Repository: jrenaud90/CyRK (language: python)

Issue:
Testing-python-versions
- Added support for python 3.12
- Fixed installation process for macos
- Added correct compile performance flags


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/build_wheels_main_pypi.yml b/.github/workflows/build_wheels_main_pypi.yml
index f7fd489..5832d19 100644
--- a/.github/workflows/build_wheels_main_pypi.yml
+++ b/.github/workflows/build_wheels_main_pypi.yml
@@ -11,14 +11,14 @@ jobs:
     name: Make SDist
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         submodules: true  # Optional, use if you have submodules
 
     - name: Build SDist
       run: pipx run build --sdist
 
-    - uses: actions/upload-artifact@v3
+    - uses: actions/upload-artifact@v4
       with:
         path: dist/*.tar.gz
 
@@ -34,12 +34,13 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
     steps:
       - name: Checkout CyRK
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Install Anaconda
-        uses: conda-incubator/setup-miniconda@v2
+        uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: cyrk_test
           auto-update-conda: true
@@ -50,30 +51,21 @@ jobs:
         shell: bash -el {0}
         run: conda info
 
-      - name: Install llvm and libomp
+      - name: install gcc-13  # openMP does not play nice with clang on MacOS; also some versions of macos use older gcc so there is a need to install latest.
         shell: bash -el {0}
         run: |
-          brew reinstall llvm libomp
-
-      - name: Install Dependencies
-        shell: bash -el {0}
-        run: |
-          conda install numpy scipy cython llvm-openmp pytest
-          python -m pip install --upgrade build
+          brew install gcc@13
 
       - name: Install package
         shell: bash -el {0}
         run: |
-          export CC=/usr/bin/clang
-          export CXX=/usr/bin/clang++
-          export PATH="/usr/local/opt/llvm/bin:$PATH"
-          export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp -I/usr/local/opt/llvm/include"
-          export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include"
-          export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include"
-          export LDFLAGS="$LDFLAGS -Wl,-rpath,/usr/local/opt/libomp/lib -L/usr/local/opt/libomp/lib -lomp -L/usr/local/opt/llvm/lib"
+          conda install pytest matplotlib numba numpy scipy cython
+          export CC='gcc-13'
+          export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
+          python -m pip install -v .
           python -m build --wheel --outdir ./wheelhouse/
 
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
           path: ./wheelhouse/*.whl
 
@@ -89,14 +81,14 @@ jobs:
           - ubuntu-20.04
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.14.1
+        uses: pypa/cibuildwheel@v2.16.5
 
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
           path: ./wheelhouse/*.whl
 
@@ -105,7 +97,7 @@ jobs:
     needs: [make_sdist, build_macos_wheel, build_other_wheels]
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@v4
         with:
           name: artifact
           path: dist
@@ -118,12 +110,12 @@ jobs:
     runs-on: ubuntu-latest
     if: github.event_name == 'release' && github.event.action == 'published'
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@v4
         with:
           name: artifact
           path: dist
 
-      - uses: pypa/gh-action-pypi-publish@v1.8.8
+      - uses: pypa/gh-action-pypi-publish@v1.8.11
         with:
           user: __token__
           password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.gitignore b/.gitignore
index 5aaba5e..83d67b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@
 
 # Include non-generated c files
 !CyRK/array/interp_common.c
+!CyRK/cy/rk_step.c
 
 # Ignore coverage files
 .coverage
@@ -17,6 +18,7 @@ __pycache__/
 # Objext files
 *.so
 *.o
+*.exe
 
 # Distribution / packaging
 .Python
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..fb26150
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,58 @@
+{
+    "files.associations": {
+        "iterator": "c",
+        "xmemory": "c",
+        "xutility": "c",
+        "atomic": "c",
+        "bit": "c",
+        "cctype": "c",
+        "charconv": "c",
+        "clocale": "c",
+        "cmath": "c",
+        "compare": "c",
+        "complex": "c",
+        "concepts": "c",
+        "cstddef": "c",
+        "cstdint": "c",
+        "cstdio": "c",
+        "cstdlib": "c",
+        "cstring": "c",
+        "ctime": "c",
+        "cwchar": "c",
+        "exception": "c",
+        "format": "c",
+        "initializer_list": "c",
+        "ios": "c",
+        "iosfwd": "c",
+        "istream": "c",
+        "limits": "c",
+        "locale": "c",
+        "memory": "c",
+        "mutex": "c",
+        "new": "c",
+        "ostream": "c",
+        "ratio": "c",
+        "sstream": "c",
+        "stdexcept": "c",
+        "stop_token": "c",
+        "streambuf": "c",
+        "string": "c",
+        "system_error": "c",
+        "thread": "c",
+        "tuple": "c",
+        "type_traits": "c",
+        "typeinfo": "c",
+        "utility": "c",
+        "xfacet": "c",
+        "xiosbase": "c",
+        "xlocale": "c",
+        "xlocbuf": "c",
+        "xlocinfo": "c",
+        "xlocmes": "c",
+        "xlocmon": "c",
+        "xlocnum": "c",
+        "xloctime": "c",
+        "xstring": "c",
+        "xtr1common": "c"
+    }
+}
\ No newline at end of file
diff --git a/.vscode/tasks.json b/.vscode/tasks.json
new file mode 100644
index 0000000..cbf1a6a
--- /dev/null
+++ b/.vscode/tasks.json
@@ -0,0 +1,28 @@
+{
+    "tasks": [
+        {
+            "type": "cppbuild",
+            "label": "C/C++: gcc.exe build active file",
+            "command": "C:\\Software\\mingw64\\bin\\gcc.exe",
+            "args": [
+                "-fdiagnostics-color=always",
+                "-g",
+                "${file}",
+                "-o",
+                "${fileDirname}\\${fileBasenameNoExtension}.exe"
+            ],
+            "options": {
+                "cwd": "${fileDirname}"
+            },
+            "problemMatcher": [
+                "$gcc"
+            ],
+            "group": {
+                "kind": "build",
+                "isDefault": true
+            },
+            "detail": "Task generated by Debugger."
+        }
+    ],
+    "version": "2.0.0"
+}
\ No newline at end of file
diff --git a/Benchmarks/CyRK - SciPy Comparison.ipynb b/Benchmarks/CyRK - SciPy Comparison.ipynb
index ffa20d4..05b0c11 100644
--- a/Benchmarks/CyRK - SciPy Comparison.ipynb
+++ b/Benchmarks/CyRK - SciPy Comparison.ipynb
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.8.4\n"
+      "0.8.6\n"
      ]
     }
    ],
@@ -146,7 +146,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -177,7 +177,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -210,7 +210,7 @@
     },
     {
      "data": {
-      "image/png": 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r1q1Vr6nE9ddfT2dnZ9/R1tYW7Y/KBLbRqtTBgxhxKK8Rd9YN5XhfOng3Za0fMjhwI7bDUG9KK7sPEM2Ew/Paa69xwgkncOqpp/Jv//ZvPPDAAxx11FF9n/dft9/Q0FB3LX+9a2666SZaWlr6jmImOVfKS+jBjOjL1lDFiBvqRTRED43oYbC1GE75tnAT22HovyTdRhweT+zZs4c333yT5cuXc8MNN7BixQr+/u//nvXr1wMMiNSMHz++L+qzfv16hg4dypgxY6peU4menh62b9/uOopHpQgPyCheRmnSwfdH9HBTqa1IErc4PFB/SisTbkVFMlmyhoYGhg4dyttvv017eztz5szp+2zIkCHMmjWLpUuXArB8+XJ6enpc17S2tjJ9+vS+a8pLpQgPlHfuWVZpGWQKx001PaStGPZi9qEpkx5DHa+THyzZMxWNjVmJqlWb0qr8eImosH9/mJ2aU1fwm9/8Jo8//jhr166lubmZiy66iNmzZ/ftxbNgwQJuuOEG3njjDd544w1uuOEGurq6+OEPfwhAZ2cn9957L7feeiubNm1i8+bN3HLLLaxcuZKnnnoqzZ+WAWTU6qbWqLUF55boxada3ZAO3k1Z20qt+jGScukx3PE6+QjPpk2bADjyyCN58803Y/1b3khnSuvII48EYOPG4I+8Sd3hmTBhAt///veZOHEi27Zt4w9/+ANnn312n7Ny8803M3z4cO6++27Gjh3LCy+8wFlnndX3tFWAa665ht7eXh566CGGDx/OokWLuOyyy9i3r/9/SNmQUbyhATNSq5bEPQp32L7ISAfvRlZpuallO1opV/2w68ZeoNfxfjJ1Y+fOnSxevJgLLrgAgFdffZXe3t4634qTscAEtOPbXeH9EUQ5cGxsbOTII4/kggsuYPHixXR1BX+OWOoOz9/8zd/UvebGG2/kxhtvrPp5d3c38+bNY968eVEWrQBIp2ZwhqWdenSjE7mb0HqUxeGp5wxLB68pY1sBsR1O6qUGjEB3pfE5Iffddx8AF154YWx/wzvNwH7oXbg3ON4fCYxD15nq+bNBWbx4cZ8OQUnd4RHiRDo1gzMsXUmPcWgjXsTtCSohHZob0cON6GGotvijfxL3lthKoJTie9/7Hj/60Y8YN24cDQ0N9b8UG58DbgB+Afwvx/sfBO4BVgJXRvbXlFJs3LgxVGTHRhyeQiN5GgZbi17MXLON0+EpC9KhuRE93EjEy1CtbuxB6zMcrUd8Do9NV1cX7777bux/pzY9aE06gXcc779tvd/S7/3skMlVWkJUiNEyVNMCyh3xqrUqKc1RZNLIKi03ktNkqDalBeW2pfkbHIjDU2hk1GqopgWUs1OrVzdAJ3GXBWkrbiQ6bKjWwUM560c1BzD7WojDU2gkh8fgJcKT3YYaPdWM1m50qB7KqYdENGAIZmlx/jq16Kk1WCpj/ajXVkaS1d2WxeEpNDJKM3gxWmXSw8uotUxGXKZ/Dc4Hy0rES6a0+uM1iTt7iMNTaEZYZzHiEuHpjziAbmRKy+B0eLr7fVZGPWoNDso8eOxvS/c43hOHR0iUJsdrMeISlu5PLQewzEa82qi1mfIkccvgwI0MDtzkN6dJHJ7C4iUsLR28JtuNNB7EiLupN6U1CJ2bUAZkcOBGprTc5HcBiDg8hcW50V5Pv8/K3Ej7h+gh6400HsThcVNNj12YHXTLoofUDTf5jWjEQ34dQHF4Ckul50bZlLGDr6VHthtpPEin5kb0MEjOihupG27y6wCKw1NY7A6+UkTDrpRNuJ8xVWS86JHNRhoPMm3hRvQwyODAjdQNN/m1peLwFBY7ablSpdzheJ3Nihk9+W2k8WDr0X+6E0SP/pRND2krbqRuuMlv/RCHp7DUqpT7cK8+KQP5baTxUMshLtu0xRDHa9HDW1spUxK3tBU3tepHtvUQh6ew1KqUkPWKGT219LAjXmVx/sCb0SqLHs5pXdHDdPCVIhq7MA/fLYse0lbceNEjm4+lEYensHh1eMrSUGuFpcumBYgRdyIOj5t6tqNsAwRpK27ya0vF4Sks4vC48RKWHgY0JlOc1BEjbrC12AOoCp+XVY96tiObo/jokbbixostzaYe4vAUllpeOMgozYnzGTBlM+KV6kdZ64a0FY0MltxIW3GTXwdQHJ7CIqM0N7X06MUsOc1mQ40eL6M0qRuasupRzQHMdqcWPV7aShPu5PciIw6PkDlqNVLIesWMHhm1usmv0YoeaStuRA83XqPDZdCjAePY5S/iJQ5PYZEO3o0kYroRh8cgbcWNtBU3tfTYS9afEB4tzodS5y86LA5PYREj7kbC9G7yu7Q0eqRuuBHb4Ub0MOR7RaM4PIVFGqkbr2H6snTytfZasbUoy+Zy0lbcSE6TG3GIDU6HR5alC5lBjLgb0cMw2Dqgsh5d6N24oRx6yBSOm1rOMJSrrUD9wVKZ6ocfO9oQf3F8Ig5PYZGltm7E4THUC0tDueqHRP/cSFtxIxEvg1dnGLIYHRaHp7CIEXcjRtzgxeEpU/2QuuFG9HAjehjqabGbLD96RByewiKN1I1MWxhsLfZhjFN/ylQ/JEfDjdgON6KHoZ4WkOXBkjg8hUUaqRvp1Az1on9QLj28tpWhlGNzOZkOdyO2w+DH4cmeHuLwFBZxeNzIZmqGfBut6PEa/YNy6CHT4W7EdhjqOX+QZT3E4Sks4vC4kcRDgzg8burpUbZHj4jtcDPMOst0eN6jw+LwFBavYekydPAgRtyJF4enjEbcy6i1DO1F2orBOYUpeuR9sCQOT2HxarRkczlNdhtp9PgJS0sHrylj/ZC2Iisa+5PvwZI4PIWlXuhxJ2ZzuTI1VEnEzHtYOnrE4XHjZ+PB7G0uFy31dhaGctWNfA+WxOEpLPn2xKNlENBovZZRq3Tw/RE93PhJ4i56dNjWohczQOxPmepGvgdL4vAUFjHihnpP+IXyaAFSN/ojeripp8cuzP5N2RvFR0u+O/joyXdbEYensOS7YkaLzMO7kbrhxkvSclmioSD1w4lEyt3ku26Iw1NY8l0xo8Xp8Oypco2txTDM9FdRyfdeGtGT791jo0fqh0HsqJt86yEOT2HxYrTKMjKxtdhd45oybS7nJUxfpm0L8m3Eo0emcQziDLvJdzRUHJ7C4sdoFb2henH+yrS5nHTwbkQPN6KHwU+0qwl3vmARybcDKA5PYZFRmsGLFlAePaRDcyN6uJG8FYOfaCgUX498txVxeAqLn91js1cxo0UcHjdSN9yIHoZBwGDrdT73WokWLx38XqDLel30+pHv/K7UHZ7rrruOF198kc7OTjo6OnjkkUc4/PDDXdfcd999KKVcx3PPPee6pqmpiTvuuIMNGzawY8cOHn30USZPnpzkT8kYYsQNXrSA8unhdZRW9M3l/Iziy1I3IK+j+GjxOlgqW/3IZ91I3eGZNWsWd911F6eeeipz5syhsbGRhQsXMmLECNd1jz/+OK2trX3HOeec4/p8wYIFzJ07l4suuoiZM2cyatQoHnvsMQYNSv0npoQ4PAa/Dk/RR61+6gYUf3M5eZaWwenwiO2QwVJ/8t2vpL7+9qMf/ajr35dffjkbNmzgpJNO4tlnn+17v7u7m46Ojor3aGlp4YorruCzn/0sixYtAuAzn/kMa9eu5cwzz2ThwoUDvtPU1MTQoWa5cnNz9v5zwiGrtAxitNx40cPeXG4wupPfUePavJNvIx4tToen2hYOUD49ZLCk8bNKaxQ6OqxiLZEfMhf+GD16NACbN292vT979mw6Ojp47bXXuOeeezjggAP6PjvppJNoampyOTbt7e2sWrWK0047reLfuf766+ns7Ow72traYvg1aeF3Hl6MlkYcQDeih0HaihvRw43oYXBGh7PlAGbO4bntttt49tlnWb16dd97jz/+OJdccglnnHEGX/7yl5kxYwZPP/00TU1a/NbWVrq7u9m6davrXh0dHbS2tlb8OzfddBMtLS19R7HyffyGpbNVKaNHjJYb0cONODwGL5FhEGe4P2WpH1702I3e5gOy1rekPqXl5Dvf+Q7HHXccM2fOdL3/0EMP9b1evXo1y5Yt45133uFjH/sYjzzySNX7NTQ0oFTlcFpPTw89PfUqcV6ReXg3YrTciB5uxOExyBSOG2krbvzoMRatR3usJfJDZiI8d9xxB+eeey4f+tCH6k4vrV+/nnfeeYfDDjus799Dhw5lzJgxruvGjx9fNe+n2Mg8vBsxWm5EDzd+8hKKvrmc1A03Mv3rJt/1IxMOz5133sn555/PGWecwZo1a+pev99++3HggQfS3q49x+XLl9PT08OcOXP6rmltbWX69OksXbo0rmJnGLtS7qF2wlg2K2X0yKjVTb6NVvT4zUsosh5SN9yIHm7yrUfqU1p33XUXF198Meeddx7bt29nwoQJAGzbto3du3czcuRIvva1r/Gzn/2M9vZ2pk6dyre+9S02btzYN53V2dnJvffey6233sqmTZvYvHkzt9xyCytXruSpp55K8+elRL4rZfSIHm7EAXTjRY+96JVrw9F6bIq7UCkhbcWNtBU3+d6XKHWH5wtf+AIAS5Yscb1/2WWX8cADD7B3716OPfZYPve5zzFmzBja29t55plnuPDCC9mxwyyVveaaa+jt7eWhhx5i+PDhLFq0iMsuu4x9+/Yl+nuyQZAwbLaWD0aLhKXdSGKqGz+d2nCKrYc4PG5EDzf51iN1h6ehofYurrt37+bss8+ue5/u7m7mzZvHvHnzoipajvH7KAWAEcDOeIqTOvlupNEjerjxo8d4iq2HDA7cSFtxk++IVyZyeISo8Vopu9Cheih2QxWj5Ub0MAxxvBY9gnVoRX70iLQVN/nWQxyeQuK1UkI5Rmpep3Cy2UijJ99GK1q8buEA5WgrfusGFPvRI9JW3ORbD3F4CokfhyebFTNa8t1Io0f0MPhxeMqkR73pcOfmcmXQQ5xhTb5thzg8hUQcHjf5nneOHtHDYGuxDzO9W40y6SG2QyNtxU2+HUBxeAqJTGm58dtIh+LO7Sga+TZa0SIdvBvRw02+IxrRk289xOEpJGK03ATJSxA9pG70R/RwIw6xoQx1A/Ie8RKHp5AEMeLZqpjR4lUPe3M5KLbhEiNuEIfHjejhxm9bGYJZJFFE8m07xOEpJGK03Igebvzu01QGLSSioZHBkhu/079Q3PoxCLN1nzg8QmaQDt6N6OEm36O0aJG64Ub0cONVj33ofc2guHrkf88qcXgKiRgtNzJqdSObyxm87tEEUjf6I7bDTdHrR/73rBKHp5CI0XIj0xZuZHM5g9fpPZC20h/Rw03R9XA6PHvqXJtNLcThKSTSwbsRo2XwMw+/m+I/ekTqhhuxHW6kfhhsLfZQ/0HTthYjyZKbkZ2SCBEiYXo3YrQMfubhofh6SN1wI3q4ET0MQbSALEWHxeEpJNJI3YgeBj/z8FAePSSioZGcFTdiOwx+BtLdmGmv7OghDk8hkUbqRvQw+JmHh/LoIXVDI3q4EYfY4EcLyGL9EIenkIjRciN6GPzMw0PxR/FB6sYQ3I5jkZC24kYiXgZxeIRMIkbLTZBRmhgtTdHrR5C6AaIHFL9uQLB8yKLq4dd2ZC/iJQ5PIZEwrBsx4ob8G61o8aPHXoq/uZzYDjdiOwxBB0vZGTyKw1NIgoZhi7q5nBgtg0R43IgebqStuJEIjyH/bUUcnkKS/+WD0SKbyxnyb7SiRSJebmRLC0Oj47U4PEWwHeLwFBI/HfwuZHM5J9lrpNGSf6MVLaKHG4kOG/xu4VB0Zzj/bUUcnkKS/4oZLeLwGPI/Dx8tooeboNHhIuohe1a5yX+/Ig5PIcl/xYwWWaVlkLrhRvRw40ePbG4uFx1Oh6fXw/VlcYa9zBxAFiNe4vAUEjHihkZMNZdRmr8cDSi+HtJW3EhOk8FvBy91w032HEBxeAqJGC1D0LB0U7/vFgWpG27E4XEjehhECzf510McnkKS/4oZHUETD6HYekjd0IgD6Cb/o/jokLbiJv96iMNTSMRoGfzOwxd9c7n8G61oET3ciB6GoM5wIzAs+uKkTv7rhjg8hST/FTM6/M7DQzn0EGdYI3q4kbwVQ1CHB0QPyGLdEIenkOS/YkaHXy2g2NMWUjfciB5uRA+DXy32ATut10V0iPM//SsOTyERo2UI4vAUeRQfJqJR5M3lpK1o8t+pRUcY2yF6ZFELcXgKSf4rZnSI0XITtG6AOIBQ7LrRAAyxXoseMh3en/xP/4rDU0hklGYQh8eNXyPejUn2LrIe0laMswN57tSiQ2yHm6AOz0iy4mpkoxRCxOTfE48OMVpuRA83EuEx+N3CAcqhh+T/afIfHRaHp5DIbroG6eDdSE6TGxkcGJwOz56qV7mRtuJG9DD0OK7Nhh7i8BQOmYd3I6M0N2LE3cgybIOtRS96xZEXyqCHDA40+bel4vAUjjDz8NmolNEiRsuNODxugkZ4hmAiqUVB6oYb0cNN/vUQh6dwyDy8m/w30mgRPdzI5nKG/I/go0Xaipv8Dx7F4SkcQRweMVpuxGi5kfph2EdxHz2S/w4tWsR2uMm/HuLwFA67Uu7F/zx8dpYPRkf+G2m0iB5uRA+DaOFGBgdu8l8/ita7CaEqJWinp0jkv5FGi4zi3YgeBmkrbkQPN35X/0LW9Ejd4bnuuut48cUX6ezspKOjg0ceeYTDDz98wHXz58+nra2Nrq4unnnmGY4++mjX501NTdxxxx1s2LCBHTt28OijjzJ58uSkfkaGCLI76G6Ku7mcjNLchDHiLRGXJQuIHoYwWkh0WNNpncV2aMThcTFr1izuuusuTj31VObMmUNjYyMLFy5kxIgRfddce+21fOlLX+Lqq69mxowZrF+/nieffJJRo8wIa8GCBcydO5eLLrqImTNnMmrUKB577DEGDUr9JyZMkEoJYsSdbLPORdMCwhlx0UNT1PoRpm6A6AHSVvpj6zE64rIER2XpGDdunFJKqdNPP73vvXXr1qlrr722799NTU1qy5Yt6sorr1SAamlpUd3d3eqCCy7ou2bixImqt7dXnXXWWZ7+bnNzs1JKqebm5tQ1CHecoEApWOvze29b3zslA78hyuNa63d9z8d3DrO+sy0D5Y/6eMr6bRf6+M4XrO/8JAPlj/IYZP0upWCsj+89an3nbzPwG6I8zrZ+1+98fq/L+t7BGfgNUR63Wb/rmz6+M9P6zmsZKH/Ux2rrt83y8Z2vW9+5M9ayee2/Mxf+GD16NACbN28GYNq0aUycOJGFCxf2XdPT08OSJUs47bTTADjppJNoampyXdPe3s6qVav6rulPU1MTzc3NrqMYBI3w2KPW0RGWJQuEHcFnromEJIweRa0bIHqA2I7+SFtxk/9oaOas+W233cazzz7L6tWrAWhtbQWgo6PDdV1HR0ffZ62trXR3d7N169aq1/Tn+uuvp7Ozs+9oa2uL+JekhRgtN0ES7bY5XhfFEbbJv9GKDnF43ARpK1D8+pHvKZzoyL8DmCmH5zvf+Q7HHXccn/70pwd8ppRy/buhoWHAe/2pdc1NN91ES0tL31GcBGdxeNwE0aMbk/QtRrz4dQO8PzsKpIPvT9HrR5C2Mgx3/SoC+bcdmXF47rjjDs4991w+9KEPuaIt69evBxgQqRk/fnxf1Gf9+vUMHTqUMWPGVL2mPz09PWzfvt11FIOgRquoIxMx4m7yb7SiQ+qGG7EdbsImcYseWasbmXB47rzzTs4//3zOOOMM1qxZ4/rs7bffpr29nTlz5vS9N2TIEGbNmsXSpUsBWL58OT09Pa5rWltbmT59et815UGMuBvRw02QaYtsGa3okLrhRvRwE0SPfciKVyfZqhuNaRfgrrvu4uKLL+a8885j+/btTJgwAYBt27axe/duQC85v+GGG3jjjTd44403uOGGG+jq6uKHP/whAJ2dndx7773ceuutbNq0ic2bN3PLLbewcuVKnnrqqdR+WzqI0XIjergJY7RGoE1Gb41r84TUDTeih5swejRTXD387PGWrenf1B2eL3zhCwAsWbLE9f5ll13GAw88AMDNN9/M8OHDufvuuxk7diwvvPACZ511Fjt2mAf5XXPNNfT29vLQQw8xfPhwFi1axGWXXca+fV4fr1AUwhqtbFTM6JAwvZsowvSboitOqgRN0pW64UZshxupH4ZsOcOpOzwNDQ2errvxxhu58cYbq37e3d3NvHnzmDdvXlRFyykySnMjergJosde9O7To9CdWlEcHung3UhbcSN6GIY4XgdxeIYAw4FdkZUoCJnI4RGixB61+ll1AsVspCCdWn/EiBtECzeihxvRwxB0C4ed6AETZEEPcXgKR5B5VihmIwUxWv2RML1B6oYbqRtuZLBkCOrwQJbqhzg8hcOO8IjDoxE93EgnbwirRRN6v5WiIHXDjUTLDXbd2GcdfsiOHuLwFA4xWm4k4mVwjtJEj+DO8A6M0Rc9ilk3ILjtyE5EIzqC1g3IUsRLHJ7CEbRiFrGRgqzEcRImLJ0doxUdQQcHCrPXShHrh9QNjTiAhigcnvT1EIencIRtpEMd9ygCYfUokhF3/r+KES/KqDU6pIN3I3oYgjrDkKXBozg8hSNoxXQ+WqNIRlym+AzOnITaz6EbSHaMVnSEMeJFrh9Bo8NFshsgES8nEuERMknQirmPYnZqMkozSAfvphhGPDrCdvCD0Xs1FQWxHYZi2A5xeApHMSpmdEjSskE6eDeih5ugeuzCrGQSPWTg2J/sRLzE4SkcYsTdBE1azk4jjQ6pG25kcOCmGJ1adMh0uKEYtkMcnsIRNKIBWaqY0RE2LN0IjIyuOKkSRQdfpA4tjBEvYt6KOIAG2cLBjSQtC5kkaEQDpKE66cI8FbwonVoxRmnRIXq4icIBLIoeUWzhMAqd11QEitFWxOEpHMWomNEhRtxQjFFadEhEw43oYYhiCweA5gjKkgWK0a+Iw1M4pFMzDMKMsPLdUKOhGEYrOkQPN6KHIcwWDnswTwUvih7FmA4Xh6dwSOKhwTlKk1FrdHWjKGZDOng3YjsMYTp4KN7gsRhtpSiWS+hDwtKGMGFpKJ4eUdQNKM5eK9JW3IgehjAdPBRPjzCLYbLj/InDUziK4YlHgzPx0O8Tj6F4eoSpGz3Abuu16FHMVVqS72YQh8dNFIthhgNDoilOQMThKRzi8BjCGq2idWoSpncjEQ03YjsMYdtK0ab4onCGIe36IQ5P4RAjbpBRmhvRw4108G7EdhikrbgJUzf2YZ7VKA6PEClixA1RjdKKoocYcTdRtJUR6M0pi4DYDkNU0WHRQ5ONiJc4PIVDRmkG6eDdiAPoJootHKAYegwmmi0cijKFI23FTTEcQF8Oz5QpU+IqhxAZMkozhFlZAMXToxijtOgIo8deYIf1ugh6hNlZGLLSoUWHDJbcFMMB9OXwvPrqq3z9619nxIgRcZVHCE0Uz9IqSpg+zMoCkA6+P9KpuSmSHrKFgxsZHLgphgPoy+GZM2cOZ511Fm+88QaXXXZZTEUSwjHMOgfp5Lc7XhehoUqH5qYYo7ToED0MzghPb9WrqlMkLUDqRn9K6PA899xznHrqqVx33XV8/etf56WXXmLWrFlxlU3wjXOPgyAVsxfYab0uQkOVKS03xTBa0SF6GGwtdte8qjq2Fk2YQVeekcGSm2Is0w+UtPz973+fww8/nF/84hf88pe/5OGHH+bQQw+NumyCb8LOw0MxjbhooZFRqxtxeAxhtdiBXn4MaXdq0SB1w00x9Ai8SquhoYGFCxdyzz33cO6557Jq1SpuueUWRo0qyrbz+eKTwGdDz8NDVipmGIYA/wgcKREeAA4CbgaGF8RoheVM4J8AcQA1VwMfCa2FwkQ1xoQtUmq0AN8AJkndAOAY4NvA4IJsWuorM/Xv/u7vmDFjBjNmzOCoo45i7969/OEPf+Cuu+7i5Zdf5pJLLuGVV15h7ty5LF++PK4yC/04A/gp0E4T3wf0CpJ9tb5Sg/w31JuBLwKTGco8IHgj3Wqdh6LD9EHD/ekxCPgtMAV4hKE8BwR3eLZa5zFhi5UaBwFPWq//haFWzSivHp8G7gReYii/BoJrAVqPMeRZjweATwAbGcoCoMx1Yxjwe/QA8t8ZyltAcD22WOcxocsVBl8Ozz/90z/x/PPP88ADD/D888+zbNkyenpMZ3Lfffdx/fXXc//993PsscdGXlihMt+2zt2hR/BgKubYEPdIjwPQzg5AU2g9tqOdx8FoPdpDlS0N/hva2YEoRq35rhsA8x2vw49a869H9LZjKnnV42i0swMwJrQedt0YjR52BB2ApseVmKzQ/UM7PFutc7p1w5fDc9BBB9W95t577+Wf//mfAxdI8McI4P3W62iM1lbrnE+jdarjdU/oKS3QeuyPHpnkz+E53fE6fP3Yap3HBC1O6nzQ8XpvyfWYChxove4J7fxB3vVwtpXwemxxvB4DbA54n/RwLkdSkekxJnB5oiDynZb//Oc/c8YZZ0R9W6EKJ6LjDzswjbQhlNHKRsUMygzr3Imzg49Cj3w6gCdb561E4fDkW4sxwPus11uAnpLrEW3dgKLosYUo9OjFbEw5JkSp0sPWYxtRDpZyFOHxym9+85s4bitU4BTrvBAYYVXKRrrZE/iO+TZatsPzA6KK8ORXjyHACdbrHxPlqHWkdffgtSwNbAP+J+AN1xYO5ZzSstvKz9BTFqBtR5BdeDT51sOuHw8RZcRrFHnUYzw6320fOj+0KIMleZZWzrEb6e+AtVYjbZKIhquRNpZUj+nodOvNwNNEYbS2YXIR8qfHSdZ5GfBmJCsa81s3wLSVpcB6y3YMK2lbGYpuLxBVBw951sNuK68BfyAKB3CrdR4TvFARIA5PzjncOr8CrLUa6dCSNtKxwDjr9VJgm6VHS0n1OMI6rwLeJgqjpTCr+PKnx5HWeSXwlsvhybcRD4pTjzZLj5ElbSvvQ093bEGvarQdnuEl1eMo67wSWEOUEZ7BQHPwgoVEHJ6cY2/3+CdgndWhlbWR2vkZbegF5JssPUaXXI8/4TZajSVNarf1eBNYY9WNQfQSfAVNfuvGcGCS9fpPmAjPyEiSlvOnh21H3wR2AZ2WHvuVNOLl7FfeJgqHZzdmW48xwQsWEnF4coy9dgjgLaDdqpQjStpInR08wCZLj3BGa6t1zp8eTiO+Adht6TE+EgdwTIh7pIPTiL8baTS0BT1yzQ+HWOct1rHe0qO5pHXD6QwDbLH0GFdSPZy24x1MdHh4zm2pODw5xm6k76F952iM1lbrnO8OHmCr1Uj3L6nR6m/Euyw9JpXQIR4BTLRev4mZ/h1GNw2B77rV8XpM4LukgdP5A9hg6VHWaGh/27Et0sFBfvX4E+4Vr5Nzroc4PDmmv9HaFElYOv8dvK1Hp9VIx+S8kQalvxG3IzzROID50sOOaGxGuymbHQn+wfcU34vZMj9fevR3hrdZeowqoTMMA9tKV990eL4jGkEYDBxsvbb12NM3eMz3Pk3i8OSY/kbL7uBHRdKhDQdXYmf2GRjRsEet5TPi/SMaYMLS4YxWPvXo36H1Oqa0xlX8hle2Wucxoe6SNP312BHplFa+6gYMHDzusvQYW0I9DkJvOrEbWAc4H0p9QM71EIcnx9i7pK6xzjscS0uDP8LVfpwC5LGhgtFjt6VHGSM8U63zVswv2FPiCM9U6/x23zsmwhPO4cmnHtOs81vWeWckKxq3Wucx5KlrGYSJaNj1Y3df/l/5psPtuvE2el2mc+AbzuFZh07ASO8xG7FsPCgkg/2MpDbrvMcxat0fs8+nPxTmcQpjgfVhipgYgzARjfes8+6+UVr5wtK2M7zW8V5vJHkJW61zPvV4t++dqCI8+XR4+tePLseU1lCCrsVxPk6hBXeOU3aZgI5o7MWOaJhoaBlXaQ20HSbCMyGUHn8b4rvRkLobfvrpp/Pzn/+ctrY2lFKcd955rs/vu+8+lFKu47nnnnNd09TUxB133MGGDRvYsWMHjz76KJMnT07yZ6SC/Qvf63unvEZ8Atp73wt0WO919xmt8kU0bGfYGK1B7LPGNweUMMdroBEvb1uBgfWju99gKRh7gJ3W6/zoYdeNdZjY9p5IV2nlRwuoZDvs7Sz2cIAV88krqTs8I0eOZMWKFVx99dVVr3n88cdpbW3tO8455xzX5wsWLGDu3LlcdNFFzJw5k1GjRvHYY48xaFDqPy9W+kd4nGH64EYL8thQbeevHWO0ehxh6eA1of/jFPJBrVFaawmNeDU9wreVrdZ5TKi7JMkoTGnFAazUwRuHJ9wUzlbrnB8tIM7BQfqkPqX1xBNP8MQTT9S8pru7m46OjoqftbS0cMUVV/DZz36WRYsWAfCZz3yGtWvXcuaZZ7Jw4cKK32tqamLoUDM32dyc3u6PQRiBaUbRR3i2Wuf8NNSBzp+ZwhlGD2OBTYHuvM3xegx6R5vsU81oAUwUh4cyd/C2Flsw8Zhoc5qmkEc9nA7PXkuP6KKhDZCT6EitwUHeHZ5chEBmz55NR0cHr732Gvfccw8HHHBA32cnnXQSTU1NLsemvb2dVatWcdppp1W95/XXX09nZ2ff0dbWVvXaLGJHNHZgFsZKhMft8Nh6hOvU9mEcwP0C3yVpas/Dh3nop1038qPFYAbmd0Xn8Gy2zvnRo1IHTyRTWpDH+mHr8Z7jvb2OR20E37bArhvpPk7BLwP1KE6EJ/MOz+OPP84ll1zCGWecwZe//GVmzJjB008/TVOTNuCtra10d3ezdetW1/c6OjpobW2tet+bbrqJlpaWviNvOT8D83cgOqNlN9T8OTyV9AjvANqxofwZ8f4RjSa6OaDC9d7JnxYT0V1ODya/K7pRa/4cHjsa6m4rUemRv/pRzwEMrkc30GW9DmeBkqRaDk8RHJ7Up7Tq8dBDD/W9Xr16NcuWLeOdd97hYx/7GI888kjV7zU0NKBU9RBiT08PPT1hwpXpUjmiEVXFtI1W/hpp9BEe0J3aoeTRiJtOzXRoI9C7LO0KdGe7gx9l3TP7bcjWog3npEJ520p8HTzk0QGsrIfbAXyToGxCJyDsh3NThKwyEjPMrTSlFW6wlD6Zj/D0Z/369bzzzjscdthhff8eOnQoY8aMcV03fvz4qnk/RaBWBx/dqDU/RrxexKtMndpotOGCymFpCNMdbcOkheejU6sc0TDRv3C/In8dfC09htId8pfkq61AfdsRLs6dL1tq141tOLc1MVo0k4MoSQ1y5/Dst99+HHjggbS3twOwfPlyenp6mDNnTt81ra2tTJ8+naVLl6ZVzNip10iDzztDHsPSlR1A06mF0yNfnZr9FOwtOKM4WoshlsMTXA9F3vI07Pydda53TfQvmraSjw4NTP2oNliKpq3kQ48GTP0wegzGfhhs2WxpvboBeoelvJK6szZy5Eje97739f172rRpHH/88WzevJnNmzfzta99jZ/97Ge0t7czdepUvvWtb7Fx48a+6azOzk7uvfdebr31VjZt2sTmzZu55ZZbWLlyJU899VRaPyt26kV4ymbEKzuAplML10jzpYdtwNtd72otGi2jFb5+jCMvethG3O3wRDU4yFcHD9Xqh9Ej3ANl8tXBj0NvNrGPgfld+lW5HMBadcM5WNpMPknd4Tn55JNZvHhx379vv/12AO6//36uuuoqjj32WD73uc8xZswY2tvbeeaZZ7jwwgvZscME3K655hp6e3t56KGHGD58OIsWLeKyyy5j3770trCOm3gjPPlqpM4pnGo5TWWM8FTq4AeHjvBA3vSo5QA2WY9hGYyZqPOH3cEPI0xmVJLUcwCHhbp7vmyHXTc2AL197w51vCpnhCc+25EuqTs8S5YsoaGhoernZ599dt17dHd3M2/ePObNmxdl0TJNMhGefDRSW4vN6AfeGcoZ8ao1ShscWYQH8qJHvQ4edJh+C0HYgd5heAhaj/dqX54yjdCXeFrNASyT7ajVVgAa6S3V4LHW4KAhEtuRLrnL4RG00bIX3Mcb4RmLPZedZSpHuxqxq3fZIl6VO3httAZJhMdCt5Vo9MhPJz8B3Sr2ABtdn5QzOlyrrTSymwbKUzeg9uCgwTE4yCvi8OSQVozR+rPrE2O0RhHmP9c5Qzsm8F2SolbCMsioVeM2WhLh0Z3avkhGrfnp5O26sZ7++/5GleBfnLYS7eAg+3UDkrAd6SIOTw6xIxrr6G+0osqm34vZXTj7DbVWwjLIqFVj61GuDn4YZl+RSqPWfSWL8FSuGxDdqjW7bjSSh1hA7WholNO/2a8bUFsPJVNaQhpU3nQQTFSjXCOTWpswwl4a2RvRKq18GK3Ky7CjrBv50cPWogvnI1ggHocn+22l8gge+q/SCr5Sazdmd+H81I/4Ihr5saNQWw8lER4hDSpP4YDtie8p2cikVgJ3NIl2ttFqJg9PTLdHafEZrfwY8XoRjd6SRbzq6RHNXit516Ocg4Nm9P7pUNl2RDM4SBdxeHLIwI2ybHTF3FPSTq1yhCcKLbaid+qArBuuFvRG9lB5pcXeSDq0/BjxehGN3pJ1avX06BE9iHYKx7kAJNvdra3FNkyMTuO2HeLwCIlSeQQP/Y1WWTbbqz2FoxvpcMLEZpy7C2dbD7tubKX/jjBRjtLy5wxXi2hEEw0tgh66fnSXrH7Ush3RRkMHhb5T3NSrG9EMDtJFHJ4cUs+I7y7RlFYjMN56XXmU1t33Thn0qGzAIdopnHxoARLR6E9lPcxQYFeJ6sf+mFR+91MX3YODcAPHPcB2x1/MLtUH0lEODtJFHJ4cUs+Il2mUZu8r0oveLdVgprTsPbnLoEe96F80ozRbi+HWkV3qjVqjiYbmo25AvZyVctkOW4s/o90Sg3sKpxEzTRyMfETLqw+W3LYj+2vvqiMOTw6pF+HZFWmeRj4a6cB9RcwybHt1TnOov2Rv0xbuuetx49VojRrwuR+2Y08V5kWPaqPWHut3hNMjH3VjMNWiocbh2VUiPeoNHPfR3Ze5VwbbUW+wZOeGhtMiXcThyRnV9xWB/hGeMhiteiN4Z4QnXEO140cH1LwqbbyGpcMbrXzpUa+tlKFuVI+G2oODXrqsLr4MevixHeFsqa1Htm1pvcFBdLYjPcThyRl2pdyFzqY3mI32dkVSMfNhtOpFNKC7bwa9DA5gPT3sKZzhhH1oSL70qDf9G01b2Z8sP4ql3i7L0Q8O8lo3THTYth3RRHiybUu9Tv+Gs6PpIg5Pzqg/KoHd1iM0y+Dw1ItoRGe08qGH1yRdKL4ewzBps/US/MNp4XwUS3YTdetNhTvbSjSDg+zWDfAW4RHbAdEODtJFHJ6cUX0EP6zv1a5IR63jgOpPs0+b6kbLTqbdVSqjVa9T29eXtRJVmD67etgP2B0YDQXbiO+OZNS6F5Pzll096nVo0sHbmC0tyhTx8rr6dxg6kTuPiMOTM6pHNEwHH828sz1KayTLCxGrGy3bAdwdcVg620arukMctQOYfT2qG3Cw68euSKKhkIdOrboeUbeV7GsBXiI8u0sT8RpFtV2Wwa4f3VZbgfxGecThyRn1IzxRGa0ezNOHsttQvRjxaBMPs6tF9a3hIb5OLbt6VHeGwdajKzKHJ/udWvKDg6Gh7xQn1fUoX3TYtqOdwM4Bn+r6sbcvHprfPB5xeHJG9QiPbbSiaqSQp4YqU1rureF3Dfi0fKP46m2lAePwaKXCG/Ds14/kosO7MN1mdvVIzgHMflupPTiI2pamhzg8OaN+Bx9VI4WsG/FGTMmSG7WOJKub7VV+pphNXFOe2awbUP+ZcwA7rQjPYML+r+anUxuoR9RtBbKux36YWuDFASz6Ki0v07/R1o90EIcnZ3iZ0oqmQ4OsG63q+4pA9I20k6xvtpes0cq2MwxeBgew0xELK2+nFscIPtt6VN9lGSq1lWimw0cT5ol+cVLbdkTtAKaHODw5o/6UVpReeLaNVvV9RSD6iAZkvZOvXjcgvim+bDp/4CW/qxfYWwoHcDB6gABJ5P9B1vVIdnCwFV3XIKvtxa8eksMjxE7tXZYHdmjhN5cTo+Um2w5gsnrYWuxHVs2Il8EBUAoH0I6G7sH8zxlM/l9ZosNeIxrR1A1F1rctkCktIXNU32UZKlVKKPaoNflGWgwjHk2nZhvwQWT1eWtepnCAUuQ02VrUjobK4EAzMD1AbAdI0rKQKF4b6R6IeHO5bDbS5FcWiANocG62l736MZxa0dA4IzzFqBvRRYezVzfAfwdflunwgXo0YmpClA5gOojDkyOqJyxD/1FrGZ4BI1NabkQPg91WdmJ2kzLE0VaK1cFDORzAWns0laWtgJcNSyFaBzAdxOHJEV4bKZRj1FrbAYxz1Vo29Ug+LJ1dPbw6fxD14CCbm+0lHx3OdgcvbcXQgt5sA2rlu0F0jx5JD3F4coTXDh6iMuJ/ts7jQ90lLpJdlQTQYZ2zp8cYzC8eqMcQTFOP0gG09ZhQ86o08OPwRLfZnl3T8qZHHBGv7NYNSCOHJ7t62FpsodKGpXHUjfQQhydH+DFa0Rhxu5EOJ4tV3O8UTvjN5Ww9WmtelQa2FpvA8Tx0m8ph6TIY8eQ6eNApwZDl+pHcYCm7WjTgffAY3RROXttK5cFB9noDb4jDkyOSn9LahcmAyFZDrb3LMjj1cD4bJhojni0twKvR2gf0SAcfS1uBonRq0UZ4sjdYOgBtP/ZhSukmjuhw3m2Hu25IDo8QO8lPaUFWoxq1d1kGp9FSRB3xyq7Rqj29V74Ovt50J5RDDz8LHqIZxWd3is+uGx3otYYDKWd0ONloaDqIw5MjxIgbnFoM3FcE4h+1toS6U9T4cYYlhycuPbIZ8RqCyTpLdhSfzU6+dt0Apy3tcrxb1Jym5GcO0kMcnpxQe5dl+wqIvmJm04jX3oMHyjbFl84UTjbrBqQdDc1W3bD/d7qBzRWviDunKVt61G4rgzHPu9qNIioH0K4bI8iauyARHiFz2Aa8i0q7LEPZRvF+RmlQ/E4tiNEaQdjN5bKpBaRlxLOpR/22Ui4H0JszDNHq0UXWp/iSjYamgzg8OaF+RKNcK0/8RniKPm0RJMIDUa7iy84U3yhMaZIN02d7CsdvWym6Hsk6PJDPiJdEeIQUkFGaGz9haSi7Hm6j1UNUm8tlc4rPdoY7MZ23mzi2cIB8dmhQrVMrpx62He1Br+MSB1DjtqPhHz2SDuLw5IT0HJ48RzSgLBEvP0YL4jDi2enUZHDgRvRw42fxB8SRxJ0dPfZD7w0O/qKhkM8ojzg8OcHvlFbRc3hq6+HeDh2KbcT3B5qs1+srXuFelg7FdgDT7+CzNcWXfr5bduoG+B8cFHlKy9ZiIybq68ZdN5yPHhGHR4gNifC48RuWLnKY3tZiA9ogDcTWY+CotYgOYP2clbg6+GxO8fm1HdFF/7LXVgZjSuNl+heK7QDWTuCGWg5gHhOXxeHJCekswwbTSIein9iUPvV3WR5otIo8D++3Q4Ni77UStIMfjDs2GIzsOoD1Izxx7cOTHS3Go/+fq29YGmeEJ3t6+I3+Qb4Tl8XhyQnphaW7ga3W62w01Pq7LIvRchOnA5i9UbxfPZyJzeV2iOOa0srOFJ+txXrs2G9/4sx3y2NbidOWJo84PDkh6Kg1mrBjtoy4312WoRzz8OkYrWzVDfCvh/PRI0WbAh6GTkyFanoMdbyOOjrsnOLLhh5hIhrljIYOzP/L8wNEU3d4Tj/9dH7+85/T1taGUorzzjtvwDXz58+nra2Nrq4unnnmGY4++mjX501NTdxxxx1s2LCBHTt28OijjzJ58uSkfkLsDMPsslw/L8FttMJvLgdZM+LeE7jjmMKxtXD+r6RLumFp+39hUs2rkiSIEY9ej4k1r0qK+huWOp8QFeWztGyypUe6gwP7r2ZDC/CzR1McDmDypO7wjBw5khUrVnD11VdX/Pzaa6/lS1/6EldffTUzZsxg/fr1PPnkk4waZeResGABc+fO5aKLLmLmzJmMGjWKxx57jEGDUv95kTDFOu+gmtEahKmY+tng0W0uB6ahZsOJtPV4r+oVI6yzeRJOdEa8BzORlg097FKkk8PTZp2z4/BkQ49s1Y36Hdpe9CRx1B1a3vRIwhkeCowLfbcoCNNW8hjhaUy7AE888QRPPPFE1c+/+MUv8s1vfpNHHnkEgEsvvZSOjg4uvvhi7rnnHlpaWrjiiiv47Gc/y6JFiwD4zGc+w9q1aznzzDNZuHBhIr8jTg60zmurXuEcpelO3t5crgldMSs7Sl7JltGy9ajv8OzseyfaRroOnTY9GVgVyR3DUL9+xOkA2nVjAtqc9Ia+Yxj2x7SGtqpXDawf0euRjbZiDw781I3+0eHKTxT3Sv71iK5u9KKntSag9dgY+o5hCWI78uzwZDoEMm3aNCZOnOhyWnp6eliyZAmnnXYaACeddBJNTU2ua9rb21m1alXfNZVoamqiubnZdWSV+hGNkY7XxR+1hjHi0fwv500Pu37E4QBuQLvWg8jClKetxZ+xd2CqhK1HHPUjW3WjfodWXQsoXnTYewc/sK0ULeLViGmx9fuWOBzA5Mm0w9Paqv87Ojo6XO93dHT0fdba2kp3dzdbt26tek0lrr/+ejo7O/uOtrbq48G08ddITRpvUUet3o14XBGe7OgxFL3UFvxN8UWnh8KE6tPXo37dgHgjgNmpGxCsg+/B7OcUnR7ZmPIM4wAWzXZMQjsAPegBQmXidgCTJdMOj41S7rU4DQ0NA97rT71rbrrpJlpaWvqOLCc5e4/wdLneLboRD9LBF22UZteNLmBz1auqO4DR6JGdUbw3h6d6pxZdRKMlkruFxbvt2Ol6t6jR4TARnmgdnvQdQKcdrd5Txj14TJZMOzzr1+sVMf0jNePHj++L+qxfv56hQ4cyZsyYqtdUoqenh+3bt7uOrBKkkUIcDk/6jbQBYzr9RHjsaFcjUWwulx09/EU04gpLZ0eP+h18E2bdYhw5PDswS7HT1yNIjgYUc7A0FvNrq8fz457CyY4eYQcH4vBEzNtvv017eztz5szpe2/IkCHMmjWLpUuXArB8+XJ6enpc17S2tjJ9+vS+a/JOkIgGxDFqTX91wQFWKfZRa2VB9Q4eimXE63fwUKYpPu9TFlCGaYsg078Q12CpIfTdwmBrUTu/K+4pnOxEQ73ZjjgT/JMn9VVaI0eO5H3ve1/fv6dNm8bxxx/P5s2bWbt2LQsWLOCGG27gjTfe4I033uCGG26gq6uLH/7whwB0dnZy7733cuutt7Jp0yY2b97MLbfcwsqVK3nqqafS+lmREiQpFaKsmHvQZmI8aa8usLVYT631QANHJfbmcqPQelTeodkr2TFa2Ril5bGD78FZg6LX4yjS1qMJsz2m3whPtBsx7gOGoIcr1bNF4iZsW7Gjw7sJQ57aCsQ7/Zs8qTs8J598MosXL+779+233w7A/fffz+WXX87NN9/M8OHDufvuuxk7diwvvPACZ511Fjt2mDH7NddcQ29vLw899BDDhw9n0aJFXHbZZezbV3nz8DwxHL3UFtIMS4NuqLbDsyKSOwYhaFIqaD1GEWVewgS0Ia/8yM4kCJukW7ScprDTv0XSw/7ru/CX3wVR6tGLdnJa0VGe9ByeoBENpzLNlM3hiTunKVlSd3iWLFlCQ0PtUOeNN97IjTfeWPXz7u5u5s2bx7x586IuXurYlXI7JjNgIHEnLYNuqO8n7YZaP9oF1fSIbtS6ER0UH4reNfXd0HcMStAprWjD0tmJeAVN8C9inkbQ/C6Iw3a0ovV4OZI7BiFoRCPa6LBdN8ah7Uf1ybW4qa/HYMyjRySHR0gAbx18eUat9fOZIP4kbshKJx+0U7O1GEkURiAbScvO/C4/mw5CMaf4/HXwoocmblu6BfOYhnTbi7/93YqRwyMOT8bx18HLqFWTVMQLsq9HA7XC0hDlFF8zaZpBW4sOak0yJlE3suEMhxksie1IYrCUnsPThNl0sP70716ckag85/CIw5NxvDk85RmlBZ2Hh7hWW6RntLzldw187Aho82U7BeH16AK2Wq/Tqx/Zi4amO4L3ZzviWuEJWXMAw+hRFFvqzO+qvgSlthbRPJg6WcThyTgHW+c1Na9Kah4e0jZath7v1LwqCaNlm80pNa+KE2d+V/VnpTnD0rtcn0Q7ik9fj4Osc7r5XWC0mEiaXYI/PeIcLKVfNxoIN6UVT/04sOZVcRJFagDkL8ojDk/GmWqd19S8KolRmp2Ye1DNq+KkEWMy19S8MgmjZetxcM2r4mSqdV5T8yqnM+zeTzXaTi0veiTRwa9Hx9AaSXOAMNU6v13zqiQGS/bwJL26MQG9pLyXcNPh0djS9PWYap3X1LyqshbRPnokWcThyThTrfOamlclkYhpN9IxwOhI7uiXKejx8i50nkZ1kojwrLHOUyO5WxDsv7ym5lWVO3goqx5JTGkpTLeal04tzsGBXYL0tXiPek9/T9KWpq/HmppX1bcdEuERIqMBE08JMoUTrdHqwizITKehTrXOtbWAZDq1vBityiN4KKsetZ3hIZiFuOGw9Zha66LY2A/T7mtvmhD3Lu2g3Yx96Hyy8XWujYep1nlNzauGYqYgk4h4Ta11UazYf3lNzasq21HI79J0cXgyzER0Nn0vtZbZQjKjEki7U5tqndfUvTKJML1digm4E4OTY2q/klQmqQhPXhyeym2l/+Zy4bFLkW5bWUe9nV6SmOLbg0lczrLtGOF4Hefg0S5F1ttK5cEBiMMjxMBU67wWr2HYOEdpkPa0hf1Xa0d4Km+WBVEbra2YrSDTyWuaap3X1LyqeoQnHiM+NZK7+aUZs2ItSDR0H6bLL4IDONU6r6l7ZVKDJbskUyO7ox/sv7qmxjXVHjsCceVDjiat9ICp1nlNzauqR3jyuhePODwZZqp1XlP3ytph+mg2l3OWJMtG3DlKk4hX8hGeyaSxgbv9P7AJ9yqSgdQP00c7xTc1krv5xf6r9ad/k8h3c5Yky20lqSkcZ3rA1Eju6IfBmFVaa2peWT/CIzk8QmRMtc5r6l5Ze+NBKIYR97ZE326k++gfzC/SqHUYesrTWYrKJNXBd6D1Hkway4+nWuc1da9MKkxvlyTLHTwks6IR0rYd9l8NEv2DOG1H8vXDHpL0AO01r0xqsJQc4vBkGG97zoCpdu6xrXNzufIYcbv73jHgk+hHJemNWu1JtO3UejAkJGfEFWnqMdU6r6l7ZXUjHm0nb2txEHr5QbJMtc5r6l5Zf3O5vEeHG/A7WIp7+hey0Fbeof9GFf2pv+BBHB4hMqZa5zV1r2yxzgMfLxptJ2+XZGokd/ODM25Q2wG0tRg4sVGkUav9F9fUvbKyMwzFMuLeOjSopUf0G3XuReeTtda5NnqmWuc1Na8ajOnU3HpEv7lcenXD3oNnL/U22rN/adx2FLLg8NTenwmStR3JIA5PhplqndfUvTIpI2430nG4d/CNnynoMGw3elu36ti/tLrRKkJYemq/ElSnvjNchCk++y+uqXGNpn5biaZT68V0r1mtH85fGnd0OP3BwXv0T0XuT/XBUnw5TVMju6NX7L+4pu6V9W2p5PAIkeAMw9aOaDRZB8TviTsnUJI14lOtc/0wbFLOn10ayHtEo0ij1jV1r0zSAUxHD+978Nha7KbS41bjmeJrBsZGckevTLXOa+pemaTtsEuTh7aShB7JIA5PRpmMDobvod5W6M4qVz1vJe8jk0Otc5gwrP1OI1FtLrfGOk9Cb1mXHIf0K0F10nAAp0Z2R69EoUdRpvimWed2tCtTnepaON+NxiHejdkfPR091tS9srozXJS6AUaPoLmhznfE4REi4X3W+W3q7cFjN9KuilcWZWRi6/FG3SurG63oN5fbgNZ9EEk/CNDW4091r0zSiK+xzsnWjbHoqAbAm3WvLr4DGEXdgOJMeXrXI41o6AEknR5wmHXOlu1IBnF4Mor/RlrbaEW/+eC0WhdFThRGK/rN5cDocUitiyLHu9FKsoO3428Hk+RePHbdeI/+z4PvTwPJ5iXYeqRTN+oPDrxFeKKvH4fWvCpq/OtRvW5E9+iRbcAW63VytnQE5nG2YeqH5PAIkeI9opG00bLH0O+reVXUROHwON+NTg+7RIfVvCpKxqH3Z91HNBGN6IzWOrTL0UiSo3jvzl/1JF3nO9HVDbv1Hh7ZHb3gP8JTua1EP4q39UiurUA0ekQfHQZ43TonVz9sV3Mzxt2qTpL5bskgDk9GiWIED3EYreQbKUTv8ETXySevh1033qPec5IgWaOlSKNT8z/duYdK2S3xtZWDMAsL4se/7UgqOpx83RiF2aAzjB7xRIez3FZAcniExMhuRMNuKoeSVPUZjy7/XrwkLSedl5Ce0apfN8BLku4ootwaL3k9ohocRF83Oqy7DibJaS3/DmDStiO5umFHNDagJ5Fq402PPDuAUdsOcXiESIgqLB19I30XHVcYRlKJurY5eBe9HXptko54ZX2UVn9pKZTFiCcd/YOk9WhBDxAgughP9BGvA4Hhkd21FsE6+OIOlrznMw3DrDyVfXiEGJmITi7rxc/SwaQaqTNzJJmGGtWoxPlu9Eb8EJJK1PUe0YBa9WM3ZiO2PE/xRbGCD+IK0yerh63FeiptUtGfpHN4NmP28UomB9B7Bw/JR7zSayvenWGotd1JdA+mToY8lbU02JXyHSptB9afpCMakJYR99bBJx3xWodemp5coq53PYajp1OgqNMWY9BJ3BAugdv5brRtJVk94hgc5DniFWWEJ77ocGukd62F/yXpO6i01Ws80eH4EYcng0SVWOZ8Nx6HJ39GK55EXbtkyTiA3ketttFyply6ic+IH0wSibp23bDdztokHdGApDv4YBGNJCNeWdYjaQewE/iz9Tr+iNdwzPMIwyzRB53U0NvvyjwgDk8GyXZEA5JebisOoGF/dFQD4K26V9fWwvlJtIm6nWjTEv9+K8HqRlKrkiCttpLN6V8oQnQ4Hgcwfj3stPktmInF6qRhO+JHHJ4MYld9MVqaYDkrxRzFO5ek195kD/wYrbxOW0RZN+x3nU+nC4/dVqaQRKKuPz2KHfEaiX7oC3jRYwg6URfSyfFKrq1EEf2DfCYui8OTQY62zq94unqMda686DJeozWVuJ8hNQm9yV4vXo34aOuc5Cg+OQfwSOv8qqera3dozk/y2qkdZZ1fr3mVjbcOHvKbqGvr8Zqnq4udw3OEdf4zsLXu1c7/8WI6gLbt8NZWJMIjJMAQTNX35vDYTx6uHKSMp1K2o5t/I3Fvi247f2/gJYF7JMYBq7yPaN47+GOss7+6UX1P1bwntdv1Y7Wnq2vrsQ+TB5TH+jEe/UyxvXh1eGrrEW9biT9R11/dGGOdd1Dt6YV5tx3B2srWqlfkcS8ecXgyxvvQXXYn0ObpG96MVvTLB5OZe/YX7bK16KFakm68YemDiHvaIpge1R2eePU4ouZVYRns+AtRODwQdwTwyJpXhcV2ht+i3lPSbdJweDrRi+Yh7voRbHBQPbslz3UDoh8sSYRHCI2/Dg28Gi2I+pm8f7TOx9S8Kiz+Gqn9zOykIxobgI3o5nRUnWvD4W+UZutR34hHq4dduumR3rU/h6CzLnbiZb8q8GPEW6peEYRV1jlePfzZjkGY6d/KesQ3gk+mfvjTo77tiKduvIqesB+LyTiKHqdlitoBFIdHCEzUDk83ZnfiaBvqSut8bKR37U/UEQ0702l01SuCEr8eIzE7/WRbj1fR0wL7Y55kFD123fgjlXYKqUR9I77VOkerh+3wZKmtjMaY/8r1Y6t1HknUW2omazuiiv5ttc5jApanMj2YKE98ehyMjj3vxsvqTvDiAG61zmMClyp5xOHJGP6M1gjMepL6FXNs1SuCkE+HZ6t1HhOwPNWx9Yhv1GqP0DrwsqwUvOhhfxJt3diNmfKMTw//gwPvRjyetnIEcSb529FQfx38Tqo9sGVrhaujIf6I13DMMmx/EZ76zvCYYEWqQfx62HXjVXSuWn28244xAcuUBuLwZIxgHXwvtTaSj6dTs434kcT1SIUJmCRMbysL0urgIQkH0N+IFbwY8Tzr4W+6E9KrH2vRsbQhxJm3EvXgYB8mAjgmYJkqE3/dOALduW2wjvqUw3b4bytp2I74EIcnQziTMKMyWs5Po62Y76ITEJuIy4jbjTSqJEznJ8Mwu25EQ/zTFlFPdzo/id5oJaeHNwewAdNtp2HE49VjHHAA2knxtmVB/Q4N4tLD/h+biJ72jB5pK27yEw2NF3F4MsQhwFB0kPldT99I0+GBuBuq/4iGt0Q7e0v0eML0kzDGIlr8TVmAnym++Eat8YTpB2HWtUSVs+L8JG962HXjbbxsSAleOjSIq37sxGSSxKuH/w6+vjPsrEnRYNeNoyO/s00ctkMiPEIo7A7+VfwmYabl8MQbio1jlAZxGfEd6O4G4jLicYzS4puHt+vGMcRhZqah8zR2AWs8fcP+3+6iWs4KJJHjla+2En/9yNpgqf7gAKJOan8LXS+HEcfmlA34XaEFfqa0xgQoU1qIw5MhjrPOUUY0IIlRfDxGy75rlB2889M86TECvyu0IN15eNuIDyeOZ2rFkYTp/DS+aGhWIhppTmlB3NHh4BGe6vXDmSkZrR6KOJfqH4xeadcNvOnpG0Mwuw3JlJYQE++3zi95/kbaRjy+Dr4Bo8fvPX8rbT3iM+LHoxvrOvSOP/Vx5qzUj/A496iOhn2Y7iZ6I27XjRWev5GVDn4acTx9yNbjD56/keaUFsQ5xdeM2bfYux5p14/4bOkJ1vkVqu0h3R/nr6v8yCKQKS0hJCda5/w5PFOJepefw9DdQhdet8mH7OhxXM2rgmDXjeWev9GCToOHWno4nzgWnx7HR35n/3qkHf3bjNk7Pdr6MQjTqXnXIytt5Vii7oZOsM7vAJs8fysrekTfVk6yzsHqRvX4qTOnqSFAudJAHJ6MsD869AjwsudvHWCda4/542ukWzB73L6/1oW+cY7gvY1KwKz4qG3m4tPDNiknEPVSfdtoeXeG7Q6+Cx3Mrsw+4hzF23qcHPmd/Q8O0o5oQFx6HIGe8tyB1ydhg5ckXYgzT+M1dPJyM1Gv8vRfNyD9+pG/tmJ/Opj87LaceYdn/vz5KKVcR3t7+4Br2tra6Orq4plnnuHoo4+ucrfsYnfwb1Dr+bT9GW+d/1zzqnhDj7+zzqdEetdgRmuCde6oedVW6xy9Hn+y7j6cqEP1/vWwtahdNyDO+hFP3RgPTEE7a96ntOy2Urtu5LGt2M7w7/GazwTp2469mNo8I9I7+x8cjMBMM6alx0toTQ5EP1g1Ovzr4a1udGO2C8nLtFbmHR6AVatW0dra2ncce6yZ57z22mv50pe+xNVXX82MGTNYv349Tz75JKNGRT9PHidxdvD2GC6exdIvWue0HZ7hmHFG7YYanx4K06lFZ8SHYpIw/Rut2nUDTDwsej1WoFdEHYCJX4bHrht2jMAb3hxAW4tmzB7m0WG3lWg7+Dhth61HPLvlZEUPu63sot5wMz49dmJy3qLTYxL6f7oXP4MDb3UDzNxCPPUjenLh8PT29tLR0dF3bNxopnC++MUv8s1vfpNHHnmE1atXc+mllzJixAguvvjimvdsamqiubnZdaRJMKPlzRO31Rrn695eyZrR2o07M2Ug9s6r8egR/Sj+WPQE2QbgPc/f8h7hsevHATWvCkI3Jm00Oj385++An+ifvU9T9EZ8mXU+nCgnifyP4MFr/YivbkAcbWUEZn8m7/XDewefjO2IzpbadeOPeN28FbJhO+IhFw7PYYcdRltbG2+99RYPPvgg06ZNA2DatGlMnDiRhQsX9l3b09PDkiVLOO2002re8/rrr6ezs7PvaGtrq3l93PhfoQVeR/F2I92POP7Dl6MD6QdjGko4DkaXtQc/S/S9RzTibaTRO4DhOrS0jXhW9PBWPxRmFB99/diMWRgcTa6GczWj9w5+GGaRgTfbEW/dOJ6o4mnHo3NK1uGl5ttkpYOP3gGMc3AAcdeP6Mm8w/PCCy/wuc99jo985CP87d/+La2trSxdupT99tuP1lY919nR4f6P6ejo6PusGjfddBMtLS19x+TJk2P7DfVoRo/5wM8S7KGYUaK3KZxBxDFtsYOoQ7F2I10J7PH8Le9GK5lR2nT0eDM84aJ/aTuA8RnxuBzAPEVE34e2H7vw+kgJMHWjm1rLjsGtRfQrcd62/sJQolq5FnfdkMGBm3jbSvRk3uF54oknePjhh1m1ahWLFi3iYx/7GACXXnpp3zVKufclbmhoGPBef3p6eti+fbvrSIsTrPO7+FlGaXdPPbj3AB1IL8bpyUOnlu8Ofh16+fFgolq5lu9Rmm3ETyIKc7MfZgPGl319078eeWgrdofmbzWjf+evkah3F7axp/mi6eTz3cGvRDuh+xHVZp1JDQ5kSismurq6WLlyJYcddhjr168HGBDNGT9+/ICoT5b5gHVeVvOq/niPaEBSnVo0RvxU6xysg087wgOmU/uL0HcaitmKLI4cDYjbaL2KjgKOwmxwHxx7IuhP1ItNOHFGQ9Pu1KJtK7abEFeH1oPJiIt3CjgfesTbVvZgYvzh9ZgITEYnHLzs65tZGSxFT+4cnqamJo466ija29t5++23aW9vZ86cOX2fDxkyhFmzZrF06dIUS+mPv7TOz/r6lvdRCcTdUF+wzqdiNrsLRiPG4fkvX9/0P0obRhx73gI8Z51nhr7TDHR3vR7zpC5vZMVo7cPUj9ND381W9Le+vuU9GgpxOzwvWeWYhH5ccDhsPfxZO+91A+LWw24r4evGGMxmEM/VuG4gWRosRaeH3a/8AT+rGSE707/Rk3mH51/+5V/44Ac/yNSpUznllFP46U9/SktLCw888AAACxYs4IYbbuATn/gExxxzDPfffz9dXV388Ic/TLnk3mjAVEx/RjxYhCceh2cFuiMZTdhpnOPRTsgW/DwHB/zo0WUdEFdDXWydZxE288E2e/6cYcjOFB8YPWaHvpOthz9nOEttZRfGAZwd6k4jMFMWcQ6W4tXjt+hJ90PRe9AEx7ajr+H1f9rGfwc/Cj1gip7F1nl26DsFsx3BoqEypRURU6ZM4cEHH+S1117j4Ycfpqenh1NPPZV3330XgJtvvpkFCxZw9913s2zZMiZPnsxZZ53Fjh076tw5GxyJXv7ahZ+EZTCbU/kzWvGN4n9jvf5QqDs5R/Denhhv40+PeBvqS+j9PPYj7LNxbD38dfBDMOnpWRi1LrbOs0PdZQhmktCfEc9SRAOi0uMv0BHRtej8P+/4cwDj1WM7ZvJ6Vqg7BWsr4Kd+dKLjcxCXHr9B29OjCLvqNZjDYzvD3qKhMqUVMZ/+9KeZPHkyQ4cOZcqUKXzqU5/ij3/8o+uaG2+8kUmTJjF8+HBmz57N6tXeFzOnjd1In8fs/+GNg6yzN1MXvyf+jHWeHeouwY2WPz3ibai9mF8Q3AEcBNibK/gzWlOs8y68pMHbdWMsUT8Qw+ZFqywTCJPHcxJ6e8kN+Hm+GpjIgbddjOKNaEBUDk/w6J8/PZKzHeEGS8H1sNuLt61J4nUAt2IyboI7gC2YdW/+bKmtxTpPV8uUluCL8B38OzWvsonfiNtG63TCdJvB9GjAGPHiOIDT0cHl7fh56jOYXY29abEZs8InHj16MBO2swPfJWlneHzNq8LwHHo1zoGEWY2TlB52HCg+PRZb59mB7zAMk7Dsz+HZD5PJt9bTN+z6Ec2uY5VYbJ2DO4AfQGdTvgm017nWjW07vPUrdt0YR9jszWQQhydl7HnnuI3Weusc7VNanPwBHU1oxiwO9ceh6PJ1E2TFWhO62/Y2MrGNwERff8cPi63zBwmax+NMSPW+5Bj8Gi2FqR/x6zE78B2Cd/D+9LBrUHxahM/jGYxZ3ek/opE1Pew8nkMwds0fM9AWoB14y9c37b/Xgde9iOPXY7F1nh34DnZb8V83/A+ke9GORHwOYHSIw5MiE9Gd/F70lJY//I3i7UYa3/aKClhivQ42MrEb6e+o9XzvSthatOHVNbD1mOTr7/jhJfSM/37oVGz/JDWChyT0WGydZxPEAWwgjBEP1lb2R6dwxsNi6xysrZyASe73N4E/AhPH86dHfHVjB2aIMzvQHcLXDW8dPCShx7PoPJ4jCepWBUvuhyCDJXvwGJ8e0SEOT4p82Dq/jJ8npINeDWVvDe8tDGvPTsdbKZ+2zh8J9G1bj7hHJZCE0dqL6dQ+GugOs61zMYz4i+haPh6ztsg709EOyE78JveD3/qxFR2DgThH8Yus80cIYoZnW+el+E3ut6d+t+F1J6P46wbAU9b5nEDfnm2d4+7gIQk9tmIcQP+2YxhBk/vB7+AAkqof0SAOT4qcbZ2f8P1N24BvwJjm2theeDNx7T0D8EvrPBO/D7FowLhJv/b9d/03UtsBjPeBIr+wzuf6/ubx6M52B373FIFsOoB7MDXdvx52W1mM3+T+Rsz/cpaM+G/R2VPjMJNT3rG7wSd9fzOLHTyYtnI2ej2ed0Zg0nv965HFtgJGj4/7/uZstNOzFnjd97ezWj+iQRyelBiE6eCDOzzeDfhOzHguvoq5Bp3L04jfkcmJ6LH/dvxuogbZnMIBeMw6n4rfGW5bvacxy2C9k9VRWnAH0Nbjcd/fnIzOeOnGz+Mkk4kA/sp67U+PkZgpC/96+K8b9mBpJCauHD2/Q2eSjUbnvXlnNnrqcQ1+nidmk/W2chZ+d/wJ3lZAHB4hFk5Ej+22ESZ/x9t0lk0yUY2fW2d/RtwewT+FnweG2gQ3WhOJ46GINusxW+d/zNc3wxmtrI5af4Xu6E/AzyZzzZgcDf96OJ1h75M/yehhtxV/o/gz0Am6bxFkBO+/buxC5wpBnHoozADBn+0oZge/Al1nR6D/x71j21L/eoxBtzYI4hCLwyNUxdnB+wvRAxxhnd/w9a1kjfjZaLPsjeDTe2CeNf+m52+sR6cFNqFzQ+LDvwPYgtl/x78eU9Ajwj143WcFkqobmzDL07138h9GT3K8jt8VOKCfJw56/O+dZPT4NTp+dxRwmOdvhevgs6xHsGmccHrY2wJ4f3CLrUUrcQ6WIEhE9BC0NdyDyRLzjq3FeryuWAOJ8AgeCNfBH22d/1jzqv4kUzGXWX+pBa8rLsZgshj86zEEY8S9P4yiF7OHRDIO4Bz0tnn1ORM9KfgqfrslMHXjDfy40skZLVuP8zx/I1yHZuvhby1TMnp0YhLbvXdq0ejh78EtySUu7wKm4XWH8sPQ3XQ3ZsmEd1rRuYZ78RMr60APloYQ94Z7TgfQm2tl143/wu9CGIBjrHMW60Y0iMOTAvthHpAZzOGxd6v1VzHtKa0pNa8Ki8J0ahd6+sYcdJbFK/jdIh+0yWtEdx7edkq1sa8OtvOHV1ai4xIj8NrJp9Gh2VpMIK5nBNn8p3U+A695TeEGB8GMuK3HwTWvioL/tM4Xe7r6SGAquoN/pvalFRiMiQ77cwCT0aMLWGi99qaH3Vaexe8DMsG0lTfxsxHGXsw0Trx6PINesTUJr4PHNGyHHUeO145Ggzg8KXA+2vT8Hj+TDjYtmCwcfxEeO2gb/hnN9fh/1vkCdKpjbf6bdf5FzauqEayRgtFjWqC/64fvW+fL6l7ZCHzCev3LGtdVJ5gem9AuI8RtxN9Ep6U3ApfUvXoG2pDuxMRC/BFMD3vqLP668WP0tNaJmGd9V+eT1vlpzANwvXMoOr13J36HFsnp8R/W+bN46Z5sPZJsK5CUHj3o+gFwad2rR6OjwxBWD3/OsG1Hx2IeO5pVxOFJATvu8aNA37ajO22YLsobdiON3+H5LXpKZRTavavOKOCvrNc/rnVhVYJFuyBJPWwjfib1Ar8fRofJO0i2g4ck9XjAOtc34hdZ50fxk1VgMwrjvgVzeCbjJxMtCJsxrr53PYK1FedUuL/de5KrG4+hNZmM2ZmrMpMx67l+EuhvBYv+QRpt5ZPUGzx+Au3OriLIL4KgenRhdmqP3yEOhzg8CTMes7fqQ4HuYHfw/qI74G6k8SbbAdxvnS+redXH0ZktrxNkQzkI08HbKc7xG6230E9BHgx8puaVdof2E/w+TsImDw7Pj9Huy3HoFVuVaSCqwUE7Zp2RNzag90AaRBLTWnan9hlqPZHoGHQMqBszEeYPu0Pz/3Dl5OpGD/Cg9bq2A3iBdf4NfiezbYJFNCBJPZ5DW8dRmHhWZWzbEaytjEBPlkK2bUc4xOFJmE+hTdoLBElIBZPM579SvotOYx1GnDvI2nwfndp3BrW6jHAjVjBGPLgDGPzxjX6wO7XLql4xFJhrvQ5mtCaiA8v+kjBtkjNa29AxG6ilx0z0KH4rQTajhDAjeEhSj8fRKfSt6H1XKnOR42pveyT3J3xE40D8bgsYBLutzKXWzj+fts7B2grkI8IDJkJc3QEch5nOCqbHkWh34M+YRyt7RxweoSLhRqxg0p1/5/ubezG7TcRfMddiFkZ+vuIVozEJqcH0aMEYreW+v51sI/0JOnfiKKrtq3E2WpO1BNl8EUzdeAW/TyODpPW43zpfitn7w43dwT9MkM0XwWyw/3KgbyenRy/wA+v1/6h6VbgRPITRowM9dZFMxOt36Do8Arii4hWHovO79gI/DfQ3DkVvSNFNkMFScvmQ4B48Vs7z+iQ6K+53+Nmcw8kp1nlFoG8nFy0Phzg8CfI+9JzzPoLOOQ/FPIk8WJdoV8xkohp3WuerqDRS+zQ6PyL4nPNfoKvwm/jZRdfmXbTBHE4SEa/twL3W6+sqXvE56/wQfjMsbOzde35b86pqJFs3fo3uaMYAfzfg06GYKYvg0b9weiQbAbwTXRs/ip7qc/MBtP3YSdDk/lZ0d7SPIFudQtJ63G6dv0SlmJI9MbwIPf3on7+0zssJMzg4CO1oxMu7wM+s19dWvMK2HcGdYVuPPLSV4IjDkyB2nONXBJ1zPhHdFXQQZAs2gD9Z5yNqXhUVj6HdmdFop8fNf7fO/yfw/e0OLZjz14sZqR0ZuAx+uM36q3Po/wDNAzGL1r8X+P7hjJa9jeVh1MokiQoF3Gy9vob+qcEXosP072AeK+mP0ZjRcDA97EnBo2peFRVvY7L6/nHAp3Zb+TFBVmeBqRsr8bvYwcbWI5m28h/oHV6m0H+J+hCMi5xWW2lHD2Ea8bNlZBi+bZ0/Tf8F4CegLWEPJk7on3B6JNtWgiMOT0IMBy63Xt8d+C729nzBOnjQ7gd43dYrLArTUK/BucPL6ejuaCdmxt4/4UbwoM0/JKXHO5iETHen9ndoJ+Npgka7wkf/1qATdYdhtnKMlx+gN2aYhF6GbLA7+H9DxyT8cyravP0Js8WkP+y2Un+xeFTYDuCFmARSvdDB3rrhO4HvHa5Dg6RtRw+wwHp9Lc5lFnPREdn16OnOYESnRzL1Yzna9W9ER70Mdlv5KUHi3KDVnIaOMAaL/tlp31PI/tJ0JQequblZKaVUc3NzLPe/DJQC9SaoQYHv80ulb/M/A5djplWONYlp26jgbavc/73v/R9Z5fhu4PsOVbDduu9xgcv3dasc9ySmx3SrzHsVHKkA1QSqwyrH+YHvO8u6b3uo8j1vleNTienxJavcrysYogA1wyrDblDjAt/3m9Z97w9cttFWOZT1Ohk9Hrf+5Hf73vsnqwxLQ913mXXfiwPf41NWOZ5PTIsWBVutcn+y7/0lVjm+Fvi+4+z/VgUHBC7fPdZNvp6YHmdaZd6hYIIC1FhQXVY5Tgt834us+74UqnxrrHLMTEwPc/jov5MvXBaPuB2eZVZl+IfA9xitoFvp2xwZuBxjMEa8JTF9r7L+5AYF+6mJoHqsMhwX+J4ft+75roKGwGX7b1Y5nktMCxQ8YpX91wpQF1tlWAtqcOB73mHd875QZfs/hO1M/B6jFHRYZf+yAtQDVhkeCHXfV617fjpU+d4lbGfi95hplbtXwftVo1UvlFVPgt3zYMc9xwcu25FWObaDakhMj69bZX9bwXB1rFWGPaAmBb7n31r3XBaqbPOssjycmBYoeM4q+wMKUF+2yvBSqHs+ZN3zplBl+4VVls8nqoc+xOGJTzDfx0etirAT1P6B73OJ0rdZHbo8thH/y8T0HaxghVX+f1cLrL+/JNQ9H7Dud3uosqVjxA9RsEuBUg18Uq20yvBPge/XoKDN0uOcUGX7e9Iw4pdZZd+uDmRynzM8I/D9jrXut0tphyp42X5JGkb8h1b5l6rLaFAK1Hp0JDDY/b5s3W9RqHINRkfdFKhDE9NihIJ3rPLfqB60/v6PQ93z19b9/jFU2T5kleVPidaNk5WODivVxEzVZpXhilD67rT0ODFU2W6yynJ3onroQxye+ATzffyWQUqB+pdQ93lYgVJ6xBOuPI/qG6kvJqrxX1rlV2oJf6EUqDMC32uIgi3W/f4yVLkGo50dBeqYRPX4mgKlRvCu2s5ItZkw0yZ2VGCLgqZQ5bKnPNsS1aJBwW8VKHUKDyoF6olQ97OjAo+ELps95XlfonpMUtCpQKmbuUwpUF8Kdb/nLT0+H7psL1h6fDpRPc5XoFQDu9SfOCRkZHh/BXssPQ4JVa4xmGh58IFskOO7CpQaxwq1h8FqDWGc4U9ZP+HN0OWypzyXJ6qFPsThiU8wX8fJzFaH8Zp6iSPU+MD32V9BlwKl4PjQZfpHfSP1k8R1vk+BUsewUv2KESHuc56lxToVZjrLPp6y9PjbRLUYpuAtBUpdyXfV9aHudZelxwOhyzUMM914cKJ6nKD0lItSP+GTIaI7DQpes/QInq9iH2dbWryWqBYoO7dpLJvU8xyshgW+zyGWFnuVnfcR5rjN0uPOxPXQUZmZ/Eb9PxpD3OfvLD2WR1KuVZYef5WoFvsp2KhAqa/yNXV5qHv9zNLjf4cu12RLi15QIxOuH+LwxCeYr2MGTyhQajyrlU7CC3KfbygibKT2KH5dwhofzAGqlXUKlDqAH4a41wuWHuEbKaQ1ikd9hDNVQ194+tKA92lVxhk+I5Jy2YnLyY7iUefyvxUoNYxOBUcEvI89Yt2iIHxbHoMZxQdPoPZ/DKdRnWDV8wP4ndJJ+kHudY9V/McjKVdao/jjOES1WAnMY7k14H0aFfzJ0uOLkZTLznn7VsJ6nMfF1u9QahAfDXifo5U9PaYXU4Qv1xqrULMT1kMcnvgE83XszwQ1mrVK14PHFL5HJ2MUbLO+/4lIyjQMVDdJz8WjngT1G2aqwfRYv+fLAe7zEeu7O1WYFRbOwx7Fv5mgFgeA2gjq63zF+j1dCk4JcK/brO//V2Rls0fx/5agHmeD2sNgNYunrd+zWsFYn/dpULDS+v5XIyvbakuPuQnqcSuodzhQjWWD9XvuU/6jmVMVfW3tA5GUaxJmFJ/UoodGUL8H9UhfZFcpndPo916XWd9drwgVYTbH5VaB/ivBunEoemXWVX2R3c0q2ADhQev7P42sbD+09PhqgnqAODxxChbgOEmZUfjPlL88C7tS/0FFMX1jH8/om6r/kZC+l1p/rwvU/vwP6zf5HWkNUyb5+ZbIyjYKk4x5REJ62IZhGQ2qgZ9bv2mrglN93OdoZerVnMjKdo5VtncS0mKU9bcUqK8xXsF71m96SenpXK/3utL63halVzVGU74FVtn+T0J6zEA7FArUyZyp7Kk+Ha3xYwN+an3viUjL94pVtqS2LrjO+nsbQQ3nJus39Sp/U5ZjlUl+/lJkZTsQ4wDul5Aei6y/+RhNyqzaaldwlI/7/KUy0Z3g23r0P/7aKtsLCWlhH+LwxCdYwOMcZa/M0QZonIfvXGBdH22HBjoJUoFamIC240Ftsv7e/+x73zZcSsE/K72Sq969/q91/XoVZnltpeOJAeWL7/gYxkieCApGKlhs/bZOBed6uM8oBX+0vhNthzYMvaJQgTo2AT3+FRNhGwFKO3Lt1m9bpbyNXk9Upn1F16EB6kzMFHDcK/mGgPqD9fe+3/f+xco4PQ8qb1N1f29d363Crr7pf3zbKt/9CdSNw0Htsv7eZ0DBIGXswF6lB0z1nMAGpaPrSukprWiiO/bxslW+SxLQ4wrrb+0ANQ2U7kdetn5bh9L7cdW7zwRlVnXeH2n5WjFTwBMS0MM+xOGJT7AQx4eV3jTK9sjPV9Ub62XKGPBvRF6Ww/SNVTd686q4fnMjZkSynP77zHzV+n1KwVJVfR65SdkrE7ThjyZXxXlcbRXktzFqAXpEuN76W992fTZcwUKHHv+uqk/ZTVFm5c27ypvz7O+wV/LFHZo+1/xg9WHXZ4crE+nZqeBqVT0y+iFl9vL5TxVlJBS0E9JplfHUmPWwnb8/03/lzwXKrC56U+lN6Krd58uOa/974LJUO07HRFyCrw6qfwxH2wwF6nHXZw0K/k2ZqvO4gvdVuU+zgh9b13WpKBZ99D++YRXkpzHXjWMw9fAa12f7KbOx5F6lcxurRTiPUHoQYQ8mRkZezhetMv5dzHo4D3F44hMs5HG8MhXOzlW4XmkDNkvBpUrvl2F//lPlLfrh//i99Ufmxfh7F1h/oxPU0RWvuViZJeZKwa+U3qhwptIO4v9UZtXNXqU7vujLORG9mZkCdVRMWjgN+O+tf7uvaVLwbYcWXUqPZi9RcJqCs5Xed2iz9fkmFSzvp/5xiVWItwmzM3jtw2nA76h4zUTldgLfU3oH5Y9Z9eOTSndmdvTjJRXlVJbzuJ/4p7Xs0btCO4IDr/lLZXYtV0on7/9PBbOt40plpjiU0k5B9OUcjNkM8b/FqIe9G3sHqCkVr7lKmSndXqVt5d9YOp2p9IDKnsbqVnBhLOW0N0PsRufmxfE39kNHQBWop6nUJkcq+D+O//ttCu5UOon/NKXbzHeV2Z2+TelBRfRltTdDTHJaSxye+ASL4BiqdNRmq8LYuH5Hr4L/paIerTqPq6w/tjqm+1/p+EHn1bz2QAU/UqbjqnRsVDphOb7/l0esP3ZbDPcejN4sTaEN+EE1r5+tTASn2rFM6aTUeLQYBmqz9cfOjuH+49Ebtil0BLCx6rUNCr6gTLSn2vE9pXO84tHDXtm4g3iSdWdhFhLU3oCyRcG/KtPRVzp2K738Oh4twKxsfCqm+8/HOBG1N0g9SsEv6tSNdxX8Rax62Csbr43h3kMxUfI3qbfnz7nKJO5XOxapqFMCnMc4TF0+IUbNnYc4PPEJFuHRorQxf1DpKMYfld6Abb7STkC8f78Fs+le1CtQnM7OfM/fO8T67Y8rHbb/g9L7b1yhwu6Y6+WwV2vtQM9FR3XfRujbIbYHP8+amaX0KqznlM49+L1VVz6q4or6OQ97tVa4ZzgNPCaB+qN177fwumlbk9KRru8pHSF9VcGLSke8op+mqHTYe67Mj/i+H8bkTHnfQXicgmuUXgTxhtKR4t8oHfFpjV2LgzER0ah3bP8qxnb8jefvTVd6ELlI6bayQmlH6GKlp4vj1eMyq7zriXYPmuHoPEuFjoZ62xy1QWkbcaeC31n14yWlV/rNVnEOou3DXpTxs5j/jn2IwxOfYIU6bkRXzFeJbj7+eozBiiNaEuex1Cr3v0d0v2ZMPkw3qE9k4Dd6PVoxHfEnI7rnUZjIzhqS3RYh7PFJTMczMaJ7fgqTlPsrCLHBYPLHv1vl/i3RTHsORi/HV9ZxXQZ+o9ejEdQbVrm/FtE9DwD1G+ue20F9MAO/0+txFGalYRKPMBKHJz7BCnU0Y5Jobw95r7HoZzAp6/jfGfh9fg87IVOhl2eHudexaEdSoTu1j2Xg9/k97KmLPxM+6nURJqL4J+pN62XzeM4q/0LCrdhqAnULpq49QrwJwHEckx3/n/8Y8l6t6NwUW48vZuD3+T3sTRn3gDol5L1Ow+RJbQX1gQz8Pr+HvSnjm8S/Z5M4PPEJVrjD3ndFoR8e6ff7Degk1w7rHrvxE4rO3nG79Tu2gfqLAN8fid551X5Ew7uEN4BpHU3oJzErdKJ1kL1GpoH6OaaOPUV8yZ1xH4ejpzwVqHsI5vScgZnSU+jVeoMD3CcLh73x3l6CPc29EdQX0J26QkfPooompnHYU9fr0Q8m9vv9/dAP39xr3ecV4ltEEfcxGr3oQYFajL3lRDyHODzxCVbIYz7GAN+Dt6XqI9Fz16sc310F6uQM/J4wx1DMaLML1H/HW4c0GdT/Qi/XtfV4mGQfSRDHcSiodkzOzWyP3zsJ1PcwuR7d6HB/Xjt3+/gUJlz/a1BTPXxnCHrl1WJM3WinXjJ/Po5/w+28eclhGYN2dN5yfPcFktv4M66jBTNA2IreO8iLU3yopZ29alGh286oDPymMMeJGGd2Jaj3x/R3xOGJT7DCHv+EGVlsQzs+F6Iz7Q9BjzTmoMPNj2DC2QrUFvSc+5AM/I4ojhGgHnP8vrfQ+218FL28/lD0E5vPQ+dBPevQTqGnsiovLc7ncSQm90aht9K/Bp1XcJh1nIIe5d+JO4Kh0Bs7BhnxZvW4AJPf1I1+EO/laGf/EHTHPRu9F8n/A7XBocVu9DL80Rn4HVEcDZjtJ5T1W+8EdT66jUxDt5mPolcx/Qqzs7lCR0OuIr7tD5I+9ke3D/v3rUYPhM5Et4FD0Tb1fFA3YfatsY+X0Kv20v4dUR1/gRkw7SWe3CxxeOITrNDHbMzOoV6O19Dz90k9VyfJowEd3fmzDz0Wo53Eohhv59EC6juYqbp6Rxd6tUbwJ59n+zgKHeHxWjfaQd2MXqWWdtnjOM4F9boPPVagN/wcuB9V/o9G9OKNbR616EVvrpjsU9eTO8ajHX+Fzk+K+v5e++8G60XpaW5uprOzk5aWFrZv3552cVKlAZgFnA+cAhwEtADdwDrgT8BSYBHwUkplTJIRwCeAs4ETgVZgCLAbeAd4FVgCPAm8m04RE2UicBG6jhwDHGC9vx14C1gBLAaeAjpTKF/SnABcAJwGHAKMBfYAG4E3gRfQejwL7E2lhMkxGDgLOA84GZgCjES3lTbgdeC36LqxOqUyJkkL8Em0JicAE9Aa7QTWAK+gbcdCoCOVEibL+9D9R9R47b/F4bEQh0cQBEEQ8ofX/ntQgmUSBEEQBEFIhUI5PFdddRVvvfUWu3btYtmyZcycOTPtIgmCIAiCkAEK4/BccMEFLFiwgG9+85u8//3v59lnn+Xxxx/nwAMPTLtogiAIgiCkTGFyeJ5//nleeuklvvCFL/S998orr/Cf//mf3HDDDXW/Lzk8giAIgpA/SpXDM2TIEE466SQWLlzoen/hwoWcdtppFb/T1NREc3Oz6xAEQRAEoZgUwuEZN24cjY2NdHS4F/Z1dHTQ2tpa8TvXX389nZ2dfUdbW1sSRRUEQRAEIQUK4fDYKOWenWtoaBjwns1NN91ES0tL3zF58uQkiigIgiAIQgo0pl2AKNi4cSO9vb0Dojnjx48fEPWx6enpoaenJ4niCYIgCIKQMoWI8OzZs4fly5czZ84c1/tz5sxh6dKlKZVKEARBEISsUIgID8Btt93G97//fZYtW8Zzzz3HlVdeyUEHHcR3v/vdtIsmCIIgCELKFMbheeihh9h///356le/ysSJE1m1ahXnnHMO775bhqcbCYIgCIJQi8LswxMW2YdHEARBEPJHqfbhEQRBEARBqIU4PIIgCIIgFJ7C5PBEhey4LAiCIAj5wWu/LQ6PhS2Y7LgsCIIgCPmjubm5Zg6PJC07mDRpUuQJy83NzbS1tTF58mRJho4Z0ToZROdkEJ2TQXROhrh1bm5uZt26dTWvkQiPg3pihWH79u3SmBJCtE4G0TkZROdkEJ2TIS6dvdxTkpYFQRAEQSg84vAIgiAIglB4xOGJme7ubr72ta/R3d2ddlEKj2idDKJzMojOySA6J0MWdJakZUEQBEEQCo9EeARBEARBKDzi8AiCIAiCUHjE4REEQRAEofCIwyMIgiAIQuERhydmrrrqKt566y127drFsmXLmDlzZtpFyjWnn346P//5z2lra0MpxXnnnTfgmvnz59PW1kZXVxfPPPMMRx99dAolzTfXXXcdL774Ip2dnXR0dPDII49w+OGHD7hOtA7H5z//eVasWMG2bdvYtm0bS5cu5eyzz3ZdIxpHz3XXXYdSittvv931vmgdnvnz56OUch3t7e0DrklLZyVHPMcFF1yguru71RVXXKGOPPJIdfvtt6vt27erAw88MPWy5fU4++yz1T//8z+ruXPnKqWUOu+881yfX3vttWrbtm1q7ty56phjjlEPPvigamtrU6NGjUq97Hk6Hn/8cXXppZeqo48+Wh133HHqF7/4hVqzZo0aMWKEaB3h8Vd/9Vfqox/9qDrssMPUYYcdpr7xjW+o7u5udfTRR4vGMR0nn3yyeuutt9TLL7+sbr/99r73Retojvnz56uVK1eqCRMm9B3jxo3Lis7pC1TU4/nnn1d33323671XXnlFfetb30q9bEU4Kjk869atU9dee23fv5uamtSWLVvUlVdemXp583yMGzdOKaXU6aefLlrHfGzatEn99V//tWgcwzFy5Ej12muvqQ9/+MPqmWeecTk8onU0x/z589Xvf//7qp+nqbNMacXEkCFDOOmkk1i4cKHr/YULF3LaaaelVKpiM23aNCZOnOjSvKenhyVLlojmIRk9ejQAmzdvBkTrOBg0aBAXXnghI0eO5LnnnhONY+Cuu+7il7/8JYsWLXK9L1pHy2GHHUZbWxtvvfUWDz74INOmTQPS11keHhoT48aNo7GxkY6ODtf7HR0dtLa2plSqYmPrWknzgw8+OI0iFYbbbruNZ599ltWrVwOidZRMnz6d5557jmHDhrFjxw7mzp3LH//4Rz7wgQ8AonFUXHjhhZx44onMmDFjwGdSn6PjhRde4HOf+xyvv/46EyZM4Ctf+QpLly7lmGOOSV1ncXhiRinl+ndDQ8OA94RoEc2j5Tvf+Q7HHXdcxYR70To8r732GieccAJjxozhk5/8JA888ACzZs3q+1w0Ds+UKVP413/9V84666yajzYQrcPzxBNP9L1etWoVzz33HG+++SaXXnopzz//PJCezjKlFRMbN26kt7d3QDRn/PjxA7xbIRrWr18PIJpHyB133MG5557Lhz70Idra2vreF62jY8+ePbz55pssX76cG264gRUrVvD3f//3onGEnHTSSUyYMIHly5ezZ88e9uzZw+zZs5k3bx579uzp01O0jp6uri5WrlzJYYcdlnqdFocnJvbs2cPy5cuZM2eO6/05c+awdOnSlEpVbN5++23a29tdmg8ZMoRZs2aJ5gG48847Of/88znjjDNYs2aN6zPROj4aGhoYOnSoaBwhixYtYvr06Zxwwgl9x+9+9zt+8IMfcMIJJ/DWW2+J1jHR1NTEUUcdRXt7eybqdOpZ3UU97GXpl19+uTryyCPVbbfdprZv364OOuig1MuW12PkyJHq+OOPV8cff7xSSqkvfvGL6vjjj+9b6n/ttdeqLVu2qE984hPqmGOOUT/4wQ9kaWmA46677lJbtmxRH/zgB13LS4cNG9Z3jWgd/vjmN7+pZs6cqQ4++GA1ffp09Y1vfEP19vaqM888UzSO+ei/Sku0jub4l3/5F/XBD35QTZ06VZ1yyinq5z//udq2bVtfv5eyzukLVOTjqquuUm+//bbavXu3WrZsmWtZrxz+j1mzZqlK3HfffX3XzJ8/X61bt07t2rVLLV68WB1zzDGplztvRzUuvfRS13Widbjj//7f/9tnHzo6OtSTTz7Z5+yIxvEe/R0e0Tqaw95Xp7u7W7333nvqpz/9qTrqqKMyoXOD9UIQBEEQBKGwSA6PIAiCIAiFRxweQRAEQRAKjzg8giAIgiAUHnF4BEEQBEEoPOLwCIIgCIJQeMThEQRBEASh8IjDIwiCIAhC4RGHRxAEQRCEwiMOjyAIgiAIhUccHkEQCs/tt9/OI488knYxBEFIEXF4BEEoPDNmzODFF19MuxiCIKSIPEtLEITC0tjYyM6dO2lqaup774UXXuDUU09NsVSCIKRBY9oFEARBiIu9e/cyc+ZMXnzxRY4//ng6OjrYvXt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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -243,7 +243,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -256,7 +256,7 @@
     "# CyRK - Cython CySolver\n",
     "CySolverTesterInst = CySolverTester(time_span, initial_conds, rk_method=1, args=args, rtol=rtol, atol=atol, auto_solve=True)\n",
     "print('CyRK (Cython - CySolver) Solution')\n",
-    "diff_plot(time_domain, y_results, fig_name='CyRK_CySolver')"
+    "diff_plot(CySolverTesterInst.t, CySolverTesterInst.y, fig_name='CyRK_CySolver')"
    ]
   },
   {
@@ -271,9 +271,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "5c43792b",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -470,7 +468,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -541,9 +539,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "697a88bd",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -551,41 +547,41 @@
      "text": [
       "How much faster X is vs. Y\n",
       "End Time: 1.0e-03\n",
-      "\t nbrk_ode =  11.0x solve_ivp\t cyrk_ode =  16.8x solve_ivp\n",
-      "\t CySolver =  21.7x solve_ivp\t nbrk_ode =   0.7x cyrk_ode\n",
+      "\t nbrk_ode =  10.6x solve_ivp\t cyrk_ode =  16.5x solve_ivp\n",
+      "\t CySolver =  20.8x solve_ivp\t nbrk_ode =   0.6x cyrk_ode\n",
       "\t CySolver =   2.0x nbrk_ode\t CySolver =   1.3x cyrk_ode\n",
       "End Time: 1.0e-02\n",
-      "\t nbrk_ode =  10.9x solve_ivp\t cyrk_ode =  17.1x solve_ivp\n",
-      "\t CySolver =  21.9x solve_ivp\t nbrk_ode =   0.6x cyrk_ode\n",
-      "\t CySolver =   2.0x nbrk_ode\t CySolver =   1.3x cyrk_ode\n",
+      "\t nbrk_ode =   9.3x solve_ivp\t cyrk_ode =  16.1x solve_ivp\n",
+      "\t CySolver =  20.1x solve_ivp\t nbrk_ode =   0.6x cyrk_ode\n",
+      "\t CySolver =   2.2x nbrk_ode\t CySolver =   1.2x cyrk_ode\n",
       "End Time: 1.0e-01\n",
-      "\t nbrk_ode =  16.0x solve_ivp\t cyrk_ode =  20.4x solve_ivp\n",
-      "\t CySolver =  32.0x solve_ivp\t nbrk_ode =   0.8x cyrk_ode\n",
-      "\t CySolver =   2.0x nbrk_ode\t CySolver =   1.6x cyrk_ode\n",
+      "\t nbrk_ode =  14.5x solve_ivp\t cyrk_ode =  19.8x solve_ivp\n",
+      "\t CySolver =  29.8x solve_ivp\t nbrk_ode =   0.7x cyrk_ode\n",
+      "\t CySolver =   2.1x nbrk_ode\t CySolver =   1.5x cyrk_ode\n",
       "End Time: 1.0e+00\n",
-      "\t nbrk_ode =  30.2x solve_ivp\t cyrk_ode =  28.0x solve_ivp\n",
-      "\t CySolver =  66.0x solve_ivp\t nbrk_ode =   1.1x cyrk_ode\n",
-      "\t CySolver =   2.2x nbrk_ode\t CySolver =   2.4x cyrk_ode\n",
+      "\t nbrk_ode =  29.2x solve_ivp\t cyrk_ode =  27.2x solve_ivp\n",
+      "\t CySolver =  63.0x solve_ivp\t nbrk_ode =   1.1x cyrk_ode\n",
+      "\t CySolver =   2.2x nbrk_ode\t CySolver =   2.3x cyrk_ode\n",
       "End Time: 1.0e+01\n",
-      "\t nbrk_ode =  93.5x solve_ivp\t cyrk_ode =  36.8x solve_ivp\n",
-      "\t CySolver = 296.3x solve_ivp\t nbrk_ode =   2.5x cyrk_ode\n",
-      "\t CySolver =   3.2x nbrk_ode\t CySolver =   8.0x cyrk_ode\n",
+      "\t nbrk_ode =  95.0x solve_ivp\t cyrk_ode =  36.6x solve_ivp\n",
+      "\t CySolver = 294.2x solve_ivp\t nbrk_ode =   2.6x cyrk_ode\n",
+      "\t CySolver =   3.1x nbrk_ode\t CySolver =   8.0x cyrk_ode\n",
       "End Time: 1.0e+02\n",
-      "\t nbrk_ode = 122.5x solve_ivp\t cyrk_ode =  37.7x solve_ivp\n",
-      "\t CySolver = 466.5x solve_ivp\t nbrk_ode =   3.2x cyrk_ode\n",
-      "\t CySolver =   3.8x nbrk_ode\t CySolver =  12.4x cyrk_ode\n",
+      "\t nbrk_ode = 122.4x solve_ivp\t cyrk_ode =  37.4x solve_ivp\n",
+      "\t CySolver = 449.5x solve_ivp\t nbrk_ode =   3.3x cyrk_ode\n",
+      "\t CySolver =   3.7x nbrk_ode\t CySolver =  12.0x cyrk_ode\n",
       "End Time: 1.0e+03\n",
-      "\t nbrk_ode = 113.8x solve_ivp\t cyrk_ode =  35.5x solve_ivp\n",
-      "\t CySolver = 446.3x solve_ivp\t nbrk_ode =   3.2x cyrk_ode\n",
-      "\t CySolver =   3.9x nbrk_ode\t CySolver =  12.6x cyrk_ode\n",
+      "\t nbrk_ode = 123.1x solve_ivp\t cyrk_ode =  37.9x solve_ivp\n",
+      "\t CySolver = 474.8x solve_ivp\t nbrk_ode =   3.2x cyrk_ode\n",
+      "\t CySolver =   3.9x nbrk_ode\t CySolver =  12.5x cyrk_ode\n",
       "End Time: 1.0e+04\n",
-      "\t nbrk_ode = 103.0x solve_ivp\t cyrk_ode =  37.4x solve_ivp\n",
-      "\t CySolver = 465.8x solve_ivp\t nbrk_ode =   2.8x cyrk_ode\n",
-      "\t CySolver =   4.5x nbrk_ode\t CySolver =  12.5x cyrk_ode\n",
+      "\t nbrk_ode = 105.7x solve_ivp\t cyrk_ode =  37.9x solve_ivp\n",
+      "\t CySolver = 465.0x solve_ivp\t nbrk_ode =   2.8x cyrk_ode\n",
+      "\t CySolver =   4.4x nbrk_ode\t CySolver =  12.3x cyrk_ode\n",
       "End Time: 1.0e+05\n",
-      "\t nbrk_ode =  96.9x solve_ivp\t cyrk_ode =  37.5x solve_ivp\n",
-      "\t CySolver = 426.3x solve_ivp\t nbrk_ode =   2.6x cyrk_ode\n",
-      "\t CySolver =   4.4x nbrk_ode\t CySolver =  11.4x cyrk_ode\n"
+      "\t nbrk_ode = 102.4x solve_ivp\t cyrk_ode =  38.4x solve_ivp\n",
+      "\t CySolver = 443.2x solve_ivp\t nbrk_ode =   2.7x cyrk_ode\n",
+      "\t CySolver =   4.3x nbrk_ode\t CySolver =  11.5x cyrk_ode\n"
      ]
     }
    ],
@@ -620,7 +616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/Benchmarks/CyRK_CySolver.pdf b/Benchmarks/CyRK_CySolver.pdf
index db79627..c172075 100644
Binary files a/Benchmarks/CyRK_CySolver.pdf and b/Benchmarks/CyRK_CySolver.pdf differ
diff --git a/Benchmarks/CyRK_SciPy_Compare_predprey_v0-8-6.png b/Benchmarks/CyRK_SciPy_Compare_predprey_v0-8-6.png
new file mode 100644
index 0000000..d9167c2
Binary files /dev/null and b/Benchmarks/CyRK_SciPy_Compare_predprey_v0-8-6.png differ
diff --git a/Benchmarks/CyRK_cyrk_ode.pdf b/Benchmarks/CyRK_cyrk_ode.pdf
index db79627..c172075 100644
Binary files a/Benchmarks/CyRK_cyrk_ode.pdf and b/Benchmarks/CyRK_cyrk_ode.pdf differ
diff --git a/Benchmarks/CyRK_numba.pdf b/Benchmarks/CyRK_numba.pdf
index 0d9ad7d..c172075 100644
Binary files a/Benchmarks/CyRK_numba.pdf and b/Benchmarks/CyRK_numba.pdf differ
diff --git a/Benchmarks/SciPy.pdf b/Benchmarks/SciPy.pdf
index f23ab24..ebdec35 100644
Binary files a/Benchmarks/SciPy.pdf and b/Benchmarks/SciPy.pdf differ
diff --git a/CHANGES.md b/CHANGES.md
index 726de9d..a335c31 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -2,6 +2,20 @@
 
 ## 2023
 
+#### v0.8.6 (2024-02-13)
+
+Major Changes:
+- Added support for Python 3.12.
+- Converted `CySolver`'s `rk_step` method into a pure-c implementation to allow for further optimizations.
+- Changed all files to compile with c rather than c++.
+  - Had to change cpp_bools to bints to make this change.
+
+Bug Fixes:
+- Fixed issue where CyRK was not installing on MacOS due to issue with LLVM and OpenMP. 
+  - Have opted to go to gcc for macOS install due to issues with OpenMP and clang.
+- Fixed incorrect type for rk method in CySolver (should eliminate some compile warnings).
+- Fixed issue in benchmark where incorrect results were being displayed for CySolver.
+
 #### v0.8.5 (2023-10-27)
 
 Other Changes:
diff --git a/CyRK/array/interp.pyx b/CyRK/array/interp.pyx
index 07920d1..fb63238 100644
--- a/CyRK/array/interp.pyx
+++ b/CyRK/array/interp.pyx
@@ -1,4 +1,4 @@
-# distutils: language = c++
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 from cython.parallel import prange
 
diff --git a/CyRK/cy/common.pxd b/CyRK/cy/common.pxd
index abe8b6d..48bb597 100644
--- a/CyRK/cy/common.pxd
+++ b/CyRK/cy/common.pxd
@@ -1,10 +1,8 @@
-# distutils: language = c++
+
 ctypedef fused double_numeric:
     double
     double complex
 
-from libcpp cimport bool as bool_cpp_t
-
 cdef double SAFETY
 cdef double MIN_FACTOR
 cdef double MAX_FACTOR
@@ -33,7 +31,7 @@ cdef void interpolate(
         size_t t_len_full,
         size_t t_len_reduced,
         size_t target_len,
-        bool_cpp_t is_complex
+        bint is_complex
         ) noexcept
 
 cdef size_t find_expected_size(
@@ -41,8 +39,8 @@ cdef size_t find_expected_size(
         size_t num_extra,
         double t_delta_abs,
         double rtol_min,
-        bool_cpp_t capture_extra,
-        bool_cpp_t is_complex
+        bint capture_extra,
+        bint is_complex
         ) noexcept nogil
 
 
@@ -51,7 +49,7 @@ cdef void find_max_num_steps(
         size_t num_extra,
         size_t max_num_steps,
         size_t max_ram_MB,
-        bool_cpp_t capture_extra,
-        bool_cpp_t is_complex,
-        bool_cpp_t* user_provided_max_num_steps,
+        bint capture_extra,
+        bint is_complex,
+        bint* user_provided_max_num_steps,
         size_t* max_num_steps_touse) noexcept nogil
\ No newline at end of file
diff --git a/CyRK/cy/common.pyx b/CyRK/cy/common.pyx
index 98ccbab..b9e2491 100644
--- a/CyRK/cy/common.pyx
+++ b/CyRK/cy/common.pyx
@@ -1,4 +1,4 @@
-# distutils: language = c++
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 import cython
 
@@ -46,7 +46,7 @@ cdef void interpolate(
         size_t t_len_full,
         size_t t_len_reduced,
         size_t target_len,
-        bool_cpp_t is_complex
+        bint is_complex
         ) noexcept:
     """ Interpolate the results of a successful integration over the user provided time domain, `time_domain_full`. """
 
@@ -107,8 +107,8 @@ cdef size_t find_expected_size(
         size_t num_extra,
         double t_delta_abs,
         double rtol_min,
-        bool_cpp_t capture_extra,
-        bool_cpp_t is_complex) noexcept nogil:
+        bint capture_extra,
+        bint is_complex) noexcept nogil:
 
     cdef double temp_expected_size
     # Pick starting value that works with most problems
@@ -145,9 +145,9 @@ cdef void find_max_num_steps(
         size_t num_extra,
         size_t max_num_steps,
         size_t max_ram_MB,
-        bool_cpp_t capture_extra,
-        bool_cpp_t is_complex,
-        bool_cpp_t* user_provided_max_num_steps,
+        bint capture_extra,
+        bint is_complex,
+        bint* user_provided_max_num_steps,
         size_t* max_num_steps_touse) noexcept nogil:
 
     # Determine max number of steps
diff --git a/CyRK/cy/cyrk.pyx b/CyRK/cy/cyrk.pyx
index 6a8d65b..c15ca34 100644
--- a/CyRK/cy/cyrk.pyx
+++ b/CyRK/cy/cyrk.pyx
@@ -1,4 +1,4 @@
-# distutils: language = c++
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 
 import cython
@@ -7,9 +7,7 @@ from cpython.mem cimport PyMem_Free
 
 import numpy as np
 cimport numpy as np
-np.import_array()
 
-from libcpp cimport bool as bool_cpp_t
 from libc.math cimport sqrt, fabs, nextafter, NAN, floor
 
 from CyRK.utils.utils cimport allocate_mem, reallocate_mem
@@ -80,9 +78,9 @@ def cyrk_ode(
         double first_step = 0.,
         unsigned char rk_method = 1,
         double[:] t_eval = None,
-        bool_cpp_t capture_extra = False,
+        bint capture_extra = False,
         size_t num_extra = 0,
-        bool_cpp_t interpolate_extra = False,
+        bint interpolate_extra = False,
         size_t expected_size = 0,
         size_t max_num_steps = 0,
         size_t max_ram_MB = 2000
@@ -148,7 +146,7 @@ def cyrk_ode(
             ```
     num_extra : int = 0
         The number of extra outputs the integrator should expect. With the previous example there is 1 extra output.
-    interpolate_extra : bool_cpp_t, default=False
+    interpolate_extra : bint, default=False
         Flag if interpolation should be run on extra parameters.
         If set to False when `run_interpolation=True`, then interpolation will be run on solution's y, t. These will
         then be used to recalculate extra parameters rather than an interpolation on the extra parameters captured
@@ -181,7 +179,7 @@ def cyrk_ode(
     # Determine information about the differential equation based on its initial conditions
     cdef size_t y_size
     cdef double y_size_dbl, y_size_sqrt
-    cdef bool_cpp_t y_is_complex
+    cdef bint y_is_complex
     y_size       = y0.size
     y_is_complex = False
     y_size_dbl   = <double>y_size
@@ -201,7 +199,7 @@ def cyrk_ode(
 
     # Build time domain
     cdef double t_start, t_end, t_delta, t_delta_check, t_delta_abs, direction_inf, t_old, t_now, time_
-    cdef bool_cpp_t direction_flag
+    cdef bint direction_flag
     t_start       = t_span[0]
     t_end         = t_span[1]
     t_delta       = t_end - t_start
@@ -218,7 +216,7 @@ def cyrk_ode(
         direction_inf = -INF
 
     # Pull out information on args
-    cdef bool_cpp_t use_args
+    cdef bint use_args
     if args is None:
         use_args = False
     else:
@@ -271,7 +269,7 @@ def cyrk_ode(
     
     # Determine max number of steps
     cdef size_t max_num_steps_touse
-    cdef bool_cpp_t user_provided_max_num_steps
+    cdef bint user_provided_max_num_steps
     find_max_num_steps(
         y_size,
         num_extra,
@@ -363,7 +361,7 @@ def cyrk_ode(
     cdef double_numeric[::1] diffeq_out_view = diffeq_out_array
 
     # Determine interpolation information
-    cdef bool_cpp_t run_interpolation
+    cdef bint run_interpolation
     cdef size_t len_t_eval
     if t_eval is None:
         run_interpolation = False
@@ -486,7 +484,7 @@ def cyrk_ode(
     cdef double step, step_size, min_step, step_factor
 
     # Integration flags and variables
-    cdef bool_cpp_t success, step_accepted, step_rejected, step_error
+    cdef bint success, step_accepted, step_rejected, step_error
     cdef size_t len_t
 
     # Integration completion variables
@@ -666,7 +664,7 @@ def cyrk_ode(
                                     # Initialize
                                     y_view[i] = y_old_ptr[i]
 
-                                y_view[i] += K_ptr[j * y_size + i] * A_at_sj
+                                y_view[i] = y_view[i] + K_ptr[j * y_size + i] * A_at_sj
 
                     if use_args:
                         diffeq(time_, y_array, diffeq_out_array, *args)
@@ -686,7 +684,7 @@ def cyrk_ode(
                             # Initialize
                             y_view[i] = y_old_ptr[i]
 
-                        y_view[i] += K_ptr[j * y_size + i] * B_at_j
+                        y_view[i] = y_view[i] + K_ptr[j * y_size + i] * B_at_j
 
                 # Find final dydt for this timestep
                 if use_args:
diff --git a/CyRK/cy/cysolver.pxd b/CyRK/cy/cysolver.pxd
index 80388b3..0d390b4 100644
--- a/CyRK/cy/cysolver.pxd
+++ b/CyRK/cy/cysolver.pxd
@@ -1,6 +1,3 @@
-from libcpp cimport bool as bool_cpp_t
-
-
 cdef class CySolver:
 
     # Class attributes
@@ -19,39 +16,40 @@ cdef class CySolver:
 
     # -- Time information
     cdef double t_start, t_end, t_delta, t_delta_abs, direction_inf
-    cdef bool_cpp_t direction_flag
+    cdef bint direction_flag
 
     # -- Optional args info
     cdef size_t num_args
     cdef double* args_ptr
-    cdef bool_cpp_t use_args
+    cdef bint use_args
 
     # -- Extra output info
-    cdef bool_cpp_t capture_extra
+    cdef bint capture_extra
     cdef size_t num_extra
 
     # -- Integration information
     cdef readonly char status
     cdef readonly str message
-    cdef public bool_cpp_t success
+    cdef public bint success
     cdef double* tol_ptrs
     cdef double* rtols_ptr
     cdef double* atols_ptr
     cdef double first_step, max_step
-    cdef bool_cpp_t user_provided_max_num_steps
+    cdef bint user_provided_max_num_steps
     cdef size_t max_num_steps
     cdef size_t expected_size, current_size, num_concats
-    cdef bool_cpp_t recalc_first_step
-    cdef bool_cpp_t force_fail
+    cdef bint recalc_first_step
+    cdef bint force_fail
 
     # -- Interpolation info
-    cdef bool_cpp_t run_interpolation
-    cdef bool_cpp_t interpolate_extra
+    cdef bint run_interpolation
+    cdef bint interpolate_extra
     cdef size_t len_t_eval
     cdef double* t_eval_ptr
 
     # -- RK method information
-    cdef size_t rk_method, rk_order, error_order, rk_n_stages, rk_n_stages_plus1
+    cdef unsigned char rk_method
+    cdef size_t rk_order, error_order, rk_n_stages, rk_n_stages_plus1
     cdef double error_expo
     cdef size_t len_C, len_Arows, len_Acols
     cdef double* A_ptr
@@ -94,18 +92,14 @@ cdef class CySolver:
             self
             ) noexcept nogil
 
-    cdef void rk_step(
-            self
-            ) noexcept nogil
-
     cpdef void solve(
             self,
-            bool_cpp_t reset = *
+            bint reset = *
             )
 
     cdef void _solve(
             self,
-            bool_cpp_t reset = *
+            bint reset = *
             )
 
     cdef void interpolate(
@@ -115,25 +109,25 @@ cdef class CySolver:
     cpdef void change_t_span(
             self,
             (double, double) t_span,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_y0(
             self,
             const double[::1] y0,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cdef void change_y0_pointer(
             self,
             double * y0_ptr,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_args(
             self,
             tuple args,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_tols(
@@ -142,32 +136,32 @@ cdef class CySolver:
             double atol = *,
             const double[::1] rtols = *,
             const double[::1] atols = *,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_max_step(
             self,
             double max_step,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_first_step(
             self,
             double first_step,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_t_eval(
             self,
             const double[::1] t_eval,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cdef void change_t_eval_pointer(
             self,
             double* new_t_eval_ptr,
             size_t new_len_t_eval,
-            bool_cpp_t auto_reset_state = *
+            bint auto_reset_state = *
             )
 
     cpdef void change_parameters(
@@ -182,8 +176,8 @@ cdef class CySolver:
             double max_step = *,
             double first_step = *,
             const double[::1] t_eval = *,
-            bool_cpp_t auto_reset_state = *,
-            bool_cpp_t auto_solve = *
+            bint auto_reset_state = *,
+            bint auto_solve = *
             )
 
     cdef void update_constants(
@@ -193,3 +187,56 @@ cdef class CySolver:
     cdef void diffeq(
             self
             ) noexcept nogil
+
+ctypedef void (*DiffeqType)(CySolver)
+
+cdef extern from "rk_step.c":
+    int rk_step_cf(
+        # Pointer to differential equation
+        DiffeqType diffeq_ptr,
+        # Pointer to the CySolver instance
+        CySolver cysolver_inst,
+
+        # t-related variables
+        double t_end,
+        bint direction_flag,
+        double direction_inf,
+
+        # y-related variables
+        size_t y_size,
+        double y_size_dbl,
+        double y_size_sqrt,
+
+        # Pointers to class attributes that can change during rk_step call.
+        double* t_now_ptr,
+        double* y_ptr,
+        double* dy_ptr,
+        double* t_old_ptr,
+        double* y_old_ptr,
+        double* dy_old_ptr,
+        double* step_size_ptr,
+        char* status_ptr,
+
+        # Integration tolerance variables and pointers
+        double* atols_ptr,
+        double* rtols_ptr,
+        double max_step,
+
+        # RK specific variables and pointers
+        unsigned char rk_method,
+        size_t rk_n_stages,
+        size_t rk_n_stages_plus1,
+        size_t len_Acols,
+        size_t len_C,
+        double* A_ptr,
+        double* B_ptr,
+        double* C_ptr,
+        double* K_ptr,
+        double* E_ptr,
+        double* E3_ptr,
+        double* E5_ptr,
+        double error_expo,
+        double min_step_factor,
+        double max_step_factor,
+        double error_safety
+        ) noexcept nogil
\ No newline at end of file
diff --git a/CyRK/cy/cysolver.pyx b/CyRK/cy/cysolver.pyx
index 347027c..05556e4 100644
--- a/CyRK/cy/cysolver.pyx
+++ b/CyRK/cy/cysolver.pyx
@@ -1,16 +1,13 @@
-# distutils: language = c++
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 
-from libcpp cimport bool as bool_cpp_t
-from libc.math cimport sqrt, fabs, nextafter, fmax, fmin, isnan, NAN, pow, floor
+from libc.math cimport sqrt, fabs, nextafter, fmax, fmin, isnan, NAN, floor
 
 from cpython.mem cimport PyMem_Free
 
 import numpy as np
 cimport numpy as np
 
-np.import_array()
-
 from CyRK.utils.utils cimport allocate_mem, reallocate_mem
 from CyRK.rk.rk cimport find_rk_properties
 from CyRK.cy.common cimport interpolate, SAFETY, MIN_FACTOR, MAX_FACTOR, MAX_STEP, INF, EPS_100, \
@@ -69,13 +66,13 @@ cdef class CySolver:
         Absolute value of t_delta.
     direction_inf : double
         Direction of integration. If forward then this = +Inf; -Inf otherwise.
-    direction_flag : bool_cpp_t
+    direction_flag : bint
         If True, then integration is in the forward direction.
     num_args : size_t
         Number of additional arguments that the `diffeq` method requires.
     args_ptr : double*
         Pointer of additional arguments used in the `diffeq` method.
-    capture_extra bool_cpp_t
+    capture_extra bint
         Flag used if extra parameters should be captured during integration.
     num_extra size_t
         Number of extra parameters that should be captured during integration.
@@ -84,7 +81,7 @@ cdef class CySolver:
         See "Status and Error Codes.md" in the documentation for more information.
     message : str; public
         Verbal message to accompany `self.status` explaining the state (and potential errors) of the integrator.
-    success : bool_cpp_t; public
+    success : bint; public
         Flag indicating if the integration was successful or not.
     rtols_ptr : double*
         Pointer of relative tolerances for each dependent y variable.
@@ -104,12 +101,12 @@ cdef class CySolver:
         If `expected_size` is too small then it will be expanded as needed. This variable tracks how many expansions
         were required.
         See Also: `CySolver.growths`
-    recalc_first_step : bool_cpp_t
+    recalc_first_step : bint
         If True, then the `first_step` size is recalculated when `reset_state` is called.
         Flag used when parameters are changed without reinitializing CySolver.
-    run_interpolation : bool_cpp_t
+    run_interpolation : bint
         Flag if a final interpolation should be run once integration is completed successfully.
-    interpolate_extra : bool_cpp_t
+    interpolate_extra : bint
         Flag if interpolation should be run on extra parameters.
         If set to False when `run_interpolation=True`, then interpolation will be run on solution's y, t. These will
         then be used to recalculate extra parameters rather than an interpolation on the extra parameters captured
@@ -231,14 +228,14 @@ cdef class CySolver:
             double first_step = 0.,
             size_t max_num_steps = 0,
             const double[::1] t_eval = None,
-            bool_cpp_t capture_extra = False,
+            bint capture_extra = False,
             size_t num_extra = 0,
-            bool_cpp_t interpolate_extra = False,
+            bint interpolate_extra = False,
             size_t expected_size = 0,
             size_t max_ram_MB = 2000,
-            bool_cpp_t call_first_reset = True,
-            bool_cpp_t auto_solve = True,
-            bool_cpp_t force_fail = False):
+            bint call_first_reset = True,
+            bint auto_solve = True,
+            bint force_fail = False):
         """
         Initialize new CySolver instance.
 
@@ -292,7 +289,7 @@ cdef class CySolver:
                 ```
         num_extra : int = 0
             The number of extra outputs the integrator should expect. With the previous example there is 1 extra output.
-        interpolate_extra : bool_cpp_t, default=False
+        interpolate_extra : bint, default=False
             Flag if interpolation should be run on extra parameters.
             If set to False when `run_interpolation=True`, then interpolation will be run on solution's y, t. These will
             then be used to recalculate extra parameters rather than an interpolation on the extra parameters captured
@@ -305,7 +302,7 @@ cdef class CySolver:
         call_first_reset : bool, default=True
             If set to True, then the solver will call its `reset_state` method at the end of initialization. This flag
             is overridden by the `auto_solve` flag.
-        auto_solve : bool_cpp_t, default=True
+        auto_solve : bint, default=True
             If set to True, then the solver's `solve` method will be called at the end of initialization.
             Otherwise, the user will have to call `solver_instance = CySolver(...); solver_instance.solve()`
             to perform integration.
@@ -707,239 +704,30 @@ cdef class CySolver:
 
         return step_size
 
-    cdef void rk_step(self) noexcept nogil:
-        """ Performs a Runge-Kutta step calculation including local error determination. """
-
-        # Initialize step variables
-        cdef size_t s, i, j
-        cdef double min_step, step, step_factor, time_tmp, t_delta_check
-        cdef double scale, temp_double
-        cdef double error_norm3, error_norm5, error_norm, error_dot_1, error_dot_2, error_denom, error_pow
-        cdef bool_cpp_t step_accepted, step_rejected, step_error
-
-        # Run RK integration step
-        # Determine step size based on previous loop
-        # Find minimum step size based on the value of t (less floating point numbers between numbers when t is large)
-        min_step = 10. * fabs(nextafter(self.t_old, self.direction_inf) - self.t_old)
-        # Look for over/undershoots in previous step size
-        if self.step_size > self.max_step:
-            self.step_size = self.max_step
-        elif self.step_size < min_step:
-            self.step_size = min_step
-
-        # Determine new step size
-        step_accepted = False
-        step_rejected = False
-        step_error    = False
-
-        # Optimization variables
-        cdef double A_at_10
-        # Define a very specific A (Row 1; Col 0) now since it is called consistently and does not change.
-        A_at_10 = self.A_ptr[1 * self.len_Acols + 0]
-
-        # # Step Loop
-        while not step_accepted:
-            if self.step_size < min_step:
-                step_error  = True
-                self.status = -1
-                break
-
-            # Move time forward for this particular step size
-            if self.direction_flag:
-                step          = self.step_size
-                self.t_now    = self.t_old + step
-                t_delta_check = self.t_now - self.t_end
-            else:
-                step          = -self.step_size
-                self.t_now    = self.t_old + step
-                t_delta_check = self.t_end - self.t_now
-
-            # Check that we are not at the end of integration with that move
-            if t_delta_check > 0.:
-                self.t_now = self.t_end
-
-                # If we are, correct the step so that it just hits the end of integration.
-                step = self.t_now - self.t_old
-                if self.direction_flag:
-                    self.step_size = step
-                else:
-                    self.step_size = -step
-
-            # # Calculate derivative using RK method
-
-            # t_now must be updated for each loop of s in order to make the diffeq calls.
-            # But we need to return to its original value later on. Store in temp variable.
-            time_tmp = self.t_now
-
-            for s in range(1, self.len_C):
-                # Update t_now so it can be used in the diffeq call.
-                self.t_now = self.t_old + self.C_ptr[s] * step
-
-                # Dot Product (K, a) * step
-                if s == 1:
-                    for i in range(self.y_size):
-                        # Set the first column of K
-                        temp_double = self.dy_old_ptr[i]
-                        # K[0, :] == first part of the array
-                        self.K_ptr[i] = temp_double
-
-                        # Calculate y_new for s==1
-                        self.y_ptr[i] = self.y_old_ptr[i] + (temp_double * A_at_10 * step)
-                else:
-                    for j in range(s):
-                        temp_double = self.A_ptr[s * self.len_Acols + j] * step
-                        for i in range(self.y_size):
-                            if j == 0:
-                                # Initialize
-                                self.y_ptr[i] = self.y_old_ptr[i]
-
-                            self.y_ptr[i] += self.K_ptr[j * self.y_size + i] * temp_double
-
-                # Call diffeq to update K with the new dydt
-                self.diffeq()
-
-                for i in range(self.y_size):
-                    self.K_ptr[s * self.y_size + i] = self.dy_ptr[i]
-
-            # Restore t_now to its previous value.
-            self.t_now = time_tmp
-
-            # Dot Product (K, B) * step
-            for j in range(self.rk_n_stages):
-                temp_double = self.B_ptr[j] * step
-                # We do not use rk_n_stages_plus1 here because we are chopping off the last row of K to match
-                #  the shape of B.
-                for i in range(self.y_size):
-                    if j == 0:
-                        # Initialize
-                        self.y_ptr[i] = self.y_old_ptr[i]
-
-                    self.y_ptr[i] += self.K_ptr[j * self.y_size + i] * temp_double
-
-            # Find final dydt for this timestep
-            self.diffeq()
-
-            # Check how well this step performed by calculating its error
-            if self.rk_method == 2:
-                # Calculate Error for DOP853
-                # Dot Product (K, E5) / scale and Dot Product (K, E3) * step / scale
-                error_norm3 = 0.
-                error_norm5 = 0.
-                for i in range(self.y_size):
-                    # Find scale of y for error calculations
-                    scale = (self.atols_ptr[i] +
-                             max(fabs(self.y_old_ptr[i]), fabs(self.y_ptr[i])) * self.rtols_ptr[i])
-
-                    # Set last array of K equal to dydt
-                    self.K_ptr[self.rk_n_stages * self.y_size + i] = self.dy_ptr[i]
-                    # Initialize
-                    error_dot_1 = 0.
-                    error_dot_2 = 0.
-                    for j in range(self.rk_n_stages_plus1):
-
-                        temp_double = self.K_ptr[j * self.y_size + i]
-                        error_dot_1 += temp_double * self.E3_ptr[j]
-                        error_dot_2 += temp_double * self.E5_ptr[j]
-
-                    # We need the absolute value but since we are taking the square, it is guaranteed to be positive.
-                    # TODO: This will need to change if CySolver ever accepts complex numbers
-                    # error_norm3_abs = fabs(error_dot_1)
-                    # error_norm5_abs = fabs(error_dot_2)
-                    error_dot_1 /= scale
-                    error_dot_2 /= scale
-
-                    error_norm3 += (error_dot_1 * error_dot_1)
-                    error_norm5 += (error_dot_2 * error_dot_2)
-
-                # Check if errors are zero
-                if (error_norm5 == 0.) and (error_norm3 == 0.):
-                    error_norm = 0.
-                else:
-                    error_denom = error_norm5 + 0.01 * error_norm3
-                    error_norm = self.step_size * error_norm5 / sqrt(error_denom * self.y_size_dbl)
-
-            else:
-                # Calculate Error for RK23 and RK45
-                # Dot Product (K, E) * step / scale
-                error_norm = 0.
-                for i in range(self.y_size):
-                    # Find scale of y for error calculations
-                    scale = (self.atols_ptr[i] +
-                             max(fabs(self.y_old_ptr[i]), fabs(self.y_ptr[i])) * self.rtols_ptr[i])
-
-                    # Set last array of K equal to dydt
-                    self.K_ptr[self.rk_n_stages * self.y_size + i] = self.dy_ptr[i]
-                    # Initialize
-                    error_dot_1 = 0.
-                    for j in range(self.rk_n_stages_plus1):
-
-                        error_dot_1 += self.K_ptr[j * self.y_size + i] * self.E_ptr[j]
-
-                    # We need the absolute value but since we are taking the square, it is guaranteed to be positive.
-                    # TODO: This will need to change if CySolver ever accepts complex numbers
-                    # error_norm_abs = fabs(error_dot_1)
-                    error_dot_1 *= (step / scale)
-
-                    error_norm += (error_dot_1 * error_dot_1)
-                error_norm = sqrt(error_norm) / self.y_size_sqrt
-
-            if error_norm < 1.:
-                # The error is low! Let's update this step for the next time loop
-                if error_norm == 0.:
-                    step_factor = MAX_FACTOR
-                else:
-                    error_pow = pow(error_norm, -self.error_expo)
-                    step_factor = fmin(MAX_FACTOR, SAFETY * error_pow)
-
-                if step_rejected:
-                    # There were problems with this step size on the previous step loop. Make sure factor does
-                    #    not exasperate them.
-                    step_factor = fmin(step_factor, 1.)
-
-                self.step_size = self.step_size * step_factor
-                step_accepted = True
-            else:
-                error_pow = pow(error_norm, -self.error_expo)
-                self.step_size = self.step_size * fmax(MIN_FACTOR, SAFETY * error_pow)
-                step_rejected = True
-
-        if step_error:
-            # Issue with step convergence
-            self.status = -1
-        elif not step_accepted:
-            # Issue with step convergence
-            self.status = -7
-
-        # End of step loop. Update the 'old' variables
-        self.t_old = self.t_now
-        for i in range(self.y_size):
-            self.y_old_ptr[i]  = self.y_ptr[i]
-            self.dy_old_ptr[i] = self.dy_ptr[i]
-
     cpdef void solve(
             self,
-            bool_cpp_t reset = True
+            bint reset = True
             ):
         """
         Public wrapper to the private solve method which calculates the integral of the user-provided system of ODEs.
         
         Parameters
         ----------
-        reset : bool_cpp_t, default=True
+        reset : bint, default=True
             If True, `reset_state()` will be called before integration starts.
         """
         self._solve(reset=reset)
 
     cdef void _solve(
             self,
-            bool_cpp_t reset = True
+            bint reset = True
             ):
         """
         Calculates the integral of the user-provided system of ODEs.
         
         Parameters
         ----------
-        reset : bool_cpp_t, default=True
+        reset : bint, default=True
             If True, `reset_state()` will be called before integration starts.
         """
 
@@ -949,6 +737,7 @@ cdef class CySolver:
 
         # Setup loop variables
         cdef size_t i
+        cdef int rk_step_output
 
         # Setup storage arrays
         # These arrays are built to fit a number of points equal to `self.expected_size`
@@ -1015,7 +804,43 @@ cdef class CySolver:
                 break
 
             # # Perform RK Step
-            self.rk_step()
+            rk_step_output = rk_step_cf(
+                self.diffeq,
+                self,
+                self.t_end,
+                self.direction_flag,
+                self.direction_inf,
+                self.y_size,
+                self.y_size_dbl,
+                self.y_size_sqrt,
+                &self.t_now,
+                self.y_ptr,
+                self.dy_ptr,
+                &self.t_old,
+                self.y_old_ptr,
+                self.dy_old_ptr,
+                &self.step_size,
+                &self.status,
+                self.atols_ptr,
+                self.rtols_ptr,
+                self.max_step,
+                self.rk_method,
+                self.rk_n_stages,
+                self.rk_n_stages_plus1,
+                self.len_Acols,
+                self.len_C,
+                self.A_ptr,
+                self.B_ptr,
+                self.C_ptr,
+                self.K_ptr,
+                self.E_ptr,
+                self.E3_ptr,
+                self.E5_ptr,
+                self.error_expo,
+                MIN_FACTOR,
+                MAX_FACTOR,
+                SAFETY
+                )
 
             # Check if an error occurred during step calculations before storing data.
             if self.status != 0:
@@ -1303,7 +1128,7 @@ cdef class CySolver:
     cpdef void change_t_span(
             self,
             (double, double) t_span,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change the independent variable limits (start and stop points of integration).
@@ -1312,7 +1137,7 @@ cdef class CySolver:
         ----------
         t_span : (double, double)
             New t_span to use during integration.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1338,7 +1163,7 @@ cdef class CySolver:
     cpdef void change_y0(
             self,
             const double[::1] y0,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change the initial conditions.
@@ -1350,7 +1175,7 @@ cdef class CySolver:
         y0 : double[::1]
             New dependent variable initial conditions.
             Must be the same size as the original y0.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1378,7 +1203,7 @@ cdef class CySolver:
     cdef void change_y0_pointer(
                 self,
                 double* y0_ptr,
-                bool_cpp_t auto_reset_state = False
+                bint auto_reset_state = False
                 ):
             """
             Public method to change the initial conditions.
@@ -1390,7 +1215,7 @@ cdef class CySolver:
             y0 : double*
                 New pointer to dependent variable initial conditions.
                 Must be the same size as the original y0.
-            auto_reset_state : bool_cpp_t, default=False
+            auto_reset_state : bint, default=False
                 If True, then the `reset_state` method will be called once parameter is changed.
             """
 
@@ -1412,7 +1237,7 @@ cdef class CySolver:
     cpdef void change_args(
             self,
             tuple args,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change additional arguments used during integration.
@@ -1421,7 +1246,7 @@ cdef class CySolver:
         ----------
         args : tuple
             New tuple of additional arguments.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1462,7 +1287,7 @@ cdef class CySolver:
             double atol = NAN,
             const double[::1] rtols = None,
             const double[::1] atols = None,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change relative and absolute tolerances and/or their arrays.
@@ -1481,13 +1306,13 @@ cdef class CySolver:
         atols : const double[::1]
             Numpy ndarray of absolute tolerances, one for each dependent y variable.
             if None (the default), then no change will be made.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
         # This is one of the few change functions where nothing might change.
         # Track if updates need to be made
-        cdef bool_cpp_t something_changed = False
+        cdef bint something_changed = False
 
         # Update tolerances
         cdef double rtol_tmp
@@ -1537,7 +1362,7 @@ cdef class CySolver:
     cpdef void change_max_step(
             self,
             double max_step,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change maximum allowed step size.
@@ -1546,7 +1371,7 @@ cdef class CySolver:
         ----------
         max_step : double
             New maximum step size used during integration.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1558,7 +1383,7 @@ cdef class CySolver:
     cpdef void change_first_step(
             self,
             double first_step,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change first step's size.
@@ -1567,7 +1392,7 @@ cdef class CySolver:
         ----------
         first_step : double
             New first step's size.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1594,7 +1419,7 @@ cdef class CySolver:
     cpdef void change_t_eval(
             self,
             const double[::1] t_eval,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change user requested independent domain, `t_eval`.
@@ -1603,7 +1428,7 @@ cdef class CySolver:
         ----------
         t_eval : double[:]
             New independent domain at which solution will be interpolated.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1634,7 +1459,7 @@ cdef class CySolver:
             self,
             double* new_t_eval_ptr,
             size_t new_len_t_eval,
-            bool_cpp_t auto_reset_state = False
+            bint auto_reset_state = False
             ):
         """
         Public method to change user requested independent domain, `t_eval`.
@@ -1643,7 +1468,7 @@ cdef class CySolver:
         ----------
         t_eval_ptr : double[:]
             New pointer to independent domain at which solution will be interpolated.
-        auto_reset_state : bool_cpp_t, default=False
+        auto_reset_state : bint, default=False
             If True, then the `reset_state` method will be called once parameter is changed.
         """
 
@@ -1680,8 +1505,8 @@ cdef class CySolver:
             double max_step = NAN,
             double first_step = NAN,
             const double[::1] t_eval = None,
-            bool_cpp_t auto_reset_state = True,
-            bool_cpp_t auto_solve = False
+            bint auto_reset_state = True,
+            bint auto_solve = False
             ):
         """
         Public method to change one or more parameters which have their own `change_*` method.
@@ -1700,15 +1525,15 @@ cdef class CySolver:
         max_step
         first_step
         t_eval
-        auto_reset_state : bool_cpp_t, default=True
+        auto_reset_state : bint, default=True
             If True, then the `reset_state` method will be called once parameter is changed.
-        auto_solve : bool_cpp_t, default=False
+        auto_solve : bint, default=False
             If True, then the `solve` method will be called after all parameters have been changed and the state reset.
         """
 
         # This is one of the few change functions where nothing might change.
         # Track if updates need to be made
-        cdef bool_cpp_t something_changed
+        cdef bint something_changed
         something_changed = False
 
         if not isnan(t_span[0]):
diff --git a/CyRK/cy/rk_step.c b/CyRK/cy/rk_step.c
new file mode 100644
index 0000000..563942b
--- /dev/null
+++ b/CyRK/cy/rk_step.c
@@ -0,0 +1,315 @@
+#include <stddef.h>   // `size_t`
+#include <stdbool.h>  // `bool`
+#include <math.h>     // `fmin`, `fmax`, `fabs`
+#include <stdio.h>
+// Create a fake struct to trick C into accepting the CySolver class (which contains the diffeq method)
+struct CySolverStruct {
+    char empty;
+};
+
+
+int rk_step_cf(
+        // Pointer to differential equation
+        void (*diffeq_ptr)(struct CySolverStruct*),
+        // Pointer to the CySolver instance
+        struct CySolverStruct* cysolver_inst,
+
+        // t-related variables
+        double t_end,
+        bool direction_flag,
+        double direction_inf,
+
+        // y-related variables
+        size_t y_size,
+        double y_size_dbl,
+        double y_size_sqrt,
+
+        // Pointers to class attributes that can change during rk_step call.
+        double* restrict t_now_ptr,
+        double* restrict y_ptr,
+        double* restrict dy_ptr,
+        double* restrict t_old_ptr,
+        double* restrict y_old_ptr,
+        double* restrict dy_old_ptr,
+        double* restrict step_size_ptr,
+        char* restrict status_ptr,
+
+        // Integration tolerance variables and pointers
+        double* restrict atols_ptr,
+        double* restrict rtols_ptr,
+        double max_step,
+
+        // RK specific variables and pointers
+        unsigned char rk_method,
+        size_t rk_n_stages,
+        size_t rk_n_stages_plus1,
+        size_t len_Acols,
+        size_t len_C,
+        double* restrict A_ptr,
+        double* restrict B_ptr,
+        double* restrict C_ptr,
+        double* restrict K_ptr,
+        double* restrict E_ptr,
+        double* restrict E3_ptr,
+        double* restrict E5_ptr,
+        double error_expo,
+        double min_step_factor,
+        double max_step_factor,
+        double error_safety
+        ){
+    /**
+     * Performs a Runge-Kutta step calculation including local error determination. 
+    */
+
+    // Initialize step variables
+    double min_step, step, step_factor, time_tmp, t_delta_check;
+    double scale, temp_double;
+    double error_norm, error_dot_1, error_pow;
+    bool step_accepted, step_rejected, step_error;
+
+    // Store values from pointers so that they do not have to be dereferenced multiple times
+    double t_now = *t_now_ptr;
+    double t_old = *t_old_ptr;
+    double step_size = *step_size_ptr;
+
+    // Run RK integration step
+    // Determine step size based on previous loop
+    // Find minimum step size based on the value of t (less floating point numbers between numbers when t is large)
+    min_step = 10. * fabs(nextafter(t_old, direction_inf) - t_old);
+    // Look for over/undershoots in previous step size
+    if (step_size > max_step) {
+        step_size = max_step;
+    } else if (step_size < min_step) {
+        step_size = min_step;
+    }
+
+    // Determine new step size
+    step_accepted = false;
+    step_rejected = false;
+    step_error    = false;
+
+    // Optimization variables
+    // Define a very specific A (Row 1; Col 0) now since it is called consistently and does not change.
+    double A_at_10 = A_ptr[1 * len_Acols + 0];
+
+    // !! Step Loop
+    while (!step_accepted) {
+
+        // Check if step size is too small
+        // This will cause integration to fail: step size smaller than spacing between numbers
+        if (step_size < min_step) {
+            step_error  = true;
+            *status_ptr = -1;
+            break;
+        }
+
+        // Move time forward for this particular step size
+        if (direction_flag) {
+            step          = step_size;
+            t_now         = t_old + step;
+            t_delta_check = t_now - t_end;
+        } else {
+            step          = -step_size;
+            t_now         = t_old + step;
+            t_delta_check = t_end - t_now;
+        }
+
+        // Check that we are not at the end of integration with that move
+        if (t_delta_check > 0.0) {
+            t_now = t_end;
+
+            // If we are, correct the step so that it just hits the end of integration.
+            step = t_now - t_old;
+            if (direction_flag){
+                step_size = step;
+            } else {
+                step_size = -step;
+            }
+        }
+
+        // !! Calculate derivative using RK method
+
+        // t_now must be updated for each loop of s in order to make the diffeq method calls.
+        // But we need to return to its original value later on. Store in temp variable.
+        time_tmp = t_now;
+
+        for (size_t s = 1; s < len_C; s++) {
+            // Find the current time based on the old time and the step size.
+            t_now = t_old + C_ptr[s] * step;
+            // Update the value stored at the t_now pointer so it can be used in the diffeq method.
+            *t_now_ptr = t_now;
+
+            // Dot Product (K, a) * step
+            if (s == 1) {
+                for (size_t i = 0; i < y_size; i++) {
+                    // Set the first column of K
+                    temp_double = dy_old_ptr[i];
+                    // K[0, :] == first part of the array
+                    K_ptr[i] = temp_double;
+
+                    // Calculate y_new for s==1
+                    y_ptr[i] = y_old_ptr[i] + (temp_double * A_at_10 * step);
+                }
+            } else {
+                for (size_t j = 0; j < s; j++) {
+                    temp_double = A_ptr[s * len_Acols + j] * step;
+                    for (size_t i = 0; i < y_size; i++) {
+                        if (j == 0){
+                            // Initialize
+                            y_ptr[i] = y_old_ptr[i];
+                        }
+                        y_ptr[i] += K_ptr[j * y_size + i] * temp_double;
+                    }
+                }
+            }
+            // Call diffeq method to update K with the new dydt
+            // This will use the now updated values at y_ptr and t_now_ptr. It will update values at dy_ptr.
+            diffeq_ptr(cysolver_inst);
+
+            // Update K based on the new dy values.
+            for (size_t i = 0; i < y_size; i++) {
+                K_ptr[s * y_size + i] = dy_ptr[i];
+            }
+        }
+
+        // Restore t_now to its previous value.
+        t_now = time_tmp;
+        // Update the pointer.
+        *t_now_ptr = t_now;
+        
+        // Dot Product (K, B) * step
+        for (size_t j = 0; j < rk_n_stages; j++) {
+            temp_double = B_ptr[j] * step;
+            // We do not use rk_n_stages_plus1 here because we are chopping off the last row of K to match
+            //  the shape of B.
+            for (size_t i = 0; i < y_size; i++) {
+                if (j == 0) {
+                    // Initialize
+                    y_ptr[i] = y_old_ptr[i];
+                }
+                y_ptr[i] += K_ptr[j * y_size + i] * temp_double;
+            }
+        }
+        
+        // Find final dydt for this timestep
+        // This will use the now final values at y_ptr and t_now_ptr. It will update values at dy_ptr.
+        diffeq_ptr(cysolver_inst);
+
+        // Check how well this step performed by calculating its error.
+        if (rk_method == 2) {
+             double error_norm3, error_norm5, error_dot_2, error_denom; 
+            // Calculate Error for DOP853
+            // Dot Product (K, E5) / scale and Dot Product (K, E3) * step / scale
+            error_norm3 = 0.0;
+            error_norm5 = 0.0;
+            for (size_t i = 0; i < y_size; i++) {
+                // Find scale of y for error calculations
+                scale = (atols_ptr[i] + fmax(fabs(y_old_ptr[i]), fabs(y_ptr[i])) * rtols_ptr[i]);
+
+                // Set last array of K equal to dydt
+                K_ptr[rk_n_stages * y_size + i] = dy_ptr[i];
+
+                // Initialize
+                error_dot_1 = 0.0;
+                error_dot_2 = 0.0;
+                
+                for (size_t j = 0; j < rk_n_stages_plus1; j++) {
+                    temp_double = K_ptr[j * y_size + i];
+                    error_dot_1 += temp_double * E3_ptr[j];
+                    error_dot_2 += temp_double * E5_ptr[j];
+                }
+                // We need the absolute value but since we are taking the square, it is guaranteed to be positive.
+                // TODO: This will need to change if CySolver ever accepts complex numbers
+                // error_norm3_abs = fabs(error_dot_1)
+                // error_norm5_abs = fabs(error_dot_2)
+                error_dot_1 /= scale;
+                error_dot_2 /= scale;
+
+                error_norm3 += (error_dot_1 * error_dot_1);
+                error_norm5 += (error_dot_2 * error_dot_2);
+            }
+            // Check if errors are zero
+            if ((error_norm5 == 0.0) && (error_norm3) == 0.0) {
+                error_norm = 0.0;
+            } else {
+                error_denom = error_norm5 + 0.01 * error_norm3;
+                error_norm = step_size * error_norm5 / sqrt(error_denom * y_size_dbl);
+            }
+        } else {
+            // Calculate Error for RK23 and RK45
+            // Dot Product (K, E) * step / scale
+            error_norm = 0.0;
+            for (size_t i = 0; i < y_size; i++) {
+                // Find scale of y for error calculations
+                scale = (atols_ptr[i] + fmax(fabs(y_old_ptr[i]), fabs(y_ptr[i])) * rtols_ptr[i]);
+
+                // Set last array of K equal to dydt
+                K_ptr[rk_n_stages * y_size + i] = dy_ptr[i];
+                
+                // Initialize
+                error_dot_1 = 0.0;
+
+                for (size_t j = 0; j < rk_n_stages_plus1; j++) {
+                    error_dot_1 += K_ptr[j * y_size + i] * E_ptr[j];
+                }
+
+                // We need the absolute value but since we are taking the square, it is guaranteed to be positive.
+                // TODO: This will need to change if CySolver ever accepts complex numbers
+                // error_norm_abs = fabs(error_dot_1)
+                error_dot_1 *= (step / scale);
+
+                error_norm += (error_dot_1 * error_dot_1);
+            }
+            error_norm = sqrt(error_norm) / y_size_sqrt;
+        }
+
+        // Check the size of the error
+        if (error_norm < 1.0) {
+            // We found our step size because the error is low!
+            // Update this step for the next time loop
+            if (error_norm == 0.0) {
+                step_factor = max_step_factor;
+            } else {
+                error_pow = pow(error_norm, -error_expo);
+                step_factor = fmin(max_step_factor, error_safety * error_pow);
+            }
+
+            if (step_rejected) {
+                // There were problems with this step size on the previous step loop. Make sure factor does
+                //   not exasperate them.
+                step_factor = fmin(step_factor, 1.);
+            }
+
+            // Update step size
+            step_size *= step_factor;
+            step_accepted = true;
+        } else {
+            // Error is still large. Keep searching for a better step size.
+            error_pow = pow(error_norm, -error_expo);
+            step_size *= fmax(min_step_factor, error_safety * error_pow);
+            step_rejected = true;
+        }
+    }
+
+    // Update status depending if there were any errors.
+    if (step_error) {
+        // Issue with step convergence
+        *status_ptr = -1;
+    } else if (!step_accepted) {
+        // Issue with step convergence
+        *status_ptr = -7;
+    }
+
+    // End of RK step. 
+    // Update "old" pointers
+    *t_old_ptr = t_now;
+    for (size_t i = 0; i < y_size; i++) {
+        y_old_ptr[i]  = y_ptr[i];
+        dy_old_ptr[i] = dy_ptr[i];
+    }
+
+    // Update any other pointers
+    *step_size_ptr = step_size;
+
+    return 0;
+}
diff --git a/CyRK/rk/rk.pyx b/CyRK/rk/rk.pyx
index 1543db6..4ad80f1 100644
--- a/CyRK/rk/rk.pyx
+++ b/CyRK/rk/rk.pyx
@@ -1,3 +1,4 @@
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 
 from CyRK.rk.rk_constants cimport \
diff --git a/CyRK/rk/rk_constants.pyx b/CyRK/rk/rk_constants.pyx
index 5e5f231..4c149d2 100644
--- a/CyRK/rk/rk_constants.pyx
+++ b/CyRK/rk/rk_constants.pyx
@@ -1,3 +1,4 @@
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 """ Constants for Runge-Kutta Integration Methods.
 
diff --git a/CyRK/utils/utils.pyx b/CyRK/utils/utils.pyx
index b28f9ea..99d414c 100644
--- a/CyRK/utils/utils.pyx
+++ b/CyRK/utils/utils.pyx
@@ -1,4 +1,4 @@
-# distutils: language = c++
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 from cpython.mem cimport PyMem_Malloc, PyMem_Realloc
 
diff --git a/Performance/cyrk_performance-DOP853.csv b/Performance/cyrk_performance-DOP853.csv
index 792d2fc..28b3786 100644
--- a/Performance/cyrk_performance-DOP853.csv
+++ b/Performance/cyrk_performance-DOP853.csv
@@ -18,3 +18,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.8.0, 06/09/2023 15:03:15, 0.9615, 0.0011, 0.0561, 0.0001, 0.1602, 0.0004, 9.8846, 0.1049, 0.4796, 0.0010, 1.4545, 0.0154, 0.4413, 0.0005, 0.0376, 0.0001, 0.0953, 0.0001, 4.3959, 0.0170, 0.2890, 0.0007, 0.7862, 0.0168, 1.4213, 0.0046, 0.0937, 0.0005, 0.2464, 0.0012, 19.5317, 0.0414, 1.1684, 0.0054, 3.1531, 0.0143, 1.6563, 0.0055, 0.0989, 0.0018, 0.4797, 0.0064, 22.9818, 0.1217, 1.2122, 0.0250, 6.4178, 0.0221
 0.8.3, 07/10/2023 02:40:03, 0.9727, 0.0033, 0.0587, 0.0014, 0.1625, 0.0006, 9.9439, 0.0246, 0.4990, 0.0070, 2.6214, 0.0527, 0.4393, 0.0008, 0.0400, 0.0006, 0.1034, 0.0063, 4.4720, 0.1096, 0.3114, 0.0025, 1.1428, 0.0021, 1.4298, 0.0046, 0.0987, 0.0009, 0.2440, 0.0019, 21.2338, 0.3276, 1.7444, 0.0077, 4.3900, 0.0738, 1.6848, 0.0280, 0.1039, 0.0003, 0.4644, 0.0006, 25.3711, 0.1346, 2.2809, 0.0329, 7.3621, 0.0222
 0.8.4, 18/10/2023 12:49:04, 0.9453, 0.0036, 0.0574, 0.0012, 0.1718, 0.0070, 9.9086, 0.1287, 0.4833, 0.0013, 1.4208, 0.0060, 0.4293, 0.0016, 0.0383, 0.0001, 0.0956, 0.0006, 4.2518, 0.0123, 0.2944, 0.0001, 0.7605, 0.0005, 1.3829, 0.0014, 0.0931, 0.0001, 0.2438, 0.0003, 19.2126, 0.0622, 1.1729, 0.0003, 3.1186, 0.0861, 1.6221, 0.0111, 0.0957, 0.0000, 0.4634, 0.0006, 22.3320, 0.0879, 1.2067, 0.0012, 6.1291, 0.0053
+0.8.6, 13/02/2024 17:08:45, 0.9814, 0.0156, 0.0611, 0.0001, 0.1640, 0.0017, 10.2798, 0.4311, 0.5307, 0.0079, 1.4800, 0.0021, 0.4434, 0.0036, 0.0420, 0.0005, 0.0991, 0.0000, 4.4219, 0.0575, 0.3169, 0.0014, 0.7947, 0.0034, 1.4053, 0.0155, 0.0973, 0.0004, 0.2517, 0.0009, 19.6548, 0.3540, 1.2133, 0.0019, 3.1887, 0.0088, 1.6764, 0.0156, 0.1001, 0.0001, 0.5250, 0.0036, 23.9277, 0.3592, 1.2488, 0.0017, 7.2941, 0.1608
diff --git a/Performance/cyrk_performance-RK23.csv b/Performance/cyrk_performance-RK23.csv
index f4e5eed..7333dd8 100644
--- a/Performance/cyrk_performance-RK23.csv
+++ b/Performance/cyrk_performance-RK23.csv
@@ -18,3 +18,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.8.0, 06/09/2023 15:00:58, 4.4794, 0.0786, 0.2262, 0.0003, 0.8199, 0.0021, 43.1476, 0.0854, 2.1466, 0.0018, 10.1946, 0.1933, 2.7552, 0.0105, 0.1866, 0.0004, 0.5903, 0.0024, 26.9694, 0.1524, 1.7797, 0.0257, 5.9678, 0.0812, 4.7003, 0.0324, 0.2855, 0.0005, 0.9508, 0.0055, 80.1011, 0.3480, 4.8504, 0.0219, 20.8530, 0.2461, 5.5239, 0.0051, 0.3178, 0.0014, 1270.6912, 2197.9259, 95.6354, 0.1723, 5.4006, 0.0797, 35.0990, 0.8415
 0.8.3, 07/10/2023 02:37:45, 4.5212, 0.1075, 0.2681, 0.0021, 0.7992, 0.0038, 43.9629, 0.3217, 2.5955, 0.0502, 9.2842, 0.4116, 2.7602, 0.0088, 0.2146, 0.0037, 0.5896, 0.0074, 27.1210, 0.1858, 2.0566, 0.0327, 5.7936, 0.0150, 4.8923, 0.0594, 0.3719, 0.0011, 0.9807, 0.0023, 82.6179, 0.6252, 6.0628, 0.0336, 20.2684, 0.2082, 5.6639, 0.0115, 0.9587, 0.2834, 1525.4251, 2635.4115, 105.9372, 0.3232, 14.0169, 0.0614, 45.0599, 0.8249
 0.8.4, 18/10/2023 12:46:46, 4.4202, 0.0673, 0.2500, 0.0004, 0.8115, 0.0034, 43.1191, 0.0775, 2.4081, 0.0282, 10.0251, 0.7074, 2.7552, 0.0436, 0.2110, 0.0039, 0.5987, 0.0058, 26.5597, 0.2133, 1.9887, 0.0201, 5.8675, 0.1112, 4.7007, 0.0078, 0.3080, 0.0014, 0.9944, 0.0238, 81.1241, 0.3424, 5.5864, 0.7125, 19.9567, 0.1538, 5.4119, 0.0106, 0.3208, 0.0010, 1322.6941, 2287.9898, 96.8733, 2.3521, 5.5968, 0.1162, 36.6713, 0.3267
+0.8.6, 13/02/2024 17:06:17, 4.5290, 0.0708, 0.2875, 0.0033, 0.9640, 0.1354, 43.6531, 0.0926, 2.8374, 0.0722, 8.9517, 0.2453, 2.7551, 0.0064, 0.2243, 0.0006, 0.6140, 0.0013, 27.0492, 0.2018, 2.1363, 0.0025, 5.9659, 0.0384, 4.6135, 0.0079, 0.3487, 0.0003, 0.9485, 0.0007, 79.4872, 0.4035, 5.9439, 0.1090, 19.9015, 0.1388, 5.6296, 0.0271, 0.3621, 0.0012, 1454.8195, 2516.6233, 96.8326, 0.5951, 6.1248, 0.0257, 38.7549, 0.4298
diff --git a/Performance/cyrk_performance-RK45.csv b/Performance/cyrk_performance-RK45.csv
index 8b9cfba..7153a1d 100644
--- a/Performance/cyrk_performance-RK45.csv
+++ b/Performance/cyrk_performance-RK45.csv
@@ -18,3 +18,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.8.0, 06/09/2023 15:02:20, 1.2080, 0.0171, 0.0582, 0.0001, 0.2023, 0.0008, 11.8032, 0.0256, 0.4807, 0.0012, 1.8475, 0.0119, 0.8318, 0.0034, 0.0569, 0.0002, 0.1659, 0.0003, 8.1177, 0.0578, 0.4684, 0.0018, 1.4517, 0.0058, 1.6145, 0.0023, 0.0921, 0.0005, 0.2928, 0.0011, 23.6490, 0.0748, 1.2090, 0.0118, 4.0150, 0.0179, 1.8902, 0.0034, 0.0969, 0.0009, 0.5533, 0.0009, 27.6976, 0.0946, 1.2947, 0.0416, 7.9394, 0.0140
 0.8.3, 07/10/2023 02:39:08, 1.2268, 0.0292, 0.0670, 0.0002, 0.2042, 0.0002, 13.0410, 0.0369, 1.0062, 0.0659, 2.6633, 0.0087, 0.8234, 0.0038, 0.0631, 0.0002, 0.1648, 0.0001, 8.8861, 0.0427, 0.7835, 0.0085, 2.1426, 0.0023, 1.6358, 0.0049, 0.1139, 0.0050, 0.6746, 0.0009, 26.8571, 0.1145, 3.0055, 0.0209, 6.6462, 0.1028, 1.9187, 0.0032, 0.1280, 0.0045, 0.9513, 0.0050, 32.9776, 0.3770, 4.3772, 0.1087, 10.9500, 0.0345
 0.8.4, 18/10/2023 12:48:10, 1.1825, 0.0070, 0.0622, 0.0001, 0.2012, 0.0008, 11.7285, 0.0223, 0.5412, 0.0092, 1.8985, 0.0613, 0.8106, 0.0029, 0.0601, 0.0012, 0.1661, 0.0004, 7.9235, 0.0105, 0.4982, 0.0007, 1.4473, 0.0029, 1.6015, 0.0058, 0.0981, 0.0003, 0.2963, 0.0039, 23.5090, 0.0487, 1.3260, 0.0138, 4.0189, 0.0470, 1.8400, 0.0072, 0.1011, 0.0002, 0.5443, 0.0013, 27.1240, 0.0951, 1.3706, 0.0024, 7.7848, 0.0081
+0.8.6, 13/02/2024 17:07:48, 1.1940, 0.0018, 0.0656, 0.0001, 0.2073, 0.0006, 12.0870, 0.1605, 0.5541, 0.0014, 1.8434, 0.0014, 0.8268, 0.0006, 0.0641, 0.0001, 0.1746, 0.0037, 8.1272, 0.0545, 0.5381, 0.0006, 1.4994, 0.0056, 1.5900, 0.0112, 0.0984, 0.0001, 0.2944, 0.0007, 23.5061, 0.1064, 1.2961, 0.0085, 3.9463, 0.0073, 1.8949, 0.0107, 0.1017, 0.0001, 0.6063, 0.0029, 28.0150, 0.1407, 1.3543, 0.0045, 8.5997, 0.0030
diff --git a/README.md b/README.md
index 4a89018..1f6b83a 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # CyRK
 <div style="text-align: center;">
 <a href="https://doi.org/10.5281/zenodo.7093266"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7093266.svg" alt="DOI"></a>
-<a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/Python-3.8|3.9|3.10|3.11-blue" alt="Python Version 3.8-3.11" /></a>
+<a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/Python-3.8|3.9|3.10|3.11|3.12-blue" alt="Python Version 3.8-3.12" /></a>
 <a href="https://codecov.io/gh/jrenaud90/CyRK" ><img src="https://codecov.io/gh/jrenaud90/CyRK/branch/main/graph/badge.svg?token=MK2PqcNGET" alt="Code Coverage"/></a>
 <br />
 <a href="https://github.com/jrenaud90/CyRK/actions/workflows/push_tests_win.yml"><img src="https://github.com/jrenaud90/CyRK/actions/workflows/push_tests_win.yml/badge.svg?branch=main" alt="Windows Tests" /></a>
@@ -11,7 +11,7 @@
 
 ---
 
-<a href="https://github.com/jrenaud90/CyRK/releases"><img src="https://img.shields.io/badge/CyRK-0.8.5 Alpha-orange" alt="CyRK Version 0.8.5 Alpha" /></a>
+<a href="https://github.com/jrenaud90/CyRK/releases"><img src="https://img.shields.io/badge/CyRK-0.8.6 Alpha-orange" alt="CyRK Version 0.8.6 Alpha" /></a>
 
 
 **Runge-Kutta ODE Integrator Implemented in Cython and Numba**
@@ -28,7 +28,7 @@ The [cython](https://cython.org/) `CySolver` class that works with cython-based
 An additional benefit of the two cython implementations is that they are pre-compiled. This avoids most of the start-up performance hit experienced by just-in-time compilers like numba.
 
 
-<img style="text-align: center" src="https://github.com/jrenaud90/CyRK/blob/main/Benchmarks/CyRK_SciPy_Compare_predprey_v0-8-4.png" alt="CyRK Performance Graphic" />
+<img style="text-align: center" src="https://github.com/jrenaud90/CyRK/blob/main/Benchmarks/CyRK_SciPy_Compare_predprey_v0-8-6.png" alt="CyRK Performance Graphic" />
 
 ## Installation
 
diff --git a/_build_cyrk.py b/_build_cyrk.py
index 8413363..bc4e5c4 100644
--- a/_build_cyrk.py
+++ b/_build_cyrk.py
@@ -19,11 +19,11 @@
     extra_link_args = []
 elif install_platform.lower() == 'darwin':
     # OpenMP is installed via llvm. See https://stackoverflow.com/questions/60005176/how-to-deal-with-clang-error-unsupported-option-fopenmp-on-travis
-    extra_compile_args = []
+    extra_compile_args = ['-O3']
     extra_link_args = []
 else:
-    extra_compile_args = ['-fopenmp']
-    extra_link_args = ['-fopenmp']
+    extra_compile_args = ['-fopenmp', '-O3']
+    extra_link_args = ['-fopenmp', '-O3']
 macro_list = [("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")]
 
 # Load TidalPy's cython extensions
diff --git a/cython_extensions.json b/cython_extensions.json
index 535c0f8..86486df 100644
--- a/cython_extensions.json
+++ b/cython_extensions.json
@@ -8,7 +8,7 @@
   },
   "interp": {
     "name": "CyRK.array.interp",
-    "sources": [["CyRK", "array", "interp.pyx"], ["CyRK", "array", "interp_common.c"]],
+    "sources": [["CyRK", "array", "interp.pyx"]],
     "include_dirs": [["CyRK", "array"]],
     "compile_args": [],
     "link_args": []
@@ -43,7 +43,9 @@
   },
   "cysolver": {
     "name": "CyRK.cy.cysolver",
-    "sources": [["CyRK", "cy", "cysolver.pyx"]],
+    "sources": [
+      ["CyRK", "cy", "cysolver.pyx"]
+    ],
     "include_dirs": [["CyRK", "cy"]],
     "compile_args": [],
     "link_args": []
diff --git a/pyproject.toml b/pyproject.toml
index 9d72547..5077c00 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,14 +1,14 @@
 [project]
 name='CyRK'
-version = '0.8.5'
+version = '0.8.6'
 description='Runge-Kutta ODE Integrator Implemented in Cython and Numba.'
 authors= [
     {name = 'Joe P. Renaud', email = 'joe.p.renaud@gmail.com'}
     ]
-requires-python = ">=3.8,<3.12"
+requires-python = ">=3.8,<3.13"
 dependencies = [
     'numba >= 0.54.1',
-    'numpy >= 1.22, <1.26',
+    'numpy >= 1.22, <1.27',
     'scipy >= 1.9.3'
 ]
 license = {file = "LICENSE.md"}
@@ -59,7 +59,7 @@ dev = [
 [build-system]
 requires = [
     'setuptools>=64.0.0',
-    'numpy >= 1.22, <1.26',
+    'numpy >= 1.22, <1.27',
     'cython>=3.0.0',
     'wheel>=0.38'
 ]
diff --git a/setup.py b/setup.py
index 77d453c..f83b66e 100644
--- a/setup.py
+++ b/setup.py
@@ -13,11 +13,11 @@
     extra_link_args = []
 elif install_platform.lower() == 'darwin':
     # OpenMP is installed via llvm. See https://stackoverflow.com/questions/60005176/how-to-deal-with-clang-error-unsupported-option-fopenmp-on-travis
-    extra_compile_args = []
+    extra_compile_args = ['-O3']
     extra_link_args = []
 else:
-    extra_compile_args = ['-fopenmp']
-    extra_link_args = ['-fopenmp']
+    extra_compile_args = ['-fopenmp', '-O3']
+    extra_link_args = ['-fopenmp', '-O3']
 macro_list = [("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")]
 
 # Load TidalPy's cython extensions


Test patch:
diff --git a/.github/workflows/build_wheels_test_pypi.yml b/.github/workflows/build_wheels_test_pypi.yml
index b205281..75f8d7d 100644
--- a/.github/workflows/build_wheels_test_pypi.yml
+++ b/.github/workflows/build_wheels_test_pypi.yml
@@ -8,14 +8,14 @@ jobs:
     name: Make SDist
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
       with:
         submodules: true  # Optional, use if you have submodules
 
     - name: Build SDist
       run: pipx run build --sdist
 
-    - uses: actions/upload-artifact@v3
+    - uses: actions/upload-artifact@v4
       with:
         path: dist/*.tar.gz
 
@@ -31,14 +31,14 @@ jobs:
               - ubuntu-20.04
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.11.4
+        uses: pypa/cibuildwheel@v2.16.5
 
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
           path: ./wheelhouse/*.whl
 
@@ -47,7 +47,7 @@ jobs:
     needs: [make_sdist, build_wheels]
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@v4
         with:
           name: artifact
           path: dist
@@ -60,7 +60,7 @@ jobs:
     runs-on: ubuntu-latest
     # if: github.event_name == 'release' && github.event.action == 'published'
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@v4
         with:
           name: artifact
           path: dist
diff --git a/.github/workflows/push_tests_mac.yml b/.github/workflows/push_tests_mac.yml
index 776404e..6154692 100644
--- a/.github/workflows/push_tests_mac.yml
+++ b/.github/workflows/push_tests_mac.yml
@@ -11,46 +11,43 @@ jobs:
     defaults:
       run:
         shell: bash -el {0}
-
     name: Test CyRK on MacOS
     runs-on: macos-latest
     strategy:
+      fail-fast: false
       matrix:
         python-version:
           - "3.8"
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
     steps:
-      - uses: actions/checkout@v3
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: actions/checkout@v4
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           activate-environment: cyrk_test
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}
           auto-activate-base: false
+        
       - name: Conda info
         shell: bash -el {0}
         run: conda info
-      - name: install libomp
-        shell: bash -el {0}
-        run: |
-          brew reinstall llvm libomp
-      - name: Install Dependencies
+        
+      - name: install gcc-13  # openMP does not play nice with clang on MacOS; also some versions of macos use older gcc so there is a need to install latest.
         shell: bash -el {0}
         run: |
-          conda install numpy scipy cython llvm-openmp pytest
+          brew install gcc@13
+
       - name: Install package
         shell: bash -el {0}
         run: |
-          export CC=/usr/bin/clang
-          export CXX=/usr/bin/clang++
-          export PATH="/usr/local/opt/llvm/bin:$PATH"
-          export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp -I/usr/local/opt/llvm/include"
-          export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include"
-          export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include"
-          export LDFLAGS="$LDFLAGS -Wl,-rpath,/usr/local/opt/libomp/lib -L/usr/local/opt/libomp/lib -lomp -L/usr/local/opt/llvm/lib"
-          python -m pip install . -v
+          conda install pytest matplotlib numba numpy scipy cython
+          export CC='gcc-13'
+          export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
+          python -m pip install -v .
+        
       - name: Run pytest
         shell: bash -el {0}
         run: pytest --capture=sys -v Tests/
diff --git a/.github/workflows/push_tests_ubun.yml b/.github/workflows/push_tests_ubun.yml
index 99c43c7..6761dbe 100644
--- a/.github/workflows/push_tests_ubun.yml
+++ b/.github/workflows/push_tests_ubun.yml
@@ -7,16 +7,18 @@ jobs:
     name: Test CyRK on Ubuntu
     runs-on: ubuntu-latest
     strategy:
+      fail-fast: false
       matrix:
         python-version:
           - "3.8"
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install Dependencies
@@ -34,9 +36,9 @@ jobs:
     name: Test Python 3.9 and Upload Coverage Report
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python 3.9
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.9
       - name: Install Dependencies
diff --git a/.github/workflows/push_tests_win.yml b/.github/workflows/push_tests_win.yml
index 82428ab..372268f 100644
--- a/.github/workflows/push_tests_win.yml
+++ b/.github/workflows/push_tests_win.yml
@@ -10,16 +10,18 @@ jobs:
     name: Test CyRK on Windows
     runs-on: windows-latest
     strategy:
+      fail-fast: false
       matrix:
         python-version:
           - "3.8"
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install Dependencies
diff --git a/CyRK/cy/cysolvertest.pyx b/CyRK/cy/cysolvertest.pyx
index 5fbb099..ca2bbd6 100644
--- a/CyRK/cy/cysolvertest.pyx
+++ b/CyRK/cy/cysolvertest.pyx
@@ -1,4 +1,4 @@
-# distutils: language = c++
+# distutils: language = c
 # cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False
 
 from libc.math cimport sin, cos