::Bank* bank,
+                         int64_t length,
+                         bool* outside) const
+{
+  // Determine shape of array for counts
+  std::size_t m = this->n_bins();
+  std::vector<std::size_t> shape = {m};
+
+  // Create array of zeros
+  xt::xarray<double> cnt {shape, 0.0};
+  bool outside_ = false;
+
+  for (int64_t i = 0; i < length; i++) {
+    const auto& site = bank[i];
+
+    // determine scoring bin for entropy mesh
+    int mesh_bin = get_bin(site.r);
+
+    // if outside mesh, skip particle
+    if (mesh_bin < 0) {
+      outside_ = true;
+      continue;
+    }
+
+    // Add to appropriate bin
+    cnt(mesh_bin) += site.wgt;
+  }
+
+  // Create copy of count data. Since ownership will be acquired by xtensor,
+  // std::allocator must be used to avoid Valgrind mismatched free() / delete
+  // warnings.
+  int total = cnt.size();
+  double* cnt_reduced = std::allocator<double>{}.allocate(total);
+
+#ifdef OPENMC_MPI
+  // collect values from all processors
+  MPI_Reduce(cnt.data(), cnt_reduced, total, MPI_DOUBLE, MPI_SUM, 0,
+    mpi::intracomm);
+
+  // Check if there were sites outside the mesh for any processor
+  if (outside) {
+    MPI_Reduce(&outside_, outside, 1, MPI_C_BOOL, MPI_LOR, 0, mpi::intracomm);
+  }
+#else
+  std::copy(cnt.data(), cnt.data() + total, cnt_reduced);
+  if (outside) *outside = outside_;
+#endif
+
+  // Adapt reduced values in array back into an xarray
+  auto arr = xt::adapt(cnt_reduced, total, xt::acquire_ownership(), shape);
+  xt::xarray<double> counts = arr;
+
+  return counts;
+}
+
+std::string RegularMesh::get_label_for_bin(int bin) const {
+  int ijk[n_dimension_];
+  get_indices_from_bin(bin, ijk);
+
+  std::stringstream out;
+  out << "Mesh Index (" << ijk[0];
+  if (n_dimension_ > 1) out << ", " << ijk[1];
+  if (n_dimension_ > 2) out << ", " << ijk[2];
+  out << ")";
+
+  return out.str();
+}
+
+double RegularMesh::get_volume_frac(int bin) const {
+  return volume_frac_;
+}
+
 //==============================================================================
 // RectilinearMesh implementation
 //==============================================================================
@@ -1938,6 +2012,17 @@ UnstructuredMesh::write(std::string base_filename) const {
     auto msg = fmt::format("Failed to write unstructured mesh {}", id_);
     warning(msg);
   }
+
+xt::xarray<double>
+UnstructuredMesh::count_sites(const std::vector<Particle::Bank>& bank,
+  bool* outside) const {
+    xt::array<double> out;
+    return out;
+  }
+
+double UnstructuredMesh::get_volume_frac(int bin = -1) const {
+  return 0.0;
+
 }
 
 #endif

From 36e99d70fb6a0d2318da327654c6aa2b0d323920 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 10 May 2019 17:56:36 -0500
Subject: [PATCH 002/139] Using a common count_sites implementation.

---
 include/openmc/mesh.h | 58 +++++++++++++++++++++++++++++++++++++++++++
 src/mesh.cpp          |  1 +
 2 files changed, 59 insertions(+)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index af02dc8cb9..d91549d63b 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -83,6 +83,12 @@ public:
   //! \param[in] group HDF5 group
   virtual void to_hdf5(hid_t group) const = 0;
 
+  //! \param[in] bank Array of bank sites
+  //! \param[out] Whether any bank sites are outside the mesh
+  //! \return Array indicating number of sites in each mesh/energy bin
+  virtual xt::xarray<double>
+  count_sites(const std::vector<Particle::Bank>& bank, bool* outside) const;
+
   //! Find the mesh lines that intersect an axis-aligned slice plot
   //
   //! \param[in] plot_ll The lower-left coordinates of the slice plot.
@@ -404,7 +410,59 @@ private:
   std::pair<moab::Tag, moab::Tag>
   get_score_tags(std::string score) const;
 
+<<<<<<< HEAD
   // data members
+=======
+public:
+
+  std::pair<std::vector<double>, std::vector<double>>
+  plot(Position plot_ll, Position plot_ur) const override;
+
+  //! Determine which surface bins were crossed by a particle.
+  //
+  //! \param[in] p Particle to check
+  //! \param[out] bins Surface bins that were crossed
+  void surface_bins_crossed(const Particle* p, std::vector<int>& bins) const;
+
+  //! Write mesh data to an HDF5 group.
+  //
+  //! \param[in] group HDF5 group
+  void to_hdf5(hid_t group) const;
+
+  //! Get bin at a given position.
+  //
+  //! \param[in] r Position to get bin for
+  //! \return Mesh bin
+  int get_bin(Position r) const;
+
+  std::string get_label_for_bin(int bin) const;
+
+  int n_bins() const override;
+
+  int n_surface_bins() const override;
+
+  Position centroid(moab::EntityHandle tet) const;
+
+  std::string bin_label(int bin) const override;
+
+  //! Add a score to the mesh instance
+  void add_score(std::string score) const;
+
+  //! Set data for a score
+  void set_score_data(const std::string& score,
+                      std::vector<double> values,
+                      std::vector<double> std_dev) const;
+
+  //! Write the mesh with any current tally data
+  void write(std::string base_filename) const;
+  std::string filename_; //<! Path to unstructured mesh file
+
+  std::string get_label_for_bin(int bin) const;
+
+  double get_volume_frac(int bin = -1) const;
+
+private:
+>>>>>>> Using a common count_sites implementation.
   moab::Range ehs_; //!< Range of tetrahedra EntityHandle's in the mesh
   moab::EntityHandle tetset_; //!< EntitySet containing all tetrahedra
   moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree
diff --git a/src/mesh.cpp b/src/mesh.cpp
index ea1dc3eb7e..856d525f10 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2021,6 +2021,7 @@ UnstructuredMesh::count_sites(const std::vector<Particle::Bank>& bank,
   }
 
 double UnstructuredMesh::get_volume_frac(int bin = -1) const {
+
   return 0.0;
 
 }

From 01f4e5674eed5107f5f61694acf559d73d95e7fc Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 28 Nov 2019 06:28:45 -0600
Subject: [PATCH 003/139] Some cleanup and removal of commented sections after
 rebase.

---
 include/openmc/mesh.h | 53 +------------------------------------------
 src/eigenvalue.cpp    |  1 -
 src/mesh.cpp          |  1 -
 3 files changed, 1 insertion(+), 54 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index d91549d63b..7149dfa095 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -318,6 +318,7 @@ public:
   bool intersects(Position& r0, Position r1, int* ijk);
 
 
+
 private:
 
   //! Find all intersections with faces of the mesh.
@@ -410,59 +411,7 @@ private:
   std::pair<moab::Tag, moab::Tag>
   get_score_tags(std::string score) const;
 
-<<<<<<< HEAD
   // data members
-=======
-public:
-
-  std::pair<std::vector<double>, std::vector<double>>
-  plot(Position plot_ll, Position plot_ur) const override;
-
-  //! Determine which surface bins were crossed by a particle.
-  //
-  //! \param[in] p Particle to check
-  //! \param[out] bins Surface bins that were crossed
-  void surface_bins_crossed(const Particle* p, std::vector<int>& bins) const;
-
-  //! Write mesh data to an HDF5 group.
-  //
-  //! \param[in] group HDF5 group
-  void to_hdf5(hid_t group) const;
-
-  //! Get bin at a given position.
-  //
-  //! \param[in] r Position to get bin for
-  //! \return Mesh bin
-  int get_bin(Position r) const;
-
-  std::string get_label_for_bin(int bin) const;
-
-  int n_bins() const override;
-
-  int n_surface_bins() const override;
-
-  Position centroid(moab::EntityHandle tet) const;
-
-  std::string bin_label(int bin) const override;
-
-  //! Add a score to the mesh instance
-  void add_score(std::string score) const;
-
-  //! Set data for a score
-  void set_score_data(const std::string& score,
-                      std::vector<double> values,
-                      std::vector<double> std_dev) const;
-
-  //! Write the mesh with any current tally data
-  void write(std::string base_filename) const;
-  std::string filename_; //<! Path to unstructured mesh file
-
-  std::string get_label_for_bin(int bin) const;
-
-  double get_volume_frac(int bin = -1) const;
-
-private:
->>>>>>> Using a common count_sites implementation.
   moab::Range ehs_; //!< Range of tetrahedra EntityHandle's in the mesh
   moab::EntityHandle tetset_; //!< EntitySet containing all tetrahedra
   moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree
diff --git a/src/eigenvalue.cpp b/src/eigenvalue.cpp
index a27009c00b..f5d8a9c807 100644
--- a/src/eigenvalue.cpp
+++ b/src/eigenvalue.cpp
@@ -585,7 +585,6 @@ double ufs_get_weight(const Particle& p)
   if (simulation::source_frac(mesh_bin) != 0.0) {
     return simulation::ufs_mesh->volume_frac_
       / simulation::source_frac(mesh_bin);
-    // return m->get_volume_frac() / simulation::source_frac(mesh_bin);
   } else {
     return 1.0;
   }
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 856d525f10..dc1f14d6e1 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -5,7 +5,6 @@
 #include <cmath>  // for ceil
 #include <memory> // for allocator
 #include <string>
-#include <sstream>
 
 #ifdef OPENMC_MPI
 #include "mpi.h"

From c95a0a914a79cd2a8aef3958e1aca5be3be788f2 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 5 Dec 2019 22:29:47 -0600
Subject: [PATCH 004/139] Start of libmesh implementation.

---
 CMakeLists.txt                  | 15 +++++++++++++++
 cmake/Modules/FindLIBMESH.cmake | 21 +++++++++++++++++++++
 include/openmc/mesh.h           | 24 ++++++++++++++++++++++++
 src/mesh.cpp                    | 31 +++++++++++++++++++++++++++++++
 4 files changed, 91 insertions(+)
 create mode 100644 cmake/Modules/FindLIBMESH.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6008b8ad1a..5f734b6d17 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -31,6 +31,7 @@ option(debug    "Compile with debug flags"                       OFF)
 option(optimize "Turn on all compiler optimization flags"        OFF)
 option(coverage "Compile with coverage analysis flags"           OFF)
 option(dagmc    "Enable support for DAGMC (CAD) geometry"        OFF)
+option(libmesh  "Enable support for libmesh unstructured mesh tallies" OFF)
 
 #===============================================================================
 # MPI for distributed-memory parallelism
@@ -65,6 +66,14 @@ if(pugixml_FOUND)
   message(STATUS "Found pugixml: ${pugixml_DIR}")
 else()
   message(STATUS "Did not find pugixml, will use submodule instead")
+#===============================================================================
+# libMesh Unstructured Mesh Support
+#===============================================================================
+if(libmesh)
+  find_package(LIBMESH REQUIRED)
+  set(LIBMESH_LIBRARIES mesh_opt)
+  set(LIBMESH_INCLUDE_DIRS "${LIBMESH}/../include/")
+  link_directories(${LIBMESH})
 endif()
 
 #===============================================================================
@@ -405,6 +414,12 @@ if(dagmc)
   target_link_libraries(libopenmc dagmc-shared uwuw-shared)
 endif()
 
+if(libmesh)
+  target_compile_definitions(libopenmc PRIVATE LIBMESH)
+  target_link_libraries(libopenmc ${LIBMESH_LIBRARIES})
+  target_include_directories(libopenmc PRIVATE ${LIBMESH_INCLUDE_DIRS})
+endif()
+
 #===============================================================================
 # openmc executable
 #===============================================================================
diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
new file mode 100644
index 0000000000..3e813e9d76
--- /dev/null
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -0,0 +1,21 @@
+# Try to find LIBMESH
+#
+# Once done this will define
+#
+#  LIBMESH_FOUND - system has LIBMESH
+#  LIBMESH_INCLUDE_DIRS - the LIBMESH include directory
+#  LIBMESH_LIBRARIES - Link these to use LIBMESH
+#  LIBMESH_DEFINITIONS - Compiler switches required for using LIBMESH
+
+find_path(LIBMESH NAMES libmesh_opt.so
+          HINTS ${LIBMESH_ROOT} $ENV{LIBMESH_ROOT}
+          PATHS ENV LD_LIBRARY_PATH
+          PATH_SUFFIXES lib Lib
+          NO_DEFAULT_PATH)
+
+if(DEFINED LIBMESH)
+  set(LIBMESH_FOUND TRUE)
+endif()
+
+
+message(STATUS "Found LIBMESH in ${LIBMESH}")
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 7149dfa095..c8641dab9f 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -22,6 +22,11 @@
 #include "moab/GeomUtil.hpp"
 #endif
 
+#ifdef LIBMESH
+#include "libmesh/libmesh.h"
+#include "libmesh/mesh.h"
+#endif
+
 namespace openmc {
 
 //==============================================================================
@@ -251,6 +256,12 @@ public:
   int set_grid();
 };
 
+class UnstructuredMeshBase : public Mesh {
+public:
+  UnstructuredMeshBase(pugi::xml_node node);
+  std::string filename_;
+};
+
 #ifdef DAGMC
 
 class UnstructuredMesh : public Mesh {
@@ -422,6 +433,19 @@ private:
 
 #endif
 
+#ifdef LIBMESH
+class LibMesh : public UnstructuredMeshBase {
+
+public:
+  // constructor
+  LibMesh(pugi::xml_node node);
+
+private:
+  std::unique_ptr<libMesh::Mesh> m_;
+  std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
+};
+#endif
+
 //==============================================================================
 // Non-member functions
 //==============================================================================
diff --git a/src/mesh.cpp b/src/mesh.cpp
index dc1f14d6e1..b26633500a 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2027,6 +2027,37 @@ double UnstructuredMesh::get_volume_frac(int bin = -1) const {
 
 #endif
 
+UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
+    // check the mesh type
+  if (check_for_node(node, "type")) {
+    auto temp = get_node_value(node, "type", true, true);
+    if (temp != "unstructured") {
+      fatal_error("Invalid mesh type: " + temp);
+    }
+  }
+
+  // get the filename of the unstructured mesh to load
+  if (check_for_node(node, "mesh_file")) {
+    filename_ = get_node_value(node, "mesh_file");
+  }
+  else {
+    fatal_error("No filename supplied for unstructured mesh with ID: " +
+                std::to_string(id_));
+  }
+}
+
+#ifdef LIBMESH
+LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+  libMesh::LibMeshInit init(0, NULL);
+
+  m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(init.comm(), 3));
+  m_->read(filename_);
+
+  point_locator_ = m_->sub_point_locator();
+  m_->find_neighbors();
+}
+#endif
+
 //==============================================================================
 // Non-member functions
 //==============================================================================

From afda211b3eac8ab405a3af65d699343b59acbd49 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 6 Dec 2019 12:35:59 -0600
Subject: [PATCH 005/139] Filling in a lot of the necessary methods for
 accumulating collision and track-length tallies.

---
 include/openmc/mesh.h |  38 +++++++
 src/mesh.cpp          | 231 +++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 268 insertions(+), 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index c8641dab9f..57d09e85e6 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -25,6 +25,8 @@
 #ifdef LIBMESH
 #include "libmesh/libmesh.h"
 #include "libmesh/mesh.h"
+#include "libmesh/point.h"
+#include "libmesh/elem.h"
 #endif
 
 namespace openmc {
@@ -436,13 +438,49 @@ private:
 #ifdef LIBMESH
 class LibMesh : public UnstructuredMeshBase {
 
+  typedef std::vector<std::pair<double, std::unique_ptr<const libMesh::Elem>>> UnstructuredMeshHits;
+
 public:
   // constructor
   LibMesh(pugi::xml_node node);
 
+  void bins_crossed(const Particle* p,
+                    std::vector<int>& bins,
+                    std::vector<double>& lengths) const;
+
+  void intersect_track(const libMesh::Point& start,
+                       const libMesh::Point& dir,
+                       double track_len,
+                       UnstructuredMeshHits& hits) const;
+
+  bool elements_share_face(const libMesh::Elem* from,
+                           const libMesh::Elem* to,
+                           unsigned int side) const;
+
+  int
+  get_bin_from_mesh_type(const libMesh::Elem* elem) const;
+
+  int get_bin(Position r) const;
+
+  // triangle intersection methods
+  bool plucker_test(std::unique_ptr<const libMesh::Elem> tri,
+                    const libMesh::Point& start,
+                    const libMesh::Point& dir,
+                    double& dist) const;
+
+  double plucker_edge_test(const libMesh::Node& vertexa,
+                           const libMesh::Node& vertexb,
+                           const libMesh::Point& ray,
+                           const libMesh::Point& ray_normal) const;
+
+  double first(const libMesh::Node& a,
+               const libMesh::Node& b) const;
+
+
 private:
   std::unique_ptr<libMesh::Mesh> m_;
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
+  libMesh::Elem* first_element_;
 };
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index b26633500a..71c9aa2149 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2055,8 +2055,237 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
 
   point_locator_ = m_->sub_point_locator();
   m_->find_neighbors();
+
+  auto e = *m_->elements_begin();
+  first_element_ = e; // FIXME
 }
-#endif
+
+void
+LibMesh::bins_crossed(const Particle* p,
+                      std::vector<int>& bins,
+                      std::vector<double>& lengths) const
+{
+  // get element containing previous position
+  libMesh::Point start(p->r_last_.x, p->r_last_.y, p->r_last_.z);
+  libMesh::Point end(p->r().x, p->r().y, p->r().z);
+  libMesh::Point dir(p->u().x, p->u().y, p->r().z);
+  dir /= dir.norm();
+
+  double track_len = (end - start).norm();
+
+  UnstructuredMeshHits hits;
+  intersect_track(start, dir, track_len, hits);
+
+  bins.clear();
+  lengths.clear();
+
+  for (const auto& hit : hits) {
+    lengths.push_back(hit.first);
+  }
+}
+
+int
+LibMesh::get_bin(Position r) const
+{
+  libMesh::Point p(r.x, r.y, r.z);
+  auto e = (*point_locator_)(p);
+  if (!e) {
+    return -1;
+  } else {
+    return get_bin_from_mesh_type(e);
+  }
+}
+
+int
+LibMesh::get_bin_from_mesh_type(const libMesh::Elem* elem) const {
+  int bin =  elem->id() - first_element_->id();
+  if (bin >= n_bins()) {
+    std::stringstream s;
+    s << "Invalid bin: " << bin;
+    fatal_error(s);
+  }
+  return bin;
+}
+
+void
+LibMesh::intersect_track(const libMesh::Point& start,
+                         const libMesh::Point& dir,
+                         double track_len,
+                         UnstructuredMeshHits& hits) const
+{
+
+  auto e = (*point_locator_)(start);
+
+  if (!e) {
+    // partcile start location is outside of the mesh,
+    // need to check for intersection
+    std::cout << "Particle outside mesh" << std::endl;
+    // don't tally for this situation (for now)
+    return;
+  }
+
+  while (true) {
+    // find the triangle intersection
+    double dist = 0.0;
+    int side = -1;
+    for (int i = 0; i < e->n_sides(); i++) {
+      auto tri = e->side_ptr(i);
+      if (tri->type() != libMesh::ElemType::TRI3) { warning("Non-triangle element found"); }
+      double temp_dist = -1.0;
+      bool hit = plucker_test(e->side_ptr(i), start, dir, temp_dist);
+      if (hit and temp_dist > 1E-14) {
+        side = i;
+        dist = temp_dist;
+      }
+    }
+
+    // make sure we found a hit for the tet we're in
+    if (side == -1) {
+      warning("No hit found"); return;
+    } else {
+      // add hit to output
+      hits.push_back(std::pair<double, std::unique_ptr<const libMesh::Elem>>(std::min(track_len, dist), e));
+      track_len -= dist; // subtract from
+    }
+
+    if (track_len < 0.0) { break; }
+
+    // get tet on the other side
+    auto next_e = e->neighbor_ptr(side);
+    if (!next_e and e->on_boundary()) {
+      // will potentially need to find the next mesh intersection here
+      break;
+    }
+
+    // check that the hit is correct
+    if (!elements_share_face(e, next_e, side)) {
+      warning("Incorrect adjacent element found");
+      // this should maybe throw an error
+      break;
+    }
+
+    // update the element we're in
+    e = next_e;
+  }
+}
+
+bool
+LibMesh::elements_share_face(const libMesh::Elem* from,
+                             const libMesh::Elem* to,
+                             unsigned int side) const
+{
+  for (auto j : to->side_index_range()) {
+    if (from->side_ptr(side)->key() == to->side_ptr(j)->key()) {
+      return true;
+    }
+  }
+  return false;
+}
+
+double
+LibMesh::first(const libMesh::Node& a,
+               const libMesh::Node& b) const
+{
+  if(a(0) < b(0)) {
+    return true;
+  } else if(a(0) == b(0)) {
+    if(a(1) < b(1)) {
+      return true;
+    } else if(a(1) == b(1)) {
+      if(a(2) < b(2)) {
+	return true;
+      } else {
+        return false;
+      }
+    } else {
+      return false;
+    }
+  } else {
+    return false;
+  }
+}
+
+
+double
+LibMesh::plucker_edge_test(const libMesh::Node& vertexa,
+                           const libMesh::Node& vertexb,
+                           const libMesh::Point& ray,
+                           const libMesh::Point& ray_normal) const
+{
+  double pip;
+  const double near_zero = 10.0 * std::numeric_limits<double>::epsilon();
+
+  if(first(vertexa, vertexb)) {
+    const libMesh::Point edge = vertexb - vertexa;
+    const libMesh::Point edge_normal = edge.cross(vertexa);
+    pip = ray * edge_normal + ray_normal * edge;
+  } else {
+    const libMesh::Point edge = vertexa-vertexb;
+    const libMesh::Point edge_normal = edge.cross(vertexb);
+    pip = ray * edge_normal + ray_normal * edge;
+    pip = -pip;
+  }
+
+  if (near_zero > fabs(pip)) pip = 0.0;
+
+  return pip;
+}
+
+bool
+LibMesh::plucker_test(std::unique_ptr<const libMesh::Elem> tri,
+                      const libMesh::Point& start,
+                      const libMesh::Point& dir,
+                      double& dist) const
+{
+  const libMesh::Point raya = dir;
+  const libMesh::Point rayb = dir.cross(start);
+
+  // get triangle vertices
+  auto node0 = tri->node_ref(0);
+  auto node1 = tri->node_ref(1);
+  auto node2 = tri->node_ref(2);
+
+  double plucker_coord0 = plucker_edge_test(node0, node1, raya, rayb);
+  double plucker_coord1 = plucker_edge_test(node1, node2, raya, rayb);
+  if( (0.0<plucker_coord0 && 0.0>plucker_coord1) || (0.0>plucker_coord0 && 0.0<plucker_coord1) ) {
+    return false;
+  }
+
+  double plucker_coord2 = plucker_edge_test(node2, node0, raya, rayb);
+  if( (0.0<plucker_coord1 && 0.0>plucker_coord2) || (0.0>plucker_coord1 && 0.0<plucker_coord2) ||
+      (0.0<plucker_coord0 && 0.0>plucker_coord2) || (0.0>plucker_coord0 && 0.0<plucker_coord2) ) {
+    return false;
+  }
+
+  // check for coplanar case to avoid dividing by zero
+  if(0.0==plucker_coord0 && 0.0==plucker_coord1 && 0.0==plucker_coord2) {
+    return false;
+  }
+
+  // get the distance to intersection
+  const double inverse_sum = 1.0/(plucker_coord0+plucker_coord1+plucker_coord2);
+  assert(0.0 != inverse_sum);
+  const libMesh::Point intersection(plucker_coord0*inverse_sum*node2+
+                                    plucker_coord1*inverse_sum*node0+
+                                    plucker_coord2*inverse_sum*node1);
+
+  // To minimize numerical error, get index of largest magnitude direction.
+  int idx = 0;
+  double max_abs_dir = 0;
+  for(unsigned int i=0; i<3; ++i) {
+    if( fabs(dir(i)) > max_abs_dir ) {
+      idx = i;
+      max_abs_dir = fabs(dir(i));
+    }
+  }
+
+  dist = (intersection(idx)-start(idx))/dir(idx);
+
+  return true;
+}
+
+
+#endif // LIBMESH
 
 //==============================================================================
 // Non-member functions

From d5e16a1ed0d9724b67553b17810d0dce74ad555f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 6 Dec 2019 17:26:11 -0600
Subject: [PATCH 006/139] Adding mesh intersection check.

---
 include/openmc/mesh.h |  15 +++++-
 src/mesh.cpp          | 122 ++++++++++++++++++++++++++++++++++++++----
 2 files changed, 124 insertions(+), 13 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 57d09e85e6..2591f7d2ce 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -448,8 +448,8 @@ public:
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const;
 
-  void intersect_track(const libMesh::Point& start,
-                       const libMesh::Point& dir,
+  void intersect_track(libMesh::Point start,
+                       libMesh::Point dir,
                        double track_len,
                        UnstructuredMeshHits& hits) const;
 
@@ -462,6 +462,15 @@ public:
 
   int get_bin(Position r) const;
 
+  std::pair<double, libMesh::Elem*>
+  locate_boundary_element(const Position& r0,
+                          const Position& r1) const;
+
+  std::pair<double, libMesh::Elem*>
+  locate_boundary_element(const libMesh::Point& start,
+                          const libMesh::Point& end) const;
+
+
   // triangle intersection methods
   bool plucker_test(std::unique_ptr<const libMesh::Elem> tri,
                     const libMesh::Point& start,
@@ -476,11 +485,13 @@ public:
   double first(const libMesh::Node& a,
                const libMesh::Node& b) const;
 
+  bool intersects(Position& r0, Position r1, int* ijk) const;
 
 private:
   std::unique_ptr<libMesh::Mesh> m_;
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
   libMesh::Elem* first_element_;
+  std::set<libMesh::Elem*> boundary_elements_;
 };
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 71c9aa2149..1c15b1e10c 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2058,6 +2058,17 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
 
   auto e = *m_->elements_begin();
   first_element_ = e; // FIXME
+
+    // determine boundary elements
+  for (int i = 0; i < m_->n_elem(); i++) {
+    auto e = m_->elem_ptr(i);
+    for (int j = 0; j < e->n_neighbors(); j++) {
+      if (!e->neighbor_ptr(j)) {
+        boundary_elements_.insert(e);
+      }
+    }
+  }
+
 }
 
 void
@@ -2081,6 +2092,7 @@ LibMesh::bins_crossed(const Particle* p,
 
   for (const auto& hit : hits) {
     lengths.push_back(hit.first);
+    bins.push_back(get_bin_from_mesh_type(hit.second.get()));
   }
 }
 
@@ -2096,6 +2108,77 @@ LibMesh::get_bin(Position r) const
   }
 }
 
+bool LibMesh::intersects(Position& r0, Position r1, int* ijk) const {
+
+  // first try to locate an element
+  // for each of the points
+  int bin {-1};
+  bin = get_bin(r0);
+  if (bin != -1) {
+    ijk[0] = bin;
+    return true;
+  }
+
+  bin = get_bin(r1);
+  if (bin != -1) {
+    ijk[0] = bin;
+    return true;
+  }
+
+  auto result = locate_boundary_element(r0, r1);
+
+  // if we don't get a hit, the track won't intersect with the mesh
+  if (result.second) {
+    ijk[0] = get_bin_from_mesh_type(result.second);
+    return false;
+  }
+
+  return false;
+}
+
+std::pair<double, libMesh::Elem*>
+LibMesh::locate_boundary_element(const Position& r0,
+                                 const Position& r1) const {
+  libMesh::Point a(r0.x, r0.y, r0.z);
+  libMesh::Point b(r1.x, r1.y, r1.z);
+
+  return locate_boundary_element(a, b);
+}
+
+std::pair<double, libMesh::Elem*>
+LibMesh::locate_boundary_element(const libMesh::Point& start,
+                                 const libMesh::Point& end) const
+{
+  // attempt to locate an intersection with the mesh boundary
+  libMesh::Point dir = (end - start).unit();
+  double length = (end - start).norm();
+
+  // locate potential elements
+  std::set<libMesh::Elem*> candidate_elements;
+  for (auto elem : boundary_elements_) {
+    // being conservative about search parameter
+    if (elem->close_to_point(start, length + elem->hmax())) {
+      candidate_elements.insert(elem);
+    }
+  }
+
+  // find nearest hit along our direction
+  typedef std::pair<double, libMesh::Elem*> RayHit;
+  RayHit result = {INFTY, nullptr};
+  for (auto elem : candidate_elements) {
+    for (int i = 0; i < elem->n_sides(); i++) {
+      double temp_dist;
+      bool hit = plucker_test(elem->side_ptr(i), start, dir, temp_dist);
+      if (hit && temp_dist > FP_COINCIDENT && temp_dist <= length) {
+        // update if we find a closer intersection
+        if (temp_dist < result.first) { result = {temp_dist, elem}; }
+      }
+    }
+  }
+
+  return result;
+}
+
 int
 LibMesh::get_bin_from_mesh_type(const libMesh::Elem* elem) const {
   int bin =  elem->id() - first_element_->id();
@@ -2108,20 +2191,27 @@ LibMesh::get_bin_from_mesh_type(const libMesh::Elem* elem) const {
 }
 
 void
-LibMesh::intersect_track(const libMesh::Point& start,
-                         const libMesh::Point& dir,
+LibMesh::intersect_track(libMesh::Point start,
+                         libMesh::Point dir,
                          double track_len,
                          UnstructuredMeshHits& hits) const
 {
 
+  double track_remaining = track_len;
+
   auto e = (*point_locator_)(start);
 
   if (!e) {
-    // partcile start location is outside of the mesh,
-    // need to check for intersection
-    std::cout << "Particle outside mesh" << std::endl;
-    // don't tally for this situation (for now)
-    return;
+    auto result = locate_boundary_element(start, start + track_len * dir);
+    if (result.second) {
+      // if an intersection is found, update the remaining track length
+      // and set the element
+      e = result.second;
+      track_remaining -= result.first;
+    } else {
+      // if there was no intersection, we're done
+      return;
+    }
   }
 
   while (true) {
@@ -2145,16 +2235,26 @@ LibMesh::intersect_track(const libMesh::Point& start,
     } else {
       // add hit to output
       hits.push_back(std::pair<double, std::unique_ptr<const libMesh::Elem>>(std::min(track_len, dist), e));
-      track_len -= dist; // subtract from
+      track_remaining -= dist; // subtract from
     }
 
-    if (track_len < 0.0) { break; }
+    if (track_remaining < 0.0) { break; }
 
     // get tet on the other side
     auto next_e = e->neighbor_ptr(side);
+
+    // if we exit the mesh, check for re-entry along
+    // the track
     if (!next_e and e->on_boundary()) {
-      // will potentially need to find the next mesh intersection here
-      break;
+      auto result = locate_boundary_element(start + dir * (track_len + TINY_BIT - track_remaining),
+                                            start + dir * track_len);
+      if (result.second) {
+        e = result.second;
+        track_remaining -= result.first;
+        continue;
+      } else {
+        return;
+      }
     }
 
     // check that the hit is correct

From 4ed227220883151d17f3de40a38868b1f31d2f31 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 6 Dec 2019 19:42:00 -0600
Subject: [PATCH 007/139] Collision-based is working. Initialization is a
 nightmare right now.

---
 include/openmc/mesh.h     | 19 +++++++++
 include/openmc/settings.h |  9 +++++
 src/initialize.cpp        | 10 +++++
 src/mesh.cpp              | 83 +++++++++++++++++++++++++++++++++++++--
 src/settings.cpp          |  4 ++
 5 files changed, 122 insertions(+), 3 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 2591f7d2ce..20831fdc81 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -462,6 +462,10 @@ public:
 
   int get_bin(Position r) const;
 
+  int n_bins() const;
+
+  void get_indices(Position r, int* ijk, bool* in_mesh) const;
+
   std::pair<double, libMesh::Elem*>
   locate_boundary_element(const Position& r0,
                           const Position& r1) const;
@@ -485,8 +489,23 @@ public:
   double first(const libMesh::Node& a,
                const libMesh::Node& b) const;
 
+  int get_bin_from_indices(const int* ijk) const;
+
+  void get_indices_from_bin(int bin, int* ijk) const;
+
+
   bool intersects(Position& r0, Position r1, int* ijk) const;
 
+  int n_surface_bins() const;
+
+  void surface_bins_crossed(const Particle* p,
+                             std::vector<int>& bins) const;
+
+  std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
+                                                           Position plot_ur) const;
+
+  void to_hdf5(hid_t group) const;
+
 private:
   std::unique_ptr<libMesh::Mesh> m_;
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index 783c221266..ce2e7b10a3 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -6,6 +6,7 @@
 
 #include <array>
 #include <cstdint>
+#include <memory>
 #include <string>
 #include <unordered_set>
 #include <vector>
@@ -14,6 +15,10 @@
 
 #include "openmc/constants.h"
 
+#ifdef LIBMESH
+#include "libmesh/libmesh.h"
+#endif
+
 namespace openmc {
 
 //==============================================================================
@@ -22,6 +27,10 @@ namespace openmc {
 
 namespace settings {
 
+#ifdef LIBMESH
+extern std::unique_ptr<libMesh::LibMeshInit> LMI;
+#endif
+
 // Boolean flags
 extern bool assume_separate;          //!< assume tallies are spatially separate?
 extern bool check_overlaps;           //!< check overlaps in geometry?
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 2c645ace1b..edc1df5001 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -3,6 +3,7 @@
 #include <cstddef>
 #include <cstdlib> // for getenv
 #include <cstring>
+#include <memory>
 #include <string>
 #include <vector>
 
@@ -32,6 +33,10 @@
 #include "openmc/thermal.h"
 #include "openmc/timer.h"
 
+#ifdef LIBMESH
+#include "libmesh/libmesh.h"
+#endif
+
 
 int openmc_init(int argc, char* argv[], const void* intracomm)
 {
@@ -50,6 +55,11 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   initialize_mpi(comm);
 #endif
 
+#ifdef LIBMESH
+  settings::LMI = std::unique_ptr<libMesh::LibMeshInit>(new libMesh::LibMeshInit(argc, argv));
+#endif
+
+
   // Parse command-line arguments
   int err = parse_command_line(argc, argv);
   if (err) return err;
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 1c15b1e10c..3446308fe6 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2048,12 +2048,16 @@ UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
 
 #ifdef LIBMESH
 LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
-  libMesh::LibMeshInit init(0, NULL);
 
-  m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(init.comm(), 3));
+  // always 3 for unstructured meshes
+  n_dimension_ = 3;
+
+  m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(settings::LMI->comm(), 3));
   m_->read(filename_);
 
   point_locator_ = m_->sub_point_locator();
+  point_locator_->enable_out_of_mesh_mode();
+
   m_->find_neighbors();
 
   auto e = *m_->elements_begin();
@@ -2071,6 +2075,49 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
 
 }
 
+void
+LibMesh::get_indices(Position r, int* ijk, bool* in_mesh) const {
+  int bin = get_bin(r);
+  *in_mesh = bin != -1;
+  ijk[0] = bin;
+  return;
+}
+
+int LibMesh::n_bins() const {
+  return m_->n_elem();
+}
+
+int LibMesh::n_surface_bins() const {
+  return 0;
+}
+
+void
+LibMesh::surface_bins_crossed(const Particle* p,
+                               std::vector<int>& bins) const
+{}
+
+std::pair<std::vector<double>, std::vector<double>>
+LibMesh::plot(Position plot_ll,
+              Position plot_ur) const { return {}; }
+
+
+int
+LibMesh::get_bin_from_indices(const int* ijk) const {
+  if (ijk[0] >= n_bins()) {
+    std::stringstream s;
+    s << "Invalid bin: " << ijk[0];
+    fatal_error(s);
+  }
+  int bin = first_element_->id() + ijk[0];
+  return bin;
+}
+
+void
+LibMesh::get_indices_from_bin(int bin, int* ijk) const {
+  ijk[0] = bin;
+}
+
+
 void
 LibMesh::bins_crossed(const Particle* p,
                       std::vector<int>& bins,
@@ -2305,6 +2352,22 @@ LibMesh::first(const libMesh::Node& a,
   }
 }
 
+void LibMesh::to_hdf5(hid_t group) const
+{
+  hid_t mesh_group = create_group(group, "mesh " + std::to_string(id_));
+
+  write_dataset(mesh_group, "type", "unstructured");
+  write_dataset(mesh_group, "filename", filename_);
+
+  // write volume of each tet
+  std::vector<double> tet_vols;
+  for (int i = 0; i < m_->n_elem(); i++) {
+    tet_vols.emplace_back(m_->elem_ref(i).volume());
+  }
+  write_dataset(mesh_group, "volumes", tet_vols);
+
+  close_group(mesh_group);
+}
 
 double
 LibMesh::plucker_edge_test(const libMesh::Node& vertexa,
@@ -2401,17 +2464,31 @@ void read_meshes(pugi::xml_node root)
       mesh_type = "regular";
     }
 
+    std::string mesh_lib;
+    if (check_for_node(node, "library")) {
+      mesh_lib = get_node_value(node, "library", true, true);
+    } else {
+      mesh_lib = "moab";
+    }
+
     // Read mesh and add to vector
     if (mesh_type == "regular") {
       model::meshes.push_back(std::make_unique<RegularMesh>(node));
     } else if (mesh_type == "rectilinear") {
       model::meshes.push_back(std::make_unique<RectilinearMesh>(node));
 #ifdef DAGMC
-    } else if (mesh_type == "unstructured") {
+    }
+    else if (mesh_type == "unstructured" && mesh_lib == "moab") {
       model::meshes.push_back(std::make_unique<UnstructuredMesh>(node));
 #else
     } else if (mesh_type == "unstructured") {
       fatal_error("Unstructured mesh support is disabled.");
+#endif
+#ifdef LIBMESH
+    }
+    else if (mesh_type == "unstructured" && mesh_lib == "libmesh") {
+      std::cout << "Making libmesh mesh" << std::endl;
+      model::meshes.push_back(std::make_unique<LibMesh>(node));
 #endif
     } else {
       fatal_error("Invalid mesh type: " + mesh_type);
diff --git a/src/settings.cpp b/src/settings.cpp
index 41b18942c3..3326f0029a 100644
--- a/src/settings.cpp
+++ b/src/settings.cpp
@@ -37,6 +37,10 @@ namespace openmc {
 
 namespace settings {
 
+#ifdef LIBMESH
+std::unique_ptr<libMesh::LibMeshInit> LMI;
+#endif
+
 // Default values for boolean flags
 bool assume_separate         {false};
 bool check_overlaps          {false};

From abf7195251d8b564864d463a3cd3301912cf0f26 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sat, 7 Dec 2019 00:01:01 -0600
Subject: [PATCH 008/139] Updates to element pointer types and updates to
 tracking for tracklength method.

---
 include/openmc/mesh.h |  6 +++---
 src/mesh.cpp          | 32 +++++++++++++++++++++-----------
 2 files changed, 24 insertions(+), 14 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 20831fdc81..7470d60527 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -438,7 +438,7 @@ private:
 #ifdef LIBMESH
 class LibMesh : public UnstructuredMeshBase {
 
-  typedef std::vector<std::pair<double, std::unique_ptr<const libMesh::Elem>>> UnstructuredMeshHits;
+  typedef std::vector<std::pair<double, const libMesh::Elem*>> UnstructuredMeshHits;
 
 public:
   // constructor
@@ -466,11 +466,11 @@ public:
 
   void get_indices(Position r, int* ijk, bool* in_mesh) const;
 
-  std::pair<double, libMesh::Elem*>
+  std::pair<double, const libMesh::Elem*>
   locate_boundary_element(const Position& r0,
                           const Position& r1) const;
 
-  std::pair<double, libMesh::Elem*>
+  std::pair<double, const libMesh::Elem*>
   locate_boundary_element(const libMesh::Point& start,
                           const libMesh::Point& end) const;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 3446308fe6..c5fb650a01 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2139,7 +2139,7 @@ LibMesh::bins_crossed(const Particle* p,
 
   for (const auto& hit : hits) {
     lengths.push_back(hit.first);
-    bins.push_back(get_bin_from_mesh_type(hit.second.get()));
+    bins.push_back(get_bin_from_mesh_type(hit.second));
   }
 }
 
@@ -2183,7 +2183,7 @@ bool LibMesh::intersects(Position& r0, Position r1, int* ijk) const {
   return false;
 }
 
-std::pair<double, libMesh::Elem*>
+std::pair<double, const libMesh::Elem*>
 LibMesh::locate_boundary_element(const Position& r0,
                                  const Position& r1) const {
   libMesh::Point a(r0.x, r0.y, r0.z);
@@ -2192,7 +2192,7 @@ LibMesh::locate_boundary_element(const Position& r0,
   return locate_boundary_element(a, b);
 }
 
-std::pair<double, libMesh::Elem*>
+std::pair<double, const libMesh::Elem*>
 LibMesh::locate_boundary_element(const libMesh::Point& start,
                                  const libMesh::Point& end) const
 {
@@ -2201,7 +2201,7 @@ LibMesh::locate_boundary_element(const libMesh::Point& start,
   double length = (end - start).norm();
 
   // locate potential elements
-  std::set<libMesh::Elem*> candidate_elements;
+  std::set<const libMesh::Elem*> candidate_elements;
   for (auto elem : boundary_elements_) {
     // being conservative about search parameter
     if (elem->close_to_point(start, length + elem->hmax())) {
@@ -2210,7 +2210,7 @@ LibMesh::locate_boundary_element(const libMesh::Point& start,
   }
 
   // find nearest hit along our direction
-  typedef std::pair<double, libMesh::Elem*> RayHit;
+  typedef std::pair<double, const libMesh::Elem*> RayHit;
   RayHit result = {INFTY, nullptr};
   for (auto elem : candidate_elements) {
     for (int i = 0; i < elem->n_sides(); i++) {
@@ -2255,6 +2255,8 @@ LibMesh::intersect_track(libMesh::Point start,
       // and set the element
       e = result.second;
       track_remaining -= result.first;
+      // advance position along track
+      start += dir * result.first;
     } else {
       // if there was no intersection, we're done
       return;
@@ -2262,7 +2264,7 @@ LibMesh::intersect_track(libMesh::Point start,
   }
 
   while (true) {
-    // find the triangle intersection
+    // find the positive distance triangle intersection
     double dist = 0.0;
     int side = -1;
     for (int i = 0; i < e->n_sides(); i++) {
@@ -2278,14 +2280,19 @@ LibMesh::intersect_track(libMesh::Point start,
 
     // make sure we found a hit for the tet we're in
     if (side == -1) {
-      warning("No hit found"); return;
+      start += dir * TINY_BIT; // nudge particle forward
+      e = (*point_locator_)(start);
+      continue;
     } else {
       // add hit to output
-      hits.push_back(std::pair<double, std::unique_ptr<const libMesh::Elem>>(std::min(track_len, dist), e));
+      hits.push_back(std::pair<double, const libMesh::Elem*>(std::min(track_remaining, dist), e));
       track_remaining -= dist; // subtract from
+      // advance position along track
+      start += dir * dist;
     }
 
-    if (track_remaining < 0.0) { break; }
+    // if we've reached the end of the track, break
+    if (track_remaining <= 0.0) { break; }
 
     // get tet on the other side
     auto next_e = e->neighbor_ptr(side);
@@ -2293,13 +2300,16 @@ LibMesh::intersect_track(libMesh::Point start,
     // if we exit the mesh, check for re-entry along
     // the track
     if (!next_e and e->on_boundary()) {
-      auto result = locate_boundary_element(start + dir * (track_len + TINY_BIT - track_remaining),
-                                            start + dir * track_len);
+      auto result = locate_boundary_element(start + dir * TINY_BIT,
+                                            start + dir * track_remaining);
       if (result.second) {
         e = result.second;
         track_remaining -= result.first;
+        // advance position along track
+        start += dir * result.first;
         continue;
       } else {
+        // if no next intersection with the mesh, we're done
         return;
       }
     }

From 941a068ed67285ef0bd55ab753c601051a3d85cd Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 9 Dec 2019 18:53:55 -0600
Subject: [PATCH 009/139] Updates to libmesh init and track length estimator

---
 include/openmc/settings.h | 2 +-
 src/finalize.cpp          | 4 ++++
 src/initialize.cpp        | 2 +-
 src/mesh.cpp              | 9 ++++++++-
 src/settings.cpp          | 2 +-
 5 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index ce2e7b10a3..b6044cf483 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -28,7 +28,7 @@ namespace openmc {
 namespace settings {
 
 #ifdef LIBMESH
-extern std::unique_ptr<libMesh::LibMeshInit> LMI;
+extern libMesh::LibMeshInit* LMI;
 #endif
 
 // Boolean flags
diff --git a/src/finalize.cpp b/src/finalize.cpp
index 7b28b5a7c2..f42e3134af 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -142,6 +142,10 @@ int openmc_finalize()
   if (mpi::bank != MPI_DATATYPE_NULL) MPI_Type_free(&mpi::bank);
 #endif
 
+#ifdef LIBMESH
+  delete settings::LMI;
+#endif
+
   return 0;
 }
 
diff --git a/src/initialize.cpp b/src/initialize.cpp
index edc1df5001..d5040976e2 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -56,7 +56,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 #ifdef LIBMESH
-  settings::LMI = std::unique_ptr<libMesh::LibMeshInit>(new libMesh::LibMeshInit(argc, argv));
+  settings::LMI = new libMesh::LibMeshInit(argc, argv);
 #endif
 
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index c5fb650a01..e65cd912de 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2280,8 +2280,15 @@ LibMesh::intersect_track(libMesh::Point start,
 
     // make sure we found a hit for the tet we're in
     if (side == -1) {
+      auto orig_e = e;
       start += dir * TINY_BIT; // nudge particle forward
       e = (*point_locator_)(start);
+      if (!e) {
+        if (!orig_e->on_boundary()) {
+          std::cout << "May have incorrectly truncated a track." << std::endl;
+        }
+        return;
+      }
       continue;
     } else {
       // add hit to output
@@ -2299,7 +2306,7 @@ LibMesh::intersect_track(libMesh::Point start,
 
     // if we exit the mesh, check for re-entry along
     // the track
-    if (!next_e and e->on_boundary()) {
+    if (!next_e) {
       auto result = locate_boundary_element(start + dir * TINY_BIT,
                                             start + dir * track_remaining);
       if (result.second) {
diff --git a/src/settings.cpp b/src/settings.cpp
index 3326f0029a..21ce932ad5 100644
--- a/src/settings.cpp
+++ b/src/settings.cpp
@@ -38,7 +38,7 @@ namespace openmc {
 namespace settings {
 
 #ifdef LIBMESH
-std::unique_ptr<libMesh::LibMeshInit> LMI;
+libMesh::LibMeshInit* LMI;
 #endif
 
 // Default values for boolean flags

From 7e6b23752edf73d418d8735f97d5313464c3684a Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 17 Dec 2019 15:36:57 -0600
Subject: [PATCH 010/139] some updates to the build and track length routine.

---
 CMakeLists.txt |  2 +-
 src/mesh.cpp   | 22 ++++++++++++++--------
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5f734b6d17..29d16bc080 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -71,7 +71,7 @@ else()
 #===============================================================================
 if(libmesh)
   find_package(LIBMESH REQUIRED)
-  set(LIBMESH_LIBRARIES mesh_opt)
+  set(LIBMESH_LIBRARIES mesh_opt timpi_opt netcdf)
   set(LIBMESH_INCLUDE_DIRS "${LIBMESH}/../include/")
   link_directories(${LIBMESH})
 endif()
diff --git a/src/mesh.cpp b/src/mesh.cpp
index e65cd912de..edf0529e72 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2138,7 +2138,7 @@ LibMesh::bins_crossed(const Particle* p,
   lengths.clear();
 
   for (const auto& hit : hits) {
-    lengths.push_back(hit.first);
+    lengths.push_back(hit.first / track_len);
     bins.push_back(get_bin_from_mesh_type(hit.second));
   }
 }
@@ -2203,7 +2203,7 @@ LibMesh::locate_boundary_element(const libMesh::Point& start,
   // locate potential elements
   std::set<const libMesh::Elem*> candidate_elements;
   for (auto elem : boundary_elements_) {
-    // being conservative about search parameter
+    // being conservative about search parameter by adding element hmax
     if (elem->close_to_point(start, length + elem->hmax())) {
       candidate_elements.insert(elem);
     }
@@ -2214,7 +2214,7 @@ LibMesh::locate_boundary_element(const libMesh::Point& start,
   RayHit result = {INFTY, nullptr};
   for (auto elem : candidate_elements) {
     for (int i = 0; i < elem->n_sides(); i++) {
-      double temp_dist;
+      double temp_dist = 0;
       bool hit = plucker_test(elem->side_ptr(i), start, dir, temp_dist);
       if (hit && temp_dist > FP_COINCIDENT && temp_dist <= length) {
         // update if we find a closer intersection
@@ -2272,7 +2272,7 @@ LibMesh::intersect_track(libMesh::Point start,
       if (tri->type() != libMesh::ElemType::TRI3) { warning("Non-triangle element found"); }
       double temp_dist = -1.0;
       bool hit = plucker_test(e->side_ptr(i), start, dir, temp_dist);
-      if (hit and temp_dist > 1E-14) {
+      if (hit and temp_dist > FP_COINCIDENT) {
         side = i;
         dist = temp_dist;
       }
@@ -2293,9 +2293,9 @@ LibMesh::intersect_track(libMesh::Point start,
     } else {
       // add hit to output
       hits.push_back(std::pair<double, const libMesh::Elem*>(std::min(track_remaining, dist), e));
-      track_remaining -= dist; // subtract from
       // advance position along track
-      start += dir * dist;
+      start += dir * std::min(track_remaining, dist);
+      track_remaining -= dist; // subtract from
     }
 
     // if we've reached the end of the track, break
@@ -2307,7 +2307,7 @@ LibMesh::intersect_track(libMesh::Point start,
     // if we exit the mesh, check for re-entry along
     // the track
     if (!next_e) {
-      auto result = locate_boundary_element(start + dir * TINY_BIT,
+      auto result = locate_boundary_element(start,
                                             start + dir * track_remaining);
       if (result.second) {
         e = result.second;
@@ -2321,6 +2321,8 @@ LibMesh::intersect_track(libMesh::Point start,
       }
     }
 
+    if (track_remaining <= 0.0) { break; }
+
     // check that the hit is correct
     if (!elements_share_face(e, next_e, side)) {
       warning("Incorrect adjacent element found");
@@ -2459,7 +2461,11 @@ LibMesh::plucker_test(std::unique_ptr<const libMesh::Elem> tri,
     }
   }
 
-  dist = (intersection(idx)-start(idx))/dir(idx);
+  // no negative distances
+  const double temp_dist = (intersection(idx) - start(idx)) / dir(idx);
+  if ( temp_dist < 0 ) { return false; }
+
+  dist = (intersection - start).norm();
 
   return true;
 }

From 781bc8adf57faf2ae94c8a5c415dba01e0e3c2c7 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 17 Dec 2019 20:44:56 -0600
Subject: [PATCH 011/139] Adding equation system to mesh.

---
 include/openmc/mesh.h | 5 ++++-
 src/mesh.cpp          | 7 +++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 7470d60527..417097df74 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -24,9 +24,10 @@
 
 #ifdef LIBMESH
 #include "libmesh/libmesh.h"
+#include "libmesh/elem.h"
+#include "libmesh/equation_systems.h"
 #include "libmesh/mesh.h"
 #include "libmesh/point.h"
-#include "libmesh/elem.h"
 #endif
 
 namespace openmc {
@@ -509,6 +510,8 @@ public:
 private:
   std::unique_ptr<libMesh::Mesh> m_;
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
+  std::unique_ptr<libMesh::EquationSystems> equation_systems_;
+  std::string eq_system_name_;
   libMesh::Elem* first_element_;
   std::set<libMesh::Elem*> boundary_elements_;
 };
diff --git a/src/mesh.cpp b/src/mesh.cpp
index edf0529e72..454cc9188b 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2054,6 +2054,13 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
 
   m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(settings::LMI->comm(), 3));
   m_->read(filename_);
+  m_->prepare_for_use();
+
+  eq_system_name_ = "mesh_" + std::to_string(id_) + "_system";
+
+  equation_systems_ =
+    std::unique_ptr<libMesh::EquationSystems>(new libMesh::EquationSystems(*m_));
+  auto& eq_sys = equation_systems_->add_system(eq_system_name_);
 
   point_locator_ = m_->sub_point_locator();
   point_locator_->enable_out_of_mesh_mode();

From 2226dc9f6e096af6a79c9216c182f81782c4b9f8 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 18 Dec 2019 16:17:51 -0600
Subject: [PATCH 012/139] Adding more attributes and methods necessary for
 writing out an exodus mesh with data.

---
 include/openmc/mesh.h | 12 ++++++++++++
 src/mesh.cpp          | 45 ++++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 56 insertions(+), 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 417097df74..f42a19490f 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -26,6 +26,9 @@
 #include "libmesh/libmesh.h"
 #include "libmesh/elem.h"
 #include "libmesh/equation_systems.h"
+#include "libmesh/exodusII_io.h"
+#include "libmesh/explicit_system.h"
+#include "libmesh/dof_map.h"
 #include "libmesh/mesh.h"
 #include "libmesh/point.h"
 #endif
@@ -494,6 +497,7 @@ public:
 
   void get_indices_from_bin(int bin, int* ijk) const;
 
+  libMesh::Elem* get_element_from_bin(int bin) const;
 
   bool intersects(Position& r0, Position r1, int* ijk) const;
 
@@ -505,12 +509,20 @@ public:
   std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
                                                            Position plot_ur) const;
 
+  void add_variable(const std::string& var_name);
+
+  void set_variable(const std::string& var_name, int bin, double value);
+
+  void write() const;
+
   void to_hdf5(hid_t group) const;
 
 private:
   std::unique_ptr<libMesh::Mesh> m_;
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
   std::unique_ptr<libMesh::EquationSystems> equation_systems_;
+  std::unique_ptr<libMesh::NumericVector<libMesh::Real>> sol_;
+  std::map<std::string, unsigned int> variable_map_;
   std::string eq_system_name_;
   libMesh::Elem* first_element_;
   std::set<libMesh::Elem*> boundary_elements_;
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 454cc9188b..849f264dbc 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -5,6 +5,7 @@
 #include <cmath>  // for ceil
 #include <memory> // for allocator
 #include <string>
+#include <gsl/gsl>
 
 #ifdef OPENMC_MPI
 #include "mpi.h"
@@ -2060,7 +2061,10 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
 
   equation_systems_ =
     std::unique_ptr<libMesh::EquationSystems>(new libMesh::EquationSystems(*m_));
-  auto& eq_sys = equation_systems_->add_system(eq_system_name_);
+  libMesh::ExplicitSystem& eq_sys =
+    equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
+
+  sol_ = eq_sys.current_local_solution->clone();
 
   point_locator_ = m_->sub_point_locator();
   point_locator_->enable_out_of_mesh_mode();
@@ -2124,6 +2128,45 @@ LibMesh::get_indices_from_bin(int bin, int* ijk) const {
   ijk[0] = bin;
 }
 
+libMesh::Elem*
+LibMesh::get_element_from_bin(int bin) const {
+  m_->elem_ptr(bin);
+}
+
+void
+LibMesh::add_variable(const std::string& var_name) {
+  // check if this is a new varaible
+  if (!variable_map_.count(var_name)) {
+    auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
+    auto var_num = eqn_sys.add_variable(var_name, libMesh::CONSTANT, libMesh::MONOMIAL);
+    variable_map_[var_name] = var_num;
+    equation_systems_->init();
+  }
+}
+
+void
+LibMesh::set_variable(const std::string& var_name, int bin, double value) {
+  // look up the variable
+  unsigned int var_num = variable_map_.at(var_name);
+  auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
+  const libMesh::DofMap& dof_map = eqn_sys.get_dof_map();
+  auto e = m_->elem_ptr(bin);
+  std::vector<libMesh::dof_id_type> dof_indices;
+  dof_map.dof_indices(e, dof_indices, var_num);
+  Ensures(dof_indices.size() == 1);
+  (*sol_).set(dof_indices[0], value);
+}
+
+void LibMesh::write() const {
+  // update solution
+  auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
+  *eqn_sys.solution = *sol_;
+
+  libMesh::ExodusII_IO exo(*m_);
+  std::set<std::string> systems_out = {eq_system_name_};
+  exo.write_discontinuous_exodusII("test.exo", *equation_systems_, &systems_out);
+}
+
 
 void
 LibMesh::bins_crossed(const Particle* p,

From 2410a4dfbad72470c90bd8d0e6e92f01e774592b Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 18 Dec 2019 17:16:20 -0600
Subject: [PATCH 013/139] Updating how the libmesh solution set is handled.
 Also applying data for a single score, single nuclide tally.

---
 include/openmc/mesh.h |  1 -
 src/mesh.cpp          | 12 ++++--------
 2 files changed, 4 insertions(+), 9 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index f42a19490f..7c4a2dbdd0 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -521,7 +521,6 @@ private:
   std::unique_ptr<libMesh::Mesh> m_;
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
   std::unique_ptr<libMesh::EquationSystems> equation_systems_;
-  std::unique_ptr<libMesh::NumericVector<libMesh::Real>> sol_;
   std::map<std::string, unsigned int> variable_map_;
   std::string eq_system_name_;
   libMesh::Elem* first_element_;
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 849f264dbc..26e8ed1f28 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2064,8 +2064,6 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
-  sol_ = eq_sys.current_local_solution->clone();
-
   point_locator_ = m_->sub_point_locator();
   point_locator_->enable_out_of_mesh_mode();
 
@@ -2154,17 +2152,15 @@ LibMesh::set_variable(const std::string& var_name, int bin, double value) {
   std::vector<libMesh::dof_id_type> dof_indices;
   dof_map.dof_indices(e, dof_indices, var_num);
   Ensures(dof_indices.size() == 1);
-  (*sol_).set(dof_indices[0], value);
+  (*eqn_sys.solution).set(dof_indices[0], value);
 }
 
 void LibMesh::write() const {
-  // update solution
-  auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
-  *eqn_sys.solution = *sol_;
-
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
-  exo.write_discontinuous_exodusII("test.exo", *equation_systems_, &systems_out);
+  std::stringstream output_filename;
+  output_filename << "umesh_" << id_ << ".exo";
+  exo.write_discontinuous_exodusII(output_filename.str(), *equation_systems_, &systems_out);
 }
 
 

From 87f9a495ef12d911d1ada29dcc1953f5fffb7028 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 18 Dec 2019 18:25:00 -0600
Subject: [PATCH 014/139] Write files using a tally-based filename to support
 multiple tallies at once.

---
 include/openmc/mesh.h | 2 +-
 src/mesh.cpp          | 6 ++----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 7c4a2dbdd0..647ed23413 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -513,7 +513,7 @@ public:
 
   void set_variable(const std::string& var_name, int bin, double value);
 
-  void write() const;
+  void write(const std::string& filename) const;
 
   void to_hdf5(hid_t group) const;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 26e8ed1f28..19b27e1cf4 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2155,12 +2155,10 @@ LibMesh::set_variable(const std::string& var_name, int bin, double value) {
   (*eqn_sys.solution).set(dof_indices[0], value);
 }
 
-void LibMesh::write() const {
+void LibMesh::write(const std::string& filename) const {
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
-  std::stringstream output_filename;
-  output_filename << "umesh_" << id_ << ".exo";
-  exo.write_discontinuous_exodusII(output_filename.str(), *equation_systems_, &systems_out);
+  exo.write_discontinuous_exodusII(filename, *equation_systems_, &systems_out);
 }
 
 

From 64cae08c43eb709029a2a147ed9c4832d450f552 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 9 Jan 2020 21:43:05 -0600
Subject: [PATCH 015/139] A lot of updates to the LibMesh implementation.

---
 include/openmc/bounding_box.h |  76 +++++++
 include/openmc/mesh.h         |  10 +
 include/openmc/surface.h      |  49 +----
 openmc/mesh.py                |  31 ++-
 src/mesh.cpp                  | 374 +++++++++++++++++++++++++++++-----
 src/tallies/filter_mesh.cpp   |   6 +
 6 files changed, 447 insertions(+), 99 deletions(-)
 create mode 100644 include/openmc/bounding_box.h

diff --git a/include/openmc/bounding_box.h b/include/openmc/bounding_box.h
new file mode 100644
index 0000000000..a1d16cac53
--- /dev/null
+++ b/include/openmc/bounding_box.h
@@ -0,0 +1,76 @@
+#ifndef OPENMC_BOUNDING_BOX_H
+#define OPENMC_BOUNDING_BOX_H
+
+#include "openmc/constants.h"
+#include "openmc/position.h"
+
+namespace openmc {
+
+//==============================================================================
+//! Coordinates for an axis-aligned cuboid bounds a geometric object.
+//==============================================================================
+
+struct BoundingBox
+{
+  double xmin = -INFTY;
+  double xmax = INFTY;
+  double ymin = -INFTY;
+  double ymax = INFTY;
+  double zmin = -INFTY;
+  double zmax = INFTY;
+
+
+  inline BoundingBox operator &(const BoundingBox& other) {
+    BoundingBox result = *this;
+    return result &= other;
+  }
+
+  inline BoundingBox operator |(const BoundingBox& other) {
+    BoundingBox result = *this;
+    return result |= other;
+  }
+
+  // intersect operator
+  inline BoundingBox& operator &=(const BoundingBox& other) {
+    xmin = std::max(xmin, other.xmin);
+    xmax = std::min(xmax, other.xmax);
+    ymin = std::max(ymin, other.ymin);
+    ymax = std::min(ymax, other.ymax);
+    zmin = std::max(zmin, other.zmin);
+    zmax = std::min(zmax, other.zmax);
+    return *this;
+  }
+
+  // union operator
+  inline BoundingBox& operator |=(const BoundingBox& other) {
+    xmin = std::min(xmin, other.xmin);
+    xmax = std::max(xmax, other.xmax);
+    ymin = std::min(ymin, other.ymin);
+    ymax = std::max(ymax, other.ymax);
+    zmin = std::min(zmin, other.zmin);
+    zmax = std::max(zmax, other.zmax);
+    return *this;
+  }
+
+  // ensure bounding box contains a point
+  inline void update(const Position& r) {
+    xmin = std::min(xmin, r.x);
+    xmax = std::max(xmax, r.x);
+    ymin = std::min(ymin, r.y);
+    ymax = std::max(ymax, r.y);
+    zmin = std::min(zmin, r.z);
+    zmax = std::max(zmax, r.z);
+  }
+
+  // check if a point is in the box
+  inline bool contains(const Position& r) const {
+    if (r.x < xmin || r.x > xmax) { return false; }
+    if (r.y < ymin || r.y > ymax) { return false; }
+    if (r.z < zmin || r.z > zmax) { return false; }
+    return true;
+  }
+
+};
+
+} // namespace openmc
+#endif // OPENMC_BOUNDING_BOX_H
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 647ed23413..bde0736539 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -12,6 +12,7 @@
 #include "pugixml.hpp"
 #include "xtensor/xtensor.hpp"
 
+#include "openmc/bounding_box.h"
 #include "openmc/particle.h"
 #include "openmc/position.h"
 
@@ -452,6 +453,14 @@ public:
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const;
 
+  bool inside_tet(const libMesh::Point& r,
+                  const libMesh::Point& u,
+                  const libMesh::Elem* e) const;
+
+  bool inside_tet(const libMesh::Point& r,
+                  const libMesh::Point& u,
+                  std::unique_ptr<libMesh::Elem> e) const;
+
   void intersect_track(libMesh::Point start,
                        libMesh::Point dir,
                        double track_len,
@@ -522,6 +531,7 @@ private:
   std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
   std::unique_ptr<libMesh::EquationSystems> equation_systems_;
   std::map<std::string, unsigned int> variable_map_;
+  BoundingBox bbox_;
   std::string eq_system_name_;
   libMesh::Elem* first_element_;
   std::set<libMesh::Elem*> boundary_elements_;
diff --git a/include/openmc/surface.h b/include/openmc/surface.h
index 0209b23246..c2f461030e 100644
--- a/include/openmc/surface.h
+++ b/include/openmc/surface.h
@@ -11,6 +11,7 @@
 #include "pugixml.hpp"
 
 #include "openmc/boundary_condition.h"
+#include "openmc/bounding_box.h"
 #include "openmc/constants.h"
 #include "openmc/particle.h"
 #include "openmc/position.h"
@@ -29,54 +30,6 @@ namespace model {
   extern std::vector<std::unique_ptr<Surface>> surfaces;
 } // namespace model
 
-//==============================================================================
-//! Coordinates for an axis-aligned cube that bounds a geometric object.
-//==============================================================================
-
-struct BoundingBox
-{
-  double xmin = -INFTY;
-  double xmax = INFTY;
-  double ymin = -INFTY;
-  double ymax = INFTY;
-  double zmin = -INFTY;
-  double zmax = INFTY;
-
-
-  inline BoundingBox operator &(const BoundingBox& other) {
-    BoundingBox result = *this;
-    return result &= other;
-  }
-
-  inline BoundingBox operator |(const BoundingBox& other) {
-    BoundingBox result = *this;
-    return result |= other;
-  }
-
-  // intersect operator
-  inline BoundingBox& operator &=(const BoundingBox& other) {
-    xmin = std::max(xmin, other.xmin);
-    xmax = std::min(xmax, other.xmax);
-    ymin = std::max(ymin, other.ymin);
-    ymax = std::min(ymax, other.ymax);
-    zmin = std::max(zmin, other.zmin);
-    zmax = std::min(zmax, other.zmax);
-    return *this;
-  }
-
-  // union operator
-  inline BoundingBox& operator |=(const BoundingBox& other) {
-    xmin = std::min(xmin, other.xmin);
-    xmax = std::max(xmax, other.xmax);
-    ymin = std::min(ymin, other.ymin);
-    ymax = std::max(ymax, other.ymax);
-    zmin = std::min(zmin, other.zmin);
-    zmax = std::max(zmax, other.zmax);
-    return *this;
-  }
-
-};
-
 //==============================================================================
 //! A geometry primitive used to define regions of 3D space.
 //==============================================================================
diff --git a/openmc/mesh.py b/openmc/mesh.py
index 8f6cccb808..534a96e9d6 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -605,6 +605,8 @@ class UnstructuredMesh(MeshBase):
         Unique identifier for the mesh
     name : str
         Name of the mesh
+    size : int
+        Number of elements in the unstructured mesh
 
     Attributes
     ----------
@@ -614,6 +616,8 @@ class UnstructuredMesh(MeshBase):
         Name of the mesh
     filename : str
         Name of the file containing the unstructured mesh
+    mesh_lib : str
+        Library used for the unstructured mesh tally
     volumes : Iterable of float
         Volumes of the unstructured mesh elements
     total_volume : float
@@ -628,6 +632,7 @@ class UnstructuredMesh(MeshBase):
         self.filename = filename
         self._volumes = None
         self._centroids = None
+        self._mesh_lib = 'moab'
 
     @property
     def filename(self):
@@ -638,6 +643,24 @@ class UnstructuredMesh(MeshBase):
         cv.check_type('Unstructured Mesh filename', filename, str)
         self._filename = filename
 
+    @property
+    def mesh_lib(self):
+        return self._mesh_lib
+
+    @mesh_lib.setter
+    def mesh_lib(self, mesh_lib):
+        cv.check_value('mesh_lib', mesh_lib, ('moab', 'libmesh'))
+        self._mesh_lib = mesh_lib
+
+    @property
+    def size(self):
+        return self._size
+
+    @size.setter
+    def size(self, size):
+        cv.check_type("Unstructured mesh size", size, Integral)
+        self._size = size
+
     @property
     def volumes(self):
         return self._volumes
@@ -670,7 +693,9 @@ class UnstructuredMesh(MeshBase):
 
     def __repr__(self):
         string = super().__repr__()
-        return string + '{: <16}=\t{}\n'.format('\tFilename', self.filename)
+        string += '{: <16}=\t{}\n'.format('\tFilename', self.filename)
+        string += '{0: <16}=\t{}\n'.format('\tMesh Library', self.mesh_lib)
+        return string
 
     def write_data_to_vtk(self, filename, datasets, volume_normalization=True):
         """Map data to the unstructured mesh element centroids
@@ -767,6 +792,8 @@ class UnstructuredMesh(MeshBase):
         centroids = group['centroids'][()]
         mesh.volumes = np.reshape(vol_data, (vol_data.shape[0],))
         mesh.centroids = np.reshape(centroids, (vol_data.shape[0], 3))
+        mesh.mesh_lib = group['library'][()].decode()
+        mesh.size = mesh.volumes.size
 
         return mesh
 
@@ -783,8 +810,8 @@ class UnstructuredMesh(MeshBase):
         element = ET.Element("mesh")
         element.set("id", str(self._id))
         element.set("type", "unstructured")
-
         subelement = ET.SubElement(element, "filename")
+        element.set("library", self._mesh_lib)
         subelement.text = self.filename
 
         return element
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 19b27e1cf4..22be31f814 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1568,6 +1568,18 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start,
 
   // sorts by first component of std::pair by default
   std::sort(hits.begin(), hits.end());
+
+  for (const auto& hit  : hits) {
+
+  }
+
+  for (const auto& hit : hits) {
+    moab::CartVect hit_loc = start + dir * hit.first;
+    // std::cout << "Hit location: (" << hit_loc[0] << ", "
+    //           << hit_loc[1] << ", " << hit_loc[2] << ")" << std::endl;
+    // std::cout << "Distance: " << hit.first << std::endl;
+  }
+
 }
 
 void
@@ -1594,37 +1606,34 @@ UnstructuredMesh::bins_crossed(const Particle& p,
   bins.clear();
   lengths.clear();
 
-  // if there are no intersections the track may lie entirely
-  // within a single tet. If this is the case, apply entire
-  // score to that tet and return.
   if (hits.size() == 0) {
-    Position midpoint = last_r + u * (track_len * 0.5);
-    int bin = this->get_bin(midpoint);
-    if (bin != -1) {
-      bins.push_back(bin);
+    auto last_r_tet = get_tet(last_r + u * track_len * 0.5);
+    if (last_r_tet) {
+      bins.push_back(get_bin_from_ent_handle(last_r_tet));
       lengths.push_back(1.0);
     }
     return;
   }
 
-  // for each segment in the set of tracks, try to look up a tet
-  // at the midpoint of the segment
-  Position current = last_r;
   double last_dist = 0.0;
-  for (const auto& hit : hits) {
-    // get the segment length
-    double segment_length = hit - last_dist;
-    last_dist = hit;
-    // find the midpoint of this segment
-    Position midpoint = current + u * (segment_length * 0.5);
-    // try to find a tet for this position
-    int bin = this->get_bin(midpoint);
+  /// IMPLEMENTATION THREE
+  for (auto hit = hits.begin(); hit != hits.end(); hit++) {
 
-    // determine the start point for this segment
-    current = last_r + u * hit;
+    // mid point for this segment
+    double segment_length = hit->first - last_dist;
+    Position segment_midpoint = last_r + u * last_dist + u * segment_length / 2.0;
+    if (segment_length < 1E-08) { continue; }
 
-    if (bin == -1) {
-      continue;
+    last_dist = hit->first;
+
+    // try to get a tet at the midpoint of this segment
+    auto tet = get_tet(segment_midpoint);
+    if (tet) {
+      bins.push_back(get_bin_from_ent_handle(tet));
+      lengths.push_back((hit.first - last_dist) / track_len);
+    } else {
+      // if in the loop, we should always find a tet
+      warning("No tet found for location between trianle hits");
     }
 
     bins.push_back(bin);
@@ -1633,19 +1642,147 @@ UnstructuredMesh::bins_crossed(const Particle& p,
   }
 
   // tally remaining portion of track after last hit if
-  // the last segment of the track is in the mesh but doesn't
-  // reach the other side of the tet
-  if (hits.back() < track_len) {
-    Position segment_start = last_r + u * hits.back();
-    double segment_length = track_len - hits.back();
-    Position midpoint = segment_start + u * (segment_length * 0.5);
-    int bin = this->get_bin(midpoint);
-    if (bin != -1) {
-      bins.push_back(bin);
-      lengths.push_back(segment_length / track_len);
+  // the last segment of the track is in the mesh
+  if (hits.back().first < track_len) {
+    auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0);
+    auto tet = get_tet(pos);
+    if (tet) {
+      bins.push_back(get_bin_from_ent_handle(tet));
+      lengths.push_back((track_len - hits.back().first) / track_len);
     }
   }
-};
+
+  return;
+}
+
+//   //// IMPLEMENTATION ONE
+//   if (hits.size() == 0) {
+//     moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5);
+//     if (last_r_tet) {
+//       bins.push_back(get_bin_from_ent_handle(last_r_tet));
+//       lengths.push_back(1.0);
+//     }
+//     return;
+//   }
+
+//   moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0);
+//   double last_dist = 0.0;
+
+//   // make sure first point is inside a tet
+//   if (!tet) {
+//     last_dist = hits.front().first;
+//     hits.erase(hits.begin());
+//     tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0);
+//   }
+
+//   if (!tet) { fatal_error("Should in in a tet now."); }
+
+//   // if there are no other hits, there is only one segment to tally
+//   if (hits.size() == 0 && tet) {
+//     bins.push_back(get_bin_from_ent_handle(tet));
+//     lengths.push_back(1.0);
+//     return;
+//   }
+
+//   // score all remaining segments
+// for (auto hit = hits.begin(); hit != hits.end(); hit++) {
+//     // score in this tet if one was found
+//     if (tet) {
+//       bins.push_back(get_bin_from_ent_handle(tet));
+//       lengths.push_back((hit->first - last_dist) / track_len);
+//     } else {
+//       // we may have exited the mesh, move forward
+
+//       last_dist = hit->first; // store last dist
+//       hit++; // advance iterator
+
+//       // try to find a tet for the mid point of the next segment
+//       tet = get_tet(last_r + u * (hit->first - last_dist) / 2.0);
+//       if (!tet) {
+//         // warning("Couldn't find re-entry location");
+//         if (hit != hits.end()) { warning("Possible missed segment"); }
+//         break;
+//       } else {
+//         bins.push_back(get_bin_from_ent_handle(tet));
+//         lengths.push_back((hit->first - last_dist) / track_len);
+//       }
+//     }
+//     last_dist = hit->first;
+
+//     // find next tet
+//     moab::Range adj_tets;
+//     rval = mbi_->get_adjacencies(&hit->second, 1, 3, false, adj_tets);
+//     if (rval != moab::MB_SUCCESS) {
+//       fatal_error("Failed to get triangle adjacencies from mesh " + filename_);
+//     }
+
+//     if (adj_tets.size() == 2) {
+//       tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0];
+//     } else if (adj_tets.size() == 1) {
+//       tet = adj_tets[0];
+//     }
+//   }
+
+//   // tally remaining portion of track after last hit if
+//   // the last segment of the track is in the mesh
+//   if (hits.back().first < track_len) {
+//     auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0);
+//     tet = get_tet(pos);
+//     if (tet) {
+//       bins.push_back(get_bin_from_ent_handle(tet));
+//       lengths.push_back((track_len - hits.back().first) / track_len);
+//     }
+//   }
+
+//   return;
+
+  /// IMPLEMENTATION TWO
+  // double prev_int_dist = 0.0;
+
+  // if (hits.size() == 0) {
+  //   moab::EntityHandle last_r_tet =get_tet((r0 + r1) * 0.5);
+  //   if (last_r_tet) {
+  //     bins.push_back(get_bin_from_ent_handle(last_r_tet));
+  //     lengths.push_back(1.0);
+  //   }
+  //   return;
+  // }
+
+  // moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0);
+
+  // if (!tet) {
+  //   last_r = last_r + u * hits.front().first;
+  //   hits.erase(hits.begin());
+  // }
+
+  // for (const auto& hit : hits) {
+  //   tet = get_tet(last_r + u * (prev_int_dist +  hit.first) / 2.0 );
+  //   if (!tet) {
+  //     prev_int_dist = hit.first;
+  //     continue;
+  //   }
+  //   int bin = get_bin_from_ent_handle(tet);
+  //   double tally_val = (hit.first - prev_int_dist) / track_len);
+  //   if (tally_val < 0.0) {
+  //     fatal_error("Negative score applied to tally");
+  //   }
+
+  //   bins.push_back(bin);
+  //   lengths.push_back(tally_val);
+  //   prev_int_dist = hit.first;
+  // }
+
+  // // tally remaining portion of track (if any exists)
+  // if (hits.back().first < track_len) {
+  //   tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0);
+  //   if (tet) {
+  //     bins.push_back(get_bin_from_ent_handle(tet));
+  //     double tally_val = (track_len - hits.back().first) / track_len);
+  //     lengths.push_back(tally_val);
+  //   }
+  // }
+
+//};
 
 moab::EntityHandle
 UnstructuredMesh::get_tet(const Position& r) const
@@ -1743,8 +1880,8 @@ UnstructuredMesh::to_hdf5(hid_t group) const
 
     write_dataset(mesh_group, "type", "unstructured");
     write_dataset(mesh_group, "filename", filename_);
-
-    // write volume and centroid of each tet
+    write_dataset(mesh_group, "library", "moab");
+    // write volume of each tet
     std::vector<double> tet_vols;
     xt::xtensor<double, 2> centroids({ehs_.size(), 3});
     for (int i = 0; i < ehs_.size(); i++) {
@@ -1835,7 +1972,7 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const {
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: ", bin));
   }
-  return ehs_[bin];
+  return ehs_[0] + bin;
 }
 
 int UnstructuredMesh::n_bins() const {
@@ -2064,24 +2201,36 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
+
+  m_->set_point_locator_close_to_point_tol(FP_COINCIDENT);
   point_locator_ = m_->sub_point_locator();
   point_locator_->enable_out_of_mesh_mode();
+  point_locator_->init();
 
   m_->find_neighbors();
 
   auto e = *m_->elements_begin();
   first_element_ = e; // FIXME
 
-    // determine boundary elements
+  bbox_ = {INFTY, -INFTY, INFTY, -INFTY, INFTY, -INFTY};
+
+  // determine boundary elements and create bounding box
   for (int i = 0; i < m_->n_elem(); i++) {
     auto e = m_->elem_ptr(i);
-    for (int j = 0; j < e->n_neighbors(); j++) {
-      if (!e->neighbor_ptr(j)) {
+
+    // update bounding box for each node
+    for (int j = 0; j < e->n_nodes(); j++) {
+      auto n = e->node_ref(j);
+      Position r(n(0), n(1), n(2));
+      bbox_.update(r);
+    }
+
+    for (int k = 0; k < e->n_neighbors(); k++) {
+      if (!e->neighbor_ptr(k)) {
         boundary_elements_.insert(e);
       }
     }
   }
-
 }
 
 void
@@ -2170,7 +2319,7 @@ LibMesh::bins_crossed(const Particle* p,
   // get element containing previous position
   libMesh::Point start(p->r_last_.x, p->r_last_.y, p->r_last_.z);
   libMesh::Point end(p->r().x, p->r().y, p->r().z);
-  libMesh::Point dir(p->u().x, p->u().y, p->r().z);
+  libMesh::Point dir(p->u().x, p->u().y, p->u().z);
   dir /= dir.norm();
 
   double track_len = (end - start).norm();
@@ -2190,8 +2339,34 @@ LibMesh::bins_crossed(const Particle* p,
 int
 LibMesh::get_bin(Position r) const
 {
+  if (!bbox_.contains(r)) { return -1; }
+
+
   libMesh::Point p(r.x, r.y, r.z);
   auto e = (*point_locator_)(p);
+
+  libMesh::Point dir(rand(), rand(), rand());
+  dir /= dir.norm();
+  if (e && !inside_tet(p, dir, e)) {
+    bool found = false;
+    for (int i = 0; i < e->n_neighbors(); i++) {
+      if (e->neighbor_ptr(i) && inside_tet(p, dir, e->neighbor_ptr(i))) {
+        e = e->neighbor_ptr(i);
+        found = true;
+        break;
+      }
+    }
+  }
+
+  //   if (!found) {
+  //   std::stringstream msg;
+  //   msg << "Incorrect tet found for location: "
+  //       << "(" << r.x << ", " << r.y << ", " << r.z;
+  //   warning(msg);
+  //   return -1;
+  //   }
+  // }
+
   if (!e) {
     return -1;
   } else {
@@ -2221,7 +2396,7 @@ bool LibMesh::intersects(Position& r0, Position r1, int* ijk) const {
   // if we don't get a hit, the track won't intersect with the mesh
   if (result.second) {
     ijk[0] = get_bin_from_mesh_type(result.second);
-    return false;
+    return true;
   }
 
   return false;
@@ -2240,6 +2415,10 @@ std::pair<double, const libMesh::Elem*>
 LibMesh::locate_boundary_element(const libMesh::Point& start,
                                  const libMesh::Point& end) const
 {
+  typedef std::pair<double, const libMesh::Elem*> RayHit;
+  RayHit result = {INFTY, nullptr};
+  if (start == end) { return result; }
+
   // attempt to locate an intersection with the mesh boundary
   libMesh::Point dir = (end - start).unit();
   double length = (end - start).norm();
@@ -2254,8 +2433,6 @@ LibMesh::locate_boundary_element(const libMesh::Point& start,
   }
 
   // find nearest hit along our direction
-  typedef std::pair<double, const libMesh::Elem*> RayHit;
-  RayHit result = {INFTY, nullptr};
   for (auto elem : candidate_elements) {
     for (int i = 0; i < elem->n_sides(); i++) {
       double temp_dist = 0;
@@ -2281,13 +2458,43 @@ LibMesh::get_bin_from_mesh_type(const libMesh::Elem* elem) const {
   return bin;
 }
 
+bool
+LibMesh::inside_tet(const libMesh::Point& r,
+                    const libMesh::Point& u,
+                    std::unique_ptr<libMesh::Elem> e) const
+{
+  return inside_tet(r, u, e.get());
+}
+
+
+bool
+LibMesh::inside_tet(const libMesh::Point& r,
+                    const libMesh::Point& u,
+                    const libMesh::Elem* e) const
+{
+  // fire rays at each triangle in the tet
+  int n_hits = 0;
+  for (int i = 0; i < e->n_sides(); i++) {
+    double temp;
+    if (plucker_test(e->side_ptr(i), r, u, temp)) { n_hits++; }
+  }
+
+  if (n_hits == 0) { return false; }
+
+  for (int i = 0; i < e->n_sides(); i++) {
+    double temp;
+    if (plucker_test(e->side_ptr(i), r, -u, temp)) { n_hits++; }
+  }
+
+  return n_hits >= 2;
+}
+
 void
 LibMesh::intersect_track(libMesh::Point start,
                          libMesh::Point dir,
                          double track_len,
                          UnstructuredMeshHits& hits) const
 {
-
   double track_remaining = track_len;
 
   auto e = (*point_locator_)(start);
@@ -2307,26 +2514,64 @@ LibMesh::intersect_track(libMesh::Point start,
     }
   }
 
+  if (!inside_tet(start, dir, e)) {
+    bool found = false;
+    // try to find a new tet adjacent to this one
+    for (int i = 0; i < e->n_neighbors(); i ++) {
+      if (e->neighbor_ptr(i) && inside_tet(start, dir, e->neighbor_ptr(i))) {
+        e = e->neighbor_ptr(i);
+        found = true;
+        break;
+      }
+    }
+
+    if (!found)
+    warning("Starting point is not inside the specified tet.");
+
+  }
+
+  auto last_e = e;
+
+  std::set<libMesh::Point> visited;
+
+  bool first = true;
+
   while (true) {
     // find the positive distance triangle intersection
-    double dist = 0.0;
+    double dist = -1.0;
     int side = -1;
+
     for (int i = 0; i < e->n_sides(); i++) {
       auto tri = e->side_ptr(i);
+      if (visited.count(e->side_ptr(i)->centroid())) {
+        continue;
+      }
       if (tri->type() != libMesh::ElemType::TRI3) { warning("Non-triangle element found"); }
       double temp_dist = -1.0;
       bool hit = plucker_test(e->side_ptr(i), start, dir, temp_dist);
-      if (hit and temp_dist > FP_COINCIDENT) {
+      // if (hit and temp_dist > FP_COINCIDENT) {
+      if (hit and temp_dist >= 0 and temp_dist > dist) {
         side = i;
         dist = temp_dist;
+        first = false;
       }
     }
 
     // make sure we found a hit for the tet we're in
+
+    // if we don't find a hit for this tet, we may
     if (side == -1) {
+
+      if (first) {
+        warning("Couldn't get hit on first iteration");
+        inside_tet(start, dir, e);
+        first = false;
+      }
       auto orig_e = e;
       start += dir * TINY_BIT; // nudge particle forward
+      last_e = e;
       e = (*point_locator_)(start);
+
       if (!e) {
         if (!orig_e->on_boundary()) {
           std::cout << "May have incorrectly truncated a track." << std::endl;
@@ -2337,6 +2582,7 @@ LibMesh::intersect_track(libMesh::Point start,
     } else {
       // add hit to output
       hits.push_back(std::pair<double, const libMesh::Elem*>(std::min(track_remaining, dist), e));
+      visited.insert(e->side_ptr(side)->centroid());
       // advance position along track
       start += dir * std::min(track_remaining, dist);
       track_remaining -= dist; // subtract from
@@ -2348,12 +2594,24 @@ LibMesh::intersect_track(libMesh::Point start,
     // get tet on the other side
     auto next_e = e->neighbor_ptr(side);
 
+    // // if our distance is zero,
+    // // check that we're not going back and forth
+    // if (dist == 0 && next_e == last_e) {
+    //   //        start += dir * TINY_BIT;
+    //     continue;
+    // }
+
+    if (next_e && !next_e->contains_point(start)) {
+      warning("Moving into tet that does not contain the current location.");
+    }
+
     // if we exit the mesh, check for re-entry along
     // the track
     if (!next_e) {
       auto result = locate_boundary_element(start,
                                             start + dir * track_remaining);
       if (result.second) {
+        last_e = e;
         e = result.second;
         track_remaining -= result.first;
         // advance position along track
@@ -2375,6 +2633,7 @@ LibMesh::intersect_track(libMesh::Point start,
     }
 
     // update the element we're in
+    last_e = e;
     e = next_e;
   }
 }
@@ -2421,6 +2680,7 @@ void LibMesh::to_hdf5(hid_t group) const
 
   write_dataset(mesh_group, "type", "unstructured");
   write_dataset(mesh_group, "filename", filename_);
+  write_dataset(mesh_group, "library", "libmesh");
 
   // write volume of each tet
   std::vector<double> tet_vols;
@@ -2457,6 +2717,19 @@ LibMesh::plucker_edge_test(const libMesh::Node& vertexa,
   return pip;
 }
 
+/* This test uses the same edge-ray computation for adjacent triangles so that
+   rays passing close to edges/nodes are handled consistently.
+
+   Reports intersection type for post processing of special cases. Optionally
+   screen by orientation and negative/nonnegative distance limits.
+
+   If screening by orientation, substantial pruning can occur. Indicate
+   desired orientation by passing 1 (forward), -1 (reverse), or 0 (no preference).
+   Note that triangle orientation is not always the same as surface
+   orientation due to non-manifold surfaces.
+
+   N. Platis and T. Theoharis, "Fast Ray-Tetrahedron Intersection using Plücker
+   Coordinates", Journal of Graphics Tools, Vol. 8, Part 4, Pages 37-48 (2003). */
 bool
 LibMesh::plucker_test(std::unique_ptr<const libMesh::Elem> tri,
                       const libMesh::Point& start,
@@ -2506,10 +2779,13 @@ LibMesh::plucker_test(std::unique_ptr<const libMesh::Elem> tri,
   }
 
   // no negative distances
-  const double temp_dist = (intersection(idx) - start(idx)) / dir(idx);
+  double temp_dist = (intersection(idx) - start(idx)) / dir(idx);
+  if ( fabs(temp_dist) < TINY_BIT ) { temp_dist = 0.0; }
   if ( temp_dist < 0 ) { return false; }
 
-  dist = (intersection - start).norm();
+  dist = temp_dist;
+
+  // dist = (intersection - start).norm();
 
   return true;
 }
diff --git a/src/tallies/filter_mesh.cpp b/src/tallies/filter_mesh.cpp
index 7897173868..ecbcf94eca 100644
--- a/src/tallies/filter_mesh.cpp
+++ b/src/tallies/filter_mesh.cpp
@@ -43,6 +43,12 @@ const
   } else {
     model::meshes[mesh_]->bins_crossed(p, match.bins_, match.weights_);
   }
+
+  double total = std::accumulate(match.weights_.begin(), match.weights_.end(), 0.0);
+  // if ( fabs(1.0 - total) > FP_PRECISION) {
+  //   std::cout << "Total weight for score < 1.0 (" << total << ")" << std::endl;
+  // }
+
 }
 
 void

From dfdedc6ede898713fa4fae38c5d89de55a7c0a90 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 10 Jan 2020 01:04:08 -0600
Subject: [PATCH 016/139] Some cleanup.

---
 include/openmc/mesh.h | 155 +++++++++++++++++---------
 src/mesh.cpp          | 253 ++++++++++--------------------------------
 2 files changed, 160 insertions(+), 248 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index bde0736539..fffe287a0a 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -446,49 +446,118 @@ class LibMesh : public UnstructuredMeshBase {
   typedef std::vector<std::pair<double, const libMesh::Elem*>> UnstructuredMeshHits;
 
 public:
-  // constructor
+  // Constructor
   LibMesh(pugi::xml_node node);
 
+  // Methods
+
+  // standard mesh functions
   void bins_crossed(const Particle* p,
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const;
 
-  bool inside_tet(const libMesh::Point& r,
-                  const libMesh::Point& u,
-                  const libMesh::Elem* e) const;
-
-  bool inside_tet(const libMesh::Point& r,
-                  const libMesh::Point& u,
-                  std::unique_ptr<libMesh::Elem> e) const;
-
-  void intersect_track(libMesh::Point start,
-                       libMesh::Point dir,
-                       double track_len,
-                       UnstructuredMeshHits& hits) const;
-
-  bool elements_share_face(const libMesh::Elem* from,
-                           const libMesh::Elem* to,
-                           unsigned int side) const;
-
-  int
-  get_bin_from_mesh_type(const libMesh::Elem* elem) const;
-
   int get_bin(Position r) const;
 
   int n_bins() const;
 
   void get_indices(Position r, int* ijk, bool* in_mesh) const;
 
+  int get_bin_from_indices(const int* ijk) const;
+
+  void get_indices_from_bin(int bin, int* ijk) const;
+
+  int n_surface_bins() const;
+
+  void surface_bins_crossed(const Particle* p,
+                             std::vector<int>& bins) const;
+
+  std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
+                                                           Position plot_ur) const;
+
+  bool intersects(Position& r0, Position r1, int* ijk) const;
+
+  void write(const std::string& filename) const;
+
+  void to_hdf5(hid_t group) const;
+
+  //! Add a variable to the libmesh mesh instance
+  void add_variable(const std::string& var_name);
+
+  //! Set the value of a bin for a variable on the libmesh mesh instance
+  void set_variable(const std::string& var_name, int bin, double value);
+
+private:
+
+  //! Translate a bin value to an element pointer
+  const libMesh::Elem* get_element_from_bin(int bin) const;
+
+  //! Translate an element pointer to a bin value
+  int get_bin_from_element(const libMesh::Elem* elem) const;
+
+  //! Checks whether if a point moving in a given direction
+  //! is inside the element
+  //
+  //! \param[in] r In: point to be checked
+  //! \param[in] u In: direction (this is arbitrary, any normalized direction will do)
+  //! \param[in] e In: libmesh element in question
+  //! \return whether or not the point is in the tet
+  bool inside_tet(const libMesh::Point& r,
+                  const libMesh::Point& u,
+                  const libMesh::Elem* e) const;
+
+  //! Checks whether if a point moving in a given direction
+  //! is inside the element
+  //
+  //! \param[in] r In: point to be checked
+  //! \param[in] u In: direction (this is arbitrary, any normalized direction will do)
+  //! \param[in] e In: libmesh element in question
+  //! \return whether or not the point is in the tet
+  bool inside_tet(const libMesh::Point& r,
+                  const libMesh::Point& u,
+                  std::unique_ptr<libMesh::Elem> e) const;
+
+  //! Returns all hit elements and distances on the mesh
+  //
+  //! \param[in] start In: starting location of the track
+  //! \param[in] dir In: normalized direction of the track
+  //! \param[in] track_len In: track length
+  //! \param[out] hits Out: set of elements and hits on the unstructured mesh
+  void intersect_track(libMesh::Point start,
+                       libMesh::Point dir,
+                       double track_len,
+                       UnstructuredMeshHits& hits) const;
+
+  //! Checks that the elements from and to share a face
+  //
+  //! \param[in] from In: starting element
+  //! \param[in] to In: next element
+  //! \param[in] side side crossed in the starting element
+  //! \return whether or not the elements share a face
+  bool elements_share_face(const libMesh::Elem* from,
+                           const libMesh::Elem* to,
+                           unsigned int side) const;
+
+  //! Searches for an intersection with the mesh between
+  //! r0 and r1
+  //
+  //! \param[in] r0 In: starting position of the track
+  //! \param[in] r1 In: ending position of the track
+  //! \return the distance to intersection and intersected element
   std::pair<double, const libMesh::Elem*>
   locate_boundary_element(const Position& r0,
                           const Position& r1) const;
 
+  //! Searches for an intersection with the mesh between
+  //! the start and end of a track
+  //
+  //! \param[in] start In: starting position of the track
+  //! \param[in] end In: ending position of the track
+  //! \return the distance to intersection and intersected element
   std::pair<double, const libMesh::Elem*>
   locate_boundary_element(const libMesh::Point& start,
                           const libMesh::Point& end) const;
 
-
-  // triangle intersection methods
+  // Internal triangle intersection methods
   bool plucker_test(std::unique_ptr<const libMesh::Elem> tri,
                     const libMesh::Point& start,
                     const libMesh::Point& dir,
@@ -502,39 +571,17 @@ public:
   double first(const libMesh::Node& a,
                const libMesh::Node& b) const;
 
-  int get_bin_from_indices(const int* ijk) const;
-
-  void get_indices_from_bin(int bin, int* ijk) const;
-
-  libMesh::Elem* get_element_from_bin(int bin) const;
-
-  bool intersects(Position& r0, Position r1, int* ijk) const;
-
-  int n_surface_bins() const;
-
-  void surface_bins_crossed(const Particle* p,
-                             std::vector<int>& bins) const;
-
-  std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
-                                                           Position plot_ur) const;
-
-  void add_variable(const std::string& var_name);
-
-  void set_variable(const std::string& var_name, int bin, double value);
-
-  void write(const std::string& filename) const;
-
-  void to_hdf5(hid_t group) const;
+  // Data members
 
 private:
-  std::unique_ptr<libMesh::Mesh> m_;
-  std::unique_ptr<libMesh::PointLocatorBase> point_locator_;
-  std::unique_ptr<libMesh::EquationSystems> equation_systems_;
-  std::map<std::string, unsigned int> variable_map_;
-  BoundingBox bbox_;
-  std::string eq_system_name_;
-  libMesh::Elem* first_element_;
-  std::set<libMesh::Elem*> boundary_elements_;
+  std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libmesh mesh instance
+  std::unique_ptr<libMesh::PointLocatorBase> point_locator_; //!< point locator for the mesh
+  std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh (for result output)
+  std::map<std::string, unsigned int> variable_map_; //!< mappint of variable names (scores) to their numbers on the mesh
+  BoundingBox bbox_; //!< bounding box of the mesh
+  std::string eq_system_name_; //!< name of the equation system holding OpenMC results
+  libMesh::Elem* first_element_; //!< pointer to the first element in the mesh (maybe should be a key?)
+  std::set<libMesh::Elem*> boundary_elements_; //<! all boundary elements in the mesh
 };
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 22be31f814..26d8f35529 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1575,9 +1575,6 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start,
 
   for (const auto& hit : hits) {
     moab::CartVect hit_loc = start + dir * hit.first;
-    // std::cout << "Hit location: (" << hit_loc[0] << ", "
-    //           << hit_loc[1] << ", " << hit_loc[2] << ")" << std::endl;
-    // std::cout << "Distance: " << hit.first << std::endl;
   }
 
 }
@@ -1606,6 +1603,7 @@ UnstructuredMesh::bins_crossed(const Particle& p,
   bins.clear();
   lengths.clear();
 
+  // track could be entirely contained by a tet
   if (hits.size() == 0) {
     auto last_r_tet = get_tet(last_r + u * track_len * 0.5);
     if (last_r_tet) {
@@ -1616,13 +1614,11 @@ UnstructuredMesh::bins_crossed(const Particle& p,
   }
 
   double last_dist = 0.0;
-  /// IMPLEMENTATION THREE
   for (auto hit = hits.begin(); hit != hits.end(); hit++) {
 
     // mid point for this segment
     double segment_length = hit->first - last_dist;
     Position segment_midpoint = last_r + u * last_dist + u * segment_length / 2.0;
-    if (segment_length < 1E-08) { continue; }
 
     last_dist = hit->first;
 
@@ -1655,135 +1651,6 @@ UnstructuredMesh::bins_crossed(const Particle& p,
   return;
 }
 
-//   //// IMPLEMENTATION ONE
-//   if (hits.size() == 0) {
-//     moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5);
-//     if (last_r_tet) {
-//       bins.push_back(get_bin_from_ent_handle(last_r_tet));
-//       lengths.push_back(1.0);
-//     }
-//     return;
-//   }
-
-//   moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0);
-//   double last_dist = 0.0;
-
-//   // make sure first point is inside a tet
-//   if (!tet) {
-//     last_dist = hits.front().first;
-//     hits.erase(hits.begin());
-//     tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0);
-//   }
-
-//   if (!tet) { fatal_error("Should in in a tet now."); }
-
-//   // if there are no other hits, there is only one segment to tally
-//   if (hits.size() == 0 && tet) {
-//     bins.push_back(get_bin_from_ent_handle(tet));
-//     lengths.push_back(1.0);
-//     return;
-//   }
-
-//   // score all remaining segments
-// for (auto hit = hits.begin(); hit != hits.end(); hit++) {
-//     // score in this tet if one was found
-//     if (tet) {
-//       bins.push_back(get_bin_from_ent_handle(tet));
-//       lengths.push_back((hit->first - last_dist) / track_len);
-//     } else {
-//       // we may have exited the mesh, move forward
-
-//       last_dist = hit->first; // store last dist
-//       hit++; // advance iterator
-
-//       // try to find a tet for the mid point of the next segment
-//       tet = get_tet(last_r + u * (hit->first - last_dist) / 2.0);
-//       if (!tet) {
-//         // warning("Couldn't find re-entry location");
-//         if (hit != hits.end()) { warning("Possible missed segment"); }
-//         break;
-//       } else {
-//         bins.push_back(get_bin_from_ent_handle(tet));
-//         lengths.push_back((hit->first - last_dist) / track_len);
-//       }
-//     }
-//     last_dist = hit->first;
-
-//     // find next tet
-//     moab::Range adj_tets;
-//     rval = mbi_->get_adjacencies(&hit->second, 1, 3, false, adj_tets);
-//     if (rval != moab::MB_SUCCESS) {
-//       fatal_error("Failed to get triangle adjacencies from mesh " + filename_);
-//     }
-
-//     if (adj_tets.size() == 2) {
-//       tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0];
-//     } else if (adj_tets.size() == 1) {
-//       tet = adj_tets[0];
-//     }
-//   }
-
-//   // tally remaining portion of track after last hit if
-//   // the last segment of the track is in the mesh
-//   if (hits.back().first < track_len) {
-//     auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0);
-//     tet = get_tet(pos);
-//     if (tet) {
-//       bins.push_back(get_bin_from_ent_handle(tet));
-//       lengths.push_back((track_len - hits.back().first) / track_len);
-//     }
-//   }
-
-//   return;
-
-  /// IMPLEMENTATION TWO
-  // double prev_int_dist = 0.0;
-
-  // if (hits.size() == 0) {
-  //   moab::EntityHandle last_r_tet =get_tet((r0 + r1) * 0.5);
-  //   if (last_r_tet) {
-  //     bins.push_back(get_bin_from_ent_handle(last_r_tet));
-  //     lengths.push_back(1.0);
-  //   }
-  //   return;
-  // }
-
-  // moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0);
-
-  // if (!tet) {
-  //   last_r = last_r + u * hits.front().first;
-  //   hits.erase(hits.begin());
-  // }
-
-  // for (const auto& hit : hits) {
-  //   tet = get_tet(last_r + u * (prev_int_dist +  hit.first) / 2.0 );
-  //   if (!tet) {
-  //     prev_int_dist = hit.first;
-  //     continue;
-  //   }
-  //   int bin = get_bin_from_ent_handle(tet);
-  //   double tally_val = (hit.first - prev_int_dist) / track_len);
-  //   if (tally_val < 0.0) {
-  //     fatal_error("Negative score applied to tally");
-  //   }
-
-  //   bins.push_back(bin);
-  //   lengths.push_back(tally_val);
-  //   prev_int_dist = hit.first;
-  // }
-
-  // // tally remaining portion of track (if any exists)
-  // if (hits.back().first < track_len) {
-  //   tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0);
-  //   if (tet) {
-  //     bins.push_back(get_bin_from_ent_handle(tet));
-  //     double tally_val = (track_len - hits.back().first) / track_len);
-  //     lengths.push_back(tally_val);
-  //   }
-  // }
-
-//};
-
 moab::EntityHandle
 UnstructuredMesh::get_tet(const Position& r) const
 {
@@ -2201,17 +2068,17 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
-
+  // setup the point locator
   m_->set_point_locator_close_to_point_tol(FP_COINCIDENT);
   point_locator_ = m_->sub_point_locator();
   point_locator_->enable_out_of_mesh_mode();
-  point_locator_->init();
 
+  // will need mesh neighbors to walk the mesh
   m_->find_neighbors();
 
-  auto e = *m_->elements_begin();
-  first_element_ = e; // FIXME
+  first_element_ = *m_->elements_begin();
 
+  // bounding box for the mesh
   bbox_ = {INFTY, -INFTY, INFTY, -INFTY, INFTY, -INFTY};
 
   // determine boundary elements and create bounding box
@@ -2275,7 +2142,7 @@ LibMesh::get_indices_from_bin(int bin, int* ijk) const {
   ijk[0] = bin;
 }
 
-libMesh::Elem*
+const libMesh::Elem*
 LibMesh::get_element_from_bin(int bin) const {
   m_->elem_ptr(bin);
 }
@@ -2332,7 +2199,7 @@ LibMesh::bins_crossed(const Particle* p,
 
   for (const auto& hit : hits) {
     lengths.push_back(hit.first / track_len);
-    bins.push_back(get_bin_from_mesh_type(hit.second));
+    bins.push_back(get_bin_from_element(hit.second));
   }
 }
 
@@ -2341,10 +2208,13 @@ LibMesh::get_bin(Position r) const
 {
   if (!bbox_.contains(r)) { return -1; }
 
-
+  // look-up a tet using the point locator
   libMesh::Point p(r.x, r.y, r.z);
   auto e = (*point_locator_)(p);
 
+  // sometimes the tet isn't quite right,
+  // make sure this tet contains our point
+  // if not, check this tet's neighbors
   libMesh::Point dir(rand(), rand(), rand());
   dir /= dir.norm();
   if (e && !inside_tet(p, dir, e)) {
@@ -2358,26 +2228,17 @@ LibMesh::get_bin(Position r) const
     }
   }
 
-  //   if (!found) {
-  //   std::stringstream msg;
-  //   msg << "Incorrect tet found for location: "
-  //       << "(" << r.x << ", " << r.y << ", " << r.z;
-  //   warning(msg);
-  //   return -1;
-  //   }
-  // }
-
   if (!e) {
     return -1;
   } else {
-    return get_bin_from_mesh_type(e);
+    return get_bin_from_element(e);
   }
 }
 
 bool LibMesh::intersects(Position& r0, Position r1, int* ijk) const {
 
   // first try to locate an element
-  // for each of the points
+  // for the start or end point
   int bin {-1};
   bin = get_bin(r0);
   if (bin != -1) {
@@ -2391,11 +2252,11 @@ bool LibMesh::intersects(Position& r0, Position r1, int* ijk) const {
     return true;
   }
 
+  // check for an intersection with one of the boundary faces
   auto result = locate_boundary_element(r0, r1);
-
   // if we don't get a hit, the track won't intersect with the mesh
   if (result.second) {
-    ijk[0] = get_bin_from_mesh_type(result.second);
+    ijk[0] = get_bin_from_element(result.second);
     return true;
   }
 
@@ -2448,7 +2309,7 @@ LibMesh::locate_boundary_element(const libMesh::Point& start,
 }
 
 int
-LibMesh::get_bin_from_mesh_type(const libMesh::Elem* elem) const {
+LibMesh::get_bin_from_element(const libMesh::Elem* elem) const {
   int bin =  elem->id() - first_element_->id();
   if (bin >= n_bins()) {
     std::stringstream s;
@@ -2486,6 +2347,8 @@ LibMesh::inside_tet(const libMesh::Point& r,
     if (plucker_test(e->side_ptr(i), r, -u, temp)) { n_hits++; }
   }
 
+  // should get at least 2 hits if the
+  // location is inside or on a tet boundary
   return n_hits >= 2;
 }
 
@@ -2497,8 +2360,30 @@ LibMesh::intersect_track(libMesh::Point start,
 {
   double track_remaining = track_len;
 
+  // attempt to locate a tet for the starting point
   auto e = (*point_locator_)(start);
 
+  // the point_locator seems off sometimes,
+  // ensure the point is actually in the tet
+  // and check the neighbors as well
+  if (e && !inside_tet(start, dir, e)) {
+    bool found = false;
+    // try to find a new tet adjacent to this one
+    for (int i = 0; i < e->n_neighbors(); i ++) {
+      if (e->neighbor_ptr(i) && inside_tet(start, dir, e->neighbor_ptr(i))) {
+        e = e->neighbor_ptr(i);
+        found = true;
+        break;
+      }
+    }
+
+    if (!found) {
+      warning("Starting point is not inside the specified tet.");
+    }
+  }
+
+  // if the point locator fails, look for mesh
+  // entry along the track
   if (!e) {
     auto result = locate_boundary_element(start, start + track_len * dir);
     if (result.second) {
@@ -2514,25 +2399,7 @@ LibMesh::intersect_track(libMesh::Point start,
     }
   }
 
-  if (!inside_tet(start, dir, e)) {
-    bool found = false;
-    // try to find a new tet adjacent to this one
-    for (int i = 0; i < e->n_neighbors(); i ++) {
-      if (e->neighbor_ptr(i) && inside_tet(start, dir, e->neighbor_ptr(i))) {
-        e = e->neighbor_ptr(i);
-        found = true;
-        break;
-      }
-    }
-
-    if (!found)
-    warning("Starting point is not inside the specified tet.");
-
-  }
-
-  auto last_e = e;
-
-  std::set<libMesh::Point> visited;
+  std::set<libMesh::dof_id_type> visited;
 
   bool first = true;
 
@@ -2543,7 +2410,7 @@ LibMesh::intersect_track(libMesh::Point start,
 
     for (int i = 0; i < e->n_sides(); i++) {
       auto tri = e->side_ptr(i);
-      if (visited.count(e->side_ptr(i)->centroid())) {
+      if (visited.count(e->key(i))) {
         continue;
       }
       if (tri->type() != libMesh::ElemType::TRI3) { warning("Non-triangle element found"); }
@@ -2569,7 +2436,6 @@ LibMesh::intersect_track(libMesh::Point start,
       }
       auto orig_e = e;
       start += dir * TINY_BIT; // nudge particle forward
-      last_e = e;
       e = (*point_locator_)(start);
 
       if (!e) {
@@ -2582,10 +2448,12 @@ LibMesh::intersect_track(libMesh::Point start,
     } else {
       // add hit to output
       hits.push_back(std::pair<double, const libMesh::Elem*>(std::min(track_remaining, dist), e));
-      visited.insert(e->side_ptr(side)->centroid());
+      // add this side's centroid to the visited list
+      visited.insert(e->key(side));
       // advance position along track
       start += dir * std::min(track_remaining, dist);
-      track_remaining -= dist; // subtract from
+      // subtract from remaining track length
+      track_remaining -= dist;
     }
 
     // if we've reached the end of the track, break
@@ -2594,28 +2462,24 @@ LibMesh::intersect_track(libMesh::Point start,
     // get tet on the other side
     auto next_e = e->neighbor_ptr(side);
 
-    // // if our distance is zero,
-    // // check that we're not going back and forth
-    // if (dist == 0 && next_e == last_e) {
-    //   //        start += dir * TINY_BIT;
-    //     continue;
-    // }
-
+    // check to make sure this tet contains our current location
     if (next_e && !next_e->contains_point(start)) {
       warning("Moving into tet that does not contain the current location.");
     }
 
-    // if we exit the mesh, check for re-entry along
-    // the track
+    // if there is no next element, we may have exited the mesh
+    // and will re-enter elsewhere
     if (!next_e) {
       auto result = locate_boundary_element(start,
                                             start + dir * track_remaining);
       if (result.second) {
-        last_e = e;
-        e = result.second;
-        track_remaining -= result.first;
         // advance position along track
+        // to re-entry point
         start += dir * result.first;
+        // remove this distance from the remaining track length
+        track_remaining -= result.first;
+        // update
+        e = result.second;
         continue;
       } else {
         // if no next intersection with the mesh, we're done
@@ -2623,17 +2487,18 @@ LibMesh::intersect_track(libMesh::Point start,
       }
     }
 
+    // if the mesh entry point is too far away
+    // break
     if (track_remaining <= 0.0) { break; }
 
     // check that the hit is correct
     if (!elements_share_face(e, next_e, side)) {
+      // this should maybe throw an error?
       warning("Incorrect adjacent element found");
-      // this should maybe throw an error
       break;
     }
 
-    // update the element we're in
-    last_e = e;
+    // update to the element along the track
     e = next_e;
   }
 }
@@ -2644,7 +2509,7 @@ LibMesh::elements_share_face(const libMesh::Elem* from,
                              unsigned int side) const
 {
   for (auto j : to->side_index_range()) {
-    if (from->side_ptr(side)->key() == to->side_ptr(j)->key()) {
+    if (from->key(side) == to->key(j)) {
       return true;
     }
   }

From 57041cee5de12f8208f5f39a1d1cf8f22b4a9236 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 10 Jan 2020 21:44:26 -0600
Subject: [PATCH 017/139] Updates for threaded point location.

---
 CMakeLists.txt        |  2 +-
 include/openmc/mesh.h |  2 +-
 src/initialize.cpp    |  9 +++++----
 src/mesh.cpp          | 35 ++++++++++++++---------------------
 4 files changed, 21 insertions(+), 27 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 29d16bc080..10cfd5aa03 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -71,7 +71,7 @@ else()
 #===============================================================================
 if(libmesh)
   find_package(LIBMESH REQUIRED)
-  set(LIBMESH_LIBRARIES mesh_opt timpi_opt netcdf)
+  set(LIBMESH_LIBRARIES mesh_devel timpi_devel netcdf)
   set(LIBMESH_INCLUDE_DIRS "${LIBMESH}/../include/")
   link_directories(${LIBMESH})
 endif()
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index fffe287a0a..da2fca2c87 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -575,7 +575,7 @@ private:
 
 private:
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libmesh mesh instance
-  std::unique_ptr<libMesh::PointLocatorBase> point_locator_; //!< point locator for the mesh
+  std::vector<std::unique_ptr<libMesh::PointLocatorBase>> point_locators_; //!< pointers to locators for each thread
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh (for result output)
   std::map<std::string, unsigned int> variable_map_; //!< mappint of variable names (scores) to their numbers on the mesh
   BoundingBox bbox_; //!< bounding box of the mesh
diff --git a/src/initialize.cpp b/src/initialize.cpp
index d5040976e2..f4874d9f23 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -55,10 +55,6 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   initialize_mpi(comm);
 #endif
 
-#ifdef LIBMESH
-  settings::LMI = new libMesh::LibMeshInit(argc, argv);
-#endif
-
 
   // Parse command-line arguments
   int err = parse_command_line(argc, argv);
@@ -76,6 +72,11 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   }
 #endif
 
+#ifdef LIBMESH
+  settings::LMI = new libMesh::LibMeshInit(argc, argv, omp_get_max_threads());
+#endif
+
+
   // Initialize random number generator -- if the user specifies a seed, it
   // will be re-initialized later
   openmc::openmc_set_seed(DEFAULT_SEED);
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 26d8f35529..a3b36e3de0 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2069,9 +2069,13 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
   // setup the point locator
-  m_->set_point_locator_close_to_point_tol(FP_COINCIDENT);
-  point_locator_ = m_->sub_point_locator();
-  point_locator_->enable_out_of_mesh_mode();
+  int n_threads = omp_get_max_threads();
+  point_locators_ = std::vector<std::unique_ptr<libMesh::PointLocatorBase>>(n_threads);
+  for (int i = 0; i < n_threads; i++) {
+    point_locators_[i] = m_->sub_point_locator();
+    point_locators_[i]->set_find_element_tol(FP_COINCIDENT);
+    point_locators_[i]->enable_out_of_mesh_mode();
+  }
 
   // will need mesh neighbors to walk the mesh
   m_->find_neighbors();
@@ -2210,23 +2214,9 @@ LibMesh::get_bin(Position r) const
 
   // look-up a tet using the point locator
   libMesh::Point p(r.x, r.y, r.z);
-  auto e = (*point_locator_)(p);
 
-  // sometimes the tet isn't quite right,
-  // make sure this tet contains our point
-  // if not, check this tet's neighbors
-  libMesh::Point dir(rand(), rand(), rand());
-  dir /= dir.norm();
-  if (e && !inside_tet(p, dir, e)) {
-    bool found = false;
-    for (int i = 0; i < e->n_neighbors(); i++) {
-      if (e->neighbor_ptr(i) && inside_tet(p, dir, e->neighbor_ptr(i))) {
-        e = e->neighbor_ptr(i);
-        found = true;
-        break;
-      }
-    }
-  }
+  int thread = omp_get_thread_num();
+  auto e = (*point_locators_[thread])(p);
 
   if (!e) {
     return -1;
@@ -2361,7 +2351,8 @@ LibMesh::intersect_track(libMesh::Point start,
   double track_remaining = track_len;
 
   // attempt to locate a tet for the starting point
-  auto e = (*point_locator_)(start);
+  int thread = omp_get_thread_num();
+  auto e = (*point_locators_[thread])(start);
 
   // the point_locator seems off sometimes,
   // ensure the point is actually in the tet
@@ -2436,7 +2427,9 @@ LibMesh::intersect_track(libMesh::Point start,
       }
       auto orig_e = e;
       start += dir * TINY_BIT; // nudge particle forward
-      e = (*point_locator_)(start);
+
+      int thread = omp_get_thread_num();
+      e = (*point_locators_[thread])(start);
 
       if (!e) {
         if (!orig_e->on_boundary()) {

From 80d35e2bb89677299216e032a47f81b24287294c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 13 Jan 2020 09:31:02 -0600
Subject: [PATCH 018/139] Relying on LibMesh's BoundingBox and Sphere.

---
 include/openmc/mesh.h |  5 ++++-
 src/mesh.cpp          | 19 +++++++++----------
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index da2fca2c87..c458caa7d2 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -24,6 +24,7 @@
 #endif
 
 #ifdef LIBMESH
+#include "libmesh/bounding_box.h"
 #include "libmesh/libmesh.h"
 #include "libmesh/elem.h"
 #include "libmesh/equation_systems.h"
@@ -32,6 +33,7 @@
 #include "libmesh/dof_map.h"
 #include "libmesh/mesh.h"
 #include "libmesh/point.h"
+#include "libmesh/sphere.h"
 #endif
 
 namespace openmc {
@@ -578,7 +580,8 @@ private:
   std::vector<std::unique_ptr<libMesh::PointLocatorBase>> point_locators_; //!< pointers to locators for each thread
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh (for result output)
   std::map<std::string, unsigned int> variable_map_; //!< mappint of variable names (scores) to their numbers on the mesh
-  BoundingBox bbox_; //!< bounding box of the mesh
+  libMesh::BoundingBox bbox_; //!< bounding box of the mesh
+  libMesh::Sphere bsphere_; //<! bounding sphere of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   libMesh::Elem* first_element_; //!< pointer to the first element in the mesh (maybe should be a key?)
   std::set<libMesh::Elem*> boundary_elements_; //<! all boundary elements in the mesh
diff --git a/src/mesh.cpp b/src/mesh.cpp
index a3b36e3de0..a3985de1d4 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -28,6 +28,10 @@
 #include "openmc/tallies/filter.h"
 #include "openmc/xml_interface.h"
 
+#ifdef LIBMESH
+#include "libmesh/mesh_tools.h"
+#endif
+
 namespace openmc {
 
 //==============================================================================
@@ -2083,19 +2087,13 @@ LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   first_element_ = *m_->elements_begin();
 
   // bounding box for the mesh
-  bbox_ = {INFTY, -INFTY, INFTY, -INFTY, INFTY, -INFTY};
+  bbox_ = libMesh::MeshTools::create_bounding_box(*m_);
+  bsphere_ = libMesh::MeshTools::bounding_sphere(*m_);
 
   // determine boundary elements and create bounding box
   for (int i = 0; i < m_->n_elem(); i++) {
     auto e = m_->elem_ptr(i);
 
-    // update bounding box for each node
-    for (int j = 0; j < e->n_nodes(); j++) {
-      auto n = e->node_ref(j);
-      Position r(n(0), n(1), n(2));
-      bbox_.update(r);
-    }
-
     for (int k = 0; k < e->n_neighbors(); k++) {
       if (!e->neighbor_ptr(k)) {
         boundary_elements_.insert(e);
@@ -2210,11 +2208,12 @@ LibMesh::bins_crossed(const Particle* p,
 int
 LibMesh::get_bin(Position r) const
 {
-  if (!bbox_.contains(r)) { return -1; }
-
   // look-up a tet using the point locator
   libMesh::Point p(r.x, r.y, r.z);
 
+  // quick rejection check
+  //  if (bsphere_.above_surface(p) || !bbox_.contains_point(p)) { return -1; }
+  if (!bbox_.contains_point(p)) { return -1; }
   int thread = omp_get_thread_num();
   auto e = (*point_locators_[thread])(p);
 

From df997644b68352bb23ebdd43a7ab59d8cc577fb5 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 13 Jan 2020 10:01:04 -0600
Subject: [PATCH 019/139] Moving bounding box back into surface.

---
 include/openmc/bounding_box.h | 76 -----------------------------------
 include/openmc/mesh.h         |  1 -
 include/openmc/surface.h      | 67 +++++++++++++++++++++++++++++-
 3 files changed, 66 insertions(+), 78 deletions(-)
 delete mode 100644 include/openmc/bounding_box.h

diff --git a/include/openmc/bounding_box.h b/include/openmc/bounding_box.h
deleted file mode 100644
index a1d16cac53..0000000000
--- a/include/openmc/bounding_box.h
+++ /dev/null
@@ -1,76 +0,0 @@
-#ifndef OPENMC_BOUNDING_BOX_H
-#define OPENMC_BOUNDING_BOX_H
-
-#include "openmc/constants.h"
-#include "openmc/position.h"
-
-namespace openmc {
-
-//==============================================================================
-//! Coordinates for an axis-aligned cuboid bounds a geometric object.
-//==============================================================================
-
-struct BoundingBox
-{
-  double xmin = -INFTY;
-  double xmax = INFTY;
-  double ymin = -INFTY;
-  double ymax = INFTY;
-  double zmin = -INFTY;
-  double zmax = INFTY;
-
-
-  inline BoundingBox operator &(const BoundingBox& other) {
-    BoundingBox result = *this;
-    return result &= other;
-  }
-
-  inline BoundingBox operator |(const BoundingBox& other) {
-    BoundingBox result = *this;
-    return result |= other;
-  }
-
-  // intersect operator
-  inline BoundingBox& operator &=(const BoundingBox& other) {
-    xmin = std::max(xmin, other.xmin);
-    xmax = std::min(xmax, other.xmax);
-    ymin = std::max(ymin, other.ymin);
-    ymax = std::min(ymax, other.ymax);
-    zmin = std::max(zmin, other.zmin);
-    zmax = std::min(zmax, other.zmax);
-    return *this;
-  }
-
-  // union operator
-  inline BoundingBox& operator |=(const BoundingBox& other) {
-    xmin = std::min(xmin, other.xmin);
-    xmax = std::max(xmax, other.xmax);
-    ymin = std::min(ymin, other.ymin);
-    ymax = std::max(ymax, other.ymax);
-    zmin = std::min(zmin, other.zmin);
-    zmax = std::max(zmax, other.zmax);
-    return *this;
-  }
-
-  // ensure bounding box contains a point
-  inline void update(const Position& r) {
-    xmin = std::min(xmin, r.x);
-    xmax = std::max(xmax, r.x);
-    ymin = std::min(ymin, r.y);
-    ymax = std::max(ymax, r.y);
-    zmin = std::min(zmin, r.z);
-    zmax = std::max(zmax, r.z);
-  }
-
-  // check if a point is in the box
-  inline bool contains(const Position& r) const {
-    if (r.x < xmin || r.x > xmax) { return false; }
-    if (r.y < ymin || r.y > ymax) { return false; }
-    if (r.z < zmin || r.z > zmax) { return false; }
-    return true;
-  }
-
-};
-
-} // namespace openmc
-#endif // OPENMC_BOUNDING_BOX_H
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index c458caa7d2..5e16c6a62e 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -12,7 +12,6 @@
 #include "pugixml.hpp"
 #include "xtensor/xtensor.hpp"
 
-#include "openmc/bounding_box.h"
 #include "openmc/particle.h"
 #include "openmc/position.h"
 
diff --git a/include/openmc/surface.h b/include/openmc/surface.h
index c2f461030e..5ef903f527 100644
--- a/include/openmc/surface.h
+++ b/include/openmc/surface.h
@@ -11,7 +11,6 @@
 #include "pugixml.hpp"
 
 #include "openmc/boundary_condition.h"
-#include "openmc/bounding_box.h"
 #include "openmc/constants.h"
 #include "openmc/particle.h"
 #include "openmc/position.h"
@@ -30,6 +29,72 @@ namespace model {
   extern std::vector<std::unique_ptr<Surface>> surfaces;
 } // namespace model
 
+//==============================================================================
+//! Coordinates for an axis-aligned cuboid bounding a geometric object.
+//==============================================================================
+
+struct BoundingBox
+{
+  double xmin = -INFTY;
+  double xmax = INFTY;
+  double ymin = -INFTY;
+  double ymax = INFTY;
+  double zmin = -INFTY;
+  double zmax = INFTY;
+
+
+  inline BoundingBox operator &(const BoundingBox& other) {
+    BoundingBox result = *this;
+    return result &= other;
+  }
+
+  inline BoundingBox operator |(const BoundingBox& other) {
+    BoundingBox result = *this;
+    return result |= other;
+  }
+
+  // intersect operator
+  inline BoundingBox& operator &=(const BoundingBox& other) {
+    xmin = std::max(xmin, other.xmin);
+    xmax = std::min(xmax, other.xmax);
+    ymin = std::max(ymin, other.ymin);
+    ymax = std::min(ymax, other.ymax);
+    zmin = std::max(zmin, other.zmin);
+    zmax = std::min(zmax, other.zmax);
+    return *this;
+  }
+
+  // union operator
+  inline BoundingBox& operator |=(const BoundingBox& other) {
+    xmin = std::min(xmin, other.xmin);
+    xmax = std::max(xmax, other.xmax);
+    ymin = std::min(ymin, other.ymin);
+    ymax = std::max(ymax, other.ymax);
+    zmin = std::min(zmin, other.zmin);
+    zmax = std::max(zmax, other.zmax);
+    return *this;
+  }
+
+  // ensure bounding box contains a point
+  inline void update(const Position& r) {
+    xmin = std::min(xmin, r.x);
+    xmax = std::max(xmax, r.x);
+    ymin = std::min(ymin, r.y);
+    ymax = std::max(ymax, r.y);
+    zmin = std::min(zmin, r.z);
+    zmax = std::max(zmax, r.z);
+  }
+
+  // check if a point is in the box
+  inline bool contains(const Position& r) const {
+    if (r.x < xmin || r.x > xmax) { return false; }
+    if (r.y < ymin || r.y > ymax) { return false; }
+    if (r.z < zmin || r.z > zmax) { return false; }
+    return true;
+  }
+
+};
+
 //==============================================================================
 //! A geometry primitive used to define regions of 3D space.
 //==============================================================================

From 9695cc2771c3cae811894a278ba3a0c5ea489a48 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 13 Jan 2020 11:34:15 -0600
Subject: [PATCH 020/139] Updating unstructured mesh constructors to take a
 filename (template). Exposing unstructured mesh addition via CAPI.

---
 include/openmc/capi.h |  1 +
 include/openmc/mesh.h | 15 +++++++++++++--
 src/mesh.cpp          | 43 ++++++++++++++++++++++++++++++++++++++++++-
 3 files changed, 56 insertions(+), 3 deletions(-)

diff --git a/include/openmc/capi.h b/include/openmc/capi.h
index a36f0e280c..64b62f3e5b 100644
--- a/include/openmc/capi.h
+++ b/include/openmc/capi.h
@@ -47,6 +47,7 @@ extern "C" {
   int openmc_get_mesh_index(int32_t id, int32_t* index);
   int openmc_get_n_batches(int* n_batches, bool get_max_batches);
   int openmc_get_nuclide_index(const char name[], int* index);
+  int openmc_add_unstructured_mesh(const char filename[], const char library[], int* id);
   int64_t openmc_get_seed();
   int openmc_get_tally_index(int32_t id, int32_t* index);
   void openmc_get_tally_next_id(int32_t* id);
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 5e16c6a62e..77c01bd3aa 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -266,17 +266,23 @@ public:
 
 class UnstructuredMeshBase : public Mesh {
 public:
+  // constructors
+  UnstructuredMeshBase() {};
   UnstructuredMeshBase(pugi::xml_node node);
+  UnstructuredMeshBase(const std::string& filename);
+
   std::string filename_;
 };
 
 #ifdef DAGMC
 
-class UnstructuredMesh : public Mesh {
+class UnstructuredMesh : public UnstructuredMeshBase {
 
 public:
+
   UnstructuredMesh() = default;
   UnstructuredMesh(pugi::xml_node);
+  UnstructuredMesh(const std::string& filename);
   ~UnstructuredMesh() = default;
 
   void bins_crossed(const Particle& p,
@@ -447,8 +453,9 @@ class LibMesh : public UnstructuredMeshBase {
   typedef std::vector<std::pair<double, const libMesh::Elem*>> UnstructuredMeshHits;
 
 public:
-  // Constructor
+  // Constructors
   LibMesh(pugi::xml_node node);
+  LibMesh(const std::string& filename);
 
   // Methods
 
@@ -489,6 +496,10 @@ public:
 
 private:
 
+  //! Setup method for the mesh. Builds data structures,
+  //! element mapping, etc.
+  void initialize();
+
   //! Translate a bin value to an element pointer
   const libMesh::Elem* get_element_from_bin(int bin) const;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index a3985de1d4..063c3a9047 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1295,6 +1295,27 @@ openmc_extend_meshes(int32_t n, const char* type, int32_t* index_start,
   return 0;
 }
 
+//! Adds a new unstructured mesh to OpenMC
+extern "C" int openmc_add_unstrucutred_mesh(const char filename[],
+                                            const char library[],
+                                            int* mesh_id)
+{
+  if (library == "moab") {
+    model::meshes.push_back(std::move(std::make_unique<UnstructuredMesh>(filename)));
+  } else if (library == "libmesh") {
+    model::meshes.push_back(std::move(std::make_unique<LibMesh>(filename)));
+  } else {
+    set_errmsg("Mesh library " + std::string(library) + \
+               "is not supported by this build of OpenMC");
+    return OPENMC_E_INVALID_ARGUMENT;
+  }
+
+  *mesh_id = model::meshes.back()->id_;
+
+  return 0;
+}
+
+
 //! Return the index in the meshes array of a mesh with a given ID
 extern "C" int
 openmc_get_mesh_index(int32_t id, int32_t* index)
@@ -1452,6 +1473,7 @@ openmc_rectilinear_mesh_set_grid(int32_t index, const double* grid_x,
 
 #ifdef DAGMC
 
+<<<<<<< HEAD
 UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node)
 {
   // unstructured always assumed to be 3D
@@ -1472,7 +1494,18 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node)
     fatal_error("No filename supplied for unstructured mesh with ID: " +
                 std::to_string(id_));
   }
+=======
+UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+  initialize();
+}
 
+UnstructuredMesh::UnstructuredMesh(const std::string& filename) {
+  filename_ = filename;
+  initialize();
+}
+>>>>>>> Updating unstructured mesh constructors to take a filename (template). Exposing unstructured mesh addition via CAPI.
+
+void UnstructuredMesh::initialize() {
   // create MOAB instance
   mbi_ = std::make_unique<moab::Core>();
   // load unstructured mesh file
@@ -2057,8 +2090,16 @@ UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
 
 #ifdef LIBMESH
 LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+  initialize();
+}
 
-  // always 3 for unstructured meshes
+LibMesh::LibMesh(const std::string& filename) {
+  filename_ = filename;
+  initialize();
+}
+
+void LibMesh::initialize() {
+    // always 3 for unstructured meshes
   n_dimension_ = 3;
 
   m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(settings::LMI->comm(), 3));

From 4d607e52b803f0b0de734f70a3e0d179c2262cee Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 13 Jan 2020 11:39:02 -0600
Subject: [PATCH 021/139] Setting id appropriately for new unstructured mesh.

---
 src/mesh.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 063c3a9047..0840d0144b 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1310,7 +1310,12 @@ extern "C" int openmc_add_unstrucutred_mesh(const char filename[],
     return OPENMC_E_INVALID_ARGUMENT;
   }
 
-  *mesh_id = model::meshes.back()->id_;
+  int id = 0;
+  for (const auto& m : model::meshes) { id = std::max(m->id_, id); }
+
+  id += 1;
+  model::meshes.back()->id_ = id;
+  *mesh_id = id;
 
   return 0;
 }

From f3f114ff0e862037ca47733a9277152a3fa5c131 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 14 Feb 2020 14:49:22 -0600
Subject: [PATCH 022/139] Removing call to set LibMesh threads and updating
 call for setting point containment tolerance.

---
 include/openmc/mesh.h |  19 +++--
 src/initialize.cpp    |   2 +-
 src/mesh.cpp          | 163 +++++++++++++++++++++++++++++++-----------
 3 files changed, 134 insertions(+), 50 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 77c01bd3aa..aa1693b69f 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -96,12 +96,6 @@ public:
   //! \param[in] group HDF5 group
   virtual void to_hdf5(hid_t group) const = 0;
 
-  //! \param[in] bank Array of bank sites
-  //! \param[out] Whether any bank sites are outside the mesh
-  //! \return Array indicating number of sites in each mesh/energy bin
-  virtual xt::xarray<double>
-  count_sites(const std::vector<Particle::Bank>& bank, bool* outside) const;
-
   //! Find the mesh lines that intersect an axis-aligned slice plot
   //
   //! \param[in] plot_ll The lower-left coordinates of the slice plot.
@@ -230,6 +224,17 @@ public:
 
   void to_hdf5(hid_t group) const override;
 
+  // New methods
+
+  //! Count number of bank sites in each mesh bin / energy bin
+  //
+  //! \param[in] bank Array of bank sites
+  //! \param[out] Whether any bank sites are outside the mesh
+  //! \return Array indicating number of sites in each mesh/energy bin
+  xt::xtensor<double, 1> count_sites(const Particle::Bank* bank,
+                                     int64_t length,
+                                     bool* outside) const;
+
   // Data members
   double volume_frac_; //!< Volume fraction of each mesh element
   xt::xtensor<double, 1> width_; //!< Width of each mesh element
@@ -271,6 +276,8 @@ public:
   UnstructuredMeshBase(pugi::xml_node node);
   UnstructuredMeshBase(const std::string& filename);
 
+  std::string bin_label(int bin) const override;
+
   std::string filename_;
 };
 
diff --git a/src/initialize.cpp b/src/initialize.cpp
index f4874d9f23..3b1abfc9ae 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -73,7 +73,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 #ifdef LIBMESH
-  settings::LMI = new libMesh::LibMeshInit(argc, argv, omp_get_max_threads());
+  settings::LMI = new libMesh::LibMeshInit(argc, argv);
 #endif
 
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 0840d0144b..6697b277a5 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -107,6 +107,125 @@ StructuredMesh::bin_label(int bin) const {
   }
 }
 
+//==============================================================================
+// Untructured Mesh implementation
+//==============================================================================
+
+UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
+  // check the mesh type
+  if (check_for_node(node, "type")) {
+    auto temp = get_node_value(node, "type", true, true);
+    if (temp != "unstructured") {
+      fatal_error("Invalid mesh type: " + temp);
+    }
+  }
+
+  // get the filename of the unstructured mesh to load
+  if (check_for_node(node, "mesh_file")) {
+    filename_ = get_node_value(node, "mesh_file");
+  }
+  else {
+    fatal_error("No filename supplied for unstructured mesh with ID: " +
+                std::to_string(id_));
+  }
+}
+
+std::string
+UnstructuredMeshBase::bin_label(int bin) const {
+  std::stringstream out;
+  out << "Mesh Index (" << bin << ")";
+  return out.str();
+};
+
+//==============================================================================
+// RegularMesh implementation
+//==============================================================================
+
+RegularMesh::RegularMesh(pugi::xml_node node)
+  : StructuredMesh {node}
+{
+  // Determine number of dimensions for mesh
+  if (check_for_node(node, "dimension")) {
+    shape_ = get_node_xarray<int>(node, "dimension");
+    int n = n_dimension_ = shape_.size();
+    if (n != 1 && n != 2 && n != 3) {
+      fatal_error("Mesh must be one, two, or three dimensions.");
+    }
+
+    // Check that dimensions are all greater than zero
+    if (xt::any(shape_ <= 0)) {
+      fatal_error("All entries on the <dimension> element for a tally "
+        "mesh must be positive.");
+    }
+  }
+
+  // Check for lower-left coordinates
+  if (check_for_node(node, "lower_left")) {
+    // Read mesh lower-left corner location
+    lower_left_ = get_node_xarray<double>(node, "lower_left");
+  } else {
+    fatal_error("Must specify <lower_left> on a mesh.");
+  }
+
+  if (check_for_node(node, "width")) {
+    // Make sure both upper-right or width were specified
+    if (check_for_node(node, "upper_right")) {
+      fatal_error("Cannot specify both <upper_right> and <width> on a mesh.");
+    }
+
+    width_ = get_node_xarray<double>(node, "width");
+
+    // Check to ensure width has same dimensions
+    auto n = width_.size();
+    if (n != lower_left_.size()) {
+      fatal_error("Number of entries on <width> must be the same as "
+        "the number of entries on <lower_left>.");
+    }
+
+    // Check for negative widths
+    if (xt::any(width_ < 0.0)) {
+      fatal_error("Cannot have a negative <width> on a tally mesh.");
+    }
+
+    // Set width and upper right coordinate
+    upper_right_ = xt::eval(lower_left_ + shape_ * width_);
+
+  } else if (check_for_node(node, "upper_right")) {
+    upper_right_ = get_node_xarray<double>(node, "upper_right");
+
+    // Check to ensure width has same dimensions
+    auto n = upper_right_.size();
+    if (n != lower_left_.size()) {
+      fatal_error("Number of entries on <upper_right> must be the "
+        "same as the number of entries on <lower_left>.");
+    }
+
+    // Check that upper-right is above lower-left
+    if (xt::any(upper_right_ < lower_left_)) {
+      fatal_error("The <upper_right> coordinates must be greater than "
+        "the <lower_left> coordinates on a tally mesh.");
+    }
+
+    // Set width
+    if (shape_.size() > 0) {
+      width_ = xt::eval((upper_right_ - lower_left_) / shape_);
+    }
+  } else {
+    fatal_error("Must specify either <upper_right> and <width> on a mesh.");
+  }
+
+  // Make sure lower_left and dimension match
+  if (shape_.size() > 0) {
+    if (shape_.size() != lower_left_.size()) {
+      fatal_error("Number of entries on <lower_left> must be the same "
+        "as the number of entries on <dimension>.");
+    }
+
+    // Set volume fraction
+    volume_frac_ = 1.0/xt::prod(shape_)();
+  }
+}
+
 void StructuredMesh::get_indices(Position r, int* ijk, bool* in_mesh) const
 {
   *in_mesh = true;
@@ -1478,28 +1597,6 @@ openmc_rectilinear_mesh_set_grid(int32_t index, const double* grid_x,
 
 #ifdef DAGMC
 
-<<<<<<< HEAD
-UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node)
-{
-  // unstructured always assumed to be 3D
-  n_dimension_ = 3;
-
-  // check the mesh type
-  if (check_for_node(node, "type")) {
-    auto temp = get_node_value(node, "type", true, true);
-    if (temp != "unstructured") {
-      fatal_error("Invalid mesh type: " + temp);
-    }
-  }
-
-  // get the filename of the unstructured mesh to load
-  if (check_for_node(node, "filename")) {
-    filename_ = get_node_value(node, "filename");
-  } else {
-    fatal_error("No filename supplied for unstructured mesh with ID: " +
-                std::to_string(id_));
-  }
-=======
 UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   initialize();
 }
@@ -1508,7 +1605,6 @@ UnstructuredMesh::UnstructuredMesh(const std::string& filename) {
   filename_ = filename;
   initialize();
 }
->>>>>>> Updating unstructured mesh constructors to take a filename (template). Exposing unstructured mesh addition via CAPI.
 
 void UnstructuredMesh::initialize() {
   // create MOAB instance
@@ -2069,29 +2165,10 @@ UnstructuredMesh::count_sites(const std::vector<Particle::Bank>& bank,
 double UnstructuredMesh::get_volume_frac(int bin = -1) const {
 
   return 0.0;
-
 }
 
 #endif
 
-UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
-    // check the mesh type
-  if (check_for_node(node, "type")) {
-    auto temp = get_node_value(node, "type", true, true);
-    if (temp != "unstructured") {
-      fatal_error("Invalid mesh type: " + temp);
-    }
-  }
-
-  // get the filename of the unstructured mesh to load
-  if (check_for_node(node, "mesh_file")) {
-    filename_ = get_node_value(node, "mesh_file");
-  }
-  else {
-    fatal_error("No filename supplied for unstructured mesh with ID: " +
-                std::to_string(id_));
-  }
-}
 
 #ifdef LIBMESH
 LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
@@ -2123,7 +2200,7 @@ void LibMesh::initialize() {
   point_locators_ = std::vector<std::unique_ptr<libMesh::PointLocatorBase>>(n_threads);
   for (int i = 0; i < n_threads; i++) {
     point_locators_[i] = m_->sub_point_locator();
-    point_locators_[i]->set_find_element_tol(FP_COINCIDENT);
+    point_locators_[i]->set_contains_point_tol(FP_COINCIDENT);
     point_locators_[i]->enable_out_of_mesh_mode();
   }
 

From 483ec403fd1883c3e348665d9d303d13b165dc95 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 18 Feb 2020 17:03:54 -0600
Subject: [PATCH 023/139] Fixing CAPI function for unstructured mesh.

---
 src/mesh.cpp | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 6697b277a5..f311448da5 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1419,11 +1419,22 @@ extern "C" int openmc_add_unstrucutred_mesh(const char filename[],
                                             const char library[],
                                             int* mesh_id)
 {
+  bool valid_lib = false;
+#ifdef DAGMC
   if (library == "moab") {
     model::meshes.push_back(std::move(std::make_unique<UnstructuredMesh>(filename)));
-  } else if (library == "libmesh") {
+    valid_lib = true;
+  }
+#endif
+
+#ifdef LIBMESH
+  if (library == "libmesh") {
     model::meshes.push_back(std::move(std::make_unique<LibMesh>(filename)));
-  } else {
+    valid_lib = true;
+  }
+#endif
+
+  if (!valid_lib) {
     set_errmsg("Mesh library " + std::string(library) + \
                "is not supported by this build of OpenMC");
     return OPENMC_E_INVALID_ARGUMENT;

From e79932e44298a1cc6174ebbf06aa99850c3d15e4 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 4 Mar 2020 18:47:19 -0600
Subject: [PATCH 024/139] Fixes after rebase.

---
 include/openmc/mesh.h |  4 ++++
 src/mesh.cpp          | 13 +++----------
 2 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index aa1693b69f..c0b1dbefcd 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -219,6 +219,10 @@ public:
 
   double negative_grid_boundary(int* ijk, int i) const override;
 
+  std::string bin_label(int bin) const override;
+
+  int n_surface_bins() const override;
+
   std::pair<std::vector<double>, std::vector<double>>
   plot(Position plot_ll, Position plot_ur) const override;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index f311448da5..e4edc3be42 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1054,7 +1054,7 @@ RegularMesh::count_sites(const Particle::Bank* bank,
   return counts;
 }
 
-std::string RegularMesh::get_label_for_bin(int bin) const {
+std::string RegularMesh::bin_label(int bin) const {
   int ijk[n_dimension_];
   get_indices_from_bin(bin, ijk);
 
@@ -1067,10 +1067,6 @@ std::string RegularMesh::get_label_for_bin(int bin) const {
   return out.str();
 }
 
-double RegularMesh::get_volume_frac(int bin) const {
-  return volume_frac_;
-}
-
 //==============================================================================
 // RectilinearMesh implementation
 //==============================================================================
@@ -1775,15 +1771,12 @@ UnstructuredMesh::bins_crossed(const Particle& p,
     auto tet = get_tet(segment_midpoint);
     if (tet) {
       bins.push_back(get_bin_from_ent_handle(tet));
-      lengths.push_back((hit.first - last_dist) / track_len);
+      lengths.push_back((hit->first - last_dist) / track_len);
     } else {
       // if in the loop, we should always find a tet
       warning("No tet found for location between trianle hits");
     }
 
-    bins.push_back(bin);
-    lengths.push_back(segment_length / track_len);
-
   }
 
   // tally remaining portion of track after last hit if
@@ -2334,7 +2327,7 @@ LibMesh::bins_crossed(const Particle* p,
   lengths.clear();
 
   for (const auto& hit : hits) {
-    lengths.push_back(hit.first / track_len);
+    lengths.push_back(hit->first / track_len);
     bins.push_back(get_bin_from_element(hit.second));
   }
 }

From a03d58cb59bdd05ee49a91fe017a7559956b9a7c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 26 Mar 2020 18:39:44 -0500
Subject: [PATCH 025/139] Updating build style for libmesh.

---
 CMakeLists.txt                  |  6 +-----
 cmake/Modules/FindLIBMESH.cmake | 13 ++++++-------
 2 files changed, 7 insertions(+), 12 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 10cfd5aa03..f5bbf44280 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -71,9 +71,6 @@ else()
 #===============================================================================
 if(libmesh)
   find_package(LIBMESH REQUIRED)
-  set(LIBMESH_LIBRARIES mesh_devel timpi_devel netcdf)
-  set(LIBMESH_INCLUDE_DIRS "${LIBMESH}/../include/")
-  link_directories(${LIBMESH})
 endif()
 
 #===============================================================================
@@ -377,6 +374,7 @@ target_include_directories(libopenmc
     $<INSTALL_INTERFACE:include>
     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
     ${HDF5_INCLUDE_DIRS}
+    ${LIBMESH_INCLUDE_DIRECTORIES}
 )
 
 # Set compile flags
@@ -415,9 +413,7 @@ if(dagmc)
 endif()
 
 if(libmesh)
-  target_compile_definitions(libopenmc PRIVATE LIBMESH)
   target_link_libraries(libopenmc ${LIBMESH_LIBRARIES})
-  target_include_directories(libopenmc PRIVATE ${LIBMESH_INCLUDE_DIRS})
 endif()
 
 #===============================================================================
diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 3e813e9d76..1039e3e3bb 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -7,15 +7,14 @@
 #  LIBMESH_LIBRARIES - Link these to use LIBMESH
 #  LIBMESH_DEFINITIONS - Compiler switches required for using LIBMESH
 
-find_path(LIBMESH NAMES libmesh_opt.so
+find_path(LIBMESH_PC NAMES libmesh-opt.pc
           HINTS ${LIBMESH_ROOT} $ENV{LIBMESH_ROOT}
           PATHS ENV LD_LIBRARY_PATH
-          PATH_SUFFIXES lib Lib
+          PATH_SUFFIXES lib/pkgconfig pkgconfig
           NO_DEFAULT_PATH)
 
-if(DEFINED LIBMESH)
-  set(LIBMESH_FOUND TRUE)
-endif()
+include(FindPkgConfig)
+set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
+pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
 
-
-message(STATUS "Found LIBMESH in ${LIBMESH}")
+message(STATUS "Found LIBMESH in ${LIBMESH_PC}")

From 1115485173e3b7c53dbd7faea89ddc4e9da92b3d Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 27 Mar 2020 13:23:01 -0500
Subject: [PATCH 026/139] Making sure libmesh flags are included if enabled.

---
 CMakeLists.txt | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f5bbf44280..fa79883043 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -123,6 +123,11 @@ if(openmp)
   endif()
 endif()
 
+if (libmesh)
+  list(APPEND cxxflags ${LIBMESH_CFLAGS})
+  list(APPEND ldflags ${LIBMESH_CFLAGS})
+endif()
+
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
 list(APPEND cxxflags -O2)
@@ -374,7 +379,7 @@ target_include_directories(libopenmc
     $<INSTALL_INTERFACE:include>
     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
     ${HDF5_INCLUDE_DIRS}
-    ${LIBMESH_INCLUDE_DIRECTORIES}
+    ${LIBMESH_INCLUDE_DIRS}
 )
 
 # Set compile flags
@@ -389,6 +394,9 @@ endif()
 if (MPI_ENABLED)
   target_compile_definitions(libopenmc PUBLIC -DOPENMC_MPI)
 endif()
+if (libmesh)
+  target_compile_definitions(libopenmc PUBLIC -DLIBMESH)
+endif()
 
 # Set git SHA1 hash as a compile definition
 if(GIT_FOUND)

From 42e8e62a6eaa119bf88543288a14298e16bfddf4 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 27 Mar 2020 13:23:42 -0500
Subject: [PATCH 027/139] A couple updates to the solution setting. Matching
 variable name to CAPI header. Correction to function name typo.

---
 src/mesh.cpp | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index e4edc3be42..375127bd8c 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -30,6 +30,7 @@
 
 #ifdef LIBMESH
 #include "libmesh/mesh_tools.h"
+#include "libmesh/numeric_vector.h"
 #endif
 
 namespace openmc {
@@ -1411,9 +1412,9 @@ openmc_extend_meshes(int32_t n, const char* type, int32_t* index_start,
 }
 
 //! Adds a new unstructured mesh to OpenMC
-extern "C" int openmc_add_unstrucutred_mesh(const char filename[],
+extern "C" int openmc_add_unstructured_mesh(const char filename[],
                                             const char library[],
-                                            int* mesh_id)
+                                            int* id)
 {
   bool valid_lib = false;
 #ifdef DAGMC
@@ -1436,12 +1437,12 @@ extern "C" int openmc_add_unstrucutred_mesh(const char filename[],
     return OPENMC_E_INVALID_ARGUMENT;
   }
 
-  int id = 0;
-  for (const auto& m : model::meshes) { id = std::max(m->id_, id); }
+  int mesh_id = 0;
+  for (const auto& m : model::meshes) { mesh_id = std::max(m->id_, mesh_id); }
 
-  id += 1;
-  model::meshes.back()->id_ = id;
-  *mesh_id = id;
+  mesh_id += 1;
+  model::meshes.back()->id_ = mesh_id;
+  *id = mesh_id;
 
   return 0;
 }
@@ -2273,7 +2274,7 @@ LibMesh::get_indices_from_bin(int bin, int* ijk) const {
 
 const libMesh::Elem*
 LibMesh::get_element_from_bin(int bin) const {
-  m_->elem_ptr(bin);
+  return m_->elem_ptr(bin);
 }
 
 void
@@ -2297,7 +2298,7 @@ LibMesh::set_variable(const std::string& var_name, int bin, double value) {
   std::vector<libMesh::dof_id_type> dof_indices;
   dof_map.dof_indices(e, dof_indices, var_num);
   Ensures(dof_indices.size() == 1);
-  (*eqn_sys.solution).set(dof_indices[0], value);
+  eqn_sys.solution->set(dof_indices[0], value);
 }
 
 void LibMesh::write(const std::string& filename) const {
@@ -2327,7 +2328,7 @@ LibMesh::bins_crossed(const Particle* p,
   lengths.clear();
 
   for (const auto& hit : hits) {
-    lengths.push_back(hit->first / track_len);
+    lengths.push_back(hit.first / track_len);
     bins.push_back(get_bin_from_element(hit.second));
   }
 }

From 48d62e75015dc33718dd4b16b01f591680564bf1 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sun, 29 Mar 2020 23:01:24 -0500
Subject: [PATCH 028/139] A few updates to address memory leaks.

---
 include/openmc/mesh.h     | 2 +-
 include/openmc/settings.h | 2 +-
 src/finalize.cpp          | 2 +-
 src/initialize.cpp        | 2 +-
 src/mesh.cpp              | 4 +---
 src/settings.cpp          | 2 +-
 6 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index c0b1dbefcd..318fa3fe70 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -600,7 +600,7 @@ private:
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libmesh mesh instance
   std::vector<std::unique_ptr<libMesh::PointLocatorBase>> point_locators_; //!< pointers to locators for each thread
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh (for result output)
-  std::map<std::string, unsigned int> variable_map_; //!< mappint of variable names (scores) to their numbers on the mesh
+  std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (scores) to their numbers on the mesh
   libMesh::BoundingBox bbox_; //!< bounding box of the mesh
   libMesh::Sphere bsphere_; //<! bounding sphere of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index b6044cf483..ce2e7b10a3 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -28,7 +28,7 @@ namespace openmc {
 namespace settings {
 
 #ifdef LIBMESH
-extern libMesh::LibMeshInit* LMI;
+extern std::unique_ptr<libMesh::LibMeshInit> LMI;
 #endif
 
 // Boolean flags
diff --git a/src/finalize.cpp b/src/finalize.cpp
index f42e3134af..084ed24b83 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -143,7 +143,7 @@ int openmc_finalize()
 #endif
 
 #ifdef LIBMESH
-  delete settings::LMI;
+std::unique_ptr<libMesh::LibMeshInit> LMI;
 #endif
 
   return 0;
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 3b1abfc9ae..e6ca245c05 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -73,7 +73,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 #ifdef LIBMESH
-  settings::LMI = new libMesh::LibMeshInit(argc, argv);
+  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv);
 #endif
 
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 375127bd8c..40bd5858c3 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2195,8 +2195,7 @@ void LibMesh::initialize() {
 
   eq_system_name_ = "mesh_" + std::to_string(id_) + "_system";
 
-  equation_systems_ =
-    std::unique_ptr<libMesh::EquationSystems>(new libMesh::EquationSystems(*m_));
+  equation_systems_ = std::make_unique<libMesh::EquationSystems>(*m_);
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
@@ -2307,7 +2306,6 @@ void LibMesh::write(const std::string& filename) const {
   exo.write_discontinuous_exodusII(filename, *equation_systems_, &systems_out);
 }
 
-
 void
 LibMesh::bins_crossed(const Particle* p,
                       std::vector<int>& bins,
diff --git a/src/settings.cpp b/src/settings.cpp
index 21ce932ad5..3326f0029a 100644
--- a/src/settings.cpp
+++ b/src/settings.cpp
@@ -38,7 +38,7 @@ namespace openmc {
 namespace settings {
 
 #ifdef LIBMESH
-libMesh::LibMeshInit* LMI;
+std::unique_ptr<libMesh::LibMeshInit> LMI;
 #endif
 
 // Default values for boolean flags

From 2c1d6162d7bbda503362e2ee8a2139de96b33ec1 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 30 Mar 2020 09:56:59 -0500
Subject: [PATCH 029/139] Release LibMesh init during finalize.

---
 src/finalize.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/finalize.cpp b/src/finalize.cpp
index 084ed24b83..7e58ba2fe3 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -143,7 +143,7 @@ int openmc_finalize()
 #endif
 
 #ifdef LIBMESH
-std::unique_ptr<libMesh::LibMeshInit> LMI;
+  settings::LMI.release();
 #endif
 
   return 0;

From 6973ce28d0be007ec4c39f03e33ede1be1e29957 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sat, 4 Apr 2020 13:06:31 -0500
Subject: [PATCH 030/139] Updates to the LibMesh methods for adding variables
 and writing.

---
 CMakeLists.txt               |  2 +-
 include/openmc/mesh.h        | 18 +++++++++--
 include/openmc/state_point.h |  2 +-
 src/mesh.cpp                 | 60 +++++++++++++++++++++++++++---------
 src/state_point.cpp          | 34 +++++++++-----------
 5 files changed, 77 insertions(+), 39 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index fa79883043..606a8518e4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -395,7 +395,7 @@ if (MPI_ENABLED)
   target_compile_definitions(libopenmc PUBLIC -DOPENMC_MPI)
 endif()
 if (libmesh)
-  target_compile_definitions(libopenmc PUBLIC -DLIBMESH)
+  target_compile_definitions(libopenmc PRIVATE LIBMESH)
 endif()
 
 # Set git SHA1 hash as a compile definition
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 318fa3fe70..e8ed7d3203 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -282,6 +282,16 @@ public:
 
   std::string bin_label(int bin) const override;
 
+  //! Add a variable to the libmesh mesh instance
+  virtual void add_score(const std::string& var_name) = 0;
+
+  //! Set the value of a bin for a variable on the libmesh mesh instance
+  virtual void set_score_data(const std::string& var_name,
+                              std::vector<double> values,
+                              std::vector<double> std_dev) const = 0;
+
+  virtual void write(std::string filename) const = 0;
+
   std::string filename_;
 };
 
@@ -495,15 +505,17 @@ public:
 
   bool intersects(Position& r0, Position r1, int* ijk) const;
 
-  void write(const std::string& filename) const;
+  void write(std::string filename) const override;
 
   void to_hdf5(hid_t group) const;
 
   //! Add a variable to the libmesh mesh instance
-  void add_variable(const std::string& var_name);
+  void add_score(const std::string& var_name) override;
 
   //! Set the value of a bin for a variable on the libmesh mesh instance
-  void set_variable(const std::string& var_name, int bin, double value);
+  void set_score_data(const std::string& var_name,
+                      std::vector<double> values,
+                      std::vector<double> std_dev) const override;
 
 private:
 
diff --git a/include/openmc/state_point.h b/include/openmc/state_point.h
index 53f072b85b..e3cd790db0 100644
--- a/include/openmc/state_point.h
+++ b/include/openmc/state_point.h
@@ -19,7 +19,7 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
 void write_tally_results_nr(hid_t file_id);
 void restart_set_keff();
 
-#ifdef DAGMC
+#ifdef LIBMESH
 void write_unstructured_mesh_results();
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 40bd5858c3..3050bdeae3 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2277,30 +2277,60 @@ LibMesh::get_element_from_bin(int bin) const {
 }
 
 void
-LibMesh::add_variable(const std::string& var_name) {
+LibMesh::add_score(const std::string& var_name) {
   // check if this is a new varaible
-  if (!variable_map_.count(var_name)) {
+  std::string value_name = var_name + "_value";
+  if (!variable_map_.count(value_name)) {
     auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
-    auto var_num = eqn_sys.add_variable(var_name, libMesh::CONSTANT, libMesh::MONOMIAL);
-    variable_map_[var_name] = var_num;
+    auto var_num = eqn_sys.add_variable(value_name, libMesh::CONSTANT, libMesh::MONOMIAL);
+    variable_map_[value_name] = var_num;
+    equation_systems_->init();
+  }
+
+  std::string std_dev_name = var_name + "_std_dev";
+  // check if this is a new varaible
+  if (!variable_map_.count(std_dev_name)) {
+    auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
+    auto var_num = eqn_sys.add_variable(std_dev_name, libMesh::CONSTANT, libMesh::MONOMIAL);
+    variable_map_[std_dev_name] = var_num;
     equation_systems_->init();
   }
 }
 
 void
-LibMesh::set_variable(const std::string& var_name, int bin, double value) {
-  // look up the variable
-  unsigned int var_num = variable_map_.at(var_name);
-  auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
-  const libMesh::DofMap& dof_map = eqn_sys.get_dof_map();
-  auto e = m_->elem_ptr(bin);
-  std::vector<libMesh::dof_id_type> dof_indices;
-  dof_map.dof_indices(e, dof_indices, var_num);
-  Ensures(dof_indices.size() == 1);
-  eqn_sys.solution->set(dof_indices[0], value);
+LibMesh::set_score_data(const std::string& var_name,
+                      std::vector<double> values,
+                      std::vector<double> std_dev) const
+{
+    auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
+    const libMesh::DofMap& dof_map = eqn_sys.get_dof_map();
+
+    // look up the value variable
+    std::string value_name = var_name + "_value";
+    unsigned int value_num = variable_map_.at(value_name);
+    // look up the std dev variable
+    std::string std_dev_name = var_name + "_std_dev";
+    unsigned int std_dev_num = variable_map_.at(std_dev_name);
+
+  for (int bin = 0; bin < this->n_bins(); bin++) {
+    auto e = m_->elem_ptr(bin);
+
+    // set value
+    std::vector<libMesh::dof_id_type> value_dof_indices;
+    dof_map.dof_indices(e, value_dof_indices, value_num);
+    Ensures(value_dof_indices.size() == 1);
+    eqn_sys.solution->set(value_dof_indices[0], values[bin]);
+
+    // set std dev
+    std::vector<libMesh::dof_id_type> std_dev_dof_indices;
+    dof_map.dof_indices(e, std_dev_dof_indices, std_dev_num);
+    Ensures(std_dev_dof_indices.size() == 1);
+    eqn_sys.solution->set(std_dev_dof_indices[0], std_dev[bin]);
+
+  }
 }
 
-void LibMesh::write(const std::string& filename) const {
+void LibMesh::write(std::string filename) const {
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
   exo.write_discontinuous_exodusII(filename, *equation_systems_, &systems_out);
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 956518c1c3..b50b752fa6 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -770,31 +770,27 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
   H5Tclose(banktype);
 }
 
-#ifdef DAGMC
+#ifdef LIBMESH
 void write_unstructured_mesh_results() {
-
+  std::cout << "Checking for unstructured meshes to write..." << std::endl;
   for (auto& tally : model::tallies) {
 
     std::vector<std::string> tally_scores;
     for (auto filter_idx : tally->filters()) {
       auto& filter = model::tally_filters[filter_idx];
-      if (filter->type() != "mesh") continue;
-
-      // check if the filter uses an unstructured mesh
-      auto mesh_filter = dynamic_cast<MeshFilter*>(filter.get());
-      auto mesh_idx = mesh_filter->mesh();
-      auto umesh = dynamic_cast<UnstructuredMesh*>(model::meshes[mesh_idx].get());
-
-      if (!umesh) continue;
-
-      // if this tally has more than one filter, print
-      // warning and skip writing the mesh
-      if (tally->filters().size() > 1) {
-        warning(fmt::format("Skipping unstructured mesh writing for tally "
-                            "{}. More than one filter is present on the tally.",
-                            tally->id_));
-        break;
-      }
+      if (filter->type() == "mesh") {
+        auto mesh_filter = dynamic_cast<MeshFilter*>(filter.get());
+        auto& mesh = model::meshes[mesh_filter->mesh()];
+        auto umesh = dynamic_cast<UnstructuredMeshBase*>(mesh.get());
+        if (umesh) {
+          for (int i = 0; i < tally->scores_.size(); i++) {
+            // get values for this score and create vectors for marking
+            // up the mesh
+            std::vector<double> vals_vec, sum_sq_vec;
+            for (int j = 0; j < tally->results_.shape()[0]; j++) {
+              vals_vec.push_back(tally->results_(j, i, TallyResult::SUM) / tally->n_realizations_);
+              sum_sq_vec.push_back(tally->results_(j , i, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ tally->n_realizations_);
+            }
 
       int n_realizations = tally->n_realizations_;
 

From 4e1a4e45deb60fda526173ca129356f81f36f564 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sun, 5 Apr 2020 15:03:31 -0500
Subject: [PATCH 031/139] Using imported target from CMake's PkgConfig for
 libmesh.

---
 CMakeLists.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 606a8518e4..ae3dc12fe3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -379,7 +379,6 @@ target_include_directories(libopenmc
     $<INSTALL_INTERFACE:include>
     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
     ${HDF5_INCLUDE_DIRS}
-    ${LIBMESH_INCLUDE_DIRS}
 )
 
 # Set compile flags
@@ -421,7 +420,7 @@ if(dagmc)
 endif()
 
 if(libmesh)
-  target_link_libraries(libopenmc ${LIBMESH_LIBRARIES})
+  target_link_libraries(libopenmc PkgConfig::LIBMESH)
 endif()
 
 #===============================================================================

From 6775574b0f31718d8d3fb18a9f32ab56ed3962f4 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sun, 5 Apr 2020 15:04:16 -0500
Subject: [PATCH 032/139] Making sure the MOAB unstructured mesh function is an
 override of the base class.

---
 src/mesh.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 3050bdeae3..1731c0d731 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2079,8 +2079,8 @@ UnstructuredMesh::get_score_tags(std::string score) const {
 }
 
 void
-UnstructuredMesh::add_score(std::string score) const {
-  auto score_tags = this->get_score_tags(score);
+UnstructuredMesh::add_score(const std::string& score) {
+  auto score_tags = get_score_tags(score);
 }
 
 void UnstructuredMesh::remove_score(std::string score) const {

From 09ecbccfa5fa3b25cd6c617b380656b54716b577 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 6 Apr 2020 09:52:45 -0500
Subject: [PATCH 033/139] Fixes after rebase

---
 include/openmc/mesh.h | 15 ++++++---
 src/mesh.cpp          | 78 +++++++++++++++++++++----------------------
 src/state_point.cpp   | 33 ++++++++++--------
 3 files changed, 67 insertions(+), 59 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index e8ed7d3203..8a1fa8160d 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -288,7 +288,7 @@ public:
   //! Set the value of a bin for a variable on the libmesh mesh instance
   virtual void set_score_data(const std::string& var_name,
                               std::vector<double> values,
-                              std::vector<double> std_dev) const = 0;
+                              std::vector<double> std_dev) = 0;
 
   virtual void write(std::string filename) const = 0;
 
@@ -346,7 +346,12 @@ public:
   std::string bin_label(int bin) const override;
 
   //! Add a score to the mesh instance
-  void add_score(std::string score) const;
+  void add_score(const std::string& score);
+
+  //! Set the value of a bin for a variable on the libmesh mesh instance
+  void set_score_data(const std::string& var_name,
+                      std::vector<double> values,
+                      std::vector<double> std_dev) override;
 
   //! Remove a score from the mesh instance
   void remove_score(std::string score) const;
@@ -363,10 +368,10 @@ public:
 
   bool intersects(Position& r0, Position r1, int* ijk);
 
-
-
 private:
 
+  void initialize();
+
   //! Find all intersections with faces of the mesh.
   //
   //! \param[in] start Staring location
@@ -515,7 +520,7 @@ public:
   //! Set the value of a bin for a variable on the libmesh mesh instance
   void set_score_data(const std::string& var_name,
                       std::vector<double> values,
-                      std::vector<double> std_dev) const override;
+                      std::vector<double> std_dev) override;
 
 private:
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 1731c0d731..5b28adc713 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1715,14 +1715,6 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start,
   // sorts by first component of std::pair by default
   std::sort(hits.begin(), hits.end());
 
-  for (const auto& hit  : hits) {
-
-  }
-
-  for (const auto& hit : hits) {
-    moab::CartVect hit_loc = start + dir * hit.first;
-  }
-
 }
 
 void
@@ -1749,50 +1741,58 @@ UnstructuredMesh::bins_crossed(const Particle& p,
   bins.clear();
   lengths.clear();
 
-  // track could be entirely contained by a tet
+  // if there are no intersections the track may lie entirely
+  // within a single tet. If this is the case, apply entire
+  // score to that tet and return.
   if (hits.size() == 0) {
-    auto last_r_tet = get_tet(last_r + u * track_len * 0.5);
-    if (last_r_tet) {
-      bins.push_back(get_bin_from_ent_handle(last_r_tet));
+    Position midpoint = last_r + u * (track_len * 0.5);
+    int bin = this->get_bin(midpoint);
+    if (bin != -1) {
+      bins.push_back(bin);
       lengths.push_back(1.0);
     }
     return;
   }
 
+  // for each segment in the set of tracks, try to look up a tet
+  // at the midpoint of the segment
+  Position current = last_r;
   double last_dist = 0.0;
-  for (auto hit = hits.begin(); hit != hits.end(); hit++) {
+  for (const auto& hit : hits) {
+    // get the segment length
+    double segment_length = hit - last_dist;
+    last_dist = hit;
+    // find the midpoint of this segment
+    Position midpoint = current + u * (segment_length * 0.5);
+    // try to find a tet for this position
+    int bin = this->get_bin(midpoint);
 
-    // mid point for this segment
-    double segment_length = hit->first - last_dist;
-    Position segment_midpoint = last_r + u * last_dist + u * segment_length / 2.0;
+    // determine the start point for this segment
+    current = last_r + u * hit;
 
-    last_dist = hit->first;
-
-    // try to get a tet at the midpoint of this segment
-    auto tet = get_tet(segment_midpoint);
-    if (tet) {
-      bins.push_back(get_bin_from_ent_handle(tet));
-      lengths.push_back((hit->first - last_dist) / track_len);
-    } else {
-      // if in the loop, we should always find a tet
-      warning("No tet found for location between trianle hits");
+    if (bin == -1) {
+      continue;
     }
 
+    bins.push_back(bin);
+    lengths.push_back(segment_length / track_len);
+
   }
 
   // tally remaining portion of track after last hit if
-  // the last segment of the track is in the mesh
-  if (hits.back().first < track_len) {
-    auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0);
-    auto tet = get_tet(pos);
-    if (tet) {
-      bins.push_back(get_bin_from_ent_handle(tet));
-      lengths.push_back((track_len - hits.back().first) / track_len);
+  // the last segment of the track is in the mesh but doesn't
+  // reach the other side of the tet
+  if (hits.back() < track_len) {
+    Position segment_start = last_r + u * hits.back();
+    double segment_length = track_len - hits.back();
+    Position midpoint = segment_start + u * (segment_length * 0.5);
+    int bin = this->get_bin(midpoint);
+    if (bin != -1) {
+      bins.push_back(bin);
+      lengths.push_back(segment_length / track_len);
     }
   }
-
-  return;
-}
+};
 
 moab::EntityHandle
 UnstructuredMesh::get_tet(const Position& r) const
@@ -2114,7 +2114,7 @@ void UnstructuredMesh::remove_score(std::string score) const {
 void
 UnstructuredMesh::set_score_data(const std::string& score,
                                  std::vector<double> values,
-                                 std::vector<double> std_dev) const {
+                                 std::vector<double> std_dev) {
   auto score_tags = this->get_score_tags(score);
 
   // normalize tally values by element volume
@@ -2168,13 +2168,11 @@ UnstructuredMesh::count_sites(const std::vector<Particle::Bank>& bank,
   }
 
 double UnstructuredMesh::get_volume_frac(int bin = -1) const {
-
   return 0.0;
 }
 
 #endif
 
-
 #ifdef LIBMESH
 LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   initialize();
@@ -2300,7 +2298,7 @@ LibMesh::add_score(const std::string& var_name) {
 void
 LibMesh::set_score_data(const std::string& var_name,
                       std::vector<double> values,
-                      std::vector<double> std_dev) const
+                      std::vector<double> std_dev)
 {
     auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
     const libMesh::DofMap& dof_map = eqn_sys.get_dof_map();
diff --git a/src/state_point.cpp b/src/state_point.cpp
index b50b752fa6..9ef6c3497b 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -772,25 +772,30 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
 
 #ifdef LIBMESH
 void write_unstructured_mesh_results() {
-  std::cout << "Checking for unstructured meshes to write..." << std::endl;
+
+
   for (auto& tally : model::tallies) {
 
     std::vector<std::string> tally_scores;
     for (auto filter_idx : tally->filters()) {
       auto& filter = model::tally_filters[filter_idx];
-      if (filter->type() == "mesh") {
-        auto mesh_filter = dynamic_cast<MeshFilter*>(filter.get());
-        auto& mesh = model::meshes[mesh_filter->mesh()];
-        auto umesh = dynamic_cast<UnstructuredMeshBase*>(mesh.get());
-        if (umesh) {
-          for (int i = 0; i < tally->scores_.size(); i++) {
-            // get values for this score and create vectors for marking
-            // up the mesh
-            std::vector<double> vals_vec, sum_sq_vec;
-            for (int j = 0; j < tally->results_.shape()[0]; j++) {
-              vals_vec.push_back(tally->results_(j, i, TallyResult::SUM) / tally->n_realizations_);
-              sum_sq_vec.push_back(tally->results_(j , i, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ tally->n_realizations_);
-            }
+      if (filter->type() != "mesh") continue;
+
+      // check if the filter uses an unstructured mesh
+      auto mesh_filter = dynamic_cast<MeshFilter*>(filter.get());
+      auto mesh_idx = mesh_filter->mesh();
+      auto umesh = dynamic_cast<UnstructuredMesh*>(model::meshes[mesh_idx].get());
+
+      if (!umesh) continue;
+
+      // if this tally has more than one filter, print
+      // warning and skip writing the mesh
+      if (tally->filters().size() > 1) {
+        warning(fmt::format("Skipping unstructured mesh writing for tally "
+                            "{}. More than one filter is present on the tally.",
+                            tally->id_));
+        break;
+      }
 
       int n_realizations = tally->n_realizations_;
 

From cb5952ba387a3338c9ce0a3a461f512351b55aab Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 6 Apr 2020 23:18:12 -0500
Subject: [PATCH 034/139] Correction to how the LibMesh unique_ptr is handled
 and updates to ifdefs.

---
 CMakeLists.txt               | 5 -----
 include/openmc/state_point.h | 2 +-
 src/finalize.cpp             | 7 ++-----
 src/initialize.cpp           | 4 ++--
 src/state_point.cpp          | 6 +++---
 5 files changed, 8 insertions(+), 16 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ae3dc12fe3..499e006a01 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -123,11 +123,6 @@ if(openmp)
   endif()
 endif()
 
-if (libmesh)
-  list(APPEND cxxflags ${LIBMESH_CFLAGS})
-  list(APPEND ldflags ${LIBMESH_CFLAGS})
-endif()
-
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
 list(APPEND cxxflags -O2)
diff --git a/include/openmc/state_point.h b/include/openmc/state_point.h
index e3cd790db0..1c728cfdb2 100644
--- a/include/openmc/state_point.h
+++ b/include/openmc/state_point.h
@@ -19,7 +19,7 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
 void write_tally_results_nr(hid_t file_id);
 void restart_set_keff();
 
-#ifdef LIBMESH
+#if defined(LIBMESH) || defined(DAGMC)
 void write_unstructured_mesh_results();
 #endif
 
diff --git a/src/finalize.cpp b/src/finalize.cpp
index 7e58ba2fe3..5786510485 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -142,10 +142,6 @@ int openmc_finalize()
   if (mpi::bank != MPI_DATATYPE_NULL) MPI_Type_free(&mpi::bank);
 #endif
 
-#ifdef LIBMESH
-  settings::LMI.release();
-#endif
-
   return 0;
 }
 
@@ -192,4 +188,5 @@ int openmc_hard_reset()
   // Reset the random number generator state
   openmc::openmc_set_seed(DEFAULT_SEED);
   return 0;
-}
+
+}
\ No newline at end of file
diff --git a/src/initialize.cpp b/src/initialize.cpp
index e6ca245c05..c85ae81b18 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -58,7 +58,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 
   // Parse command-line arguments
   int err = parse_command_line(argc, argv);
-  if (err) return err;
+  //if (err) return err;
 
   // Start total and initialization timer
   simulation::time_total.start();
@@ -73,7 +73,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 #ifdef LIBMESH
-  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv);
+  if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv);
 #endif
 
 
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 9ef6c3497b..c9f436a6ed 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -167,7 +167,7 @@ openmc_statepoint_write(const char* filename, bool* write_source)
         tally_ids.push_back(tally->id_);
       write_attribute(tallies_group, "ids", tally_ids);
 
-#ifdef DAGMC
+#if defined(LIBMESH) || defined(DAGMC)
       // write unstructured mesh tallies to VTK if possible
       write_unstructured_mesh_results();
 #endif
@@ -770,7 +770,7 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
   H5Tclose(banktype);
 }
 
-#ifdef LIBMESH
+#if defined(LIBMESH) || defined(DAGMC)
 void write_unstructured_mesh_results() {
 
 
@@ -784,7 +784,7 @@ void write_unstructured_mesh_results() {
       // check if the filter uses an unstructured mesh
       auto mesh_filter = dynamic_cast<MeshFilter*>(filter.get());
       auto mesh_idx = mesh_filter->mesh();
-      auto umesh = dynamic_cast<UnstructuredMesh*>(model::meshes[mesh_idx].get());
+      auto umesh = dynamic_cast<UnstructuredMeshBase*>(model::meshes[mesh_idx].get());
 
       if (!umesh) continue;
 

From 074f7f7cd396f1dbbffeb2a676752056d0287b85 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 7 Apr 2020 15:48:35 -0500
Subject: [PATCH 035/139] Passing the number of threads in use to libmesh.

---
 src/initialize.cpp | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/src/initialize.cpp b/src/initialize.cpp
index c85ae81b18..8053246cdb 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -58,7 +58,24 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 
   // Parse command-line arguments
   int err = parse_command_line(argc, argv);
-  //if (err) return err;
+  if (err) return err;
+
+#ifdef LIBMESH
+
+#ifdef _OPENMP
+  int n_threads = omp_get_max_threads();
+#else
+  int n_threads = 1;
+#endif
+
+  // initialize libmesh
+#ifdef OPENMC_MPI
+  if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, mpi::intracomm, n_threads);
+#else
+  if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
+#endif
+
+#endif
 
   // Start total and initialization timer
   simulation::time_total.start();
@@ -72,10 +89,6 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   }
 #endif
 
-#ifdef LIBMESH
-  if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv);
-#endif
-
 
   // Initialize random number generator -- if the user specifies a seed, it
   // will be re-initialized later

From dce646af8ba52f930ef32832119db2f54f32d2d2 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 7 Apr 2020 18:11:34 -0500
Subject: [PATCH 036/139] Making sure ligmesh is released on reset.

---
 src/finalize.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/finalize.cpp b/src/finalize.cpp
index 5786510485..255cc7f30b 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -68,6 +68,10 @@ int openmc_finalize()
   // Reset timers
   reset_timers();
 
+#ifdef LIBMESH
+  settings::LMI.reset();
+#endif
+
   // Reset global variables
   settings::assume_separate = false;
   settings::check_overlaps = false;

From d2dd262846d2411108dc2e55ea2b92a665792fdd Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 7 Apr 2020 18:11:57 -0500
Subject: [PATCH 037/139] Making sure libmesh is found in the OpenMC config if
 we build with it.

---
 cmake/OpenMCConfig.cmake.in | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/cmake/OpenMCConfig.cmake.in b/cmake/OpenMCConfig.cmake.in
index 9f491ce88b..94078f9092 100644
--- a/cmake/OpenMCConfig.cmake.in
+++ b/cmake/OpenMCConfig.cmake.in
@@ -9,6 +9,12 @@ if(@DAGMC_FOUND@)
   find_package(DAGMC REQUIRED HINTS @DAGMC_DIR@)
 endif()
 
+if(@LIBMESH_FOUND@)
+  include(FindPkgConfig)
+  set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:@LIBMESH_PC@")
+  pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
+endif()
+
 if(NOT TARGET OpenMC::libopenmc)
   include("${OpenMC_CMAKE_DIR}/OpenMCTargets.cmake")
 endif()

From 9711d70b3352794aef2d37749445550cbf5a9f52 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 7 Apr 2020 20:36:58 -0500
Subject: [PATCH 038/139] Removing tracklength methods and unused methods from
 the LibMesh class.

---
 include/openmc/mesh.h |  85 ++------
 src/mesh.cpp          | 444 ++----------------------------------------
 2 files changed, 34 insertions(+), 495 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 8a1fa8160d..5b25ae73bc 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -292,6 +292,9 @@ public:
 
   virtual void write(std::string filename) const = 0;
 
+  virtual Position centroid(int bin) const = 0;
+
+
   std::string filename_;
 };
 
@@ -338,7 +341,7 @@ public:
   //
   // \param[in] tet MOAB EntityHandle of the tetrahedron
   // \return The centroid of the element
-  Position centroid(moab::EntityHandle tet) const;
+  Position centroid(int bin) const override;
 
   //! Return a string represntation of the mesh bin
   //
@@ -488,31 +491,23 @@ public:
   // standard mesh functions
   void bins_crossed(const Particle* p,
                     std::vector<int>& bins,
-                    std::vector<double>& lengths) const;
+                    std::vector<double>& lengths) const override;
 
-  int get_bin(Position r) const;
+  int get_bin(Position r) const override;
 
-  int n_bins() const;
+  int n_bins() const override;
 
-  void get_indices(Position r, int* ijk, bool* in_mesh) const;
-
-  int get_bin_from_indices(const int* ijk) const;
-
-  void get_indices_from_bin(int bin, int* ijk) const;
-
-  int n_surface_bins() const;
+  int n_surface_bins() const override;
 
   void surface_bins_crossed(const Particle* p,
-                             std::vector<int>& bins) const;
+                             std::vector<int>& bins) const override;
 
   std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
-                                                           Position plot_ur) const;
-
-  bool intersects(Position& r0, Position r1, int* ijk) const;
+                                                           Position plot_ur) const override;
 
   void write(std::string filename) const override;
 
-  void to_hdf5(hid_t group) const;
+  void to_hdf5(hid_t group) const override;
 
   //! Add a variable to the libmesh mesh instance
   void add_score(const std::string& var_name) override;
@@ -534,7 +529,7 @@ private:
   //! Translate an element pointer to a bin value
   int get_bin_from_element(const libMesh::Elem* elem) const;
 
-  //! Checks whether if a point moving in a given direction
+  //! Check whether if a point moving in a given direction
   //! is inside the element
   //
   //! \param[in] r In: point to be checked
@@ -545,7 +540,7 @@ private:
                   const libMesh::Point& u,
                   const libMesh::Elem* e) const;
 
-  //! Checks whether if a point moving in a given direction
+  //! Check if a point moving in a given direction
   //! is inside the element
   //
   //! \param[in] r In: point to be checked
@@ -556,61 +551,11 @@ private:
                   const libMesh::Point& u,
                   std::unique_ptr<libMesh::Elem> e) const;
 
-  //! Returns all hit elements and distances on the mesh
-  //
-  //! \param[in] start In: starting location of the track
-  //! \param[in] dir In: normalized direction of the track
-  //! \param[in] track_len In: track length
-  //! \param[out] hits Out: set of elements and hits on the unstructured mesh
-  void intersect_track(libMesh::Point start,
-                       libMesh::Point dir,
-                       double track_len,
-                       UnstructuredMeshHits& hits) const;
-
-  //! Checks that the elements from and to share a face
-  //
-  //! \param[in] from In: starting element
-  //! \param[in] to In: next element
-  //! \param[in] side side crossed in the starting element
-  //! \return whether or not the elements share a face
-  bool elements_share_face(const libMesh::Elem* from,
-                           const libMesh::Elem* to,
-                           unsigned int side) const;
-
-  //! Searches for an intersection with the mesh between
-  //! r0 and r1
-  //
-  //! \param[in] r0 In: starting position of the track
-  //! \param[in] r1 In: ending position of the track
-  //! \return the distance to intersection and intersected element
-  std::pair<double, const libMesh::Elem*>
-  locate_boundary_element(const Position& r0,
-                          const Position& r1) const;
-
-  //! Searches for an intersection with the mesh between
-  //! the start and end of a track
-  //
-  //! \param[in] start In: starting position of the track
-  //! \param[in] end In: ending position of the track
-  //! \return the distance to intersection and intersected element
-  std::pair<double, const libMesh::Elem*>
-  locate_boundary_element(const libMesh::Point& start,
-                          const libMesh::Point& end) const;
-
-  // Internal triangle intersection methods
-  bool plucker_test(std::unique_ptr<const libMesh::Elem> tri,
-                    const libMesh::Point& start,
-                    const libMesh::Point& dir,
-                    double& dist) const;
-
-  double plucker_edge_test(const libMesh::Node& vertexa,
-                           const libMesh::Node& vertexb,
-                           const libMesh::Point& ray,
-                           const libMesh::Point& ray_normal) const;
-
   double first(const libMesh::Node& a,
                const libMesh::Node& b) const;
 
+  Position centroid(int bin) const override;
+
   // Data members
 
 private:
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 5b28adc713..2deeaa540d 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2002,9 +2002,11 @@ int UnstructuredMesh::n_surface_bins() const {
 }
 
 Position
-UnstructuredMesh::centroid(moab::EntityHandle tet) const {
+UnstructuredMesh::centroid(int bin) const {
   moab::ErrorCode rval;
 
+  auto tet = this->get_ent_handle_from_bin(bin);
+
   // look up the tet connectivity
   std::vector<moab::EntityHandle> conn;
   rval = mbi_->get_connectivity(&tet, 1, conn);
@@ -2183,6 +2185,13 @@ LibMesh::LibMesh(const std::string& filename) {
   initialize();
 }
 
+Position
+LibMesh::centroid(int bin) const {
+  auto elem = this->get_element_from_bin(bin);
+  auto centroid = elem->centroid();
+  return {centroid(0), centroid(1), centroid(2)};
+}
+
 void LibMesh::initialize() {
     // always 3 for unstructured meshes
   n_dimension_ = 3;
@@ -2227,48 +2236,27 @@ void LibMesh::initialize() {
   }
 }
 
-void
-LibMesh::get_indices(Position r, int* ijk, bool* in_mesh) const {
-  int bin = get_bin(r);
-  *in_mesh = bin != -1;
-  ijk[0] = bin;
-  return;
-}
-
 int LibMesh::n_bins() const {
   return m_->n_elem();
 }
 
 int LibMesh::n_surface_bins() const {
-  return 0;
+  // TODO: Return number of faces in the mesh here
+  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
 }
 
 void
 LibMesh::surface_bins_crossed(const Particle* p,
                                std::vector<int>& bins) const
-{}
+{
+  // TODO: Implement triangle crossings here
+  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
+}
 
 std::pair<std::vector<double>, std::vector<double>>
 LibMesh::plot(Position plot_ll,
               Position plot_ur) const { return {}; }
 
-
-int
-LibMesh::get_bin_from_indices(const int* ijk) const {
-  if (ijk[0] >= n_bins()) {
-    std::stringstream s;
-    s << "Invalid bin: " << ijk[0];
-    fatal_error(s);
-  }
-  int bin = first_element_->id() + ijk[0];
-  return bin;
-}
-
-void
-LibMesh::get_indices_from_bin(int bin, int* ijk) const {
-  ijk[0] = bin;
-}
-
 const libMesh::Elem*
 LibMesh::get_element_from_bin(int bin) const {
   return m_->elem_ptr(bin);
@@ -2339,24 +2327,8 @@ LibMesh::bins_crossed(const Particle* p,
                       std::vector<int>& bins,
                       std::vector<double>& lengths) const
 {
-  // get element containing previous position
-  libMesh::Point start(p->r_last_.x, p->r_last_.y, p->r_last_.z);
-  libMesh::Point end(p->r().x, p->r().y, p->r().z);
-  libMesh::Point dir(p->u().x, p->u().y, p->u().z);
-  dir /= dir.norm();
-
-  double track_len = (end - start).norm();
-
-  UnstructuredMeshHits hits;
-  intersect_track(start, dir, track_len, hits);
-
-  bins.clear();
-  lengths.clear();
-
-  for (const auto& hit : hits) {
-    lengths.push_back(hit.first / track_len);
-    bins.push_back(get_bin_from_element(hit.second));
-  }
+  // TODO: Implement triangle crossings here
+  throw std::runtime_error{"LibMesh tracklength tallies are not implemented."};
 }
 
 int
@@ -2378,79 +2350,6 @@ LibMesh::get_bin(Position r) const
   }
 }
 
-bool LibMesh::intersects(Position& r0, Position r1, int* ijk) const {
-
-  // first try to locate an element
-  // for the start or end point
-  int bin {-1};
-  bin = get_bin(r0);
-  if (bin != -1) {
-    ijk[0] = bin;
-    return true;
-  }
-
-  bin = get_bin(r1);
-  if (bin != -1) {
-    ijk[0] = bin;
-    return true;
-  }
-
-  // check for an intersection with one of the boundary faces
-  auto result = locate_boundary_element(r0, r1);
-  // if we don't get a hit, the track won't intersect with the mesh
-  if (result.second) {
-    ijk[0] = get_bin_from_element(result.second);
-    return true;
-  }
-
-  return false;
-}
-
-std::pair<double, const libMesh::Elem*>
-LibMesh::locate_boundary_element(const Position& r0,
-                                 const Position& r1) const {
-  libMesh::Point a(r0.x, r0.y, r0.z);
-  libMesh::Point b(r1.x, r1.y, r1.z);
-
-  return locate_boundary_element(a, b);
-}
-
-std::pair<double, const libMesh::Elem*>
-LibMesh::locate_boundary_element(const libMesh::Point& start,
-                                 const libMesh::Point& end) const
-{
-  typedef std::pair<double, const libMesh::Elem*> RayHit;
-  RayHit result = {INFTY, nullptr};
-  if (start == end) { return result; }
-
-  // attempt to locate an intersection with the mesh boundary
-  libMesh::Point dir = (end - start).unit();
-  double length = (end - start).norm();
-
-  // locate potential elements
-  std::set<const libMesh::Elem*> candidate_elements;
-  for (auto elem : boundary_elements_) {
-    // being conservative about search parameter by adding element hmax
-    if (elem->close_to_point(start, length + elem->hmax())) {
-      candidate_elements.insert(elem);
-    }
-  }
-
-  // find nearest hit along our direction
-  for (auto elem : candidate_elements) {
-    for (int i = 0; i < elem->n_sides(); i++) {
-      double temp_dist = 0;
-      bool hit = plucker_test(elem->side_ptr(i), start, dir, temp_dist);
-      if (hit && temp_dist > FP_COINCIDENT && temp_dist <= length) {
-        // update if we find a closer intersection
-        if (temp_dist < result.first) { result = {temp_dist, elem}; }
-      }
-    }
-  }
-
-  return result;
-}
-
 int
 LibMesh::get_bin_from_element(const libMesh::Elem* elem) const {
   int bin =  elem->id() - first_element_->id();
@@ -2476,213 +2375,7 @@ LibMesh::inside_tet(const libMesh::Point& r,
                     const libMesh::Point& u,
                     const libMesh::Elem* e) const
 {
-  // fire rays at each triangle in the tet
-  int n_hits = 0;
-  for (int i = 0; i < e->n_sides(); i++) {
-    double temp;
-    if (plucker_test(e->side_ptr(i), r, u, temp)) { n_hits++; }
-  }
-
-  if (n_hits == 0) { return false; }
-
-  for (int i = 0; i < e->n_sides(); i++) {
-    double temp;
-    if (plucker_test(e->side_ptr(i), r, -u, temp)) { n_hits++; }
-  }
-
-  // should get at least 2 hits if the
-  // location is inside or on a tet boundary
-  return n_hits >= 2;
-}
-
-void
-LibMesh::intersect_track(libMesh::Point start,
-                         libMesh::Point dir,
-                         double track_len,
-                         UnstructuredMeshHits& hits) const
-{
-  double track_remaining = track_len;
-
-  // attempt to locate a tet for the starting point
-  int thread = omp_get_thread_num();
-  auto e = (*point_locators_[thread])(start);
-
-  // the point_locator seems off sometimes,
-  // ensure the point is actually in the tet
-  // and check the neighbors as well
-  if (e && !inside_tet(start, dir, e)) {
-    bool found = false;
-    // try to find a new tet adjacent to this one
-    for (int i = 0; i < e->n_neighbors(); i ++) {
-      if (e->neighbor_ptr(i) && inside_tet(start, dir, e->neighbor_ptr(i))) {
-        e = e->neighbor_ptr(i);
-        found = true;
-        break;
-      }
-    }
-
-    if (!found) {
-      warning("Starting point is not inside the specified tet.");
-    }
-  }
-
-  // if the point locator fails, look for mesh
-  // entry along the track
-  if (!e) {
-    auto result = locate_boundary_element(start, start + track_len * dir);
-    if (result.second) {
-      // if an intersection is found, update the remaining track length
-      // and set the element
-      e = result.second;
-      track_remaining -= result.first;
-      // advance position along track
-      start += dir * result.first;
-    } else {
-      // if there was no intersection, we're done
-      return;
-    }
-  }
-
-  std::set<libMesh::dof_id_type> visited;
-
-  bool first = true;
-
-  while (true) {
-    // find the positive distance triangle intersection
-    double dist = -1.0;
-    int side = -1;
-
-    for (int i = 0; i < e->n_sides(); i++) {
-      auto tri = e->side_ptr(i);
-      if (visited.count(e->key(i))) {
-        continue;
-      }
-      if (tri->type() != libMesh::ElemType::TRI3) { warning("Non-triangle element found"); }
-      double temp_dist = -1.0;
-      bool hit = plucker_test(e->side_ptr(i), start, dir, temp_dist);
-      // if (hit and temp_dist > FP_COINCIDENT) {
-      if (hit and temp_dist >= 0 and temp_dist > dist) {
-        side = i;
-        dist = temp_dist;
-        first = false;
-      }
-    }
-
-    // make sure we found a hit for the tet we're in
-
-    // if we don't find a hit for this tet, we may
-    if (side == -1) {
-
-      if (first) {
-        warning("Couldn't get hit on first iteration");
-        inside_tet(start, dir, e);
-        first = false;
-      }
-      auto orig_e = e;
-      start += dir * TINY_BIT; // nudge particle forward
-
-      int thread = omp_get_thread_num();
-      e = (*point_locators_[thread])(start);
-
-      if (!e) {
-        if (!orig_e->on_boundary()) {
-          std::cout << "May have incorrectly truncated a track." << std::endl;
-        }
-        return;
-      }
-      continue;
-    } else {
-      // add hit to output
-      hits.push_back(std::pair<double, const libMesh::Elem*>(std::min(track_remaining, dist), e));
-      // add this side's centroid to the visited list
-      visited.insert(e->key(side));
-      // advance position along track
-      start += dir * std::min(track_remaining, dist);
-      // subtract from remaining track length
-      track_remaining -= dist;
-    }
-
-    // if we've reached the end of the track, break
-    if (track_remaining <= 0.0) { break; }
-
-    // get tet on the other side
-    auto next_e = e->neighbor_ptr(side);
-
-    // check to make sure this tet contains our current location
-    if (next_e && !next_e->contains_point(start)) {
-      warning("Moving into tet that does not contain the current location.");
-    }
-
-    // if there is no next element, we may have exited the mesh
-    // and will re-enter elsewhere
-    if (!next_e) {
-      auto result = locate_boundary_element(start,
-                                            start + dir * track_remaining);
-      if (result.second) {
-        // advance position along track
-        // to re-entry point
-        start += dir * result.first;
-        // remove this distance from the remaining track length
-        track_remaining -= result.first;
-        // update
-        e = result.second;
-        continue;
-      } else {
-        // if no next intersection with the mesh, we're done
-        return;
-      }
-    }
-
-    // if the mesh entry point is too far away
-    // break
-    if (track_remaining <= 0.0) { break; }
-
-    // check that the hit is correct
-    if (!elements_share_face(e, next_e, side)) {
-      // this should maybe throw an error?
-      warning("Incorrect adjacent element found");
-      break;
-    }
-
-    // update to the element along the track
-    e = next_e;
-  }
-}
-
-bool
-LibMesh::elements_share_face(const libMesh::Elem* from,
-                             const libMesh::Elem* to,
-                             unsigned int side) const
-{
-  for (auto j : to->side_index_range()) {
-    if (from->key(side) == to->key(j)) {
-      return true;
-    }
-  }
-  return false;
-}
-
-double
-LibMesh::first(const libMesh::Node& a,
-               const libMesh::Node& b) const
-{
-  if(a(0) < b(0)) {
-    return true;
-  } else if(a(0) == b(0)) {
-    if(a(1) < b(1)) {
-      return true;
-    } else if(a(1) == b(1)) {
-      if(a(2) < b(2)) {
-	return true;
-      } else {
-        return false;
-      }
-    } else {
-      return false;
-    }
-  } else {
-    return false;
-  }
+  return e->contains_point(r, FP_COINCIDENT);
 }
 
 void LibMesh::to_hdf5(hid_t group) const
@@ -2703,105 +2396,6 @@ void LibMesh::to_hdf5(hid_t group) const
   close_group(mesh_group);
 }
 
-double
-LibMesh::plucker_edge_test(const libMesh::Node& vertexa,
-                           const libMesh::Node& vertexb,
-                           const libMesh::Point& ray,
-                           const libMesh::Point& ray_normal) const
-{
-  double pip;
-  const double near_zero = 10.0 * std::numeric_limits<double>::epsilon();
-
-  if(first(vertexa, vertexb)) {
-    const libMesh::Point edge = vertexb - vertexa;
-    const libMesh::Point edge_normal = edge.cross(vertexa);
-    pip = ray * edge_normal + ray_normal * edge;
-  } else {
-    const libMesh::Point edge = vertexa-vertexb;
-    const libMesh::Point edge_normal = edge.cross(vertexb);
-    pip = ray * edge_normal + ray_normal * edge;
-    pip = -pip;
-  }
-
-  if (near_zero > fabs(pip)) pip = 0.0;
-
-  return pip;
-}
-
-/* This test uses the same edge-ray computation for adjacent triangles so that
-   rays passing close to edges/nodes are handled consistently.
-
-   Reports intersection type for post processing of special cases. Optionally
-   screen by orientation and negative/nonnegative distance limits.
-
-   If screening by orientation, substantial pruning can occur. Indicate
-   desired orientation by passing 1 (forward), -1 (reverse), or 0 (no preference).
-   Note that triangle orientation is not always the same as surface
-   orientation due to non-manifold surfaces.
-
-   N. Platis and T. Theoharis, "Fast Ray-Tetrahedron Intersection using Plücker
-   Coordinates", Journal of Graphics Tools, Vol. 8, Part 4, Pages 37-48 (2003). */
-bool
-LibMesh::plucker_test(std::unique_ptr<const libMesh::Elem> tri,
-                      const libMesh::Point& start,
-                      const libMesh::Point& dir,
-                      double& dist) const
-{
-  const libMesh::Point raya = dir;
-  const libMesh::Point rayb = dir.cross(start);
-
-  // get triangle vertices
-  auto node0 = tri->node_ref(0);
-  auto node1 = tri->node_ref(1);
-  auto node2 = tri->node_ref(2);
-
-  double plucker_coord0 = plucker_edge_test(node0, node1, raya, rayb);
-  double plucker_coord1 = plucker_edge_test(node1, node2, raya, rayb);
-  if( (0.0<plucker_coord0 && 0.0>plucker_coord1) || (0.0>plucker_coord0 && 0.0<plucker_coord1) ) {
-    return false;
-  }
-
-  double plucker_coord2 = plucker_edge_test(node2, node0, raya, rayb);
-  if( (0.0<plucker_coord1 && 0.0>plucker_coord2) || (0.0>plucker_coord1 && 0.0<plucker_coord2) ||
-      (0.0<plucker_coord0 && 0.0>plucker_coord2) || (0.0>plucker_coord0 && 0.0<plucker_coord2) ) {
-    return false;
-  }
-
-  // check for coplanar case to avoid dividing by zero
-  if(0.0==plucker_coord0 && 0.0==plucker_coord1 && 0.0==plucker_coord2) {
-    return false;
-  }
-
-  // get the distance to intersection
-  const double inverse_sum = 1.0/(plucker_coord0+plucker_coord1+plucker_coord2);
-  assert(0.0 != inverse_sum);
-  const libMesh::Point intersection(plucker_coord0*inverse_sum*node2+
-                                    plucker_coord1*inverse_sum*node0+
-                                    plucker_coord2*inverse_sum*node1);
-
-  // To minimize numerical error, get index of largest magnitude direction.
-  int idx = 0;
-  double max_abs_dir = 0;
-  for(unsigned int i=0; i<3; ++i) {
-    if( fabs(dir(i)) > max_abs_dir ) {
-      idx = i;
-      max_abs_dir = fabs(dir(i));
-    }
-  }
-
-  // no negative distances
-  double temp_dist = (intersection(idx) - start(idx)) / dir(idx);
-  if ( fabs(temp_dist) < TINY_BIT ) { temp_dist = 0.0; }
-  if ( temp_dist < 0 ) { return false; }
-
-  dist = temp_dist;
-
-  // dist = (intersection - start).norm();
-
-  return true;
-}
-
-
 #endif // LIBMESH
 
 //==============================================================================

From 651ef1255c515adc71e21dda74e457baba2e7dbb Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 8 Apr 2020 12:54:18 -0500
Subject: [PATCH 039/139] Some cleanup of the bin_label functions.

---
 include/openmc/mesh.h | 17 +++++++----------
 src/mesh.cpp          | 22 +---------------------
 2 files changed, 8 insertions(+), 31 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 5b25ae73bc..e9d10b6c8a 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -108,6 +108,9 @@ public:
   plot(Position plot_ll, Position plot_ur) const = 0;
 
   //! Get a label for the mesh bin
+  //! Return a string representation of the mesh bin
+  //
+  //! \param[in] bin Mesh bin to generate a label for
   virtual std::string bin_label(int bin) const = 0;
 
   // Data members
@@ -219,8 +222,6 @@ public:
 
   double negative_grid_boundary(int* ijk, int i) const override;
 
-  std::string bin_label(int bin) const override;
-
   int n_surface_bins() const override;
 
   std::pair<std::vector<double>, std::vector<double>>
@@ -274,6 +275,7 @@ public:
 };
 
 class UnstructuredMeshBase : public Mesh {
+
 public:
   // constructors
   UnstructuredMeshBase() {};
@@ -282,10 +284,10 @@ public:
 
   std::string bin_label(int bin) const override;
 
-  //! Add a variable to the libmesh mesh instance
+  //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;
 
-  //! Set the value of a bin for a variable on the libmesh mesh instance
+  //! Set the value of a bin for a variable on the mesh instance
   virtual void set_score_data(const std::string& var_name,
                               std::vector<double> values,
                               std::vector<double> std_dev) = 0;
@@ -294,7 +296,7 @@ public:
 
   virtual Position centroid(int bin) const = 0;
 
-
+  // Data members
   std::string filename_;
 };
 
@@ -343,11 +345,6 @@ public:
   // \return The centroid of the element
   Position centroid(int bin) const override;
 
-  //! Return a string represntation of the mesh bin
-  //
-  //! \param[in] bin Mesh bin to generate a label for
-  std::string bin_label(int bin) const override;
-
   //! Add a score to the mesh instance
   void add_score(const std::string& score);
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 2deeaa540d..955c264ab6 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -133,9 +133,7 @@ UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
 
 std::string
 UnstructuredMeshBase::bin_label(int bin) const {
-  std::stringstream out;
-  out << "Mesh Index (" << bin << ")";
-  return out.str();
+  return fmt::format("Mesh Index ({})", bin);
 };
 
 //==============================================================================
@@ -1055,19 +1053,6 @@ RegularMesh::count_sites(const Particle::Bank* bank,
   return counts;
 }
 
-std::string RegularMesh::bin_label(int bin) const {
-  int ijk[n_dimension_];
-  get_indices_from_bin(bin, ijk);
-
-  std::stringstream out;
-  out << "Mesh Index (" << ijk[0];
-  if (n_dimension_ > 1) out << ", " << ijk[1];
-  if (n_dimension_ > 2) out << ", " << ijk[2];
-  out << ")";
-
-  return out.str();
-}
-
 //==============================================================================
 // RectilinearMesh implementation
 //==============================================================================
@@ -2033,11 +2018,6 @@ UnstructuredMesh::centroid(int bin) const {
   return {centroid[0], centroid[1], centroid[2]};
 }
 
-std::string
-UnstructuredMesh::bin_label(int bin) const {
-  return fmt::format("Mesh Index ({})", bin);
-};
-
 std::pair<moab::Tag, moab::Tag>
 UnstructuredMesh::get_score_tags(std::string score) const {
   moab::ErrorCode rval;

From b1af80697caca15e47cdb08463fd3ebde1526cce Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 8 Apr 2020 14:07:37 -0500
Subject: [PATCH 040/139] Restructuring unstructured mesh inheritance a bit.

---
 include/openmc/mesh.h |  44 +++++++++------
 src/mesh.cpp          | 128 ++++++++++++++++++++----------------------
 2 files changed, 86 insertions(+), 86 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index e9d10b6c8a..1505556805 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -284,6 +284,11 @@ public:
 
   std::string bin_label(int bin) const override;
 
+  //! Write mesh data to an HDF5 group.
+  //
+  //! \param[in] group HDF5 group
+  void to_hdf5(hid_t group) const;
+
   //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;
 
@@ -296,20 +301,24 @@ public:
 
   virtual Position centroid(int bin) const = 0;
 
+  virtual double volume(int bin) const = 0;
+
+  virtual std::string library() const = 0;
+
   // Data members
-  std::string filename_;
+  std::string filename_; //!< Path to unstructured mesh file
 };
 
 #ifdef DAGMC
 
-class UnstructuredMesh : public UnstructuredMeshBase {
+class MOABUnstructuredMesh : public UnstructuredMeshBase {
 
 public:
 
-  UnstructuredMesh() = default;
-  UnstructuredMesh(pugi::xml_node);
-  UnstructuredMesh(const std::string& filename);
-  ~UnstructuredMesh() = default;
+  MOABUnstructuredMesh() = default;
+  MOABUnstructuredMesh(pugi::xml_node);
+  MOABUnstructuredMesh(const std::string& filename);
+  ~MOABUnstructuredMesh() = default;
 
   void bins_crossed(const Particle& p,
                     std::vector<int>& bins,
@@ -324,27 +333,26 @@ public:
   //! \param[out] bins Surface bins that were crossed
   void surface_bins_crossed(const Particle& p, std::vector<int>& bins) const;
 
-  //! Write mesh data to an HDF5 group.
-  //
-  //! \param[in] group HDF5 group
-  void to_hdf5(hid_t group) const;
-
   //! Get bin at a given position.
   //
   //! \param[in] r Position to get bin for
   //! \return Mesh bin
   int get_bin(Position r) const;
 
+  std::string library() const override;
+
   int n_bins() const override;
 
   int n_surface_bins() const override;
 
   //! Retrieve a centroid for the mesh cell
   //
-  // \param[in] tet MOAB EntityHandle of the tetrahedron
+  // \param[in] bin Bin to return the volume for
   // \return The centroid of the element
   Position centroid(int bin) const override;
 
+  double volume(int bin) const override;
+
   //! Add a score to the mesh instance
   void add_score(const std::string& score);
 
@@ -364,8 +372,6 @@ public:
   //! Write the mesh with any current tally data
   void write(std::string base_filename) const;
 
-  std::string filename_; //!< Path to unstructured mesh file
-
   bool intersects(Position& r0, Position r1, int* ijk);
 
 private:
@@ -492,6 +498,8 @@ public:
 
   int get_bin(Position r) const override;
 
+  std::string library() const override;
+
   int n_bins() const override;
 
   int n_surface_bins() const override;
@@ -504,8 +512,6 @@ public:
 
   void write(std::string filename) const override;
 
-  void to_hdf5(hid_t group) const override;
-
   //! Add a variable to the libmesh mesh instance
   void add_score(const std::string& var_name) override;
 
@@ -514,6 +520,10 @@ public:
                       std::vector<double> values,
                       std::vector<double> std_dev) override;
 
+  Position centroid(int bin) const override;
+
+  double volume(int bin) const override;
+
 private:
 
   //! Setup method for the mesh. Builds data structures,
@@ -551,8 +561,6 @@ private:
   double first(const libMesh::Node& a,
                const libMesh::Node& b) const;
 
-  Position centroid(int bin) const override;
-
   // Data members
 
 private:
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 955c264ab6..0f92032e37 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -136,6 +136,30 @@ UnstructuredMeshBase::bin_label(int bin) const {
   return fmt::format("Mesh Index ({})", bin);
 };
 
+void
+UnstructuredMeshBase::to_hdf5(hid_t group) const
+{
+    hid_t mesh_group = create_group(group, fmt::format("mesh {}", id_));
+
+    write_dataset(mesh_group, "type", "unstructured");
+    write_dataset(mesh_group, "filename", filename_);
+    write_dataset(mesh_group, "library", this->library());
+    // write volume of each tet
+    std::vector<double> tet_vols;
+    xt::xtensor<double, 2> centroids({this->n_bins(), 3});
+    for (int i = 0; i < this->n_bins(); i++) {
+      tet_vols.emplace_back(this->volume(i));
+      auto c = this->centroid(i);
+      xt::view(centroids, i, xt::all()) = xt::xarray<double>({c.x, c.y, c.z});
+    }
+
+    write_dataset(mesh_group, "volumes", tet_vols);
+    write_dataset(mesh_group, "centroids", centroids);
+
+    close_group(mesh_group);
+}
+
+
 //==============================================================================
 // RegularMesh implementation
 //==============================================================================
@@ -1404,7 +1428,7 @@ extern "C" int openmc_add_unstructured_mesh(const char filename[],
   bool valid_lib = false;
 #ifdef DAGMC
   if (library == "moab") {
-    model::meshes.push_back(std::move(std::make_unique<UnstructuredMesh>(filename)));
+    model::meshes.push_back(std::move(std::make_unique<MOABUnstructuredMesh>(filename)));
     valid_lib = true;
   }
 #endif
@@ -1590,16 +1614,16 @@ openmc_rectilinear_mesh_set_grid(int32_t index, const double* grid_x,
 
 #ifdef DAGMC
 
-UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+MOABUnstructuredMesh::MOABUnstructuredMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
   initialize();
 }
 
-UnstructuredMesh::UnstructuredMesh(const std::string& filename) {
+MOABUnstructuredMesh::MOABUnstructuredMesh(const std::string& filename) {
   filename_ = filename;
   initialize();
 }
 
-void UnstructuredMesh::initialize() {
+void MOABUnstructuredMesh::initialize() {
   // create MOAB instance
   mbi_ = std::make_unique<moab::Core>();
   // load unstructured mesh file
@@ -1637,7 +1661,7 @@ void UnstructuredMesh::initialize() {
 }
 
 void
-UnstructuredMesh::build_kdtree(const moab::Range& all_tets)
+MOABUnstructuredMesh::build_kdtree(const moab::Range& all_tets)
 {
   moab::Range all_tris;
   int adj_dim = 2;
@@ -1672,7 +1696,7 @@ UnstructuredMesh::build_kdtree(const moab::Range& all_tets)
 }
 
 void
-UnstructuredMesh::intersect_track(const moab::CartVect& start,
+MOABUnstructuredMesh::intersect_track(const moab::CartVect& start,
                                   const moab::CartVect& dir,
                                   double track_len,
                                   std::vector<double>& hits) const {
@@ -1703,7 +1727,7 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start,
 }
 
 void
-UnstructuredMesh::bins_crossed(const Particle& p,
+MOABUnstructuredMesh::bins_crossed(const Particle* p,
                                std::vector<int>& bins,
                                std::vector<double>& lengths) const
 {
@@ -1780,7 +1804,7 @@ UnstructuredMesh::bins_crossed(const Particle& p,
 };
 
 moab::EntityHandle
-UnstructuredMesh::get_tet(const Position& r) const
+MOABUnstructuredMesh::get_tet(const Position& r) const
 {
   moab::CartVect pos(r.x, r.y, r.z);
   // find the leaf of the kd-tree for this position
@@ -1807,7 +1831,13 @@ UnstructuredMesh::get_tet(const Position& r) const
   return 0;
 }
 
-double UnstructuredMesh::tet_volume(moab::EntityHandle tet) const {
+double MOABUnstructuredMesh::volume(int bin) const {
+  return tet_volume(get_ent_handle_from_bin(bin));
+}
+
+std::string MOABUnstructuredMesh::library() const { return "moab"; }
+
+double MOABUnstructuredMesh::tet_volume(moab::EntityHandle tet) const {
  std::vector<moab::EntityHandle> conn;
  moab::ErrorCode rval = mbi_->get_connectivity(&tet, 1, conn);
  if (rval != moab::MB_SUCCESS) {
@@ -1823,13 +1853,13 @@ double UnstructuredMesh::tet_volume(moab::EntityHandle tet) const {
  return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0]));
 }
 
-void UnstructuredMesh::surface_bins_crossed(const Particle& p, std::vector<int>& bins) const {
+void MOABUnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector<int>& bins) const {
   // TODO: Implement triangle crossings here
   throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
 }
 
 int
-UnstructuredMesh::get_bin(Position r) const {
+MOABUnstructuredMesh::get_bin(Position r) const {
   moab::EntityHandle tet = get_tet(r);
   if (tet == 0) {
     return -1;
@@ -1839,7 +1869,7 @@ UnstructuredMesh::get_bin(Position r) const {
 }
 
 void
-UnstructuredMesh::compute_barycentric_data(const moab::Range& tets) {
+MOABUnstructuredMesh::compute_barycentric_data(const moab::Range& tets) {
   moab::ErrorCode rval;
 
   baryc_data_.clear();
@@ -1868,32 +1898,8 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& tets) {
   }
 }
 
-void
-UnstructuredMesh::to_hdf5(hid_t group) const
-{
-    hid_t mesh_group = create_group(group, fmt::format("mesh {}", id_));
-
-    write_dataset(mesh_group, "type", "unstructured");
-    write_dataset(mesh_group, "filename", filename_);
-    write_dataset(mesh_group, "library", "moab");
-    // write volume of each tet
-    std::vector<double> tet_vols;
-    xt::xtensor<double, 2> centroids({ehs_.size(), 3});
-    for (int i = 0; i < ehs_.size(); i++) {
-      const auto& eh = ehs_[i];
-      tet_vols.emplace_back(this->tet_volume(eh));
-      Position c = this->centroid(eh);
-      xt::view(centroids, i, xt::all()) = xt::xarray<double>({c.x, c.y, c.z});
-    }
-
-    write_dataset(mesh_group, "volumes", tet_vols);
-    write_dataset(mesh_group, "centroids", centroids);
-
-    close_group(mesh_group);
-}
-
 bool
-UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const {
+MOABUnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const {
 
   moab::ErrorCode rval;
 
@@ -1928,7 +1934,7 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet)
 }
 
 int
-UnstructuredMesh::get_bin_from_index(int idx) const {
+MOABUnstructuredMesh::get_bin_from_index(int idx) const {
   if (idx >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: {}", idx));
   }
@@ -1936,25 +1942,25 @@ UnstructuredMesh::get_bin_from_index(int idx) const {
 }
 
 int
-UnstructuredMesh::get_index(const Position& r,
+MOABUnstructuredMesh::get_index(const Position& r,
                             bool* in_mesh) const {
   int bin = get_bin(r);
   *in_mesh = bin != -1;
   return bin;
 }
 
-int UnstructuredMesh::get_index_from_bin(int bin) const {
+int MOABUnstructuredMesh::get_index_from_bin(int bin) const {
   return bin;
 }
 
 std::pair<std::vector<double>, std::vector<double>>
-UnstructuredMesh::plot(Position plot_ll, Position plot_ur) const {
+MOABUnstructuredMesh::plot(Position plot_ll, Position plot_ur) const {
   // TODO: Implement mesh lines
   return {};
 }
 
 int
-UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
+MOABUnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
   int bin = eh - ehs_[0];
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin: {}", bin));
@@ -1963,18 +1969,18 @@ UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
 }
 
 moab::EntityHandle
-UnstructuredMesh::get_ent_handle_from_bin(int bin) const {
+MOABUnstructuredMesh::get_ent_handle_from_bin(int bin) const {
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: ", bin));
   }
   return ehs_[0] + bin;
 }
 
-int UnstructuredMesh::n_bins() const {
+int MOABUnstructuredMesh::n_bins() const {
   return ehs_.size();
 }
 
-int UnstructuredMesh::n_surface_bins() const {
+int MOABUnstructuredMesh::n_surface_bins() const {
   // collect all triangles in the set of tets for this mesh
   moab::Range tris;
   moab::ErrorCode rval;
@@ -1987,7 +1993,7 @@ int UnstructuredMesh::n_surface_bins() const {
 }
 
 Position
-UnstructuredMesh::centroid(int bin) const {
+MOABUnstructuredMesh::centroid(int bin) const {
   moab::ErrorCode rval;
 
   auto tet = this->get_ent_handle_from_bin(bin);
@@ -2019,7 +2025,7 @@ UnstructuredMesh::centroid(int bin) const {
 }
 
 std::pair<moab::Tag, moab::Tag>
-UnstructuredMesh::get_score_tags(std::string score) const {
+MOABUnstructuredMesh::get_score_tags(std::string score) const {
   moab::ErrorCode rval;
   // add a tag to the mesh
   // all scores are treated as a single value
@@ -2061,7 +2067,7 @@ UnstructuredMesh::get_score_tags(std::string score) const {
 }
 
 void
-UnstructuredMesh::add_score(const std::string& score) {
+MOABUnstructuredMesh::add_score(const std::string& score) {
   auto score_tags = get_score_tags(score);
 }
 
@@ -2094,7 +2100,7 @@ void UnstructuredMesh::remove_score(std::string score) const {
 }
 
 void
-UnstructuredMesh::set_score_data(const std::string& score,
+MOABUnstructuredMesh::set_score_data(const std::string& score,
                                  std::vector<double> values,
                                  std::vector<double> std_dev) {
   auto score_tags = this->get_score_tags(score);
@@ -2126,7 +2132,7 @@ UnstructuredMesh::set_score_data(const std::string& score,
 }
 
 void
-UnstructuredMesh::write(std::string base_filename) const {
+MOABUnstructuredMesh::write(std::string base_filename) const {
   // add extension to the base name
   auto filename = base_filename + ".vtk";
   write_message(5, "Writing unstructured mesh {}...", filename);
@@ -2172,6 +2178,8 @@ LibMesh::centroid(int bin) const {
   return {centroid(0), centroid(1), centroid(2)};
 }
 
+std::string LibMesh::library() const { return "libmesh"; }
+
 void LibMesh::initialize() {
     // always 3 for unstructured meshes
   n_dimension_ = 3;
@@ -2358,23 +2366,7 @@ LibMesh::inside_tet(const libMesh::Point& r,
   return e->contains_point(r, FP_COINCIDENT);
 }
 
-void LibMesh::to_hdf5(hid_t group) const
-{
-  hid_t mesh_group = create_group(group, "mesh " + std::to_string(id_));
-
-  write_dataset(mesh_group, "type", "unstructured");
-  write_dataset(mesh_group, "filename", filename_);
-  write_dataset(mesh_group, "library", "libmesh");
-
-  // write volume of each tet
-  std::vector<double> tet_vols;
-  for (int i = 0; i < m_->n_elem(); i++) {
-    tet_vols.emplace_back(m_->elem_ref(i).volume());
-  }
-  write_dataset(mesh_group, "volumes", tet_vols);
-
-  close_group(mesh_group);
-}
+double LibMesh::volume(int bin) const { return m_->elem_ref(bin).volume(); }
 
 #endif // LIBMESH
 
@@ -2407,7 +2399,7 @@ void read_meshes(pugi::xml_node root)
 #ifdef DAGMC
     }
     else if (mesh_type == "unstructured" && mesh_lib == "moab") {
-      model::meshes.push_back(std::make_unique<UnstructuredMesh>(node));
+      model::meshes.push_back(std::make_unique<MOABUnstructuredMesh>(node));
 #else
     } else if (mesh_type == "unstructured") {
       fatal_error("Unstructured mesh support is disabled.");

From 58d477c657140c46e05d4ed2d35b26cbf8986a48 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 8 Apr 2020 14:13:16 -0500
Subject: [PATCH 041/139] Renaming UnstructuredMeshBase class to
 UnstructuredMesh.

---
 include/openmc/mesh.h | 12 ++++++------
 src/mesh.cpp          | 10 +++++-----
 src/state_point.cpp   |  2 +-
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 1505556805..f4a6dcc33e 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -274,13 +274,13 @@ public:
   int set_grid();
 };
 
-class UnstructuredMeshBase : public Mesh {
+class UnstructuredMesh : public Mesh {
 
 public:
   // constructors
-  UnstructuredMeshBase() {};
-  UnstructuredMeshBase(pugi::xml_node node);
-  UnstructuredMeshBase(const std::string& filename);
+  UnstructuredMesh() {};
+  UnstructuredMesh(pugi::xml_node node);
+  UnstructuredMesh(const std::string& filename);
 
   std::string bin_label(int bin) const override;
 
@@ -311,7 +311,7 @@ public:
 
 #ifdef DAGMC
 
-class MOABUnstructuredMesh : public UnstructuredMeshBase {
+class MOABUnstructuredMesh : public UnstructuredMesh {
 
 public:
 
@@ -480,7 +480,7 @@ private:
 #endif
 
 #ifdef LIBMESH
-class LibMesh : public UnstructuredMeshBase {
+class LibMesh : public UnstructuredMesh {
 
   typedef std::vector<std::pair<double, const libMesh::Elem*>> UnstructuredMeshHits;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 0f92032e37..538c3661dc 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -112,7 +112,7 @@ StructuredMesh::bin_label(int bin) const {
 // Untructured Mesh implementation
 //==============================================================================
 
-UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
+UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
   // check the mesh type
   if (check_for_node(node, "type")) {
     auto temp = get_node_value(node, "type", true, true);
@@ -132,12 +132,12 @@ UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
 }
 
 std::string
-UnstructuredMeshBase::bin_label(int bin) const {
+UnstructuredMesh::bin_label(int bin) const {
   return fmt::format("Mesh Index ({})", bin);
 };
 
 void
-UnstructuredMeshBase::to_hdf5(hid_t group) const
+UnstructuredMesh::to_hdf5(hid_t group) const
 {
     hid_t mesh_group = create_group(group, fmt::format("mesh {}", id_));
 
@@ -1614,7 +1614,7 @@ openmc_rectilinear_mesh_set_grid(int32_t index, const double* grid_x,
 
 #ifdef DAGMC
 
-MOABUnstructuredMesh::MOABUnstructuredMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+MOABUnstructuredMesh::MOABUnstructuredMesh(pugi::xml_node node) : UnstructuredMesh(node) {
   initialize();
 }
 
@@ -2162,7 +2162,7 @@ double UnstructuredMesh::get_volume_frac(int bin = -1) const {
 #endif
 
 #ifdef LIBMESH
-LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMesh(node) {
   initialize();
 }
 
diff --git a/src/state_point.cpp b/src/state_point.cpp
index c9f436a6ed..e8767947c5 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -784,7 +784,7 @@ void write_unstructured_mesh_results() {
       // check if the filter uses an unstructured mesh
       auto mesh_filter = dynamic_cast<MeshFilter*>(filter.get());
       auto mesh_idx = mesh_filter->mesh();
-      auto umesh = dynamic_cast<UnstructuredMeshBase*>(model::meshes[mesh_idx].get());
+      auto umesh = dynamic_cast<UnstructuredMesh*>(model::meshes[mesh_idx].get());
 
       if (!umesh) continue;
 

From 8b7a09eee126342a98aa447f97c8fcb0d243b568 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 8 Apr 2020 16:36:13 -0500
Subject: [PATCH 042/139] Some I/O cleanup.

---
 openmc/mesh.py |  3 ++-
 src/mesh.cpp   | 21 +++++++++------------
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/openmc/mesh.py b/openmc/mesh.py
index 534a96e9d6..2559a0f56d 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -832,7 +832,8 @@ class UnstructuredMesh(MeshBase):
         """
         mesh_id = int(get_text(elem, 'id'))
         filename = get_text(elem, 'filename')
+        library = get_text(elem, 'library')
 
         mesh = cls(filename, mesh_id)
-
+        mesh.library = library
         return mesh
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 538c3661dc..1fc5169c52 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -113,6 +113,8 @@ StructuredMesh::bin_label(int bin) const {
 //==============================================================================
 
 UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
+  n_dimension_ = 3;
+
   // check the mesh type
   if (check_for_node(node, "type")) {
     auto temp = get_node_value(node, "type", true, true);
@@ -122,8 +124,8 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
   }
 
   // get the filename of the unstructured mesh to load
-  if (check_for_node(node, "mesh_file")) {
-    filename_ = get_node_value(node, "mesh_file");
+  if (check_for_node(node, "filename")) {
+    filename_ = get_node_value(node, "filename");
   }
   else {
     fatal_error("No filename supplied for unstructured mesh with ID: " +
@@ -2384,11 +2386,10 @@ void read_meshes(pugi::xml_node root)
       mesh_type = "regular";
     }
 
+    // determine the mesh library to use
     std::string mesh_lib;
     if (check_for_node(node, "library")) {
       mesh_lib = get_node_value(node, "library", true, true);
-    } else {
-      mesh_lib = "moab";
     }
 
     // Read mesh and add to vector
@@ -2397,19 +2398,15 @@ void read_meshes(pugi::xml_node root)
     } else if (mesh_type == "rectilinear") {
       model::meshes.push_back(std::make_unique<RectilinearMesh>(node));
 #ifdef DAGMC
-    }
-    else if (mesh_type == "unstructured" && mesh_lib == "moab") {
+    } else if (mesh_type == "unstructured" && mesh_lib == "moab") {
       model::meshes.push_back(std::make_unique<MOABUnstructuredMesh>(node));
-#else
-    } else if (mesh_type == "unstructured") {
-      fatal_error("Unstructured mesh support is disabled.");
 #endif
 #ifdef LIBMESH
-    }
-    else if (mesh_type == "unstructured" && mesh_lib == "libmesh") {
-      std::cout << "Making libmesh mesh" << std::endl;
+    } else if (mesh_type == "unstructured" && mesh_lib == "libmesh") {
       model::meshes.push_back(std::make_unique<LibMesh>(node));
 #endif
+    } else if (mesh_type == "unstructured") {
+      fatal_error("Unstructured mesh support is not enabled or the mesh library is invalid.");
     } else {
       fatal_error("Invalid mesh type: " + mesh_type);
     }

From 26fafd319e8bc5a94952fffed20e95b661c6b2e0 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 5 Dec 2019 22:29:47 -0600
Subject: [PATCH 043/139] Start of libmesh implementation.

---
 CMakeLists.txt | 16 ++++++++++++++++
 src/mesh.cpp   | 31 +++++++++++++++++++++++++++++++
 2 files changed, 47 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 499e006a01..d104405424 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -73,6 +73,16 @@ if(libmesh)
   find_package(LIBMESH REQUIRED)
 endif()
 
+#===============================================================================
+# libmesh Unstructured Mesh Support
+#===============================================================================
+if(libmesh)
+  find_package(LIBMESH REQUIRED)
+  set(LIBMESH_LIBRARIES mesh_opt)
+  set(LIBMESH_INCLUDE_DIRS "${LIBMESH}/../include/")
+  link_directories(${LIBMESH})
+endif()
+
 #===============================================================================
 # HDF5 for binary output
 #===============================================================================
@@ -418,6 +428,12 @@ if(libmesh)
   target_link_libraries(libopenmc PkgConfig::LIBMESH)
 endif()
 
+if(libmesh)
+  target_compile_definitions(libopenmc PRIVATE LIBMESH)
+  target_link_libraries(libopenmc ${LIBMESH_LIBRARIES})
+  target_include_directories(libopenmc PRIVATE ${LIBMESH_INCLUDE_DIRS})
+endif()
+
 #===============================================================================
 # openmc executable
 #===============================================================================
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 1fc5169c52..47f9a1d611 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2372,6 +2372,37 @@ double LibMesh::volume(int bin) const { return m_->elem_ref(bin).volume(); }
 
 #endif // LIBMESH
 
+UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
+    // check the mesh type
+  if (check_for_node(node, "type")) {
+    auto temp = get_node_value(node, "type", true, true);
+    if (temp != "unstructured") {
+      fatal_error("Invalid mesh type: " + temp);
+    }
+  }
+
+  // get the filename of the unstructured mesh to load
+  if (check_for_node(node, "mesh_file")) {
+    filename_ = get_node_value(node, "mesh_file");
+  }
+  else {
+    fatal_error("No filename supplied for unstructured mesh with ID: " +
+                std::to_string(id_));
+  }
+}
+
+#ifdef LIBMESH
+LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
+  libMesh::LibMeshInit init(0, NULL);
+
+  m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(init.comm(), 3));
+  m_->read(filename_);
+
+  point_locator_ = m_->sub_point_locator();
+  m_->find_neighbors();
+}
+#endif
+
 //==============================================================================
 // Non-member functions
 //==============================================================================

From abda65d0385ba082b478d48f997b4179a39e3548 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 7 Apr 2020 18:11:57 -0500
Subject: [PATCH 044/139] Making sure libmesh is found in the OpenMC config if
 we build with it.

---
 cmake/OpenMCConfig.cmake.in | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/cmake/OpenMCConfig.cmake.in b/cmake/OpenMCConfig.cmake.in
index 94078f9092..b584de5bc9 100644
--- a/cmake/OpenMCConfig.cmake.in
+++ b/cmake/OpenMCConfig.cmake.in
@@ -15,6 +15,12 @@ if(@LIBMESH_FOUND@)
   pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
 endif()
 
+if(@LIBMESH_FOUND@)
+  include(FindPkgConfig)
+  set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:@LIBMESH_PC@")
+  pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
+endif()
+
 if(NOT TARGET OpenMC::libopenmc)
   include("${OpenMC_CMAKE_DIR}/OpenMCTargets.cmake")
 endif()

From c4087307f0ac79b8ae3208ec4302ad37a733ab8d Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 8 Apr 2020 16:59:46 -0500
Subject: [PATCH 045/139] Updating unstructured mesh test results.

---
 .../unstructured_mesh/inputs_true0.dat        |     2 +-
 .../unstructured_mesh/inputs_true1.dat        |     2 +-
 .../unstructured_mesh/inputs_true2.dat        |     2 +-
 .../unstructured_mesh/inputs_true3.dat        |     2 +-
 .../unstructured_mesh/inputs_true4.dat        |     2 +-
 .../unstructured_mesh/inputs_true5.dat        |     2 +-
 .../unstructured_mesh/inputs_true6.dat        |     2 +-
 .../unstructured_mesh/inputs_true7.dat        |     2 +-
 .../unstructured_mesh/results_true.dat        | 50956 ++++++++--------
 .../unstructured_mesh/test.py                 |     2 +-
 10 files changed, 25487 insertions(+), 25487 deletions(-)

diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
index 58d6f0b1ed..897d79917f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="2" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.h5m</filename>
   </mesh>
   <filter id="1" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index 1ccfda9ba0..5461b8df1f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="4" type="unstructured">
+  <mesh id="4" library="moab" type="unstructured">
     <filename>test_mesh_tets.h5m</filename>
   </mesh>
   <filter id="3" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index 7f25eead89..3902f36b58 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="6" type="unstructured">
+  <mesh id="6" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.h5m</filename>
   </mesh>
   <filter id="5" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index d0e329b60f..2bb59617bd 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="8" type="unstructured">
+  <mesh id="8" library="moab" type="unstructured">
     <filename>test_mesh_tets.h5m</filename>
   </mesh>
   <filter id="7" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true4.dat b/tests/regression_tests/unstructured_mesh/inputs_true4.dat
index d2fbe4197b..ee89864654 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true4.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true4.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="10" type="unstructured">
+  <mesh id="10" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.h5m</filename>
   </mesh>
   <filter id="9" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true5.dat b/tests/regression_tests/unstructured_mesh/inputs_true5.dat
index dd2d9d59bb..0348bbd68e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true5.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true5.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="12" type="unstructured">
+  <mesh id="12" library="moab" type="unstructured">
     <filename>test_mesh_tets.h5m</filename>
   </mesh>
   <filter id="11" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true6.dat b/tests/regression_tests/unstructured_mesh/inputs_true6.dat
index c14e9052b4..eb7f5e4153 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true6.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true6.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="14" type="unstructured">
+  <mesh id="14" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.h5m</filename>
   </mesh>
   <filter id="13" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true7.dat b/tests/regression_tests/unstructured_mesh/inputs_true7.dat
index 6eff949061..931c6a3ee8 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true7.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true7.dat
@@ -70,7 +70,7 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="16" type="unstructured">
+  <mesh id="16" library="moab" type="unstructured">
     <filename>test_mesh_tets.h5m</filename>
   </mesh>
   <filter id="15" type="mesh">
diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat
index 94e6a11f77..b967cb6497 100644
--- a/tests/regression_tests/unstructured_mesh/results_true.dat
+++ b/tests/regression_tests/unstructured_mesh/results_true.dat
@@ -1,4379 +1,4381 @@
 tally 1:
-8.089363E-03
-6.543779E-05
-3.146002E-02
-5.860332E-04
-6.418397E-02
-1.377443E-03
-1.276874E-01
-3.908566E-03
-7.611981E-02
-2.088880E-03
-6.036441E-02
-1.216457E-03
-6.691217E-02
-1.980841E-03
-9.722686E-03
-9.453062E-05
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-7.667324E-02
-2.007092E-03
-1.298554E-01
-3.654454E-03
-7.693092E-02
-1.456468E-03
-1.236752E-01
-2.398141E-03
-7.467512E-02
-2.941183E-03
-1.441027E-01
-4.192347E-03
-9.236718E-02
-1.966420E-03
-4.195794E-03
-1.760469E-05
-3.487809E-02
-1.216481E-03
-0.000000E+00
-0.000000E+00
-2.909677E-02
-5.473222E-04
-8.525987E-02
-2.547029E-03
-1.158490E-01
-2.304292E-03
-2.922003E-01
-1.469044E-02
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+3.601221E-05
+1.050704E-02
+1.103978E-04
+1.200877E-02
+7.692591E-05
+8.162339E-04
+6.662377E-07
+4.883879E-03
+1.202608E-05
+2.918152E-02
+4.573956E-04
+1.275018E-02
+8.651241E-05
+8.080520E-03
+3.488253E-05
+5.660160E-03
+3.203741E-05
 0.000000E+00
 0.000000E+00
+1.462026E-02
+1.182318E-04
+4.456366E-03
+1.985920E-05
+5.381590E-03
+1.487181E-05
 0.000000E+00
 0.000000E+00
+1.025368E-02
+1.051379E-04
+1.629496E-03
+2.655258E-06
 0.000000E+00
 0.000000E+00
+2.321848E-03
+5.390978E-06
+8.162674E-04
+6.662924E-07
+1.225150E-02
+1.248128E-04
 0.000000E+00
 0.000000E+00
 6.756013E-03
 4.564371E-05
+4.704899E-03
+1.643119E-05
 0.000000E+00
 0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
+3.211018E-03
+1.031064E-05
 3.677964E-03
 1.352742E-05
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
+1.233996E-02
+1.522745E-04
+2.760766E-02
+6.300858E-04
 2.555184E-03
 6.528967E-06
-0.000000E+00
-0.000000E+00
+5.546849E-03
+3.076753E-05
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index f4461cd117..39259a2fec 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -190,7 +190,7 @@ def test_unstructured_mesh(test_opts):
         mesh_filename = "test_mesh_tets.h5m"
 
     uscd_mesh = openmc.UnstructuredMesh(mesh_filename)
-    uscd_mesh.mesh_lib = 'moab'
+    uscd_mesh.library = 'moab'
     uscd_filter = openmc.MeshFilter(mesh=uscd_mesh)
 
     # create tallies

From 700d62c9ef0dbea6d38ac8a38364ff2448f72813 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 9 Apr 2020 12:37:33 -0500
Subject: [PATCH 046/139] Some cleanup (reordering of methods to match in
 inheritance) and including libmesh collision tallies in testing.

---
 include/openmc/mesh.h                         |  97 +++++++-----------
 openmc/lib/__init__.py                        |   3 +
 openmc/mesh.py                                |  12 +--
 src/mesh.cpp                                  |  64 ++++--------
 .../unstructured_mesh/inputs_true0.dat        |   4 +-
 .../unstructured_mesh/inputs_true1.dat        |   4 +-
 .../unstructured_mesh/inputs_true10.dat       |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true11.dat       |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true12.dat       |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true13.dat       |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true14.dat       |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true15.dat       |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true2.dat        |   4 +-
 .../unstructured_mesh/inputs_true3.dat        |   4 +-
 .../unstructured_mesh/inputs_true4.dat        |   2 +-
 .../unstructured_mesh/inputs_true5.dat        |   2 +-
 .../unstructured_mesh/inputs_true6.dat        |   2 +-
 .../unstructured_mesh/inputs_true7.dat        |   2 +-
 .../unstructured_mesh/inputs_true8.dat        |  92 +++++++++++++++++
 .../unstructured_mesh/inputs_true9.dat        |  92 +++++++++++++++++
 .../unstructured_mesh/test.py                 |  20 ++--
 .../unstructured_mesh/test_mesh_tets.exo      | Bin 0 -> 354408 bytes
 .../unstructured_mesh/test_mesh_tets.h5m      | Bin 481444 -> 0 bytes
 .../test_mesh_tets_w_holes.exo                | Bin 0 -> 353440 bytes
 .../test_mesh_tets_w_holes.h5m                | Bin 480220 -> 0 bytes
 25 files changed, 824 insertions(+), 132 deletions(-)
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true10.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true11.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true12.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true13.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true14.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true15.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true8.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true9.dat
 create mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets.exo
 delete mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets.h5m
 create mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.exo
 delete mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index f4a6dcc33e..eecb06a204 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -41,6 +41,8 @@ namespace openmc {
 // Global variables
 //==============================================================================
 
+extern "C" const bool libmesh_enabled;
+
 class Mesh;
 
 namespace model {
@@ -277,17 +279,12 @@ public:
 class UnstructuredMesh : public Mesh {
 
 public:
-  // constructors
+  // Constructors
   UnstructuredMesh() {};
   UnstructuredMesh(pugi::xml_node node);
   UnstructuredMesh(const std::string& filename);
 
-  std::string bin_label(int bin) const override;
-
-  //! Write mesh data to an HDF5 group.
-  //
-  //! \param[in] group HDF5 group
-  void to_hdf5(hid_t group) const;
+  // Methods
 
   //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;
@@ -297,14 +294,31 @@ public:
                               std::vector<double> values,
                               std::vector<double> std_dev) = 0;
 
+  //! Write the unstructured mesh to file
+  //
+  //
+  //! \param[in] filename Name of the file to write
   virtual void write(std::string filename) const = 0;
 
+  //! Retrieve a centroid for the mesh cell
+  //
+  //! \param[in] bin Bin to return the centroid for
+  //! \return The centroid of the bin
   virtual Position centroid(int bin) const = 0;
 
+  //! Get the volume of a mesh bin
+  //
+  //! \param[in] bin Bin to return the volume for
+  //! \return The volume of the bin
   virtual double volume(int bin) const = 0;
 
+  //! Get the library used for this unstructured mesh
   virtual std::string library() const = 0;
 
+  std::string bin_label(int bin) const override;
+
+  void to_hdf5(hid_t group) const override;
+
   // Data members
   std::string filename_; //!< Path to unstructured mesh file
 };
@@ -331,27 +345,18 @@ public:
   //
   //! \param[in] p Particle to check
   //! \param[out] bins Surface bins that were crossed
-  void surface_bins_crossed(const Particle& p, std::vector<int>& bins) const;
+  void surface_bins_crossed(const Particle* p, std::vector<int>& bins) const;
 
-  //! Get bin at a given position.
-  //
-  //! \param[in] r Position to get bin for
-  //! \return Mesh bin
   int get_bin(Position r) const;
 
-  std::string library() const override;
-
   int n_bins() const override;
 
   int n_surface_bins() const override;
 
-  //! Retrieve a centroid for the mesh cell
-  //
-  // \param[in] bin Bin to return the volume for
-  // \return The centroid of the element
-  Position centroid(int bin) const override;
+  std::pair<std::vector<double>, std::vector<double>>
+  plot(Position plot_ll, Position plot_ur) const override;
 
-  double volume(int bin) const override;
+  std::string library() const override;
 
   //! Add a score to the mesh instance
   void add_score(const std::string& score);
@@ -372,7 +377,9 @@ public:
   //! Write the mesh with any current tally data
   void write(std::string base_filename) const;
 
-  bool intersects(Position& r0, Position r1, int* ijk);
+  Position centroid(int bin) const override;
+
+  double volume(int bin) const override;
 
 private:
 
@@ -468,7 +475,7 @@ private:
   std::pair<moab::Tag, moab::Tag>
   get_score_tags(std::string score) const;
 
-  // data members
+  // Data members
   moab::Range ehs_; //!< Range of tetrahedra EntityHandle's in the mesh
   moab::EntityHandle tetset_; //!< EntitySet containing all tetrahedra
   moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree
@@ -490,40 +497,35 @@ public:
   LibMesh(const std::string& filename);
 
   // Methods
-
-  // standard mesh functions
   void bins_crossed(const Particle* p,
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const override;
 
-  int get_bin(Position r) const override;
+  void surface_bins_crossed(const Particle* p, std::vector<int>& bins) const override;
 
-  std::string library() const override;
+  int get_bin(Position r) const override;
 
   int n_bins() const override;
 
   int n_surface_bins() const override;
 
-  void surface_bins_crossed(const Particle* p,
-                             std::vector<int>& bins) const override;
-
   std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
                                                            Position plot_ur) const override;
 
-  void write(std::string filename) const override;
-
-  //! Add a variable to the libmesh mesh instance
   void add_score(const std::string& var_name) override;
 
-  //! Set the value of a bin for a variable on the libmesh mesh instance
   void set_score_data(const std::string& var_name,
                       std::vector<double> values,
                       std::vector<double> std_dev) override;
 
+  void write(std::string filename) const override;
+
   Position centroid(int bin) const override;
 
   double volume(int bin) const override;
 
+  std::string library() const override;
+
 private:
 
   //! Setup method for the mesh. Builds data structures,
@@ -536,43 +538,14 @@ private:
   //! Translate an element pointer to a bin value
   int get_bin_from_element(const libMesh::Elem* elem) const;
 
-  //! Check whether if a point moving in a given direction
-  //! is inside the element
-  //
-  //! \param[in] r In: point to be checked
-  //! \param[in] u In: direction (this is arbitrary, any normalized direction will do)
-  //! \param[in] e In: libmesh element in question
-  //! \return whether or not the point is in the tet
-  bool inside_tet(const libMesh::Point& r,
-                  const libMesh::Point& u,
-                  const libMesh::Elem* e) const;
-
-  //! Check if a point moving in a given direction
-  //! is inside the element
-  //
-  //! \param[in] r In: point to be checked
-  //! \param[in] u In: direction (this is arbitrary, any normalized direction will do)
-  //! \param[in] e In: libmesh element in question
-  //! \return whether or not the point is in the tet
-  bool inside_tet(const libMesh::Point& r,
-                  const libMesh::Point& u,
-                  std::unique_ptr<libMesh::Elem> e) const;
-
-  double first(const libMesh::Node& a,
-               const libMesh::Node& b) const;
-
   // Data members
-
-private:
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libmesh mesh instance
   std::vector<std::unique_ptr<libMesh::PointLocatorBase>> point_locators_; //!< pointers to locators for each thread
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh (for result output)
   std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (scores) to their numbers on the mesh
   libMesh::BoundingBox bbox_; //!< bounding box of the mesh
-  libMesh::Sphere bsphere_; //<! bounding sphere of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   libMesh::Elem* first_element_; //!< pointer to the first element in the mesh (maybe should be a key?)
-  std::set<libMesh::Elem*> boundary_elements_; //<! all boundary elements in the mesh
 };
 #endif
 
diff --git a/openmc/lib/__init__.py b/openmc/lib/__init__.py
index 8a2d3cf7ba..9a72403ff4 100644
--- a/openmc/lib/__init__.py
+++ b/openmc/lib/__init__.py
@@ -45,6 +45,9 @@ def _dagmc_enabled():
 def _coord_levels():
     return c_int.in_dll(_dll, "n_coord_levels").value
 
+def _libmesh_enabled():
+    return c_bool.in_dll(_dll, "libmesh_enabled").value
+
 from .error import *
 from .core import *
 from .nuclide import *
diff --git a/openmc/mesh.py b/openmc/mesh.py
index 2559a0f56d..4d65db2a96 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -632,7 +632,7 @@ class UnstructuredMesh(MeshBase):
         self.filename = filename
         self._volumes = None
         self._centroids = None
-        self._mesh_lib = 'moab'
+        self._library = 'moab'
 
     @property
     def filename(self):
@@ -644,13 +644,13 @@ class UnstructuredMesh(MeshBase):
         self._filename = filename
 
     @property
-    def mesh_lib(self):
+    def library(self):
         return self._mesh_lib
 
-    @mesh_lib.setter
-    def mesh_lib(self, mesh_lib):
+    @library.setter
+    def library(self, mesh_lib):
         cv.check_value('mesh_lib', mesh_lib, ('moab', 'libmesh'))
-        self._mesh_lib = mesh_lib
+        self._library = mesh_lib
 
     @property
     def size(self):
@@ -811,7 +811,7 @@ class UnstructuredMesh(MeshBase):
         element.set("id", str(self._id))
         element.set("type", "unstructured")
         subelement = ET.SubElement(element, "filename")
-        element.set("library", self._mesh_lib)
+        element.set("library", self._library)
         subelement.text = self.filename
 
         return element
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 47f9a1d611..a967828fe4 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -39,6 +39,13 @@ namespace openmc {
 // Global variables
 //==============================================================================
 
+#ifdef LIBMESH
+const bool libmesh_enabled = true;
+#else
+const bool libmesh_enabled = false;
+#endif
+
+
 namespace model {
 
 std::unordered_map<int32_t, int32_t> mesh_map;
@@ -2205,25 +2212,10 @@ void LibMesh::initialize() {
     point_locators_[i]->enable_out_of_mesh_mode();
   }
 
-  // will need mesh neighbors to walk the mesh
-  m_->find_neighbors();
-
   first_element_ = *m_->elements_begin();
 
   // bounding box for the mesh
   bbox_ = libMesh::MeshTools::create_bounding_box(*m_);
-  bsphere_ = libMesh::MeshTools::bounding_sphere(*m_);
-
-  // determine boundary elements and create bounding box
-  for (int i = 0; i < m_->n_elem(); i++) {
-    auto e = m_->elem_ptr(i);
-
-    for (int k = 0; k < e->n_neighbors(); k++) {
-      if (!e->neighbor_ptr(k)) {
-        boundary_elements_.insert(e);
-      }
-    }
-  }
 }
 
 int LibMesh::n_bins() const {
@@ -2243,15 +2235,6 @@ LibMesh::surface_bins_crossed(const Particle* p,
   throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
 }
 
-std::pair<std::vector<double>, std::vector<double>>
-LibMesh::plot(Position plot_ll,
-              Position plot_ur) const { return {}; }
-
-const libMesh::Elem*
-LibMesh::get_element_from_bin(int bin) const {
-  return m_->elem_ptr(bin);
-}
-
 void
 LibMesh::add_score(const std::string& var_name) {
   // check if this is a new varaible
@@ -2309,7 +2292,7 @@ LibMesh::set_score_data(const std::string& var_name,
 void LibMesh::write(std::string filename) const {
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
-  exo.write_discontinuous_exodusII(filename, *equation_systems_, &systems_out);
+  exo.write_discontinuous_exodusII(filename + ".e", *equation_systems_, &systems_out);
 }
 
 void
@@ -2328,11 +2311,10 @@ LibMesh::get_bin(Position r) const
   libMesh::Point p(r.x, r.y, r.z);
 
   // quick rejection check
-  //  if (bsphere_.above_surface(p) || !bbox_.contains_point(p)) { return -1; }
   if (!bbox_.contains_point(p)) { return -1; }
-  int thread = omp_get_thread_num();
-  auto e = (*point_locators_[thread])(p);
 
+  int thread = omp_get_thread_num();
+  auto e = (*point_locators_[thread])(p, false);
   if (!e) {
     return -1;
   } else {
@@ -2343,29 +2325,19 @@ LibMesh::get_bin(Position r) const
 int
 LibMesh::get_bin_from_element(const libMesh::Elem* elem) const {
   int bin =  elem->id() - first_element_->id();
-  if (bin >= n_bins()) {
-    std::stringstream s;
-    s << "Invalid bin: " << bin;
-    fatal_error(s);
+  if (bin >= n_bins() || bin < 0) {
+    fatal_error(fmt::format("Invalid bin: {}", bin));
   }
   return bin;
 }
 
-bool
-LibMesh::inside_tet(const libMesh::Point& r,
-                    const libMesh::Point& u,
-                    std::unique_ptr<libMesh::Elem> e) const
-{
-  return inside_tet(r, u, e.get());
-}
+std::pair<std::vector<double>, std::vector<double>>
+LibMesh::plot(Position plot_ll,
+              Position plot_ur) const { return {}; }
 
-
-bool
-LibMesh::inside_tet(const libMesh::Point& r,
-                    const libMesh::Point& u,
-                    const libMesh::Elem* e) const
-{
-  return e->contains_point(r, FP_COINCIDENT);
+const libMesh::Elem*
+LibMesh::get_element_from_bin(int bin) const {
+  return m_->elem_ptr(bin);
 }
 
 double LibMesh::volume(int bin) const { return m_->elem_ref(bin).volume(); }
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
index 897d79917f..916b77f0fb 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
@@ -70,8 +70,8 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.h5m</filename>
+  <mesh id="2" library="libmesh" type="unstructured">
+    <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index 5461b8df1f..a8633ab75d 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -70,8 +70,8 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="4" library="moab" type="unstructured">
-    <filename>test_mesh_tets.h5m</filename>
+  <mesh id="4" library="libmesh" type="unstructured">
+    <filename>test_mesh_tets.exo</filename>
   </mesh>
   <filter id="3" type="mesh">
     <bins>3</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true10.dat b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
new file mode 100644
index 0000000000..cb5a96a0ce
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="19" material="19" name="fuel" region="109 -110 111 -112 113 -114" universe="7" />
+  <cell id="20" material="20" name="clad" region="(-109 | 110 | -111 | 112 | -113 | 114) (115 -116 117 -118 119 -120)" universe="7" />
+  <cell id="21" material="21" name="water" region="(-115 | 116 | -117 | 118 | -119 | 120) (121 -122 123 -124 125 -126)" universe="7" />
+  <surface coeffs="-5.0" id="109" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="110" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="111" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="112" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="113" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="114" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="115" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="116" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="117" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="118" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="119" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="120" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="121" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="122" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="123" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="124" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="125" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="126" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="19" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="20" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="21" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="13">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="14" library="moab" type="unstructured">
+    <filename>test_mesh_tets_w_holes.exo</filename>
+  </mesh>
+  <filter id="13" type="mesh">
+    <bins>13</bins>
+  </filter>
+  <filter id="14" type="mesh">
+    <bins>14</bins>
+  </filter>
+  <tally id="13" name="regular mesh tally">
+    <filters>13</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+  <tally id="14" name="unstructured mesh tally">
+    <filters>14</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true11.dat b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
new file mode 100644
index 0000000000..f721422f1d
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="22" material="22" name="fuel" region="127 -128 129 -130 131 -132" universe="8" />
+  <cell id="23" material="23" name="clad" region="(-127 | 128 | -129 | 130 | -131 | 132) (133 -134 135 -136 137 -138)" universe="8" />
+  <cell id="24" material="24" name="water" region="(-133 | 134 | -135 | 136 | -137 | 138) (139 -140 141 -142 143 -144)" universe="8" />
+  <surface coeffs="-5.0" id="127" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="128" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="129" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="130" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="131" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="132" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="133" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="134" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="135" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="136" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="137" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="138" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="139" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="140" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="141" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="142" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="143" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="144" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="22" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="23" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="24" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="15">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="16" library="moab" type="unstructured">
+    <filename>test_mesh_tets.exo</filename>
+  </mesh>
+  <filter id="15" type="mesh">
+    <bins>15</bins>
+  </filter>
+  <filter id="16" type="mesh">
+    <bins>16</bins>
+  </filter>
+  <tally id="15" name="regular mesh tally">
+    <filters>15</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+  <tally id="16" name="unstructured mesh tally">
+    <filters>16</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true12.dat b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
new file mode 100644
index 0000000000..f29b4ca6af
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="25" material="25" name="fuel" region="145 -146 147 -148 149 -150" universe="9" />
+  <cell id="26" material="26" name="clad" region="(-145 | 146 | -147 | 148 | -149 | 150) (151 -152 153 -154 155 -156)" universe="9" />
+  <cell id="27" material="27" name="water" region="(-151 | 152 | -153 | 154 | -155 | 156) (157 -158 159 -160 161 -162)" universe="9" />
+  <surface coeffs="-5.0" id="145" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="146" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="147" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="148" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="149" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="150" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="151" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="152" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="153" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="154" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="155" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="156" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="157" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="158" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="159" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="160" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="161" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="162" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="25" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="26" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="27" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="17">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="18" library="moab" type="unstructured">
+    <filename>test_mesh_tets_w_holes.exo</filename>
+  </mesh>
+  <filter id="17" type="mesh">
+    <bins>17</bins>
+  </filter>
+  <filter id="18" type="mesh">
+    <bins>18</bins>
+  </filter>
+  <tally id="17" name="regular mesh tally">
+    <filters>17</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+  <tally id="18" name="unstructured mesh tally">
+    <filters>18</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true13.dat b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
new file mode 100644
index 0000000000..e2337e1ae0
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="28" material="28" name="fuel" region="163 -164 165 -166 167 -168" universe="10" />
+  <cell id="29" material="29" name="clad" region="(-163 | 164 | -165 | 166 | -167 | 168) (169 -170 171 -172 173 -174)" universe="10" />
+  <cell id="30" material="30" name="water" region="(-169 | 170 | -171 | 172 | -173 | 174) (175 -176 177 -178 179 -180)" universe="10" />
+  <surface coeffs="-5.0" id="163" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="164" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="165" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="166" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="167" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="168" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="169" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="170" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="171" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="172" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="173" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="174" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="175" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="176" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="177" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="178" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="179" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="180" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="28" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="29" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="30" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="19">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="20" library="moab" type="unstructured">
+    <filename>test_mesh_tets.exo</filename>
+  </mesh>
+  <filter id="19" type="mesh">
+    <bins>19</bins>
+  </filter>
+  <filter id="20" type="mesh">
+    <bins>20</bins>
+  </filter>
+  <tally id="19" name="regular mesh tally">
+    <filters>19</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+  <tally id="20" name="unstructured mesh tally">
+    <filters>20</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true14.dat b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
new file mode 100644
index 0000000000..b8f90877fa
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="31" material="31" name="fuel" region="181 -182 183 -184 185 -186" universe="11" />
+  <cell id="32" material="32" name="clad" region="(-181 | 182 | -183 | 184 | -185 | 186) (187 -188 189 -190 191 -192)" universe="11" />
+  <cell id="33" material="33" name="water" region="(-187 | 188 | -189 | 190 | -191 | 192) (193 -194 195 -196 197 -198)" universe="11" />
+  <surface coeffs="-5.0" id="181" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="182" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="183" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="184" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="185" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="186" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="187" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="188" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="189" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="190" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="191" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="192" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="193" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="194" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="195" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="196" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="197" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="198" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="31" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="32" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="33" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="21">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="22" library="moab" type="unstructured">
+    <filename>test_mesh_tets_w_holes.exo</filename>
+  </mesh>
+  <filter id="21" type="mesh">
+    <bins>21</bins>
+  </filter>
+  <filter id="22" type="mesh">
+    <bins>22</bins>
+  </filter>
+  <tally id="21" name="regular mesh tally">
+    <filters>21</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+  <tally id="22" name="unstructured mesh tally">
+    <filters>22</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true15.dat b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
new file mode 100644
index 0000000000..4ef681bc80
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="34" material="34" name="fuel" region="199 -200 201 -202 203 -204" universe="12" />
+  <cell id="35" material="35" name="clad" region="(-199 | 200 | -201 | 202 | -203 | 204) (205 -206 207 -208 209 -210)" universe="12" />
+  <cell id="36" material="36" name="water" region="(-205 | 206 | -207 | 208 | -209 | 210) (211 -212 213 -214 215 -216)" universe="12" />
+  <surface coeffs="-5.0" id="199" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="200" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="201" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="202" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="203" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="204" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="205" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="206" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="207" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="208" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="209" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="210" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="211" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="212" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="213" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="214" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="215" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="216" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="34" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="35" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="36" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="23">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="24" library="moab" type="unstructured">
+    <filename>test_mesh_tets.exo</filename>
+  </mesh>
+  <filter id="23" type="mesh">
+    <bins>23</bins>
+  </filter>
+  <filter id="24" type="mesh">
+    <bins>24</bins>
+  </filter>
+  <tally id="23" name="regular mesh tally">
+    <filters>23</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+  <tally id="24" name="unstructured mesh tally">
+    <filters>24</filters>
+    <scores>flux</scores>
+    <estimator>tracklength</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index 3902f36b58..a93b8a160e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -70,8 +70,8 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="6" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.h5m</filename>
+  <mesh id="6" library="libmesh" type="unstructured">
+    <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
   <filter id="5" type="mesh">
     <bins>5</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index 2bb59617bd..baa197a68e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -70,8 +70,8 @@
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="8" library="moab" type="unstructured">
-    <filename>test_mesh_tets.h5m</filename>
+  <mesh id="8" library="libmesh" type="unstructured">
+    <filename>test_mesh_tets.exo</filename>
   </mesh>
   <filter id="7" type="mesh">
     <bins>7</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true4.dat b/tests/regression_tests/unstructured_mesh/inputs_true4.dat
index ee89864654..a39452da7c 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true4.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true4.dat
@@ -71,7 +71,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="10" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.h5m</filename>
+    <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
   <filter id="9" type="mesh">
     <bins>9</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true5.dat b/tests/regression_tests/unstructured_mesh/inputs_true5.dat
index 0348bbd68e..288b7d1fcb 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true5.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true5.dat
@@ -71,7 +71,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="12" library="moab" type="unstructured">
-    <filename>test_mesh_tets.h5m</filename>
+    <filename>test_mesh_tets.exo</filename>
   </mesh>
   <filter id="11" type="mesh">
     <bins>11</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true6.dat b/tests/regression_tests/unstructured_mesh/inputs_true6.dat
index eb7f5e4153..883e6cbf48 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true6.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true6.dat
@@ -71,7 +71,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="14" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.h5m</filename>
+    <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
   <filter id="13" type="mesh">
     <bins>13</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true7.dat b/tests/regression_tests/unstructured_mesh/inputs_true7.dat
index 931c6a3ee8..267966e3ca 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true7.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true7.dat
@@ -71,7 +71,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="16" library="moab" type="unstructured">
-    <filename>test_mesh_tets.h5m</filename>
+    <filename>test_mesh_tets.exo</filename>
   </mesh>
   <filter id="15" type="mesh">
     <bins>15</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true8.dat b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
new file mode 100644
index 0000000000..ff3d3dc8d5
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="13" material="13" name="fuel" region="73 -74 75 -76 77 -78" universe="5" />
+  <cell id="14" material="14" name="clad" region="(-73 | 74 | -75 | 76 | -77 | 78) (79 -80 81 -82 83 -84)" universe="5" />
+  <cell id="15" material="15" name="water" region="(-79 | 80 | -81 | 82 | -83 | 84) (85 -86 87 -88 89 -90)" universe="5" />
+  <surface coeffs="-5.0" id="73" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="74" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="75" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="76" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="77" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="78" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="79" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="80" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="81" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="82" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="83" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="84" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="85" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="86" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="87" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="88" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="89" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="90" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="13" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="14" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="15" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="9">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="10" library="moab" type="unstructured">
+    <filename>test_mesh_tets_w_holes.exo</filename>
+  </mesh>
+  <filter id="9" type="mesh">
+    <bins>9</bins>
+  </filter>
+  <filter id="10" type="mesh">
+    <bins>10</bins>
+  </filter>
+  <tally id="9" name="regular mesh tally">
+    <filters>9</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+  <tally id="10" name="unstructured mesh tally">
+    <filters>10</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true9.dat b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
new file mode 100644
index 0000000000..953750e5ca
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
@@ -0,0 +1,92 @@
+<?xml version='1.0' encoding='utf-8'?>
+<geometry>
+  <cell id="16" material="16" name="fuel" region="91 -92 93 -94 95 -96" universe="6" />
+  <cell id="17" material="17" name="clad" region="(-91 | 92 | -93 | 94 | -95 | 96) (97 -98 99 -100 101 -102)" universe="6" />
+  <cell id="18" material="18" name="water" region="(-97 | 98 | -99 | 100 | -101 | 102) (103 -104 105 -106 107 -108)" universe="6" />
+  <surface coeffs="-5.0" id="91" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="92" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="93" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="94" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="95" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="96" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="97" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="98" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="99" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="100" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="101" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="102" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="103" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="104" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="105" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="106" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="107" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="108" name="maximum z" type="z-plane" />
+</geometry>
+<?xml version='1.0' encoding='utf-8'?>
+<materials>
+  <material depletable="true" id="16" name="fuel">
+    <density units="g/cc" value="4.5" />
+    <nuclide ao="1.0" name="U235" />
+  </material>
+  <material id="17" name="zircaloy">
+    <density units="g/cc" value="5.77" />
+    <nuclide ao="0.5145" name="Zr90" />
+    <nuclide ao="0.1122" name="Zr91" />
+    <nuclide ao="0.1715" name="Zr92" />
+    <nuclide ao="0.1738" name="Zr94" />
+    <nuclide ao="0.028" name="Zr96" />
+  </material>
+  <material id="18" name="water">
+    <density units="atom/b-cm" value="0.07416" />
+    <nuclide ao="2.0" name="H1" />
+    <nuclide ao="1.0" name="O16" />
+  </material>
+</materials>
+<?xml version='1.0' encoding='utf-8'?>
+<settings>
+  <run_mode>fixed source</run_mode>
+  <particles>100</particles>
+  <batches>10</batches>
+  <source strength="1.0">
+    <space origin="0.0 0.0 0.0" type="spherical">
+      <r parameters="0.0 0.0" type="uniform" />
+      <theta type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </theta>
+      <phi type="discrete">
+        <parameters>0.0 1.0</parameters>
+      </phi>
+    </space>
+    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
+    <energy type="discrete">
+      <parameters>15000000.0 1.0</parameters>
+    </energy>
+  </source>
+</settings>
+<?xml version='1.0' encoding='utf-8'?>
+<tallies>
+  <mesh id="11">
+    <dimension>10 10 10</dimension>
+    <lower_left>-10.0 -10.0 -10.0</lower_left>
+    <upper_right>10.0 10.0 10.0</upper_right>
+  </mesh>
+  <mesh id="12" library="moab" type="unstructured">
+    <filename>test_mesh_tets.exo</filename>
+  </mesh>
+  <filter id="11" type="mesh">
+    <bins>11</bins>
+  </filter>
+  <filter id="12" type="mesh">
+    <bins>12</bins>
+  </filter>
+  <tally id="11" name="regular mesh tally">
+    <filters>11</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+  <tally id="12" name="unstructured mesh tally">
+    <filters>12</filters>
+    <scores>flux</scores>
+    <estimator>collision</estimator>
+  </tally>
+</tallies>
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index 39259a2fec..d1a013561b 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -62,17 +62,20 @@ class UnstructuredMeshTest(PyAPITestHarness):
     def _cleanup(self):
         super()._cleanup()
         output = glob.glob('tally*.vtk')
+        output += glob.glob('tally*.e')
         for f in output:
             if os.path.exists(f):
                 os.remove(f)
 
 
-param_values = (['collision', 'tracklength'], # estimators
+param_values = (['libmesh', 'moab'], # mesh libraries
+                ['collision', 'tracklength'], # estimators
                 [True, False], # geometry outside of the mesh
                 [(333, 90, 77), None]) # location of holes in the mesh
 test_cases = []
-for i, (estimator, ext_geom, holes) in enumerate(product(*param_values)):
-    test_cases.append({'estimator' : estimator,
+for i, (lib, estimator, ext_geom, holes) in enumerate(product(*param_values)):
+    test_cases.append({'library' : lib,
+                       'estimator' : estimator,
                        'external_geom' : ext_geom,
                        'holes' : holes,
                        'inputs_true' : 'inputs_true{}.dat'.format(i)})
@@ -81,6 +84,11 @@ for i, (estimator, ext_geom, holes) in enumerate(product(*param_values)):
 @pytest.mark.parametrize("test_opts", test_cases)
 def test_unstructured_mesh(test_opts):
 
+    # skip the tracklength test for libmesh
+    if test_opts['library'] == 'libmesh' and \
+       test_opts['estimator'] == 'tracklength':
+       pytest.skip("Tracklength tallies are not supported using libmesh.")
+
     ### Materials ###
     materials = openmc.Materials()
 
@@ -185,12 +193,12 @@ def test_unstructured_mesh(test_opts):
     regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh)
 
     if test_opts['holes']:
-        mesh_filename = "test_mesh_tets_w_holes.h5m"
+        mesh_filename = "test_mesh_tets_w_holes.exo"
     else:
-        mesh_filename = "test_mesh_tets.h5m"
+        mesh_filename = "test_mesh_tets.exo"
 
     uscd_mesh = openmc.UnstructuredMesh(mesh_filename)
-    uscd_mesh.library = 'moab'
+    uscd_mesh.library = test_opts['library']
     uscd_filter = openmc.MeshFilter(mesh=uscd_mesh)
 
     # create tallies
diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.exo b/tests/regression_tests/unstructured_mesh/test_mesh_tets.exo
new file mode 100644
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diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.h5m b/tests/regression_tests/unstructured_mesh/test_mesh_tets.h5m
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diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m b/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m
deleted file mode 100644
index 971de0ad3cc1cf1c08166a7670b749422a8fb181..0000000000000000000000000000000000000000
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From a5a98fdfcd0cf7f1a66f73c26f1331dd401760e7 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 9 Apr 2020 13:02:32 -0500
Subject: [PATCH 047/139] Removing some old code that git re-added on rebase.

---
 CMakeLists.txt | 16 ----------------
 src/mesh.cpp   | 31 -------------------------------
 2 files changed, 47 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d104405424..499e006a01 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -73,16 +73,6 @@ if(libmesh)
   find_package(LIBMESH REQUIRED)
 endif()
 
-#===============================================================================
-# libmesh Unstructured Mesh Support
-#===============================================================================
-if(libmesh)
-  find_package(LIBMESH REQUIRED)
-  set(LIBMESH_LIBRARIES mesh_opt)
-  set(LIBMESH_INCLUDE_DIRS "${LIBMESH}/../include/")
-  link_directories(${LIBMESH})
-endif()
-
 #===============================================================================
 # HDF5 for binary output
 #===============================================================================
@@ -428,12 +418,6 @@ if(libmesh)
   target_link_libraries(libopenmc PkgConfig::LIBMESH)
 endif()
 
-if(libmesh)
-  target_compile_definitions(libopenmc PRIVATE LIBMESH)
-  target_link_libraries(libopenmc ${LIBMESH_LIBRARIES})
-  target_include_directories(libopenmc PRIVATE ${LIBMESH_INCLUDE_DIRS})
-endif()
-
 #===============================================================================
 # openmc executable
 #===============================================================================
diff --git a/src/mesh.cpp b/src/mesh.cpp
index a967828fe4..c309f8a96c 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2344,37 +2344,6 @@ double LibMesh::volume(int bin) const { return m_->elem_ref(bin).volume(); }
 
 #endif // LIBMESH
 
-UnstructuredMeshBase::UnstructuredMeshBase(pugi::xml_node node) : Mesh(node) {
-    // check the mesh type
-  if (check_for_node(node, "type")) {
-    auto temp = get_node_value(node, "type", true, true);
-    if (temp != "unstructured") {
-      fatal_error("Invalid mesh type: " + temp);
-    }
-  }
-
-  // get the filename of the unstructured mesh to load
-  if (check_for_node(node, "mesh_file")) {
-    filename_ = get_node_value(node, "mesh_file");
-  }
-  else {
-    fatal_error("No filename supplied for unstructured mesh with ID: " +
-                std::to_string(id_));
-  }
-}
-
-#ifdef LIBMESH
-LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMeshBase(node) {
-  libMesh::LibMeshInit init(0, NULL);
-
-  m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(init.comm(), 3));
-  m_->read(filename_);
-
-  point_locator_ = m_->sub_point_locator();
-  m_->find_neighbors();
-}
-#endif
-
 //==============================================================================
 // Non-member functions
 //==============================================================================

From f7220b62db48a89351b3c2d34f97e08d89e21bde Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 9 Apr 2020 13:29:55 -0500
Subject: [PATCH 048/139] Some cleanup from self-review

---
 cmake/OpenMCConfig.cmake.in                   |  6 -----
 include/openmc/mesh.h                         | 19 ++++++++------
 openmc/mesh.py                                | 25 +++++++++----------
 src/finalize.cpp                              |  3 +--
 src/initialize.cpp                            |  2 --
 src/tallies/filter_mesh.cpp                   |  4 ---
 .../unstructured_mesh/test.py                 |  5 ++--
 7 files changed, 26 insertions(+), 38 deletions(-)

diff --git a/cmake/OpenMCConfig.cmake.in b/cmake/OpenMCConfig.cmake.in
index b584de5bc9..94078f9092 100644
--- a/cmake/OpenMCConfig.cmake.in
+++ b/cmake/OpenMCConfig.cmake.in
@@ -15,12 +15,6 @@ if(@LIBMESH_FOUND@)
   pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
 endif()
 
-if(@LIBMESH_FOUND@)
-  include(FindPkgConfig)
-  set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:@LIBMESH_PC@")
-  pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
-endif()
-
 if(NOT TARGET OpenMC::libopenmc)
   include("${OpenMC_CMAKE_DIR}/OpenMCTargets.cmake")
 endif()
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index eecb06a204..674c3cb6c3 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -24,15 +24,14 @@
 
 #ifdef LIBMESH
 #include "libmesh/bounding_box.h"
-#include "libmesh/libmesh.h"
+#include "libmesh/dof_map.h"
 #include "libmesh/elem.h"
 #include "libmesh/equation_systems.h"
 #include "libmesh/exodusII_io.h"
 #include "libmesh/explicit_system.h"
-#include "libmesh/dof_map.h"
+#include "libmesh/libmesh.h"
 #include "libmesh/mesh.h"
 #include "libmesh/point.h"
-#include "libmesh/sphere.h"
 #endif
 
 namespace openmc {
@@ -109,7 +108,6 @@ public:
   virtual std::pair<std::vector<double>, std::vector<double>>
   plot(Position plot_ll, Position plot_ur) const = 0;
 
-  //! Get a label for the mesh bin
   //! Return a string representation of the mesh bin
   //
   //! \param[in] bin Mesh bin to generate a label for
@@ -285,6 +283,13 @@ public:
   UnstructuredMesh(const std::string& filename);
 
   // Methods
+private:
+
+  //! Setup method for the mesh. Builds data structures,
+  //! element mapping, etc.
+  virtual void initialize() = 0;
+
+public:
 
   //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;
@@ -383,7 +388,7 @@ public:
 
 private:
 
-  void initialize();
+  void initialize() override;
 
   //! Find all intersections with faces of the mesh.
   //
@@ -528,9 +533,7 @@ public:
 
 private:
 
-  //! Setup method for the mesh. Builds data structures,
-  //! element mapping, etc.
-  void initialize();
+  void initialize() override;
 
   //! Translate a bin value to an element pointer
   const libMesh::Elem* get_element_from_bin(int bin) const;
diff --git a/openmc/mesh.py b/openmc/mesh.py
index 4d65db2a96..969a776917 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -616,8 +616,8 @@ class UnstructuredMesh(MeshBase):
         Name of the mesh
     filename : str
         Name of the file containing the unstructured mesh
-    mesh_lib : str
-        Library used for the unstructured mesh tally
+    library : str
+        Mesh library used for the unstructured mesh tally
     volumes : Iterable of float
         Volumes of the unstructured mesh elements
     total_volume : float
@@ -627,12 +627,12 @@ class UnstructuredMesh(MeshBase):
         (1.0, 1.0, 1.0), ...]
     """
 
-    def __init__(self, filename, mesh_id=None, name=''):
+    def __init__(self, filename, library, mesh_id=None, name=''):
         super().__init__(mesh_id, name)
         self.filename = filename
         self._volumes = None
         self._centroids = None
-        self._library = 'moab'
+        self.library = library
 
     @property
     def filename(self):
@@ -645,12 +645,12 @@ class UnstructuredMesh(MeshBase):
 
     @property
     def library(self):
-        return self._mesh_lib
+        return self._library
 
     @library.setter
-    def library(self, mesh_lib):
-        cv.check_value('mesh_lib', mesh_lib, ('moab', 'libmesh'))
-        self._library = mesh_lib
+    def library(self, lib):
+        cv.check_value('mesh_lib', lib, ('moab', 'libmesh'))
+        self._library = lib
 
     @property
     def size(self):
@@ -786,13 +786,13 @@ class UnstructuredMesh(MeshBase):
     def from_hdf5(cls, group):
         mesh_id = int(group.name.split('/')[-1].lstrip('mesh '))
         filename = group['filename'][()].decode()
+        library = group['library'][()].decode()
 
-        mesh = cls(filename, mesh_id=mesh_id)
+        mesh = cls(filename, library, mesh_id=mesh_id)
         vol_data = group['volumes'][()]
         centroids = group['centroids'][()]
         mesh.volumes = np.reshape(vol_data, (vol_data.shape[0],))
         mesh.centroids = np.reshape(centroids, (vol_data.shape[0], 3))
-        mesh.mesh_lib = group['library'][()].decode()
         mesh.size = mesh.volumes.size
 
         return mesh
@@ -810,8 +810,8 @@ class UnstructuredMesh(MeshBase):
         element = ET.Element("mesh")
         element.set("id", str(self._id))
         element.set("type", "unstructured")
-        subelement = ET.SubElement(element, "filename")
         element.set("library", self._library)
+        subelement = ET.SubElement(element, "filename")
         subelement.text = self.filename
 
         return element
@@ -834,6 +834,5 @@ class UnstructuredMesh(MeshBase):
         filename = get_text(elem, 'filename')
         library = get_text(elem, 'library')
 
-        mesh = cls(filename, mesh_id)
-        mesh.library = library
+        mesh = cls(filename, library, mesh_id)
         return mesh
diff --git a/src/finalize.cpp b/src/finalize.cpp
index 255cc7f30b..35e52743c0 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -192,5 +192,4 @@ int openmc_hard_reset()
   // Reset the random number generator state
   openmc::openmc_set_seed(DEFAULT_SEED);
   return 0;
-
-}
\ No newline at end of file
+}
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 8053246cdb..9c2861be0b 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -55,7 +55,6 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   initialize_mpi(comm);
 #endif
 
-
   // Parse command-line arguments
   int err = parse_command_line(argc, argv);
   if (err) return err;
@@ -89,7 +88,6 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   }
 #endif
 
-
   // Initialize random number generator -- if the user specifies a seed, it
   // will be re-initialized later
   openmc::openmc_set_seed(DEFAULT_SEED);
diff --git a/src/tallies/filter_mesh.cpp b/src/tallies/filter_mesh.cpp
index ecbcf94eca..eb9275bd44 100644
--- a/src/tallies/filter_mesh.cpp
+++ b/src/tallies/filter_mesh.cpp
@@ -45,10 +45,6 @@ const
   }
 
   double total = std::accumulate(match.weights_.begin(), match.weights_.end(), 0.0);
-  // if ( fabs(1.0 - total) > FP_PRECISION) {
-  //   std::cout << "Total weight for score < 1.0 (" << total << ")" << std::endl;
-  // }
-
 }
 
 void
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index d1a013561b..1ba89264e0 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -184,7 +184,7 @@ def test_unstructured_mesh(test_opts):
 
     ### Tallies ###
 
-    # create meshes
+    # create meshes and mesh filters
     regular_mesh = openmc.RegularMesh()
     regular_mesh.dimension = (10, 10, 10)
     regular_mesh.lower_left = (-10.0, -10.0, -10.0)
@@ -197,8 +197,7 @@ def test_unstructured_mesh(test_opts):
     else:
         mesh_filename = "test_mesh_tets.exo"
 
-    uscd_mesh = openmc.UnstructuredMesh(mesh_filename)
-    uscd_mesh.library = test_opts['library']
+    uscd_mesh = openmc.UnstructuredMesh(mesh_filename, test_opts['library'])
     uscd_filter = openmc.MeshFilter(mesh=uscd_mesh)
 
     # create tallies

From d00dd061ca95cab068a8cd8cff154a04a36534bf Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 9 Apr 2020 15:50:29 -0500
Subject: [PATCH 049/139] Updating test to reset IDs so tests will work with
 any combination of mesh libraries enabled.

---
 .../unstructured_mesh/inputs_true1.dat        |    68 +-
 .../unstructured_mesh/inputs_true10.dat       |    68 +-
 .../unstructured_mesh/inputs_true11.dat       |    68 +-
 .../unstructured_mesh/inputs_true12.dat       |    68 +-
 .../unstructured_mesh/inputs_true13.dat       |    68 +-
 .../unstructured_mesh/inputs_true14.dat       |    68 +-
 .../unstructured_mesh/inputs_true15.dat       |    68 +-
 .../unstructured_mesh/inputs_true2.dat        |    68 +-
 .../unstructured_mesh/inputs_true3.dat        |    68 +-
 .../unstructured_mesh/inputs_true8.dat        |    68 +-
 .../unstructured_mesh/inputs_true9.dat        |    68 +-
 .../unstructured_mesh/results_true.dat        | 51922 ++++++++--------
 .../unstructured_mesh/test.py                 |    13 +-
 13 files changed, 26344 insertions(+), 26339 deletions(-)

diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index a8633ab75d..d23b617384 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="4" material="4" name="fuel" region="19 -20 21 -22 23 -24" universe="2" />
-  <cell id="5" material="5" name="clad" region="(-19 | 20 | -21 | 22 | -23 | 24) (25 -26 27 -28 29 -30)" universe="2" />
-  <cell id="6" material="6" name="water" region="(-25 | 26 | -27 | 28 | -29 | 30) (31 -32 33 -34 35 -36)" universe="2" />
-  <surface coeffs="-5.0" id="19" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="20" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="21" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="22" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="23" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="24" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="25" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="26" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="27" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="28" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="29" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="30" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="31" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="15" id="32" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="33" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="15" id="34" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="35" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="15" id="36" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="4" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="5" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="6" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="3">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="4" library="libmesh" type="unstructured">
+  <mesh id="2" library="libmesh" type="unstructured">
     <filename>test_mesh_tets.exo</filename>
   </mesh>
-  <filter id="3" type="mesh">
-    <bins>3</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="4" type="mesh">
-    <bins>4</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="3" name="regular mesh tally">
-    <filters>3</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="4" name="unstructured mesh tally">
-    <filters>4</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true10.dat b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
index cb5a96a0ce..ce9e9160ba 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true10.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="19" material="19" name="fuel" region="109 -110 111 -112 113 -114" universe="7" />
-  <cell id="20" material="20" name="clad" region="(-109 | 110 | -111 | 112 | -113 | 114) (115 -116 117 -118 119 -120)" universe="7" />
-  <cell id="21" material="21" name="water" region="(-115 | 116 | -117 | 118 | -119 | 120) (121 -122 123 -124 125 -126)" universe="7" />
-  <surface coeffs="-5.0" id="109" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="110" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="111" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="112" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="113" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="114" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="115" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="116" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="117" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="118" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="119" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="120" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="121" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="10" id="122" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="123" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="10" id="124" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="125" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="10" id="126" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="19" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="20" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="21" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="13">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="14" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
-  <filter id="13" type="mesh">
-    <bins>13</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="14" type="mesh">
-    <bins>14</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="13" name="regular mesh tally">
-    <filters>13</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="14" name="unstructured mesh tally">
-    <filters>14</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true11.dat b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
index f721422f1d..d58da3042a 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true11.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="22" material="22" name="fuel" region="127 -128 129 -130 131 -132" universe="8" />
-  <cell id="23" material="23" name="clad" region="(-127 | 128 | -129 | 130 | -131 | 132) (133 -134 135 -136 137 -138)" universe="8" />
-  <cell id="24" material="24" name="water" region="(-133 | 134 | -135 | 136 | -137 | 138) (139 -140 141 -142 143 -144)" universe="8" />
-  <surface coeffs="-5.0" id="127" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="128" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="129" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="130" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="131" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="132" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="133" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="134" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="135" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="136" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="137" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="138" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="139" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="10" id="140" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="141" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="10" id="142" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="143" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="10" id="144" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="22" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="23" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="24" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="15">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="16" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets.exo</filename>
   </mesh>
-  <filter id="15" type="mesh">
-    <bins>15</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="16" type="mesh">
-    <bins>16</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="15" name="regular mesh tally">
-    <filters>15</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="16" name="unstructured mesh tally">
-    <filters>16</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true12.dat b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
index f29b4ca6af..e9b5736801 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true12.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="25" material="25" name="fuel" region="145 -146 147 -148 149 -150" universe="9" />
-  <cell id="26" material="26" name="clad" region="(-145 | 146 | -147 | 148 | -149 | 150) (151 -152 153 -154 155 -156)" universe="9" />
-  <cell id="27" material="27" name="water" region="(-151 | 152 | -153 | 154 | -155 | 156) (157 -158 159 -160 161 -162)" universe="9" />
-  <surface coeffs="-5.0" id="145" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="146" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="147" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="148" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="149" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="150" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="151" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="152" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="153" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="154" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="155" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="156" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="157" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="15" id="158" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="159" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="15" id="160" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="161" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="15" id="162" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="25" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="26" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="27" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="17">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="18" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
-  <filter id="17" type="mesh">
-    <bins>17</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="18" type="mesh">
-    <bins>18</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="17" name="regular mesh tally">
-    <filters>17</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
-  <tally id="18" name="unstructured mesh tally">
-    <filters>18</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true13.dat b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
index e2337e1ae0..bd8026e712 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true13.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="28" material="28" name="fuel" region="163 -164 165 -166 167 -168" universe="10" />
-  <cell id="29" material="29" name="clad" region="(-163 | 164 | -165 | 166 | -167 | 168) (169 -170 171 -172 173 -174)" universe="10" />
-  <cell id="30" material="30" name="water" region="(-169 | 170 | -171 | 172 | -173 | 174) (175 -176 177 -178 179 -180)" universe="10" />
-  <surface coeffs="-5.0" id="163" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="164" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="165" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="166" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="167" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="168" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="169" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="170" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="171" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="172" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="173" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="174" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="175" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="15" id="176" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="177" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="15" id="178" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="179" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="15" id="180" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="28" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="29" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="30" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="19">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="20" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets.exo</filename>
   </mesh>
-  <filter id="19" type="mesh">
-    <bins>19</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="20" type="mesh">
-    <bins>20</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="19" name="regular mesh tally">
-    <filters>19</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
-  <tally id="20" name="unstructured mesh tally">
-    <filters>20</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true14.dat b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
index b8f90877fa..07ec3f063f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true14.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="31" material="31" name="fuel" region="181 -182 183 -184 185 -186" universe="11" />
-  <cell id="32" material="32" name="clad" region="(-181 | 182 | -183 | 184 | -185 | 186) (187 -188 189 -190 191 -192)" universe="11" />
-  <cell id="33" material="33" name="water" region="(-187 | 188 | -189 | 190 | -191 | 192) (193 -194 195 -196 197 -198)" universe="11" />
-  <surface coeffs="-5.0" id="181" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="182" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="183" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="184" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="185" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="186" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="187" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="188" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="189" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="190" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="191" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="192" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="193" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="10" id="194" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="195" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="10" id="196" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="197" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="10" id="198" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="31" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="32" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="33" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="21">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="22" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
-  <filter id="21" type="mesh">
-    <bins>21</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="22" type="mesh">
-    <bins>22</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="21" name="regular mesh tally">
-    <filters>21</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
-  <tally id="22" name="unstructured mesh tally">
-    <filters>22</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true15.dat b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
index 4ef681bc80..7f4c499a21 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true15.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="34" material="34" name="fuel" region="199 -200 201 -202 203 -204" universe="12" />
-  <cell id="35" material="35" name="clad" region="(-199 | 200 | -201 | 202 | -203 | 204) (205 -206 207 -208 209 -210)" universe="12" />
-  <cell id="36" material="36" name="water" region="(-205 | 206 | -207 | 208 | -209 | 210) (211 -212 213 -214 215 -216)" universe="12" />
-  <surface coeffs="-5.0" id="199" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="200" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="201" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="202" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="203" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="204" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="205" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="206" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="207" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="208" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="209" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="210" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="211" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="10" id="212" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="213" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="10" id="214" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="215" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="10" id="216" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="34" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="35" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="36" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="23">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="24" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets.exo</filename>
   </mesh>
-  <filter id="23" type="mesh">
-    <bins>23</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="24" type="mesh">
-    <bins>24</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="23" name="regular mesh tally">
-    <filters>23</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
-  <tally id="24" name="unstructured mesh tally">
-    <filters>24</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>tracklength</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index a93b8a160e..4f95feae91 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="7" material="7" name="fuel" region="37 -38 39 -40 41 -42" universe="3" />
-  <cell id="8" material="8" name="clad" region="(-37 | 38 | -39 | 40 | -41 | 42) (43 -44 45 -46 47 -48)" universe="3" />
-  <cell id="9" material="9" name="water" region="(-43 | 44 | -45 | 46 | -47 | 48) (49 -50 51 -52 53 -54)" universe="3" />
-  <surface coeffs="-5.0" id="37" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="38" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="39" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="40" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="41" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="42" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="43" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="44" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="45" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="46" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="47" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="48" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="49" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="10" id="50" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="51" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="10" id="52" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="53" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="10" id="54" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="7" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="8" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="9" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="5">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="6" library="libmesh" type="unstructured">
+  <mesh id="2" library="libmesh" type="unstructured">
     <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
-  <filter id="5" type="mesh">
-    <bins>5</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="6" type="mesh">
-    <bins>6</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="5" name="regular mesh tally">
-    <filters>5</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="6" name="unstructured mesh tally">
-    <filters>6</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index baa197a68e..a22a8f3cd4 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="10" material="10" name="fuel" region="55 -56 57 -58 59 -60" universe="4" />
-  <cell id="11" material="11" name="clad" region="(-55 | 56 | -57 | 58 | -59 | 60) (61 -62 63 -64 65 -66)" universe="4" />
-  <cell id="12" material="12" name="water" region="(-61 | 62 | -63 | 64 | -65 | 66) (67 -68 69 -70 71 -72)" universe="4" />
-  <surface coeffs="-5.0" id="55" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="56" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="57" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="58" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="59" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="60" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="61" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="62" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="63" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="64" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="65" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="66" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="67" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="10" id="68" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="69" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="10" id="70" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-10" id="71" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="10" id="72" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="10" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="10" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-10" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="10" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="10" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="11" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="12" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="7">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="8" library="libmesh" type="unstructured">
+  <mesh id="2" library="libmesh" type="unstructured">
     <filename>test_mesh_tets.exo</filename>
   </mesh>
-  <filter id="7" type="mesh">
-    <bins>7</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="8" type="mesh">
-    <bins>8</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="7" name="regular mesh tally">
-    <filters>7</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="8" name="unstructured mesh tally">
-    <filters>8</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true8.dat b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
index ff3d3dc8d5..8b094e9d50 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true8.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="13" material="13" name="fuel" region="73 -74 75 -76 77 -78" universe="5" />
-  <cell id="14" material="14" name="clad" region="(-73 | 74 | -75 | 76 | -77 | 78) (79 -80 81 -82 83 -84)" universe="5" />
-  <cell id="15" material="15" name="water" region="(-79 | 80 | -81 | 82 | -83 | 84) (85 -86 87 -88 89 -90)" universe="5" />
-  <surface coeffs="-5.0" id="73" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="74" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="75" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="76" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="77" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="78" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="79" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="80" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="81" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="82" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="83" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="84" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="85" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="15" id="86" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="87" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="15" id="88" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="89" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="15" id="90" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="13" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="14" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="15" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="9">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="10" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets_w_holes.exo</filename>
   </mesh>
-  <filter id="9" type="mesh">
-    <bins>9</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="10" type="mesh">
-    <bins>10</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="9" name="regular mesh tally">
-    <filters>9</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="10" name="unstructured mesh tally">
-    <filters>10</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true9.dat b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
index 953750e5ca..e7d1f69a32 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true9.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
@@ -1,34 +1,34 @@
 <?xml version='1.0' encoding='utf-8'?>
 <geometry>
-  <cell id="16" material="16" name="fuel" region="91 -92 93 -94 95 -96" universe="6" />
-  <cell id="17" material="17" name="clad" region="(-91 | 92 | -93 | 94 | -95 | 96) (97 -98 99 -100 101 -102)" universe="6" />
-  <cell id="18" material="18" name="water" region="(-97 | 98 | -99 | 100 | -101 | 102) (103 -104 105 -106 107 -108)" universe="6" />
-  <surface coeffs="-5.0" id="91" name="minimum x" type="x-plane" />
-  <surface coeffs="5.0" id="92" name="maximum x" type="x-plane" />
-  <surface coeffs="-5.0" id="93" name="minimum y" type="y-plane" />
-  <surface coeffs="5.0" id="94" name="maximum y" type="y-plane" />
-  <surface coeffs="-5.0" id="95" name="minimum z" type="z-plane" />
-  <surface coeffs="5.0" id="96" name="maximum z" type="z-plane" />
-  <surface coeffs="-6.0" id="97" name="minimum x" type="x-plane" />
-  <surface coeffs="6.0" id="98" name="maximum x" type="x-plane" />
-  <surface coeffs="-6.0" id="99" name="minimum y" type="y-plane" />
-  <surface coeffs="6.0" id="100" name="maximum y" type="y-plane" />
-  <surface coeffs="-6.0" id="101" name="minimum z" type="z-plane" />
-  <surface coeffs="6.0" id="102" name="maximum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="103" name="minimum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="15" id="104" name="maximum x" type="x-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="105" name="minimum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="15" id="106" name="maximum y" type="y-plane" />
-  <surface boundary="vacuum" coeffs="-15" id="107" name="minimum z" type="z-plane" />
-  <surface boundary="vacuum" coeffs="15" id="108" name="maximum z" type="z-plane" />
+  <cell id="1" material="1" name="fuel" region="1 -2 3 -4 5 -6" universe="1" />
+  <cell id="2" material="2" name="clad" region="(-1 | 2 | -3 | 4 | -5 | 6) (7 -8 9 -10 11 -12)" universe="1" />
+  <cell id="3" material="3" name="water" region="(-7 | 8 | -9 | 10 | -11 | 12) (13 -14 15 -16 17 -18)" universe="1" />
+  <surface coeffs="-5.0" id="1" name="minimum x" type="x-plane" />
+  <surface coeffs="5.0" id="2" name="maximum x" type="x-plane" />
+  <surface coeffs="-5.0" id="3" name="minimum y" type="y-plane" />
+  <surface coeffs="5.0" id="4" name="maximum y" type="y-plane" />
+  <surface coeffs="-5.0" id="5" name="minimum z" type="z-plane" />
+  <surface coeffs="5.0" id="6" name="maximum z" type="z-plane" />
+  <surface coeffs="-6.0" id="7" name="minimum x" type="x-plane" />
+  <surface coeffs="6.0" id="8" name="maximum x" type="x-plane" />
+  <surface coeffs="-6.0" id="9" name="minimum y" type="y-plane" />
+  <surface coeffs="6.0" id="10" name="maximum y" type="y-plane" />
+  <surface coeffs="-6.0" id="11" name="minimum z" type="z-plane" />
+  <surface coeffs="6.0" id="12" name="maximum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="13" name="minimum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="15" id="14" name="maximum x" type="x-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="15" name="minimum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="15" id="16" name="maximum y" type="y-plane" />
+  <surface boundary="vacuum" coeffs="-15" id="17" name="minimum z" type="z-plane" />
+  <surface boundary="vacuum" coeffs="15" id="18" name="maximum z" type="z-plane" />
 </geometry>
 <?xml version='1.0' encoding='utf-8'?>
 <materials>
-  <material depletable="true" id="16" name="fuel">
+  <material depletable="true" id="1" name="fuel">
     <density units="g/cc" value="4.5" />
     <nuclide ao="1.0" name="U235" />
   </material>
-  <material id="17" name="zircaloy">
+  <material id="2" name="zircaloy">
     <density units="g/cc" value="5.77" />
     <nuclide ao="0.5145" name="Zr90" />
     <nuclide ao="0.1122" name="Zr91" />
@@ -36,7 +36,7 @@
     <nuclide ao="0.1738" name="Zr94" />
     <nuclide ao="0.028" name="Zr96" />
   </material>
-  <material id="18" name="water">
+  <material id="3" name="water">
     <density units="atom/b-cm" value="0.07416" />
     <nuclide ao="2.0" name="H1" />
     <nuclide ao="1.0" name="O16" />
@@ -65,27 +65,27 @@
 </settings>
 <?xml version='1.0' encoding='utf-8'?>
 <tallies>
-  <mesh id="11">
+  <mesh id="1">
     <dimension>10 10 10</dimension>
     <lower_left>-10.0 -10.0 -10.0</lower_left>
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
-  <mesh id="12" library="moab" type="unstructured">
+  <mesh id="2" library="moab" type="unstructured">
     <filename>test_mesh_tets.exo</filename>
   </mesh>
-  <filter id="11" type="mesh">
-    <bins>11</bins>
+  <filter id="1" type="mesh">
+    <bins>1</bins>
   </filter>
-  <filter id="12" type="mesh">
-    <bins>12</bins>
+  <filter id="2" type="mesh">
+    <bins>2</bins>
   </filter>
-  <tally id="11" name="regular mesh tally">
-    <filters>11</filters>
+  <tally id="1" name="regular mesh tally">
+    <filters>1</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
-  <tally id="12" name="unstructured mesh tally">
-    <filters>12</filters>
+  <tally id="2" name="unstructured mesh tally">
+    <filters>2</filters>
     <scores>flux</scores>
     <estimator>collision</estimator>
   </tally>
diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat
index b967cb6497..ef138626b5 100644
--- a/tests/regression_tests/unstructured_mesh/results_true.dat
+++ b/tests/regression_tests/unstructured_mesh/results_true.dat
@@ -1,26002 +1,26002 @@
 tally 1:
-1.074269E-01
-5.112021E-03
-1.402567E-01
-5.594461E-03
-2.065009E-01
-7.650770E-03
-3.077823E-01
-1.715967E-02
-2.477947E-01
-1.298310E-02
-3.987454E-01
-3.308666E-02
-2.445906E-01
-1.230984E-02
-1.749595E-01
-5.935790E-03
-1.515503E-01
-7.205949E-03
-2.303173E-01
-2.543151E-02
-9.707717E-02
-2.370007E-03
-2.381603E-01
-9.599927E-03
-1.348382E-01
-3.836659E-03
-2.126834E-01
-6.708818E-03
-2.210697E-01
-8.138725E-03
-3.293776E-01
-2.035616E-02
-1.331165E-01
-5.283753E-03
-1.366089E-01
-3.311060E-03
-2.633057E-01
-1.656548E-02
-2.811285E-01
-3.270571E-02
-1.889198E-01
-6.050995E-03
-1.160538E-01
-3.979174E-03
-2.285327E-01
-7.784698E-03
-2.354272E-01
-8.644888E-03
-2.423694E-01
-1.195944E-02
-2.029082E-01
-8.298675E-03
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-1.462026E-02
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-2.655258E-06
 0.000000E+00
 0.000000E+00
-2.321848E-03
-5.390978E-06
-8.162674E-04
-6.662924E-07
-1.225150E-02
-1.248128E-04
 0.000000E+00
 0.000000E+00
-6.756013E-03
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-4.704899E-03
-1.643119E-05
 0.000000E+00
 0.000000E+00
-3.211018E-03
-1.031064E-05
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-1.352742E-05
-1.233996E-02
-1.522745E-04
-2.760766E-02
-6.300858E-04
-2.555184E-03
-6.528967E-06
-5.546849E-03
-3.076753E-05
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index 1ba89264e0..9d39e30761 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -9,10 +9,6 @@ import numpy as np
 import pytest
 from tests.testing_harness import PyAPITestHarness
 
-pytestmark = pytest.mark.skipif(
-    not openmc.lib._dagmc_enabled(),
-    reason="Mesh library is not enabled.")
-
 TETS_PER_VOXEL = 12
 
 
@@ -84,6 +80,15 @@ for i, (lib, estimator, ext_geom, holes) in enumerate(product(*param_values)):
 @pytest.mark.parametrize("test_opts", test_cases)
 def test_unstructured_mesh(test_opts):
 
+    openmc.reset_auto_ids()
+
+    # skip the test if the library is not enabled
+    if test_opts['library'] == 'moab' and not openmc.lib._dagmc_enabled():
+        pytest.skip("DAGMC (and MOAB) mesh not enbaled in this build.")
+
+    if test_opts['library'] == 'libmesh' and not openmc.lib._libmesh_enabled():
+        pytest.skip("LibMesh is not enabled in this build.")
+
     # skip the tracklength test for libmesh
     if test_opts['library'] == 'libmesh' and \
        test_opts['estimator'] == 'tracklength':

From 7323ae526fd4e61c97989bd303ab7aea7f2d124c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 10 Apr 2020 17:17:45 -0500
Subject: [PATCH 050/139] Updates to get the timing of variable addition and
 score setting right for libmesh.

---
 CMakeLists.txt                  |  4 ++++
 cmake/Modules/FindLIBMESH.cmake | 11 +++--------
 src/finalize.cpp                |  8 ++++----
 src/initialize.cpp              |  8 ++------
 src/mesh.cpp                    | 24 +++++++++++++-----------
 src/state_point.cpp             | 16 +++++++++++++++-
 6 files changed, 41 insertions(+), 30 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 499e006a01..8bd7988289 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -415,8 +415,12 @@ if(dagmc)
 endif()
 
 if(libmesh)
+if (debug)
+  target_link_libraries(libopenmc PkgConfig::LIBMESH_DEVEL)
+else()
   target_link_libraries(libopenmc PkgConfig::LIBMESH)
 endif()
+endif()
 
 #===============================================================================
 # openmc executable
diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 1039e3e3bb..8d3097f649 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -1,11 +1,5 @@
-# Try to find LIBMESH
-#
-# Once done this will define
-#
-#  LIBMESH_FOUND - system has LIBMESH
-#  LIBMESH_INCLUDE_DIRS - the LIBMESH include directory
-#  LIBMESH_LIBRARIES - Link these to use LIBMESH
-#  LIBMESH_DEFINITIONS - Compiler switches required for using LIBMESH
+# Finds the libMesh installation using CMake's PkgConfig
+# module and creates a libmesh imported target
 
 find_path(LIBMESH_PC NAMES libmesh-opt.pc
           HINTS ${LIBMESH_ROOT} $ENV{LIBMESH_ROOT}
@@ -16,5 +10,6 @@ find_path(LIBMESH_PC NAMES libmesh-opt.pc
 include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
 pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
+pkg_check_modules(LIBMESH_DEVEL REQUIRED libmesh-devel IMPORTED_TARGET)
 
 message(STATUS "Found LIBMESH in ${LIBMESH_PC}")
diff --git a/src/finalize.cpp b/src/finalize.cpp
index 35e52743c0..3afd9e1861 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -68,10 +68,6 @@ int openmc_finalize()
   // Reset timers
   reset_timers();
 
-#ifdef LIBMESH
-  settings::LMI.reset();
-#endif
-
   // Reset global variables
   settings::assume_separate = false;
   settings::check_overlaps = false;
@@ -141,6 +137,10 @@ int openmc_finalize()
   // Deallocate arrays
   free_memory();
 
+#ifdef LIBMESH
+  settings::LMI.reset();
+#endif
+
   // Free all MPI types
 #ifdef OPENMC_MPI
   if (mpi::bank != MPI_DATATYPE_NULL) MPI_Type_free(&mpi::bank);
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 9c2861be0b..fcb6d98b11 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -67,12 +67,8 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   int n_threads = 1;
 #endif
 
-  // initialize libmesh
-#ifdef OPENMC_MPI
-  if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, mpi::intracomm, n_threads);
-#else
-  if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
-#endif
+// initialize libmesh
+if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
 
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index c309f8a96c..72abb6d169 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2238,12 +2238,11 @@ LibMesh::surface_bins_crossed(const Particle* p,
 void
 LibMesh::add_score(const std::string& var_name) {
   // check if this is a new varaible
-  std::string value_name = var_name + "_value";
+  std::string value_name = var_name + "_mean";
   if (!variable_map_.count(value_name)) {
     auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
     auto var_num = eqn_sys.add_variable(value_name, libMesh::CONSTANT, libMesh::MONOMIAL);
     variable_map_[value_name] = var_num;
-    equation_systems_->init();
   }
 
   std::string std_dev_name = var_name + "_std_dev";
@@ -2252,7 +2251,6 @@ LibMesh::add_score(const std::string& var_name) {
     auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
     auto var_num = eqn_sys.add_variable(std_dev_name, libMesh::CONSTANT, libMesh::MONOMIAL);
     variable_map_[std_dev_name] = var_num;
-    equation_systems_->init();
   }
 }
 
@@ -2261,15 +2259,18 @@ LibMesh::set_score_data(const std::string& var_name,
                       std::vector<double> values,
                       std::vector<double> std_dev)
 {
-    auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
-    const libMesh::DofMap& dof_map = eqn_sys.get_dof_map();
+  auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
 
-    // look up the value variable
-    std::string value_name = var_name + "_value";
-    unsigned int value_num = variable_map_.at(value_name);
-    // look up the std dev variable
-    std::string std_dev_name = var_name + "_std_dev";
-    unsigned int std_dev_num = variable_map_.at(std_dev_name);
+  if (!eqn_sys.is_initialized()) { equation_systems_->init(); }
+
+  const libMesh::DofMap& dof_map = eqn_sys.get_dof_map();
+
+  // look up the value variable
+  std::string value_name = var_name + "_mean";
+  unsigned int value_num = variable_map_.at(value_name);
+  // look up the std dev variable
+  std::string std_dev_name = var_name + "_std_dev";
+  unsigned int std_dev_num = variable_map_.at(std_dev_name);
 
   for (int bin = 0; bin < this->n_bins(); bin++) {
     auto e = m_->elem_ptr(bin);
@@ -2290,6 +2291,7 @@ LibMesh::set_score_data(const std::string& var_name,
 }
 
 void LibMesh::write(std::string filename) const {
+  std::cout << "Writing file : " << filename + ".e" << std::endl;
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
   exo.write_discontinuous_exodusII(filename + ".e", *equation_systems_, &systems_out);
diff --git a/src/state_point.cpp b/src/state_point.cpp
index e8767947c5..0eb7334b65 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -799,6 +799,20 @@ void write_unstructured_mesh_results() {
 
       int n_realizations = tally->n_realizations_;
 
+      for (int i_score = 0; i_score < tally->scores_.size(); i_score++) {
+        for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) {
+          // generate a name for the value
+          std::string nuclide_name = "total"; // start with total by default
+          if (tally->nuclides_[i_nuc] > -1) {
+            nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_;
+          }
+
+          std::string score_name = tally->score_name(i_score);
+          auto score_str = fmt::format("{}_{}", score_name, nuclide_name);
+          umesh->add_score(score_str);
+        }
+      }
+
       // write each score/nuclide combination for this tally
       for (int i_score = 0; i_score < tally->scores_.size(); i_score++) {
         for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) {
@@ -828,10 +842,10 @@ void write_unstructured_mesh_results() {
           if (tally->nuclides_[i_nuc] > -1) {
             nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_;
           }
-
           std::string score_name = tally->score_name(i_score);
           auto score_str = fmt::format("{}_{}", score_name, nuclide_name);
           tally_scores.push_back(score_str);
+          // set the data for this score
           umesh->set_score_data(score_str, mean_vec, std_dev_vec);
         }
       }

From b54bbad9d96fdbbb5d72fbcfc04bb2e8baf6e3d9 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 24 Apr 2020 17:22:54 -0500
Subject: [PATCH 051/139] Adjusting how the LibMesh comm is stored.

---
 include/openmc/settings.h | 1 +
 src/initialize.cpp        | 6 +++++-
 src/mesh.cpp              | 5 +++--
 src/settings.cpp          | 1 +
 4 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index ce2e7b10a3..e873e3d3e2 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -29,6 +29,7 @@ namespace settings {
 
 #ifdef LIBMESH
 extern std::unique_ptr<libMesh::LibMeshInit> LMI;
+extern libMesh::Parallel::Communicator* libmesh_comm;
 #endif
 
 // Boolean flags
diff --git a/src/initialize.cpp b/src/initialize.cpp
index fcb6d98b11..7ec7ab0f5f 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -68,7 +68,11 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 // initialize libmesh
-if (!settings::LMI) settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
+if (!settings::LMI && !libMesh::initialized())
+{
+  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
+  settings::libmesh_comm = &(settings::LMI->comm());
+}
 
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 72abb6d169..7306d40f08 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2190,10 +2190,11 @@ LibMesh::centroid(int bin) const {
 std::string LibMesh::library() const { return "libmesh"; }
 
 void LibMesh::initialize() {
-    // always 3 for unstructured meshes
+  // always 3 for unstructured meshes
   n_dimension_ = 3;
 
-  m_ = std::unique_ptr<libMesh::Mesh>(new libMesh::Mesh(settings::LMI->comm(), 3));
+  m_ = std::make_unique<libMesh::Mesh>(*settings::libmesh_comm, 3);
+
   m_->read(filename_);
   m_->prepare_for_use();
 
diff --git a/src/settings.cpp b/src/settings.cpp
index 3326f0029a..4a2b3432db 100644
--- a/src/settings.cpp
+++ b/src/settings.cpp
@@ -39,6 +39,7 @@ namespace settings {
 
 #ifdef LIBMESH
 std::unique_ptr<libMesh::LibMeshInit> LMI;
+libMesh::Parallel::Communicator* libmesh_comm;
 #endif
 
 // Default values for boolean flags

From dd609b4191ea4fb95ad4d762eb42b4748e79b72e Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 28 Apr 2020 00:10:40 -0500
Subject: [PATCH 052/139] Adding check for tracklength estimator with libmesh.

---
 src/tallies/tally.cpp | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp
index 038bcec649..04fc238967 100644
--- a/src/tallies/tally.cpp
+++ b/src/tallies/tally.cpp
@@ -282,6 +282,21 @@ Tally::Tally(pugi::xml_node node)
         "Invalid estimator '{}' on tally {}", est, id_)};
     }
   }
+
+#ifdef LIBMESH
+  // ensure a tracklength tally isn't used with a libMesh filter
+  for (auto i : this->filters_) {
+    auto df = dynamic_cast<MeshFilter*>(model::tally_filters[i].get());
+    int32_t mesh {-1};
+    if (df) { mesh = df->mesh(); }
+    auto lm = dynamic_cast<LibMesh*>(model::meshes[mesh].get());
+    if (lm && estimator_ == TallyEstimator::TRACKLENGTH) {
+      fatal_error("A tracklength estimator cannot be used with "
+                  "an unstructured LibMesh tally.");
+    }
+  }
+#endif
+
 }
 
 Tally::~Tally()

From 210d99aed3292f6da79429cc16b3c35dc4f504ab Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 28 Apr 2020 00:11:06 -0500
Subject: [PATCH 053/139] Some cleanup of libmesh control flow and file write
 during state_point.

---
 CMakeLists.txt      | 11 ++++++-----
 src/initialize.cpp  |  4 ++++
 src/mesh.cpp        |  1 +
 src/state_point.cpp | 10 +++++-----
 4 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8bd7988289..e4d81b2327 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -71,6 +71,7 @@ else()
 #===============================================================================
 if(libmesh)
   find_package(LIBMESH REQUIRED)
+  list(APPEND cxxflags ${LIBMESH_CFLAGS})
 endif()
 
 #===============================================================================
@@ -415,11 +416,11 @@ if(dagmc)
 endif()
 
 if(libmesh)
-if (debug)
-  target_link_libraries(libopenmc PkgConfig::LIBMESH_DEVEL)
-else()
-  target_link_libraries(libopenmc PkgConfig::LIBMESH)
-endif()
+  if (debug)
+    target_link_libraries(libopenmc PkgConfig::LIBMESH_DEVEL)
+  else()
+    target_link_libraries(libopenmc PkgConfig::LIBMESH)
+  endif()
 endif()
 
 #===============================================================================
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 7ec7ab0f5f..3260e051ee 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -70,7 +70,11 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 // initialize libmesh
 if (!settings::LMI && !libMesh::initialized())
 {
+#ifdef OPENMC_MPI
+  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads, comm);
+#else
   settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
+#endif
   settings::libmesh_comm = &(settings::LMI->comm());
 }
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 7306d40f08..3b6891907f 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2217,6 +2217,7 @@ void LibMesh::initialize() {
 
   // bounding box for the mesh
   bbox_ = libMesh::MeshTools::create_bounding_box(*m_);
+
 }
 
 int LibMesh::n_bins() const {
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 0eb7334b65..29430ed6cb 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -54,6 +54,11 @@ openmc_statepoint_write(const char* filename, bool* write_source)
   // Write message
   write_message("Creating state point " + filename_ + "...", 5);
 
+#if defined(LIBMESH) || defined(DAGMC)
+      // write unstructured mesh tallies to VTK if possible
+      write_unstructured_mesh_results();
+#endif
+
   hid_t file_id;
   if (mpi::master) {
     // Create statepoint file
@@ -167,11 +172,6 @@ openmc_statepoint_write(const char* filename, bool* write_source)
         tally_ids.push_back(tally->id_);
       write_attribute(tallies_group, "ids", tally_ids);
 
-#if defined(LIBMESH) || defined(DAGMC)
-      // write unstructured mesh tallies to VTK if possible
-      write_unstructured_mesh_results();
-#endif
-
       // Write all tally information except results
       for (const auto& tally : model::tallies) {
         hid_t tally_group = create_group(tallies_group,

From 03f3d7ac73688a53e7a34f54298a1282ae3191cc Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 28 Apr 2020 12:11:38 -0500
Subject: [PATCH 054/139] A slightly safer method for writing out meshes
 independent of library.

---
 include/openmc/mesh.h |  1 +
 openmc/mesh.py        | 14 +++++++++++++-
 src/mesh.cpp          |  8 ++++++++
 src/state_point.cpp   |  5 +++++
 4 files changed, 27 insertions(+), 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 674c3cb6c3..0be9f65386 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -325,6 +325,7 @@ public:
   void to_hdf5(hid_t group) const override;
 
   // Data members
+  bool output_ {true};
   std::string filename_; //!< Path to unstructured mesh file
 };
 
diff --git a/openmc/mesh.py b/openmc/mesh.py
index 969a776917..ef6455b96a 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -618,6 +618,9 @@ class UnstructuredMesh(MeshBase):
         Name of the file containing the unstructured mesh
     library : str
         Mesh library used for the unstructured mesh tally
+    output : bool
+        Indicates whether or not automatic tally output should
+        be generated for this mesh
     volumes : Iterable of float
         Volumes of the unstructured mesh elements
     total_volume : float
@@ -626,13 +629,13 @@ class UnstructuredMesh(MeshBase):
         An iterable of element centroid coordinates, e.g. [(0.0, 0.0, 0.0),
         (1.0, 1.0, 1.0), ...]
     """
-
     def __init__(self, filename, library, mesh_id=None, name=''):
         super().__init__(mesh_id, name)
         self.filename = filename
         self._volumes = None
         self._centroids = None
         self.library = library
+        self._output = True
 
     @property
     def filename(self):
@@ -661,6 +664,15 @@ class UnstructuredMesh(MeshBase):
         cv.check_type("Unstructured mesh size", size, Integral)
         self._size = size
 
+    @property
+    def output(self):
+        return self._output
+
+    @output.setter
+    def output(self, val):
+        cv.check_type("Unstructured mesh output value", val, bool)
+        self._output = val
+
     @property
     def volumes(self):
         return self._volumes
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 3b6891907f..a2aa823183 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -138,6 +138,13 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
     fatal_error("No filename supplied for unstructured mesh with ID: " +
                 std::to_string(id_));
   }
+
+  // check if mesh tally data should be written with
+  // statepoint files
+  if (check_for_node(node, "output")) {
+    output_ = get_node_value_bool(node, "output");
+  }
+
 }
 
 std::string
@@ -2294,6 +2301,7 @@ LibMesh::set_score_data(const std::string& var_name,
 
 void LibMesh::write(std::string filename) const {
   std::cout << "Writing file : " << filename + ".e" << std::endl;
+
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
   exo.write_discontinuous_exodusII(filename + ".e", *equation_systems_, &systems_out);
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 29430ed6cb..0e569a0700 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -788,6 +788,8 @@ void write_unstructured_mesh_results() {
 
       if (!umesh) continue;
 
+      if (!umesh->output_) continue;
+
       // if this tally has more than one filter, print
       // warning and skip writing the mesh
       if (tally->filters().size() > 1) {
@@ -857,6 +859,9 @@ void write_unstructured_mesh_results() {
                                          tally->id_,
                                          simulation::current_batch,
                                          w);
+
+      if (umesh->library() == "moab" && !mpi::master) continue;
+
       // Write the unstructured mesh and data to file
       umesh->write(filename);
 

From 9a82c2f76c5fe36d37db8291ccd01a10488493fb Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 30 Apr 2020 11:18:33 -0500
Subject: [PATCH 055/139] Swapping MPI and thread args.

---
 src/initialize.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/initialize.cpp b/src/initialize.cpp
index 3260e051ee..ca392073df 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -71,7 +71,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 if (!settings::LMI && !libMesh::initialized())
 {
 #ifdef OPENMC_MPI
-  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads, comm);
+  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, comm, n_threads);
 #else
   settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
 #endif

From c92bc54b3d113393b287ee4a6d01864b022f6f40 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 30 Apr 2020 11:19:43 -0500
Subject: [PATCH 056/139] Making the unstructured mesh source isotropic to test
 more of the mesh.

---
 src/mesh.cpp                                  |     2 +-
 .../unstructured_mesh/inputs_true0.dat        |     1 -
 .../unstructured_mesh/inputs_true1.dat        |     1 -
 .../unstructured_mesh/inputs_true10.dat       |     1 -
 .../unstructured_mesh/inputs_true11.dat       |     1 -
 .../unstructured_mesh/inputs_true12.dat       |     1 -
 .../unstructured_mesh/inputs_true13.dat       |     1 -
 .../unstructured_mesh/inputs_true14.dat       |     1 -
 .../unstructured_mesh/inputs_true15.dat       |     1 -
 .../unstructured_mesh/inputs_true2.dat        |     1 -
 .../unstructured_mesh/inputs_true3.dat        |     1 -
 .../unstructured_mesh/inputs_true8.dat        |     1 -
 .../unstructured_mesh/inputs_true9.dat        |     1 -
 .../unstructured_mesh/results_true.dat        | 51274 ++++++++--------
 .../unstructured_mesh/test.py                 |     3 +-
 15 files changed, 25639 insertions(+), 25652 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index a2aa823183..9593f0b94a 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2326,7 +2326,7 @@ LibMesh::get_bin(Position r) const
   if (!bbox_.contains_point(p)) { return -1; }
 
   int thread = omp_get_thread_num();
-  auto e = (*point_locators_[thread])(p, false);
+  auto e = (*point_locators_[thread])(p);
   if (!e) {
     return -1;
   } else {
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
index 916b77f0fb..db78bacb2a 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index d23b617384..07e58a738b 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true10.dat b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
index ce9e9160ba..6aa7efc03c 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true10.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true11.dat b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
index d58da3042a..76c3d0912d 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true11.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true12.dat b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
index e9b5736801..6f1f4cc65f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true12.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true13.dat b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
index bd8026e712..29f2d33fc4 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true13.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true14.dat b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
index 07ec3f063f..582769d5cd 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true14.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true15.dat b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
index 7f4c499a21..3e5655e438 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true15.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index 4f95feae91..04e06d6740 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index a22a8f3cd4..e53ac42bd2 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true8.dat b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
index 8b094e9d50..02902d921a 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true8.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true9.dat b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
index e7d1f69a32..01a96b6215 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true9.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
@@ -57,7 +57,6 @@
         <parameters>0.0 1.0</parameters>
       </phi>
     </space>
-    <angle reference_uvw="-1.0 0.0 0.0" type="monodirectional" />
     <energy type="discrete">
       <parameters>15000000.0 1.0</parameters>
     </energy>
diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat
index ef138626b5..116b5a47c7 100644
--- a/tests/regression_tests/unstructured_mesh/results_true.dat
+++ b/tests/regression_tests/unstructured_mesh/results_true.dat
@@ -1,21861 +1,4205 @@
 tally 1:
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-2.861816E-02
-4.189930E-04
-9.750783E-02
-5.588106E-03
-1.928763E-01
-1.894948E-02
-1.053266E-01
-9.047354E-03
-7.884086E-02
-2.185071E-03
-4.430043E-02
-1.962528E-03
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-5.140511E-02
-1.569878E-03
-8.736501E-02
-3.416650E-03
-1.489750E-01
-1.167806E-02
-1.502841E-01
-4.762081E-03
-9.369291E-02
-4.122978E-03
-1.902638E-01
-8.212537E-03
-1.942473E-01
-1.566214E-02
-1.320380E-02
-1.743404E-04
-1.012081E-02
-1.024307E-04
-0.000000E+00
-0.000000E+00
-2.802730E-02
-3.938858E-04
-3.562887E-02
-6.581713E-04
-1.366496E-01
-1.058783E-02
-3.113034E-01
-2.496231E-02
-9.535492E-02
-4.957886E-03
-1.722337E-01
-9.940917E-03
-1.151368E-01
-5.786808E-03
-3.621743E-02
-8.299913E-04
-1.225075E-01
-8.118250E-03
-1.782919E-02
-3.178800E-04
-3.173278E-02
-3.691953E-04
-3.250594E-02
-5.348920E-04
-2.573925E-01
-1.780616E-02
-2.082849E-01
-1.162374E-02
-6.779461E-02
-4.596109E-03
-1.249213E-01
-6.710452E-03
-1.536299E-02
-1.197865E-04
-4.676578E-02
-9.961893E-04
-8.667198E-02
-3.951164E-03
-9.964266E-02
-2.979278E-03
-2.036376E-02
-4.146828E-04
-4.669965E-02
-8.973174E-04
-3.738354E-02
-8.838855E-04
-2.643849E-02
-4.000739E-04
-2.115400E-01
-9.750700E-03
-7.955778E-02
-5.239001E-03
-1.083829E-01
-4.343811E-03
-1.548817E-01
-5.393982E-03
-7.397521E-02
-3.082958E-03
-2.842457E-02
-8.079563E-04
-7.174113E-02
-2.829013E-03
-6.419693E-02
-2.969986E-03
-6.269216E-02
-1.973968E-03
-7.411297E-02
-3.388565E-03
-2.342008E-01
-1.103187E-02
-1.438412E-01
-1.210264E-02
-1.062298E-01
-9.549989E-03
-4.823977E-02
-1.217530E-03
-2.398767E-02
-3.058849E-04
-1.051726E-01
-6.032055E-03
-0.000000E+00
-0.000000E+00
-1.088701E-01
-4.939745E-03
-1.729408E-01
-8.328575E-03
-1.868437E-01
-1.190196E-02
-1.442641E-01
-5.335163E-03
-1.462085E-01
-7.662300E-03
-2.132254E-01
-1.172785E-02
-5.772551E-02
-2.452594E-03
-8.910897E-02
-2.688961E-03
-6.186834E-02
-2.081871E-03
-0.000000E+00
-0.000000E+00
-1.824626E-02
-3.329260E-04
-8.886665E-02
-2.496374E-03
-4.818053E-02
-8.506930E-04
-2.154898E-01
-9.214701E-03
-2.268066E-01
-3.536496E-02
-4.520862E-02
-5.728405E-04
-9.076363E-03
-8.238036E-05
-6.853355E-02
-1.868456E-03
-1.077541E-01
-5.805747E-03
-9.468064E-03
-8.964423E-05
-6.543014E-02
-1.684384E-03
-9.329183E-03
-8.703366E-05
-9.482039E-02
-5.262671E-03
-1.050048E-01
-1.102601E-02
-8.356963E-02
-2.222700E-03
-1.380391E-01
-4.123409E-03
-1.264948E-01
-7.962280E-03
-1.808794E-02
-3.271736E-04
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-3.268020E-02
-1.067995E-03
-2.771697E-02
-4.303759E-04
-7.001685E-02
-4.902359E-03
-3.336472E-02
-1.113204E-03
-5.430581E-02
-1.289322E-03
-3.886771E-02
-7.608844E-04
-5.411350E-02
-1.464254E-03
-5.231252E-02
-1.372152E-03
-0.000000E+00
-0.000000E+00
-5.725022E-02
-1.300895E-03
-9.832317E-02
-4.588776E-03
-1.975813E-01
-1.583557E-02
-1.967262E-01
-1.480373E-02
-1.067110E-01
-6.085002E-03
-1.763754E-02
-3.110829E-04
-3.091901E-02
-9.559854E-04
-7.988838E-03
-6.382153E-05
-2.104331E-02
-2.569468E-04
-1.827175E-02
-1.719734E-04
-2.862687E-02
-8.194977E-04
-1.038073E-01
-4.583121E-03
-9.586303E-02
-4.716686E-03
-1.532801E-01
-4.763968E-03
-2.082541E-01
-1.038985E-02
-2.432446E-01
-2.175431E-02
-8.683497E-02
-6.165123E-03
-7.834311E-02
-2.475613E-03
-1.026893E-01
-3.346623E-03
-2.479611E-02
-6.148472E-04
-1.213308E-01
-7.834498E-03
-5.824509E-02
-1.391647E-03
-2.058077E-01
-1.484144E-02
-2.879298E-01
-1.839890E-02
-3.160947E-01
-3.005913E-02
-1.112245E-01
-6.378826E-03
-2.794493E-01
-2.251843E-02
-1.356585E-01
-4.181534E-03
-4.634504E-02
-1.075698E-03
-7.020932E-02
-1.755047E-03
-2.706521E-01
-3.533663E-02
-1.349813E-01
-6.070011E-03
-1.210356E-01
-4.337737E-03
-2.369640E-01
-1.511249E-02
-1.539189E-01
-7.116246E-03
-1.779643E-01
-9.608876E-03
-1.960215E-01
-9.569550E-03
-1.160214E-01
-8.343891E-03
-4.509401E-02
-1.382073E-03
-1.239878E-01
-9.141212E-03
-1.276252E-01
-6.372971E-03
-2.048962E-01
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-0.000000E+00
-0.000000E+00
-0.000000E+00
-1.424729E-02
-2.029853E-04
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-2.847319E-02
-8.107224E-04
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-4.668524E-02
-2.179512E-03
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-1.808241E-02
-3.269735E-04
-9.039068E-03
-8.170475E-05
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-9.040298E-03
-8.172699E-05
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-9.046017E-03
-8.183041E-05
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-1.807726E-02
-3.267872E-04
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-8.679719E-02
-7.533752E-03
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-1.102844E-02
-1.216265E-04
-0.000000E+00
-0.000000E+00
-9.147212E-03
-8.367149E-05
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-1.053675E-02
-1.110230E-04
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
-0.000000E+00
+4.059522E-03
+8.862555E-06
+2.525650E-03
+6.378909E-06
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
+6.047444E-03
+3.657158E-05
+9.682018E-03
+4.409643E-05
 0.000000E+00
 0.000000E+00
+4.439952E-03
+1.971318E-05
 0.000000E+00
 0.000000E+00
+7.425338E-03
+5.142130E-05
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
+6.685143E-03
+4.469113E-05
+1.269745E-02
+1.612253E-04
 0.000000E+00
 0.000000E+00
+9.316601E-03
+8.679905E-05
+4.724634E-03
+1.681000E-05
 0.000000E+00
 0.000000E+00
+7.462754E-03
+3.189003E-05
+3.363922E-03
+1.131597E-05
+1.040956E-02
+1.083589E-04
 0.000000E+00
 0.000000E+00
+6.187170E-03
+3.828107E-05
 0.000000E+00
 0.000000E+00
+2.669653E-03
+7.127046E-06
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
+1.250017E-02
+1.562543E-04
+4.004688E-03
+1.153670E-05
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
+2.478125E-03
+6.141102E-06
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
+5.646087E-03
+3.187830E-05
+6.961443E-03
+4.846169E-05
+9.932593E-03
+9.865640E-05
 0.000000E+00
 0.000000E+00
 0.000000E+00
@@ -25996,7 +25986,17 @@ tally 2:
 0.000000E+00
 0.000000E+00
 0.000000E+00
+3.756288E-03
+1.410970E-05
+9.251745E-03
+8.559478E-05
+7.941224E-03
+6.306304E-05
 0.000000E+00
 0.000000E+00
 0.000000E+00
 0.000000E+00
+3.817426E-03
+1.457274E-05
+2.566730E-03
+6.588105E-06
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index 9d39e30761..a201e0641e 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -232,9 +232,8 @@ def test_unstructured_mesh(test_opts):
     phi = openmc.stats.Discrete(x=[0.0], p=[1.0])
 
     space = openmc.stats.SphericalIndependent(r, theta, phi)
-    angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0))
     energy = openmc.stats.Discrete(x=[15.e+06], p=[1.0])
-    source = openmc.Source(space=space, energy=energy, angle=angle)
+    source = openmc.Source(space=space, energy=energy)
     settings.source = source
 
     model = openmc.model.Model(geometry=geometry,

From b9ef22128c8d2a0a75099232e891eb103763efad Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 30 Apr 2020 12:53:36 -0500
Subject: [PATCH 057/139] Allow direct setting of LIBMESH_DIR for CMake.

---
 cmake/Modules/FindLIBMESH.cmake               |     2 +-
 .../unstructured_mesh/inputs_true0.dat        |     2 +-
 .../unstructured_mesh/inputs_true1.dat        |     2 +-
 .../unstructured_mesh/inputs_true10.dat       |     2 +-
 .../unstructured_mesh/inputs_true11.dat       |     2 +-
 .../unstructured_mesh/inputs_true12.dat       |     2 +-
 .../unstructured_mesh/inputs_true13.dat       |     2 +-
 .../unstructured_mesh/inputs_true14.dat       |     2 +-
 .../unstructured_mesh/inputs_true15.dat       |     2 +-
 .../unstructured_mesh/inputs_true2.dat        |     2 +-
 .../unstructured_mesh/inputs_true3.dat        |     2 +-
 .../unstructured_mesh/inputs_true8.dat        |     2 +-
 .../unstructured_mesh/inputs_true9.dat        |     2 +-
 .../unstructured_mesh/results_true.dat        | 51994 ++++++++--------
 .../unstructured_mesh/test.py                 |     2 +-
 15 files changed, 26011 insertions(+), 26011 deletions(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 8d3097f649..96ece4a0a8 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -2,7 +2,7 @@
 # module and creates a libmesh imported target
 
 find_path(LIBMESH_PC NAMES libmesh-opt.pc
-          HINTS ${LIBMESH_ROOT} $ENV{LIBMESH_ROOT}
+          HINTS ${LIBMESH_DIR} $ENV{LIBMESH_ROOT}
           PATHS ENV LD_LIBRARY_PATH
           PATH_SUFFIXES lib/pkgconfig pkgconfig
           NO_DEFAULT_PATH)
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
index db78bacb2a..ac1f74c0cf 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index 07e58a738b..d4c846c51d 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true10.dat b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
index 6aa7efc03c..6da13f40de 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true10.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true11.dat b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
index 76c3d0912d..7dc204669f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true11.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true12.dat b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
index 6f1f4cc65f..7cbd41221e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true12.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true13.dat b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
index 29f2d33fc4..ebf1585b8e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true13.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true14.dat b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
index 582769d5cd..a2ee56ede5 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true14.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true15.dat b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
index 3e5655e438..e23078e52c 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true15.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index 04e06d6740..1aafe4b2ec 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index e53ac42bd2..33a93a280a 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true8.dat b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
index 02902d921a..c5cde96f01 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true8.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true9.dat b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
index 01a96b6215..818f7521ac 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true9.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>100</particles>
+  <particles>10000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat
index 116b5a47c7..b99a4a8787 100644
--- a/tests/regression_tests/unstructured_mesh/results_true.dat
+++ b/tests/regression_tests/unstructured_mesh/results_true.dat
@@ -1,26002 +1,26002 @@
 tally 1:
-2.803162E-02
-4.039469E-04
-4.372331E-02
-1.328278E-03
-7.003143E-02
-1.199941E-03
-1.843585E-01
-6.065337E-03
-1.459853E-01
-4.668408E-03
-1.025156E-01
-3.547014E-03
-9.781742E-02
-4.756125E-03
-6.723374E-02
-1.329451E-03
-1.040913E-01
-5.015187E-03
-2.203592E-02
-3.221646E-04
-3.796439E-02
-5.743879E-04
-4.706428E-02
-1.377701E-03
-1.310681E-01
-3.681928E-03
-2.330130E-01
-1.059428E-02
-3.287721E-01
-1.798864E-02
-2.096247E-01
-7.781713E-03
-8.066133E-02
-1.546582E-03
-1.019530E-01
-3.724690E-03
-9.044067E-02
-2.945102E-03
-7.321531E-02
-2.288338E-03
-9.093940E-02
-1.607582E-03
-1.188361E-01
-3.561068E-03
-1.597633E-01
-4.338707E-03
-1.958937E-01
-7.417725E-03
-1.279867E-01
-5.895089E-03
-1.610675E-01
-5.391704E-03
-1.528744E-01
-4.506962E-03
-1.734256E-01
-6.360573E-03
-8.855633E-02
-1.718467E-03
-9.267083E-02
-2.032859E-03
-1.267542E-01
-4.454106E-03
-6.965984E-02
-1.175356E-03
-2.368609E-01
-8.373527E-03
-2.130323E-01
-1.259042E-02
-1.180848E-01
-3.673216E-03
-1.351779E-01
-4.240354E-03
-1.294290E-01
-3.778812E-03
-1.386471E-01
-3.625126E-03
-1.192514E-01
-3.869782E-03
-5.800893E-02
-1.058446E-03
-8.280902E-02
-1.578525E-03
-1.066590E-01
-2.276927E-03
-1.161112E-01
-3.583450E-03
-2.695348E-01
-1.414719E-02
-2.232008E-01
-6.555604E-03
-2.458240E-01
-1.105062E-02
-1.476800E-01
-4.156562E-03
-2.119453E-01
-8.039788E-03
-1.169448E-01
-4.423428E-03
-1.141932E-01
-5.441588E-03
-7.934996E-02
-1.454219E-03
-2.422882E-01
-1.274550E-02
-1.536180E-01
-6.693946E-03
-1.576712E-01
-8.863333E-03
-2.652854E-01
-1.238154E-02
-1.795479E-01
-5.135060E-03
-2.752749E-01
-1.473778E-02
-1.461326E-01
-3.260001E-03
-1.902740E-01
-6.661618E-03
-1.441770E-01
-4.024364E-03
-1.333282E-01
-4.018144E-03
-2.376511E-01
-7.460185E-03
-1.807673E-01
-6.646280E-03
-1.990602E-01
-9.957415E-03
-1.658597E-01
-7.984084E-03
-2.258122E-01
-9.350930E-03
-3.263721E-01
-1.508211E-02
-2.474348E-01
-1.364363E-02
-1.592951E-01
-4.881711E-03
-5.696997E-02
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+2.166581E-06
+7.303672E-03
+5.652246E-06
+5.841624E-03
+3.814512E-06
+5.287923E-03
+3.279433E-06
+5.639718E-03
+3.508385E-06
+4.585531E-03
+2.271304E-06
+5.551188E-03
+3.585035E-06
+4.143775E-03
+1.853931E-06
+3.130756E-03
+1.180119E-06
+3.776458E-03
+1.887037E-06
+3.889790E-03
+1.589109E-06
+3.646583E-03
+1.555075E-06
+3.701654E-03
+1.503157E-06
+5.013249E-03
+2.719762E-06
+4.504204E-03
+2.331604E-06
+2.704016E-03
+8.576372E-07
+4.772037E-03
+2.649795E-06
+2.945537E-03
+1.227748E-06
+3.379927E-03
+1.450525E-06
+2.294221E-03
+6.808754E-07
+1.946359E-03
+4.719737E-07
+2.975332E-03
+1.007291E-06
+3.290602E-03
+1.224533E-06
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index a201e0641e..fbbc3178e6 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -223,7 +223,7 @@ def test_unstructured_mesh(test_opts):
     ### Settings ###
     settings = openmc.Settings()
     settings.run_mode = 'fixed source'
-    settings.particles = 100
+    settings.particles = 10000
     settings.batches = 10
 
     # source setup

From 1780c96051622f6930fb5adec774decb54bf1b3f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 20 May 2020 21:04:01 -0500
Subject: [PATCH 058/139] First take at adding libmesh to CI.

---
 tools/ci/gha-install-dagmc.sh      |  2 +-
 tools/ci/gha-install.py            |  8 ++++++--
 tools/ci/gha-install.sh            |  4 ++++
 tools/ci/travis-install-libmesh.sh | 16 ++++++++++++++++
 4 files changed, 27 insertions(+), 3 deletions(-)
 create mode 100755 tools/ci/travis-install-libmesh.sh

diff --git a/tools/ci/gha-install-dagmc.sh b/tools/ci/gha-install-dagmc.sh
index eabb9d3821..82759c9bcc 100755
--- a/tools/ci/gha-install-dagmc.sh
+++ b/tools/ci/gha-install-dagmc.sh
@@ -19,7 +19,7 @@ cd $HOME
 mkdir MOAB && cd MOAB
 git clone -b $MOAB_BRANCH $MOAB_REPO
 mkdir build && cd build
-cmake ../moab -DENABLE_HDF5=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$MOAB_INSTALL_DIR -DENABLE_BLASLAPACK=OFF
+cmake ../moab -DENABLE_HDF5=ON -DENABLE_NETCDF=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$MOAB_INSTALL_DIR -DENABLE_BLASLAPACK=OFF
 make -j && make -j install
 rm -rf $HOME/MOAB/moab $HOME/MOAB/build
 
diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index 2f573b1a97..026cd6ca2c 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -19,7 +19,7 @@ def which(program):
     return None
 
 
-def install(omp=False, mpi=False, phdf5=False, dagmc=False):
+def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
     # Create build directory and change to it
     shutil.rmtree('build', ignore_errors=True)
     os.mkdir('build')
@@ -49,6 +49,9 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False):
         cmake_cmd.append('-Ddagmc=ON')
         cmake_cmd.append('-DCMAKE_PREFIX_PATH=~/DAGMC')
 
+    if libmesh:
+        cmake_cmd.append('-Dlibmesh=ON')
+
     # Build in coverage mode for coverage testing
     cmake_cmd.append('-Dcoverage=on')
 
@@ -65,9 +68,10 @@ def main():
     mpi = (os.environ.get('MPI') == 'y')
     phdf5 = (os.environ.get('PHDF5') == 'y')
     dagmc = (os.environ.get('DAGMC') == 'y')
+    libmesh = (os.environ.get('LIBMESH') == 'y')
 
     # Build and install
-    install(omp, mpi, phdf5, dagmc)
+    install(omp, mpi, phdf5, dagmc, libmesh)
 
 if __name__ == '__main__':
     main()
diff --git a/tools/ci/gha-install.sh b/tools/ci/gha-install.sh
index debf833145..08bcd2ca00 100755
--- a/tools/ci/gha-install.sh
+++ b/tools/ci/gha-install.sh
@@ -17,6 +17,10 @@ fi
 # Install vectfit for WMP generation if needed
 if [[ $VECTFIT = 'y' ]]; then
     ./tools/ci/gha-install-vectfit.sh
+
+# Install LibMesh if needed
+if [[ $LIBMESH = 'y' ]]; then
+    ./tools/ci/travis-install-libmesh.sh
 fi
 
 # Install mpi4py for MPI configurations
diff --git a/tools/ci/travis-install-libmesh.sh b/tools/ci/travis-install-libmesh.sh
new file mode 100755
index 0000000000..96b2abb2fa
--- /dev/null
+++ b/tools/ci/travis-install-libmesh.sh
@@ -0,0 +1,16 @@
+
+#!/bin/bash
+set -ex
+
+CURRENT_DIR=$(pwd)
+
+# LibMESH install
+cd $HOME
+mkdir LIBMESH && cd LIBMESH
+git clone https://github.com/libmesh/libmesh
+mkdir build && cd build
+../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
+make -j4 install
+rm -rf $HOME/LIBMESH/build
+
+cd $CURRENT_DIR

From 80e62611c13fc4a231d4ce78475374eaf787ae91 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 4 Jun 2020 14:48:52 -0500
Subject: [PATCH 059/139] Corrections after rebase.

---
 include/openmc/mesh.h | 9 +++------
 src/mesh.cpp          | 8 ++++----
 2 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 0be9f65386..208399c51f 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -344,14 +344,11 @@ public:
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const override;
 
-  std::pair<std::vector<double>, std::vector<double>>
-  plot(Position plot_ll, Position plot_ur) const override;
-
   //! Determine which surface bins were crossed by a particle.
   //
   //! \param[in] p Particle to check
   //! \param[out] bins Surface bins that were crossed
-  void surface_bins_crossed(const Particle* p, std::vector<int>& bins) const;
+  void surface_bins_crossed(const Particle& p, std::vector<int>& bins) const;
 
   int get_bin(Position r) const;
 
@@ -503,11 +500,11 @@ public:
   LibMesh(const std::string& filename);
 
   // Methods
-  void bins_crossed(const Particle* p,
+  void bins_crossed(const Particle& p,
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const override;
 
-  void surface_bins_crossed(const Particle* p, std::vector<int>& bins) const override;
+  void surface_bins_crossed(const Particle& p, std::vector<int>& bins) const override;
 
   int get_bin(Position r) const override;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 9593f0b94a..7d7ecb6291 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1743,7 +1743,7 @@ MOABUnstructuredMesh::intersect_track(const moab::CartVect& start,
 }
 
 void
-MOABUnstructuredMesh::bins_crossed(const Particle* p,
+MOABUnstructuredMesh::bins_crossed(const Particle& p,
                                std::vector<int>& bins,
                                std::vector<double>& lengths) const
 {
@@ -1869,7 +1869,7 @@ double MOABUnstructuredMesh::tet_volume(moab::EntityHandle tet) const {
  return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0]));
 }
 
-void MOABUnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector<int>& bins) const {
+void MOABUnstructuredMesh::surface_bins_crossed(const Particle& p, std::vector<int>& bins) const {
   // TODO: Implement triangle crossings here
   throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
 }
@@ -2237,7 +2237,7 @@ int LibMesh::n_surface_bins() const {
 }
 
 void
-LibMesh::surface_bins_crossed(const Particle* p,
+LibMesh::surface_bins_crossed(const Particle& p,
                                std::vector<int>& bins) const
 {
   // TODO: Implement triangle crossings here
@@ -2308,7 +2308,7 @@ void LibMesh::write(std::string filename) const {
 }
 
 void
-LibMesh::bins_crossed(const Particle* p,
+LibMesh::bins_crossed(const Particle& p,
                       std::vector<int>& bins,
                       std::vector<double>& lengths) const
 {

From ba6816d4df177e3610bb1386ebab106188eba018 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 26 Oct 2020 13:14:25 -0500
Subject: [PATCH 060/139] Corrections after rebase.

---
 include/openmc/mesh.h |  18 ++++-
 src/mesh.cpp          | 166 +++++++++++++++++++++---------------------
 2 files changed, 100 insertions(+), 84 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 208399c51f..3f7029b72b 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -222,8 +222,6 @@ public:
 
   double negative_grid_boundary(int* ijk, int i) const override;
 
-  int n_surface_bins() const override;
-
   std::pair<std::vector<double>, std::vector<double>>
   plot(Position plot_ll, Position plot_ur) const override;
 
@@ -294,6 +292,9 @@ public:
   //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;
 
+  //! Remove variable from the mesh instance
+  virtual void remove_score(const std::string& var_name) = 0;
+
   //! Set the value of a bin for a variable on the mesh instance
   virtual void set_score_data(const std::string& var_name,
                               std::vector<double> values,
@@ -324,6 +325,15 @@ public:
 
   void to_hdf5(hid_t group) const override;
 
+  //! Count number of bank sites in each mesh bin / energy bin
+  //
+  //! \param[in] bank Array of bank sites
+  //! \param[out] Whether any bank sites are outside the mesh
+  //! \return Array indicating number of sites in each mesh/energy bin
+  xt::xtensor<double, 1> count_sites(const Particle::Bank* bank,
+                                     int64_t length,
+                                     bool* outside) const;
+
   // Data members
   bool output_ {true};
   std::string filename_; //!< Path to unstructured mesh file
@@ -370,7 +380,7 @@ public:
                       std::vector<double> std_dev) override;
 
   //! Remove a score from the mesh instance
-  void remove_score(std::string score) const;
+  void remove_score(const std::string& score) override;
 
   //! Set data for a score
   void set_score_data(const std::string& score,
@@ -517,6 +527,8 @@ public:
 
   void add_score(const std::string& var_name) override;
 
+  void remove_score(const std::string& var_name) override;
+
   void set_score_data(const std::string& var_name,
                       std::vector<double> values,
                       std::vector<double> std_dev) override;
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 7d7ecb6291..27e49107dd 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -162,7 +162,7 @@ UnstructuredMesh::to_hdf5(hid_t group) const
     write_dataset(mesh_group, "library", this->library());
     // write volume of each tet
     std::vector<double> tet_vols;
-    xt::xtensor<double, 2> centroids({this->n_bins(), 3});
+    xt::xtensor<double, 2> centroids({(size_t)this->n_bins(), 3});
     for (int i = 0; i < this->n_bins(); i++) {
       tet_vols.emplace_back(this->volume(i));
       auto c = this->centroid(i);
@@ -180,90 +180,90 @@ UnstructuredMesh::to_hdf5(hid_t group) const
 // RegularMesh implementation
 //==============================================================================
 
-RegularMesh::RegularMesh(pugi::xml_node node)
-  : StructuredMesh {node}
-{
-  // Determine number of dimensions for mesh
-  if (check_for_node(node, "dimension")) {
-    shape_ = get_node_xarray<int>(node, "dimension");
-    int n = n_dimension_ = shape_.size();
-    if (n != 1 && n != 2 && n != 3) {
-      fatal_error("Mesh must be one, two, or three dimensions.");
-    }
+// RegularMesh::RegularMesh(pugi::xml_node node)
+//   : StructuredMesh {node}
+// {
+//   // Determine number of dimensions for mesh
+//   if (check_for_node(node, "dimension")) {
+//     shape_ = get_node_xarray<int>(node, "dimension");
+//     int n = n_dimension_ = shape_.size();
+//     if (n != 1 && n != 2 && n != 3) {
+//       fatal_error("Mesh must be one, two, or three dimensions.");
+//     }
 
-    // Check that dimensions are all greater than zero
-    if (xt::any(shape_ <= 0)) {
-      fatal_error("All entries on the <dimension> element for a tally "
-        "mesh must be positive.");
-    }
-  }
+//     // Check that dimensions are all greater than zero
+//     if (xt::any(shape_ <= 0)) {
+//       fatal_error("All entries on the <dimension> element for a tally "
+//         "mesh must be positive.");
+//     }
+//   }
 
-  // Check for lower-left coordinates
-  if (check_for_node(node, "lower_left")) {
-    // Read mesh lower-left corner location
-    lower_left_ = get_node_xarray<double>(node, "lower_left");
-  } else {
-    fatal_error("Must specify <lower_left> on a mesh.");
-  }
+//   // Check for lower-left coordinates
+//   if (check_for_node(node, "lower_left")) {
+//     // Read mesh lower-left corner location
+//     lower_left_ = get_node_xarray<double>(node, "lower_left");
+//   } else {
+//     fatal_error("Must specify <lower_left> on a mesh.");
+//   }
 
-  if (check_for_node(node, "width")) {
-    // Make sure both upper-right or width were specified
-    if (check_for_node(node, "upper_right")) {
-      fatal_error("Cannot specify both <upper_right> and <width> on a mesh.");
-    }
+//   if (check_for_node(node, "width")) {
+//     // Make sure both upper-right or width were specified
+//     if (check_for_node(node, "upper_right")) {
+//       fatal_error("Cannot specify both <upper_right> and <width> on a mesh.");
+//     }
 
-    width_ = get_node_xarray<double>(node, "width");
+//     width_ = get_node_xarray<double>(node, "width");
 
-    // Check to ensure width has same dimensions
-    auto n = width_.size();
-    if (n != lower_left_.size()) {
-      fatal_error("Number of entries on <width> must be the same as "
-        "the number of entries on <lower_left>.");
-    }
+//     // Check to ensure width has same dimensions
+//     auto n = width_.size();
+//     if (n != lower_left_.size()) {
+//       fatal_error("Number of entries on <width> must be the same as "
+//         "the number of entries on <lower_left>.");
+//     }
 
-    // Check for negative widths
-    if (xt::any(width_ < 0.0)) {
-      fatal_error("Cannot have a negative <width> on a tally mesh.");
-    }
+//     // Check for negative widths
+//     if (xt::any(width_ < 0.0)) {
+//       fatal_error("Cannot have a negative <width> on a tally mesh.");
+//     }
 
-    // Set width and upper right coordinate
-    upper_right_ = xt::eval(lower_left_ + shape_ * width_);
+//     // Set width and upper right coordinate
+//     upper_right_ = xt::eval(lower_left_ + shape_ * width_);
 
-  } else if (check_for_node(node, "upper_right")) {
-    upper_right_ = get_node_xarray<double>(node, "upper_right");
+//   } else if (check_for_node(node, "upper_right")) {
+//     upper_right_ = get_node_xarray<double>(node, "upper_right");
 
-    // Check to ensure width has same dimensions
-    auto n = upper_right_.size();
-    if (n != lower_left_.size()) {
-      fatal_error("Number of entries on <upper_right> must be the "
-        "same as the number of entries on <lower_left>.");
-    }
+//     // Check to ensure width has same dimensions
+//     auto n = upper_right_.size();
+//     if (n != lower_left_.size()) {
+//       fatal_error("Number of entries on <upper_right> must be the "
+//         "same as the number of entries on <lower_left>.");
+//     }
 
-    // Check that upper-right is above lower-left
-    if (xt::any(upper_right_ < lower_left_)) {
-      fatal_error("The <upper_right> coordinates must be greater than "
-        "the <lower_left> coordinates on a tally mesh.");
-    }
+//     // Check that upper-right is above lower-left
+//     if (xt::any(upper_right_ < lower_left_)) {
+//       fatal_error("The <upper_right> coordinates must be greater than "
+//         "the <lower_left> coordinates on a tally mesh.");
+//     }
 
-    // Set width
-    if (shape_.size() > 0) {
-      width_ = xt::eval((upper_right_ - lower_left_) / shape_);
-    }
-  } else {
-    fatal_error("Must specify either <upper_right> and <width> on a mesh.");
-  }
+//     // Set width
+//     if (shape_.size() > 0) {
+//       width_ = xt::eval((upper_right_ - lower_left_) / shape_);
+//     }
+//   } else {
+//     fatal_error("Must specify either <upper_right> and <width> on a mesh.");
+//   }
 
-  // Make sure lower_left and dimension match
-  if (shape_.size() > 0) {
-    if (shape_.size() != lower_left_.size()) {
-      fatal_error("Number of entries on <lower_left> must be the same "
-        "as the number of entries on <dimension>.");
-    }
+//   // Make sure lower_left and dimension match
+//   if (shape_.size() > 0) {
+//     if (shape_.size() != lower_left_.size()) {
+//       fatal_error("Number of entries on <lower_left> must be the same "
+//         "as the number of entries on <dimension>.");
+//     }
 
-    // Set volume fraction
-    volume_frac_ = 1.0/xt::prod(shape_)();
-  }
-}
+//     // Set volume fraction
+//     volume_frac_ = 1.0/xt::prod(shape_)();
+//   }
+// }
 
 void StructuredMesh::get_indices(Position r, int* ijk, bool* in_mesh) const
 {
@@ -2087,7 +2087,7 @@ MOABUnstructuredMesh::add_score(const std::string& score) {
   auto score_tags = get_score_tags(score);
 }
 
-void UnstructuredMesh::remove_score(std::string score) const {
+void MOABUnstructuredMesh::remove_score(const std::string& score) {
   auto value_name = score + "_mean";
   moab::Tag tag;
   moab::ErrorCode rval = mbi_->tag_get_handle(value_name.c_str(), tag);
@@ -2163,16 +2163,14 @@ MOABUnstructuredMesh::write(std::string base_filename) const {
     auto msg = fmt::format("Failed to write unstructured mesh {}", id_);
     warning(msg);
   }
+}
 
-xt::xarray<double>
-UnstructuredMesh::count_sites(const std::vector<Particle::Bank>& bank,
-  bool* outside) const {
-    xt::array<double> out;
+xt::xtensor<double, 1>
+UnstructuredMesh::count_sites(const Particle::Bank* bank,
+                              int64_t length,
+                              bool* outside) const {
+    xt::xtensor<double, 1> out;
     return out;
-  }
-
-double UnstructuredMesh::get_volume_frac(int bin = -1) const {
-  return 0.0;
 }
 
 #endif
@@ -2263,6 +2261,12 @@ LibMesh::add_score(const std::string& var_name) {
   }
 }
 
+void
+LibMesh::remove_score(const std::string& var_name) {
+  return;
+}
+
+
 void
 LibMesh::set_score_data(const std::string& var_name,
                       std::vector<double> values,

From 66d246cf44c24d81f4ea1d9a6711d287d6dcfd4f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 26 Oct 2020 13:15:12 -0500
Subject: [PATCH 061/139] Reducing number of particles in unstructured mesh
 tests.

---
 .../unstructured_mesh/inputs_true0.dat        |     2 +-
 .../unstructured_mesh/inputs_true1.dat        |     2 +-
 .../unstructured_mesh/inputs_true10.dat       |     2 +-
 .../unstructured_mesh/inputs_true11.dat       |     2 +-
 .../unstructured_mesh/inputs_true12.dat       |     2 +-
 .../unstructured_mesh/inputs_true13.dat       |     2 +-
 .../unstructured_mesh/inputs_true14.dat       |     2 +-
 .../unstructured_mesh/inputs_true15.dat       |     2 +-
 .../unstructured_mesh/inputs_true2.dat        |     2 +-
 .../unstructured_mesh/inputs_true3.dat        |     2 +-
 .../unstructured_mesh/inputs_true8.dat        |     2 +-
 .../unstructured_mesh/inputs_true9.dat        |     2 +-
 .../unstructured_mesh/results_true.dat        | 51994 ++++++++--------
 .../unstructured_mesh/test.py                 |     2 +-
 14 files changed, 26010 insertions(+), 26010 deletions(-)

diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
index ac1f74c0cf..e5f7fd6132 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index d4c846c51d..58e2ee468e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true10.dat b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
index 6da13f40de..0d41154b33 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true10.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true11.dat b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
index 7dc204669f..cbdfd518f8 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true11.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true12.dat b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
index 7cbd41221e..0e726216e0 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true12.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true13.dat b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
index ebf1585b8e..897e451008 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true13.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true14.dat b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
index a2ee56ede5..6f965dc7e5 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true14.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true15.dat b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
index e23078e52c..df0a8d0b03 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true15.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index 1aafe4b2ec..751a8e0b47 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index 33a93a280a..bbfbc6402f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true8.dat b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
index c5cde96f01..9d67b8909e 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true8.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true9.dat b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
index 818f7521ac..f81a476124 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true9.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
@@ -45,7 +45,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <settings>
   <run_mode>fixed source</run_mode>
-  <particles>10000</particles>
+  <particles>1000</particles>
   <batches>10</batches>
   <source strength="1.0">
     <space origin="0.0 0.0 0.0" type="spherical">
diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat
index b99a4a8787..ea0d9bd97b 100644
--- a/tests/regression_tests/unstructured_mesh/results_true.dat
+++ b/tests/regression_tests/unstructured_mesh/results_true.dat
@@ -1,26002 +1,26002 @@
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diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index fbbc3178e6..4b09a10de4 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -223,7 +223,7 @@ def test_unstructured_mesh(test_opts):
     ### Settings ###
     settings = openmc.Settings()
     settings.run_mode = 'fixed source'
-    settings.particles = 10000
+    settings.particles = 1000
     settings.batches = 10
 
     # source setup

From 4fc694c87f95dded56060a1708c7c7564d13df9a Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 26 Oct 2020 14:04:52 -0500
Subject: [PATCH 062/139] Making libmesh communicator pointer const.

---
 include/openmc/settings.h | 2 +-
 src/settings.cpp          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index e873e3d3e2..69231321d4 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -29,7 +29,7 @@ namespace settings {
 
 #ifdef LIBMESH
 extern std::unique_ptr<libMesh::LibMeshInit> LMI;
-extern libMesh::Parallel::Communicator* libmesh_comm;
+extern const libMesh::Parallel::Communicator* libmesh_comm;
 #endif
 
 // Boolean flags
diff --git a/src/settings.cpp b/src/settings.cpp
index 4a2b3432db..d54543f728 100644
--- a/src/settings.cpp
+++ b/src/settings.cpp
@@ -39,7 +39,7 @@ namespace settings {
 
 #ifdef LIBMESH
 std::unique_ptr<libMesh::LibMeshInit> LMI;
-libMesh::Parallel::Communicator* libmesh_comm;
+const libMesh::Parallel::Communicator* libmesh_comm;
 #endif
 
 // Default values for boolean flags

From bf54f4a0eee88243c69646d2daefcb93d21ec7d9 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 26 Oct 2020 15:31:37 -0500
Subject: [PATCH 063/139] Placing appropriate ifdefs around OpenMP calls in the
 LibMesh class.

---
 src/mesh.cpp | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 27e49107dd..efd627b6de 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2210,7 +2210,12 @@ void LibMesh::initialize() {
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
   // setup the point locator
+  #ifdef _OPENMP
   int n_threads = omp_get_max_threads();
+  #else
+  int n_threads = 1;
+  #endif
+
   point_locators_ = std::vector<std::unique_ptr<libMesh::PointLocatorBase>>(n_threads);
   for (int i = 0; i < n_threads; i++) {
     point_locators_[i] = m_->sub_point_locator();
@@ -2329,7 +2334,12 @@ LibMesh::get_bin(Position r) const
   // quick rejection check
   if (!bbox_.contains_point(p)) { return -1; }
 
+  #ifdef _OPENMP
   int thread = omp_get_thread_num();
+  #else
+  int thread = 0;
+  #endif
+
   auto e = (*point_locators_[thread])(p);
   if (!e) {
     return -1;

From 4326bca9628b258e0d4b2dd240b882f23a1ad51c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 26 Oct 2020 19:20:38 -0500
Subject: [PATCH 064/139] Close if statement.

---
 tools/ci/gha-install.sh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/ci/gha-install.sh b/tools/ci/gha-install.sh
index 08bcd2ca00..88e6ab03d6 100755
--- a/tools/ci/gha-install.sh
+++ b/tools/ci/gha-install.sh
@@ -17,8 +17,9 @@ fi
 # Install vectfit for WMP generation if needed
 if [[ $VECTFIT = 'y' ]]; then
     ./tools/ci/gha-install-vectfit.sh
+fi
 
-# Install LibMesh if needed
+# Install libMesh if needed
 if [[ $LIBMESH = 'y' ]]; then
     ./tools/ci/travis-install-libmesh.sh
 fi

From 22507b3ac139ce69ddd34e66acb855832deb22bc Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 27 Oct 2020 00:22:08 -0500
Subject: [PATCH 065/139] Getting libmesh submodules as well.

---
 tools/ci/travis-install-libmesh.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/travis-install-libmesh.sh b/tools/ci/travis-install-libmesh.sh
index 96b2abb2fa..e35568685c 100755
--- a/tools/ci/travis-install-libmesh.sh
+++ b/tools/ci/travis-install-libmesh.sh
@@ -7,7 +7,7 @@ CURRENT_DIR=$(pwd)
 # LibMESH install
 cd $HOME
 mkdir LIBMESH && cd LIBMESH
-git clone https://github.com/libmesh/libmesh
+git clone https://github.com/libmesh/libmesh --recurse-submodules
 mkdir build && cd build
 ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
 make -j4 install

From 19560dbc7bea590a5f832f53c35a0b89ad9927df Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 27 Oct 2020 12:29:23 -0500
Subject: [PATCH 066/139] Cloning specific version of libmesh.

---
 tools/ci/travis-install-libmesh.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/travis-install-libmesh.sh b/tools/ci/travis-install-libmesh.sh
index e35568685c..e3603ba474 100755
--- a/tools/ci/travis-install-libmesh.sh
+++ b/tools/ci/travis-install-libmesh.sh
@@ -7,7 +7,7 @@ CURRENT_DIR=$(pwd)
 # LibMESH install
 cd $HOME
 mkdir LIBMESH && cd LIBMESH
-git clone https://github.com/libmesh/libmesh --recurse-submodules
+git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
 make -j4 install

From 21d3c14d86c2d83c722cabdb5e113454c0b1f540 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 27 Oct 2020 22:54:33 -0500
Subject: [PATCH 067/139] Cleanup of CMake files.

---
 CMakeLists.txt                  | 12 +++---------
 cmake/Modules/FindLIBMESH.cmake | 13 +++++--------
 2 files changed, 8 insertions(+), 17 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e4d81b2327..7c8ec5ff5b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -66,12 +66,12 @@ if(pugixml_FOUND)
   message(STATUS "Found pugixml: ${pugixml_DIR}")
 else()
   message(STATUS "Did not find pugixml, will use submodule instead")
+
 #===============================================================================
 # libMesh Unstructured Mesh Support
 #===============================================================================
 if(libmesh)
   find_package(LIBMESH REQUIRED)
-  list(APPEND cxxflags ${LIBMESH_CFLAGS})
 endif()
 
 #===============================================================================
@@ -389,9 +389,6 @@ endif()
 if (MPI_ENABLED)
   target_compile_definitions(libopenmc PUBLIC -DOPENMC_MPI)
 endif()
-if (libmesh)
-  target_compile_definitions(libopenmc PRIVATE LIBMESH)
-endif()
 
 # Set git SHA1 hash as a compile definition
 if(GIT_FOUND)
@@ -416,11 +413,8 @@ if(dagmc)
 endif()
 
 if(libmesh)
-  if (debug)
-    target_link_libraries(libopenmc PkgConfig::LIBMESH_DEVEL)
-  else()
-    target_link_libraries(libopenmc PkgConfig::LIBMESH)
-  endif()
+  target_compile_definitions(libopenmc PRIVATE LIBMESH)
+  target_link_libraries(libopenmc PkgConfig::LIBMESH)
 endif()
 
 #===============================================================================
diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 96ece4a0a8..2359f904d7 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -1,15 +1,12 @@
 # Finds the libMesh installation using CMake's PkgConfig
 # module and creates a libmesh imported target
 
-find_path(LIBMESH_PC NAMES libmesh-opt.pc
-          HINTS ${LIBMESH_DIR} $ENV{LIBMESH_ROOT}
-          PATHS ENV LD_LIBRARY_PATH
-          PATH_SUFFIXES lib/pkgconfig pkgconfig
-          NO_DEFAULT_PATH)
-
 include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
 pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
-pkg_check_modules(LIBMESH_DEVEL REQUIRED libmesh-devel IMPORTED_TARGET)
 
-message(STATUS "Found LIBMESH in ${LIBMESH_PC}")
+find_path(LIBMESH_PC NAMES libmesh.pc
+          HINTS ${LIBMESH_LIBDIR}
+          PATHS ENV LD_LIBRARY_PATH
+          PATH_SUFFIXES lib/pkgconfig pkgconfig
+          NO_DEFAULT_PATH)

From 61d8d80e6f79a966ad19ace915b2b01571e3ae12 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 30 Oct 2020 09:56:42 -0500
Subject: [PATCH 068/139] Fix for checking tracklength estimator on libMESH
 mesh filter.

---
 src/tallies/tally.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp
index 04fc238967..6a35835c2a 100644
--- a/src/tallies/tally.cpp
+++ b/src/tallies/tally.cpp
@@ -287,12 +287,12 @@ Tally::Tally(pugi::xml_node node)
   // ensure a tracklength tally isn't used with a libMesh filter
   for (auto i : this->filters_) {
     auto df = dynamic_cast<MeshFilter*>(model::tally_filters[i].get());
-    int32_t mesh {-1};
-    if (df) { mesh = df->mesh(); }
-    auto lm = dynamic_cast<LibMesh*>(model::meshes[mesh].get());
-    if (lm && estimator_ == TallyEstimator::TRACKLENGTH) {
-      fatal_error("A tracklength estimator cannot be used with "
-                  "an unstructured LibMesh tally.");
+    if (df) {
+      auto lm = dynamic_cast<LibMesh*>(model::meshes[df->mesh()].get());
+      if (lm && estimator_ == TallyEstimator::TRACKLENGTH) {
+        fatal_error("A tracklength estimator cannot be used with "
+                    "an unstructured LibMesh tally.");
+      }
     }
   }
 #endif

From 23f2926af1a757f1dc7d24c68cd1772e6b2fc195 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 30 Oct 2020 13:58:15 -0500
Subject: [PATCH 069/139] Removing unused bounding box methods.

---
 include/openmc/surface.h | 18 ------------------
 1 file changed, 18 deletions(-)

diff --git a/include/openmc/surface.h b/include/openmc/surface.h
index 5ef903f527..def7cb1eba 100644
--- a/include/openmc/surface.h
+++ b/include/openmc/surface.h
@@ -75,24 +75,6 @@ struct BoundingBox
     return *this;
   }
 
-  // ensure bounding box contains a point
-  inline void update(const Position& r) {
-    xmin = std::min(xmin, r.x);
-    xmax = std::max(xmax, r.x);
-    ymin = std::min(ymin, r.y);
-    ymax = std::max(ymax, r.y);
-    zmin = std::min(zmin, r.z);
-    zmax = std::max(zmax, r.z);
-  }
-
-  // check if a point is in the box
-  inline bool contains(const Position& r) const {
-    if (r.x < xmin || r.x > xmax) { return false; }
-    if (r.y < ymin || r.y > ymax) { return false; }
-    if (r.z < zmin || r.z > zmax) { return false; }
-    return true;
-  }
-
 };
 
 //==============================================================================

From e11c7cb6d40fb77dde9951bdd56f9c2fe120e7d8 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 30 Oct 2020 13:59:12 -0500
Subject: [PATCH 070/139] Reducing ifdefs and inducing fatal_error if a write
 of unstructured mesh data is attempted in an unsupported build.

---
 include/openmc/state_point.h |  3 ---
 src/state_point.cpp          | 10 +++++++---
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/include/openmc/state_point.h b/include/openmc/state_point.h
index 1c728cfdb2..f93fa00aaa 100644
--- a/include/openmc/state_point.h
+++ b/include/openmc/state_point.h
@@ -18,10 +18,7 @@ void write_source_bank(hid_t group_id, bool surf_source_bank);
 void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool distribute);
 void write_tally_results_nr(hid_t file_id);
 void restart_set_keff();
-
-#if defined(LIBMESH) || defined(DAGMC)
 void write_unstructured_mesh_results();
-#endif
 
 } // namespace openmc
 #endif // OPENMC_STATE_POINT_H
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 0e569a0700..263fd0ae8c 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -55,7 +55,7 @@ openmc_statepoint_write(const char* filename, bool* write_source)
   write_message("Creating state point " + filename_ + "...", 5);
 
 #if defined(LIBMESH) || defined(DAGMC)
-      // write unstructured mesh tallies to VTK if possible
+      // write unstructured mesh tallies to VTK
       write_unstructured_mesh_results();
 #endif
 
@@ -770,9 +770,13 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
   H5Tclose(banktype);
 }
 
-#if defined(LIBMESH) || defined(DAGMC)
+
+
 void write_unstructured_mesh_results() {
 
+#if !defined(LIBMESH) && !defined(DAGMC)
+  fatal_error("Unstructured mesh is disabled for this build of OpenMC.");
+#else
 
   for (auto& tally : model::tallies) {
 
@@ -868,8 +872,8 @@ void write_unstructured_mesh_results() {
       for (const auto& score : tally_scores) { umesh->remove_score(score); }
     }
   }
+  #endif
 }
-#endif
 
 void write_tally_results_nr(hid_t file_id)
 {

From a1bbdfb94082a0b4a91b36e0937e6e714d2d2661 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 30 Oct 2020 16:14:49 -0500
Subject: [PATCH 071/139] Polishing the mesh class

---
 include/openmc/mesh.h     |  53 ++++-----
 include/openmc/settings.h |   4 -
 src/initialize.cpp        |   2 -
 src/mesh.cpp              | 232 ++++++++++++--------------------------
 src/settings.cpp          |   5 -
 5 files changed, 90 insertions(+), 206 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 3f7029b72b..44309faa5a 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -51,9 +51,11 @@ extern std::vector<std::unique_ptr<Mesh>> meshes;
 
 } // namespace model
 
-//==============================================================================
-//! Tessellation of n-dimensional Euclidean space by congruent squares or cubes
-//==============================================================================
+#ifdef LIBMESH
+namespace settings {
+extern std::unique_ptr<libMesh::LibMeshInit> LMI;
+}
+#endif
 
 class Mesh
 {
@@ -302,7 +304,6 @@ public:
 
   //! Write the unstructured mesh to file
   //
-  //
   //! \param[in] filename Name of the file to write
   virtual void write(std::string filename) const = 0;
 
@@ -323,16 +324,10 @@ public:
 
   std::string bin_label(int bin) const override;
 
-  void to_hdf5(hid_t group) const override;
+  void surface_bins_crossed(const Particle& p,
+                            std::vector<int>& bins) const override;
 
-  //! Count number of bank sites in each mesh bin / energy bin
-  //
-  //! \param[in] bank Array of bank sites
-  //! \param[out] Whether any bank sites are outside the mesh
-  //! \return Array indicating number of sites in each mesh/energy bin
-  xt::xtensor<double, 1> count_sites(const Particle::Bank* bank,
-                                     int64_t length,
-                                     bool* outside) const;
+  void to_hdf5(hid_t group) const override;
 
   // Data members
   bool output_ {true};
@@ -341,25 +336,19 @@ public:
 
 #ifdef DAGMC
 
-class MOABUnstructuredMesh : public UnstructuredMesh {
+class MOABMesh : public UnstructuredMesh {
 
 public:
 
-  MOABUnstructuredMesh() = default;
-  MOABUnstructuredMesh(pugi::xml_node);
-  MOABUnstructuredMesh(const std::string& filename);
-  ~MOABUnstructuredMesh() = default;
+  MOABMesh() = default;
+  MOABMesh(pugi::xml_node);
+  MOABMesh(const std::string& filename);
+  ~MOABMesh() = default;
 
   void bins_crossed(const Particle& p,
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const override;
 
-  //! Determine which surface bins were crossed by a particle.
-  //
-  //! \param[in] p Particle to check
-  //! \param[out] bins Surface bins that were crossed
-  void surface_bins_crossed(const Particle& p, std::vector<int>& bins) const;
-
   int get_bin(Position r) const;
 
   int n_bins() const override;
@@ -502,8 +491,6 @@ private:
 #ifdef LIBMESH
 class LibMesh : public UnstructuredMesh {
 
-  typedef std::vector<std::pair<double, const libMesh::Elem*>> UnstructuredMeshHits;
-
 public:
   // Constructors
   LibMesh(pugi::xml_node node);
@@ -514,16 +501,14 @@ public:
                     std::vector<int>& bins,
                     std::vector<double>& lengths) const override;
 
-  void surface_bins_crossed(const Particle& p, std::vector<int>& bins) const override;
-
   int get_bin(Position r) const override;
 
   int n_bins() const override;
 
   int n_surface_bins() const override;
 
-  std::pair<std::vector<double>, std::vector<double>> plot(Position plot_ll,
-                                                           Position plot_ur) const override;
+  std::pair<std::vector<double>, std::vector<double>>
+  plot(Position plot_ll, Position plot_ur) const override;
 
   void add_score(const std::string& var_name) override;
 
@@ -552,12 +537,12 @@ private:
   int get_bin_from_element(const libMesh::Elem* elem) const;
 
   // Data members
-  std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libmesh mesh instance
+  std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libMesh mesh instance
   std::vector<std::unique_ptr<libMesh::PointLocatorBase>> point_locators_; //!< pointers to locators for each thread
-  std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh (for result output)
-  std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (scores) to their numbers on the mesh
-  libMesh::BoundingBox bbox_; //!< bounding box of the mesh
+  std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
+  std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
+  libMesh::BoundingBox bbox_; //!< bounding box of the mesh
   libMesh::Elem* first_element_; //!< pointer to the first element in the mesh (maybe should be a key?)
 };
 #endif
diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index 69231321d4..650940c779 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -27,10 +27,6 @@ namespace openmc {
 
 namespace settings {
 
-#ifdef LIBMESH
-extern std::unique_ptr<libMesh::LibMeshInit> LMI;
-extern const libMesh::Parallel::Communicator* libmesh_comm;
-#endif
 
 // Boolean flags
 extern bool assume_separate;          //!< assume tallies are spatially separate?
diff --git a/src/initialize.cpp b/src/initialize.cpp
index ca392073df..515442f80f 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -75,9 +75,7 @@ if (!settings::LMI && !libMesh::initialized())
 #else
   settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
 #endif
-  settings::libmesh_comm = &(settings::LMI->comm());
 }
-
 #endif
 
   // Start total and initialization timer
diff --git a/src/mesh.cpp b/src/mesh.cpp
index efd627b6de..37f6a3a7ce 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -53,6 +53,12 @@ std::vector<std::unique_ptr<Mesh>> meshes;
 
 } // namespace model
 
+#ifdef LIBMESH
+namespace settings {
+std::unique_ptr<libMesh::LibMeshInit> LMI;
+}
+#endif
+
 //==============================================================================
 // Helper functions
 //==============================================================================
@@ -116,7 +122,7 @@ StructuredMesh::bin_label(int bin) const {
 }
 
 //==============================================================================
-// Untructured Mesh implementation
+// Unstructured Mesh implementation
 //==============================================================================
 
 UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
@@ -147,6 +153,12 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
 
 }
 
+void
+UnstructuredMesh::surface_bins_crossed(const Particle& p,
+                                       std::vector<int>& bins) const {
+  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
+}
+
 std::string
 UnstructuredMesh::bin_label(int bin) const {
   return fmt::format("Mesh Index ({})", bin);
@@ -175,96 +187,6 @@ UnstructuredMesh::to_hdf5(hid_t group) const
     close_group(mesh_group);
 }
 
-
-//==============================================================================
-// RegularMesh implementation
-//==============================================================================
-
-// RegularMesh::RegularMesh(pugi::xml_node node)
-//   : StructuredMesh {node}
-// {
-//   // Determine number of dimensions for mesh
-//   if (check_for_node(node, "dimension")) {
-//     shape_ = get_node_xarray<int>(node, "dimension");
-//     int n = n_dimension_ = shape_.size();
-//     if (n != 1 && n != 2 && n != 3) {
-//       fatal_error("Mesh must be one, two, or three dimensions.");
-//     }
-
-//     // Check that dimensions are all greater than zero
-//     if (xt::any(shape_ <= 0)) {
-//       fatal_error("All entries on the <dimension> element for a tally "
-//         "mesh must be positive.");
-//     }
-//   }
-
-//   // Check for lower-left coordinates
-//   if (check_for_node(node, "lower_left")) {
-//     // Read mesh lower-left corner location
-//     lower_left_ = get_node_xarray<double>(node, "lower_left");
-//   } else {
-//     fatal_error("Must specify <lower_left> on a mesh.");
-//   }
-
-//   if (check_for_node(node, "width")) {
-//     // Make sure both upper-right or width were specified
-//     if (check_for_node(node, "upper_right")) {
-//       fatal_error("Cannot specify both <upper_right> and <width> on a mesh.");
-//     }
-
-//     width_ = get_node_xarray<double>(node, "width");
-
-//     // Check to ensure width has same dimensions
-//     auto n = width_.size();
-//     if (n != lower_left_.size()) {
-//       fatal_error("Number of entries on <width> must be the same as "
-//         "the number of entries on <lower_left>.");
-//     }
-
-//     // Check for negative widths
-//     if (xt::any(width_ < 0.0)) {
-//       fatal_error("Cannot have a negative <width> on a tally mesh.");
-//     }
-
-//     // Set width and upper right coordinate
-//     upper_right_ = xt::eval(lower_left_ + shape_ * width_);
-
-//   } else if (check_for_node(node, "upper_right")) {
-//     upper_right_ = get_node_xarray<double>(node, "upper_right");
-
-//     // Check to ensure width has same dimensions
-//     auto n = upper_right_.size();
-//     if (n != lower_left_.size()) {
-//       fatal_error("Number of entries on <upper_right> must be the "
-//         "same as the number of entries on <lower_left>.");
-//     }
-
-//     // Check that upper-right is above lower-left
-//     if (xt::any(upper_right_ < lower_left_)) {
-//       fatal_error("The <upper_right> coordinates must be greater than "
-//         "the <lower_left> coordinates on a tally mesh.");
-//     }
-
-//     // Set width
-//     if (shape_.size() > 0) {
-//       width_ = xt::eval((upper_right_ - lower_left_) / shape_);
-//     }
-//   } else {
-//     fatal_error("Must specify either <upper_right> and <width> on a mesh.");
-//   }
-
-//   // Make sure lower_left and dimension match
-//   if (shape_.size() > 0) {
-//     if (shape_.size() != lower_left_.size()) {
-//       fatal_error("Number of entries on <lower_left> must be the same "
-//         "as the number of entries on <dimension>.");
-//     }
-
-//     // Set volume fraction
-//     volume_frac_ = 1.0/xt::prod(shape_)();
-//   }
-// }
-
 void StructuredMesh::get_indices(Position r, int* ijk, bool* in_mesh) const
 {
   *in_mesh = true;
@@ -1441,32 +1363,35 @@ extern "C" int openmc_add_unstructured_mesh(const char filename[],
                                             const char library[],
                                             int* id)
 {
+
+  std::string lib_name(library);
+  std::string mesh_file(filename);
   bool valid_lib = false;
 #ifdef DAGMC
-  if (library == "moab") {
-    model::meshes.push_back(std::move(std::make_unique<MOABUnstructuredMesh>(filename)));
+  if (lib_name == "moab") {
+    model::meshes.push_back(std::move(std::make_unique<MOABMesh>(mesh_file)));
     valid_lib = true;
   }
 #endif
 
 #ifdef LIBMESH
-  if (library == "libmesh") {
-    model::meshes.push_back(std::move(std::make_unique<LibMesh>(filename)));
+  if (lib_name == "libmesh") {
+    model::meshes.push_back(std::move(std::make_unique<LibMesh>(mesh_file)));
     valid_lib = true;
   }
 #endif
 
   if (!valid_lib) {
-    set_errmsg("Mesh library " + std::string(library) + \
-               "is not supported by this build of OpenMC");
+    set_errmsg(fmt::format("Mesh library {} is not supported "
+                           "by this build of OpenMC", lib_name));
     return OPENMC_E_INVALID_ARGUMENT;
   }
 
   int mesh_id = 0;
   for (const auto& m : model::meshes) { mesh_id = std::max(m->id_, mesh_id); }
-
   mesh_id += 1;
-  model::meshes.back()->id_ = mesh_id;
+
+  model::meshes.back()->id_ = mesh_id + 1;
   *id = mesh_id;
 
   return 0;
@@ -1630,16 +1555,16 @@ openmc_rectilinear_mesh_set_grid(int32_t index, const double* grid_x,
 
 #ifdef DAGMC
 
-MOABUnstructuredMesh::MOABUnstructuredMesh(pugi::xml_node node) : UnstructuredMesh(node) {
+MOABMesh::MOABMesh(pugi::xml_node node) : UnstructuredMesh(node) {
   initialize();
 }
 
-MOABUnstructuredMesh::MOABUnstructuredMesh(const std::string& filename) {
+MOABMesh::MOABMesh(const std::string& filename) {
   filename_ = filename;
   initialize();
 }
 
-void MOABUnstructuredMesh::initialize() {
+void MOABMesh::initialize() {
   // create MOAB instance
   mbi_ = std::make_unique<moab::Core>();
   // load unstructured mesh file
@@ -1677,7 +1602,7 @@ void MOABUnstructuredMesh::initialize() {
 }
 
 void
-MOABUnstructuredMesh::build_kdtree(const moab::Range& all_tets)
+MOABMesh::build_kdtree(const moab::Range& all_tets)
 {
   moab::Range all_tris;
   int adj_dim = 2;
@@ -1712,7 +1637,7 @@ MOABUnstructuredMesh::build_kdtree(const moab::Range& all_tets)
 }
 
 void
-MOABUnstructuredMesh::intersect_track(const moab::CartVect& start,
+MOABMesh::intersect_track(const moab::CartVect& start,
                                   const moab::CartVect& dir,
                                   double track_len,
                                   std::vector<double>& hits) const {
@@ -1743,7 +1668,7 @@ MOABUnstructuredMesh::intersect_track(const moab::CartVect& start,
 }
 
 void
-MOABUnstructuredMesh::bins_crossed(const Particle& p,
+MOABMesh::bins_crossed(const Particle& p,
                                std::vector<int>& bins,
                                std::vector<double>& lengths) const
 {
@@ -1820,7 +1745,7 @@ MOABUnstructuredMesh::bins_crossed(const Particle& p,
 };
 
 moab::EntityHandle
-MOABUnstructuredMesh::get_tet(const Position& r) const
+MOABMesh::get_tet(const Position& r) const
 {
   moab::CartVect pos(r.x, r.y, r.z);
   // find the leaf of the kd-tree for this position
@@ -1847,13 +1772,13 @@ MOABUnstructuredMesh::get_tet(const Position& r) const
   return 0;
 }
 
-double MOABUnstructuredMesh::volume(int bin) const {
+double MOABMesh::volume(int bin) const {
   return tet_volume(get_ent_handle_from_bin(bin));
 }
 
-std::string MOABUnstructuredMesh::library() const { return "moab"; }
+std::string MOABMesh::library() const { return "moab"; }
 
-double MOABUnstructuredMesh::tet_volume(moab::EntityHandle tet) const {
+double MOABMesh::tet_volume(moab::EntityHandle tet) const {
  std::vector<moab::EntityHandle> conn;
  moab::ErrorCode rval = mbi_->get_connectivity(&tet, 1, conn);
  if (rval != moab::MB_SUCCESS) {
@@ -1869,13 +1794,8 @@ double MOABUnstructuredMesh::tet_volume(moab::EntityHandle tet) const {
  return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0]));
 }
 
-void MOABUnstructuredMesh::surface_bins_crossed(const Particle& p, std::vector<int>& bins) const {
-  // TODO: Implement triangle crossings here
-  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
-}
-
 int
-MOABUnstructuredMesh::get_bin(Position r) const {
+MOABMesh::get_bin(Position r) const {
   moab::EntityHandle tet = get_tet(r);
   if (tet == 0) {
     return -1;
@@ -1885,7 +1805,7 @@ MOABUnstructuredMesh::get_bin(Position r) const {
 }
 
 void
-MOABUnstructuredMesh::compute_barycentric_data(const moab::Range& tets) {
+MOABMesh::compute_barycentric_data(const moab::Range& tets) {
   moab::ErrorCode rval;
 
   baryc_data_.clear();
@@ -1915,7 +1835,7 @@ MOABUnstructuredMesh::compute_barycentric_data(const moab::Range& tets) {
 }
 
 bool
-MOABUnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const {
+MOABMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const {
 
   moab::ErrorCode rval;
 
@@ -1950,7 +1870,7 @@ MOABUnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle t
 }
 
 int
-MOABUnstructuredMesh::get_bin_from_index(int idx) const {
+MOABMesh::get_bin_from_index(int idx) const {
   if (idx >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: {}", idx));
   }
@@ -1958,25 +1878,25 @@ MOABUnstructuredMesh::get_bin_from_index(int idx) const {
 }
 
 int
-MOABUnstructuredMesh::get_index(const Position& r,
+MOABMesh::get_index(const Position& r,
                             bool* in_mesh) const {
   int bin = get_bin(r);
   *in_mesh = bin != -1;
   return bin;
 }
 
-int MOABUnstructuredMesh::get_index_from_bin(int bin) const {
+int MOABMesh::get_index_from_bin(int bin) const {
   return bin;
 }
 
 std::pair<std::vector<double>, std::vector<double>>
-MOABUnstructuredMesh::plot(Position plot_ll, Position plot_ur) const {
+MOABMesh::plot(Position plot_ll, Position plot_ur) const {
   // TODO: Implement mesh lines
   return {};
 }
 
 int
-MOABUnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
+MOABMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
   int bin = eh - ehs_[0];
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin: {}", bin));
@@ -1985,18 +1905,18 @@ MOABUnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
 }
 
 moab::EntityHandle
-MOABUnstructuredMesh::get_ent_handle_from_bin(int bin) const {
+MOABMesh::get_ent_handle_from_bin(int bin) const {
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: ", bin));
   }
   return ehs_[0] + bin;
 }
 
-int MOABUnstructuredMesh::n_bins() const {
+int MOABMesh::n_bins() const {
   return ehs_.size();
 }
 
-int MOABUnstructuredMesh::n_surface_bins() const {
+int MOABMesh::n_surface_bins() const {
   // collect all triangles in the set of tets for this mesh
   moab::Range tris;
   moab::ErrorCode rval;
@@ -2009,7 +1929,7 @@ int MOABUnstructuredMesh::n_surface_bins() const {
 }
 
 Position
-MOABUnstructuredMesh::centroid(int bin) const {
+MOABMesh::centroid(int bin) const {
   moab::ErrorCode rval;
 
   auto tet = this->get_ent_handle_from_bin(bin);
@@ -2041,7 +1961,7 @@ MOABUnstructuredMesh::centroid(int bin) const {
 }
 
 std::pair<moab::Tag, moab::Tag>
-MOABUnstructuredMesh::get_score_tags(std::string score) const {
+MOABMesh::get_score_tags(std::string score) const {
   moab::ErrorCode rval;
   // add a tag to the mesh
   // all scores are treated as a single value
@@ -2083,11 +2003,11 @@ MOABUnstructuredMesh::get_score_tags(std::string score) const {
 }
 
 void
-MOABUnstructuredMesh::add_score(const std::string& score) {
+MOABMesh::add_score(const std::string& score) {
   auto score_tags = get_score_tags(score);
 }
 
-void MOABUnstructuredMesh::remove_score(const std::string& score) {
+void MOABMesh::remove_score(const std::string& score) {
   auto value_name = score + "_mean";
   moab::Tag tag;
   moab::ErrorCode rval = mbi_->tag_get_handle(value_name.c_str(), tag);
@@ -2116,7 +2036,7 @@ void MOABUnstructuredMesh::remove_score(const std::string& score) {
 }
 
 void
-MOABUnstructuredMesh::set_score_data(const std::string& score,
+MOABMesh::set_score_data(const std::string& score,
                                  std::vector<double> values,
                                  std::vector<double> std_dev) {
   auto score_tags = this->get_score_tags(score);
@@ -2148,7 +2068,7 @@ MOABUnstructuredMesh::set_score_data(const std::string& score,
 }
 
 void
-MOABUnstructuredMesh::write(std::string base_filename) const {
+MOABMesh::write(std::string base_filename) const {
   // add extension to the base name
   auto filename = base_filename + ".vtk";
   write_message(5, "Writing unstructured mesh {}...", filename);
@@ -2165,14 +2085,6 @@ MOABUnstructuredMesh::write(std::string base_filename) const {
   }
 }
 
-xt::xtensor<double, 1>
-UnstructuredMesh::count_sites(const Particle::Bank* bank,
-                              int64_t length,
-                              bool* outside) const {
-    xt::xtensor<double, 1> out;
-    return out;
-}
-
 #endif
 
 #ifdef LIBMESH
@@ -2195,10 +2107,7 @@ LibMesh::centroid(int bin) const {
 std::string LibMesh::library() const { return "libmesh"; }
 
 void LibMesh::initialize() {
-  // always 3 for unstructured meshes
-  n_dimension_ = 3;
-
-  m_ = std::make_unique<libMesh::Mesh>(*settings::libmesh_comm, 3);
+  m_ = std::make_unique<libMesh::Mesh>(settings::LMI->comm(), 3);
 
   m_->read(filename_);
   m_->prepare_for_use();
@@ -2227,7 +2136,6 @@ void LibMesh::initialize() {
 
   // bounding box for the mesh
   bbox_ = libMesh::MeshTools::create_bounding_box(*m_);
-
 }
 
 int LibMesh::n_bins() const {
@@ -2235,21 +2143,22 @@ int LibMesh::n_bins() const {
 }
 
 int LibMesh::n_surface_bins() const {
-  // TODO: Return number of faces in the mesh here
-  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
-}
-
-void
-LibMesh::surface_bins_crossed(const Particle& p,
-                               std::vector<int>& bins) const
-{
-  // TODO: Implement triangle crossings here
-  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
+  int n_bins = 0;
+  for (int i = 0; i < this->n_bins(); i++) {
+    const libMesh::Elem& e = m_->elem_ref(i);
+    n_bins += e.n_faces();
+    // if this is a boundary element, sure to count boundary faces
+    // twice
+    for (int j = 0; j < e.n_sides(); j++) {
+      if (e.neighbor_ptr(j) == NULL) { n_bins++; }
+    }
+  }
+  return n_bins;
 }
 
 void
 LibMesh::add_score(const std::string& var_name) {
-  // check if this is a new varaible
+  // check if this is a new variable
   std::string value_name = var_name + "_mean";
   if (!variable_map_.count(value_name)) {
     auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
@@ -2258,7 +2167,7 @@ LibMesh::add_score(const std::string& var_name) {
   }
 
   std::string std_dev_name = var_name + "_std_dev";
-  // check if this is a new varaible
+  // check if this is a new variable
   if (!variable_map_.count(std_dev_name)) {
     auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
     auto var_num = eqn_sys.add_variable(std_dev_name, libMesh::CONSTANT, libMesh::MONOMIAL);
@@ -2309,8 +2218,7 @@ LibMesh::set_score_data(const std::string& var_name,
 }
 
 void LibMesh::write(std::string filename) const {
-  std::cout << "Writing file : " << filename + ".e" << std::endl;
-
+  write_message(fmt::format("Writing file: {}.e for unstructured mesh {}", filename, this->id_));
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
   exo.write_discontinuous_exodusII(filename + ".e", *equation_systems_, &systems_out);
@@ -2322,7 +2230,7 @@ LibMesh::bins_crossed(const Particle& p,
                       std::vector<double>& lengths) const
 {
   // TODO: Implement triangle crossings here
-  throw std::runtime_error{"LibMesh tracklength tallies are not implemented."};
+  throw std::runtime_error{"Tracklength tallies on libMesh instances are not implemented."};
 }
 
 int
@@ -2366,7 +2274,9 @@ LibMesh::get_element_from_bin(int bin) const {
   return m_->elem_ptr(bin);
 }
 
-double LibMesh::volume(int bin) const { return m_->elem_ref(bin).volume(); }
+double LibMesh::volume(int bin) const {
+   return m_->elem_ref(bin).volume();
+}
 
 #endif // LIBMESH
 
@@ -2397,7 +2307,7 @@ void read_meshes(pugi::xml_node root)
       model::meshes.push_back(std::make_unique<RectilinearMesh>(node));
 #ifdef DAGMC
     } else if (mesh_type == "unstructured" && mesh_lib == "moab") {
-      model::meshes.push_back(std::make_unique<MOABUnstructuredMesh>(node));
+      model::meshes.push_back(std::make_unique<MOABMesh>(node));
 #endif
 #ifdef LIBMESH
     } else if (mesh_type == "unstructured" && mesh_lib == "libmesh") {
diff --git a/src/settings.cpp b/src/settings.cpp
index d54543f728..41b18942c3 100644
--- a/src/settings.cpp
+++ b/src/settings.cpp
@@ -37,11 +37,6 @@ namespace openmc {
 
 namespace settings {
 
-#ifdef LIBMESH
-std::unique_ptr<libMesh::LibMeshInit> LMI;
-const libMesh::Parallel::Communicator* libmesh_comm;
-#endif
-
 // Default values for boolean flags
 bool assume_separate         {false};
 bool check_overlaps          {false};

From 16e05739a9858a94da2af146e17fb2b5114784fe Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sun, 1 Nov 2020 13:54:03 -0600
Subject: [PATCH 072/139] Using libmesh element references where possible.

---
 include/openmc/mesh.h |  2 +-
 src/initialize.cpp    |  9 +++++++--
 src/mesh.cpp          | 10 +++++-----
 3 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 44309faa5a..d4255877e4 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -531,7 +531,7 @@ private:
   void initialize() override;
 
   //! Translate a bin value to an element pointer
-  const libMesh::Elem* get_element_from_bin(int bin) const;
+  const libMesh::Elem& get_element_from_bin(int bin) const;
 
   //! Translate an element pointer to a bin value
   int get_bin_from_element(const libMesh::Elem* elem) const;
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 515442f80f..c6d8250c1e 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -67,13 +67,18 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
   int n_threads = 1;
 #endif
 
-// initialize libmesh
+// initialize libMesh if it hasn't been initialized already
+// by a call external to OpenMC
 if (!settings::LMI && !libMesh::initialized())
 {
 #ifdef OPENMC_MPI
+  // pass command line args, empty MPI communicator, and number of threads.
+  // Because libMesh was not initialized, we assume that OpenMC is the primary
+  // application and that its main MPI comm should be used.
   settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, comm, n_threads);
 #else
-  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, n_threads);
+  // pass command line args, empty MPI communicator, and number of threads
+  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, 0, n_threads);
 #endif
 }
 #endif
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 37f6a3a7ce..ccaf96efd6 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2099,8 +2099,8 @@ LibMesh::LibMesh(const std::string& filename) {
 
 Position
 LibMesh::centroid(int bin) const {
-  auto elem = this->get_element_from_bin(bin);
-  auto centroid = elem->centroid();
+  auto& elem = this->get_element_from_bin(bin);
+  auto centroid = elem.centroid();
   return {centroid(0), centroid(1), centroid(2)};
 }
 
@@ -2145,7 +2145,7 @@ int LibMesh::n_bins() const {
 int LibMesh::n_surface_bins() const {
   int n_bins = 0;
   for (int i = 0; i < this->n_bins(); i++) {
-    const libMesh::Elem& e = m_->elem_ref(i);
+    const libMesh::Elem& e = get_element_from_bin(i);
     n_bins += e.n_faces();
     // if this is a boundary element, sure to count boundary faces
     // twice
@@ -2269,9 +2269,9 @@ std::pair<std::vector<double>, std::vector<double>>
 LibMesh::plot(Position plot_ll,
               Position plot_ur) const { return {}; }
 
-const libMesh::Elem*
+const libMesh::Elem&
 LibMesh::get_element_from_bin(int bin) const {
-  return m_->elem_ptr(bin);
+  return m_->elem_ref(bin);
 }
 
 double LibMesh::volume(int bin) const {

From 69de477bb5013f04e57273eba4e288d97396ccd7 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 2 Nov 2020 13:51:44 -0600
Subject: [PATCH 073/139] Making the point locator a thread private variable.

---
 include/openmc/mesh.h |  3 ++-
 src/mesh.cpp          | 27 +++++++--------------------
 2 files changed, 9 insertions(+), 21 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index d4255877e4..1817809ff3 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -538,7 +538,8 @@ private:
 
   // Data members
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libMesh mesh instance
-  std::vector<std::unique_ptr<libMesh::PointLocatorBase>> point_locators_; //!< pointers to locators for each thread
+  static std::unique_ptr<libMesh::PointLocatorBase> PL_; //!< per-thread point locators
+  #pragma omp threadprivate(PL_)
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
diff --git a/src/mesh.cpp b/src/mesh.cpp
index ccaf96efd6..194d3e72fb 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2088,6 +2088,9 @@ MOABMesh::write(std::string base_filename) const {
 #endif
 
 #ifdef LIBMESH
+
+std::unique_ptr<libMesh::PointLocatorBase> LibMesh::PL_ = nullptr;
+
 LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMesh(node) {
   initialize();
 }
@@ -2118,19 +2121,9 @@ void LibMesh::initialize() {
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
-  // setup the point locator
-  #ifdef _OPENMP
-  int n_threads = omp_get_max_threads();
-  #else
-  int n_threads = 1;
-  #endif
-
-  point_locators_ = std::vector<std::unique_ptr<libMesh::PointLocatorBase>>(n_threads);
-  for (int i = 0; i < n_threads; i++) {
-    point_locators_[i] = m_->sub_point_locator();
-    point_locators_[i]->set_contains_point_tol(FP_COINCIDENT);
-    point_locators_[i]->enable_out_of_mesh_mode();
-  }
+  PL_ = m_->sub_point_locator();
+  PL_->set_contains_point_tol(FP_COINCIDENT);
+  PL_->enable_out_of_mesh_mode();
 
   first_element_ = *m_->elements_begin();
 
@@ -2242,13 +2235,7 @@ LibMesh::get_bin(Position r) const
   // quick rejection check
   if (!bbox_.contains_point(p)) { return -1; }
 
-  #ifdef _OPENMP
-  int thread = omp_get_thread_num();
-  #else
-  int thread = 0;
-  #endif
-
-  auto e = (*point_locators_[thread])(p);
+  auto e = (*LibMesh::PL_)(p);
   if (!e) {
     return -1;
   } else {

From 257d08624ffb144f4a38812dd079349ca2acb526 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 12 Nov 2020 12:24:01 -0600
Subject: [PATCH 074/139] Initialize one libMesh point locator per thread.

---
 src/mesh.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 194d3e72fb..24a574ba4b 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2121,9 +2121,15 @@ void LibMesh::initialize() {
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
-  PL_ = m_->sub_point_locator();
-  PL_->set_contains_point_tol(FP_COINCIDENT);
-  PL_->enable_out_of_mesh_mode();
+  #pragma omp parallel
+  {
+    #pragma omp critical
+    {
+    PL_ = m_->sub_point_locator();
+    }
+    PL_->set_contains_point_tol(FP_COINCIDENT);
+    PL_->enable_out_of_mesh_mode();
+  }
 
   first_element_ = *m_->elements_begin();
 

From 7329eb90d70d74fa7a73f2562af29e6b0fc8419e Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 12 Nov 2020 15:40:54 -0600
Subject: [PATCH 075/139] Adding CMake prefix path to configure line for
 libMesh.

---
 tools/ci/gha-install.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index 026cd6ca2c..5f7c70660e 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -51,6 +51,7 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
 
     if libmesh:
         cmake_cmd.append('-Dlibmesh=ON')
+        cmake_cmd.append('-DCMAKE_PREFIX_PATH=$HOME/LIBMESH')
 
     # Build in coverage mode for coverage testing
     cmake_cmd.append('-Dcoverage=on')

From 4b03aaa93d0180164b52590ae165d72563f3691f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 12 Nov 2020 18:15:02 -0600
Subject: [PATCH 076/139] Providing LIBMESH environment variable.

---
 tools/ci/travis-install-libmesh.sh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/ci/travis-install-libmesh.sh b/tools/ci/travis-install-libmesh.sh
index e3603ba474..5e75f9be57 100755
--- a/tools/ci/travis-install-libmesh.sh
+++ b/tools/ci/travis-install-libmesh.sh
@@ -4,13 +4,14 @@ set -ex
 
 CURRENT_DIR=$(pwd)
 
-# LibMESH install
+# libMESH install
 cd $HOME
 mkdir LIBMESH && cd LIBMESH
 git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
 make -j4 install
+export LIBMESH_PC=$HOME/LIBMESH
 rm -rf $HOME/LIBMESH/build
 
 cd $CURRENT_DIR

From fbbc421a0c7d07ec05e1511cb8d2fde2f75a8e8f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 13 Nov 2020 11:01:52 -0600
Subject: [PATCH 077/139] Modifying path to libmesh pkgconfig.

---
 tools/ci/travis-install-libmesh.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/travis-install-libmesh.sh b/tools/ci/travis-install-libmesh.sh
index 5e75f9be57..ae3e234992 100755
--- a/tools/ci/travis-install-libmesh.sh
+++ b/tools/ci/travis-install-libmesh.sh
@@ -11,7 +11,7 @@ git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
 make -j4 install
-export LIBMESH_PC=$HOME/LIBMESH
+export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
 rm -rf $HOME/LIBMESH/build
 
 cd $CURRENT_DIR

From 02df8567b6321dda7f169bb452541eeb318f317d Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sun, 15 Nov 2020 23:22:27 -0600
Subject: [PATCH 078/139] More explicit path to libmesh install.

---
 tools/ci/gha-install.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index 5f7c70660e..812370928b 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -51,7 +51,7 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
 
     if libmesh:
         cmake_cmd.append('-Dlibmesh=ON')
-        cmake_cmd.append('-DCMAKE_PREFIX_PATH=$HOME/LIBMESH')
+        cmake_cmd.append('-DCMAKE_PREFIX_PATH=/root/LIBMESH')
 
     # Build in coverage mode for coverage testing
     cmake_cmd.append('-Dcoverage=on')

From 341101e6f7c638b1047a6860f335a6820192b63c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 7 Dec 2020 18:30:03 -0600
Subject: [PATCH 079/139] Adding a version check for libMesh.

---
 cmake/Modules/FindLIBMESH.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 2359f904d7..7b3a64ad00 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -3,7 +3,7 @@
 
 include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
-pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
+pkg_check_modules(LIBMESH REQUIRED libmesh>=1.6.0 IMPORTED_TARGET)
 
 find_path(LIBMESH_PC NAMES libmesh.pc
           HINTS ${LIBMESH_LIBDIR}

From 040c5b9bd42ff1ddafccd71c060400310fd1fa3f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 7 Dec 2020 19:29:09 -0600
Subject: [PATCH 080/139] Removing libmesh include from settings header.

---
 include/openmc/settings.h | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index 650940c779..af74a442fb 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -15,10 +15,6 @@
 
 #include "openmc/constants.h"
 
-#ifdef LIBMESH
-#include "libmesh/libmesh.h"
-#endif
-
 namespace openmc {
 
 //==============================================================================

From 6b911389571fa1f993fc7364562b5a44827e5ac5 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 7 Dec 2020 19:29:35 -0600
Subject: [PATCH 081/139] Some cleanup of the mesh source files.

---
 include/openmc/mesh.h | 24 +++++++++++-------------
 src/mesh.cpp          |  8 ++++----
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 1817809ff3..846345e22a 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -53,6 +53,7 @@ extern std::vector<std::unique_ptr<Mesh>> meshes;
 
 #ifdef LIBMESH
 namespace settings {
+// used when creating new libMesh::Mesh instances
 extern std::unique_ptr<libMesh::LibMeshInit> LMI;
 }
 #endif
@@ -274,6 +275,7 @@ public:
   int set_grid();
 };
 
+// Abstract class for unstructured meshes
 class UnstructuredMesh : public Mesh {
 
 public:
@@ -286,7 +288,7 @@ public:
 private:
 
   //! Setup method for the mesh. Builds data structures,
-  //! element mapping, etc.
+  //! sets up element mapping, creates bounding boxes, etc.
   virtual void initialize() = 0;
 
 public:
@@ -297,7 +299,8 @@ public:
   //! Remove variable from the mesh instance
   virtual void remove_score(const std::string& var_name) = 0;
 
-  //! Set the value of a bin for a variable on the mesh instance
+  //! Set the value of a bin for a variable on the internal
+  //  mesh instance
   virtual void set_score_data(const std::string& var_name,
                               std::vector<double> values,
                               std::vector<double> std_dev) = 0;
@@ -316,7 +319,7 @@ public:
   //! Get the volume of a mesh bin
   //
   //! \param[in] bin Bin to return the volume for
-  //! \return The volume of the bin
+  //! \return Volume of the bin
   virtual double volume(int bin) const = 0;
 
   //! Get the library used for this unstructured mesh
@@ -330,7 +333,7 @@ public:
   void to_hdf5(hid_t group) const override;
 
   // Data members
-  bool output_ {true};
+  bool output_ {true}; //!< Write tallies onto the unstructured mesh at the end of a run
   std::string filename_; //!< Path to unstructured mesh file
 };
 
@@ -343,7 +346,6 @@ public:
   MOABMesh() = default;
   MOABMesh(pugi::xml_node);
   MOABMesh(const std::string& filename);
-  ~MOABMesh() = default;
 
   void bins_crossed(const Particle& p,
                     std::vector<int>& bins,
@@ -361,12 +363,7 @@ public:
   std::string library() const override;
 
   //! Add a score to the mesh instance
-  void add_score(const std::string& score);
-
-  //! Set the value of a bin for a variable on the libmesh mesh instance
-  void set_score_data(const std::string& var_name,
-                      std::vector<double> values,
-                      std::vector<double> std_dev) override;
+  void add_score(const std::string& score) override;
 
   //! Remove a score from the mesh instance
   void remove_score(const std::string& score) override;
@@ -374,7 +371,7 @@ public:
   //! Set data for a score
   void set_score_data(const std::string& score,
                       std::vector<double> values,
-                      std::vector<double> std_dev) const;
+                      std::vector<double> std_dev) override;
 
   //! Write the mesh with any current tally data
   void write(std::string base_filename) const;
@@ -489,6 +486,7 @@ private:
 #endif
 
 #ifdef LIBMESH
+
 class LibMesh : public UnstructuredMesh {
 
 public:
@@ -539,7 +537,7 @@ private:
   // Data members
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libMesh mesh instance
   static std::unique_ptr<libMesh::PointLocatorBase> PL_; //!< per-thread point locators
-  #pragma omp threadprivate(PL_)
+  #pragma omp threadprivate(PL_) // each thread has its own point locator
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 24a574ba4b..388d742080 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2037,8 +2037,8 @@ void MOABMesh::remove_score(const std::string& score) {
 
 void
 MOABMesh::set_score_data(const std::string& score,
-                                 std::vector<double> values,
-                                 std::vector<double> std_dev) {
+                         std::vector<double> values,
+                         std::vector<double> std_dev) {
   auto score_tags = this->get_score_tags(score);
 
   // normalize tally values by element volume
@@ -2182,8 +2182,8 @@ LibMesh::remove_score(const std::string& var_name) {
 
 void
 LibMesh::set_score_data(const std::string& var_name,
-                      std::vector<double> values,
-                      std::vector<double> std_dev)
+                        std::vector<double> values,
+                        std::vector<double> std_dev)
 {
   auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
 

From 19cea16ef9d5b1bd9c501899aa4c07f6be8a6740 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 7 Dec 2020 19:29:59 -0600
Subject: [PATCH 082/139] Adding check that the LibMeshInit object is set.

---
 src/initialize.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/initialize.cpp b/src/initialize.cpp
index c6d8250c1e..d30581e066 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -68,7 +68,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 // initialize libMesh if it hasn't been initialized already
-// by a call external to OpenMC
+// (if initialized externally, the LMI object needs to be provided also)
 if (!settings::LMI && !libMesh::initialized())
 {
 #ifdef OPENMC_MPI
@@ -81,6 +81,10 @@ if (!settings::LMI && !libMesh::initialized())
   settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, 0, n_threads);
 #endif
 }
+
+if (!settings::LMI) {
+  fatal_error("libMesh::LibMeshInit object isn't set.");
+}
 #endif
 
   // Start total and initialization timer

From 6284375f53e2bf304b9fb8f8db867bc2093b67cb Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 7 Dec 2020 21:44:44 -0600
Subject: [PATCH 083/139] Removing unecessary line.

---
 openmc/mesh.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/openmc/mesh.py b/openmc/mesh.py
index ef6455b96a..54eba9a9db 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -846,5 +846,4 @@ class UnstructuredMesh(MeshBase):
         filename = get_text(elem, 'filename')
         library = get_text(elem, 'library')
 
-        mesh = cls(filename, library, mesh_id)
-        return mesh
+        return cls(filename, library, mesh_id)

From 3f5bceb6ebd0d08e8cee1875d8c79ad1f2fe1b27 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 8 Dec 2020 09:02:08 -0600
Subject: [PATCH 084/139] Updating libmesh syntax to match their repo.

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7c8ec5ff5b..1937940799 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -31,7 +31,7 @@ option(debug    "Compile with debug flags"                       OFF)
 option(optimize "Turn on all compiler optimization flags"        OFF)
 option(coverage "Compile with coverage analysis flags"           OFF)
 option(dagmc    "Enable support for DAGMC (CAD) geometry"        OFF)
-option(libmesh  "Enable support for libmesh unstructured mesh tallies" OFF)
+option(libmesh  "Enable support for libMesh unstructured mesh tallies" OFF)
 
 #===============================================================================
 # MPI for distributed-memory parallelism

From c749b00bf75df083ad7253ee446fe90d0a556432 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 8 Dec 2020 09:48:31 -0600
Subject: [PATCH 085/139] Updating formatting of unstructured mesh functions.

---
 include/openmc/mesh.h |   4 +-
 src/mesh.cpp          | 171 +++++++++++++++++++++++++-----------------
 2 files changed, 105 insertions(+), 70 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 846345e22a..f063fd182e 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -528,10 +528,10 @@ private:
 
   void initialize() override;
 
-  //! Translate a bin value to an element pointer
+  //! Translate a bin value to an element reference
   const libMesh::Elem& get_element_from_bin(int bin) const;
 
-  //! Translate an element pointer to a bin value
+  //! Translate an element pointer to a bin index
   int get_bin_from_element(const libMesh::Elem* elem) const;
 
   // Data members
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 388d742080..f8ffc70a95 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -132,7 +132,7 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
   if (check_for_node(node, "type")) {
     auto temp = get_node_value(node, "type", true, true);
     if (temp != "unstructured") {
-      fatal_error("Invalid mesh type: " + temp);
+      fatal_error(fmt::format("Invalid mesh type: {}", temp));
     }
   }
 
@@ -141,8 +141,7 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
     filename_ = get_node_value(node, "filename");
   }
   else {
-    fatal_error("No filename supplied for unstructured mesh with ID: " +
-                std::to_string(id_));
+    fatal_error(fmt::format("No filename supplied for unstructured mesh with ID: {}", id_));
   }
 
   // check if mesh tally data should be written with
@@ -183,7 +182,6 @@ UnstructuredMesh::to_hdf5(hid_t group) const
 
     write_dataset(mesh_group, "volumes", tet_vols);
     write_dataset(mesh_group, "centroids", centroids);
-
     close_group(mesh_group);
 }
 
@@ -1363,10 +1361,10 @@ extern "C" int openmc_add_unstructured_mesh(const char filename[],
                                             const char library[],
                                             int* id)
 {
-
   std::string lib_name(library);
   std::string mesh_file(filename);
   bool valid_lib = false;
+
 #ifdef DAGMC
   if (lib_name == "moab") {
     model::meshes.push_back(std::move(std::make_unique<MOABMesh>(mesh_file)));
@@ -1638,9 +1636,9 @@ MOABMesh::build_kdtree(const moab::Range& all_tets)
 
 void
 MOABMesh::intersect_track(const moab::CartVect& start,
-                                  const moab::CartVect& dir,
-                                  double track_len,
-                                  std::vector<double>& hits) const {
+                          const moab::CartVect& dir,
+                          double track_len,
+                          std::vector<double>& hits) const {
   hits.clear();
 
   moab::ErrorCode rval;
@@ -1669,8 +1667,8 @@ MOABMesh::intersect_track(const moab::CartVect& start,
 
 void
 MOABMesh::bins_crossed(const Particle& p,
-                               std::vector<int>& bins,
-                               std::vector<double>& lengths) const
+                       std::vector<int>& bins,
+                       std::vector<double>& lengths) const
 {
   Position last_r{p.r_last_};
   Position r{p.r()};
@@ -1772,30 +1770,35 @@ MOABMesh::get_tet(const Position& r) const
   return 0;
 }
 
-double MOABMesh::volume(int bin) const {
+double MOABMesh::volume(int bin) const
+{
   return tet_volume(get_ent_handle_from_bin(bin));
 }
 
-std::string MOABMesh::library() const { return "moab"; }
-
-double MOABMesh::tet_volume(moab::EntityHandle tet) const {
- std::vector<moab::EntityHandle> conn;
- moab::ErrorCode rval = mbi_->get_connectivity(&tet, 1, conn);
- if (rval != moab::MB_SUCCESS) {
-   fatal_error("Failed to get tet connectivity");
- }
-
- moab::CartVect p[4];
- rval = mbi_->get_coords(conn.data(), conn.size(), p[0].array());
- if (rval != moab::MB_SUCCESS) {
-   fatal_error("Failed to get tet coords");
- }
-
- return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0]));
+std::string MOABMesh::library() const
+{
+   return "moab";
 }
 
-int
-MOABMesh::get_bin(Position r) const {
+double MOABMesh::tet_volume(moab::EntityHandle tet) const
+{
+  std::vector<moab::EntityHandle> conn;
+  moab::ErrorCode rval = mbi_->get_connectivity(&tet, 1, conn);
+  if (rval != moab::MB_SUCCESS) {
+    fatal_error("Failed to get tet connectivity");
+  }
+
+  moab::CartVect p[4];
+  rval = mbi_->get_coords(conn.data(), conn.size(), p[0].array());
+  if (rval != moab::MB_SUCCESS) {
+     fatal_error("Failed to get tet coords");
+  }
+
+  return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0]));
+}
+
+int MOABMesh::get_bin(Position r) const
+{
   moab::EntityHandle tet = get_tet(r);
   if (tet == 0) {
     return -1;
@@ -1835,7 +1838,9 @@ MOABMesh::compute_barycentric_data(const moab::Range& tets) {
 }
 
 bool
-MOABMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const {
+MOABMesh::point_in_tet(const moab::CartVect& r,
+                       moab::EntityHandle tet) const
+{
 
   moab::ErrorCode rval;
 
@@ -1870,7 +1875,8 @@ MOABMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const {
 }
 
 int
-MOABMesh::get_bin_from_index(int idx) const {
+MOABMesh::get_bin_from_index(int idx) const
+{
   if (idx >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: {}", idx));
   }
@@ -1879,24 +1885,28 @@ MOABMesh::get_bin_from_index(int idx) const {
 
 int
 MOABMesh::get_index(const Position& r,
-                            bool* in_mesh) const {
+                    bool* in_mesh) const
+{
   int bin = get_bin(r);
   *in_mesh = bin != -1;
   return bin;
 }
 
-int MOABMesh::get_index_from_bin(int bin) const {
+int MOABMesh::get_index_from_bin(int bin) const
+{
   return bin;
 }
 
 std::pair<std::vector<double>, std::vector<double>>
-MOABMesh::plot(Position plot_ll, Position plot_ur) const {
+MOABMesh::plot(Position plot_ll, Position plot_ur) const
+{
   // TODO: Implement mesh lines
   return {};
 }
 
 int
-MOABMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
+MOABMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const
+{
   int bin = eh - ehs_[0];
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin: {}", bin));
@@ -1905,18 +1915,21 @@ MOABMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const {
 }
 
 moab::EntityHandle
-MOABMesh::get_ent_handle_from_bin(int bin) const {
+MOABMesh::get_ent_handle_from_bin(int bin) const
+{
   if (bin >= n_bins()) {
     fatal_error(fmt::format("Invalid bin index: ", bin));
   }
   return ehs_[0] + bin;
 }
 
-int MOABMesh::n_bins() const {
+int MOABMesh::n_bins() const
+{
   return ehs_.size();
 }
 
-int MOABMesh::n_surface_bins() const {
+int MOABMesh::n_surface_bins() const
+{
   // collect all triangles in the set of tets for this mesh
   moab::Range tris;
   moab::ErrorCode rval;
@@ -1929,7 +1942,8 @@ int MOABMesh::n_surface_bins() const {
 }
 
 Position
-MOABMesh::centroid(int bin) const {
+MOABMesh::centroid(int bin) const
+{
   moab::ErrorCode rval;
 
   auto tet = this->get_ent_handle_from_bin(bin);
@@ -1961,7 +1975,8 @@ MOABMesh::centroid(int bin) const {
 }
 
 std::pair<moab::Tag, moab::Tag>
-MOABMesh::get_score_tags(std::string score) const {
+MOABMesh::get_score_tags(std::string score) const
+{
   moab::ErrorCode rval;
   // add a tag to the mesh
   // all scores are treated as a single value
@@ -2003,11 +2018,13 @@ MOABMesh::get_score_tags(std::string score) const {
 }
 
 void
-MOABMesh::add_score(const std::string& score) {
+MOABMesh::add_score(const std::string& score)
+{
   auto score_tags = get_score_tags(score);
 }
 
-void MOABMesh::remove_score(const std::string& score) {
+void MOABMesh::remove_score(const std::string& score)
+{
   auto value_name = score + "_mean";
   moab::Tag tag;
   moab::ErrorCode rval = mbi_->tag_get_handle(value_name.c_str(), tag);
@@ -2038,7 +2055,8 @@ void MOABMesh::remove_score(const std::string& score) {
 void
 MOABMesh::set_score_data(const std::string& score,
                          std::vector<double> values,
-                         std::vector<double> std_dev) {
+                         std::vector<double> std_dev)
+{
   auto score_tags = this->get_score_tags(score);
 
   // normalize tally values by element volume
@@ -2068,7 +2086,8 @@ MOABMesh::set_score_data(const std::string& score,
 }
 
 void
-MOABMesh::write(std::string base_filename) const {
+MOABMesh::write(std::string base_filename) const
+{
   // add extension to the base name
   auto filename = base_filename + ".vtk";
   write_message(5, "Writing unstructured mesh {}...", filename);
@@ -2091,36 +2110,31 @@ MOABMesh::write(std::string base_filename) const {
 
 std::unique_ptr<libMesh::PointLocatorBase> LibMesh::PL_ = nullptr;
 
-LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMesh(node) {
+LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMesh(node)
+{
   initialize();
 }
 
-LibMesh::LibMesh(const std::string& filename) {
+LibMesh::LibMesh(const std::string& filename)
+{
   filename_ = filename;
   initialize();
 }
 
-Position
-LibMesh::centroid(int bin) const {
-  auto& elem = this->get_element_from_bin(bin);
-  auto centroid = elem.centroid();
-  return {centroid(0), centroid(1), centroid(2)};
-}
-
-std::string LibMesh::library() const { return "libmesh"; }
-
-void LibMesh::initialize() {
+void LibMesh::initialize()
+{
   m_ = std::make_unique<libMesh::Mesh>(settings::LMI->comm(), 3);
-
   m_->read(filename_);
   m_->prepare_for_use();
 
+  // create an equation system for storing values
   eq_system_name_ = "mesh_" + std::to_string(id_) + "_system";
 
   equation_systems_ = std::make_unique<libMesh::EquationSystems>(*m_);
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
+  // one point locator created per thread
   #pragma omp parallel
   {
     #pragma omp critical
@@ -2131,17 +2145,30 @@ void LibMesh::initialize() {
     PL_->enable_out_of_mesh_mode();
   }
 
+  // store first element in the mesh to use as an offset for bin indices
   first_element_ = *m_->elements_begin();
 
-  // bounding box for the mesh
+  // bounding box for the mesh for quick rejection checks
   bbox_ = libMesh::MeshTools::create_bounding_box(*m_);
 }
 
-int LibMesh::n_bins() const {
+Position
+LibMesh::centroid(int bin) const
+{
+  auto& elem = this->get_element_from_bin(bin);
+  auto centroid = elem.centroid();
+  return {centroid(0), centroid(1), centroid(2)};
+}
+
+std::string LibMesh::library() const { return "libmesh"; }
+
+int LibMesh::n_bins() const
+{
   return m_->n_elem();
 }
 
-int LibMesh::n_surface_bins() const {
+int LibMesh::n_surface_bins() const
+{
   int n_bins = 0;
   for (int i = 0; i < this->n_bins(); i++) {
     const libMesh::Elem& e = get_element_from_bin(i);
@@ -2156,7 +2183,8 @@ int LibMesh::n_surface_bins() const {
 }
 
 void
-LibMesh::add_score(const std::string& var_name) {
+LibMesh::add_score(const std::string& var_name)
+{
   // check if this is a new variable
   std::string value_name = var_name + "_mean";
   if (!variable_map_.count(value_name)) {
@@ -2175,7 +2203,8 @@ LibMesh::add_score(const std::string& var_name) {
 }
 
 void
-LibMesh::remove_score(const std::string& var_name) {
+LibMesh::remove_score(const std::string& var_name)
+{
   return;
 }
 
@@ -2216,7 +2245,8 @@ LibMesh::set_score_data(const std::string& var_name,
   }
 }
 
-void LibMesh::write(std::string filename) const {
+void LibMesh::write(std::string filename) const
+{
   write_message(fmt::format("Writing file: {}.e for unstructured mesh {}", filename, this->id_));
   libMesh::ExodusII_IO exo(*m_);
   std::set<std::string> systems_out = {eq_system_name_};
@@ -2250,7 +2280,8 @@ LibMesh::get_bin(Position r) const
 }
 
 int
-LibMesh::get_bin_from_element(const libMesh::Elem* elem) const {
+LibMesh::get_bin_from_element(const libMesh::Elem* elem) const
+{
   int bin =  elem->id() - first_element_->id();
   if (bin >= n_bins() || bin < 0) {
     fatal_error(fmt::format("Invalid bin: {}", bin));
@@ -2259,15 +2290,19 @@ LibMesh::get_bin_from_element(const libMesh::Elem* elem) const {
 }
 
 std::pair<std::vector<double>, std::vector<double>>
-LibMesh::plot(Position plot_ll,
-              Position plot_ur) const { return {}; }
+LibMesh::plot(Position plot_ll, Position plot_ur) const
+{
+  return {};
+}
 
 const libMesh::Elem&
-LibMesh::get_element_from_bin(int bin) const {
+LibMesh::get_element_from_bin(int bin) const
+{
   return m_->elem_ref(bin);
 }
 
-double LibMesh::volume(int bin) const {
+double LibMesh::volume(int bin) const
+{
    return m_->elem_ref(bin).volume();
 }
 

From 7fd78f84b4b21c8af7f3bd6fdc935430f4218043 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 8 Dec 2020 14:49:37 -0600
Subject: [PATCH 086/139] Updating documentation for unstructured mesh XML
 elements.

---
 docs/source/io_formats/tallies.rst | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/source/io_formats/tallies.rst b/docs/source/io_formats/tallies.rst
index 6875e45049..974a3d093b 100644
--- a/docs/source/io_formats/tallies.rst
+++ b/docs/source/io_formats/tallies.rst
@@ -276,7 +276,7 @@ should be set to:
       <filter type="azimuthal" bins="2" />
 
 :mesh:
-  The unique ID of a mesh to be tallied over.
+  The unique ID of a mesh to be tallied over.z
 
 :distribcell:
   The single cell which should be tallied uniquely for all instances.
@@ -338,7 +338,11 @@ attributes/sub-elements:
   :z_grid:
     The mesh divisions along the z-axis. (For rectilinear mesh only.)
 
-  :mesh_file:
+  :library:
+    The mesh library used to represent an unstructured mesh. This can be either
+    "moab" or "libmesh". (For unstructured mesh only.)
+
+  :filename:
     The name of the mesh file to be loaded at runtime. (For unstructured mesh
     only.)
 

From 4e04e8f4fba59b1531063aac18c4b41b9b798ba8 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 8 Dec 2020 17:38:16 -0600
Subject: [PATCH 087/139] Small adjustment to the install location of libMesh
 in CI.

---
 tools/ci/gha-install.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index 812370928b..5f7c70660e 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -51,7 +51,7 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
 
     if libmesh:
         cmake_cmd.append('-Dlibmesh=ON')
-        cmake_cmd.append('-DCMAKE_PREFIX_PATH=/root/LIBMESH')
+        cmake_cmd.append('-DCMAKE_PREFIX_PATH=$HOME/LIBMESH')
 
     # Build in coverage mode for coverage testing
     cmake_cmd.append('-Dcoverage=on')

From 50dbd50f2bb851ff57126eaf7c89d0b2a5733c49 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 8 Dec 2020 20:59:24 -0600
Subject: [PATCH 088/139] Ensure that the CMAKE_PREFIX_PATH is checked for the
 libmesh installation.

---
 cmake/Modules/FindLIBMESH.cmake | 1 +
 tools/ci/gha-install.py         | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 7b3a64ad00..62ab223387 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -3,6 +3,7 @@
 
 include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
+set(PKG_CONFIG_USE_CMAKE_PREFIX_PATH True)
 pkg_check_modules(LIBMESH REQUIRED libmesh>=1.6.0 IMPORTED_TARGET)
 
 find_path(LIBMESH_PC NAMES libmesh.pc
diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index 5f7c70660e..3dac29407c 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -51,7 +51,7 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
 
     if libmesh:
         cmake_cmd.append('-Dlibmesh=ON')
-        cmake_cmd.append('-DCMAKE_PREFIX_PATH=$HOME/LIBMESH')
+        cmake_cmd.append('-DCMAKE_PREFIX_PATH=/home/travis/LIBMESH')
 
     # Build in coverage mode for coverage testing
     cmake_cmd.append('-Dcoverage=on')

From 7cd3e388a4f85990f9311f5243889aa62e73d132 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 9 Dec 2020 12:34:56 -0600
Subject: [PATCH 089/139] Updating to the '.e' extension for Exodus file
 formats.

---
 .../unstructured_mesh/inputs_true0.dat         |   2 +-
 .../unstructured_mesh/inputs_true1.dat         |   2 +-
 .../unstructured_mesh/inputs_true10.dat        |   2 +-
 .../unstructured_mesh/inputs_true11.dat        |   2 +-
 .../unstructured_mesh/inputs_true12.dat        |   2 +-
 .../unstructured_mesh/inputs_true13.dat        |   2 +-
 .../unstructured_mesh/inputs_true14.dat        |   2 +-
 .../unstructured_mesh/inputs_true15.dat        |   2 +-
 .../unstructured_mesh/inputs_true2.dat         |   2 +-
 .../unstructured_mesh/inputs_true3.dat         |   2 +-
 .../unstructured_mesh/inputs_true8.dat         |   2 +-
 .../unstructured_mesh/inputs_true9.dat         |   2 +-
 .../regression_tests/unstructured_mesh/test.py |   4 ++--
 .../unstructured_mesh/test_mesh_tets.e         | Bin 0 -> 354408 bytes
 .../unstructured_mesh/test_mesh_tets_w_holes.e | Bin 0 -> 353440 bytes
 15 files changed, 14 insertions(+), 14 deletions(-)
 create mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets.e
 create mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.e

diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
index e5f7fd6132..7411e9b940 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="libmesh" type="unstructured">
-    <filename>test_mesh_tets_w_holes.exo</filename>
+    <filename>test_mesh_tets_w_holes.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
index 58e2ee468e..e85aab1193 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="libmesh" type="unstructured">
-    <filename>test_mesh_tets.exo</filename>
+    <filename>test_mesh_tets.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true10.dat b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
index 0d41154b33..628e5e7bcc 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true10.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true10.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.exo</filename>
+    <filename>test_mesh_tets_w_holes.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true11.dat b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
index cbdfd518f8..1c0a579e54 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true11.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true11.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets.exo</filename>
+    <filename>test_mesh_tets.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true12.dat b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
index 0e726216e0..a75344435f 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true12.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true12.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.exo</filename>
+    <filename>test_mesh_tets_w_holes.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true13.dat b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
index 897e451008..c62c3c9113 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true13.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true13.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets.exo</filename>
+    <filename>test_mesh_tets.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true14.dat b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
index 6f965dc7e5..b7ac4c048c 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true14.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true14.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.exo</filename>
+    <filename>test_mesh_tets_w_holes.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true15.dat b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
index df0a8d0b03..1d96efc7d3 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true15.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true15.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets.exo</filename>
+    <filename>test_mesh_tets.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
index 751a8e0b47..a2a2d4a27a 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="libmesh" type="unstructured">
-    <filename>test_mesh_tets_w_holes.exo</filename>
+    <filename>test_mesh_tets_w_holes.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
index bbfbc6402f..c96f902016 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="libmesh" type="unstructured">
-    <filename>test_mesh_tets.exo</filename>
+    <filename>test_mesh_tets.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true8.dat b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
index 9d67b8909e..9a7ad57113 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true8.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true8.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets_w_holes.exo</filename>
+    <filename>test_mesh_tets_w_holes.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/inputs_true9.dat b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
index f81a476124..8aedad2377 100644
--- a/tests/regression_tests/unstructured_mesh/inputs_true9.dat
+++ b/tests/regression_tests/unstructured_mesh/inputs_true9.dat
@@ -70,7 +70,7 @@
     <upper_right>10.0 10.0 10.0</upper_right>
   </mesh>
   <mesh id="2" library="moab" type="unstructured">
-    <filename>test_mesh_tets.exo</filename>
+    <filename>test_mesh_tets.e</filename>
   </mesh>
   <filter id="1" type="mesh">
     <bins>1</bins>
diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py
index 4b09a10de4..c921a78482 100644
--- a/tests/regression_tests/unstructured_mesh/test.py
+++ b/tests/regression_tests/unstructured_mesh/test.py
@@ -198,9 +198,9 @@ def test_unstructured_mesh(test_opts):
     regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh)
 
     if test_opts['holes']:
-        mesh_filename = "test_mesh_tets_w_holes.exo"
+        mesh_filename = "test_mesh_tets_w_holes.e"
     else:
-        mesh_filename = "test_mesh_tets.exo"
+        mesh_filename = "test_mesh_tets.e"
 
     uscd_mesh = openmc.UnstructuredMesh(mesh_filename, test_opts['library'])
     uscd_filter = openmc.MeshFilter(mesh=uscd_mesh)
diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.e b/tests/regression_tests/unstructured_mesh/test_mesh_tets.e
new file mode 100644
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literal 0
HcmV?d00001


From b982704d4fa030140c9468b9709f22ec50d67f4f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 9 Dec 2020 13:10:53 -0600
Subject: [PATCH 090/139] Addressing @aprilnovak's PR comments.

---
 docs/source/io_formats/tallies.rst |  2 +-
 include/openmc/settings.h          |  1 -
 src/initialize.cpp                 |  3 ---
 src/mesh.cpp                       | 11 ++++++++++-
 4 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/docs/source/io_formats/tallies.rst b/docs/source/io_formats/tallies.rst
index 974a3d093b..8c1ad91b58 100644
--- a/docs/source/io_formats/tallies.rst
+++ b/docs/source/io_formats/tallies.rst
@@ -276,7 +276,7 @@ should be set to:
       <filter type="azimuthal" bins="2" />
 
 :mesh:
-  The unique ID of a mesh to be tallied over.z
+  The unique ID of a mesh to be tallied over.
 
 :distribcell:
   The single cell which should be tallied uniquely for all instances.
diff --git a/include/openmc/settings.h b/include/openmc/settings.h
index af74a442fb..85468a7330 100644
--- a/include/openmc/settings.h
+++ b/include/openmc/settings.h
@@ -6,7 +6,6 @@
 
 #include <array>
 #include <cstdint>
-#include <memory>
 #include <string>
 #include <unordered_set>
 #include <vector>
diff --git a/src/initialize.cpp b/src/initialize.cpp
index d30581e066..5e4c86fd13 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -82,9 +82,6 @@ if (!settings::LMI && !libMesh::initialized())
 #endif
 }
 
-if (!settings::LMI) {
-  fatal_error("libMesh::LibMeshInit object isn't set.");
-}
 #endif
 
   // Start total and initialization timer
diff --git a/src/mesh.cpp b/src/mesh.cpp
index f8ffc70a95..6f4fd73274 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2123,10 +2123,19 @@ LibMesh::LibMesh(const std::string& filename)
 
 void LibMesh::initialize()
 {
-  m_ = std::make_unique<libMesh::Mesh>(settings::LMI->comm(), 3);
+  if (!settings::LMI) {
+    fatal_error("Attempting to use an unstructured mesh without a libMesh::LibMeshInit object.");
+  }
+
+  m_ = std::make_unique<libMesh::Mesh>(settings::LMI->comm(), n_dimension_);
   m_->read(filename_);
   m_->prepare_for_use();
 
+  // ensure that the loaded mesh is 3 dimensional
+  if(m_->spatial_dimension() != n_dimension_) {
+    fatal_error(fmt::format("Mesh file {} specified for use in an unstructured mesh is not a 3D mesh.", filename_));
+  }
+
   // create an equation system for storing values
   eq_system_name_ = "mesh_" + std::to_string(id_) + "_system";
 

From a4add365dd2d73c673679f03c2cf448947ff2ce0 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 9 Dec 2020 13:50:23 -0600
Subject: [PATCH 091/139] Updating the mesh dimension check in
 LibMesh::initialize.

---
 src/mesh.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 6f4fd73274..7e60dc00ec 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2132,7 +2132,7 @@ void LibMesh::initialize()
   m_->prepare_for_use();
 
   // ensure that the loaded mesh is 3 dimensional
-  if(m_->spatial_dimension() != n_dimension_) {
+  if(m_->mesh_dimension() != n_dimension_) {
     fatal_error(fmt::format("Mesh file {} specified for use in an unstructured mesh is not a 3D mesh.", filename_));
   }
 

From 324d3494593f1aa587fc1fdac7c7b6c5e8ed9099 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 10 Dec 2020 03:30:19 -0600
Subject: [PATCH 092/139] Back to a vector of point locators.

---
 include/openmc/mesh.h |  3 +--
 src/mesh.cpp          | 32 ++++++++++++++++++++------------
 2 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index f063fd182e..99b43637aa 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -536,8 +536,7 @@ private:
 
   // Data members
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libMesh mesh instance
-  static std::unique_ptr<libMesh::PointLocatorBase> PL_; //!< per-thread point locators
-  #pragma omp threadprivate(PL_) // each thread has its own point locator
+  std::vector<std::unique_ptr<libMesh::PointLocatorBase>> PL_; //!< per-thread point locators
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 7e60dc00ec..bb10001166 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2108,8 +2108,6 @@ MOABMesh::write(std::string base_filename) const
 
 #ifdef LIBMESH
 
-std::unique_ptr<libMesh::PointLocatorBase> LibMesh::PL_ = nullptr;
-
 LibMesh::LibMesh(pugi::xml_node node) : UnstructuredMesh(node)
 {
   initialize();
@@ -2143,15 +2141,17 @@ void LibMesh::initialize()
   libMesh::ExplicitSystem& eq_sys =
     equation_systems_->add_system<libMesh::ExplicitSystem>(eq_system_name_);
 
-  // one point locator created per thread
-  #pragma omp parallel
-  {
-    #pragma omp critical
-    {
-    PL_ = m_->sub_point_locator();
-    }
-    PL_->set_contains_point_tol(FP_COINCIDENT);
-    PL_->enable_out_of_mesh_mode();
+
+  #ifdef _OPENMP
+  int n_threads = omp_get_max_threads();
+  #else
+  int n_threads = 1;
+  #endif
+
+  for (int i = 0; i < n_threads; i++) {
+      PL_.emplace_back(m_->sub_point_locator());
+      PL_.back()->set_contains_point_tol(FP_COINCIDENT);
+      PL_.back()->enable_out_of_mesh_mode();
   }
 
   // store first element in the mesh to use as an offset for bin indices
@@ -2280,7 +2280,15 @@ LibMesh::get_bin(Position r) const
   // quick rejection check
   if (!bbox_.contains_point(p)) { return -1; }
 
-  auto e = (*LibMesh::PL_)(p);
+  #ifdef _OPENMP
+  int thread_num = omp_get_thread_num();
+  #else
+  int thread_num = 0;
+  #endif
+
+  const auto& point_locator = PL_.at(thread_num);
+
+  auto e = (*point_locator)(p);
   if (!e) {
     return -1;
   } else {

From 1b05cdec16a71291f23ae3190f93b6a29d311f2b Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 11 Dec 2020 13:00:37 -0600
Subject: [PATCH 093/139] Adding check for unstructured mesh file existence.

---
 src/mesh.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index bb10001166..3a2f768618 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1,5 +1,4 @@
 #include "openmc/mesh.h"
-
 #include <algorithm> // for copy, equal, min, min_element
 #include <cstddef> // for size_t
 #include <cmath>  // for ceil
@@ -21,6 +20,7 @@
 #include "openmc/capi.h"
 #include "openmc/constants.h"
 #include "openmc/error.h"
+#include "openmc/file_utils.h"
 #include "openmc/hdf5_interface.h"
 #include "openmc/message_passing.h"
 #include "openmc/search.h"
@@ -139,6 +139,9 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
   // get the filename of the unstructured mesh to load
   if (check_for_node(node, "filename")) {
     filename_ = get_node_value(node, "filename");
+    if (!file_exists(filename_)) {
+      fatal_error("Mesh file '" + filename_ + "' does not exist!");
+    }
   }
   else {
     fatal_error(fmt::format("No filename supplied for unstructured mesh with ID: {}", id_));

From 8fa814d142db60b8e1f6026d4eda6113ce8797db Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 23 Dec 2020 12:20:45 -0600
Subject: [PATCH 094/139] Updating version of cmake.

Adding netcdf libraries to CI.

Adding libmesh job.

Extending prefix path for libmesh to the  directory.

A couple more tweaks to the libmesh paths.

Updating how libmesh is installed. Relying on MOOSE install scripts.
---
 .github/workflows/ci.yml           | 14 +++++++++++++-
 tools/ci/gha-install-libmesh.sh    | 24 ++++++++++++++++++++++++
 tools/ci/gha-install.py            |  3 ++-
 tools/ci/gha-install.sh            |  2 +-
 tools/ci/travis-install-libmesh.sh | 17 -----------------
 5 files changed, 40 insertions(+), 20 deletions(-)
 create mode 100755 tools/ci/gha-install-libmesh.sh
 delete mode 100755 tools/ci/travis-install-libmesh.sh

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 8486c0bc6d..5f9356040c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -30,6 +30,7 @@ jobs:
         mpi: [n, y]
         omp: [n, y]
         dagmc: [n]
+        libmesh: [n]
         event: [n]
         vectfit: [n]
 
@@ -44,6 +45,10 @@ jobs:
             python-version: 3.8
             mpi: y
             omp: y
+          - libmesh: y
+            python-version: 3.8
+            mpi: y
+            omp: y
           - event: y
             python-version: 3.8
             omp: y
@@ -63,6 +68,7 @@ jobs:
       DAGMC: ${{ matrix.dagmc }}
       EVENT: ${{ matrix.event }}
       VECTFIT: ${{ matrix.vectfit }}
+      LIBMESH: ${{ matrix.libmesh }}
 
     steps:
       -
@@ -73,7 +79,11 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-
+      -
+        name: Set up CMake
+        uses: jwlawson/actions-setup-cmake@v1.5
+        with:
+          cmake-version: 3.12.x
       -
         name: Environment Variables
         run: |
@@ -88,6 +98,8 @@ jobs:
           sudo apt -y update
           sudo apt install -y mpich \
                               libmpich-dev \
+                              libnetcdf-dev \
+                              libpnetcdf-dev \
                               libhdf5-serial-dev \
                               libhdf5-mpich-dev \
                               libeigen3-dev
diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
new file mode 100755
index 0000000000..c5cdc5c408
--- /dev/null
+++ b/tools/ci/gha-install-libmesh.sh
@@ -0,0 +1,24 @@
+
+#!/bin/bash
+set -ex
+
+CURRENT_DIR=$(pwd)
+
+# libMESH install
+# cd $HOME
+# mkdir LIBMESH && cd LIBMESH
+# git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
+# mkdir build && cd build
+# ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
+# make -j4 install
+# export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
+# rm -rf $HOME/LIBMESH/build
+
+cd $HOME
+git clone https://github.com/idaholab/moose MOOSE
+cd MOOSE
+
+./scripts/update_and_rebuild_petsc.sh CXX=mpicxx CC=mpicc FC=mpifort
+./scripts/update_and_rebuild_libmesh.sh CXX=mpicxx CC=mpicc FC=mpifort
+
+cd $CURRENT_DIR
diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index 3dac29407c..d3fdfe2efd 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -51,7 +51,8 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
 
     if libmesh:
         cmake_cmd.append('-Dlibmesh=ON')
-        cmake_cmd.append('-DCMAKE_PREFIX_PATH=/home/travis/LIBMESH')
+        libmesh_path = os.environ.get('HOME') + '/MOOSE/libmesh/installed'
+        cmake_cmd.append('-DCMAKE_PREFIX_PATH=' + libmesh_path)
 
     # Build in coverage mode for coverage testing
     cmake_cmd.append('-Dcoverage=on')
diff --git a/tools/ci/gha-install.sh b/tools/ci/gha-install.sh
index 88e6ab03d6..3fb66ca764 100755
--- a/tools/ci/gha-install.sh
+++ b/tools/ci/gha-install.sh
@@ -21,7 +21,7 @@ fi
 
 # Install libMesh if needed
 if [[ $LIBMESH = 'y' ]]; then
-    ./tools/ci/travis-install-libmesh.sh
+    ./tools/ci/gha-install-libmesh.sh
 fi
 
 # Install mpi4py for MPI configurations
diff --git a/tools/ci/travis-install-libmesh.sh b/tools/ci/travis-install-libmesh.sh
deleted file mode 100755
index ae3e234992..0000000000
--- a/tools/ci/travis-install-libmesh.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-
-#!/bin/bash
-set -ex
-
-CURRENT_DIR=$(pwd)
-
-# libMESH install
-cd $HOME
-mkdir LIBMESH && cd LIBMESH
-git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
-mkdir build && cd build
-../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
-make -j4 install
-export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
-rm -rf $HOME/LIBMESH/build
-
-cd $CURRENT_DIR

From 9eb53a1af968004a834cd85cef05e021eee6cb93 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 5 Jan 2021 22:20:19 -0600
Subject: [PATCH 095/139] Apply suggestions from code review

Co-authored-by: Paul Romano <paul.k.romano@gmail.com>
---
 include/openmc/mesh.h | 4 +---
 openmc/mesh.py        | 4 ++--
 src/initialize.cpp    | 3 +--
 src/mesh.cpp          | 8 ++++----
 src/state_point.cpp   | 4 ++--
 5 files changed, 10 insertions(+), 13 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 99b43637aa..5bdb5353fc 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -340,9 +340,8 @@ public:
 #ifdef DAGMC
 
 class MOABMesh : public UnstructuredMesh {
-
 public:
-
+  // Constructors
   MOABMesh() = default;
   MOABMesh(pugi::xml_node);
   MOABMesh(const std::string& filename);
@@ -488,7 +487,6 @@ private:
 #ifdef LIBMESH
 
 class LibMesh : public UnstructuredMesh {
-
 public:
   // Constructors
   LibMesh(pugi::xml_node node);
diff --git a/openmc/mesh.py b/openmc/mesh.py
index 54eba9a9db..459b48a694 100644
--- a/openmc/mesh.py
+++ b/openmc/mesh.py
@@ -652,7 +652,7 @@ class UnstructuredMesh(MeshBase):
 
     @library.setter
     def library(self, lib):
-        cv.check_value('mesh_lib', lib, ('moab', 'libmesh'))
+        cv.check_value('Unstructured mesh library', lib, ('moab', 'libmesh'))
         self._library = lib
 
     @property
@@ -706,7 +706,7 @@ class UnstructuredMesh(MeshBase):
     def __repr__(self):
         string = super().__repr__()
         string += '{: <16}=\t{}\n'.format('\tFilename', self.filename)
-        string += '{0: <16}=\t{}\n'.format('\tMesh Library', self.mesh_lib)
+        string += '{: <16}=\t{}\n'.format('\tMesh Library', self.mesh_lib)
         return string
 
     def write_data_to_vtk(self, filename, datasets, volume_normalization=True):
diff --git a/src/initialize.cpp b/src/initialize.cpp
index 5e4c86fd13..c3c7411ab9 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -69,8 +69,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 
 // initialize libMesh if it hasn't been initialized already
 // (if initialized externally, the LMI object needs to be provided also)
-if (!settings::LMI && !libMesh::initialized())
-{
+if (!settings::LMI && !libMesh::initialized()) {
 #ifdef OPENMC_MPI
   // pass command line args, empty MPI communicator, and number of threads.
   // Because libMesh was not initialized, we assume that OpenMC is the primary
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 3a2f768618..4cc7efc1cf 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -1780,7 +1780,7 @@ double MOABMesh::volume(int bin) const
 
 std::string MOABMesh::library() const
 {
-   return "moab";
+  return "moab";
 }
 
 double MOABMesh::tet_volume(moab::EntityHandle tet) const
@@ -2138,7 +2138,7 @@ void LibMesh::initialize()
   }
 
   // create an equation system for storing values
-  eq_system_name_ = "mesh_" + std::to_string(id_) + "_system";
+  eq_system_name_ = fmt::format("mesh_{}_system", id_);
 
   equation_systems_ = std::make_unique<libMesh::EquationSystems>(*m_);
   libMesh::ExplicitSystem& eq_sys =
@@ -2167,7 +2167,7 @@ void LibMesh::initialize()
 Position
 LibMesh::centroid(int bin) const
 {
-  auto& elem = this->get_element_from_bin(bin);
+  const auto& elem = this->get_element_from_bin(bin);
   auto centroid = elem.centroid();
   return {centroid(0), centroid(1), centroid(2)};
 }
@@ -2302,7 +2302,7 @@ LibMesh::get_bin(Position r) const
 int
 LibMesh::get_bin_from_element(const libMesh::Elem* elem) const
 {
-  int bin =  elem->id() - first_element_->id();
+  int bin = elem->id() - first_element_->id();
   if (bin >= n_bins() || bin < 0) {
     fatal_error(fmt::format("Invalid bin: {}", bin));
   }
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 263fd0ae8c..33c682a1e9 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -55,8 +55,8 @@ openmc_statepoint_write(const char* filename, bool* write_source)
   write_message("Creating state point " + filename_ + "...", 5);
 
 #if defined(LIBMESH) || defined(DAGMC)
-      // write unstructured mesh tallies to VTK
-      write_unstructured_mesh_results();
+  // write unstructured mesh tallies to VTK
+  write_unstructured_mesh_results();
 #endif
 
   hid_t file_id;

From 6c7c0a6816cf8940402810255440b7c99b0141d1 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 5 Jan 2021 22:23:19 -0600
Subject: [PATCH 096/139] Addressing initial review comments from @paulromano.

---
 include/openmc/dagmc.h      |  2 +-
 include/openmc/mesh.h       | 24 ++++++++------
 openmc/lib/__init__.py      |  4 +--
 src/dagmc.cpp               |  4 +--
 src/finalize.cpp            |  2 +-
 src/initialize.cpp          |  9 ++---
 src/mesh.cpp                | 66 ++++++++++++++++++++++++-------------
 src/state_point.cpp         |  6 ++--
 src/tallies/filter_mesh.cpp |  2 --
 9 files changed, 72 insertions(+), 47 deletions(-)

diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h
index 859a229f0a..9f409d8730 100644
--- a/include/openmc/dagmc.h
+++ b/include/openmc/dagmc.h
@@ -3,7 +3,7 @@
 #define OPENMC_DAGMC_H
 
 namespace openmc {
-extern "C" const bool dagmc_enabled;
+extern "C" const bool DAGMC_ENABLED;
 }
 
 #ifdef DAGMC
diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 5bdb5353fc..e57f09e857 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -40,7 +40,7 @@ namespace openmc {
 // Global variables
 //==============================================================================
 
-extern "C" const bool libmesh_enabled;
+extern "C" const bool LIBMESH_ENABLED;
 
 class Mesh;
 
@@ -54,7 +54,7 @@ extern std::vector<std::unique_ptr<Mesh>> meshes;
 #ifdef LIBMESH
 namespace settings {
 // used when creating new libMesh::Mesh instances
-extern std::unique_ptr<libMesh::LibMeshInit> LMI;
+extern std::unique_ptr<libMesh::LibMeshInit> libmesh_init;
 }
 #endif
 
@@ -95,6 +95,9 @@ public:
   //! Get the number of mesh cell surfaces.
   virtual int n_surface_bins() const = 0;
 
+  //! Set the mesh ID
+  void set_id(int32_t id=-1);
+
   //! Write mesh data to an HDF5 group
   //
   //! \param[in] group HDF5 group
@@ -285,12 +288,6 @@ public:
   UnstructuredMesh(const std::string& filename);
 
   // Methods
-private:
-
-  //! Setup method for the mesh. Builds data structures,
-  //! sets up element mapping, creates bounding boxes, etc.
-  virtual void initialize() = 0;
-
 public:
 
   //! Add a variable to the mesh instance
@@ -332,6 +329,13 @@ public:
 
   void to_hdf5(hid_t group) const override;
 
+private:
+
+  //! Setup method for the mesh. Builds data structures,
+  //! sets up element mapping, creates bounding boxes, etc.
+  virtual void initialize() = 0;
+
+public:
   // Data members
   bool output_ {true}; //!< Write tallies onto the unstructured mesh at the end of a run
   std::string filename_; //!< Path to unstructured mesh file
@@ -534,10 +538,10 @@ private:
 
   // Data members
   std::unique_ptr<libMesh::Mesh> m_; //!< pointer to the libMesh mesh instance
-  std::vector<std::unique_ptr<libMesh::PointLocatorBase>> PL_; //!< per-thread point locators
+  std::vector<std::unique_ptr<libMesh::PointLocatorBase>> pl_; //!< per-thread point locators
   std::unique_ptr<libMesh::EquationSystems> equation_systems_; //!< pointer to the equation systems of the mesh
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
-  std::map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
+  std::unordered_map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
   libMesh::BoundingBox bbox_; //!< bounding box of the mesh
   libMesh::Elem* first_element_; //!< pointer to the first element in the mesh (maybe should be a key?)
 };
diff --git a/openmc/lib/__init__.py b/openmc/lib/__init__.py
index 9a72403ff4..82dc92ba48 100644
--- a/openmc/lib/__init__.py
+++ b/openmc/lib/__init__.py
@@ -40,13 +40,13 @@ else:
 
 
 def _dagmc_enabled():
-    return c_bool.in_dll(_dll, "dagmc_enabled").value
+    return c_bool.in_dll(_dll, "DAGMC_ENABLED").value
 
 def _coord_levels():
     return c_int.in_dll(_dll, "n_coord_levels").value
 
 def _libmesh_enabled():
-    return c_bool.in_dll(_dll, "libmesh_enabled").value
+    return c_bool.in_dll(_dll, "LIBMESH_ENABLED").value
 
 from .error import *
 from .core import *
diff --git a/src/dagmc.cpp b/src/dagmc.cpp
index 58d83f54c9..5939f3f400 100644
--- a/src/dagmc.cpp
+++ b/src/dagmc.cpp
@@ -26,9 +26,9 @@
 namespace openmc {
 
 #ifdef DAGMC
-const bool dagmc_enabled = true;
+const bool DAGMC_ENABLED = true;
 #else
-const bool dagmc_enabled = false;
+const bool DAGMC_ENABLED = false;
 #endif
 
 }
diff --git a/src/finalize.cpp b/src/finalize.cpp
index 3afd9e1861..45dd0fc7cc 100644
--- a/src/finalize.cpp
+++ b/src/finalize.cpp
@@ -138,7 +138,7 @@ int openmc_finalize()
   free_memory();
 
 #ifdef LIBMESH
-  settings::LMI.reset();
+  settings::libmesh_init.reset();
 #endif
 
   // Free all MPI types
diff --git a/src/initialize.cpp b/src/initialize.cpp
index c3c7411ab9..c480392cba 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -68,16 +68,17 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 #endif
 
 // initialize libMesh if it hasn't been initialized already
-// (if initialized externally, the LMI object needs to be provided also)
-if (!settings::LMI && !libMesh::initialized()) {
+// (if initialized externally, the libmesh_init object needs to be provided also)
+if (!settings::libmesh_init && !libMesh::initialized())
+{
 #ifdef OPENMC_MPI
   // pass command line args, empty MPI communicator, and number of threads.
   // Because libMesh was not initialized, we assume that OpenMC is the primary
   // application and that its main MPI comm should be used.
-  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, comm, n_threads);
+  settings::libmesh_init = std::make_unique<libMesh::LibMeshInit>(argc, argv, comm, n_threads);
 #else
   // pass command line args, empty MPI communicator, and number of threads
-  settings::LMI = std::make_unique<libMesh::LibMeshInit>(argc, argv, 0, n_threads);
+  settings::libmesh_init = std::make_unique<libMesh::LibMeshInit>(argc, argv, 0, n_threads);
 #endif
 }
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 4cc7efc1cf..06f8701519 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -40,9 +40,9 @@ namespace openmc {
 //==============================================================================
 
 #ifdef LIBMESH
-const bool libmesh_enabled = true;
+const bool LIBMESH_ENABLED = true;
 #else
-const bool libmesh_enabled = false;
+const bool LIBMESH_ENABLED = false;
 #endif
 
 
@@ -55,7 +55,7 @@ std::vector<std::unique_ptr<Mesh>> meshes;
 
 #ifdef LIBMESH
 namespace settings {
-std::unique_ptr<libMesh::LibMeshInit> LMI;
+std::unique_ptr<libMesh::LibMeshInit> libmesh_init;
 }
 #endif
 
@@ -103,6 +103,35 @@ Mesh::Mesh(pugi::xml_node node)
   }
 }
 
+void
+Mesh::set_id(int32_t id) {
+  Expects(id >=0 || id == C_NONE);
+
+  // Clear entry in mesh map in case one was already assigned
+  if (id_ != C_NONE) {
+    model::mesh_map.erase(id_);
+    id_ = C_NONE;
+  }
+
+  // Ensure no other mesh has the same ID
+  if (model::mesh_map.find(id) != model::mesh_map.end()) {
+    throw std::runtime_error{fmt::format("Two meshes have the same ID: {}", id)};
+  }
+
+  // If no ID is specified, auto-assign the next ID in the sequence
+  if (id == C_NONE) {
+    id = 0;
+    for (const auto& m : model::meshes) {
+      id = std::max(id, m->id_);
+    }
+    ++id;
+  }
+
+  // Update ID and entry in the mesh map
+  id_ = id;
+  model::mesh_map[id] = model::meshes.size() - 1;
+}
+
 //==============================================================================
 // Structured Mesh implementation
 //==============================================================================
@@ -1388,12 +1417,9 @@ extern "C" int openmc_add_unstructured_mesh(const char filename[],
     return OPENMC_E_INVALID_ARGUMENT;
   }
 
-  int mesh_id = 0;
-  for (const auto& m : model::meshes) { mesh_id = std::max(m->id_, mesh_id); }
-  mesh_id += 1;
-
-  model::meshes.back()->id_ = mesh_id + 1;
-  *id = mesh_id;
+  // auto-assign new ID
+  model::meshes.back()->set_id(-1);
+  *id = model::meshes.back()->id_;
 
   return 0;
 }
@@ -2124,11 +2150,11 @@ LibMesh::LibMesh(const std::string& filename)
 
 void LibMesh::initialize()
 {
-  if (!settings::LMI) {
+  if (!settings::libmesh_init) {
     fatal_error("Attempting to use an unstructured mesh without a libMesh::LibMeshInit object.");
   }
 
-  m_ = std::make_unique<libMesh::Mesh>(settings::LMI->comm(), n_dimension_);
+  m_ = std::make_unique<libMesh::Mesh>(settings::libmesh_init->comm(), n_dimension_);
   m_->read(filename_);
   m_->prepare_for_use();
 
@@ -2152,9 +2178,9 @@ void LibMesh::initialize()
   #endif
 
   for (int i = 0; i < n_threads; i++) {
-      PL_.emplace_back(m_->sub_point_locator());
-      PL_.back()->set_contains_point_tol(FP_COINCIDENT);
-      PL_.back()->enable_out_of_mesh_mode();
+      pl_.emplace_back(m_->sub_point_locator());
+      pl_.back()->set_contains_point_tol(FP_COINCIDENT);
+      pl_.back()->enable_out_of_mesh_mode();
   }
 
   // store first element in the mesh to use as an offset for bin indices
@@ -2188,7 +2214,7 @@ int LibMesh::n_surface_bins() const
     // if this is a boundary element, sure to count boundary faces
     // twice
     for (int j = 0; j < e.n_sides(); j++) {
-      if (e.neighbor_ptr(j) == NULL) { n_bins++; }
+      if (e.neighbor_ptr(j) == nullptr) { n_bins++; }
     }
   }
   return n_bins;
@@ -2289,14 +2315,10 @@ LibMesh::get_bin(Position r) const
   int thread_num = 0;
   #endif
 
-  const auto& point_locator = PL_.at(thread_num);
+  const auto& point_locator = pl_.at(thread_num);
 
-  auto e = (*point_locator)(p);
-  if (!e) {
-    return -1;
-  } else {
-    return get_bin_from_element(e);
-  }
+  const auto elem_ptr = (*point_locator)(p);
+  return elem_ptr ? get_bin_from_element(elem_ptr) : -1;
 }
 
 int
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 33c682a1e9..8e1c6e55d9 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -806,11 +806,11 @@ void write_unstructured_mesh_results() {
       int n_realizations = tally->n_realizations_;
 
       for (int i_score = 0; i_score < tally->scores_.size(); i_score++) {
-        for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) {
+        for (auto nuc_idx : tally->nuclides_) {
           // generate a name for the value
           std::string nuclide_name = "total"; // start with total by default
-          if (tally->nuclides_[i_nuc] > -1) {
-            nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_;
+          if (nuc_idx > -1) {
+            nuclide_name = data::nuclides[nuc_idx]->name_;
           }
 
           std::string score_name = tally->score_name(i_score);
diff --git a/src/tallies/filter_mesh.cpp b/src/tallies/filter_mesh.cpp
index eb9275bd44..7897173868 100644
--- a/src/tallies/filter_mesh.cpp
+++ b/src/tallies/filter_mesh.cpp
@@ -43,8 +43,6 @@ const
   } else {
     model::meshes[mesh_]->bins_crossed(p, match.bins_, match.weights_);
   }
-
-  double total = std::accumulate(match.weights_.begin(), match.weights_.end(), 0.0);
 }
 
 void

From e9a13d67abcbcf06db52866296dcda5f1d3be40b Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 6 Jan 2021 10:25:56 -0600
Subject: [PATCH 097/139] Updating unstructured mesh examples to include the
 library argument in Python classes.

---
 .../jupyter/unstructured-mesh-part-i.ipynb    | 62 ++++++++---------
 .../jupyter/unstructured-mesh-part-ii.ipynb   | 67 ++++++++-----------
 2 files changed, 61 insertions(+), 68 deletions(-)

diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb
index f307469de2..10f0155537 100644
--- a/examples/jupyter/unstructured-mesh-part-i.ipynb
+++ b/examples/jupyter/unstructured-mesh-part-i.ipynb
@@ -136,7 +136,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fe9650cd320>"
+       "<matplotlib.image.AxesImage at 0x7fdb2a1bd860>"
       ]
      },
      "execution_count": 6,
@@ -145,7 +145,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 2160x720 with 1 Axes>"
       ]
@@ -175,7 +175,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fe967252e48>"
+       "<matplotlib.image.AxesImage at 0x7fdb2a2d0c50>"
       ]
      },
      "execution_count": 7,
@@ -184,7 +184,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 2160x720 with 1 Axes>"
       ]
@@ -246,10 +246,11 @@
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
       "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
-      "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
-      "           Version | 0.12.0-dev\n",
-      "          Git SHA1 | e7eceff2aa3a9bbfd4dd553f585d1a31b051ddb3\n",
-      "         Date/Time | 2020-03-27 11:18:32\n",
+      "           License | https://docs.openmc.org/en/latest/license.html\n",
+      "           Version | 0.12.1-dev\n",
+      "          Git SHA1 | 8ae407c90e927af62bfdc8f150e96bfd5d7b2ec2\n",
+      "         Date/Time | 2021-01-06 08:55:22\n",
+      "     MPI Processes | 1\n",
       "    OpenMP Threads | 2\n",
       "\n",
       " Reading settings XML file...\n",
@@ -269,11 +270,11 @@
       " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
       " Reading B11 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n",
       " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n",
-      " Maximum neutron transport energy: 20000000.000000 eV for U235\n",
-      " Minimum neutron data temperature: 294.000000 K\n",
-      " Maximum neutron data temperature: 294.000000 K\n",
+      " Minimum neutron data temperature: 294.0 K\n",
+      " Maximum neutron data temperature: 294.0 K\n",
       " Preparing distributed cell instances...\n",
       " Writing summary.h5 file...\n",
+      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
       " Initializing source particles...\n",
       "\n",
       " ====================>     K EIGENVALUE SIMULATION     <====================\n",
@@ -294,20 +295,21 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 6.8458e-01 seconds\n",
-      "   Reading cross sections          = 6.7039e-01 seconds\n",
-      " Total time in simulation          = 1.9462e-02 seconds\n",
-      "   Time in transport only          = 1.7538e-02 seconds\n",
-      "   Time in inactive batches        = 9.2816e-03 seconds\n",
-      "   Time in active batches          = 1.0181e-02 seconds\n",
-      "   Time synchronizing fission bank = 5.2594e-05 seconds\n",
-      "     Sampling source sites         = 4.6704e-05 seconds\n",
-      "     SEND/RECV source sites        = 3.1580e-06 seconds\n",
-      "   Time accumulating tallies       = 2.0290e-06 seconds\n",
-      " Total time for finalization       = 1.0320e-06 seconds\n",
-      " Total time elapsed                = 7.0439e-01 seconds\n",
-      " Calculation Rate (inactive)       = 53869.9 particles/second\n",
-      " Calculation Rate (active)         = 49113.3 particles/second\n",
+      " Total time for initialization     = 8.7173e-01 seconds\n",
+      "   Reading cross sections          = 8.4809e-01 seconds\n",
+      " Total time in simulation          = 1.8461e-02 seconds\n",
+      "   Time in transport only          = 1.6362e-02 seconds\n",
+      "   Time in inactive batches        = 8.5893e-03 seconds\n",
+      "   Time in active batches          = 9.8714e-03 seconds\n",
+      "   Time synchronizing fission bank = 8.9632e-05 seconds\n",
+      "     Sampling source sites         = 4.7833e-05 seconds\n",
+      "     SEND/RECV source sites        = 9.6690e-06 seconds\n",
+      "   Time accumulating tallies       = 1.2050e-06 seconds\n",
+      "   Time writing statepoints        = 1.8772e-03 seconds\n",
+      " Total time for finalization       = 1.0670e-06 seconds\n",
+      " Total time elapsed                = 8.9499e-01 seconds\n",
+      " Calculation Rate (inactive)       = 58212.0 particles/second\n",
+      " Calculation Rate (active)         = 50651.2 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -322,7 +324,7 @@
     {
      "data": {
       "text/plain": [
-       "0.20706788967181938+/-0.019653037754288005"
+       "PosixPath('/home/shriwise/opt/openmc/openmc/examples/jupyter/statepoint.10.h5')"
       ]
      },
      "execution_count": 8,
@@ -382,7 +384,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 2160x720 with 1 Axes>"
       ]
@@ -437,7 +439,7 @@
    "outputs": [],
    "source": [
     "download(pin_mesh_url, \"pins1-4.h5m\")\n",
-    "umesh = openmc.UnstructuredMesh(\"pins1-4.h5m\")\n",
+    "umesh = openmc.UnstructuredMesh(\"pins1-4.h5m\", library='moab')\n",
     "mesh_filter = openmc.MeshFilter(umesh)"
    ]
   },
@@ -478,7 +480,7 @@
     {
      "data": {
       "text/plain": [
-       "0.2311872124292096+/-0.00017018932313754055"
+       "PosixPath('/home/shriwise/opt/openmc/openmc/examples/jupyter/statepoint.100.h5')"
       ]
      },
      "execution_count": 13,
@@ -696,7 +698,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb
index 92089cabc1..478417970e 100644
--- a/examples/jupyter/unstructured-mesh-part-ii.ipynb
+++ b/examples/jupyter/unstructured-mesh-part-ii.ipynb
@@ -244,20 +244,24 @@
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
       "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
-      "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
-      "           Version | 0.12.0-dev\n",
-      "          Git SHA1 | c9cbdb7c70b202e847c7169f9e5602f110a43853\n",
-      "         Date/Time | 2020-04-24 09:25:02\n",
-      "    OpenMP Threads | 8\n",
+      "           License | https://docs.openmc.org/en/latest/license.html\n",
+      "           Version | 0.12.1-dev\n",
+      "          Git SHA1 | 8ae407c90e927af62bfdc8f150e96bfd5d7b2ec2\n",
+      "         Date/Time | 2021-01-06 09:17:05\n",
+      "     MPI Processes | 1\n",
+      "    OpenMP Threads | 2\n",
       "\n",
       " Reading settings XML file...\n",
       " Reading cross sections XML file...\n",
       " Reading materials XML file...\n",
       " Reading DAGMC geometry...\n",
+      "Set overlap thickness = 0\n",
+      "Set numerical precision = 0.001\n",
       "Loading file dagmc.h5m\n",
       "Initializing the GeomQueryTool...\n",
       "Using faceting tolerance: 0.001\n",
-      "Building OBB Tree...\n",
+      "Building acceleration data structures...\n",
+      "Implicit Complement assumed to be Vacuum\n",
       " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n",
       " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n",
       " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
@@ -291,12 +295,11 @@
       " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n",
       " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n",
       " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n",
-      " Maximum neutron transport energy: 20000000.000000 eV for N15\n",
-      " Minimum neutron data temperature: 294.000000 K\n",
-      " Maximum neutron data temperature: 294.000000 K\n",
-      " Reading tallies XML file...\n",
+      " Minimum neutron data temperature: 294.0 K\n",
+      " Maximum neutron data temperature: 294.0 K\n",
       " Preparing distributed cell instances...\n",
       " Writing summary.h5 file...\n",
+      " Maximum neutron transport energy: 20000000.0 eV for N15\n",
       "\n",
       " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
       "\n",
@@ -311,20 +314,19 @@
       " Simulating batch 9\n",
       " Simulating batch 10\n",
       " Creating state point statepoint.10.h5...\n",
-      " WARNING: Skipping unstructured mesh writing for tally 1. More than one filter\n",
-      "          is present on the tally.\n",
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 4.3651e+01 seconds\n",
-      "   Reading cross sections          = 2.1907e+00 seconds\n",
-      " Total time in simulation          = 3.4743e-01 seconds\n",
-      "   Time in transport only          = 2.9555e-01 seconds\n",
-      "   Time in active batches          = 3.4743e-01 seconds\n",
-      "   Time accumulating tallies       = 7.6575e-03 seconds\n",
-      " Total time for finalization       = 2.2273e-01 seconds\n",
-      " Total time elapsed                = 4.4224e+01 seconds\n",
-      " Calculation Rate (active)         = 2878.27 particles/second\n",
+      " Total time for initialization     = 2.4196e+01 seconds\n",
+      "   Reading cross sections          = 2.1428e+00 seconds\n",
+      " Total time in simulation          = 1.9512e-01 seconds\n",
+      "   Time in transport only          = 1.9269e-01 seconds\n",
+      "   Time in active batches          = 1.9512e-01 seconds\n",
+      "   Time accumulating tallies       = 2.0220e-06 seconds\n",
+      "   Time writing statepoints        = 2.2461e-03 seconds\n",
+      " Total time for finalization       = 1.8940e-06 seconds\n",
+      " Total time elapsed                = 2.4401e+01 seconds\n",
+      " Calculation Rate (active)         = 5125.02 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -350,7 +352,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "unstructured_mesh = openmc.UnstructuredMesh(\"manifold.h5m\")\n",
+    "unstructured_mesh = openmc.UnstructuredMesh(\"manifold.h5m\", library='moab')\n",
     "\n",
     "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n",
     "\n",
@@ -399,8 +401,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "manifold_flux.vtk  tally_1.100.vtk  tally_1.200.vtk\r\n",
-      "manifold.vtk\t   tally_1.10.vtk\r\n"
+      "tally_1.200.vtk\r\n"
      ]
     }
    ],
@@ -495,7 +496,7 @@
      "text": [
       "<?xml version='1.0' encoding='utf-8'?>\r\n",
       "<tallies>\r\n",
-      "  <mesh id=\"1\" type=\"unstructured\">\r\n",
+      "  <mesh id=\"1\" library=\"moab\" type=\"unstructured\">\r\n",
       "    <filename>manifold.h5m</filename>\r\n",
       "  </mesh>\r\n",
       "  <filter id=\"1\" type=\"mesh\">\r\n",
@@ -562,16 +563,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/vtk/util/numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
-      "  assert not numpy.issubdtype(z.dtype, complex), \\\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_dict = {'Flux 0 - 1 MeV' : thermal_flux,\n",
     "             'Flux 1 - 5 MeV' : fast_flux,\n",
@@ -596,8 +588,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "manifold_flux.vtk  tally_1.100.vtk  tally_1.200.vtk\r\n",
-      "manifold.vtk\t   tally_1.10.vtk\r\n"
+      "manifold.vtk  tally_1.200.vtk\r\n"
      ]
     }
    ],
@@ -647,7 +638,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

From 33158e78158b1cb5d800350fdd5b8d35e359f926 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 6 Jan 2021 10:48:46 -0600
Subject: [PATCH 098/139] Adding notes on libmesh CMake option to the install
 guide.

---
 docs/source/usersguide/install.rst | 27 ++++++++++++++++++++-------
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/docs/source/usersguide/install.rst b/docs/source/usersguide/install.rst
index 1a038d0842..0776d71508 100644
--- a/docs/source/usersguide/install.rst
+++ b/docs/source/usersguide/install.rst
@@ -230,17 +230,25 @@ Prerequisites
           sudo apt install mpich libmpich-dev
           sudo apt install openmpi-bin libopenmpi-dev
 
+    * git_ version control software for obtaining source code
+
     * DAGMC_ toolkit for simulation using CAD-based geometries
 
       OpenMC supports particle tracking in CAD-based geometries via the Direct
       Accelerated Geometry Monte Carlo (DAGMC) toolkit (`installation
       instructions
-      <https://svalinn.github.io/DAGMC/install/dag_multiple.html>`_). For use in
+      <https://svalinn.github.io/DAGMC/install/openmc.html>`_). For use in
       OpenMC, only the ``MOAB_DIR`` and ``BUILD_TALLY`` variables need to be
-      specified in the CMake configuration step.
+      specified in the CMake configuration step when building DAGMC. This option
+      also allows unstructured mesh tallies on tetrahedral MOAB meshes.
 
-    * git_ version control software for obtaining source code
+    * libMesh_ mesh library framework for numerical simulations of partial
+    differential equations.
 
+      This optional dependency enables support for unstructured mesh tally
+      filters using libMesh meshes. Any 3D element type supported by libMesh
+      can be used, but the implementation is currently restricted
+      to collision estimators.
 
 .. _gcc: https://gcc.gnu.org/
 .. _CMake: https://cmake.org
@@ -248,6 +256,7 @@ Prerequisites
 .. _MPICH: https://www.mpich.org
 .. _HDF5: https://www.hdfgroup.org/solutions/hdf5/
 .. _DAGMC: https://svalinn.github.io/DAGMC/index.html
+.. _libMesh: https://libmesh.github.io/
 
 Obtaining the Source
 --------------------
@@ -318,10 +327,14 @@ openmp
   being used must support OpenMP. (Default: on)
 
 dagmc
-  Enables use of CAD-based DAGMC_ geometries. Please see the note about DAGMC in
-  the optional dependencies list for more information on this feature. The
-  installation directory for DAGMC should also be defined as `DAGMC_ROOT` in the
-  CMake configuration command. (Default: off)
+  Enables use of CAD-based DAGMC_ geometries and MOAB_ unstructured mesh
+  tallies. Please see the note about DAGMC in the optional dependencies list
+  for more information on this feature. The installation directory for DAGMC
+  should also be defined as `DAGMC_ROOT` in the CMake configuration command.
+  (Default: off)
+
+libmesh
+  Enables the use of unstructured mesh tallies with libMesh_. (Default: off)
 
 coverage
   Compile and link code instrumented for coverage analysis. This is typically

From 9fec4b4b3e32e6a160316772e2295543c775ebb6 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 6 Jan 2021 21:50:28 -0600
Subject: [PATCH 099/139] Some improvements to readability and moving to
 removal of all scores from mesh instances in one step.

---
 include/openmc/mesh.h | 17 +++++++------
 src/mesh.cpp          | 59 +++++++++++++++++++++++--------------------
 src/state_point.cpp   | 48 +++++++++++++----------------------
 3 files changed, 59 insertions(+), 65 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index e57f09e857..ab1c1c406e 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -293,8 +293,8 @@ public:
   //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;
 
-  //! Remove variable from the mesh instance
-  virtual void remove_score(const std::string& var_name) = 0;
+  //! Remove tally data from the instance
+  virtual void remove_scores() = 0;
 
   //! Set the value of a bin for a variable on the internal
   //  mesh instance
@@ -304,8 +304,8 @@ public:
 
   //! Write the unstructured mesh to file
   //
-  //! \param[in] filename Name of the file to write
-  virtual void write(std::string filename) const = 0;
+  //! \param[in] filename Base of the file to write
+  virtual void write(const std::string& base_filename) const = 0;
 
   //! Retrieve a centroid for the mesh cell
   //
@@ -369,7 +369,7 @@ public:
   void add_score(const std::string& score) override;
 
   //! Remove a score from the mesh instance
-  void remove_score(const std::string& score) override;
+  void remove_scores() override;
 
   //! Set data for a score
   void set_score_data(const std::string& score,
@@ -377,7 +377,7 @@ public:
                       std::vector<double> std_dev) override;
 
   //! Write the mesh with any current tally data
-  void write(std::string base_filename) const;
+  void write(const std::string& base_filename) const;
 
   Position centroid(int bin) const override;
 
@@ -484,6 +484,7 @@ private:
   std::unique_ptr<moab::Interface> mbi_; //!< MOAB instance
   std::unique_ptr<moab::AdaptiveKDTree> kdtree_; //!< MOAB KDTree instance
   std::vector<moab::Matrix3> baryc_data_; //!< Barycentric data for tetrahedra
+  std::vector<std::string> tag_names_; //!< Names of score tags added to the mesh
 };
 
 #endif
@@ -512,13 +513,13 @@ public:
 
   void add_score(const std::string& var_name) override;
 
-  void remove_score(const std::string& var_name) override;
+  void remove_scores() override;
 
   void set_score_data(const std::string& var_name,
                       std::vector<double> values,
                       std::vector<double> std_dev) override;
 
-  void write(std::string filename) const override;
+  void write(const std::string& base_filename) const override;
 
   Position centroid(int bin) const override;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 06f8701519..3990131343 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -25,6 +25,7 @@
 #include "openmc/message_passing.h"
 #include "openmc/search.h"
 #include "openmc/settings.h"
+#include "openmc/tallies/tally.h"
 #include "openmc/tallies/filter.h"
 #include "openmc/xml_interface.h"
 
@@ -2050,35 +2051,39 @@ void
 MOABMesh::add_score(const std::string& score)
 {
   auto score_tags = get_score_tags(score);
+  tag_names_.push_back(score);
 }
 
-void MOABMesh::remove_score(const std::string& score)
+void MOABMesh::remove_scores()
 {
-  auto value_name = score + "_mean";
-  moab::Tag tag;
-  moab::ErrorCode rval = mbi_->tag_get_handle(value_name.c_str(), tag);
-  if (rval != moab::MB_SUCCESS) return;
+  for (const auto& name : tag_names_) {
+    auto value_name = name + "_mean";
+    moab::Tag tag;
+    moab::ErrorCode rval = mbi_->tag_get_handle(value_name.c_str(), tag);
+    if (rval != moab::MB_SUCCESS) return;
 
-  rval = mbi_->tag_delete(tag);
-  if (rval != moab::MB_SUCCESS) {
-    auto msg = fmt::format("Failed to delete mesh tag for the score {}"
-                           " on unstructured mesh {}", score, id_);
-    fatal_error(msg);
-  }
+    rval = mbi_->tag_delete(tag);
+    if (rval != moab::MB_SUCCESS) {
+      auto msg = fmt::format("Failed to delete mesh tag for the score {}"
+                            " on unstructured mesh {}", name, id_);
+      fatal_error(msg);
+    }
 
-  auto std_dev_name = score + "_std_dev";
-  rval = mbi_->tag_get_handle(std_dev_name.c_str(), tag);
-  if (rval != moab::MB_SUCCESS) {
-    auto msg = fmt::format("Std. Dev. mesh tag does not exist for the score {}"
-                           " on unstructured mesh {}", score, id_);
-  }
+    auto std_dev_name = name + "_std_dev";
+    rval = mbi_->tag_get_handle(std_dev_name.c_str(), tag);
+    if (rval != moab::MB_SUCCESS) {
+      auto msg = fmt::format("Std. Dev. mesh tag does not exist for the score {}"
+                            " on unstructured mesh {}", name, id_);
+    }
 
-  rval = mbi_->tag_delete(tag);
-  if (rval != moab::MB_SUCCESS) {
-    auto msg = fmt::format("Failed to delete mesh tag for the score {}"
-                           " on unstructured mesh {}", score, id_);
-    fatal_error(msg);
+    rval = mbi_->tag_delete(tag);
+    if (rval != moab::MB_SUCCESS) {
+      auto msg = fmt::format("Failed to delete mesh tag for the score {}"
+                            " on unstructured mesh {}", name, id_);
+      fatal_error(msg);
+    }
   }
+  tag_names_.clear();
 }
 
 void
@@ -2115,7 +2120,7 @@ MOABMesh::set_score_data(const std::string& score,
 }
 
 void
-MOABMesh::write(std::string base_filename) const
+MOABMesh::write(const std::string& base_filename) const
 {
   // add extension to the base name
   auto filename = base_filename + ".vtk";
@@ -2241,12 +2246,12 @@ LibMesh::add_score(const std::string& var_name)
 }
 
 void
-LibMesh::remove_score(const std::string& var_name)
+LibMesh::remove_scores()
 {
-  return;
+  equation_systems_->clear();
+  equation_systems_->init();
 }
 
-
 void
 LibMesh::set_score_data(const std::string& var_name,
                         std::vector<double> values,
@@ -2283,7 +2288,7 @@ LibMesh::set_score_data(const std::string& var_name,
   }
 }
 
-void LibMesh::write(std::string filename) const
+void LibMesh::write(const std::string& filename) const
 {
   write_message(fmt::format("Writing file: {}.e for unstructured mesh {}", filename, this->id_));
   libMesh::ExodusII_IO exo(*m_);
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 8e1c6e55d9..890075e928 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -805,52 +805,40 @@ void write_unstructured_mesh_results() {
 
       int n_realizations = tally->n_realizations_;
 
-      for (int i_score = 0; i_score < tally->scores_.size(); i_score++) {
+      for (int score_idx = 0; score_idx < tally->scores_.size(); score_idx++) {
         for (auto nuc_idx : tally->nuclides_) {
-          // generate a name for the value
+          // get the name for this nuclide
           std::string nuclide_name = "total"; // start with total by default
-          if (nuc_idx > -1) {
-            nuclide_name = data::nuclides[nuc_idx]->name_;
-          }
+          if (nuc_idx > -1) nuclide_name = data::nuclides[nuc_idx]->name_;
 
-          std::string score_name = tally->score_name(i_score);
+          // get the name for this score
+          std::string score_name = tally->score_name(score_idx);
+
+          // combine the score and nuclide into a name for the value
           auto score_str = fmt::format("{}_{}", score_name, nuclide_name);
+          // add this score to the mesh
           umesh->add_score(score_str);
-        }
-      }
-
-      // write each score/nuclide combination for this tally
-      for (int i_score = 0; i_score < tally->scores_.size(); i_score++) {
-        for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) {
 
           // index for this nuclide and score
-          int nuc_score_idx = i_score + i_nuc*tally->scores_.size();
+          int nuc_score_idx = score_idx + nuc_idx*tally->scores_.size();
 
           // construct result vectors
           std::vector<double> mean_vec, std_dev_vec;
           for (int j = 0; j < tally->results_.shape()[0]; j++) {
-            // mean
+            // compute the mean
             double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations;
             mean_vec.push_back(mean);
-            // std. dev.
+
+            // compute the standard deviation
             double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ);
+            double std_dev {0.0};
             if (n_realizations > 1) {
-              double std_dev = sum_sq/n_realizations - mean*mean;
+              std_dev = sum_sq/n_realizations - mean*mean;
               std_dev = std::sqrt(std_dev / (n_realizations - 1));
-              std_dev_vec.push_back(std_dev);
-            } else {
-              std_dev_vec.push_back(0.0);
             }
+            std_dev_vec.push_back(std_dev);
           }
 
-          // generate a name for the value
-          std::string nuclide_name = "total"; // start with total by default
-          if (tally->nuclides_[i_nuc] > -1) {
-            nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_;
-          }
-          std::string score_name = tally->score_name(i_score);
-          auto score_str = fmt::format("{}_{}", score_name, nuclide_name);
-          tally_scores.push_back(score_str);
           // set the data for this score
           umesh->set_score_data(score_str, mean_vec, std_dev_vec);
         }
@@ -858,18 +846,18 @@ void write_unstructured_mesh_results() {
 
       // Generate a file name based on the tally id
       // and the current batch number
-      int w = std::to_string(settings::n_max_batches).size();
+      size_t batch_width {std::to_string(settings::n_max_batches).size()};
       std::string filename = fmt::format("tally_{0}.{1:0{2}}",
                                          tally->id_,
                                          simulation::current_batch,
-                                         w);
+                                         batch_width);
 
       if (umesh->library() == "moab" && !mpi::master) continue;
 
       // Write the unstructured mesh and data to file
       umesh->write(filename);
 
-      for (const auto& score : tally_scores) { umesh->remove_score(score); }
+      umesh->remove_scores();
     }
   }
   #endif

From 6d9c2ee92b1960b81f685e4851f23b1093cabb00 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 7 Jan 2021 12:48:28 -0600
Subject: [PATCH 100/139] Corrections to nuclide variable.

---
 src/state_point.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/state_point.cpp b/src/state_point.cpp
index 890075e928..4e4c3849f3 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -806,10 +806,11 @@ void write_unstructured_mesh_results() {
       int n_realizations = tally->n_realizations_;
 
       for (int score_idx = 0; score_idx < tally->scores_.size(); score_idx++) {
-        for (auto nuc_idx : tally->nuclides_) {
+        for (int nuc_idx = 0; nuc_idx < tally->nuclides_.size(); nuc_idx++) {
           // get the name for this nuclide
+          int nuclide = tally->nuclides_[nuc_idx];
           std::string nuclide_name = "total"; // start with total by default
-          if (nuc_idx > -1) nuclide_name = data::nuclides[nuc_idx]->name_;
+          if (nuclide > -1) nuclide_name = data::nuclides[nuclide]->name_;
 
           // get the name for this score
           std::string score_name = tally->score_name(score_idx);

From dddf2613a027e25796e5b96679be887cad3b5b14 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Thu, 7 Jan 2021 17:14:54 -0600
Subject: [PATCH 101/139] Calling clear and init on the equation system instead
 of equation systemS.

---
 src/mesh.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 3990131343..40b8834a2e 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2248,8 +2248,9 @@ LibMesh::add_score(const std::string& var_name)
 void
 LibMesh::remove_scores()
 {
-  equation_systems_->clear();
-  equation_systems_->init();
+  auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
+  eqn_sys.clear();
+  eqn_sys.init();
 }
 
 void

From 02f8804fe3240bfad0f5913a9c7ef8ac7e2faf9c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sat, 9 Jan 2021 08:39:13 -0600
Subject: [PATCH 102/139] Updating umesh example notebook to include libmesh
 route.

---
 .../jupyter/unstructured-mesh-part-i.ipynb    | 218 ++++++++----------
 1 file changed, 98 insertions(+), 120 deletions(-)

diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb
index 10f0155537..636cdc69e9 100644
--- a/examples/jupyter/unstructured-mesh-part-i.ipynb
+++ b/examples/jupyter/unstructured-mesh-part-i.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "Here, we'll apply an unstructured mesh tally to the PWR assembly model from the OpenMC examples.\n",
     "\n",
-    "**_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled._**"
+    "**_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC or libMesh support enabled._**"
    ]
   },
   {
@@ -23,7 +23,7 @@
     "import openmc\n",
     "import openmc.lib\n",
     "\n",
-    "assert(openmc.lib._dagmc_enabled())"
+    "assert(openmc.lib._dagmc_enabled() or openmc.lib._libmesh_enabled())"
    ]
   },
   {
@@ -45,7 +45,8 @@
     "\n",
     "import urllib.request\n",
     "\n",
-    "pin_mesh_url = 'https://tinyurl.com/u9ce9d7' # 1.2 MB\n",
+    "pin_mesh_moab_url = 'https://tinyurl.com/u9ce9d7' # MOAB file - 22 MB\n",
+    "pin_mesh_libmesh_url = 'https://tinyurl.com/yysgs3tr' # Exodus file - 9.7 MB\n",
     "\n",
     "def download(url, filename='dagmc.h5m'):\n",
     "    \"\"\"\n",
@@ -136,7 +137,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb2a1bd860>"
+       "<matplotlib.image.AxesImage at 0x7f85befdd8d0>"
       ]
      },
      "execution_count": 6,
@@ -175,7 +176,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb2a2d0c50>"
+       "<matplotlib.image.AxesImage at 0x7f85bf12ddd8>"
       ]
      },
      "execution_count": 7,
@@ -248,8 +249,8 @@
       "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
       "           License | https://docs.openmc.org/en/latest/license.html\n",
       "           Version | 0.12.1-dev\n",
-      "          Git SHA1 | 8ae407c90e927af62bfdc8f150e96bfd5d7b2ec2\n",
-      "         Date/Time | 2021-01-06 08:55:22\n",
+      "          Git SHA1 | ad587f222432f9ba8d58b1a00bf9eb176585807b\n",
+      "         Date/Time | 2021-01-08 07:54:16\n",
       "     MPI Processes | 1\n",
       "    OpenMP Threads | 2\n",
       "\n",
@@ -295,21 +296,21 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 8.7173e-01 seconds\n",
-      "   Reading cross sections          = 8.4809e-01 seconds\n",
-      " Total time in simulation          = 1.8461e-02 seconds\n",
-      "   Time in transport only          = 1.6362e-02 seconds\n",
-      "   Time in inactive batches        = 8.5893e-03 seconds\n",
-      "   Time in active batches          = 9.8714e-03 seconds\n",
-      "   Time synchronizing fission bank = 8.9632e-05 seconds\n",
-      "     Sampling source sites         = 4.7833e-05 seconds\n",
-      "     SEND/RECV source sites        = 9.6690e-06 seconds\n",
-      "   Time accumulating tallies       = 1.2050e-06 seconds\n",
-      "   Time writing statepoints        = 1.8772e-03 seconds\n",
-      " Total time for finalization       = 1.0670e-06 seconds\n",
-      " Total time elapsed                = 8.9499e-01 seconds\n",
-      " Calculation Rate (inactive)       = 58212.0 particles/second\n",
-      " Calculation Rate (active)         = 50651.2 particles/second\n",
+      " Total time for initialization     = 9.0121e-01 seconds\n",
+      "   Reading cross sections          = 8.8066e-01 seconds\n",
+      " Total time in simulation          = 2.4026e-02 seconds\n",
+      "   Time in transport only          = 2.1537e-02 seconds\n",
+      "   Time in inactive batches        = 1.0994e-02 seconds\n",
+      "   Time in active batches          = 1.3032e-02 seconds\n",
+      "   Time synchronizing fission bank = 1.3601e-04 seconds\n",
+      "     Sampling source sites         = 6.5548e-05 seconds\n",
+      "     SEND/RECV source sites        = 1.2944e-05 seconds\n",
+      "   Time accumulating tallies       = 2.8990e-06 seconds\n",
+      "   Time writing statepoints        = 2.1043e-03 seconds\n",
+      " Total time for finalization       = 9.6200e-07 seconds\n",
+      " Total time elapsed                = 9.3122e-01 seconds\n",
+      " Calculation Rate (inactive)       = 45479.8 particles/second\n",
+      " Calculation Rate (active)         = 38366.9 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -432,14 +433,57 @@
     "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run the simulation using a MOAB mesh, run the following cell:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "download(pin_mesh_url, \"pins1-4.h5m\")\n",
-    "umesh = openmc.UnstructuredMesh(\"pins1-4.h5m\", library='moab')\n",
+    "# ensure that OpenMC was built with the optional DAGMC dependency enabled\n",
+    "assert(openmc.lib._dagmc_enabled())\n",
+    "download(pin_mesh_moab_url, \"pins1-4.h5m\")\n",
+    "umesh = openmc.UnstructuredMesh(\"pins1-4.h5m\", library='moab')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To instead run the simulation using a libMesh mesh, run the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ensure that OpenMC was built with the optional libMesh dependency enabled\n",
+    "assert(openmc.lib._libmesh_enabled())\n",
+    "download(pin_mesh_libmesh_url, \"pins1-4.e\")\n",
+    "umesh = openmc.UnstructuredMesh(\"pins1-4.e\", library='libmesh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regardless of the library used to represent the mesh, we can apply this mesh object in a `MeshFilter`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "mesh_filter = openmc.MeshFilter(umesh)"
    ]
   },
@@ -452,14 +496,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "tally = openmc.Tally()\n",
     "tally.filters = [mesh_filter]\n",
     "tally.scores = ['heating', 'flux']\n",
-    "tally.estimator = 'tracklength'\n",
+    "# Only collision estimators are supported for \n",
+    "if umesh.library == 'libmesh':\n",
+    "    tally.estimator = 'collision'\n",
     "model.tallies = [tally]"
    ]
   },
@@ -474,18 +520,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "PosixPath('/home/shriwise/opt/openmc/openmc/examples/jupyter/statepoint.100.h5')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "CalledProcessError",
+     "evalue": "Command 'openmc' died with <Signals.SIGABRT: 6>.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-a0aec3a123a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minactive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/model/model.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0mlast_statepoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m         \u001b[0mopenmc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0;31m# Get output directory and return the last statepoint written by this run\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/executor.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(particles, threads, geometry_debug, restart_file, tracks, output, cwd, openmc_exec, mpi_args, event_based)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpi_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m     \u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/executor.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(args, output, cwd)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         raise subprocess.CalledProcessError(p.returncode, ' '.join(args),\n\u001b[0;32m---> 29\u001b[0;31m                                             ''.join(lines))\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mCalledProcessError\u001b[0m: Command 'openmc' died with <Signals.SIGABRT: 6>."
+     ]
     }
    ],
    "source": [
@@ -504,9 +554,9 @@
     "  - flux_total_value\n",
     "  - flux_total_std_dev\n",
     "  - heating_total_value\n",
-    "  - heading_total_std_dev\n",
+    "  - heating_total_std_dev\n",
     "  \n",
-    "  Where \"total\" represents  \n",
+    "Where \"total\" represents the nuclide entry in the tally. If a set of nuclides are specified in the tally, a different score will be added to the VTK for each one in addition to \"total\".\n",
     "  \n",
     "\n",
     "Currently, an unstructured VTK file will only be generated for tallies if the unstructured mesh is is the only filter applied to that tally. All results for the unstructured mesh tally are present in the statepoint file regardless of the number of filters applied, however.\n",
@@ -516,17 +566,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tally_1.100.vtk\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!ls *.vtk"
    ]
@@ -540,25 +582,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Image(\"./images/umesh_flux.png\", width=600)"
    ]
@@ -573,25 +599,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Image(\"./images/umesh_heating.png\", width=600)"
    ]
@@ -605,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -623,25 +633,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.43381086e-04 1.48043747e-04 1.60408339e-04 ... 7.04197023e-05\n",
-      " 7.04197023e-05 7.04197023e-05]\n",
-      "[[ 2.88485691 -2.55429784  9.97768184]\n",
-      " [ 2.87565092 -2.60469781  9.8884092 ]\n",
-      " [ 2.85832254 -2.65291228  9.97768184]\n",
-      " ...\n",
-      " [ 1.46082175 -1.15569203 -3.62914358]\n",
-      " [ 1.4443143  -1.1321793  -3.65475081]\n",
-      " [ 1.46884412 -1.15657736 -3.68206543]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(umesh.volumes)\n",
     "print(umesh.centroids)"
@@ -658,25 +652,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Image(\"./images/umesh_w_assembly.png\", width=600)"
    ]

From 7b6bf93afd294f66dd21ffdf3c07eb35e46c6dab Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sat, 9 Jan 2021 18:37:37 -0600
Subject: [PATCH 103/139] Adding function for retrieving the name of a nuclide
 from a tally.

---
 include/openmc/tallies/tally.h |  3 +++
 src/tallies/tally.cpp          | 12 ++++++++++++
 2 files changed, 15 insertions(+)

diff --git a/include/openmc/tallies/tally.h b/include/openmc/tallies/tally.h
index a87946bf79..1de372f3c5 100644
--- a/include/openmc/tallies/tally.h
+++ b/include/openmc/tallies/tally.h
@@ -75,6 +75,9 @@ public:
   //! A string representing the i-th score on this tally
   std::string score_name(int score_idx) const;
 
+  //! A string representing the i-th nuclide on this tally
+  std::string nuclide_name(int nuclide_idx) const;
+
   //----------------------------------------------------------------------------
   // Major public data members.
 
diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp
index 6a35835c2a..b3a259f79a 100644
--- a/src/tallies/tally.cpp
+++ b/src/tallies/tally.cpp
@@ -677,6 +677,18 @@ Tally::score_name(int score_idx) const {
   return reaction_name(scores_[score_idx]);
 }
 
+std::string
+Tally::nuclide_name(int nuclide_idx) const {
+  if (nuclide_idx != -1) {
+    return "total";
+  } else if (nuclide_idx < 0 || nuclide_idx >= nuclides_.size()) {
+    fatal_error("Index in nuclides array is out of bounds");
+  }
+
+  int nuclide = nuclides_.at(nuclide_idx);
+  return data::nuclides.at(nuclide)->name_;
+}
+
 //==============================================================================
 // Non-member functions
 //==============================================================================

From 7a2f1d6d0a926c919e52238ff4bd1b8ad1d9cf0d Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sat, 9 Jan 2021 18:38:30 -0600
Subject: [PATCH 104/139] Updating unstructured mesh writing to use the
 nuclide_score tally method and putting variable addition back into its own
 loop.

---
 include/openmc/mesh.h | 12 ++++++------
 src/mesh.cpp          | 23 +++++++----------------
 src/state_point.cpp   | 33 +++++++++++++++++++--------------
 3 files changed, 32 insertions(+), 36 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index ab1c1c406e..6bc0843861 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -299,8 +299,8 @@ public:
   //! Set the value of a bin for a variable on the internal
   //  mesh instance
   virtual void set_score_data(const std::string& var_name,
-                              std::vector<double> values,
-                              std::vector<double> std_dev) = 0;
+                              const std::vector<double>& values,
+                              const std::vector<double>& std_dev) = 0;
 
   //! Write the unstructured mesh to file
   //
@@ -373,8 +373,8 @@ public:
 
   //! Set data for a score
   void set_score_data(const std::string& score,
-                      std::vector<double> values,
-                      std::vector<double> std_dev) override;
+                      const std::vector<double>& values,
+                      const std::vector<double>& std_dev) override;
 
   //! Write the mesh with any current tally data
   void write(const std::string& base_filename) const;
@@ -516,8 +516,8 @@ public:
   void remove_scores() override;
 
   void set_score_data(const std::string& var_name,
-                      std::vector<double> values,
-                      std::vector<double> std_dev) override;
+                      const std::vector<double>& values,
+                      const std::vector<double>& std_dev) override;
 
   void write(const std::string& base_filename) const override;
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 40b8834a2e..5e035b20e6 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2088,19 +2088,11 @@ void MOABMesh::remove_scores()
 
 void
 MOABMesh::set_score_data(const std::string& score,
-                         std::vector<double> values,
-                         std::vector<double> std_dev)
+                         const std::vector<double>& values,
+                         const std::vector<double>& std_dev)
 {
   auto score_tags = this->get_score_tags(score);
 
-  // normalize tally values by element volume
-  for (int i = 0; i < ehs_.size(); i++) {
-    auto eh = this->get_ent_handle_from_bin(i);
-    double volume = this->tet_volume(eh);
-    values[i] /= volume;
-    std_dev[i] /= volume;
-  }
-
   moab::ErrorCode rval;
   // set the score value
   rval = mbi_->tag_set_data(score_tags.first, ehs_, values.data());
@@ -2250,13 +2242,13 @@ LibMesh::remove_scores()
 {
   auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
   eqn_sys.clear();
-  eqn_sys.init();
+  variable_map_.clear();
 }
 
 void
 LibMesh::set_score_data(const std::string& var_name,
-                        std::vector<double> values,
-                        std::vector<double> std_dev)
+                        const std::vector<double>& values,
+                        const std::vector<double>& std_dev)
 {
   auto& eqn_sys = equation_systems_->get_system(eq_system_name_);
 
@@ -2278,14 +2270,13 @@ LibMesh::set_score_data(const std::string& var_name,
     std::vector<libMesh::dof_id_type> value_dof_indices;
     dof_map.dof_indices(e, value_dof_indices, value_num);
     Ensures(value_dof_indices.size() == 1);
-    eqn_sys.solution->set(value_dof_indices[0], values[bin]);
+    eqn_sys.solution->set(value_dof_indices[0], values.at(bin));
 
     // set std dev
     std::vector<libMesh::dof_id_type> std_dev_dof_indices;
     dof_map.dof_indices(e, std_dev_dof_indices, std_dev_num);
     Ensures(std_dev_dof_indices.size() == 1);
-    eqn_sys.solution->set(std_dev_dof_indices[0], std_dev[bin]);
-
+    eqn_sys.solution->set(std_dev_dof_indices[0], std_dev.at(bin));
   }
 }
 
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 4e4c3849f3..80349c9f72 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -770,8 +770,6 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
   H5Tclose(banktype);
 }
 
-
-
 void write_unstructured_mesh_results() {
 
 #if !defined(LIBMESH) && !defined(DAGMC)
@@ -807,18 +805,24 @@ void write_unstructured_mesh_results() {
 
       for (int score_idx = 0; score_idx < tally->scores_.size(); score_idx++) {
         for (int nuc_idx = 0; nuc_idx < tally->nuclides_.size(); nuc_idx++) {
-          // get the name for this nuclide
-          int nuclide = tally->nuclides_[nuc_idx];
-          std::string nuclide_name = "total"; // start with total by default
-          if (nuclide > -1) nuclide_name = data::nuclides[nuclide]->name_;
-
-          // get the name for this score
-          std::string score_name = tally->score_name(score_idx);
-
           // combine the score and nuclide into a name for the value
-          auto score_str = fmt::format("{}_{}", score_name, nuclide_name);
+          auto score_str = fmt::format("{}_{}",
+                           tally->score_name(score_idx),
+                           tally->nuclide_name(nuc_idx));
           // add this score to the mesh
+          // (this is in a separate loop because all variables need to be added
+          //  to libMesh's equation system before any are initialized, which
+          //  happens in set_score_data)
           umesh->add_score(score_str);
+        }
+      }
+
+      for (int score_idx = 0; score_idx < tally->scores_.size(); score_idx++) {
+        for (int nuc_idx = 0; nuc_idx < tally->nuclides_.size(); nuc_idx++) {
+          // combine the score and nuclide into a name for the value
+          auto score_str = fmt::format("{}_{}",
+                                       tally->score_name(score_idx),
+                                       tally->nuclide_name(nuc_idx));
 
           // index for this nuclide and score
           int nuc_score_idx = score_idx + nuc_idx*tally->scores_.size();
@@ -826,9 +830,11 @@ void write_unstructured_mesh_results() {
           // construct result vectors
           std::vector<double> mean_vec, std_dev_vec;
           for (int j = 0; j < tally->results_.shape()[0]; j++) {
+            // get the volume for this bin
+            double volume = umesh->volume(j);
             // compute the mean
             double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations;
-            mean_vec.push_back(mean);
+            mean_vec.push_back(mean / volume);
 
             // compute the standard deviation
             double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ);
@@ -837,9 +843,8 @@ void write_unstructured_mesh_results() {
               std_dev = sum_sq/n_realizations - mean*mean;
               std_dev = std::sqrt(std_dev / (n_realizations - 1));
             }
-            std_dev_vec.push_back(std_dev);
+            std_dev_vec.push_back(std_dev / volume);
           }
-
           // set the data for this score
           umesh->set_score_data(score_str, mean_vec, std_dev_vec);
         }

From 2897a8188adc939fea65d7648c04e78eff41fc60 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Sun, 10 Jan 2021 10:44:04 -0600
Subject: [PATCH 105/139] Updating example notebook to include some information
 about using libmesh meshes.

---
 .../jupyter/unstructured-mesh-part-i.ipynb    | 205 +++++++++++-------
 1 file changed, 129 insertions(+), 76 deletions(-)

diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb
index 636cdc69e9..1b035fa845 100644
--- a/examples/jupyter/unstructured-mesh-part-i.ipynb
+++ b/examples/jupyter/unstructured-mesh-part-i.ipynb
@@ -23,6 +23,7 @@
     "import openmc\n",
     "import openmc.lib\n",
     "\n",
+    "# ensure one of the two mesh librares is enabled\n",
     "assert(openmc.lib._dagmc_enabled() or openmc.lib._libmesh_enabled())"
    ]
   },
@@ -137,7 +138,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f85befdd8d0>"
+       "<matplotlib.image.AxesImage at 0x7f5f451a0860>"
       ]
      },
      "execution_count": 6,
@@ -176,7 +177,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f85bf12ddd8>"
+       "<matplotlib.image.AxesImage at 0x7f5f452f0cf8>"
       ]
      },
      "execution_count": 7,
@@ -249,8 +250,8 @@
       "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
       "           License | https://docs.openmc.org/en/latest/license.html\n",
       "           Version | 0.12.1-dev\n",
-      "          Git SHA1 | ad587f222432f9ba8d58b1a00bf9eb176585807b\n",
-      "         Date/Time | 2021-01-08 07:54:16\n",
+      "          Git SHA1 | e62681221a625ce7eb635df966826379fb4e9453\n",
+      "         Date/Time | 2021-01-10 00:51:10\n",
       "     MPI Processes | 1\n",
       "    OpenMP Threads | 2\n",
       "\n",
@@ -296,21 +297,21 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     = 9.0121e-01 seconds\n",
-      "   Reading cross sections          = 8.8066e-01 seconds\n",
-      " Total time in simulation          = 2.4026e-02 seconds\n",
-      "   Time in transport only          = 2.1537e-02 seconds\n",
-      "   Time in inactive batches        = 1.0994e-02 seconds\n",
-      "   Time in active batches          = 1.3032e-02 seconds\n",
-      "   Time synchronizing fission bank = 1.3601e-04 seconds\n",
-      "     Sampling source sites         = 6.5548e-05 seconds\n",
-      "     SEND/RECV source sites        = 1.2944e-05 seconds\n",
-      "   Time accumulating tallies       = 2.8990e-06 seconds\n",
-      "   Time writing statepoints        = 2.1043e-03 seconds\n",
-      " Total time for finalization       = 9.6200e-07 seconds\n",
-      " Total time elapsed                = 9.3122e-01 seconds\n",
-      " Calculation Rate (inactive)       = 45479.8 particles/second\n",
-      " Calculation Rate (active)         = 38366.9 particles/second\n",
+      " Total time for initialization     = 2.1129e+00 seconds\n",
+      "   Reading cross sections          = 2.0943e+00 seconds\n",
+      " Total time in simulation          = 1.1595e-01 seconds\n",
+      "   Time in transport only          = 1.1219e-01 seconds\n",
+      "   Time in inactive batches        = 5.8272e-02 seconds\n",
+      "   Time in active batches          = 5.7678e-02 seconds\n",
+      "   Time synchronizing fission bank = 2.3802e-04 seconds\n",
+      "     Sampling source sites         = 1.3992e-04 seconds\n",
+      "     SEND/RECV source sites        = 2.9613e-05 seconds\n",
+      "   Time accumulating tallies       = 1.4423e-05 seconds\n",
+      "   Time writing statepoints        = 3.0105e-03 seconds\n",
+      " Total time for finalization       = 4.0100e-06 seconds\n",
+      " Total time elapsed                = 2.2442e+00 seconds\n",
+      " Calculation Rate (inactive)       = 8580.51 particles/second\n",
+      " Calculation Rate (active)         = 8668.78 particles/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -430,14 +431,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run the simulation using a MOAB mesh, run the following cell:"
+    "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter. We do this by specifying a mesh file and mesh library on an `UnstructuredMesh` object. The specified mesh library will be used to load the mesh file during simulation initialization. OpenMC must be built with support for the specified mesh library enabled."
    ]
   },
   {
@@ -446,29 +440,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ensure that OpenMC was built with the optional DAGMC dependency enabled\n",
-    "assert(openmc.lib._dagmc_enabled())\n",
-    "download(pin_mesh_moab_url, \"pins1-4.h5m\")\n",
-    "umesh = openmc.UnstructuredMesh(\"pins1-4.h5m\", library='moab')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To instead run the simulation using a libMesh mesh, run the following cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ensure that OpenMC was built with the optional libMesh dependency enabled\n",
-    "assert(openmc.lib._libmesh_enabled())\n",
-    "download(pin_mesh_libmesh_url, \"pins1-4.e\")\n",
-    "umesh = openmc.UnstructuredMesh(\"pins1-4.e\", library='libmesh')"
+    "mesh_library = 'moab' # change to 'libmesh' to use libMesh instead\n",
+    "\n",
+    "if mesh_library == 'moab':\n",
+    "    assert(openmc.lib._dagmc_enabled())\n",
+    "    mesh_file = 'pins1-4.h5m'\n",
+    "    mesh_url = pin_mesh_moab_url\n",
+    "elif mesh_library == 'libmesh':\n",
+    "    assert(openmc.lib._libmesh_enabled())\n",
+    "    mesh_file = 'pins1-4.e'\n",
+    "    mesh_url = pin_mesh_libmesh_url\n",
+    "    \n",
+    "# download the file and create the UnstructuredMesh object\n",
+    "download(mesh_url, mesh_file)\n",
+    "umesh = openmc.UnstructuredMesh(mesh_file, library=mesh_library)"
    ]
   },
   {
@@ -480,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,22 +505,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "ename": "CalledProcessError",
-     "evalue": "Command 'openmc' died with <Signals.SIGABRT: 6>.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-a0aec3a123a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minactive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/model/model.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0mlast_statepoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m         \u001b[0mopenmc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0;31m# Get output directory and return the last statepoint written by this run\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/executor.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(particles, threads, geometry_debug, restart_file, tracks, output, cwd, openmc_exec, mpi_args, event_based)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpi_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m     \u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/executor.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(args, output, cwd)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         raise subprocess.CalledProcessError(p.returncode, ' '.join(args),\n\u001b[0;32m---> 29\u001b[0;31m                                             ''.join(lines))\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mCalledProcessError\u001b[0m: Command 'openmc' died with <Signals.SIGABRT: 6>."
-     ]
+     "data": {
+      "text/plain": [
+       "PosixPath('/home/shriwise/opt/openmc/openmc/examples/jupyter/statepoint.100.h5')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -566,9 +547,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tally_1.100.vtk\r\n"
+     ]
+    }
+   ],
    "source": [
     "!ls *.vtk"
    ]
@@ -582,9 +571,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rr5S1nCMtH76PgmBAr9NDkCAIgthYkEi6wNjCyDPOHeDX4Fwlly5SYtZIVndCY4bEwtZDhsEAjgkFAJAaE7NDimd8TW6sq8s7f73GQhCtZfi4Jbj+6FN/jH/4u4cWOyyCIDYQlJNEdCokki4gX/jeLwAAuuhNlOYUJyzmdoZ0zSuohIQjkFSl8ZRqCCkWrlAay3hCbZWyFX5zh9sAqxmvYQD1mnNbqArXFEq5115c2oERBLGhoHAb0amQSLqAzL35HPp2vd8jigDAZJwHjik74xmYYOMA6xM3ACCIBnSNhe57VgmNdZt1QJGcy51IOrZUPOHkMhUXJAg1X+u3GqAVrFicmEou7eAIgtgwUJ0kolMhkdQm6Hz4U4bVLUHExp1xhksJ2YJJ16zluUaDNl21BJIacy6xvRSjm6iVLPcqltYhVwXHiYKTsM2UG7lKhTwq9anzPDKCIDodcpKIToVE0gVm7rWfInPpZYilBoITi45jZCIoouyuRnhFhwBHUNnUUoLHFXILJgDg4wa0Rgivu7cW3PykCsWoL/lYCILYmFBOEtGpkEi6gPz3+/8b7r7nywCAWn4GmaE+7wyer42w2YQJnQ//1Wb4fs1pgjfXiWmIqGSXt1RAvc5hYV5CV3cd8+Wh5ngOZz3z8fEkzITU8pgIgth4kJNEdCokktqISr6K7IijjDS5inrO+xNNhA5Os8ROJdNovpYQYKcx2UnWAMDDgFL3CiXWMFFZEJpCSWIvhdQI5RVKBYDx5iOJrERuEkEQBLEhIZHUxqhaH5KD3nb5Rl8/isd+Fb2Mr0sRAMhe8i7Pd61ahFT2Cp+SWvB85055XSQAYIVYYBxBEAQlbhOdComkdmAiB25si2eUXEqEzmrUCshcfF1gPC+JgXHMQhmAp7ySs/6UhHg56BCxJg/D5ybJxgIJJIIgIuEo3EZ0KCSS2ozCxJwn5OZGSwWFEABwrAZT1cAI4cLqfKn1ZqwP+QqgWoJLSFAJAIIgvJCTRHQqJJLaBbORGMTIntFybQHxmC/kViuAjWWDq1CrTaFku0gAwFdr0BKWE6RVi866G26SO9QmMiIAESVbILm3WzyzvGMiCGJDQInbRKdCIukCo5sa0MtCUwrgxSxgxiGX1MUXbMCxYcG0IG6h5EZOSRAXfA7VzBQQ980opAAAGshJIgjCC4XbiE6FTNI2hUnEwDREjVxbCITajFohbDHLTXK5SDYSBDCoQUiInoGpBucFgLRsPfTq+cnmOBJIBEEQxEaCnKQLDKvpLafbQmn7LqfY44k3rXG2izQ06l3HxCkRKW/UDgAwMlrExJlgGM0UeTBK0JFKywzs1O6+YeBcruWuEgRBEERHQSKpjdCUAhL9/QAAszoNJjEIAGB5b7N+WzCNv8ph8CrBM23iVHhyd/dOS+EsKpRmvN2P9A07nzN9IopzyjKOiCCIjQDDBPuTJIhOgERSG2CoCphFClkff1XHjqsdsTT+auvQVzmOUDfJjTaXDx1fG3BEVD1fQzYVPh9BEAQAMJS4QXQoJJLaCFE00NVo/b8wZ/31u0h+pg+pATfJxhZKtotk43eTkkPW8tWjJZ9AmnHWNWXlJim5YJFJgiA2NgxLThLRmZD+v8AYjYeLKBqe8V19VsjNzfFXw/OXpg9ZreGiQm028bTQHAS2iuSQ0BRINrFadCsVpZgCn+7HJz/5yZbbIQhiY8GwnTMQhBtyktqEdA+LUt7A3FvT6LvIykUa3GIgV7bicKbuVMdeLNQ2NOoIrtIhBfF0uNO0VArlHujF0orWsVp85YC3S5ZEQkOx4BWH7sJ2uu6IPo6zBClbccTmf7392jXYS4IgCKITIJHUBigh/a35YThLLB1/tY4YAKbfW1373CnDI47cTLwgYuR6J+H69JuWWDAMDixrCYbqUSfvKFZjUItZ8/Rtsv5On1viwawi3/z5L0LGWoIvkXBa42WyCooFMbLqry2ObIwk1xRKD77w755pf3L9O89/hwlig8JSuI3oUEgkXWC++Vf3457/8S2ce2MKmy4bQilvCR0pHi54uvqAOhPe/cj0GRODo45zUjq0eEs0t1Byk45NI73J+c7FEtBrVQAAy6/MmWrFV/63JVpUkQMQzGZPZa3QotshkqsCWBbgeO85q9cs8SnlvcU5KxkRkHjE4hqUuldZffXZlyCOW/P/ycf/08oOhiA2CBSmIjoVEkltRrqH9Qik3tRbyJUvAgBku62QGw8ZmqsktlZ35vcLJRvbTbJdJD9ad8rz3VRTEHmr0OTc63OeaXwivZxDWpSv//MvoQ56b0VB0aGKHOIVr8DRKwDXEG9yNSjWbGHkppIJz9WqydY2Y3GtKYzcPPjtXwIA/viudy9+EASxgaHEbaJTIZHUJkgJR9iUy0mkUhXPdFsg2fiFkpvpMyYSC8GX/sQLItBbR7rb+7Nv8sTSujbhYgkwcrBrk/Plaw874TRhWoM6yIPJWueB40zw0GEkgj9RdYUBFkxwcBwwReKgqywYmOBc2ikWd46tlveuyy7toisM5LQI1vA96Lusffnqsy/jTz/4jvM6RoLYCJCTRHQqJJLajN6UjnlYQglAQCy54SGjVndCUumSI7S0reEhubSweL9wnGoVlFQ0x00CADOdBOYXXXxJPPD4S0Cv1+GJTStQB3lPDhHLAkbDKDNNl0PWEDBYMKFIXvdIbxxiPKXDMJxlxC5rvdIJr+B0U+hPQFD05rrtbZFQIohoWComSXQoJJLaiFo9BqQq6DZOYp4dA2CJpWyPAkQ8g7buqCD/sjdUxoyqEKBCNbxOE2+UoNRZiJITnps80chbYgXACBdQZtoSbLXZ1cnefuDxl0LHGxwDbk5HNRUMj8XimiNa7PlZBmAZ8KoBqdc5JkHw5SbVLRElipb4MXdatz1zxHKZCv3hghKwHCosUpSTIDY65CQRnQqJpDbB3VrLTdM9YRjA9IqEd11ewOlidDkAgZUDQglAQCg18QmloVEN+TM1zyxcfGX5SF96NCiQWBEQXeHBRNkSbtWU6AmXMd3WX3O+IZBcqCqLeFyDrjPQNO8024mqlL05THq/JcZMXwoXrxiorrBsAkEQBPH2h0RSG1A4dRqJy0YAABO5JEZ6I0JsLqH0rssLzdE97yg33SRmNNwN4o1gnaOmi+RiZHh80f1lxdXJS2IaOUBmDYGQmcGxYA0TSj2YiM2xluphXV3QxRtiyg7V2a3fVNX5iWu3ftM1FnrdUUaM6QilRElt/nULpcKRXwJ7rl7+QRLEBoASt4lOhURSGzKRSyLR77hIcm4e8d6GjcIwHoFk4xZKblq5SVIyKEA0MwWeKXvG9Y8wmJ2wHoJdAyymZgKLLZn8qz9B9+4PAABMzjo+VjdhMgxMNqTat8tg4zSv+8U0yiUoMR5q1RuicztNiaSzEk1jwLAm+Hjwoa5XGNQSzr+ELZgmJ8LDgwRBWFC4jehUSCS1CYWChCzeApO1mvuXSxKSqfA6R2eLPLZkguG5Wq+OTDyYzzPYPYdjr/cGxmtaFQLvJDF3Jada7mPXwOo/Cd3Ch9Et4aIJXvHmDrkBAHKOwImXFMRLznkq9CcCobhqhUesqoJXdGAwGEZrulW8d59qCR5qmTr3JYjFoGKSRKdCIqkNUfMMuE1ApSwGhFIsrqE4p+EsJGzJaNiaqbTMS4pCU6rR0xpuUv7MQnMca8pAQ3ske8JLDyyV+Vd+gqFL3hc5PdaojVRPWIJGV/xJQwziAzoqczzUXu++MIaJZNkJOeq8d1lx2plW7rLChrGqN0RZSwgwFpzaUGw8BUP2umsEQTiQk0R0KiSS2oFGi3PDYGDkrZe6fm4c3KbNHqHkd1TcjtK5M5ZYKM5pyPQ5l7Wn22qzf/EVuVA3aTFiabvEQLSoWi4mC9SLVsxOygwAAHg1WPVbqnrFUnzAOw+XDA+ZeebRGjlKPANN5FBLtE7I1vp6wQNQFBmoRpdfIAjCgXKSiE6FRFKbYSRYsNVgy7NAyMmFLZD82ALJj9tFUjUJAl8PDbU5AslCMbKoT70Jho9uMr8YX7vvz/CZL/xN87tZKcIcHoECQK97hZiQts4DBxNakUF11huG0wTOqWnUgIUJJcZB562ftmLNe96kmHf+5JZLAADFidmW+13LLZ7QThAEQXQWJJLahHQ6up81Iay5foN/fyONdK8lHko5SwD43SSbpbhJimoJoIW8jESw67RVo6KXkeS8ieacZG07e9EgAKB09q3Acobk+PoszEYfbw5+0aTEvOeha8uO4L6c84pJZWG6+bm6MAkASPQu34UjiI0ChduIToVEUhtgMkCpKCKdsYSS203q6q21WhQAIM9XEe9ONMUSAGwdziEWCyZxl7eEu0vFyaA7VK2nkJDKmHjTWWYlLlIUzOQEzGGrBIIZc1yx9JaLmp81ri90WZMJL0dg1KrIFkOS2zkZuh4MDXAMD930zZ9IAgtAKsujTAUlCSISqrhNdCqk/9sAJtgSH0aCXVQgTY0vL4G6WJLR2xPskmP69NL6buvfzICPW9v8zJ//12VtezksTBQ931MZHV3J6dB5eSZY/ymKWp+ldDjOyVtyu0gc4/3NwEsSUln6HUEQi8GwnTMQhBu6JdqAhx64H4DlJgGNBG6DQSIR3c+aXyDJ804+z7ZNVrP1Wi06hOdHVhyHqJrPOZ/rKfRvZtC/2ZsQzYgra+EGAOjt93x1u0hLgWPDz49Rs85FIbN8gaMsTIOXJPBSI9YoBmtPEQThhWHNjhkIwg2JpDbD3SErgFChlEyp2HZJrTm4sQWSn2LJiRe53aTFXKRENpiYZLtJ5wtjKOAFA2ZhHkpXGkpXGszkhGcev5sE4LzcJFso2S6SjdtNElixOYhpp4y3UfbuE0EQ4Vxo94ecJGKtoFuijUimVGzaHHzh20IpmVKRTAVFky2WNg/M4eoBr3tyvm5SIis1BRLDe3ObVHNl/beFoXSlwcVEcL48qlQmWBoACLpItlCyXaRW8JIIXhKB8SoENpi3Fbo9ScKf/JfPLGlegthosKzZMQNBuKGEizYh212H1uhnbNPmEs6Ne4VItRrzCKRKvoRkjzOPwDgt4PxC6d8nglnHvT11vPFyMBmqms953CN1/iwASyhNHT4L1UxDmTu9nENbEowUg5KfhdjTHxBKbrqS01ioDIZOM2pV9G0Odm2SnwTi0uJiqKZYYUZWlGAojttmxvoB5CEkKfRGEASxkSCR9DZAVVlABGZn0ugfCDpNboHk5+i8AZYPXubpSRFKPSgoBgZE1IO53dZ+rJKD5G5dZhbmwWS7Q+czVBkVuScwPp5iAARFT09/GZoR3vpOrnKIJxxXqn7UStgWNQYKH/7r0WStsGJlehIhufUEQTSgMBXRqdCt3SbM/sc4eMERO5s2l6CqbLMXe045Z803k8bsTLiD5OfovDUtLYZ0HBsCL1rzS1JQNPDxBOJpp1o1I6zMVWHNCCW2BErT0e3xedYbbstPRie/R9G3CWBMavNPEEvlQidbU+I2sVaQSGpT8rmYJ7nYz+xMekkCKYzpyaALYwukMPh40J1hmNWrlzSyy3G6lLxV+TqZaXQnUvUmosulcE8nlVy8bzW5ai1ru0g2osagpuTQt8kSSGHobAYMH16TiSA2Ohc62ZoSt4m1gsJt7UKjGJuqss1SAIDVCius+OElW0voijuO0rzsiJylCqSRi0xMvBUuxCTJRK2QDwikeFqAmktAWVidvKT+7VbrvE3bNZw7sfTbsTQtIz0Y3sqOZ6vQjESoiyRXObAAUju9fbhVTmYBFLzricehyeQoEcRikLggOhUSSW1EtcKjenQKiaFRz3i3ULpka3hz9+649ZQ6cJhFpodDbywoEMIcJCDaRcrNpdDbZzk0U0da9222HATBKjtgC6T6zFlIA1uwabs1Xja8olCv5sElegIuki2UwlwkK+wmgBWC7o9fIE2fXLxFnBCXcP4BQoLobJYW0CeItx8kktoEXXN+iqUzisdNAiyhlOr2NudfyJXQ1eu4ScfOOcvkal4hMHHKabGWiDuv+yiBVK7GAzE0gwAAIABJREFUASgeoQQA8ayEYAWj5WOCwfRsDwb7g3WdChMlJIasFmwCt7iAAYCeYe9j+uwxLlQg8RNzqIFDbHuwtMD8fBbd3QXMnlxojlOKeQjxNezEboX83S9+DQCQKzyEkGupLDBAPPgzX9Os8/V///Zla7uDBEEQb2NIJLURmZSGYtm6JHY/bgCg644AmC5KGMwEPQ23QPKTm/ZOq8rWS3/zthK6kyUcOeJtQWYJpCDxRmkANhEDFkJnWRb6wglMY7tHKE2dTXrmUfVGuI9JQMwgAGPIAYHU3N+EDDniWBbDbtnWt4nD3DkdtckJ8NKFKwHw1WcONT/Hk04BULkS/i+sLLjOiWw0hZItjmz++sAbnu+JsoI/vn33SneX2Giw5CURnQmJpDaB44MuQL3GeVq82UwXLbFii6WlCKRY3ERNdh5km7c5YbudOy2RcuRIT6hAys2lEM9a/tHssUkAABPi0iwV0wRSWRPlgrU/07M9GB0ICiQ3qqxCiAuh084eZbHlEuc8nT3mhOXcQomfmGuOr52w3CR/qK1rSMLM6VXocmWFfGX/6+DrLrcr6X0J+cWRIQN1mYXON1wj16nq6rbuk/IM3/yH1wTnHCXKXofyoX2vND+TYCKWRItGJgTxdoZEUpuRSWlISY0Xfv00ZGwNFUqAJZZm60t3kBZj5848TvxHcPyOsSqEgoFj09YLONUjgqmtn5AwdAB6CSrSHqEUkxxHzS+UlkLtBIexy7zduhz7dTCXq28Th/FJgEuubeu2r+x/3fNdkzhHKFVMqKIlbNQFEZzmHCuXNME19CUHHUrdms8WR2EwppP3JScFmIz3JZcsWsLpm//bEkyfvoPEEhENQyKJ6FBIJLUZCwUR/b5xmsoGhJKqWIJlaNjrAsznLWHUSiC5XSTPdlgeHKdAdxkYO8aWlhO0HOz8q1TWhN12rKs+gylhJ6B6X+yGL3VoMUcJTPBhHU/IUI9VIO7wihxWzQXmBQBGiMNUZSSZk81xaymQvvzEawAAKRbMk6qb4SUPdJ6FKIV32dLVXUcxJ6KY890DHGBGvMsY00S8okKUw/vy+8f/51cAgE/8wbXhKyA2NhRuIzoUarjZJnzjC1/EQsF6qZ07HEwU1lQW5ek8VIVtCqQwunsUTI0LyPaoyPYEXZFWAsmGiygvffGggVTP8typpXDVjorzRZAAQUJ1ajogkGxUWfW4SAMjzlCV06HD4LvCi8RVK4vXexKT1vWoz55d+kEtEVsgAVZ4tV7jPN+jSJQV8Dkd7LzhGdQai0JeDIghUdYgyhqkqjW4iVdUxCvWvaLEeShx728nTWCbw9//y8vne6hEJ8OxnTMQhAtyktqI3i4FuQVHKG263OuqzE4nsDm8B48mE+Pe5GK3UEqp4dWn3QLJhuOCLpIBBokUUF28buPSMAFOChcCjBDdoswwGAyMBMeXCwb6+hYwN9flGb950+SSdscdamOEOETJ2YdUTwz11auCAAD42iMvgwk5fkVmkVqood4dDGn684fYRrkEOSkgWaiDV7zKUhM4KGkeStp7jRkA8QUVqmSJn8B2ipZzqYkseM3rYj74s9fwJ++7cvEDJDYMFG4jOhWSzW8D5vMxzOetcE8iqSGRDA+J+AWSG6GR51SrLv2SF+RoJ4MVz79Z/De++DnAZewcPhPSbA2A2eL2PHE4OvzV1xfe9K67xykM6Q61+d2kngETPQNr2z3B1x6xHBmxrkOse4UNq5uopiWk52XPIMV06H0cagnBMzAGkCipMFnv+bKTs8WSBrHk3DPxBRXxBUsQCnUDQt26NzK5WnPwU9siNgeCIIiNAjlJbUZvl4JaI/o0tyBCiWhFlkhqqDZaOLUSR4AjkGxsoRRLGKEuEgD0DdYxP2EJpWzc+xJPpAB5DvijT/1n/MPf/f2ixxSGprPgufBEa/dvUlsoMbDmNQxn6onDMWy/3Hqhlwvh6/K7SN09MubzQYdm9s0+9Ayca35Xi9MAkwRnVpA72egqJb16AkHo94owARrKJcGTkF1NW0I0UapDGXa2LSSsedQqi3jZEjuVjHffBCUYq+RqBqSqinp/8DgY1kQxE4c47XUbjUZrOXbW2S+jn35bET4oJ4noUEgktSHiiPMC3dyosD1+Oh2YL5HU8OZrg8hEtGLyiyM/83NxFGY5jGz1xs/6Br3rK8gcelzShatZjkyqa/XqBh0+kwGXjq7ca4L1CCQbt1ByExZ2s2HVHCpnvO5RdnQGpfnQ2QEAsZSA4FZWTqXs6jSYgdOEH47QqSVFcIxXVJnzgAAdmsiiHnP+jVOF4L1Qj/OQqo74kWadkJ0y6E2CVwaFplAy+HAxlP+3fwMo3Ea4oXAb0aGQSGojZo+fQWxkDFu3KZhb8P7a37y15BFKRkP/DA9OYHLam6CT6a4vKpDkqhMumzidagolv0CyqWhAcpXvFk33voSV+SlI3UOh86bSCmKnysgNB4WZrsgw2GxgfDJeQn4+KJQ0XQAQnp8VWDeTRCxlhe/Y5OqJQnXWeqmIcMJgSpwPdYAAQG/UlOKyJkyfmJNqGhjDRLykYH4wWGuKYaxyAoxPaMWKKvicjmrKe68pXRwEV2tKYT48vEsQNkwnJTwvr5JIgHPnzuHZZ59FKpVCtVrFTTfdhMHBwcjpH/zgB6HrOg4ePOgZ19/fj3/8x3/Epk1Wr9sf+MAHUCqVcPDgQSSTSciyjBtvvBGyLOOFF15APB5HpVLB9ddfD4Zh8NprVqOQfD6P3bt34+qrr17ZgW1QSCS1Ee4uy/q6woWSYfQuup5sl4K5KQl9Q+GCxy2QbCZOp7BzRzm0f7LhjIG8rxLAwFAKZwvnn5dUOHUK2W3bINaOQold0iwLwAkGdHXtHriaXAmMy47OAADS3TxKPkGQ6RFQm1n9/Zh58zkM7Ho/AEBqtCyz/wJAuTsYZjVYBqoMoDEp5nKH4iXLHeqeto7PFkvuighmo8mbVFGbCd+AkwzuFktq4xoIggG123pMKMU8zOOr07Ex0WF0kpO0QpH06KOP4kMf+hBGR0dx4sQJ/OAHP8CnP/3pyOn79u0DAOzZs8cz7vd+7/dw2WWX4YYbbmgu+8///M/Ys2cPtmzZ0pxPlmXcdddd6O/vx8zMDB5++GH82Z/9GcbGxgAAjz32GC699NKVHdQGhkTS24R4wxkayuqYLEQnVG8Zc5r4z005IsYWTG6BVJj1tpQCrGrY7hfrcMZ5YlQ0J9RWY8JrFS2XUlEE7wvr+IVSKu3sZ++k103aerFlqySlAip1x02qlywBwUCDGXKb8yMCtIlwN0ktTgOwBNLUsdyatW6olmYhdQ2gmgnmCLG6dd7j5eA1sik1Wr9JNQ3VrFewSjUNTKNT5LAYpuJrVddM8q67nC2Jh6qyEAQDSjHYxx5B2DCUk9RkdnYWw8PDAIDNmzdjdnZ2SdP942ZnZ/H666/jhRdewNatW3HzzTcHlp2bm8PAwACOHDmCZDKJY8eOIZt1noPFYhGCICAej8M017YxSqdCIqnN6Eo44Za+LusFGfeFzoaz1jx+seQWSH7mpiToqgFRCv9H2bnDyUvyCyU3JW4TBGPlbeE1jY3sdwywhBLgFUg2vZNlLIx0NwXSkrcZ4iL5SXfz0HivADS4NFg9+tyeL6pcaH6Wugasv7I33Ga4ilaxriqflWzM4waFYXKMJZQiZnN3TRJGvFHGO548i9xq9GpMEBsA0zQhCNYzRBTFgDiJmu4fx3Ec3vve9+LSSy/Fz372Mzz++OPNZU3TbM63Z88efOtb38LBgwfBsiw+8YlPNLf1q1/9Ctddd916HHbHQiKpDTn95jS27hpEnAeEmAE94iU33HCVWokjAKhWLJdBEmUodSYglNwCycY0ASkW7TuXpsKrVS8V05WEPbylhMmzwcT03n45sqDiwHAeC8UedGUcoWS7SbaLZNPKTUpyE858wiKt1/g17IqlVITQPQhViqhwnmEAcNBCQpEquGYukzuJGwBSC1a6ueobz+kmJDncpTK6FynGRRB+Oijc9rnPfW7Zy3zhC19ofuZ5HoqiQBRFqKoK1leaI2w6y7KBcX19fehu/C/ecMMN+NrXvtZcVhCE5nxPPPEEbrrpJuzevRuvvPIKfvjDH+KTn/wkNE1DLpfz5EMRy4dEUhvhFkPuosccg0ihxDAMpJiKei08/GULJDdK3XmghQkkAIhxgAwgrwB2ke2Tp9fOrnULpcRM4+W9LQkppgeEknidsx8LRe8L3VC8WVVDWy3xcPw1FkBQ5KRiXmG0cK4AII1kzCs8C3N6ZMu788GIsOo40Wl1p8fk0HlsMjPO9GqjPIFY16BIzs1T7oohtVCDUPPmWrlFE7/7cs80bWIWhuYVmr3DLHKTlmium9EhQGKD0kGJ21/84hdXFJoaGRnB+Pg4tm/fjsnJSfT3Wx1Nzc3Noa+vL3R6LBYLjDt48CBuvPFGdHd3N8dxHIfx8XGMjY1hamoKfX19KBaL2LFjBxKJBC6++GL8/Oc/BwC8/vrruOKKK1blnGxkSCS1GQtVSxBM5AWMuKpl+4XSVNF76aSYNa9bLIUJJDepjIbxmRg2D3gbt8d85k3e905U2X4AOcS6gi3KloOmseB5x60a3lKCejbYGs0WSrVtCmKnHFEjsXnUjR7vzAyDodFga6xURkW56BWSer4IBA2sAPGMhJKVpoTP/Pln8fUvf2nxhVaB5OaLQsf3jYuYnXjFMy5RUjxCyU/s6t/wfgfALMxD6fV1QTLhDaXG005XLObx01D708A0xd4IL5ST5LBnzx48/fTTOHToEIrFIm655RYAwIMPPoj7778/MP3mm2+GIAh45plncOjQIZRKJdx8883o7u7Gk08+CY7jIMsy9u7dC13XPfPdcsstqNfr2L9/PxKJBCqVCvbu3QsAOHLkCD7ykY9cyFPREZBIaidCeh9Vp6YgDFnN4jkGmCqyaFUo3XaVFhNIl22v4j9OWJ/DhJKfwaEsTo87rlNdOf+WbVFMnklh82UGam8Gj88WSm4XyY+hhjscUy0aZM2c7sLA1mCF7kotjdLZU4hnvMdp9+O2WqhyAamUZYer89MQuq3PZk8ccn4e8R6vU8Y0rPv+kd2e8QuXxpCRw/uhS04vICobS8xpAaEEACwvBNwktX8JipLYmHRQuG2lDAwM4A/+4A8C4++///7Q6bZrdeeddwaWuf322wPj7rzzzoDTZbdkc/PRj350eTtOhNI5HmkHYBgM3Pf+RN7lCqkMqqr9IGrdRpWBAbSwiy/bbuW9XLrdebCNz8QwPhPzuEjd/eG5KdmEJSq40IIBS4PlrP3TNBaTZ1KYPOO0WIvtCj++clGEyUgwGUeoSKzV6sotkKbOhGv/VMZ56ev5oBtihdoc/ALJ5FavTpKhL61OUxi5EKcMAJh4MJ8pOW1dq+S8dxqzEEx697tINjU1hZqaQrqPuiQhIuCYzhkIwgWJpDZnPCe6xJGboJBQahwUd/6OaTpDA1sghbF9uI50SGrToBhe4HA1CGu9BlhCqfyi9SLPzcSRm/HmE9liyWSkSAcJaO0i2cyc9ob4GCEJRkhCM7zbFBLR/cWdLyYDsIIEttGhrzo/DbPH2a6cd8QMw4b/uy5c6uxXmFBaDDEXFF2brjLAZ6qoqasnDInOheHYjhkIwg3dEW3EN75otZCwNY1d/C+qhZcllIygOApheHMZQyPRicDbhx1XKC2gKZb8Asl2kVYDWyD19Efvl18c+amUOFx0SdUzAJabFCaQUhk11EUCHHHkxhZKayWQAKBiWvtsiyVWWx3HxnaRmt8bblKUi7TpKqM5BKYXnH7tuIj+BAmCIDoNyklqQ5JxvSmQbOo1zsrLWchB6nKqbvO8CVHSoNSjL+XwFicjJV9n0OMrAeAWSG5k1cBaBVhMA81ChYAllPKzjiBSFRbcRcC7ri7gxV+HJ4hXSpYwPHo4hktc/bfZQuncRHj+UM/uoNtilsoowHn5yzNOaYCZY9MQEjGUJ8aXenhLgmEUmKb3DOupJHigKZQM3hKSfhcpN6qh9wzvcZGa641XYUbkJwFAQgsK6kp3FkBQPHGiBN3VYjDdJ6JOdSUJP5S4TXQo5CS1Gcm45dzEEsEQiNtR4nkTPO+IHVHSIEreZYa3VDwCySZfZ5BvlAGIEkg2Otjm8K7rUtBMDprJYfMV4X2sLRU7J8mN7Sipive2fNc1hcC8tkCK4tVf94SOn5sMyr6abAnM4eHw5HU+5ayLEdY+/FQvzjU/s5qI+kwFurG0Gk2anoCmJxCrKNBTicBg1qMT9CfOOjloU8eDtbfEuAgxTnlJRBCGYzpmIAg35CS1GTWFRUy03JVYQkOt6r1E9RqHJB+dlG0LpTBxFEZVNZEQgg+GmWow5GIqTrKxXDYQj608V0n1FUfUdQ5hJaJtoXS20L9kgTQ7nUT/oHMewgRSK5Ld1vyNXkoAALH06rRuk0SvoK2YVSSZcPfnot1VnDmHgFAq7NCh6RGO0YgKTITXziqnRaRKTh7XbLp1EjknSmAb4qgwMd1yXmKDQuKC6FDISWpzwhwlrUWn7KbJwDQZcLwBjg9vJdYjmZ6QW1U1UVWd72ECiVXOvyXbcmFbPHDVcgmX7QovgAkszUE6e3ZT87PtItnYbpItkPwUx1c35OZGTyUjp41uOrXk9SQTDWE44hU/stG6zANguUm2i1STU6jJKRSnlp8MTmwwWLZzBoJwQXdEm1E+cwo1V7iJ5wyM9QZ/6WtaUCz585gABMSSPx/JTVU1IWtegVROBIs7ymVnnj/61Kci17cUkkkNhh7cb79QOjERw4kJJ//msl3l5gBY4mg5ITYgKJBsuuMzgXEmm1g1FykMsaYBMef47JDbRSH5U4BVNwoABPiSsxNLcxDLjcKTfhcpluab4iiKZB8lbhNeLnSIjMJtxFpBIqlN4TkDPOeIkTChBFhCyXaPWsHxBrJi61L7tnmSFp0BiHaR1BaO1mL8zefvRdzlkrUSSm5xFMbWi8rYtsM72CxXIFXqXiE0ecQSKyYT7fKcD0rduhYMo1gCySYW84ilpeAXSh4ablKYi1ROi4ilec8AAFI2eG5KZSdfSTNX91wQBEG0K5ST1Ga0KhM51qviZM6bZ9KX1LGt18DhXLTeVZVGDo+kQjfD0wciokvIz8ZQaDhbW5Kti1iuNixv4vCxDAAgngivhVSJKJFkC6VC/vx+B2QzJgpF60TZAomTVt9JEsSIcxqLQehJAHCcpNFNp3Dm3Lami+QnykUqxkxk+4MXeH5cDrReVIrepmtKweuqkUAiQiEHhuhQSCS1GYu90sd6VeRgiSM3l/daL1u/WGoKJBd2H3D2c80tkAb6s5iZtZKkT/scnLMVFtdd1odj/2HVzDm+Cuk5hsvcst0kNiQxXa6KAaHkFkgxsYqa4iQxFxfsWzsoQi7eWsYvXrDmHd7iiBC/i5TNmDjnc5BiydVr3WULJCORAFutwjh7EuwWq3sBTjMhj0/jDJwevEdH50LXA1hu0uYd4S+qN3LR/+ZKlYcYkvcmZXnUC97xpXyjg9tVLoWwlvyvf3kVnBYuROVt3uv9n9+zaz12qTOhEgBEh0IiqQ0xTWDm2BQGLg42s5c1QEpGtyqzxdKbxXCB5EY1ADAMwlqT+QUSAFza5X3ZMIyJiM7szwvTZMD4VCKnT0LnhgFYQsnCiHSQALdACnLxVm/S9+RZSyz19wRdIlkVAFhhKqb6VuudPw+yGaDQqGtpC6VWnDnTB6UuI9UV7EOtXjNx/BCw4ypvWPaNf7OuY2GWQbbfuc7z404BzyihZBNvhN/k8F5L2oIHHz+MWNl77EqMB2I84mXvzVIaaYhpFeAF53/p73/xJtSSdQP6lwGAT9x25SrvdefArOaDgCDaCBJJbxNk3zuspCK0CxEA+PWEgGu26QB0HIrIyXE7OPOubk+6BTNUIPkpNNKUtu0aXXTeKPJvnUXPRVuQO3kOPdtGAACVqUkkh4bBsC3yp0wgKQCVkDQtv0BiWBamYYk7v0Cyed87S5ie1jFfDorKkW01TJyyzkcsHUMtvzrhplaNaDgt/NiVemP/zBrAhF+j44eEgFCy8QulwPpdobZ4ioeEeVTa8BHx4OOHW05XYt59llMi4mXFEUcuNJWDWQtxLhvLAIAmWuf973/8RnP6H/0f5Dp5ICeJ6FDa7wm4wfEHBrangwLJxi+Ufh1SF+eqYetB7xZLRgv9cWRSRP+AFW6bnXFexH4XaTUwdAaVcrjSMw0mIJSu29WwXWpAPZZC0rVoRV2eg7QYlotkMbKthvzZ1W3RZSfaZzNAqXF9jUQCQoRAakU95CUPOC6SH7eLZKNUecRTiz8OxEQCWjqGP//Cffjy5+9b1n6uFLc4EnqDx1xNxsCVgy5reqsKgEXqsPe4F/oaoolnwKve5WINBV5Lht+fDz31H+BV63+CBBOo6TzRsZBIalNSMQPbG1EVMTcNpXcwdL6SCjzzKxM9w61zZa4aVnBiAYi3iMBNz3lfCP0DVqhJynsFUmGVSia5RRDHG9A174PWNCwhcd0V4X2teeZlGAxt9oarpsatl2ArgfS+dzqVpbtT4W7SWmNUT4BNbAdrmIDQuI6+TnubLpLNCtykrqHgC02vzkMLCWEmswIqBRXy7IUpIvk/v/8KWMNENeO9v9UcA6HXRL3mfYTpKa4plCxx5MBfzkA7bDriyIUmcOBVvSmObPxiyRZGbv7h8TcBkFgiiE6ERFIbko5bD+JpGRhs0RvF9JQT+ikVRaQz0Yk6r06ISCdrkBs/mP1iyS+QbMplHhKAYsiqM9LqWux+obSl3xJp5+QkNsWjHZayFr4fQ5ur6JOAyQKD7pDl3QLJpjulY7roPRcGGAxmWEwXDUipiBjnCshmgIri27+GWJLHp8H1bwpZCoBZQ72RbM4I3htl9qwGKRPSBUsxuqAkL3LQFOsGMcrBBCQxZa1vvUpL/s/vv2LtC8sgEXYDFgEtHbz2djWM4pngtTKyDBIh+UZGI1zE6uH3GdMYzUbYsGo3j2++cBSfvv6S0OkdDzlJRIdCd3ab8dV7v+QJh00HIyOYnkp6BJJNqSiiVPS+GF+dEPHqhDWuVHF14KpbQyIZaymQ/Cg6ILDWUF95rySI+Zqzc7yBLf21pkCyOSczOCcHX4hRAgkA+ly52POuZX/xghAqkACgK86iJ7EKB7YMZuacHVVnznqmDV3cuhiVpgkBgWSTSYUfY2He16luNdixrZtkVmgKpNLEFLj42vZf99C/vtYUSDZGRM5LvORz3CQOqugM7uXtdSgSB0XyTmsuH+ehxBuJ6mmxOdj416t281C7nf+Tb75wdMnH2VGwTOcMBOGCnKS3EbXGuzuRtEIA1Uq4uCkVRbx2rgbDbF3XZ3zcElrdXXMoLXhfnG6BlBNH0KtMhK5jNYSSDdfo9DZX5dAbIVTOyQw2xc2W4gjwCiSb+RCRFUVPQke+ysGAs0yWqyNEs54Xf/25/4Z7vvKXYLsvBgD0jWmYO+n9dxzaZnk2YsxyFpWa9zdNYV4CLwKqrEGIO8tGiSO3i1SYF5HtDjoqbjeJFaNDuFHCbDUQ6hpMFlCl4OOJVw2YvpZU8ZICOS02hU92znuVWN3E/HDwR4V99Ol80F0zOQbJYh2F/mBojucNqEkeTNkAUw6G3/7Xo6/j/9x7ReTxdSTkJBEdComkNsXtJp2rAqlg7yBIJNWAUOrpcx74lzWStt8IaeFmCySbdJfzwpw/G35bKC7dYjf55bnzfziajWNMp9WmQLJZTCipEJCK6Jg1TCC5ufiKEqZkYMj3nu+Ke4+lJ6FjskUx69UmTCi5EWNGUygV5pdW2DKTKqFYDpYMaIWhah6BVJrKgRPj0JXVkojR/I9/ebn5WahbLpotlux8IMY0PUKp2IhJMwAy00GBBADdk1ahTVssca6budRjOazpfA2mryhidtYSqoX+BHhfX4hmim2KJDGqdcVGgUQS0aGQSGpj3OkRHON8j6nTqAlWIrdbKLkFkhu/WPILJDebE0DW1QXKqUaFb2WNIlCCYL1kdJ1ZllACgHLJKxCzPUZLgXR1v/X3141aiG6h5BdIAJCv8uiWgPk17NuX0QHTlR/WN6ahUHFcJD9izAgVSLabFOYiZVIlzJwLuo6FeRHZ9Dw4v2NUaX3AsZQIeY3yuJV4SB4Ry0ATWNTjQLKRm2QLJbnLO3+xVRIfAAYmkikVwtGgwLYFlF/wCDUNyXnrf0sTvcl8qsjBZBlowvon/LcVFKYiOpSOEUkf//jH8e1vf/tC78aqoRkAzwBahFBy864dFZxWF7+Uv3NxHS9ORU/fHIwsYFuviimZQUwFar4fy73bt6BwOjwMt1rkqhwyIS6an11DCs7VOMzJQF88+MC2BZKfKdlyxVptolsC8pW1d1Fskt3RwlAQDCSTCoqFoDuoyhr6d4aXaqhWI37pL0P8cmIcsdTqVRxvRVQOUqWRjJ5aqIEBkMzpqPo6Hjb9y7r+NZIpSxypl1jiyhZL7nCc4gpd2uKouSpFbwolOzeJadi+ge1uJMhJIjoUurPbkOljZyKnuaMBVwwruGK4RenpBqMpE6MpR12NjVYwNurt58stkNJ9jmSYcuXxNAoYQ9ZW71dzTXbWpfs6ua2UBFRKAnoloLeFQ7RryHsO5mQTc7JzvFECCXDChq++5XXX8lVfUUqBByPwGB7piV7ZecI0hArLmWC56FZ8tusGAJlsyHWvazjyy+CJmjrDQooF3aFyvoZCwXvc8rzlRBmN7lhKU7nmtOK0U0ohnlmbPtxMxhoY0/QMvGogtVBrDm4SpToSJev4woRKZlpGZlqGYTDNxg32YPAMDJ6xwmqG6Rk4RUctKTSTue3B4BjoPAvWMD2t3WyxVHnt/1uTc0MQxPrTEU5Sp7lIbvxuEgDUdAZXjC7HBnJ1AAAgAElEQVQujgB4xJEfWyilItIppkISnbVG7aKqujb6WtcZVErhCem9EpDzvev9AsnNnGwi1aJMgb8rhVffSuLqi8I7idVTGXDlIqbGc6HTV4pfHJUKEtLZxeN8mqKCFwWgvrycmHJIsnIr+EQcgCWSarNr5x7OHn4O/Ze/v/ndbnrPmGYzFMeENMOPVVWk3eLJN8tCSAJ215w3pJnNWW5hOesVmgbLBJr+M42EOpPxTlNUbwfBGwZykogOpS3v7Lvuuiv080bCXY6Hb7zLKxqDSqNVV4yzhii2d+s4VWpt/1+StYYTeb452IQJJD/lkohSfeW3kO0m9aY09Ka0yF7uAa+j1EogAWgKpHMVE+cqvpdcRF9Tr76VRNXnaM0trE2ojWGAeEZHPKMjNxssDFkqOAfrdpFsmm6STyC53aSpM871CXOTADTdJNtFcsMn4g2BZKEYWeuvvjZOEmAJJamqQqqqEOXgYLV+YxCrqs0hAGMNC/2JpkASFN0zLPQlUOqKBQaTYSDWdc/AqwZYzQzkHtliSdHnoejzqE5Ortl5aWsudLN9KgFArBFt5yT5BdLDDz8M04x2Qz7+8Y97/i6Ff/qnfzrv/bsQTC8IuG4MqISYBTHOKQ0AWOLIzfOT1kvyhmHvS/aSbPi2TuR5pBMaeMaEZnofGLaL5OfTd/8JvvnfH1zkKKJJJnT0+uwsKaajXgtXgZrC4dLNdQDRD7QwB8kWSq0647TFSEzSUfNXuV5lNF+F8dxsDL2++lClggQhFd0lzNBwGdUz3ajyS3eSluIiGUwMUMOb9q2lQAIAgwXkklXMMp72xkrdid1yIz9KCKlDUe22prEwwdWiz5/OB0U+pznzGyH3ilsoddet/ZmL3MIGgZwkokNpK5Fki6Iw96hVSC1sfCth1e7YDWjOHj8HsW9rc/yWpImzleBDO8YB21OtM3BtsXRVV+v50q7e4HnGOYd+gVQuOQm8K+kAPNlovVbVgYRPk/iF0nTJO4PRCHOwvl9/rUJsszKHaoVHIhkUFX63JibpcBdG0lMZADn8l8/8X/jbr38jchurhR3FGei3diI3G91yK6E5/8pV3spN6toUdFikWB1hnbQUCknwTHhJhTBqc9F5c6uJXJpFfGg7dMUKjbkFjI2dI2QXllyshZuN7ShF4a/yXc5KEGUNSpxviiMAKMl5wLVJIx3xC6STIZFEdChtJZIefvjhC70LbUMp4n3lF0q2i9Tb0CwLLSJQScFsrjcdkvbjFkhuZmfjuKgRwfGXg+H58xejpsnAhOMHRQml+boJRYm+VW2xFCaOeN3Z4VlXkni1Yq3PFkth4SwAYFQV/UlrXqNWQ0lcXXfJMLzvl9xsDOmh8E6Ie/tlj1BKJKyLnRidR/VMtzNe45HaUUGmN3g+lHIN2V3BlecmFFQKwW1KCQH1qor8ScvZEZJxqFJIM8hVwmici1q8gphsOVaV6hySib7mPLZg8rtAclpEvKQEaiXZ09xVtm38oiuq2xEA6O0es/atugDA68ZJsoh63LoecaOtHqvrA4WpiA5lA/43tz+yysDu+GFzr4rxnFfRbEmanhCbmy4xKJSSQvDB7xdLrQSSmzgP9CZM9CbqOD29tIKGUXz13s/jb77/Lc+4qg7YR5t0uT2iqLUUShmRwZZG/2xvFoMPbLdA8myvYq8zqEo5Qwd6+4Dc+gVTOM5EucAjkQm/HpZQArr6o89FaoeVfF7MmaFCyTQYT+fCYbBKsFaEkFy7KttRGJlMYBwnJiD2efuzK8+cWHRdoi8sp0gc6nEekkv5GyyDxDt/AwBQWygiOxueyB9F3OBRlFvU2SAI4m3F20YkdWrrtTAEX0unza7ijnZoSzOdhG4/XQ1XqbCEkOPRAiDGNNQNQPI55n6BBABlDejZOoz86UlsHazj5CpUpPbvZTIkFAaEC6WMGDwJuzJesRQlkGxYFujpryEfkjy9lpgNE8No/HUX06wWo4WSKGmIp0XIJccF8btJYSjl8Fyk3ISlqpNZBpVC8J6REkJAIMXT61MvycbtJpmxGOrlPKSUU44hNbAdANCbCRfuM5sSkBA+LTa3gNLWpYXILBcJgBAD1JoVamtgTgQ7Bd4wULiN6FDa+s6285OWk5TdqRw5Nh3I/dFMpzxALu/NNOFZgIM1RDHpK+pcN6wBCBdIfsZzIhJrILNZmGAD0slCFDWIoiUewgSSm10ZE6Op1vO4n+09rqRpzvC6DkbNmda9fUvLdS4XQTCQzgTjpNWi9+SWigJKRcdVjKfZ5mBju0g2xVz4eTQjkvD99I+w6B9hMTTa6C/w3BrnIplLz4vykyqqqCthGVfRxOYiktMXigCAQv/SktRFRoCRXl73Lx0Fy3bOQBAu2vKOcOcmPfzwwxvKRQKAv/78l5qfF+rWAASdHht3HSWetQY3YWLJL5DcGCawfZOM7Zu8uR3liAZUK81G0M3wcgZRQknV2NCcKj/FRrmEa/sNXNsfzDkKex729NeQbpFnVZHP/yUeRlQulI0tlNziKIx4moXRU0f/Jg39m4IXajEXySaZZcAqU01xBADzZ73hIyEVg84Ew2CrTZGRoSe9+U9mzHH76uWl1SSa2WSto47oulPp0yEJWQ1sodR0kWyEGERGgMgs4WbsdC50s30qAUCsEW+bcNtGJCTPFBLruD1uFhRgcBHJywFQDWCuRQtwwfeMsIVS2ddX13hudcIts8fPoOeiUQDBcgaAVyipvibzdgtGf5N+Wxz5ubbfwEuz1jqifjAW8hyAAfSLM81x5VoN83bxTIZxeuZdKYy3z7qwDotZ3kRfl4FSsfWqZie9oUK3UMpNS6jXfB0apyoeN6kw40wf2Rx+coZGVZwtrkNI0lSh1FgIGSucZgulSmUOidjm0EVSRUe81pUyJDEVOl8d9WbYbTEXKQqtUW28KuciAngbEHJgiA6F7uw2xRZIYa9jt6NUVq0BAF7OMXg557z4tsWdF2WctwYVLPpiQJ/rXdeVsUJrfoFk0+rHFcMAI5eOtjqUJXHurXPNz/lxryLjGTMgkNyYptkUTFECyebafgOp1PLcoHlfdXEmwuFaDRJJFaxi9cDLuhytsdE6xkbDnRC/QHKTi0iuL5WTKBZjKMwkPQKpe7iO8bPhLlFdXaem7Uq4u8ZmuqFmU1Cz4QIoDNtFWgpRbpJk8Mh3c9AyyaZAsqn3WCG2BcWqxG6k0+DE9c1tawsudIiMwm3EGvG2vyO+/e1vd2TOkjspO+yVnBEcceTHL5biEX6hLZb6YssTSCkeGOtXMNa/tK5RFsNdC8kdditXeZQbfajFhdb1neI80B/XcFE6WgA9fyKG509YL7BEQkUi4Z3XcpG8CFod27dbBQ3r9dUNtdkX1t9nHcOYHoH0qxec7bqF0uxk7LwEEgCYRvB8dg8767aFkh1qswVS3/D6hiO0arhwscVSvZz3uEg2rXKT6qgHXKR6Twb1ngzEc0VIBu8ZlgMnbFBv6UKHyCjcRqwRb3uRtFGwX5n2/3FhZh6L/Ug+shAtkGwWFGu4cZOGG325LFECyU1Y6G+l1OtcUxwBwPRpq7uMMKFkO2Ru3EKpzljCxxZHfmyxFCWQ1hO5ykO2j5uJ/tccG62jXGr9Ml6uQGqFLZCM4unmuFhEK7K1QuCs7ek5r8uoZlNQMknPYNN0kaRYYLBFkT20ompYgktJO/dQVQ724ccJEhT5XGA8QRBvXygnqY1xu0kSGy5aNiWAc74k7LirKflkxXrZDieDaias8KQtlF6sAu6yMumhfpSmvE2chcZ7XF9B9MletF7jUKg6QiUR11CVg7enWyi1EoC2UHqrJEQKJJvCgoTBXsexmF6lfKulIsV0SJLuCCQbhnXqBLiQNRPdqUkAw864snNznI9AcrtINuNnM+DVtem3LpKIUFsUfEgrPVsodU0UsLB9MDC9u9cS3fK471dGyRov1DSosWU8Gnf0gju2vFZ1HQeFqYgOhe7sNmX+hPWrXWKdHKQEbyIR0vJqU8Ia4pzpEUhuJitsUzDZ7lEURwvWC0LivMnjfhdpteB5AzwffDkm4uHN6cpVvinQWvGDNxN4eVzAUF90pnphISgoBnsVjG7KYl5xXsB2qK13bGTxDS8Dd2e+Xd0hzpXLUZI1E7IWfn3jKRPxlIlKSUDs/2fvXYMkuc66z3/e617V9+7p7rlfpNFIlmQJEbbXJrAC3ggLgT8sBBBB8IVPLAthbFhgwTaX3Yh3Y4nY4NsSQch8eN8gFgPhwAS2ZS3GXhkQlmxZI82MZ6Zn+n7vrntVXvdD5sk8efJkVs1M1cx0df4cCZqq6qqsrKw8v3rOc54nxxeNexEkABAkGfWDaCkIVTjgPn5QaFMLgBHsU9yUG6EmRAs+amxpeA7ZhfglnkqH//d6MeMmbI+r/lbbCK+yUwvDq0j+2PKo84jSnKSUIZFGkh5TBLgrvVROkcec7KBFJSiTlIxiLr6dCcFygHEN2I+ZSSKCRKNJQF4GCkUDd+vu6qt+JKUf6Fwo3sIxOqLUYCItpKMEp0cp/vaD8EBFRGlz140q8eSIsJALdoKIEsmViqt0fj84jgC9K0HVkp/0+acaaJuVns9nXrWwB2Di40JElDKae2IcHoRXzyUJEg8lN9yq23ZMUryQdz8vp9mFtbcFaWKGG0ViqdzeCkWTSBQpQp1/O5lqAwBRcc8dTSkACP/KcApFCA33OXLlY1gSYKRyeY5u38+UwZNK0mOKbgsgk0S8atg52QEnX9WvH8TK0jQzaI5TjkCEiRakylQFhztucmueOktOedNYSwa//9v9oCoOdMO9yPJESZSciCDRmHYgSqwcscxOdrC8nIEo8C+EtCARyooDkuarioN737zWIJWxLg4P3A/n+cvBUvRdbwXa5DS/TYZ5NV60ttYzADKYmamjMhY+MTbuuAdO0+KnueoHWRTH2kMXpH4Q8hqcZjciSDWhiZLjHqO4KBJPkLILrei0G9zikGoX6OTubaXasRQkYMQiMENItEw5sqSS9BhDz6zQCdLrLXeAUDJAIeaaTAbyUo9ihYAbzeFFkICwIBF+6PWSqxsPXkiSByl71NGDub4Zr0XHVo2/n6YN/NNK7wHKcoD5RQFrK+6/aVniCdLDQO+Gk8bLY12cPcev1bO7nY+IUm9B4uPmQLnHtdsVfVHiRZH0Wg1ZyiUKk8MtB6DlbahFFZ16dF5YyGsoeKlSjT68jY0mEVob7vL9TsZAuRE9d6pqF2xGgrDtJY7XVaCoh6ba9PY6ZFXBwd1j2LttpCQpJSUgPbMfc2p3g9Uy6y3BFyRCXBkAkk/kOMm1DzljkE+SIBHOXZofSOK2SjXhLWUtbiFNIJAlmpoRTDnmCgZyMXWQePtpOwJsR0BT5+temdov0iC+Zgz+a3Nwdw0OqFWMCY/d3c5jdzsP86r1gIIUptsVMVfc5jw6SnV5ra/H3Q9KgSoJwekRN7bWhrPpfi8KbfjCVBOasVEkTXeliN76QTb7iyooWRWiNtnXY1NSUo4OaSTpMcWmBnTJG79Vkb/4p2GEI0o8wXAchHq/JckRAMzmbFzb4YsSYdB920rZ8IBfzlqocprTElHiTTcSiCi1GkpPiZsab+NgG7h+GKjJpYodEqRh4jjhWlEEEfGBf90QoVyiPoDrYTm4V0ECgJeeamNrK3p7YcKGcQi0Wxlkcx3ImTyAAXQ27oG+swR16kzotrE1/mq7QhuwJBF2gd9rTbfaUKVo2KmjuOdJdVJFeTf4UrhRJBfZtGHKYhBFItRVKNmHuxLycUVII0kpI0p6Zj/mLBbC4qDGfGINA2iafEEikKhSP4JEaJrBxkaRCHkZ+J9/6zeSnzSBkuJuc5x0onI2GikRANysAdvMeLmgRa1Jkm0sJPQonRp3n2RsOhxZuFlzk7QHmajNI0etbuNF/NiPWzdE6LxI1iXZ3zLZeHuMEySay+eD3J3CRFjT2vvJK80eFFJE06b6oWWKKlqbK7GCBLiCBABVTjHuetH9UujWYMoZlM5IKJ2RIBwOXxRTUlIeLWkk6THmTNEdQE8VgLtUGRY2okSiSE24SdjjMQu3SHIzvbqZXZQyG7N8fLsDaNTZMqgokii4uU0kh2ouF22+S0SJ3lWx1YCdK2C7DUxz8lLYyAwRpVUqnYcIEoskho9BxwIqVMBgI/bd3CMcKWIjfoArSoIEdDu9f9OQUgqnzkWTlA+24z+0l54KH4vL5+v4z28bKEz0fMmBYjv8aU9RzQJ9Ok61AJRjyhbRESUSRfL/zosm0VEkQqakQsvH/wLpbLlnRadThpIdbomEx5KE4qcpKUeZVJIeUywHqOoSymogSjdubuHieTcBVRWBfEyeMlmtRssSb5k8EEzrxckR4AoSS8Vbtn7YTQhd9YHdx4zW+k4GM3Nuc14eJKI0neVPW9EQWXrmUgv/uRMdkFlBAsLRnK4jQOasSrsfiAzlsxaanGlFANgh47UAaBk7VpR4daZoDvayECX3g7St8GuxgkS48GQTG9ucOkkZAZI2/IrbJJpEEtvrM25l7OJWOKmdRJF4kCgSjW61YccUi7QyKuRK9Lww6ofQOJHOXF1Eqxgc+8qciN0ezYhHklSSUkaUkTizR7F/23/9oz+NvU+33W1cdTdCnulXtd8FXpy2YgWJcGXchmWL/kbDE6QqNV3XNJILU/YLXbKATLut72SwvhPk1iTVZprLublbL57oXUSQ8MKUgxemAuHhCRIhu3Ci7+ftl/beDtbeX43c7jiuHO1wShhpGRtaJryf/QgSjSgFU3xxglTVXYmYmw7u1/UHE+L7gV35B7iyRIQpTpDYabesrYQ2LWuj0EBkq2Xi51cbrcCS6u9E25JU5kTs3Nrt522NHoI4OltKCkV6RjzmVKmB6blJk5u4Pc7JHf3EnIlPzJne31l4bjJ68b8ybuPKePQJiSwlCVJ1bCH8NzHTJL34337/C5Hb1pvhlig0PFFic5lePGHesyxlJQG1WvRADvML8n/9+Z/7/53PWjjcz/jbzmZyvSciSvcqSARRskKylMTcdBtKZaqvxw6CxsoSjK4EoytBTvgEWnpyfpQpCb4UhW6fSH7fOhOtM+rJuUe5uojK3DG/lD7qKtlpxe2UIZFOtx0BxjPBgM+uZPMfowL7Onwx4kFE6bvrAleOaM6VOzh5Lrhg/Pdbwy+wZjvAJpWPNDHewd5+dJUWEaXxHt0fiCjVe/SpvbofvE9alMaYx3U9EZQSpnfuF0kA2HTrnc0cpmb5rTPaTRmV2eCzNvTwPsXJEeHivPtq79903++T89TKLj16Wbi82MFbXnWA5uFwG//KTOsdGSJMZp2fbLjncrOcQb4atfl8TQcUDeh0gQx/atCYsqHsUJ99QhSJ0GjlUMi5n4lw2stturEN+ivpyMkNc1NSUo4OqTY/xkiCu20zK5J4tZFsuMnFOTl8oXeM8INFwcFH521MxfRFA1xBYvnFcyIMO5ywvdYYzPSLu9/8+ybGo/tCVtvFRZtodjvAVtvdeNCCRPPcvO7XmkpaMTgI/BIPavQN7WzmQlGldlNGu+kerMPNQFYU1fa3fgWJ5oM1V5Z4gsRDKz86ESCCRGiWM2iWA5nO1zzhI/3fOlQfOCaKZEzx5Z9Ek9goUiYvo3HQAhEk/3l2gvk9Sbi3Kt0jwaOeIkun21KGRHpGPMZkqakUnijdWjmAjWgtnZxsRWQJQKQVx1TW9GWpWNJwrtzhChIAfGsjeJWcDJwrBf9+0DYdP3p/2f/vWU50iIgSkSOarsWXpd2Ou9GwspQkSCwbNTmUZP6Zz36G+7cPCk+UAKBZV3w5SmJuto12S/Q3FlqQ3r8Zfq3rWyqWb0eLLI5pXv+63JDrIQBQvQrxghONJjVqGxFBommWM4Eg3SO8KJLeEZHJy6ENALpdTp2B444gjc6WkkKRTrc9xtx4bxWLT5zk3ne+3HuFVU620DKl2D5lhKmsiZtVFaIjYzITjTDRgkRjUE976onFnvvTL7M5gGSbkKTzvW7QZJYHESVWjHjcrgt4e8udgpljepnxBOnOQfA1sXtUML8fqk0Z5Xxw3GlROuV1ql89BGxbgJiwsm5uNhouI6KUzdncCBJBpK4Ey7eLOHnWLSFABInQaI9DkoeXnGxZAiSJ/x5zHRtICpIJ5P9w/r7ThfqEBhWck6gIZO5GhbJrtwEmTa26EV4c0bkRVCjfubULScigfTi8auSPLWkuT8qIkp7ZjzlsNOl82QkJUlLBw+uHGlYaMnbayS58sxqMBLsdGbud4PE8QZpjygWIwoM3AVdF4Nb1oAVLZn8rsiov6b22LXezHH77EULTDO/oxoGCjQM3FMYTpMh+SsPoVhfl1ELLFyQAWKjcAeCKks00d52bbXMFiUZRbYgxdidyTg9eRIlGqPfXvuRBoKNJqlfcyxqPOZd7fCz2vMDtAwcArVvxn3un3n+pg9wEm8V2jHjUU2TpdFvKkEgjSUcEzUtmNS0BMueXdscKIi3XD6MXdiJKdC4SLUcsux0ZP9johH6Xzy5OQdgLt2aYzAArAOQe0ap+oafOMhJfiuj32o6RJssJcn0IrCDRGA6w7jnJCWrKj44iDZNqU0axErRTWd1w81oW5vihMRJV6iVHAFCqBJ+56DiwqWqVPEEiPCwhTELtRCOb1rgMaT9p9WI4mmTPx78PIkiCacKRg4PRtZOPa7dbgKa5USWj4uVnNYdbjfyxJpWLlBFlpM7sUauVJIqufLCxHNPiX/Q7Fl+QaHbabmQpSZDquoQ6VXpAoLZhkPd6pNU5s0FxU2xNU4gVJAKJKjVNIVGQJivhF15vudtKLfziw5AGx1sxV8yb3Ma8RJZYFs/UMX+qAadHNU5akAii47hbgiDNzrvGSOeA1XYSmuUNEMsSIGsOZM2BEBMWDEWUuB9LzHekV0+eBNipNoASpJSUlJEkjSQ9xlieHTWWV4CTi8hSS6PpiBLdMmOPU22b5i2vyvSY98nPM33NaDk6u1DG7dXg1/HdhoA5StAmB7SIx7IFf7/XmgLm8+GBMSMBnb0DaBNjoXpMpOF7NuYsJtI1lw2eb6MdHjxZQSK0TLLcPzpIVzRgUFk5mmah6OUjqZIDnSPAqxsZP6K0eCbaboSIkkDNefLkiGatJeGy95zvb4SFmQhSQ55GwdxG0ww3OrakSWhytJjioLCs8G83wXLgcATVGpd7mLvQVxTJf7QXTWKjSJ26hkwxXPZAElW0atFzQxvPottKXl04kqSRpJQRZeQkiRdNeu211x7+jjwEdFPATExAiG1N8hanBQcArFG9zGQ5PjP6biP897YjICsNp3YST5Raugg5pmAlT5Z4USkgEKZD9BYkmitTFt4ZUhpOoyWh4K0cixOlrX0Vi5x+bDRElniCdGEyiIKstcKf8+U5VxZYWaJ5746KxUX3eG1tDm+Vm6w4sDhPz4qS03DfqyTbsVW3dVWAgPBySQktdOo67Hv8LKsbDUhi/PFxmisAgMqMjFq0iProkyZup4woIyVJX/rSl/z/dga9BOkRwPZva3uDNz3DsttJjujsd4H3G72nic6XgG/fdR/341PhY8cKEkvTAMrx40dfnC9ZuFmLStp+O7j4bm/mMB1TXBFwZcno41q93hZQnlEwJ7mDPt1Qlxak8ZOT2F/exZWp8KhtOsD5y4NbzceiSg5IJpKiBSLqODaEHr/YW7qEs14rkducvmusINFcntNh1KM5UEurD/jh3g+CA8RIMREkgmRFRUlX3b911g8hnKj4t1ueNE3t8RusrZ2SUOEsVqhP59A9iMqnlNFgeXWYKjMjdTm9N9Kl8ykjysjo/yj2b2ORBHcAZeHVBALcKNK4BvyXRRP/ZZE//XK+5G40/7Yj+BtPkNhO7R96eg5V/f7zdXitVrp2WJAI2zHtOi6W3W0ykyyN6+3ofs7l3I0XQWIhTYXbMXlh90uDkRdBcEKCRHAcG44Tvb2lS2gxvdXOTrd9YQKSBQlwc7fGSw8n7+h+6BSUiCARJCs4JkSQ4rBNHVsXoifJ2in+HHV9OlphXG+HpxuJIG1e24o89ljwqFekpavbUoZEekYcAaq65K/UWl7aRkbmDxRElIgcsbCyxMoRzbTmbhOa429AVJDI0KQO4Ifk+ZKFyayDSW9KrJzhT+dtb+Z8WSJyxMLK0npb4AoSYaMF5GTH3whsFGnQsEn4thNECs0E8SSixJMjlrPTbVR7CCCd2D5eMnxZYqNI9FTbb//O5xKf84HxVky2yyraXqjSSDjRJMtGey4fud1ZD6pm22bvxO0q2wzwHlHzx7Di9qPut5b2bksZEukZ8Rjzv//hn6JmADUDOGB+zPJEaafjRjq6PVKFPjZr4yOzBqZz/KjBdEzS99NjTmyFawD4jd/6zeQXTsDxthazS3GiNDfXxNN9lKWZzABnK8mCsMGZwcvJDp4Zt7HTCf42/4CVxZOQJAe7e9HBNUmUqgcqprLJEvetf5vGt/5tGgBQUG0U1OjxjFv5Z/JCfB7droHp84OfcvzCZz4Hjap+rXOWN8aJkqRbsDf3YE3k/I2GFSQ6mtRPFEnzVjvQUSTV0WC3m6G/yRT6r600Mjzq6E8aSUoZEsd4Ev3ok5EdGHDliLC02cCZ2QK6NqBxvu8NJmmHiNJ2yzWAOEE6wfy47lqAbguQEypA90uvQpTljI2q10trbi48IJH3yBPDBSr5+yPTwQPe3A6OAU+QAOB8KfhbWpSGQVyFaYKpC5BV9zG1w6hITWUt7LSj4kDkiKWg2mh4DXGTSiMAwOkTgSR88D0L5bIrMLWdxD97IPSuiF6aYagSFN3dF0mPF0UiStJOExgbrOWqTrCXBzsljE3x85xSUlKOLqkkHSEOusAYNXrM5QxkKyZ2NvkfIy0OrByxTOcMzGbCSeEEVpAAYLMN5ACY9mAEomsJ0DxZaBlAju3sTkUAACAASURBVBnPLlZs7GSbnL900cTg/S7kk6WDCNNbK+HbsxMVtPcOQ4JEyEnAhjdTaQzoPQNudL/VlJHzygDs72cwzmnqOzfexeHhROzzkIjSTluKlSOagmrj+oaKhQl+NHF7Nypdc5fG0doM7IiT4z0UpK4Fi9Nl2BGSBcn/+5r7Hh09KklbFwBTj37e1bkcROsgcrs2JgOteIXLFDQcLh3DtiRpBCZlREkl6Yhx0AVeYqbJPjbrDrDf4cjS2aKJH1Xd6MNcji8PChUNoqM6tsMXJJaGIYTyeO4VjRNJaRlABsAMFTh51nOE78eU6NFE4M6BiIV88sBJIkNPBYuecNVLW+EJEosy5PHAFaW7MLRTWJwNTKRU6XAjSTRFzUJlvIPD/f7yYlb3XHGgZYknSIeNaPLyMDE6IhRqqpUVJdLoVhh399/Z58seEaQ4MjsWwEmEBwBhPnpbh2nx0zzc8P/78IMukNzNZXRJJSllREnP7CNIXG8yIkuAK0dni+EVbRstARutcBRESWqYmgMOdHej2fQWTLUmToRuFx4wwNKlkphlESjFzI4QWfrunaD2z1u7At7aFQDLwHc2JHxng5+3Ejd1RgsTS456qrjClfcLaT7caoafeH21EBIkQqnCD990bHcjVMY7qHAiUnGs7ilY3VO4gsQimI9mBZzUtSAbli9INESWQo/vIUjZavzxMTnfCyJI2dnofVm2E+5xQ5RGZ0tJoUgjSUeAqYyDiQywR13TSW+y9u4espPBNMwzEzamGDk6m+3gdjuILGy0BFw6YWOvFW81M0yJHSJKbC5vwxhsvg7b1LZtAVnOdevZCTei9NZu/OsTUfrYnNUzr4hEkg6pROmK6oQEiTBxZh47t4czpdJpBV/JkuIm7bMQUSJRpU5Cov69iJKWNWFmgyfreCUYHnYUiWB0RGheJNNS3X0R8yLsZkzkhxKlQQsSwem4043ZWQftTfdcOfaCBKSRpJSRJT2zH3MOby3H3kdHlGqGiJqXdyQKycnQP3HCKzZYU3G7Flzgf+KcawSsIBEuj7kDD6/EgBVT+K8f/tfPfQFtU0TbFHGyEB0AeT3auhbwZAUoKr2nxyqahdlsvElcPeTfvtcVYAOR3nnDgOQkscRF0wBAkqxEQSKomoWWJaCVUNtJy0ZfP5O1sdWZg0MNgCSKVD690PuF7xN69mtssesLEkHMx1+2VNVEvmgik9CO5H4EiZ1m85+LFaR6FlL+wUoIHEke9Yq0dHVbypBII0lHCDaaBAC3aipOlvlfbCJKJBmbyBELEaWzJb2nINFoEqB5BQ/3e+x7LxzAb8N1smBjuRF+T0SUeOUHin6D3PDA+Mrp8DwhLUqbXpQkTpDYKU0bQGZI35b5KR0HLXd/MjkzFE0CohGlZj0wp1nv89qMaVqvauED1rIE5JgcMJ4gAcDhQSAARJTIcRlCr98QmR55ZbyIkqqG3wctSp01d8fvRZCMjQKUuUasICn5A3Rjzp+UlJTRIJWkI0BODgaDCW/W7E49GMAMS4SS0EetYwmxgkQoKjZ22jJMxx2cZGoQ5AlSkxlX2yZw5snB1c05WbBDERxJBDipKCGKioO6IUTkiEfTAK7t8sWHl/NV857SAZAb4Lem3yTwWleAUI8PK81mw6LEyhENiSjFyREQFiRCTrRB1hfKApDtI4p3P0jM807OtLG7FbV3ElFi5YhHZl6ApBjAVDB/at70jpEgwhT4xytOkEzTPT7avIjumo3Orjv9KisZIPmrNpqkRRh91tfX8c1vfhOFQgGtVgsvv/wyZmZmYu//5Cc/Ccuy8MYbb4RuEwQBX//612EYBl588UVcuXLFf47vfve7+PrXv47Pf/7zuHPnDr7zne8gm82i2WziYx/7GM6ePQsA+Lu/+zu8/PLLKJUSKgenJJJK0mMOmwO00wY+OFQjeTqG1zmdliW6zcb3dtxB5sNT0St4UYkKlumNUxN91MXrmO5rNzhLqftl/cYyTlw8GbqN17dURPz010/Ouybzw4McLo/FD5y3asETd6iHZeT4pPhhQSJjYzkbJA2djiatUm1hLszo+NFWfP4LiSqtJggSYW8rg4lScC50qXOFJ0hcHjRTPwFFsWFQZSt4opTLG4Cxga5zApoWiLxer0EtBoOCdcc9YyzoUC8E700+736J9C7w1IUqdz/efaf/S6SsuL9gZOcY5iil01Q+f//3f4+f+ZmfwcmTJ3H79m387d/+LX7913899v4vf/nLAIBXXnkldNvi4iJeffVV2LaNv/zLv/QlaX9/Hzs7QSmOL3/5y/iVX/kVTE1NYXt7G3/913+Nz372s3j77bexsLCQCtIDMlJn9ij3b7tzYx07lN/w8nQAV5ZaphDbh+x7O1lfmIqKzRUkQt4bH7Zair997EolEkUaFssxjXVFhE/cn5zXfUEivH8g4/2D6ABHCxLL6QJwsRKNmtV6B6YGgrF3x//vykQnJEiECzM6Lszwd8hy3O3UQhOnFuJrSu1tRcsDaLIDTXZiBSknBucJr0jpw2Byxv0C5PKGK0gU3S4/ykYEKQ49ISf9h1fHYegCDKbqOYkiEbR50RekenU18fVGlkedR/QY5STt7Oxgbm4OALCwsBASmrj7ebd96lOfQiaTwa1bt/z7HMfB66+/jk9+8pP+8+XzeVy7dg2tVgs/+tGPUC6XcXh4iK997Wv413/9V7z22ms4PEznhe+XNJJ0BDjo8pelsiu/KpprL09U3Cv/tcP4MNCPqhlMZbvYiZlOyMecGWsNBScLji8wJIr0oFgOUNWBf//BJl760Kx/+3JDwMkCP7wjAhE5YiGi1ElosQEAT1AlAGhReuNu+HFkqq1XlfD7YSxnw6SmwAxdgqLybZiOKsVFv4go3V11+5mxcrRXG8NEKSiYOK7aEOYDE7+15so0LUgPE4USeMmbci7mdVgxCehElEhU6UEFicbQBSiqExEkAMgVgmlIACjOH8O2JOnSeR/HcaAo7rmoqiocx+nrfvY2URSxtbWFd955By+++CIA4J133sGlS5eQzwc9Cl955RX81V/9Fd544w2Ioohf+7Vfwze+8Q18+MMfxic+8Ql8+9vfxle/+lX80i/90tDf+ygyUpGkUYQuNFzi/MhvW64cEUGiIbJEU1IdlNTgSzuVNf2NQAtSZSqwB5Mac04WHJwsOJjLDaYBLP3c+133tFxsrQOIRpQOjWB7b1/Fe/vx0xsLeQcLeSfS8JbmiZgaSfsdGXM5+Ft+fq7/N3SPCAgS12mMhOa1+aLR1/TgqYUmxnrkD41zerqdm28jmzOwl5ALNQxEKoFakm1fkPzberRx6XaVgQiS3qiHbpfVeDPuFo5hU1sKYYS2B0WWZei6++PNMAyITL4W737ebY1GA4uLi/j5n/95vP7666jX67h16xaeeeaZ0PP90z/9E15++WV87nOfwyc/+Ul85Stfwc2bN/HRj34UmqbhpZdewvLy8gDe2fEkjSQ95iiig5YZRDBoUSp7A1/TFJGX+YMCEaXrzd55Eks1EfOaha4VHZhNztM3DQEKgLmchRu8fiYDZLkh4McKBt4/4A/Y7+2ruDIejirx2pPQorSCZEFimcs5+I+GgIWYyNb9Ql+YizkTdWZ1GxtRWqfqW1W8j/UwJqBGpl3LEnB+wY0S3VwN5/bQgjQ2VcLBTrQHGREl24mf6h0GrYaCXCE6BSpJDjeilPcem50Pnyet68FAdS8RJJaLZxu4cbvg/ztHlazoFjIoHsfmtiPGH/7hH97T4//4j/849O/5+Xmsrq7i7Nmz2NjYwNTUFABgd3cXk5OT3PszmUzktn/8x3/EL/zCL6Db7UKSJCwtLeHq1au4evWq/1pf+cpXUKvVcP78eeRyOVy4cAFvvvkmJiYmsL+/j3w+j62tLUxOTj74gTmmpJJ0BFH31pGdDUc1kkRptalA9wZLNaZ9yH9QCcGaFIyCXUuKFaRBk5GAjvfS+10R4155gafHgh24PJYsSgCwUO4tMTUDOFMCtxEwT5DcdW0uqw0BWdnGZz732/jz/+P/7PlaSfByn3miJIhhOWLhyVJcXhotS7wIEmGdyenKZE0/udwRBLT04QSiTabPYC9RynPuY8ldct/n4br7A0Adi5pekiBlAy+KiBJB1gB0gd1bG5H7Rh0nprXLUeRP/uRPIlNk98Irr7yCb3zjG3j33XdRq9Xwsz/7swCAv/iLv8AXv/jFyP2vvvoqFEXB66+/jnfffRf1eh2vvvoqarUa/uEf/gEHBwd45ZVXcP78eT+K5DgOvvjFL+LVV1/F0tIS/vmf/xm5XA7NZhOf/vSnUSqV8PrrryOXy6HVauHnfu7nBnJsjiOpJD3mfOH3/wx//t//b7RMYDobXNgzsoMOMwg2mfyg1WZUJlhZ+o+E1VKA2zaj4c3EFbyzhRWk1YaIc5fmsXVzsCHd3Y6Ip+eDi+/1pTounSnGitL5krujH9TcHT0d04eNLcZMNwLmC1KYSxUTyzs9H9YXf/o7f4A/+q9/hpNPhFf2FXPue1EzwWd+wuu910uWVuv9Cez8XBure+77XSiFp2tZQXrYNBtKSH54otRYdfdRTWgpQ2BXqOsH4WjprTt5xJGN+hAunm1g9W5UDHTjGK5sA+A8lJKrR4Pp6Wn88i//cuT2L37xi9z7iZCxOUPz8/N48sknY1/n85//PADgzJkzOHPmTOT+X/zFXwz9+0HE7ziTStIRYTZnQZNsdK3gas8TJQD415s2zs4lh/11U4DQ43pu2OGRhciSbgPqgIMIWU/aMlJQ/DIJWpTOl/jL7e7U3GNDZKlHpwrsd4CcJ4D5UA4Pf4ckYXgXna4loJzhz2udyDlcUXp3371N5ATaqpaEshch5E2XrdYCWeIJUoZTU0lWhlxRkqHVUJDPB3JEaDbcNxwXUepVwkcque8jl++i1Qx/b2hBWqwEK9dWt/MQpTpszpRfrnT8LqvpAJwyqhy/b/MRJKmpaoaaPmtT0xSkAGWLs/psz5uSmcsBL826//j3Tao4pR0/qhA5IovFthqDsyVSF4muvp3E85M6Elq3+dypCXhmzMB7CdGRfaYQM4mWWXa4XtOlynDrH9BNfg1dhBIzHUaLEpGjfuiVTzSVFVDyZKPmyQcrSM4Q6yPRNBsKskENPsiKDS1joRFz2WKjT0D/gkTI5d2EpVZT40aQWETJgW0J7lTbMSaNJKWMKunqtiOGxqmsvVQTQ4JEQ1fr3tMDQWJ5aVbHS7N6X4JEU1JtfwOAX/+N/ylh7wdDRnKQoVY47Xfk2GmyA8PdAODKmIkrnCKTrCARtr3bLVvwN4JhC7AfoF8djQ332PKOr5GQ9zOXdVBMyCmi0SQnUZCem7Hw3Ez4AaWCgVLBwKmCg1MDTlZPQmXek6zYkKlyABPn4zOvmw0FzYYCUUwWJKkkRASJhsgSj9Xt8NSc2GO13XHAGaH/paTQpJGkI0KVWgquSTZ0O3yB32wHFZdZcrKN7d65rXhrW0WrEQyUdH0i3gD+7ISN73k5qnsdMVGw+qGi2jj0pIAXTepYQkiOWPY7MsYzrgQdJLxfIkpX28BOD0Gi0WQHW+bg68H0qrnERpTYmY2Pzlr4/zb5+6X1MYCzckQzpgJEK08VHCyfnEdtZb3ncz4o5TFXUixLgMyZPpw438XezWj4ZnzC/eAEMbi0Ocx3JUmO3Nd233FbdPehXg9ehxUkANjZygNoAQDMdhvQklfIjSKjlLidkkKTRpKOAOs3goTopiGhaUg4WQhCQvmm216W1+T0+3savr+n4bBloqTwu8q/ta3ire1ogtJyQ8ByQ8BEJjrQPjvBvyje70yMaUf/kLxqxxLQ8aah/uZaDn9zLb7Lej+J1wDw+pqMDSeLqQwwxZS4iRMkmkFXPDB6PN90xoEkRAWJ8NFZCx+dDcvOIASJi4O4NK2BQEfs7OoSuh2+ANIRpfGJji9ILILo+Fu/gkRTLHZRLHa5gkRgV+SlpKSMBuk3+whABsbbtbDh0KJE2Gy7G5EjHkSW3jvMcOWIIApBlKOoOv5GC9KHr5zAXmdwp1HFi5h0THcrxLRN4YlS2wpybsYUd+Px+lpUpIgs7TOzLN3xqchjFa/YofQQUnPmSxbmS4HIyD0ONRGlfgRpPg+sNNyNhSdIy02qPpMmYJimZDPSHCdK6rQTK0cslilCkkx/Y+EJEqHdVqF3owffjSIxzzN+/KZsHvUUWTrdljIsRm66jfRve+211x71rgyMjuX2D4sO164oLTeCEW3eu2a/NNfGv27EzL8BuOsNjGU5aLIKALliFq16O3YKKCfbeHMrGCw+MkO1jhiAKzUNEQ4zVk1mLOxyBsm/uZbDJ59MTkYmovStbRWGkTwlcKGiY2Xd7XBPMJ1oFIlw7vJC4vPdK3Q0aT7vQMrz35gs8ot7vrsXfAAkAZwnS/OcgAgtSr0E6VFBi1K9Fuxkoez+WGhU+cJvxbTOIaJkWXJPQSLoXRGqxj+PVKmGzDEUJCCdbksZXUY2kvSrv/qrj3oXhsJWO/qRnSzomM9HB7+Pz7Xx8bnoHNxdJnKgScEGxOfI5DjFKuuGNbA+Zk1vyqLGKVQ5yVkO/+E5s6/qzztdVyqyCelEFyr8jPaLZRtNQ/BXuyni4AdBOiI1n3cwT1UK78a8P1kMokrv7okhQaLpWkJoxRxPkGiW1rN+BJF8rqwguVEkQBxGAzsAptdQlo0mAa4c0YJEQ2SJJk6QaCZn2mg3wydHu+p+b2hBIpCIEi+KBADVjf2erzlqOLBHZktJoRlZSRo1LAe4dd1NmKVFabmhYrmhohLTCBUIZOluIypILBUV0DlPxROkp8eD7GhRAFoPsDr+87/7eUiUgCSJ0ofnTHx4zn2xbj35De0w02dZKSxLFyp6rCCdLYXfc9MQ/BkmZcDfnLzsIC87MDkOFidKW23gxmF/O2LYQL0db4lL61ksrUcjj6LgRtbkRx9IAuD2q8sX47PyC2Xdl6V+BGlsMnguVpRS+udRT5Gl020pw2LkptsIozTdlufk1mw0FRjMEvSKauGQ0xC15q0Y+4kT7uDxL+vRX8dslIUWJZ4gsSheVOHClVM9H9svtCh9d9s9Vc8u9jaxkmjiVjv51M5KbluSr62o+OnFQJJ+8nIOb7zfiggSQCVrD/g6yiaBm05USmhR2mKCgxnvs+twZEpkCl5e23Uf/MRk8GCeHBEU6ryQBbf0wcNCToja5YsGmjGNd2XZgagaaNSTixcRQTKocCQtSrwoEgB/ui2XN9FquudZLnMIALDVCQDHMJKUTreljCgjK0lx021HWZ4sByipwcChCA5XlFwkX45YWFlKmoY6XQSqu8HzkERqOoo0SCTRAa1BRI5ottvAdMy4/rSXxPu8quD/uR0/SJ4pBf/9tRX3OBBZShSkIWHaQqIUmA5wY1tFuRhT6Arh3ndAVJBoiCztJ+StKTHnxUH8LgwELWOHjoVtCxA5x4YWJZmTN1YoumFEVpbo6FEcnY4CQXDg9KiFRYsSAOSKQF0c2ctqSsqxY2S/zUdZhnhMZvi380TpUJdws6pimtNKgub5SQuyY2G5wT8NThejtzUMEYunppBrr/kNVJUh5KaMeTI4pjnY7Uaff9uLphBZepqzwul/POsOkrQs0XLE8rUVFR+fs3C7JoZEiRUkcrzrQ2jySzAdQPL+P0213luU9psSsj0+e8CNiPzExSAs9S83AmHiCRIdRZrKAOYQrx66IUKlVjbGidLUbAuyHC96QFiWegkSESpZcP+GFSVe0vbcfBU1r3RUI6lA1wiTTlOljCojK0mjhExFBKq6gLIaviAp3v3sVNu2N+XEk6Ua9diTBfd+WpZoQSpPjqG6ewAAOKCEJef9eifTQyXtwaRh6YMVPPuhEyiVHOxRzjMZI0oAMJszsdBjCTiRpfd2Y2oCeHx8LgjD3K49vHQ9ImF0rSjdAmY1frHLat2NfLGypFEff9v77HmyFLc6iwjTKqeJ/cOcZiPwRImQywcysnonj4XTzcTnWltxT+hJzYAYk2AVNz0neN+vuOOWkk63pYwuaeL2EeAPf+/PQnlBVV3Ayp1t/99dS0QpIXF7m8rPqelSSJBoThZMnCyY3AgSEBYkwkbLfW7TGUwJAMIEEzmb1MJi+MyEiWcmXAHY7SjY7cQL0H+7qeG/3dSw1gTWYsZSWpDY18kpjr+xUbtBo1vhfDC20CUNkSV6ZSJLuy37wgT0HuibFnCuZPobj6R9GgQmp2kzIZc3QoJEWL2Tx+od/mozIkgE23RgMyG6XvlLB7tZbsHIciVZzo4LjzrZOk3cThkWqSQdUaq6gK4lomsFHyGvuCRhtamGHsujboioGyJmc7q/EZIEiWYQolRbXubePqk5ITliYUWJyBELLUsfn7MSBYlFER0oooOps4OtkSSLDmTRwelidF+IlGT1rdDtl2d0NDmH4kwpGn46OMjg8lS8SDctd2M5VzIxkwMWOL3bGsPt9QvAjSYJAvztcD9ZZmhRWlsphgRpciYsV0SW+hEkgmmIviyxgnRcp9qAtARAyuiSStIRgkSTcpK78VYzEVHa23enTnRbhE71VNMkh1tgsB7TVmE2pyOvWPjQRHzDTwAY95bnSwnJwv1w0A3EayIDHKy6YqBI7rbdkrHNkTMCiSrx5IhlrQlstyVsc5bG8wTpTj0QxbWmgHKfzWV74dYkCo5bkigBrhxdngkEdr8brRRO6HRkdKhWLedLDs6Xwp8RT44I/3g9kA6eKA0Tx+G3YeklSrevlyAnRFZpJmc6UFQLSszjaUGiSWxDYg05s/0xxHGckdlSUmjSnKQjxlTG9osuAq4oZZgx/mRBx1JNDckRCxGlQ05xSpq6ETw5LUrv3ng4p44DfhLxdkvGdC4qMiXFve1TJ90/+upyf/tJi1IvQXoYnC5aod+0DcMrWzATPwATURr3HKKT0MeOiNK1LWCxwH8MLUg01kMYR3RdhOLlI2mahW43fBIc7muojIfNUGcec+rcIe7eqnCff3ImGm0jomR409FxggQAxVILW7eD46MfbKBUdPe3mjBdOKqkEZiUUSWNJB0RSmr8R0VHlJZqKpa8isQZKfnCtVRLFghakGi22zJsB/5GokhnL80lPl8vyCK5g66M3Q6w641j01n+qExHlEqK6QsSzadOmvjUyf7nha6MdyJVqllBulN3P4t2H8UK+4EkbrNL9peXttAwXEEiVFR360WSIBHa3vPy+rfxBGm1ERwHw3ZXHg4DXpNkTYtGekhESe9KEUEinDp3iFPnDv1/T850uIJEo6hWT0FiUcfnE58zJSXlaDKSkvSlL31pZNuSAECe0/Q1J9u+HNFkJDsiS0s1OVGQ6oaUKEjR2/hTVg/CMxPh/I44UVpvytw+ZpHnG+f3OyNcGe/gynh48CSyVEheFDcUNlpSbDI2wBelcS2IIk1PdDAds+qvbQSCRENkabUZ/YxpQXpYGMyUFitKliVgbyeD2fneydOnzh1idyfaFJlHvmhAzZhQM1G55gkS7KCMwvbm8aza7Tj2yGwpKTTpdNsR4XvfX8aHnz3p/5uIEplqe/NaGx95Ari6z196lJFsTGYcvLWdvDRpoWBAlN0pnduMdPEEyUEweFq2EFrGfq/06D+L6ayDpdUDnFkYi9QP2vdymca16MC2QpU2IKIkU+MvK0c0JMpDRCmpQOODstcR0T2QfNHpRUUFqg4SH09EaXvP/dx5ckQzkwXMLvDsQjCVtbMafkyvz+lB0RJyvSwrphTEfBOba/zpwQZ1Hl97bxwA8MSVaFVsXrsTIkp6R+YLUgqAtE5SyugykpGk48ByQ0SWU2X4Kc6Af7HcxcVyF7Kl48XpDl6c5kvBQiE8SJwt6f7WS5BofuO3frOft8CFjvaw0SRFcDCfs7n9zQj7VOL3SkMOCRL7OqYNHCTko8dV2T5fsnC+1F9icL/sde79q3i77jbHrfaRJ3xquoM7dWArxgdnsu7G49aujG5HQrcTjpLYQy6HAATRJJJcXSjEWx4bUWrU1JAg0Vx7b9wXJoAvSDRTs03IMiCzp5MdbSB9HHnUK9LS1W0pwyKNJB0R6FVjy41gQOW1TiCiNFGOH3iJKL21ncHTY8kX+vcPNMzla/6/N5pyRJAmBlBojzfkPjNhhIppAsHS80LM2bvaVKElzVV5fOKEju8sAd/ecKMs/8NcYBA8QaooABbmUVtdA8Cf9rwfbACTGRu7nijtd+OjQ7fr4X/r9QbUYgFVHShzfIDXcoaI0owXVIyTIyBaV+p00UGrZoeaLA+LTNYVUXblWaFgoNHgz4ESUWrUEkqrU1x7bxxjY8nTdeVK2KSJKI0v5rF/N51qA5CuCksZWVJJOkLstB3wLv2sKCmCO4C9vZPD81PJUwRnSwau7mt4ilopZGfyEDtNvH/AH6nPFB1YWfeX92ZbwROXZrFzx+3LMIjKzCSaVFTclUm5jIk9TiJywwyLUotJpC4o7oW7wbQP+cQJfuiFyFKz3sHJQnhgrgwxL4mnG6wokeiZ5LjRIx4kolRWk/vxEbY6yYUh4wpvAsBM1sbdmCrog8C2RADuZ1DO2KgykbY4Udreco1v4ZwFPaZoKmFuwc1W7zQFmIYImSO9rCAR2p4kSpyXEIXjF41IIzApo0o63XZEIMuub13fwMlC9IIkCA4UQfQFifD2Tg5vcxJW9zoS9qjpk6v7Gq5S9WfiBIllNmugawo46A7uVNIkxxckwgQniRZwRallihFBoikoji9McYIEAD+92MZPL7qRgeWG5G9JgvTbv/1b8XfeI5MZG43ldf/f+11XjtjpxV5L8FeaAq7diVnX70FWJtL/TUfPaEFavDAGALg0FXwGzaQ6QQ8I7/2VM9Fznky9bW9l/Y1GVS2onPpHcwsNX5Bo6EKRQLwg8Wg2ZMiKDVmxI+UKUlJS9mPNhgAAIABJREFUji5pJOkIIQhBcT1alE54bRo6to2MyB+8SFRpr5N8Ab+6r+FQBz5cbOFOnbNaTgz//V3qMQddEcUHLLBI6jcddA8xpkVFiY4odfwxm0Q04u3BtAS8vODOM/GiXUSOWCY0oFwJBtobh8H7l4b0GyNPfSvPlmxuHzmLE1GqMREzIkpPnA4LQVyuFblvuw0oYjhBmxakYUPO8VZDQY7KQSpnbNB7UfVEfvZUC1ub8SvXaFHiyRGLaYg4c3Ef+zGLHNoPYarxqJFOt6WMKqkkHRF4UyyOA8wziaxxojSdM7DaVLBQMbEVU7H6kAmynKYaqB50ooLEUtZsbCOod3SvZGUHM1kZW213KIwTpf3Y8VoAK0omZzWU4nWTJ7KUJEgsy01A0MWBVdumicvzSRIlICpHLESW5udqiYIEhBvqKt5LDns1271QjYlwzswmi5KWsXDx8j7qMYnchGwu+D6NUwsciDDxBKlJLQ4Y5urHx5l0ui1lVEl/Eh0xBCE8vbTGycvo2DY6tnvRms4ZmM6FRWomZ2KGqVbNChLNf2xnsNpQcJMZqO9yIk2D5qAbFAJcb6pYb6r4semklUgCSGSJJ0g0iujgyTkNktDfb4XX1wJJrOoiOtbgBga6LQyvLtPZUvi1LCfYxvso6vjC+QbOFoHzMfnMO52wINE0OhKubSm4tuXuGJlqK6kOfvf3/5eer/0gtLzzu2kKaJoCymPxU2Azs9H8Oy1jQcsEkaRiSUexxD/Zs7n482p8ugNRspEvhL83zZjVk8eNR92UdpD/S0mhSSXpCDGX1TCX5TRs5YhSVpJR4lQppiGy1EuQaG7WRNysibheDQtSmVrddvnp+6s+/Huf/YK7X1l6Gb/iyxFNkiiJgoNLlQ6emghHiFaaQc7KZkvBZis4bpIg+xvAjyLRnMy7xzauDMK98Ce/8we4cy3c1DdOlIgYsYxrDleWXjjfwAvnw1NM50uBLO0J2Vg5AoCDVjh66IBfEXtYyIqNJtPmo5cozcy2InLEQstSNmckChIAGFTieL5g+ltKSspok0rSEeGPfu/PcKiHB/31u9v+fxNRykoyshKVt2OJ6Fj8j1kTHWiig4/PdfyNhhUkQssQsXhyCh0b/kboNZ3TLysNBSveeypzCkQCrijRsiQKTmS646mJdkSWaDli+da6gu0O8EE1fDsdRRoWuYSgxPf3RHx/T8RYD3kjssSTI5bzJXf5vx4TEGMFiUYQ4lfZDQJBcPzVZoecKbYkUQKAdkITZJr5iS6q1fiDanTEkCDR1A8VXDzT8Ddy7mVzg62hdRSgI5tHfUtJoUljxSODgI2WirNF/ojXsUS/PYkmxl8JiCj9v7fCgtTOjyPb3EeLs6qpbQKOl99j2gLkhOfvxWbbHWAuaXZoxVxZM1Ht8k/XZrWBYoVfbZnw1EQbWcnBD3bipwi/tR6WJyJKE+osgB3/dhJFAoCLl4fXs2si48oRy5gWXwSz6L0FtjwCS50JnBBRIi0CeYJ0aSb4o5W6CBFBkvUgMTnn2OGBhgojRuWxbmyOEhCIUpbbCDm847Qolcvu68TJEeAKUmS/veldtm7ZceBhFBZNSXkUjGwk6bXXXhvp/m0BQQ4OANyux3+kux0F1w+T25Lcqmm4VdOwkLf8jRAnSCwP0ppEoHICxpgClXREaaOl+BsA1HUBdT143fe2w4aQ9VbNbbZEbLai74MVJMKE51SXx2x/87EHn6yakwFNgt+3LU4CxjSEokpFJRAkQsMMCm/SsIJEo9vA6QLw3HQ4GkIL0sPCYqbZ2IjSWMHE6cVmz6hRuyX7jykpTkSQWKpVDXd+FF+MkidI5UfQ3y8lJWX4pJGkI8ah3kYZQd2gjGyjw9QIul0XcbZoY8auY0ssosE0q313383NeWY8PA11q8YfkBfyFrKyjf2uiV2qPQkrSOMZC+t4MLZvLmPq/KnY++cL/BYphLouoKgGgyCRIxZalJabyYJE0zZFLHki6oBfJfx+cJz45PnqgRY7vZSRonLEQkTJ7GNnX543sbTi/jctSqvLMX8wJBqrSygsnOHeN8bJBWq3ZG7EiGCaQXHKXlw43cA7eyrWVoJaU/OL7tRlL0HK5mz00SVm5EinqVJGlVSSjhgtU4RguFMxhDhROuiIqIzH55UQWTLM5IhIVg7un8wGA9GNe9nxe4AXTSow1ZAlwYEVE+Kv6wKyUrwgEc54/de6ovue7tTdr8OVUxVsbBxGHt/mFKwc1NhAN9yNFjKIihItqMsN9zicLMTvzVZNxuXJYPhuMoGhl+eDJzyzWMDSSpDPVFQtrFDFJfeoaOXk+cXY1xwEbDRJTUjG5omSyXxmpFwCL5p04XR8DtfaSgF2s4tc/uFH1I4Cg8pFTEl53Egl6Qhx4+oqFi6d5N6XoUTmgMqlKKkWagntGZqGgK2mO3AscgZZWpBo1psyTuSD+9oJg9f9cP3aJi49MQtJdFDtyigo0d/nPFGi23F8f8+VwGcnonWQznAa1J4uugPseh9JvxMZGzUAwgCXes1kgS1vV+NEqX0i/u+XG0JElLZq/PeS96IfTSMsSCxFpmK1ZYnIeMK69xB8gbdCrWkKyHOaOwNBHlJGSc4kYGUpSZAA4MlLVVx9G2hRUcdc3kin2TzifrCkpBx1Ukk6oizVZZwphge3/c19CJXJyGNL3kBHy1KTU4BwpRHcNi8mCxLNqYKM97yHXnlqFu9d3ezvTXCo6gKmAWQlG5KXAN7aXMfa7AnMF/iiBABqwuIzWpZ4ckRDBInu7uLY/CjSsKE/ofNzbhRp/cAtrnlijD+pQ6JKhtXfV/vymIWvXlPwUyejxsMKEktBARpDEiVBDARJ06xIq484UVqc9SwzL2FnNzn/DgBKCjC3kNzg9slLVe7touzgveXgNbI590tg28fvsppGklJGleP3bT7ilFTLz6xYqst4FoBMLXsvazaqMX3USqoF2TGxayYPHi9M6Rj3LvS36+HBmBUklv2uBFW6/4RmRQQy3jTZhCZirxs811pDjYgS+QU7nTOwnbC0HwC+uZaDbto4FyNKcRGkF2e6uF53/6ZhSJjg9BEbFDNU+zHdBhamushwdmv9QI0VJQC4MuMK9HtbwR+/v6v6U26nmAa+X18Ojt1PnTS4gmRRpSQOqyqy6K+R7r0icE7fXqLkyxHF1KS7UpMnSwu58KjeoKb1Ct5zxskRABTK0WPPTg0eJ9JIUsqoMrKr20YRlSlMIwLIcaI9ZS16myw4vkzN5w3Mx+RWvDAVvvifLar+xhOkU0nrzIfAWsPNprYcIXJh5lUXB4C9joC9TvDYWzXJ3whxgvTCVDhhuqBYfj2mM08s3Pf7YKHzp3Q7XLuoEzMbtn7Qu+L5lRnTFyYCK0gsqw0Z1a6EKiUlVkytrWGQ9Vq+sC1ENKY46trdAm7cKiKTTX4/RJYAV45YQWJpmAKUhLYzPEHq7AXHymilwpCSMiqMvCSNYhmAJytG6INTOQnKZc1GWbNDcsRCZGkmL+KFKT0iSDSG3UFFs/0NiApSzVtFZ9vAb/7Wb9zjuwrQqEjUhBY+RZsmMNajkjgRJVaOeLRNoW9BAoB/3wgG7vGMih457/eE7bgbr6xCnCipIvDiZO+5jiszJi5Pm4mCdHncweVxpn6QJ0uLMfW3HjYr7x1i7W4Ba3cLodt3qsnCODXZ8Zsn96JVd6NqN5eKuLlUDN3XS5COK+TcHYUtJYUmnW47YigisHRjA5gOJ3CrkgOd6lVGptwuaCb2Y4owEl6ez0A3RSzVo72vDJvfs+L5qS62V9zntZzBtWfgKY3juHJEeOdmHc+ddwev3U70vTUMETnFgibJsUUXAeBELn7g7yVIg4a9NrdNIMu8tY4JwJsVOz0RHqxfnHTw1m68EJKL/42qO6BfLIdliZWj0Ot6h4mI0vUHrfNwD9RrKgongFMTJEJo4Gad/9idqoopjsRUa+5Bm5x0sON9rFOcahdEjliIKGVyO9z7aYRj6kvpdFvKqHIsJIkXTXrttdce+n48KCb1M+dSxcB1pmaLKjncfKRxrwgjK0tPVsK/ks8U3S7qS/UW9kURRTMqTQBwthSe0pIEGZOnTsC8s+E33n1QNMnGLtXvRAS4fcYnM6YvSg1OsUtScJGWpSQ5AoDZnImNDffYzsT09Kpo7hMbGNyoaNiuBCfx8VM6lmMWYpGIEi1Lcb+MiSwBwOXx+AhihzlUkxkb1wGUSjqEpoP2AxQO7YfxiQ4lSC6FkoFGjS80JKI0lQ/kiPs4RpbiBIlQr6ooZcLVwGXFTqNIHmkEJmVUGXlJOooyFIftADVdRMnLl7hUCQaPmax78bbyJjZikqvHvagSK0csZ4o5GHYHVU7eKitIALDWDJJmc7KNrvVgAycpFlk2xVDOVZwozWQNXChX8M5ufNiIyJKUkGsCuIJEs+UlgxeVhz/d1DaBDIAcM36fLCBWlABXllZ1fpNclu028GLRjSqtN8MDPitIPLIxS/EfFFFwMD7hRjFvrGdw8UQ4ohknSuNe01pV7C/qZwPISQ74Pwdc6jFTebYlws643zuxYx/bKBKQRpJSRpeRz0kadfKy7QsSYS7Pn/4qqwLOFC107eSaMHFTbP0QtAa5v8Ez02NlHP1OJcHxSwAAwHOT8X28KpqJimZiqS76GwsrSISLZfc9vXyyg5dPdnpOXz4IBvX2D3XgbIl/HE8WuDfjP/fcDXCX58ct0d9uuxvNibzlbzxBmqRW9Z0ZYo5SR49+NjfWoyvUCpSwj5d0X5AIUxUDUxX+AZjQ3I0wWdExWYlG1OIESWR20c6I/imv5I9fWOVR5xGlOUkpw2KkJWnU+rf90e/9KQA3miSLDmTRwebyFhpGdHCfy5u+LJVVAWU1/EuvazcismTYnVhBqurAhbIDSZAhCYEk0FGkQdA0xVB3ed703eWxbkiOaJ6b1CKyVNH48kNkaTZn9hQklts1C7drg+/2npEcHOrhFiWz2XhRIrJEyxELK0usHLHcqUtYa8LfgLAgEU5fTKhsOQB0RpZ4ojRzIikG5MKK0kS8S/uy1NqT+xYkAFg86X2XHMB6BDW1HjW2I4zMlpJCc/y+zUecnGwjJ9vISuEGnDxRapkicj2miYgsJUWPqpyUFUmQcaPaRZMaEOgGs09cnk983XuBiNITY1084bXm4K3oo3luUvOjR0mcKdpQRcffaHiCRPeoEoTBTjeR+lCTnDJWcaJU0UzU+izoeNgF3r6Zj73/Tl3CnXp0zkgEsFwXsZzQPPlhki8Y/kbYr/VY3VYx0LWEREEi1DsiLr2kQzJtSMzyxURBSklJGTlGPidp1MjKGtomP/emYZjIIhp9MRw34qFwkiZM2zUgh/oFJVCP4wkSABzqQRSFiFJettE0RSh9NhJNgkST6l5l8I9MdKAzScJElHQmB+rb664YfXTR3fnbdf4AypsyIqLUS5CGQcsUkJMDUdplvHU26+D63QNcOjUWkb+LnjPfqEWfly1TsLrmitLCfFBpmidHALDIONVyXcSph1gOQNdFqADaXp7dD35UwNnn+Y/dr6mRKTfALToJAJICrHs1jE5waiXVO3wJJKIkKzbsHvWiZE5PuONA2uA2ZVR5PH4aptwXbDRppyPjQrkS+3giS4ArR0SQWBzHguNYPQXpQ5eC1yINaPOyjc4ACg+OZ0xfkAhspMe/3ZOlb6+bviDRnC3qOFsM3syZop2YU6OIDrp219+A8CDwY09PgrRsEwbW4jYKG1Ea00w8WTESo2MXw6dEYh2n1bU8VtfysYLEg7R/sYEHTtCP448/91lf2mXF9gWJsHonfuHBfk31o0pNU/AFCQAsKtq63hJ8YQLiBYlw4bkDAIAo2f4GpFEkwqOeIkun21KGRSpJR5CsHMwZZKUSdjoydqh6QR+aCEbKHyyHB9Tv7aq4dph8IbC8/41pkr8BgFIphCJIhAJnSq9l3d9Sny/87uf9Zr3znAR0nijttmVstRu4OJaccD6VMfFTC21cKPMjcYroQOE8f9fuoqJmUFF79wK7X8jFucW0tnjzvW2MaSbGKDHabcvYbccHgS+WgDOFZEECgBcuNPHChSbOl0x/o2GjSDyGpYiO4woSAEzNRvOOVu8UubJk2+52825MZjvD9U0NN26VEh9DBIlleqKDra0ctrbc0hnHNYoEuD8iRmVLSaFJJekII9nudqYYlZ4PTZRCsvTuXhbv7gWNwW7WNNysRRM0rJipsjHN7ck2poYHZ1aQDrqDXQcdJ0pEFFhZiBOlOlND6UK5G5IlnhwRVhvBUvOKmoEmOX7+0LAwbXerJKTa8ETJdNxtq2HiQgm4EDP+v3CB39SVyJLJeXu8JsJx1dwHDU+UgCCqROSIZmMtj421eNOj255srBWwsRYVqzhBYlfCbW3loHvnvjjkc+Nx5FFHf9JIUsqwSCXpiPGDH9yCZZvQ7fCgwRMlADBsISRHLESWSPQojqYZJMmOqbK/PQxoUdrtyNjtyFASBqKLYx1fluqGGBEkmgvlLp6cy8XeTwsSALStYGlYRnIjT5/7nc/0fA/9okmAzHyUtxOKIhJRInLEg5YlEj2K4411GW+su8+51Ag2VpCMIU210QjMS/BEqVDSuVW2aVhRqtfUSF+44LGuLBUzdkSQpk+75wmvVICWcb87eleCPKTaUY8zjzr6k0aSUoZFKklHDJtq3NFGVJS+efUuAOA/d+r4zx23f0NJtf0ClDz2OhLe3MzhzU2+LNCCRKOIGeRl298GGUVik8+JHIVenxKlm+vhgetM8QS+v5fFLSZaZjHJ6znZFZC9juRvBFaQWE7kBtumZDITSGqGOZQ8UWpZ7rbfBWrJngAA+PCUhVOF+POAyBHLbBaYyDj+9iiZmm2hUNL9jTAz1luUWg05Vo5oLp5zs99FRC+QSYKUkpIyeqSr20aM2ZzhyxFLSbVRo2rP0EJAoEXporYf+zqKGM7P2e1ImPK6sc996ATe/sHGPe03zZ0PVnD6yUWUqfDFTE7AVis6QBNRIpGNM8Vo7R4iSudKwfQakSMeex0JRUfAfN7EGpU0TEeRBk0/Rexu1xScLRloxYzJNR0ocRygxDQEpkXpbsM9H5IEiUUWHMiyg/ZDqAdER5Om8u5+i9MtrG9EhX5mTMfWQfQAlKlp1YwnNB3OuU/kiIW8y7WVAmaKh4n7K8sPb+Xf40Q6TZUyqqSRpCOIbgeDHokmtS0dbcv9lfv0eHxEp6TaOJ3pcAWJ5sdnWigqWX+jYQWJR78d13nEJVDP5OIvxBaigvSxyXLo37dqGm7VtERBmsmZmKEKS87nTcznTVhOOIIw6CgSoahY2LztdpBlo0lTWRt1I/lzq+lBVKmkWRFBYjlVsPHv2/0LkmkLKC8uuM+v2nC4LYkfHM2LfGYlIKfZviARTszxc5RmxnQ/qlQud0OCRJPJWL4wAfGCRFhbcfOVikqwAWkUifCop8jS6baUYZFGko4Yqlft+sYHG7j45Bw6lgWBM1ARUfrhfnARv0CtXjrrTVXc9qYfNEVE1+uJ8eMz0QGIiJLBW11GCZc8QO1++4ereP7phdBtRJRIenaHyo3Z66wCACYy4b8htL2VY5teKGY2FxaOmZiq24YNjM+PYX8tmsQrCuJQf0WPZxxMZe8tOnHWyz+ayxnYaMUL4aWKKxBvLQN56krQNPmC9DAxLQHZHrO3J+b4EaWDloTxiQ4ss/fnYpkiTp3uT5BYJjPAtldaolFXj20UCUgjSSmjSypJRxRRkNCxAgHKSRpaVvRX89PjEgql+Lo6tCzx5IjmezsOlGaQ9LuQz4cEKbyDD2ZLWclBOyE5WBKcULVvmr3OakiU2jGDJS1LSYLEUjfsoU69FRULOWo5+WpTxEI+fgDO6Q201IIvRzRzOTefjJUlIkg8fnpRx7984P63Rn28pv1wB8J6R0TRa4fSNMMiR3AjShoOWtHzUPISqONkqUnlnHW9r5LGPE2cILGPKxR1SHULrY4E9RE0Q37UpBGYlFFl5CWJ9G977bXXHvWuDAQbNjKSG/1pGGJoCT4rSnXDlZ4peRFtMz6X4mJ5BhfLwFaTXxjvezv8K2DDrGGCmm5oDXHy9s7tHVy5+IT/7wNYfoVvHnudVRSUiVhBojnUTb9qN93uJE6QaERIfeUT9cu+l/w+xdyeJEo1Hbg4mfy8RJaUXPL00MfmwtOKRB5kRpAU77CbD3F05ImSKLhTcvyF+i6S7IREqZmQkE/er2GJfQsSAJjeeZjLWKj3cc6NGmkkKWVUSXOSjhiWY8F04lfy5CQNdaPlCxIhK1eQlaPVuC+WZ2KfS8k7sYJU4eS6OAgXF/zMZ34z9rnvhc2Wg01O0jbgVvhmKSgTKCgTAID5vIH5PH91XkZ2kGGWa+uWAN0SsHBirOd+kam+S1f403v3wh/89h/gvR+u+f+2OFGb1Wb460rnH63Fr+r3udvQ8N5+Bu/t83PKWEEiaBJg2eHtYcFWwm6arhiRjXCimCx/kuxgZzOXKEgEw6sYf+5MA+fOhH84JAnSccZ2RmdLSaEZ+UjSqNOgagB1vSa101IBbYs/aBBRmsr2rkicl0u4NLaN6wfhQZUnSBNaBcvYBOCG3sUB1GK2HCEkR+8f7uNyZZyzn+6oTcSIBxGltaY7SLJyRDPrpbnQUSXdEiJRpGFj2QIkJgdstSkiH5MvRERpnqmfeLcRLRpKROnKuHvOJAkSS8cGWgm1pwYBTxJV7yVLCrhNfU8ULaxzWqxsrgYH5HDfPRaVcf50o8FpqUNEyXEA9JDRdJBNSRkt0p9AR4zf/90/AwA/mtQwJLzxgy1fkAhZSUJWig4YN2tV3KxVcdBdw0F3LXI/4MpRXg4SXC6NdfwtTpAA4LnLTzD33P+I0fB+nU9nw6/3/mG0LMFhV3Y3Pb5kASErO9jvxv82mI2pK/ntDQ3vHyh4/8CVrM6QiinGzV5VlGDbi08nAuDK0lrTlSOeINHIooOCAnx/N7paL06QCE0D6A45T2m6YMO0A0EilGICQieKlh9V2lzNhwSJ5nBf84UJcOWIJ0gEx/tc3lvJ+BuQRpEChBHaUlIC0kjSEaVhiMhQy8HzchlNsxp5HBGlm7XofQB8URrT5r3nSe5j9eZmEFX6yGy0BcigfkkXFduvlD2dtbDdDt7r+4f7uIxxLEnRCxoRpYoajjixYrTuRZROUFNxcYL0zdVwJO39AwV5L8LTionYDYqlmoDnoqWffFGa4DgQaUy80nCPz2KB/6E8OxlOPqdFSZOSk/iHjSLbmE4ofAnER5S2WiK6dn+XtuqBCqPrYGwyvu+fE3NO3zmUoHg5gYYhHuso0sOcgk1JeZikknQEIVNsp4sO7tQDUYgTJTdpO/kX0tWDLaiQuCuI4nhzM4N5wUFVP0BZDefwsC0l7gVBSP51fr6UgW63cGlCwvVDfn7Nob7vi1JS5Gi9qcBygHGlDV5glRUkABjXABLMGcbAWFBsLFOpMCcKBtZjcmn2uoEoVWNS1VhZYuWIZTYLfLcd/jcQjiI9DDZrEmZLroS2TCDH+RhJRGmLs2qg7E2pVff50TSB6jt3sOt+zqwsxQlSoRi2M0WxcdCWkcuaxzIYka5uSxlVUkk6gthOkLTKEyUe8zn3KrbWil7B11vBabDRcpMu5nK9W8A/P9XF1rL731X9ANerKhRhsNEkQtMQcb4UFZZLlU6sKN2o1mBJyUUf6Ys7nXNUVMRYQWIZ1JioiA7KXhHFvOwmKROSRMm04wWJZqUhYFZL/nB49ZE220DNkzYSbWt6jtDWH5458URpuyOgfquA6ZjikkBUloSEprxElgBgpsxPQGIFCXCbLgNAqy0jrxw/Y0glKWVUSSVpxOhaTbStYigf6fCghcqYO7oRWQKEkByxbLSaqOkSxkSANw4+PxWfGCMK7pLgQVw3l6ieZRutJlfeWFHaboffFymT0GCSjZMu7PtdB58+4x6zv19yB2BWkA4TIlTDgBWlMuVMT3hufI0/qxrih3vusXp6Ihw1iSsgmZcBUm5x03ORwXXpS2azJmFuLvh3y3TFiGV7I5coSoQkQSIUvdphK+vu5794InjeJEE6zqSSlDKqpFmHR5C1GyuhaM25koSu1UTXCn75ti0rdoVb07Tx/kFya5GaHgyDqhhsrzyjhQRp5qRboOd6NYjYdL2k5qeeXuz/TTFoYg6amMPl8XCiEIl0sVyqdGA7UUGiKSg2Cords/2AwSQjf/pMDp8+k8NEJr4o56Cge+vxpj7n8gZemLJDgkTzRDkQpl78cC/jC1OSILEUFFecdRsQHmReNQFe3R1JcLc4KdneyGGbU4G7uq/5UaRGTUUjocltsRQNya2s57CynsP6du92PIqatilJSRkl0kjSEUYSgo+va1vQxOjv+7Zl+VGlphkOCZFISEULD/60ILE4DlBS3L+rGe7f0YI0GBzodhuqyB+56YjSNSqC9GIFmMyY2O3wT+tDrzr4sxNBws0HB+HwECtIhHf3XDkjovQfDRkFBZg7t4DNW6v9vKn7gkgKmxtzoWLjR4fxv3GeKAOHffy6/86WhAkHWPeCJScox4gTJIL6EMJJjg00mwpKhXAERxUd6DGf1fZGDuWZ+FwkAL4oFTwp4skRjeSVmZjKBxK005T+f/beNEaO7Lr3/MeakWtl7cWq4r41l97bkm3JkKCmDDyjuyVLIxuWAKExgAHDgvS0tbyNntTSPGPmecZvBvo0hkfq0YeBYXVLsjza3Iv11HqWF5ndTZFNdpPN4lZk7UvuGet8uHEzbty4EVlkVZHNzPgRARQzconI7f7y3HPPSaNIPv2ctJ7S26SRpLsQzyPNRR0vLDdtv/Ht2nw1dLnlORFBYqFL6FfbSqIgtbkfySVNxaCexQPDTK2mLVga73FGwEeTAMB2zZAgsYwYNka4qM9aTPuUI4OQa2AQAAAgAElEQVRtHBlsw3KlroJEoXlDNQswlK3LyaHNYmk0qe2S7fy6OGx0sCx+7CHDxpBh49ighWOD4kKaABEkAFiWg+f3eoNsewVltArd6zBuGRnFg8ecXkWQjxUnKLm8jQM74lersQyNtrA4H7Os0UeJ6clWMBzUmgpq/srLfo4i3emmtGmD25TtIo0k3eU4nh0bUWKbzQ5lyNL+lXa0mecjI+QX92tLdudLgl1dz8sRRZGCgYuK0oVlDRXLEtbY2ShPfe5p/OAf/hqmG0R8qCiNGsFj7iuRab+LFXHEYMSwcblloGUli1vNklFl5LDIDHa8IInIbKEoAUBR89Dm7vJCRcOBUlR42IjSUMx0IBWlM36NJypHcRwccPBWBRhjHXTXKKo3Fjd4BluDrHhwu0g3FaVcPnruDx8jCVr/fkY8/7hrf/BZuHg+qEa/72DQwidOkJrcZ6LWVJDXPNQtCWv125Wx9fYhlYuUXqUvJIn2b6N/9wKaHJ8f4Xp2SJBYqCwBFcy28x1B4qFfehsRJIrkR0JKmoZrcCFtUaDyzTUyF3RoVDz9tq/UjojSeJZk+15uVWH4q414WeITuSlUmLL2GvgUZRpF2g4UyUPRP9aiBlQ5JxKJki4Dx4ZcDOesrg1ojw1auLTeXZBETOaAy/5NW04w1XY7ptwolVp02s1yJdiWAiA+X4yXJVaORFBhGiqLpZAXJAAYKjhYWQbymofVtMFtSkrP0BeSROkVQZL9ZNlzZ6/iniM74Xg2NG7VTsEvClmzxQNCXlXxSDG5GvPekgm3EfzCXmmTX9hJgsTiYXODRdWSsNyqgH2bzjZsTAkK5pCoUq4jRyIMZml2nCBRiroDux6Whkur4fMZ8TvUb8kyPp+85qDuFwmNE6XDQ9EK1ACpng2gqyzdPxQc8GsrwXXjBIkXbkMBlG0WAXqEshJ+cis1DVnB+VXWMiiVk0uR12sa9hxOFiQAeOjBJQDAwkIgXo06ec/FCVK/k+YkpfQqfSVJNJrEcjeLkyYbcDwyisqSCteL/pouqKWQKLWdYDmzA/LlrvjRkvtH8nhtqY69MUmsQ5ky5hoVOJ4lFKXOcfnjZ7fBOgnT9RBXgUgkSlnFgyFLWG/PYSAzEXu/Ky0JZl6KTfwtxuSVOLBRvwPd3Yvc07wj5+JgycTlWnyyvCp7G37u7x/ysK9k4sbyxgSJR1D0fNugOT+NuiqcXqusEennZemn/zISXMdP1C8NioWKChJPLm/jwQkbpy91OcY0y7PvuX79Ol588UUUCgU0Gg2cOHEC4+PjsfsfffRROI6Dl156KXRZoVDAiy++iJMnT+LLX/4yAGBmZgY/+9nPkM1mUa/X8e53vxuyLEcu27dvHwDg29/+Nk6cOIFSKbmTQko8fSVJd7MQ8ciCqawkUWLliIeVpThBAoC5RiBbVM4AcRTp/sN78O9nL8feVzf+/dXrOH7vNACyomyZW7E227BxHxdlWFmtYWiwECtKK1x9HZrPQmUpSZB4fvW+UfzzqcUgmrQF0MTxvBY+jh256GPsLiSL0lJTxbQBdKtWtc9/vdlIAC1U2k2QltvbP9dWyrrAymWoE9Ohy+NECaCy5ITkKHIdTpbi5Ijy4AR5LFaCLDeNIlHS6baA73znO3j88cexa9cuXLx4Ec8++yw+8YlPxO5/7rnnAACPPfZY6LI//MM/xBNPPIGTJ092bvvcc8/h4x//OEZHR7GwsIBvfvObkCQpctnnP/95nDx5EtPT06kgbZK+kqRewWWW/WTkPNpukFzMixKN+Kgy+Xa3XbEEtR1SUvlKlQy8u4rh67GCxLLadrBmBiuJiDBtzTcmmxDN1iiixSFn6xqm8uLVW+vtOf+vfESOfrGcxyPDwXOmyx7uncrizbk6dE68RILE1vDRJDd2KfpmyfoOUtJsVKzoR3V3gbxGrCzN1cOhJ7pijpelfQkyTIXJdruL0nahSL4g+QxmXaw2wwcTJ0pjO+pYMQ0cf3AJp1+JFyUAUGQvlKgtggoST9OSQYu0a3J/R5FSSQpYXFzEDr8C6vT0NBYXFze0P+k2lEKhgDfeeAP5fB7nz5/HwMAAHMfBuXPnQpetra3hxz/+MTRNw+uvv44PfvCDGBjYYAG1lBB9/LG+e/mTP/rPaLtBZCgjh6tQy5IKRdKEU2KqrEOV2cKPtY4gsVyp6rhS1SHnWomCxELr6tiyBFveGnE4d3au83dG8TqCRJmta5iti6f+hrQJDGUsHBiIF4KcqiGnBrc3HQmmv6JKJEjbDS1FsI9L0C5p8ceyu2Birq5FBInFgwQPEvYMOImCBAAzVQUYILlothtsLGwU6ak/+ePE+7tVrA0E6Wiu0NiOemdjOZ4QIRpkKo6v1dTOxhInSJV2+KvTcoG1toS1tgQICmH2Ond62f7bqQSA53nQNPJZ1HU9UtJEtL/bbSiPPfYYXnrpJfzFX/wFXnrpJTz++ON47LHH8I//+I+hy55//nk8/PDD+OQnP4np6Wl8//vf3/yJ9SmpJPUIFy/eCP1fs1xoCaOMKutCOWLJqQ6WmgqW2yqWuRYcvCDxtNw2NFnCZz/7qS5HHvfY5EtCkTysthWs+oNyXC0jVpSGtAkMaWS67bBBmtweGDAjssTKEU9WdXFm2cCZ5fAqQjaKtCO7tRKly16ogOOO/MZFKae5yHVJpl5uKVhqqciq8SPBTFU8hba7aKHloLNRPPn2fYUMZqPnN1G2sG9/ci+W4w8uhWRpcLgVEiSetZoKy924IAHAKlPLKr+NKyDfrrhu72ybRVVVmCb5rrEsCzL3GRHt73Ybyg9+8AOcOHECTz31FB599FF873vfE1524cIFvOtd70Imk8E73/lOXLlyZfMn1qek0213MTSa5HgWVEmcn6JZLixuHoDmE634eT6jgvo6OTUqQVSUDNcC79ei6sybwfWiid9n3ljGscPDsFwJmqCQoOmiI0dxUFFKEqSBTPjcqSg1/VVnOa52TtPy8NnPfhp/+Zf/R+JjbwVJogQQWWpwK/eWBYU0WVFq2lKsHAFEkFiGMoDtv97L3Q74FqmbEkogERr27TuYdTFQjk6xFnI2agm9CAEiS7vLKs7NJVeIz+fIc8yuLKQJ9N0EqV/ppem2L37xizd9m6985Sudv6empnDt2jXs27cPN27cwOjoKABgaWkJIyMjwv2GYQhvw1OpVHDw4EHkcjkcPHgQ//RP/wQAOHDgQOiy4eFhrKysIJ/PY35+HiMjydPOKfGkknQXI0EOJVDrsgHTjf5CJhGl8HVZFhlZEskRy56iibk6kGeiFqwg7T4wiDfeJFNktfhizzfFZN7CdW4qyXIl0AIGo9nggRoemW7JSdFGuCyeR85Tkrg6SBnx+Q8bDmjzkYYtY3fBwA3F60zNbQWKoPkqG02q+ZLW9B8zq4hHpiCiJAsFiWeuoWN3sY7L1agA8ILEM5RxcTt+o7Kzt5IUbdMCEFEC0FWW7pkgoiySJSpIPFULWNpAEW+lT9uU9JIkffWrX42d7toIjz32GJ5//nmcOnUKlUoFH/jABwAAX/va1/D0009H9j/xxBPQNA0vvPACTp06hWq1iieeeEJ43x/60Ifwox/9qLOS7bd/+7chyzJ+9KMfIZfLdS4rlUp44YUXkMvl0Gg08MEPfvCWz6ffSSXpLqduW8gzURFelE43SIuSwjrpM7EvYaGD4wGOn+irCJKv9xSjuSyLTRWSFAyunrcF8Wp6XwiKAPCilFVc7Cp4aDriQe1mZQlIFqQ4dMVDdosrbrPS6XgSZCncT+/cTBX37C2i6UixolTQXFiugpLuhprm8swwRTh3F4PzuFyVhYI0xJTWonWZ1G0UA9cTFzSNEyUgkKVu3DNhdkQpTo4oVJCmCsFzNFuT0yhSSoSxsTF87GMfi1z+9NNPC/dTIfvoRz8qvD+6/B8A9u3bh3379kUkbu/evZHb/d7v/V7o/5sRv34mlaQeQCRKVI4otfUaCgMFXKxERWkoEx0gWFkSyRFABIlnrqlAFzTavVkyggI8k3krIiRZRY0VJQCoed2LB55bJ+cxyeQBtPzkBFaQpneN4NqVJewuBHlKOVXQ5GwLcJjcp8szi9i9d1TYV48XJT6xHSB9/gCEZGkmppUL5XDZwdoiuT7tTzeUfJMtRZbI41E5aTrBaj9KkiiV6PRYl2jmO3aaeGUOaJkyjJhcorgI0lTBxbr//mi3lL6NIgG9FUlKE3VTWFJJuks5e3oWR45Pdf5ft8loQKePjhtkID/din7DX/S9Yd9Q91/c04UsllZJVIb9HhQJ0o1GMLDrcg7FLVgTTaJJwf0stjSMGuGRL6vQasjB+bgI5OZImczvn12LLqulgsRjyDLGcjYcLi+KFaTtoO6fgip5sLlVUiXdiRUlKGJBCt+e7O8mSAYXnWo5MmyXLMungyEbPdp3ZGfi/W0VcaKUREmLF6USFwRq+RLJytJGptgAIGM4nZy1xh0oOnqnSStup/QqfSPNbP+2XqRhy1huRwtGUlli2V+SsL8kYVwZw7gyJry/3QUjIgSSv3UTpK1iSM9jSI9Oly22yOg2czW8vDurqHDhhASJ5Uh5tCNM59bVWEECgFUTmJ4sQpG90MbCRpF2Hdm1sZNKwIWEhh1YgCrIUSoxBS/Pr2U6G9C91YrtSn4yvAdRLStD8SKCxKNIZKNJ9dslBJL/XI90cdJRAzh/Ibn+S0kLCxH/f56WKYeiWBvh0GAgVgN6/xnDnV62/3YqAZDSW6SRpLsUCRI8D1hpq53pEABYalUxYhQxs1TB3hEyr3bcMHC61cL+knhAo6I07ywASI6WDGWm8d57gH+9cLZzWWN0BLgcrHPS5dytn5iPyXxbSZDgcYM6H1E6t04EYaDcgK4mP/6w4YC6wEo7+hFYjSkj5HrAjXpzI4d/S/C1iABxRMlypY4Y8VBRYqNK8S1Kguc0SY7446IvDZWu7cxJ4mk6wH7B25OK0sED8eUAFAm4UpNwfDD5eCf93Kz9zLT0Wwmztqwg9SupXKT0Kqkk3aXIUvxLR0WJctWtY0AHrtYd7MzH5wsVtUEUShIswQo5gAiSCEX2OmUEFpn2Ieumh+nDtxZh0WTA8trQJCIDcaLUXo9GT0ybRNR4WVpozUWuS/OxVtpqrBwB0emEsayEszapM9V2t7Y9R8NWcPbsHI4cCcoZ8G1ZGg6QS3jYmiWjqG2sf15ec2Ga5ARdTshE4saz3T3tRozwNJsqAXbMoHz+wkBElOrcrPLpVXK8vCxNFuNPdn8JeH0VeOuagaPT8SEmWXG3st/xXUMqSSm9SipJdznVy1eB3TtD0SSAiJIqGLzjRKkhB1EZTQ5+qlNhihOkNjcIl3UHS9s0TNAecWyNzMmchesN8dwJlaWFVnJ3eIDIABUEmZvm2ki+RSG59M4tYyjxU2gNf+aNlyUaRfIsF6pfxVckS3lBDhN77iJBYgfDLvVEtwQ9ZuVgN1F6x684ETniobIEeImCBBBB6vx9Lfh8pFGklJTeJpWkuxXXhSZnYLlEAFpOMJiOGCRXZjCTwWo7KghX63R0W0Yhm9z8UJMNlDRx3hIvSDzDmc2bg+UFx3+1LmNKMCbFiZIik8t2+Mc/a0UjSQCwwOVYsbIkEqSxbHDeWTWbfAK3QNMGmrYSEiTLk6AJcpSAQJaSErfplJjdaaIbf116nUWmxtKo4dzWaIEHYKEFTPspacMGsMwFcESidM9BEkVqO4VEkaK8d9LFSoNc6UZMD2hWkFhyBRtvLQfvBXmLS0HcTaSJ2ym9SipJPUJOdVHSFehyeNCOE6WS5mLddLHsVLG7UIzsBwCXqSHENsZVZV0oSGzLkE7hyk3OxHiei6v1QBam8pawV9tkjkbC9I4c8Uz51bipLPFyxON6EvYWgym7mWojJEhbzRc+/Wf47vP/D5p+8vZMVcHeYvAaJIlSTnXheuGiiyLKGRv7CibeilnhFjc9l1ECYZSl2xNF4okTJSCQIx66XyRL750MS80O/6VmZSlJkHhc/4eKoHpFz5NOt6X0Kn2zuq1XqVhyqE2G6UYTiwczwYBY0lyUuCjC5VoVl2ukrhJdVcQKUhQJE1kZE1km0rGB3Jeb4anPfbkz7bfcCt/3VN7CVD66rrukyZhrdn9LT2kTmM4nF5kUMWQ4kCS5UzxzO6JIF16/lrjf6tI8Ne4XfUFzUNCC13R/qY39pbA8xwnSdD4sBK4HTOQcTOS2z5Q0/1Cu1cOv5zCTtP3ymzm8/GYOaCx0vT+VObX3TroRQWLZkQN25YFX5sVJXyJBcpi8rMbt74t8x7nTK9LS1W0p20UqSXcpT/3R01huE1EYzoRfRpEojWXLoaiIiMu1KmbrlURBUuVwBGIiK2NAd2EogOGPKXHtTzYDL0oAOqJU0mSUmJpMs3UHs3XxOdyw5nDDjyQ9NJLBQyMbq5C4boWLc0qSDNNtCdvAbBa2gCXfU+1yVcKFiowDpfBzfO56cByuF5YlVo54qCxN5MSvGS9IQLhVyWpLQsO8vaGTjhwl0K5GmzdPZIGHR7qPghpzOm1TQZupTdVNkPqVOy02qSSlbBfpdNtdTMuWYTBRpJNn5/HQkXEAVJTy0Ljpt2E/X2m5FR5E2Jo8i81g6mI0S5ZW83JE4atdGwrQsLsnSt8KrCjR1V5uS0MpRgJm6w6m/CT1GzH5SAA6olSvSrhWr0b284IEAOtmIAqyIqO5jdNPM1UFTiY6EB8oWbhQiS/4YyguXllV8I6x+INji1NOF8iU6rUaySXrJkgA8NB9O3DqzPXkE9gk1+oyBplT+A/Ha/jh6Y1XOudrLVV9qSty9Yy0BNdpmwrGCg5aho1GK/5rs2g42J53/9sbp0uEMyXlbiWNJPUIbDSpoI2goI1ASXBgKkuAuGghZbG5DjcrLhIjagcyz0x37T08FFtOYKPoSjhisNxSI8vhLyZ0sNflLJaceEECAMc14fg5V9P5YmcDugsSANx/fDzx/m+FYcNBORNsD42IIz18RAkg+UnsFOy/Luj414VoEr2oejdAZOkXiwq8LgNfvUvxys2iy8HG8x+ORyNFPCNGcjHKqil1hClJkABgrBBYWs6wO1saRSJIPbSlpLCkknSX07KDl3A8m0VBGwntd934qa/BTBZT+eQVaLSjfMNe62xAd0HaCv755DkAwGTe7Wx8qQMKL0p7iwXsLQYiqMlk43Hc+OJI44YKTfagJRRL3I7wvONJwl/mSaJ0oGRF5IiHylLFVGIFCQC+OxNEHz1P6myihrfbheMB0/lATNYEL5NIlK7PZzvb2RvdV1dWLGChCczGrGwDwoLEMmIAA1m3sxUTGiGnpKTcnaTTbT0AOxV2pbaAXYXwkv1AlMj1+Jyj8Ry5fL4RTBTkEpaIr5k3kFPINE/DEQ+cur/MSnFv/S2myR4qZg3jWgEVKxjUDcUNlTygXKwqaJk63jcZPziyopQkSJYTzuuiorTatruuINsMbFQvp7po2OHzfGjEwsmlYIptnJlN/fmyjgeGEypiAjg+ZOP0Ssb/OzoxxAoSiyR5ociRtc2r3fmeeQARpTL30tKpt+vz4uOmonRkR/h5EfVzo6I0xQQvkwSJp+mQY5b6sJyk46QxmJTepK8iSb3Wv20yL2Eka8PhokVXauLVPnl1KDEpezyXwXgukyhIfFJ4TtE6wjSeFd/u05/+ZOz9bYTz5+YieUd8ROnYYB7HBvN4OOfgpevJokDqS7Xg+v94eEHicT0JridB3qaPD9vZXhQZemjEwng2LEiUV5d1vLoclcTjQzaOcw2NT69kOsIEJAuSCF1xY4s9bhVsNAkIR5R+ctHATy4ayOZsDI4kT+uyUaW4hreU2QagKUApu3FBWq729+9N15V6ZktJYenvT/Zdzi9PzeDwcVIJ23GtUH2gK7UFDI1Nou6EpySKGmnwWrUWI/dXt4gcsIM0GzURrZoDgKw6AIDk/YxnXew+egBn37jUpYzAxiAFHclBiETp2KB4KT8VJTaqpMUknweiJHcVJB5NklHtVrFwk1BRYqfIzq6pOFKOX2tORen4WPf16OfWMnAbDo6UPZxdCw8SIkHiC1FuZ2SNp2JKePlKRpgLNDjSwupSfBKS7QLjORuVhMbGALCHKRvWYt5yhtJdkHbmHbyReO+9iZtGklJ6lFSS7nL0mMKJE9l8p54P5c0br+PQjqMAiCxRUaJyJIIuJU8WpABDCfKAZEkBtmGlm8R8H1+pkaTyXQVx5fCXrps4MKxgdyH8VtdUFZYdCITjWbCcjUdFhjMZLDH/T+oqfzO0XfKasaI6W9OQEeQQnV0j5ySSpV8dJ5GVRSsXWgHJo3LBsCNl5oEr3QUJCPdV2w6m8w6WuTIDiuptWJQOjocjiwcHyPN1npOlPeKaqh10GZD9m3RredJvpBGYlF4llaQewnEtTGTDkZWCWkLNFq9OUyQVBXUIdSu+gKEqkRFwQB/qXLZukuk8XpB4ZP/txTem3Sj0a5f2ExvMDMCab0GfiP6cv1KrRETp3iF/CsYDLvsBNV6WgKCuk1GU0aqGJWBQGsKqtxK6bNgvznn48C688caVmzqnjVK3ZczWNmZebFSJyhELTe5nZYmXIx7Xk/D6SvBc8VN1t4OGLXfaqbxrwsR/nwtPIyaJkmXKODiebG8dWYKK4YQpZiAqwXn/qdluQbxbSCNJKb1KX+UkUXolL0nyQyoNx+xsV2vBMp0b10lPhYJaQkENBKJhr6JhB/0WxrPTGM9GG9hSQeIZ0MdIiQEpPHKwUaStQpHINpgJhCyuZAGNKt071AoEieNyzcblGhkcHc9KLHy56BfrHJSGOttwJjxlR6faVHlrOtx6HhEkADBvIt3nv89rQkFiadkyWraMe4eTo3uuYGXd6RUVtidh3Y9oHT689WUPWMoZF9c2IKCKGn4vWKYMyyTP30RyvUkAwHxDRUkDFhOeurgoIf3ynC7bmC7byMr927stJaVXSSNJdzmuZ6NmyaHmpldrDewsREeIQX08JEc8VJSWGsmtMUynicFRHauLZkeUaAsRirwFby2bGaybdhVZNZgPUSUvtB8ARg2gaVcwmZ3EfDP+PAHAgwtdzsZOI1JBYln31tCwg+vntqEtCQBM5YHZOvnbdMV1gigr7eA5YHuriWgxv/aPDBJROrsalj6RIAFAiXHAdVOBobgwXRn6NolB1ZJQ1DzYrpQYTQKIKFEx4qGiNMct8Z9vRN+frCiNGslTqPyjLfqFTpeaEpw+LNucTrel9Cp9KUnPPPPMnT6ELYHmrSzPXAf2TsaKElt1+0KFjBYHSuKf2WvmDQwZOwEAK62roX1mTFLzfPMGBqfyWKUjO0iURpE0mLKMo8d23uSZBTTsoDedSJQAMqDxjGcH/WMLy9KuQnh4ow2BqSyJ5CgO2zNR1smgHU2D3z7qloxxxcONhlgMeFlqJUyFUFn6ZT3+OiXOS9iVhbYnQbmN8eh3TZj4hf8SsVNtu8sOLq/Fz31RWRLJkYjlFrDcBoYFuf59GX7vgqhkQ0pKL9CXkhQ33faNb3zj9h7IJlElFbKkwvXI9FHNkjHI7LfcVqQtCeVCpYEDpRzWGysYyA1hzbwRuQ6VJQBYbMwL72e+Gb0djS5ttodbUiVwKidTOTL6ma64GuB4dhDzzdWIHPHochaSJKNpW8gKEp3XvbXw9ZVgQK7bbTiejS984bP4L//lLxMfpxv0jKfyAF1YZbri6tY7cm6sKAFElpIEibLeVqDKwTIuttEtL0h3AvZ4zq1p2LvLwoX56IEliRJNtKZRuVzCNx/7jC4zM5PDGbEgLTLtcrQ+zVFKc5JSepW+lKS4SJLn3f1h8olc+Gt81ZzHoC7OH7lQaeBIWSxILHVrJVRPiNYHShIkAKjQpJpNfn+yBRWbdhWTehBNuja7gOmpMehyTihKbaeOsq6joI2gZi1F9lPYlYBN5vFy8CKCdDvZ57cd2VE0caUaFQORKJX14LWaKJKVXf++FL3tels8otPprXeMmTi3Fr5dXMXz7WSxJeHcWnju68C4GStKADqyFLcKreFfzspStwjReBZoNj3/9lLn2FLS6baU3qUvJanXkCUVhkK+vBVJi0RwVk0SBWJl6Ug5WK1W9csnF7m+HXUrvKqL4sJFzVpHTlXRYJbR84ncW8VwJhwNazltGEp0HoQVpbZTj+ynLVtYWeLLJLCMGDZWqkGzXyqHfBRpq2nZZKDfzTWY3ZUgSkBYjngeHgnLUpwgUQ4MtHCtDtxTDpbPX17j6yP5SeuCgpxbge1JHQnJKECbK7sVJ0oA8OguC6t295wxKkvdBOloOfz/nJ8wXtA81KxUENJIUkqvkkrSXcxTX/gyvveD/ytyuUiUACJLR8b3xt5ftdNr4joUL74oX80KxCGnBm+hCoIBtXIzS7MSoIIkS0qoOGWcKC22gKK1DF2OP/6OLLnxEaIRIxqCcOFi/75RXL9SgeOF9ytblKxbUGW0HKkjvTwiUdpdIM/7SjuDoUxyAc/JvIULa+R5G82KwywHBqJLvVbaaqifnJIwFboViEoUbESUDpSC993Jq+Tyh3bGV2Bf8NPsSv7bWOXGel6OWOYa5MoFjTwXlT5OVkojSSm9St99rHutNYnsR0Lor/pXz5Fl03xUR5MUaJKCdXMe66Y4vwgAduenMaRPIt8SD7asILEstVwoktrZRrNdKvNtANOVMN8KksVlriRBywmiOLONBmYbDea28Wu6ZyrzmKnMo+XUOhsLK0hDo0HpAbYNiSKpsF0HXsxqsK1ClFO0y59C210wO4JEWWkrWImJEt1ohu9rsalisUnsYHJYx4GBllCQRNQsGattsm0ngrJWESaGTBwouSFBYqGyxLLQDASJxfbIBmxMkCg0muR5QG57AqopKSl3gDSS1EOwLTwAwHZNaJK4EvW6OY8BZvptdz5aJ4kVpaufO9AAACAASURBVBqSBYnlRpNEaEazRYzdfxi/PH1po6cQ4s+e+jL+3//v/47d37AlNGwTUkOctE1FiUaVZirxckhFSRRBApDYp83zpHAZ8E3AR6jIsQX3TVuTvLMkJdZRoqI0mYnKEc9iU8WDow1YtQyqgunDlXb4a6LJrCrbW7Dxym1M5cv4/lfjAqUvzyn4jYn4KBoVpX2TG4twZlXg3/yFkfcNhvfxgpSSTrel9C6pJPUAMpNXI0teKB/H8aeoFEFhSBpR2l3al3j/7VwWynoDAxrJY1pncpV4QdoO5ltN0ElCWVI6SbMU6idxefcXqw4O6cmL9P95nqySG2YkhQqTSJDaTBRrMFPCDSxA2myGOgMrRi2HPH6Fa02i+73oTFecF3VyKYNFW8FYNnmV4YOjgWQWVXKfVJZ4QboTsNGkmgWMykTaeV6eI8+PSJZyBfIcSP7nIO69UhREgU4xVSSKAkFic5LMbY4svl1JSwCk9Cp3/hswZcsY9vt+5dVB1LmikY7nRETpSo38ujYys+T2manIfbZzJCeoOJBDdZ0MplSWVr1ocjSNIgGA5C9ol+TNhxrY1iZFzUVVsCReksKD37V6eMQbMchteLGjgsSz1FKxI2ejYoULC7KCtJ0UNBdLreAjWtKdiCgBRJZYUTq5FM7VWmiSg+dliZUjnqKagSRVMWzYWGaOoSloA7Jd0BQ5RY5GjoYz4eX5LGxUicoRj0isRYLEcs9AMK0NAFdrUpq07ZPmJKX0Kqkk3fWQL6dhOQcweThxogQEcsSz3A5kicpRHLQI43Am+HL8ZXPrl8oP6C5USUfLsWEowds1SZR4OeKhsvTfZpP7VuzIBVGlSkwwZjATTGceuTc6ZXkrDGbip/aSROlKU8W1evyqNSpLgJcoSBe4/nXDdApSd1DWgRt+Qca9BXK5vU0Vptt2UKjS8Uh7mtBxJYjSPy/IsYLEIkmA6WxMkHh2FjyYBXLuV2r9LQnpdFtKr9J3idu9xi9PzcTuy6tBMsWAPo4BfRyKpGFvMXlQM90WHFc8wOhytiNILCW5jKGM1tlwi01tk2g54XydIteUdDyrYTyrYSjjdF3lpctZPDLaxCOj4irirCCxrLUVFLSBzub5x2RJyY+3Ueq2HEq6p1N+Z88vdy4r6Q7+5U3y/1eWpc4GANP55ONo2hKgyKiYilC2eEGinF3VsZAnkcYdORs7cjbWze0bGP/kU09FLhO5GFsRW5O9zgYAnivB6xLhMP2nS5PIxnPPgFiQAODsWnCDXQWvE56y+/Br1XWlntlSUlj679Pcy6jRZe+6nAklaNs1cp29RS8iS0VtGEVtuPN/x7U6G7kvcXSpJIuXAXkIVOlzn/v0Rs8ixKlTs4n7pzMDHTniEcmSSPKoLFFhShIkFlqSwHNseFtUK0i09F+UTH64PNgRI57pvBORpaYtCafKWFlKEiSe8+t+vSVTQiamXMFWMFcJP+eOB8xcDCfgV9pSR4xEiGTJdAJBYqGypEnxcgSEBQkIShPocrSMQEpKyt1LOt3Wa/iipNrBgNe015BVxSKzt+ih6NcNSsJQCmhLfg6SF9y3SJBcz8HO/SVcfasCAJA26eK2Fyxzp9GkATmIkjVtMs2YFUgiQGRJl7uXJMirWewuSAD0SPXuOEFi4UsUbAZRYc4dufDrNGLYoZwlnum8g6a9sY/4YktFViEy3HSC1ytJkACgIpjyvB3MVDjp8SVI7yJLIjESIUtBw1u+NyAvSCnpdFtK75JK0l2OKgUvoaOSQdrxLKggf69fa2JgOisUpaxSAMCu+ImOIHltMHIZ/NV0pXob4NxDJA9bwbXzyzh+jKzCi1tF1rRbEVGizXHpedSt1cjtACJILLoc5CuttePzdwCg7Ymn7DbLxTeWMLF/DEBwHjw0ysTLEo2gTZTIsZ1cEkcB24LphazfesQURIhYQbpdzFUU7AFgMA4al21kupJQlI4Nk3Nqy+S9uxaTyyQL3lqLTOmoFYEgsQUu9T6NzafTVCm9SipJPYAm6YBmAEyV7bbiIOOEIxtUlKgc8fCyJBQkiuM/VjWY+ogTJG8T+UmaTFY5WQgXTay4a8IoVtNuIdswkCuJRytelng54nE9O1JpertEkKViRYs6Xqis4EBpSHBtIkseEJuL9dBIVJZEgkQp6Q6WADwwEtjEq9yque2OIqmKB8UXtmpLhpEPRDGreWjGrCxjRYnKEU/ZPxVWlkSCxFK1gApTPLOUcSMVwPuVtARASq/Sp797egh/DfPZ1y9FpmjaSvgbvCANQHE8rFnJq9D0ehW5ahXeyhXxFRzx7/imY4e2rUBmImXs3wARJR7bA6pWC5ocbVnCktcGMZgZgq6IJcn1bLiCwo7HBodwrd7sbFsdRfrcf/wzrJuBIPH1fC5UxP30TFfCZL77aq6HRpr49XELb63HPz8lnbxvRsbChUjbDtDyNxbL256vkW73mtXi5dt0JXzy3uQIIEBk6XA5WZCqFtl4LDdcobtfo0gAmW7rlS0lhaWPP9a9gSOx6dFR2oqDgjSAghTOQl2z1iKypNXWoNXCl3krV8KyxAjSxKGjnb/X82FBK+sDcD0HrudsOqk5q4SneBauMj3ifFFiBytKnCjVrBXUmIKYupINyZJIjgDgvqFoZM10XZiui/uPHEw+iZvgl78MJ6vHiZLpSp2NYqgujJipOUNRQ2UUrlV1XOP6wFFB4vmX+fBzqUp+hG+ba4kaenDyi/Xo11WSKAHA+ybj26yMZ8kGAFfrwcYikiPyuOETtz0gr3nIdzmeXuVOr0hLV7elbBd9KUm91r+NhUaTbM/qbFUvPnK0Zq2h0axF5IjHs9qomPOoONFIBhWkg/tGARBBoqyaJNLixZU4vknYaBIVo9WEjvaanOnIEi9HPLqShSJr0ATRJZEg8SUItrLidtLT1bJlnF5ZQz5GhoCwLPFyxHOtqiOvuhsWJB5tm5vdsvCiNJgBJgse9hbij+F9k62QLLFyJILK0kYFCQCGM8Hj87LeD9zp6E8aSUrZLvpSknqJP/7CV/2/yDdz01nHihmdZogTpQknD6NlAXo+/kGK46H/VpyVzsZHkLYaz19JR6NJnmNBcr3IQFQ1FVQFdX8A4Mwq8I9XrkVyozwpiBhdqq7hUjV4jjQl25GljQgSALRipiG3AtOR0LJltOzwRzZJlM6uGvj5bPfX5/Bgy38MubNRRILEtgkpiIoLbTFsNAkAdhYcDGaIILEkiRJAZCmzgQWIsr8N6sGWBCtIKSkpvUWauN0DrPjRmqYTNKCtWBZKWniApKKUgYYJRyBFVJRMf86BkyOWkv/DvLEezE+sD+SFUSQA2Htkc9Wobc+Ex0jIkDGBldZc5HpVU0HRj4icESxko6LERn1YOVqTbZTd4GOhyBqA5OzcopIgmLdATiXHuNRUsJMJ/2uyB0swHZBXXdRtdtl+eIXfP82T98Gvj0cljgoSj+nImMwNYl/pKi4ytYoKd/Ab4yHm+PcWHcxUo8azt+Bhhqt+/d9uMM+HCqz5s7VlgfzE/WqkorTgyIBAkCn1Pm1Tkk5TpfQqqST1AIN6NiQky1eXMbxzWChKb64Dj45NAfWE6TU9DzVbhr0yCwzsiOwuxaR5DLh5rNTmI5fXrVtfAiRB7tRJsmBDY96ycaJkOrJQkFg8eJhvNtFOSKqJy02ayCkAFNSt7YkceR6w3CICsNJWQivWkkTp5KKBbMInmpWlODmiTOaC6Nm+UvD4Z+aDytRsFOlP/+yP8Of/+X9NvM9bgSZDjxWir1OSKAGcHAlgZalbSH1mKfgc1ZhyC7vTKBKAtE5SSu+STrf1OBXLwpvr6GwUN1+GmxcUmFQ0slHWb5DNJ06QUBzjLpBR1MiWVbduILEQFpchYwIAkFG8zgYgktDMM98kUjlqRIsFAvGCtG4FEpjXNBw+PL5tdZLi4KtL08riD2SX0OyyqDCrenhlWUVBc1GIiYiwgsSS969veWTLqX7FcXl7vkZU7m5Ffen2FqMCfmDAxoEBG69vsOjjTBV47VJ8Hz9WkFgyuoOlltTZ+jWKBIC0kOyVLSWFIY0k9QiDejQTlV42XLTxVlX8UndEie8eyrN+AyV5EMgBaHBhmoggRblVUeq2Mq5mLQFrAIZLwv18JWYqRzysKLXr3QUJAGyXqd0kK8jIW1dxW5UBWjSdjyYBwP6SHFsTiYoSH1USvQZUlGqWHCtHQCBIlB05G5dARMndxmCKqLEtz96ig7EB8WtGReloOXqQq1xBSSpK9+8JcvqSBImHrmzrx9pJkrPNyxxTUu4QaSSpx8jIeWTkfESa9hdt7C9GBxLZtiDbFlpywtRRfpBslNxgsAkFKXhbSX7lb3sLv0NbXhM1a4kIks+YkcOYER8NOD4ooaSPJt7vkLETQ8ZO5FHobBRekG43K/4Kvl0FBbsK5O+r9eSk7KZNtqzqdZXUxaaKa41VXOMFGGJBulPw0aTXlhW8tqzgwnry7z02qrTajgpS6D4v5RIjSyJBOlwOPydPPRVt0NvLyK7XM1tKCksaSeoBXjv1Fh44MoWsrcD1E0xNpwFdiX7RU1GS7agUsaJkuP4AzMhRKSej0ggGTEsWmc/2ebfDVBQvKIOoOdEBfczIYaEVRAIeGg7vPziwCwBwfj1cKHPI2Cl8zDwKqEo1DOqTWDWvdy5no0iZmArmtwrNOaLTTbsK5LxdwVwAFaWdgkKSJ6bJa131K4RfqUUzlWcq0ctYUeIFiafb/s1AyyA43Lj1ixt6RxRZLqyrOBATUQJIDlLDBPQuAb/hcT/a6AB7R4LndWZJ25AgAcCu+7aublZKSsqdI5WkHkD2xPMRIlEqZ3ZAbjeBhLo5AGApgDM4AdUU/+QWCpJrwy0EUiWBDLZHjuzGL89cTny8OD732f+EF3/4dQAk2ZpdlZYkSvvF/Xw7UFmy7OS5EW/xLUAiq/wG9UkAwJpzEVXJjFxX2sJE7gk/WkMFiXKp5mJPISqiV+taR5SoHPHsKpBjprIkEiSW8WwWSwgS2VxIdyyKNFcJm40sQTjNRyNKrCxN5sJXpE1uRbLUESQBY2ULa4vixHlKv+Yl9VQEJp1fSWHo+7dDrxWVlM1wZrXpkKhKObMD5QxZqbbScAHHJhuHImmh9ia2noGtBwVpLNmNFSQezXLIttm2FVrw+Hyto4ISSFnTqXW2vDaEvCbucwYA3twb8ObeAFavko3fv/gWESSOirkAACh6ebI1LKBd6+z//Oc/s/HzikFlErP5KApARInnUMlAVinif9gTf84UWQLWY2pKAUSOxrPRHLfFpgrPQ2djo0j7j4ojcZuBPfehfFRmk1qJXFhXMZnzIoLEYjqBMA2PNxMFiU1T02Svsw3c/n6/b0tkx+uZLSWFpa8l6cknn8Qzzzxzpw9ja5ADsaGiVJLLKMlllPWJ+BLOjCzxvd9YbD2D6+4atLYgsiQQpLodjvAYSnLV5s3gujaaTk24TyRL3twb0SsysiSSIyAQpOG9gum1do00Gd4ilphl5kmidKhk4FAp/LhlNT6R/lo9GNWHDAdDRlg+RHIEAPPN6HvDciVYrhRZhbaVeDcpSjk12PIa2ZIoZ8h1P7QnPkkpJo8f+/21AnMNFXMNtW+jSAAguV7PbCkpLOl0Ww/gSOHBo6FYKOjDgD+VNHvlOqZ2TZIRRxJ8kRtFZFUympiOeI3/QitIXA6J0gYEaUvgokkVazG0u+0CmYTBOq8NieWIw3ZasMf3AgDU+RkAwLg3j/OiAbAZ1Jrytvn3BitKWYUI0v5i/GNSUVqzidixcsRDRUmXxNElkSAtMhJnOhKK+u1Z3TSUd7BSj0695RK+yfIaUBfMhJY5b2dF6duXyM5ugiRC7UNX6qnptpQUhr6VJNq/TTTd9o1vfOP2H9AmcRUZZ95awN5DwfSTqyqQ+ZwbVpSMYuR+dIVEJagssXLE09YVmEYw0uitdkiQJo+M4Pp5ktMiuQ4++7n/iL/83//PmzsxDtqHzVBLaNmV8PH44zQvS7N1UnDy0MRhAOJIkl2KrnyjsvTz+SomsBze2UzudbcZND88stRSscu/bNgYhqxERcTxTChSvAA5roWMmvwxb/mFAHUECemqTO6zuyBtfzA6Lgj6q2NBXtgvKsDBBHGhEaWGFZUjEb8y7GF+TSxJIkFa5H5bGH32zZpKUkqv0mcf5SjsdNtWNWG93SjMy6jJBiw3+MYWilJtCShOJt6nrhho2OIpLIAIEo9pZCBJwaBJ+65tlldOXcCD9x9GVsrBZlqEiEQJCGSJyhGPxMmSSJAoP5+vAiDPaxzbGUUaNoLleSOGh6VWNEzh+BXJWVlaNmdD19nvVwF9qxI+j1ZMpWTbNaHKOibz5L6vJ0SitgtR2YKcCuQGnJAgUc77b4U4WaqagA1gqQWMJMyMXq0G5zrOrHuYb3QXpH5sbpuS0sv0vST1Ag5sSI0KvJx4dHBVBagvR3dYTUCL5qCstskAm8EARg0ybbPYWujsFwkSAMw3w8JSlAfgyOvC694qqqfAluJFSfN9ZbF1A7pCpoLiMEemsdqaB6x5DGjRPnVUkHgstwVIUTGyHQfHju/Z4JnE43kudD85vmVXYKjB6xonSgCRJV6OeKgsrTST54RoJIlCZamS1bHYur2FAyUJmMx7WGsHx3y9Hggcz3kuqlQVXG3JFxtWllg5ErGnCLj+vGfdJsfCR5D6lTThOaVXSSWpB2GjHnLbX7GjjgC2IDnV8vcbWkeORFBZarbbAKL3wwtSQQqm8ky3CUhSaPn+TWM1cfaN6zhyOBoBM9RSR454dMWLiFLbrkeuR4tFUllKFCSGalZBVSIJL2wdp81S1FxULXJS3UTpeiOYE7p3eNC/jfj4yT4ZGV/C2oLl7LwgdS73r3p0kBzXaze2N/nGciVhCh0lSZRKuocb4l0hqCx1E6Qs97sg70e5dvl94q7U+jARiSFNeE7pVfp6dVuv8Mdf+J8BAFKDiIosqVAkLRAkiipOxlh1V3Bj/RJW2/E1cGzP7DSazTD/xsfLEUHablSPjFiuZ3e2ojoQe31d8aD7Pd1EghS6b8VA017HA8PRaIlIkCKPFVOz6mZo2N0/liOGh7eqbkiQWAw1mm/WsmW0uPvOyB4yTMmBboJEoc+FJnvIqtsXWdKZwy0Lmsny04APj3h4eIRcbyhDtiTePeHg3RMOrtSBKzFvDcHLDADYNxD8WNhV8FC10LV3Xq9yp6tkpxW3U7aLNJLUQ1jZLGQpeEklowSvxQmMmulElFbdlch9UFEazAT3Q+VIhCZnsVPJAwCuOnWUJ4dhMz/hTXfzzV9dLrfJlcQNaKkoVe11VNbqKJXznX0lrYRXl64BAI6UxT3Kho3dof+zojQ3lzyvUhRIyWYpMnWIyJTiEFQ5GPXbAJqO3FntxkNFaVkUQeSYqehQ2hYAGw8Mh78Wuq3WOn50CqfOJE/zbRXlTHjaDQB+NqfjXffED25UlFaYp+HdE+IiolSUduXj5QgICxIA3KgHx7RN/X7f1qTTbSm9SipJPYKUHwLcJtbMKsp6EW+cv4TDB/cIRemqZGKluYjhTPwosNq2May1QonYLK5gJNip5AGjgBnnIrlACVZGuZv8heapOuA5SCh23KGoDqBqr6OkiXO0zq75lcB9WeLliEeChFIrOP6KIQmjSNuF67cVKepjaNrRHK84Uar7uVqLLSKLo4Y4zHFuLRxueXU5uF5B8HyzEbVst+6zmyDjR/907q1Wzni4yM0m/nKFHMe9Q8my9MDgxrrP6qrXqXo+1wh/TfKCJOQuXQRyq6QRmJReJZWkHoSKEkUyiCxcdcIjy7LfrpyXpb2F6c7flXawpJ8Kk0iQAABGuMhi1V1HVqGisoTjR3dFb7MBFKZ+jyypcD0bV2dWsXOvOCK0ahKRyLk6GnJ8FOzs2iocR8VwzEqnuByqvDYIVQ6el+2IItFcnHXTRlELjiOrDnQVpbpgxR8QLN2nssTLEc941sa11eC5H8w4kSnH28kKk4f1qwds/OtC9OvrlyuSUJSKOrnsB379o9/aE/++ODAQvv0E04rF8KKCxEaRttEZ39ZI7u1N5k9JuV2kktRD6HJ0pdrVGklIHttxAGiIk3mX205HlFhB4vE8F2isAYXh6E4jucmrJwPuxn7Ei2/P9G2josRD5YglTpSafn0fHS5OLQdTRfcNTwGIFySnKGj74V9V92QAm/9FrckeGraEnJ8cXLW8DYkSaTdS7VoBe7GlQuryYoxno89vxZKx1g7ufDujSBTq7+fXJQxzTveOse6iROWI5weXSC4TK0u8HPGstxWcnAuM+tcnWiFBSklJ6T36cPa8N3n1tTc7fw9nhqBIWkeQKDtyI7G39zwHe4rxgiRLMmQ/kiTXljsbAKEgVaVGcN/OJuwIgJnwK9VwVBiOipYTn5Cdc3XkXDIoNh25I0giTi3PwoX48USCxIrp8QcPAZtZweejyd1FK+vnX+VVt7NRbFeCHTMvOZa1MeYL0HjWjsiQ6DIAUATHJElSZ9sOJACGHx1T5q5jWTDL9Y4x8RTiq8sSLmxgPcEPLunYX2pvSJB4vjtjQJXR2fo1igSkidspvUsaSeoRZI/UySkw7TumZQPXuOkRKkoVkITqIW4xE12cJTHfFXJMXhIAyGYTqDMJ4PmhkCBtJUFzWw+ypMBwwm/frEL+3xQ07l3DOiZ04GxN7Qy8AGBChQ5y/bLuy1wLcJiilQqUroIk+VWyZX3retQ17GDUrVoefnnuDO695xh+sUCe78fv34uL1aXY29uu1GmWOyYQHwqVIk2x4G5wdV5W9UBrjjdsF5k7aAjvGLNxDuIed68skeN6cEQ8+H3yPhJdbdpspC64rkiO4qDlw3Sp/wbaNHE7pVdJI0k9ghvzxTwtqBStXHsTB/SBiCCxeBIAz4sIkj7ByIKeQwTbxJWahis1krS92ShS6K5dE6HpLEdcl4jKEkDkaA3hqamWI6PFRZM6giSgKEUTwEVTm9uP1xEkAPj712awrziCfcX4CGFGzqCUcG6Uo4PkfSJLXmej8FEkUSXs7YD+qGelVhRNWmqqGO/yclBZonzyvmpHkHiatoSmLSUK0gK3aHOQcePpA1NoWf2Vo3Onoz9pJCllu+hrSaL923qNthueeqKipFx7E8o1Mi23/MZpDGEAQxDXF6LF4SRJ7mwhRIKUCU+7XalpcCQPjuTh2NG9t3Iq5KFkudNTLFKOIEaURo3xiBzxtBwZr68qUUEyAnssS+LkcEXSE3umbRUDuoKiBhT9hYJTBfH58qKUV3Xk1eD4Wo4kbEFydNDoCBKPLHkYzDgoaeIBf8feUTTs2y8Dy20iRnSjTArekiyvLEl43047Vo5YxrIOXE/qbCy8IKWQ74te2VJSWNLpth6i7QQ/s4koFdDSyRd8Uh+1IQxgBbQRbfyXhCTJaNk1SBsQJAA4VsrhzFzQDkWUbL0RZC+crG17JlRWUBwLtVmgMKWjoAVJ5XyzVp6LFfLc5HwJaFhcdClGkOpWEM1RJB01v8ddUd+6VW5l3YDpNnH27FUcvidcZXyqYGG2Fm08u684Ak1NLjPNihIvRwfHR3B+nkzf8XlRVJTO2wqy6u2vmGgoLsaZp3epDYwIZjapKF3nZnzfOUbf/wrOrJKw07HBqO2MZcVRNypKRS05ipRX+nOQTafbUnqVvo4k9RJ8E1tDKaCpBV9cV67dgD11APbUAeHthzCAtpOcS9TyZWAZq50NQKwgUegSfjUht2kztHUZbcUJCRKL7ZodYQKIHFFBYslpbmfbiCABANxAGNbatS1pTfLpT/1Pof/LUnTah40o0WrotmfizCpwZjVy9RCOK+FXRieQV8vC/XGJ46MGaQS82FKx2FKRUxXMN7e3XpTlSp2NZymhXBGVpXeOuYwghTmzmu0IExAvSABZOeinnWF/KdgGty4FLSUl5W1IKkk9hCwBr79+CYYSSIsoghQnSgDQ8ppoeeGfyi271hEkHkfXUXHXOhsQFqSt4DOf+08AEKomDkkmcsRUGlSaNShN8XECwIA+JpQjliF9EkP6JGqodzZKkiBtNa++dj6U9yQSpUMDVmw1dJEsOa4Eh5ONvFrubEB3QRLRdiQmqX7rMF0JDjPVZQpcRyRKhkK2X4mRI54fXs1hPSEAJ8e8ZYYNJ5TDVY6ZluwH7nQeUZqTlLJdpNNtvYIS/4ve89xITpG043Di3VFRUqUMALF4lPXxyGUVdy1UaXurUZlE9LZTR0bJR66jNGtwsoEoDhtBaYP/8R5yzF8/Nx+53ZAebZ4LADXUUbEWyX0j+jw/cvQe/MuZNwAA0jb+7pAlBXnu4R3XgiLHP99nVoGhfHSUP7M+j2MDwet3auUGAGBPntSJuloPakeJBGl34c4MJqYbrcC91AbGQcSIh2qL6FXhm9KeXyd3cHAgiCglCZIITfZwuQ+b3aZykdKrpJLUK8gqDGTQQvwchOaGRxE7Q4RDbUerKL+xRnKU7ikNd6axalaQXyQSJAB+bzE2EuXBYXKR/uJ/expPff5Liacivl8dsJqA5wKM8CWJ0uBwfN0nKksAYJri9iUAULHCU5C0NEDDJlGznBx/261Al7OhGM21horpXDiC5bhk6o2XpRG/snbJr8i4FrNaiwoSy05flv690sIol9d9uwSJVhVwPAkKs9rOdAE6yzWVJxpUzCQ3lw0ykqJyxENlqSh5ENW9EgnSfIO73jbVjnq7kiY8p/QqqST1CE899SX8+AdfF+7LKiTjddZcxJQejQpQWVLgdeRIRCfnJ5Pv9BNjYZuvAgAcG8cO7caZNy+DpEdJuD4bX9dnwySIkpcNpKVqkscq6uIl8rZLhFJemAEAuGPhFXi8IFHOrhidNYENt4KsMoCiZqBqbV3LDkVSIccUphSJEkBlSevIEU85Q14zVpZEgkQ5StgLTAAAIABJREFUszoHAPjXhfiqjGX99k8xaXIgRyxZ/9ssTpZsV4bdcjCcUYWlBCg0IZz0emYHf2ljgtSHpInbKb1KKkk9huH/xrZdE1mnAOjhKEucKNWsZcACSjpQMcURhyCSlI/kyIgEacvRmGI4ngt2EsXy2iFBYhHJEhUkFlaWkgQpjrKSwQ1p6z9S800P49nwQCwSpflWA5ODOZhdugCXMw7yqrchQeKZzlvCoo3bjSZ70Jg5s7olIx+TA5RVw6Jku9HJNtrihJcl0Yo5ymILWF7zp+TK4um2fpxqA9LpNpbr16/jxRdfRKFQQKPRwIkTJzA+Ph67/9FHH4XjOHjppZe6XjY6Ooof//jHaDabqFQqOHHiBCzLws9+9jNks1nU63W8+93vxr59+wAAP//5z/EP//AP+NKXvoSZmRmcPn0aALCysoIHHngA999//x15ju4mUknqMVpod1ZyvXl1BYd2IlGU2Ck0Ci0+yMqS6HqUnVNTWJ4nCc6mG11WTRfZKa4J+RanIU6degv33bc/dJksh9++6ybJMxqImQqsmktQ9bGujyWbbUxq5Pm57uciAWJByiriOlNbge05UH0ZjROlbCsqc7qffC2SpTxTCFKXmcrjjEjECdLBcguzTKBxtq6i5EeSxLfYHLYHVExgLEseY7mFUDPibqIkkiMeKktyl4DYYovLX/JlqdruTyniSSUp4Dvf+Q4ef/xx7Nq1CxcvXsSzzz6LT3ziE7H7n3vuOQDAY4891vWyX/u1X8OuXbtw9OhRXLt2Dd/5znfQbrfx8Y9/HKOjo1hYWMA3v/lNfP7zn8fKygoWF4Pvr71792LvXhIt/7u/+zscOnToNj4rdy+pJPUQDa8NWQIcz4IiMfkpZj0iSm3ZgtpqQJCH3KGkO1BaDawr4up5on5pupzF3nHg4hzZt9XdwT2mr1oBBdQESeXr5nxElEouiUK1bFJI0FAFNY3K0cRtKkvQDIArTrmdgtSN6RzJQyJTR+Konc6sVMt3qZKtyy4M1cOZ5XhBYpmtB18dWaUI14uPSm0nIlHK+hW6Z/2351Q0ZS3Cmr+6rSwoqcULEoWutqPtSBYtCYMZYDVhKi+l91lcXMSOHTsAANPT0yFRSdq/kcsmJydRLpOVqKqqwrIs5PN5nDt3Dvl8HufPn8fAAPleevHFF/Fbv/VbeOWVV0KPX6lUoGkastk70TXg7iOVpB5FKErIYMQI18YpO1msCSRod4Ek7rq1dQw45MPEylJcQ1m1xd2X1YIXUxX75vEgQd6wKI2g3JEjnogsCQSpQ7uGyaKEX2Wqk7dHVNy4ElRuVvxf0i5c/NHnP4P/5S/+csNnJXxIl0TzbCb3a910cG8u+lwmiZIqe9jtZgATWNLjc6YMX6LuKQdTkufWyDQlK0hTu4cxezk+qriVUH9faMqx0SSAiNKhnBWbmL4ZWeomSJQWM/s2VZBwvc++WdPE7QDP86Bp5LtX13V4nreh/Ru5jE7b2baN559/Ho8++iiGhobw9a9/HS+99BJkWcbv//7v4+TJkzh06BDy+eib/he/+AUefvjhbTjz3qTPPsq9j+i7qq2SL3rbkjEiSKkp+xJEZYkKEgDIhQG4NRJBobJUqa0C2ehPbl6QpDYzFeS58DI5bLacztmzV3DkyK5EUdIVcpxZdQAWAM2ML4Kz1r6O8qifsG0JQgDtqIC1fUFsu8H5ObLUEaWtZLltYZTpXVZ3JGFVZ16UVCaK9JZdw361gBGTvPisLBkJEaZ7yiNYWVzDii8fQ37iNx9FAtCZFrwdLLeAIQA7uJyscsaJFSWAyNJGRAkAcpoDKC6m88C1evg+RfWaeJobmOrrJXpp0vGLX/ziTd/mK1/5SudvVVVhmiZ0XYdlWZDl8HtBtF+W5Q1dBgCO4+DZZ5/F0aNHcd999+Gv/uqvcOLECTzwwAN49dVX8d3vfhfDw8P4yEc+EjlO27axsrISypFKSabvJYn2b3vmmWfu9KFsmjfOXMfhYyQistgyAZjYrYarUF+uVrC7KE5wLrt5ZHMJzVCZfKJsMxCPZlaPRpC2A6vlT3tF6xEVUOjIUeRmuh4RpZYj6N+l+QkqVJYEgiTCUIoAFuDIUuyKtJtFk4kgAaTHnLFBUSqaRTSM+NVoADqytKbGv2YNO7rKcaWtYCqXwe5CBZdrtzdysNCUsQPBCra4/rFJojRiAG0HsDwgl/DNN5kPfwammf/fEDwuG0XKab2kCxvnnndeu6nr89GV7brNrdzuq4989ZYfCwCmpqZw7do17Nu3Dzdu3MDoKJmyX1pawsjIiHC/YRgbusxxHHz729/Gvffei6NHjwIg02cHDhxALpfDwYMH8dOf/hTz8/N4+umnO8f0ve99D48//jjOnDmDY8eO3fK59SN9L0mUJ598Et/4xjfu9GFsCgkSclwT04wtoc1FCy5XySC6I++HlbhCkzWbTKcUWMFKSLh2XAvtTPBWyrTtcBSJ4b7ju5JPIobY3nPMsntFHvSPJxo5snQS+Wo5G5gqciy0ZRvIGsg0w1NUbW6a0VCC3CaF9ohTtiaKYChep9daN1EqMq/VYIYc02o7ppGrv0qwUiXPU4lr8MsK0gN7R/HqDMmPmMoFS792FyRIkoLXrm5MJLeCLPdtdaWmYlchOs3Ii5Ioetrwb8bKEi9HPCttBWVm9dtaOyxIKSkASbZ+/vnncerUKVQqFXzgAx8AAHzta1/D008/Hdn/xBNPQNM0vPDCCzh16hSq1WrsZT/5yU+wtLSECxcu4MKFCwCAD3/4w/jRj36EXC6Her2Oj3zkI9i/P1jk8vTTT+OJJ56A53k4d+6cMMKUEk8qST7PPPPMpn49vB2QYqIYIlE6ZJRgZYzEqaiavYwSSnATBKmWie5bVRuY80g0ZkLK+Mv1AcnegtwkqwU0ySD+1rl17N8/FbmKIutwXBPzSwsYHyGr2c6vEzk6NEyiaE0nJtrSCg/67SxT4XstXOOJFaQ7wY2miiOquF8dQGQpJEqaONJGVzGWdEcYQQLCgiRmGz47EuB4QVFJQ4lKSZwoZRQP+wsO1mPKWVCoLG1EkHjKGWCgEIjrmcR7SOkXxsbG8LGPfSxyOY3s8PvpuPPRj340chv+sqmpKTz66KORsYquWhPxpS8FxXt/93d/dwNnkMKSSpLPk08+Gbnsbo4sjRq6P+VGyNgSbBA5olTn11Ac93t2CWSpBHJdzwhafEitGkpDZVyviwfTBi8fRgFN1R9IuCKQN40cfbu2MyoygqRlKkpUjniyCidLrfiIiOtXDJeZY3e5yFYnirRF0OlEg4kWtRw5UsPqQnUBAHCgKC5tMJgp4ifXbeyf7r6SRZU86H7bF9NlkrUFgiQxOUhx/d42iy643zhRmvD/btrh99eAHyWLkyXas22uFuy/bzj8ACJBAoCaBbApTroiwXYBtb/SkVJSeppUknx6IpIkyaFpsVHDH7hNMkV0qG0Chjgfic3boXIkwjMKkNauC/dFBEl4B7dWEsAFM3DZJqAGUiISJU3SoSk69GwVS834t3lWKcH23NgPguuJV41lXRUNezW6YwurblOGM8ExTOclXKtH36cXqgsRUfrJ9eB2Z9vktTmSib62Q5noOVJZulyrRJ4b6TYmaQPhaJKIPUULl6oaxrPx0aABPRxVSmpoe2rZT1T34s+zxgVFF5qMQLuA0YffrH/6p3+K973vfThx4kTnshdeeAEvvfQS/vzP/3zLHmd2dhYvvPBCp8ji+9//fkxMTMTuP3HiBBzHiRR4nJiYgOd5eO655/D+97+/s3Te8zw8++yznctmZmbw8ssvd4o1/sZv/AZkWY5cRgs4pvQWffhR7n3eOnUJ++/bA726DjjBdMzsioUp+I1LB6LTVGiuI1eaBux4kbkZQZrIBo9NagrNAfKtDbCKJ8OUbOie/5YViJImiaM5I1lbKEq2R0ZKBQZsI9e5XPULNMYJUsYJhwoySh5okufVVlXcd09COYEN4nouiv70mCQBrL93EyVWjnh4WRIJEuVyjVz3/HrYCNhnmUaRbPf2JecYChEjnvmm0lWUVttKoiBRjg66qFeD57hqBZbGC1JKQKVSged5kCQJnuehWo3Ji9sEzz33HD7wgQ9g9+7deOutt/C3f/u3+NSnPhW7/1vf+hYA4Iknnghd9slPfhInT57Ezp07O4IEIHLZt771LTz55JMYGxvDwsJCZ5EPf9lTTz215eeacudJJanXsEkukF5lpsMUDeBrFa3PdkRJWw8XO7P9+QKVkaU4OQKAgpvBgBYsKV235kOCtN3QFiNth8hNRslFrjOSDWSAylHs/Rk5ZPznpJnhhMjZwFyKFt+65OYIGqxuRJRcD3izsoDBXAmrCUvhASJLbSc+PEMFiSeremgzLV1sD1Bvw4IuxwMqzFuYT2SnzDfJefOyxD4f1/1ea5O5qGgeHRT/QCj6ZeP/aVnFVCkslmwUqd9n2nbv3o3Lly9jz549uHTpEnbv3o1/+7d/Q71exw9/+EPYtg1VVbFjxw58//vfx7ve9S488MADGBsbw9/8zd/gd37nd6DryVPXtKgiAOzcuVNYrFG0n79sdXUVP/zhD6FpGk6fPo0Pf/jD8Dyvc9mZM2fwoQ99CIVCoVOs8c0338TAwAAcx4lcltKbpJLUQ7ArwK5rWUxazBJvkSgpWkSQWKgsXV6fxZ6YpPCCG81XyatlrJlBBWZamfrIfYdx9vSFrueRhCn5A5Q/bSjqwdZ2GkJRGpNHgRZQKO3AWltcIVrlolHZdvCcigSJNtYFACezVXK0MabzkrAuFgAM+jWNusnSjUZQcHSHX6gySZDC/y8BmIPtAVaXfnG3Su3SFRw6Ng2ATGOxJWfiRAkgsjRZSD5/XpbiBIlysUKuP1sJvjbLzX7XojDHjh3D888/jz179uD06dP4zd/8TTz77LN47rnncO+99+LBBx8EQKI1APD+978f3/rWt1AqlfCe97wHuq7jv/7X/4rPfOYzsY+xVcUaf/zjH+ORRx7Be9/7Xvz0pz/F9773Pei6jkceeQTvec978PLLL+Pv//7v8fjjj+Ov//qv8cILL0CWZfzBH/wBLMuKXJbSm6Sf8B5CAuBlE37RKBowtCfYADSLJTRj6ibVrRXUrRUAwCXVwyVukBQJku1FpcXxLDieJdx3M2i1GrRaLZRXVdCCiNXV2aClBo0qAUSOxuRwU99yZgfKGVLyvw0iBbwgsczLdXi60dmAsCBtNUon7yd4zmm6WcO2OtuOXLIEUVnaCG1Xxi+WxKUbxILkH+E2VVtu2t3FqxUT2Ts4YEKWgGGj+/kP6MBv7IifQ7tYkTqCxDLIFbNc28A0Xq9jGAZs20ar1YLrushkyHfEzMxMp64PgM7fmqbh4MGDWFlZwc6dOwEgUZD+f/beLEaS5Lzz/Jsf4XFH5J1ZWXd13dVVbFJsUqIEzghqzQKzFEUOQQiCCJDvuiiSTY1mlxwR1C60i1lJ2AX4puZgFgOBaGr0MKLIbmnFwYwoiUezWV1d1d11dN2Vd2ZExuWn7YP5Ye5u7pFVFdnNirRfwVAZHh6HR0Sm/+Kzz74PiIoxAsgt1shfL9p29epV/PzP/zyKxSJ+9md/Frdu3Ypt++AHP4hbt27hr//6r/Hcc8/hi1/8In7pl34Jf/VXfyXcJhlPpCSNEV94/g9jl+9zS75JZRKkMgl0xZEjXpZ4OUpyU6OgzX2o9tO5LEIJsuIFC13lESMOjgW36q+20/XYVbwo8WxZy5il+WHwprGAklofKkhJLI3lSQVjt6NIKtGgEg3L/XS0Y6GsZsrSyYaFD872UdXzZWGSk4nVgRYOIC1I7zQWNy0oagUYiNLxhhUOnixROt2kON2ksf2S+4rkCEgLUsCGFCWcOnUKf/mXf4lTp06F24LIUsDly5cBAKZp4q233sL8/DzefvttAEhNnyXZv38/7ty5AwC4f/9+WKwxuJ3o+v379+PuXVbwMijMOD09jY2NjcxtS0tLmJmZQbvdDlt8nDhxAu12W7hNMp7I6bYxJIgm7TPKAKUg1YREdFeBykzqdqXWFrR+Gw0ArXp6ugqItyyBycuDwLet0VXh/uwXv4rvvPwfw8tU10Hs6Nt/VZ9Cx15HX1RJ27UANS1BpsM9f35qUi+hPr+IqytvCZ+L7aVXsKnLLIqlPE6JgwQOZceXrC6+bauoCaRnoaziQY9tP9lIn60DUerYkVBNDom0LPX01DY+ivRuwIvSSo8di1YCfrBawPtnxJYSyM/6gO3Py1HWvksbOxekXmJTQRltY+cniRMnTuDb3/42nnrqqXDbJz7xCXzrW9/C1atXoWlauBrt5Zdfxi/+4i9iZmYGf/EXf4HFxUX86Z/+Kf7oj/4o8/4/8pGP4KWXXsJPfvITtNttfPzjHwcA/Nmf/Rm++tWvpq7/2Mc+Bk3T8PLLL+PVV1/F9vY2Pvaxj0HXdbz00kuoVCrodrvh/bz00ksol8vo9Xr42Mc+hk6ng29961thscaPf/zjUBQltU0ynkhJGjM8v6fZPoNJzvV7bRxbRK4olVpb4WbT8mAUFDTabNolkKWYHCWpz6LXinKNku1Bzuxv4PW7bLk8gYLf+9zv4P/6D3/2CEeHMJoERBGlIBerok1AUygcTzB14vonT1+WYoKUxO6DGOKpNJEg9by4mHl4/BOkBw86KcDOSDLPEqVD1SoKtfxwRiBLw8obuYI8oxPUwWu+iJbUWjjVpo3gmPOwXILgU9UoADNFB69vRNO9zvomtKmJXFECAIUAPzfnYdPcWURzhvsor2Y4f1KQ9irBMn9VVWNTZsF2vtIzpRQf+tCHYrf/jd/4DQDIFSQAmJubw6c+9anU9q9+9avC64OcpOD+RY+Z3MbnOc3OzgqX9ye3PeklZCRi5HQbov5t44Dq+idOK55bQjuCoorddehK/jRRo93DXF6eUz1dxNBy+yxJXNWisQs4ng2PpkVBU9LRjxC9iIGTvyw5EKR6oREOYLggObNzOHlqEXBGvxw+WbwSYKIEAAWlFA4AmCoewlTxUOZ9rfR1rPR12C7CweN6JFOQeBSo8EDY2KUThBvUIQVBo8AEKeDspDjH7Qer8ajhpqmGI2DCoJgwHu45n55woaoe2mb2n83J0dYUlUgk7zIykjTOWD2gEE2b0c46UElPlTgTLPStbS7Fr6gGAmRj4LKK1EU1qr7NC9JTB57CtTssmqQkiw3ag1DGBu7jJW1oRIfpdqFyOUQUNNWSRVP0eEQpUUST+pEPfiorK3oEAHOlA1jajHIlLJipCNJuwNd+SoqSSyGUxIBAlNYHtwAwOcoiECWRHAFpQRLxxS9+AX/8x//n0P12SpCoTnOaBp+dNGMRpYAfrBbwL55ycLWV305lp6I0nZiWDERpRmV1mzYeb03CWJBXTBLAYxWUvHfvHl5++eUdF5B87rnn4LrujopKzs7O4m/+5m8wGAzQarXwy7/8y7AsK1UsMuiH9g//8A/4zne+g6985Su4ceMGLl68CABYX1/HM888E67gk4wHUpLGjIuv38V5f8l0DD/nxqrtR2FbnGToTMwzUaqKW1wACGUJ5Qnh9YEgzZ2Yx/JbS7EK1DQoOPiIQYdLr93Ce84/JbxOJEqTmADaK8Cs+Dbsdr4s5QhS0MKEpwAD7g7EYdRMGx6WB/FIxsDtxOU1wXJfxcHm8KDxpqXh7GQ0Dfn6BhNbkSBV1GZ0u8HuVOBW/d5tw0iK0jPTgRzrON4wh4pSWaO43WECebCanqpNClLAzy04uHqd/TxpAD0NMD3A2MPx+axiksNWrA3jxRdffKgCkt/4xjcAAB/96Edx8ODB3KKSH/rQh3Do0CGcO3cOd+7cwTe/+U0MBoNUscjnn38eGxsbscTyo0ePhtNuyWR1yXiwh3+d9wArK8Ddm/GkZABWxpJ/jejA5AGgGBeGGo0iEDoxoBP/pOPY0YA4grRbuIJcHerbV92rou5x0rCSXZvJ8HQYng7bHcB2089XJEgA4JH42bs304BFB7Do6I+5ojXhUS+MJM0V01NvA7cTCazP5c27uLx5N7x8uOricDV9wt+0NGxa6e9LZycH+MBsCW+R+HW8IPEcO3tg+ME8Als7iNKYHpOjSJAijjdMHG+k76SsUZQTK/dud/RQmIB8QeKp6NH9rJtAy9qbf1qDYpIAwmKSAEuqBli06dvf/jZefPFFvPDCC+EqtGE8SgHJnW5bXFzE8ePHAbDyAbZthwUku91uWCySUoqXX34Zzz33XOr5tdtt6LqOUml4j0TJk8Xe/E0eZ/yVXLTEndwFrUCsWj2UJY3oTJB4ipWULIVyJODG9n1c2XTDkYSOqG2FxZUZ4EWp77TRd9owFPGqPKxci8lSIEdJAlmy3cGOBanWHeCZfUwcNBTwyKGyBJY3AEm8bjduLAMQixLAZCkpR0l4WRLJUUBVi6KFbxEtHGtm1LNut6JIO+GVNSMcAHC9lZOLBoSiJJKjJFc2dTzoAa9tpI8vKUgi9D36l/Xs2bN47bXXAACXLl3C2bNnAQAetyTx3Llz+MQnPoEPf/jDePHFFwEAf/Inf5J7v49SQHKn2+bm5mAYBhzHwXe+8x0899xz+MhHPoK/+7u/wx//8R/jb//2b/HRj34Ur7zyCk6ePIlKJR11/v73v4/3v//9D/16SX76kdNtewVFBRIn3EKvB21iCjkpH0yU2ivQizXh1Tf64pomjmcBBj8FxCIsxgiTuItqDcQy0VPiUQJT82A44rNUy17BrLEIiFbA+WjBd4dBtOoP/vGLBGl3SDyOPontRO2qpCjZfn7SrN+CZSWnsW9BLeF4g70/319NJ/XzghR/Hi62gFCUDteYoKzQ3bWCLRNYAFAvsGPuZLy/11s6jjXE720w7VbT3TDpXUQ3cXNelESCFESRTp6cx48uLaWu30tkFZPkCSI5Bw8exPo6++zttIBkoVDILSDJX68oCizLgq7rqW3J+3FdF9/4xjdw9uxZXLhwAV/72tfw3HPP4ZlnnsGPf/xjfPOb38T09DQ++clPpp6b4zhYX1/H3Nxc6jrJk88e/b4z3jh+Dgk9lMjF8SNKhV4PhR63+s1zUwIFgOXztFfYLnDDEZAlSMcbiftyTIB6APWguh7Onc9eeZUHIQoKnoK3Ly+jqEbSVhZU/ja1uEC0JupoTXCRIU1nI4GW9Ssx2AasLhRTXJGa3ZYlWbt4/OjK7/zWvwMAmLBx5RJLfKnpk6n9ums6bOqGgsQzW0qf0AtqKVWi4dmZKTw7E5WIyBMknkkjej1tj6BSGG1rks/95hfgUmCySDFZpGEid8CJpliGrrf0WFTpastI5SXVdDd1PF07LUg8z0y7UAkNBxCfZpMwRMUkeQIx0TQtjAjtVgHJYFtQLFJUVNJ1Xbz44ou4cOFCGA1KFotstVp4/fXX8eUvfxlf+tKXACCssv3aa6/h6aeffoRXSvIkICNJY4aXiNQEomRWmVT0nTb6BaDhT7Ns3LuDyUU/l4QXJV+OhI8BF/AcHDUmcIObegEyBCnEb9b6iEvkCd/+YnsVqEUFMcueIYwoDSbmYXrZYhOIkmZnn+w2yFbsciBKNaUJ1ABsv3PRg5o+iYYR/8Y6bSxizbwn3D8QpUJOYneA5RJYCrBhbWKyEBelPEF6NznRtPHWlkB2CVBQKF7fzC9xERzXrZzIEsAEKYlKKLp2ZG5SmBiiYpLDGFZA8ld+5Vfwne98Z8cFJD/+8Y9D0zS89NJLsQKSoqKSf//3f4+1tTVcvXoVV69eBRAVvwyKRX7yk5+MHc+XvvQl/Oqv/ioopbhy5Qp+7dd+7RFfLclPO1KSxpQ3Lt/G3DG/zUh5X/hGry+bmJoz0Co4oSjFIAqw6Z9wVdHJh30jr04Z6Kxv4qgRnUydxu4viYdmJMQrIogoLfWj5zExUYWhlDNFyfarWj8YsIa3+4oLseuTghTgGiUALXahNg/YfWjKaFe7Xb50F2fO7YcJGze4FYnz9gaqiahSlih1bPatfbPDpgUPVcXS8D+W4lN5G1Ykv6KilTwr5u4l6ANMdiy/NMG2raCmx6OEvChpiWjT2Qn23LJkqe+3NKlzH/V2IpokEiQAWCg7eCOxbd5vlntDeIvxZlgxyeTPQFQ4ctQFJAM+9alPpfKXkgUkFxcXY2ULAJbjJCogGfCVr3wFAEAIwa//+q+Ht5GMH1KSxhDN9QDEV385nglNiU85xERJ1E7D9c8WanACyllK3XoAQ42Spk23lykzI2N7FdfvAMcO+MKgKH7idlrWgoRuXpYCQeK578sSAFgkviqwsKDCeuD6ggQcPDaP29eXYqsHievBFdzvo0BAMot9djJECQA8bIZylORWQpaScpSkpGqYK8Vb2HSN0Rzfo7KdOLaG7uL9My6WEoL0w5sOfuYw+3yfnRjERKmf0RgXiIRpNkOOACZIPHwUqaBQ6MPKmUskkicCmZM0ZihEBTz2B3xSj0+JOF5cWhq0DuhlYON27n3S1jIKgxzhaT1Ibapv90DLzXC03Zwprx1CBc1xXeoAisKGz6FKNB115X78uRlKGdvOhlCQeBqFGspaMxzh4xnvzhLffeX0r2pH0ITYdDvY2sHy81udAdRB/ntSEiTZ3+6uw3Q74WjZo+vPl0chIR22S9DQXTS4KFfD8NAwstujnJ0YYLFi5wpSgOkB+ypWOHiSgiRCHW16lkQieZeQkjRmfOELX45dFolSg9aZIPGs3xTeH+1EJ2Kl34bS5wpRth4IBcnYWotdNj1WoLDtduF1O0Avp2/aTlBUuNUJuFU21dfyNlO78KIU4Pn/AGDgbmMgaIbbKNTQKKRX8pW1JibJFGu5wpMhCc8///mhh7ETeDfIEiVeWgIcyoaIc66Dcy470X+4WQxHQEnVMgVJhKFQNIq7EzkJlurbHoHtIRwAsDYQB8Kqe+JHAAAgAElEQVRForRlqdiyWN7RwFUwyBAl02MjSSBLtqAieTKKJJFIxgcpST5f//rX8ZnPfObdfhq7RsUrhsMtZDSYWr8ZyhLtbMQEiUfpt6E4dtgslicpSElOPnv2YZ52DAISRsnURPJ3ligdUkoxOUrCy5JIjgKKbpTYa7l9WG4flHpoccnixG80puf1jhsRhloJh0KyZ815WeLlSMSHm8VYfzOeLEHiISNY1ZeEUoRiMimQn2GixMtRkqQsieSI54crLIoYFJy83dHhSSeSSMYaKUnjiueAqDqIqqOynZ5WyRSlQgWYWAQ5kLOkVeemnNRCOESCFESRAKCssmiFXcpuAZKHSx3AzI5CxUSp32YDQJnkP16NljFXWshs7cELUoDO5Xe1FBPQiyNv5uv4FbaD4ITtWZgpOjDU9PHkiZJHCY5lVFnnuTRgkaj7PRoOYLgglTQPJ04t5O7zOPDRm52I0oOeggc9BT0XmC4Onxo7M6FCV7Nt54crpVCQeCxfsLYtgm2LwBkiWRKJ5MlDJm6PIbTLIiP07jWQ/U/hxqaKo9gAKvFE31CUvC6TIx9z5S6M2f2hKNE7rIJuTI4SbFVVoBbv+WbmVH0eBarjwtUigakXZoHeljAJPRClHo0kq0bT1bl5UXLdlvBxdSW/FxgrdTCaEENQvNL2oryYrr2BiqBmkkI0eFyfNY/Gp4b6TXab0lY8QhjIkYimMoWqFu2fVcTx3WZtoKGtip9bIEpJmXrvdPz1qXLTZh1/ab9IjoBIkJLc7igZMUuJRPIk8tP5F0/yWJBixkm8Gz85qmu3oa7dBr1/P//+DjydEiyerWo60kI370EjRjgKhKQSxx8ZLppEiIJ6YZYJUvjg2aepMqlAJbpQkHhUCmB7JRo+IkFqqI3wZ8UZ3XJ4lzsMI1EAsitI2gaYKN3vFlKCxNNvTobClBSkai16nDPV6dRtq5oHAiUc7xTJaNKrb7Do1vW2Fo5JI19M+ahSUpCSVHWK1b6GQ7V0gr9IkEqJNidyFk4iGQ+kJCUYt7wkejfR3LW7EcpRbL+bV0FvXk3dfm1wC2sD1rAShVI0fLIEKcmFs2cAALY3gJozNbRTPF2H5/dgIjevhNvvbvknQoEoeYoCT1FAHAM9xUZPEa9wE868bK8ANL28nxek3aDDNQnOE6W+2w4HACz18vOi1sxNPFC2cahawKFqeupVJEgAUC/Ej3dSL6FEKigNmdJ8VJLvoukRmB5BRae43k5/joaJ0oGKg4KSH+vhK2oDwKGaHcrSMEGSUSSJZLyQkjSGXHz9XiyaRO9eA53YF45OMzs/JRClmBwJ8DQVKJTQtKITbHFeEQqSpqRPwp/97G/v6Fh4Pv97Xw6n/PhpJSAuSiF+K5RAjkQEsmRQDyrNECQA4KTBpXY4+Crlo4wi7YRtW8FSbyszQrfU01OytGZuxhrUBvCytFNB4pPhXbJ7sROFsHGtHX8PzYwyRpMGjclSSY1GwKUNF5c2xFW0szhUs0EIDYdEIhl/ZE6SgGQ06YUXXniXnskjYvhTSRNRNWxi9kG5Gj+BKFW32rGbolQEXb6D6fp5rEG8Us3z4nVjAlGir/wQ/Ua8nQUvSHTEkxAedWIJy+TmFdDDp6MdqqwfmeI6qXYtPJvWFjYBzBb2gYh62GVIA0ugXg1FifrFM1WF4D3ns6v17hTDL7bDR5M2TQ3TgkKROjFg02xZOjdbxd3undzHWxmYKGnAbX968WAxmsJMCpKIvjv6OMqwJfWmCxgZi+o0EhcjEYEo5clRFoRQeAC6DnufKpoUJ4lk3JCSlOCJEyIRjgUYNZhFAwZXBDIpSgCTpYmlbaCUruw8jUgO1uzbqKvzmQ9JX/kh+6Eb1Q06PlPH2+uCE+cu1pIhZh+oHk4/pL/0PSlLm1a87QhVEmfVXEGK2AaX22P34BVGV3SypKlY6kXTfHOlOpb77dR+IlH6wGz0PPdXWI8+kSytCIqF3h4wWapoFbieA1WJXrsgirT/eBO3rqUjU6OkrLnoOex9udMhOFCNf354UWpb8Vyjl++x9+G5xeyil29sqYAXfU4PVHYme30n/lirfYI5/21/57r5SSSS3UROt405ZiKJm5jRyaKo1VDUaiAnngY5cDz3fqZbFsi9ZKcqRihI/ONM+8Uc+y02Wg/AVn2NoBQxt8rOow6wcDgaAGhO4rbiOlBch0WPLHFfNnYf7iMJkm0MW/m2c2yPouSv3lusxKVrriSeMtWJgYPKNj4wW4kJ0nYvWqm3v3IgFKaVgSkUpICFMjfN6DlwPQeW9+62JbnTSX+GSmpakHgCWeJ5Y0tlgpS8/66CO938P41JQeIcFrMlG46g6KREInnykJGkcUXQnDbApQ4q+kRqOzlwHPROInm7FV/5FogSXTzF/hcI0jAcz2JVAh+Bi6/fwvmzh6DxPc0KJcCKRwoCUSKJcgC2H8VaLB8GANzr3kw9BqVsCqY6PYHOWjxKYtCM+lK7hEdd1moGTJTucZG6ZERpw4pW/d1f6+B90+K6TwCwMbiHsqpgpjKFW93V1PW8HPHc7bHHd/wpxoajQbWs7Lpbj0nQg66sueBVzvEImomHrPgf+W6GwwWi5KrDi14eqHi4+CD6HTo/Fd1pUpBEaLLytkQyFshI0hji+vViblxmQX+zaKRWpnXt6OS/wn0NJgeOs6hS635KkHgoAKg6yPt/NnVdGEUKsKJilmVBIcSHgRICqmpR892AjOktSj3YCg1HksXKYSxWDvv7uqEgiWgTX0IcOxx8naZRY5Dh960qTI54QQr40VoHP1pL10DaGMST6w9VZsIBDBck4fMYdFHMKcg4ChbK0QCASkH8eJWM7wcaYeN2h+C2IBoVIJpuu7iu4+K6joNVK3VdMookkUjGBxlJGmcUFYbFTvpdt4WKv1y9s6KgOuuha2+mI0p+hMedPQIAUFfeTt2t50eRAnhRIjcTK+Ksx29sm4lrxyNmiYhS0MtMX1+DPbWYe1eLrQ6s6QWIFnGHcpTg7T4TzbLGXlevtL0LjTlYNClgsVLCmhnPR5oyWDuVdTPdiw5gsvS+6WpKjkQcIyU88Nh7VlCiWlIiQWo4u//no1EgYbPYo3UFN9rx96dSoOgKptkqOosoaTlBn0CUDvo5TsNykfZXKDAATjajmNb1Gzs5ColE8qQiI0kcL7zwwvjUSRLk5XQFFaS79iaLKlEqnAJzZ4+EwuQtnooJUmWWizi0l9kAmLwEg+tjxkeR3nNm30MfUoDtcUvtkxGlNy+lmr0CgL5+D/q6QBLuX2IjROHGcEEKcLgVf6fPHo2JzShY6vfCkTVTGchSkvNTOmxqwvKyJUDrdaD14q+Z5fVgeb346+0TEyQ/R0kluzvlBjBRSiKKKB2tm3h6ykRzBylitztEuAru9EJ04/2V9GO8uWVgoULDcag22vdcIpG8+8hI0hhy984aLpw/BgUKOpqNqhOJCh9RKmvNcLtlGCiYORWxSw241IZKBHMZgRwlCaI8vii5hJ1siyPoCnr5jbs4c2p/tOG1H4Q/Fj0dg4xCkaEobd0WXs+jEA113w/aXApUUpBEaCP41fIIRUFl0ZzFcgP3uORrSgEiiJJMGbUwonR+Kv1eBaJU4OpGJeWIJ6iqPVeMHmz9HYgg5ZEVUTpaF39+mwawlfHRrnOtSNYG0WsyXfRX7wnkCGCCJGJzoKHrkKGlCyQSyZOBjCSNIf/P//212OWOlhAGz40JUoBlGLASq7PU7Q2o21Fl51ghRWC4IPn0uK/q1PNAc6IaeRCbne0oPFCFgCoEuJeuFF70dBS9tCQ40wfgTB8AGgtsCFCIAiWR8F0fsPFGJ70ijo8iKfZoc1L41h+L5XitIlFESSEqZopNfGB2Kvd+Lc/DhLEPqpKd4J/VdmRVcWNjNzH92ksdQW0oALiyScJxNUNcACZKfFSprtOYICVZGygwHiLHKlnF2xzeV1cikTwBSEkaY/iKyBVtAhVSRYWwFU/61krWzWAZBlTHjcmRCEo9YO4EGzw5gqRYTHJISTw1NBRChBW8MZmWPgChKIVylCQhS0k5imGw6ULTJeG408mOwjwuZAflEgJRUogaroILKKniMgFzpf2YK0VROFqdAK3Gc9OyBElXinj6+MHw8kShhnVFxbqiomW9M0nL373v4XaHCVKSPFECgPmyi8+czE5AD1isMPmrFbxwBCSjSMPanEgkkicXKUljjO2ZKKn16GSZkBd9ayUlSz23hV6Qu1RqsJFAgQolmaLsyxJNJm7vIn0nkagsEKU+seBNH04/3ySNRSh2znSjkV6VN1dm4YIHPQsPehaU7U1gMHppyoomTRUrmCpWUMxxk9j7D8TkKAmtTmDb8DBw43lY+/exIqK6Ei84OlGIRLeyizNwKmHj6paH796PhGR/xkLJq1tGSpaKqouiGkW9/s3RAf7N0XSu1WLFDQUpSa3g4bUNA1aOE3V3UB5AIpE8OUhJGlNcv7eZ2mXTQ29dy5YXfWslLkdJOFnKlY2tuwAAev1aONxqA4bKoldBFOmxULSYzPSdbVwzo1VYmGyiT6xwxG4qfO5RkjYAtjouGAE5gsRz8hyLqNkqAHsX+rj5yfWNgoapYvw5FSwXBSt76qusNjFp5E/Bdez18Oe+ux0OIF+QdhNC4usNF8vx67NECWCtRpJylISXpSw5AoDDNQuHa9HnyfLYqGquzD+SSMYYKUl7gECU2AUumqSXwtFUp9FUo9Vqy4N4hGmgOCi01qG1BNN0W3dDQeIh74lKAxhqFdAMQDNw5sLJRz+YDPrz8+Eo6Q2U9Cji8mD1QfhzPAo25OPfWUe3rKOrxmVLJEiH9ezcnsfB8vrh4MlqasuLEoESjgDb68NO3FfHXo8JUpKy1oCqaLG2JDxBFKmsvTvTTklROjUxwKkJJj5PT7KRR99VMF30YLriKBAvRzxtbnNBoWhZKg5UHRyoyoQkiWRckKvbxpQrF2/j9PmDmdfTbhukmS7A2FSnseVGjW0HSvoPfkyUtsX5HbwgAcBmorbP4y6RV2wTZds/qdkdAOn8m5LeQN+OR8fI2z9hP8z4BS/L4lymZH2nQJQWyl6qkhIvSHaFRc3yWqM8DAVSgEXZY1t0gAKJIjqOZ0JT0jk4BdvLzCkKn6cvSj07e3pwykhPzamKhoLlou+y9zMr72lUiLRlsQzcS5Tf+oUFG0cmsqN3T08CryVS7Ppu+jXiRclQ6Y4ECQBWB9Gf0tW+MuJWzhKJ5N1CStIeQbXtVE0hurUM0pxL7dtUp4HV6xjsH/IVfPIgXCWKUqktttJtmCABgPKIq9ugqCwiBbBaUH6ita6WYLtpYQtEKZSjJD3/+QeylFP8sqJN4OTBCbx5+0q4bbciSDsljCgl6gEEr6+niGXJAHsNqV+dPZCeAJEgAUhN6bnUxrb/scpNen9Mkp+WxTJwpB7/PF9tMYE83hDLUhBR2hDIkYiS5uF799kXiZ+bjz5bSUES4bpSkySScUBOt40rngvdUwAQoODPRwiiG3QrsYR/9TobAIort1BcEeQyTR5kI4HbmAM5eYY1tBUwdaT+yD3bogdxQDNO/LoqaE3SXkGpb4KUxG1LQnpb6cKUHBUt3esOAI4caLJiip4NKCp0cwdn0IekwBVptOgASzcjkVOgoqNlP6bieTEhNfx/SYIE75Ja37EgFZToNW0Wdv/7lqbEx622WFADWUpiqFUYahUzBsWMkf05bBoumkb8WL+3VML3lkr4wGz6tU5GkSQSyfggf6PHFVGlQSBblMxOKEdJYrIkkKMAddDF/IKfHNxvAf0WVKJjujiF6WI6afhzn/2t/GPYCYnj0dUS0F6JBkeWKFkTs7AmZv2D0KPhkyVIDUucxD4qUfqt3/r91LaeM8CWvZVaYdhR85PiL20uC+UoyYa1hNXB3XAE5CWF7yaaQqARCo1QDARPYSeiFMhREpEsJeWI5z3TLEL13H4rHLwgSSSS8UNK0jij+CfR5BSSLxZk/9lwAADq6am3AG/+FJTpA/A8C54XlwB10IU6ELTvSERmplotQCsAWgGPu1I6Fk2iHmxvEA4Ua2wIIKVSKEsxOcqgQsuAoDRASpC06KRMsvKcHpG23cePX7uJnsNNIwkkuKOaKVm62FrFxdYqAOCNzjLe6IiLf25YS9iwllLbVwd3Ybpd3HTinyE+ihSySzNMDa4s1k5F6ezEAAUF+Mel4d30ZgwKhexMkJJ4NBolTU6xSSTjhpSkBOPSv81TEicHXpQ8l/1VF1Gfi8mSN38K3vyp1G6BLAnlCAgF6cg0ex50mesEGiyPz4p25fDZz385urCyEg6F+yhfe3Cf/ZAhSnR9E4Nbt1BorQmv559/9JzNcOQJEs/v//4Xsu9/h1y8GL1uqbcs4/XrqGZMjpIEshQIk0iOAupcA+SbTg83nR6sQrxw5Tsx1caTJ0pnJwY4m0jg/u697OKRNzsqbnbYsbQtNRw8WYK0Nki//pZLUNe9R/loSySSn0KkJI05V95miclmqejLUXSGobejxq4biVwKb+44sHAm975d6santoLprZzcnlFAHIet3irGK28roo8zJ0p0fRN0Pd53rdBai8uSm05w56HL1wDPiw/+uflRJLewg86qo4AQLN1j73EwBadAxdmJGZydmMm96ZFqEzY1UdPF04l1wfZ95ej1VIgKlWhwqRPW5Ro1nh/1bOT0zt122LgwlR0J+u69fkyWeDkSEYjSTgWpkmpxIi1JIhkHpCSNM64NUI8JEoA+1/T1vsfqCNHbl2Ky5JXq8ErRsm5RHzOXukyQBNhGEdD94SOKIimjWlg5RJS8//7f4P3glaFRq0JrDV4xnbcSQJevMUFKUp8FXCsctJNdb+hRKWnRyVwUAFSVQmaRT5EoHak2caQanxKs6ROhLNX1iaGCJMIdUdmDLFq3WI7UvoqDyaITyhGPltOLDgB+uNpBSRtetqBeYJ/vvqOEI2C4IEkkknFBZh2OKQoXMTK6XZgVtsKtr9goCRq/YnsF3uTR7PvzRSlLjgDAnkkkdetFoLMOMnsk2rYmTg4fJQoUeP/9v6W2k2m2BpyuxQvm0KV7AAB96R7s4++L7qfLok5COQKYIMUegL1GtLMOzXEAd3eiKxPGFFQSfw8VombWngpESdWHS8xs8SA2TbY60fHyE9D53nIbu1BgPGCpFwnK6Ur0mh6uebi5nf6eF4iS40VfCqqF+Gtzusl+H65sxaeL64Xsz3ffUfCPy0zKjzfE0cbp4ruT4C6RSHYHGUkaU77w+18FHPGKpyCiRCZmwwGkl4vzeNSDRz3om8vQN6Pk39Ixlq+UEiQASERVBl4HnqbC01TAMeFm5UUN4eLrtwGHO4H70SRDq4VD+YUPZ94+lKWle6EgifAqE9imbXRmBcndGYIEALBHWQaAiUhdL2OqOIUpf5VgwUyfpEVNbgFWS8nxTBieBsPL/l5UUuORIk0phGN/OdkA952ZTiIAFO6hNs348R2uZYufpuioFtyUIPGcblZCYcoTpLfbBbzdjqKWV1s6rrZ0VHUPRJaOlEjGFhlJ2iMY3egbc6nlR0iOvA+knW4zonheWITQy5hCCUSJtjdgzxxK7yAQpADSY8/l3KnFhziCDGwWWTAsN/VpVn7hw8KIEmaOgcwcA9m+C7ounh5TrETrDl6UNoavmBoVHih04hd+7K6DVKJSCgXThmWko4KBKGW1LglEyfSrqSfliGdlEAixnjlFGkaR3p2uJKmI0vV2ZFXvO8QE5tJGttRV9CYO1djvRFLCAMTkiGfDBAJ1JKBoGi5WMlqbSCSSJxMpSeNOEE2qTKLkakAnvuKJ1meFokThAQ8uA4KVbeE+bTZtpa9F9XTs6f0pQRo5rg1YXcCYDiUJACsImVh+H0aU6AYwcyx1V2SKSQcvS0lB4ql5JXRWrsQ3ZkSRzp07PORAHp+kKGkKO6Gra7fhTGaXdAAAw/IwINnRk0iQ4mhKAXe70WrJ+TKLxIj3fjxcyqRDIVE+1qapYiKxXP9DcwNcbzeSNw85N0lTolTR06Ua+PvdNNVcQeLhyweoCoEqm95KJGOBnG7LYBzKAIRFEStseun6rTWgmk7kpdzUkauwEaAsvQFl6Y34/u2NUJCS6OYAzuS+cADxKNLIqSbawve2UrvQV/4JqEyntvOQqSm4p9+XEqTFWnS7mpeuDUQm9oMWq+GAnxDtCaa9HhXajlbf0W5aQAumHU6L8RgbyzA2MtSFm67sOlvhCMgTJJ6FcnQ53dVu99g0VSyWrXDshHOTFOcmKSp6UyhISeZKDg7VLBxK9G9LCpKISkFGlCSScUBGksYYW1OgOx47IWrcya06k4oooTEPrN8EZg4L7ysQJa+Qc3KpCUSEKChyUQfS28Yo3JzaNq7cWMPpo4LH7G2BvvKaYLvfLqUcjzg4s4ejCyr3Orns5CiSI4AJUoylt9n/1SZg7kZchUG762ircQmzuxvYXxH32gtFae5wPJdLQNfZAqXAjDGLVTMeYcwTJNKNl1bYDeoFF4dr0XGvDTRMF3eeHP9f3mZ/7g402evxPx8SR9oKgihQIEpv3k7vn4wiSSSS8UFKUg6iaNILL7zwLjyTESAQJSJoSKqu3oTri1Jv4gjKm+zE3/KnpSrbLsjkAgCAbjyIbigQJG0zXqTQqU6ATcp4OHNqP157NWPV2DDURB5OEE3iyg6Q06dBrySmxQJ6rVCUYoKUepwCeujB0lUU7Pj0TkqQBLhGdkmBnRKccml7DbR8INxeRxVtxCN0d3NECYUKW21HFGFrGoDPYWI5UDNGFGFcUzKKhib4t3/wPP73/+3/2NG+O0FXFMyWPPQc8fTVTkQpkKMk//VWWpZEghQQJHZrnAflVemWSCRPPlKSMnhiZYjj1Z/cxHsuHI42OBbQ56ajtleBWnr6TV29CQBwZw6HciSCTC7ArU+htX4HjcS5IilIQh7zWze17SgHabAdkyRgiChRD7TfE1/n00N0vaVHEQyhIAVRJAC0K27w+ygQ1wH8qBHpd0BLkXhliRIAzJa0qLFx6k59OeZkKSvJGwB6zha27fg0Jh9FeifRiA6Hxlf2iURpfcCO8UZ7+H3+11vL+NBsGW0rWwSzVr45XvQZ3jTln1OJZNyQv9VjjOFHXKghni4CwEQJSMtSfQ6q2Ufp/k20jx0W3tStRwLVUqNCOSJBYlEkH/XxTrAUFKgL5E0gfeT06ehCKb2KS711GQDgHoqqi/NylKTgAp6WiGRxghRw5sQ8Xrs1mlwsUqqD9sVne5EozRSLGExOodgZ8vhEwaa5jZqe/X70nHSO15blwuMqbBO/HpSabIUzQsoaCaNJWaJUq0dyxHNhysFP1sV/6v71QfZ52Rqsos5JJS9MWYJ0sEphJ3L8+w6B5RH0H7c5oUQi+alAJm4LGJv+bdSD7ikgLS4/ZjMjdySQpUTvNgCoX7+J+vWb4WW3PhUTJJ6qPgVMHYyNmCCNiKANBjVY9Oj6A/9stZ3uV0brs4CTvWINYLKk3roMDLJDD6JzpUJUkKl94aC2BRRypPQxIf20+NRRxUyxGI6AQbWKQTVjyi9I6gewbVvYTtR26jlbmYLEoxBfPoo1aI73jtVP0rhimoSwMWFkT7tdmHJwYSq6/l8fnAkFCQCaxbhc1wsVbNtKriDxJKNIMjVJIhkPpCTtEYaKUn8LtJsfeahfv4n7enbib1UXiROBtr0RDpQaTCIeQyRIYposEKWQ7VXQ+mw4ou0rbIjus1gCKbLnVB44KA/iJ9wsQYqhc8ekG0BvB3M9w9DSUZ5QlFwnHBVk5z/FRImToySBLHkZfdiSgiTiwoV0mYVRUfaTgVYHXawOuqEc8TzVyE9M//C+ARbKw3sL3usyqTbUaAQMEySJRDI+yN/usYbCVTO+0m5uArPpnBX6gLUNIQvxkx05cg4AcBDA7X6UsD1XYkncWYIUQzcAsNwXy+2DGuX0TR6H5r7okR0LVCAYAGKiFIiRiPLAAcoNON42kpUSRZWtdxMS9NML++ql39cKquhCLLpWtQ5LHaCG/N5mM6VJbFt9FNVIrAZuRyhIYRQJgJKcghwhm2Z0rKuDaBqsa7PveJVEu5VAlK61ove/mYgyvbJ2HwDw3ul9se2BHIkwVGB/BfDC2k3pJG/LkyEkiWSckJGkPQRpLbOVYP6gM8dABQUWASZL9MF1kCPnQkES0XfaaNAqVCtK/J2ZqyOrC/qpg8Obi+4Ulzog7VWQ9ipgbqeuJ44F4i95X6eJx61MAc6Q5eN8qQCihEMoSHwUyU+IVoY0hN0RnguHeHCIx6JwPIKIUAVVVFDFql8cU/H/BWz7/wIOl5kkzJQmMVMSr4yzqYX5UjMcQFyQdpOeQ1HUIgla7qUfN5ClJE81LDQNJyVIPK+s3Q+FKU+QgPQn2qMEW5YKQiiIQJgkEsmTj5SkPcCVO91wmsWcTPchC0Rp0+L+0Hf7bPQ22BBQ1+ORKNUy2VgTFJPRjfBHy2UnIwoPv/Pbv/mwhwNaKkMhGjtR86KzfEO4P+FrA1Wm2Ajuq7MdjpByI1VLKaS9FPZCC0ZMkHaBZJJyjIypM8czY3KUhJelpBzVGtHxbDvp936xPI0taxCO3YwiidRjmCj9cLUQjrWBjrVB/vNr2xq++2AFM0Xx9QRi5V+oxN+XkuahprMhkUjGAylJYwwBgSpoIhuI0v1XfxBuCyNKgRwl4WSprldSghTS9nOfPDccRw7NgJaHVzjeKTSvsrNIlLrrIK0HMTkS3m9nG8hbodVOr9rTOlug1A3HqWNzrG3KqPCS01yC07UvSi1vPRwAUFArKKgZ7xOA2toysHYNGjGgESN1vUiQRNieBduz4BVUOHSUzX0j+GiSiNmSg21LxQ9XxVOsIlFq2xradly4ZorRALLioWlBKqrR75kuW5JIJGODlKQx5vnn/1IFPYkAACAASURBVDC6kJCUWETp0j8Dl/4ZdHkN5Knzufe5SVrZktIWVJluRflLtNwEbW2gYNkoWDaUXn6doiw+93tfjm9ITpsFotRdZyMgeZmDzCyCzPgNdwcdNngEgpQHaW0B9mD4jjskHk3iTt3UA6iHDs2uzRSKks1Eoba2zAQpAS9LWYKkJqbZHBrXiHcqV2u5p2G25IQj4GSyYBdHEFUSyZGIiuZBV0rQlXikMClIIqQoSSTjgUzc3kskoznL60yQEgSiRK9dDLdtVuInv5QoDRGkJDSoWZRR/fmh8UWJLBxnd/vmq+zy/gPpfTlRCsVIRCBK/biA7DdM3DUNaJ34EnnS2Vnk5XG53WlhTjFQdePzQyrRwtIISQpqBX1nC02BHPGYLkuMLnIRqIG/LVeQ3oF8ZUP1cKAaCUpJBfoCJzrZcPFmKy1rHX9KzvOTqyu6WGR6TvozGYjSoXobFtfc8NzxCVy7scHtJ+VIIhknpCTtMRxqofLG9fAyOX4O9Ool4b7kqfPYLOZHe2qFWWAWaHf8JqzB9JBIkB6Ic4YeC64dij27D8Fky4OJp7CweQ307h2hKN3Wgf2HTwAASl1xpWV6603/MebieUd2f0eCpBZHs3pPI+yoOs46NgbRr2xHHexIlPpcvSNz9hAAwFi5lXqcQJCSFNUK1h22IrCqZ7Q92S0oUNE0dGyx/OWJUkAnI7G7a6dlSSRIAbbHpqELarTP9VZ6mlIikYwPcrpt3FF0NoKaOgC6pxLL+4+fAzmeWMFmDwB7gAllChOKOJenVkgngUNRgYkDQC1xXYYgnT+9T7h9GMqgA4UC24if2IOkcB569074822dDQC422Ji169U0K/Ec3dCQUqiG0C5Cbp8PxxHp+PfNUhnZ33OdoJJCNycxO2Omp7SU4kGlWjoO1sxQYrd7+yhUJhMt5sjSDUU1WiVXsfeQMfewFxxAouleGTyzJlDQ4/nYTE0iu+/khY6npJghm/LUrFlqbjXGT7917UJzk0MdiRIIrZMBVumgvmyl5ctJ5FInkCkJI05NvVPoj1WQHLpGjtpJkUJYLIUyFESXpZqhVmxIAFAg5Oe2iwbpTrI5DzI5DyAaKqNaKPJXxkmSuTMs0B9Aeul/KmQfqUCWN18QcqA3rkdDtRHsPTfx6Dx5zwpaObKi5JLnXCoZPiqM3P2EGxvANtLv+e8HPGUtWjl32KpCdMzYbomNLp7+UhVPT/oHYhSIEc8LZOgZYrnAz8w28cHZvOX/mcJUlYUSSGAKmgeLZFInjzkdNteorcJlKMWIYEo0ZWb0T7zH2T/Z+TXNC9fgzvnL5efOxG/siGICjnxky+pNUE2/OiGkb3yahivvrWE95yYByCuu2S5fSZHCSoaRVfQV6u0xqYHHa0IshA1sKUP7rIfMgSJzMWPmRw+Arxxi4nSiri696PAR5OSoqSRAgy1KmwjEohSXjQqIBAlXSnuSJAAYODFBfWdaksS8L2VaDXb6XK+mLRMgobBpHOYGAFAVdexlVE1PSlIpyYSU5yuzE2SSMYB+XUng3Hp33bl0v0omsRRsTVUbC29iiugGs89oZf+GTSZ5L38VjREgrQDaHcErTsQRZNURQsHMrq6VzSKisZOYqW1B6EgAYCWlLqF/aCtjFYmIkEK8Kfcfv/5zz3UceyUateCRgphvhIAlLXsMgsq0XcUWQKAjtPCtr2GbXsttj0pSCLoqBLxBQTRJJcSuJTgVif9HU8lFOqQwo4zpSFFRMEECQAurpfCETBMkCQSyfggI0l7iQerAFZROfRzsc2kPg3aXkvvX53E4PUfwKA5PbuqfuL0Ore0PqhHlBCO2DTeY64ECwol1pzoIywsTWB1gUI6YlXRJ1HRATSrwGpGQjm3mouu3YlflSdIu4CmFKASLRapKXY6qQa2Za2ZiiiZXpR8f3ebvZdHa2mhMj0ztS0QpaKW7g3HR5EqyohbzPAQICgr6SZKDiyWPdzrpb/rqYTG9g0iSAE3tpkEHa3FI2yBHIm4uF7CSiv9GvFosiuJRDJWyEjSuGObrDJ1Yz7atpleeUbq07HL9I0fgL7Bik2SA4dADgiScqvT6W0A0N8CXCtq5QGMtGYQAMCxhI1yHU9QzJCLKDE5SqzQmjnKBk9O2w0yuQioWnzw+FEkuv34Cdy/+bv/CwpKdkXvYicdCSxrTZS1JkyvFxMknhvbW7ixza16EwhSQEVjU7Qe3HAAQFFh8nnm1MHhB/KIWG7cOpL5RgATJREqoWgYNCVIPDe29VCY8gTp9EQfpyfSU3QKgGtbGq5tye+bEsk4IiVpr5IlSmYvlKPU9YEsVaezBUlQsZreugq6vhYOrLLHPnfg8aMPV67c3pkoba9gyh7yeDNH4c4dzRQkMrnIBCmJ1QeMajSmMpLaH5Prb66B+hGV15ej2k1JUbq+vYXr21vQdjC91rE3c6fIAkHi4af4ikoFLnXQ8XroZAjZKLC9aEprmCidn4zGs7Mqnp3NTygvqh7u91QMXBsDQbV0kRwBgj+elML2ANvD0Ck/iUTyZCAlaQ+gBtMOp56OXxGIktWLBgBy5GzmfdGle6woZTB48lp6JOmbLO/Je/QcFrrFTfGJRMnsRsOn2Bug2EtHtVyFDQCgb10JR4BQjgAmSBz3gka7U7NwJxoQdx97OH5y8SoURK8tFdznymAjlCMejehCWZotUswWo/spED0cAJOjYYIEAF03krVNU00VnNwtkqI0W3JQ0T2czyjjJJKlouqhqKY/f7ws7VSQnmpEcuVROecmkYwLUpLGnWSUIBAlxwUcF3RNXBWbHDkbkyW6dI8JUhKRLPEPfzOxnL6+i7krALpOOxx5q+d4UXJzfgvoW1dQ+v41oJIWhkxBAjAgI+zfJnpeoLBqzXAAyG2sGshSUo5ElNWqsCxAUpCEjLJvXQI+mjRpOMK2JNe386X72Vk1U46SuNSGpgAa9/n48CljR380HU+KkkQyDkhJ2iFP+kq3S5dvhAUlvX3xGkm03QJti3t/kf2HQS++FtumvpFuZQIg1tQWGCJIMwsAAMXYwYk3D8dm0SjPQ4EKppeyRMm1UNxup0SHx750H/al+9GGykQ0cm43ahQ/OqO6LgpKORwDJ70yMEuUlvttLPfbqOlTqOnZjX41Jb5yK5ClAtLvUzKKtFsQsKrZHVvBpOFg0mBSJCoiCTBRypOlgatgkGPGusIGTyBLLcFUn4wiSSTji5SkHfCZz3wGL7zwwrv9NB4JTzcAz4Xqxk8a3sETqX1jomR32QhY2WBjJ1CK+YYGMjUZjpFHkPQiSDVdI2lCFQiAUYlkybXY4FAHfaiDuPTE5CiJbQIT+2MjK4p07kK6aOejQFxBnhUQipK1Hq1ArOleKEuBHCVJypKmGClBCghWDSo0GmZOBepR8rnf/AIIl9+TlJcsUQKGR5VEspS8f571AXuwasELx2xZLv+XSMYZKUljjgICV/W/3SbqBnkHT+D+SnRyJ9ML2HhwPy5HSVY24J59f/b1NGMqp9MLx6n3HM2d4toxQWkCf0rx+lW2TF8oSnoRqM3k3p066EO59ipg50wZ2elVYD1nC3XdC0fQykN5h/JzkhGlxdICFksLmSvbeEpaHQMv+0QfCFJtljuWLpPloE6ToZRxoLK706gqF6DZiSi1LdUfw1cYDlwFcyUHSk6ydSBIPGWNvTbU/+dSiratoJ3RK04ikTx5yN/mITzJUSQA+MLz/z6+QSBKZHoBZHoh3EYf3AZ9cDt1X+6//Fdw/+W/8u+nH43whhknmWrG9I6mAoqKz3/ud4YdRhonEVlJ5F5NqFNMjIIR0FxkQwC9G00P6idnoJ/kpMo2MwWJp9aNnpdCKZsOHAFUzZ+WHDjtUI54moaGpiGWNYerwt1xuug48c+GsO6Uz6m5dBTvQKkIaI85ffqIlFRejOJC07a6mbJ0qGrhUDV6z1zKZIdHJEh5zBRdlGRFAIlkLJC/ynsUyp0I6P27IPsOpPfhRCmUIxFWH3aNJTbr7cSUnC9IR599L258/xWQ2uj6mrEnyRW6pB7Q4Ypi9g2gJG5bEoqS14vJURL95AwwPYH+nSsgB07HrhO1AQlx8osOjgJdTdRPMjusBIGApqFhy2QRIyevYa7TRU2vobT6AL2ZufQO3fj7+06tZmOPFf0cRJMe9COB+eAB9vOP1sTFTyNRKsXESIRLKa61DGimBUWQZhREkQK8xPeDvi1LAEgk48CelqRPf/rT4c9f//rXM/cTJW0/cdElq8f1EhPkI92/IxQlcuJnQGZvQlu5CWf2cPp+N/3eZgarm2TXuTXYiRMRL0iqXy5ALWQ3jd0JdGkNmObuQ6kD81zhzCAfp1THxto6Jqe5qJbnAP0tkKk50PVl8QNMR6va6J2oJECvkG7RwUeRRklQx4iqBTg0egzL7aGgJqa5ckTp0JW34HxwIirwKaCmR+9ReTV6TXozczsTpFEXDQVi3eAczk2sjMjl+6bVTFHqOQSGQrHU0zFfzpbFa1zrEV6AFDJckCQSyfiwpyUpT4yAqH+bSIhE4vTnf/7no3pqIyWosePe2YR6gJ30CSGxaBIQiRI58TPC+9H8RrihLAWClNzv/jX2g8vJy6h7eikqEyQAGFhAkZvmWVqKixLAZKlUZ2IkgEyxqEkoS9OCJf/BvtP7UL4Vn47cLUECAA8eiC82hqvA5JavDxOlxquX03cYvBecLPFylITU51AxgQfFaP/qIPF+Pka9q50w8CtvU8SlaaroYH2Q/jOWFKWeoKnxUo+thuRl6VorX9pnS4DNNRhOPvZMMaeFj0QieeLYs5K00yhSFrw4JWXjp523/7//gSO/+PMpUSJzs4Brgt66CHLofObtNcWA042v/poy17BuTEeCJMQ/kZYnQdYuPs4h4Hd/73/F3/3FfwB6O5QTv5YTvX8ZZN+ZzN3I1Bxw5D3AWz8SXz8taOSrGUAxkgyXrgKqgtMnF/H65Vs7e35DGLgdFFVxhEgkSi5caMNSDn1ZahaacDP685G6YMoNgFWpQlP4qU4XILtXBiCPLFF6Zhq42y1g0sjPCwtkqZUTBJvPyEvfGKiAK5f9SyTjyp6VJF6MPv3pTz+SKD1J0FId6A9i0SQAUKnC5Ci5/y0mMTFZmj8Z34mvcm31swUpK4pUMkC7a6OZrxBEk6gyADn5vvTTyRIllauzdMK/HSdLmYLEU6wDWAUAWISCqqMXh2Q0CWCi5BrpHm/b7zkHAKi9eil1naqw4/U8G6ovOLwsZQlSKnLl+pEV6sLeJU+yPIKCwj4nyWgSwEQpIOgtF7Bh6rmi5Pi1jfZXmHDf7cbrbWUJ0tWt+Htf0aKP8u7G1SQSyTvFnl3dxkeSxp3XXr0a3zDZhOIRKH5VYHdiEe5ExoqvWxeZHCUFKUljHthosRG7g8TpopzRN+JRKXNiNLCAxQPRAEDfFEeE6P3LoPf9qShVjwsSz4n3AeUi3MMCqUoKkgCvm25A+7CIcn8Mv4aCQtRw5MUzAlkCmBwFgpR+LBUqUbHSFEetMgUJgPd64nO2i/BarRMKnVDcvHM3JUgBG6aODTN+zA4loSDx7K/Y4XgYQQpomXv2z6pEMnbs6UhSIErjHkUCUeBQG4MPHkel75/Utu4Czf2x3dyJRaibUesRaz8rhGh1N1AuVcTFBkvpBOZAlKijgPCJ3KMWJACY2QdMch9jSgHCTnz3SQ376Dbomz8SRpRIsQp6l+UWkUMZRR/vvh7+6FS59iu1AdBPCGExWknneqPPTRm4HaAfSWHTeAptayW2D0F2t7jOMxdQy5CjAI+T2mUlWjY/51XSgiTAdEY/9Ry0+LC4Vh9TxiSmi/NomfG2Oq7HIkZZErhh6nh2oYFLm+kCmzzBY725EX22np5kvztJQRI+Z/fJmoKXSCRi9qwkAXtAjoaRIUpWU1zrxvESy9pFggQABf8E5bigXEkAuskSrcnEPFBiJxpSLOH8uYOP8OQB0miCArAnpqBvcs1uOVEKN3GiRIrpKAm9dZ1dF8gSJ0dJwiKR/PF70Un3zKljeO3yWw9xJPm41InykarTsTIH9cLsUFEiO8wV8nKS69e1LjxO/BRFe8eiSA4FLP+hTVfBPq5XYMNYSIkSwGQpKUr8yrhzE0xoRbJkZfRde21Dw1zJwcAlKKrRKyyjSBLJ+CJ/o/cIuv+Hv5uscrcVrVBTbl+BcvsKjPYmjPZm5n1pDoXXnIcnyIEJBSmB+8r16IKiAaUmUGqCagXQRyxAePFy9NztiUTBymQy/amfASUEpBHPv7Km4pfpresgOTlSgSDNHuLkMpCH3gbQ2wB9+3XoVIVOVZCC4DV6BLyMxGqAiVIS1XFQN3cmSB71cgVJE/yVKGyuAIN2OJTD+dXMd5OGsSDc7no2HA/hEHFuoh4Kk+WRTEECgDmuke7AJRi4BB+cnUBVm0RV24UoqUQiedfZ05GkPYMZX7YTE6XKNNTVt0H76aU9RnsTZj1K8tYEUykxUdqJIIno5U995EGsnNYbqg48JShnYPjTRmbitvxKPy6xO8hdErYZyZtWc0dTbVtN5s0koklAJEqKEy9xUBuw57ddTMtS0LbE5QRJJdF7KJIjACi2EgVD/YiacngGpTuPn4MlYq7sYrnHjuF+bysWTQLSEaVV/+M82WSvx4aZ/6fOpcCP1ww8My0uAsoLUsC/WIg/h7VB9IIFSeYSieTJRkaS9hgVtYmK2gQq02z4kFJRuL/R3kTh2mWhIPHYX/vPIPP7whGQFCQyxUVgRiQR4XOYmGJtMYIBgJg5jVgDWaI0u6UKgP78AajLN0CWElNonCAdOcKOmS6np35GRSyaVI3eOzrogA466DjZDYgDWQrI6uvmUhsutR9akACAZrWf2QXu99LVzhu0htVBJEg8k4a4PtZPNtr4yUYk6T9eM8IBMDnaiSDxLJTl2jaJZFyQkaQ9AC2VARDcvrKB0+eCP+4eko6cFCW6Ep3wg6XdeiJwYn/tPwsfk8zvA27cgfpeluPjvnI9LkijwPNAH6xAP7kImH6ScYsCjfjUDzH7oKKpwQ474ZMzz4Je/r7wIXreduxyIEpk+khq35gg+QJIWzmtSx6RAWXiZ7l96Hb8DWmjjTrErVhqAxdKWUffbQmvD/fTpkET04Tr1r20IL1DBPk/c+XEh2+Qjlo9VZ/HtfaS8H4CUfrevU1URF1xE6z0NRzzG/fe6UZSKRKkm534+0yIrJ0kkYwDMpK0ByCeB2j+NEqs4nTGN97abEyQeGyVjQJIpiDRK2+AXnkjtk197zHQldvhCFaGnTkursWzIxT28b383R/Ht7dWU7sSsx9FlToboSCF1595FuTMs+y5Xvln9LztlCCF+/qCZBeL4UChDHL41KMfSw5UUaAQNkIZ9LH19Mm+jfj0ZbdUCAcAlNQGSqqgrYo2jZo2ndoOAM3CHHqNJnoNThAEUSTP3L2edUUVmDBIOMyMhXpP1efFVwDQFIrZkg2N5EdGFytxITtQKYcjCS9IMookkYwXMpK0B/j883+Il176fzOu9XCvU8DiwejEsrHRxuSxCwAAev0n6Zv8pxdhAzA+9n4AgPlffhBelZSjAKILPmpWD16lAVIS1+TZCdRyQQoqYFTiAtFajUWU6AO/0OU+sQSEz/PMs6AX/xFlR0FPS5/wRBEk3Yu+a5DDp5iArd4EAHgT2dMyD4XrAKr419XWVWFECfdaMJ46mnmXgSh1nM1MOQIAF/Hppl6jCY+66G+uZdxi9AQpVUW1ykoh+Jg6IKoTGYjSCtrQMvKDAlHiayUl5Yhn1Y9cebvcgkUikfz0ICVpCHn9255YgmhSoRJtay8DggrL5NiFSJT+04vCuwtkaXAz3stt3uxiyaikBIk0JoGlEST4JlduGZX45a1lUC0eeWG380+Kyd51F/8xdrnsMPkJZEkkSLnUpgHkT23thGTQo2h5GBTiQeAgolTfjD9eZWMN3clsAdIHXRhaBZY/hVcg8Wm2pCAB6ZV2pU4vfC3JDgpsPgpdm6Cis8cQiRKQlqXr20twS3VMF/Nz3zRCMWG4GFSyp+BWBVN7AOBRB3Pl6HbLsj6SRDJWSEnaS2g6nOBbtSZI1M4SpZMfgHbpnwSnSwZd4qa36lxUqN0RCxKAkycP4spddkL33BEWXpw/BjzgavZkCBF/XVKOkpRRRot6oKvXQWbiRSf5KBKA1DTeSOHqEhWtKJqh32f94UqL74NIyiobLOLDy5I+EMgjEMqSSI6A/FIEcHZvqi1JUa3i6rXbOP5UVGPL1JkYJVkbMIvKkqUJgx1Tz4lkp6xFx5knSDwaYdOZmho15JVIJE82Midpr9DbBGwTCrhvy6Ll6+3l6GetGJMp7ZlD0J45FNs9JkgJyIm4UASCNErIfBOYrAGdFhsAsHBcsKPgpDVzjA1dY0NEQibp6vVw6MncpwxB+vznPjvsMB4eVYdu26EgBdjTBzJvUtlYg3bz9ZQglZ24PGjEQM/eCkeASJBKnZwSDCPCUP0aX7ZYPBzPCkdFy5a4tYEeChPA5CgQpCQ9R0XPUfHKuriBcpYgSSSS8UJK0l4nKUqlBmAPgG5OMclnDkH/n94PNMRF/Mj8HMh8OiJ15CAnSaoOAgXE/wh+9nd/++GfO18AscnqBF3/p39ml/NEKZCjJLwsJQQx9rALp0AX/CTt7eVo8EnxNRa1SdU4ehQ8J+ovVyjF+8zNpJvuCkWpNgPUZmBNHgBdugm6dFP4UBpJT5f17C2o7XWoRI/VUXonBElE8LkpqXU4Xlpi8kQpIEuOAg5UTRyossjYnW4hHEBakERUpDNJJGOBlKQ9iAIVb77N5Q95LpOjZJuRlniFG5mLKjyTk+dBTp6PLgvkCEBUkyiB49rs8R9lyTT1QAo5OTCcKJFSkZU4mDqcXwASANk3B0+N56c0nvIT2RcyVrAFr53nANVJ9hieC28US8EVDZSP4iQTuDNEyZ4+EMqRCF6WNGIIBQkAav3466USHVV9KhI3VQ+n2lwy2tpX7PE01Asq6oV0zlDLEj9eUpSmi3Y4hhHIkYiZYhUedWKixEeRtJ11gJFIJE8IUpL2CBffWgXs6Ju/tnQf2LwbjU7GtFnrQShLZG42Jkg85OR5NM6dFTexTQqSmt9k9ZFpJp5bZx0wCuJCmQJRIjNTIDNRQURPVcMB7ECQ3k04UVKJFg7o+W1R6JEL8EoNKP142YCFKRaNSgqSEEUNC3KqRMMf/MEXH/75D4Fy3ehoonRFniidbmbnIiXho0ci3jsVDw951MG9nolr7WhIJJLxQiZu7xVsCzCqUJbuRNuKNWDA1QIKRKkajzzQN18HZidBt7ZAmoIl7W5iyoMXpekam4oK4ATJGVHF7Vg0qdNjcsRBPQ9EEXwf8EWJF6Ms6NKbUBQmQ14latWSEqQ6J2r+lOX5M4tD738n5EaTBh3oGw/gTQmm2gJRsqPq4/TIhdRuvCh5pXqmIJU0cbHKAKK880nLLctGw2+LU02ssJsp6lgdZH/W2haTYIOw6FBygVpSjgIeJF6fouph1WafM0ORISWJZByQkaS9QsGPplQSkYViLb1vZxX0zdfDwUO3tkC3uOrCSUEK9rt9FfS2v8qsNgfU5kA7bdClu6BL8VIBrjMCWbp7kw0AXjUtctSvbbNxnTueoH1JdTrW5oOH3LsMcu9ybJvS3YTS3QQmDgBFThjq4ijb4/KbvyWIzCgqYFRZ1Wlu9ZWyfie9b4BeQls1hYIUwzahtFdB15dA1+OrxVKCxMmAIqpqPgIcz69enhNNKmkeLM9MCVLATFHHTDEewWxbaihIPCqJxsMIUkDPkX9WJZJxQf4270XyRImoAFFBTpwGOXE63GyuxFdutSYaWFPbWCukG2WFcpSHUYVmmdAsEwQE50+Lk8Dz+N3P/3tA9ZNpj8QTtfNEie/tFiMhS0k5CilPsBFQrLPhv3YAwiiS4ogl8mEhFCBExZW3lmJigka6urRIlNreFtoek1sPLjxkTKPZ6SmjQJZKrgKY3HL4dzFasmG2sG5uoqR5KHFFP8taE2Utu4DnTFHH7Y4hlKMkcyUDA7cbjoCkIIkgRP5plUjGAfmbvJfgc4F4UXJtFhVJFmcEYqIEAO0jh9A+Ei8DsFYYhLKUJUj0QYY4WTkNaB+SXFHyc2ao68LyBB1QefQSyGv/CGwICkHycsRBr/wwukBU1gZG08ME7sfl4qtcc12/5tP1a35ELkOUlPU7MTlKEpMl2xQKEgCQShmEb8dhdgCzA9PQYRrsMxVEkZRdmGpTiBImS9/c7mDDjN6XaoYQiUTpR2sKfrTG/uQ1DRfNjBVucyUDc6V0RGrgdtFzt6En/mrKKJJEMr7InKS9SmUqbMIa0twHbN1P7UpOnMZ2pQCzkH8CMCtVEK4NB91kJ+ekIJFDI+5xpoqnRJTtTXg334Zy4f9n781j5Mjy/L7viyPvzLrvYrF4N9lk93RPT8+9B8YrGbBsC2sbMGD4jx0DPuCVZrTb0q60AtY2vJKANew/BMFeG+iBLcBYSNiVVrvW2rOr3dH09PRcPd0km002m0VWFYusuyrvIzIinv94cbwX8SLryiKHVe9DPLAyIjIzsiqr4pO/93u/X7w1hy9KKY1L6u5ICiz6ojQ8sD9BAuLf137hOsnRm4FJAF6UJx0W9CzV2qgW5aUMfLRWBe7gFFCOr2Ykkl5lAFAl4feqkzZBm17krGvFKoT3g7SWQ8dlCw+qlo5SKhScgjGIuh0XwZwxiMpuGQ+c5LY3viiVO+z7KpMjH5eb4vNFaSqbRyO9AQDY6ag8JIXipKE+9uwDvzXJr/zKrzzvUzk8zQZAXdxdaTFBAqTVtTEoLiev5VOoeUVf8uYQ8mZcFIrmKIpmPKeHDA2iSy3Yk2Hkqe+CxKF12sD0GWi1XWg1dtFemz8H9+bDxPtYbpvJkUyQeDoWrQKYUwAAIABJREFUHOLAIWL0oacg2V1cv3Q8eUqxCuJbi+z/dFwISrU2SrV49Mxo1WG0uOmzwalwYH+CBACpqjgVe+OSvOTAcRKNKK22NrDa2kCla+EXpvZOzAeAmXxyQroraQY9lRXb4PBRpFp371pKCoXiZx8VSToAL3T/Nr6AS7saJhyXJsQq2wATJaeJWinSC83DF6VWtyGVIwDojs6yL9bYBdSePAtj6VOgthEe5E21Ef3wrk5XV4FMClpzLIwopXKAJRY69EWJjyqldrzISYaPREXOJZq/BQSiRBduAikTSFiCfuy0KsBW5GLcqUtFCUAgSoa+j7552RIw4b1H1j8JNvcSJCc3AKD/TW/9JO20FkpbNJoEAKZmYrW1ARm+KH1nVVz5WI5EfwwtjCTZLpt+3I8g8Yxlbawn7lUoFC8SSpIOgCyS9EKJU6cRbwILhBGlBncBKWVRrDYSRcnYWsbI+gYAtvrJunw92BcIEn/8kjjlRpe5FW71PaI4Pbi9UsGNi5LIhUSUACZLqfFekQXugigRJB994xH3XCwvh25zLUL6sWKPx0iF5RranASNngO2HonHJonSkPdz8Za6Q1KtWhpRm7jCDl/dQAEZ1Lvb8WOeA1VLR8cVz7dla0Iid5RfmBrBd1a3Y3Iko9ZtgcIFAbDXDOJKQ1w5l9ZVo1uF4iSgJOkAvFBCFOGt3/pH+Pa/+l/DDW1WE4d4OS5PN+qY5nxop9rCsCdKAAJZMraWg2PMiXF019kn99T9j9jDPloDJJLUi+uvnsXdJUmS9EFxLDE/iRMlbZ4TKX/FmWyFGwBaLYNMTQVf8whyxFPIAbvclJZfsdtIA/1e6ZQpBT8/AIEoPXmyhZkZL7Lnr0I7cz1+fx/Ne/2+LPWYcmw54fMVTCaZaZ0CQwXQ3WUvinS8bLbb+LliAffKofxut12MZMTvb8ub9pLJUsVax2sjQDaTwWIteUpstyP+3Pl09GgUiReksayaZlMoThJKkk4ZtLYDUC/KEG2ZYXkX+VQ80bdIijB65FmQIS9X6dEajPfeCXece7lnFIn0o21Hm4uI8HWbdBPIDoiCxGNbgJFCp20hnUnFhAgASCnMdekpSDyRFV4koSXLgYkW/+SJRpT8ViTbi6wVSw9IfgToyiNfvBzxaFxvPzI0B6wvsBtmBsTsnSh+UCilqHVbEFUlRCZKgBhVqljxCbD5Ivvzx8tSVI54CgYrklonyX0NFQrFyUJJ0mnCj2g4dlCxmVI3XtPFaoeiFF1ePuAldlfCVXCBIEUp5TG7vYi1XHjRpB99Ij/2sPDXzZwnNKuLwCzXt22EnR/dllzcbAvIDoBW95hCMlLAKLdKbstLBu8lSAmFDftCNJoEoDZQlPdp215k/0dkiWQjhURT3PSi1dqXIAEQaycB0mnOfvLSYE6IJgFyUdposff4rESQeHxZ2m3vLUhRnjZrKJhhxIrox1NQU6FQPB/U6jYFKBWnJajVAa1XEhOAATBZGpjuKUhRSDoPTI6GY6sCulkG3Uy+OO2JroHMnsHdR5EL90q8LpMvSwFcU19y4bMgFz4bf/ykwpOj5zE5XgQcl40IV87LE9oPjb+ajS/86diodbeCAQCOJAoYsL0IbC+CZItxQYpCNGTbNrJtMXrYS5AodXHppYMXBd0LzavfleHyfF4ajEfnttsuNlpGMHzSeg5pPTma994awXtrBFVLCwZPL0ESjjNZInnLaal6SQrFCUH9Jp9WHBsLN28FNyl1mRxZkYKC7T1WQhnpcPgkCRLP6haunGGRH5LSAZqcbLtvtp+ItyWihIGiIEex8+RlKSFnCQDow5viBscFujbQ6QKdLuwfLABu//JTKAkHGjtsACi24s/hpDJyWRqaDZO3k5AUlfRlSavvJNzp+eCL0mQuF4w3x5PlLypLvhzJ8GWpZMpLOCQJkkKhOFkoSTplEKG/FgFatWCQ8XPyO0X6gyVipIHBIhBpJhsTJBm6ufcxCdCNeBHEAE+UqG2D2p5QcLWApI938x04o3NwBifhDIrTjfThzbggAYmSp4EKPcf6AYnUt5KJEsBFlfYjR0BixW0AoA/veV/QcFTX4lNtx8ztOwto2OVgTObiEaJeogQAhmaiHP0wEKGUclFKsZ+pRoxgAHFBkt7ffPZNfhUKRf9RknSKuHXrEbuY62YoJZG8liRRsv/sO7D+6R/A+qd/IN1P156CrnHVujUtGPaPPwo2n780B6yGtXRIKlyK/dav/c2DvqQ4fDSpXGUjiYgs0ZvvgN5kSecprkikL0tSOQICQTo3x6Zl7A+W5ccdEcK1jdlLlHTKBkYTxJeDNrZBH32YvN8XJB6+sW+nDod24cKBUa/0jMAdBgINNgWyRjyF0qUOXBqP4rw5XhRkyaHdYPhcHW7j6nC8yKYvRzKaTgUaaDAAMYrUcvrXZkehUDx/VOL2KaSby8Nscsu9+eKSCEXJ/rPvSO/vi1LqP/+PAECQo1Ipi2rVu1CUw0/cvigZv/gLwKCX61TuXxSCbqwCeS8PaLIQ67tGDCOMJEXptgC+xpEEAgCpyMXfsmIRJJkgGX0IKuyn1Ufx7gL0uYvxHb4QR9ql0IaYrE5X7obPN3tVLkeAKEgAkCnixoUibi+wxrrOMTd3zegFtB3xveNSJ8hd4rk62BHESIYvSsTtPeVbt+PJ/WMZE7vcz7dm6Sim1NSbQnFSUJJ0SpGKEryVa97KN/1zl+H8+H78zj6NHdByQoXlcnxKwvjFXxBuk/PngBZbJUY3Nw9XT0jXgYK4omjh03VcuDSxtyhtiu1KyOwcO5eVUHR6+s3GLrDDfQ+nxZVlWlKftSPAR5Ogm8BiRO66bSBpCb4nS1E5kmJ3QOaZcNHFB+H2qCA9A6jXhLfWtVE02XszSZQAoGaJ78l3vdnYLyfMsBY8i/1wk+XVXRoQp+JkcgQAIxlRmhtesnbN0tHtQ1NjhULx/FGStE/8/m0vckFJ6DpgJixRttqgC7dBLr8m3uVzlwFAkKXUL/+ieF/+YtG29idIw5xQtHrnh/TEdYGuA5g6q5Dd4KY7hgdiogRCgNo20E4uXunLUk9BWokvKyepFIxXZoLbtNJnSfKKYNIGl0B9/Q3go0j/uK43hRSVpZb3mjXvAm7KSxSQSbEhsC9LE8URrD+OvG5utV2q/mzzk3jKXo2vO7sVzCWkwL27KopSISHE92kl/L6cMw8mSACQV0ncCsWJQUnSKcWEyeoKWWJOBr3/QUyUACZLNFNAWpIoG9Bij+V6PcI0r/t8VJCOFZkoAaBO5MKV8Va3yWRp+AwAQGuxWjwu3/dOIkcAQOZnhNvO1FmgsgJX10BbfZIHqw6kCkB+OFjdBkAuSkAoS60EIfQTtT1ZisoRDy2yKtsNwtUncrtAcwfIyZfIHyddtw2dGIEc8Sw3TMzl5VNsflTpK2f2ngO9OtRGtxHKkEOZpEYFSYahsj0VihOB+lU+bdgdmO3wwkImL8UOofc/EG6TTAEkw/KI6NYq6JZkNVkrfrFya23YKxXQux+A3g0fUxpFyuxjBVwvupwE5bMsmuUPo0eBvwxXCmD4TCBIPJrtQLOTowNSQTpu8hExuf6GeA5eGYCPyz1W/vlkB0Gm4+8DH1+QhKfnawk1d4QSEGQfq78OiqlRDKRS0IiGrhu+1yQlqgAwUYoyV7SC8f5WGu9vySNpV4fauDoUfz/rJIVPyjru7VLc2w2TxFQUSaE4uShJOk1oBqht4e79VaG9RZIokdnrgRzF9vuy1GpLBQkA3Io4jUbvfgD66S2WQOyP9hGm2jhILgfkC+GI7k8XQJKKY2YKQGePC3tjBwNn5oDZCWHsV5B+4zff2s/LOBov3ZDWSPq408THnYQq2PwqOU0Ph0cvQTp3xXvflLgVgsdZZdwjrYs/x16itNwwAzGSEZUlmRz5fLwrCtC9XYqnzW5QEUGhUJw8lCQpAISiREZGggEAGJ1PvlOlDuRz4eCIChIAwIjk6PjRkHYHBNqhrjTf/I3/EdC9t7EdXgit2xvS40VRcrzhsfoxGzxc4cYY9SboblUYPK5+DL9eFjd1lx8G7E44AOiS6ScfQZZKE6IgRXAzBdj5ATiRgph5a+/X5Gj9ncV3JDWo9hKllwYLwXja3Ht6jMLBSMYWKnXzRAUJAEpp8XsznR3EQGoAA6mBoKaSQqF4sVGSdIp46+/89+IGL5oU1I9JcpTReVGWKnU2oniydCBBAtj0GIBXrk73PP99sZcoZQrYqbYgyFEUX5aS5AgA6vHIDBkbg6GlgqE5LjTHZQnzfcCPWNhuOxgydKudKEvjWRvd4jC6PZbFuylxetJxbTiujY4jiUY9wyhS0+4ddRxMZQIxivK0mZLK0uujTbw+Kr6uaFuT/QgSD1VhJYXixKA+7pxKKPCE1Tai03lghFVjXimdwewWq3VDRkew88lNDF95NbiXe+sOyBs3QGWCBABNdmE2PhtJAH6cUCbgGWDd3gB+IX7RpI/Z8n9yRpKs7EcB+KlGv+K4RI4AJkgCkQrle9Xg2RPXBskNgTZ3YZA0bLr3NKVutYEMEyMZviiZhOXvROWIx/aSlunKo2AbmZwEJQTkOUlBWi9gJCNOLS7VWDTvbFFeqsAXpc9P792EN2cUsGRtYzoPPJXkOPHM+M2VPSy7d20mhULxYqAk6bTRaAIDA8DsDLDCqlNr2ytwR8S2FXRrG2SUTbm5f/H/CPvIWZbcTJcew6jUYA8UA0GKYVkg566I2+5yRQrz/emaTjvc89sWUj9/ObydtCQeoSxhciSUIxmZAuiPvw/38S60c+I0VU9BavcpiTlSd2c/okQyRRhaBTsdHcPp5MhZl3aBdC5xutMXJOGxJ8N2LZQQkFY16FVH+lwfyiAGLIcFva1IQUzb7cDQ4hGspVpVKkp+/v1SncnS2YI8V2m+KL5XprnVckMDNta5txsvSCqKpFCcLNR0mwIAEyWBSg10YRHo0dSUnD0DMjPeU5Bim/7oL9kXtXU2shkgmwGtl0Elx+8HkiuC5Ipwbi0FA9wF8sldrxhiV36e5PLrQHESKIwmPgf98feDr91H68HAYO8+Yf2G5IaCrw0SlwOSKQYDAMZctnpvp5MkLhT+PCshJBg+MkHqySF/hr1w4WIgFQqs44rPYbtyWfSjSgCTI9kCxaV6KhAmgMlRVJB8BlMZDHpJ8ROZcLSd/q/mUygUPxuoSNJpZlZcmaVZFlCJ/8HX3vwi3B+9J27k80Ny3EXFF6ZeguRBNyIC1k3O8+jFrQ8e4JXXLkKfKMBZ56I4xRJQi/Ru8ypSk8uvyx/MF6U6myLk5SiK/ubLwNoOKCdfdGERZMZb4eZFkXjp6Dsfi/3k6Lm5xOf0RYm1NOsd8SCEQF97AB1AZ2JO3MdFkQCwKNJzRhZR0jVgpVHFxeEs7u0mVCH3WKqn8PGOhvkE5x1Mye/vP2fbqcHm8rx0TTW4VShOAiqSdAqhlbC44MLDDaA4zgYA8tqb0vtob34R2ptfFFZSyXCvvQGkTaAY1j2aaK72FiQjhWuv7N2IdT/oE5H8o8iUC93dAN1YBuq9W3M0Rsaw9dvyZr6AJ0hRnrBCk/TJEuiTJdbeRTdAOxaun4svpT8wVoONdhVBBOjyS9JDk6Z9xrM6zGZdbEkTgVhtEC7xO72+HIyeguSJsasfz2evXtEkgImSriEYPC/1WNo/metiMscE54+XXPzxkpg/tpcgRWnbz6/yuEKh6C8qknTKuP1oBzfODQN8FWjqAHyHeU+U6Ac/CrbtGCzRdfwHrD2J/jqX8+Phzl8XN3iiRIojML9ko/v9B7H79I1uB8iwK2NMlKp10F3JSjdflAqiwDTSYRRg+0/CYowjf21KLkdAIEg+7vnLwK0efe8OiqbDSaWhWxJBvfwScD/ejJZSCkIIxrPyqTZflLq5UGhJwqo4Mu297rVI9E9vxBrnPisc10JaF6dIB1IOdi356/VFyY8q+WIkwxelop3Ga5JZ2Kgg2Xs00VUoFC8mKpJ0APz+bScSGk/YIK+9iR2jGQgSj/PT+3B+yiTAnb8eFyT/MbhihOaXLsL80kUgMwAydw5k7hwuF/uQ5GtHpunSJlu95w8A6DX94clSI00EQYpCLp8H7bRAOy1xh0yQfJr9iSr86jd+S9wQTQjvEVEytN51gsxmA8R19hakCEEukG4CjgV7cBj24DC0aAuYPnDn9jIMGBhJZUC5fx0nHhEbSjkYSiWfw+vjTVwf6t2o92zBwdkCe4wPtqgw9iNI7e4RVzMqFIqfCZQkHYKTLkr0yUIwhnZ655vQbhf61jL0reXYPiKp1hxbQVXIwUrpgGOBDAzGj98Pug5aq+HjpxUmSACq76zFj9OIXJa6LXTKm8jXkoVm9Ne+LNwOZKljAaNDCffqP06Ku0DvU5QAJItSusAGABhmODz2FKTo89A+N/UFazRMuLcNjeRTyUQJgCBKOdMNhs9oegSj6fh71JcjGZ8fz6Jut4TBo6baFIqThZpuOwTf+ta3nvcpHBqaiVfHBgCS8T5Zu/ELhC9Ku8Php2/tRjyHyBelwrXX0Xi8JHlydnGbz3ex2DBBd+TNYo9ELgs02YWr+s4aSl+djB+jEaDbim8HAlFqFJk4ROVIgK+w7YkSyU1C73qRhT5FkXzu3FzEy6/O4+69FVx9ySvZkMqBclKDihc5G4gX5vRFad1tYCKTXG2bHWyCDM0ArTKQFeU1JkhWcn7Ts6LjNJDW4/3/dEIFMZLhi9LZQkIjYDA5kkEpsFwPv/9d74NG21GfPxWKk4D6TT6tdO1gBILkQabmpXcZ2qlCe+WCVJACjBT08lq8D1gkgsQLUqrlCQt18dZbf+vALwUASEHely0aUSLpNEh678rQubUVjH7z80Htnxi78QgbGZkSN4zMACMzuPK5631p7uUSCt0FayabKQKZIhaWtkBkhQt9WYqQ0YsomCPoRgI+Br8arllhw6dVDsa+BEk7pj8r3ONGo0lAGFGqdrVgAIABDUaPP3VpLYe0lsPFgU4weHoJEs95b2XcSl199lQoTgrqt/kQyKbb3n777edwJgeHuDZIfhh0YhZknUV+/ARf4bipedDVxXDDKlsS3wGQPsNWwiHaU8uQT+lYf/BvQX7pq8FtuvZIetyhibb9yGUBsAtm6b/4JQAASe8v2kEb5fhGX5Q0QypHQFyQ6O4agPD74fSpNUlAdQMojYfPb3fFiBLARMmLKGX0+Np2X5SExvXN5GgKMiUY0VYomZK32u54ptp64YuSTUORtXoEjXxRssEOSmvxiKqPL0okoQp5kiApFIqThZKkQxCdbnuhquy223BoF/furuClq3O9RWlwCGh3gN2EYnnE+3SeIEcAYP3zv4jfbfYl4MF32Nej48Baf4rxBdEkQlD6xRng/LVgX2WQRcsGysk5VlJB4vfv7gJl7lwHi5hME6wXJiPHhdErp98rvzRdOiUKwIsocT/DVB5oVZAZ6j211tUBSh1otYSfQyYhybm2Ee43s8D2u+z2Max2c4lnP5qGKleDq2K5yEfcMKO7e053WW67pyQBbFb2TlmcMr02WNhXULCqFrspFCcCNd2mCAhkr1Vhw2co4WPy8iobmhkODpkgIakJ6m4V7uZOogD0xLKAVJYNXvQefhw71JclH9pqBMOdvQp39mqwT/ciFbRSA5UU2US5hq1/vgS6+AB0MV7e4PJA/6tPC1TjZQ2s4iCTo1Q8P0cGpQ6ol0dTT5NgBOwlSFEyx9PklhAdbaeOtlNHLvLRrtGN/xnL6C4yuhhWsuEGUSQAqNk7qNnxivJJ+f0AsNEuo2hmgwGIUSQ11aZQnCyUJJ1y6MQcwtYUlEUq8sPxA4eKoSz5ciRDM5mopCSf0iOCREbH48cchpQZypXJRG2h7AlKgij5YiSDlyWpHHls/Zl4gaWLD4BUHmTiAhtjZ6G1+5fUrMkiGM0y4FjhAGClezdjBUQ5klFPE6C5C+xIEvCjgmT2p//eUZCJEgBMZYsxOYriy1IvOQKA0Uw8Py2r67DcbjCuDGZwZbB3dW+FQvHioD72nDLe+u3fxbf/3/8LoA5I1xOJTpM1OOXJDwMNUQLI9EVgGsDje0AlYeUWlx+S+s/+Wrj5p++DzIfLyfsmSDJME+hy8x0PPwambghTbeQiq+tEn34ifQh9+wmQLYF40TG6uyvsjwoSAJC5eZwD8GhTlA9tdwOkHzlJhIQJ7+dDkbVMHamu+JxW2kSqE5/z2ekwuT3TysAqSWQYQKHsTTsWMqjW26IoRafknqEgpbQsLJcl+ecMoBlxlkZXwyiAodSAsL3uCVShxyq3h9UOJnq4jUyQ7EjV77YTGlZpb09VKBQvAEqSTiN2GzAyQHkDGPRkxROlJ1stzIx6F778MNBaY3IUZcDL//Flifbou1b12nUs3gm3+flMlAbJ0KSQAdaPkJ8km6qbYv3pBloJeTzTV9hpeLKkbz+RHzfElvhv/dlt+f65+ci5HK4P3b6pVYBiKAN7iZIvRzypKhM9XpYCQZLgDM+i0YpElta22Uo7IJhqo05/X/t/81//bfxvv/c/x7bz024dLwcp3yM/rt7VYqL0sJrcYgeQyxHQW5DWW8ktUBQKxYuFkiRFCBdRojsrAIAdAMMbiyDj8/L7DBT2JUg8ZHQCaGyyG5oWLu3WNdyYl0c39o2/HJ4gEKSAdB7oJEx/LTyGPtS7v1pnbAajf118zK1/+aS3IHlTesTszxQMKQ2CVuUiY5k6NhfXMTbPErVTeh7IAUZlC+jx9KnqDjB8BtrGSuIxzvBsbFvOGASwHRa19JsD04Sp2CPgTw2mNCbwFYtJyZVsHit1seaV5baC46L4UaWdPeQIANZbBiYyYYR1vc2iiVFBkqFHm8cpFIoXEiVJp53yBgBv9dPKAgCAavFChFQmSgMsgqC3KnCy4hSHTI4AT5D6zDf/9v+Av/jX/wdbZde1AdN/W1MIq70AJkoc9J0/3/PxO2MziftG/8sbWG9x0YnjqhEkIxJNAoC24clRhPE2m//ZyESm4IbPhF/zJR28SJ9MjgBfkDgSlsr3C4e6sLzyAxrRAK5O0mwhKxUlAIIsDaXCKc9bVfYemcjGBX+9Jf+zOJEZwtNmGQ7YfXTvz6eKIikUJxf1ceeAnJj+bbb3x7xaZ93qPUHqBd1YBN1YZHI0kFwYhn54E+5H94PhExMkfiXasX3yTlivTemRBQnjkqhXqwq6ucTGwsdM2Ewj/H4fBU9cSCkUlFZah0udYAAAysmRHF+WMHxGFKQo1AXd2IDuxL9/PQWJ9Mh8PkZmC3JJs9wWhlK6IEg86y1DkKIkQQKAp00xgufAxnaHIqWFQ6FQnCyUJJ1GKGXu4NX8WfiLd4CzV/a8m3vmKtwzV0EyOZBMfPUa/fAm6Ic34/f76D6cT1dhv/ch7Pc+ZBuP82LajUYH4hevnZVFkK98DeQrX5M+BO3UQTt1pJbuI7UUil7m53+OfSETpI48mZ3U4w2CD4NDbZYobWbRGh1Ga5SdQ8aQLNNPECW6s4axp9ugt7+X+Dx0cwt0cyu4rTs0GPl2/5vX7heN+3M1JGlEzIuS7VrBECJ9CRBCoUHHVFYuOlFBAoByRxSvnJGFRig0QjGUfraFNRUKxfGgJOkUcusjybJuQC5KLg3kKAovSzI5Co6bEVey2e99CHTb4QAAxwWsI1bgs7lcka4NFMfDYSWLCi9KvhxF8WUpvfF4f4JUPd5+ZlkqllNIFCVPlujOGuiO2KKF3v5eTJZ4OYrSBPsekvp2MGCkwoT5ZxxFiopS22bSL8sZWm+5UlmazHUxmRPfd1NZKsjSfgSJx3afTzRNoVD0HyVJpxbvIuBGPjn7ouTSYJ+2/DG05Xi9IQCsLlBpDOTaZZBrl4VdZGY8EKSZL1wKtutDXBaxn+wL4NpnzoMUDtnfgWhspMxwREoYwGomy9LMOOjNW72fIssiFWRoShj7ESRd70/6X60brv7bU5RcB3AdOANjPR+T3v4esLsCpJO/974gCfA941yHTSnabZAeK8wOi+YJGB9N6roO1ppNtO1aIEhA2MhXhi9LMjmK8rJtwaZdjGf3LsyZM8TpPstVJbcVipOAkiQFk6HdJ+GoyaMggiglVHX2ZYmMDcb2ARFBknHIlha37jxmzx9NNPZE6YnFnQ8nSvTRbdBH3LL+ew/Z4CDZbCBIUejG42AKzB9kJsz14afafvPv/NqBXlMUPSq0EjJ6IZAjHvfcDbjnbsSOJ7PzILPz3O1zILNhA+Om9y9GtKlu5Of29/7eb+x5rgeBUoqdDrDTYXLU5V5f2YpHdXqJEgCcLRg4W0gW1+Ga+HrGs/lgRNuZ8IKkokgKxclCSdIphdoWavOTwPlLwPlLcMfmxAMa8oiL3mnt3fLCjw5ZVjggESQuioQhuVQdhiRR8qEra6APHwLbi8kP4skSmZhPPIRuPI5v9F4TmTnDZMm22aAOm1LsA0I0qd5gq9L8AQBmcmsQXpR4OYpCZs+BXLyB7MpifGcvQdL7H0WKktHjop0kSgeVpeFaNyZIPiuNJla834uJbDoYjxtdPG6oyJFCcRJRJQBOIwkRCXdsDtrmcrjBF6VSBmTinPQ+Arz0RCCjIyDd8FO2XWF+brjAla9+Hp98JK98fSC45f0xUbKaoBtriEJKRdCqpIDlJU4aS96qPL8opkyOgPjr96ed8jkWAjkqXQs5wxMEXvx2VoDo6zXTQDf+nNrdnwAAyKtfBqrx70dAphB8yYtSle6v3pPbJyHkGclo2G6zx83oGbQdccVg2dLBr9crmOzn0SG1pLd8wNmCAeJmAexK968kfGjouOF2y22hkGIRrtX+v3yFQvEcUJJ0GtEI/FbmNbeGohbmorhjc0CDVZ0ml15n/w8XE1duBfe7dRfal74a3B547bOofPA+yPRU7Nj24AjODAJnYENuAAAgAElEQVSPl3Zha4BR3QA2vIRh7ZBvSVnScLSAo+NKSw2Qkvf6N7ZEOYpSmgAcC+T8HOjDD8R9SYLUd7zaT9G2MUmi5OHLkUBpMvzaFyZOjqKQbBboUlYywsf23hdEE6JIr74yv8frODqyiFK128V0Lv6e83uyyWSp7EU6hwwTzcGhYHuuzIRpP4IknIMkqqVQKF5MlCSdYh7ffIwzr55BzQ0jKdrmMpAmIC+9IR6c9i6enTo6SCENdmGh74cXX/f774SP86WvJgoSj8F3sh8fBSoJ3eX3grqglU3ceW8T1/y2KoOXgUIkaTlBlNpjs0j7bTU6CcndTrhqipx/LfjaerqCFB85OC5BMiINwfYSJa8Str72IKlaVIhmAPWtREmS5mTlhwF4K7+oG4gq0TTofU7NcSh7wJGM+LOTufHT5qpUlACxga0vR0k0B4dwZ2kXgI6BlJjjFRWkaFHKjKH+tCoUJwGVk3Sa8aJJhY+XoW0uC1Nt9J4k8gAEskTf/4kgSLGHfnAHtFkHbfaOQEVx9RT+1jd+9UD3iXH2fPh1fTO+33EBx0V7bDYYPCSdA+Eb/jqWIEgCXtkBS7ODASMdjrz3OGaujwUzOeXJR8oRVNeZHLXFKUQyNAiSlPdV55b9766wwd83UZA42pW9TvpIGBqBQ2041EbbaQdjOispfQAmSkmMZYAPtntb3KN6A4/q4QKGiqUHYy9BUigUJwclSaeQt/7uPwDRdBBNR+FjT4wkEQSpKBECZIogn3kV5DOvxnePD4NE6gjRZh20UgaMNDL1UJpiUaSj4iQUOoyIkrNShrNSRvquuOS/UhSjTq2ps6B8jlYUOy5O5pNFcQNhg7broMeQp8NkzAwHAKTihT6D0+Flqb4lChLP7oq3gtGMN6ztIUha63hlCQj7r/nsR5TGMuHweVRLB4OHl6Morw4XkdEKwkhzbWDUVJtCcbJQknRKodtegio/L5IkSoSEIwIvS1E5CohUwM7U69A0A8gNhyPaY+2o8NEkALh3O5AjnvTdWzFZAoDmcChMfiHGoBijbe1LkOjyfeH2K3NDOBLUhe79Q7vKBAlAIyeZ3kvl5LLk/RzpkzugFXl/PQDAhFhYlDo2qGPDyifnLB03Dg0jaPsRpa5LsFRfw9OmGdsX5VEtjY12LVGQXh0u4tXheB0p4rWKSet5pPU8JrMaJrMaOlGxVCgULyRKkg7Bienf5hMRpacdCrQ7waDLt0GXbyff37GgfeHLIOfjVbnjLUIAqxSZ9gmSwgn7+igtsPxo0voTtjpvZY0NgCWkaHIZ82WpOTwmCFL8wCLo+++Avv+OsLmnIA2KFccPjaZLV6wBCaIEhKKUILm0si7K0sSVmCD5kCHWx87i/sFqApoJaOYziSLxyESp65Jg8NzcMXFzRy5Lo5kuRr3Gv3nDRd4QI34yOQJCQfJZbfoNeFU0SaE4KajswlMM3d4Fprku8nUuysL9nd/5dAXDl2YDUSJzXq0dSZ6OIEoP7sX2JwsSQN3DT0fRZhswvYvg+pNwx1AJ2K2KB2tEWOZEZsIVbdn33gMAtL74ReEuZOoSoviilJq5BkxdAF1lTYKjEaRjo74pJKY3cinkm9zPRGM/RDrFompk7dPkxzKz8SR3Dl+QeFIR/6Veg12N2qB9DqTolAkJH00KTy4UoozO3kNtR/757+aOiVeHmRD5YiTDF6U3xkbRdeJimiRICoXiZKEiSUfghY4mFXJAIQcyOQIyORJv1zE+woYEunQLsFqJD01v/oSNlXVhxAQpgesvTe59UASSz4FuepGMIssRWXjo5SINSXJWNAIyMycIEk/2vfcCYZIJks/aK2ErFjJ1gY3BYjBQHGLFFht9vIjy0aRoYrqRZnKkxaMZdFL+OsjofHijsRMOf/8+BEloL3N2GKRHXtThcGESHSbRsVgLx0JNvjDAlyUZWb2E4XTv4o8lk6BkMvky9bQwooIkI63tPcWnUCh+9lGRpFMKyRXYyrN8EWh4K6FSqaA6dsD4CLCxzb6Ofor3p7Z0HUa3Ctssgd6Ur3jTXn4JqZp4ZaW1LZAUy6T1o0hOv5Kbi3mxvcpQCdgoQ/9cKAr2nzMJMv6dL0bvzZgaAzlzzTtYvKjycsRDnz6Qbielvft/HQqiwdDSSLfDnxtdZcnmZCougIEobdwX5UjGxqcgI2fZ1y772c0NFbC2GRETvkZU5/grT18Z7OKTcighC9UaLpTiU2IZ3RUiSl8YE1fp+cHE6AysL0cyCDSYmpjozUeR1FSbQnGyUJGkI/Ctb33reZ/Cofnwe2LD2oUHXl5KSsxtIdk8yNk5ELPHJ2PHgfODmz0FKQrdZlNi1GqzkSuB5kogZgYkWgTyAATRJJ7tMrBdhv7Zi9L72H/+XiBMAICpMTYA6EteLha3giwqSHSECURMkAphLtLVqYI0unMQfvUbvwXabrDppeI4K+Do0cnEc5J8WdpdXgq2kVaNjdIYYCWv4kJjW7ytGWw0y0AleXn9ceIiXL14ZVCUsQVZ1XQwUZrMdmOCJDyu18uZjx7JIJI/lwQaZvOFYGjEgEYMWG7vGkwKheLFQEWSjoBsuu3tt99+DmdyOEjOW6nER5MAYHgGJCtZyZZKgUYiTc6PPwpvnJ0Ov156CqC3ICVBWz0u3nugzUwCHe8cUyYTJB6XJiZv08WFQI56MdUVhWTVtHoKEnYktZr6QWFUWMLfyaSEiBLARIlMzYG05BIRiJLfjy8qRzx8uQBflHJDQTPdK595BZ/88P0DvYT9QiWCKROlayPy1XeL9SrmC/JSATsd9gFgPB1+7zQuKU8mR7LtHYd9L+1DNmlWKBQ/eyhJOgJ+JInKkkl/xtGGItM/uYKQuEtrFZDiAKIQLtIkCFIUl4JMjYLubIEMh9MxvQQp6WK0H77567+Nf/Mv/jG7UW+ynCuARVtoZAovIkr6xVBoSIm9Zlrd/0qtyeV1rA/yU07h9MuVl+bxyff7J0m3H1Vwo0cbvU4mBXhPT85wOUipfO/IkdUAOsl5ZtJ6StkSUOZy2arrQDYLdC3A7kOvOgl8NMlnux0KzWiG/Ry22vHzXayzBH5elnxBSnqeT3Y38PJQPEcuSZAUCsXJQk23nVZsm+WZ1KthrpEbzRmqgNZEWaDlKmi5ChAC/c0bkPJIFCG6swW6s4XJrAVSTKil5HH91eQk6UMjS7RttkDypiBIwl1KA8HoBV2NN7ul5dVw7K4GSfK0278pmLs3vZWDkZ5x6VYH5PJnRUHyScnzolr5PFp5bx9fLdwnSZB4IsfQxvFIg05M5I1wzCSkevmyJIVouDo4mihIPhmNRaXu7K4FA9ifzGuu+tOqUJwE1G+yQsSNr92mtUooRxH0N2+EsvToSUyQfEgpvKiS4jCTJa4WE7HtxCKNB4G2vYtzPbJazxeldpsND3d1F+6qvPM7zAxgZkBv/QD01g/E51l9LBUkZAdw/jOfiW+vHKw9y76hLtLIIN3qIN3iojcJVahZFW1mFoIcydBNtHQbrYHIqkTusc9NRVax9VEEeSh1oRMmNd1Ivs8rI2F06V98EP5MoqI0XxzEfDF8LV+eGMCXJ+IS7FfSlnFndw0O7QYDEKNIaqpNoThZqOm204phAF5dGzRqLC/Jx7UBHXDmwqKCRPca2srEAID+H/zHoMuscjV9ILby4AXJh9bLsW0AgE4dpNA7erNv6k1gKKxybU+xeSrj0V0AQHlsCIObTJDc1V1oU96xCYnjvijRpM8W2ch5R2dhO/0TCNqqB9OIC4/WcOHcJBAt5pgtAa2I2OpsurSTLkID4EanIoMnELf7opTtAtiNVOpOam3SZyh1g+X3XdeCqYVTv6+MOLi1Hc9bGs2MYqCY7Vmf1Bel7VYjUY6SWG9twODOwxcnimNoQaNQKJ45KpKkYPiJ2526V/VaflkhU2fEDZNX2OCPuTgHcpEtP9+PILkrkWThzuGmakgqDega7Ec7wPAAG/n4qib7nKQyOADnk3XQCfkKuOA5JrzaUaYujl6CdFxRpFok1yl6DkAY9dFTgSDxaESDxk9HUjeew+U/lCRIYuXyQJobz5BeEaWu0wkGsHfTmx9ulFHu6FhrtbDW6pGbxbHVFiOQKXL4VZkKheJnEyVJpxhKgY+XdoChCTYiUzT6UrxiNsBEqV2pxuRIeOxGE6WBeEHBJEG61NgSqm8fhlt35FGuvUSJVi3QKldnaHgWdHhWOJ5MjASCNDAR78FGnz4IBspeS5Tjgq9M3kOU6M3vs3H/oz2jPRrR4A5Pwx2Zle5PFCQO2igD2SLIwATc6BRdn6ARgVt6yF5XWssjreXxubGMtEI2EPQaFvjhRhk/3IhHNfeSpV6ClKaqVpJCcVJQknRIXvT+bW/95u/IdwxOCzf1pXuCLNHKJmiFXZidP/lDOH/yh7GHoAmCQF0HZHgqGNNffP2QZ7839vuPxA35LDb+8rvBTePd7wJPN0GM5F8BOjyL7re/D+fB2uFOotFky+MHCsBAAWSfFcePRHMXaFVBb34/vq++FcqSzV3USRZpEoqkOzIryNK+BYmDuMe34jOlZ4JBiIa0lo/szyGlJ1f8JgAWaltSOeIpmC4IocIA4oKkUChOLkqSTjEk7eVfrK+IOyKiRIbGgVQ2kKMovizRRrOnIMnQBjJsjJWCqR56hNwdoocrlmKi1GrDePe7TJD4cxiWT5PYv//Pwq/fvQP73Tt7P38uoWihpNHvYaD+PJ7rhqvQWlUmR83w4k0uX09+EE+UonIUO2ywBEPLoJtmwycqSM8Cl7oghIAQgq7bjuyTf2+jomS5rWAAwGjWxmg2ft+C6aJgyqccZ/IUTVsLBqCiSArFSUZJkoIRESUyOg0yNM4Eyd82NgcyFm91oZ2fgnZ+Cshlw+FBXUcqSG2Te+vxVb69ZqXf+MbfOPhriCRcG1cmgK3dcADApbPSu2rDmUCW7N//Z4Ig8djv3gEGZ0Dy8UhFTJCy6dgxR4U4Nkh+ACQfyT/KxHO/ZKJk5wpsuG3QJ3dAn4Til+IEs+nEVzJ20xmg00TK0ZDi2n3wUaT+92wLMbtcztEBRIkXIxm8LCXJEQBMS15a0Uyh47aCAc0ENBOuStxWKE4EanXbKcePJtFagxUD9IoAPtkBZs6dBZz4xYeMzYFuLjMxSiKXZVGd9+8AF0SxEgSpn9gdQNPx6cgQro564jU3DSw/FY+7dBb4dEnY5Gx7F9Gf/FsYFydgP4is4AJg/Ht/RbjNixKxM2JOFS9IXhSJlvs0TUMdgOjA7jYwxDUhzpSAtig3vijZueRVW74okZmXpXLkY1Z3hNspRwNcGyQfTiPS7SoI2StN+uAYks9zXbcNUwvF2KU2NGJ4+0Ipur3J5O9zY8nndbE0jq0m+6Bgu/HjZII0kRX/fG51jqeApkKheH6oSNIphuo64NpMkJLQ2YVgZz2caqNbj1nEp0elcX7aCwvLwaC7q0hvcLWUJFEkko6vwjowJU4K5qbj+y+dBS6dhbPdCgWJw7g4AePiRHg7IkghXDpwugCkC6CuC1IYDAa4qM9v/savH+LF9GA3sjIwU8KTJe/7669Uoy6MRkJbEg9SGAYqq0EtoihRQQIQr6nlRbNc1z1yn7ok+GgST9upo+3U0bTLgiDx/HiT4seb4nv2YmkcF0tiQVFDo8EA9idIPKu9KpcrFIoXChVJUoAMlUB3vQiCkY63lPBEiW5JVo9FREmQI377F36RfXGb9fZKbzwByUSSf/Ns1Rh1n4AcotULNdPsfGSRDD6ixPVzMy6Nw/50I/ExjbEcyNXLQG2DNZUViD8PjdYlypQAbDFRMtek9zkQ7Xbv/WVOkCL4omTzNbHgCRKHKEr2gQTpWeFXvbZdC21HXBVpu+z9a2jy6c4fb1L80ry80jrP04aJM/nw5+XQ5Fy5aBRJU58/FYoTgfpNVgBgovTgnQ/ZDb4lhdUIR7XHEv1rnwe6Nmg7/ik6ECR+W0SQyGyPhmSHhYsmdf70Njp3tuMNb8FEybgUuWhWamzw1DbYALB/QfKfpM/5SdRhpQ3yWcBqMjkqcxE6LbnlhtGowWjUQArDMUHiybopmA6A/DAbPpKq7FGcSp9XgGk6LEMDXEeYYgOArCEvPurLEs/14RauD7dQ6ayj0olPqfo8bcS/fzpJYadDMZPPCwLGC5KKIikUJwsVSTrF3P7wU9z4jKTHl+sAmgFkivGmqL4o+QJy7fPBLuPCZ2AvfCiI0kEFiTTYxfXGZy4c5KUIUNsKqlt3/vVt0WcGvChKVIDAZMkaHUFqYbH3E7SroNVQiGQFM48LWigCppcgnsoxQQJY0no3EmXyRcnl1vBnuGlIvyVJOZKzBSZIMfLD2HY3gbb3GH4OFieDz6rZs6GlYHPFJLPGAFp2vCmx7XZwfThZ6qKiJJMjn822KF2GlsZyvYmRdHifYe/btppUyVyhULxQKEk6zRAC7GwAF1nvNTI0zgSJpzQJVCV1ghwH0HvnnZBcAXTtvrgtc3zLxwmloLLeWRTxwM9AURAlMhN2eievsd5r9IMPxfsktCtBbhBwNZDiGLvfxoI8itTpT1+vLrVgEonEyEQJYLKU6dFuwy/5UH4qlyOPbTdSAiJdYO03KnfDbV4ZApLub+TMkaxe6yVKhdRYsH3YqwS+0UkoNgpgBEPYIVXMF4DFejzvKSpIALAc6Q+Yof1fzahQKJ4vSpIUoKk0iOVdBLKDQCsyJcWLUrR69Sc/Yf9feSPYRBJWUpHBSaApfnIns/Ph114UCdmD9c8KaDRAzGnQbofVEdL4dhuIiRKZmgLJJ4ueL0toNxMFiUzGSyKQbAmdGhep8nK8yNAgsNK7gOFekOiL4KNJgCBK9CFX14nMsP9H5qWP23LqQLGEeofVUBrSWHPYUimHarUZFyTE+5M5dx8D6eMrAQAAVio50qNBQ94YEgSJZzzNWurwsjSCsHr6sFbCjlvFfCF8T9RqtlSQepHWD/n+VSgUP3MoSTrtZNin3z1FafgsCKmCluUFJX1ZSqoqTQYnY9vo5gaAAoJGZ94UGenXjI3rYuXSGGY/9c7Ze1xB4vxVWAnFLnd/99vIfyXMx9HeDKuEywQp2iYk9XQFR07WltDlk4j92kRLYWNhmhQQ2l6MiVLLieea7bqhLO1bkIKT618j3yR8WTS19IGTpMfTZ1DHhiBIMjRioOuWMeh9L8tW+DwqiqRQnA6UJJ1mSI/psuygNNmYDI7JRcmbuqLc8n3iRRWSBYkj0nyV1uP5JfuFmNx5uy7SX+FynjIJFaYjsrT7u9+WHub+6Kfsi9n4a5ILkocX7XnluryY5UExXQKUw1VnzsYq9NFwKpNMs6gJfSqZYtpeBAC0Mr3PZchkJRCGUmEJhV3r6b463JM9pmIPik4MgBAQEDTtDvJGGNlrOVVk9f3lhe1aLCI66egAyux9LsGvtzQ6OIqtMhPGwRR73Y+tGgZSQMVir5EXJBVFUihOFmp12xF40fu3xUjl4KYzwaD33we9/37sMDI4BjLoTWnIVoF50E4T9nv30f3TsA3I5PREb0HqWLg+ergL7Df//j8EXdsAXdsA0qlwDIhL3pOiRgBAN3fg/vSD5CcxNDYA0PJ6MGKNZo8L6sL0E+NHxWKezla83pUvSwGTV4ORpWlkEyIgviBFGUxNwnKawQCeTRTJpeLPrGGLuVetHkUwASZHviCJdywLUVONGIEgRSlbmhBNGkg5GEg5cHQSjI5TR8epM6lTKBQvPEqSTjO2BaJpuPfTOyBgYqI14xcbmSjRjacsebuwvxyU7p9+F90//S7o1grIeI9K3UfEbXrJ0e4e0Q7XCWSJrm8Gw2fga2MY+BoTwcb3vIhNQjNcMjAOur0hjHSzATLoTef4OUP9WPllRRKz9ytKvhxJ4GVpyJxIFCRpBOnpp9A/cy0YSGXZOKbq0xQUjz9lkZ29RImu3gNdvYdyd33vKuCtMnZIGXZCLSRejniq3XA7n0SuUChOBurjzqmGhsUXO3VWMRpMlNycOH1B778POpSwMs0Xpbq8ua2P8bnzwdeBKG1xEQI/8gMA+tESnAGIyduR1WwAQEfOgq7fldwxZOBrYyDjI+iubkn3k4F4UUJSKgE1dv5kcAhY9Z6337WSEghEqRO+Xgq2Oowk/MpXaBmggGl7MmGICdIyQUqtPxGPKcu/R8+KvMlyx+jqPel+Qoi0RMFOxPN9UTK8FYT7ESRx+75OV6FQvACoSFIf+PrXv/68T+FQvPV3/0HiPj+iREbHwjE5JyQrd2zxwknOnQUeLLPhMfxXXwUgCpKPWxiJbQMA7Bw+HwngoklRBopMjLwRwE2h8ZDxEZBxdo6kVAhGsJ8TpKmXX/KOi8hlSxRHWutTkUV+RZsXTdK//Llg0Lr8eSjsQJgAJkcVKhFSuxuMgwoS2dmOHn4sbLQMPKrZyJvDgSABACYvJ96HEBJElXZycUHisamFplPHk4aJJ5H6SVFBUlEkheJkoiJJipBOHUAW7ge3AABW1Ub6l+PLqcnkHOhaKEJkTLLkmhOl/QgS3V7GlfkRfLLILrBOJqEm0UEYnmX/73IRjpWP2f+z1+LHe6Lki1ESpFQAVtZBV9ZBXr7BbU8WJHdgBFcGgHs/PKIkGWZYg6kSTg/qkqKgtGuDmAm/4pVVbJUcmFpCIjsQtDZpO2FEKqMXY4IkQ08nL9U/DC51QUBgei1TNlrh6/q0UsalgUgCti9K0Tpd3qq4TmkQeQANWx6xtCTS44vS+cwoqt0waZ4XJD+K1LBVMUmF4iSgJKkPvP3228/7FA4Ntb0O9Q8X2AYjvLBvlAyM/+F7AID0L39RuB+ZnAN0E8RNblVi3fYu4q9z00zFnCBIZ+fHsfj+T8L9R4wiabMjgGUBjRbgBxfmrwKLkWm1lY/jojToyYffH60bL2BIN7yLqsFyuOid2+z21Cjo4jLIvKQsQD9xbBZFSuVYc2CLu5g3ykBelIWoKJGKmGDuN4ONyVJCxWjLbYEMiw2D6eLN8PGPKYoUTSk6X0zhYS187VJRAjxZasbrS3nkDXYfXpZkguQzkbWBpo7zxTBy9cHTcCFC0QRqarpNoTgxKEnqA7LpthdFnIhpgna7ICODoF5fM21sAO6mKCudP3wP+K/+fXaDa2JLa960XFGMogSCFH2+bA6axV1FKquA1QV6FAk8CLfvPsWNC6PxHQmitDN7DcOdhMiILxddO5QjGVPh89FFFkHrDEwBOZbDdbnZwv3FFbhTs/t+HftmH6KE7CDI6n0gl1wXyJcl0OQInmzVF83k4fL1osreyr89cr0OS5d2g2jSXqJENh4AAAaHXgMAVLrJjYzzxiAyRhHbDfl7YSIrb21S7Ybv85z3e1E0gZz6y6pQnAjUr3IfeFGE6CBERSn1+jS2f/w+Rr7wBenxvizZP/c1uP/k96XHkHGJvPhYXaDVCZLAjcoRGoWmvOTvx58CZ7hpqPmrAFikg8y+HGx2py5DWxWnZXjIpVdANA10Id7jjBek4PjPfgV4sBDbrjUqidXID4zVBOAl1KQilSMbZZAxiZA1dxNFabTDon3VNJOBnCP+aUgSJOGYpQcA+jBNegS6TiuQoygD5rhUlDKGWCIi4zXQbbssorgfQRLvf3ytdxQKxbNFJW4rQEz2CZiMiBEIbXIIqdenkXo9nF5xf/Qe3B+9F38Q1w2W3Wv/7X8aDIDJkVSQKqvh1y1uybh30f/mN/7GYV6OyONP2f+dOtCp42k7LQhScPpTl+FOiQm/ZP4ayHw4JUcuTAcDQLIgAXjpImvQm3q8LOx37aMl+P7qr/22fEe9Jo5ugmQ2d4P+agCTI1+QhMN0Oxj7FyR/5/Hm43Qpi0TudFoYTDmYL6SDAQDO2LnE+w6Y4xgwWcJ9xijGBIkno2WQ1XM4X4znqEUFKaf3NwdLoVD8bKAk6ZRz6xOul9rQBBbcEqBrbACsFpKEQJY4OZKR+tI8kB9mg4cXpH6zUwVGx4GZc8DMObjfe1fcvymPNABMlqJyFGPNSwRf3WLDwxckH0GQSskX44PipnMAKNDtshwlx4b7VJILlCRKAJAuYOSRpLgiR86iyFkUWZIXRlSQngU6DJgOhf9vpxO+trIVLz3hjJ1LlqWNB3Cpi2Y3Of+NEA2EhH8ezxdHgtGMJHvLBMlBv3rrKBSK54mabjvtuA7sdBYocBfxM5fCCAwQihLXakK7foV94RffsyRd2j/4gbjBE6WO3QSGxpDe9T6NS6JItHzIBGBHLmzuj29C+9yr4QZflMYuBpt0/8Jrckm+0SiKJ0iltI5qx/u+rG4B0+OgPwori5M3f+5w578PPvrgE7xyLT6d5j7dhjYdiXr4opTyfnZDYgVu+nQJAECmxRYlOSvhIm93gHVxKlFvdUMl8KNIWn8/f3Vc8f1VMCnq3fDnVLaaGEzF1/MHopQwBeeLUs4cCLbxchTF8nK3DBKKEV+1XE21KRQnCyVJCjlRUQIAx4H2irxqM1LsrZRZvgt7W/4JPSgbkGXTVJ2hMaRJBihx0aw2W2VFclkAhywomc/g4+9+iGs/9xkgHV60YqIEALuPoed7rEij3sV5rUehxOl4QcnKP/4juCNMGGr3LWDG0whHnt9yaFwKaKEsSEUpNwS68RDkymuJD+PLUm7mYuIxsONVtMnYeWDrE5Bxr5fdY1YugJjHk5uUcgCvZdqeolTvsp/ZCAaA8QvQNuJ5Yj7NboWt3EsQJF+OotT5FZBmBg6YOOv972msUCieA2q67Yh861vfemGLSQIAunbYsqEbmbbwk56nxsNRmmBDAhmdB5m+CIwOscHB11XySZPIhdTtwxRFtLFqR2zT4f74Jqt87Q8AdOke6JK8SrOVzsBKZ0BefgnEKxgpIBGkakItJGr07zOJm87j7rInkZHvm/t0myVo+wPA7th50E8+AP1E3peOvBJ+jEcAACAASURBVPQmG/nhYAgkCRJPpEnx3/+t3zzAK+oPNu2i3t0KBAkAlrzIlzt+IdimcY1/LbcVSNBupx0Mfr+MuqREBAC4UIUlFYqTgookKUS6TWDtUXg7k/AWKU0AVRYBIqPz8mM8UTqwIHlTRDcuxwXkMGifvS7c7vzLHyD91+Or9AJRungVVloeCQlEKVMAFh/F9kcFqXb/GC6YkvYm2sVZ0Ifc8vVUFrDkF/dAlIo5kJfeTHwakh9GpbMGevc2yFkxR6unIPWjR10PUt4sZ0rLYTgNFEwxJ0jXTDiuvFiRL0raxkKi/PjsdtrImS5yThZNXRSimCAdU+RMoVA8X5QknXYyOWjVMkCHAH/l1eQ1UZQ2d4Cx4fh9c8PAVlyAAryEbjIrVoNO7xxhef9+aHcAnYAusSkkOFXgwgXhkCRRInOXgdoWkO5R0yjDlvGTaXE1XPV//4lwmxckP4rkNCINag+BQ7vQXIddmEts6nJhi+LC+Zm4KAFSWSLnL4MM9CjJAKDSCRO76dLHwdeN4WH0qZDBvtEIxQ9vL+Fzr7HpwJQWTqvluxoappiLpmtMnGSypBEDmLiCUkpD1ZIv489FHo8viaARHYA4zRYlY+6v8bNCofjZRknSaadRA1CKb588FxclALgM1tOLgz5mNYbIGU4aEla80cefgD6NTEd5RSwxNBBEkWjCqro9sW2QoRLobhWkVACtehXBFxZ6ihKZE4UntbUSfG2NesKU6aEG5SfInxOnmwZeY2JZ+XNJfaV+0dgRVg6SqCgBQlSJnI/0NeNLEhjs/Hk5ikIvvgasPETdCfPF7AwnBF4UibaSK7H3BwpwVbRlogSIUSVZKYNSirXU4WUpKkg8LS+iVEqF0byqG34v1FSbQnGyUDlJCpBUCh/ffhRcJAMmwyXU5MIlkAuXQFciydwc9PF9oFNPLJhIH38Sf+6z3EV7t8IuspSC6Ab6vop6QUzcTf3SNdBGNZbH1EmL+VSprRXQjeRqzSjHqzTrF8KpwoG/MgvkMmwk9VI7KERP3nV+RrhNb/4A9O5NkKnJ3o9pW6AJU3SAJ0gR0rq4msvx/pFsHgmdQI7ETofPjRLfIPlu/M+Z49oAiFSQeEqpMVA4+xIkHsL9CbXcFjqOg47jqIa3CsUJQUWSTjlv/Xf/E779R/8k3MCL0soDkM++FkvapWuLAAAyOR9sI+NiPy9SYNEpWmeVuHlBmpwewtrTXVGQAJCZWeBB79o9B4WUCniwsouLs15+1Mf3kPqleA0k+mTBOwcx2kS3w1IEdPlh+Lhz56VyBIiCJO44pl+3SDQJ1Tro3fdZFfMorSqQlUQOW96KRLOAEgmXw1cp274fQdI39258e9xkjaInRvunYoXvOdsNzc7QmITJ5AgQBYnHoWppm0JxUlCRJAUAgORKrMYNdYGnD4EVrq6MJFEYYLJkjc7FBElgpwJ6/xarhcTVQ5IKko9j4foV+Qq6/UKGSkGhS1LIgXa6oB1PGrbKbEjwZYlubwuCFKO+DbgOGxwxQZLUC3rr139t/y9EQhAV8aNJXRsobwDVOhs+Sf3wWlU2ACZHreSiiqVmF2R4FNpOuBrs/Oz5fQnSK9eTK18fBT6aZGoZuNl8MAAgre8vH6hirQmCFMV2CTJ6AeutRmxfVJD2SgJXKBQvJkqSFKAtyR/46MU9IkqkkAcp5IH6JmizDNqMSwddZhfWUpa7WLc6IOMzoNV1UH913EyPJOkD8s3f+oeg1TrobhUYC6eXyAXJc0hEieRzQHmVSVYCZHhA3ODJkn5lCjC4aTD+e+hFkUjm6KugiG0BTpcNfpXVgKSqd5IoZYfgGibcYo/k7YZYG0rbeQxt5zHcP/5jGPduH+LMD4+ppZA1TIxqBKMagallYWosMV3rxMsTpPVcoizV7S3U7S18tNP7z1/BDEVwvdUIhkv3zpejON7WLAqF4tmgptsUIeki0KmFtzUNT97/ADOf9aZajDTQcUAG5ALhixJdWZfuBwByVlw6TqvrAF8pmTqApgP1BmAfMnk76bkvzIIurIgbt8og4/KIhyBKG7txOeKwv38HGPWON3SQoUHgCZcD1fZWtREN1D3663INA5ptA2NngM0wyoOBIlCpiQenTDb1lpU3t/VFSat5UtRILpxpv/th8LUvSkZHA7ySCbofUemRM9UXui3AzAY3tU4Hbjoe8UzrOXQcVv+rbsdfly9K14dDqeHlKMpoJoNV1OHQcCrTICYMrzWJQ/s7XaxQKJ4vKpKkEEnHoxF06dNgAACtVBPvTvJDINffALn+RnxfRJAAAIvhNA2tbODKbLKIHIoxMVnZjyhpr381GJi6xkYSZh7k5S8AU/Fq4/b37zBB4p9jSGwUjOIxt6oYE1uN8BElMjHOxpkZUYABdLfFZHS3OAr6yfuJT8MLUvD4c/PSY2m7DhwwN6gXy8tMvLsGl+8T6U0niyhpREfWKGK7vdTz8T/a0UCg7SlIUaLTjkUzjaKZhkmPWRIVCsUzQUWSFAAAWtsF4LXniOYY5Wwx1wWhKPlRJZKPRyl8UaIrn4CMD8b2+4I0X3mMxYEzsd0kI8+F2hc65/++KPk5UZKGqABCUVr1agLJLpicKNn/5/99sHPyVra9cqF3faK9oHt1nN/cAS5OgeQlqwx9UYrIsFZlwuQAoCuhUJBZ1tNtX4KkpwAcT27O7/zOP8Lv/d7/sudxWqcDjcgjnR27gbQR/5ne4HLe80b4Pm7YrFSFTI6AuCBRb7Vd0z56LSyFQvGzgYokKeA8LoNuNwDDZEOSX4QSu+DubEWmcjIlkMkriY9NV9iqNlreFAYfQQJYFCmgHk+UPQi01QUKeWB7IxyRKUD3u/9f8v2fbsQEyShHpulW70L//GVhAM82iuQaBrC2yIbjMDny6lk9fbAK2uhRq8iTJa26EQhSErTTgP7GJehvhEVB5YIEXDkzALgOrr3SowfcEeGjScv1CixTE0aqXku8b8duoGOH768bkhqpPnljCGvNFmwqy3mSC5JCoThZqEhSH3j77bfx9a9/HW+//fbzPpVD8THVcR1glaaLo1goG7iAMpCLXPBLBaBaB5mQJEEXvVVdNXbB9eVIBpm+DMIHc3QTeIerVl3wIiCpo9WaoZUaCJ/MvF0GRsLXFIjSf/ifsOPf/0543zLLXyGDkajP6t3kJ2y2QZtcTgpfENOLItHm0QQQAAh/Pc5mgJYXucikWbVxDl+UhKgSF/XbyXQw3JZPDflRJB79jUsg45dAb7NpOVLsYRnHgNFg37/lDv/eEAtLAghEySrEp481YqDrdPDlyTOoWsmCuFANi4DyomTTLvaKcTpElQFQKE4CKpKkAAC4TS8RtcYlt3IRJZIy2RgdijeR5SmOw/2LfwN6fyW2i0xfjrXyQGTqiFy54Z0Hm8775t/81f2/CI+P1sMICo0mMW9HomTnZoHdZUGQeGh5Kxg9BQnA5I358IZ/kSxX2bAdNkyjPyvcduXtNJAwRUkbdSZHkmnRnYwTDPPcJZDZs1JBAgAyLraYobUdnJsbAdwuG7oO1zCB7vFOOY1no3+65JEcPqqkESNWVLKUGkcpJZZtWKg+FQSJp+OJb8MuB2O7U8FOp4qdTlVNtSkUJwwVSVIks7QKYBXkSzfYqjMeX5T4aEljRzjEFyVyeTYuR0CyIPUBulkGCvIoB207IFdDCdh+/AQj51lSOX34MHY8mfHOfat38m94h0gU4bAtVpKwOwDfPY2PJgFCRMmd45LNs15SfEJdpMG//AlcAHRuEmR8KrY/KkgAQFI9Yiqk/5/BurkczCYLQ45nNWy0+KX28YgSzAxSnS5Ica+K2+No2/VEOQJCQeIhkefzz4Ycw2tXKBTPHvWb3Ee+/vWvP+9TOByuA+PKDJDOs2G1PEHiSFrSretMjjhBSv38l8VjHBf0zk+Cwe63R/IxANpMXkXXEy1yoSwWgLHBcACgd+XCQ86HK/DIzOVQkGRs7LIhPEBvQbpxYaz3uR8AIZqU9aJTusZGPisKEk9WXEE4+Jc/weBfis156cZqMIB9CpKk0etxI40omZlw7JO2zaKP07lCMHj2I0gdh3jbFQrFSUFJkiKkzU1NvfHl+H5elBq7waCP7oM+uh8//MI0yIV4NW66+BGrucQNIYpUO6Qc+Y/fdUDyGTjrTZBZLyKyG7621UGvZUqCKCFtAqkekQfdFCWPkyVypn+FMZOgLQvuahXuahVY2wpHZAWivvkI+uYj+YNkB0A3l2NyFIWMjIOkc0DlCRv+9h6CpLWSE6ePgktZSYFuLqyrRUCQ1vPCQHr/yfJtux4IUpTpXAEXSvJq8kmCpFAoThZquq2PvKiJ27TeDGvatGtAxkt2fePLwE/eFY/d2oX13j2k/t3X44/DiZJMjgAAecmn+8Y2LO8aY+5ugda9qSNNk7b1OAj2+wswPuv1Y9utAUNiIi+9uwRy9Szok8ex+9JNrlfbmBdhSoqAjYe5Pr4oube4qRtu+oU2E0oQHADtzDDcxzvA1DiwyiUf71aB/5+9N4uRJEnvO//mR9xX3mdlZWXdZ1/D7jl6yeEpSEOQBHcJPi4wL7srzO4MOU2BC2EpAhIgrcSVVpD2ZRfTAB+12BVEQtRSQ4IckTOcg31MV1XXnVWVWVl5Z0Zk3Ie72z6YH2bu5pFZmZHVkwX7NQwd4eHuEZERlf7Lzz77vlC1cH3rCeyxoGAmXQ6qZZOr7jTjp+I0IxmJ6T+39xywekCaS+pvyVu8DBqHOnDAIjq8pNBeGyQcNfJEqRNNlDe0ROQcMtJG8HNM9svDk9CzoyviFArFyUNJ0gCRTbedJHG6+3gHlxdGIhElXhY8un/6kVSUyAyrd0S4aAKtuReqGEGSQUx2UbpxiB5uxDQA08CDnoUL4XxeTpTIBTfqY9voffAU5tvxvcbo1mPQzQrI9GT0wfFoMjR9JiauX7p+FvduLUan4w4BSYZE7SCitL0M2ogm0/vn5GQpVpAAJkhhhueA5Y/dJzKATZb8TxtHX8kXByEaKA3ykaSiBDBZckXJkyOetMG+Cy2rxm2Lb0mz6ZaB4pf8j5kFwP1I1rAmOUqhUJxUlCQNkJMkRFIcC7ToSkkzSO4lp1k+ildx26P7px8BABKfX/DlSAbJZ4OM1gZXbDAkSGY5vh3GUbA+XAQ+zxK1e4+2AWyDfOW1yH69H7Gpqb6ytBpqO9FHkM7emMbizVXoN8R8HpIeTO6OdqrP8vtyFSAz4vMmmCTQrry0AimMgXxhDPaOm4eUDZWAkAlSOkYoTAMkfbBGsy+CTky/JciBRMmxATOFrJFFx4mP4HmyZCC+SvimpE7mkDGOCoKIYd5kuUyGdoRCqAqF4icGlZOkwO/8k38JulcH3eNyM/SoP3uyxGP+7FVA10DXo13gAdbHTejllk2z0akBRvCXPS9IXhTp0Bg6W+1lWTAWRmAsjACUuoLk8mydDQmeLPWl20UhpUeSt8MRJEGQjrN2ztQ4cPGaMGhVvoqNJBK+MAGuHBWiCeW0UQFtVIDh0wcTJMl35rgJryKjvbbfcBihHnlJrY+0EQ0gGnY7K/7giRMkniZl/37K7T5FPBUKxYlCRZIUAABSzLPii9VN0IL7y183AFv8y5qcPg9zVt5WwxMlMskiGHGNbkl2GJNZYH110xclwiXb0g57TmLsvwLuRTDPjqC3GJree7YOnAqm0IjBLrr0Htea41KoZpAsErNZBpmZApkJls7T58c09WLo/go++84S9NOu4FQ3gII4PUmreyCFmH547Q4TifjZJZDZa0CtBYxzFbQ3H/UXJPN4fq0QVzJ1EnwvLKcTKTVwy5XD65L37YmSH1Xqs1R/t7OCpJZF3hzFZkuMcsYJkkKheLVQkqSIQKqbANypHPfiR3eCv6yJO8XkTdt0Ekkku1yiaqYA689/wA4/LQoVyUqmiKxAOuiWKFbUiOaR7EuXi3okTOG+VJQ04suRDF+YcunYfXg58slnQVdZUjNd2wXddiM7iSO2KrFsVhSyUY4+dgBRolVx9RldZ0nrZDKYMiWz1/zbhXwa1RoXSimMAytiFK6ZTiDTOlqF9P0g0GC7K9x0YqJjMzGpOXvIa1EhulXdk4rSSpN9/g08w+m8vGgmACS14HM6Wwi+x88bjmx3AdseXHNfhULx2aEkSeEjtPAAgF2Wa/F8dxXT49FmqSSREPJbyEK0Lo+95Lb3eHtGXmvJismPKWVxrQTcWh/AKqGEGJEyz44Ap0WpMd5hK+CsHy5GX4sXSVpz5SpUM0cmSLQqL2NAZieBR7vSxw6LvbQVRJMAqSiB2kBpFnQ5vmq4L0tnXo9/srbkfWnsc22mE0ivbQDe92g7pir4EWhZQFryW+sgouTJEc9SjQkwL0u8HMXRRfC9tQhFgrBjyu34YpQKheLkoXKSBoTXv+3EYiRBy3tAs8XGRqiG0OIqGyFIIgEyfloqSP6p372OQsJhF2pvABFBCkeRBsliG0E+VDaN8h9+Kn+t75z1hQmQTLUBrICmOw4iSHQtVIm8Ic8VOihfe+/32Q2uxYi9FBKS6gZIPucPDzJ3GSSuyGQuw0ZjOxge7WpfQYpwmAjgC5LURXGvOdGf6+WhIiwdSOxT9HyptoSl2tKBBInHccSIkaFRGJpqdqtQvCqoSJJCgG5VQNyq1Oj1ADOUF7S4GqzoKnGCwCerptwVPu/GtxmhtR1AC75+9Dk3nVfiLlTtQywjpzRIkk5yF+tUAmgHYlb+w08x9N9ejR5fqaGbSiJ5Zhqg/adW6CMxyZtC/HnxgkRmJeUDBoS+MBGpKUUrVZCSPOGIzF0Ookq5PgnNjW2QwiTo9jOQ0dAKxpAgpddCkhtXpf2QaO75WpyXyETp8pB8deJCcgSPO/KSE7NZ9l3u9oLvR8fu/90LC5Kusc8+bxYAxDfOVSgUJwcVSVIAAG5+uuzfpltcccCeuKrJ3mxg849+JApSmHYdxk/Lp2xovQJaDxUfpA4WPnfRv3txpghnuyYKzgvwjX/4T2Fvuhe4Sqj6c0o8Z/kPPw2iSpVadH931dNBIBfOgcyeD0YuA1IMhr9fJtqZ/kUhFCCUQn/zGhOkGGilClqJmfobGQdZuNH/eQqB2NHtZ/5ohOa8BEF6CVEkDx060kZBGEatAqMmL3C5kBzBQnLEvz+bnfIFKUxSz+JOpYe9bjTCGSdICoXi1UJFkhQ+ZGwcAEvQXVzexdm5YWCI5brYi+JKrda/+X8AAOn/8b8RtvulAEpDYqTBsaNyBEQjNaH8IbQGkJNUqQElTkw4URr6BTeXp7ZPxIpoILPToCvynBNy4Zx0u08xf+R2Kzw3P7qLG29eYndKY0Clf/6PL0qS4p3EXblGNx8F2wrxUS86ewV4/gSNJIvWZctl1CcD8cDO+rGUO3CohbTBprJ0YrCIIQADBixJfSOjVoGVL0W2I5HFQiKLRFKDE27czPHxTvB95UUpgSTafeopAYAeNw2pUChOFEqSFAzdBKw2HG+F0t4m8FoQ3dE/twD7g2jlbU+WEv91n2RfANSxQSbci/HGI2+juBMnSNrMCI6KvdkAzkjKFWyWMfzf/RTo4lNxu+5GjGzxdRFvuXunBTIbtFuhK6vxclTh6kbxCfG2pN7QoTmYiJAFb6Va/6lDMn4OoA7oB98FeScqSXT2yr7Pldtwpxd1EzAHX1DR1JLoOVFxPpAoSVYVelN4vCzxchQmrefRRRcpruaSoQVFKk1NQ9tW5QAUilcFJUkKAf3aKdi33T5muRGgHkQe9M+x1hW8LJlzTCDIpbcBAPTej4TzUcnqNTJxDqiug3LRKTLSp3r0IdDPuVNQI1wkYVNcMk/OzkdFCfBlicRVk/ZImnA+uQMA0F7jBKIiL6zp02v3f/wFuPtwHZfPT7JoEk8iC7KwED3AS/aWlQ/gpNX54Z8DALR3foE9FCNI2XJwHl+QXhaE+NEkIEaU0kUYFsWus4NhxCdle7K0nyCFqVry9ywTOYVCcfJQOUkKhmMBCZYEq1/jEnRz0YiOfn0W5lzBFyQecultkEtvQ6+UpYIEAKhKKl1TB2TmDBvTp4FEho3DFiZ0259U+BwkSeCFnJ2PP0cqHzT75V/q1hbolji95XxyhwmTmQLGzrIBxEaRvvn1/2nft9AXbzqrXQW6TTZGTrNoiRcx2V1mQ0Z2KBAm6sQmqDs//HPQxi7Iyh2QlTviKcoS0eKgA688zd6zybf8CE3rGTCAdDEYHLvtmJ8FgGp3C9XuFt5OEX/wHESQbOrWDdu/jJJCoTghKElS7E9uBOh0gwHA+OWfh/HLPy/dnd7+PmDZoB/eB/3wvvigRJDIsCRvBGCSBODrX/v7L/ySbz0JImAHEiW9zz8FTpbCchScYxbk7Ky4MVNilbG9Ua2zYSRAtAHk7LQlpQTykulFmSgZSTa2nwA7S9HHAZBrb4JcE5sYe7KU1QpMylyEKNJLSty2aBcW7aLc20TLrvkDuhHbImW3vSzIkidHMt5OEbw9NIxiIpoYHydICoXi1UJJkkKKfnoYqG35g0zIV1DxokRvf58JUghflnajF+OIIO2z5P6FaEgabgG+KNFnz/0BoK8o0fu30Pn3P0D3ryXFJsNyBEQTpOnga+dQt5yB9SFrPLz4yS32QIwo7VY7gRyF2VkSZCksRz7VBhseI6dBexbIxJw/0Gmw4Qw2pKI5FLoD6A4iNbbajuSzjhElBw6220+x3nzW/wnTQR2qhJ7xR4fuP12a0gfTxFihUHy2qJwkBYNogNXG3SdtXB51L6KawabhvF1cUaIb4pJo/d3LwMaD2FNrX3iD3ShXACvI1SAjw6IU8bcTR+wgH6qKXUklgKFgetAXozCh5G16/5b/UOaLZ9D8myeCKJF3L0TP0U+Q0ikAg0zeZlgfPoTxFtdMNz8KwM2vGTrlv449p4yiNhQ53ieZFfO4eKrRFYB0PSSN3qqu5BFbr8RANQPEka8sazstpLRQ6xiu/6AjSVxvWkx4MgYnNen4n49eZZ9tUg++n3vdoFaWqSfQcVRUSaF4VVCSpAAAvPc7v4//71//I+hnJoFUkjU/BSKiBDBZohVJ9e2xIH+JbrGLiS9IYZJ5AHW/BpFz5zFQ4C5OnjA5FEQ7RMAzYYLksig9ec6qbANAcSK2QvTuTh3DI1xhwuEi6F58g9rEz7KVf61HIdmaie/vNlCsPkvQd93CnBO5QJA49hyWSyTI0rSYmE3XguR8MrUgFaQIx77sXYzGGTZghZ4yElEiOmDocBATVXTxZCmbHo/dxxMknq4kgpUzE6gNdBWjQqH4rFCSpNgfvjJ2zc2DcRJAJn5KgcyfB7FiatAkxSRY545YWoDV9DmmvBbHBjQdq9TENJFcyML96ybdqav1oEWHJ0hhyEgJaIZWR60Fxzm1NlBrg5aruHE9vrHqgUgkQdxcK+MNt8K0YwWC5FF+JhUlgMnS+PT+y/rp4zugW0GSNhlzGxyHo0jh444pN4ly30dD9hXTjBee3pwyWckDmysKSXtB1PMgguRQ7//79EBRKBQnBiVJA8Tr3/b+++9/1i/l0NhP1oHZAosmAUwqACBVAN0K5XA03dwMXpay4lJ+uspFWkr7t+Xgq0PbhEWTrs/3mR7qh5cb3WgF0STAFyWP7o/diNGvx+ThAMDkKEh2CImYKuDEnaKa0GxsOOzcvb++C5iDL6oYIVUI+qoZJmCF5K/sfm6cLGUMd0qNnxrMhlYytuS5N2RyErSnAdPc9N4jTs6OaaotTIuw12eQPPLdULQxVB4gDk+OZBAzibZdh/bgJjA5IzwWJ0gKheLVQkmSwkebdJdMp918iyeLwOl5/3EydioqSgDQbKOTSCFZjC8ASW58CbQaWhK+shaJIg2au3WKyzkiJnFbNgAb3buhlXZe1e28eJEnXCNZwkWa6B5rYUIkOTy9v74rnuPq68Din0MbLQBrg6u8LUUmSgDog0+Q+dJ/FX8cL0wxgqRdjzbHJb1uINNAIKCDTlbXTdiUvS9bJ0JtzFrCObAojabmAQB1Gj+dCkAoCulXkgeg52eBfabv8rpqU6JQvAooSVIEeBeVdgtIuZGXpafA6Xk8X17DzNwUyJgbjWiy5G1S4gQh505N1YMpJnLjS/Kngo7Ja29hjWsoivUqSJEJit10pzqcQ05d6Aaom7CLnCd/WaE1SOLSCLr3JA1PPVkqZAVBirCxA6fSBirr0M8GEQmpIA2aUE89IZoEMFECQLfEpermHlvu3iuGik9ykOIwUATouph3FitIwk7sVwrVjmeqjRAN1M1XS2hpIaKznyh5csSzY7NGtCN6kIvUt2L2xTeA1S2k9aAGU9YIKm4b1i4AAgtqyk2heBVQJQCOga9+9auf9UsYLEtPhbv08T2gXBUFiSc3CuRG+wpShE3xYq7bDnR3hRnJDDAZOi8WwExcGkHiUigCNlQEhoogugnEFESkD54K9+3FddiL6yDjYzC/8mWYX/ly7Eu4fl1SMmAQpApAdS8YJD6R2pOlfpDJaX+8iCAdFwfJ9aklxBVslc4aKl1J8dIQO/YmduxNdO0+EaKL0UUIhCtmaVD161SheNVQkSSFiF/JuQVk3V/6M1OA1WFyxEGfsKJ85MycuP2T77L/G8HXi1x+B8VCDtWq5CIUEiT7/ko0P+ZF6XZAt/ZAxopAIQ9Ua8Fj+UKk2Wzi0giTIxmeKKVyETniMb5wXbhvfuXLwF/8Nej9mwDAptoAIJMFyAFWi/XDMOCssp8bTT0Mtv+dd4GtIAJExlnEiG5Gpcjc20JzZAiZXp9psZKbi8P1JwMAwiWkv2wICWQkEVryn9eK6DhNtHrRQpvb7acA5BElykV+NFfiHT4atI8g8TjmyymmqVAojh/1p88xcFITt2/eXgFJJoF6E6g3sbhRZ4LkwWWn7naCCwh9sszGJ9/1BSkMvftD0MWPhYzudgAAIABJREFUQO/9SOzvJhMkD6Lh+hdeO9qb8iiEVq15EaWxoWDMXWUjjlSR1U+yo/V2woIEgBVUdCEXbwgP0fYRJQkAybHkenIptEJtbDq67zg3vVaYDAaApknQlCWYl2ai2wDQ2z8CWmVxEM1vb+JNtdm14230qtsUuk1BCEFeKyKvBZKbNmOEF4EsAUyOaMzUmAYdz+plNM8uoGmJ0hUWJBVFUiheTVQk6RiQTbedKHEaLgK77KLg3HwE7QbX6d4TJa6tBrnCRIYk3XyUR2KPLySiSaz03o9AV7eBHFc0sjr4Inx0aw+4FExvOfeDwn84F1MTZ+4qsPxpcF8mC64oDb21gJqkuzwvSAKZAa38CtUkIpeugN7jfu5j03j+eBUzC0xyabsJFLKgrSpIQb6iyxOlRIwcAa4ghQlP67k5Q7Q+2M9TgwbLbRyrExNAcP46rSJHxKnUtFmURpQyRhFNqwzN0GD3mcJbqovRMk+UmhYr85A14/PVjH51rBQKxYlBSdIxcKKEKI5+ogSAnJoDGZW0vwBAzrmRjacPpIIEgAlSiDPzU3jccy9aD908El0D9ENMX+g6iOlevAtuS5WWZFVZswXIcp7mrgLE6d9aY4q9/+63PxK3/9Q1/+bCl97Ek212wbz0xbdx78efYpB40SQpk/Og7WgTWrrxAGSCVQrXe0nYJhOPps1+PuZSMH1HTgcRsgMJUsONDIarjg+ASMVsIyG0J4kTJUYHGSMaXWKyBX/VHBCVI56sMQyAJXs3euxn27CC71WZWkDXkrd+USgUJw4lSQofki/EJt86Nx+BfOWLwja6xqIyZOpM9IDCOJz7f+nf1a7PB8dJBIkMBzlIpDSBSz81gXt/+wnbv92M7H8QyHABdLcKVFaB0jQW2wWcnQOc5U1xR16UHnDCc/F1wKv2zcvSlFwOASDxaz8PPA+WltOHj0D5FVfuaj26138J+QvTroLMz0aE0po5B+P5o8ju1G0jQyYu+HIkgy65bVlk01dxgvSS2VisYuIsk6M6rWIUwWutdpnITGgxiwxcdGKimJjED9bXMRzjN0yQRNa5j3GrrX6dKhSvGmoiXSGnZwH5LLSFCX+EK2V7eLIEACiMsxHCufUUWNkA2tEpGF6QBPo0nN2Pb/yDfyxu4NqoaHPi6yPTEyClAvDBX8WfUNPY2E+QOOjDkJzUXdlrHl2QLNgghSzI0BCwxeVxbUfrWFkz5yLbAIDkJ4DmHka1PuUAcnmQHPvcyfyCP7q5IrrZXOxxL41QVe+cVkK5U0a1W/YFyaPrtKRtRACgmAimIHc7hj889hMkniGiZEmheFVQkqQIsHsAtYEdrrVGMZR3ESdKzx5GcpGSv/Gb/m1tKJjSIsOFYBTECy0pTRzyxcshwwXpdu3GNSZH06Hnq9bZANC+/+PgPMksG6WCPzwSv/bzBxck/wUcrRK30e+fbowoebJE8hNMkDhGtTFBlng5ilCaxtw0y/PqZnNMlqwukMyx0WSJ3GQ0h9/9nW++4DuLh8KBqaVhamlQOMEwDOS0EnJctMjus2CPF6ViYlIQpDC7HQM6MdG2a8L2sCCpKJJC8Wqi/mUrAtrtoLFstQ54AlMcAva4v8o9UWpJ8l0++g4AgLz5ZQCiHIUhVz8H3LsFWEGPLMotX4dfULJPXlAfqNc2xWuT0doDJgM5cG6y3Bvtxvnwof77J31abJBSAbTXA3quAJmZ2H19BhBFklKrijWgtp8Bo0EbEtMVJzI5FT5SYFQbgzWiAZ2YlWml6Mq5hMa979Bx11+XR7EOS9tuIqVnQFp10DQn2FYnkgdkU0CXLdrTWTTQ1gy0rSpShlyko8/NRKnQtlEgwAMaX4eq6Qx+EYJCoXj5qEjSgPH6t51k7lYkK3O8iFKzHIxS/MWFfvQd0Ccfg3zx5yKPkaufY4J0EMbZRfnrX/v7B9tfeCL3Csnlzjgf34zs5smSeKwG1JpwXOkLQ3s9Jkg8vSbQa8L56FPQWge05kpeOIo0SGzuYszXfiqMAN0mzO1nviAJx9jRi3hbd9DWXSH1okJJTkT2EySezCH77b0ApBUSOVe27e3gbz+bBlGlkj7qCxJP26qibR2sVQxf0PICsXGB2DA1A9MZYDoDZA0dXZ1K5UyhUJw8VCRJ4fPe7/8Bvv1H/0ewoVoHulzkg0oiOp4ocY1pw0UZeVEi1WMUhn4QnU0lgomS9oZYt8i5+RBYkE+7eKKkudGxiBy50OcrkW3WsyroDVYiQWtVgRW2uo2kjtjbSzMArz/Y5Ly/uTc+ArPNfWZeU9uypOeeK0q+GHmveWgIRpmLEqYKsP/6rwCwpHb9l34ZgESQ4qJPA6ZtNwGwHKH9IkoTKRY5s5w9yAq981hOBwuFDh5X5Znbsorf7WweAJueDhe2VCgUJx8VSVII0F4PtBtMf/l9zDzSMUX6SgWQ+bPxVasB2H/yJ2wFFD/64UaRSK7/yqQ4bi2Vg2hSCC+iRLLJYCwsgCwsxJ7PufVd0PtPgMdRGZIJkl3pRLaxnQfQ+LXXZjlkAJyPPxYfSkku1kOnIpvoo/ugj+4j5ZhIOTHSRqK/Iuxv/0c41IZNe/74SYEksyC6gYnUlC9IHtu9VWz3ViPHGFoShhaI0UKh4w+AyVG8IEXR9rMxhUJxYlCRJIUA3amCjBRYHpC3/L3WAPJcbo4vSlWhRQQAkCKbzqB7wTJ/+0/+RP5kyQwwclrcZnLtMzbd6IdzyMJ8YRkhOrShIOpAimlAUvTPEyX6+LH7miT/TDhRosmolIQFSQvXaJKd8wg4H38M7Y2gdUYvlRYjSgAwdAr0UXydJk+U2lpPKkce5Bf/bnRjMsMGACfTAuDg+uVp3Lq3Ft33kFBKYbhTp3XTQg7ssySWFckdq/a2UDDlq/Y8URpLRqcPwywUOtDb7PvesYMoaFiQVBRJoXg1UZKkOBjhiFKvDRhO7DJ9T5b6ClKYnaXgdjlaKfmFMVMgmQzQawHbbnHDuWFW3sDD6y8nq5DcbgOF+IufU2YSQvW2sL2vIA0iigTga7/1e/irP/2/WDQpxmf8iFLzBeoX9RpIAbCSGaATnRqVCZIm1Es63mQcjejSqA7tNKSiBCAiS0md7Zdw2A+uqx1sYUBSZ9/ZetoEHPaZG1pKKkgxAUyFQnHCUJKkiEB3qsBoWlxVRimef/ARZl4T+4RRm8kF0Q109naRLLJckca/ZXKU+I3X/X2d264E7SdIPKkEgJhpq8NiGqIoAYEsAaB3uFIGJjcNxeUieYIUhsxNQt8V5YLuctEx27vAD0aWeJyPPwbO/ALMlcfBxmQrWN3Xj56kjQr3OUmjRwgLEre9WZFuHyR11P1oEiAXJYDJ0qgen0j+IrL0vfU98PEny2nDaARC37ItABZ09atVoXglUDlJCgFSzICMFZlE9Czg1AUx+hHTk8yTpca//RNfkMLov/Jr7E/sbstPCJ8cSkQFSRJFunEmpuDkfujuxWo0dHx4umsoj+b/+Z9FQQpjmiDVbl9Bimw7xZUceDbgVh2JLKhlQ7twGtrCFLSFKZgdiVDG9ZEDQGan5ILEQZ/ehba5KAxAJkgsfHLl0vyLvIsXRiM6Fu8x8axDTBan3HstkqI/yp0Wyp3+5Rc0zUDRjK/T9b316Pcyt8fVT3oJK/oUCsXLRUmSQuDW/Q12Y4RLlp67KO4UvujOXgFmr4BOXUDmV68i86tXI+clC29EtqHbAt14Crq25Q9BkFKsmjKtHnLqzQlNy/CiNHsGq6VZYCjPBoDWz51H67vP0PpudCWY/eky7E+X2XtJ6P7w398+giSQZEnC7/32N17k3US4+dED+QNjkga1oc+MzE4xQQJA798CvX8rcgh9ehf06V3pUzQssUbWxFw2KA3BP89L7GHWMzT0DA1du4UikS8gkImSZWiwjOBXYdGc8AfA5GhfQeLIqr5tCsUrg4oJK+IZKWHxP/8Fzv6dn2OitHw/eKzTwO7qDoa/+GXpoZ4o2SE5KszMo/r8KQCAljfEg0p5EMIt526wBFtSKIDsEwWQ0mqC1qu480kVVybdRNsvvCYsESdDw6DlaM6OJ0rGtXbkMR7trfMgLXfqrhXsGxakgUeRvOfJuCUYQs1eMTYDbD0Xd04VYD+6CePLYvkDD16UqBlfoqA2LLboaIVrDLUqQGYY6DblZSMOiaElYFMLtnvOlM6+KxaYIAmYKfb/XvTz80SplN0/OjmamMMXJ4Kpw7/ZYO81IkgqiqRQvJIoSVKIaBrQjVnSPXcRdCd04b39Q/b/a+9E96cU9MEH/l1ygRWQjMgRAJTE1UK0MYDE7ThCtXRkopT+lesAAPt7rL2IPhXtU0auh8oFpNmFmRgasObmIU2NiYLkRpGgD26Z+J3bT3Hl2nykjxnGZoBiNJplfSdelMgpt1TAk6fs/+57AqJyJD/B8WUsOzR+laOhJWDJqlybqYgo5U32szfabLuVSkUPI/Jo0BcnCtjYcGCPsEiVvrMiCJIXRdI0VQZAoXgVUJKkEPESmL08pOEiUOWWcRdSQsTE5/YPA1GKWcFFH3wAcultYLsCjHLTef0EiWi4dnEKn958jBfGNEByBdB6lZUw4FfoSUQJCOQojL3Gcl/0qVxUjjiIJkY0et99CJLkLphe8jeloIdst+Lj9n8jmQJQcaMd+llgg8vxGkkBmejUk/UdVifKkyVfjsK4n3V3/iySADqOmJQeiSJ5dI+/aKjR6cBKBp9hP1HKm30EKyRLcYIEAGutLWgIomz2yCxQCyTYe37SG/BiA4VC8ZmgcpIUEUg2B4zNBhvC0pOO/uWNXg/4+Lsgl38q/ryX3g7ubFfY0HWQ8TmQ8bkjvuoDkA+tfLI6bIqKG2Q02rbCI/mbPw3jp98Eak02OIim+YI09Q5bAdhbFFd4EVOMLtw4dcQpGk1nkZtw9GYiVHuquceGjOIkyMKFvk9DXvsiRotMtJJaxh8RIXlJ6951roaTEUpUNzQummZ1/JHWc0jr0WigcGyrCYt20XKiuUZrrS2stbYi27VHt/3blW70cYVCcbJRkaRjwOvf9v7773/WL+XF6fVAW27+z9gssOUWTaRUvAh6oiRp0UGuMBmid37E7vNyxDMh5oSQ8TnMm6t40nJlosVERMvmAOfwS+ZJjusx1xUTmGknBZIUIweeKNHtoCBm8jd/OnpiT5QcAqT6J+vygmQvRRsDH5lSKYgmAUyUNkKrBl1RMn71l6LHm26tn16Q+0Ve+6L0qVp2ED0yNTf6ApO1SgFYIUp3KT61jr/RqydKpNt2XwuEpsk8aT2Hlh1qnyLJm/JEKa3lpXIEiILE4/1MFArFyUdJkuLgeBGlXS4qsTAl9m3jIJffQsOuA71d5MxQPstENGmWloOWEaQwiktvjeLeh+xCpKUSkf33fbntLqjX9PWSO520tQuMia+FdjoRUQIAcv51JLJJkKH4BN/e9xeBd86BtsWLcjiKdGzwldFLofYtfETJnXIzfu3vsftx+T1mGihNgxanQSReyguSfwhikrypAyqriXVIeg6FTkxohE14Ea5FDgmVI9hz2M+/qEVb2vgRpQMkldesbYxyTX633f50YUFSUSSF4tVESdIxcmKjSQDufnIPl1+7xKJJfRJmAUSb3HIVkUt6DhW7jnqPS4zeR5B4tGz/KZJ9cQtHOsk0NG+F3H6iNDQrPlZmOSe8LPW+vyh9OjLEEqUTvxQU0dSeB2JhffR9tp+hA4UjCoSkdcjiDz7E2c+/BdTZa7bmr8CQJeITI/q5lsQ2HY4bONRcWTqQIPVpZ3JUEqFk6J7TFqM2yWyk1MGeU4mIUqrFolvbOfb/UUeSs+XII1GjyRyeW1vAfDA9XHkcfHf916O/uNQrFIqfPFROkkLgvd/75wABaLvFLnhEA0bPiDsNy2vQkOuf79sCI/vkMbJPHoOcui6MsCCRQnxe0FFwkmksOq50bYmvk4zMArkxcRl9CFreAS3v7CtIwjErcvkbDBS0sQdaKwOpPBuTp3xB8rAScc1rDTZK0xFB4nEIgPIq8uYY8lyLj76CNMCl/5HX0+/ckorbe04FqVbXH2G2tT1sa0F0NE6QAKDrtDE2ESw0MLQkckbBH6vNLlabXWnpAYVCcfJQkqSIQFJuhKPNJbDKRMmVJXLqCsgprl1Ja48NjuwT+eo0unQzSISuNUEMA3RvKxidFminBTRevE7Sb/3e/8puyJrJnrkI5MZARmaZIPFsPWYjzPIasLwGYybvDwBI//DRgQSJrrDaS/pQClfz8XWIDsV6UACzU4ou1fdEabccRIPo9hM2qA0q6YcGANruKrRd8X3kzTEUExOgGvHHy+LjT8TPpefEy0jRGPHHitbEitZ/xV0zQaATw+/txtN12uj2eS5AHmlTKBQnGyVJx8xXv/rVz/olDA5elCpVNuJqKgFAaw/0yW3kIO8dRpduCvfJUEG8P+bm1BwhaZtu7IFu7EGr7UCr7bCl6WeCCuJ06xnoVrTCNoBAlFw5kmHM5GG+MQva2AVtBNGpOEF6WcSKUrfly1GYsCyF5cjfLptS67WAVpmNRJpVO3dsaEctcxADH03qOW20rWowdBtFQ55HJpOljFFCxhCn5JJ61h9xcmRo8mT9U7LVnwqF4kSicpKOES8fiRelk5KjFIkm3fsIAEAdG2RclBm6+Ak75uxrwbZ1UQrIGVZ/iD65FZEjoI8gefdLJbz3za/jD/63f/1ib8QggEWBrR1gzL1wLt4Dzl4S38PWM5CxoFYQXd9kz3t6CrhyFvROdIqNjEenHWljF6g2gEKQSzVfrSOqJAPACgnq+jM23ebiiZJRFyMcJDcCWo+vAN7sVWA+fQgKgBRE2ZIJEunERGjKm6DtwdVLoqAwNQKLsumwMve80+H8pH1Y0ZqYMvaf1n3W2IUJUfQItIgghaNIdkLlJCkUrwJKkl4CJ0WMPGivA2Ikgt5nO0/FxzerEVECXFk6fx10+V7sucn4FEglSK6ltX2Wwx8hihThAKJEISYzt5fWkDo9BXLlbLDxg/tSQQLABCkEuXgVJMumH6+Oj+D+8h5Qr4FuSSqPH5X1Z0jOBq/NKG8Ey/v515RjPwdellpWtD4QrQbRMW0kKhURQSrG5zYNmp5DYGqUu7+/KM2awXtIdoOoWTMhJoU/a8Tn1hWdLPZI8D3Z6zFBSmgqiqRQvGooSVLE02oAaXeqbGwY2NrFmqZjyrFBN92/nKdd6bj4Fvu/0wWZY/IhyFIjqDkk8GzNr3btQdIZoC5ZUh1TyXtfjJicmcV7wOVQpWmv1EBDHgGxP3oA7c3L7OWEk7JlgjQVRMRoRdzf3jliVWbDBCmOu7e5n02vHfQuA4LaR31kSSZI/j4Z9zsQkmXSCbXe4AWpvNn3pR8FgyRgUZaAfSBR6jQwPTIDrU8V7IwrTE1DixWkkpOPbFuqBz+3rgO0KCsR0K+FikKhODkoSVL0p9UAIIqSh/b5z4EkuqBTQcXmtpZAymvN4MpSnCDRO3eiG9MFAG6xv/w4YLO8oMNO29x6Wsb1ebeyte0A61vA+SDJmgxPg8pyb7LudKMrS/ZHDyK7kFlXChwLWIm+R16QBOrxQvKi0LVlkKk5IJ0GWlxye1iUACZLrijRJh/BO4W0wQQgLEu+IIVJFUGX7gubepSvds2kg2SjEcfjpue0I6UAAMAxk31FqZtMYb22CdN16h4N5Ho/QQKA9AD78SkUip8MVOK2IsKtxXJ8peTz16F9/nPQPv85fxNZewCyFpUIeu9j0HsfA4WpYHiPxQqShLgl7AeAFDOs2vUo1wIktFKODE+DDMuniZznu6A7fQpDOm7EwLaEES5AGY4iDYRwE9V0KFIUWoZOG1XQykZIkEKnMPJIG3mQTLavIEWwgsiJabHIjj0xFd3vCPwP//17AJi4GCQQspypC4nWST0LZKJFJAEmSo4p5hN1kyl0k7ImtxQmoVJBCsMLUkIbXAFNhULx2aIk6ZjwWpOcSKiNXjrNKmvv7mHxbx8CpRk2ANRi0i48UfLlSEZhCkhmQd4IerxN7O1GBSk/fuS3EYEvYikpKeCJkvNgxR8e2ulxaKe51+RYgSCFGXIlol1nA4StAuz24Nx5BlpvgdZb0IcGUwaAri3HP5guMjlqBInFJDMEkpH3jSPNCkizAhQng8GzjyAdNxQOWnYXLbuLoaSGoST7Fbbdrkd3jhElAEAiiz2jK5UjfxedIqFTNIyeMIBoFCmMc9ipYYVC8ROFmm5TxDMzDTx3IyDbS8BoMH3ki1I7geFEEHUiaw9AzpwDffIocjr6zG3lkGCy4okS3V4BatxFjhckL4p0lOapuZi/7DlRoj/+EABgVTrQSvF92LTT47DHzwPrd6MPDkkEIiWPQpCxIWDliK1LKAXtsGkt5wG3mvCNa/7N5893MDNzCnQ7WoKAFyXS7PNaipOgKw8ASFZshQTJiyL59JneOiopPYc214dtu13HaCpUoT0kShoRf+V5q9LSuijpCV0uOVknBWjAeT04z00NsN3SCQZJwqG2vEyCQqE4cah/yYooVi9Ifp3hpqG2lyK7hpf6e5Az5/wBcIIEIN8NVlTR7RXxQCMZFKNs7bFkbUpBdAPXLkQLNu5LuI6TF006fwk4fwnWt//KFyQPp9KBU5Fc3G2HDQCYvBwM4ECC5DxaF+6TbMx01gGhmgZtkiW9awtcxGdZUq5g9FRkGwDQjRWQtbvAnrwOFABXkFwau8HodIOfB0RBGvRUG0/eDH6uKV2UIllEab25AgonIkg8LbuKll31o0cysk406tRMqjwkheJVRkWSFC/G9hJynVBEYcJN3N6Q5CXdvQOacXNlQhEdqSDxjzcG0CRW11jFbUqBbTcXZ05cTWdvNqCPR4XFFyW7f0FEursHusg1570431eQyJh8qmugLC8Cc2eFTZ4o0Y0V2RGBKBWZ4AhyFIKucsJnO3DmL4H+p2/7m3TL/XkaL79eUNuqY70ZfY+2uyJOJ9HXVLSYAKVTwXdjs/3cv30QQUpoaQBlOFAr2xSKVwUVSVJE0XRQOAAoGzNTwM6eP8iIvJqxL0tw5ehuKDm73gTqTXRmz6CW2Ec8eEHqV9X7gNx5GKpJtMSiLRvXrgJgomRvNlB1p9q04ZQ/6NYu6FZ0WTi98wPQOz+IPlmrDbq5LAxt/IiNeg+AEE0CxIiSG5EDpSCFfRKR99ZAn0qmE71TrYoRMWderDflR9XicraOAHF/ZeXNPJpWBU2rAodayBnwBwBsteLzvTxZApgceYIUZjw1g/HUDNJ6NEooFySFQvGqoSJJiihdN4KyvgFMulNcPUvogUZGRkB3olWbabcLJOMvwp1LV/3b9elgKo/evA9ixOcCHRoKEDMF2muzFW7b3MqupUXgtBhtQcKENizPf/JESb/zfViQ70PmJiPb6Cqr+aSN50BbXdDl+GrXh8GbcgM4UfJyudaXgeloixJPlGi1hvZWBakxlrtDn7kClEn7y/gBAIYekSMpsmnHAdOx2fJ+nQzBpkygU0Ye7VD5Ak+UxtISyXYsFDNTgBW/0k+re2UdMpEVcfuhqV+tCsUrgfqXrJCiOQ4+bSdxdR9RAsR8I/+x199hj/34hwBEOeLJt9iFuA6ANlhV6vkhDU91t9s83QRMAxdunMO92wNo7jEamuqq70CfDSX3zrP36zyNVsQmp9xcm4p7QU4FF89+ghQ5z1AWqB+tISqxLUB3ZWhoGvB60J19DXj2kHsRbtRO1lKkkAet1gI5iiOVBJnhSjg8Z1NzkShSmGOIJmlEhyNpyCsTJYDJktetT6ehn0HW/T40RFkKBEmOJ2cAKwrfttkiAAICHSYoPZ6edQqF4uWiptsUEd77vX8R/2DPvehlhv1BhqNy4DMxAiOfQaoWFQJPkAQ07itpJHHxp97079r2IZZVOw5ouwHYNrC5y0YyAYyU2ACAUflScU+WACZHviDxtDtAuwPt8+9GHgoLEm2Jtaec8tGLSsYW2Tx1XrJz6MLtLvEnp86DvPEuyBvR94BcRro6kMxMAbsVaB/9wB8wuL+5XDmiXXlz2EGhE3FaLWWIUcz5PMF8nqDndKKCxJMdArJD0KDtK0g8y5LKAwqF4tVBRZIUsfgd3FMFAIHQ0PIuSCbUSsQVJbobRCTomlgGgBelfQWJwy7E5EAdAKcZUxSz3hQv/qMlYDuaKK7NjYNk4qda/KrbAMj0ueC2tQxSGoZzh1Wl5gWJDB1tVVssY6eCaBLARImPKAGg928DX/hC7Cl8Ubr/YXzpBNnnNDsJbD8Gam6la1vy+Q4AzT2vRoKcoLAoFZITKOSjkZw2ZdGeFInmD/VsJnMEOpDjSlDU49urhAWpmBjDCirQyTFMGysUis8EFUlSyNFMgOjAvBuRGBZzTejzaB0kgLWhaJlRQfIpzgDFGdDCqDAiF95B5ie50a/2vVD+ST0UhRktseHQYHg4TjBceEHioXeDQpralYsgp6dAUqY/MDYLjM2CJDS8981vHPptfe3r/5A9Hx9NGgst9T91HvTBHX8AAKobbMSx9xxk9hxISfL+4gSJx4soHUORSaobsJweLKcHh1r+MDQTCT2DhM7EjhANJKZWUZu2fGHq2W1fkKTkxmE5HViOWBJCJkgKheLVQ0WSFP0hJGgsO1xkVbhdfFG6dAkIdYNvz7GoSmqZk6XijPw5Ht8ULr4kUwR98sC9nYHWc6Mwkim7F6V9rwxc5KbN6k2glBJW0JE3fw709vfiT/J8A+SLvxBp+AqIgiSDzJ0GlmugD+/33e9QeFJw58fi9munpVEyX5QK7rTi3vPILoIoVRtAJzRFGCdILwGbOtD3KdpIiCbND0rpeZTbrExAzhyVHlu3xRWNnihpRMdaU8dUJj5a1u7FRDAVCsWJQkmSYn/4atd8RMmN0NDyBkhGvqKtPXcOqYd3hL5tAo9vik8gZNFwAAAgAElEQVSViVkdtRe/CulA9EJRjV1OuKaiz0mufQkARFlaCyVhj8z7N+n3/kJ4aKKxg43siNgOZC6m4e1R0DQQwwTaVSDt5lbNTADPQ5GimOlEAGxqTCJIPk1PFpLiysVwEjyPG0UixmBar/Ak9BS6kuiPQ3vQSPT5vIhSSpd/R+s9loPkyVJYjnj4ab61Jru9YA6DuonklFJYtA0jZupYoVCcLNS/5GPkRPdvA6KroRpNoNHE6mYtIh20WQNtilGG1MM7TJD8nZxgPL65ryCRjJgTo2kEv/3bX3+ht/Db/+QP4GzWgJEJGAUdRkHH/b+URHF0+T8Fcu1LLDcnLEg8hgkMFcQBCIJ0XNx6yL2uFidBM5Lq5N50IgBkR4MBAMXpYPA05cJAa+7zuonraHdAJidARkdARkeATJEN53hXedmhKJFDxeX+OSuBnJWAQUxYTv8k8npvGy0nPpmeFySPL00Ef2f26PEmqSsUipePkiSFlJt/exuwO0C3xUZFvODHFZSkzRq0SkOUo/A+jz5idXy8IUEQpKNGkVyMyaCgI4VkpRwvStQOBgCcP82GcEKTDQlk7hTI0FQwLt4A0kU21pZAcimQTAbXzx29kS+1YopthkWJaGzk9smfKU7DKI3tL0j8qYtcIr9zPEnbAOC4CwgSelAAkhcly+miazeQM0aQs6KVteNEqev00HXYz1Enpj889hMkHul3S6FQnEjUdJuiP702YLoXpJg6Sf79IW6l1zC7aNLd4EJLH30kfw7TAEmngtwnAJSXspwrN9ks4BxNmIzJHKx1lnVLQUH4opBJlixu/dH/C+NXfk1+Ak+U4uQoH526o3sbAJj00Sf3DvfCD0qrApRcwex2gbEhaX0kfwo13K2eK5rY4/q9mW6DXF6QJufGsL68JQoST/voJQ7C6MSE5bB8n71uIC4duwk93JstlfdfQ3V9G4VJFjXzRUmDL0ZxNK062nYQNc0Z7L2GBUlFkRSKVxMVSXoJnNQpN3/6rNfGYocTJX6fSgWkOC4IEg8ZHgYZHo4XpPBFGgAtc5GKxGA83rkrX23n9GwmR0lxNZ31x/9BfqKHS8DDJdCV2/7wiBck2RMPKNpANAAUd24/ZsUp61X2M+1yJQcmz4JMno053pUlMykIUgS7B1htkLSY1xMRpGOMInkkO+y9TWbEpfw2laymS0XzkGzag017SGhp5IxC7PPUelEhr1u70IgBAvhDhoomKRSvBiqSpJATt0qp6LazqAT5Lzv37mHkklt5WROPo+6KNJLPctvcWjr7CRKHFnc1OgDaWA4km/FzhYyhAigve940W6iRrSdKiS9cZnIUA125jUIpi+riMgCAzM+x7SFBOvYoEo9pAj0xSkImz4KuL4r7eaUWep1YSTI3HovncUWpky+BPvmAbRseFQXJiyJp0WmqQTKZSWO92fLv29SKjSjZVB418kSpbrHvqkyOPApmdHq0ZQdRT8NtnmtKmugqFIqTh4okvQTef//9z/olvDi9Hki2yJKsdZNdAIvBX+VkPKbKtmMBjgVaq/qCFIbks6yUQLnKBoCJtBUVpFAUSU+moR3Clm6tuNNrm8HFT5uT5AJJkrf1G2fQabRBblyXnzxUOwkA6NNllhDN1VaSCdK1ySxIYgD1oHqunCRDF2ZTstJr8iwTI28I5+kEA0yOwoLk0cmLVcrp7jZor+cPaDprlGwMWBZ6+09reRGlcud5MEgVi9W9vsdVuzrSRhHj6Xnp4zJB4msxaWE5UygUJx71r/olIJtu+9a3vvUZvJIXIC2pCp0fBWpBywZPlOgm1/fL7SVGJs+wx9ZD/dby7EJTfH0cez92awqVqyCTE6DrQXVoMj4JWmHPdZQoUhi6WQYZZ0vXtblxOMuhisq6Bv3GGemxnijRm7fYhpiVW+Tqm9Ft01yRx5Y7danrQPnpwV/8QYgTJe5zw/gV9v9+OUO1bWB4DthdjjwUFiQAoOs7gMGmv0gyFXl80HhTbkAw7ZbWuamzjrxdiydKZwvB1OhqsxHZjxelSidG9mNqNPVbIadQKE4WSpJeArJIEpVMNf2kQVefA6cLgNUJog4hUQIAurwBXJVHWjxZQnnVF6ToPuIqLDI1BtQBUmKJtvQhy/uhlR3ceF3Sk+wg76XRBBLRi7cfUXrEyYD3HHHL1+emgD235HJeLFMgEyTs7mEewFOtBLr0GIC8Xs+h8C7UvVAu0GogfzR3B2RY8rP38nV4WQqL0/Ccf7PTlIsHXd+Rbu+lMwBiajMdFjOFXopJUdduIWMwYUto0VYjQxhCGfKps8XqHnLp/acCG71daFybEa+xbliQVBRJoXg1Uf+yFX25u1TFZU+UPJotOB98Iu6YclegtaMdP+nTe6AbuwCeAgDIW2+z/09Ga/mQKXF5Ot1x83qKJQB9ahX1o9MCwF4fKeVYdW2+H1w3ZoWTVxDQlSUvsiVQC8QhTpDCXDRquE/lTXVfGKsb9ElzK2cv7gHhNG26uykXJcDN2dln1WCvg2To/XdKoxFBkkWR/uff/Qf4p//sn/c//wvgUFu6JN+mvUgfN5koXSix75ilBd/VzVDz4UZPXv5AIzq6dgPJHhW2ARQAQcupsbwku/+qOYVCcTJQkqSQ8s1v/i/49r/7l6B7u0DdvSBtBM1TtXd/Bs53/0v0QE+WaAf0qTxRmX74I5ZEPf8msBmsOosVpAHgbNfh6Bp0102c23ehXbsc7DDnVgRfXoscS0qjQGEckEkS4M8H0nuiOJJUARRcG5cleX7PkdF1JkrdFpBwIyrT40I0CWCiBAA46063tTh5MPgaUVwErSf2LONJPHkAMs+VfahwJRyyJRgAiGPjxmX5ysfDEG4x0rQqfjQJiBclK2HgdCl+Jdt4OpimjBMkAOja4tRcTedzpKifuK1QKF4NVOK2Ih4z5NBzl4W72rs/A+3dnxH32Vtno1UGmZBUfQb8VWYAgPFzwPg50PEzQLsbjEHjLrm3l4MLoHP7bnQ/V5ZIYdgfHmTuCsjclWBfjcQmTJHX32X/n55m4+LrLPG9mAettoHKBlDbhr20eqS3RZuhROZusNIL05JE48nT2OuuiYIU2cktOhkjSLS8C1oWRYIkB9iQ+AB4014AEyUem/ZgJQxhAIAGHRrip9h0YqBl7UmjVF27ERGkMButwTf0VSgUny0qkqTYn/EZYNPt7TV3GVgW5UJ792eYGEnwRMkAYHXlFxGKaG2d05U1PKWugJTY1NiVs2O4u3GIon06u+jd3aji8oQYTXBu3wU+x5rxVqeYVIyMunk4tVBStwuZuwKysSgWvPQec+VIICYXCwCM4vHKhVNrg0xGe8btuRGQoi1Jsm64U2gZrvGr7fXpi0ZZwoLkpLIAyiCNAecjIVoLKamzBQaRHCFocCDPKdOg+5W7AURLBkCssN2KkSMxihRg0uMte6BQKF4eKpJ0zJz4/m0e4zPBbS+i1K74g967B3ovpg4Q0UAmp0CuvA1y5W3hIZkgUa4RK8nncXEufprkMHjRJO2dL0F750tY1Sd8QQKAJ9tuErdEbujyHdBl1nKFlAr+AA4mSPTZIfOqYqCFPNDpApYNWG02mmU495fh3Gfvg2aHQLPyZrR7/IW+sRMIUhjdAEbmQc6JeVdyQQq4dk7evuYoGJoJQzMFkSGSso6a+58MDToyRlEqSDyyhHAgKkgqiqRQvJqoSJIiFuL1JvP+Sm8G8kI3Wb4QPx0FAPTePRCvsGTMEmlflKpNwBD/6qZxneorMRfvg9DtQTvPZEUfYhd12hQTawt3H6F6+Vz0WFdyPDGKg65uge79wL9Prny+vyDpxxBtMNNAj023afkUnJp4IafZIZBGGfZGG/pEEEFqmxqS1XVAk7daQdqVVPfcvii1KmjuMOHM7FVwZiKFxf6liAZC1+4goUcjcAREWumajyp1nWA60kDaj0R1QtGiODkCgISe9Y8DgJ6zjmH35VQB9MjxVx1XKBQvByVJing0gxWHrG8DuVGxFIALre5GRAm6Cbr0GGQ+Os0DANZ/+CN24+d+EcTiLiiWBVLKgVbYqiOSH9BS+VQSJJEA7XZBezaIyQTF/svvQP/ZL/u7FdzWJZ4s0Yc/9h8jV5gYeLKkdzqwk0k4d0IVrD0qq6CPxWlJMsq9n3VvZdVRi0BR1lpEUlKinygltUxkf/B9zDxhSgdRvEIhjWrVlYyWKLPNYgmZehNam01BXj43jXsfbrEVggOsuh2O/PScNkwtEL5+osQLUhhPetpOK1aQEnq0dlhC9nNUKBSvDGq6TfFiuKUAVtuBLNEqiyaQq9dBuHpJ9OmSP/zDPUEK0SXBdAUp5eA0eqB7NdC9GotIlYYBTYOzEb/y6DDYf/mdyLbCw6eCIPHwydtxgkSm5yQbudstJi6k1j8R+FCY4gVey3M5R2v3gbX7oNVNpFrxwgAAGD2DhhlTJ6oVjfZl6lwNJTM+CjMounaQVN5zRBHUqFw8LdqBRYPjmm3xZ0Cpg3StCn1vA3qopcxBBCnrrrKz6DEsPFAoFJ8JKpKkiOXmp8u4cdW94Ne3gQl3KbcXUeJXoU2Nge6ugwzHtCsZPg00onMxvBx52M/3qdlzSEgiAcAB7dlAKphasv7sL4G358WdU+5SbslKO9s0gbOvgbj97ej9p8FzHFCQPDRjQH+nEO5JzDQwGlzUSaMMWo0moXui1E5zUjN+Qdinwa0cyxql/QWJRxv832CEaEi7xR35qFLLqiLDVdyu9dj0bN6M5kR5omS45wmXFfDwRGm50ca5gihJcYKkUCheLZQkKeLpcEvAHQeLD1dwdta7GNSBjB7JrfFEqXtqAYlnj0He/GnhceMrv+Tf7rajBffCgkRKRfH+SO4Qb4QAKTe5epYJgT6dh/VJEOEiC5dAH0sSz1NB3Rtb0gsNAMjFeXZjcw90ex1klBPFAbZUkaFRgIy4q9BMbtppbBZ0K6jLRArjUlECXFkKyVGYrJYDHAvdRPDzSHS7UUF6CVEk6AnA7sIkSfT4yJAtihLAZEkmSoHUxH9Aq4lAnh5Vg1IN58wMqt1NFBLxqxYVCsWrgZpuU/SF7lVg57mLTGlK3MGWrE7bXQfdWIkIEo+VTCEBUxjhvBpBkKpBBOOb733jhd7DN977/aBDPRdxMV4Tc6bIwiWQhUviwc832ei2oDdienhNXgSZvOjfp9vroNvrcJ5tw1lmA4AQRRrYVFuvGyTIP7kvvq6xBfF+YRykEEom314F3V5lFaK9ESKrxYhps8Kiit7QCLqa5Q/03GEPbuVXON/IJGKOXNOOfkZeVAlgchSO+hCig4RqI/GCxHPODCJI1e4mql0mng2r4kfdDJJAV4uZqlQoFCcKJUmKF0ciSrtb1eCi2HMvit7Fk8NKpmBJWldYf/wfAQD6dImNC9MAtYKRzbKRzoEMoO3d4irrURYWJYDJki9HIfRG1ZelsBwJhGsodXsgp+bYmDsNjJbYSMSsKDss+4gSAHTHZwM5kuHKUlbLxQpSoiKPSnmY2+7jmdyxTLtBj69szYvSRotio0XxqLoNss+vO0J0GNmhAwmShwMbc/Ojkr0VCsWrgJIkRX8yTGjs4pj0YTJcYqOYBxmPaT/hyhJ9LF9G7wlSHH4zYC8K5BxyibUkmgQEokTv3fIHuXolfLSAXq+A/ug70Qcq1YggaZPF6H4AUI32uTs0fLmFkCg9/+gTdFIJfwAAbIcNCTSRAk2kgNpOMDikgsQ1D/YF6SXBR5M6Thsdp420kcdGK2rTTUseDTQ7HZid+BYscYLEk9fY50zIMc+xKhSKl4bKSVLEE4oA2MUxoOtOE2UK6F2YR2L7ubAPGZ8G3QxFKHae+Df1ux8F26evCII0Vm1hq5AGcsEFiUqWth8GWt4CGXJFz8vd4Woy0YdRgfNEiX7KPRZK8hVEqR2dVooIUvgC6jj43fd+C//sD/5V/zcQA00k4c9ATQRTaXTxprBfYm0Z3SlJUjknSjQhqb7tUdsBfbaIxPTV6GNxx2UOkz/WH2L1/M+gQ1t+rzQCgnZoif98voCntagUeaKUMQp9xchjMp1EHTZyVjAlFydICoXi1UJFkhTx2DZIKgt98zn0dhN6u8nqJXF0R2cih5HxaRZV2nkiCJJ4YBf5pz+GfmHKHwAEQRJPyuSCOIfL9SCZFGh5S4xCWaGIVEyBR3L1CtDeiwiSwNwUi7pxY/qtM6ETcYI0oCgSaTSCOkQN7pxj0b55ibVl+TkmTsd/Ti70GVfuoDgdjOFTQC6IMr6MKFIvZol9SlLfaD4vr9beseoot1dR1mqxzzOVmcVUZta/Xzds1A0bdiqLkpX2h4yE1kc4FQrFiUFFkhSxfPMf/Qv82X/6Fu5udnEZZaDgtrbIjbKSAC7d0ZkgotTmLjpZV3hCeUnoyi9y+uUZkNypYMPuzWDqondMtWcsW6z6zYlS9d/8mX977Ld+Eei47y3JFYWcC+VnebhRODJ/1t9E/4aLoqXci2h3B5AUPzw0jTqQdSM4YxPAVqjejytK5Ey0ujhZC4pf0inWekaQo8hz7QBew9jcGFDfAcbZ1CXZWweaG6B7dWmhy8NCdfFXlkW7fjQJYKIkiygBTIxkeKI05LDPlRejMGkjKl2lxBTKzU8AuL3lqNiAV6FQnFxUJOkl8Mr0b+PhIkqeIDnhhG4PIwkYSbQuXpYLkq6xwUEf3gpuxzTGfRFu3l0DcfOr0AmtLAtHlADQ58/iT9apAZ0ayIXz8sclicp0d6Pv40cmrqo1F1EimZw/+HIBMsjaXSAVM10m6/FW79M25hhydPhoUrh4Ix9RSuhZfwzr8rw6j7JWQzdhxE7xygQJGW6lXHJAFeIVCsVPDCqSpNgf76JRdWsYlZkUmU+3QS7MC7s6kxegrT8Qjx91V1h1ymhdf83fnL71SUSOAFGQhO3ruwASuPpadMXW/oQu1J0GoAO9RVbBu7fbgnlDnDrM/zorB1D792L9JOqVI8gUQASR2NpfkHiabsTjCIGWr/3uP8Zffef/Fjc26gDGgbIb7TN0JkZhPFHqhbrZcz3cyPng86IPP5E3wA0JEtlbP+jLf2H66dYOl180B3mVbE+Udm2x0XDWEBsAh0VpX0ESj+7zKhUKxUlCRZJeIicymtTrgG5vBUvwd8W8FvrgaeQQZ/ICnMkLTI5G44WGzM4CO3vBQFSQBhFFYi/KBu1ZwNYuUG8C9Sa6D7aFXXo3n0sPzf/6JWz9qz8DrVYCQQpBb99Bod0Cmb8MMn852B4WpOOIIvVabNl+YZhNtWVzcG6KfeO8aJ4UT5Y0M77JLQBy9YtAcSoYQF9BonsDXL3HPwcITJJAo9eBSZIwSRIaiUbSunZ8LaphfQzD+hiyxlBEkHg0aEhoKdhO1x8AooKkokgKxSuJiiS9RN5///3P+iUcHkqDaRNqA9xFiT54KkSUyLNP2Y0htwjlCFtVVUgOodopI1OWT82QyWngGf+YBpJwv6K2AzJWAn2yPVCzp7YDwkWzejefixElN0cp/xtXgbYbqUiFmvzelqyMm78M7K0C5UAa7BVOsKbmgCGAfro+sBV8YZxnW9BOhaaYjKSYI+bm+HTyc0g8ZSviyFjomEQ0IgMAdOkx6I4oIrQYlIEgY2vsRi3ajmZQVLpVlBIsyjOWymCrLVYA90SJjypt24G4zmhM4ruh/m8AEyQZe911VLrBqrmcMQLoTC5TNOEeO7imvgqF4rNDSZLicIQTU4kOzeqBrj2I7rsTRJ8yMQnY5Opl4T6tNXEawFI6Ok1EjMMVYCReW5FWG0gHOTk0XC9orwbMjQGdaPVpAL4sxdV9YucQyyDQjficnWvTA+j75bgRN7dlh79ZJkqJjC9HMuiWOxWVm4gXpJvfi2wjk5OA54HlFfb/QdaC4rCo/LORiRLApul4OQrjrUbrOu1YOQKAuiU2WM4Z0ZYnCoXi1UFJ0ktENt32rW996zN4JS8GKbr5GHzEY68GcvVCpC0JmbogF6VRt2DjcjCdRtxpHpkgifcbuDBTwIN6AldeH8Xd231WXMURFiuuRUjic6dhFpJMjniSplSUrEtvshuV/xJsbLrn24tWsQ4LkvHWJWC1CfpA8nM6BEIjYiBSjbo7eyaa2O1GPmRtSAAAmga6EhSlJAtv+rdjBcnDEyQP+5hWJrpUulXMYdi/P5ZiqyqToZwkL6+oFVNQEo0yEgCsuvsZjswLD4cFKUK/EhEKheJEoiTpJcJPtx3XFMuguXlnBdfeuuTXF+qURpFcYjV1nn/6GDNXF6SiBABo7/hyJGVsAZhaB93dAhlm0Q6ZIA0E6gCpAu52gPNunlDi7dNAmps6K+blogQAnV4gRzIyKaCQA601QPLBxZkXpLOXx7CUOYbIg+OAdlqAkQD2WJ2i7uffhEa4f97tGpCS5M2EZSkmZ4o+dssXFE5FHhME6ZhxuKTorBHIYFLPYqMVJGPnu2UAZYyno8v500ZBFKVGObIPAGDnKQCgBQpbErmSRZEuXZrDj+8/2uddKBSKk4JK3FYcjFSwuqdzOlQkMVyEkRCAENDyFujDD6SnI9Ni2w+6uwVAAzl3ShgwzWBoBqAZoK3odMqhaYVqOBWjIkHSaZBSAcbK3chjPoVgWpDWGqC1Bjb+94/gbDf9EQcZPp6kX4eGkt7bMYUT21WW/N0vqbzV9qNv5OJr/sBICZRfHcdHkY5hqk0jOojbgsVBELm58+AhJtLRJf6brZXINoCJ0khqNl6QXHrDLEFdJ6YwIoKkokgKxSuJkiTFvmjewutUASufsCkYqSjVt4SaON1ptyDhww98WSLTVyKCBABkOFohmu9YTybm3RuHrLnT7QI9JirGhVDko9XBapWTJVeUSDoNkhYrKhsrd0VZKuQEQfLY/Nb9yDaia6Arz0BXnuFCyQJ1KK584Tys3ZjpnxeAJFO488EtoDgubJeKkidL7Sob/eDkKEKSE8NeG0ikQQqT/oChsxFXw2kA8KIEYF9RyhgFfwCAnS3AzkaX9/eGp3xBCpPQM3Bg+UPXDNRpDXVaAzVMpGgCNga0KlOhUHymKElS9EfvkyQ9NglY3WAAQE3eloLkMsDaHWD9Phv8YwcVJO/+AHqCRUQJCIpa6hp2n+/0vbibtV3Qch10KVoTSCZI+gT3moePuc9XP1FKZNgIlXKQQab61KNKip8ByYbqDG2zYpz22PFMxRGuoe9+ojSUHEPP6SDbixdsT5bsbKGvHCV0sW2OqdqPKBSvNConSdEf3QDadaCVBBqs+GGyybV9yAwBTTZlsbuxieGJ8UCU8uNMjmS4okSKJcDuAHqQG8QLksBRKjcTDXSX5alQzZ3emjoFFDlhGU4Du6Hl6p4ouTlZpBgVOl6UNv94O/K4IEj8S5oZlW5/YeLyiPiihulSNEm76RXFFFfXkfH/n703+5Uky+/7vifW3DPvvtbaVV1dW3fPDDnD2UjJJA0/2SZs/wPTA4OAIM1QzbFoijQMSAZEyLBfbWB6oCdDsA3DL5JlmqIgDobkqNnTPV3dXV3Vta93zby5Z8Z2/HBiOSfiRN4tq2vh+RQO6mZmRGTkUhWf+zu/8/slkkDOJlE/euezjBxJn3cnVa38OaTfFXUbBUkBSDLsghbl05utMauT1TCSPCY3YBHGtBwBWUEqm2Hy+DOoMK5QKJ4PSpIUk0lXYwaAdhOoJ6uJeFGKCQKgvcHyiNJTPgDoMBKtMPnWD6e7tpug28kqonQUie18+PwP6nqAbQHjfVZazdazogSAdntANUf4AETBjMX/clW4++ldsbLzs4giUd0ErBKIVUJUk7rQ62FYSS3f1035arZQlng5kkFOvCb07Ita06SjSBHPIooUUA+668AzDNz6fBNXz+Wf88jr5IoSgFiWIjmSYWo2KibLP3K4nnC5ghTy1qXTk1+IQqF4KVDTbV8SL3X/NiuUgzKXn9NOLYcuzQAzJ5gcBSmJIQYbIYkgidDbYid6srqK4JcfIPjlB6B7j4HOFvCEbfOj3//h0V4LAGJxU4htSaFDTmRouw3KbUNbm6CtcHXcd77J7sxxNv8eW9lGxx7o2AM5vcZyt3QdsEzAKgKuC1KVd5I/HFH0IgnbFHuSlYGp6VPaaXGjCdpJPlfDCROv3TEbadpPQQpltsQ/HGM4cOaX4MwvQe80oXeaSQ2nKRAcclXoKG+5f7EOAg2fNOXTwwAw9LsY+kmyu6UVYWlFFLUKCDQQ9d+nQvHKo/6VKw7E9TvhUva0KNmVZAAT83hopw/UljLRIXr7rlSQpsnv/Xd/Ih4/T5R299igFMG1bG5RBG1tgna29xWkCO3ChFIIO8dbBUacnMRqyEWJ7mwAhg3aka/sEmRJJkdATn2l5L8TWz9+3thB8Kz9C4uOvE4iS8U6GxyP+yYe98Xj8HLEo6cqaRNo6Lm7IM6Qjb3H8DUCYljS/RUKxcuFkiTFRN79g/8B8EYs72W7xYaGZAwkvcw0cUVTOhoDsGk0snQahVNngHIyjbVctTKCRE6J0zbUzOlBdhDs5OJ169OHoLt7bNy5D+xlL4z0wQbog1RydpTgDQAmSQaYHO0rSJKmvtOF4vZmMq1Y7PVBNx/GI4LMTpgO0zTQnrxP3X6CJJAzFXdcDC+JTk0SpScDLx4jc/L7/rhvstpSRL5dWpAAoOty06mDfYpNKhSKlw6Vk6TYH6sMOFxEwg/EC31OAjBKdYCOAch7dzl8D7RQlMj6KdD77EJOzIIoSHxUJFXA8iCQcgUABU6y8gUEAHpPxY10XXps+mADePPc5OPXCtC5vCX/i83JgtQ//tJ/dkwjyR3b5V7PIleFe68DNCT5OaEo0WYogvs04KW//BDkypXUveI+6SgSlU37HREjR8Y8yxSiPyvGSdzqZJPoRx7bpmCIydsnKuI0XtFg0aahx767+woSf44TmgQrFIqXCyVJioNTKwOd8MnXeFgAACAASURBVIKXFiWAyZJkZRpZZJEhusXaPTgFeSSIrKeEolgDMbgpobD3Ghkfr2Gq97OPYHz7bXasM+ugd1MFBzlRIq8n50QMdqGkXqrCeFH+evRvXQS9mTr2Y+7CGtbn0arTm5oZz83B3g0jWe4Y4KNue6GUSWQJD56CvHkBdJwzvcYly9NPPkl+brLvA7lwAUBKkJ5BFMmDD88wwpV7FGOfL9IpFng8V5uXihKQyFJajtIUm9vi2oUaW92YESQVRVIoXkmUJCkOR41bMRU1hm0kK4zoL/4SAEC++o3MruTUJcD3YbkBnNTUR1qQMmUAwj5vyBGSg8OmxfYTpfG/v4MetVB9PZtLFMkSkC9IWJjN3icpOonb+9cr2o9A10F7bZCKZOVcWpSAWJboRx9mNic221aQpbzVhGdWsXwG2PjgC9AbYf7WLFdbaTdsKHuAvKGj4gQUlsY+0yItYEiY0dx5+ABnT5zEuRpbgcfL0jhIxGrRZxGlpi7JC9uStBfpbMKp1FHVxc93pxGK895D+IGHtjvFqvAKheK5oXKSFPtDA8AsAobBxuo5oFpjI6ebPP3Fz5MbxQYbHJYbxCMTQUpjZgv2XT2frVd0WLyffcR+qC6BvPk1jP/8i3hEdP+FvK0KqstAdRnk7Il4xBxUkEK0KUvEeC7VMoNLvqZ7Azbu3wZmJBGlEGLbIGe+AtqVTynhTDaxnlx9O7nRD1f29fOTyo8DgbwWUZHKizueq81jHAwEQeKZ9SuY9cPPaOuWXJAAOBIRdcAJZSPb206hULy8KElS7E94kSXzOReA7jYbaTpboLti0rPB93mL9vM9YZDFc0KvuJgwakOdnM71+/DxR3cA34Hx9Qvwv3sV/nevAtVEtuzfvgD7ty9kX96/+JtElkI5kkHOnmBtOFqp6cA8QZpCFAkANJcladNe8rzjuTnW8DYanSHonkQQZKK0eomNENrbjQeAAwvSs+Kzj+8Lt50glU8UitLI7wnD1AoTK2QPLIKCXkW7XpY+vq8gKRSKVw413abYl48/fYA33z4v3lldzLYgCUWJnBP7utFHn7P7198QtssQJX5HElSohRf+8OK/HeYnjRyQmvxCdmi6m4IoAUyWxv9fsvy/eJYlY1PPB1qPQWbWMofxfvYp+2E9jOKEokQufE3YjlxkJRTo9Wvs9lwZ6MjrRh0G0mCtOGyHq0lkWEm7GABEI6CBJAcnEiVJTz3hOawCyDkx6kd9VxQkGcb0erfpxIRHXRgaE+YZK8l7iqbaIkqdNga1rNiYWgFukGw7sLJRKV6UyKh/KEFSU20KxauDiiQ9J955553nfQqHQxZNqi7i8f3HQjHBtCDx0Eefs+k52RLr9Mo4APTuNZxZYRendKPZI2Oy49h85e3uZmYz+7cvoHi2FAuScF6tx6AtVqXZ+9mniSCl6J07i/JsIi30Ojd1Vy4wQZoGhMSNar1C6n1K1eshGgHRCHa/YBE+sn4yHvT9Pwd9/8/lT2HlRGCebINevxYPOEPALLFRrYAsz4MYx80jOxhDLyubpY48yd/UCrC1klSQeDSiY1guwqeOMFQESaH424GSpOfA9773Pbz33nvP+zQODiGAboKOeqCjHtB8yNpT9HaY8NQmt7MAAHLqCsgpbuk40ZKRI0hSijYuXThiPlLqepgRJdcThvH2SRhvn0QefrkG49uXYXz7snB/79xZ9M6JzWEFQeIJIx1/8O7RK4in2VeUvvkdaN/+Lsi6/LXxskSsglSQ6MMnoA+fiMf9yreTx3eepHeZKqNouX+qHc5BREkL/wBAyWygZGa/fxrRoREWAStVxZyx4siDQWxhRPI09nso6sCMrhrfKhSvAmq6TXFg+rqDsm/h9raP1xaGrK1GRG0F6DzN7COIURo/lJTURYreF1ddRVGkC994Azc+FitzHwqigbaaQDAHjMPpFpJMo5ALb7Hnv/ELYbdIlPp/+kuU/+O3EKxfzBw6EiWiNQCI/eHSgkQffCHchjGlf4ajDgA2FZgRpVoR5LI49QcrjGQ52TpG5M1vAOM+670HCK1F0nIEiIIk4O7TK28aDFqsLU7I0BuiaIiv36MOTK0AP5CfDy9KYzd/+rM4ml6LFYVC8eKjIklfIi9t/zaig4YJ132du8g4qYtJGFGiDx/HY/Qf/kJ+TD/n4tkMk5k1AmgEZOkEizSVGqxGTWMWKBVBj1BMUgrNHodc+Gr2vtcuwfwv/hPgaU4+FQCyehn15TVUfSsZex2AUjaQEiQ+X+Y4Vbg1HTBswLDRcfrxQLeVDAD00w/k+1tc/s2b32CClHkOg43aEsjlrwgPpQXpWUaRfvd3/+G+24x8gtZ4hE61GI8IXcuvSfWwdw8Pe/dQ1RvxiCiOvH0FyQsFrAAVRVIoXhWUJD0HfvKTnzzvU5geoSjRx9fZGPSBuez0RQS99RFQzFl63hRXe5GF9fzjHP5MWcRG1/DZB18ANnchmyBK5LVLIK+JCc3a+38B7f1E/sjqZZBVccoNANBNFTKkFP6pc8m4dg/+tXugpWP2OQt80DH7HGZtrvzAfHY6LU+UaGsHg7feyn8OGgj1ksjlrzBZskzQu9fi6VFBkKIo0j5VvA+LAR0FrczOp1BjI/Ax8glGfjKn6gXyVZC6ZgmyFMmRjKreACEaNvXJyfVeToRKoVC83KjptimRFyHaT4jeeeedQ+Unff/738ePf/zjQ53bsRkPMfIHKHtR/R0fsJILO330aZyM3eyOMVu1mSjtJr2/6K2PxGNW5pOfn2aXwmcEKUr+TUevjoNElOiDz+K7mldOAehgjsqlTvcC5MYW0oIEgKyvA9vsYmqEVbv1y2vA/Sm1JwmZtWfRHIcVoOdPAjsP8MTXsKozyaGffgBy+Wugm/cz+/a5FWLlqOZQTjFJ+sUn4u271wCLi9S4X8LU1KgTl4uYL1SxMxL773mBK20T0nPZ+1Py85Pne4H4ufCiNAr71xV0sb2JQqF4tVCSNCXSMvS9730vV5DSQhWtdDuILH3pggQgjts4A8AKV3s1VoE9LmpAg+yqtbkGyMkrWUHikax8Gq6to+RM3uZIBD7QCyNfd1l/ODpOprxoWZeuvNsloiiRr3xHeJw+SVa3kdXL+YIUYnhTmipMEUWTMoSiJGz76QdA2ZIXvozo7QDlWfZ3SE0boBOUMoIEhO1ntm6yn61n/F8L0aTylidKEZEc8SwX2UKAjWGyyjEtSBE32+y7uAZ2zCiB3NaZbA29NgJNg+aq1W8KxauAmm6bMt/73vf2zTv6yU9+EgsR//c777wjjAj+5+9///vCsb7//e9n7ps27/7Rn8DQDPzi/i4TpYhGqqggPx1z8grISZa0TU5fikdMmEOTZjTL2pEMLDaCYhVe+Ae9HVZCQDfEpPEDk13u7T5ILoaPP76fmVaK2CUdoLOVESQeSimCx5+AtveEwQuS9KxGx6yro+li1Ka3jVnXB3YfJoPkLHXfbrLBUe72UO5ybToq88lANoIEJP35MpSYVP/jP/xHB389h2UkCs18IYnuzOqL8TDb+flkAJOlE6QKtOQ5VZEg5RE1w1UoFK8OSpKmCB89OkzeUSRI7733XjyA/WspRVNvP/7xj5+5KOUSihKpL7BRmwOZz7lgggkTH5ngiQQpIm8qgxbZSqbf++E/OPTpkhUWNSBmUuCQF6XkSQLgEzYdNdceYa7NpqHoxg3QjRvZzWlOlhTRhGrihmazaJxVgv7Wa4BRANE0vHkx/z3bj7/3D/4QsAwmSp2kwjldOSduuLLAhoztZlaO0phFYOYEyMkz8QCygvSlRJH4vwFg1BFa3azqZczq2WbLMwMfM4NsJM8cDWByslpp9+IB7C9IPCqKpFC8OihJmjKTptkOAy9Kk6bhIjl61tNw+v0nMKLl4M0dVsk5GkPJRWHQzHRGp/c+A70X5vzsPU4GREFalVzIjd6ecNvvdTPbHJZcUWq22dhtYfbDrBABoixJBSmqAcVTrOPMSli9+/HN4538JIJEAjKiBAiiRPujeHgzy/HIYMojd2RtLenNV2zg7FuXQT2fVSev1gHdBAk8YFqrEQFQUDjBEE4wBCnPxSODYbIhIZKltBzJqHQH+NZSA99ayl+QoKJICsWricpJmjIyQZJNv0VRouNU3o7E6MsQJW15Hrj/BJgJLzq6waIjIXTjEciyZFpp0ATdfpR/4OoialUAj5Mk4tHckhBF4gUpiiIxJldLzhBeMFk0KclTsS5xxSmb2YsdvXMf5KzYjiNuTdJ6AsJNZVFK5RXFi9m2FgCAJ9naUkcmJ4JDV86BPOUatjouMNcA3c3m50TEojSeIDfpMg6SekvPCpPYcOkY1BmC7Df1apiAJ650exB+jWc0JsfzgSQ5X7Iq71tLDdzvbSPYE9+XgpHsH5hN4TuhUCheXpQkTYlJeUi8OPHbpSNEfNSIT+bOiyY9j+k2utsGmQsv+JNEyU4kh6wmgkGfcCuqqtnpENrpwO50oFVOx/cF1WQl3NC/zo6ZLpZ4AH7ww3+Mf/ev/ld24zU2RWR5qQt7MVzRNRT7gNE77LzJr0hqCPEEAUgtuWDSTicjSFEU6fU5AzefABfrJq63j9a0N/88kos42bkPmGbSEy+6/zwrEUC/yK4u1H/6ZwAAbz5J7CZnwxIBshpXKUEilVkAfWhHK9ZwKA4qSg/kQSUAwA4vSxNKFtzvZfOaKuYctsDymEjetKtCoXgpUZI0ZdKylBdZOshKtoNs8zxykeguF23Rk69QJEP0yX2QM/JK22T1FEB1Yb/4uJ1sbpATJFMhWjhlUzLqAI4ZgdlrA41QXoZjoJjKOSkWElGqc3WMumHOT5VFWoyZVXitJ/nFLTUC2uEaAe91Y1khc3Ps2OUKMJicVLwf125u4+rZWRbFCqWMDNrAgJumPHUFuC9Juj6f1FOK5EgGvfNLNm0WJdzHZRlkggS8ceEEbt5gAvb2W6cP+5JyIf7BSgv0vF3h9toME/jH/Q3Z5gAAt1ACscNptVTdrjxBis9LCZJC8cqhJGlKHCYP6Sc/+Yk0j4WXoryf+Sm1L7UcgF2BtgwgqgzEFSUkb78mtIUAAHqXXYwFWYqiS6OBEIGCbhxYkHjevJLfV+1A7LVxu1HHa15fKkrk1BqQVySQk6U8QaLpvKm9/DwqOpiQMH0cSo2sKAEZWSKzazA8CnzzN0H/6t9mjyN7jd4YsMvieySJwmh2Ufy8j4umg4DAIgWMtPC5PQfUFnv6lY1Z9L3slOJamQkuL0t1S5KHNZt8v+4/yuaQ8YLEo6baFIpXByVJigPx8Yef482vvAFScUGHIwTtAbR6Kdkg1T8rgt79BHhdrORsFErwuGRZ2noEsriW3N56LAiSFKKxpe/TYold8Mhi6sIXNcG1c9pZdDdAwpYtvCxlBCkFmZNfYI8DiYpjDtviFF9alADg1BUQUz7FR775mwCQyFJelMyWFGLUDGAcCl+xDlqoAsjPfTouBSfAyGJidqvTxLmaWPcpT5QAYLE4g9I+03SOz6Jky0XxtXb64n4qiqRQvJqo1W1fMi9t/zYJQZuJzOOPbrM7Bq24Kzu58I14eLYVDx7a2QDtZKc+yInXYY2ceGi9FjDsAMMOCnoFAfUBTYdLD7/Umvo+aLsLBAEbzVYsSADgvX8D3vuSFW1jJxGmHIiug35+F9QZAVYqAYaLIgmCVD5mS5I8hqkE9FIDJPCFgVKdjZBxW5QJ8tVvAJUSUJeUYpAJksF9vpykXXpjcp2oo+Dn1Dq/1ckKUdkQxckNxnAD9t0xtx/Eg8fx+7EgpXGDEXrubjwG3h4GfgcDv4Ou1wSZ4ko+hULxfFGSpDgYlAJhJWNSTNp5RKLEQ+aXMvdFeLYFOH2pHAHIXnz56ZtpTmNUuecpZy/4GVGyLcC2QO88Ar0jX60XfHpLvMMyAcsEmT8Ncu5qPOKGt5SyBOvABykds72FZgDUx/WbT5KCm4/us0KL4aD3PpXvW0qtvBv3kmhQRL2ajP0EiSeYvjD4RCz2WXDE22lR6rrbCKgPP3BjOZIRyVKeHAFMkHg0MsVopkKheOFQkqQ4GKlE67ujANpsBdpsRd7BfvceGxxGtwUj7EhPirV4AGAX3hxBOvN6avm97+PKpRNHehnXbu2w6AhwMFEK5SgNL0vBp7eyggSA2EUQW5yWofevJz970VTeM4omAcBOS3z+SaLkO1k54iCGBWJYQHleHGlByit3MGX4aBIvSmslA0Ovg667ja4rJltr4R8Zo/lVjOZX0XOb8Yhwg9G+guRTdj5UV+KkULwqqJwkxcExC8BMjSssmPQKo3ss8Zo0UvVmdu/B13TYpRLyGCyuAgMuaXrcyyYATyuKpE9YB14uw/ja18X7Pv1r9vdStseZc20DuLYB7eunAQB0K7mopuUIyBGkiGAaic0EAAXabaCeLyqxKM38GtAVJSIos5VdWl/MYSJ5kaJ+E1tBMrW3aMzBNdhnZ/pAoBFowx5oZ3rFFiml0MOctgGXPH21UMZAO9gUrAYNAZhYjSZUiO+5TVh6EXV9Dm1/N3e7SJAUCsWrhYokKQ5OWlzWs6vLIlli2+txcjXdfAC6KeZ99KsV9Ks5UZTudjzotY9YrzariItnF4HuLqiuwxgfobZQEIBElZh1HWjUmIBFo3Vfvt9mkw0wOXKuSXKpFmdBFmeBXnYKkhckgWlFkWhqWqvNSUkqmgTPY6OziTyCcgNBuYEbpfpEQeJZNKafjJ5HUGCRP5Iq3FkKDtY+xHIDFFwAQf53yNKLsPREduv6HOr6HComE+aA+iw/LgWlNL9NjUKheKlQkSTF4XGHSTRp/STwiJMf18PuZzcxd+WidNdIlPonzsiPnZruofce555G7sX7sNRXgDZXdykSpRlxmg9n1zH6vz+EVpM/L1kMi14+2ADdTaIO1NkFyuH71R9mo0gAaFfedf7AxBGyVMTt/JvJ+VltUWKBpJlvSjbIX/95uP/r+JxbAfaGM8zIEZAVJDN0B234jEobcBCigXJNiUuBnRtRstxs8+LCFlftfZF95rwc8fhaVn7GwSCeejOIBaCrcpUUilcEJUmKgxGkLi7uEPDD38JXVgBXjJ6MamzaptBJLT0P2EWm7CUXkb4RXlEPKEi0ka3UfSii3CeiJZKQFiWAydLZ7MqsoJNITiRMsSClqZWBHS4R2PPQ+dd32UO/tQ5AA3bzixseikiUoijG+qnMJqRRy4oSwN4HoiVylMNwbgm4E65mDOte5QnSsyISkCiaJCOKKNXM5LviUQeeAZS8/P/2CqSIPpE/LhMkfoUlEySFQvEqoabbFAfi3T/4p6CjHj67uYFBqYBBqcBylCLmGmykGNUaTJgCGgtSmrJWYyvnohVfmBxBOhaj1MolPoJSX8lsTpbOgCwlUS/yn39FeNz6uxdBVrL7AWCCxNM+flPeiUTtQmbYdNDtDz7NlgKAJG8MYII66oC8cRHkjWwU8FS1hlPV1H7jLujmE4yJIwzoZjLM8HuSrmh+DDTPBeXanXRdHz2PJtO74dgaZqNGADAwJPlDS2+wEeIHbjyA/QVJOL9Q4v6P//1LLPaqUCieCSqSpDgwtFQDGXRQcgIMwgJ+MAuAy636mWsAu3uZfUkoDLSTkhRLEg0gGlDiBKxYAAbJc5Aoqfgoy8sNA/BdXL/bwsX18Ln59hPnzgH9newpLZ0B3bwb3y78Z2KBTCEhPCVHy/NlbNwWo0UsisTvf8zpGS91wZ6ZBVrhtFi6uCRCUZq0ki0SJd/PylEIlVQQtzstxFN+UT6aYYLKygYcEwoKWyuhC3YeAwxQgrhA4FZnA+dq2WrakSjZnBjlPYcXOOhz3/GyOZMRJFkUaWukkrkVipcdJUmK42MWgHClEFl5A1gBeu6utG1DJEtSOQL2r6I9aXXaUUlPtZXDqbOULJGlM6Af/jwrSBx02AMG4gWUnPsqwEmSIEjTmmqLkDWfBZgo2WK+Eh2y1YmkKM+/ob0mLmwm+Uejq8nrzhekkGlWQ5egQUeArCQfRJScIFmV6YJFikxkv1c0pznvwOsIZQSKRg2GlghR3+ugbByz7pVCoXghUJKkOBS0xKIKpaguTRjBIPPnxek35IsSAKDFFWQMG49KL6xc4cqMIKWe70B4PuCOAdMGqvNANxQhWU5SKEv0w58Ld8uiYjQnQZmc+yp7/ClXHoBvnVFhq9tI+fDVwwUoBY1W++lhVGN+GTCS94g6PZB0JXBkZYn25G08Ctd+CQBwJVXCBUHij208A6k1xKm7ebuKnXEibTJRut97iqpRwnxB/l8eL0t5cgQARCVkKxR/q1A5Sc+Jd95553mfwqHxqQuNgjUr9b3sdJc7yuwTtW6YSOsR9FmL7R8eY+W7vy4KUg6/98O/f9DTl1PlEq7rK3h8ixWFpM0nbGzdA9bkFcRJrQxSK8P90/eF+2vzLIoQCRKP9p2/E/8c3H3IfhiwpPf/9g9+dMQXER08jBTtcbWPWmJuF3VcUEe+7J16bn4kCgBZXgBZXmA/N6rxsAcDUVxksusMs/cdEw3J88zbYuSm6bdgajQeETsTpsAGXhttbwcNx0LDyU6fyQSpaMinIhUKxauBiiQ9B176/m2lmbhHGzRDLITojoQIz/ww9PD5c+zvHbEydb8uuci4I8Abg6yeTu57wkVqooiSRMr2JQhYzSVnmJwn36G+sgDafJLdb20JeCzWFaJuIhveT5M6SMZ3Lx5ckL4MWo+BmTXhLuq4gGUBZva/AFJkkSI67MFYmoW32YzlKA11HCCaqjJsDIoWsJ1Elci4D5qzWuzIEIIgrLbdcZOVektBAR5Xn0gjFAHNFiGNRCmKKg08eaHLSJScUhFjncALRIHMEyQ11aZQvDqoSJLiwFz/5Ek8LYHSTPKAJl4E6V/9W8zvDRNB4pk/B8yfQ79ekwuSN84mIXO1f+jGHfaDObl7ey7lKuD0mSiF+D/9a3GbXp8NAM09rrTB2hKwtgTquoIg8RjnF4CNHWDvSTIgCpJAGEWC706nqrgsmgQIESUyf4KNlddA5rNlAuLtihXWOmaiICUMivlL4P0p11Yc+ew70rDESuhG6j3UCIVG5E9uagU8GWxNfB6nlHxPDM2KR51WpDWXYtwx5gvqv1eF4mVHRZKeI7Ipt/fee+85nMkBGUumTKJoTvMB6J1UdGTcz/ZjM9mF1KBirooXuBI5AqSClPP4oQlrAwFMlPTv/pr4eK8PVMLKzjY39fcwzKc6IU7DGeflMoHNG0BPvKBqZ1jvOf/ffMhORRLxOAyBYQI1lv/lL3AlCWzufQ58kMXTmX3J/CnQnXSlce4CPyfuQ3d/mTnGJEGaOqlp3oY1iz0nyaMyCBEiSgBiUVopZfOp9pxIuJIpQ16O0thaGQBbYWm5AaAZ0ENh9EwTcMfw9Ck2Y1YoFM8NJUnPkRdaiHIwmi0mM+4IaA2ACptaeLzVx9rZE3JRAmI5iihbNfSdZKqkHNjo8HWK2k9xIAE6ZPuHH/zwD/Hv/tX/wu2fyEtGlLp9oNsHyVsm/pBNv+XK0VhSF4mTRrorT5A+CnwZH5960KMpru0dYCHJu6Jb93JFCQDgHqCWU2r13kDSHDh+vmlPtXFE0SQZfERppcTksRlMTo6PZInaBciUj8lRCk39F6pQvMqof+GKI3O76eI1dGNRAgBy9oS4USUUiChx1xJ/Q7e9nCmJ+jLQ4uoX9Zsg1bBYZTPMISEEb17J9o87EI5Yr0lfCCMMn38CLByspo93j0kOrYmrrchcZV9BEp779Dxwd5/k9uMgESUAwMwVdvv+58ljYUSIzOSIXwpy8jxKm6m6WPxUaJTQPZhe4rZPKMpGFX0veY+jabealZx3xfRRNmeEfR+G06gnKtnPYiFM/nY8F46fnK+lFw8kSJ7JonYqiqRQvDqoSXPFwdH1JOeFX37fE4WADkcsX6UiudA6w3jkClJ6CbaVLOdumcdPbqFjF9QP4tV099ZSeTn1Khs5ePeasSBJKZdA97rxAJARJFkU6eqJ6a2UynSl307VfJo/geZuWxAk4fxa26Ctbelj8TFOns/eWeDet6ikwmj6lca7bpclSPsOe57200yzWwDou/LSBA97/ViYFuxqLEgyNOjQfB+az03z5QiSQqF4tVCRJMXBSU9tmQWgkfymTofiajPaZMnCZFZcWYWwwavPiZbuusjIESAIksBIviLpUPCVqM+eAe7cFR+vV4VWIjTsVWd88wIAwPurG8Lm5KS8pxz1PThmcgE/s1bD3TKLfOh7YQK454H28pff78twkLlLEKW5WZC59WxyeFRmYZhdKUhOX2U/bHzB/p4/mzx2UEEK0aYYXNEm/G7XdXdRTdXm6rutTERpEPYOPG1ZcOkYJsm2TSno2fwlzffhaRR3U9+/tiv2wwsQgB5yKlihULx4qEiS4uDoBkilzpb8P33MBs9YfpGnzccYdztMjrwcEdAMOP4gHgAEQTpz+WIminTlgrx+0YHwwqgA39vs7JnsdrN1UN+NBYnH+OYFGN+8ANIZ5woSquL0orX7VL7dcdE0kGEXJAhgjUawWk1YrSYwN8tGRN6Fu1iIhYmcvopYkHh27rBRK4PupcokFHIiMc8gipShIkpRV1KXq++2cLY6i4Gnx4LE49Kx0GpEJkgA4El6uM3ayX01U76fQqF4OVGRJMXhiIoNLi0Bm5tsGmnM5feEotTc7WJ2rorOciQPLhZK7GcyEH/r1iXrwykCoMdNEekGGja7GD7VukCpAYoAvj6F5OdhG0ANGHSA5TlAT/3u8DBsHXIi2wOss74K/FerKLthtepb15IH9xEkevMTvF4Abm5lo0DPFEozEaXBKssl6+kDwN/AvJ59rXEkqfMpO0woSu6/+RthM+/qZaDKIjekxOTSreZEBI9B1+0CCM9TIko1kq32fmWmjk9a+VFIUy8Bwg3UCAAAIABJREFU1EEAXyhWCShBUij+NqIiSYoD8+6P/nsAwPWbqX5j6YTk178KnLrCCZIILdVASzXoPpUKkqmnKm3rostfOnOwpOI8rn2xxaaX9rqsce5gxAQpwjDYSPNwIxamzvoqE6QU5NxVkFPnQZ9ug958kHk8gt78RLh9ab1xtBcT8mAzPP9IWItZKXl8m5tOpBSgFIPVE7Eg8ez4G9jxw895/qww1SbQbGP9u9zU23/6m/GPxpCdCzlKI+IJkMBHUbdQ1C2MiRuPqrWAgRfEQ9Z0FmCidGVGbPhbMmZQMsQpuQB+PPYTJHG/CfWTFArFS4WKJCkODx9NAoCdcNn/r35L2KzeZzkx7XLqa3bnI/b3iTe5Y4a9s/YRJDJiF1467QuRrgN+6mJuGICX5PUEX/91dg66hjqANk2t6gIyVcDpzQcg9TKslRVg4bXkgXm+iGED8ByQujzJ+CD8yT//n/DT//cn4p0SUUrXoiKoTexVRp9+Drp6mW3rclOlTUk0hhOkNAXjiMU/98HWyhgHSSRzqbSEzUFSGT0SJY9mp3mvzNRRNivZJHeOqBo3hZizVDUD8CUqVBRJoXg1UZKkODR0NIb3c9aGg1y9HF9iSZNFTuisuCw/kiVsf5R/UKcPq9sGEF6UGtl8o0iQpkLUjsNxgajpa44oBW+L8hdRJyz606Z7uS1SSD27dJze5abk+IxmSYuQw0B9D6RUB8rcNNPsirjNw9sgJ06L5xhe7HlZmtvKCiCNal2NJfWGUoIURZGeBX4qC3w/UQIgRJX6XvLayqjENaXSspTXrqTvMZl1A/Z+lYwiAsra5lBQaETLtDBRKBQvJ2q67Tnx3nvvvZRNbiO0v/P1+GeyJhZbjGQJAFBbigdZOQeyci57sKEkIgMACFjUynfQMscsqqQbgF2GHhDovg9tfPj+bZkGr/xtXQdZOCEMnVjQc6ZusHsf9Wab9ZPTxWXghxKkacInVfezOVv04T3pbho0zGuLUkGKIFFl7nIhHk/PrEEnyWvnBSmeapNWU392LJUSyb7XG+FebwSL6oIgpdGJAZ0YGHjtfQUpovSMImQKheLFQEWSFFOBiRL7bdqfWQOoD9Tlq89iUcqRI1kUKfd59cN/hYmhg5TLwOoJIBIGfgl8P0wYL88L+yWi5AG76TYe0UZMFsj6EtDtCQ8JgsSTt+LvCNBBG0CJiVIjzJnqN4Gy2OOMPrwXR5RIqrI5WX6dbbNxM7kv07aE8XQxiVpFokT7SX4XfQar+WRqGRV75CNIK/rruNEWV7rN6Ox8W352BVwAJnSNfhJR2gunitNyBGQFKYrEqSiSQvHqoCJJisOhGSAFlp8RRZPooM3G7EkmSBFbt9hI02+yEUVfuAiMTJBaBS5nxsjWs5kG9MSF7J39nex2Tz4B3bolLQkAgCVOR8nT1UoyOpsgc4vxEKJIgQsELohVkB/zgFy7mWrWmhdRCpf7052NiflIZPl1NjYlnyFEQYrQvOR9IcUwT6c6vSKZEQZ0GJqFod+GEwzgBAM86m1mtrtQz54jwGQpEqYoOVtGo++hQatoWMtoWJIVfyGT3keFQvHyoiJJikPhmSYMF9DCJN6R3xNSWjWiI6CpC04kSpUJF0vdRG1pGZ0HrHAhXWLRplxBmpBsuy9BABgmrn/8BS6+eT6OJtETF0AeigUi0d8BfSKv9ROJEokkbyzPwyH1OtDiogulBojBlpfTbrjP6PDThpIzSuogRUvii9wqrsBPikdyRKux+CKNHZJEwUpAnFgfcWBBitCn2QA3f5rSDQAz9avfhfocbrR38bMHD/Dtk2G+3Ii9vqA04bxKM5m7GtYyAhrA1Fpwg+QzC2jy2jWiY+D5ExPCFQrFy4GKJCkOxWefPAIQdp0PGdtiPRmNpIr1LV9go1Rj4wCQzVtYev0NlI1GPOjW3XiA6OHQ8Hs/+PvHe1FcInMcUYqPr4PYFRA7ueiPndTFj3qg9z8H3RCb+5J6nQkSTylZ6h8LUkjQF6fnjsqgzInQQJwmIvPLIPPyiEiAAB3SEwQpu5GH+kIDq+ZCPABRkASeQRQpORcm4z4Vn9uVLHyMI0qjXixIE5EIEgAENHvwupVEP0vB8aKBCoXixUJFkhSHJupTNUmU3GAI2toDufiN7AEiUcqZoYiiSDzWKInEkKXXgJ1bgHPEIozFIlxDBwwdGLC8KNJrAuNwhVKdRaxIWYyGELsCOk4usESSeB2JEn2wAXI1X5AEphJFis6RLfsf0D5KJEwcH7QyF30yvwy6k9S7Khvs3GyPnUvHkJxTII+MLBWXsWkk+WWkyj1Xug3KMyJPlPhoz9lqGaOijcIwP4n86eAp5utM/ExXfL1KkBSKv32oSJLiUHhBNmLgBy78wMWt+7cx9nsY+4lI0Os/lx/ILGNsknjE2x9EkHgkF679oN0+zAPsRiWRHWJXEHz0hVSQkh2Z/QXXbsQDlXmhKWo6igQAKE8n6nLvgzvZOwfZxGNSq8VRujQ1rxAPBF6uIPmGKMdkI8lfovfvpjefChSUCbIzQHHogNIAlAaomgHWzWo8JPUfAQCjoo1RUcxtezp4iqcDMcncNY147CdIPANvusUzFQrF80NFkhSHxggArbMNWl/Cw7strJ5KIi6FsY9RKqoUi9KFK4CZLIsvExv9sF/W2CSwxx5In2soW64LgiTAR5GOE60wrGR1mV0Bxj08aY+xGkaTaL8HzDXg//xD8TX1h8nTl8NVTjl90bRvfgvLADZaLhOl5gNAC38/0XWgUMxPBD8Mqc70A9oHEH42RAsb+oplCugDtuKOnJT0aiuxSBjlakcRnX22aTkCREESH5ju72L6aAyYRcAdZh5ro406kgieTVh0Z0yzkbFR0UZnMHn13TAsBdBzxahlWpBkUSTjCCsvFQrFi4X6V6w4FBohmRVmll5KmtKCiZKAF/4W3g5XH6VaXNhjeZSCUIKgmDROdRdWgCB8nvA3ezqN1W4Gl7wbipL/YRIFGf0FWwpvnpfnqdDhGM7rV2DdyC7x176ZKkTZzG9VMhV0E6RUR2k3rPMzVxKSt+mwD1KU1G8KZQnr52M5kkF9H3A9kPf/DPQ3fiu+Py1I6SgSzUlqnwalkYdBIfmvLC1KAJOlSJQKXNJSZ8L/gMOcWkmmZgMj/jEKPywESmkAnwJViUQqFIqXDyVJz5l33nkH77333vM+jSNB2pugYS2ktChh1AY5cxH0i0+zO+4k00F2JacmUip/R2smSdHmVvjbf6EGoIXLV08d7sR1AgQBm3LTwlVmlVmQKOG8ugC9ZsH/6J6wm/tFSxQlTYyQOBeSaEwdQDdVeTwjSLp4IfWn0GmFjnsgdgX+4hr0rcfszmE7I0oAMrJEimXA6bPRyPalQypHh/z7P2N/2yZIjVvtZgLk3CUAgH+LJfojeAb9zMykTlFpJJ5bG22sIak23guY1BRkWd0p8uQICAVJQC39VyheZVROkuJQUACUW+JP2pswNBuGZqNkzrDfsLnfssn5yyDnWe+v0SOujo1ZzA6AydEEQZoGP/yjfxb/5s8nNNNU6QL97dMAgE41maJyv2hBu3I2I0g8dmsHZHGFLXvnh3DwRJDiUgLHvODSCeeEYfbCH0WVogEA42643d4Tsc6SmxPts1PTd3v5Fa2nxj5RmqJRQ9Go4ZP2LnpBOxYkANnXxVE2dJQNHbpmxYMnK0giNIxuqiiSQvHqoCTpBeBlak8S9bkaV2ss14Ro8PkKw+W5uHdY83EiN5EoCUIkwaVOPICsIMVRpJCA+tB2j94YNk2eKAGA/Ttfg/07X2Pb3XoYDx67JSlA+fmHbDzeiAd0PRlB8EwiLf7imnjHsA1SmxUGPGdixe8g8NBemEN7VdJLLyVIEac1JkrBhyyPS6dTXuFm2PA1yqq6G3Y86toMioaY/H6vm1OriJOlSI5k6JqF2pCi5poojtOfUSK19AgLCBQKxYuPkqTnSDTNFvVxi8aLzLvv/jFIEOD251sYV5KEbT/dioFrskqLVdBiFXjz19CtV9CtpwoNukNpEm4Aina1EI9TpxeTBzUDcAbQdlsg6eMdEFk0SYb+9mkU/uvfyX2c3noI67OPcgUpQyPJs8IgTChus4T1H/3+P5x80hMgAEh5BjBMeMEYXjBm+Va+n4y8f/IpUQpqiwhqi8J97dWleMgEiY8iRYL0ZWMFWdnJE6XdRgk9V8dm9qsXU+6L5QKK4wDFcQDPsuBZdjyiaKqh2bD0IqBRUKLESaF42VE5SS8IL2teEg8TpXBKwnMAO5QjCZEoFUfyKxTfMBUAbJ0TITP52l796il8cjvbh2s/9GEPKM0CHTYFeOejTbx2IpnmI6+dBd1+JOxDFk6Cbot5Rc5v/iY73rVfgD5Nticr6wcXpClC+y2Q8gw0YiCgHj7b6uPSYhkY8++zBkByAfccoLKQkSOehsumoDrf+LvC/cH/+X+BrM3LdgEAELuIP/6jP8A/+af/7DAvZyK+Lhc+K9DhaGJEMBKl8lpJuk8kSkthkDMtRxF9yYybF4y5n1XfNoXiVUJJkuLQ0G4rLhg4rlTEqYYwwhPxdMCSh1dK4tRP2WD7B3zbkRFrjDpRkEIunKjixq1sr65DU2rEBSUxfwbYSVZlkYV1qSgBgHNW0ust2ub8W+wHKxVtaciFMYoiIfCPl5U0aam9XZSIEgD4QGVB2HTE1bkqcO99JEhp6PX32d+PWSSNLDVAwxWN+vaGdJ9njRXo6AO4kurdZuo+HD8/dFQ2Ghi5I/TLdkaUlCApFH/7UJKkODRerQaj0wIJVw8ZnR14NS6KYJUy1bAjWTrVOJ1/YN+DRnTQsNkogZ4VJFPyldXluTFHYpIojZIaToWwvtOoJiaZx4KUgiyvA3c/SO4YcHWRVk6wv3fvHf28JUTRpBg7yQWjn3zCfvjt3wLc/Iv7yO/Bg4t5V1I2IJQjHrKUvB/6xRPAtemXPAgQhEVNKUYmUNBZHhI1TOw5SZPfC/VsYVIAbDoMEGTpbDVbULNfTqyo72en7HhB4nHoESvBKxSKFw6Vk6Q4MhZJLiJGJ5WPYyXTGnP2bDy0IIAmS1Lmu9SHaJ6LwmAQj5HfBZk9ATJ7ApgLxaK2kNnvIHz8+QYwCJ8z3S5k/oxwkyysg95LNb4NKXT2UOjsgW7sSgWJLK8zQeJZS5KgycnX2Q9hNO6ty6myAYeA0gBuoQDHH8IkJkxiwtbEFYT00f1EkCJMi40UJbOBksnemx2zHw9gf0GKuHD1JDAn7xU3be7c3kDDEqcKXXhwIc9JsvQiLto1qSBF9L0O+l4nriofjbQgyaJI+62GUygULz5KkhRHwqux395zRckdAURD2ZBP0cSy1G9KBUn3RJHas/NbPfhHXjwV7jhss6rdUfPTUQ+jgiQxeSO7dJwO9kAHezCvnkfw0z+PB4CsHAGxIJ25JHnsmBDfSy7M/JJ/44DTfqEs8XIkpboA6FoykBUk/eIJyQlOu48bwefXH2Hkd4R706IEQBClKvcHAGwngO1kxb3vdTL3AUBNm0FBK8fDNpJop0tHCKgPW8tG3hQKxcuHmm5THJrrHz/Am5fWMfDZ9JNXS/qpjTQ3U3F75PeE3BYA6AXsAkRTbTLQC/YVpHhF+RGjSAKP7wGz4eq24R5QTC72o4KJwkhsFxKJEl2YIBEA6N1PQTtc65LldSGCBHBRJH6/0TOaqjFMwONeS6MK7CXTh0JLk6g6ti1e6OcLp6WHbp5bw7iefEYFJwAeh5JGNCC4A+gm3ryQUzh0Coz8TjztBuSL0izy++NForTry+UIYILEQyf18FMoFC89SpIUR6ZkNDDw9mBQDR633Hlk61JRAiCfauMoVDQMuvnbyEru6P7RGorS3gBYKALN1sFFKXwusswSt+mGOA1H1nNEQNNB+HYf4wHobpivs8nytS5XKD6l+TWkDk2q0jYME+Bfyrd+Hb5dhO7m9I3jWonkCtKZFeF2QRKRmTYUgdBmJE1UOX0c9LMRnajqeboaOhB/PoudJOq0RZKyBmlBSuOG51PUp9OoWKFQPH+UJCmmgkyUAKAMoK4nK4xsjUWOokjSJALqYebsOrAdNsH1+yCjcMpr1BYSqQ9NugI2z3APwCw730f3QAGQ1QXQ7WwdpEiWAIC05K+JrKbapoy5aFG3jwtn5nHjbvbYh4ZSoL0Ds84l0Q/bABpxsUqysgxvRhQb32SfiUyWxnqAuVtboGAJ0eTC1+PH9hWkaLWd08fFUxVc/2KK/dvcAWyT5b2RMGtg7PeS1jLR+ctECUhkaeGEKK8pFmkDLa2LjbGDJrZxuphEL/kokpsjbAqF4uVGSZLi8AR+nJhdMpKoi0E1bD7pYGm1Fl+Y6maNbZ+iorHftr1hG0M7WwBQWJU1CVe+wmhffAdkhitL0GwBryW37Z/+FZCSALIwLxUlej9sDstXbfbCiNMkQeKPMZzC8nFdB2ksgu5tAWQWsMKo1IM7wPrpeDOj9TQjSgCTpUiUxro8IkRv/AcE59+G//pbqANou1vS7SaWI5gyplaAGzBJ6btNlM1Z4fE8UWo1SijoPWDcw4y9lnkcAFqaKOL3htsAgAdd8X6f+75SUJD+HrQv8T1QKBTPBvWvWHF4Ah8+dQBvDHhj3L71CL4G+BpAiJa9IGl63Eg2pv0UaD+F4Q1QHPvxAOSCNPa5KMRI7ENGrALeffcHR3sthABzc2xUUsUQ7z7Nbr4wD7LAtqP3ryWClEbTgFoZtLcD2gvFKi1IXUlk5Vm1t3h0T7hptLKvbeT30NfG0kT6iOD828LturmIuaAGSy8hKFbiMQ7/UE0DNBNeqTo5endIPv58A3Dlwtl3s+c/DvoYB320GqV48LTGj9EaP05ua92MIAHAabOG06Y4nbZaSr7bFWMuvYtCoXiJUZEkxeEJIxSO5sdtIPRBF36JrRZywqkHixTE/TSdydEEhl4HdpBcwMaaly9IURSJBji07wcB4Lu43jJxEU1gNow+VOaBHhctuvtUjCjZYQJ6+ylIYx50TzJNFkhKQpo26J378U1y7gxQY8cixSrIzhCgFLR5/OKLpBEmLTvDJJokwWg9he7uoD+fzaOyO0kuzvjCFSCQ/1dhjLPTTLnRpSmhIRt55KNJABOlOmbh+uL56aQEn+bkYAGg4z7Gto4SGhi4YrPetBwBSpAUilcdFUl6zkR92142dJ+JAN/+QR+Iv3k7fJ6G77Ix7iaDo6n30dSzkRVbr4AC8UB1KR7DcgHDciGzz7FJR5S2dtmydztb+Zs05uOBgEoFSZjWe5ZIpjXhcNWlH91LGtpyjW3LOxMql9sl2GMH2vt/kXloP0GytCkmoafw4YPaZVCNYBwMMA4GCBCgaFTjUenIc6B0YmaqujfCPzxRKYSS2dhXkHhIf096v0KhePlQkqR4JpBw+PCZHMkYd4HAk8oRzAIbHIV+IlabDkuStnS2jUYO2dDD80GbreTcmuEUzbgPjPugv/EfAWWLjQguKjN2k+dzG0twG/JVbTJBIueSYpUk6m1Hj9WQJCagAZvWowFrbmtYQLmQDLPERkhzj7335Z1NUZbsEhsc2vt/EY+hn52KyosgeYXpiuzv/u67wm1bS85zeyhOtc30fcz05asfdWLCC5yMHAnbwIQOM/k+hiMtSLIoEn1WU6cKheJLQ0mS4tC8++4fY0QcfPrZXdh6GcQsAOUZoDwDnRjCNNv27i58XYubkY5rYSJzVAUawMzmbjzYY9mLqkyQIvSj1KoJz4c2W8CJc2z0xKkz+ivfye5nFWNZmiRHAAA/AN15CLrzML5LKkj8cxaOXoTw7/3gj+QPNCQNa015o9fy3l6mpYzA+jIbALqGGw9t5z5m2kPMtFnkio8iGcSCQSxQb7q9zfK8OC1KAARRqvXdeADATrCFnSAreDrk7W5a3hbK5kw8Auqj626j624DvougXIdfqkzLexUKxXNESZLiSBR08QJPUit5zHQ+EljXdof4sRzJCOCzZevc4AWJJ4oi+YQirp59COKVZ73d5E6XXeSf3mVJvFJRGvcA6sP4JNuaAwDrL5fqMUd3HgqCxJ5rzA2PjSkQWOF773LTYZNEybCTEdHdYYNnXd5epDoUIzUNaxnYugds3WOfZ3gu1JaL2TTgo0lAVpTozjYa9zcm5iNFshRHjyS0PFGmus4USjcoFIoXFiVJimPB97CaJEpRzysAcGaW4cyIF9zdpQZ2Jb2/MGgCRiEegaZhodDAQkHc9s0rp7L7TuAH/80/Ee+QiFIE/ZXvYGSbTI7GPeEx45P34yGTIwBAp88Gf8wBd5xymDReKoPkCOGRSYnS41v34lWJ8MYA0UGvfZa/f3cHjl0AefNXpQ+nBYkuJCIYLEpak0wJAg0e9YRoUiRKRZ0NlGdBd7ZBd7aFfVs5rqYRAxoxoGtmPIT9vMkJ6VWNfScpPVpxU4VC8eKhVrcpjo1UlG59BAAwAej6Ofi12cx+zswyNM3AsFIH/Hbm8bgBbcjINrF2qoHH9/egjZhkGOMj1kmKzjddxwgAKiy/pOclz1/BMka1GRQ6LfmBGhXgXrhyb4VrlyJJHpYK0pTQAJbhblfgkDBqwlfdLtaBogYMxSnLSJTI1Uvxfc682F+OzCbSU93ZRRpekATcZ1do0UuVi1gozqLnJJ8bOXUB9H62OXEkSjN7FP0ZedQIAHTNRN9tAZqNGWsFLSdZnclHkSJBUigUrxYqkvSC8NKtcBt3UfA06K4H3fVAx4N4pCNKAGB1ROHRNAMa1y+spNfjgUFTKkjxviMxmnNkaAD4DlCosqHpsSABQI0kbSg2dtjS/FFtBqMa156iUWGD5+k28HQbZGYF5NS5+O6lYCgKEk9pOg1RP/7oi8x9LpXkAhXlrTPotc/gzK9nBEnAcwHdFEZakGRRJG3KskTBEqPTEZ805NQFkFMXMvc37BXQmRXUJH3eIvquKMUz1gpmrBUQz0VNq8eDnx7WNRsEBhzveAKvUCiePyqSpDgWnqnDcH1WPJHry0bOfxX0i18I21qdJjCzBm2Cmhf0KprpZGh3BPhZubh8agE3bj6C7h1zesMPL/oAMNgDSklUoEZm0KHihbKglYBGCWjmLJ0/zVa0VdFFF9VYlGhnE0TjphnHXM2oqNjmcV8LEKdnWdSMo0kudWCSVDHHYi2OKJFf/bX4bmsrqefkLHKRNi8nn8d3Aa52ERn1ERfs7jfZ55e375TQNTOezq1YYjQpPq9TF9Cw5Yn2kSh1HDallpajmF42B2kAbnq2Mg/gYWYbhULxcqIkSTE9JKLE6ADcUvihl0z1FA0W0UgngkeYT+8AxioKehit8RwMQ58p6jWQYh0X39Bx/foRL0xRNMt3cfvhHl470ZCK0lhzmRyl6YUXyEqYjH5aXhOJdkShog8+AxBGpHphzowj5kJNA4smUZa0KNFHd9kPp1fz9w+FySvmFEpMlXcgo/z+bPSIjYhlkH0S9SsWm8Yc6OJ0XK/3BCcq+a93VptH1+8J0pXsfBBBUigUrxJKkl4g0lNu77333nM6k/358PPH+MobTAg8k6sZE4aJnCBZRt5vlJE3mTT0OijRImQVZcynd8Q7uCXkz7rTuhOMMNZSF8lOmLhby07PkPoS6OAu8NltkEuvxfen5QiIBCmkJyYVk9IxCzD6HquTFAkQAPM0f75j0EfZqt50+wHIwsncwwY//8v4Z+0b3wqfa5/o0IT2JsclgA9KA1AEoGERTQ0aNJqEKR9vD1Bf0qAR8b+5hz3WJJmXJcPLfgP5abxgP0Hiz01W1FOhULyUKEl6QXiRhUiGHgCwqzCc8EJpWMCjmwCA4VoVuiZ+tfoeq0Jc5hri9lwWdSiSIjRuOiYjR4AgSDx0fPT8JNpvgegGRsYcCiPWi84pJNNh9nCEcVFSCLGzFYsSWTudPe5nt9ljZ9ZAVi+DPvk0eexBzkqyaUeRdA3w2YXfNQhML1kG5l94i21y45fCLnT7AfuhLo/qxRgmaOdefJOEyedCFIkXpGc81QZnEDdcbrlbmDFFiQ2olxElgMnSSZRg1PKTrrvhtNsTiK9BS91OR5GIM4KW7leoUCheOpQkKY6FZ5mJKK2/HouSH7BpjrQsgVIYxMKeI8/52A22YK5fEZ/jyVNAA4yATbEIUaQol+gI1Y2v3dzCmxfFqRer14ZTSVaD2UOWbCzIkmkDwzbIbP7KNHImmXYjq5fDnyhI66Nko8e7iNdORNEpQ1x1dlSub49xccHOFSWAyRIvSt019l6MbHZOjd3sikPt27+RuY/2mwgwC9jJezQqc7HDOssDCgrTLW9gBQSOlq3YuJ8onURq2jSSmVQEqJuTlzRn62jBhRZ+dhRgDZ8B6K4H4owQmNNr5qtQKJ4fanXbC8DL2L+NajqrswMmSjHrr6P5wbX4ZiRLZb2Osp7IR8OaQcNKVontBlvYDaseu3YytUE//ln8s6dRYNAChntshMeGrMnsQV+Hz44xKiQXNauXlQN7OGJyZNriA8NOMhBGj87I8pJSF/NakkBMd8JpPMmUzqFJN7TVk3/iriHJ46lW0F1bjQWJZ2+ujr059plp3/4NqSCRxXMgi+ewtpgIY8dNphDjIo/tTejuNCNKqdeSqhLeSrVIeTTw8KA/wmp5QoV0TQc0HV23NVGQeFRRbYXi1UZJkuJI0NTl4fZn3NLz9ddRNOvxKO/tAbv3IaNhzYBYRcwXsvWKIkFa22HTb0aPaxyabjZrHaWas/gaBFEKNNDHd8Wx9wR074n8UFYR2luXQaRTN/mCJONH7/7wICcvZ0I+jEZ0+KYB/cnteACArZdh6/KsMYPY6M0vwreL8G1RwMjiucz2UkF6BmhEz0aRnAF0aPHouDt4NPDwaCAmb3tl+XRiP+iiH3ShEyKMiLQgpdHDiukqiqRQvDqo6TbFkQiojzHxYHgU1DRBbSOwMcSFAAAgAElEQVTOOwIAuA0gfbHYvQ/MJTJUpGFUhrrwNVMQpb33/1TYVRAknmNEkdj+XbZyLorilE7HD5GFZdBtSZLz3hOQRhh54SI3hl2ANx4lomTaQGdTTPROCVIcRYpuD7o4SosV4Rh2KDxrrFYRIZqwGoxYJqiTjepEohRgCIPYmccBxKI0WFgCaBdlkggHL0gC7ZxSCVNibJK4maypF+D6ST2m9ZKRkSQgESWjz8RoEibRsVxMJNIN2PF5RdOlLWVUnEmheNlRkqQ4MpYuRhYKegUjvp6R62REyaMuylR+AQaAnssSfoOrSRuMrrsL7HJTevYYcffQOSYrweYd/Oj3fw///H/8n4/yUhIMO55GBJgoAcjIEr1/Ezh1Oj8Xip+Wi1bFDdugt+4l95MCyEIoTbc2AN0CsY4fhSC+C6qb7D0KIyEUNCNKADKyZDse9CJLQh55WXlId7bvU7ZN2dUxYyQyuEc2ks8o7NlG/cltPQ5LgCDOC+KRiRKAjCztjZ8CBlDwbRRyImnS42sFPB2K03FdkiStB9RD2/Ng6JOLXCoUihcfJUmKY0NcF9RkFwSpKGmAN5tfmwZI5ChN1xXbX7jUSS6+YWXvuInqYaEUZC7Mi6rMJ9GklCgBoSy1JUnVfHVxGmRzliKGYp4TWVsD7mdbexwXmq7UybWiT4sSwGTJduSNdQsGi7ZEspQWpIiym5qGaj9FFA1zEL6PhX1WzB2SINWOhBBNOL+0KAFMlvb4Ap4cI1+s7ySTIwDYlaymtPTkeVk9r5xClAqF4qVD5SQpjsQ/+hFrEPvRDVaPh3BJuQW9AnijZJRnYIyTJe791JLwLrooGXWkkQrSNDFSURt+GbdhA9QXR7nIRh5WGdJ/UkNJXzoAZ06FBRrD3mpuGN25enHloK8gF5JTw4iCZlqKgFJBptJUdrag3fgF9JsfZR6TC5Icz55eZEULf78LuApb6XY4VWseFVIXxqy9jlk7v+XKnL6Im20Ln7ezvz8eTJAUCsWrhIokKY4MpQFMzUZgsK+RkMzNtfpotvqYnSnHouSFeS1diNM5vCj1fHF6KyNIkv5w6WTyA72G0Qif/eXf4NLXLrI7PCcpkrgURr86qQt/uQj0ubpGZlqcuHOTXFjJGrf6rZiVQwyP2eMsmuYJ5ez+tTZeO8O1Q1k4AfQlUSxueg4AjI3b2UOHorSy/ho6/ZSIpQQpjiI9M9jnbWpJ6YG0qAwxRBFZsZ2119EcP4pvz+nZAqG8KF02i9iF+FnygsTT9uSROYVC8fKhJElxZPacERpWIW7hQGkg/jbP90QLGZoECEaAZkNWZlsjSWTC1tjFrWjUMdK46ThCEF0gBz4TAc05wgWZaCCNedC9HaC/B5TDhGvdFKtJ11YyokTWT4PuboAsi41dY4ZcojlXVJCcEFfxjcILr2fbIADoYAAU8nO29n1JvW78vgflBrR+eB5cnzYAQDmMYqVliRjwO33Yg2zCerzJOqsoPrKSz7pAbWAmaWrrNJlgjf0BArsAUBemf7yE9DS2XsbY74OAxIJMnDGoJb5/k0RJIwZsLX+6drEYCo8HnCgkSfd+MRGhXacvyNlcmIO1qRK3FYqXHiVJiqkiFSWEcpSiqrEoSjcIRYdkl1gX09Nw3BJ3/8ucLK6tgBSzEQK6kbT/iIVpmF2JR++GJRBmuSgS9z5dObOET+5u4urXXse1T+XlEo7CRFECElnSUlNhq5eSn5+wKuGRHKUppBLxR14HY5/VLSr94haQblg8BdLfpoOIEgA00MhW3+5zKyTLyZTrouTzBgAnVW17rbCA65SVqRhGtcOCZx1FUygUXwZKkhRHpmEVsOewqSG+z5UbjIDSsrCtLGk4oqrV0XHZxbtsJgUmJwkST0mvAcipX3QASCO8MPY5uYkiYHoibqNimJw+zGkhEniggxYIEV9nLEjCk3JRGJdd3I3RMafZACAq7JnXV63IqpXzjV9nrdLk1iHVRZC5M8Agm1uVFqQ8tOHR28dMIq++Ey9KfJRn4O+hYkxoRNvfwet1B3uO3MDTgiRteqxQKF4ZlCQpjowGDQ2rgLbDpmxqroayEU5rjMJ8I25VU/yb/oQ6QP2w0nHBqIIiAJEkQp85u4pb90Qpck0dl66eyGw7EUpBu+E03slwGmzUQVBhojYmYyDIFkUcFYsoDIdwGsuw9jZAFsXnpXwSdLroJSAVpKkStoLRxqF0GQVg417yeOMMUE5d3I1QrnhZ6qZqUJU4aR20pYI08pJIVekXt4THfH16/90ECBBQJs1jfjVlNxFpAqCw+tXMvj2Pva60LH1O2GcxA6BhJXPBe46WkSMgK0gqiqRQvHqo1W2KI/Phx6zKdm2YXED6XirKMkolZ5MyiqTEiixKKDkUJYdfth7ADcasAaznAJ6De4NdOP4Ajj9AibLl+gQEZnC4r/MPfviHoNWwnUY3OU+tx0Rta4MtCx8Hg8y+o2IR9MZnGUGK8JfPwV9mFanJG5fjQZtboLsb8cComwzDgl8oAbY8OnIgfJ+tKATE1Xupt6bUz74mto8JIMgKEsf4x/8S4//t/4nbeESDFyQebdjL5KZNg2iZvjB9NifmfJFhByQ9xRgSydLnhMaCJONkeR5dbzceMoZeVozohBWDCoXi5UBFkl4Qov5t77333vM+lUMRXagmidLA72CWSJa186JULApyFOEF4m/w95zpNknNQ+u14ogSwESphArsjz4Qz6+abGN0mVxFcpQhJYykXAF2Q6HhoizEyRGYo2BYTC4BJkpcsnwsSrPI1IVCOXxdfbHmz/jH/1L6NMOgiwKSVWaVR1vAYtiIuDf938XSy/0F5k5l2uCQYQe0mDRGdn32HW35D3GmsgAAuJuq6l4zsyveACbuBQ8Aks+pYrKIIYEGj27AJAWMA7XKTaF42VGRJMWx8FJF/apGGVWjDGg6BibFwGTiQ3UNVJd/3bzGIkq9HuD02YjuDyY3RC3qVVy49BpAdFx94/SRX4MsmgQwUbI9xKPQH4Ccv5h/IN8FkfVliyJFHKTMTcNNcRoKhXAKyMvJb+I/ArPERuuRfFuAyVJ5Bj6RC9Iw6GKYbuvBN+odMfmiTk4e15TIJGOnIkoAgI2bcP1hLEhpzlQaOFNhKxzzBKlizGTu+//Ze7MYubL0zu9/zl1jXzKTTCZZJIusYm1dpW51a6yRMcYMYMAPA8MvMuB3yYbhgdRqdbdbmvHMaOwXCzbgJxuwp+rRhh8EwwYszAiCPcA0pJZmWu6uldWsKhbXZC6RkZGx3fWc44dz93sj18gqJvv8EgeMLSNuLMnzi+985/ue8VnFLRUKxYuAiiQpzsy0rqOJJgKezzWqMz2XIAwgJ0pht3oigj/DMEgnnrYhv+lno0g1rVzBmdGTbzGn3lzmCHWiRHOjBkwzPcioDkQRgeGco1+niSiJz+4CAPRRvt2GPk+PU0wHIHo+dycnSNnj15fw58h5mlc0zhzXm99JT1t2OQl+Ej3n1lr+ci+Klmg69P/0P0kuDv/5/16WI6BSkJJDO2Vh9CqEEHDYHDUtzQvKiRLnQO8liIN8KYOeISOa+8HiytuW1oAXiY9F06XPowSpKn9OoVBcbNRf9XPGb/3Wb33dh3BsKChEZv1m/miGYbRUdH9bTpZ1lp/4ycEuyMEuWPdydQJ3XAU6wzjYBQIHN4kuByjgzeSYDmX0ab6gAe5xGcvf/+LeA6BZEAVaLS/ktTeTJbYqplbUNy30kkEuXQca/XRoOkA0gGjQuIDGBbh5SFXv4xAnYduZ3KZiUUu6oKP9ZFcOb54KUgXWf/x3YX/0UTIA5AUpQxxFIov63J0Bh81BiQYR/SD0pSBFBJevV/5ez7iSCBMg5ShuTUJouuzr8Rk8PsOKVc49WxRBMohdeblCobh4qEiSYilMMUUTMkIy9OdyW3lETe9g391E3ysn78aiJICcHL1sd/GlO0JLl3V8hnh65DEQdtoebtGkOh4B7aigZHOtOqJUFa3S8tvuYzkqHV+jD/hp5fCp5klBAqKWKCPAm4IGS9wdZTcAN5rMvWl+t10iSlHkKVs5fBJFYFr5Ug7aqLrIpEYMiPuZmlFXb6ZX3o/l6Ywim4EAYALQql5qHubENhYlY/tR7maMcLTNywj54tILdU1+HqbwYGVKCQTmGKtIE+Pd8CnihC+dmtG/KnFbobjoqEiS4vRE9YAaehcNvQuh67iitXBFa8FiGoTVgMjs1Bpa1TlGGjUxbdQwtXVM7XRyiwUJAPo3oyKM2WhELBOH1fg5gg8+OSQfp7kGBG46WJgmQVcQdNZK/eC027cARIIEYL0nr59qGRFqHlK357ToC3aTFSNKRMO406xorRIx2UqEaZEgBW9/K3+X73wHL9+W/dF41PA33q5/XmjFnKSKpOlYlhjhYCQf1dKJAZ3kX7NYkIrsuo9z553Mrj5fl38TTKikbYXiRUBJ0nPIRVpyA/LNbcEyElGxBXpoBYksadSEFn3r7tbThOeprSNodDBnB8mQ91chSBlOk5MEQsGtOnDpJTnsFmDVkxHWKooFRqUIkkNpdhE00wm1yYx0jEaJICXPT1sQKaro83ZS/sHv/iEQehCZhsKwG4BmJSOJYGUqnI/rGsb16iU4culV0EefQIyHEOO0PUzw9rcqBemrgESfLZb5iC0SJSccJwOCY0HLNQBSlkLuo6n1K68/jiApFIoXB7Xc9pwRlwDIitLzWhbgB9//J/jz/+t/xAf3HuGdO9chjHjZyQe0KKJSEKW2KZO1feYAzEHNyFfVXlRBecsZwsy2zmjJ6MvIH4C3+vBDB2dazvFmaX2in/0E+NbfTq4Ka3XoTjk/x6+3QA/rsxYlQweZMgFGEEKf5KNIPIo6xEImgtNHxnLodrJzLqQCeiaxfkrnaPKyAI7rGtpzBnLp1YV3K8ZDTDc2YD6QUTh/VUaNioIUR5HOixaRnzEtEwFyST7SZ9I6HJTrJGm83NYmzkmKoZnvkEU5AvKClEVFkRSKFwclSc8pz6sYnQaPz1CnOpp6+du5E6StLqoEaeyXl2lca7kf26Rn3BGiBAB+u6oNRSyCkYRMdituIwUpi831RJCoK6NIuljynyQLTyRKGjUxawKOJ3PAetZVFJlubOTOm4MnGPe6mCGVjMbcQygiYZkNQQGwqo7Gp0QQCuhRIVHGIKL2MT6bw9Tyz6ljruPALy8VxhGlKQ7fwj8OdmBr+eVIxgNkCwkUo0gaMSCEiiwpFBcdtdymOBMzU4fPA0DTQbiAYxI5tACE0GQr9fyAYxoOMQ3lcg0P0o9evDPpINhOBnC0II38wm4qRvB7v/e7Jzr+sJCbAi8zYf7sJ9CplRvmgkhX/EzAmdy1VqBKkBZBGu2F1x0XsqgmVSGZeErnCB/t5pY+s+x7T7EfCdN0Y6MkSAAw7uVzdxrzNFJmsq9GFLJJ+z4rR/06Zj4BfcJHyWgZK2gZK6XfGQc7GAc7pctj2rSDNu0gECF0YkInJgABjRgYeBOk8qxQKC4qSpIUZ0IvVD6eBvlCfXZFPaNYlpJt2xV4bIa2SXNjkSD5oQNrPAHDKZODQxcY7EDUmhC1JvD2t9Oxfa90c1NrlGTJ2P4Sxna6uyu7zf8oQYqjSMvig3tFeUwfX58McfDZMzQn82QAQIMuFrOub8G4fAP7not9L78T7DBByhJEkbj/6X/4b479PI6FXr2kVxSlUPhoGH1M+AEmvHpZNitLh8mRQqH45UFJ0nNE3JrkIrIXpPkZh4lS01hJxmcHU3x2kBcEmzZh03KxRZ1Y2PMmyZj4NBnW+PStSnRiQBR2pOWiSUClKAGA8fRzGMOtvBxl4LUmeK2JwNCTERoGYNaTQd0pmF0Hs+vQ94fAaOfQHXTHxu7InXZRXzVds6FP0qRr0btW+pUGbedkqetb6PplCYlliV26deghxFGk1995Pbns8fZy2sowEYAJH0z4MqFfcJAwQNvXkmE7cskvWfZLIMCCJss+d2BpdYx8PxmHscfSXm6hUI1tFYoXDSVJijMTR5OqRMljU3hsCgKCjlldYTuWpUVypJP8RH21lu6Eu1KPIjqt6u3aJ4E8vp+eqRKlSJbEo/chHr2fXmfUki30+kzmI/Fa+bkUi2cyEciGtgDo5gN5Ybt6V9WpyRaN7OaXyqpECQDq9z9H92BxIcmOaaBjymTphtFPh94D2uvJYLoGpmuA4KBEx6+8fRvfeOflsz8nAIQQsGj3mkfSRPexmY8m6tHPgnsBIMUoHlV0rPXKyxcJklxqQ6kCvUKhuHioxG3FmbCEAa5RmFGui5fJQRl5z1DT88ttGtHACjVzbrWlBNVEXizGpDxRVwpShE5P32n+7kjgjS6RorT+trzQmwE1An81TV4WTz5YfCdGDeHKBuCUIwqV1cUP4Uc/+iH++I//uxP9ThaxqBxCdwMYbaa3i0RJfP7/lW/rRNXEa3J3XixGRbSwYpnznHu1FfFIAEvI4xubDG0/X85Ah44QxT6DUkg58cBE9Y7CG63qit0hBTo0zWMak1SYuoYJPQiws2ApUKFQXByUJCnOxPufPMLbb9+EqdnwmYtrjTU8maW7u5xwUilKQqRyVMWEjdD08vlKvUXtOlpdOMQH4yHe+ZXDl4BKCA4S11zq9ZPL0IwmQKNiuSUWEJ4eX7iSj9LQzDb+KkHKTspJFCm+vWHhbEm/hd8ttCAJ2ysgew/zj3n9NYhHv6i+O2cff8kb+Fu9FRyEe7mrjhIkLXodtHMKWrOKopHAYlFq6dW5V9kyAkwEC+WIkvLzcFm6ZBxvTFAoFC8GarlNcWYoy+8Qu9bI9z5zwgmcUC5BCJGWTop3BOkkzQmasBEmrJxYOzHlY8zZGHM2xqu3+8mOLIecLYdHTKIyBKw6mmAe7MM8qOjRRgnGnXpJkBLmQ2A+hE7N3Ai5ByE4hOCgmw9Art4BuXoHvHcJXNMXRjVOgs8c/PzjX4ATAU4EMPgSIfcQ8kgI18oySa6/BnL9teT8X/JGMmI6+koyqoShSpByj3FIP7iTIITAXiFgl112A4BdnSHNP5Jjxg4wYwdYBCUUBrXw4bBciV0JkkLxy4eKJCnORDZKYmppY08pSiG6RipMxHUgrOpokE5MCKKhaoNaLEgxB5mJWERVuP1lbzWf7qXRpAjzYB+wALKazasJMA+l1NX1TF7UvGrCLB/jrW+9hS93KmTiNNXDs0fFOQxKQQMf3Ih6iQ03EfYzQrd2C9i9X/pd0unIQpQL3HOeNHbV0gKiAEIeAHrmv5RM/z4OAcpCcHM5zV8p0dC3BPY8AkOrYRbI90CjBuaZNjUPZlu42SjnFMWi1NA60f2VBSgrStf0HpyCXGUFKYu+rGKgCoXia0dJkuLMCE2DRnRQaJiHBwiihqFXtdfkklRmwqdRuwweyVKNpNIUCLn7LWbTzDckBaoFKebm6+t4XNz+fhTRUpSMJrXz0aTpHlBvQbjpjixy+9fkCbdcgHAejtAOdIRhVdHEsvToTjrJukw+Bidnr9YsqiI88WNWiRIAHDwr3fY3oiTov8wsW6WClIcUCie6LFONekGE7ryo62VRAlApS6NgG+u1G3DD6l13L7ek6AUzoKal1eEnZB9NPa2k/mX4SdLLMP5MEaIStxWKi45ablOcjULbkacPxjCojBZ88jjKceHl/BpK9FKuUpGWT3OjPvdwJaTJ8PgMHp/hnTdfQ03X0TTshXWXFvHd7/4hSLMF0l8H71wC71wCJkPAbssRzEG0ip5mdj5pvB3oaAfZKEojHccUpBh6xigSKbwnNMiHhPThJqbBIDdGvcXvxW+YDL/RsjCuWE4ignytgtS35HNtGPndjfWKBr+xLAXcTUaMrbdgFz6PsSAV8YqiSEgiSCaV0k/M6t9VKBQXCxVJUpyZeLcaBwOFFAqD2rlJCFzI5rFFopYZ2YKHlRSiRmGzC+ycoVdbBdmlKew+ANZu4qnTwNXaDETTIFhhLdBuwApCWHBL9wUgEiQAB/nChPow3V2GWht2tBsuhAcIwGP8hHvhyjRDDoCnk3VnBVxPvxM19D5mBemJRam7n5E2Kz/ZJzlNAIgoVx/PCVIGetT7e0KqZHiRKLWN9LNjaY3857KArbdwb/ToZIKkUCheWFQkSXFGFkduDGpDaFoyXDYpRU0SOIfP5/B5mtjb2ojq+FQJUkT8zb3UAX4Z7D7InU0iSixMBwBQU44s5oL2JYXnIj75uPJmt94o90w7CdyW5RREmEoNLSwDNip66QFSlmbNekmQsth6CyA0N4b+HubZyFH2tG7BJT60JckShQYCAgGBkHuYBaMkL4nxMBmX7HIOXFzMtMi90SPcG8klXo1oyYg5SpBUFEmhePFQkqQ4E9//wT+FHsjdWnrIQcP88gqpqB6dE6XAlyNDLEu77mPAytdOqhKk3O+yU1Y99uUEWFyawu4DzPqryfDYDB4WPAY1ZW6PX5G3E1WFzl30ZaazvGB451oflrM4ynFsxGJxPUyUug5JBgDMDDmyVC1LAcDY305Oz1kAS2vKPnacLWz4e15oNC/M9oJ+e7EsZeWoCoeNEXIXWuanSPGzGHIfAVcVuBWKi45ablOcGWZa+PyD+3jlnVswmJxIs4mwJPRLrT+4CDELxmggncAaxMZMSEk48DP1eGxZ22b9zhq2PvxpcnHA0+UvbT5GUDus+exiQt2AzkUaAagDaK6mxzWZY1ZYfolFyUJU62k/X3cI08wSmygnchcFKct3bq/i335xwgT0DCwq7FkVTQIyovTjPwcANAB0//5/CKCizAGkKJlaA3ZYXUwyK0gA0DPThGbMvppt8ZZWg8cWF7C0tQZclsqrnqmLdCPaxfhwmq8BNQ0XvB7hCNOwupTBR8MANvfhMud8opsKheIrRUWSnjMucv+2LMVoAwl92I4Hk9Zy37pnmR9AylFOkGKMfOFJr59W3o4TxQFAJxVJ1sck5DKKdH8PwDQvKY2oEezOXv7YGEVZkDKIRw8Ab5IfzVWQt7+VDFA9GVPLAK93YFcli58AAQ4BLhvA6hY2/RE8GiQDjyuW+Ro9OTJkm/ma+7u5ARwhSBkWLrOeEZr5L8zS0s9VMZoEAANXw589PsgJUpYbzZVEmA4TpNIxnOEzp1Aonm/UVx3FmXGZB9uUEhNE8wX1PdRh4mmQRhKejoC3AfBGuc8aJToceOibcpv20N9KrzymIJ2WTz54gDffuQlAipIe5xdNB5URJZ0WKoWvZOom7clmt+LRg+oH610HJmn9nbmeiSIdsnX/JGiOB5krRhByFzq14Wwx7K3PsJLdlXdzA3iwWb6DRg+mdkSLl0YPjjOGkRGTkiBlokgmlZE4YSyrVYeASWsQ4MlnIOBuIroxdWHj3jwvNn/6SArw37++iiIen2O9XgPhMgqXlaWjBOmjoaqPpFC8aKhI0nPKRYom1bQaXOYhrmpMn32RXHfJ7uOSnU8QprORHKwOSnTQimWJvrmOS6KLId/F0EulIitIWeKlNo6qGkVHcJicTAfwO71kGNSCx6prBQEAmpcOF6QM5yFIR7FXrO90M62ZFNJ0GONBMnJURJsAoLa3k0SvBDi4CCG6VyC6V4C2fM8o0SAOyZc6LTuOLPJoUBstc610/Z16dfPjWJYAKUceLy+hNfUemnoPc3aQq3u0cqm+MILkRst+XFRURlUoFBcKJUnPKe+9997XfQgnxsgGJvfybR2KogRvCjp8ArJADnQvn8A89J4ALIQ1myQjFAEcNoHDJtCIkeu/dVK0+RjaZA+UUHARArVOMuIoSO7w2SwvS95Mjirq/cMFKXtTbQk7o5IdXVJIwsKW96wokUYL5K3XKitvxySyVCFHgBSkLMUin18FsSgBOLYo/b0NA/PwAM4hS4E2qcHOFDwlhIAQAltrgGZ+fDbHnU6AO50AHiMwqYGq+lgKheJioSTpOeUiRZIAGU0CDhelgBPAm8qRgRCayJLuuSVBAoC+nq+WfE+k3+LjHJO4fMB3f+93T/ksMozzuTZVogQA2t4TgC+WAu+td6IT02Qwy4alNZIBuy138VlNMBGAco7wDFEIRgQ0YkZDh0Z0CBFiQ28lAy+9BdLI543p2/ehb1fIktUCrBbG1ElGTFaQruvl941Er8155e2QBf+FLRKlv7dhJCMLFywZQFmOsliFz0I2NnYQKDFSKF4kVE7Sc0pVAve77777NR3N4Xz04UO8/tYV+HGzr8u3k+sIIXiSyU3xujIx1hrlE6DjIoUk09stlqXjCFJ6P6f4/s5DkKjUABnvQrSjCXa8nSwVAako1UdVieXRcQdSIBI5AmCuNuAPougN1XC138DToTyfa2Y7SYXjLAno8gBmgNUA2d+C6MnXT5gWiJ/udBP96yDD8tZ3ffs+0F2RcrQIu4WaS4BsH7vBl7koEinKY+jlev2dBSJ4rngpIKNJt3AlOd8y1wA3HyUyqKw/dVhByba5Bs/3QAsRsaIcAUqQFIoXHSVJzzHFJbfzyOdYJj535M61g2dAJ52srjX6OVECUlmih9SSGdARdGJizOTvtrV+TpDyjz2HgIClnfwjLQq/Q8a7QCdaWhpvA15+qXDY09GfVhdFFKNnIK++DdsXcM3CpEnzx75IkM6TI0XJkEnQ3mwGS4ik/EKWtnmp8r4n3TbEbkY+4qXU0ANCD1zTl9rPjIkQIMBlaiSNc6nvA5kmugcWR8crR5uyyd4xXSsv4zyzFBxwBqtwN4v+GluG+m9VoXhRUH/NijPDRACOwvLQMUQJkGIFlIvxjYKt0m33wx3csdIJ8DOa7tQ6ac+2IsKbImzdgE7kzja/n+bfOBpDjeUFZ9iUfzqxLImKnB7bl8fkmORwQcpgLJDAk5Bsi/dmANq5aBIgRanE2i1gVLHTzU3bjLRb5QaxADAJy5E1ezQAYJZvfE5Q301ECb5bEiUAlbIUcg9fHtmRlJsAACAASURBVFD8O5cWVzgf+nLZeJqp/WVrNdTmaW6XywCLxp9BKYKi+DehUCguHEqSFGcmmxcipSeaoA6eAddkwuz98T4AAo/NZB5OgViWpuFB6Togihpk+NR1k4WWJJ8pEpzbb1470fEzHoA3ZIf3UPjQiYmAu7nyAo4mJ7yiLIn5BMEVY+EfkhNFk4aZQo1NfQU6yYhKNooUtfLQ2emTnwPB0jXHaMs/cSa5qAk52AaK1cm7G9Wi1JU74IbztABm37qKdreJp4NyjSgpSBHh+VadLn4uEgqiBEhZagFwKj5jf73zFAByshTLURFbywv9+5nGxl2ziS0MYUAHf84jvwqF4miUJCmWwtinSX7QrNVAYyYnZJdNsTnLR02qRIlGotPQ8zuoXOxVChIAsJU69GE5t4SeckXn3k8/xp3vvJWcL4oSIGVpY6dcaDC8+hoAQH/6C3m74lJbRFPP9wyjw8xEbNYBjIH5Egovhp4sJDncA/ryMU1iwxeZ10uzqkUJkLLU3cAiht5T1PV2EsGLc41ygpSBazoooUtr1RFWlHqgfuGz4LsAzFxhSQMGDH0V47D6OP965ymuNgJoCxLnDxMkhULx4qF2tynOTBzJmYVSZh7c38asIeXi4N4mNhoGNhr5BOt4Cz0lNBGkIk4ol3riHVoa0bFPp5W3BWQUyGUhyAmX3vSQgYDkokkxAXdRQz037rZWcLdVbpAKSFnSDRNWRV+5owUpe1BLXKoapsthJikU39TyS29bwbYcDQ0Dr7qfWV1vo67nc5UEBFq0C3Q20uHNkkbAi97js8BECJ2Y+OSLAWA1AKuBOfXATTMZiyJNbb1cSPJqI8DVhhT6ht7LDeBoQeqaMvl/cVxRoVBcNJQkKZZCw5Df7GNRApCIUkwsSkywZHw6IsmIccJxIkhZplGz3Mv1EJfrIV5pd0rFKG+/sTi3ZBGBLpfQCAiEEBBCoDEYwtKasLQmiGGCGGVpyYqS4QfJiLFoLRl9K78EmBOkLEuIIn3vd/6hPLFgqasoSn53DRhtYivYLt124D1KBoCSHMW0aT4COEemMCON/psRZ88dOw7ZvoEA4LPqnWxtfRVtfTUnR1VMgn30rcuJHFa9BrEgxQQIVe82heIFQEnSc8hF6992WPHAWcMG40EyyCFb2z8dEUyC6p5ZsSDFtDJRGeLO8K3bNzEPT79zigZSKHgm8qBv5yMpVaLkXb0FQ1vcGsXnTpqcrtWiUYffW0sG66yB1RpgtQbCdg9huwf41Q1Uj4uodQBqgG1cB9u4LqNJVEuG12rDj0bM+mxxonHX2kDfuFyqH9SmveMJ0jkRV8kuRoyqRCkrS77wktE2Gmgb1c2Rqz6PD6e72HU9UIJkPJ3P8XQ+x447Q4AQOjWXupNPoVB8PShJUpyZH/7gj2BSHU5IwQTBOGC4u49kFL9R69RM+6NFuIzCZfLjGAqWjHrLOFKQiph0eYULq0TpVruTjIRaKx3Iy1Ge/MSZjepoY7kspk+rk9dPQxAdA9u4XiqQWRXTWZ+xnCx1rQ10rXJukk1qsPUO5tFPTE6QKh7MIMvp3UYIxScfPs5ddpQoWVod+8E2fFEdYYtlqW00MAn2FwqSQqH45UHFgxVLgQmG4YNtrN+WNXRe73F8up86uEb00iSmUwNvdCl+VlGbEQDahoxQXMo0Zb07mwNIl+J0Uz4GAYWtCRj05K1JfvC9f4p/9af/S/KNQUaTonO9KxDOGMJKjyHuzWVrC4otWnXY01G5TtIhglSEm9XVnk9DwB0YcY5UoUCmKB0VwFeu4xIAo1gYKEYrR9TmmMOktbwQZ57D/JDWH6eFEAo72gAQt6R59nCOKzfS/K6G3kFYKLcQN6pt6OVWJS1DCngQpGLuRrsCjxIkg6rdbArFi4aKJCmWis/SKff1Xn4ZLp1ABbJxjG+tyAEA9yYNtI1eIkhZxkG+4OJb3erk6cOW9BZS7wBhAEo0UKLhYbMD9GSdp6ebQ5CKvmwum8DNTv6cyRFh+yIZGOePvShIcRQpy4/+4Acnfx4RH374AMJa0AeuKqLUvw6+IsehVAgSUK5zZYmMrEaRQIMuLxn9v/jPf7jwurreyZ0vVmWPmYWjRJhaxkoiSEVsasMJJ7hk27hkLxbb5PGi5zkNVJ0kheKioyRJsRR8JqNJ8nQqSr92qQ+D2smYjvyknlGRb60A3+g5+RpCEbEgfeuWnPizgrSof9dpyCYWPwzyS0dVotQ1N4Bnn8pxFONtYLwNh40xDfeTwQVD0OoiaHWhQwOafYiKLe4nfR4kI2zxstvEJpjYBBap4clsnAwAOCwQMvI2EYIjrEiCPo4gxWSPaSmEMko0DsqFSrNUidKHQw0fDjVsOw4+H1cn0u+4AXbcfCTqkm1jzTLABZJxuWagb5noWyYC7oASDTb96hv9KhSK5aIkSbEUxoEGnwHzkGIeUhAQtAy5zbqqT5ZOTFm00WfRbZxkIpfXW9CJhWm4n9uSDyyOIMVLbafuQl+XEYijRKlrbiQDALxe1Ktu+zM5DsFp5IVCJwau34iWeOLnzxbvtDopti9gzxzYMwezWl4Ubjcvl25PRV6WRt4mRl6+wGTIXITMBQFKeVc5QcoQR5ECcn7RlawoFaNJgHytdWokclTk8/GTRJaq5ChGFGoo3Wyl5weuX7y5QqG4wChJUiyVS7U072gapEtIAXdLsvTMGWPXLRdsjMm2JqnrXdT1LkY+lT3BouGxOVw2hcumMLU6FnfUOhyfOUBQTui94bvAbAjU2ungoRyL4CzdSZahSpDOk6QnnZG+vnG5hE8fyshXLEr/9vEX6S/uPQAdPIB+SIuU7I6+aTjENBzK+/bnyfDhw9cBX5eNjpe924tCA/xZEk0qYmp1iMLPZOTjW6uH/7d34O9g1aJYLeRkCcGUICkUv2SoxG3F0hgHclItilIbaYPYSXgAxylHE+pRwb55uF/Ztw0ANueF2knzUXoys/0/OG0kCYA5mwJhgPBgH7dvpb3KJv4OWsXGrrEoxRWd126V7zASJdbqwQTgL+g+HxQiMloYnkkqSCkdO3NIRM+VOrjdvCy36u89KN121XwJADDw5U6yReUO2kbhtTEbQNSKJd3Rdk5b4v0Zxr6MWo79EW6ItFCkHfX3c3l+qTQWpZ8N5Gdlza5eAo5FyWMUIVssSFksTdZMMg8pDaFQKC4GKpKkWArbnz1AxwzhMQKPEXw5QTKaeh8u9+BGLSnckMINyx89S6ujZ11FU++hWWhPclxBOi13P67oWVaILE18mRc1PMgswbljYD7EfLW6+euUepjS9H5MagOCo0masGHJobeT4pqgOmDUctGf00K8zHFm7q/BbbREU4pRPACgf3jStsPGGPs7pcurBamaRflop0XU5LLaej2N0lVF6GxafUzr9QC9Iw5pHCVg65qdjL8ZUDyZackIhY+uBXQtQIQ+RKiiSgrFi4CSJMVSqelyuavY23PVvFK6bSxKllaHpZV3YjX1Ht7fq4FCw7V6RpoygpTFZzNwwSAA/OAH3zvxsftxoq2emWQrRMkJRlKO3Ly4zcUcc5GKSVaO0mMs1BHS0yT1Yj+7N9966SSHX8ndXzwFmA8wHw000OAZ+QoqJvL+9ZwsDfzHSRQpZuzvJKNrFuSwIEjFukjnWWDxuKL0bO4lIybkPkKefz3GAUsEKctfb+dv92onfc51TRbndIWHPac6aqhQKC4OSpIUS8EgRmkx5ShRmoYaBq4BLjh4xRLZX2znJ5lr9R5aBscOdZKRjSLxBU1JjwMBkTlNrTV5gW4A66/KsXI9STRPIiH64qKII7aLEZmUEs5LglTBy6+uwmHlliyngZuREGVlqFhos0qUAPDVmwiwOIG8ubuD5q6MKsX99w78LYQUyeCCweNzeHweVVxfbnSFRO93HE0qEouSy2fJAIA7YvF/eyH3c0uRRQ4TJIVC8eKhJOk55iK1JmFIJ5Y4mgTkRekg2IVBdHBIQSoSy9JfbLslQZK/n68ldG8/FRU92pI1Z2eLVPjckaLUWgOGaQQl+/zSB7VysuTzGfxC7ksofITCh0YM1Io7rg6JIgXMB2fnVJywSpQCH5zSZMR0zHV0omgRI25OjrLsF3bACXC8cktGwmxHRmzIOTS5JRAgEOCCgQuGSzUT8/AgGYtewTuClmSp2Gy5bWjJAI4WpGwUCQCaKuNTobjwKEl6Tnnvvfe+7kM4MZZm4NFEjvtf7OJGM8SNZogvpjs4CPLViut6OeoTck8ORpIRcxxBimGgOKlfcMHgMSk41e1EpCgVZYkbFnitjfq4upVIN7TRDdMlrpreSWUp9IBQPmeN6NCIjmGUR/Tq6+Xt+Sch4D4AUh1NAgDB4RR+isU6s3TMdazWXsbBar90XVGQvkr8KM8tm+s0KTxXizZgLchJuiNoSY6KdKcBaqjh717u5MafP0lFPhakR9MJOBg4VCFJheJFQH3Xec4pRpPefffdr+lIDseL5oRv9Br4aF/Kxp5HsWJx+IMhBqt9rFr5KE8sSiGv7qUFADdbPvbn+ehMVpCyaFRHiwKhWGLey/Ax0M/nBllaA9woH0N3mgrUwKY5OUqoKEh5vhCwrkysnmKCJssIG6NwtPwyZyxK2WRsQ8uXLsiK0sFBWZCyhTDjKFJ63VfTumMS+GgVGhJbtAEvjvTZaWPfeiYHbR6mstudVi83fjJJhf/Pn7hYrwmMp7Lyes9i2Acw9ulX9lwVCsX5oSTpOScbURLFJJ/nDJ/LSaVKlABg4Mnjr1nA69215PeYkN/ItUKy7Svdsjz9fFDDd9bSb+k/xQaa0+qSASeBgMLSGmAilEUuuYdZK+rtFezhipbf3k9HzwAAvFtOSP+r0MCv6G0EcZkiN4pMVQhSdpPfbpwIvoS3WYCAgEBAQONA1DsYU809UpQAwGNTzPwpVmsvV99/lA9U7H/26SxtCGxqHNClqOhUSiU7rL7UCWEk/0LpxMzlgWUjSlvus+T0RmtxQnxchFIbVUfVsoKkUChefJQkKZbCP/7Rf43/81/8z5XX7XkUL2cm4hvN6kgQixqRfvvSHAd+OWfp54N8ROOnu+mE29A6AHaSfllvvn31RMdPKibvxuNNzF6SVbXpVNb84c18aYJYlmBq+KuwujhkYNdQA/DxREYpbplyIq4UpAhbmPlmsWck60FVogQAFi3vMBw4XwJATpaKBRUB4MmCHYfnjUZ1MDDMMhGgRqbv34ztYxLk89tCU75Pul+OFP1sT0aEvrN6M71w8ABAtSCt11JR61lqiU2heNFQOUmKpRFygr/64FPcn05QNzheNq1k0MK3ficcwwkLYqC3YOstAEDHZMkAjiNIeQx6OsF4cG+Ui0Y0HueXk2JZSh+oJkfnCn59ZRXH4b5/gL0GktwVDoaOaaBjGmCCwBamrCh+hqiLyTkICxYWlZxqLrhp50btkC3rA+dLPJw8RFcvP8cqQTKzVma3Tv4EjkncL7BhpBGtrAACQGtBzanQNBJh+tneJBGkEqs3MWIDbNRJMoDFgjT2KS5r6r9WheJFQEWSFEvD1ghcJtC3GIaehiehg2t6KjeUCPBCvpATjtE0Wknn9CKr9DIOyEP86lqaTP1sa4Y7mYDOcCavu/nKCh58voeeRcEO2cZdySFLmY3Hm0A/fUC69xjYuFZ521iUHFa93d2pKKJpVkRwlgVhAVzI18eil3MCKFCugR2LklNLxWJWOOaekSaV/3z+oPSYhwkSO0OZhpPQZDamWip9sShlo0oOk1JkcQNv9Qx8vF+OLE39QeX9r1gM3dVUzO6OtpPTlzWK0NBhHNLWRaFQXAyUJCnOlSpRAoBdN5WeZq2VFPKLZWmVphPx7d4NfLH/EACw7+UnnoZuYYh8rs+d19dx9251V/fFiCRyU4t2KqGXWRYM5gDPTP5BdPxGPsIFAAHhYBkZ0aKdV0cJ0tCLxG4ZW+UJBUIf0E1YWiPZuVfM26kSJSCSpd4qtpxhxbVAGCVn32iu5S7X6nkJeiJk5C3uGWd6y2veSwqB8IbRxSxIo1rFiBLjAeqalshRkbd6Mqr08X6wUI48VpbvXTfNXzIX7KJTKBQXEyVJiqVia3LK7WeWH6aaD9dJJ48N7qNnatj3q8oA+JgyAwf+ALft/NJOlSDFpNv2T5/1zAkBzUaUIslIoDQvSkAqS5ByVAUTPgjR8FYvTfL+eP9ZtSBFTLiTq1d0ViytgXufP8CdV25WilJyHEZ6eR/Aek3uZMvKUojq50mQf3+y0bz6tTbmT5ZTJDOGCwYhOELBse/FOUkEgZEehwa9VNIhLjIZirKwDdw5LteAliklfZKRJSVICsUvH2rhXLE05mFU0DGkmIcUPqfoWnLCutNOhebnj2UtoJ6poWemE9qT2T6ezNKcny/cAb5wB5gTF6v2RnK53estEKQUvqyNgMUeXAVxYd3LYN3LGIbbmGRqQVmGFCBCNBBSXnZ5td2Gx6bJWK+1sF5r4apuY8Id1A6p6H0cpGCJ8vFH6MSEJpAb2SW2Iuu1Pm40rgGzcmSJQDtUkB7PZOQmrmT9D//Rj076dBYSlxuoaWk8jBXkx6TlaB+Qb10ycOcYuOWK6C1zFS1zFddWrmPg5r9TZgUpS2jo2JyF8MnydvIpFIqvByVJiqVjZ3JSdpzUVu60rZwsAXILeeAxOGF1McYrtWZy+tXOZbzauYwVm+HRJB0xGtGw5wIHPgc5YbmE737/jwDIaFKJjGi4nT7c3iqgG2DdcsHHSbCbyFKVHPlsBp/llwctVv1nKBZEbI4DAQGzagAECOMgjMNGDRo1klG1rFfT26jp7dxlFq3ndr7ps1EyinIEVAtSzJu/9sbCZPKTclgF7+OK0rbjYBzMYGrVnxfG/Vw7lYGrY+DqWLM3cNkTyQg4wywcYxaOsTkLcb1ZW3ozX4VC8dWjltsUS+PzT57glTdlQnNRlLL95ftWWKqvAwArlpz09jyek6Msw0J15zc6fXywe/Y6SYCcWGW/sUhiGpeBmhSGYcNDvSAP2lRGVVgzX4W6m/Soy9d5KsoRkBckLVhevk6O0EtaoFAO8KxbEApU9M2r6W0IAPZhSeXuFNSd5i7aLBT+pNFi3pV6A7/AEJvzc3qOkNEkJ1NqfZEoPZ1XL/vFouRHld4X9Zp7o3spd35gp9JnakqMFIoXCSVJiqVBiUDbtNC35E6w2+00qZeDY+ily2LjKNrSNvKJvz1rAz0LGMyfAshHAKoEKUaLojY6PX30JWYaeGgWKmo7AwtYHZdECYhkqZWVozIBd8EKrSoWCVI9TnRfZu3Qo0QpojZIiy6Kay/D5XIJKidLBTGKmdbzgrC0Jc8FUFAYNP8+ZZfdns09+FO3lMv2t17WMPYXR6F6lo5t5wEAYNXayF2nBEmh+OVCLbc9x7z33nsXqsltTJwULAqJrn2rvOQRy5KUo43S9T53MGcHeDjZwsSnmESTW5UgpZDlFWJ08lGHeTjGPFPfaUv3sKV70KmJabiHaZjvMRdwFwEv1x/y2Aw7/CAZU+JgShzMqBc9p+rClMeFCAHNmURLbmUSl5zuJaO2u7gHm8vnGAVbQKO6FtRhgnSlLhOa4yjSN965ccxncXwMaqNh9NEw+iAgeDZPo3hVRR7bJkfbzAt1QzfQ0POv+8DbxMDbBCX6oYKU5XpTvuaiIkKnUCguFiqSpFgaY59i6OWXKAQLQbT0Y9a3armIkq034QsHI18u0xSjMdldWDE+BzYzSbP3J1KcahqHTCM+/eREI+GaBpmlMmecLLvVtKjujzvFVrNaZGJRKm6EA5Bsxc/ihtWT7ZmbpNa60JwRhHUzvSzzXgz8Z1hFIfoR53JlcrNcVlieWknvb7pfFqujIkg6W06tJJ8zyAIGix9wb2sfK+s92VPNK+dPDVwD/QZHTV8spZdsuVic7THosDEaerqbTaf74FENqH2/OtKmUCguHkqSFEvDyMQlc1vMWQhEk/GjqQ9Awzyk6NvlfJeR/6z0+1ksvdCQ1av+CAfs5LkvBrUAwdMKzZQgMKLJk7voa/mltvWoAepW08D+bIJeQwpU67MvAADspdeS206N8EhBOiwR+SwQ34Mwo2UpzgCaysLA9LHqVywTCQHOA/iivHMwJkCQSGXMzVa6fHp/vJtEkQDgSl2DEQS4e8rncRQ+m8PU5Gdqo25jc56P4MWidH9c3jnohPK9zMpSLEel2xakcTezK66hdQFsokcMPIJCobjoqOU2xdKwNIFZQDENZiCguHf/CTgVcogwEqSUYcWWa4/58JgPNyTJAKQcHSZItYomrb///e+e6Pg/+NkvAACmkPd799E2jGMkU69PA6yTPlqffZEIUpGmT9E113OjagNeQ+9DCxkQuGdacvsHv/MH8kRNJsgTPxMZ4/lIzsD0MTCj92b9tWRorA6T1GCS/JJdEP0UGRRylV5upUtzHi+/12dFpwRfjkNky2H6LH2cjbqU3VvtVjK+vVbHK+1y4+QYJwzAOINNq8shHCZICoXixUNJkmKprNr5PCQaSnkZ7TBca1Bca+Q/ckN3jqE7T+SoCovWETCSGwDQtuIK2fIxbF2ga+pomWapGvOxiaI5sSgBqBSlpyKKRrQuywGAvPnrIG/+ev6GglfuHhv7crnQ0gQsTaBprKKh90u3+8EPfv9UTwMAUMgJqxKlkKcD669hEYksLShwWRQkkTHAWJCOI5yn4ctxCJ+7yZiFs2R0TFaZn/ZK2yvJkkkpzMzzG/s7yQDKglRERpGAXiS3WlD9eVYoFBcHtdymOBcmwQQtQy4/0ZCD6+nkE4uSTtOJ1NbkN3eXpUskHbM66bhtUsRlG9tWiD1d3s8oyjnRyekKMS4oVwQgneDjBrwAgNZqSUQAJKLkeiPYFUGLeNKNWbPXgJvAwwcDOGHUVuOMxSQBQEz2QVo9hIYGPWoDM2Np246+dhkH/Hj5M3XI9yIESjvKDhOkKsIlNX8lILB1ATcksLUmXCaPw9R0+IX3RSN6ZT+/V9oemkYXPlu8rKhRE7NwBLMQXTJovKw3REPrpkvECxrqKhSKi4eSJMXS+MMf/jP833/2z5Pzk2ACQCZi05DjwSSdHK/WAL1iroxlySPlSattln+BcYLrN1bw6OFe6brTEPIAHglg0QZMmICZ5tQcBJO8JAEyETozIT+cPk5OrxgtuFaaszOelys0r9lpDk8iSBH0HHKUHBagpqXLeB1D1qM6CMqyNAn3sKqVo1sxIfdhaQ1cbaSv0YPRw+R0TevA43OE8fKeEAhMc2Ez47NyElFq6pkOyRAwNSmBRVnSKo716TwqixBFKw0q4EWCFLc6YSKEWGoNB4VC8XWgltsuABepDAAB8OW9Lex7mtxNpOnJKC46zUOCeWFn18f7Ah/vy8llEjjJyEadYhhPf3dU2LlEyOnq9JBixW03XzF626nYJq/pAHhOkIrYWgttX8uNllGDy6bJMGgNBq0h1AlC+Aj52ZenxES2eQntNCrnVCS1x7I0CfeSAQBDPsCQ55u9htxPGhJnYRXHGwtSh1QXBz0LXKTLrFWYmZ18bjiGG44RsHlFyYjs79RgajVo1DxUkBQKxS8HKpKkOBfinUQDfxerZraoZNnMt+YGXBJWbtEGgCczE204mIdpwrFFAUTiZGsGLtdkhOBDJ4RcFAJe+0b17qRFaCJ9fI/PYMUNS90JYKcRpG1nE5drG0Ahh+r6vowEPeql1cRtrRB5iu9Dy0cseMVSEAC89fZLx38CBT58eIC3b3SS81WilK1Kvek8QcuoXuKMRclEo3I5Mxakl65s4PGzTdS09HHPQ5BiWoYUpW0n3jlI4IT519INy7lEDhujppULg7IoIpRd9o1vVyVIu24awSw2zCVLWDJVKBRfLyqSpDh3Bv5u7jwHMPb1ZMT0LJYr/PdkZuLJrPxtvrVg8vlk/2wFGAUloMiLUoI7wa5jJOOj4W5OnLJc3x/hRusGbFGdm3KYIMXS4oSLd2CdhINGAzqn0EMBPRSoaWZmlCUhjtwVaRmraBlyt1oovGS0WnYpgpQVpCyBKd/LuJ7QWaGEoKbL96uTKQxZDAY2jVU0jXIRTIeNk2RsJvxEkKpu1/ALSe7IC1IWLtjS+tMpFIqvFxVJuiAUl9zefffdr+lIDuezAw1rNY5JQHPCM/B3wWtpxWI32qFmVzQWnYcUn+zLiEbblPcx1jq4SsqVq20tL0balatwNp+VbncSNKKnk5xZB+ZyyeqN3jXc3S804o1FKVqWI9d/JX99mApEUY6AakF69c4GPvjkS9x+Yx1f3D3bcyliUDtXAbyprwBAqVJ4LErX6lcX3pdNZKRNzyRy/81gAMsbYNWS73WHNMGj2kOEEFCioWLldCl0TI6DqCI7ISiVWGgaq5gGg9x5AGg6cwAaRnZZ3lZZWSQBKfTNTD7ZX3mf5q73+OzQjQAKheJioCTpAvDee+8BOHrX0PPGJKAwiEAgpHBca8hv6k9mJpzxDLV2I5ElAHg6S4VH1wnCUGDsy0jBnY4LFBwpK0jFKBLRBBzvNLOUwL27z3DnjSuV0YA3ejJKUpQlz6Dw1zdQHVsCfjwYYr1wd/t+IUk4qYskYGlGqVDjaWgnhTG1ZNt/UZQAKUsu5HPaoOlyYSxxtLCNPhakLH8zSAVk4O3gEhqJIFW1ZjkrcWJ0HE0qUhQll02gUwtNY6Xy9l03vZ8Q1XK0ViuLlCfS59Y2dCxnC4FCoXgeUN91nnMuav+2h5N0wjFIXu5iWYqp6xzjOcOlWjnx907HlYJUwNZMyDRxgl2X4TtrHN9Z4+hZAhoRuPP64mazC1kkofVe6aI3eh14bJaMmIm/i0lmefHHgyF+PBiWfr9WaIZaFVE7K6zwumcrbRuZ7eyEUhBKcWU0yQlSFi5CcBHCpI0jBQkALqF8m/NACAYhGLbmOrbmOpyQoqHXktE0hZtYYQAAIABJREFUanDZBC5LE/DjBPkqnuounuouAruWjJjjCJJCoXixUJKkWCrB44fYdeTHapEoWZqB222Bus5RL1TRvlQLElmqkiNKdVCq48oVGQ3YddPlqi/GZ8sDERAg3gzEmybnY2Y1A9qje7lh4ZDEXGcCUIK/cymNWmwJGYU6XJDk6WVEkQC5FR2aDoQ+EPowaS3Z6XX3wScgheKQYvgYYljepZfd0cZJOoCjBSmOIi3rOSXHKgRYJLYbmYcc+/nPzaKSA1lZiuWoisCu4d/MPNT1Dup6mm+VFaQsHp8h4EtssqxQKL421F+xYum80glhRPPSTwc2WORB/9EGoNN0WWzgytOrdj6C1DE5OqaHz/fTye3llg9Kj/dxLVb9PgncqoN6c+hb0Vb/V29hFkSRoOtvyX8ffZzcPhYlDzLRusXKydqpKFFsjXiuUWqVICXnBAc7cyd5gQ8+/gzvvPUq4MndWW3zMsb+NgDAjaJgttbAqHYdXUd2HItFidUX7xDccp4CAG6185HBsQdEmw0RcDfNu4qf3in66h2HjQawGQX1xr6Ltpm+F7EoFUsXzMN9/I3DAZi40Sonbv9sb1S6rK53MPYPkj5xgHwf/SiBPeAEJiU48FXFbYXioqMiSYpzoWrCAVBZ+2fgGhi4RiRH1VLw8z0bB/4sN6qiSG90a0keU3GX00nY66cRg4ZRKKgYy1KGaTBEQy8vy6Wkf2o6taBTC9++9SZaRjcZOjGgEwOzcAIhOAxOzvQcTrLDys0sGZLLd9JBNJCKCFAsSFlW6BpWqExmdnRgrvNEkCxNhnpkdG45O78S51qwTFqMKAFy59k83E9GlocTEw8nqZhXCZK833w+2kEknAAwXFDGQqFQXExUJEmxVFyGZJnhRsvHHgCNIokmAVKU4ojSJ5l5aq0lfy+ueQQg2a2UZb22AQC4G34OAPAYwRtduWyy5aSVo6ua3h7G937vv8L/8//+b8n5vX4Ht6PTVaK0F5R3nnErk+sSBFj0PSS7bAMAjJ8++rWI0lZ7K418ZKNJ82A/+Zf1myhvlkciStT3sBkOStfHchQjSqVDz4cgWvMT0XPdaMjjHAc0+tfHRqGK9v2JFMJbreq8KS6Ap/NdXKoBO05+Q4ASJIXilwslSYqlIgSBE5YTXON2XbFACSGiZbZybaNtR95mxQ5x4OfzSWJBivEyu+OygnRaCChgt9A1owKIQbo0RlgA7Kf5Oo3uHQAyilSEhiH49mfpBZdl89iiHAF5QTqI7svgS4q2CAFAJE12Z+F+khtkaLXK6tODYBOrRv513pzLekI39S429FSjNsNBSZCK5KNIy4MAaBs1jAMHdb2LeTiCEAw9q4lxkBZ+vDsK8Ea3/DmLZQlRccyXmuXoZ5wf1zQ4tvY8ZLrM5AQpi0mJ7Ct3QklXKBTPH0qSFEslXh6q6U1szR3og03cvCUn0X2/CUBg1UoF4N+7Iiehf/1MTmLZKNK+k893WaeLBSlLXFupqpXJcdGoBcY9BJTD4JloUO+lnCgBQDMTZaKZas/6+h2EW/fkme1fAJoB35BLcnGvsCpBigmoyBW3PCmEUOjUlHk4QiRvDhcsl0TdMlcx8fPRoUEgc7Kc8JDkdN/BBhrYKxxiwN0kB8jSGqBRCYgkF0s/n95tsSgBwI1mHQ+neVECUJKlj4YmfvNVC0M/334mS9NIZSf+zM1Div1MiYlsFEkVklQoXhyUJCnOHVMT8BnB9uY+Lm/0MPDyogQA/9kbNRgGsOstnqweTLeS005IoWV2zD2bp1GkT0dR8cIlHX8sSk+f7uHq1ZWSKFlT+dj7VP67ol0q30mh6KXPHJjEgpZJRt+KImg3mjoCKkAIRcCXU526yGGi1NDTMgA9U95msxil88uFMWeZHJ+Q+6jrHVAhf48RKRqW1gATW6XfPR3yHW4vaKVSFCVAytKeVr5930wrXMXClJWjLPMwv4SajRht1E2MAcxCDidUKZ8KxUVH/RUrlso//i//KDm9Xk8nI7NQB2jgCbzebScjZs1qYc1aVJJRUpx8spPZritFw4xu8nvf/96Jjh9Ii3ZqmUrSAS1MmHYHFqOJIGXZYzvYYzvyjGaUBAkAzEL/s/MotqiBgrIw3QJfSHCOc5YINBBoaJuX0TXXK+9ro9bERq0JzPaOFKSvEiIESOZ51fV8nadXGx3oVOTGb77cxm++nH7m/uSz/Gu/YnWxZjcr27YcJkgKheLFQ0WSFEtHAPjpB1+g/tJLeLNXgxUte1lUoG+mkZEdR06sl2rlXWEOo3i1I2Xps4M0WfY4ghQT73I7CaHwoRE92fa/62rY0GNpY4BTaJZaa5cvAzBnHHO2hXlNiuJtR4pFUY6AvCDdaMrnQMjyvr9QFkJQGXUxqA0zSF8znViyllKGqorc9YGM/oR6Dcjk+8CoVwpSNvcqG0VaJhrR4QkPVvSa3h1JYb3MbTRJKjhPZxquNsoRud98uY0/+TJ976p28WVFaV5o3VIUpI26lNFZ1NzNOK/+KwqF4itDSZJi6cxCikZUJPKTfR+v9Du42pA5IVX5LzvOfiJKj+YzFHm1IyfcP9uWE91aJpm2CpMCXVNg6xRz1M/ff4Bvf/MVWFodHptj8HgXeAmJKI1tgbZbWMirxRPpGHNWfUxf1GpYMSm29jeTyzbsjUpBAgB9InNrDHoGWSIUoAbAA1CiJ9vxfYPmREkjeqUoAUB9kM+/0kNHilKEV7fxxU7+GNf0JjgYKDQwwuGzWKqinKhz2vn2RreZiNJUjI8tSjq1wUj5cxdjRgUnX29fTi7bCzbh2Gku2kdihMcz+bn0VNM2heKFQf01K86Nhp5aSrYvW8vMbzK3dY5xsId/M/CxNS8vTT2YjPBgktas2XUMvNJuoW+zZHzztVV0TIaOydA108fVi605jgFLavukW+Y3wzRXamwLjO30fn1NDpO3UatoL7JiUqyY5T+1B7MtDD0/GXHrDyNe5lsgXKcl23/NN/LHk60O7bBJMtC5IkcBr9OH1+mXLv/mShoV5GCpICUFn5YfXfGEV3n5VOQjfE9nGp7OZLRIp3YyANlcOG4wHGPSWiJIWfaCzdz5OFkcAF5qyM9v12KlJWaFQnHxUJGkC0Dcv+3dd9/9ug/l2MxCijXkRSnLo5mBb/Y1BKJcXDIWpZdqyMlRzN++nK9qrVXsADvtzjaLHr1ENwvlMc+oB6Ni+SwrSjWz+v78QrThan0NY5SXrcQpJC8m4D6GgYN+JrH50ZcDXH9ZSmosSiIqD0BBoYesOuM9EqW5SSAqyhNk5Sjm8bQ6CZ8u6bsZE0GySzDmjW4zd34qxvgPXrqdu2zkb2HVvrnwPgEgYM6JBUmhULx4KElSLB0uAC4Ins3lZPqr0VKbnIoJPt5PJxaDRF3iC7JU1xn+13sOELX9+HevyGjBcQQpve/TxS24YPDYHJZWh6XVsWGn/deyE/yD7S3cvLwePVZelpqGFBGekbV5MCrJESAFKWaZeTt6FB0aBk5uWY9lW3N4c8DMv6YtISNoE5LmHs3NVIz23DQ3Z8VeOVqQzlI2/BBk7W4CAYFZOEJdl8trThjmJJmg/DkYuA8AoCRLX07k67RhIW1HA1lMtChIRbJRJGBxv2SFQnFxUJKkODdaBjAJgD1Xx0qmn9pbvS4+3h/h54/n+OZLckKOZamuV295H/sarqxoeDZPk7irBGnb0cAEwBgBJcD11xf3HjsuMz5Gg6b5LS3dwiTML/EE0ZJPLEdVvD/UsFKIDHUzkaZYkLhgCJsyD8t8vLgkwkmYBDO0DHn/W06A9Vq6rEl8F8Is95xriTrmR5Q0snyGA55/LUb+AVomMImqpdtalPjuyvwmjZ+PPczDcSJKISdHihIgZUmnJraDxdGttilzkVbCvMD+IkxLGYQc+HIiRV8n51MHSqFQfPWonKQLxG//9m9/3YdwLHY/fwRakIG9ws6zt3rpVu33hxreH2romSEsKpLdcN+8LGNPb6+UW3b0LR0O48n4y20D287Z20JwiKSlhsfSSMqM5/NbWnoaOfpi7CTjk/0DfLKfb13xk+0JfrJdlp01Wy4NzcIhZuEQXDBwwTANZPIxPcbS30mYBGly8paTj9yRTJ8zV+PJWMQG7WKDdkuXZ/O4WiaHHUlLHFZhVlnGzsqi4o1hYVmQIF1J1KmZDABYsxnW7LKgx4KU5cDwcWBU9ya8GbXWoUQHOacImkKh+OpQkSTF0hGZdYZWJg87FqXPx3IZw9KAWRgCUURoayKw3pITi0UFns0MvL1Snoz6Vv5j+7NBKixXGwx47TJ+8sHOqY6dEJqLBEhRkjIw42N00ENTk+ebGvDZgQNaMRfGorTvVE+UsSDFhCLA2ksWdh/nozJCP/33mOKOtSJbToBXABBbRkgIgHvOENebZfmJISBHylGMTSnsq8DgaV4+fLKchHSNaKWimPNwjE6m+1wsSp+P0x2VrzQWVxGPRaltlJPVAVTKUVKHSqFQvHAoSbpAXKTEbTck6NsMJhXQiUjSUu6Py33OXml7+HycTlzPMjvhXi20OpuO04/sy1dX8X+8n0Zoilu8G9evAs/yW9iPIo5KPJ1puN6UpxssfcxJ6KFZCPBwgZIovbMi84w+GuSrS6+JvBwBUpBi4ihSlh/9wQ/wx//tf3/8J5Ghb9Yx9Odo6HZ0rAyXa9k/+7KwPJrKnLGsLGWjNWOaFpNs89pCQUo4x+Scoeehb1kQ4MkOypc1ilZm2fPpfAfFoHkY5WUVBSd+NZ7M8vlH1xobRwpSHEWKoRV1lxQKxcVCSdIFomq57XkUJyYIGkZ+Ysy0Dkumq+z0/Erbw6cjOydIWf7FY/lR/dVME9KduQEvcydaFDUIOMGtdgiTCvzNo5NHYkLhA0+28ejaZVxvEkyohxbPSJy3jStWfhmGC+Dt/hpMa/Gf1NbchKXnJfH+GMg2+Z1FxTInYQAgSJb+zkrIPehRaYE6tzCnacRKuBMQu1zl/NF0hFf6K6XLs5BaB9zL7/BatdJE9GmwB4fJqFpgmoDgyXGcJ5NgkBOlml7dJiTkPnRqHvkqz8IhNJL/bFZFkAQ4Rn4I4PAonkKhuBgoSbpAPI9CVIWfyQXJni4GFCiAT0f5HJV5JhhUj76Ix4KUZadQT6lnAtXVck7Gvlc9uVWJEgC8vfFK7nZ+mOb9mHqUKL0g+3nfy0ca3u738RfPdpJlSe2MFZu/+zv/CD/+13+CvikjPUeJkrwCuaRugWzyc/pedqzq9iXa7kMAaZL7JMzrh15RMuGsDD0PNwBcbQS5elxHiVIsPUIIdAwZ4TsoRPJ6C3KomsYKHrJnyfkbLQoBjgM/KppqICneqVAoLi5KkhRLp64L9CyWS5x1oq3vbZPhg7104vn378ilm5/vlWvSMAB/+ihOhE0vrxKkmCB6TPOUglHTBSyaLh89mgrE++Mm1MNLRj4fZ9d9hjW7On/l/b0BvLC85FKUI0AK0vmQ39cVZnai1WPpm6XRrTvdaxgv0M1YmA4XpJRnzm5yOi7auEy4YMkOypi4snvMJBigAblme60uj7uuE7isusJ2LEsBKdfvAqQcFVkJLWxjObsQFQrF84WSJMW5MPYpXrFDDFwdX97fxfqNdHnqnRU3J0oA8M0VJxElqyKVgwvgm6s+/tV9eZu3evJbepUgxVBoCE6xWsXBQUGxXg+j+2XQot16Q89B38oL3a4rIwqxLN09yDeAbWf61VX9wWUFqbgL0DljMMLjM1i0gXpAgVqU4JXZ7j/VPBSzpNpMytNYS2XpwE+X1Nb0fDSoKEfA4YI09+fFm58ag1poGR52HT1Zzl0PBdpGeow900RDz0uoHZVbKMpSM3o1ZpnnHlcNXyRIMXEUKeY8omYKheKrRZUAUJwrq1F9pBtNKQrvfyEnpXdW8g1Uh56G600/WWIr8s3VfNLsx/s6dhwdb6+4yTApTwaFhoErSw2ehB/9/j9JTn86SiNWTKQCNvQcDL28CAGybtNhfclutuTEu1ZjybhUC7Ht7GDb2YEAx42WjxstHys3NtDQz2mSLTTPnbarC1i2mQWPTXOCVMRHAKy+nBuTYICmTpLhsRk8NoPPPMzDcjPg82Ac5KNhs7C8YQBIZakZ/VRhanUwESatS+Kq3FlBytI2yMKyBAqF4mKhJEmxdH74/X9WeXksSjE1HfjxszqGheUnLTO+ueqXBAkA1gqrN9NDigGehUWiBEhZWrevJSPmtY6F1zrpBHqz5SWC1GilB+4v8KkdJx9N+sbbZyuI6fEoWuJk6jdViFIsS56pJwMAbE2HreWPyUcgBalIoSHv5wfVifjLJO51V8x5O0qUQhEgFAF0auJfPsvXtopxwjGcCrEb+9tAYyUZJq1hzW5jzY6qfjN+ZAkGhULx/KMk6YLw3nvvXZhikoBc+vrkk234nKBtsqRQ4ksNH58d2PjsIJWFj4flb+RNk6NpcjybmcmIOY4gDdzTf5MPuQ+Pz3G9GeJ6M0Rf6yXjWuMKvjiwkjFjB5ix6gn22701tI0QQzcvgT4vC9KlWrlS955bnRdzEj768DG0bAXoClEyaC0Z+3TxUlgsS5VyBBwqSC1Pvh91rbyL7rRQooHy/At5VLWBgfcM+/5BruxCzL98dpAMAJVyBOTblQCAz9PX7MtJda6TQqG4mKicJMW58sXYwO12gJGvoRvl5sRRlQeTVI5iUbrWXbxc1TMZ/s6V/DLdjz+VxvRqV0abRLTMEe+q08+w6tGzTPz/7L1ZjCRJeuf399vjvvLOyso6uvqe7uZwhj0cjkRwh1xRoBbYlSg9SHqb0TQlgMMTSw4pcomVhAUkSIuVoIcheh4WkPSgFUBAC2IHvMQZcUl2s3u6u6rPurPyzoz7cg8/9WBu7uZHZFVmRlZ1luxXMFRmZBweHpFpv/jss+/rTCyYwgS6Hx3rT6/U8YOd+EQ5cnsoSCTnp8i0MKFQUVrKS9BEM1a6gBWkZBSptH4Bw40HJ38SAS3RRUEm992b7EIM+rotSldSEQ+6JJaXy5DcPFxpHGu38pdbrdj1f7yYXqbKEiSKNMNK1BYcqJ6MxZwH0yWGlJN8KEzjWw8eJm4yoXuMkpKu7wQAX1oYY8hUJ6e74JJyBHBB4nCedngkifPY6FrxiAqVpYrmh2OtaGGtGF9ee7Fm4MVaOgdoaxhNxHtjJRSkzSF5W2sikbJv/+q3T33sphBfuvnplXgisO2Z6Nr7MN0BmvZ25n0sMQlXmkjGj61cxP1BG/cHbdjeBMt5B8t5Byv57B52J2HskPNJqpunkQQZkpD+vDR2+tA6h0f2o/uZhRq61l5sNE0BVc0JxySnY5LT8cX1JcA2p97XWaJJ6WW/gT3GwCaS86WFcTiSuL6NvbELWdRiNZ5YQWLp2z5sT4DlzabGFYfDeXLwSBLnTMjJHrqWhBpINInubetaElpMBGl1nshAcsmMipKppddPWDl6GK57solKCD4/1LRoqcoUJtjZ2MXKOtnF9uUFEYfGENWMApJUlF6uraIZ1FRKUvent/+YFYqQfa483wmjSRQiShZqg7gQKhKJ1tluJDg/s1Cb8ojx12sxJ2PreId8LD7+cBMvvryGjU9bWH++EUaTbNeIRZMAIkrJiJIs+jDcEVRJhjQlwHVoiInbaGhNhuGuNyAdRZrTC7h30ifF4XA+N/BIEudMKchk/N2hgkNDxqGR7eVFJS4zGwMVGwMiKIYrhkOX0tJUUsll1ZWFVBTJ8Y+/tDN2fJiuD10qQpeKeOvAheGOYLgjdLw2xk4XYyfa8dWdUoByY6ji3c4hHgx9PBhGx133qylBWi1EO8zozqjWJHgu02bvR8CHjzKTqMNGk2ixQ9nxwlHz8kAhW4AUSYci6fiJhWm9ytKCRNHGJBI4Ek+fZ0X5L3/pN4HELjKdOVe2m44+1rR5yKIfDhbXJ4NyaIgpQQKA1iRecLI5iaJ+7G5K1+d/Xjmc8w7/LeacKUNmTrw/TE/2u4fRRLY9EmJylKQX7IJ7pmKFw/GBzkRAZyLAcARoEqmzVFE9VNSTRZHufkL6vdEclDXDiNV1KiCdy9KdOKEsbQxVbAzTz+HB0Ed17OO+3Q8HkC1IRaWBggxcLs5mh1RBllFUNBQVDTm5gpI6j5I6D9s1YQgZHe0TojS22uEAgBHzj8jRwwUpwsd3vvNbM3hWACBAFTVAlOBn/LPcMfJyNRwAyftK5n6xbI8UGE5a3IFHFyQOh/N0wJfbOGfC+x9s47VXVwEQUaLKcH8ogM3mUSUPn3WPbgTaY0oE9MQyKl4f90ORIktDOnMXDd3DCMD2mLy9r70Ybc9/VLxgvhvZbRQUcsTXW3pY34mKUh/k+4oa9Y1bydvYSVQFf2XKx5GO0MfmiCzVrOVzYcHCkdMJr9MzTzn5BpEk33chBE1Xac8yiiFYyPkJsSvUcNse4LKVXWMIACx3hGQ056peijnTgzy535FtQxIEfOELV2Bm9FGbFaQ6VnQAQ4skmhfVeDFIKkoLOSKie+P00iQVJc8XYBgD5GVg7JDnywoSy5xegO2ZyPO/rhzOuYf/GnPOBFUEHE/A8xUSSlqcj0JKquSBnV7+48sa/tU9Ijs3Wx6ebZAJdDlvAxDDOkp1jdzqfiLSpD+k2TpdtpolAz/YAabIUDK6va/ko+c7P04/fmXaitWMocUt793YxOUvrMV+5ngWmvvA3CLJ3SERJRW37XiLjU97JPfm+UoUQbOmtPUoKwswEfU0M6XHkLwsSIDvomeJYfQwKUoAkaWkKM3pLjxfwPZImZqT5CWWbPOyj/sDFRWmkvrFYvSCfuo9meR0Docze7gkcc4U2jmsNZHR0KKlIwlIiRIA/Kt7k0CO0rQnEr66qOJ2sORxaKbfvg09mpRX8w52RR9wj5/TU1F1WJ4ftAnp4WsXaRKwjhv9Hi6VmNwXnzwTRZAgOB58OS5FnVx0TDVDzBSktXyUZEyjSAv66ZfaREjwtWiLvu/Hox+OZ6FnR4+zklvG1VwJd/o7qfuisgTPw1pGke6yshD7nhWkkU1eUzZ6NQssL/58elZ07qeJ0lw13ScQiPKRqCwl5YiSlPTbPQ1lkCXFA4MIUnvig6ckcTjnHy5JnDPBA1ANlqDoVHOUKP2vH0c7qv7ovoZ/dCm+w+qri+nJ1fGAl+pMw1bZx9vBRrL1a0vYuLWHq89fwPs3srfkT0MA0LVMrDKX2Z4JhelBdn/g41JJgNFzkKuQXyPbd9E0ZCylyySF7KgOZDdePHNOd2F4NNdFQEEm+UCWlxaV06BL5eARBLCxvEV9Ffvmbuy6V8srMVHKyZFsGBawOYoMYEHMQU8UicwSJIoIGbo8mwiTLPiwPSIoi7nI3EQx+tP2o2b8vfOLi+SV3Ztkvy8OTRnNoM7W5VI8XytLkChUkDgcztMDlyTOmZCXfdwfClhLXN6ayFgOvqYFH2/2hwDi+SB/dD+afH7pufRy1m6Qb2QV5qCOmsgzk3gjiMB0Jg9Zh3sEGroTNp2dJkrNRBLwvWAiZSdYI6MUgZzYgq+JeSCISOwbJI+mIOdQVE+ek2S6DgRBgO/7aE3aaGgkv0qElBClZeybu/hk+yZeWH0WABGlod3GoTNdaF6o1tHqtmG60RLdWLRiOUlzSmCNkoID9CHMsJhkFoqow0Uk4+tlImkb/fh7bEmLy1J7kv5zeI+RoqVENXJWkFjaEx8ezq5VDofDeXzw3+JzxnlpTWIFS1yuL2BnpGDz7gEGloCBJUCXvVCQKD93Ib3E9lrDx2sNHw+Gw3AAkSBRWEHKYlrT3OnEj63BLHvZnon5nB2OkWPhWiXdHR4gE+xCbiVM/GbJFqTZo0nxX/HWJErCFhE/MYs60deh3Q4HACiiHw7KC9U6XqiS59WoRs+vZw+Qa0Qy0rPS2/BnD3m9HD+SUinj8x+VpSSikC4HwJKT/TCStlKwsFKwcKlk4VolGnf7Foa2iKEtHtHimMPhnDd4JOkc8eabb54bSQIAI5FS81rDxPvBVvpykPTaZ6pw/9wFG+2JBGGK9NzsabjZs7E3jG7zlYUJfrgZRXeKSgcTV0TPkqBJPmwfx560nODhVVHAMHgOA1vEhQLJudke+VgtxJdhrlUauNWL2nW8vhCvUj2nR993zC6Ogo0iAdFOu5NiukQudakMM7Eh66OOBat8wFwioWwZqKjZeTtLOQETJaNkAIggxb5nBSmoeD3rKJKcED3Ht7B5v4u1S1VIkGMRJYCI0oGVXd5yIUck6sAgx5qb8j5UEx8t2UDbasHBDoADQ4Ak8JIAHM55h0eSOGfC7//WH4RfL+ajieq1honrd6LJtKy6aE+kcABAQ3fDQbmZsbTxlYV43lLlFMtSSQoy8N6HuyhO+RixPUrnSF2rNKBKXkqQKKYzgOkMoEml+GCiSPcGHYwdEWNHhOc7YcHH00C3rANkJ6DlGbjRIQMALpfSSVQ9y4hJTkMT0dCiPxc9ez8cI6fzSIJEESBA8Wfz+Wzie5AFFbKgoTOJy1vfnmBku/B9xIYu6tBFfco9EllSRB9Zq4xHCRKHw3n64JGkc0hWNOnNN998AkdyNKTQo4SG7mAx7+CjDpmYFDEeQZIVMvPoGcX7dsYCBC9dv+YoQeoF922f0JlUMXt9bmt0EEaTgEiU1OFh7Hr3Bnvh15dLSwCIICURJ/Ecl4EdiUVe9jAEIIoiTG82v6b7hoTFHBHP5yrAZ73oZ5dLZdwbpLveW64BTZYx7fOULJBzoIvRzw9NAUUlOmYzqHztH25CgITJlPIBs4CIkoWKzTS4Dd4HYiKIRUXJDLbsqxkvOytBSUFKslogQntgkAcqZLyfORzO+YJL0jnk8yhEWawVPLx9fRf//k/MQxT82LJXTXNTidWmLYaidKMdf2uWmAmnVnXQs6SwTk2WIAGA79MCj8c7bgGA5ivQJEAUZJQVYOLc20/YAAAgAElEQVSSGfJOv4kvXpuPXb+Wy2HfyM69mThD+JCgyUVMnKha88MEaVZ4iS3/+4aEq8HXWaIEAKqUXk4bBu1MijJ5XagcJTk04ybi+2cvCq2JgYaWgyRE2/iLioyhHY/CeX5alAAgJxUwwvSGwvQ+R3Z0Y03yyY6+4O22VuyAfnOpRO7rwR44HM45h0sS58xpmXIs+ZmSFCUXwMgWcXNPRiljDqaFAkdBEKlnSehZErZHkWBcCvJKaA2lriVAPkEaTM+zURGjCNZCDjgIHsZxiUTIUnSQizkSuaCytJKLahNRNJlctjUhOTHzIG0yWEF66VoN9+6RnCRRFLE/dqBl9Ks7Dq4vYGALeKkQPR9dIsfyah0wnXgE6bNOBV+oxSN14c96Il6qkBIFyYjQUYLkH27GbuNPidYdFzdxah5FlACElccp7A7FuVx0m+T9A0i9Hj0rihIOAyPXJS9WKZ7D4ZxPeE4S58w4MESsFcik0coo/AgAe4aAvOKlPscPrGgAyOzDxkaNAKCTnU98bH71V383/FpkJtOFRC4zlSWK4Y5QVj2Mpmz9vt3v4nY/Sto+RBcfmwNYnhCOvCzD8QU4voD9cTRZ/9Zv/+aJnw+NwqliLjiO+CRPe5qx3OhouNGJ8sC2R2oqD0uTCtCkApqmC1WKn5wsQaLYvhfbiXYadElEWRFgeyZKSlpKioocO7+WJ2BnbGN7NL2mUdOQ8YW6dCxBmr+Q3RSYw+Gcb3gkiXNm6DIRJXVCIgxfrkbRid1RDV1mR3ZJ8TDIkIutjoKq4mEv6Mb+XIWIw1GCxEaRAEA/YSSm55EDPEqUetYEups+7lv9aDntmVIpJkfRccafQ5VJipGDnVEDe/Y1hdRWGx8CeLkWFYCkojR24sdZUuZQgo9tZC8nbo+iKNRqgeT4TNwRDnpMMck5ct+KoMFu3oN8hnWSskRprUDO5eYo/rjbIzM8ZsprQfkC27HxWiN+X3vto99HbBQJACra9CU8DodzPuCRJM6Z0wnc6JN2fIdaNZGPXVI8lBQPWx0lHEn+YkdGy5TheEI4qipwOVjdOjRlFBQPBcWDgKji0S//yrePdcwk50TA3tjG3tgOd5rR3WYj18coCDU0DTlVUDJ8jpqLgd3EYs7BIrOM8yiCRDlNd3l6y2Q0CQA+7KSTyfNyFV9dKIWD8pViDl8pRrfdHvVjgkRJLsEpYnbBxVnStcizZPOvxk66JhKVJZbtkQlJkPFaQwkFKckPdskQhfgQAFTVEqpqCZ5PljVdn7xafPM/h/N0wCNJnDNj/9YDLF67GLvsk7aGF5hWIlSUtpg2F68tRxPc+7vRxLWdsSnqdh+g2T+Xi4AabEfaDwpOlhUfrfTNHpmG7qJlSrjd1/BMOTrugiSEkkShoiQqRI6yWMw5GNgId5kBQDtrWxWAiStAFU834TpB0U5Z0NCedMLLnymS0FvfbiFZY3FRIMUxLT+dl7Sct3GnbwMQUhG6owRJEbTgOGYbRTITlcw93w0jf2PHRl6Oi89awccEwMViPGfM8qJIGSuSP4h3awEAzLuk/ckGSCJ+exLJphGUW9DEPFzv7CJmHA7n8cAliXNm2B5guUAOJJq0Hlz+SVvDC1ciSXBHAyzly9gbpyeV15ZtWA6wO079CLcTgYzlvAvkF9DaOYhdbnunicWwj3e0KNE6Tyu6j7EjpnapdYOfs0rUMiV83I4EcbVoY3eUjmhMa7Z6HOp6A22TKGPfksKCnoOgemYpkeGuClooShtedLJrOaBjAGZQVf1SScd+uxk2hgWmR5CEGQevZZG0tzkwgWsZPx87NsrqYvw2eSPVYobF8gy8fSCnCm8CkSBxOJz/f8CX2zhnTkEG5nVgawzsm2TsjtLRk6V8XGW6FhkAsJyPRkN3sgUpgI0ijV3hRLvDSAd5IjlsUcvb/WjyFwQBRVmELPqhILHQopDdiRQKEgC4GploW4klt9ViFNK5XLbw1VcbZ1prp5/I66KyxGL5BrbRS11OuVSKRMP1oyFCDofreTBdAwObJEsLEI5dluEoysE5kkUNsqhBFGSooh+OkZ0dS7S9dPK2Lhagi+T10aX4yBKkaVEkAFBE/ueVwznv8EgS58xwvajFB8X2SDFJIBKl5UIkIXMaADjYsqK35tgTkRfJRHhoAoco4fWFaHLqNaNcn6YhQw2kaOyeLvpiuQK2RzLWisDzVSAnkxydQRMox0slYSFHCwlGx01Xlv5qLx5V+drS5KGCxNK3xRNHwn7zV34Hf/qX/xJOEBFio0lZ5JQKhl46yZztbeZ4QkyOjuLdQwfpYgizg23ZYromdCn7uKgoFZR4nz0qSkVx+lFWVPLamomlu4E9CN/LA1sMhbblPI5+dRwO53HAP+qcM85T/zZ2oxGzmz0VRdgYSHBcAQ4jNa8v2OEAAjnK2LXdYTq3s8nT3WBHXT/wjWRu1MO4cSPd38tgqmZXTAEVMy1hCzkHghAJUpKXag46Eyms8dSzJLxQs1FWEA7aokUWJXgAKoqHZ168cKzjZ1FEHa4P7I3b2Bu3YXkCaqqHmupBgoC6lsNCroqFHNmF1rdG6FvZVbE10UdB9o68DuXdQ7YxMDkhQpBKr2ZVdZwBpjt9az8QydLIsWJj7PbCAQD/7goRWypISe4O0knvQFTqQhV9+Dx9m8M59/BIEufM8HygohBB0iTgQj5qGuoh3vKBthBREnPnX+6QT++/eJl8Ov+/7pGkWlaOAEzdXXZSRPjQAsnbHAK4u4/LVxZhOIMwogQQWWrmgGcrq+FlA38nOq6JjK+tOvirbRkv1dIT7qv1IoAi7m1vAAButLIrWc+C7ZEcts6QRBWuF0WsHG8COZFHRCVI1WRoYvaE37dGqGlVKEFxSNsjUUFWkFiSDWdPiywAOVlC7iEv/y2mNtRPNnRUVR1dK707DwDGbg91bQ0VNZ21nSVHWaUrOBzO0wH/7eacKZudaPb69PZ+mLcBkKTbJFSW/nJHCQWJ5RcvG8hJJGmbjiTdiYD5nIdnn1uCCGDxmbVjH7fhiOhZQmal8EOjDbtYCcdaoQTDyZ5w5zQHvo8jBCmCFSRaj0kTfVx5buXYx5+ELkNtj6LXQxLjQuZ48d1sJSWHkpJD05CxnZFMDgA1LV6IUhElFJUKfmJRD4csSsjJIoSgtIEkKHBm2K7EdMm5NRwBhiOgM5lgz1DCsW9mi1lVTTf2rWtrqGvk/XKlbIcDeLggsVEkAJjwMkkczrmHS9I55vO+7PbffucPwq/ZCWOaKG0Mybg9AF5p+HilEY9e/PGDHP74QbyaY10HmtocDgzSNuT9poj53Owm4IFNGvSOHQdD28bQJhPmntFOXTcpSoemHBa2zMlebDyKIEX3KyJ3in5uIzu7xQiQFiXLHYVylGR7pISyVNOqKUECAE2KP6/OZHrS9yxgzwqb5J7M+Sqrbribj6WqllFVyzE5ysL2BDxfWYyN2z0N+2MF+2MFVc1FLRieL8xkNyKHw3ny8OU2zpmyXncyW4farTbcaj38fpCu/QcAoSgl5QgggsSyM0o7f/mEq1d5xQ8n3YEtoaS4cH3EtrnvGW0s5eqx233c2UG1IMLMqMINAFdKZIdUrx81u32/5eBrjejJXB+QM6aKORjO6T/H3P54E6+9eg1rRQc1NQdAwc44elVW4MXaiLhBwUxJSP95WC2QF+qdQ1KT4Uvz+fBnRwmS6brBfQYVrc+ohlBBidrCtEwptjMRQEyUxk50fH+3TZ7/z67Oxa7/WTf7DXR/eHT3WlkUYfE6SRzOuYdL0jnlm9/8Jt58880nfRgPxXQBfW8Hc5fJklGbBjUmAl5cANrMRq56kBLTZgIf11tkovnaQtyiriekKkuQWPLy8ZJonSmBmyxR6tk+HD1auumOPFSZ3eKmK4ZylOT9VnwpaChkFIQ6A9aLeWwMo8cSBDEmSkAkS4AUylGSdw7HeKUOSJDhBEnTsqRnRpDysjq1NtFJkQURkgDYXnQeC4oHmlLeMiWsAygrkRyZTnbSNQD82XYTAPC1RTVTkLLk6Lna0cniHA7n/MIlifNYoN3ZPcTXeOtqXJQAYK0A3O0LGGbMyzQHpMS8c8uqi0+YpZaNDrlONZjjOpaA4Ql7oM3pDka2GFauBsjzyCWk63KpiHuDYfLmAEgpgV2D/Gw5F0VbjhIkturzLBjaJuYBdCwjiCY9XJRuB55TL/o4NGXMZ+RnvVJPXQTLHaOmVcLvW0IfeVlCe0Ju37WGMKZE2k6CIiqwPRudiYTFHHnRL9ci05aEbEEVIcJDXAy/thiJ0Y/PR6/Vu4fDYwsS7eHG4XDOL1ySOI+dLFFSMt6JxSBXmMpS1i6iZJ5JQcYRpQ8fHcdPF1tkc4PeOSARkS8tRJPk5RKZVO8NhqnbUnaNITTRw8g2cKUM3A2KU04TpFLw/FqnSB+UhKh2FEBEibJejJbLaLHHO81SLPJCoflV87qTKUcAUtLBLmmdBbaXLSKaXMTEiaR1FCxbFmQPh+0h5oOcMDE4r6wcZdHQHWhW/LFernsAotvRxG1RALwZJqZzOJwnB5ckzpliuiTyQlNg6sxcxBaaDHaOh9vuWQwX+KPbRCZ+dj0KL2UJEoWNIp2GTz/Zw/MvLIV5Lskk6ncO9Jgo/Z93LQAq/qNrbqwfHUUT45PnlfIEqghs70bP69AkURDXp7WFgFm0TNUlP+ydNmKSzPt9HQu5eNiub5MXIilLednDyBGxM4iO59kKuc+jBIlGkShV9WwkYt+wwmhSUpQAIkuF4PUTmGJWtD5SXqrErn9/kC6sCdBedJFs3gvOR072MES8aCqHwzm/cEk6x9DdbechNykvEVEaWkAxEJhPusALiQ1S7C64rOKRf7ZBwkuOaYPGo/7eBTtTkABSpwkA+tmBnal4voDuRAgTv9mdU2wy9Uv1CQxHCOQozoUCuc3WSEzJEUVNTKSmczbJvjTHyvGJKOlSHqYbRa8ODCUlSgCRpTr8VB86lo5lo2nEJchjBGnlYhnjuyTX58bAQLx73elwfGDkOKAqCRBRuhJ8rckkYjSvx3OhJFGB56eXD6ksAcD+KPu1oM16KfcGvGgkh/O0wiXpnMKK0VGlAD6PApUUJQB4KUgbKVOpsUgidzuxe/3Hl4hFvXU/umxrJKHElPHZD+Y+KiBD+2STmO0DDwYynqOFLpklqzndw2I+koqfWiJG92/34pNx3/ZRVt1YNXE5qBd0lCDFo0jxr47NlPLfWaIEAAhW4KgYHQyA11fI+tp+ovTBfC4tGqIAvN+KRILuMKPb4ouqj3QBhZPheAKqahFda4i2KWE5KJbJ5laVp/S/EwU5U5QoO0xtqJWCnZIjIC5IyTIN4zMSXg6H8/jgkvQU8HkUIUr/7gNcfJ60BMkzAYShBSBPJlSALE3kMgIMdMfbc6XsynzP1uITkz2lgB8Vk5NwvaXilYaFouxhyESR9sdKTJSASJb69tG/Wg8G8RwYISFItFubM4N6O57vxrajO350LnQpH+4EozvYGjUTt/rZOTqLQckD22lm/jzZbSS5BT88hhlGk7JoTwzUtSi3axLIoCblY9cTwzIHR1d+lARAEOLHLAoyrjL1KJvBe4zLEYfz9MBXzc8h56l/GyWv+MgrPp6rusjJQE4GLkfdPXB9awjDJflHLPS6fTs+gKMF6bRRpP/6N38v8/JiIlqwP46iDY4nhGN7pGJ7FBcNWfAzZY2NxvQsKRSkqhY09TWkWO+7k3Ljoz3YHsmXkQQZoiBBFCSsFezUFv9rZQvXyuklxL/eH+Gv90dwfSEcAJGjowQpWVzRndEKFc0vqqpkWW2XqSjenqQbzU6YyJkmFcPRtSR0M5Lt14o21orpZUjaMJiSk4ktTQIZLSsyRPDkbQ7nvMMl6XPKN77xDXzjG9940odxalwAouDj+l4kExeL0eS5ntF83XBJDs1R/bieqXrwfIQDIA11FYmsLtk+GbpEBgD88rd/+VjHvpL3wurd15mK2FSUrpQtXClbKMg+amr2rE9lqaZmt/XIWq6aNaIgoRiUNleZHmwCs4QnIvuPAZUlKkdZFBUP87oYGz8xL+NqSQuH5QmwPAEVzYN0iqheFnf6Bu70IyGaJkqqqEEVNfi+myp8SaGy1LWkTDkCpgsSh8N5+uDLbZ8TqBB973vfi/1/3mGLMl7fU3DpMvn6YtEN9wZRUWLzjxQ4GFjk7VlSAVES4bnZn8yLCkD3amkisHZxHpsPDjGYkMlYl042KY9sEQXFw3qw1CcIPioqqci8MldDz9qPXX9eDyI/ZqQbVY1KkICSEonWfC5d0LDHRDLYKJIiniojCZY7XcQECOgeOKguBNvXg8uTy23LJSIMu0zkrJLR5gMgy3vxByH3mg/qBg1n2BB24gmY110cmhIEASgF+UdsyQNJkFLVw/fGHQDAUr4Wu5zNK+owgTRZTMsRuX4kSJNEhW3H459BOZzzDpekzwmsFH3jG994aiRJEkg17No8mTgfDCMRWKsCHzSjieSZKfUTBxZZnmmbAi4VyeSnFHLQrPhyinZGcxJtSwIAPasZilJFXQwui8tSeyJjrWrDdLPVZs9wMnuxUemwlpYgdcl9zmob+cD2QVWLjSZRVDFKSt4YKlBFP7OtxnKQg1XJqKMETBekJEftlpsFnYmEmhYdy7RWK1SWivmH50hJiT+XtmfCtkgOWlldCEXW8QS0eXdbDuepgH/U+Rzyve9976lZbqNcDOTmg3vxxNlX546eLF0/nr9yfyjg/lDAtYqL1hHdINgo0sgGJs7Jo0kAESVKz4onLVfURXzWU8NB0SU/FsXaMxzsJbbKdyYSOpPovg+NKFJDSyDIog/rFHOuLAphTSrbE2B7ArZGY9jeBLY3geNZ+LSbJU7py/Kyh7zsYXOoxIbnu0cKUj5RfTqrL9xJyEnkMeaD/Ce24Ch7Xikus5utqHjhWNUvYFW/kLq+LMabMFP+dCd+Ic11WgnKPmiiC9PmOUkcznmHR5I+pzwtkSSPmWcvFn08GN7HB/cu4dXLUQLtq3NeLKI0F+yi77rAMGOl6OdWowtbJmmOu6xFD3SjE0VArIUVYHsHALB07eKxjl2XPVRUL4ye3OlF2/tXrH18fzP6/u9dI3KUlfxruQI+6wGAjIoaHXtyEmcF6awYO2IYxdk3XCwyWwo/7fp4vhqPHqmijzndgSpP/1Mxrzv4wd349viKGS0n3u3LsIIkdtsToJ1w+XMaauKUp0Rpq4VLFxr4853o/P7DF7MFhhWlgbmVeZ2kIE3Lb+JwOOcfHkninCn/3e/8AbyMZSc2otS3gcsVD4YbCRKlKJMBEDliBQkggsTCChKdBk87JctBROW5avyxf34tHcqqMnk6fUtE34r/ivUsGT1LxoetHLaH01th0CjSoQnsjoXwGGbBmC1jkNhO+GnXx5xrYE53wgEABdlHIdGvbl53Mvu5/dRSelcZAFQDQSxOqVt0Eoa2Dz2QlKrq4WrZwdWyg7LihcP1hJggkduJU3OjmtYmmtYmdsdyOAAiR0cJEo0iUarabGWQw+E8frgkcc6chaKH+z3Srf1yyUe5YqFcsfDD64NwOz9lM2MDVUkFlvOkozsdQFyQdpVaTJCSLF1bO/Zx3/l4C72E5FBR+n8/ISKQJUpzuoPb3XThQQAQ4ENgtG17qKJpKNgdK3B8UkFalTwYjgAjUW/n13/z14/9HABSWFEO7mrkiGEfM4AkKvdtG9cqbjhc38mMiAFElub1SqzhL0tSkO72syNQbnJpbsZsJOpQXS55uFxKyxmVJSpGTWsz8/4mnoB/Z8mIjbxchSTI4dLhBy0ZH7RkzOs5lFUeXeJwnga4JHEeC537O7jTJW+3SiGKPpSVqMo2ZXNERkklI4v/7e0yFvMIR0UlfeHoKAb3e9jUUVKAniUcuzYPG72RRR83b5Mu8MmI0lKOLLPJoh+7zcFYCQeAmBxdnCeXJZee2IRmGnG5Wjp9mYCKKsfus2tJseTxnXFaepK1g/JyFXk56iOzO1bCsVJwjxSkqhp/DrOqk0TPH40mtZidhUlRAhATJVrbSRSAOX0dc/p66vp13UU9oyBmcqn0/oAsH7/WINe93jJnlnfF4XCeHPy3mPNEqBQc9Jh6NmUFaAKx9iI02Vhm5u9/c72Qui8z4RAf3iliLXeQup5zglUe2xOwM1KgBPIzCTrxXiq7eL8Z3z7eNOVweYpl7AItI5pUr5TJdR5FkCjerKyCYXeshPlWABGllXz6ce4PVDzTWIHhpMsWkJ/LmANwj5GSht5DIzgXDW0FfzMgeWFXn13E/r3dGT4LwAqSpgsgzWlbpgj6LtkYqFgvkb389aAcQ10DVEnKjIaxolTXs6NKSUG60+9nXo/D4Zx/uCRxzpyWCSwVPVSKHgYOMAmWsGTFiyV2vxhU0N4cxgOcjk9EKZl/BGQLEuXOLklw2prSqPRhuH5c0ABgb6xgKRCLvBK0obCjKzXN6FdqzAQgcqoIwyLP725fxsQD1pRoqe4zQ8AlpgI5jUitXl6GfUASiFeOmXhOoa1NktEkIFuUAMD108ttOZkcIJWl+4PsPx9Xyx62M5ZNaXVuRZzdnx3XBwzXQ04iy4i0cvh8wYLhkvdR3xaxKKUNWRb9TFG63t4GAPQYGapobuZuOVaQaBSJYnvZxSg5HM75gUsS57ExtoG8AuiKBzNImh07QD7xLlwreqEoJSXlay9Es2+LlLgJt2izgjRrbE8Io0msKAGRLAFRtGIEGWUAe+P4xDrJiGa1gxyr+wPy/2phtlvHfR9wfBJNoW1RrpYm+FGTSGRvouJL8/EIUlVz0c2QAkpNc1HTXLzXjHKvrpbTx93QVjJvL506nf74sDv7KFRGqRhNoyB7GCUO+WZvAFp7ShJ89Kzg/WGMsZAHdmZz2BwO5wnCc5LOKeexfxuLzkQzkn3JPu6IGNjAfGKnG6VtkkFxPGBzKGBu0QgHAFxdNlGquBAEQFYklKdvJsvE8QSYroBysGPNZqIOe2MFB4YQjls9KRQklqW8G46jBOnSKsn1YQXp4zZZe1REH8+/tIL8DHaFUUn46GYLX5yLTuJKPv2noKq5qDIFGQ1nkFpy+7G5CX5sbgKtkMOdfvw+WEFymd5tPcuFIMymCawWeJwRVGPfHkXrtbmM6BG7s28xtxyOyyUdl0vpN9xKwcZKIR0Rmrjx53qrR0yfelRV5TWSOJynAR5J4pw5rVubwLU1rIBEk2i+CCtKBwawAuDDTnzyqTGbxD4ZZt//biLpWMyYHAFgLE2xrilQoWgaMuaCHmslptL0AhPxAoC3DsjBvr6Qbl8BRKUMKG0jHqnJEiSK5wupcgKPSlbl7CyoKO2Mo+NYyttYytv4tLuD9WIp83YHRvTEqCjlBaBjkVhKWZmHFZSB2B49vLL1cRAhw/PjZR62Rwrmg69zkhcuu5nM/y9o9cz7Y0VpX8x+wyUF6W4/+znNyAM5HM4ThEeSzjnnJZpkGnLYbPb5qg9JIC1LuhYwZD6ov1zz8HItLjnvNslgdyNRjhIkuqtMkk8+MdOiyc1ABEqJaM5akXzvj6IJ9a0DDbmMPl+UsuqjrPrQpGgs5m1cKEajZYpomSJWi14sCnMSNMnHISMy7JITG02izOluKEcsG8MBNoZRJOnAkGOCBAAXixYuFi3MLZAk6rIyH/6MCpIRLLX+1m//45M+pUw0yUNdc1DXHCzkitBlH7rso6a5YaSHxfayX6OJO8TEHWJnpIQj+tl0QUouIPq8TBKHc+7hknTOefPNN5/0ITwUMdjFtdOJJptrlWgGobvY3n8QVeF+ueaFcpSFJgHbQxGeJ4QjS5BOgx/km2zejmeXTBOlL89b4QCAzaGEzWFc0MpqeuasJpbp3m9m11g6Kb/x7d+B6wOHhhw0e5VQUgQoog5F1PH6AmB5djgAQBEkKEK2XI6dDqqai2erEzxbjUTjYtHKvH4WtfV0C5BZU1bika/khgAAYWsWKkYTNzt6NLRFuL4QlnmQRR8v1ebwD9Zr4ZAEH5LgQxGidjYcDud8w5fbOGeOLgGXGg66IKL0UsZ16A62v9qPJpd5Jg+b3dmW1fRVln1cLEUCcj3wmOEoj9Uc4Koauke01sjCcgWIAtlBJYlAZxKPnHTH0YHIIjB0JBTldE2dzaGEr6+OoQtSKvJylCC9Mkd+RuNIyYrXJ6FpCpjTyf3sjh0sB1nzFaUBAOjZrdj1FUGCHRR+zKquDQDPViekKGOfHGkuOM6sKNLYARZyLjqWgKsvnmy3Hovtx8/RyBFROKJ57uZQDKW2a7XDyzcMsgj8Yi0eWSur2UUvLxYkjJhAVMciZQ1opIkHkTicpwMuSeecrOW2z1t0yXCBngXUl4np7ARb8gtyelu/MiWIUlKAy/nsitxyQh5uTYnErM7nMy8/iobuhhW+gfgut8tBkcd7zFb4oUOuS2Xp66tRdAwAFoLcpqEt4uZQwIOhglca5CRkCRIAFBQPtidAOmWOCy21NE2UACJLrCi5vgsRwIUCydVJRlqyWnsYjoDWREavR7YfSqIfJud/bcnFzd7pngeLJKR3R7IVxctKCX2bLBHS+lQAMLY7mff3cSfKSSqr2ZGxi4V4hI0KUhI2SZzD4ZxPuCSdcz5vQjSNoSliXQX6Ftm1RnuTtdtkUqrX07kxh0MH80UZ7eATe3si4seZfKV7IiCK0wVpOCJS5Koabn+8dWxJEoXovgVE0QFWlAAiS6wovVwjz2mpHBek8LgSYnG9pcDx4uUO6GPP6S7uHuuos1nJ+2EhzCS7YweXgq9v93sAZNSntA2hvcoct58pSK1J/E+KxJynry2R++xYZ5PRPKe72A8idR93JrjALP99dcnE3jh+bKogwMpIHKKJ7veZ4piXgoKURwlSMl9JmWG/PQ6H82TgH3U4Z47P7K6i2/CT27uEi+AAACAASURBVPupLFEsW4Rlixg72RPqW4eAVtRiOUl9W8BihUxmVJAoq/N53L+1g9/43d9+5OP+zm/8PraGEhpBWwr2SNhyADXNwRfnTLxc00NBYq9HrzutqWqyEvhJKoM/Krrsoay6sDwPPVsKx6fdfiBIBFXUoYrZuwF/uOvhXl/FoSHHEsKTgnQUNRXQT582lmIxF0WLthINhJfy6eVCVRCILHnRyOKDlpoSpIHdhCwo4XA88toNbZHnJHE4Twn8N5lz5vwPv/dPIDPJztc/2wcQF6VqzYTnR3LEoksC9GCt6a1DMpJ8cS66/8WKBUVxoSguSmULQ0dA1xEBRYUnHLcMALA1JKUPfQC3eiJqmoOa5qCouKgxOUVss1OWQ1PCzljGYt4JB+UoQZrT4/WZ7BkHJvREW5Ss6tNUln6464UjyaEhQ5d8HIzl2CipbriT7uurOWhSEZpURE0F9ozZlALwHrKF7ChRGjrReK1h4LWGkbw5ehYZSW4P4ufhblB4myb7qxKPInE4TwN8uY3zWHBsEQeGgEGPTFrtYOJZUXYxLkY90ObyQW2icdrfPV+ILdVRWEECgMGU6FOpXkT/ILv/2MPoTOI1myjJpTcAoSgdmtNFYHMo4Wo5vqT1QZRHDNkVsTUi50AUfNzqnu5X9cMbG3j5C6QvmeUJUEXaGNaH6Ubny/GEsAr1n29Hr8Fry+QFu0NfP1tFXbGwVkwXWvyZlRwA4MYG+f5SsYKNJuml15nMts8ZkRIBPhhJzsUjRltDFasA3m9GgrxgCshnJMKzovTH97OFepogJRHPZlWRw+E8RrgkcR4bnY1t1NZXMeip2OrJuFAhk9mVMplN7vajSWsu74WilKzITSNQ73xYgeAOgbkomZgVJD1HJGRknm5dhyYGdyakEObbByp+YiEKL7CitDuOEoJNnxyozpQmeOcgO6n8s24kVALjh4roIx3fOD6uL8BDJGWWJyAXfE0jSstMZek9wwaQPtarleh5F8XpgkRZ0CMBpoI0qyhSkk86CuZz5FybQwEv1qI3zuYwXW597GSL0vst8hxeX4hsnBYKPUqQfMStyHK5JXE45x0uSZwnBitKAJElGue5/SDIKRJ9aBntPt75sBJ+/W8+IMnES5Wjt02NrMKRP8+iZQqoaj7mdB9NM5r03j5Q8Q9XASPYwWQAaE3aANITI630vJBPr9uwcgSkBWnW7I3lMNKiSx4+DSJUX7iSFp6X6iRj/qN2JEsLTHBF9KLzOXRGRwpSEkmY7XMTgsyBQ0MMRenjjhwTpYbuoWXGI5Q0522jFT/2JC/WbBSVBl5rRJf9xUEv7BvIspInRzSLhHsOh/Nk4ZJ0jqH9287LDjfHFqGIPuq1CRZLLvYHErZ6Mh6MoyTrfD0HexIXjQmbEKy4MUGi6Hk68ZPJ96ULURGbDw4BdzwGUIBen0/d9lEY2mTbfCkXRVNkMYeS6mJgRaJDc1HYKMIzQeNXywG+fiHa8fZ/XH+0iMqtroyqBvRPWXnbcAQUAewbcmpJCojyoZIT/0v1CRYekmXds0QY4274fU0FDCcKs3zSHQMgjysJ/kz60AF4aFmEo0SJ5nwBQG8cvG6JvKzBlATsvhVPjNscSpAArBZE3ALQsnjvNg7naYAnbnOeCE1DwGLJxWLJxfpqfEFJ0XwoWsbWbFPA3kjChcvxWj1UkLr2IgCSu0T54H4BnitAqc8BAOz21qmPneblUEoZBQf/el/HW/tyKEhJ3m/mMXIRG19o2Hi5RobtChjbIsa2iKoWb91yEswpSz/PV6fL0tZIDcftvhgOlu2xge1x/PWrJVa2Pu6c7Wex9kSAJhVQDd4zh0b8GOnjXyl5uFLy8OV5B307+5hU0Q/HcQSJw+E8nfBIEuexYjoi9LBxrIC5HJnY1lcNbGznMG6Pka+TyJKi+bDM7MmdilJrJz7Je0dEW8zu8asYTh5soHtxHRdAokm00QURpR5WV0lUq6S6+Mud9JLN/36LGMN/do1En95vpms1vdYIrCQ4vF2mVxirWGZ26aJHxgdwsWhDEQXc6ctYCi7/8vwE9wfV2HUX1OkT/+2+iOcqErbG6WypowSJRq9oFMmd8WqiKvqoBiuDqyUPbWZprTcRgVIin2ig4Uop3b+NJly3mQhmPVjyPUqQVgtMBXYBUPgONw7n3MMlifNYcF1SywggokTn0qYhYCX4mkaUDsaRbFimDy0vw/eChFxDgZ4jYZWLRRvjXDTxGUxC8KjUwO0bJPFW9AYoFRW0SyXI5vH7oqmSj04wly74wBfqzFIes2OqFHhGsoo4ALz5qYr/5JKN56sGPu1Gzy8UpAAqSPMX5rG/FZ+QZznlXi07WM6rGNrknK8Vab+5yHLKCrGyvh2d1+cq0dcNPW5to0TK1bQIUndC7qNpzi6QvTse4zIqUEU/XDKr63FReq8p48fm4lJ9d0DeD+JD/hKarogf9Zpgc87ebcZFUhqT17Jn86U2DudpgS+3cR4L/Q2yH3w8UDAeKDjYLWAh72Mh7+PBng4PCIdjp6MYgihCEMnb9WLRxsWMrefGUESzpaPZ0nH/HiMvxdNXLXy+RoTgs1v7uNE+WrRKChkAacliJCJAz1cNPF818A8umlgrWFgrELtgI0iUvg1M3Ox+dcdBAEkE/+jjPdhepFtFJR79WstoUltWXNzryzFBYvmglcMHicTnmz0ZiohwXCvLKCs6ckFZ8aRgnZT/6pf+8ZE/r+txYXmvGdmQCD8cX12cDwdLXvbCWk8suUSPvlfr5PuDYKmvM+F/WjmcpwH+m8x5LLhBTow3JRyytRevSZMlSgCweb+Ej7ajJavn18hbuH2YFgxVc6Fq8clMLBZT1zsKz4/qI1FRAnCkKL29I+PtHRn/+TUb/8XzaZl75zCHdw7jUrGct3GtOgkHQASJop8y7UVIrEI+TJTaphwbAPD9TQHf34zfUVKOACJILPoRDWdnwd6tTRiOAC1Y3lITuwKpKLUtMv58R46VZUhCZSlLjoDpgpRkwksAcDjnHr7cxnksiKIf9lnzfGLnH94p4uWrURL21p6OC0tRbRoqShqAB3fj2/dZUeodxvNKhkztmtaOBWARK/V9SKoC95hFhwQhPekmqQWT8K0BcDhI/0pRUeqZiMnRbauGZ9QOnEQe1a1uJGANzQfdM6ad8iMN7S83H7QSmTej8/a8pOEu06tMLASvVUYV7u9vCnipbmF/mC62eJQg2T55PBpFKp1BW5JCkO/UnYioatFjZ8l51k5EAPjrfbLMKWeUKThKkA6YhPHLJREPuCRxOOceLkmcx4cP2MF2+WcuEjka2sASU7TP8YBi2UabyfVp3iGCJAjpT/aN2gjWgExGxnh6uGVokNsK8vHaktBpr29J6FpAnpnYb7Q17OsCvsrc5XyJ5LwkZelGW0LPyMVEZ+LhoYLE4gP4td/4dfzz//F/OtZzoOgSSf42HAE52YfvxyNMV0pWTJQApoFw8N9L9fhyHCsSPjxcq1i4Fez+mxZBygUvkzjDWknVIGLIVgxPilJVAboZ+WKq5IdiNI2eJUETfNhME9ufWozfWUUNxFLlf1Y5nKcFvtzGeSxoqgfLksLIxISZP2/dLuPW7XLs+vW5RO8RAL4fvV0btREatVHs57m8C1cqolSOJq9cUUKueLq1qqFNjrka+EMyP+iv99P3P19ycKMthQMAKszKlCIBRYXkCdGRk0S80rDDUVVdVFUXBQWQzug3Ndn67EopnZO0ORTQGTn4+mp2GO5aZYJrlQnzvYViog7SztjCoSHgThBpuj+Y3bZ53yeP9d4N0hOQ7UHXTeQGVRnJHTtiOMqqEw6WniWhZ6WPNal/9Hu6M65rpUsrcDic8weXpKeAb37zm0/6EB7KaBR9us4SJQCPJEqLKyZ8ZK/TGONoxi+VbbiIR0Uaa+kilA9DnPIbMk2U/sPLRjjmc044YrfN8IMf7MQjXLf75Eq7Y3Le3FOm9ciCj7rmhblNBtO+xfeBTzeiNco53cErDQObQwGbw3ikaynnYilo95KUIwo9ZspR+T+zwPEFSAJZxssSmu5ExIt1MxxfXTTDHXZZUFnKui9guiAlOW3JBg6H8+ThksR5LIiSB4HJ7bn7IzIpTzxgtXI/vJyK0tZGGVsbZYxHKhaXh1haHWNplVSrFn0T7baOdpuIxfJFPyZILFo5B8XdgqSo6G3tQy0erzWJ6QBVzYPnAzsjYMyssCgikJP8cAx7Q/ztfnZ7i/mcA1n0MZdR6XqaIFEqa6RIQjL5+rgMbRF1zYMkkErVh4aCxZyOxZyOBV1H3xbRZwoo/sL6OPN+8nIVV0ol6JIIPRHiOkqQ+oF00CgSjdDNmp4loSADa0UnHJ920suslitk9lf7UVPFj5oqhrYQG3T3JQv7PdvQtiB7mNPPVg45HM7ZwyXpnHNeWpL883/6+3AdEbLiQdMdKKqLiSljYsroj2XU6lY4Nu+lRYbWSUrSbuvY2S2nLu+3SBSqXBcgKVFEyey0jn3sE1dA+35UqbvALCU9m35o/O1+Dn+7n4PqkShLZyKhw0QuKqoTjv97I5q8357MxwSJFQ7PBzTRx9KzF499/EkW8pGofdiOwh2ljFYhv7A+DmUpL1eRl6up61BR+qSroWtJ6AYylBSknOwhd0Y73Whhyh1mBZbuyqNkiRJAZImK0Y+a6Ua4ALBScFFR4+OHuwr+ihktU0DLFGB76SVLDodzPuEZhk8JySW3z7M8ua4ISfJweFPF/LMW8o0KgAksK0pa9j0yeQuixFwWTLACYE7ik1mvHU2AjQqRI0p/1EC50IKokOv88q/8Cv6Xf/EvHulYJ0xkZSkP7AXBlYLiYRT8jIrSTWZXXVkBPm7n8MJC9pqLzeTN9ILoVEmOLi8qXthD7bQtSYBHj0IlRaltkvP/k4sT3B14uFIqpW5jZqwFlhQXtxlJoXJEd5nN5Vx01dkkbjuJHXg7I4TVxNumjLoeSSEVpbkGsJCLTuwzTFHPH+xG76WVQvbrtz1SQNoaE76y4OKdNrCcd9EDMPGEI6u/czic8wGXpKeEz7MUsYx2H6CwHEVDDm+qWP9J8rWqxkUJiGTJdqI8JFGxoWk2JpN0blK57qFYsDEMWnxsfzIAQERJCPp+iHp6op+GqnhomgLmgnlziekqUkgIhe0ROUqyECyxHRhyTI5YSsxvIpv0TAVp7AooX76I4a3NRz72+LEJUKWoGnUymvRyncgQPToRQNNM5+TcHQzCr8tTco1occzHCY3UXWZEltI2Zby8GjdN6YgGuz+9TKKQu1N6sm0nin5+ZYoIczic8w9fbuM8NoplC7m8g2LJhhtspa5ctNHrqHhwr4IH9yrY29ZRqdmxsb+TnUekaUSW9u9ZKNc9lOvRxFesMH20NAXeqAlXKANSDqL66GUA/pvf+j34AJqmgKYJNE2gbYrhWC44+KgLfBQUM7J9MpJ81tXQmUiwPSEmSiU5LkgPQ39IzaajKATRHN8nY6VgwfNJdOd6y0WHSVTudcaY013MZVTGlkUytkdKOChHCRIbRZo4YioCNCuW8sCDIUDqFpAxr6f/1I0dCWNnegK368dH8rkCcUFazkdfm46IscMjSRzOeYdHkjiPjX5HQ7lG8nSKJRu5BQteMJFUqgZ6QU+zapUklnS7kRy1D8jyWn0hPgmbVg6yNkHWHqPuYB6yFu2QE44hR1ks5oB9gyRss+25fnbVwZ9tx3+VbB9QBCJHWVBZ+g/W49vq37sb3fFOEBHJSUDPAsZDGcfvPJempET95S4ULWwx/draloS6GhcjKkqDKbsKAeD/2clhXemH+UgAUGOqnS8/U8OHt4hJTpzZfzYryD5GjgBF9EMJfTAUcLEYSeW8LuLQTL9Pxo6E3Y6O52rp3ZSUkuKiVo7v5OvXfYyD53JvIEAPxH/arjgOh3P+4JEkzmPHTewo8sdNAESUWKgssSiKANPKhYMyDj7h1wpb2NpqYGurAXO3Cd+J7zbz5YxM60cgGb9JlgD42dVo+cpyyRg5wFv7It7aj185GU2ifNLV8F5TwntNCb0jtqifBFo1nEaT2GrXFxL92trMJP9+Kx+ORV3Aoh4/7ps9OVVlG0Cq7AGFJnMni2iehmQ/WYWJtj1IlDCY18UwqjS0xXAAwGcdHZ8lkrtLiouSko6mvXuYvYuRLq2Kgo/SjHKuOBzOk4NHkjiPDUn20O9oKCxHoiTKfhhNAgDf9bG3G99Btcjkk7RuBZP8Ovm+vR19ujcmJYw6jdTj+k4OgqrDONjB3KKA4fbxjtt2BZRVH5ZHokmULFF6uwdcymgPR0XpqwvZcsRyhUmZotWpx8PT/6o6ngDDFXC7T+5rcPcQl66Qhq4XihZ2mKWknZGC7U4ez2aUlqKidKedLXJJQTrrOklAICWKHy5x0bNcVoGuJWBnHD8GJ2P5jfJZR4fpAYrvPZIg3RvwZTUO52mFSxLnsTHauIvC+hVIwdyq0ORZBThoRZ/gjUMHuXny1lxcIYnCYyM+Ww82IlFiW400Fmy0DqLJXvNljIeHYFeK1OrciY6fRix0yUfXIhPjn2zJ+PsX4lJwP2hHx8rSczVy40868QnV1qYLEksuSLRu751+Qq6qHrqWiH1Dxuu5Wni5VJ7gbj9+PDd7SInSW/v0ZHqYY64+r6ajR6wgsVGk9mS2xRaHtoiiQupZiQLwhYaDDaY1TM+SUFEf/oDJ1bhtZimy2U1Hj1hBWmDkMCf7GJxRHSgOh/P44MttnFPxrW99C9/61rdSl7/xxhtTb2ME7e2dSTSpXLzaS12PChIA5HM9tG55yK0o4XBsJSZItSIJETUWbJi7TWh+NEkKdg5zi0Gl78Oo5tFJqTJLKX+yJeNPttKfNzZHRI6oICXp2gL2xmI4bFfE4QThKMhkwi8r5LEEwYMwtb7z0fz6t38n8/KNYfy8XymnK2jf7AF1zcFb+wojSHF2DeDQkGPjUqmIpXwZS/kyNMmHKJDn056kl8hOw7RFrfXSo7UYAQBZ8lOCxFLTXbxUn8SG7Qm4UAAuFIArZQ+fdBR80lFSux45HM75hUeSOKfiD//wD1OXvfHGG/jud78b/h+iHZ04ffFqDw/ukLCFcehgB/GIT3V9iInNTNL2EKUSMBjE77dQkVBf1zC6z0QO9BwMu4LmzbuP+MwiRgMFKBGxYyf3quqjy1zvT7ZkNOrx2xrMzvNccOjdjAhDI5GRrZ5BEGLsiMgHOUk0mgQQUVovRuEiKkprevy87jApYytBUGU3u50bLhVFdINQUWcSidiAqTs1q6dYVOKa5DHfrpfiESUgqMitxxvs+sz9sJXAaxm7+wBgMEW2ahqpnjR0JKgy/wzK4Zx3uCRxZk5MjBJoLRujooJRD7g4R6JJspZ+G2pVD97hBMI8sYdSgaxhacoYEzsfu26pZKJS7gKIT1yFSxIOP9wD9GiZpLJUQXcywHEw9+9gubGGfHCYC3kHO0NiPEt5wGbm0Y5BJsZaLh1NWM77GJlkJ9a2EU3ERwlSUY6iSKdFk7ywoSsAqMwcvjHsYSLMx67v5WU8W8lOwH6hbuJmW8Z6EdgYRnd0qRgXA1aQklS12UVcCooXiyi9/0kLSxcXAQCaCNhu/LjebZLvvzyflqCi4mPiCkCGH2XJkfoYcq44HM6TgX/U4cycrKW2N954A0JiYcRxFTKCZbf2YQ7FsgWtGk06/mF6+UdTxlDVCdSCEo52KxKh5gbZFdfrVyDnSYREsLcg69ktJx4ViYk8rBRJiEjudjIb1lJZAoArFR9XKvHnvprzsZrzcaXsoKKRAWRHkPoWERpVTCeLn4Rpk/ozFQvPVOI73ZK7176+auLrq/Gt8utFD+tFDxcKDhx/ep0kGkVa0H2s5me788sJK5Vn3+9Czo5V2Kb83WFQRNMhxz1xBSJIAR1TCkeyRhIQP5e1QHaHDqmHxTOSOJzzD5ekp4A333wz1ZbkSZK51AZAEMiEVDocoSxWMPyQCJDjKmgzO4bq8/E1HMc08frLdjh692T070ZTkNElUSYqSr1+Bb1+tHwki2pMkCT5eE1udcXHbovcXhJ8XP+0nbpOUpRoJOmjHR3/+pN0wu9PLU/wU8txAZRED8uFaHiej74F5IO5eZLRjPW4aMGkflT0IylKS3kPfVvEhSmnbc8QscdIoeNbcHwLf7Ft4r2mhveaGv5mTwsb6wJA6RGSqB+VrCa1ADDICIJNE6XORDry/EpBWQHaBLhvi1gtWpjPOZjPOTgwRMiiD5mp08ThcM4/fLmNcyZMXXJTst9ynmNDlKNP6vV5A82uCrdEJqfv/yCHn//puDz17wooXyE/93dI/szuMJrgveYeAECWVJj9IpTiEGLn+NEkRQCKVQsfHcp4aT6aeVeKdrjstjsmE+OPrXnYTJd3CkXpm691Mh9jaMc/r7BRKxbDOvnnmr4loj4lxyZJ0wRenEuL1Gddcp6fq5JoEitHlOvhTkUigSYjKxOm3IMzw91tFiMmRcUP84oGTrqi+ULOhpBxGl+bi95f7zfJ6yVNqXDuMo/3UTt6TxkOjWi5mZXXORzO+YJLEufMoctvo51bqF4leSKG1QeWloEPR9BfWQEQF6XNuwVAclCtRFGY7/8AEIMmsqJM1jZG3RGEnXiCcb7sY9wXIEtk8jL7B+S6+w5kAP/97/3asY6fTnYXKi4+OiS/Mn6TWXpJrAiuBREXVpb+vWtEKt5vxhOQlkRrqiC9/lIDN262yEPMIIq0ffMB6q+sommK+NI8yevaZuovdVoqxozQXAhajGyN0mLZNGVcLlu4HNTm/Jt9HX/Tr6NgxxunTRMkYDbPicXyBLhBkcqcTJb1KFvD+FLZhUViaIdGdgJ2QfFhwwvvj+IeI0pU4bvcOJxzD19u4zw2aHRpdGkxvMyoFzDYNVGu+SjXfHiOTQQpA7nrQ/T8UJAo/gqZuKxyP7zswrPxj/G1y6d7q9MK1RcqZHK9VIpPgLqUDhusFYgcUUFKMvGAdw9lfNYVw3G9JYZVt+8PJAwsYGCRZO+C7EPKeJzj0E40rXUSd5fP+Nh0oWCFwtTQHTT09DrWTy6aWMp5aE2EcJiJqx2YZHQtAZszKI5J8RHlarE72w5MJjk+56KRS4eu5hOX3erpuNWLpFsS/HBUVBf1xHPPiiJ5HtCfccV0DofzZOCS9BTxecpLYkkuvU0Od+BWCsDebuq65ZoPOZeDnCMRpG6wx9zaSCdwC+UoVOOvCKgvIRx7d8YQg0qHrCD97m/8p8c+/mQEhDJNlCqaHw4XCEfsPjOCDGw17xrjgaVgHu5PTvfrqjGC9c5hFPF5mCjRRrArBSvMaWK511dxrx+PNg0swPKiMU6cAO0UjXqPAytKADJFqaG7aOhuTI5YTEeEyfSbq+sO6rqDvOzhywsmvrxgYjnvQpP82DnmcDjnHy5JnDOHLre98cYb2L7zV+HlbqUAWSI2sP0xkSBJlTBf3wGAUJaGezaE5UI4YJkQyiOoeTUcymcDmO+m386CEeWZ/JNv/MKJjl+WfHQs4JMHORgO0Ci4uN0XcbsvwvGBi8Vo3B0A7zWzpcoFyfVpMoGluVoOi7m4IB2Ff4o52PYETDxSpBLIFiW6i66qAt2JjLGTjohokhfKUlKOACJILKyElVVg5coyAGDizGY5yvfJyIomAdmi5PkIB0UVvXBQzIxmvGsFD2uF6DrsUp4cyN9qwUsto3I4nPMHz0niPBaS0aR/+kdvQckpKDSiiURSowl5vr6Dw/YKYJLdSJO2Aa1OTEJYLsDoWVDz6UnWfFdEtzpEsUrua7AJmFsF9LqbwOsnP/6aCrSY78dOFHGhrTBYqCi9Gqws0iKU6xURGz0vFIkrJeDtA+AZpu/uUVEkbwY7p5omMKdHz+NqmURX1ms2bvfiuTuWK0BNREf2xuQ6n7Xjfz6erXgPFSQK2eU2u5ykgU2WREtMHtCrC1H08dCQcJXZtacFu+u6U4pC/ul9BV9eJAfPXoOVo4dh8ZQkDufcwyXpKeLNN9980ofwyPz+P3od/+z7P8LgoI3SAilTfbAFiDppbjs31516W6NHJrteU0RlzoP/HtGX3gXS3FZo+wCicM2v/eKrAF6Ff8IwDI1QiOM7uL9zFZdWSHQqKUoAsIgh9oVieNndIE1qLaPpLdun7XafCInpCjCCAE9zP4da4BFuRkTjuDgeUAyOlxWlO30pFKVnKtmiBAC9cXZLEoA810+7IgxmNevaxSr8XlQu4d8GjYULM/6rw0rcyBFRCKqKy4IAh3nN7/TUmCgBQDUhS398N1HZE9FSqSz62DUELOeyE8Jl0cf9gYi5S8vYfZBeSuZwOOcPLklPEVk5SU9anNilNpbvfve7+M7PfxH/7Ps/AgC4QjwfpNmsYnG5hYkTNWDtbhvwgu64YtDiZDAA3AvMDdtNAMDoQIe3vx0I0ukwbAH7A/K4ch64v5PDs0vkZ+xusLpGJs/djHqKm0HT27VidhPbZK2f5n60/jaXF3EPgOOdrhjmo3bJeKZCawnpuFCM6grtWpEQsEtJyeUtALiUeI4HQakAKkizbG6brEt0XFH64S45r79w1QGQlqRwVTB4yrtBtfS9sRKe03cOJRgDvrzG4TxtcEl6injSQnQU0+omfefnvwjf9/E/v78PAPDMbhhNAgBN7oSiVF2VMOj6cP1IIKp3SS2k1gqzXgXgV/6/9t48SJL8uu/75l13VVefM9PTc+wxC+wBLAASIIMwjbBompAlOWxJDpsMOUjPzmJxLLDAgiRIWqGQSUIiTgIEiJ3dkI8QHKbkUNghirJDweABUAEQWAKLBXYx90xP30fdVVmVp//45fHLrKyePqp7pqrfJyJjurKuzO7pyk+/937vvXcKiM1+OwiqZsPoSRC9qMUjBWDdK3f60YaM954OZWLGO7wNHah3LBS9cNNbWF4fTwAAIABJREFUJthzNcGLXngrzXYSpDhyKoXf+p1/gt/+zX+yr/OQRRc5JYyAPTkZHnd8+Gs+MzgdllMcKLKEXsKk2kGCdJiczVu43Uz+OJOF6Hn8m9saTk0lC+cvP84WA/wvP2IrLJPKpv7NVdY+4afybLzNdzfD71vZcyzDRl+HeYIgRg+SJOLQ2WmWm8/zb2fFO74szZzon5zarHk1IoIO200HggQAkysN/NLPFTBMMfL59Kd+E5/7v/53AEC+YKCZUKz8jbtKRJQAJksbeihHcUopG4stCSe4ER3f+XFUkKYyTDAOGkXycVwB1dvLyDx8CgCw2FSwkO/vQu1zu8ne92w+jL78YDuUgnfPRJfEX4sXTXOC5EeRqgYwk3Jwe1g1Oy6LbJ3NW+h5EaqeLeIO12bAKQFX6qEsta3oMcU5V7KCaFSda+DpC5IPL0hxUtQFgCBGHpIk4tDYKdU2CF+W+LTHv6pXsbVVijxOEnRPio4GdjFlV+B8gQnDbDqMJgFRUbqyHqamlkrMHOazoUEstvqvoA8XHTz8PhlXF1khkwERa3fYfQXvPat1EYOno+0evp7qXqIEABu6jJopYTGhmzgAfHuDvdgpKXwdUXAx441neW1bRNU78Lk04NSAhcfm+17noOQVF02v27YSGxFyoehGRAnol6U/X+5PtxVVdg7fXVGxUGJPWKz1f3SWNfZ6k5qL2jB+SARB3HdIksYEf37bg5hy200kaSf+YbEEFKP79luEvV9kgV1Iiyl2wSx4Bb8FFVjXwmjK7aYAc0C2bKktYKkNnMsmCxKPwXXnUL3BcB0jD2ATYiYazdgttsOW7/vi0LEAXz0XmwoeLkcfn5H7Qz0LXp9PX5Z8OYojDhirMuenIrvs/H71138Vv/fPfm/3J5GAElt9t1dRslygbgKvbvYLEsBqnOLkMxZuN6Wg+N1xgS0uqLaQtXFtPydDEMQDBUkSQeyBlS0NJ6d6WGxJWMgxUfLLchTuWjpdYhGVzRqLKC1xERg/uuJT3IUg+WQmMhCM6OiP3eKX5iiiGyxPX+Fe6iE32vRyq8s+HqYSOmw/UWaPFBwb392MfozEBem17eSapO2OiNMXFvZwBjvTMkWUEvYrohuZHNNuKnh4Dljrz+gGLRf8VgZJglSNRYn4wvVJLbzRoR4ABDHykCQRh85e0m0PMnKsjpkXJYDJkhK7pi5MmtAtBUgo4m16vY++cSuMYDw0ZUI3XaSV6JuxKBKjsrS5zzMA7jQlnMnbkAUWQcnKYcoJYD2B4gvPfFkSJKCg9S9Le5c39LduSLi6Bvj9jxZybkSQ5tJh7yBjiKvbbFdA1xaRkhz8YEPGu08aSMvA97jaKcUFms1oXddcOlmUAGA+CzgQkJbZz80fObJbQQIATRrubDqCII4ekiTiUBlVIYrTsZkEiWDRpNPn2P7FlgQx7FKApbqEswk9kSY1dsHc7rELaTNhxMhDU2E9j266kMVwqfxU2YQoCthe3f9KMd0UkJSou5co+YKwrgNoSXjbZL/hxFfGAcBKR8A5z+1UEbjideMcUqPte/L+0138yd2wtUQ+bySKEsBkqTi4DRTSsouFnAs+7nXNiw7637vtNvs+tQ0Rky4JEkGMA9TYgyD2wNnZHmYne9juCVjusO3GagbrTSnopWS5/fPQfM7kgG+s9QsFL0hAtKfRZjWMNLl6B9ns/la65TUXquRitcNenI+MZWXgGz+uAwBSsoOs7GCtIwSCxPPatoTXvChN3ZASBYk/fpX72hckXxJ7Q4goyV56r2uz1/z2Svj9ef/p6HDhfN5AnlupN5ti29smAHHAp+H1uoDrfQXfAtoWL0jR70HSjDuCIEYPiiQRh8J+VrY9yGxdX0TxfLR+JiWFkZ5WS0MuFx3Ca7mhiOSUqDV9vxpedOOCdJjYLhsJstxgF/WffQh4k2tunooVa3vNzVGMeVlJZam7O43w8W+fdHfdsBIAVNnB/qqr+lG8mWmPTdn48ZaEb6+oeOoCu+/9p7v4D17UZ84LpZU1QEkI9vCidK2aHA1qx8SRF6Q21y6g2V/KRRDEiEGSRBwqoypFcbo9EVPe9S/N/dakJAD2CgzxJFqt6Ooof+VTa4fXfWrCxRuxCRZJUSTRGw6XSqmoZQ7e+uBE3saqF/l6SykUpZU2O7mT2egVvm4AEJgcDaJjCTiZicpghTv5kpfOUiQXrYwTCNhBEQUXG7qAmbSLomrjMa9V1t12eLAXimwFG4/pHWpclhzP+8rcj7Pi+e9uBWmlLWEmRc0kCWLUIUkiDo1xESSfti5hs+1dCG9u4MT5GQBMlAwXOO81wFyrT+CJ2agBNLwl6QUvovTUgAaTpgPcXWGFMoYhYbrMZp/9+LUMTp1gcRdXTCU+9174l3fbe+sT+TDXxYsS4MlSJvp4xbVQM9hHBi9Lj8baMwCsFQIA+Do3zDEku6VnC9C49gB+zVGSLClCKEdJlFRARwavrYQ/1/NpYC4dnlhOZam97a6M19YPfvwEQdx/SJKIoTNuqTYA0DR2BW01VeS8mpY2V3xdPhmtffnhOrOIuCxJAE6m3ciAWZ+ECR8RatXegX9hZ9IuJAEocSux/LqahVy0lsqf3LEZzSKyY/FOaydB8uEFye9p5EeRtCF0pW4YIsoANnQBjwEoqnZQJxUXJSBaoH2Ga6LZE9jPs2VEc4ZJs+kEMbozvtq/oAkYsHCOIIgRgiSJODRGWYriWJYA0bsw+qJUzjiodMILas0UUFJclMUqKg5b8vbDdRVnZ11McDVJBU1Eo+dgy/OqJDkyuGLoWlsFwB5spYsABrS93gNlzUalJ/Vd3Dd1YDrWDHNai4oSv7rNtqMv8OrmYEHimdCAaoJ87QdBACZTLNX2eoV9354sh2/cswWoAGYz4bF2EBUknpzXYXt7gOXsJEjb3fAjdaNLH68EMerQbzFB7BJR7A8pDBIlAMhkWLhkowlMTitw7P7nX1lV4HBNgwo5I0GQQoxGFeb28sFOJIYiRkVtkCjJkouJbLL1rHq1OY8Uw/tXdQnXOZ8zewAgBA0bDwO/ncHrFQk/fZK1IQCASRu41RRxLh+e6J0mCynFZcnylu/fbUd/XqezuxckAYAmU00SQYw6JEljyP0cT8Kn2PivRz2qpHgXvKnJLtq6DNsWscSNsZjl0kZdR8CJktlXmCx6zQV9Wbqyyi7SopqCY7BI0fqahoX5sFnkym32bzZVxZlJGdcaAJT91SQBQNsUkPUkrqzZ8K/xSaIEtDA3lY08v9ELT9RvLLna7s+ZrerRfW8r2/juIdXpxBfU8X2fTmbcQJSAflECQlmayg/ubXQqCzgA/osz4Q/1zpIbGWLrZxmTxrkQBDGakCSNEQ/S/LZRl6Ikep4gZNMW2rqMMzM93NlgorRZYeGR6XJ4ES2qAupGfzThegPoJTST1FvRi/T6poRshl3tpbkiYLKwTLa0v9ltv/axT+F//XdfR9sUsNjyfvVb63j04ZnwmGML51TRheEky0PHErHsmUGak4UkQfLxo0jVHkuTDavzdkpyUTckJI3NO5mJjiW51RQxOwksx7KWhQw7FzVWw3Qq6okAgGpXRmFqAo2tKgDgzZqAeYSCJCR0WCcIYvQgSSKIXaB3JSRliXhRApgsDRKlq9XohTPjpa46balPkAZhWgOm5+6RjMwG3ALAdk8IRmostlgBN4/qpZh8WZKlfgHQPdmpd0JBEoRkQfLvm8+62BqCS7hgwpaRHUieexZUIK+E771QiNpYTwTO54Gbzf7XM2x2nj89a+FOqz9KVo3VGr1ZS/7ZUc9tghh9qOP2GHLx4sVgO0wuXbo08L5nn322b3XbKCMrDrq6jKvfrcB2gRQ39PXMTBinkGQXlYaCrY4YbKYl4bXVwX+PtJsyJNmF5KX0Kvo01jfDi3O3wZoNVVNlAEDPSZh7spdz8X7rM9whbffCS/pii208S20RG7oAdUCdzaNFC48Wo72VuhZwpSYFm09SE8fD4Btr4TK2dV3Cut4vPOfzfbtwo8E2ADiTsyPbToLkR5E6lojtnoCiRtEkghh1KJI0hvDptosXL+Lll18+8GteunQJly9f7rsd3+/jp9t8URqH9JuWstDjLpI3llOYnmG1RMXpHlqtMFQiyy6sWONBvcsu2umUCU0Dej1gcy3agNIXJb6U2JHYYzqbmyjOpdHeru/7HCpdtkovJTnBGA+f7Z4A2cvkPe5FgJba/X9HeX0t4bjoEyOf+LgRWQQOqwG1P1S4Y4mIt9n8xpqC986F3811XcJsOnpw5/Os+7cvRkm8UWWCNRXb77cwmM8CnQ1vLMqSiiF0NiAI4gGAJGnMeeWVV+C6B/+LNkmEBsEL0TjIEQB8+lO/if/5f/s/AACV7RTKk0yONjdCUcrljD5RApAoS7quoNMYUJDjWjC8+qe1lTJmpsOK545+sCiSz2ZXxHTKQUoCVO7wFvIuskpYeDznddBe60TPoaiyY//LpfAj5D2zTIOSBMnHjyLNZ13c3WWK8V64ANZaAuZyyf/Pv7Gm4N2cPa3rEkoaEyOeFmdxOe+0fDnaLd9eYj9/WQB0qt8miJGH0m3EQC5durRjSi3OoCaS44Km2dC8NFtlO1xhtrkRfp3LGcjlosvaZNmFriuRDQAmp3VMTrNmPLbgyY/bH2/Z2JwF4AlSaxGlmYOtoXdihdiLsQLmttn/sTCXcTGXYb2IfEGKk1ds6BaLMPlbXmVjXNIy4LpsybzhAHdbArJK2M37oExmXKy1BJQ1Fy0TaJnhMc9lXPyoyno2+VtGsYPBuEm0LOB7W7sTpPmEwm6A9WciCGK0oUgSMZB7RY92SrXdS5RGMcLkN5FU/EiLEF4EN9c0TM+FcrRyN425+bALtyKzlI9pce2ePXxRqq1HBamyEqaJthbDzobZbDRFtxeqt+5g4twZdsxeNAlgorTAXezbphhElApcAXTTZO/NF0UDwM/N93eGXNGBae/rsubCz2ZZR1SqM6UBW9xh3WgAD8Xycb4o+b2R4lrER8WenrIxK+h4fTssnucF6dtLKk5PWJBEYJkb8UIQxOhCkkTsi3sJ1ChK0G6RFReWKaC+JaM4FYrN5poaEaW1pVRElAAmS6alIK2xymiF853JEruib9fCnSVvdX5nMwOr00E2q2H52saBjj8lu+iYbGZZ3RChppggXKsDC6fDx7VNESt1Ae+IF+IAaJpejU7axdOTyYLkU+YKmOOClB5C8Y4/NmTSSw0u5Fwseqm83YgSAEylLLxNVvDDSnL05+mp0JaenGQn96fLaax1/OcDpycOq+qKIIj7BUkSQeyBVlMFXxW0V1FqNFgUYmu1gPMXkiuFJ0s9CKYBx6tl6mwyKZIzmWFMJOmjpLmo9XxREvBIMWoyf7PFPibewZ3nctuXCRff2w6lbkXf/cSydvJUkD0jCkDTBKZS4XEvcPVJUxrAL9a70QBSBQlvnYh1+wTwRJk9z5clXo54/nQ5jCbxM/j8FgS2C+qURBBjAEkSMXTGabBtBBdB8xtZcdHznKG+JSNTBha4IbePnOtEnnrzag75UvSCe/MKC2nwstTu+RdfHRPTLqreLDTdK9jW9QlASmjus0sUIYzeKAKLJgH9ogSgT5bmMia+VRnc7fv1VQWOERUPRQSappfKmp5Ga2UTBa+kynQE1NrD/QgSAHiNzZHiCrN/4bSJb3ItARbXWwByiaIEsAaSJ1L9gsTL0W6OhSCI0YYkiRgqYyNEOzCRs7BZVSGlgLKXeskVorVG3/t+AU+/PRopatakPlECmCy5po6Z2f4L8MS0i+1FL3bVXhrK8csDlmuUYn19rtUF/IMn7x0Zen21v85KkjxB8f6pDWmY7V7o2QI0r/HlrbsN/MzpQkSUAOCNKjM2X5aqvcFqs9aRkPee3vSiYPEoUs8C3lo20UoB+2/UQBDEgwKtbiOIA1DhingrLfbr9MbNUwCYKMVp1iQ0vcaKCw/pwQYAG+usSVGvUQEALC1OYfWGiWwp/FvGkdIQtDw++vFPHOi4qx0J5RQwmwZqPSHYGiZwIhNuf3QjjT+6kRw9ycrAageYKkbzZoEgefCCxEeRACCXGUIdj+d2W132mvyKufgKs5+ZM/Ezc9Hj/cGqgv/zjSy2u8mCtNaRsNaJFk/lFRYlW26zTbcQRBYBPh1JEMQoQ5GkMeN+zW/zWwWMbaoNgL52A+m5hwAA0xMGVhOCLL4o+fgRpRML0bROOVtH2+xfpbaxnkFzM/lvF0disiK7B2sBsKkLyAO4UZHwUNnGI0U3SLEBwJU6cKEYfc4f3Ujjv/VkLpvwqeGL0uqqANtiQqFqdl8EyY/sTHoRmFhj730jANB7Iu60BJzJuX2ipHGjVN6oKihrTI7iOLFCorgcAayFAE+Z+zG+tRwK2LDaGxAEcf8gSSKGxjgJ0SDUlI2lVRbx0VKLsFIL6PXkoMgaABTZwvxDoRTVdQGa3ETPYjMwylmWiMkqPbRNDXd+7D1QZlfbiZkmqhvRC3ixUEO1C3Q3VyBDhaztr6lk1xSR9Mx7idLd5Qw+u5zB+fMGHklcHcb+XeX2vXvWwV/fCG9PaoCNMKWliS5a7eH2pn6ybOH1CvtYO684uNP0X1+AXo1+TyfyTGiqzX5ZKqjRPkknvE7kOwkSjyy6QSdwgiBGF/o1Jog94pgsomKIJ5DNsQvtuidOPks3WLRHQHhV1eT+guutzTyTI0+QZHsLADAxw173sUdMPPaI6e2TDhxFAgBFcqGoDkTRxY1KKAJ8ofbpLBuAu11XcHc5em7XGuEGROtyfN67EE1pTe6/tdM98YvP0xpL8z1Z5ubq5cPv/xNlE0+U+5fU+bIEMDkqJHyLVztAQXVRUBDZFDHcTuUc1A0J39/qly6CIEYTiiQRB+Y4pNoAAFIKekuAwkVYmhUgz+bOYn01g9kT4aq2pRsqTj+kY2OjHL6E2EOzHjYeEqwmclmg1U6wCD263r/eKEKbbqB67ea+T0GWHWRloLm4jMzJeYiii7QcLts7kWEjNXgyOROdVvTCv1Vnt5/ihvv2HODJcwpKdnRlHy9IfBRpqSkgkz54TZLpAKrIOnkncSZvcxElJks/rLDjf+8JT6JO2FjqamgY/ZJ0odSfN4un5So9oMzdthyqSSKIcYAkiRgaYydFA+j1FGjQE4eYrq9moHpdmAURaNbDMIskMqEQ3B5cISpFuWyPidIt7gotGvjRm5N4/C3bqDdCM1Pyk7D1/bcBCI5HdmFbYb8jX5T8ho+8LGW8iJkvR0kse063ZYRPVMVwJZgiACuerDSaw5UI02XvtdiSoVvRaBJP3WDvfzrn4ImJ5B5IDa58bLeC5HOrEQbnSZMIYvShdNuYcvHixft9CGOHaNhIZ9kVstdTkC85yJccNCvRxwki23zSWjSyAjBREiwmOpWtIipbRUhO+LiqGJY0X7nOLuyCfhda9mDpNgFAg8s4STJ/xY9e/X1Zmp80g22qaPatZgOAmwnO1hzQLLJe8VORh1fZfKUmo2mEGnsmb+OvY3VeKclBSuoPP02nXUynXcykWeTL34DdC5K0w1w4giBGB4okEQeCH4A79um2tHeRtS1AkqE3ekgXNORLDnKFFjbWWbRH1AQoavQieX5+E69fmY/sszqhRaQ1ZlrueRnCTRYFUQQmEy6A3IQESwwFSSsOmKp6D0QvvGG74UqzuhnGPCwD+NHVMGr1n7zHhJ4QcPFFaasblS6fuCApA8Iq1hCWgP3jj30SL/27Pwprk2S2JB8AmoaEPDeQ90pNxIVSVIx8UZpODz6WnAyYthBJ6W13gfP58Pb3vXM0B6T9CIIYPUiSiKFw+fJluO74//Vs6CpkLpjji1Ic0whF6cR0/ywR19IhqYBt9P8K1nKAonuCJLORJG5NC/I36ZKCzvpBz4SFkR0AGzoww7VCevzRekSU/A7dvCy1PbFSZVbA7OO4QCV2urwg1Ssqzs93UOmcRPX2KtQhLW4zHURWk6W5b+tOovQflsKDN0T2Aj8zF7WcXMKn5HZ0JB9aVngcAJBTgFiAkSCIEYTSbWPKUfVJuteg23Hin//6x4KvBVGEqUflaGY22mPZNARUb4ehhicvsI7ZrhU2WJJUC2mtgl4jG2wAYKVDE3NrGuT8CrSchs42Gy//2x9/fl/n0O0l/8pvxHo+Pf5of7/oUxkmDG0u8lS1QsvxU1GbW6lgS0usw7cssijW+fn+1OMwqN1YRFoCLAcwbbYJghtsLVPESksKtj9bUmDYyeGtb66J+OaaiLN5a0+CFKdD824JYuShSNKYklST9PLLLw/9ffx0G592A8Yw1cYhKKHAmLoGUwdy3oK1mdk6Wr0U8nnvSprv4o3vzAWPz8w00GxFOzVmiy30vOX03W4oJ06pCCyFt00nC0fMQ3T2X7QtKy4cTg54ZdrQgTLXA+mRh+soJ/RjmuXSUuu60FenE7y2GN7BP6bSYe9aTNvoxp90ABomMMXdjg/rfce0jb/ZjIaufvYEM5m/WE3+KJyKrb67Fovg8YLkR5HqLRnWkIb3EgRxfyFJGlP4SNJhp8H8aNJxSLcBgFvZgjQ7j25zE6n8NBRBRe1OWCOUOtlBUpBWm2FRlHyuHojSZIml08qndVTuspyXqLF/hfU1QFHhmgZMJ3z93/jFv3+g41dlF4YlQPBcSTcFPDXJrvBX2hIKaa63k3cavYQ6G0UA5jNu0FXb1w9eju72ZnFKDc3CFySfw6jfEQTA/6+4G1ECgEeLwA93cM+7LfZRKQqh/Vxd1TDPpV7r3mOcAREqgiBGD0q3EcQe+KcfCAVFVFJBcbXZrkUe5yqZYEuf1ANB8snn6oEgAYDpZpEptwJB4nFmwg48v/Xf/fyBjt/hpKTDdQn/wTb7KLggbqKh90uEJobCpAjJhdg2gLvbGlJpO7L94Hou2DotBZ2WgpTqoGMDpjmcjyBVika4BO74+E7iABOld0zbWO3IwQYA75xyg43HFySeq6vRVKvifct8QUoPt5E4QRD3CYokjSFHNb/tOKbaAOAfX/p7+J3/51UAQGqO/Qp111jeJXWSXfS7tRZSJZaqai0KAATYxeg8j0Khf3JZadJAbVuFsL4Ge4I9X3WWgbSK7evLBz52SXLR6UrIpFi0qGOxOW4AEyU/ohQXJdm72fRcrxzrRLCy3V+8zkeUAODCuRbeeO1gx38vMjLQ8wJh05xv1gwBW7Fvd7Mj4V1zyb2S3jnlotJN/ngcJEhxNPF4RFYJYpwhSSIOzHFLtwHAb/69d+LT//5HqC7XMXGq6MlSC+32KWSzyXkbqd4IRMk1O6hviyh6UmJ3GsHjMqaE1kR/LdBBo0g+rgN0uhKULru6c6vY8YNtEXOnQnFY7wKnEroNVLiGi2u7FCSfcpnl5yzrcALZmsREqWMCGW7lXcMUUFCix/XdNfY98GXpDidS57xpLP5A35oBVKNziiOC5NgC3n7KxJ0W4CQMPyYIYvQgSSKIffKpX3gcn/73Pwpu2wLrrt1u571/gfIpFUD0yuqaYerNFyVjLQx7iFtbKABoTCpQnWU89xNPHN5JAChobiQ9ZHhjPnz8Ttq8LLWM8AEOF4wRpd0Jko+iDKcoyfZeZvPWMqbPnQr2J4kSgD5ZutsGnsyGXcfj1Lgf4fQkOwfHBfz+n7YDvP0UVWsTxLhBNUlEH88880zkdjydFt936dKlxMfEiTebHAc+9QuPo7pcDwRJcreD+woTJqxOG9kFNdgKxS7SXSmyzc5uJ752YdscuiANKiqON4xMmoO23GaixAsSz0+f6+E9Cz3M5OxgW1/JYrmiBltOZm0ELEGCJLpDjSZZjhDMTNM46eskuMt8FpjN28Hm81jJxWPcKJKaERUkH361ns19r/xIlD1oyR9BECMFSRLRx8svvxyRnp16IfH33UuCxrVW6VO/8Dg+9GS4+JwXpTjd2yYcK7zqnn7HGgDg7E+xdJu4tYVf/s9ngu0wME2pL9oDDBalNT3cblQsXJhwcGEialE/fS4aIQKA169Ga7BULbn+Z9jkFRd5xcVkismSJjGReWvJDTbAiwQNcJlHi8D/+2Ya37rBNp+dnsPTNIfTTZwgiPsLSRKxI/eKEF26dCkQJV6CfGHixSn+tb+NCx96cirYChNh+MLqsHxV93a4jxclAPhbxgT+x3eIhyZGPr/9a78efC1JLjptBT98tYGcwrpESyJgOkKwiQJwMpP8WhcmHPw353uYn9mbINVsFuYx7f5apoPgj1mpcQN2c1yq7VaTbXF46XFcAY7bH2371o00dBsoquEGhFGktY6IhglMaIA+pBV7BEHcf6gmiUjk8uXLEQHa7eN9UXrppZcGCtCzzz4bEapxLPj+H04qsT0G8Pb4o3qAMXFER7QzNzcVnJ9mErd0J4/5M1Gb8EVpxavBeWIimsNa9OqWGqYA2wAWzjaweJuJUpIgVW8sAdBgYX8z6OJ0Yp3Ea4YAv/Q9pwAt7nBvNYFzXLV63vtRNTy5ysV+dG87m1yF7bhMjgBAig3KVcmTCGIsIEki9sS96pPiJKXY/H2+RH3ta18b0tERuyFXMNBqsFBIvmCg6X0dFyVGE4+cYCKTk4FHC0BnQNasYUYjMAtnG0jJwOLN0Bhq3kq4dEpCGhbcSQe/8du/i9/9rd848Hm1vb5PPTtakwSEs9TOeObkuKEcxeGFKkmQqrHAGS9IK63wXC0adEsQIw/9vUPsiXhk6fLly8HG40eLkqJJ/r5xrVF60Om0ZeQKBjZX+xtX3txk5vCuxxp412MNzKXdoNia59UtBa9uscc2TKFPkAAgFXtOOsusIZXxZAYsPCWlhxNN2m6HH2c9m81YK2lOsL1rOmotosC2JNoW8PApHW0TaHPStFtB6lhAz6LO2wQx6pAkEUMjSYp22hdPuxFHy8LZFjZX0+i2JWSyVrDlC9EUJBA+AAAgAElEQVR6qesNtsX5m5sZvPzXRTw14eKpiWjKdJAgxTHEInq16oHOAwBEKXx/g4t03ahHP+Km0/3H4ctS2wq3OG2TzbZ7qhxuExowkw63guagoDkwB6z+Iwhi9KB0GxHwzDPP7HkILp9qe+mllyI1Sfz+QfuI+0OnzX71F862sHg7h0ZNRaEUytGVlRQunIyOn/VFaW07uZL7qQkXf3k9hZTchemJRj7lRATJjyING0EAHCd87bgoPVQMj2E67WBTZyJT4jqHX/GW7y94KblyRkSl4yRK09129PZ6J3petWpq7ydBEMQDB0kSERAXJD+FllTAnVTQPY4F2OOIsX4T6ZPn+vbvJEpZ7pPi4dOsTuf63Wi67i+v94vBTAroqkxQ3rhSxGSetUe4eW0SACChCjml9j1vv2w2JbynzKxG52qO1jsiJK64WxWBnAaYCf9lFz1ZKqrJUaWdBGm7GX6j8hr9PhDEqENx4THFn99GEIOQBdZf+szZFiTFgaQ4aLdlrK1kg+0vvjuJ11+fTHz+w6d1PHxax19eTyUK0kOlASk2bxwKBBFGu799wH4QhHAV3RtVL0qWixpON+Fwkob18kv8/VScX7u0G0FKpS1kc9R9myDGAYokEQPxI0h81GjQUNs4lE57sJnIRpeolQsWKg32cfATT1fwne+VI/d/63uTeM/T0SaZusUiJaXJUHT81WtxQXrjSnE4Bz4AUUiO2izkLCy2wo+5rgOkEv40VIRQjJLwu4xbnHeVNeCpcvi+f+UNOebTfgRBjDYkScRAdttp24fSbaMB3zxREgC/MXRclABEZOlb32MRpbe/sz7wtR852YXZdtA2gayX7uIFKYgicaj54QiUFIsm/exptj9JlAAAsVTao9xhXOVOMWkMSznWB/O2l6LzBUlVbdCMW4IYfUiSxpx4ym2vhdnEeGK6QNsUkVUcSFzgo1yImsNPPF2BkYqmxHoOC7loYjRaNB2rwWmbwHfeKKJQ2Az26RUZqbQFCEw8tJwMTewXp/1gmAJUxYXiOc23NmQ8UWbHWE45uFqPRniyAB7KI5FHi8B/XBfQbcuQ1eh5DhKkODS+jSBGH5KkMeeVV14ZWoQnPtTWZ7dduYkHC8cW0PbKEt12KCrdioZHZ0Mx+vEa62M0MxctyOk57LnTWkJ1M5ggRRCYpHR1T5QAWEMq3bG4wb2OG9YQ/bAiBqJ0Ps9+D242w8fe8BqL87L0H9ejMmVxkaSzOaDLnS4vSI4joFHTUCp3I+JJEMToQpJE7BmSotHGskRYDqCBiZIouWjUNRSKoRhdXY+KEgBsrGUjotT1RnLctcJlZKfLzHoGCRIAaGkHLQCS24QFoHJ37cDnJEvRPwQcN1yVwosSwGSJFyUAsFzgWxtInNsGAO89zcxotRr2gFrthONHcgrwo9pwZ9ERBHH/odVtxK4hORoPRMFN7DTdqEcv8lfX+y/6taqGVkMNBAkARDl8sbsVJUh37Qar5wLigAm6e2Q66w4svv5hJXpQ75xycSaHYPNh3xs3KAR/72krECSe1U74NT/rrVRmLRNkiiQRxFhAkSRiz/ipNpKm0aaUtrG8yZbu5086sEwRjbqGKW/mbjZvYLkjYHJGR7MeHXRmWeyjQ5ajApFKsz5LP/1EWPn8/R+7sLzhsZrX8Vpym4A0vMiL4wqo9Fi9UMc/pOV1PPTQbHhsEpDmPvG2vF6ZUwl9H/2I0l/cYef9s2dYhIyXo52PJyyIJwhidCFJInZNvKkkydJoYlkieoaI1ZsrOHX+JJY3U5E6oUzWhCBGr/D5ohmIktk1oaQU77VkyLIVyBEAbCKFaTAD+avX8shqvaD4eWvFC/VwgpQpDyeS5JOVwyaQ6zowy/W81K2oKAGhLAGD021/cUdBtaphciK6Zs00gKp36sWJHlxXQFZ2sUuXIgjiAYckidg3JEejiaImN3nkRcn1lrLzssSLUvCcjghARa/L5OfEqWZw31+9Fl06VtkIi8MFtQDXaEDNyLAGrA7bE4ILWQAqPaCIvYmSn6KbUADAxWIrKkrVanLEi3+UFksx9qhXEkGMBSRJBHEMMU0Rpsmu7Kemu9jwhKKrRz8S3NjFPl9kaafaAHFYXWZidHUrKlO8ILk9NgRu5mwOy69X0DOz+zyLfmQBmMswsbvRCI99XQdOAWh4q+kaJqBm+qNKALCQY89fbAmJghTXH16QsnIoldQCgCBGH5IkYlcM6rRN0aTRo9eVIHUlaCk7mF02PduB6K0Q21iZwFvOh6vY1lsKTk8Zkde4ssLEZvZktC2AZbDXSGfCjt7tzhSAauRx0ycdrFxtDOV8AMC2RFguk6StroCplIuHCi5OTIZRs5vN/vqjpPQbADgOMJ9x0ZsO02tbm+ldC1JOdtGiYBJBjDwkSWOMP79tmA0kSYrGi64lIOVd3P12AADw5s1sRJTubrGcVFyW1j1ZKpXbgSDFMbsuUikmTZ0OEyQf25oD0Ex83l5ID0ghfmtVwXtOhM2YtrrJogQA6Xv0tHzqoRYkriuC5QCvL4bL6YwuMybbFmjZMEGMCfS7TOwaEqTxIZW20fNGhHStWCdqZQkAE6U4vizxPPpEDZUNpW8/ADRiS+8LXL8iq52CJN6BWd/a28EPQATrdwSwaJLPt1ajx7bVDYu1lxtSsK3oCDaekwUbJwvRWXdWzMlsO/o9bCaMMiEIYvSgSBJxTyjVNl4IoouuLiGVtpHxokidtoJM1oxEkwAmSoVpQNFCCVja1vCT79pArRsVpkaN3S6UDEDOoLHRRRKGW8Da90MxktO5xMfthU5PxCkFaJmA6QnMakfAyUkmN4stEanJ6HM2uoPzYSs6oOsyThaMvvt2EiT+a4tqkghi5CFJInYNSdH4UWmqKOcNNBYlYCHc76jR3JPZcyOiBACllNEnSgCTJUGUkM030W6GURwZNQBAKqsgP6+hudRDKjug++MBmEqFkSJ+REkS/ko/k4v86Fzx+mOlqBG9uRp9flyQOm12vt2EQb4EQYweJEkEcdwwk0McjUUJhQUbUwsZAHVsrEVHiwwSJUYGQsKg2mye1QO5rRpSWSYQ23cqkcdISkI3xz0iedEvv/v1XkQJAG5dLSJTEDB3qp14/6tbTKJMbtbcfCZ8XwCBIAlUsE0QYwNJ0jHgmWee2Xfx9qChtgBFlkYVVxQgikBXl1AEiyb5NBYlTHkRpZk51jXbRDm4nxel5a2wZmn+LGt2tLzIxCqjbITvBxXVxixOZKNypOWThWQ/iKKLtRr7OHs04f6k5fhXfzjRt29tmZ0TL0u+IPHMx/pflgsGthEKkgAgNXtuN4dOEMQDDEkSsSOXL1/u67RNjAG6AzEtwvaKtk892UHLaxS5vDiF+bNcC4ClNCamwvEjStrBaiW5T9KpBSZW1VUmRzyrq2WcOFFBaZrd3qgDFgoAhtFNEoArAIKLdZ2NIInPkJvPRmXJfRuTttdeKyPO2nIWP/VkHX96W0UpFx29kiRIcSTZQTmbvOKOIIjRgSSJII4bQmgK7YqMbLl/gOvS7WxElKpbckSUAEAULThO/0dIq52FkrJgeOmu6kq4xH/lioDZ0+FjLQxnJInRk5CUtPN7JwXHLPRHld7mydK1W5P4qSfriFNrsXP8yVMWvrEmo+K1d3pqzgoEqawBM1PshHu2gK3tg50PQRAPBrRO9RgwjD5Jly5d6ku3EaPJ7/1Pv8a+0Fmko12RsfJmFbmiGXnc0u1oC4DqVr8Q+aLUameDzUdNhcvms0UR2SL7uFm/y5aZWcigubwO20xeBbdXbFtg0SQAXW7FfnyVWbw+qaFLaOgS3vl4HcaA4M9PnuoXyesNJkdlDVisU6E2QYwjFEk6BjzzzDN9+3YrTvEhtjTUdjzQJpJtIFc00eWCKVvrGiZno4JgWdGYzfpdZh25kmcjRvgCpx8xYLT7/xarbw03FaVqNrQU6/3kL8zr2oA/so0XJX8J/2ZNhZaK9j8CEIiSKibLEQBkMuF+XpB63mo3SrURxHhAknQMOGgkiRcikqPxwDJdyIoArcDswbIkbKyy1JeSs6FoXEqu7gRRoGZdBuoOyrP94tOqCYEozZ5m8rF9V0dpEqhtcx81WljPJIsqovGrg7OlC5hKu1ClaKrtR1XgQnTBHnreeJY4tzbYMerlsLPk6VxUjnaiZwNb28Nvb0AQxNFCkkQQxxjH0CGqaVjybGS/2RMGihIAVNb9/kIsijIzz26rQh1apj+KUpq0sPT9ekSQyqeyWF6tQpKTi8D3dB6xQby+KLUsIMd9yl2pJ4uS9yqBHCXx59dTOFuItuO+cjfsA2U6AgQB2N5MBREsgiBGG5KkMecg89uo0/b4Ur+ziOKZBShgogQZaNVc5EqDm/w0F6NF1kbKxezp/shKryP2iVK2JELIFeCaPaTEJWhZBevXKpCLGRgHH90GUWRClxQVShIln0IxXJm20VGRzZlot6JjTG6s9ZeEr8ZSiIYhRYbfUhSJIMYDkiTinpAUjR+ipMEyFeg6u5hrnv+0ai4mvCkhfDTJ2BRh5YBUbLX7+l05ECVVCO2j1xHh2jqyJSYTV7/L5EVQNGipUEIccTir2wAgm7bR1iVIohsMsc14QSLHBWrVqOxohWQhzObCBOBuBclnezN8fCo97EQiQRBHDa1uI3aEBGlcYfKjyMx6ZEnH/Dm2+YgSYFsCjM3wY8KfQmKkwlTc+l32t5aWlSNbdbtfgCRNQ6XCmjj6giT7jZMOAb65uKI6wRgSn1pVQ60aTbHlVCfY4txLkIoTPRQnejBowC1BjAUUSSL64FfDUart+KG3XGSLYZRFnnRhbYe3TdcApHzkOSllDeASTqu3WOSoup3B5q0mJK8WqVdlg22ZKK3Cri+hu33wpkK2xaQkm2bvu9VFEE0yXUDhgkaK6kRmtQFMlt71kI3VTvLrz3k9kL7/3bCgqVjqwbZCqSxO9AAAeps+VgliXKDfZmJHSIrGF7nHzCFTmAFgYm0RmPNGkrTrCaLUA6QeizwJrSbcHBOlM2fWAIQjS3xBAgBBTiFVtGB2w6hMpijDqK2hB8AVhlPiLMkO2rqE2Xx/TRLQP65OUR3MT/dij5Jxwgt+8bLkCxJPsRR97sRUF9W7oSAVivHXJghiFKGYMEEcQwQx/PvI2VyD21XgdqMFy+161CwkTQEKYbNIodVfcd1o5CDIqWBrbLCRI0qKfdRkiuH7Zkvs/X7nxecOeDZRbG+l21YXSEvh9q5TBtJZM9iu3cni2p1s4mucyAAlFZgs706Qkuhs0t+gBDHqkCQRO+J32qZu2+PF7/7ahwEAjlSK7DdMFemcgHQujCIJyyaEZRPq8ibbUcgG2+REBXovH2xrdxw0alHZAgA5pcLpRJfPG1YOqcJwIkl8bRDPrc3oKrPHS/2TbuOypFts8zEdttkucP5CFZOzHUzOslATL0iuA6TS7ImdTRnIDF4pSBDEaECSdExI6ro96D6/XcDly5cjGzFeuJoGa30JZiEHZ3Ot7/50ToBhqrBOFYJ9gSgBKBdrkcev3QlTao2agsZGC3JKhZwKRcXsajBqazCsHNzWMj75X/+toZ1PSrOx3mSyZHN9k3YjSnMnO3h9RcXrK8lL9+OjTACg1wvFrNuhqBFBjCMkSceEl19+uU+G/NvDmO1GjB6/+St/O/jaLOQweY6ZgILNyOPsTh3WqUKwibVWRJDsXjT9lC1JyJYkiN1w1Vh7pQoAyPVE5LYOp9Wi5QhIaTZk0YUsugNF6Y2aAAFAo65h7mQHcyej1drXN1Rc32CPF4VkQVpZDAvXeUHq6t7XFEUiiLGAJGmMuXjxIi5evBjcThIl4njji5Ika1i/bqBaYVEjBZuwnMENERudYmTTX+0FcgQA+iKrV3JqoSzkeuHHTblmDzWKxOPHiWTRharZ2N5MY3szjTdrTJB4rr45kfganY6MH9zOYGkpG2wAkyNekAZhWSJkhea3EcSoI3z96193Xbc//Hwvjuo5R/le43p8vigNihg988wzkfv2IlJ8Gm5cv3/H4fg++//9GACglqcAAKmuCGuG9S+SRRZpkTJFoBY2SBSnmTikboZNJK2H2bp7X5LsQg5WvYJMjb2G66X1fuUfPjHUc/qtf/55zDw2DwCYOn8CAGtIoE6dBACsLuXw+LtDSXMh4M7S6chrLJzvohNLm5m98DhOnmpj5Xb0fdffaAdfi3cXAQDOvITKrVWk0hY+9nd/ft/ndJDnjdr/v8N6zlG+Fx3f0T/nKN6LEunHBD+KlCRKSfuoBul48eLPPxaIko+8sQlrZhqWw9bFiz0FSCsQdCY8zmY7EKXgOde7qE1pQCEX2d8pZZCpde4pRwfBttjstCROzLcAhLVVghdveudTlWBfzUihqLIWB/VaGEU7eSoUIR9JjkaJlKIL++5+j5wgiAcVkqRjwE6CRBA+L/78YwCAy9+o9N0nuC5Mow1FzcJNh520HaOHXjlMPwm5FjIw0OkwybDq4Wv98vumDuvQI1QrKeS9mWx5LeybtHiriEceDqNesjw4HVYsseerstF3X5Ig+TjzLN3or3IjCGK0IUkiIvipNuq0fXy59N4yACZLfjTJxxclABDUfoHwyWRCUfrQ08ObzzYISUoOn8sCYHF3XbseFaXX3mAtEN721hpKahc1IwWHcyDDjJZtSnJUfnhB8pmY7KK77qLX0fruIwhitCBJOkbsZTUbSRHhyxJg4w9/HJUFXpDsjAmpo0DIscaR/+iELw49ANl91xrsBReAqjrBzLRmXQ2iSfcSJYDJ0tveWosIEs/JPLtj+UbYAyqbN7GwEDbUrEgmABOm0d8niiCI0YQk6Rjhy9G9ZOnll18+kgsbMTo895jD/Z9oJDwiuev0UdJsqMgXDLguIAhMlGa8++RYrdK160U8/c5a32vMFlikaL0RfjT6gsSTzZuR22trGQxeC0gQxKhCknQM2WmVG/+vD0WViFEimzPRbrFoTtcU4Xj9knIFI9KZe63KHjM3Yfa9xmzBwmZLxvpyFusAnr4QRoySBMnHjyL1Ohq6m9cAvG8o50QQxP2BJInogyJJxKhhW0yEmg0VmRNRUeJRVbtvhElcljZb/R+L37uSh22JcNx2ELECkgUJABxRBOR791MiCOLBhiSJIIixQJQcOHZ/f1xRdINoEsBEicGkpuPJ1M2WAlvQMD2jx18CthV93WZDxdSMjgI37HZ9m32cdusagP4VggRBjB7UcZsgiJHnd3794wCAUtGA0ZPQqGkRsRFFFhnNF41g63XlQJB4NjfS2Nxgo1NsS+wTJACYionUnLfKjQkSoOVU5GcOZ/wKQRBHB0WSCADROiSqSSJGlUZLRpGbNOI4AixPcjJ5G0ZPgsr1TpJkJjd+us6nWOrBMESYXGRKkVgB9yBB8mlv34WWozJughgHSJIIAP0NJ6kmiRhlUmkrGDYry04gSgD6RAlgsmRbAoql6LDe6GMcTJajw3B5QeKjSARBjAckSQRBjAXt5VsonT0T3Oa7Xse7axs9CZCj3bMlGRBFVtQd75eU0vo7aGuqjZu32ay6ajWFMxduAADuXmHiJAo04JYgRh2SJAIALf8nxgdJcoOVbSs3wtly6UwGc28J57C5YDVH8TEjACB6gackOQKYICVRqWoAWujVJeh1Gk1CEKMOSdIxYTfz2/j7KN1GjCL+gNugBYAgAN7/Zb2zjbU3JyOiBCSLkt5mEaUzZ8P+SHdusyX9cUGqVlNDPQeCIB4caHUbQRBjgdkb8HEmRIuy195k0SXRDjtu+6vY9LYUCFKcM2eb0DsyarVwJhsvSCyKxEPyRBCjDkkSERBPtRHEqNFuKei0ZWSyJrI5rjN2giitvZmFbQmRrTTZQ2lycPG2T62modFUkc2awdasa2jWNfTqyZJFEMToQek2IjLLjRclqkciRplM1oSkmLDM8G/BE49GU23ZSRfVzXgECPcUJVGKpqP5Lt7ZKQvNihF/CkEQIwhJ0jFjt3VJVJNEjBoCFxff2kgH/YxkxQlESfBcxuXKiiammRDxsnTl1RIAIFNi0ajzD9eD+3YSJLVhATn2tdWsHuBsCIJ4ECBJIgAMHnpLEKOCG1uktrWRRv4U+5oXJYDJkhtboHZmvoPvfXcm8bVvXi8CAForXcw9EvZKis+B80nnZGA6m3gfQRCjA0nSMWfQ0n+A0m3EaGK2RWgFZkD5PEt7NZsqZCW0qC1vMO2Jk1FTKp1ky/ZrK+yjsVNTgmhSKmWgBWDtWjjU1nFNiAKLLKkN9txmxWCSRBDEyEO/yceMQRGj+H5KtxGjiGsC3doigAVoBRuNuoZCsRfIUrcjo9UIO2K/caWEt16o9b0OL0upVHJ9UX62i+oa4LgCMmkd9TL7OE232L+mTbPbCGLUIUk6ZtAKNmJcEQas1fVFySdXMPpECUCfLDVrMqQMIKuA5XnS1HkRWzcd5Ge7g49DzcM1mgPvJwhidCBJOmbEI0ZJ6TaqTyJGFTnNUmqKYaPXkJDxSozuJUqAH1Wq4Adv9NclydxD44KUSYcDbwWDiVandaDTIAjiAYEk6RjDL/0niHHAdQQIIksVK4YNvSLB1ViPpE5bxum3hGnkQqmHjpFDu60E+179/hQkWYQo9o8q6bS81+mwx2cyJibmNPTqeuRxhpEB0IS+tTrUcyMI4uihZpLHGJIjYpyQlVCAhMmweaTQC/ebZv9HXjZr9u1zHBGOEz7WFySeTkdBoyahtamgtanAbBmeIDEKs6W9nwRBEA8UJEnHiHizSIIYJ1yHpdF8dhKluCwliRIAZHIOGrXkZf6WEY02iWnWIMlsrUNJKUlPIQhixKB02zGFl6W4OFGEiRhlsrlwJIluhANueVECwqhSqxXtuJ0rWH2vaVnssbI3CDcuSIbJeieJ6RwUq3LQUyAI4gGBJOmYQ0JEjAu2LUBvK0hnzaBQ25YAyQ4jSt2OAMMIpUjS3aBDNgDMzIVF2YYZLewGmCy5tg2gP/2mZdnHqe4tbDNsaiZJEKMOpdsIghgP7AG7Y9kyVY3OZVNbycv5VSVM3RmGEmwAIElusBlmJxCknHwb0/MirOrSPk+CIIgHCYokHXMo1UaME47Doklqhi37F70oEYsohY9T1V4kohQXJf2aX1Pkop1VoWgDmquKCnKTaZjdsKbJFVIQ3ClYtavDOCWCIO4jJEljxMWLF/HKK6/s6TkkRcS44TgCLJ2lw/KiBXeCfe2qMzC4WiKxo8HIReuR6rccqP0lSTB77DUCWRKjhdlKSoHZNeEKqWBfulw+8LkQBHF/oXTbmECCRBAurK5XKyT31wwBQK8T/chTElJthsw2AGhmY6+jSxFBctrbwdei2w6+zpygvz8JYhwgSRphLl68iIsXLw68TRDHigEZMaEavWM3ogSEoqQ4YrDJqRpUxYHKDcs19RRMPYwgrb9Gq9sIYlygP3dGGD5yFI8i+ZElX5r4qFHSKJJBXL58eeB9ly5dwuXLl4N/CeJ+IzosmiQDkWiSUHWBiQ24Khs50uuI4MfP+qI0+UQ0jba16sLtJP8t2W0YEIxUZJ9u5JE50QAAmLS6jSBGHpKkMcWXpldeeQWum/wn9k7ptkHPIYgHFo0Ji+i4SGdYYZGhOlDS4cecZIbV28VHe2g288FtVTXRrFjIl9nj/akiQsaC25Ehp9hcNkH2xagFV1UgGCZErAEAet0MrE4BZv0qAJIkghh1KN1GHAiKIhEPEqJhQivOoLve67tPSE3CTUVrjPJ51tRIVcPVac1Kf+W2k1YgyClOkKL0uhkmSJV1tiM1vd9TIAjiAYIiSceYnZb/X7p0CcDO6bZx4IMf/CC++tWv3u/DIA4RU7ci0aQkWp185LZwuwF7rhDcdo0WuhCQSrMIa329Fdw3t2XjLteQUsimgB7gbG8M4egJgrifUCTpmOGL0Msvvxxsg+AF6dKlS4E47cSzzz6LZ5999uAHesh88IMfvN+HQAwZwWXF1KLtwE7JaNfD1Jqph9EhPprUbigQ3Wjhdn6D1RRl1xp979H1WgucWOkGW3VBR67MPkqFbBhpklOUbiOIUYck6ZjyzDPPBFt8XxIHiSjFpSlJpD7wgQ/gAx/4wD33EcROyE5syb4t9bfcBhOldoMVaZ95tIsLb1lLfL3sWgOuwaJGp85ZOHXOgvzjevJ7N1kLAKOyCjmlJT6GIIjRgiSJ6MMXJT96tJsIEs9LL710GIcFAH3S9Nxzz+G55567574kKNU2XgiCDCPD5ERpG9BKU3Acnd1pS3AtE64jBNvJhWbk+RfesoaUbsPMZyObL0c8RpoJVnVBh97NQ++ydJ1myIEgOVmKJBHEqEM1SceQ3TSRjK9u80UpSZji++6VbnvppZd2nZL72te+tqvH3YvnnnsuIkRf/epX+1Ju/m3+cf6+r3zlK0M5jmHxkY98BADw5S9/+T4fyYOHvbUGaWouuO04OkTRW/DvGIAYDq5losSE5+qreQBdaFzX7K5qYel2GvNnmWzV/zysRdKnS9C70Y/QtCWhI2Uh2G0QBDH6kCSNKX5/pL124R7EbtJtg9oGDKpTSto3Cum1D33oQ33SlLTvwx/+MP7gD/7gKA/tWPNPX/wAPv0v/i0AQDMF4PYqjHMLEFxzx+e1/jiFlRNhf6Sey0Spy80nWbqdRv72JuxcGB3SltcwB2BtPgdhfQVpK0zruWZyg0qCIEYLkqQxgm8gOSw5irOfJf/3Sr/t1JMpSZqS9u0mvXaUfPjDHx64jxenpH0f+chHKEJ0AMRUJnLbFRRkyy4MUwYkKxJNunUtD5wBTt6JilQzbWK2YUf23TldRKbV3x5gsgnoniBtSw2keyJ0AJ/6pV8Y4lkRBHE/IEkaIw5LjHzuR4ftQem23TS7jItT0oq2Qfv4lNthp9ruFW3abWrt+eefx5e+9KWhHddh8nshBXAAAAZ/SURBVMILLwAAvvCFLwz1dT/1K38HX3jlTyBoKbi9hGiO7X3k2Q4TJI+VM8DJO+zrZrr/aXdOM6nq5GRkWha05TWYRbbu32hVIIkKbMdEukdlngQxTpAkjTiHGTXaK88+++yhFm3vlT/8wz+M3N5LF3Fenj70oQ/13b/Tvp2k6qjTb3Fxev755wEAv//7vx/s++hHP9q3bxwQtBTUW4tIvescAEBVtmCYU+y+lAJHyEUev3KmhTzX2qhakDDRsANBAoBCR0AlC5SK0ecCgGUbwdcv/N3/dIhnQhDE/YIkacRJEiR/34MkUA8ySavc+Nt7HdHCC1Q87bZTGg7YWyH2flJyX/rSlwJR2okkcfrYxz6GL37xi5HH7XbfUfLCxffjC6/8CdKFWXTn51C8xfb3mCtBSLH6I0evQUyXgudpDRFabMFvpyCj0Ol/j9psDqX1FoxWBfDTe2YbqqDiV/7LR4Z+TgRB3B9IksaYoxIkP/32IEWRBhEXIv/2MNsBfOUrX9nX7DvXdYPUmv8vT3xf0mOGyX4iSx/72Mf69h1Wam0nXrj4fnzhz24AANbTOmb1NLRbgKo1IedKqE8wGfJFKV1nJtRMOch3Q1GqeQGjtDflpJJmtUtXX3oBjz77BWSsaEqPBIkgxguSJGLX+HVI8XqkURpdslPE6EFgUHQoXszN3/aF7Pnnn0+MEiXt8yNFPvcj1RaXphdeeKFv38c//nF8/vOfj9wGENk3iBfe9xB7H0+WeIpVJ/jaMqKhIl+UalxGTddCUUp5RdoTt1hurqeK6DU3SZAIYgwhSSKONaPSUPLLX/5yIEaDVr8l1R4NKuSOR7ri0jRoX1KkiOeLX/xi4mMOM4rki9PnPve5YN8nPvGJ4PYL73sIX/izG1j646/iHe//DVitGuRcmGaDaSGDaIfsWs6FKqiRfVp1Gymlv0GkZjj4RRIkghhLSJIIYgQYJEgf+chHgjojXoj8r5MKt7/0pS/hox/9aBA98r9O2sczKIWYJEXxfX7KLb7vqFJwL7zvIXzij/v3//W/+lW845c+jw56gSh9819+CD/zDz6H3gSTpO9cvoSf/TufBgBMbLRRnQlF6e//XPnwD54giPsGSRJxrPGjSA96NGlQGu7LX/5yn7zs1ArAF6qk9BovSntJv92rSHun+qy4PCXJlB8piu/jU26f//znd1UH9ov/2WTw9de/J+zwSECrNtGbyPft/0dvMfAi+gXpk5/8JD7zmc/c8xgIghgdSJIIYkQYFE3abR3SbvBFyf/6sBkUSdqN8PDylCRSn/jEJwbu+9znPodffNrF3/xL4L9/u1+fpOP//nYoTv/Vu10ADXwHwF/820/hs5/97D2PiSCI8YIkiSBGAF6M4vVJ8WX98SgSH1nyv06KJvFRpKR6pIPgp9biKbZB+3dDvHh7LysKeYF68cUX++7/5r/+BL75r6P7/MeRLBHE8YEkiSAecHZa6u+n2wbVIPHw98VrkPh9h8EgATrKtgA8fJH3Xts1+LL0yU9+su8+fh+l3ghi9BG+/vWvu/vt6XIUzznK96LjO/rnHOV70fEd/XPizxsUMYrvp+/f0b8XHd/RP+co34uOb3/PoUFDBEEcOkkF2Tvtf1CJp+b8yFFSVIkgiNGHJIkgiEMnXnfEd+E+ylYAByGpdokgiPGGJIkgiCOBF6FBXz+ovPjii1SwTRDHEJIkgiCIXUCRJII4fpAkEQRB3IPPfvaziZGkz3zmM9REkiDGGFrddsDnHOV70fEd/XOO8r3o+I7+OUf5XnR8R/+co3wvOr6jf85RvBdFkgiCIAiCIBIgSSIIgiAIgkiAJIkgCIIgCCIBkiSCIAiCIIgESJIIgiAIgiASIEkiCIIgCIJIgCSJIAiCIAgiAZIkgiAIgiCIBEiSCIIgCIIgEiBJIgiCIAiCSIAkiSAIgiAIIgGSJIIgCIIgiARIkgiCIAiCIBIgSSIIgiAIgkiAJIkgCIIgCCIBkiSCIAiCIIgESJIIgiAIgiASIEkiCIIgCIJIgCSJIAiCIAgiAZIkgiAIgiCIBEiSCIIgCIIgEiBJIgiCIAiCSIAkiSAIgiAIIgGSJIIgCIIgiARIkgiCIAiCIBIgSSIIgiAIgkiAJIkgCIIgCCIBkiSCIAiCIIgESJIIgiAIgiASIEkiCIIgCIJIgCSJIAiCIAgiAZIkgiAIgiCIBEiSCIIgCIIgEvj/AeM0DNFcVsiAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {
+      "image/png": {
+       "width": 600
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Image(\"./images/umesh_flux.png\", width=600)"
    ]
@@ -599,9 +604,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {
+      "image/png": {
+       "width": 600
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Image(\"./images/umesh_heating.png\", width=600)"
    ]
@@ -615,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -633,9 +654,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.43381086e-04 1.48043747e-04 1.60408339e-04 ... 7.04197023e-05\n",
+      " 7.04197023e-05 7.04197023e-05]\n",
+      "[[ 2.88485691 -2.55429784  9.97768184]\n",
+      " [ 2.87565092 -2.60469781  9.8884092 ]\n",
+      " [ 2.85832254 -2.65291228  9.97768184]\n",
+      " ...\n",
+      " [ 1.46082175 -1.15569203 -3.62914358]\n",
+      " [ 1.4443143  -1.1321793  -3.65475081]\n",
+      " [ 1.46884412 -1.15657736 -3.68206543]]\n"
+     ]
+    }
+   ],
    "source": [
     "print(umesh.volumes)\n",
     "print(umesh.centroids)"
@@ -652,9 +689,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {
+      "image/png": {
+       "width": 600
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Image(\"./images/umesh_w_assembly.png\", width=600)"
    ]

From df4c8ca40890f07ff2c631b866d01c21a52f4eb5 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Mon, 18 Jan 2021 19:12:28 -0600
Subject: [PATCH 106/139] Adding omp header.

---
 src/mesh.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 5e035b20e6..f335cd3a22 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -9,6 +9,9 @@
 #ifdef OPENMC_MPI
 #include "mpi.h"
 #endif
+#ifdef _OPENMP
+#include <omp.h>
+#endif
 #include <fmt/core.h> // for fmt
 #include "xtensor/xbuilder.hpp"
 #include "xtensor/xeval.hpp"

From 49fc6d0590a93194bb8dc248b63fc257fc570b12 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 20 Jan 2021 17:50:03 -0600
Subject: [PATCH 107/139] Only updating scores on local mesh elements.
 Broadcasting tally vector to other procs to ensure scores are written
 correctly.

---
 src/mesh.cpp        |  8 ++++----
 src/state_point.cpp | 11 ++++++++---
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index f335cd3a22..74664eda48 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2266,18 +2266,18 @@ LibMesh::set_score_data(const std::string& var_name,
   std::string std_dev_name = var_name + "_std_dev";
   unsigned int std_dev_num = variable_map_.at(std_dev_name);
 
-  for (int bin = 0; bin < this->n_bins(); bin++) {
-    auto e = m_->elem_ptr(bin);
+  for (auto it = m_->local_elements_begin(); it != m_->local_elements_end(); it++) {
+    auto bin = get_bin_from_element(*it);
 
     // set value
     std::vector<libMesh::dof_id_type> value_dof_indices;
-    dof_map.dof_indices(e, value_dof_indices, value_num);
+    dof_map.dof_indices(*it, value_dof_indices, value_num);
     Ensures(value_dof_indices.size() == 1);
     eqn_sys.solution->set(value_dof_indices[0], values.at(bin));
 
     // set std dev
     std::vector<libMesh::dof_id_type> std_dev_dof_indices;
-    dof_map.dof_indices(e, std_dev_dof_indices, std_dev_num);
+    dof_map.dof_indices(*it, std_dev_dof_indices, std_dev_num);
     Ensures(std_dev_dof_indices.size() == 1);
     eqn_sys.solution->set(std_dev_dof_indices[0], std_dev.at(bin));
   }
diff --git a/src/state_point.cpp b/src/state_point.cpp
index 80349c9f72..da13e4f071 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -828,13 +828,14 @@ void write_unstructured_mesh_results() {
           int nuc_score_idx = score_idx + nuc_idx*tally->scores_.size();
 
           // construct result vectors
-          std::vector<double> mean_vec, std_dev_vec;
+          std::vector<double> mean_vec(umesh->n_bins()),
+                              std_dev_vec(umesh->n_bins());
           for (int j = 0; j < tally->results_.shape()[0]; j++) {
             // get the volume for this bin
             double volume = umesh->volume(j);
             // compute the mean
             double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations;
-            mean_vec.push_back(mean / volume);
+            mean_vec.at(j) = mean / volume;
 
             // compute the standard deviation
             double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ);
@@ -843,8 +844,12 @@ void write_unstructured_mesh_results() {
               std_dev = sum_sq/n_realizations - mean*mean;
               std_dev = std::sqrt(std_dev / (n_realizations - 1));
             }
-            std_dev_vec.push_back(std_dev / volume);
+            std_dev_vec[j] = std_dev / volume;
           }
+#ifdef OPENMC_MPI
+          MPI_Bcast(mean_vec.data(), mean_vec.size(), MPI_DOUBLE, 0, mpi::intracomm);
+          MPI_Bcast(std_dev_vec.data(), std_dev_vec.size(), MPI_DOUBLE, 0, mpi::intracomm);
+#endif
           // set the data for this score
           umesh->set_score_data(score_str, mean_vec, std_dev_vec);
         }

From b11e03846072c30b373ee0887d5f00f3b411a6e6 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 20 Jan 2021 18:32:03 -0600
Subject: [PATCH 108/139] Writing unstructured mesh results after the
 statepoint file write is complete.

---
 src/state_point.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/state_point.cpp b/src/state_point.cpp
index da13e4f071..d0fabf0e3a 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -54,11 +54,6 @@ openmc_statepoint_write(const char* filename, bool* write_source)
   // Write message
   write_message("Creating state point " + filename_ + "...", 5);
 
-#if defined(LIBMESH) || defined(DAGMC)
-  // write unstructured mesh tallies to VTK
-  write_unstructured_mesh_results();
-#endif
-
   hid_t file_id;
   if (mpi::master) {
     // Create statepoint file
@@ -315,6 +310,11 @@ openmc_statepoint_write(const char* filename, bool* write_source)
     if (mpi::master || parallel) file_close(file_id);
   }
 
+#if defined(LIBMESH) || defined(DAGMC)
+  // write unstructured mesh tallies to VTK
+  write_unstructured_mesh_results();
+#endif
+
   simulation::time_statepoint.stop();
 
   return 0;

From 894fcbe23ca1716e9b3deb7bb6dee40a2bfeb4dd Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 20 Jan 2021 18:15:30 -0600
Subject: [PATCH 109/139] Updating CI to use a libmesh installation relying on
 its native distributed vector.

---
 .github/workflows/ci.yml        |  2 --
 tools/ci/gha-install-libmesh.sh | 22 ++++++++--------------
 tools/ci/gha-install.py         |  2 +-
 3 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5f9356040c..39651c6434 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -98,8 +98,6 @@ jobs:
           sudo apt -y update
           sudo apt install -y mpich \
                               libmpich-dev \
-                              libnetcdf-dev \
-                              libpnetcdf-dev \
                               libhdf5-serial-dev \
                               libhdf5-mpich-dev \
                               libeigen3-dev
diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index c5cdc5c408..c9f3de2866 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -5,20 +5,14 @@ set -ex
 CURRENT_DIR=$(pwd)
 
 # libMESH install
-# cd $HOME
-# mkdir LIBMESH && cd LIBMESH
-# git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
-# mkdir build && cd build
-# ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-mpi
-# make -j4 install
-# export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
-# rm -rf $HOME/LIBMESH/build
-
 cd $HOME
-git clone https://github.com/idaholab/moose MOOSE
-cd MOOSE
-
-./scripts/update_and_rebuild_petsc.sh CXX=mpicxx CC=mpicc FC=mpifort
-./scripts/update_and_rebuild_libmesh.sh CXX=mpicxx CC=mpicc FC=mpifort
+mkdir LIBMESH && cd LIBMESH
+git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
+mkdir build && cd build
+export METHODS="opt"
+../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+make -j4 install
+export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
+rm -rf $HOME/LIBMESH/build
 
 cd $CURRENT_DIR
diff --git a/tools/ci/gha-install.py b/tools/ci/gha-install.py
index d3fdfe2efd..9c163c1dcb 100644
--- a/tools/ci/gha-install.py
+++ b/tools/ci/gha-install.py
@@ -51,7 +51,7 @@ def install(omp=False, mpi=False, phdf5=False, dagmc=False, libmesh=False):
 
     if libmesh:
         cmake_cmd.append('-Dlibmesh=ON')
-        libmesh_path = os.environ.get('HOME') + '/MOOSE/libmesh/installed'
+        libmesh_path = os.environ.get('HOME') + '/LIBMESH'
         cmake_cmd.append('-DCMAKE_PREFIX_PATH=' + libmesh_path)
 
     # Build in coverage mode for coverage testing

From ec41b43624a0fefb33eba7ea1a85e31251bae0a4 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 26 Jan 2021 22:41:02 -0600
Subject: [PATCH 110/139] Adding explicit compilers in libmesh configure line.

---
 tools/ci/gha-install-libmesh.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index c9f3de2866..751c42fb06 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -10,7 +10,7 @@ mkdir LIBMESH && cd LIBMESH
 git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 export METHODS="opt"
-../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
 make -j4 install
 export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
 rm -rf $HOME/LIBMESH/build

From 9915e506fffe2d392022b3bada048b37ae1e6ca6 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 26 Jan 2021 23:00:06 -0600
Subject: [PATCH 111/139] Re-adding the netcdf libraries in CI.

---
 .github/workflows/ci.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 39651c6434..5f9356040c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -98,6 +98,8 @@ jobs:
           sudo apt -y update
           sudo apt install -y mpich \
                               libmpich-dev \
+                              libnetcdf-dev \
+                              libpnetcdf-dev \
                               libhdf5-serial-dev \
                               libhdf5-mpich-dev \
                               libeigen3-dev

From 0feb12e2ce3e446186fdbf30700b6b4ec5ba8523 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Tue, 26 Jan 2021 23:16:54 -0600
Subject: [PATCH 112/139] Updating unstructured mesh test files with files
 using correct element connectivity.

---
 .../unstructured_mesh/test_mesh_tets.e        | Bin 354408 -> 18 bytes
 .../unstructured_mesh/test_mesh_tets.exo      | Bin 354408 -> 354408 bytes
 .../test_mesh_tets_w_holes.e                  | Bin 353440 -> 26 bytes
 .../test_mesh_tets_w_holes.exo                | Bin 353440 -> 353440 bytes
 4 files changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 120000 tests/regression_tests/unstructured_mesh/test_mesh_tets.e
 mode change 100644 => 120000 tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.e

diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.e b/tests/regression_tests/unstructured_mesh/test_mesh_tets.e
deleted file mode 100644
index 90e74736037cc1d2c7fd1e9bc99a93b3576d2c92..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.e b/tests/regression_tests/unstructured_mesh/test_mesh_tets.e
new file mode 120000
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--- /dev/null
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@@ -0,0 +1 @@
+test_mesh_tets.exo
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diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.exo b/tests/regression_tests/unstructured_mesh/test_mesh_tets.exo
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diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.e b/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.e
new file mode 120000
index 0000000000..a2e2dd1d48
--- /dev/null
+++ b/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.e
@@ -0,0 +1 @@
+test_mesh_tets_w_holes.exo
\ No newline at end of file
diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.exo b/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.exo
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From a1c29c1f6d326412e571b57e32bab3bc557f412b Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 27 Jan 2021 09:09:28 -0600
Subject: [PATCH 113/139] Adding a serial build of libmesh to ci.

---
 .github/workflows/ci.yml        |  7 ++++++-
 tools/ci/gha-install-libmesh.sh | 10 +++++++++-
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5f9356040c..08f815c3a4 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -49,6 +49,10 @@ jobs:
             python-version: 3.8
             mpi: y
             omp: y
+          - libmesh: y
+            python-version: 3.8
+            mpi: n
+            omp: y
           - event: y
             python-version: 3.8
             omp: y
@@ -58,7 +62,8 @@ jobs:
             omp: n
             mpi: y
     name: 'Python ${{ matrix.python-version }} (omp=${{ matrix.omp }},
-      mpi=${{ matrix.mpi }}, dagmc=${{ matrix.dagmc }}, event=${{ matrix.event }}
+      mpi=${{ matrix.mpi }}, dagmc=${{ matrix.dagmc }},
+      libmesh=${{ matrix.libmesh }}, event=${{ matrix.event }}
       vectfit=${{ matrix.vectfit }})'
 
     env:
diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index 751c42fb06..7195b2781d 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -10,7 +10,15 @@ mkdir LIBMESH && cd LIBMESH
 git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 export METHODS="opt"
-../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+
+
+if [[ $MPI == 'y']]; then
+../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich \
+--enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+else
+../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich \
+--enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+fi
 make -j4 install
 export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
 rm -rf $HOME/LIBMESH/build

From a0aa431744071e790494a637f57a6e4d2bd95609 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 27 Jan 2021 09:17:40 -0600
Subject: [PATCH 114/139] Correcting bash syntax.

---
 tools/ci/gha-install-libmesh.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index 7195b2781d..684286180b 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -12,7 +12,7 @@ mkdir build && cd build
 export METHODS="opt"
 
 
-if [[ $MPI == 'y']]; then
+if [[ $MPI == 'y' ]]; then
 ../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich \
 --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
 else

From ccae5f93a5cb86c9aa27912fd6ba762c52f612f6 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 27 Jan 2021 09:33:20 -0600
Subject: [PATCH 115/139] Disabling MPI in the serial libmesh build.

---
 tools/ci/gha-install-libmesh.sh | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index 684286180b..bd94de0584 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -16,8 +16,7 @@ if [[ $MPI == 'y' ]]; then
 ../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich \
 --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
 else
-../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich \
---enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack --disable-mpi
 fi
 make -j4 install
 export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/

From 025011ee3d8ffc177488fff448be4e1c42ba67ff Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 27 Jan 2021 10:50:47 -0600
Subject: [PATCH 116/139] Removing mpich specification from compilers in
 libmesh config.

---
 tools/ci/gha-install-libmesh.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index bd94de0584..95407ba31c 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -13,7 +13,7 @@ export METHODS="opt"
 
 
 if [[ $MPI == 'y' ]]; then
-../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx.mpich CC=mpicc.mpich FC=mpifort.mpich F77=mpif77.mpich \
+../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx CC=mpicc FC=mpifort F77=mpif77 \
 --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
 else
 ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack --disable-mpi

From c50fb6a86c3772bf5da528cdfdca61d51dd84236 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 27 Jan 2021 12:08:59 -0600
Subject: [PATCH 117/139] Small formatting fix in the libmesh install script.

---
 tools/ci/gha-install-libmesh.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index 95407ba31c..084e85686b 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -11,7 +11,6 @@ git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 export METHODS="opt"
 
-
 if [[ $MPI == 'y' ]]; then
 ../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx CC=mpicc FC=mpifort F77=mpif77 \
 --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack

From 30d533d1c049ec475de8f323ec380b5f9167e1cc Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 08:48:49 -0600
Subject: [PATCH 118/139] Removing redundant check in
 write_unstructured_mesh_results

---
 src/state_point.cpp | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/src/state_point.cpp b/src/state_point.cpp
index d0fabf0e3a..d1663c0b90 100644
--- a/src/state_point.cpp
+++ b/src/state_point.cpp
@@ -311,7 +311,7 @@ openmc_statepoint_write(const char* filename, bool* write_source)
   }
 
 #if defined(LIBMESH) || defined(DAGMC)
-  // write unstructured mesh tallies to VTK
+  // write unstructured mesh tally files
   write_unstructured_mesh_results();
 #endif
 
@@ -772,10 +772,6 @@ void read_source_bank(hid_t group_id, std::vector<Particle::Bank>& sites, bool d
 
 void write_unstructured_mesh_results() {
 
-#if !defined(LIBMESH) && !defined(DAGMC)
-  fatal_error("Unstructured mesh is disabled for this build of OpenMC.");
-#else
-
   for (auto& tally : model::tallies) {
 
     std::vector<std::string> tally_scores;
@@ -871,7 +867,6 @@ void write_unstructured_mesh_results() {
       umesh->remove_scores();
     }
   }
-  #endif
 }
 
 void write_tally_results_nr(hid_t file_id)

From 4ec3530a36320f122d90d663d6b0efa66360c078 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 10:27:14 -0600
Subject: [PATCH 119/139] Updating comment on the number of surface bins. Using
 a range-based loop to check for boundary faces.

---
 src/mesh.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 74664eda48..5963546cb8 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2211,10 +2211,11 @@ int LibMesh::n_surface_bins() const
   for (int i = 0; i < this->n_bins(); i++) {
     const libMesh::Elem& e = get_element_from_bin(i);
     n_bins += e.n_faces();
-    // if this is a boundary element, sure to count boundary faces
-    // twice
-    for (int j = 0; j < e.n_sides(); j++) {
-      if (e.neighbor_ptr(j) == nullptr) { n_bins++; }
+    // if this is a boundary element, it will only be visited once,
+    // the number of surface bins is incremented to
+    for (auto neighbor_ptr : e.neighbor_ptr_range()) {
+      // null neighbor pointer indicates a boundary face
+      if (!neighbor_ptr) { n_bins++; }
     }
   }
   return n_bins;

From 314e87b9904d91fd99b5cd0ea3bf1492c3edea97 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 10:41:28 -0600
Subject: [PATCH 120/139] Updating comment to be more general (not only tets).

---
 src/mesh.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 5963546cb8..32aa7c16a0 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -207,7 +207,7 @@ UnstructuredMesh::to_hdf5(hid_t group) const
     write_dataset(mesh_group, "type", "unstructured");
     write_dataset(mesh_group, "filename", filename_);
     write_dataset(mesh_group, "library", this->library());
-    // write volume of each tet
+    // write volume of each element
     std::vector<double> tet_vols;
     xt::xtensor<double, 2> centroids({(size_t)this->n_bins(), 3});
     for (int i = 0; i < this->n_bins(); i++) {

From 6156e1b3ae8e44f4be37c0cf92b82b2aca040251 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 11:14:12 -0600
Subject: [PATCH 121/139] Storing the first element ID rather than its pointer.

---
 include/openmc/mesh.h | 2 +-
 src/mesh.cpp          | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 6bc0843861..6c052b3e28 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -544,7 +544,7 @@ private:
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   std::unordered_map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
   libMesh::BoundingBox bbox_; //!< bounding box of the mesh
-  libMesh::Elem* first_element_; //!< pointer to the first element in the mesh (maybe should be a key?)
+  libMesh::dof_id_type first_element_id_; //!< if of the first element in the mesh
 };
 #endif
 
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 32aa7c16a0..f746913344 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -2184,7 +2184,8 @@ void LibMesh::initialize()
   }
 
   // store first element in the mesh to use as an offset for bin indices
-  first_element_ = *m_->elements_begin();
+  auto first_elem = *m_->elements_begin();
+  first_element_id_ = first_elem->id();
 
   // bounding box for the mesh for quick rejection checks
   bbox_ = libMesh::MeshTools::create_bounding_box(*m_);
@@ -2325,7 +2326,7 @@ LibMesh::get_bin(Position r) const
 int
 LibMesh::get_bin_from_element(const libMesh::Elem* elem) const
 {
-  int bin = elem->id() - first_element_->id();
+  int bin = elem->id() - first_element_id_;
   if (bin >= n_bins() || bin < 0) {
     fatal_error(fmt::format("Invalid bin: {}", bin));
   }

From 9c3ee5daa537801ceb1bb4998fd9259b5cc0d669 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 11:17:57 -0600
Subject: [PATCH 122/139] Removing superfluous public section.

---
 include/openmc/mesh.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 6c052b3e28..340ea44fdc 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -288,7 +288,6 @@ public:
   UnstructuredMesh(const std::string& filename);
 
   // Methods
-public:
 
   //! Add a variable to the mesh instance
   virtual void add_score(const std::string& var_name) = 0;

From c798b6c6515a612b39a7af63fa47fbfef50d8148 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 11:38:22 -0600
Subject: [PATCH 123/139] Fix docstring typo.

---
 include/openmc/mesh.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index 340ea44fdc..d787db15ae 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -543,7 +543,7 @@ private:
   std::string eq_system_name_; //!< name of the equation system holding OpenMC results
   std::unordered_map<std::string, unsigned int> variable_map_; //!< mapping of variable names (tally scores) to libMesh variable numbers
   libMesh::BoundingBox bbox_; //!< bounding box of the mesh
-  libMesh::dof_id_type first_element_id_; //!< if of the first element in the mesh
+  libMesh::dof_id_type first_element_id_; //!< id of the first element in the mesh
 };
 #endif
 

From c996a1476a3eb28c0c0e243bc682b22580363ac1 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 11:56:56 -0600
Subject: [PATCH 124/139] Update installation notes for DAGMC and libMesh in
 the user's guide.

---
 docs/source/usersguide/install.rst | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/docs/source/usersguide/install.rst b/docs/source/usersguide/install.rst
index 0776d71508..695240406a 100644
--- a/docs/source/usersguide/install.rst
+++ b/docs/source/usersguide/install.rst
@@ -236,19 +236,28 @@ Prerequisites
 
       OpenMC supports particle tracking in CAD-based geometries via the Direct
       Accelerated Geometry Monte Carlo (DAGMC) toolkit (`installation
-      instructions
-      <https://svalinn.github.io/DAGMC/install/openmc.html>`_). For use in
-      OpenMC, only the ``MOAB_DIR`` and ``BUILD_TALLY`` variables need to be
-      specified in the CMake configuration step when building DAGMC. This option
-      also allows unstructured mesh tallies on tetrahedral MOAB meshes.
+      instructions <https://svalinn.github.io/DAGMC/install/openmc.html>`_). For
+      use in OpenMC, only the ``MOAB_DIR`` and ``BUILD_TALLY`` variables need to
+      be specified in the CMake configuration step when building DAGMC. This
+      option also allows unstructured mesh tallies on tetrahedral MOAB meshes.
+      In addition to turning this option on, the path to the DAGMC installation
+      should be specified as part of the ``CMAKE_PREFIX_PATH`` variable::
 
-    * libMesh_ mesh library framework for numerical simulations of partial
-    differential equations.
+          cmake -Ddagmc=on -DCMAKE_PREFIX_PATH=/path/to/dagmc/installation
+
+    * libMesh_ mesh library framework for numerical simulations of partial differential equations
 
       This optional dependency enables support for unstructured mesh tally
-      filters using libMesh meshes. Any 3D element type supported by libMesh
-      can be used, but the implementation is currently restricted
-      to collision estimators.
+      filters using libMesh meshes. Any 3D element type supported by libMesh can
+      be used, but the implementation is currently restricted to collision
+      estimators. In addition to turning this option on, the path to the libMesh
+      installation should be specified as part of the ``CMAKE_PREFIX_PATH``
+      variable.::
+
+          CXX=mpicxx cmake -Dlibmesh=on -DCMAKE_PREFIX_PATH=/path/to/libmesh/installation
+
+      Note that libMesh is most commonly compiled with MPI support. If that
+      is the case, then OpenMC should be compiled with MPI support as well.
 
 .. _gcc: https://gcc.gnu.org/
 .. _CMake: https://cmake.org

From 23e6e1b8b49286dae0aa4c08024c1886f9e417b0 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 11:57:16 -0600
Subject: [PATCH 125/139] Fixing indentation of CMake options.

---
 CMakeLists.txt | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1937940799..aa66ea7aa5 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,12 +25,12 @@ endif()
 # Command line options
 #===============================================================================
 
-option(openmp   "Enable shared-memory parallelism with OpenMP"   ON)
-option(profile  "Compile with profiling flags"                   OFF)
-option(debug    "Compile with debug flags"                       OFF)
-option(optimize "Turn on all compiler optimization flags"        OFF)
-option(coverage "Compile with coverage analysis flags"           OFF)
-option(dagmc    "Enable support for DAGMC (CAD) geometry"        OFF)
+option(openmp   "Enable shared-memory parallelism with OpenMP"         ON)
+option(profile  "Compile with profiling flags"                         OFF)
+option(debug    "Compile with debug flags"                             OFF)
+option(optimize "Turn on all compiler optimization flags"              OFF)
+option(coverage "Compile with coverage analysis flags"                 OFF)
+option(dagmc    "Enable support for DAGMC (CAD) geometry"              OFF)
 option(libmesh  "Enable support for libMesh unstructured mesh tallies" OFF)
 
 #===============================================================================

From 3e787e682a45e329d1e88a2cc4bed6ea19aaed4c Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 13:05:17 -0600
Subject: [PATCH 126/139] Adding warning to the openmc-plot-mesh-tally script
 and ensuring unstructured mesh filters aren't included in the app's combobox.

---
 scripts/openmc-plot-mesh-tally | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/scripts/openmc-plot-mesh-tally b/scripts/openmc-plot-mesh-tally
index fad2df4187..2a4fcd7434 100755
--- a/scripts/openmc-plot-mesh-tally
+++ b/scripts/openmc-plot-mesh-tally
@@ -2,6 +2,7 @@
 
 """Python script to plot tally data generated by OpenMC."""
 
+
 import os
 import sys
 import argparse
@@ -19,11 +20,18 @@ from matplotlib.figure import Figure
 import matplotlib.pyplot as plt
 import numpy as np
 
-from openmc import StatePoint, MeshFilter
+from openmc import StatePoint, MeshFilter, UnstructuredMesh
 
 _COMBOBOX_SELECTED = '<<ComboboxSelected>>'
 
 
+def mesh_filter_check(filter):
+    """
+    Check that the filter is a usable mesh filter
+    """
+    return isinstance(filter, MeshFilter) and not isinstance(filter.mesh, UnstructuredMesh)
+
+
 class MeshPlotter(tk.Frame):
     def __init__(self, parent, filename):
         super().__init__(parent)
@@ -289,10 +297,17 @@ class MeshPlotter(tk.Frame):
         # Create StatePoint object and read in data
         self.datafile = StatePoint(filename)
 
+        meshes = self.datafile.meshes
+        if any(isinstance(m, UnstructuredMesh) for m in meshes.values()):
+            warn_msg = "Unstructured meshes are present in the" \
+                       " statepoint file but are not currently" \
+                       " supported by this script"
+            messagebox.showwarning("Unstructured Meshes", message=warn_msg)
+
         # Find which tallies are mesh tallies
         self.meshTallies = []
         for itally, tally in self.datafile.tallies.items():
-            if any([isinstance(f, MeshFilter) for f in tally.filters]):
+            if any([mesh_filter_check(f) for f in tally.filters]):
                 self.meshTallies.append(itally)
 
         if not self.meshTallies:

From f013f4d877d035637f7db4289e579f3137a9279b Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 13:47:27 -0600
Subject: [PATCH 127/139] Adding CMake version check for libMesh builds.

---
 cmake/Modules/FindLIBMESH.cmake | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 62ab223387..a9e79a8476 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -1,6 +1,10 @@
 # Finds the libMesh installation using CMake's PkgConfig
 # module and creates a libmesh imported target
 
+if (${CMAKE_VERSION} VERSION_LESS 3.12.0)
+    message((FATAL_ERROR "OpenMC builds with libMesh support require CMake version 3.12.0 or greater."))
+endif()
+
 include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
 set(PKG_CONFIG_USE_CMAKE_PREFIX_PATH True)

From 8d42eafe1b064731af1a81bc428ab0807d95fd1f Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 13:48:38 -0600
Subject: [PATCH 128/139] Removing custom CMake version temporarily.

---
 .github/workflows/ci.yml | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 08f815c3a4..a384abb52c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -84,11 +84,6 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-      -
-        name: Set up CMake
-        uses: jwlawson/actions-setup-cmake@v1.5
-        with:
-          cmake-version: 3.12.x
       -
         name: Environment Variables
         run: |

From dbe4c66c0f754a2667ad24b05f939afc89394ad5 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 14:19:54 -0600
Subject: [PATCH 129/139] Setting CMake version to 3.3

---
 .github/workflows/ci.yml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a384abb52c..b3145c553f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -84,6 +84,11 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+      -
+        name: Set up CMake for libMesh build
+        uses: jwlawson/actions-setup-cmake@v1.5
+        with:
+          cmake-version: 3.11.x
       -
         name: Environment Variables
         run: |

From 6723a8f64a0df3c8bbd73903dbdc4f3d392bb903 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 15:38:12 -0600
Subject: [PATCH 130/139] Fix for CMake message syntax.

---
 cmake/Modules/FindLIBMESH.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index a9e79a8476..71d78e620a 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -2,7 +2,7 @@
 # module and creates a libmesh imported target
 
 if (${CMAKE_VERSION} VERSION_LESS 3.12.0)
-    message((FATAL_ERROR "OpenMC builds with libMesh support require CMake version 3.12.0 or greater."))
+    message(FATAL_ERROR "OpenMC builds with libMesh support require CMake version 3.12.0 or greater.")
 endif()
 
 include(FindPkgConfig)

From 12bab4a54920b2c8109861704212ff29a433ac3b Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 15:49:51 -0600
Subject: [PATCH 131/139] Removing custom CMake version.

---
 .github/workflows/ci.yml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b3145c553f..499d241b76 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -84,11 +84,6 @@ jobs:
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-      -
-        name: Set up CMake for libMesh build
-        uses: jwlawson/actions-setup-cmake@v1.5
-        with:
-          cmake-version: 3.11.x
       -
         name: Environment Variables
         run: |
@@ -118,7 +113,6 @@ jobs:
         name: before
         shell: bash
         run: $GITHUB_WORKSPACE/tools/ci/gha-before-script.sh
-
       -
         name: test
         shell: bash

From cb6c24ba9b739a8de18f26f6484ee13446df80d1 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 10 Feb 2021 16:00:44 -0600
Subject: [PATCH 132/139] Formatting correction in FindLIBMESH module.

---
 cmake/Modules/FindLIBMESH.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 71d78e620a..b236592fe5 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -1,7 +1,7 @@
 # Finds the libMesh installation using CMake's PkgConfig
 # module and creates a libmesh imported target
 
-if (${CMAKE_VERSION} VERSION_LESS 3.12.0)
+if(${CMAKE_VERSION} VERSION_LESS 3.12.0)
     message(FATAL_ERROR "OpenMC builds with libMesh support require CMake version 3.12.0 or greater.")
 endif()
 

From 0abf803bb771cb60efa71e80c2ec56f8f2b6bbc0 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 3 Mar 2021 18:08:40 -0600
Subject: [PATCH 133/139] Adding ability to specify the libMesh pkconfig type
 with the METHOD env var.

---
 cmake/Modules/FindLIBMESH.cmake | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index b236592fe5..395c472422 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -5,13 +5,16 @@ if(${CMAKE_VERSION} VERSION_LESS 3.12.0)
     message(FATAL_ERROR "OpenMC builds with libMesh support require CMake version 3.12.0 or greater.")
 endif()
 
+set(LIBMESH_PC_FILE libmesh)
+
+# if the METHOD variable is present, check specifically for
+# the libMesh .pc file for that build type
+if(DEFINED ENV{METHOD})
+  set(LIBMESH_PC_FILE libmesh-$ENV{METHOD})
+  message(STATUS "Using environment variable METHOD to determine libMesh build: ${LIBMESH_PC_FILE}")
+endif()
+
 include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
 set(PKG_CONFIG_USE_CMAKE_PREFIX_PATH True)
-pkg_check_modules(LIBMESH REQUIRED libmesh>=1.6.0 IMPORTED_TARGET)
-
-find_path(LIBMESH_PC NAMES libmesh.pc
-          HINTS ${LIBMESH_LIBDIR}
-          PATHS ENV LD_LIBRARY_PATH
-          PATH_SUFFIXES lib/pkgconfig pkgconfig
-          NO_DEFAULT_PATH)
+pkg_check_modules(LIBMESH REQUIRED ${LIBMESH_PC_FILE}>=1.6.0 IMPORTED_TARGET)

From fd00bd94b213f3c75af8cbfd9dde4ad0141fe6ee Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 12 Mar 2021 08:45:38 -0600
Subject: [PATCH 134/139] Relying on the libmesh communicator to create new
 mesh instances.

---
 include/openmc/mesh.h | 1 +
 src/initialize.cpp    | 2 ++
 src/mesh.cpp          | 7 ++++---
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index d787db15ae..cc0169ce0f 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -55,6 +55,7 @@ extern std::vector<std::unique_ptr<Mesh>> meshes;
 namespace settings {
 // used when creating new libMesh::Mesh instances
 extern std::unique_ptr<libMesh::LibMeshInit> libmesh_init;
+extern const libMesh::Parallel::Communicator* libmesh_comm;
 }
 #endif
 
diff --git a/src/initialize.cpp b/src/initialize.cpp
index c480392cba..f6e927b688 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -80,6 +80,8 @@ if (!settings::libmesh_init && !libMesh::initialized())
   // pass command line args, empty MPI communicator, and number of threads
   settings::libmesh_init = std::make_unique<libMesh::LibMeshInit>(argc, argv, 0, n_threads);
 #endif
+
+  settings::libmesh_comm = &(settings::libmesh_init->comm());
 }
 
 #endif
diff --git a/src/mesh.cpp b/src/mesh.cpp
index f746913344..30553c8c74 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -60,6 +60,7 @@ std::vector<std::unique_ptr<Mesh>> meshes;
 #ifdef LIBMESH
 namespace settings {
 std::unique_ptr<libMesh::LibMeshInit> libmesh_init;
+const libMesh::Parallel::Communicator* libmesh_comm;
 }
 #endif
 
@@ -2150,11 +2151,11 @@ LibMesh::LibMesh(const std::string& filename)
 
 void LibMesh::initialize()
 {
-  if (!settings::libmesh_init) {
-    fatal_error("Attempting to use an unstructured mesh without a libMesh::LibMeshInit object.");
+  if (!settings::libmesh_comm) {
+    fatal_error("Attempting to use an unstructured mesh without a libMesh communicator.");
   }
 
-  m_ = std::make_unique<libMesh::Mesh>(settings::libmesh_init->comm(), n_dimension_);
+  m_ = std::make_unique<libMesh::Mesh>(*settings::libmesh_comm, n_dimension_);
   m_->read(filename_);
   m_->prepare_for_use();
 

From f673a8dad277138a4eca3a874b2f44ffb4001751 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Fri, 12 Mar 2021 10:32:14 -0600
Subject: [PATCH 135/139] A couple more changes for robustness.

---
 src/mesh.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/mesh.cpp b/src/mesh.cpp
index 30553c8c74..333a5dc331 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -60,7 +60,7 @@ std::vector<std::unique_ptr<Mesh>> meshes;
 #ifdef LIBMESH
 namespace settings {
 std::unique_ptr<libMesh::LibMeshInit> libmesh_init;
-const libMesh::Parallel::Communicator* libmesh_comm;
+const libMesh::Parallel::Communicator* libmesh_comm {nullptr};
 }
 #endif
 
@@ -2155,6 +2155,9 @@ void LibMesh::initialize()
     fatal_error("Attempting to use an unstructured mesh without a libMesh communicator.");
   }
 
+  // assuming that unstructured meshes used in OpenMC are 3D
+  n_dimension_ = 3;
+
   m_ = std::make_unique<libMesh::Mesh>(*settings::libmesh_comm, n_dimension_);
   m_->read(filename_);
   m_->prepare_for_use();

From 1262bcde7d0db05c4ee68e047727764a10a681b4 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 7 Apr 2021 17:36:02 -0500
Subject: [PATCH 136/139] Apply @paulromano's suggestions from code review

Co-authored-by: Paul Romano <paul.k.romano@gmail.com>
---
 include/openmc/mesh.h           | 12 +++++-------
 src/initialize.cpp              |  3 +--
 src/mesh.cpp                    | 12 ++++++------
 tools/ci/gha-install-libmesh.sh | 12 +++++-------
 4 files changed, 17 insertions(+), 22 deletions(-)

diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h
index cc0169ce0f..e9519ca0a1 100644
--- a/include/openmc/mesh.h
+++ b/include/openmc/mesh.h
@@ -329,16 +329,14 @@ public:
 
   void to_hdf5(hid_t group) const override;
 
-private:
-
-  //! Setup method for the mesh. Builds data structures,
-  //! sets up element mapping, creates bounding boxes, etc.
-  virtual void initialize() = 0;
-
-public:
   // Data members
   bool output_ {true}; //!< Write tallies onto the unstructured mesh at the end of a run
   std::string filename_; //!< Path to unstructured mesh file
+
+private:
+  //! Setup method for the mesh. Builds data structures,
+  //! sets up element mapping, creates bounding boxes, etc.
+  virtual void initialize() = 0;
 };
 
 #ifdef DAGMC
diff --git a/src/initialize.cpp b/src/initialize.cpp
index f6e927b688..e84678feb0 100644
--- a/src/initialize.cpp
+++ b/src/initialize.cpp
@@ -69,8 +69,7 @@ int openmc_init(int argc, char* argv[], const void* intracomm)
 
 // initialize libMesh if it hasn't been initialized already
 // (if initialized externally, the libmesh_init object needs to be provided also)
-if (!settings::libmesh_init && !libMesh::initialized())
-{
+if (!settings::libmesh_init && !libMesh::initialized()) {
 #ifdef OPENMC_MPI
   // pass command line args, empty MPI communicator, and number of threads.
   // Because libMesh was not initialized, we assume that OpenMC is the primary
diff --git a/src/mesh.cpp b/src/mesh.cpp
index 333a5dc331..19cbeae10a 100644
--- a/src/mesh.cpp
+++ b/src/mesh.cpp
@@ -192,7 +192,7 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) {
 void
 UnstructuredMesh::surface_bins_crossed(const Particle& p,
                                        std::vector<int>& bins) const {
-  throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."};
+  fatal_error("Unstructured mesh surface tallies are not implemented.");
 }
 
 std::string
@@ -210,7 +210,7 @@ UnstructuredMesh::to_hdf5(hid_t group) const
     write_dataset(mesh_group, "library", this->library());
     // write volume of each element
     std::vector<double> tet_vols;
-    xt::xtensor<double, 2> centroids({(size_t)this->n_bins(), 3});
+    xt::xtensor<double, 2> centroids({static_cast<size_t>(this->n_bins()), 3});
     for (int i = 0; i < this->n_bins(); i++) {
       tet_vols.emplace_back(this->volume(i));
       auto c = this->centroid(i);
@@ -2182,9 +2182,9 @@ void LibMesh::initialize()
   #endif
 
   for (int i = 0; i < n_threads; i++) {
-      pl_.emplace_back(m_->sub_point_locator());
-      pl_.back()->set_contains_point_tol(FP_COINCIDENT);
-      pl_.back()->enable_out_of_mesh_mode();
+    pl_.emplace_back(m_->sub_point_locator());
+    pl_.back()->set_contains_point_tol(FP_COINCIDENT);
+    pl_.back()->enable_out_of_mesh_mode();
   }
 
   // store first element in the mesh to use as an offset for bin indices
@@ -2303,7 +2303,7 @@ LibMesh::bins_crossed(const Particle& p,
                       std::vector<double>& lengths) const
 {
   // TODO: Implement triangle crossings here
-  throw std::runtime_error{"Tracklength tallies on libMesh instances are not implemented."};
+  fatal_error("Tracklength tallies on libMesh instances are not implemented.");
 }
 
 int
diff --git a/tools/ci/gha-install-libmesh.sh b/tools/ci/gha-install-libmesh.sh
index 084e85686b..132963820d 100755
--- a/tools/ci/gha-install-libmesh.sh
+++ b/tools/ci/gha-install-libmesh.sh
@@ -2,23 +2,21 @@
 #!/bin/bash
 set -ex
 
-CURRENT_DIR=$(pwd)
-
 # libMESH install
-cd $HOME
+pushd $HOME
 mkdir LIBMESH && cd LIBMESH
 git clone https://github.com/libmesh/libmesh -b v1.6.0 --recurse-submodules
 mkdir build && cd build
 export METHODS="opt"
 
 if [[ $MPI == 'y' ]]; then
-../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx CC=mpicc FC=mpifort F77=mpif77 \
---enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
+    ../libmesh/configure --prefix=$HOME/LIBMESH CXX=mpicxx CC=mpicc FC=mpifort F77=mpif77 \
+        --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack
 else
-../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack --disable-mpi
+    ../libmesh/configure --prefix=$HOME/LIBMESH --enable-exodus --disable-netcdf-4 --disable-eigen --disable-lapack --disable-mpi
 fi
 make -j4 install
 export LIBMESH_PC=$HOME/LIBMESH/lib/pkgconfig/
 rm -rf $HOME/LIBMESH/build
 
-cd $CURRENT_DIR
+popd

From 97e4e692f54a7aada8ad44d4f3f1e65e7f568763 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 7 Apr 2021 17:34:03 -0500
Subject: [PATCH 137/139] Correction to index check in Tally::nuclide_name.

---
 src/tallies/tally.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp
index b3a259f79a..5fd034f92c 100644
--- a/src/tallies/tally.cpp
+++ b/src/tallies/tally.cpp
@@ -679,13 +679,12 @@ Tally::score_name(int score_idx) const {
 
 std::string
 Tally::nuclide_name(int nuclide_idx) const {
-  if (nuclide_idx != -1) {
-    return "total";
-  } else if (nuclide_idx < 0 || nuclide_idx >= nuclides_.size()) {
+  if (nuclide_idx < 0 || nuclide_idx >= nuclides_.size()) {
     fatal_error("Index in nuclides array is out of bounds");
   }
 
   int nuclide = nuclides_.at(nuclide_idx);
+  if (nuclide == -1) { return "total"; }
   return data::nuclides.at(nuclide)->name_;
 }
 

From 0b61d14380fb6f689e7c48c52fed1c99367505ea Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 7 Apr 2021 19:17:12 -0500
Subject: [PATCH 138/139] Correcting unclosed if statement in CMakeLists.txt.

---
 CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index aa66ea7aa5..596f5c7b8b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -66,6 +66,7 @@ if(pugixml_FOUND)
   message(STATUS "Found pugixml: ${pugixml_DIR}")
 else()
   message(STATUS "Did not find pugixml, will use submodule instead")
+endif()
 
 #===============================================================================
 # libMesh Unstructured Mesh Support

From 18a1ba1c414c0a3481e612efac108de15e462a37 Mon Sep 17 00:00:00 2001
From: Patrick Shriwise <pshriwise@gmail.com>
Date: Wed, 7 Apr 2021 20:56:18 -0500
Subject: [PATCH 139/139] Ensuring that all necessary information makes it into
 the OpeMCconfig file for linked applicatoins.

---
 cmake/Modules/FindLIBMESH.cmake | 1 +
 cmake/OpenMCConfig.cmake.in     | 5 +++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/cmake/Modules/FindLIBMESH.cmake b/cmake/Modules/FindLIBMESH.cmake
index 395c472422..82d806343a 100644
--- a/cmake/Modules/FindLIBMESH.cmake
+++ b/cmake/Modules/FindLIBMESH.cmake
@@ -18,3 +18,4 @@ include(FindPkgConfig)
 set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:${LIBMESH_PC}")
 set(PKG_CONFIG_USE_CMAKE_PREFIX_PATH True)
 pkg_check_modules(LIBMESH REQUIRED ${LIBMESH_PC_FILE}>=1.6.0 IMPORTED_TARGET)
+pkg_get_variable(LIBMESH_PREFIX ${LIBMESH_PC_FILE} prefix)
\ No newline at end of file
diff --git a/cmake/OpenMCConfig.cmake.in b/cmake/OpenMCConfig.cmake.in
index 94078f9092..e23ec26a62 100644
--- a/cmake/OpenMCConfig.cmake.in
+++ b/cmake/OpenMCConfig.cmake.in
@@ -11,8 +11,9 @@ endif()
 
 if(@LIBMESH_FOUND@)
   include(FindPkgConfig)
-  set(ENV{PKG_CONFIG_PATH} "$ENV{PKG_CONFIG_PATH}:@LIBMESH_PC@")
-  pkg_check_modules(LIBMESH REQUIRED libmesh IMPORTED_TARGET)
+  list(APPEND CMAKE_PREFIX_PATH @LIBMESH_PREFIX@)
+  set(PKG_CONFIG_USE_CMAKE_PREFIX_PATH True)
+  pkg_check_modules(LIBMESH REQUIRED @LIBMESH_PC_FILE@>=1.6.0 IMPORTED_TARGET)
 endif()
 
 if(NOT TARGET OpenMC::libopenmc)