diff --git a/Makefile b/Makefile
index 7154c63b1..63cfb0ee0 100644
--- a/Makefile
+++ b/Makefile
@@ -51,7 +51,6 @@ SWIFTEST_MODULES =   swiftest_globals.f90 \
                      rmvs_classes.f90 \
                      helio_classes.f90 \
                      symba_classes.f90 \
-                     module_nrutil.f90 \
 							lambda_function.f90\
                      swiftest.f90 
 
diff --git a/examples/symba_swifter_comparison/1pl_1tp_encounter/init_cond.py b/examples/symba_swifter_comparison/1pl_1tp_encounter/init_cond.py
index b292ed42f..86c13a50e 100755
--- a/examples/symba_swifter_comparison/1pl_1tp_encounter/init_cond.py
+++ b/examples/symba_swifter_comparison/1pl_1tp_encounter/init_cond.py
@@ -55,7 +55,7 @@
 p_tp = np.array([atp, 0.0, 0.0], dtype=np.double)
 v_tp = np.array([0.0, vtp, 0.0], dtype=np.double)
 
-Rhill = apl * 0.0100447248332378922085
+Rhill = np.double(apl * 0.0100447248332378922085)
 
 #Make Swifter files
 plfile = open(swifter_pl, 'w')
@@ -126,6 +126,7 @@
 plfile.write_record(v_pl[1])
 plfile.write_record(v_pl[2])
 plfile.write_record(mass)
+plfile.write_record(Rhill)
 plfile.write_record(radius)
 plfile.close()
 tpfile = FortranFile(swiftest_tp, 'w')
@@ -171,7 +172,8 @@
 print(f'MU2KG          {MU2KG}')
 print(f'DU2M           {DU2M}')
 print(f'TU2S           {TU2S}')
-
+print(f'RHILL_PRESENT  yes')
+print(f'MTINY          1e-12')
 
 
 
diff --git a/examples/symba_swifter_comparison/1pl_1tp_encounter/param.swiftest.in b/examples/symba_swifter_comparison/1pl_1tp_encounter/param.swiftest.in
index 36937896f..f2a1422d1 100644
--- a/examples/symba_swifter_comparison/1pl_1tp_encounter/param.swiftest.in
+++ b/examples/symba_swifter_comparison/1pl_1tp_encounter/param.swiftest.in
@@ -27,3 +27,4 @@ GR             no
 MU2KG          1.988409870698051e+30
 DU2M           149597870700.0
 TU2S           31557600.0
+RHILL_PRESENT  yes
diff --git a/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swifter.in b/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swifter.in
index 95513c9fd..17d461561 100644
--- a/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swifter.in
+++ b/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swifter.in
@@ -2,7 +2,7 @@
 1 39.476926408897625196
 0.0 0.0 0.0
 0.0 0.0 0.0
-2 0.00012002693582795244940133 0.010044724833237891545
+2 0.00012002693582795244940133 0.010044724833237892
 4.25875607065041e-05
 1.0 0.0 0.0
 0.0 6.283185307179586 0.0
diff --git a/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in b/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in
index 6f4bc1337..c94c6ae61 100644
Binary files a/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in and b/examples/symba_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in differ
diff --git a/examples/symba_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb b/examples/symba_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
index f1f15c4cb..71a2c4da6 100644
--- a/examples/symba_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
+++ b/examples/symba_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
@@ -21,9 +21,9 @@
      "output_type": "stream",
      "text": [
       "Reading Swifter file param.swifter.in\n",
-      "Reading in time 1.355e-01\n",
+      "Reading in time 1.348e-01\n",
       "Creating Dataset\n",
-      "Successfully converted 199 output frames.\n",
+      "Successfully converted 198 output frames.\n",
       "Swifter simulation data stored as xarray DataSet .ds\n"
      ]
     }
@@ -75,23 +75,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2ad64892e950>,\n",
-       " <matplotlib.lines.Line2D at 0x2ad64891d090>]"
+       "[<matplotlib.lines.Line2D at 0x2b2e7ff9c6d0>,\n",
+       " <matplotlib.lines.Line2D at 0x2b2e80051590>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -103,12 +103,12 @@
     }
    ],
    "source": [
-    "swiftdiff['px'].plot.line(x=\"time (y)\")"
+    "swiftdiff['vx'].plot.line(x=\"time (y)\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -465,91 +465,33 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;px&#x27; (time (y): 199)&gt;\n",
-       "array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "...\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  3.33066907e-16,  4.44089210e-16,  1.11022302e-16,\n",
-       "        1.11022302e-16,  1.11022302e-16,  3.33066907e-16, -1.11022302e-16,\n",
-       "       -3.33066907e-16, -4.44089210e-16, -2.22044605e-16, -5.55111512e-16,\n",
-       "       -3.33066907e-16, -4.44089210e-16, -5.55111512e-16, -4.44089210e-16,\n",
-       "       -7.77156117e-16, -4.44089210e-16,  0.00000000e+00,  0.00000000e+00,\n",
-       "       -1.11022302e-16,  0.00000000e+00,  3.33066907e-16,  1.11022302e-16,\n",
-       "        1.11022302e-16, -2.22044605e-16,  0.00000000e+00, -5.55111512e-16,\n",
-       "       -3.33066907e-16, -1.11022302e-16, -4.44089210e-16, -4.44089210e-16,\n",
-       "       -3.33066907e-16,  2.22044605e-16,  0.00000000e+00,  0.00000000e+00,\n",
-       "        2.22044605e-16,  1.11022302e-16,  2.22044605e-16, -1.11022302e-16,\n",
-       "        3.33066907e-16,  6.66133815e-16,  8.88178420e-16,  1.22124533e-15,\n",
-       "        3.88578059e-15,  4.10782519e-15,  1.02750769e-03,  1.03179676e-03,\n",
-       "        1.03606675e-03,  1.04031758e-03,  1.04454916e-03,  1.04876143e-03,\n",
-       "        1.05295429e-03,  1.05712769e-03,  1.06128153e-03,  1.06541574e-03,\n",
-       "        1.06953025e-03,  1.07362498e-03,  1.07769985e-03])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vx&#x27; (time (y): 198)&gt;\n",
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
        "Coordinates:\n",
-       "    id        int64 2\n",
-       "  * time (y)  (time (y)) float64 0.0 0.0006845 0.001369 ... 0.1342 0.1348 0.1355</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'px'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time (y)</span>: 199</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ca480486-584b-48e9-8388-92fa8104966a' class='xr-array-in' type='checkbox' checked><label for='section-ca480486-584b-48e9-8388-92fa8104966a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 ... 0.001061 0.001065 0.00107 0.001074 0.001078</span></div><div class='xr-array-data'><pre>array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "...\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  3.33066907e-16,  4.44089210e-16,  1.11022302e-16,\n",
-       "        1.11022302e-16,  1.11022302e-16,  3.33066907e-16, -1.11022302e-16,\n",
-       "       -3.33066907e-16, -4.44089210e-16, -2.22044605e-16, -5.55111512e-16,\n",
-       "       -3.33066907e-16, -4.44089210e-16, -5.55111512e-16, -4.44089210e-16,\n",
-       "       -7.77156117e-16, -4.44089210e-16,  0.00000000e+00,  0.00000000e+00,\n",
-       "       -1.11022302e-16,  0.00000000e+00,  3.33066907e-16,  1.11022302e-16,\n",
-       "        1.11022302e-16, -2.22044605e-16,  0.00000000e+00, -5.55111512e-16,\n",
-       "       -3.33066907e-16, -1.11022302e-16, -4.44089210e-16, -4.44089210e-16,\n",
-       "       -3.33066907e-16,  2.22044605e-16,  0.00000000e+00,  0.00000000e+00,\n",
-       "        2.22044605e-16,  1.11022302e-16,  2.22044605e-16, -1.11022302e-16,\n",
-       "        3.33066907e-16,  6.66133815e-16,  8.88178420e-16,  1.22124533e-15,\n",
-       "        3.88578059e-15,  4.10782519e-15,  1.02750769e-03,  1.03179676e-03,\n",
-       "        1.03606675e-03,  1.04031758e-03,  1.04454916e-03,  1.04876143e-03,\n",
-       "        1.05295429e-03,  1.05712769e-03,  1.06128153e-03,  1.06541574e-03,\n",
-       "        1.06953025e-03,  1.07362498e-03,  1.07769985e-03])</pre></div></div></li><li class='xr-section-item'><input id='section-e7b57a6c-03c7-4f23-88c9-0ec50c0a5fa5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7b57a6c-03c7-4f23-88c9-0ec50c0a5fa5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-cd463064-0272-495a-97e1-4dfadae4f6a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd463064-0272-495a-97e1-4dfadae4f6a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4e1d8fc-4cae-4f82-96b7-c9f44564f760' class='xr-var-data-in' type='checkbox'><label for='data-b4e1d8fc-4cae-4f82-96b7-c9f44564f760' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time (y)</span></div><div class='xr-var-dims'>(time (y))</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0006845 ... 0.1348 0.1355</div><input id='attrs-8d7ed962-c464-48de-8797-d3c5222c9b28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d7ed962-c464-48de-8797-d3c5222c9b28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6399728c-4d12-41fd-833d-81a84cd8fb5a' class='xr-var-data-in' type='checkbox'><label for='data-6399728c-4d12-41fd-833d-81a84cd8fb5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.000684, 0.001369, 0.002053, 0.002738, 0.003422, 0.004107,\n",
+       "    id        float64 100.0\n",
+       "  * time (y)  (time (y)) float64 0.0 0.0006845 0.001369 ... 0.1335 0.1342 0.1348</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vx'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time (y)</span>: 198</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df9a1333-5858-4f67-9c94-9c31460b1824' class='xr-array-in' type='checkbox' checked><label for='section-df9a1333-5858-4f67-9c94-9c31460b1824' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></div></li><li class='xr-section-item'><input id='section-72086983-bbdf-4e0d-b0b1-028b5bab118f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-72086983-bbdf-4e0d-b0b1-028b5bab118f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.0</div><input id='attrs-a8f02ba5-f777-43a1-8723-db6ae6544406' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8f02ba5-f777-43a1-8723-db6ae6544406' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d431a522-6da0-4b9c-98f1-78a921d629a2' class='xr-var-data-in' type='checkbox'><label for='data-d431a522-6da0-4b9c-98f1-78a921d629a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(100.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time (y)</span></div><div class='xr-var-dims'>(time (y))</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0006845 ... 0.1342 0.1348</div><input id='attrs-1e376867-2a05-49c2-b249-daa9012d7da2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e376867-2a05-49c2-b249-daa9012d7da2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce825c25-12a7-4e9b-a9d6-9e2ac27b9088' class='xr-var-data-in' type='checkbox'><label for='data-ce825c25-12a7-4e9b-a9d6-9e2ac27b9088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.000684, 0.001369, 0.002053, 0.002738, 0.003422, 0.004107,\n",
        "       0.004791, 0.005476, 0.00616 , 0.006845, 0.007529, 0.008214, 0.008898,\n",
        "       0.009582, 0.010267, 0.010951, 0.011636, 0.01232 , 0.013005, 0.013689,\n",
        "       0.014374, 0.015058, 0.015743, 0.016427, 0.017112, 0.017796, 0.01848 ,\n",
@@ -577,63 +519,34 @@
        "       0.119781, 0.120465, 0.12115 , 0.121834, 0.122519, 0.123203, 0.123888,\n",
        "       0.124572, 0.125257, 0.125941, 0.126626, 0.12731 , 0.127995, 0.128679,\n",
        "       0.129363, 0.130048, 0.130732, 0.131417, 0.132101, 0.132786, 0.13347 ,\n",
-       "       0.134155, 0.134839, 0.135524])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f328b8e-a1b9-4175-af53-1260915ac199' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f328b8e-a1b9-4175-af53-1260915ac199' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       0.134155, 0.134839])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcbf2a56-6adc-4212-9e81-ac2e670ee01e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dcbf2a56-6adc-4212-9e81-ac2e670ee01e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'px' (time (y): 199)>\n",
-       "array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "...\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        0.00000000e+00,  3.33066907e-16,  4.44089210e-16,  1.11022302e-16,\n",
-       "        1.11022302e-16,  1.11022302e-16,  3.33066907e-16, -1.11022302e-16,\n",
-       "       -3.33066907e-16, -4.44089210e-16, -2.22044605e-16, -5.55111512e-16,\n",
-       "       -3.33066907e-16, -4.44089210e-16, -5.55111512e-16, -4.44089210e-16,\n",
-       "       -7.77156117e-16, -4.44089210e-16,  0.00000000e+00,  0.00000000e+00,\n",
-       "       -1.11022302e-16,  0.00000000e+00,  3.33066907e-16,  1.11022302e-16,\n",
-       "        1.11022302e-16, -2.22044605e-16,  0.00000000e+00, -5.55111512e-16,\n",
-       "       -3.33066907e-16, -1.11022302e-16, -4.44089210e-16, -4.44089210e-16,\n",
-       "       -3.33066907e-16,  2.22044605e-16,  0.00000000e+00,  0.00000000e+00,\n",
-       "        2.22044605e-16,  1.11022302e-16,  2.22044605e-16, -1.11022302e-16,\n",
-       "        3.33066907e-16,  6.66133815e-16,  8.88178420e-16,  1.22124533e-15,\n",
-       "        3.88578059e-15,  4.10782519e-15,  1.02750769e-03,  1.03179676e-03,\n",
-       "        1.03606675e-03,  1.04031758e-03,  1.04454916e-03,  1.04876143e-03,\n",
-       "        1.05295429e-03,  1.05712769e-03,  1.06128153e-03,  1.06541574e-03,\n",
-       "        1.06953025e-03,  1.07362498e-03,  1.07769985e-03])\n",
+       "<xarray.DataArray 'vx' (time (y): 198)>\n",
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
        "Coordinates:\n",
-       "    id        int64 2\n",
-       "  * time (y)  (time (y)) float64 0.0 0.0006845 0.001369 ... 0.1342 0.1348 0.1355"
+       "    id        float64 100.0\n",
+       "  * time (y)  (time (y)) float64 0.0 0.0006845 0.001369 ... 0.1335 0.1342 0.1348"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "swiftdiff['px'].sel(id=2)"
+    "swiftdiff['vx'].sel(id=100)"
    ]
   },
   {
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/init_cond.py b/examples/symba_swifter_comparison/9pl_18tp_encounters/init_cond.py
index 321c79932..18ef4ce48 100755
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/init_cond.py
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/init_cond.py
@@ -37,6 +37,8 @@
 sim.param['OUT_STAT'] = "UNKNOWN"
 sim.param['GR'] = 'NO'
 sim.param['CHK_CLOSE'] = 'YES'
+sim.param['RHILL_PRESENT'] = 'YES'
+sim.param['MTINY'] = 1.0e-12
 
 sim.param['MU2KG'] = swiftest.MSun
 sim.param['TU2S'] = swiftest.JD2S
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swifter.in b/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swifter.in
index aa33eeaa4..d87472e35 100644
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swifter.in
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swifter.in
@@ -21,6 +21,6 @@ CHK_QMIN_RANGE   0.004650467260962157 1000.0
 EXTRA_FORCE      NO
 BIG_DISCARD      NO
 CHK_CLOSE        YES
+RHILL_PRESENT    YES
 J2               4.7535806948127355e-12
 J4               -2.2473967953572827e-18
-RHILL_PRESENT    YES
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swiftest.in b/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swiftest.in
index 6504c9637..e9ed6376c 100644
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swiftest.in
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/param.swiftest.in
@@ -25,6 +25,7 @@ DU2M             149597870700.0
 EXTRA_FORCE      NO
 BIG_DISCARD      NO
 CHK_CLOSE        YES
+RHILL_PRESENT    YES
 FRAGMENTATION    NO
 ROTATION         NO
 TIDES            NO
@@ -32,4 +33,4 @@ ENERGY           NO
 GR               NO
 YARKOVSKY        NO
 YORP             NO
-MTINY            0.0
+MTINY            1e-12
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.in b/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.in
index bd980fc4b..fea43297f 100644
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.in
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.in
@@ -1,33 +1,33 @@
 8
-1 4.9125474498983623693e-11
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 1.6306381826061645943e-05
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 4.0453784346544178454e-05
--0.7188115337296047125 -0.0118554711069603201795 0.041316403191083782287
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 2.265740805092889601e-05
--1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
--0.0051275613251079554117 -0.011607719813367209372 -0.000117479966462153095864
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 0.00038925687730393611812
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 0.00016953449859497231466
-14.856082147529010129 13.007589275314199284 -0.14417795763685259391
--0.0026158276515510360365 0.0027821364817078499815 4.40781085949555924e-05
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+-0.0026245225263081049631 0.002774730265364384104 4.416262654344997005e-05
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 0.000164587904124493665
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diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swifter.in b/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swifter.in
index 701e9a14f..79614abb4 100644
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swifter.in
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swifter.in
@@ -2,35 +2,35 @@
 0 0.00029591220819207775568
 0.0 0.0 0.0
 0.0 0.0 0.0
-1 4.9125474498983623693e-11 0.0014751243077781048702
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 1.6306381826061645943e-05
-0.33206272695596028566 0.07436707001147663254 -0.02438290851908785084
--0.0115920916602103591525 0.028710618792657981169 0.0034094833969203438596
-2 7.243452483873646905e-10 0.006759104275397271956
+-0.065841771551149230746 0.30388831943526661838 0.030872485461978960153
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+2 7.243452483873646905e-10 0.006759080797928606587
 4.0453784346544178454e-05
--0.7188115337296047125 -0.0118554711069603201795 0.041316403191083782287
-0.00021427347881133320621 -0.020313576971905909774 -0.00029114855617710840843
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 4.25875607065040958e-05
-0.35677088372527121507 -0.95189300879814897627 4.4027442504036787155e-05
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 2.265740805092889601e-05
--1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
--0.0051275613251079554117 -0.011607719813367209372 -0.000117479966462153095864
-5 2.825345908631354893e-07 0.35527126534549128905
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 0.00046732617030490929307
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-6 8.459715183006415395e-08 0.4376527512949726007
+4.1148395833578952363 -2.8938323061728068453 -0.080043092204059404504
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 0.00038925687730393611812
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 0.00016953449859497231466
-14.856082147529010129 13.007589275314199284 -0.14417795763685259391
--0.0026158276515510360365 0.0027821364817078499815 4.40781085949555924e-05
-8 1.5243589003230834323e-08 0.7812870996943599397
+14.816779495279050138 13.049265812461410263 -0.14351615042000470668
+-0.0026245225263081049631 0.002774730265364384104 4.416262654344997005e-05
+8 1.5243589003230834323e-08 0.7813355837717117843
 0.000164587904124493665
-29.55744967800954015 -4.629377558152945049 -0.58590957207831262377
-0.00046987400245862169295 0.0031274056019462009859 -7.51415892482447254e-05
+29.564459991843019537 -4.5824598513731222837 -0.5870359532621901577
+0.0004648344125208179762 0.0031282868879460171488 -7.5042704502708602616e-05
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swiftest.in b/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swiftest.in
index bd980fc4b..fea43297f 100644
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swiftest.in
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/pl.swiftest.in
@@ -1,33 +1,33 @@
 8
-1 4.9125474498983623693e-11
+1 4.9125474498983623693e-11 0.0014751238438755500459
 1.6306381826061645943e-05
-0.33206272695596028566 0.07436707001147663254 -0.02438290851908785084
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-2 7.243452483873646905e-10
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+2 7.243452483873646905e-10 0.006759080797928606587
 4.0453784346544178454e-05
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-0.00021427347881133320621 -0.020313576971905909774 -0.00029114855617710840843
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 4.25875607065040958e-05
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 2.265740805092889601e-05
--1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
--0.0051275613251079554117 -0.011607719813367209372 -0.000117479966462153095864
-5 2.825345908631354893e-07
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 0.00046732617030490929307
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 0.00038925687730393611812
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 0.00016953449859497231466
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--0.0026158276515510360365 0.0027821364817078499815 4.40781085949555924e-05
-8 1.5243589003230834323e-08
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 0.000164587904124493665
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diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb b/examples/symba_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb
deleted file mode 100644
index d0d223ce7..000000000
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb
+++ /dev/null
@@ -1,753 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import swiftest\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.swifter.in\n",
-      "Reading in time 3.652e+03\n",
-      "Creating Dataset\n",
-      "Successfully converted 333 output frames.\n",
-      "Swifter simulation data stored as xarray DataSet .ds\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile = 'param.swifter.in'\n",
-    "swiftersim = swiftest.Simulation(param_file=inparfile, codename=\"Swifter\")\n",
-    "swiftersim.bin2xr()\n",
-    "swifterdat = swiftersim.ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swiftest file param.swiftest.in\n",
-      "Reading in time 3.652e+03\n",
-      "Creating Dataset\n",
-      "Successfully converted 333 output frames.\n",
-      "Swiftest simulation data stored as xarray DataSet .ds\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile = 'param.swiftest.in'\n",
-    "swiftestsim = swiftest.Simulation(param_file=inparfile)\n",
-    "swiftestsim.bin2xr()\n",
-    "swiftestdat = swiftestsim.ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftdiff = swiftestdat - swifterdat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftdiff = swiftdiff.rename({'time' : 'time (d)'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftdiff['rmag'] = np.sqrt(swiftdiff['px']**2 + swiftdiff['py']**2 + swiftdiff['pz']**2)\n",
-    "swiftdiff['vmag'] = np.sqrt(swiftdiff['vx']**2 + swiftdiff['vy']**2 + swiftdiff['vz']**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plidx = swiftdiff.id.values[swiftdiff.id.values < 10]\n",
-    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['rmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric position differences \\n Planets only\")\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-planets-rmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['vmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric velocity differences \\n Planets only\")\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-planets-vmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['rmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric position differences \\n Test Particles only\")\n",
-    "legend = ax.legend()\n",
-    "legend.remove()\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-testparticles-rmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['vmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric velocity differences \\n Test Particles only\")\n",
-    "legend = ax.legend()\n",
-    "legend.remove()\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-testparticles-vmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
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-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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-       "\n",
-       "html[theme=dark],\n",
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-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
-       ".xr-text-repr-fallback {\n",
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-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
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-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
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-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
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-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
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-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
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-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;rmag&#x27; (time (d): 333)&gt;\n",
-       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "...\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13180114e-12,\n",
-       "       6.30252092e-12, 1.12657932e-11, 1.70947866e-11, 2.35410127e-11,\n",
-       "       3.01486367e-11, 3.63634702e-11, 4.16224366e-11, 4.54289913e-11,\n",
-       "       4.74142910e-11, 4.73824194e-11, 4.53327404e-11, 4.14594589e-11,\n",
-       "       3.61300773e-11, 2.98446324e-11, 2.31845539e-11, 1.67548923e-11,\n",
-       "       1.11262399e-11, 6.78147816e-12, 4.07218435e-12, 3.25977426e-12,\n",
-       "       4.52137637e-12, 7.66342713e-12, 1.23344633e-11, 1.81013732e-11,\n",
-       "       2.44264806e-11, 3.07065663e-11, 3.63320360e-11, 4.07478190e-11,\n",
-       "       4.35128453e-11, 4.43475549e-11, 4.31649567e-11, 4.00801554e-11,\n",
-       "       3.53984592e-11, 2.95862328e-11, 2.32329074e-11, 1.70175537e-11,\n",
-       "       1.17040422e-11])\n",
-       "Coordinates:\n",
-       "    id        int64 2\n",
-       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 ... 3.63e+03 3.641e+03 3.652e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'rmag'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time (d)</span>: 333</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3692e8f7-8e63-4397-874d-ca335f0335ef' class='xr-array-in' type='checkbox' checked><label for='section-3692e8f7-8e63-4397-874d-ca335f0335ef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 ... 2.959e-11 2.323e-11 1.702e-11 1.17e-11</span></div><div class='xr-array-data'><pre>array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "...\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13180114e-12,\n",
-       "       6.30252092e-12, 1.12657932e-11, 1.70947866e-11, 2.35410127e-11,\n",
-       "       3.01486367e-11, 3.63634702e-11, 4.16224366e-11, 4.54289913e-11,\n",
-       "       4.74142910e-11, 4.73824194e-11, 4.53327404e-11, 4.14594589e-11,\n",
-       "       3.61300773e-11, 2.98446324e-11, 2.31845539e-11, 1.67548923e-11,\n",
-       "       1.11262399e-11, 6.78147816e-12, 4.07218435e-12, 3.25977426e-12,\n",
-       "       4.52137637e-12, 7.66342713e-12, 1.23344633e-11, 1.81013732e-11,\n",
-       "       2.44264806e-11, 3.07065663e-11, 3.63320360e-11, 4.07478190e-11,\n",
-       "       4.35128453e-11, 4.43475549e-11, 4.31649567e-11, 4.00801554e-11,\n",
-       "       3.53984592e-11, 2.95862328e-11, 2.32329074e-11, 1.70175537e-11,\n",
-       "       1.17040422e-11])</pre></div></div></li><li class='xr-section-item'><input id='section-0774eb86-e07d-42e2-a849-c819e3e57bbf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0774eb86-e07d-42e2-a849-c819e3e57bbf' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-cd565590-0ab9-465f-8b17-8b70177e0e8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd565590-0ab9-465f-8b17-8b70177e0e8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-817c1952-dcbb-4f45-8c25-97fe440b8671' class='xr-var-data-in' type='checkbox'><label for='data-817c1952-dcbb-4f45-8c25-97fe440b8671' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time (d)</span></div><div class='xr-var-dims'>(time (d))</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 11.0 ... 3.641e+03 3.652e+03</div><input id='attrs-07ccb926-0a7b-4274-bd2e-c44cf8b7f23b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07ccb926-0a7b-4274-bd2e-c44cf8b7f23b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e81aec9a-1cd1-4af7-819e-223d5d283d94' class='xr-var-data-in' type='checkbox'><label for='data-e81aec9a-1cd1-4af7-819e-223d5d283d94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0.,   11.,   22., ..., 3630., 3641., 3652.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fcfd1eae-6bea-400b-88cf-526cf12b1a38' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fcfd1eae-6bea-400b-88cf-526cf12b1a38' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'rmag' (time (d): 333)>\n",
-       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "...\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13180114e-12,\n",
-       "       6.30252092e-12, 1.12657932e-11, 1.70947866e-11, 2.35410127e-11,\n",
-       "       3.01486367e-11, 3.63634702e-11, 4.16224366e-11, 4.54289913e-11,\n",
-       "       4.74142910e-11, 4.73824194e-11, 4.53327404e-11, 4.14594589e-11,\n",
-       "       3.61300773e-11, 2.98446324e-11, 2.31845539e-11, 1.67548923e-11,\n",
-       "       1.11262399e-11, 6.78147816e-12, 4.07218435e-12, 3.25977426e-12,\n",
-       "       4.52137637e-12, 7.66342713e-12, 1.23344633e-11, 1.81013732e-11,\n",
-       "       2.44264806e-11, 3.07065663e-11, 3.63320360e-11, 4.07478190e-11,\n",
-       "       4.35128453e-11, 4.43475549e-11, 4.31649567e-11, 4.00801554e-11,\n",
-       "       3.53984592e-11, 2.95862328e-11, 2.32329074e-11, 1.70175537e-11,\n",
-       "       1.17040422e-11])\n",
-       "Coordinates:\n",
-       "    id        int64 2\n",
-       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 ... 3.63e+03 3.641e+03 3.652e+03"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "swiftdiff['rmag'].sel(id=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "swiftestOOF",
-   "language": "python",
-   "name": "swiftestoof"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/swiftest_symba_vs_swifter_symba.ipynb b/examples/symba_swifter_comparison/9pl_18tp_encounters/swiftest_symba_vs_swifter_symba.ipynb
new file mode 100644
index 000000000..b3b31d703
--- /dev/null
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/swiftest_symba_vs_swifter_symba.ipynb
@@ -0,0 +1,753 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import swiftest\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading Swifter file param.swifter.in\n",
+      "Reading in time 3.652e+03\n",
+      "Creating Dataset\n",
+      "Successfully converted 333 output frames.\n",
+      "Swifter simulation data stored as xarray DataSet .ds\n"
+     ]
+    }
+   ],
+   "source": [
+    "inparfile = 'param.swifter.in'\n",
+    "swiftersim = swiftest.Simulation(param_file=inparfile, codename=\"Swifter\")\n",
+    "swiftersim.bin2xr()\n",
+    "swifterdat = swiftersim.ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading Swiftest file param.swiftest.in\n",
+      "Reading in time 3.652e+03\n",
+      "Creating Dataset\n",
+      "Successfully converted 333 output frames.\n",
+      "Swiftest simulation data stored as xarray DataSet .ds\n"
+     ]
+    }
+   ],
+   "source": [
+    "inparfile = 'param.swiftest.in'\n",
+    "swiftestsim = swiftest.Simulation(param_file=inparfile)\n",
+    "swiftestsim.bin2xr()\n",
+    "swiftestdat = swiftestsim.ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "swiftdiff = swiftestdat - swifterdat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "swiftdiff = swiftdiff.rename({'time' : 'time (d)'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "swiftdiff['rmag'] = np.sqrt(swiftdiff['px']**2 + swiftdiff['py']**2 + swiftdiff['pz']**2)\n",
+    "swiftdiff['vmag'] = np.sqrt(swiftdiff['vx']**2 + swiftdiff['vy']**2 + swiftdiff['vz']**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plidx = swiftdiff.id.values[swiftdiff.id.values < 10]\n",
+    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['rmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric position differences \\n Planets only\")\n",
+    "fig.savefig(\"symba_swifter_comparison-9pl-18tp-planets-rmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['vmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric velocity differences \\n Planets only\")\n",
+    "fig.savefig(\"symba_swifter_comparison-9pl-18tp-planets-vmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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f7yiFDjro4P2C7Q8/wDe/8AnioZm2fvf8D/+FXY8/zPaHH9C+mz6hLA1eKhaYPqlnmfBzi3cyXfUelNW1VgohxoQQXxJCfFUI8dV3ag4ddNBBB5UY27+Xk7t3ntXv7vv//oQzhw5o3+1/4VkAXn2g6UJ/NXD6/AAcfGmuo/n0qRPa52Rktu35vVW8k1lJn5JSDkgpzVLKBVLK70opvyWlrAk2Syk/L6W8952aWwcddPDLj1wmzRtbfkGpOBfGfOq7/8Bjf/9XlEotQ5saZKnEA//rv3Ny904OvPCM8p2UJGeVpEtVQehBsVAgGY0AEBo7rc0tePI4C1avBSBV3v5O4j1DJXXwy4sjr0/x6i+OUyp11v7o4K1j20/vZvzwwbZ/t/ORB3nmX7/NyT2Kh5CMzBIaO00qGmlrvEwyQS6dBmDy2BEAIpPjpOMxfH0D5DNp8pmMrrGSkTBIyYI1aynm84THx8hl0sxOjjN83nqEwfDL7TF08G8T+144wxPf3cf2R07y0r1H3+3p/FIhl0m33CefK7J/2zi5dKHpfqEzo1WWtJRS1/hvN47teI2nvvuP2t+hsVFeue8e3tjyi7bGKRWL7HlaqZsd3fem8v/+N7XtR159SfdYqgXv7e0jeOoEhVxOUyxLL7pE2ScWBaBYKNFsMbR4KATAkvM3AYqnMH3iGEhJ35JlOL2+jsfwy4hMMkFitlVdnz4UcrkqTvOtQErJ8Z2vU8jnz+r3pVKRTDKh/X3m4H5iM9V1iVJKdj01Su8iD6s+MMCbz4+RSdYeL5/NcOjlF7QXKDo9xdTxs1MipVKRbT/9IbMTZ5S/i0Umjx5u+nI2w6sP/JS9zz2l/R2bmSabSp3VWHuefpz7/vy/IUslALKpFIdefvGsLMJTe3bx91/8FNMnjwMKJfHMv36bl++7R9unWCjx8N/t5tkfHOS+/72joXJIRSPc9R+/xpZ/+H/adw//zf/i77/4SYqFs3s+KpHPZnj15z/TbUVXYveTj7L7iUeYGVU6RB95dRugPG/tYOr4UeKhIEazmdN7dwMwuncPFruD/qXLtfH1QL1fSy+8hFKxyPTJY4wfPoDF7mDBmnUApGIRQuMJfvDHL/Pzv3qDTKL+dUyEFcUwsnYDRpOJ6VMnmDh6GICB5StxeP0dxfB+RyGX46Wf/ahKyP3i//0FP/sf/+WsxjvwwrNs+ce/JjQ2CsCrP/8pP/6T/0R4/EzbY4XOjPKTb/wBL/3sRwCcenMXD/zlf2fX4w+3PVYhn+cH//nrfPe3vkKxkCeXTnHvn/1XHvnb/1MlgCeORolMpVh71RDrrh6iVJQc21lb1P7iPd/n4b/+S07u2gHAff/fn/DDP/xtghUBOL04+vorvHLfj3ntQSVEteORn3P3f/mPPPb3f9W2ckjForz0s7t54Ud3USoWCY2d5ju/8UW++YWPEzx9su257X7yUU7u2sHYwX3IUol7/uvv8fBf/wU7Hn2w7bGO73yNUrHAG1uU+/f8D7/LG1t+wUs/vVvb58TuGcaPRFh79RDh8SSHXp2sO9b4kUMAHHjxOfK5LKf27OLwyy9QKhaJTNX/TTMc2PY8x3a8pv297ad38+I932uLeweFyx8/rBhCh195EYAjr70MQDwUrDFEmiEViwCwcP35TJ88TioWZXT/HhasPg+Hz0+6bOHrgRoTWLbpUkDxQCYOH2Rg+UqcPp9yvEiEx/9pL6ViickTUV7+ef3MokRYyWDy9vbTv2wFo/v2MHnkEN7ePhweLw6fr0Mlvd9xet9uXr73Rzz813/J6Td3M33yOKff3EX4zGjbXkMhl+PRv/8r9j33FNsfvp9iocCbZVf4xBvtL0z07F3/xNiBvbx83z0UC3n2la3g/c8/3fZYu594lJnRU2TiMU7t2cWR116mkMsyfmi/Zo0BnHxzBoNRsOzCXnpG3Pj6HBzdUf0yR6YmNeF28KWtJGbDmrW/9e5/bXtu239xP6BQA4VcjoPblEyPAy88SzaVbGusw69so1QskopGOLXnDQ6+9IK2baZNpZWYDWspiHufeYKJo4cJjZ0GIHoWwnf0gNKL8uC250nFohx44TkAjCaT5pEcfGUCp8/KlZ9YQfewi/3bxuuONVm2UNXx3nxmrlVZZLL+b5rh0b/93/z8f/0p6XiMUqnIoXK2TTQ41dY4oTOjZJNJDEYjh1/ZRiIcYvrkMVZediUAZw7s0z1WJqF4t6svvxqk5M2nH2d2YpyRtRtweLxtFZKlIhEAuhcupnfxUg6+tJXg6CkGV6zC4fEBMHl8itnJFJd+eCkrNvVxZPsU+WxtgDsemsFktWJ1Olm88SKmjh/l2I5X6V+6AgCn168poncSHcVwDhGfmWvPcWDbc+x45OegFOy17fomZpWgFMCR117i0MsvkIzMYjSbObGrfcUQnZ7EYneAlBzctpWjr7+Cw+sjePpkW240wOzkOGabHavTycGXtrJ/6zN4enoxmkxVqX/jRyL0LfJgthoRQjC8JsDk8SilYknbZ2b0FFKW6BlZxNHXX2bvM08A0LNwMdHp9gRJIZ9n4sghBlesJptK8vov7mP65DF6Fy8FIDnbnuV1bMer+AcGsbk97H7qMY68uo2+JcsAiJcpAL1Q79nQqvM49MqL7H7iEQxGE31LlhEPtdfWJZ2IEzx1gqUXXUohl+Xxf/xrMok4izdeSLFQIDEbJpPMc3pfmJWX9GEwCFZdNsDMaILIVC0NNnH0ED2LlhAYXMAbWx7m2M7XWFEWvrNteqe59Nz4r//ifsb279XoknY9QNVb2HjTBwmNndY8q4s+eCdAW8+HqhgWrj8fpz/Aiz/+PgDD563H7vaQjsd0e5TJ6CwGowmb08WSCzYxc/okSMng8lU4PF4ARg+cwWQxsOzCXlZfPkg+U+T4G7UeTjIyi8sXQAjB4vMvAhRacPi89QA4fD5SkchZU6Fni45iOIeIh2YQBgNrrryWfc89xf6tz7Bh8y2YrFbOHNRv3cCci3nBrXeQTSZ57Jv/F//AIOs338zY/jfbS6+TksRsmDVXXYfd7eHJ73yTQj7H1Z/9EqAI+naQiszi6e5h9RXXcPDF5zm9dzcbbri1ig/NZQoET8UZWO7TfjewxEshV2JmbC42kYoqwvqyj/4quXSabT/9IUOr1rBgzdq2XWiVDlh95bV0jyzipZ/ejcls4cJb7wDKGSBtIBEOERga5oKbP8Sx7a8SGjvN+s03Y7HbScy2pxgikxMIg4EbvvKbFPN59r/wLIs2XkD3yKK2C6JUQbTxxlvpW7KM4ztfx+p0sn7zLYBiBEwciyJLkpHzugAYWRMAYPxopGa8qeNHGFi2grXX3kDw5HEK2Swbb7wVm8vd9rMRPjOmfd6/9RkOv/oSJouVFZdc3rZiiAWnEQYDm27/CKB4g06fn76lyzHb7G1Z+Wo8zOp0svSCiwEleNwzsgi720MxnyevM9ieikRw+HwIIVhx6RUYTSZWX3ktI+s2YrbZMFmthMeDDK8OYLGZGFjqxe42M3qw9nnOJBPYXC5AMYZWXHI5l330U6y7/kZA8RhKxUJVPO+dQEcxnEPEZoK4Al1cVH6QQbFu+hYvY+pEe9WLqkW69tobtEyHTXd8FF/fIMVCQbOA9CCXTlHIZvH29HLT136bUqHImiuu0fKk2+FXARKRME6fjys++Tnc3T34+ga44NY7cHh9WjbG1MkYpZJksEIx9C9VrKnJ43PHU93yxRdsYsMNt2AyW7jhy7+J0+snl06Rz2V1z0tVSk6/n6s//QUMRhPXf+lr9C9T3PJ26bx0PIbd7eX8Wz6E0x/gvKuvZ921N+IKdJMItacYsskENqeLrqFhzrtmMwvWrOXGr/wHPN09JGbDFAvNs4YqkUnEAXB4lXuwaMMFfOyP/4zA0DCgWNITRyMYjIK+RR4AfH0O7G4z40ciVWMVcjmyySSe7l7WXX8T66+/mQ/+9u8zvGYd/oHBtqmk0BklHnb5xz9DcjbM7iceYeH68xlcuZrkbLitQGo6HsPmcuPyB1i4/nwAzr/5QwghFCu/fB30IJtMYHU6MRiMXPqRT3LdF7/Kr/7ZXyEMBuxlK1+voklGZ3F6fQD0jCzi69+/j1t/83cxGI0A2F1esqm49uwLIRhY5mOijlJW5uXS9vvQf/xDPvCxT2MwlMfyKPcvHXtneya9l3olve8RDwVxd/XQM7KIr33nbmLBaXz9Azi9PoJt0jWq++3p7uH2//hHTBw9zOCKVVp1ZDoe09zW1mMpAtHpD7D0wov5d//nm3h7+7Xt7T50qcgsA8tXYXU4+exf/A1SljCZzTi8Xs3KD55SXlpVMAG4AzZcfiuTx6Ksv1YRYsloBKvDicls5vovfo0PfPwzODxeJsoB0VQkgre3T9e8VAXn8PgYWrma3/iXe7DY7FoWUbINxSClJB2L4fB4sDldfPmb38VoMgPgCnRp90cv0okENpcbgJu/9tva9+7uHpCSRDik+zxV69HmdNG7aAmLNlwAKFQaQigewwkvvQvdmCyKgFGF03zFoI3lcmFzurjhK7+pbfP3DzK6v+m6WjUInxnFYDRywa23s+uJR8imUpx/8we17KbI1CSOslBteZ6JBPbyNbv9d/+IQi6nPfN2t5tMOx5DIo6tLIDdXd2cf9MHtW1295zwrXwvGiEVieD0+7W/VYWgwmh2QilVZRQNLvNx/I0g8XAGd8CmfZ9NJnF39zY8ljrnbMdjeP8iHprB0610e3V4vPQvXQ4oWr+dhxggEZrBbLNjsTswGI0MrVytWUqg37qBOQrF5VfohK4FI5gsFkwWC2abXbPy9UBKSTISwVkey+ZyaXNyeOZyrmfGErj8VmxOc9Xvu4fd86ikiCYohMGgvfiOcnZHO3SSeh4OrzKGxWZX/rfbMVttbXkM2VSSUrGgWZOqUgBwB7qIt0klqR7DfHi6FKFQGZ9qOVZCpUWqxzOZzbj8ASJTk0yfijOw1Fe1fWCpl3goQyqWq5pXvbFAUYDJyGxb/HZ4fAxf3wAWu4Nf+7vv8h/u+ikL123E7lKekUxSv5WfScQ0ZWqx2asMIZvL3ZbHoFA27rrb1Oc3Fdf3HqRikabKTUo7kKZ7wdw1VZXE5LHqY2RTSWxNmu6p96VDJb1PIUsl4jMzuLu6a7bZXB4yiYSWLaIHiXAIV6CLcrdZDWejGFSB6PR31WxzeDxtUUm5dJpCLqv1d6kay+cjFY0ipWRmLFH1YqjoGnISmU5TyCsxkkYvmdOrjJ+MtqEYykppviclhMDp97flMcx5H7VemSvQTXI23FacJ5NIYHXVXg932ZCItRGAziSTCIMBi91eOzd/gOhUiFJR0jNSLQjV+xGqUMwqJVlPaVmdLkrFAoWsfjovGY3gKr8DJotFs6ZVHr0dCjSdSGBzNxbmmXh7iqGe8oO5e6zXc26mZAAKBQvCkMVgnBOvgUEnBqMgOFo950yZ4moEW0cxvL+RikUpFQvai14Ju9uNlKW2CqPi4RncgVpBXun26oVKe7j8tcLc7vG25TGoFnxdxeDxUioWSEVjRKZSdNVVDC5kSTI7oVyLVCSi8bWVUMdPtekxGE0mJftqHlz+rra8D1Xxqte7aqxAF7JU0uIjetDIY3AFFM+rHaWVScSxOl01RgMowjwVU4RP11D18boXKMIsODYnnCppqfk4G6GUTda3gFVBmmnHyo/HNSqp3njpRDtUUv3rDxU8vg5jq1jIU8hmG1r5siTJpY3IUnUxn9FkwD/grFLKhVyOYj7fUGHBnELNtqFQzwU6iuEcQRU6Ll+gZpv6UrTzICfCYVx1FUN5rLaopFmNlpoPh8fblpJRaSnVoq8aqyzgJ49NIUtSE0SVUIVVaFx50FPRiEYbVUKlcJJtCN9UNIrd66srMJ0+f1uZRKnyNbHX8RhUfrkdRdPIYjVbbRiMxraEbyaZwNbAyrQ5XWQSCYwmA76+ao/C5jLj8lurhFO2IltnPqxnwW9nU8kGYzlBCNLtWPmJeBP6x002maxq49F0XhXZP/Oh0LUmXZ6zatw1svJjoTSyZKFUyNYkFPQscFXRqGpdTaN7CWhrO7Rbg/NW0VEM5wjNuFrV6mzH9U0nYnWFktmqpMO1pRhmw1pFZu3cvLq5VajwGOp4H6pimD6lFGz5B2oVka/XjtFkIDSWoFjIk0km6lJJRpMJu9ujpbPqQToebRiQd/raay3QjErSAoI6X1ZZKimWdJ1nQwiB1eFsT/g28D5AsTBzmWSZuqh9vbvnCadMk+f2rDyGRH0FaDAYsTmcuj2GQj5PPptpGRfQM56Usir4PB9K7M6t651q9p4DhMaSIJTg8vzno2uBi1Qsp8V4VFqt2cI+JosFk9nSoZLer8iUH4J6lkS7HkMrd1XhV9vL4VaDfzVjlWMMegOMqaga4PXVbFOF6OzEDAjw9tRy4AajAV+fg8hUShurnvehHqM9j6FxUNDqdJJLp3XHBVR6TaUZqsZSrbikPsWQy6SRstTYyne5yOgcC5rz5Vani1IhTWCo/rECQy4ikymK5SLDZjEGLS6gc26FXI5CPtdQ0Nlcbt2KQd3P3iDGYNNiba3Hy2czlIrFFpSNW5fwVe95o3OcGYsjDNbyvtXjzY/xZFPNlYwKq8vVVmzmXKCjGM4R1Aem3suvPtx6PYZW7qpaqal7bg3ce1CEeTvFPdrDXOfFUIVyNBjG7bdhMhtr9gHw9dmJTKe1c2gUYLS5XG250KlYFEedmIAyX+Xly6X0nWc6HsNksWK22mq2aZZ0St/Lqgq5Rtav1ek6Zx6DyWIHJJ6u+tfe3+egVJLEZzLaWBa7vSblEioVoL65zVEjDbwZdxuKQX02GnkMbRhbmvJrEjDW67U1MwABZqdSOLxqBlb1eP4Bp7YPVMqM5orB1ubzcS7QUQznCHOWRB3Lqw3rRhmruSVhd3vaigtkUyksjbyPspWf0jleNpXCZLViNNWWwKgPeDISq+G3K+HrdRALpkk3sVZBfVnbsKQTce1a14zlVLlafS9YOhat6y1UjaVzbhpl0IDjtjld7cUYEo358lLRAoDDU98D9PUp9J4qnJp6H21mEmVbCM32PIbmwrwdenZOMTcP8mYSre+nJswbvE+RqRSebp+y77zr5vBYMNuMWluSTJP4TiWsbT4f5wIdxXCOoAoci6NWINocToQwkNFJJc0pmfoLgLftMSQTTWkp0J8t0ijrBBQ+1Gi2kI4l8PY2XrzcV7ZaIxNK/KCRW251unQL8lKxSC7deCF29eXTS4s0S0m02OwghG5vplnmjzI3/RahlLKpMM/nFIVtc9RPjVYVQ2RyTjg1VszKvnrvgfbctgiM64HqCTS6B+1kOWVbXH/QL3w1o62OkpFSEplO4+vzKXObN54QAn+fg8hUsjxWc1pKhc3p7CiG9yuyyaSS3WCodcmFwYDV5dLvMahUUhNhfq6opHat32yqsVACRZgU82l8LRQDQGSqrBjOgceQLTdva8jjtxkXaKYAhcGA1eFoY6zmgsnmdOoWmPlMGlkqNRwrm1KeP7OlfosNm9OM3W2uEE6NFYPBYMTq0C+UNKFZx2uG9lJM1evRKF11TtHroH+Szb0PKNM1OhSgNladc0xFcxSyRQIDXeV9a58PX59jjkrSPKx/w1SSEOJfhBDTQoi6NfZCiE8LIfaU/70khNjwTs3tXCCbSjbV/PY2KjVbBaWsThfZdEpXwZyWK90oIOhoj2LJJpufp8niQMos3t7mVBJAdCYCNLMwnWRTKV2B8TlrtfE1gzbOs4kyBUX46X1Zm2X+qN9nU0ld59lqrExSeaVz2cZKq1I4ZRpkEVXNTafSauUZaSmmOhIAWtGpVrtD8dr0KIYWlKV6nGyq9TuVTSltwE1Wa802lSLqHu6qOodK+PocJMJZ8jlloSuTxYrJbK7Zr2puv+TB57uAm5tsPwFcLaVcD/wP4J/eiUmdK7QSJO1o/UwLF9PmdILOpRdbWSVq7EEvxdLqPA1GG8is5hXUg81lxuo0kQgr1qO1Tn0FKOcvZUlXYFw7z0ZU0ll4DI0sX1CUWUYvlaTx5Y1jDKVikXy29QpnrcZKRpQajmbnqWaFKfs1jldA+TzbDD43izGAvphFNp0CIbDYaoP/oHhtNp3eTGU/qEZQ36lWRaiqYVSvViYyrfy2a4GvYYqp+l5Ep1NaY79WsDld5NKptirt3yreMcUgpdwKNCzvlFK+JKVUk9ZfARa8IxM7R2hlSVudTt2cdKvCl3aEXKsAl00LyrYjMJs8zMIKZPF01X+hVfh6HaSi8YYZMdCe0mrFb1vbPc9Ui1YFDic5nZXs2WQCg9FUN8NJmZv+IG8zWkpKSXxWVu1XD74+B+l4nkwyTyaVbJ7G6dSfSpvREWNoNTcV2WQSq92BMDQWUXot6WwygTAYMNsae7F6exI1M4wiUymMZgMunxWrq74h6O8vB/8nU63fpTLm6mbObknZs8F7NcbwJeCxRhuFEF8RQmwXQmwPBttb5OTtQqYV9eB0tWWtCtH4QW6HX22VRWG22RHCoD/DpoUgKRXNCEOubnFVJfx9DoXGaGKVt6O0mqXRAlpfIT1jtQpkg6K0dHuA5SyielYmVJxnG9ZvvXuQiuXIZ02AaPps+MtWa+hMTKmXaaEY2klXNZpMmMyWutvbaQjXyjNtZ25qcVuj66+OBa3vQTbZ+JmNTKfx9tgRBtEwNqMmZUSmUi1lhgqNBn0H6aT3nGIQQlyLohh+v9E+Usp/klJeJKW8qKentjfRu4FmwUpoL7NAtVYbPcjqg6lPYDa34oQQZW+m9dyklORSyYbZUgCFvBlk66Zr3j4H+WwaS5OxtPM8B4VHBoMRi11fwLhVIFvdpptKapJFBO0JzGY8fnQ6hRACs9Xe1JJW6YyZ0zMNx6qcm27FUD7PxgpQv4DLplINKcbKuem5B5lE86Z3lXNrdQ+aCfPIVEq7to2UltlixBWwEplKNQ38V+Ld6LD6nlIMQoj1wD8Dd0gp2+tr/C6jdbaOkmGjN5Da3PvQTyXNce/NeWQ9YxWyWaWCtEkDsXzWSKmYaXmevl4HyCxGU2PKqR36p5UCVLfpGqtFIBvay5hq1tsIKq1VHXNrEmOITCmxGKur+Xl6euwYDIKZ8VB5/1YxBv1Ukp6GcPo8hkTD2httPJ2B8WYpuSrUa9DaY6jf2qRULBELzmXjNauL8JdjPPqpJP0MwbnCe0YxCCFGgPuBz0opD7fa/70EWVI6p7aikkrFAgUdK5K1ynBqi2JpUROhbNMnMNVK30YvWTKaRUoLslSkkM/V3UeFr0/JXhIGHYpBVywlCUI0tTKVYKWea9a6h43SYkNfQLCVZdiOwMyUaUZLHZoxMpXCaDLgcLubCjij0YCnx05kUgn5taKSCrmssghQCzSrl4FKy1ePom/+Pinj6Qw+N2h5XjWWQ5/wbRQwjoUylEpSK+xsVhfh63MyO5Ui08KYVPFuLNbzTqar3gO8DKwUQowJIb4khPiqEOKr5V3+BOgC/kEIsUsIsf2dmttbRS6TBimbC5I2AsatFENbFIsOS9qm0ypsFeCNTKXKwefW5+nttYNUFEkjWNsJPqcSrYOVOnPVdXkfbbTY0Esl6Y0xWJ3OuucZmU7h7bWXrdXmY/n6HMSCEeX4TY2Q9p41PWsL6A4+6/EYkomW3qkeykbvehGN5hWdVp4DzWNoQsH5+hzk0oWG3sd8tFuBfi7wTmYlfUpKOSClNEspF0gpvyul/JaU8lvl7b8mpfRLKTeW/130Ts1tPkqlIs/c9W3OHNyva/9WArNym14rv3ngU7FK9Fq/BqMJk6U271qbm86c/FY8fmQ6jdCpGMwWI5ClVGycwz3XclinINFhYepSzDoqUttpsZFt0sIC5nLy9VbeNhImkakUvl6Hripeb6+dZEStLm4yt3bon5Y1Lvo7hbaKZYGiUIuF1l640r67uQBW2583ew/UJoH1rr+a/qvGGJrVGileRR5ZKulOV4V/o1TSewl7n32KNx77Ba8+8BNd+zerhlRha8P6bZWtoAVSdXLvzQLZ0Ab33sKSjkynMJjU7J/mD7EslZClbDmLpj6MJiXF81x4WXDuKDPQ32JDlkpkUsmmwU+1klqPRdjI+yiVJNFgGl+fXVH0Lc7T16tUqENrKgnOjccANEzjrIQs1xPo8RigucDUrn8Ly1xJwmh+3ZrVykSmU1gdJmwu89zcGtRFqPG1RmPNh8mi9Cb7paSS3k949YGfIISBk3ve0LW6mS7qQQ1u6bR+mwUr1WPpox50jKUzkNoqkB2dTuMOKL2XWo2Xy2QASS5Tv4ZBm1sbAeNz7jG0qGOo3LfhWKkUSKmvg6ae82zgfcRDGUpFibfXoXSlbUUllak8aB1kh9bWqrLmgQ7KRoc3ky+3KW+pGHSsbpZNl69/i6wkZW7NW5M0e88jUym8vQ7NAGuWUu4K2BCGrHbMVtCUVhsNJd8qOophHrKpFLHgNCs/cCWyVOLY9ldb/0YP9eBo/RCDkkOfz6SbZhFBG4FUPZa000k+m6lZcapmrBaBbOXl8AG0TCNUFWQ+ZyKbbnzcdpRWq2tmdehrJZJNJctVt60Lolop+lYtLCrH0x9jqENllKtufb0OJWCcz1HINU4A8PY6kDKD0VS/U64KvUtLFnJZSsVC6/PUUf/RqleYNpbW/rzx86F1ttWZFtpMaTUzGCLTqaqOws08LYNBaN1v9cxLHa9DJb2LiEyOA7D84sswW20ET59o+Rs9zbC0lLOWArO1taoeS5/30TrzQW9dRDNXulQsEZtJ4+9XFt3JtRqr/JIJYSU63biis5V7Xzm3lpy0w6GrlYjClbcIZOv1GHR09lS366WS6lmZlRy3nrx3l8+KEFmM5sbKT50X6Hhu9XYK1dHeWr3fzWpcKufWTNFo11+Xx9BcOTdqEljIFUmEs1WNI7Xr1uCe2l3V+7VCO61JzgU6imEeZsuKwT8wRGBomNDYaMvfNFs3V4VFtyDR94K1Q4voaesLrRVDJpnAZLZgstRmEsXDCpURGGrcWbJqXuqxhE3L6Gg0t3NJJan7Nh+reUV21VitrpmOBm7qeHromkbB5+hUCovNiN1t1nWewiAwmfPaamPN5gWtPYZ2DJrWKaHNq/Xnz62ZQp27/voqjJvNrVFxYTRYzkiq6A/WKmhvcyhpzvXWYa+7/zvcYbWjGOYhMqEoBl/fAF0LhgmfmVMMslAi+vhJJv7X60x/aze5UaVbqtYjpslNNpnNmCxW/Va5Hpdcj8BM68sHBz18eZOqz7Jw7xr0KoEynecphFWjQepBT7WyLJWU82xJv7316lYVc5lErZUpNC8iA30vfrMlKhUqQ+G49WaxGIx5SqXmisFoMmOyWltfs3O4Glk2rXoM+oLPzcbLJOO65jU3Nx3B53nPhuat1fEYGs3NbFUUQyHfOCuvEu/0Yj0dxTAPkakJXP4AZpuNwNAwiXBIa4k8e+9h4s+OYu51UAxnCH7nTXLjCbKpZNNmcCr0BIy1BnrvoMeg0SJjMSKPHif2zGlKqdqCpmZjzacy9FSQAjh9rqYeg562B1qAUa8C1EGLtLr+wmDAane0pPPmqAw9MQad3keDqmdvRQ595bEbQmYoFc2Uis1jLnr4bT1es7JdoQabFQZmE3rHah0Y17OsZ+V4mSZ1EY2aBKqGTWWr+VbK2WhSgs8pfZ34dSUUnEt0FMM8zE6M4xsYBKBrwQgA4TNjpHYFSe0K4tk8Qvfnz6P3NzZisBkJ//gQ2UTrwCfoC6TqyYhRxmodSC3k8xRyWV1BvEHHMgyPp0i8NE7siVNMfXMXxVh18LKZxxCdTmNWqQwd56m+ZN5ef1OPwVZWps2KmPTQb7JYwhw10WUdavmC5XQ3N2t9nuoaHHos6VYB40bxikKuSHw2U5VDD609hlIxA9iIh5vXAeiy8hvw7/XGgrkAcz2o8260SI8KNW27uWJQrn8rj02dmyw1bvPeqElgZCqF02vBYpsL4pusVgzGJimmMgPCRnymdScEUBbnyiQTlIrvTOvtjmKYh2R0Fpdf4cn9/YqCiIxPENtyAvMCF+7rFGVh9Frx3b6UwnQKT9ijW5C0sn4zLbqEqtD6x6cbv2A5nbyvOW/mkp4PUnQVGfjDS+j56npKiRyhew5WCeRsk86qkWmluGquKZ++rKTAgL+5x+BwKi9rk7UKWvHbxXiO6W/tofiLMJsHP4Pp+RylXOMXLKMjw0mdW2vvI6EIkyYFhqCvLUajDKdoMA0SLStGb7fWQi4NLag89XgtFX2LVvEq9GQ56c3kUsdrNZbRbMbc4vpXHq/RPWjUJDAyla5Zf0R9DxqNVcglEQa7RsG2gsPjA2hr5ca3go5imId8JqOlKWou+9EsxWgO782LEYa5h8J2XheWRR76Uguw2vVymPooltYeQ+u4QEZnEC//0iwCQXh5BKPTjHWRF+8Hl5A7ESW9K1g1t0ZxlOh0SsmNR79nZLba8PW5lXUBkvV78eiJfzSzVmVJEr7nIIXJJM4PDrMz9CTGkGD2J4eazu1ceQyZBsKkZixHa2HeaInK2KsTbHabcDx6gtjTpyvWsWg8llIxnEEIW9OsMOV4+gPGeqikVnPLJhOYrbamabSV47UaSw+NBBVKq8E9bUSlRqZTeOssTNVsLYt0PIbZ6mx57VU4vF4AUtGIrv3fKjqKYR5y6TRme7VisI2aMS9wYV3qrdpXCIH7qgVYpZ0B86KWY+sSmDpy6EEfX95qiVBAiZHsm+VIbDup4hzh6byoH/Ogk9jTp5ElqR2r3otfzJeIhzLay6ErLlAeS+Vl1cyO+dDTFyrTxGNIvjZB9ngU3x1L8V42zJHYTqJDMdL7QqT3zdTsXyoVyaVbV92qc2up6HUUfUHr9EZ1rMp9AZKvTWJ+bZKslFiG3cSePEXiqbFyxXjrYi2jpbXVqmfdZ3WZSqOpeTDVpkPRV7bJzk0kCf1wP9Pf2k3i1YkaSrFVYaCeorvKsdRzqTtWnQZ6mWSeTCJfd41zm9tNJl6/QDYdj2FxuNv2GPQU3J4LdBRDBUqlIoVcVhPKRpOZQfcyzBkzrg8M1rX6bKsCpEpxegsLWo6vS2DqyKEHfQJTD/eeePEMwmLkRGF/VSBVGATua4YpzKTJ7A9pqZL1lEx0Jo2UFQ3E9BQxla0vbf3nBpaTFmBsct3UldTm0xgyXyT29CiWRR4cF/ZhMBox2+zMeCYw9dqJPnFKU3pzY6XrjlUPetZkyCRbd/aEysr4Zh5gdfC5EEoT+cUxkk4zOw0Ger5wHs6L+0k8P0a/d2nTegFVATl9ntYeg84YQ6sAuzpW5bnUnVu5ViN3JkHwW7vJHo8is0UiDxwl9sSpqn2tjubVyq0aGM4fq9nc0vEYdren6jutsLCOx+Dw+EjF6lM/6XgMr7uLBbEsM3ftI/n6ZNM4ml31GDqK4Z1HPqPw2JXrzC72rKcoCjjWddf9jTAIRtMHcWU9FOPNW03rC6S2zqEHfQKzJfeeyJHaE8RxYS9Gh7nGirOv7cbos5J8fZJCPkexUKjvSs9L19MbY7A6XXh6bCBoaDnpaT3RiEpK7pimFM/hvXFhVauCTCqJ57oRClMpMgeqV5vV42WpsOpY3jOTTLQMooI+gZlNJqpabkefPAUCDpuNePuUflje25Zg9Fk5z3GZrmItd8Cr3b9GsDpd5NLppoFPpVGdnswfHR5gIoHN6SH8k4MYbEZ6v34+vf/hfBwX9RF/dpTM0Yi2r61F76WMToWljgWN4x/pWAyHp5o1iGrPfq2H7/B661I/UkpKyQIXGS5ksUWQnUwye98Rog8fbzg39bjpDpX0ziOXVoSTWnRSyhbpNy8ibJlGmOunospSiWOR3QgEqd3Buvuo0BNI1bvcnx6XvBXvm94dhILEdclA3UCqMAgcG3vJHJklPRWpOm4l1OCxtyLGUMznm2bYqD2cTGYjbn9jnlsPZabyuGrXWVBevsTL45iHXFgWz73MtjKdZ1/Xg9FnJfHyeN2xdFFJZQXYNPVSp8WqZ93nypbb+WCK9O4grssGCYYymmAyWI14rhvBK7qwxRvTkaoC8vb6iIcyFAuNs9tsOr2ZtiizFplEI4YVFKbT+H5lOSafDWEQ+G5fiqnLRuQXxzRPz9qEx1fH0k0llRWbmsk0H+l4DFuNx5BGGASe7jqKweMjHYvVZA7mMmku9G3GIi28mChS+OASnJcNkNg2TnpvLb0JynUTBkPHY3g3oLZKUGMMmUNhTMLMZOlUw99kUyniuRB5V4HUrumm4+sLpLZu7QCVVFJrj6FR19fU7iDmfifmfmfDNRkcF/RCqaxEqC8wI9MpbC4zNqfCL8/1EWpO/6hjeXsb89x6xtLqSAxzyjt3KkZhKoXr0oEqClCtJRFGgXNTP9mjEQqhuWPrDaKCvpXX9HLcuoLPFWMlX5kAg8B8YR/peL4q+Om4oJecIUt/drjxWOXj+PsDSAmxmWbV53qEuT7LXE3jbDZWPpmmPzOCdbkP+8qA9r3BYsRzw0IKUylNgLZaSEjv8pmgGIQGo4lUncyffCZDIZet8RgiUyk8XTaMplpR6vB6kbKkpSyrSOyaYNCxjOTiPLNFSWQ6je+DSzAPOok8fBxZR0kLgwGHx9tRDO8G8qrHUKaS0vtC5EWO6fTphr9JJ5SHqLAA8mMJ8s36/ugJpCbi2F2ehttVqNZxswKrTCKOwWjEZK1N1SuEM+ROx7Fv7NHmVneN2l4H5gUu8gfK+eB1FylJVbnSqmJramFWeEbeXgfRYAOPQR2rWVA2WZtemtoVRJgN2Nf3VH1vc7k1oeS4qA8EJHfOKfSsznRhQLMe0/H6FmapVGya4lsJo8mE2WZvyb1bnS5koUTqjWnsa7qIJ5UGhJXBT2EyMOueIUB/ldKrhHo9uxYoqdnNiwxbt8XQSyUJIVqmmPbJhZhKZjzl1PBK2Nf3YOq2k3jxDNC8mK+Qz5NLp7G59WUlCSEa0j+qQLZ7amMM9eILUEH/VAhzKSXpbdPE82HMG12YbUai02mE0YD3lsUUI1mSr082HC8V7SiGdxw5LcZgR+ZLZA6EiTujWll9Pahup3G58vKk9zVeqlqP5aW4q60fZD2L26fLL2u9oHlqj+IBOMqCs1lcwHF+L4QKeMzddV/+2clUlWBq5Rlpgeyy8PX12skmC3VTVo0mM2arrTn3nqrOFpFFSfrNGWyrAhis1RSgzeXWcsFNXiuWRV7Sb1an5II+xWDXqIf6AcasFhTXnxXT1AMsW+XpA2FKqQKOi/oaBj+TPWlAktwxVXcs9Xr2jij3v1ktg54U08pMolZolmKaz2ZZ7FhLzpHDsqjWQBIGgfPSAXKn4+TOJJqubpYuZwSpGT164PD66ioG9Zmxu+c8BimltjhSo7GgOsU0eywCMwUORF7B4fPi63VoNKp1mQ/LiJvEi2dqkiIA7B4vqVjt3N4OdBRDBVQqyWJ3kDkWQeaKZAKZ5i9E2Vq09bgxL3CROdBYMbTiy5We9nFdwUp1vKZWeTxek0WhIr0riGXEjSmgeEfNUmkd63uQSIadK2vGy6bypGI5/ANzgnQu9bK+Qs2mkshSSRMk6ovVKAhq93iaFvZkKpQMQPZ4hFIyj2NDT82+drdHu2fKuXVTmE6Tn0pqcwOdVFJZgTfyGLJNWljUHa9FIz3VY0htn8TosWBb7icylUII8M7juE1eK9OZUdJ7ZuomO6jppU6/A5vTzGyTAHQrg6aQy1HIZdtQgI3PM3lsBr+1j9xwqWHth/PCPjAZSO2Y0t6VdB3lnC5nBM2nf5pBUQy1Vrlq9TsqPIZkJEchV6pqhVE1lqc2kyj5+hTSLDmV2I/N5cHXa9eUshAC1wcGKYQyZI7M1p9bpPb7twMdxVCBfLmK2Gyzk947g7AaKfUYKGSzFAv1OUxV+NlcbuyrAuRG4xQT9YOurSxpNfNjfoCrEVqtyZBO1KbXAeSDKfKTSewVgnNuTYY6VrvbQt5TYMixrMabmZ1Urpm/f85qUo85n1tVoQpmdT9f+bfhifrnUmnlNxqv8jxTu4IIqxFbBT9dOVYhn9MSAOxru0FAao/CWWcScRCiaUNEFSrl10gBtlPBq+7XKsPGZfWSOTyL48I+hEEwO5HE02PHaK5+lW0uF6PJAxRm0uTrXNdsRftu/4CD2QbXHlrHUuan0bZCs/TX9O4gRVmAxY3XAjfYTdhX+UntCWoWfDv0TzM4Gljl2lgVz9nspHI9Ko2iqrHmeQyldIH0vhCprgwlijh9fnz9TmKhDIVyJb59bTcGp4lUHU/P3dVNIhxquZ7IuUBHMVRApZLMViuZAyFsqwNYPc2zRdIVTbpsq7tAQuZQfa3eanF7lZLQ7zG0ECTx+rxv5qCSomlf06V9p75gjazfhDOO39qPMVdNzWgvR//cy6GN1SSHG+ZeWE+3HaPJoCmZ+Zhv5c9HKhbVxpKFEul9M9jP60KYax9v+zwr3+i2VNFJ6XgMu8vdso4EWmexZOLt389GlrRCvyXxF3pBonlD4clU1bXX5uZ0MZY8DALSe2ozXTKJuWwpf7+z4bVX5tU8v7/SONKDRucpS5LikQSTqRPYfM2VjH1DD6VEHktMSXioa+WXBbIqoPXA4fWRjkZrvKy5Z3bO+5h79usbETa3G4TQlEpqTxAKJULWScw2O1aHQ/mtRPPYhMmAfV0PmQNhStnqbDdPdy/FQoHkO+A1vGOKQQjxL0KIaSHE3gbbhRDib4UQR4UQe4QQF7xTc1OhUUlmG6VkQcnWUV/+hi9FeUF1pwvzoBODx9KQTmqVedJOJ0hQBGYzSzqdiGuCsOo4B8KY+x2Y/HP1GqqL3Gi8WaMSoM0eqH4oZydTGEwCT/fcWGqBXqOxtJesbHEbDAJfn0N70eaj2XlKKasKj7LHo8hMUfEEGow1/zwr6aR0LFb18jeD1eFACENDZZqqI0yaoVkX01w6RalYwJ3yYAzYMPU5KBZLRKdSBOpYrFani2wpjRi0kNoTrBF0mWRcs/D9/Q4yiTzpBnU4rdYc1hSDU3/riXrB59ypGKQkp5MHsLcQ5vZVAYTFiDyqvLOpaK2wVIvL9F5/UBRDIZ/TUtdVpGNRDEZjFWUZnlDWeXZ46ns3BoMRu9tDKhJR5rNjClOfg1DqDO6AYpSp967y2Xds7FFinPur5YinpxeA2Ezz7MdzgXfSY7gLuLnJ9luA5eV/XwH+8R2YUxXUh8EolR4tBpuxZasCxfJyYjAaEUJgXxUgczhSN+XMaDJhsTtaCky9WRTNuHcpJZk6edelVJ7sySi2VV1V32sCs4GVH80FScoo6XkPqxp4NhjnHiVhMJSFeeN2AJXHhOZ0ht3tqcshw1y8Qpv/gRDCbMC23Fd/LJX+qRDm9vMUJZLeFyIVjzaMy8yHMBjKK5I1uJ8aL61TMbgax3lS0QgmYcESsWBf04UQglgwTakk8Q/UWqyqlV8YgmI4Q/5M9fOrFGv5gDkqpJFiVtccbmwctUcl2d1eMslEzVKyqd1BpEEynjqq9QZqBGE2Yj+vi/S+MHZ3o4BxVLlHOhIJVMzFBarHS8UU46My7jE7kcTf72zaB8sd6CYeClKYSZM7Hcd5QR/x2RlcZcWgNJ6E2Yk5j80y4sHos9akv3u6FS8xFvwlUgxSyq1AuMkudwDflwpeAXxCiIF3ZnYK8pk0ZqsNmVOEusFqapl5ko7Hqix82+oAMlcke6K+UHR4G+ciq5y8nnRVUF6wdLy2gEY9l2KhUENjZI7MQkmZZ/VYqiVdf26ZeJxZU5Ds8SilijWalZejVjA1s/LnU0mg0BmVXGslbC432WSybuVtpZKRUpI5EMa6zNewIFELGFfcT6PHgnnYTXp/qG51azPY3J7GHkNMEUx6AtmgWPnqfasdK0a/fTFCCuxrlHunxmTqeQyqQZMOZBU6aZ5CT8WimvBVfx+eaB6AbpRiOkcl6VMMTp+vPIeI9p0sSdJ7Z0h70xRkXtc9sG/oQaYLDPtW1qWSUjFFyeuhBVXMxQWqx0tGwjh8/qrvZieTdZVyJTw9PcRmgtr1t6/vJhEOa4rBaDbg6bFXGUXCILBv6CFzJEKxIlPP3a16DM0Lac8F3ksxhiGgch3NsfJ3NRBCfEUIsV0IsT0YPHcXKZdJY7HbkWVuT9iMDR8UFfNbHtiW+RBmQ02rBRV2j7cqr7lqLE3I6fMYHB4PslSqa8mpwmq+95E+EMbgNGEZrv7e7mkdF0i44lCSZA4rbnshXyyv81wrmFopBoPRWLWsocq11kubVBVIPS4/HZtTMoWpFMVIFtuq2qBz5byAmpiFfU2A/FiCUqKg22MAJX7QzGOYb2U2Q7O1t9OxKEOOZWAVWBYq90q1Muvl0WsB43wCyyJPFS1RKhUV+q18z11+KyarsaHHAMr1TTUyGtqMMWjvVJliAYVGKiXyRGwhbE5Xy2Z8ALalPoTFwIBtCcl6wedotK34QtXc5lFTidAM7q45elKh3vJ1n/1KeLp7iQWnSe8LYR5wYvCaScyGqsby9zsJz4vxONb3KO9aRfq71eHA6nT+m1MM9d6euk2FpJT/JKW8SEp5UU9PbUri2SKfyWCx2yllFIvNYDVpQqmRlZ+Z5zEIsxHrUh/pA6G6aYKOJopBE+Z6Ywx10uG0edXxPmRRkjk0i21loKp9eOUxGwrzRJyiDwwOkxa8jk4rzfMaegwNlIwaFK8UmBrXWsdqtTeZm/qdw+0lXY7t2Fc3VgyqVTufmlID8YFSX1tZLDaXS0tAqDe3tryPJjn5qUiUAcdSzEvdCKNy3cITSVx+a9UCMdpYFdSgfU0X+ckUhXBmbnwptbkJIfD3Nc9MUlIlIw3P02A06V6/2OFVLO9K+id9IARGQbA43jK+oEKYDViX+wmU+kjXySRKx6JV6aV64PIrz04iXO1hxUMzuLvmZE14srG3VglPTy+GvIHcqRi2NV2kolFkqaSt+aKOEZ1KUaxYSc886MTos2rPtDZedy/xX7IYQyuMAZU1/AuA8Qb7vi3IpVOYbXZkZs5jMFttmK22ug8e1C/ssa0OUJzNUqiTG253e+uW3CtjxbE6nC2XCNXGUq38JgKz0mPInYoh0wUle2oejCYTVqezrpKRUioCxuvBtjJA5lAYWZIVqartewzzrXKVaw3XsVrnMqYan6fd41GC6kMujJ7Gi7IYTWYsdnvNWKZeBwafhUH70qoiplZoljGVaiOQrY4F1DUciuNprEY7jnW92nezk8mGgslkNmNzukhGZzWlp9IZqkCunJt/wNE0M8nh9Tc0jpLRCA6vV7dn5CwL/korP3MgjHWJl2Q81JYyta/uwlKyYkrUKsd0PNrWvQRFAZrMliqrPJ9RapnUgDGgKdFGGUkqPN29DDqWKnNd06UpHFfFWP4BB6WSJFbRel4IgW11gOzRSNWiUv7+QUJnKomVtwfvJcXwEPC5cnbSpUBUSjnxTk4gn8lgttq0NDG1alYpk28cSJ0v5OxlKiN9sJZOcng8pGO16XDqWHp5WmguSOrFK9IHFausUWDW4fHWFb75TJpSUYlX2FYHKKUK5E7HNOrBV89jKAfGG53nfKvcaDbg6bbX9xiaZEyp31kNdnKj8abegjZeHWEuhMCwyE6ffSF2RxtUUtkDrHuesWhbisHpq7WkVZinBEVZwLlGUQyyJIk0SFVVoVr5pi47pj6HVpWvPsuVFcH+fieJ2Sy5TG18AxRhnknE68c/opH2UkLVGEP5PPPBFIVgGvvqrqrYhx7YVvmRSHqMC6qaNkopSYTD2jXVCyEE7p5eYtNzdQTxsJLu6+6e8xhmJ1KYLAbcAVvNGJXw9PQy5FhOya54AWpG0XwqSR2zEvY1Xch8iWxFN9m+pcuJTk2+7Su5vZPpqvcALwMrhRBjQogvCSG+KoT4anmXR4HjwFHgO8Cvv1NzU1Eo5DFZLMjyyyHKLrrSV71W+OZzWbKpZM3DZ/RaMQ+5NMqlEg6vj1KxWDf7RHkpfLrn62gSF6jk3lWoVpmhDvUAaiC1iVXu9mBb4QeDIHMgzOxEEnfAhtlS6+HY3Ur8oy5fHo/VDbD7B5x1ee5mNFc6HsNoMlE4qSxvWc8bqplbg2ZkhQEwChPOhD5KBBSBWcjnGsYF2olXqM/R/Dx1KSX2qINQcUIzVuLhDIV8qWnw0+nzkyxz5fY1XeRORikm8xWtIubmFlCFUwOvQePe6xV/takYLDY7ZqtN4/HVeJxtdUAZq40WFkaXhaK3xJBjGcnI3PuWSSbIZzNaimc78HT3VKWExkNlxVBh5YcnyxlJhuZeksvTRZ99ERl/BiEEodHTIASBwbn1W/wNCjyti70Iq7EqcaB/6QoAJo8dYf/WZ4hMvj228zuZlfQpKeWAlNIspVwgpfyulPJbUspvlbdLKeVvSCmXSinXSSm3v1NzU1EqFDCaTDUeg93jqStIVM5VtYAqYVsVIHcqVpVVoIzVOC6QnG3Pwmk6ViSMwWjU+Pn8TJpCMN0yMFvP+0iWz9PuUZSKdbGH9MEwofEkXUP1Ldam3kwdLwsgMOAgMpWiVMG1QuNMEXV8u9tD9kBYyS4abJ0B5PQFSM7WKu2MPUW2mMY0rY/KA3CWOenkbLUwLxYKZJKJtjhuu8eDEAZNmKsoTKWwFm1EzHOFaqoQaVR1C+Dw+edoozXl4suDYe27SmGuKpjweIO2KKqVXyfOkIxGNHpILxw+n/ZcpfeXA7MeM+lEvC2PAcCwyE7AOkDsdIWVX6aCKi1zvfD29BEN1lEMXZUeQ/1svPkQk0VMBjMzZVZ8ZvQUvt5+zBVrvlhsJlx+a41RJEwGbCv9ZA6Gtd5JfUuWgRAc2/4Kj3/rb9j91GNtn58etFQMQogRnf/ai/K8B1HM5zGazJQyRTAJRLmVrlINGanZX7XsXL5aYWtfFVBexMPVL7mjicBMRiM464zVCCZzfb4cFEHl8Pm1VD3VKrM3sagbBcZVS0wNzNlWdVGYSpGdStG1oD71pXoz87NFioUCqVhUE6iVCAy6KBVlTd8ek9mMzeUmUUeYK9ZqgMyRCLbVAV08t8vvJ1GnejSdiDGROgajWWSx8WJKlVDv13wrXwv+t0ElGQxG7B5PzVhabMAzJzhC5bqEriaK0On1aWOZh1xK8eX+kFL4JURV/Mnb68BoNmjj1hsLamkuKSXpNj0GmGtWV0zmlcDs6oDyHEvZlscA4CzHXdIVmYDxUFkxdLefnOLp6SUdi2ptUxJlxaDGBbKpPInZLAEdRkhmf4gCeU6M7wIUxdA1vLBmv8Cgi9CZOvG1NV2UEnlyY2p3YwfdwwvZ/eRjlIpF1l9/U9vnpwd6PIbvoRSnfa/Jv7uAD78dE3wnUSwUMJhMyGyhim5R+qfU8uWqZTc/vxnKL6LLXFMFrVn581L/ioU8mXhMl8eQz+eZnJwkGAwq9E8DYe6qGCtzIISp16E1zasHh1ehzObXRagCWX0x1BqIXqOga6i+YlD3nZ/dkYzMgpS4ArWKobusZOoJJ5c/QHK2tqI8Hg4x6F6KzBV10UigWPmZeKymh39iNsSZ1BFkpkT2pL72xk6/Sv9UKy3NKm9XyPn8Nd5Men+I2eI0Ju9cs7aZsQTugA2ro3Fap8PnJ5dOk89mlNz41QEyR2bJRKLYXe6q9SsMBkHXoJOZsfqKQc0kmq/os6kkxUKhbcXgLCuGzMEwyHJgtnze7cYFPCuGSOQjiLG5+6kGjz3dZ0ElqRXG5VT4yNQETn8Ak0WpcNaUcoNnX4UsSjIHw6R9GaZPnyCbSjE7cYbuOoqhe4FCo85fMEmhbiGzf+6ZuPbffRmA3sVL8Q/Uzeh/y6hPNldASnnt/O+EEP1SyvpNw9/HKBYKmMyKx1DZrtnu8VIqFsimklUdJNVOh/UeZGEQ2FYFSO+dQRZLiHJlsJZJVFNAUx7L3/ilyGQyPPvss+zcuZN8WaiZAkMUwxFKpRKGikKe5GwYT28fMFft7L6q8cItoFRplopFxaKvOKfkbFipZi7TIuZuOyWXmf58SRPm8+EKKC58jWJQlYy/Voj7+h0YjILQWAI2VW9z+gNVHsPMzAyTk5OEUhlWe/qUauel+qxz1cpPRWarOOhEKETEFAKTILM/hG2pT8dY/qrzUqEFLLv0KavK8ZIVdE0xliU/lmAsdhB3YO7+hcYSDb01bawKK9/b2499TRfJVycxzNTvH9S1wMWJXUo31vmeVyOPQf27XSrJFehmdN+bZA6EMHgsmAddxHYo3XLajQuYLRaCpTEWxtZQyhUxWIzEQzMYjKa2MpxUeMvvTWRqnK4FwwRPnaRnZJG2XVWe3Quap5XnTsUopQpY1nopvVFg/wvPIEsluodr15noWlD2lieTVeMaHGasi5RUbO/NyhxG1m7gs3/5t5oH/3bgbGMMnzuns3iPoFjIlz2GohZ4hsYcd2J2FoRo+PDZVweQmSLZk3NUj9PnByFIzNaxpGlsLc3MzPDtb3+b1157jTVr1vDRj36UO+64A7vZxKQ08cMf/pBMZm7J0ERkVhsrfTCsVDuvaf4gqVa+yqnOnWcYp9dXZWHGnWa6TAK3p77FanU6MZktNfSPet71qCSj0YB/oL7V6vIrFuXMzAzf+973+OY3v8m9995LtGuQx3IHeM1/krxsvMRm9VgB7bzmz80R8GFb6iN9INx0bW7tPB3l85xH/yRCalpiexy30xeoijGo9MiZ5BGNLy/kisxOJZDuKNu2bWP79u2EQrXe1PxgtnWpD2E14ow5tfYKlehe4CKTzJOK1vZMMttsVQFjFVqczduele/t6SWfzpA+NIt9tVJXEy/z+mdD/yQcMQzSoGXwxGeCuLu726p6VtE9sgghDEweO0qxUCA0dorueYrB5jTj9DXuAAtlCtAo6LlsGQDbfvIDTBYrI+s21h5zSFEGoTrPvm2NQt1WLrrUu2hJ215aOzhbxXCHEOI3hRArz+ls3mUU1eBzplDlMajW7XzrNxWZxeHxNqw7sC7zg1FUZScZTSacPn+N8FWDl/ViDOFwmH/9138ll8vxhS98gTvvvJO1a9dy/vnnc8HIII7pMU6ePMk999xDoVCgWCiQjkW1sTL7QxjcFiwtLJyG9M9sGOc8C38qX8IoBPnj9dPmhBC4Al01Y6l/V2Z4VKJ7gau+YggEiGYLfPvb32ZycpIbbriBz37i4/SemWJ5cYDdsSN897vfJdagqK4SDa380AyuQADbmi6K4UzdOpR65+n0+2v65MdDQYQwtG3VOX1KiqlK52X2h8BtIJqfK7B64/W9hLpfZ/uxp3nyySd5+OGH+bu/+zvuv/9+0hXN3xzzFIMwGbCtUArCKgOpKlTvrxGd5AoEiM9TQEktkN2eZe7t7afPthDyJWzlOotocBqT1ao7k2t2dpatW7dy//33c9JV4KSY0uIxsZkgnjrnqAcWm52u4RGmjh1mduIMxUKB3oWLte2qt9YsniWlJL0/hG2ZD89AH2uvvYFsMsl5V19X15D09SkdhutdezUFO72/Nsb2duFsFcOvoKSV3imE+OdzOJ93FaWCEnyW2SKiQjGo1pUa0FKRjM42daENVqUKen7aqruru1YxNPAYstksP/7xjykWi3z+859nZKTaDfV09WAMTXLbLTdz6tQpHnroobmguD+gdGk8PIt9TW2183yoFmmNMJ8N18QETgUzFA2iKuA3H65AV43wTcyWs6UavPxdQy5S0VxNp8+UwURqwRL8Ph9f+9rXuPzyy7EZBEsNC7miuJpf/egnmZ2drfGcGs0LIDEvLpAIK60K5l7ExosuVcLpC9TEGOKhEE6/X3ex4txYfkrFAulEnFKmQOZohHyvoiTcXd1s27aNR558AFEy8qFb7uQP/uAP+PrXv84VV1zB3r17+c53vsNs2chQ72fls2Ze4cFmcNJlq+WmVc58Zqx+wZ6np49YsHqdgGQTD7AZ1Px+aUKj7GLBaTxdPS0TCEqlEs888wx/93d/xzPPPMOpU6eYyRV4yrqXn+x9lHAoTGjs1Fvi3/uXLmfi2BGCp04A0F1WDKViidB4siGFqqIwlaIYzmhK77ovfpUrf/XzfOBjn667v8FoINAgxqPWocyPV+rxaM8WZ6UYpJRTUsotUsq/kFL+2rme1LsFJSup7DFUUEmu8gs2v91tMjJbN/BcCfuqAIVgmnzFYuvurm4tnW5urLBCS1UoGiklDz74IMFgkI997GPUa/+hctgjvT1ce+217Nmzh5dffglQ4hXKSnRzVlkzqN6Pyo9rc5uXRpuK5UjF8xR67FoVdD3U8xiUsQINXXzNaq0IQIdCIV4/fBxDNsMHr78WTznWkQiHGHKuwDBgYcXaVXzyk59kZmaGe++9l2KdhnsqtLTQihTTQi5HOh7D5e/C6LFqTfX0wOn3kwjXxhjcbdJIMEejxILTZA6FoSiJOZR5Hhsb58knn6TXPUJP4iIu2LQem81GIBBg8+bNfP7znyeVSvH973+fVCqF3e3BbLMTnZoLB+a7ipRkCW+m9rm1Osy4Ata6dAaU0zinqxVDu1a+dp49vQw6lpH1ZbXsv9jMdMv4QqFQ4Cc/+Qlbt25l3bp1/M7v/A6/8zu/w69cdxWDoSxhGeefvv1tUoUSvYuXtDWnSvQvXUEmHmP7ww9gdTq1uoNoME2xSWxNRXpfCMRcqxWzxcrFd3y0Kf3TtcBF6EyirsC3r+kiezJKKaXEFtPpND/84Q/Zu7fuKgZvGWelGIQQfy+EuKv8+cZzOqN3CVLKclZSrcdgMptxeH01wjw+E2yZJ63WDVQ21VNa8VYvuZgIK60AjKY5hbR7927279/P9ddfz9KlS+uOr3LY8fAMV111FStWrOC1N3ZTtNhw+gJk9ocQFqOuQKowGHD6AlXCvJDPawJThSo4rCv8Val086EGjKvOczbclF7RMpPKxygUCtx3331KVs3oUfIVDQPTZ2bxWXq0ttlLlizhtttu4+jRozz33HMNj2EwGHH6/VUeoJZ5Vb6f9tVKU71iLNtwHBXe3n6iwamqbK75Tdf0wtenNBSOTE2Q3hfC4DITzk2Cr4vHn3qKZcuW0S/X0z3oqfEAR0ZG+PSnP00sFuMnP/kJxWIRb28f0QorP5EIE8ycxhKqHxvqXuBmpk7aJJTTOOMxbd0SgFhwCm9Pn+52GCqMYYnd5GLWPHcPYjPBpllEUkoeeOABDh06xC233MKdd96Jt0xhDa1YTWbmMHfkN2EsQnpkBfae/rbmVIllmy7F5nIzfeIYF9xyu/ZeaoHn4RaKYX8Iy7Abo7t5HKIS3UMu0nFlqdz5sK0OQAnSh2ZJJBL8y7/8CydOnNCSUM41zpZKyqFUKQNcd47m8q5CbelsMpsp5aoVA6jVkNX9U5KRWe1FbgRTeVGVSjfQ3dVNPpupqn6OTE3i7Zt7kGOxGFu2bGFkZIQPfOADDcd3d5cVw8wMQgg+9KEPYTAIMoOLcQa6SB8IYVvp16yyVnB1dZGo8Bi0fPAKIada8/5NfSCoW+ENShyhkMtWdX+NzwSr+sTMh91tweGxaIrhmWeeYXx8nFtuuBFDIVdNZZTTE90bB7WvLrzwQjZu3MiLL77I2NhYw+P4+geqqkbn56pr/YWaUGXaWH0DFPN54mWFKqVU4hVnU1xVfgaiE5NkDip9jsIzQZJ9C+np6eGjH/0ooTOphhbr8PAwt99+O6dOneKpp55SlFaFxxALTXMmdQQRLVV5sSq6F7iITKUo5Gs9LjVbp7JdRDQ4rX3fDjIHwpRkiYm0QtXk0iky8VhTj+Gll15i3759bN68mUsuuaRmbkanFZOlyObCOqQw8PTLr5LL1QpZPXD6/Nz29f/EwvXnc/4tt2vfz4wlMBhF01YkhYiy/oX9vPYy0uYbRZWwLHBjcJtJ7J3i7rvvZnZ2ls985jOcf/75bR1DL85WMaQArxDCDNTmXr0Poa51bDCaoCAxzGvz4O7uqfIYotPKy6bnpbCf10X2RFRbC9pdJ2YRmZyoUjKPPvoohUKB22+/vSoNdT6c5SI2VZi73W6WeJyU7E52v7KHUjxftYRnK7gD3VUKcHbiDEAVXxs8FcPlt+LsdWJZ6KlZaUqFV7V+J5Wqz1KxSGRqEv/AYN39VXQvcBEcSzAxMcFLL72kCPuLLsJitxMeP6PtZ52xEJezmLuqF2O/+eabcbvdPPDAAw0Fg79/UDs3QLOq1XiSqc+BMWBreG6V8PWr56kommwyST6baRhgbwaLza6sInYqqdRmrAlwLJ5CGgQf//jHySUluXShKZWxYcMGNm3axCuvvAIeH5HpSc1ri88EOZM6BlD33LqGXMiSrFsB7elRnvXKquDY9NRZtZ1I7w+RtMSYHlfsy2mVyx+pzfEHOHnyJE899RRr1qzh8ssvr9kuhKB/6XLGkofoyXoZzhaYmpri0UcfbXtuKhZtuICP/pf/UdVWPzSWwN/vxNjE0FJbZeuhbyvR1ST4r6a/P330ZSYmJvjYxz7GkiVnT5W1wtkqhjBwDPh7YNu5m867B7U5mEkoLqOwVF8aT3cPsdDcEomRshXWymOAcm91Cem95YwcLWahCOBCLkc8PKONdeTIEQ4ePMg111xDd3dzq9NgMOLu6tbmA2CMhnGUCmzdvo2UIYdtpf5UwsDgENGpKa34a3ZcEer+wTnFMH0qTu8ihVO2r+0mP5kiH6zN4OkaUnjZ8BnFco9OT1IqFvBX9Imph+4RN6HxBI8+8igOh4PNmzcrraEHhjRhXkzkcOU9JJy1WUg2m4077riDUCjEiy++WPcY/oEh0vGY5s2Exk5jMJq0eyCEwL6mi8zRCKVs/cZy2lj9iqKLTJXbHoydAiCwoHndSCN4+/qxhawIq5F9seOkDGYW+z10d3czfVKh7XoWNuf0N2/ejN/v53A4Rj6f14ogZyfGMfmtmAecdWMovQsVITh9sva6qkaQGmfIJBNkU0lNYehFPpiiMJ2mNGQgOjVJKhZl+vhRAPoWL6vZP5vNcv/99+P3+7n99tsb0laLN17E4TOvAXDpyBVceeWV7Nq1i8OHD7c1v0aQUjJ9Ok6PDhrJ1GvH3KO/5xaAzWnGHbARPF2fmj1sneSwGOfytZewcuXbmxDalmIQQviEEP8KfKT81feBi875rN4FlFTFgMIJivkeQ1cvhWxWaz+heQz9rRWDqc+BqceuLTjvKwuS2XFVYE6BlPj7BygWizz++OMEAgEuvfRSXXPvWjBCaOy09nd0apwlPjeFYoE3/KMYmlTHzkdgwQhSljQBPDtxBpvTNbfATTJPNJjWBIh9XXlZzDoLznt7+zEYTYTLbYLD5fMNDDbPFulb5CFjmWZ0bJTrr78eu13xCCoVQ2pvECEEcrj+uS1dupS1a9eybds2wuFaOshX9loiE4owD42dJjA4VJVFZFsdgKIkczjSdL6uri6MJpPmMYRGFcVQr8JVD3y9A/jy3chlTp58+kmMyRgbzlsDwPSpGAajoLtF1a3VauXDH/4wqWyObPcgkSllbtMnj9OzcAm2NV1KL69EtUfl7rJhc5mZOlUrnBxeX9lrU+6jusRku1SSWsXrOV95DqaOHWHy+FGc/kBdmvHpp58mFotx5513YrM1rtzfeOOt+JeNECkG6ZIDXH311fT29vLQQw9VpfGeLRKzWdKxnGYU1UMplSd7Iop9Tfs0IkDvIg9TdZRyIpHg2Te3MSgDXGisVZ7nGm0pBillBPgL4L8Dr6Ksz3z/uZ/WO49i2UI2lj0Gw7wYQ1e5WnHm9ElACQ5aHc6qSuhGEEJgX9dN9rhCJzk8Xpw+PzNlAaK+tL7+QV5//XVmZma46aabMFUEopuha8EI4TOjlIpFCrkcsZkgi/xLOK8wzIHkyaZce81YQ4qVqyqa2Ykx/ANDmpUWLAuM3rLFavJasSz0kH6zVjEYjEb8A4OEyh6DSgO18hh8gzaS7uP4XF1VHGpgcAGxmSD5XJb49nES+VncSxvTGDfeeCMGg4HHH3+8ZlugTI3NTs4phq4F1ayodZFXWZioBZ1kMBjx9vYzOzHXKM1id9StFdCDftcirAY7rxUOksvlsU6epnehQhtMn4zRvcCF0dz61V24cCFr16wmH+jl6L43yWczzI6foWfh4qqmepUQQtC70FPXYxBC0LdkOZNHDwFzFKG3TY8h9WYQ85CLvg0rEcLAxNHDTB0/qjSIm4fTp0/z2muvcfHFFzM83NwDMxiNfPxP/ozBzespnElCssiHP/xhkskkTz31VFtzrAf1mjRTDGoxabvxBRW9i9zEQ5madO0nn3ySfDbLZWELmTenG2YCniucDZX0JWCJlHKHlPJfpZS/ONeTejegxhiMKAph/prBaoGLmtccmZzA29uvOxvDvra7ik7qHllE8NTJ8ljKC2bx+HjuuedYsmQJK1as0D337uGFFAsFZifHFU9GSgKZXs5nKS6Xi0cffZRSnXWh68E/OIQQhjkrf2K8KiYwdar8ciyc413t67rJTybr0kmBwQWahRk+M4bd461Zh3o+du19nZIxx7BzfVV8xT8wCFIye2yU4liGU4kDTekaj8fD1VdfzaFDhzhy5EjVNuXeGQiNjZLPZIgGp2sUgzCKuYWJWjTV6xoeYerEUaSUzIyeont4YduZOiq6S4NMy1nePHWA4YAXYy5D1/AIpZJk+lScviaCaT5uvvU2hJRs33eQ4OmTSFmid9HiuRXC6hRN9S1yMzuRrLs2w8DylQRPnSCfyzJ57AgGo6luU7hGKITS5McSONb3YLHZ6V64iDefeZzwmVEWrDqvat9SqcQjjzyCx+Ph+uuv1zW+0WTGfb7yvGb2hxgcHOSSSy5hx44djI+/tXW/pk7GMJiae2vpN2eULr9N9imE6q/uCGj3ttJrOH36NLt372bV8RPY9r9EKVUid/q9tx7DLPBVIcRfCyG+IIR4e8Li7zA0j6HcPkpYqy+Nw+vD6Q8wffI4slRi8ujhtvKkzQNOTD12UrsU97tn4WJCZ05TKhaZOHoYlz/Ay6+/Tjab5eabb25LqKiURWj0FJPHjiAQWKZNeFf1cMMNNzA+Ps6ePXv0zdNixdvXR2j0NMnILInQTNWLHzwVx9trr2re1oxOCgwNE5kcJ5dJM33iGN0LmucqhMNhtm3bRpdtmMxk9UpsvYuVlN3wSycQwKnU/qq+9vVw6aWXEggE2LJlC4WKRWZMFgt9S5dxet9uJSYgpeYVVsK2potSqtCyqd7weeuJzwSJTE0wc/rUWdNIsljCMCZ5wbQXk9GAL5vE1zeAxWYnMpkiny3Su1i/YnC5XCxw2ogVSuzeoXSy7120ZG6FsCOzVSuEKds9SAkzo7V00sDyVZSKRaaPH1PegUWLMZn1U5WpsmdpX688M1d88rMkwiFcgS423nhb1b47d+5kamqKG2+8Eau18ap882HuValb5VjXXHMNTqezLQOpHqZPxugeauytlVJ5pZh0fU/DYtLkK69w5PIrOHHnr1CqU4jZM+IGocTxAIrFoqIcXS5W79hBYWoPspgntfvtXfe5bcUgpfxz4MvAN4ATwFXneE7vCtTgs0GqwefaitXehYsJnjzOzNhpMskEC1av1T2+EALHhX3kTsYozKQVKz+fJzw+xui+PXStPI8dO3awadMmenvby/IILBgu93Y5wqk9bzDSdR6kS9g39LJu3ToGBwd5+umndafu9S9dwej+Nzm+83UAFq6f0/3Tp2IajaTC5LViWewh9cZ0jSW0cP1GZKnEm08/wfTJYyzaeGHTYz/xxBMYDAY2rrqUyFSKbGouT9s/MKQEh49nictZvMsGqvra14PJZOLmm29WiuRef71q26L15zN55DB7n3kSYTDUvZ+2FT6lrcm+5nTSyHkbANj24x+QScRZcN66pvs3QuZIhOPZcWbNOVyJWUZ37WDh+o3AnBU5//q3woZ1azFkUmzfdxBP/4AWLNZWCDsSqdpfHX/qZB3FsEzxZEf3v8nk8aP0L9Pv2QKkdwexjLgx+ZX7tuT8Tdz41a9z+3/8o6p7mU6nefrppxkZGeG8885rNFxDODb0KJmA0Sw2m43NmzczNjam20Caj/neWiEYJPnyy1XCPb0vBEWJY0NjCjH5yisAZA8eJLNvX812i81EYMCp0Vbbt29namqKq4aGMBWLeO+4jcLUm6R3v710UtuKQQjxp8AdwA3AGSnl35zzWb0LmFMMyiVR01Uj0ylN2PUuXkrozChHXlUqi4dWtffAOs7vBQHJnVPab1/7+c9IRiNMCQtWq5Vrrrmm7bmbLVYWbTifAy8+x8k9b7Cy9xKE3YR9VQCDwcDNN99MPB5n2zZ9CWQrL7uSdDzGc9//Dk6fn95FimeUjGZJzGaraCQVzgv7KMykyc3LqBhatQa728Nz3/8OAEsvvKTmtyqOHTvGwYMHueqqq1i4Qsnnn64IggohWLn2A7iKXo7P7mLFJbVpi/WwYsUKli1bxnPPPUeyonZk4YYLkLLEnqe3sGjDBXV72BisynVM7Q4ii42tzcDQAlz+AIdefgGT2cKyTfoSB+YjunOc1yxHcdus5E8eoZDPaZb09MkYZpsRf1972S6rLr0CbyJM0WjCs/FSzRu1LvEibCbSe6s9PYfHgjtgqxtncPr89C9bwbaf/IB8Jq2tKKYH+WCK/EQS+/pqwbnu2hsZWF6dZfP888+TTqe55ZZbzoqSs29QMgFTexTLesOGDQwNDfHkk0+2bJlSD5XemiwWOfbBD3H6C19k9u4fafukdgcxdtkwN0klzry5F2N50aPMwYN19+ld6Gb6VIx4PM4zzzzDkiVLGB4bQ5jN+D78YQpndlBKFcmdevvopLPxGP4E+FsgDnxECPGdcz6rdwFaHYMsxxgsRqLBFHf/t1c4tlN5uFZfeS2lYpGX7/0RrkBX29kYJq8V6zIfqZ3TeHv6GFm3kQMvPkfR5SUYiXLttdficLT30qs475rNJMIhiokcvmwXzvN7EWWXV7W6tm3bRrTB2tWVWLTxQowmE7l0mmWb5gSJKqTrBd/s67oRZgOpHdUtEwwGI8svUQr0AkPDBIbqUz/FYpHHHnsMv9/PpZdeqlmt0/Me/sWe9UgpOZM7xnKdigGUQHQul+PZZ5/VvhtcvkoT4OuaLHjiuLCPUjJP5lDt4j4qhBDc9NXfwt3Vw4UfvBOzRT/1oaKULfLawZ0kyPArn/gEl975cS645Xats+fUScVba9Xzaj5cgS4+9Xt/RK/LwZEzE9ozIIwGHOu7Se+b0VYtVNG7yF1z7VWsuPQK7fOSCzbV3aceUm9MgwDHuuYZO8FgkNdee40LL7yQgYHWWX/1YO5xYF7gIrVLeXcNBgO33noryWSSF154AVDSTyM//znxZ55tNhRQ7a3lTp2iVL6G6bIHUoznyB6L4FjfuNeTlJL03r24b9iMwesle6h+Gm3fIg/peJ4tjzxOPp/n1ltvpTA5hWlgAOvq1RQm94AoaUrv7cDZ1jH8e+CNcq+kL5/LCb1b0DyGknJThdVIZFpZR3isLBC6hoZZfL6SnXvdF796VpaM88I+ipEs2eMRNt3+Ecx2B4bla+nu7uaii84+83fpRZey8gNXcf3VX0RIgfPi6nYAN9xwA1JKXdkZJrOZD/+n/8pNX/0trv7sl7TvJ49FMBgFvSO1HoPBasK+rluxrOdVzV7z2V/jV//s//KpP/3fDa9ZZTaW2WzG5jTj7bUzdWJOOMmiRBwrYFvp5/P/+I9tdS7t7e1l06ZN7Nixg6kpRXkZjEbu+L0/5j9872cs33RZw9/aVvoxuMwk5ym9+Vi08UK+8g//yuWf+IzueVVi6rUT7BYnWb1oBYsXL+GKT36Oaz//FUBptR06k6BvUfPAfSP0LlrCp7705ZpnwHFhHzJXqskq613kITaTqdueYeVlV2Jzurj1P/ye7h5JsiRJ7ZjCutyP0dtYaUop2bJlC2azmeuue2tNFRwbesmfSWhJEUNDQ2zcuJFXXnmFUCjEzN99k4k/+EPGvv71lmNVemuZAwcAsCxerNFBqd1BkDSlkfKjo5SiUWzr1mFbuZLMoQYewyIPeXOUfQf3ctlll9Hd3U0hGMTU24PR5cLUF4DiBOk9zb3Yt4KzVQz/AnxNCPG/hRAb9f5ICHGzEOKQEOKoEOIP6mz3CiF+IYTYLYTYJ4T4wlnOr22UNMWgUkkGkhGlT85Eucc7wAd/6z/zhf/3raaCpBns53VhcJhIvjzBovXnc+GXf4tkJstNN92Esc1OnJUwmc3c9vX/RCDVg2XYjXleyb7P5+MDH/gAb775pq701UUbL2TttTdgts7xvuNHovQudGOqE3+BspDJFrXMKxVmm42BZSuxueq72IlEgmeffZalS5dWFe4MLPEycSyqUXmZg2FK8RyuSwax2Nv3rK655hqsViuPP/54VSzEYrM3+VXZsj6/l8yBcE3ef939zzIb6akXn0UIuOnDt9ZsmzoRo1SUDOjoedUIfr+fyy+/nDfffJPTp5V0ZMuIG1O3vUbpDS5TjlP57KvwdPfw69+9h9VXXKP72NmjEYrRHM6LmnvZR44c4dixY1rAWA+SL73E6V/7MqV5tQqODd0gyp5KGddffz1Go5EnnniCpEqtFgoUZmoTJyoxcSxC/2LFW8sePARmM54PfZD82BiF2VlS2ycxL3DVvHeVyJ1Srrl16VKsK1eSPXykZrVEAP+gg4T3GFaTg6uuUkK4helpTOUmmpYFwxRDb1JKFmqWDj5XOFvF8HWU1d9MKLRSSwghjCiV0rcAa4BPCSHWzNvtN4D9UsoNwDXA/xVC6O9C9RagZiWJogABmAwkZhXFEB5Pkkkq2y12R8tMmGYQZiPOi/tJ7w8RHQuzdetWli9fzvLly9/yOWSPRylMp2u8BRVXXHEFLpeLLVu2tN2yt5ArMn0q1lQwWRd7MXXbSbzUXlrgM888Qz6fr8nGGljuI5PIMzupWHzJ1yYweCxaY8J24XA4uOaaazh+/Hjb1bDOC/ugJKuEzLnEke0HOJY9w6YlG/GVOehKjB+NgIB+navUNcIVV1yB2+1my5YtlEqlclJEL7kTUQrhOe69Z8SNyWxQjlsH7Sq/5PZJDA5T0/YshUKBLVu20NXVxaZN+iiq/MQEp7/4JZIvvkhq586qbUaPFdsKP8ntU1q6sdvt5sorr+TQoUOcikawrloFzFFC9ZBJ5gmNJxlc7lP+PngQ69Kl2DcoCQfJl/aTn0zh3NS8aV9hWnl2TL29WBYtRKbTFOsUX77xxk4KpgRdpZVaNlYhGMRcTkox9fSQH92JwWEie0LfErTt4mwVwzHABjwopdSblXQxcFRKeVxKmQN+jBLEroQE3EJ56lworTea9yM4R1BjDBSV+IIQguTs3Isyefzc3QDnpYMg4KkHt5DL5bjxxnPToDaxdQyD04xjY3131mq1ct111zE2NtZ2u97pU2WLtfxy1IMwCFyXDZAbjZPVmWd95swZdu7cySWXXFLTVrzSai2EM2QOz+K8qA9hPDuLHGDTpk10d3fz+OOPV6WvtoK5X+kLlXh54pxng5RKJR5/6gmc0sY1d2yuu8/4kQhdgy5sTv2pofVgsVi0FObdu3cD4DhfaYaYfL2irYrJQN9iDxNH3/pzX0zmSe8L4Ti/t2Ezx2IkwqO//huEw2Fuvvlm3cWdiYqWJ+kdO2q2Oy/upxTLKS3My7j00kvxud3sWLUa94c+BEYj6d2NFcPEsShIGCg/j9kjR7CuWI61bMyl98UQZkNTGgkU4Q6KYDeVhbyqLLTzSSR4+umn6XIPkJtwkU3lKSWTlJJJzWMw9fRQCE7R+9sX4Lv17emXdLaKYR/wDPAlIcTrrXYuYwgYrfh7rPxdJb4JrAbGgTeB35JS1vhaQoivCCG2CyG2B4PnJgCjxhhEUWipqolIFn95HeJz8YKoMPmsJJYZ2Rs8zKYLN9VdZ6Fd5CeTZA7N4vrAYE1xXiU2btxIf3+/UknZRsve8fL5DyxpbrE6LupDWI0ktrX2GtQCJpfLxdVXX12z3dtrx+42M340QvyFMTAInJecXTBShdFo5KabbiIcDvPaa6+19VvXFYMUw5mqFurnAq+99CozmVmuXHAhVm8tRVYqlpg8EWNw2VvzFlSsW7eOBQsW8NRTT5HJZDD5rNhWd5F8bQKZn3vdBpb5mBmNk0u/Ndss+fokFGVTi/rkP/wDe3q6GY7H2/KecydOIiwWrKtXk9peqxhsq7owuC0kX53rpGs2m7l60WJiPi+HPG6sy5aROXig4TEmjiixtb5FSkZSIRjEPDCIqasLLHYKs3bs67qr1nCph0IwiMHjwWCzadZ/fp5ieOqpp8jn81x75fUIKZg4Fp1TKJrH0I1MpxGGt6flNpy9YlgBGIF/AvTGAeqZefNNr5uAXcAgsBH4phCiJrolpfwnKeVFUsqLzoVQhTmPQRTm2mEkI1l8fQ56RtxMHIuck+OAIhCfS+3GKs1ssrWXB94I8a1jCLMB12XNBafBYOCmm24iFos1bDBXDxNHo/gHnNhczS1Wg9WEc1M/6TdnKESbr2Wwfft2xsfHuemmm+r2wBFCMLjMx8zhCMnXp3Cc34upSeBSL5YvX86yZct4/vnnq9JXW8G+phujz0pi25nWO+tEPB7nmWefYagY4Pyb66fyBkcTFLLFpt5aOxBCcMstt1Rl6LguH6SULGgFmKB4bFK25y1P/Nc/Yfz358KHslAi8dI41mW+hvy7LBR4/tgxSgYD57+xqy7v3gi5EyewLFyI8+JNpHfvRs7zAoVR4NzUR+bwLIUKBmA4GqV3aoptx45RWrCAwmTjxILxoxH6FnkwWYwUZ2ehWMTU04MwGrGu3gzS2JC+rURhehpT75zVr36nYnR0lF27dnHZZZexcuNiDAbFIFWVR6XHoPz2vZeVtAp4A/g94Cs6fzMGVPYvWIDiGVTiC8D9UsFRlAK6VWc5x7agegwUpNZZNTGbxeWzMrDMx9TJWN0e9WeDV199lcmZKa7uuYDctiClOq0H2kE+mCK1K4jz4n5dDfMWL17MunXreOGFF5iebs2Zl0qSyWMR3Rar6wNKS4L4s6MN94nH4zz99NMsXryYtWsbFwoOLPPRm85DoYT76rOP7czHTTfdRC6Xa6uHjjAqVFn2eJTceP1VztrF41u2UCwWuWboIqzD9TN81ADw4DlSDKBk6GzYsEHL0LEu8WLud5DYNq7Fn/qWKMHWRnGG+ciPjxO57z6ijzxCsbz2dvrNGUqxHK4rGzdOPLpzJ6eHh1kfi2EfH9eyfvQgd+IElsWLsSxZisxmNeu6Es6L+0FA4uU5cZMfPc2FR46QyWbZ1dNDfnKy5ncA+WyR4Km4RiNV0kGyJDEvuBKZD2LRUXRYCAbnhHu5a7Iq3NUKZ7fbzVVXXYXZYqRnoVuhUWs8hp6qubwdOFvF4AN+H/jPgN5qkdeB5UKIxeWA8ieBh+btcxq4HkAI0QesZG5BoLcVpSrFYCSfLZJNFXD6rQwu81IqyKrUybNFJBLhmWeeYfny5Vzw4SuQ6QLxrfqb3NVD9LGTCJMB9zX62zzffPPNWK1WHnrooZZtAkJjCXKZovZytIIpYMO5qY/ka5MUQvW7Wj755JMUCgVuu+22poHMgWEXS6wGCoOuttsYN0NPTw+XX345b7zxBseP63/EnBcPIGwmYk+eestzOHbsGHv37WN9YSHDN8zPw5jD+JEInh47znPgLVVi8+bNGI1GHnvsMQBcHxhSKMmyMLLYTPQMu3TTqLM/+xmUSlAokNj6grJg0YtnMPXasS2v3/q9UCjw+PNbcSYSXFGmEzN7ayuC60Hm8+TGxrAsXox5QLHY8xMTNfuZfDbs63tIvjKpLY1ZCAbpsTu46KKL2IckaDRSStX2+po6EaVUkppSrlQMmQMhhNlHYXybrmB8ZWaRsFgwBgKax/DKK68wOTmpvZegGEVTp2JkJ+eOWfn/e1Ex/ClK4PkQoMvvk1IWgN8EHgcOAD+VUu4TQnxVCPHV8m7/A/iAEOJN4Gng96WUzfPIzhHU9QfIK4pBTVV1+awMrvAjBIwdfGupYVJKHnnkEQBuu+02rAvc2Nd1k3hxnGIL2qURsscjZPaHcF+7oK1lBJ1OJzfffDNjY2Ns37696b6j5Q6cC9pY18Fz3QgYBLGnT9dsO3z4MHv27OHyyy9vud6EeX8IE3DGri8Y2Q6uvvpqAoEADz30kO52IQa7CfdVQ2QOhHUH2Oshl8spPXCEg02D67A2iN2USpLxIxGGzqG3oMLtdnP99ddz9OhRdu/ejeP8XoxeC7M/3smR664nd/o0gyv8TJ6Iks+19pYzu/dgW7MGY1cXieefJ3MwTP5MAvcVCxoW5b3wwgvMJBNcsHMn3ksvBaOR/IS+rLbc6BgUClgWL8JcLoSrpxgA3FcPI3NFEq8o2xXrvZvrr78eh9nM65s2kanz2zNHIoiKbLA5xdBNfOsZEBlyR15oOVcpZVVmESgeQGF6mtnZWZ599llWrlzJmjVzBsLgcp9ikI6lETYbBre7fOz3iGIQQhiFEBNCiF8DkFKOSSmfKn+uqUdoBCnlo1LKFVLKpVLKPyt/9y0p5bfKn8ellDdKKddJKddKKX/Y/imdHVSPQeZKGKxGEmXF4PTbsNpN9C7yMPoWg447duzgyJEjXH/99VpKovfmRUrH0AeOtp1CKgslIg8dx+i14r6i+RoH9bB+/XqWLFnCU089xexsY6U3diBMYNCJ06ffYjV6rbguGyD1xnTVmtCpVIqHHnqI3t5eLUe7EfJTSZKvTRByWzl2It729WkFs9nM7bffTiQSqaqIbgXX5UMYnGZiT5y91/D0008TDoe5PLuSwI2LG1qc06diZFMFhtecXYpuK2zatInh4WG2bNlCIpPEfd0IpaQFY2Al0Z8/yPBqP6WCopxaIXdmDMuihdhWrSJ38hTRLScxddlwXFi/99fExAQvvPACKwwGhsKzmAYGMPf1UWgg3OcjX+6WahkexlRWDIUGlJBlwIltpZ/EtjOUckWN1rHZbNywbh1Rv09Z8W4exg6E6V3kwVo2TApBxU4txizkTsUwd8coJeIUE82pxWIkgsznNaEOYOrtIT89zcMPP6xVZlc+B0PLfQiDYHLGqMQ0ytsMHg/CYnn3FYOUsgjsBeqvSP9LgGKhgDAYkPkSwmzQUlVdZWE4vDrA9MkY2bPM0JienmbLli0sXbqUiy++WPve1GXHc+MiMgfDpHe1d6Ojj58kP5nEd8fSpplIjaCuES2E4L777qNYrLUKC/ki40ejLFil31tQ4bluBIPbwuy9h5GFkuYxpVIp7rzzzqYpibIkiTx0DGExYb6kn3g4Q3T6rS+2Mh+LFi3ioosu4pVXXtGKvlrBYDXivnaY7NFITZ8hPThx4gSvvvoqa0rDLFu9HNuyxtd27EAYBGd1/Yvx1srUYDBwxx13UCgUeOSRRzAPC0rJINbVHyb64IMMLPViNBlaGkWyWCR/ZhzzgmHMQ0OU8j0UplJ4blyEMNaKmWKxyIMPPojD4eDiySksIyMIITANDpA/o89jqKR1jC4XBpeL/ER9xQCUlZ5C3RaDM5qQXrNxI0OjY2w7epRQaK44M5vKM3UixvDqOaVcCAYxuD3Enj6D0WfFuqxcZzDVvCp+fpxA/XzUYODYsWNcf/31eL3VXqPFbqJ/iYeprK/qd0KIcsrqe4NKcgD/uZwm+lD534Nv18TeaRQLeYxmMzJXVNItNY9BufELVvmREs406ZfTCNlslnvvvReLxcKHP/zhmjWcXZcPYhlxM/vgMQp1Fmivh8zhWRIvnMF56UBbazrPh9/v54Mf/CBjY2M8//zzNdsnjkUp5ktVL4deGOwm/B9eRn4yRfy5UbZv386+ffu49tprW/bASbxwhuyxKN5bFjFUzg9/qx5bI2zevBmfz8d9992ne6Uv12UDmAeczD54VOOt9SCVSvHAAw/gNbvYVFqG97bmeein94fpGXZjd7VX51mYneXI1dcw+4MftNy3u7uba665hoMHD/LG00+SPfAQRt8ImJci4hEGlnkZrbNuQ9XxJiehUMA8vABT/wLMI5sx99u1luzz8cILLzA5OanEmE6dwrJQaXluHhhsSAfVHFMVtmU60jzQ3/S31oUehbp9fhSMzjnOvq+PC3fswAg88MADmoE0dmgWKany1grBIJblV5EfT+K5aRGWQaWSu1HwWvvddHWcACDT1cXrK5azYMGChgV9w6sDREUXpe7qddLfS4rhMpSU0wuAD1b8+6VAsZDHaDJRyhYxWIwkZrNYnSbM5ZqG/iVeTBZD23GGUqnEAw88QDAY5CMf+Qhud22vG2EQBD65CmGAmbv2UYw357vzk0lCPzqIqc+B77bFbc2nHtatW8fGjRvZunUrhw4dqto2dmAWg0GcdUaMfU0X9o09HHt2L1se28KyZcvqLuZeidxonOjjJ7Gv7cJ5cT/eHjvuLtvbphhsNhsf/ehHicfjPPjgg7ooK2E04P/oCkrJPJGH9QWvS6US999/P8lEgmsTq+m6bjGmQOO24blMganjsbNSysltLyFTKUL/8q9IHfUql112GSMjIzx9/Djh6H6M3jzW8z5C5vBphlcHCI8nSTaJg+XGlBRey4IFlHKLEXY/zouddWMLJ06c4Pnnn2f9+vWsXr2awsQE5kFF8JkHBshPTSHreK/zUZgJYnA6MZQbT5oGBlrSUN5bFiOLEuuaOzGWFYrBasVlt3NlSTI2NsbWrVsBGN0fxmwz0lex/kVhJoJp8DrMA04cGyoK1VoI6fkeQ6lU4ulcDikEt199dY2xqGJ4dQCEIOyuNiDeS4phcZ1/b0/Z3buAUqGAyWSF4lzw2VXBqRtNBgaX+9sWTs888wwHDx7kxhtvZOnSxkycKWCj63NrKEazBL+9p+5qaACZYxGmv70HYTHQ/fnzzopCqodbb72VgYEB7rvvPq3JHChWet8SD5YWxTvNYLiuhycte7CVzHzw8psbvgQAhZk0M9/bh9Fjwf8ryxFCIIRgeE2AM4dmKb5NTcOGhobYvHkzBw8e1ARDK1iGXLivGSa1c5q4jtqGrVu3cvToUS7Jr2Bo6UjLLLIzhyOUSvKs4gvJF7aCEBQmJ0m80Do4ajQa+chHPoJRSl664goc1/WAMBJ/IaLRWGNNnv38mJKaLOkmP2klf/QpKNQKrng8zn333UdXVxe33XabUtWbSmmWtHlwUOldpEPoVaZ/Apj7B1pa7qaADdtSgXnkMmRx7rqaentZND3Nhg0b2Lp1KydOnGD0QJihFX6MlVSY/QKE0Ynvw8sQBqG7pqCS9gLlWTiTTnPBjp24M40VbnePCVMhRZDqOon3jGKQUp6q9+9tm9k7jEI+j8WkNFM7tGOaE7tncPqqrbmRNQEiUyliOumerVu38uKLL3LhhRdy6aWt+/NbF3np/tJaSuk803/7hhJDmEoqC4yfjhG+9zAz//wmRpeZ3q9u0BY7ORewWCx86lOfwmKx8KMf/YhIJEIqliM4Gj8ri1VFOp3mnnt/QtEMt5gvIvm9Iw0bf2VPRpn+1m4oSbq/cF5VTcbwqgC5TJHpc5Ay3AiXXXYZ69ev59lnn2VfnUVU6sGzeSG2NV1EHz5Oel/jeMPu3bt57rnnWGEY4jzrIgKfWNmyffbogTBGUaT0rT9r6zyklCS2vYTr2msByB4+0uIXCrxeL1cnk0Q9bh479iaZ/fdTnDVjORDC7jZzuoliyI2OIpxdxJ4OYfSbyR54kPyZamWZz+f58Y9/TCaT4WMf+xhWq7VGYJoHy9lFOpbhLASDGo0EYOrrpRgOI1tkmJm6whSjoyR3liiWu8eaenspBIPceuutdHV18dOf/JTZcISRCqWc2h/C1HMBBts41nLdgsHhwOBytfYYpqcxuN0Y7HYOHjzIc889x9qRhSw6eZJCsHEtUSk8g3/2MFNJV5Una+rtoRSL1V0F7lzgbNNVf+lQKhSwmBVBOzUWpyTyc0VvZSxcp3D5J+osYVkJKSXPP/88zzzzDOvWrWuZq18J6yIvfV+/ANvqAPHnRpn6fzsZ/9NXCP7DblJvTOP6wCC9v3l+UwribOHxePj0pz9NNpvle9/7Hge2nwQJi9Y3TylthFQqxQ9+8ANmZmb4xCc/waqvXYHRa2HmX/YSuucgmcOz5IMpMkdnCf/sMMFv70FYjPR8dQPmvuoq2eHVfgwGwcm9zVdSeysQQnD77bczPDzM/fffz9GjR1v/xiAIfHIl5gVuQncfJPHSeA0VdeTIER588EEGRYArSmvo+eLalqnFUkpO7pnBF9xP8rFHdFn9KkqJBMWZGRwXXoixu5vcqL6gOsDA2BkuDoU5ePgwO/tSIEZJPDfGukEnp/aGKDXw2PKTszgu/21kvkTX59aCsVq4qzTamTNn+MhHPkJfn8LNz1cMpj7FMm4VzFV/q1YSwxxN06pTajEUJPP6d6AIoXsOIgslxQKfnsZqtfLJT36SQr5I1L+PvqXKc5gPppj96SGK0TEsw9WGofrblnPt6WFqaor777+fwcFBbr3pJgS1/ZKqfjc9TSC8n2TawOzEHIugeSotzvVs0VEMZRQLBcxGhTrKkCfc8xonCi8Rruh+6Ot14O93cLKJYsjn8zzwwAM8++yzrF+/vm6wuRWMXitdv7qa/t/fhP9jK/DetoTAr65i8L9cgu9DS7WWHW8HBgYG+MxnPkMymeTxFx/AHMjR3WRFqkaIRqPcddddTE1N8YlPfIIlS5ZgCtjo/Y2NuK8dJnMgzMy/7GXq/+5g5p/3ktodxPWBQfp+63zMvbWFbFaHmYHlvqbX/lzAZDLxqU99ip6eHu655x6OHGltbRssRnq+tBbbch+Rh44x/fe7SLw2QfZ4lN1bXuWeu+/BX3Bwo+0iBr62Ectg6+sZOpMkHsrQE1Kau83+6B7d56AKVVN/H5bhYfKnG1eg1/vteoedTZs2cWDhQl5PvYZlmZeBqSQLC6W6bbhz4wlKxYsRti66PrcGy4ALc38/+XGF7y+VSjz44IMcOHCAm266idWrV88dr6wYVL5fFfR6aJLKzCKo32ai7jlOB5HFKL47l5E7ESX0o4MYe3ophELIYpHu7m5GbBdQNKd49OkHSU/GmPnnvYAk/eo/YO6rbsOjehvNjzlNanCQH/zgB1itVj7xiU9g7+sFg6G5YggG6Q4pDS9PVCzM83a3xTibpT0/9HZM5N1GsZDXPIawZRppKJLOx/n2t7/N9u3btUyFReu7GT8SqZu2euLECf7xH/+RPXv2cN1113HnnXe+tTUWfDacF/bhvnIIx/oeXe0uzgUWLFjA5z77OQqFIlPW7ezZs6etGoIjR47wrW99i0gkwqc//emqNRYMFiPemxYx8MeX0P2ltfg/voLuL5zH4H9VlV7jWMbi9d2Ex5NEG8RfzhUcDgef+9zn6Onp4Uc/+hGvvvpq67RPm4muz5+H/6PLkekCs/cf4dnvPszPX95Ct3Rxm+xlwb9fV+MJNcKJ3UFA0j3zJra1a8mdOKF7/vly3x9zXx+WkWFyo/oUgyyVyAeDWPr6uOWWW1iZz7Pb72PXggksa7tYbTeSvecQiZfGyR6PkN43Q/hnh5n+5i6QRkg/h63cll0VloVCgfvvv5/du3dz9dVX11Cq8z0Go8+HMJtbCvf5sQlgrjGdjkCwqacH5wV9+G5fSmZ/iGLsPAz2HorhMNlUnsQJK+uGL+PkyZP86FvfJ5/L4brUgExVKyPtXFvMdzYe5/HBAYrFIp/97Gfxer0IkwlTV1dNI71K5Kenseai9CxwcGL3nFH0dhe5nY3H0B7h+T5Bpccwa5+mr6+Pr33ta/T39/Pwww/zj//4j7zyyiv0LrNTKkpOlxeHT6fT7N+/n7vuuovvfe97SCn53Oc+x1VXXXXWC7a8F1CI2PDNnE9XoIcHHniAu+++m4kWGR+zs7Pce++93H333bjdbr7yla+wZEn9/ASDxYhtuR/nBX3YVgaaKgQVizcoVmXlC/J2weFw8PnPf57ly5fz2GOPce+99xKPx5v+RgiB86J+HP9+JdvOO8Mr5iMsW7iE28wZsj/7c6b+55/qPv6J3TMEDBEcXU6cl11GbmyspkFcI8x5DP2Yh0coTE1RyraurC+Gw5DPY+rtw2AwcF2gi6VHj/HSKy/zlGEXh/xGcpkCkYeOEfynNwn94ADpPUGcl/STfvX/YOqae95NvT1EIxHuuusu9u7dy/XXX8+1115b804UZmYQZrO2DrIQAlNvb1NhCbWehnLM+q2sa347MyfcXR8YpOszqynlLTiu/wahHx9j/KeHWWURXBL2c0V+FaPM8HjvPlKpSe3cKqEGghsZD2NjY2xZu5a8wcBnPvMZeutUPzec63QQYbWy+II+pk7EtMywt1sxnE2qyftX2jVBMZ/HbHBDCbLGBEuWnIff7+fzn/88Bw4cYOvWrWzZsgUAQ6+Z+x/fheUFoa2f6/F42Lx5MxdffDEWyzuyttDbipN7ZrCZHXzhK19i564dPP3003z7299m4cKFLF++nMHBQaxWK7lcjmAwyKFDhzh+/DhGo5GrrrqKK6644pxfB0+3na4hJyd2z7Bx88g5HbsebDYbn/zkJ3nhhRe0jKJLL72Uiy66qG7acTQaZefOnbz88ssUCgU2b97M5ZdfzukvfhGA2MMP0/9f/xijt3kzwsRshuDpOCuzx7AMD2NZtBAKBfLj41hGWp93fkoVYL1YRoZBSvJjY1ibZMUpv5ujoADM/X1cuH07y7/4BZ548UVGzWPsyizk3/37zbhMBoTViGXABbLA5O+f0YRVqVTicFcXr9jtMD3Nxz/+8apWD5UoTAcx9nRXKQyFs29h9Ze59Urr3RgIgNGoK0PIWuHF2td24/+Qm8m//BlG582Yz4RZajVidpi57OprGHSu5/4H7ufumWku7Otjxbw2LqbeHmQ2SykWq7q3pVKJV199laefegprIc+dQ0sYGhqa99veprUXqnezZEMPrz10gpN7ZjjvyqG5c30PKYZz25fgPQLVY8gXipQoacsKCiFYs2YNa9asYWpqimPHjrHnpSNEZxOMrBqgqyvAwoULGRkZeUu00XsJpZLkxO4ZRtYEMFtNXHLJJaxfv56dO3fyxhtv1O1I6vP5uOKKK7joootqKjjPJRat72bnllNkEvmWLcDPBQwGA1dffTWrRkZ48C/+kuezWbZu3Up/fz8DAwOYzWay2SzT09OMl4OtK1eu5MYbb6SrqwtZLJLZ8yam/n4Kk5PkTp7UVv5qBNUj6p7YgWndQiwLFwLK0pB6FENhahqj34/BYsG8QEmJ1aMYClOK5WouB4ZNvb0IYENvHwu++EUeevAXTGcP8P1fTHHltZexbNkyLCZBYWIGCcQ9Hg5v28bOnTsJFQr0zs7ykX//VfoWNp7z/JRT9bjZY8eaz3UeBQUgDAZM5fWRW/3WOa+WxjLcR27/A3g/vpGfv9LH0gt6uPZzijJbQz8+v4+ffuc7PH/tNUSefpqrrrqKrq6uqjkUgkGMXq+SOHDyJM888wyjo6MsGxrivHvvo7fOGtam3l7S5QWT6s51ehpTby+BQSeebhsnyopBGAz0/eEfYmugcN8qzn1nsvcpSoU8JquFjFDS1+qtN9vX10dfXx+D7hU88vd7uHD5+rPO2HkvY+KIkqq6tKLHjd1u5/LLL+fyyy8nkUgwPT1NPp/HZDLR3d2Nx+N5R6izJRt72PHYKY7vCrLmisHWPzhHcI6Pc+XzzxN3uQh95ctMW60cOnSIQqGAyWSit7eXq6++mg3/P3tnHSdXdf/99x3bWXf33WyyG3cPCUmAhBDctbQUKFCg8ittn7bUaClQChVKcXe3oHF32WR3k92su/v4ef4447aTkKRCPq/sazJ3zrnn3Dtzvy6TJpGQ4ApxNFZVYRscJP7aa+n8178wVlePyBgqd7URlxqBblMZ2rNmujGGWlgwf8S9Wlpa0KTJ6B6tXfo3hxLl49A07IzBwSAsbW1kjR/Hrd+7haf++B6dQ9XOYpBhYWFoAOMlF2OpqoKqKrKzs5kdG0vka68T938/Cb5meztaL2anSUlhcMuWEecBx2zvtw0PYxsY8J1n1wIaq4cxGqzkT/Gs75SRkcFF/QNs6+nmoEbD/v37yc3NZdSoUUTZbPSkpDC0fz89tbVUVFTQ1tZGdHQ0559/PqOHh6k3Gn3WdOzf2tWFMJtRtL6CjqW9nbAimc+TPymZA+saMA6ZCYvQknDtNUHv0dfBacZgh9VsRh2mxYDMEo2ICFziObskgbAIDUd2tf5PMoYjO1vR6FTkBShnEBUVRVTUsUcqnQgk50QTmxzOkZ2tx8UYul5+GWN5OUl3fB9tqv/ibv5gOCAjQ6IHBsgbGiLl5tDakDhyCGLOOZvOp5/GVFMTdPxAt4Gmyh6mn5mK8roZTWoa6qQkVBERkjGEAHNbm0d/YBTFqQ0EndfaCmq1k0hqnIxBMhWVSsWs2TPY8GYsl9xRQO9wB+3t7Rjq6hjef4BRV19N4ZzZJCUlMbh1G3VIphJWEDg739LeTvi0qR7HNCkp2Pr7sQ0NObOa/c3DzTfhPtccxNnuzwQFrjLYta06wiI0/nN32tuZaTJx7t13s337dsrLy13a8+IzYf9+FEUhNzeXFStWMHnyZLRaLb3vv+93TblfV9ip1k+ZGEtbm1O7KZqeyr6v6jm6t4OSuV+vk+FIOB7GMLLo8V8Iq8WCWmicGkMwxqDWqCiYkkzlrjYsZiuaE5R9/J8Am9VG1Z528icmoT2JYbHHC0VRKJqRyq5VNQz2Go+5R0HH3/+Btbsba28fWX99NOR5hoOlUnJXqTAdDT1CyCGFa3Ny0GVlYaquCTq+clcbCMhNt9CNlPgVRUGbmYGlJcQaQi0thE+cCCAdu4mJTuIefK9taJKSUOwmUU1iok845ahpKWx86wi9tTBjhazv0/3qq7Ts2cOoRx5B6ww7dZSKCMyQhMmEtafHj9TvMs04tCWfvbZ3yL16aama5CSGd+8OfI1uZbO9oSSn02RIZMz8FNR+elNbWlsJKy4mKiqKxYsXs3jxYgYGBuhta+PIt24k/coryPvOd5z9FBzw7sDmea0uh7k3Y7ANDXloNyl50cQk6Tmys/WkM4ZjjkoSQpx1Mjby74bVakGFhiECm5LcUTQtFbPBSl3pyanf8+9CQ0U3hgEzo6an/ru3EhAFY8IRAsrePraezdaBAdmaEY6pSxjAcOlB9OPHoysowFQdemMfc2srqogI1FFR6PLyRgw7PbKjleScaCLNMurNkfClSUl1hqEGg81oxNrdjcZNG9KmpIRsSnI4ngFXOKXb3Mi4MDKL4jiyo9UZhWNpbwdFQZPoWWICRkjeslcy9WEMIeQjeGc9u69r7e7GFiD72V8xOwc6UydhRUPRDP+/fUtbm4+WGRUVRWZBAelDQ8R29/gwBcdeVZGRqPzQlEC9nx3z5DXZm/soCqOmp9JQ3s1QX2j9Q44XpxPc7LCazSg2DcPKyKYkgMwxcYRHazm8I3htlv82HNnRik6vJmfcyan/fyKg2riKqIEGyr6owDoQes9mY3k5APqJEzE3NGALsZKqbXgYS3MzYUVFhBXkY6qpDT10tKXVae/X5eVhqq8PGNbY2z5EW20/RdNTnTV/HARek5YasNeAx3ptDgeyq7aOJjU1RFNSG9oUT6Lob+6o6al0twzR2Sh7EFja21EnJqK4lVFXR8nidiMRd/Al0toQCtP5c1q7z7UGmBtoTYCmsCLCzP1+C0ZaBwZk3kSKf/NjsNpFlrb2wPOCMFB/ex09IxVhE1TtHvn7/Do4zRjskKYkNUOYUKvUfjm/O1RqFUUzUqne34FhIPSyy//JMBksVO5up3Bqyikxj1m6u+n/8ssRa9t4w3i4gpS2nfTFFtC+M3TJ31AmGUPseeeBECEnjTnyArTpaejyCxBms08doGBzHQ5gbXoaYngYW5//ek/lW1pAgVHTU2RzervEDtIRbOnoGLFSqjOHIdVF4DWpKaGVmGhp8ZgH/p25o6amoFIrcr/YCV8AM0mwfARX2WxfBzL4l6Kdczt8E81g5Ph+S2uLNK8leAo+hkEzrZYkUtp2ogjfsh/O+5riX5sI5vQOxMQgeIite6KiAwkZkSRkRHJ4+8kVSI+LMSiK8kO3/48JNva/BTaLBZVNzTBmwiPCQ4qwKZmbjs0i/me0hspdbViMVkrmnfxoH+vAIFVLltJwx/fp/ejjY5prqDhMTlgrCBtlW0K/94byMtQJCUTOnQMwYkikA44HVJOahi4vDyB0R3Brq5OYOMxC/kxCNpugfEszOWMTiE7QS7NOSrLL3p+aBkKMWBvHSUzcTELa1FSsPT1Bk9ysA4PYBgc95sl1fZmKPkpL/qQkKra1YLXYfGoWOeempATNKfA2lTigiolBCQsLOFeYzdJcFsRmH4ipmFvb0KSm+jzfFdtasAkV6U2bZaKf914dfoIAAQvBNYa2gBqDolIFrLXkED4cJclBmpOKZ6fTcrSPrubQteVjxTExBkVR4hRFeRa4TFGU2xRFmQ+E3NrzPxlWsxmVUGNQzE7/grWvj8af/MRZa94bSVnRJOdEU7bZ1ylo7e1laIReyoFwoltYhoryzc3Ep0WQVhAz8mA3GKuqMIdgqvCYc/iws/m64dChkOcJsxljVRVJZ0wnqbeco/WagIXdvGFubEKXa88L0GgwHhm5SB64OZDTUl3VP4N0CnPu1WpvIemWMAb4dSLXH+pioNtIyVxJBMwtrR7mIOfckTqF+dMYUlxhpwHntfnOAztT6e31YSol8zIwDJip3tcRUCIeqYaQpc3hm/BsNOXIfg4mgSPECM7cAETaj1YkhKBsUxOJ8YKowSa/e3b4WbRBTEKWtjafZ9fR6zmQxuCc6+d7NTc2ok5I8InMGjM7DZVKoWxTaJ3ujgfHxBiEED1CiBuB3wPbgAXAOydjY6caVosFlVBjVExOxtD254fp++BDel4LXMCsZG46HfUDtNe5yiUIIag67zxqr70Oqz0zOlTYjEZqLrmU1j89cOzX0NdH6/1/OmYiDdBZ30NzVS/Fc9OPKR+h5733OLriPJp+es8xrWeslGGc6uQkDOWhm4NMNTVgNqMvLiZXU4/BpqPuYGgBADK+PxVFq0WbmeHsITASXBpDqnR4qlROZhF0vY5OsFpdCWNpgTWGsk1NTkkcJIF3dwQ7iNlIDmhza4uzFLT33GBMxeG/8DaVaJL928CzSxKIig+jbFMjls7OwIzBD7F0rtnejjohwcM34TE3AFNx+F+06Wk+n6nj40GjCTy3tdXDNAPQVttPZ+MgYybIe+bXEexwWgfxMTiyn91h7exEGAweUr83tGn+O8+ZGxvRemVKA0TE6Mib6NLYTgaO18ewEBm2OhsIOUpJUZRliqJUKIpSqSiKX01DUZRFiqLsVRTloKIovr0mTwJsNitC2FAJFUaVmYiICKz9/fS8I3leMIm2aEYqao2KQxtd3HtgzRqs9qbhQ0FC5/yh/dG/Yjh0iK5nnz2meUII6r77Xbqee47eD46t46q1t5ctdz2KIqyMnnhsWctdzz4HwPDuPcek6RgrK1HCw4lesgRjWTnCFtoP3GH+CRs1iuy8MHTmfg6FIDkJIexEwZH4lR6S1A9SY1DFxqIKD5eROsnJIUUIuaRwe2RRAKYy1Geiel8HxbPTUGtUPnuV5/DMKQi8V19TiSOSJlhkkkOY8DUl+WcqKpVC8Zx06sq6MWhjA8boC4MBW4AaU5aODr+RRRC8lLWTiaX5MgZn9rOfuUIIO8P1nHdoUxMarYrRsyXx9usIbm2V/RQCBKU4Qk29+0iYGxrk59lZfucB6HJzZC0sr6515qamgAylZF46w/3mk1Zt+HgZQxxwD/ATIKROEYqiqIF/AMuBscBViqKM9RoTBzwGnC+EGAdcdpz7OyZY7Q49lVBhQpqSTDW1sqBYejpDu/cEdJDqI7UUzUihfFuLs+Lq8IED8kNFCRpT7Q8Da9c6/z9SITF3mGpqMOyTJZoNB0M3zQB0vPUejfHTSG7fh6ohNPMKSFOJqboaRa9HGI0jtlX02G9lFWEFBejHjsU2OBiyM9dBzLWZmYRlZZLetJma/R30dQaPMLL19SGGh93MOmkjdvtyrtna5iFlhhoh5B1ZFIipHNzQiM0mnAl7zr26ramOi0PR6UZkSA6tyB0u4h7ElOTHBAUupuKPWI6dn4ECNGYsCBohFIjAm1t99+rcc0pgxuD8DQToGx7IDGXt6UEYjR4hp8YhM4e3tzJqegoR2WmgVvttEmRubkbrhxE54OhZbar17H1hqpeMQZcVmDFoc3LAbPYQVIQQkjH40RhANg2LSgijs+nk+BmOlzH8FnhfCFEBhKrLzAQqhRBHhRAm4DXgAq8xVwPvCCHqAIQQJzcmyw5XQx4Fs2IhIiLCSajiLroIYTAE1RomnpmNxWil3O5rMNc3oM3MJHzSJIZ27gp5H8JsxlRXR8SsWQAM79kb8lxHhI02Kyto7RV/qDw0gEUbQVbjWob3Hwh5nrmpCWEyEXv++XK/IXY9A6kxhI0aRVhRkfN9KLC0tKBERKCKjkabkU5mk2zDWbo2OGNxOWXt0nt6mjRzhNJb2IvYhtJCEtxqD7kRFG+mYrXYKF3XSM64ROLTIr326pZToCiy3tIIPgaZ9exJbFXR0Sjh4UHnmh1akd6zAZTTmeuHqUQn6MnOhMaMeShx/nMKIDBjsDQ1ByTu2pQUbIODfsORzS0tqCIjUfspZAiBTTMuB7Lr+zi0qRmL0crEM7OliTE93W//CnN9nU/pDo81s+2MwaspkrnRrjEEIPAAuhyZxGeucwU0WDs6JBPL9K8xqNQqrvn1bGae9/V7vvs9//FMEkI0CCG+tP8/VOdzJuB+xxvsx9wxGohXFGWtoii7FEW53t+JFEW5WVGUnYqi7Gw/AdUFbXbGYEESCckY5BcatWghAKa6wF2wknOiSSuIZf/aBoRNVrLUZmURPmkihrKykE0spvoGsFiIOW8Fik7H8N69IV+Dw8QSe9GFWJqbQ0poAimZVPalEmPrJClymOED+495zZhzl4NGg6E0NMZgGx7G0taGLj/f+cCEIoGDJApau6lEk5aG3thDbo6KQ5uaMBsDE3mfOkBpaWC1htQBy9uso02TeQYjfa+W1hZZtiE+3jU3Nc3ju6nc1cZQn4lJi7M85+FJwMCRqBb4PgmrVUbAeEn9iqLIuUHMUBYvrcgBVUwMil4fkLiPTunFoo2iusW3kq5Di/Cn+dqGhrD29KBN90/4nJ3c/Djq3WtB+YM2OwtzQ4OPedLBLBwanM1q48CaBjKK4kjOkUzGX/8KIQSm+gZ02YF7dKujIlEnJGD2ohOmhgZZ0iQ8POBcl7bhYgwOTSMYQ9HoTl5I+fGGq/5DUZTn7P8/O9Rpfo55P1kaYBqwAjgH+KWiKKN9JgnxhBBiuhBienIQb3+osNhNSWZFEpbIyEhMDQ2oYmOd5XlHMnVMXJxFX/swNaWdmBob0GZlos3KRhgMfsPf/MGRUasfMwZdbm5QZuQzt+oompQUIu2NUBzJXCOhvqyLAXUCRQldhE+c6KwJFOqaAPqSEnTZ2SPWAXLA6TzMSJc2Zq3W2e1rJFhaWpxOR4e0WZTci3HIQsXWwOdwrunQGOyvI5m/hMmEtaPDyxGchrCXKwgGc2sr2pQUFLcOftr0NCzNzQghEEKw76t64tMiyHbrLezaq5dJKC0tuDmoUzq7/ZlnRkpys7S2+jAUsGsqQfIg4obqiBqo5+Dufh9G6TRh+XOsuv0G/EFnt8n7azJkbmkJbtbJzkaYTD7MzKEJOAh89f4O+rsMTHRjytrsHB/ibmlrlw7kIH4CAF1ODiYvbcNc34AuCHEHexXbsDAPM5Tx8GEAwkYVBZ17snC8piQT4KgL4FtL1j8aAHeWmwV4G/MagE+FEINCiA5gPRC8FOUJgM1iQYUKk0pqDhEREZgbGtFlZqIKC0OTnIxpBMZQMCWZ6AQ9uz6pxtLegS4ry8ntQ7WfG4/KW+qQpEOd55irKyxAa//Rj7RfB3Z9WInO2Evh+GjCCgowNzaGnHBmrD6KOikJdWzsMe3X6TxMTUNRqdCmpgatSe8Oc0uLy5lrJzxxhgZScqPZ+2V9wNBVS0sL2B2T4OYsHEFTMdujUdylaQdjGmnPlhZfYqtJTXPWwGko76a9rp+Ji7M9nMWW1jYZxuldKsJOoANG+ThCKv0QeMkYgpmSWgPG6GtTUgNqoNbmJvL6d9PVPEStVz9ulV4vnx27A9ZjPbsgENCUZDfbmOv9zG1pRuMnIsk51/EMeEvvdXXSBJWYiBCCPZ/XEZ2oJ3+S6z7rcrKx9vZidYsuMtvNQyOVPNfmZPusaa6vRxvEvwDSYa7LyfFIuDQerkAVFRXQlHSycbyMYQiIVRRFC4TaMWUHUKQoSr6iKDrgSuADrzHvAwsURdEoihIBzAKOrajNccBqsaBWaTG41UlymIOAkIieWq1iytk5tNb00xNXhDYr28UY/DwY/mA6Wo06OQl1dLRcs6EhJDOUEALT0aOE5RfIwmI6HeYAuRfuaDnaS1P1IDkNXxExqlDu1x4REwrMDY1O6etYGIPLeeiS/ENhDMJikWWa7fNUOh3q5CQsLc1MW5ZHb/uwLELnb82WVnlv7KWNtc7Q0eCMwTuyyP3/oeQUeEv9rlyGFnZ+UkNkrI6SOZ7E0dza4rFX59zUNGfhuUDree/VNTdw6Kgwm7F2dnqYyzzmBvluzY1NZEd3EZ2oZ+cnNT7n12Zl+SfuzVImDMQY1PHxsqKsV0ixzWDA2tEZcB64NALvdU21tWhzc1AUhYbyblqr+5h6Ti4qlVsEl5OpuNY1eWkaAdfNycXS0uLMz7H29WFuaiJstI/Rwwf6ceMYLi113j9DeQVhY8b827pAHi9j6AKqkFFGm0KZIISwAHcAnyGJ/RtCiIOKotyqKMqt9jFlwKfAfmA78JQQInTbxnHCajGjUXQY7HWSwvV6GUPswRhGDoksmZeOXg81OedIU5Kd24cqvZsbGtDZnVjazExsAwMByyd47L+nB9vAALrcHCmBZ2SERKR3rapBp7GR0bQRXV6uc7+hXCvIh9tJ3DMzZfGywZGjJMx2u7GzZ0BGupNQBIOlowNsNg/Cp01Lx9LcQv6kJBIyItm5qhZh8yV+3nZpVUyMdMiOELJq8WPWcfY4CMJUHCGnPnkB9j3U72um6UgPU87ORa31fAz9aRrgZpoJsK6rhIKv5K9JTZMMwI9Z09zaCkL4zQsAabMP1B7U1NRIWEY6U8/JpbW6j4aKbp+5/gQjS3Oz1OACZQQrCtrsbL/EHSEIyw/sdNWmp4Na7eMINtXWOqu1Ophy8RzPa3ZoBe6OYFN9Hdifq2DQjy0BIZwFGh0lWPRjS4LOAwifNBFrZ6fU2IXAWFGB3q3L3KnG8WY+X2I/9AIwPdT5QohPhBCjhRCFQoj77MceF0I87jbmQSHEWCHEeCHEI8eyv+OF1WJGo9I6S25rBwYRJhPaLCnxazMzMTc3jxjBotGqKU7rozuhhC5bPOqoKNSxsaFL0m5xy461Q5nrCK/TOOf6fxjd0V7XT82BTopi29AoVjTJyc61Q1lTCGEvECclNydT8RPq5w1LcwvqxERU9tafmvR0LK0jRwg5tApPIi3DThWVwrTluXQ3D1K1x3/mqrtdWlGUkEJWnclt7pFFjh4HQZiKra8PYTD42PsdZp59u4cJj9YydoEvsfGucuq61uBNdyytsr6S2iuTGAhq1nTG2gcweeiy7e1BveYKi0U6rTMzKZ6TRmSsjp0fe2oNuqxs6aj3Mk+aG5vQJCf7bU7j3HN2lk8SoqNsuaM0iT/4iy5y1LfS5ebSdKTbyZS9a4LpCgpAq3USdQBjeQW6vDyUEVrV6sePB1zh6oYyGcmoLwmFMUiL+fC+fZiqa7ANDnq0Hz3VOObMZ+B+4DfIzOci/gcyn60WCxpFxzBm9Fo9Vrv0qnPTGDCbR2wyDpBnO4LO1Mf2NZ0IISSRDkECF1arJF4O4m5/kP3ZZ73hIMbuc0ci7lverSQsUkOe4QDatDQUtVoSTkUJibhbu7tlOJ2jcqhjv6EwslZP56E2LV1GCI3UktHZk9hlRtC4OXNHTUslPi2C7R8e9fE1+Ivv16anObWXwGv6ZhIrWq3MRwgSIeSvABpIptKVUEJLl5ap5+Si9RNZ4l0Owzl3JI3BUV9J5ftYO525/kIxR2AMzvagXo5gS2urzOzOyECjVTN1WR5NR3o8MtG12dlgs/mYCo011QF7LTj3nJWNqd7TnGqqkXb4YIwBZAKk4XCF6xobG+Ves3PZ8u5RImJ0fpmySqdDX1SE4aDLUGEoLSV8wvig64GMGtOkp2Owh3wbDh1Ck5ISMInPY7+jR6OEhzO0cyf9X3wBQFQI3fpOFo7HlPQdoEAIsUsI8awQ4sMTvalTDavZpTFE6MN9HpRjcSKL5jpGDWynubKX2gOdIdveLW1tYLE4ibvOuWYIErg3Y8jKxNrTE7AkdX1ZF/Vl3UxfngfNdU57raLTyYqYITAGp/Mww6ExhH6PLM2eZh1n/aERIpOcvgmvnALb0BC2vj5UKoXZFxbS3TLkUb/KOjAgC8R5EVtNWrqsYhpsTXvZbJ+GMGlpQec6beje5geNlqqii4lQDTNhoS8htg0OYuvv9x9ZlJwsI7gC3GNLc0tgP4H9t+yvDIipoQEcgoEf6HLsdndvs45XkbdxCzKISQ5ny7uV2OzmPFd0kWuu9IlVoyss8Luec92CfITB4KH9mqqr0aSlBcxAdkA/bhymo9WuelwVMsqnVZ1Fy9FeZq7M98uUQUr+w6UH7ebANizt7ejHjcwYAMLHj2d4/36Ezcbwrt3ox40LaZ6i0RC9ZAk9b7xJ96uvEj558oimq5OJ42EM3cCtiqI8oijKjYqiTDnRmzrVsFksaBQtBkWWw3BI6Q5i57K9j0z0TA2N5Md3EZsczpb3qlBnZDrthsHglPrta6liY1FFRYVsSlIiIpxtDnVBiLSwCba8W0VUQhjjF2b6pN2H6p+wOP0Ekqirk5JQwsJCYmTe4YbOCKER/AyWlhaU8HBUMa4if06mYpei8yclkVYQy/YPq515Da4SCr6OYEt7e9DeCpYA0Tra1NSgZihHiKbGy0l6eEcr/eEZjDHv8fEtgHtpCj/lHtRqdJmZfqV+kAQ+UEilKjxcRgj50xgam6TW6KdmESB7LYSH+2gMjrh7h11erVEx+4ICOhsHObxN3htnv+qjrkq2lvZ2bP39hBUU+l3PAX2JLIzgnslvrKlBl58XdB6AftxYsNkwlEutYXjvXkSYnl27LcSnRQTtgKYfPw5bXx/m+noMpVL614egMQBELpiPuaGBzieewNzYSMzyZSHNA0j87nel5tzSQuwlF4c872TgeDq4/RH4LvBroBo44wTv6ZTDYjGjUekwY5GO54ZGNMnJqOw9GRyEcyQziRACc309+qws5lxUSFfTILWqIoTRiNUrkcpsM7OlaQsWmyRK3uYg2c4xNG1DEndX8btg0nvZ5mba6/qZfUEhamyyK5VbSJw2MzM0jcErskhRlJCYinVASsTujk4H8Rwpp8DhJ/CoA+SILrLPVRSFuZeMYqjPxK5Pa+RnXlnPznXT0sBmC2oiNDc1SVOXFzTpaUGT8sxNzaDVepgRTAYLW96pJFbpIaVhs9953ol43pAOWV/ibjOZsLS0OIMXjmWuewSePyiKIvNUvEqNm+z1rtyTsEZNTSElN5ot71VhGragSU5GnZTkJNAAJntYdtgIGkPY6CJQq53OXGGxYDpSOSJDAZySuqNiwfC+fbROuoSetmFmX1iISh2Y9EVMlT2oB9auZWDjRhSdDn1x8YhrAsSuXIkqNpb2Rx5FFRlJ9NKlIc0D0I8ZTeZfHyXnueeIu/TSkOedDBwzY1AU5bfIUhZnAY1CiNAb5/6HwqExmLAQFq73eVAcuQwjET1bby+2gQG0WVkUTEkmuySe/fWxmLTRHnNNVhO3fnErN39xMw/tfAjwH9ftCFkdCebGJo8MUpfZwHOuYcDMlnerSB8Vy+iZ9th0m81XY2hpCckR7Gig7rHfEe6Rt6YBoI6KQhUTM2JRO0tzs28dIAdTcSPS6YWxjJ6Vyp7P6+huGQycSTxCLoMwmWR4rB+VXpuaJks2BCgQZ25ulhnabvb+7R9VM9hnYlpmCxY/RdPkXgLnIoB0BPvrAmduaAQhnGafgHP95RQ0NDiDHQIhbPRoDBUVHseMR44QVljocY2KSuGMq8Yw1Gdi+4fSH6AvLvaooOvImNcVBifwqrAwwgoLnU5cQ3kFtqEhwqeObKTQpKSgTk5iaOdOhMlEb0UdhyNnkT02wVnBNuC1jhqFfuJEul5+mb73PyDm3HODZi577Dk8nJS77yJ8yhTSfn3viCYvb8ScfTaRs2f928JUHTgejeFXgNE+9xJFUZ484bs6xbBaLGhUOkyKBb0fxgChhaw6+jZoszJRFIUFV4zGaoXKwos8tI3Paz9ne8t2pqZM5eWylznYcRBzUxPquDiPH5KD0IZihnInXuqEBKn6exHpLe9VYRy2MOHCZO7ZcA8HDnwl1/FiDFgsIzraLfYkIw/pPQTG4M9PIN/7r2/jMbe11Ud61yQlgUbj45+Yd0kR2jA16149jKmpBRQFrVdDGG9tw2e9lhYZxumPMYxgXjS3eNYB6mgYYP/qBsbNzyCtKAlhNvuvwe8IjQyY+JWNrb/fJ5fBkYSlDRJrr83JlnH2bi1NrQMDWNrbnfV6AkFfUoyluRlLtysc1XhE1rvyRmpeDOMWZLJ/bQMdDf3oS4oxHal0RiYZKw6jio4OGKrquW4JhtKDdpu97G8SMW3aiPMURSHm7HMYWL2a3g8+4HDu+dgUFWdcOTokoht/5ZWYa+uwDQ0Rf/VVI473mHvVVeS9+gqxK1ce07z/JBxvHsMzQAmQiKyG+l8Nq9mMWtFgwopeHyZt4F4SVEhEr8GzkmJ8WiRTFmfQkjaL2kM9znHvVb5HZlQmf138VzQqDauqV/ktsavLynTWlAkEZ80Zry5P2swMzE2u/dYd6uTQxiYmLM7g7v3fY1X1Kl5e84i8tgxPUxKMHHZqbm7xIdLajIwRcxkc0rvKy24/UpKbsDMrb41BUatlHSCv6KKIGB2zLyigsaKbymohyw54hRtqApRKdl6j0+/jK00Hyq51wNLU7PR/WC02vnr+EPpIDbMvLHQ5c/3Y+021dWgzMgKGRjqTt7wkf2cVzyCMIWz0aBDCo2Chs/TCmOBJWGF2U4rRrjVYe3qwtLcTVuTLGABmX1CAPlLDV8+XoRldLBss2U1IQ9u3EzFtWkgEOnLBAqxdXQzv2sXQzl1oMzODlsNwR+yFFyJMJvb/5Q3aUqYxbXkecSmhSfCxF5xP9lNPkfPcc4RPnBjSnP8lHC9juBNZ10gD/NebkqwWCyq1FqEINGYT2Gw+ZXJDyWVwVlJ0mzvjgtFEDTezvToBw4CZjuEOtjdv5/zC84kNi2Vexjw+q/0Mc1OjT/q7K3M6MEPy9k24z3VoMMYhM6tfKCc+LQLj1Ebq+uv42cyfEd8j/RsaD/NVaI52c7NvZcxQmEpT1X5swO37f8mwxSW5ajLSndFV/uBIbvMbxpmejsVPRNO4BZlkjoln/2AR5ixfG7E6Kgp1fLzfzFxwRYT5K0vgTITyY7N3hB477uuOj6vpqB9g0TXF6CO1AStxgmcSlj84GVKNp73fXF8nAxCChEY64ukdNntwEfqRkqkcNnZHfL/BXovLUR3XZ3yklkXXFNNRP0B5n3weBjduxNzSgqmmxllBeCRELz4TJTyc9n88xsDatUSesSCkeSCdyOHX3Eh5ybXEx9iYtjz0SqSKWk3U/HlEzg5tn/9rOF7GUAXokaW3/+udzzaLGVT2Ugn28DZtpi9jGMnE4ii8514OWK1VMdmwAZNVw5qXy9nVshuBYF7mPACW5i6lZaAZU2OjX+IOwYm0dzSTAzo3M9S6VyoY6jOx9MaxfFjzPon6RC4bcxnjLan0RqkQWlc0SqCGI+5wSu9embIjZXrbhI3t+1fRF61iR8ce/rXvX27rZsgWkgG0DWf/W39EOivTr+1cUSksuaEExWZlX9xZfusoabOz/RJosN8DRfFr71dHR6OOjfUf5dPcImPmMzNpquxh96e1FM9Np2Bysv1a02TYqddcIYSdMQR2IIfl5YFW65T0HTBWHUWXkxNUCtdmZqKKivIosGioqEAVExO0WimAJjERTXq6s7/I4KZNoNEQbnfU+kPB5GSK56Sxd2Mnw1OW0vfpZwxu3gIQMsFVRUQQs3w5Q1u3Isxmkm65JaR5DhxMXIZVH805d81GrTlecvfNw/HeqYPAauA7iqLsOIH7+bfAYjaDWhJHtb0EhT8fA4xApO2F97yRmKZndPd6ju5pp3R1I+GacMYmyFC86anTiR4GDMavxxh85mZh6+tj36eVHNnZxsyV+cRl6dnUtIll+cvQqrTkDUXSGmNjd5urmZAqPBx1YmJQf4qlvV1K796mpBH2u6t1F/rOQfTpWSzMWsiHRz/EarN67D8QUwmWhKXNzglYsiEqRsPoI6/TZY1n87tVPp/rsrP91t8HeW/9maCc6+bkOG377nBUybWm5vLZE6XEJIWz4DKXZK2o1egyMnyifKw9Pdj6+4NqDIpOJ5O33Ii7EALDoUMjZtgqikJY8RjPrN6Kw+hHh2Z3j1q0kIGNG7ENDzOwcRMRkyejdkv884f5l48mOlHP3qSV9B6uo/0vf0GXn39MWb1p9/6KtHt/ReZfHw3ZjASw5/M6avZ3MOfCQhIzg+/zNDxxvIyhEGlGegK48cRt598Di9mMUMlkF3VXtz3Zx8s56pCGg0QJmevr/Tr/tFmZZJZ/QMGUJMJ2ZjNXLEWrlhpKZlQmo42yXr93zLs6JkZG6wRjDI1NYO8M5rnfTHpiCtj8YT15E5OYdk4uBzoOYLQamZk2E4DIriE6YhV2tuz0nDtC2KmzNEWGryNY0ekCahvvV75PSr9CQm4RKwpX0DbUxo5WKVc4pGR/phmw29DtIbze0OXmyJIN/iJuWlpJa9nO6CwD+76s58gOT4evNidbmgj9VJQ1NzQETTLSZWf79xNUV2NT1KzbJDAZLCy/dQK6cM8cgbDRRT5Sv6NsuXaEjGD9mDEeUT6WtjasXV3ox44NMss+d+xYDGVl2AwGbMPDkqGEmIQVc/bZiOFhup5/AWNZGZHzR87MDQvXsPzWCZjRUjbj+5g6u0j7za/9ZmcHgiosjPirriLm7FAr/Mskzq3vVVE4NYVJS4IXvzsNXxwvY6gXQnwAVHIKqp+ebJgNJoQ9rtnWJm3n3sk+I0nDwmazF97z46jMzASTielnRdKjb6No+5l0Nspa/oqiMB1JCPxHv2RiagzCjJqanCUt3DEYmcb+CbcQFSFY+q0SFJXCrlbZTW5a6jSEzYatuRVbSqLzuHPNjIygpiRXzSJP6c1VwM93rhCCzQ0bSekFfU4ui7IWoVVp2diwEXA5Tb1bIzrXbGhAk5rqrK/kDl0QR7CDWcxaFEd6YSxfvVBG05Eet7k5smSDn+s1VlejKwhSrC0AUzEcraZswndoqR1k8fUlfqXVsOJiTLW1HqYzh1M4WIE4kBFC1vYOZwkRR6x+KMXaos5YiDAYGNy8hcEtWxBGI1ELQ7MGR8yYgTYzk/ZHHkEdG0vsRReGNC8pK5pF15bQpc2g8fbniJgxI6R5x4vOxgE+e7KU+PRIFl9f/G8P/fxvxPEyhmWKomQBjwN/OYH7+bfANGxEKJKwmhrr/JorVI5yEQFMLJb2doTZ7Le3q8O8dKRyPR+XPI42TMOHf9vn7FNcbJJFzzrifL8ObeYI0rufaKaBbgOff9yHImwsHNVEWITUTna17qIovojYsFgsHR0Is5mY3EL2te/DbDW7rWl3tAeq++/IJPYTUhkoequ2rxZbWwdqiw1ddg56jZ4JSRPY2Sq1FXVcHKqYmID2flNDfcBYe2ftfn+Mwc5U9TlZLL91AtEJej5+bD8dDZIxu8o9eEr+1t5erB0dhBUETsIKG1UEVqsz2gYkA9zTkERrwiTmXFRI0XT/+Qj6EnslTjetwXCgFFVMTNAWkgBhdpPR8H63Ht+KQtiYkZOwImfOQBUdTf+XX9K/ejWqqCgipodWB1PRaMh98QWiliwh8y8PB8y18Icxs9KYuTKfIwf62PJOVchdDY8VfR3DfPDXvWi0KlbcPhGd3n8292kEx/EyhjjgHuAnyJyG/2qYjWas9prsGrceA94IFrLqiBLR+sk8dWgb9eU7Mej7Off28VhMVt59aDc9rUNk9moY0sFeg2/fY12mLMIX6EEyeTUC6W0f4p0Hd2My2Jhy+BnCOiXBs9gs7Gnbw7QUGQPuMNlkFE3BYDVwsNPVllObkeE3W9u5Zl096rg4v/blQPdoR+sO0nrs9XPsxHh62nTKusoYNA/aj+cEtvfXN6DL8v+9qOPjUUVG+jXrGKuOymqbGRmER+s4/67JaMPUvP+XPbTW9KGzS+fGSk//g7NpUhDGoC/xjNQRNsH6Vw9ToymhUFfDlLMDE3hHlI+7I3i49ADh48eNKOGGT56MKjKSgbVrARjYsB59SQnqqMig80D6KKKXLqX3ww/pfe99opcuHbFqqDu0GRlk/+PvRM6dG/IcB6afm8f4hZns+aKOzW9XnnDm0NM6xLsP78ZqtrHyrsnEJIaWlHYavjhexvBbZERSBTByN/X/cBiHDdjsdyKyeyBgca+gjMHefclfmr9Dou+qqWBs4liy8pK58IdTsFpsvPPQLozNalqS1OxycwK7rymGh/3W0bf29dmlWkncWqv7eOeh3ZiNVi74wRQSExTnfss6yxi2DDM9bbrHfsdMkQ34HJK7+34DmZMcDU/8QZuZibWry1m8zIGdLTspHJSMRGtPppqWOg2bsLGnbQ9g77frR+q3GY2ydEeAsg2KoshWqG4dsBwwVlWiKyhA0WgQQvBZx0dEXNKOLlwyh6YWWZzOuxWqq2xD4OxcXV4eil6Psbwcs8nKF88cpHR9Izl1nzNtvDUogdekp6OOjWXYXonTZjRiPHwE/fgJAec4oNLpiDxjAf1r1mJubMSwbz/R55wz4jwHUn/+M8LHjkVfXEzqL/5fyPO+LhRF4YwrRjNhURZ7v6xn9YvlWM3+O+4dK1pr+njnoV1YzTYuuHsKiRmnnc1fByExBkVR1IqiNCuKchOAEKJBCPGl/f8/PZkbPBUYGh7EqgIEaCwW1HmBiZ65uRlhNvt8Zqo+ihIR4bfGjSoiAlVCAqKphampMrwvKSuai340FW2Yms3as+jOXcbuVj+MIUhfBgch1OblU7q+kXf+vAu1RsWFP5pCSm6MR0kNd/8CSPu5otORnD+WwthCDz/DSPkIwWLt/c0VQrCzZScTjLI6qKNO0uTkyWgUjXNtbU4O5qYmH5u9qbpalnsIUjwtbMwYn5INgKytY8/OfXz/4/xmy2/43aFfor6wkZgkPR/+fR+1JZcwXO4511h1FEWnC9qMXVGrCSsqouNwE2//aRdHdrUxdZLCqKPvEzE5eEdaRVGImDuHgQ0bEDYbhv37wWIJuVhbzFlnYe3ooOH7d8r3y0JnDOroaHJffYW8118bMaroRENRKSy4oojp5+ZRvrmZdx/eTX+X4bjPJ4SQv/2HdqHRqrnoR1NJzokeeeJpBEVIjEEIYQVKkdFI/3MwGoaxKAI1CgpQF+9figkrLACr1W/Te+PRanR5uQGjLcwZiaR2Wp2EGWRm9KV3jSOhq4zesHOZsGUF1TWexNiRT+HXTFJdzWB4Cl9tD2fdKxVkjY7n8p/NcEpL7u1Bd7XuIi8mj6RwmQBlqq6RHd/UaqalTmNP2x5nQb9gSW42gwFLc3PAEgr+orfq++tpG24jt1+PLiPD6SiP0EYwNnGsMyrKn80eXNm5+iAtEvXFY7B2dnr0dLANDsrWikWjGDIP8cLBF1icvZiZaTN56ug/ueDHkxk9M5UK1QQ2R51PW5VLKzOWl6ErLPRx6rvDarZRk30267UrGOg2cN4dkyg07gO1mvDxIxP46DPPxNrRgaG0lN6PP0bR64mcE5qJJvqcc4havBjDoUMk3vSdEXsbeENRqYJe28mEoijMOr+AZTePp6tpkNd+u40DaxucpbpDRW/7EB/9fZ/87Y+J5/KfzyA+bWRz2mmMjGMxJUUAP1EUZaeiKB/Y/94/WRs7lTAajVgUGzpUmDSwR/Fv53b0bjV4hRkCzp7LgdCapienXUrJHmhrYOKBxxk1ppXEwUw++VMZnz1ZStORbmw2u5Ss0WA8csQ5xWYTNFX2sH6TjW0zf0lHu5mFV4/hvDsmoY9ydcTS5eVhGxrC2NLMrrZdHkzJVF2NLk+aoKalTmPQPMjhbnldzqJ2fjQGh28iECFyNFAxVbls9g4zVXzLgE+DlWlp0yjtLGXYMuyy2XuZdYyHD4NWG7Q5S1ixPavXTfJ3FGsLGzWKVdWrGDAP8K3x3+KakmtoH25nW8cWln5rLPOmmDCExfPmg3v55J/7aSjrYHDvfmeVTW8M9ZnY+2UdL/5yC4eGCkjsLOWCiyLJHZfI8N596MeMCal4WtQZZ4BaTeezz9K36lOily4NyU8AUlvJfOQv5L3+Gik//nFIc/7TUDg1hSt+MYOUvBjWv3aYV3+zjUObmjAZApdBB+hsGmDNi2W88pttNFf2Mv+yIs673fO3fxpfD8fisp9jf51q/wM4OaEFpxhWkwVzhI0wq6A9Scu+rgN+x+kKCkCt9iDSIKVoc1MTsRdfFHCN8vhhFhsgsscAblGepuoaFARzl0/kt5u+x6XGW6g7qKFyVxv6KC2peTEok2+gvkJLxJtH6G0borWmj+F+Mxoiye/bwaIHf0REjK8DUV8sk4hqdq2h39TvZAzCbMZUX0/0WWcBOM1bu1p3MTZRxsLrsrJ8yi6AKyRUl+efMWji49GkpnoQ6B0tO0jRxCNqGwlb6lmffnrqdJ4tfZb97fuZmTsNJSwMY1k5XOgaYzh8mLCCgqBtIB3Xaqwod3a+MhyUDvWwMWP4ouI+cmNymZw8GYuwkKBP4JPqT1iUvYixKyag+ttKuq74JZWVGqr3daCd/EsyRARJ71cRFqFF2AT9nQY6Gvppqe4DAZmj41h0aQ7DN9yJ2KnGOnEUw3v2EHfF5QH36Q51XBxJt9xMx2P/BK32mIu1qXQ6Z0vI/1bEJkdw/l2Tqdrdzq5Pa1jzYjnrXztM1ph4krKiiE7Uo1IrGIcs9LQN01jRTU/rEGqtipK5GUxfnkdUfNi/+zL+53AsjCH0QiP/ZVAsAhMWtCYzhrw09rTtQQjh4zxU6XTo8vMwHvZkDIayMhAiYL0Zs9XMlohGFiNr07jH/xsOHULRaoksKKKopoBtlg954dsvUXewi5r9HXQ0DNATNRGbUFBvaCQ2OYLskgTyJiZh/X83ETU63y9TAJzZpQ17N0G2JMJgj7ixWJwF0NIi08iKymJX6y6uG3udc+7A+vU+53RE7wQzXYQVj3E6c4UQ7GzdyRLraLBschJwByanTEZBYXfrbmalzyKsqMi3vPPhIyOGVKpjY9FmZTG0dy+OjseD27ejSUvDlp7MrnW7uGT0JbLAoKJlbsZcNjdtxiZs6LIyiczNIK76I+b98wkO/P0djm48SnfqXOo+q0PYTRxhERri0yKZcW4eBVNSSMqSJrvaKVPo+3QV6ugohNFI3MWhN1lJuv12VNExRM6aGVKC2v8iFEVh1LQUCqcm03Skh6N726k/1EXdoS7nvQd5/1PyYpiwKIui6SmER4ceTfXfCptNoFJ50qEfvr4XvU7NHy4aOVDheDEiY1AUxeGJ9asduH3eI4ToG+Fcy5BF99TAU0KI+wOMmwFsBa4QQrw10h6/LhQbmBQLYUYz+okT6Bj+nIaBBrKjfcMj9aNHM7xvv8cxgz2eXB+gCmNpZylHEqRD1XD4MFELFzo/G96/j7CxJah0OqamTOWZ0mewqE2MmpbCqGmyAmnHk0/S/ueHGb19G2p79zJLdzdHaisJv+yCgNeljo5Gm5mJoayMjOIM0qPS7WvK/YZPcP2wpqVOY13DOidD1JcU0/vuu1ja2z2yqg1lZWizs5378Ad9cQmdmzZjMxppMrXTMtjCtAGplXjH2sfoYhgdP5pdbdIBrS8ppu/zLxBWK4pajbm1FUtLS0hEM3LePPo+/FA6r7VahrbvIHLeXPa278VgNTA3w2W/n50+m4+OfsSR7iOMSRhD1Bln0P3SS6jMBuK3vskU0xCFf7wHq9WG1WxDURQ0OpXfSKPE795E/S230vbnhwmfNCnkpi4gTUKJN34r5PH/y1AUhczR8WSOlpUArBYbQ30mhE2g1avRR2q/Uclq7+1p5HcfHeL1W2YzKkU61Bt7hnlnj/T9zS5I5PxJJ6f9Zyg+hueB5+yvgf6ew0P594WiKGrgH8ByYCxwlaIoPk+7fdyfgM9Cu4SvD41NhRkLWrOFrDlLANjbttfvWP3EiZgbGz2auwzv24cmPR1tgPry25q3MaxXoc7McDIRkMXoDAcPET5RmgOmpU7DKqzsbfdc26GJGA65kswNBxwtB4OXBNaNGUNETRuz0l1Fywz7D8hEKjepf1rqNHqMPRzttYdpjnHY+z2l91Bq8uiLx4DFgvFIJTtaZMmL3DZQ9Hq/BeKmpU5jf/t+zDYzETNnYevtdWbzDm6xF12bMzvomgBRZyzANjTE0O49GI8cwdrZSeTMmWxu2oxGpXFqTCAZA8CWJnn+mOXLEGYzjXffjeHAAeKvuRoAtVqFTq9BG6YOSJSiFi4k6Y47iFl5Hhl//nPQPdrEiQnP/CZArVERnaAnJimc8Cjdfz1TsNkEa8rbsHgVc3xsbSWXPb6ZtRWuAp2tfQbufn0vnYMmXt/h8nm+Z2cKOo2K17b7TwY9ERiRMQghzhRCLLa/BvpbLIR4YYRTzQQqhRBHhRAm4DVkJzhvfB94GwjeKeYEQi1UmBQLWpuVUVMXE62N9ikT4UCkvVzw0LZtzmPD+/YHrdm+o2UHxQnFRM2ezeD2Hc7S3cbKSsTwMOETpeQ+KXkSKkXlE7YaPmUKqFQMbXdbc/8BUJQR69z0j0olvdPGXL2LBw+XlhI+frzHg+Ygmo7rdph83GvyWPv7MdfVjSi9h0+eDMi6+9tatpGgT0B3oBL9+HF+I2Gmpk5l2DJMWWcZkfPmgqIwsGGDPMeWLajj40MquhYxazaKTkfPO2/T/eJLKDodUQsXsqVpC1NSphChdTmEUyNTKYgtYGvzVrnniROJu+xSBjduRJuZSdwFgTUxf0i+43YyH3gAXYDs7I7hDm767CbOfutsjvYc9TvmNP530NA9RH2XZy7PJ6XN3PjcDv613vX922yCpzZUs6Omm99+5Optva5CRtflJETw3t4mJzP5/FArU3LiuHZWLrtquzFaTk4a2amsQ5sJuIf7NNiPOaEoSiZwEbLURkAoinKzPTpqZ7tbeOLxQis0mLCg1+nQhOmZlT6LjY0b/WZmho0Zgzo2lsFt2wFprzc3NgbsKmWwGNjbtpeZaTOJnD1bSsN2+/vgJtn3N3yKbFUYpYuiOKHYI9kMpEkofMIEZ8likIwprKhoxCiW0nz5FY+vlddi7e3FePgw+kmejCwrOouU8BRXiYrYWLTZ2Qzv2eu6FrvGoh8XnDFo09PRFRYysGkjW5u2sihyMsZDh4ia77+WvsMpvrt1N5qEBPTjxjHw1WpZxXPDRiLnzA6p6Jo6KpKEb32Lvg8+pOfdd4m95GJ6ohTKu8o9zEgOzMmYw67WXRitMnk/9Re/IOeZpyn45GNUkSc27PGBHQ/I0iM2M99f/X1nVdnT+O/Gjpou+g2eeU37G3qY/6c1nPc3Txry4hYZzPGPNZV0DsjfXHlLP12DJiZkxnK0fZDaTlkFYFt1FwmROn58zhja+43sb+xlyGThYGMvcwsTmVOYiNFiY09dz0m5rlPJGPzpgd6U9xHgHnveREAIIZ4QQkwXQkxP9qoqejxQKWpsikBnL6Q3P3M+rUOtVPb4lqhQVCoiZs9mYN06bEYjfR99DIoSMPN0X/s+TDYTM9NnOpuTDNqdun2rVqEfN86jvtLcjLnsa9tHv8mzl3DE3DkMHziAta8PS3s7Qzt3htRo/KvwGoYiVKh2SNNT/+o1YLUSvXix53UpCtNSp7GzZafzxxw5Zw5D27Y5E/oGt2yWMfoTRnZ6Rc2fx9COnfT3d7CwKVaeb4H/apxJ4UnkxuQ6/Qxxl16C4eBBGm6/HWtXF/FXXz3ies5zfe9Wos85h7iLLiL5zjudGsGcjDk+Y+ekz8FgNTjNhqqwMCLnzkUVdmKjXOr76/ms5jOuKr6Kn8/6OXX9dWxs3HhC1/h3oHfIzD/WVNIz5JmQKIRgc2UHzb3DHsdb+ww8sb4Kq1e+Qu+wmWGT7yNvMFup6xzyOd7WZ/AxxwTDiSi98cG+Jh750jNM/UBDL5c9voVJv/mchm7XPv/8uRzXO2ymtFG6Xeu7hthW3cWKCekMmaxsOCLLzWw4IgXbX54nha3V5dJQsr2mk5l5CcwfJfOONld2sKeuB4tNMCMvgZn5CagU2FLV+bWvzR9OJWNoANy9uVmAd6D8dOA1RVFqgEuBxxRFufBkb0yxl9zW2xvWOJroBHp446+4HGtnJ90vv0Lve+8RMXMm2lT//oXtLdtRK2qmpkxFm5JCxJzZdL/yKsMHD2IoLSXm3OUe4xdkLsAiLE7btwPRS5aC1UrPm2/St+pTEIKY5Z6hn94wWo3s7thL18Rc+levxjowQP+nn6LJSEfvJwFrTsYc2ofbqeiWfoXI+fOwDQ4yvG8fAANr1hIxZQrquLig6wJELVkCJhNL9gqyvzyEJiUlqG9iaspUdrfuxiZsxF16KWHFxQxu3kLU0iUhF3kD2U8i69FHSP/db9HEx7OlaQtxYXGUJPiuPT1tOhpF43OvTzTeq3wPgGvHXsvinMWkhKfwesXrJ3XNU4FnNlXz4GcVXPzYZgxmF2H/6dsHuPqpbfz0bc+w7/s+LuMPn5Tz9i5X8qPNJljx1w1M/M1nTiIJMGSycO5fN3DGg2to6XVlRncOGDnzobXc+tJuD4J/oKGXm57fyR8/8Sz2XN81xPw/reHdPZ4Vij8tbeH2V3bTO+Qp7dd1DvG3r47QNehidkMmC7/+4CCPfHmE0sZe5/HPD0k/o03AR/tlxWGz1cbOmi5WTspAq1b4cL8kcVuOSgJ+55IiYsO1bK6SjGFjZQejU6OYmZ9AQXIkq8vb7GaoYWbkJ5AQqaMkPYZNlZ1sr+5CpcC03Hhiw7Xcs6yY+UWBO/Z9HZxKxrADKFIUJV9RFB1wJfCB+wAhRL4QIk8IkQe8BdwmhHjvZG9MZW/SExYmw9/SItMoii8KyBgi5sxBP348bQ88gLm1laTv3Rrw3BsbNzIhaQJROhnamHTzzVja26m99jrUcXHEnn++x/iJyROJ0cWwoXGDx/Hw8eOInDuXziefov1vfyN88uSAbRUd2Nu2F6PVSMR1V2Lr7aXxhz9kYN06Ylee79eRtyBLmnrWN0iNJnLOHNBo6PtkFab6eowVFUSdeWbQNR2ImDGDutFxXL/ahmX/QZLvujOoOWh62nT6TH2Ud5XLKp4vPE/+B++T9ejxd44VQrClaQuz02ejUnzXjtRGMjF5Ituat/mZfeKwpn4NU1KmkBKRglalZVn+MrY2b3UWD/xvhBCCd+2O0KMdg2y1E75+g5m3d0sivP5Iu1ObqGzr58P9TWjVCg9/cdgp8e+u66ahexizVdraHXhlWx1H2+X9cZwP4LUd9QyarHxZ1uokxgBPbDjKl2Wt/Gv9UQ/b/j1v76exZ5hHvzzi1FSEEPzq/VI+3t/M9152+RJ7hkyc88h6/vzFYV7YUuM8/ubOBroGTYRpVDy21mVF+OJQK7PyEyhOi2b9YcnUDjT2MmiysmxcGnMKk/iqTPb/2FHdRXyEltGpUcwpSGRzVScGs5Vt1V0sKJJWj8VjUth2tIv390pmcuYYeXxeYSK7art5c2c9U3PiidbLfJ5bFhYyIy8h5O/sWHDKGIMQwgLcgYw2KgPeEEIcVBTlVkVRAlPWUwF7ye2wcFdc9PzM+exu2+334VUUhewnnyDxe7eS+dCDRM72HzHTMtjCoc5DLMpe5DwWMXs2qb/4BWFFRWT+9VGfBjsalYZ5GfPY0LDBJ4Il5af3oEmWzXAyHnpoxMva1LQJjaJh0oKLib34YgbXbyBs9OiAjCwpPInxieNZ17AOkL6NuIsupOfNN2n++f9DCQsbUUtxwGQz8chZRprmFBJ//XXEXhQ4+Q9w+gAczFgdEyM7i32Nsg2VPZW0D7f79S84MDt9Ngc7D9Jr7A045uugob+BI91HODPbxVAXZS/CbDOztWnrSVnzVGBPfQ91XUPcd9F4wjQq1tqdpRuOdGCxCX553liEkJI5wCcH5OsvVoylpc/AvoYeQH6uU6u4ckY2W6o6GTTKrOc1FW2MTo1iVn4Cb+2SZV2EELyyrY65hYmkxehZVSoZg8FsZXVZq9Ps8tlBuVZd5xCbqzqZkRdPTeeQk3jva+ilrd9IZlw4m6s6aeuTGsm6w+0M2zWfLw65Gjp9Vd5GUUoUl0/PZm1FOyaLjebeYcpb+llSksIZo5PZWdPNkMniNO3MKkjgjKIkqtoHaewZZntNFzPyElAUhXmjEmnoHubFLbWYLDan1L+4OAWT1caDn1UwJjWagmQpTF4/Jw+NWqGp18CtC09NVaJT2gRVCPGJEGK0EKJQCHGf/djjQggfZ7MQ4lunIocBALskG+7myF2QuQCLzRLw4dXEx5Ny113ELF/u93OAdfWSwLoTBUVRSLj2GvLfeJ3ImTP9zluQtYBOQydlXZ5qsX70aPI/+ICi9esCRr84IIRgTd0aZqTNIEoXRcYf7qPws0/JffEFVHp9wHlnZJ/BgfYDdBlk3aCk229HFRXF0I4dJN/5/aAdzdyxq3UXDTFm9L/5CWk///mIzuOk8CTGJY47obZ3h+bjz7/gwOyM2QiEM6z2RGNN/RoAFme7fDqTUyYTrY1mbcPak7LmqcDqsjbUKoXzJmQwpzCRdXaiu7q8jRi9hhvm5JKdEO4ksOsOtzMxM5YLJ2eiUmDd4Q6EEHx6sIUFRUmcPykDk9XGxsoOBo0WdlR3s2hMCisnZVDdMcjRjkEOtw7Q2DPM+ZMyOGN0EhuPdGCx2thc1cGgycpNC/IpTot2MoZ1dtPU7y+cQKROzZd26f3DfU3oNCoevlyGia+17/2rsjYSI3X8ZNkYDjb10dgzjMVqY1dNF7MLEpk3Kokhk5W99T1srpQMYP6oZBaOTsZktbH+cDtrytsYmx5DUlQYZ4yWQt/TG6qp7RxidoFMvXSYme77pAydRsWsfCn1T89LIClKCqcrJrp6neQkRvDw5ZO5fk4uS0r8m6xPNL7x3bGFEGB3OuujXZUmJ6dMJjYsls9qjz+d4ovaL8iLySM/9tiSxudmzEVBYX29b+axoighSdHVvdXU9NWwOMdFkHS5uahjY4POW5i1EIFwEmhtWhqjvvqSgo8+JOHb3w75GlbXrSZcE86MtNC7dc3PnM++9n0nTHpf17COkoQS0iID9wkenzSeCE3ESfMzrK1fy6i4UWTHuNxrWpWW+ZnzWd+w/pTlNQyYBgLm5hwPVpe3MS0nntgILUtKUqnuGGRffQ+fH2xhSUkqGrWKJcWpbKzsoLXPwJ66bs4YnUxshJZJ2XGsP9zOwaY+GrqHOWd8GjPyE4gN1/JpaQvrD7djstpYNDqZRXZzypryNmec/8IxySwcnUKfwcLe+h4+LW0hOkzD3MIklo1PY2dtN+39RtZVtJOdEM7o1Cjmjkpi3eF2KTCVtzGnIJGZ+QmkxoSxtqINk8XG2oo2zixO4Zxx8vfyVVkrpU19DJqszMxPYE5BIioFNlV2sKmqg4RIHcVp0cyy+wKe31zLrrpuzh4nKywXpUSRFR/OM5uq0WtVXDRFCnNxETrOnyT////OLSFCJ03ZOo2Kr360iDdvncPNZ3jWXVs2Po3fXjD+lOVyfOMZg3u/54g4F9HUqrScnXs2a+vXMmT2jYwYCW1DbWxv2c7y/OXH/GUmhicyJWUKn9V8dtwRFZ/Xfg54aiuhoCShhOTwZNbWr3UeU0VEEDZqVMjXYRM2vqr7ivmZ8wnXhN4sZUHWAmzCxuamzce0Z3/oMnSxt22vhxnPH7QqLTPSZjijl04keo297Grd5fc7WJi9kC5DF6UdpSd8XW9UdFVw7jvnct2q63hk1yNf+3zt/UYONfexqFgS7fMmpKNVK9z84k76DBYumCy1yiUlKRgtNn7w+l5sApaWSIJ51thU9tb38PAXh1GrFJaWpKJVq1g2Lo0vDrXy0rZaUmPCmJmfQFZ8BEUpUXxxqJVPD7ZQnBZNemw484uS0GtVvLGzni8OtbKkJAWdRsU549IQAt7YWc+GI+0sHpOCoigsHJ1MQ/cwXxxq5WjHIIuL5fGzxqayuryNjw800WewsGJCOoXJURQkRfLFoVY22rWOWfkJxEZomZoTz3t7G9l4pEMyCpWCRq1i2fg0thztRAg4e6xkLIqi8OiVkwnTqLhyRg7xkS5T9e8uHMdbt87hhrl5Hvc2NlzLjLwE9Np/T+VbB77xjGFoaNDZ71kfF+/x2fL85Qxbhvmq7qtjPu+q6lUIBMvzA5uaguHc/HOp6q1yVjw9Fggh+Pjox8xIm0FqZOjtF0H+mBfnLGZDw4bjdo7ubt1Nx3AHS3NGDqd1x/jE8cSFxZ0Qc9KGhg0IBAuzF444dnb6bOr662gcCNxC9XiwvmE9VmH1yxjmZ85Hrag9GPDJgBCCP+34EyB/U0+XPv21mVF7v4zBL0iSptf4SB2Li1No7TOSFKVjnt3WPys/kYKkSDZXdbKgKIlJ2XEAXDNTZtyvLm/jzDHJJNgJ5nmT0hkwWthU2cll07LR2J/LS6dlsa26iz11PVwzS2bOx4ZruWBSJm/sbKB7yMyy8ZIYF6dFk5MQwYOfVWC02Lhujlzr7LGp6LUqfvD6XhRF2vPlubMxmG384PV9JETqnPb+s8amsqmyg7+trmRWfgIpMdL8evMZBdR2DtHWb+SKGS4t8Ma5eZw1NpXbFhVSku7qBzEtN4HNP13sDEd1IEKnYfpJchyfCHzjGUNfXx82e8RKRILnFzUtdRo50Tm8dfjYXB1CCN46/BYTkycesxnJgbPzzkajaHi/6tgrm5d2lFLTV8N5Becd19rnFZyHwWo4LoYI8OHRDwnXhI8orXtDrVIzL1M63s0232ZIx4J1DetICU9hbMLINZbmZkrntMMncaKwpn4NyeHJjEvyzU6PDYtlcspkp6P/ZGFX6y52tOzglkm38Ks5vyJaF81TB576WuccNEkHcWSYq9TavSvH8adLJvDyTbPR2gm6TqPixZtmsWJiugdhjI3Qct9F47l4aiaPXDnFeXz+qCR+dd5YVkxI5/o5rnItNy0o4IzRycwpSOTqWa7j31mQT2Kkjqtn5bDEro0oisKvzx9LtF7DWWNTnTWGUmL0fG/hKAZNVn6wdDTZCTILflJWLGPTZd2vy6ZlOfd+0dRMbAIP5gKSYZw/KYOfLS92+hAAilKjefL66fxkWbGPZp0YFYZadWpMQCcMDm//f+vftGnTxNdBael+8ew9fxa/+9VvhLG21ufzZw48I8Y/N14c7joc8jm3Nm0V458bL96vfP9r7e2Ha34o5r4yVwybh49p3i82/kLMeGmG6DP2Hde6NptNLH97ufjWqm8d89wh85CY9fIs8f82/L/jWnt17Wox/rnxYm3d2uOaL4QQRotRzHxppvj15l+HPOf8d88X3/n0O8e9ZqA9/GbzbwKOefbAs2L8c+NFY3/jCVvXG/duulfMeGmGGDQNCiGE+MvOv4iJz08U7UPtx33O1eWtIveej8TOmq4Ttc0RYbPZhNVqC3n8sMkijGarxzGL1SZ2VHf6nKdv2CSae4aFzeZ5/EBDj/jz5xXCZPE8z/8KgJ0iAF39xmsMA4P92BTQ2BS/FUMvGnUR4Zpwni19NuRzPlv6LAn6BM7OPftr7e3K4ivpM/XxSfUnIc/pMfSwqnoVKwtWEq07vhaHiqJwSdEl7GzdSWW3b/Z3MHxy9BMGzYNcOOrC41p7ftZ8EvQJx6UpObCxcSNDlqFj8q8syVnCztaddBu6j3tdd2xq3MSQZcjD+e8Nh5nrZGkNJquJz2s/Z0nOEmedqBUFK7AJG1/Wfnnc5x0yypDOKDeN4WRDURSf8tPBoNeq0Wk8yZtapTA9L8HnPNF6LWmxeh9Jf3xmLD88a7RTi/gm4Zt3xV4YHhzCqoBGqFBF+xLSOH0cl42+jE+qP6G2z7dxjTdKO0rZ1LSJ68Zeh14TOCw0FExPnU5JQgnPlD4Tcm2dl8pewmg1clXxsTV98cbFRRejU+l4ufzlkOcIIXip7CXGxI/x6BZ3LNCqtJybfy5r69ced3TSR0c/IkGfEDRM1RtLc5diFdYTZvP/tOZTYsNiParaeiMvJo+c6JyTxhg2NGyg39TPioIVzmOj4kZREFvApzWfHvd5HbkGEbp/r4P0NE4evvGMYWhoyK4xEDAM9MbxNxKmDuOhncGTyoQQPLDjARL0CVw55sqvvTdFUbhpwk3U9tXycfXHI47vMfTwctnLnJV7FqPiR32tteP18Vww6gLer3yflsGWkScgQzMreyq5duy1XyusbmXhSsw2M59WHzvx6jX2srZ+Lcvzl6NVhd7qsSShhMyoTL6sO35J2gGDxcDa+rUszVkadA+KorAweyHbm7cfV+TbSPi4+mMS9AnOEuOONZflLWN3627aho6vgLHDx3AqNYbTOLX4xjMGw/AwFpUNTZCo0KTwJL478busrV/LZzWB8xreOvIWe9r2cNfUu5wlML4uluYuZVziOB7d9eiIxONve/7GsGWY70363glZ+zsTvoMQgsf3BS12C4DVZuUfe/9BdnS2h4R6PChJKKEovoi3j7x9zOG67x55F7PNzEWjgmdae0NRFJbkLGFL0xb6TEH7TY2IDY0bGLIMsSx/5CzxRVkyC/pEhOi6o9/Uz7r6dSzPX45G5UnAz8k/B4Hgi9ovjuvcDo0h8jRj+J/FN54xGIcNWBBoRHAJ94ZxNzAhaQK/2fwbjnQf8fn8QPsBHtj+AHMz5h63fd0fVIqKn878Ke3D7Tyw44GA47Y1b+PNw29yVfFVFMUHr6EUKjKjMrmq5CreOfIOBzsOBh375uE3qeiu4I7JdxyTpO4PiqJwVfFVlHWVHVNGssVm4fWK15maMpUxCSP3b/DGioIVmG1mPqz68JjnuuPT6k9J0Cd4NAYKhCmpU4gPi2dV9aqvtaY3vqz9EpPNxIp8XyZdEFvA6PjRx73moMmKVq342PD9wWQ18futv+f8987no6MfHdd6p3HqcZoxGAxYFGtQjQGk7fvBhQ+i1+i56fObnJmywl564pYvbyEpPIn75t/nt2Db18HklMl8e/y3efvI27xW/prP57V9tfxk/U/Ii83j+1O+f0LX/t6k75EcnsxPN/w0oMZS3VvNX3b9hVnps447b8Mb5xeeT6I+kSf2PxGy1rCqehUNAw1cP/b641pzbOJYJiZN5PWK1487sbDP1Mf6hvWclXuWj6TuD46iemvr1/qUWv86+Pjox+RE5zA+ybeKLsgcnX3t+2ga8C5wPDIGjZaQtYXH9j7mrCT7sw0/O2mlR07jxOIbzxgswxYsio1QZNzMqEyeOucpYnQx3PzFzax8dyXnvnMud665k4zIDJ465ymSwk9OGdw7ptzBouxF3LftPv6888/0mfpkhnHtV9yw6gYAHln0iEeXshOBaF00f1jwB2r7avnRuh9hsnrW3m8faufO1XcSpg7j9/N+f8JS9sPUYXx34nfZ1rItJDOL2Wrmif1PUBRfxJk5x5bt7Y7Lx1xOdW/1cROwD6s+xGA1cHHRxSHPWVmwEpPNdNymHW80DTSxvWU7KwpWBPw+zsmT/UOCmUYDYcBoIVI3MmNo6G/g2YPPcnHRxbxx3hukRabx551/Pt3e9L8A33jGgMEs+z377SPki4LYAt5Y+QY/m/kzCuMKKUks4d459/LyipfJjApe2O7rQKPS8OeFf+bS0Zfy3MHnmP/qfGa+PJO7195NvD6eZ855hoK4gpFPdBzICJtAlu06NjZu5LpV17G5cTOtg608vPk1Vr5zGW1DbTy6+FFnTaK2PgNv7qzHZPEkANuOdjrr0LujqWcYm81XQl+SeQGZkVn8cfsfMVhcNfl7h32T314ue5mavhrunnr319LYzsk7h9iwWF6r8NXMRoJN2Hi94nUmJE1gbOLIiXUOjE8aT15M3tc2YTng6P8QzKSZHZ3NhKQJx2VOGjJaiQwbOSLptfLXUFC4bdJt6DV6bpt0Gwc7D56U8iOncWLxjWcMwmjDotjQqUOXdMM14VxdcjWPnPkIDy96mEtHX0qY+sR2/fIHnVrHvXPu5c2Vb3LrpFu5csyVPLjwQd447w0K405OOV6z1cbNL+7iUMVYkoduoXWwlVu+vIWlby3l2SP30TekppifMiVFZrA2dA8x9/7V/N9b+3l9h6tZeUP3EDc+t4Mbn93B4VaXyWTjkQ7m3r+a+X9a7Sx/DLJQ2ew/rEPdfSm1fbU8vOthAN7a1cDk337uce4j3Ud4ZPffiLFNYqjH07/yxs56lj2y3uPcAH9cVcZTG456MCQhBJuO9FIcuYSv6r5yhicLIXh8XZVHkxaAYZOV3XXdzjr/a+rXUN1bzTUl11Da2OvDGI0Wq09XM5A+lfMKzmNn606fshzHatKy2qy8W/kuczPmkhEVvBLusrxllHWVUdNbc0xrDJpGNiUNmYd4p/IdluYudZZlWVGwggR9gl9z6Gn8Z+Ebzxgwywcv7GvU/T/VKE4o5rbJt/HjGT9mWd4ytOqv5+wNhg1H2ilr7mPFhHSO1ubzf2Of569n/pXlabczVPtdcoZ/xYZSHa12wvvGjnqsQqDTqHh+S62TsD34mb0rXJiG+z52lRN/eqNsjN7Ua+CNnbIluM0muOft/QAcrEpjScalvFr+Ks+VvsSDn5WjUhR+/m4p9V1DtA21cftX38di1tFUeR5/+KTcSagPNPTys3cOUN7SzwP29UFqLv9ad5Tff1zGP9dVOY+/ur2e7zy/k6+2lqBRdDy29zEAXtlex/2ryvnuCzs9On796dNyLn5sMxc/tgmL1cq/9v2LrKgsth3I5ry/beSBT8s97uWdr+5h4YNr2V7d5TxmMFu56fkdfLApA5Wi5pWyVwCwWG18/9U9XP6vLR5tLLsGTdz47Hbe3+vJQPoMZp7eWM0/tn5Cy2CL05TVOWDkD5+U+TDGhu4h8vSyiq97AqXJYmO/vVeCO9r7jU5NbTAEU9LH1R/Tb+rn6mJXW1adWsfFRRezrmEdzQPNQWafxr8bpxmDxc4YdCePuP434+3djSRE6vjz5ZNIitLx4d4Ozsw5k4MV4xgdO4XHrp6GTcA7uxux2gRv725kQVEy9104nsq2AbYc7aR3yMyq0hYun57NNbNy2HCkndY+AzUdg6w93M6dS4qYU5DIGzsbsNkE26q7aOge5k+XTCA5OozexnM4M/tM/rzrT/REvM7/Oz8dq83CI5vf5eqPr6Z9qJPhhuv58dLp1HUNse6wjM9/aWstYRoVV83M4e3dDc6+vI+trSIpSsf03Hhe2VaHzSbLALywpYb8pEjCVLFkKGfzSfUnbGvaxQOfVlCSHkNLn8HJyHqHzU5Gtq+hl0e2vk5ZVxkX5t/IC1vk8Re21NLWLwny5qoOPjvYigLc/doeJ/P6cF8TX5a1Ud6gJkmZxVuH36LX2Mtja6v4cF8TO2q6eXVHvfP7eGL9UdZUtHPXa3vZVOkyy/3w9b387qNDPL7nZeLD4jkz+0yEkAz2ifVH+dGb+5xM2mYTfOvZHVz/ZAXJmvG8W/kuFpuFPoOZlX/byPl/3+TsaQDSp3De3zaw4q8baOs3MDiCKUkIwStlr1CcUOzUJB24fPTlALxx+I1j/CWexqnEN54xqGRINmE6nf8B7YfhBDQT/2/DgNFCeUsfXxxs5fxJGei1ai6YnMlX5a18eaiVg019XD0zm4LkKGbmJfDilhre29NIY88wV8/MZuWkDOIitLywuZb39zVisti4ZGoWF02Rxcne3t3Ai1trUSsK18zK4cqZ2dR1DbGmoo1XttcRFabh/EmZXDYti3UVndwz9Q/EmZegi9vGXyquJ7r4F3zeeT86lQ6l+TYW5U7nuwsKSIkO44UttQyZLHx8oJkVE9K5bVEhQsC7uxup7Rxk3eF2rpudx/Vz82jsGWZTVQd763sob+nnpgX5XDkjh/LyGSSHp/Dz9b+i1zDEL1aUsKAombd3N2KzCd7YUc+Qyco7t80lLsrEK5X/YGziWFoaxqJVK7z9vbmYrDZe2SZNXs9srCY5Ooz7L5lAU6+BjZWyUc0zm2oYkxrNjXPzqDs6kyHLEK+WvcZLW2s5c0wyM/MS+OeaSqw2Qe+QmRe21LB8fBrxEVrnueu7hviqvI1phWZUkYcoiliCVq1l69EuvixrY0ZePBuOdLDe3oB+3eF2KtsGAGism0LLYAvrGtbx+vZ6Kuxmvke+POI0s/19dSWtfUba+438/qOyEU1JO1p2UNlTydXFV/s4v9Oj0lmYtZB3jrzjE8hwGv85+MYzBsX+4w8L9+MjaCuHf8yA/SNINwPt8MccqBjBkddZBYO+zlcfrHsQ9oRQisJqAdvJifC44ZntLHtkAzqNiu8tkv6L62bnolGp+O6LOyXhniyd7XcsHkVTr4Gfv3uAUSlRnD02Db1WzRUzsvn8UAsPflrB1Jw4xmfGUJAcxYKiJP65poo3dtSzfEI6qTF6zp2QTkasntte3s2H+5q4dnYu4To1l0/Pxibg/94spb7yLK7LfowfT/8xZ6ZdxXDDtVjr/4+u7hRumJuHTqPi6lk5rK1o51fvH2TAaOGKGdlkJ0QwpyCR13bU8/i6o6hVClfMyObssakkRYXxz7VVPLa2ihi9hgsmZ3L9nFzMFi1Twm+mzVhHQs7HzM5P4NJpWTT2DPNVeRvPb6lhZl4Ck7NjSSv8AJMY5PpR/8c7e5o4e2wa03LjWVCUxBs76mnqGWZ1eRuXTcvi3AnpxEVoeW17Hduquyhr7uPGeXncMDcPiyGNTN0Unj/4Em0D/VwxI4fr5+Y6GclbuxsYMlm5Y/EoLpySyeeHWugYMPLK9joUIG/UdlRoqDwyBZtN8PqOOqL1Gp66YQZxEVqnhvPUxqOkxej54I55GHrHEKVO4rXy13h1Rx1Tc+J46LJJlDX3seVoJ8MmKy9vrWXlpAyumpnDp6UtNPYMBzUlvVz2MnFhcQFDl68svpIuQ9dxRUQdK450H+HlspdDKmdzGi6cZgz2aKTwSD8NZertTeJL3w5+kr0vgbEXNv8t8Bgh4J9z4cFCaNwVeJzVDBv+LP+CQQh4fB68fm3wcTWbYN0DI2s9R9fC298Fmw0hBLtqZTG5+y4aT6q9Fn1eUiR/unQiRSlRvPidmcSGS/PbgqIkrpiezYTMWO67cLyzSNntZ45iflEyigIPXz7ZKT3ed+EEbEKQHBPGD5ZKZ7FWreLH54zBZLWRGKlzMqO8pEhuWVjA5qpOJmTGcseC2dww7gYePeenTIifT0XLEOMzY5z9fq+emUNUmIa3djWwYmK6s+b93UuLaOkz8Or2Oi6cnElarB69Vs1tiwrZXNXJF4da+c78AqLCNBQkR7G4OIV3Nkdh7DgTc8RW/rb3r5xVkkJ+UiTff3U3Dd3D3DA3hz9s+wONpp1Y21fw89e76DOYue1MuferZubQ1GvgqidlFM4VM7IJ06i5dlYuq0pb+NX7pcRHaLlwSibZCREsLUmlpW4BA5YeEjM3srg4hbPGphIfoeXvq4/wwpYapuTEMS4jlmtn52K1Cf74STkvbqnljHFWvqz/mNnJK6htV/P8lhpWlbZwweQMYsO1XDg5ky8OtvL5wRY2VXbyrXl5TMyKY0ZeEqbumWxt3kp1bw1XzczhvInpxEdoeXlbLR/ub6LfaOG62blcNj0Lk9WGEIGzno/2HmVN/RouG31ZwFphs9NnkxuTe1yRX8eCbc3buPzDy7l/+/1c9uFl7Gnbc1LX+1/CKc1pVxRlGfAooAaeEkLc7/X5NcA99rcDwPeEEPtO6p7sAnd4jJ8SFk275WvVajD0gt5PW0whYNdz8v9m34gTJ3obwBFyuedlyAxQZK7lAFiGoasKumsgPs//uNK3ob1c/gkBgfIHPvk/aDsIkUkwPUhrzh1PQ9kHsOCHdITLsNd7V47lgsmeIbjn63ZxftY7kOEqk6EoCn+6dKJrUH8LvHMzMSsf4fkbZ2C02Dw6UuUkRrDxnsVEaSxoV/8W5t0FMelcPDWLs8elYbHanEwH4KfLipmWE8+MvARnG0RFUXjsmqkcaOhlflGSkxmlxOhZddcCPtrf7NFIZVZBIg9dNpHKtgHuXjraefzqWTm09hmI0Gk82ik+dNkkfvleKZG67xCZmcjTpU9T0V3BrWdfw8Mf27hwopV3m+9lW8s2bhx3IzEFF/L0xmoumpLJuAz5O1k2Lo1Lp2Xx1q4GfnP+OHITZWOb284s5J3dDVS2DfCr88Y67833FhWy/ol2lPBJaOLW0DBYQ0FsAb9YMZYfvbkPrVrhdxfIhLXC5CgunZbFGzsbAIEp7l0i+yK578wfcKh0L7/58BDReo2zefxNC/J5a1cDt7y0i0idmqtmyIY3dywu4lvPTyEy6nMSMzdy4ZTr0apVXD49myc3HGXjkQ6K06KZkRePoijMyk9gW3UXw/Z6Sd54rvQ5dGod15Rc4/93hszmv2LMFTyw4wEOdR46ptDeUNFv6ucn639Cbkwuf1zwR3649of8v43/j7fPf/uYugp+YxGoHveJ/kMygyqgANAB+4CxXmPmAvH2/y8Hto103q/bj+HZHz4k7r33XlHz6Ze+H/5zvhB/zBbi3hghKj71f4K+Zvn5bxKF+G2yEBaT/3Hlq+S4e2OEeHZF4A1tecw1bvtTgce9eLFrXG+Qev6PTpZj/jI+8BirRYg/5shx254Qu2q7RO49H4kvD7V4jmvc41qzfmfg8+14Ro55YnHgMUIIceBtOW7DX4KPay0TYtsTQgx0BB/39s1y7ZFQv0OItoqRx9lhs9nES4deEjNfminGPzfe+Tf75dnizYo3g861Wm2irnPQ53h7v0G09Rl8jrf1GcQnhyrEvFfnias+ukqY7L+nd3c3+PQ/6B40imc3HhUPbX5OjH9uvHit7DUhhBDNPcPix2/sFWsr2jzGf7K/SVz1xBaxp67b49oe+LRMzH/ybjHhuYmisrtSCCHEoNEsrn96m5j2u89FVVu/c3xFS5/Ivecj8ebOep+9Nw80i8kvTBb3bb0v6D0RQoheY6+Y8dIM8cuNvxxx7PHg0V2PivHPjRelHaVCCCG2NW0T458bL57c/+RJWe+/EfyH9GOYCVQKIY4KIUzAa8AF7gOEEJuFEI6C+FuBrJO+K7ugHR4fyzu7G7j6ya28tr0OzAZoOwTjLwHgyXdWUdnmWbLgz59X8MrH9mzVSVeC1Yit9RDv7mngoc8qPMIMba2y1lBr1jIp5dsxYLTw0f4mVzhh3VaIzUZEp9NcupbOAaPHmgNGC3vquqG9AmLst6c1QB0j0yCiqxqh1kFPHRgH/I9r2guGHvn/mg3Ud8noHUeXKyeq3Tqctez3fy6QewNo3Am9Qdplln0gXx0mO3+wmOBfZ8AnP4adzwQeN9QF+1+Dj+6GYCXKTUPw1BJphguGnjo4ug5sNhRF4ZqSa/jysi/504I/8YNpP+CBMx7gy8u+5FJ1Iqz+fcDTqFSK730EkqLCSI729WslR4exvGQ09865lwMdB/jd1t8hhODCKZlMy/VsPRsXoWPGmCFerfobs9JmcdmYywBIi9Xz4GWTWOjWYQxg+YR0XvnubCbbW2yC1Lz+75xiPrj2V0Row/nbHmkOjdBpeO7GGWy8ZzEFyS5tenRqNId+ew6XTPVN5nzu4HMIIbhh3A0B74cDMboYzs0/l0+qPznu8uqB0DrYyouHXuTc/HMZlyi7581Mn8kZWWfwbOmzJ7T0yP8qTiVjyATq3d432I8FwncAv95cRVFuVhRlp6IoO9vb27/eruyMoV+j56dvH2BnTTe//egQ3XWlYLNQHTOddhFLzEA1d766F7Od2Dv6wZYd2ClPMEmW2d668St+8Po+/r6mkrd3NziXqTywjQaRxDO1iTDYLgkZ8IdPyrjjlT0seXid7KXbcgCRMZlD1iw6qvfz/Vf3OMMMhRB876VdXPvYV9BbD+NlrLqpcR8X/GMTY36xyiO+fbB+PwqCj2yydSUdkmC39Rs46+F1/OydAwybrFAn6z4N5yyiv2I9d722F4CseC+Vu70cIlMgLAZag/QNbtkPjuzjpgB2XYsRDn8u/1+/LbAPpOMwWO3MsXlv4DXd/TZVawKPK7W3abWagjOQT34CL5wPH97pPBSti+bcgnP59vhvszx/OZHaSNjyD1j/oDT7BcOWx+DvM6UPKRCsZsl8bTbOyj2L7074Lu9Wvsvvtv7Os9WpEFD5FQfa9nHLl7eQoE/gT2f86WtlfMfr4/nWuG/xVd1XznIgiqL4bUofodP4RBtV91bzevnrXDjqwpArAFxVfBVGq9GZqX2i8Ni+x7AKq0/dsDsm30GfqY8XD714Qtf7X8SpZAz+jOB+qYGiKGciGcM9/j4XQjwhhJguhJienJzsb0jIcBRVfW5vJwBPf2s6BrOVdRuldPzs4TBqlCyWJHdzqLmPj/c3Y7MJfvfRIXISIjg/o59+EU515CREWDQt5VuZkRfP5Ow4HvnyCAazlbZ+A6LtEF2RhXTo7T2g2yuoah/g9R31TMyKZcBo4bn15dBdTYMmly19SYxWNbOlqp0P98tkoLd2NbDhSAdnJvYA0BI7EWJzqDm4nX31PSRFhXH/qnIMZknwvlgjewu8b5njXBPgvo/LqOkc5NXtdTLBq60MEZnCq12jibZ2k4iU4CK8I0/aDkHqWEgdL30h/mCzyc8mXG5fs8z/uPZyMA9C4WIY6oTOAJ3iHNpQcklwLaXBrbbRkc8Dj3OPMGsvDzzOcb6KVYGZltXsZKoc+iDwufqa4LOfScbcFuB+ABx8D55fCR//EIDvT/k+3x7/bd48/CbXfHwNX9V+RftQO0f2Ps9fPr6RGz69gShtFE+f/TSJ4Ym+5xNCBh+M1OTJPAxCcP3Y68mOzuYXG39xTFK1Tdi4b+t9hGnCuGPKHSHPG5Mwhump03nu4HMnrB9FVU8V71W+xxVjriAr2tPgUJJYwlm5Z/HCoRfocWjIp+EXp5IxNADZbu+zAJ/SjoqiTASeAi4QQnSe7E3ZFAWNUPFmeTfXzcllQVEyl0zNorVqLzZFwyuVWrRpxSQO11CUHMk/1lTy3t5Gylv6+cFZRUwKb+MoGTzw2WGawsdQYDnCnUuKuGdZMc29Bl7cUsvTaysooImckpnMniWl96pDO3jw0wr0GhXPfGsGKydmsHnrFhA23qqLpjUsjzCMnJE8zF+/OoLJYuNvqyuZkBnLHxZIx+zzR/SYkkpQtR3k7LGpPHTZJJp7DbywpYbWPgODtfsYVkUxa8mFGIWGlsq97G/o4f29Tdy6sJBzxqXy3KZqrK0H6Ykq5KsuSVxGqxqI9o46sdkkY0kugbQJkmD7C5XtqQFjH+TOgdicwITQcXyqvRJq094A4w6CWidNej11MByg9WbDDkidAFkzJAPzByEk08pf6JrjD33NMNQB8fnydaDV/7jmfWAaAJUGyoLUOTrwluv/joCGQNcAsOtZ6K5BURR+MO0HPLzoYbqN3dy99m4Wv7mYi/f/mWfiYjlbk8BrK14jOybb//nevwOeOxf2vBR4TasFHhwF791GhDaCPy74I61Drdy37T7fchyf/MTzWux4tfxVtrVs40fTf3TMRSTvnnY3HcMdPFMaxEx4DHh096NEaCK4eeLNfj+/ffLtDJmHeObgiVnvfxWnkjHsAIoURclXFEUHXAl4iFmKouQA7wDXCSEOn4pN2RSBRqjQ6vXcZg+RvGtpEaNooNKaSnZSLMXjp6MYevnFokQq2wf44Rv7KEqJYuXEDHRdhwlLK2ZVaQufdKYxTlXPgvxY5hQmcsboZB78rIJ1mzejVazE5U3mvAWz6CeCXdvW8+nBFm4+o5CkqDD+75wxFNgtbava4pg5UzKQ28dbqGwb4OJ/bqKua4jvLx5FTF8lVkXDE6WCD1oSyKOJe5bmOdf8x5oqfvjGXsYodShp47hm7ihqlQwaDu/m9x+VER+h5eYzCvj+4iIGjCYsreWs7kpkIFaGjj62VM/Hdy7wvFE9tWAegpQSSBsvCWJ3te8NbbZL9WkT5di2AFJ5q53gj14GKm1g01TrQUgaA5n2DFp/mooQ0mSVOQVSxso5/qT8vibpSylZCeEJ0LDT/5oOk9UUeyhwS4C91WyUrxMuk0wikJmo5QBEZ4A+DhqDMIam3eCImGl2BeOdlXsWqy5exZNnP8nPZ/6c+/osfFbfyP0NtcTpfPuUA9BdK8OoARq2B16ztVR+l/tegZZSJiVP4pZJt/Dx0Y957uBzrnHth2H7v2DrYx7Ttzdv56EdD7EwayGXFl0qmera+4Ob8wDKP4FNjzIpeRLL8pbx/MHnaR30w4DNBnnOELC3bS9r6tfw7fHfJl4f73dMYVwhKwpW8GrZq3QMh5BT9A3FKWMMQggLcAfwGVAGvCGEOKgoyq2KotxqH/YrIBF4TFGUvYqiBHhyTxxsKtnv+cM7F5AYJZ2BWfERLIhtx5pUzFM3TEefJUMxF0Y388gVk7lqZg6v3TwbzWALDLRSNGk+3188ivjCGWgxO80nD18+ifMmpnNlrr0jWMpYwsM0qDImMV5Vy/VzcrlloQyRzE6I4DujDVhQM2HiVJYukIR5ekQbN87Lo7Sxj1sXFnLW2FRo3oeSMpaitDjW9CSjUWwUIv0ZP1tejE0INle2M1HbgD5zIhE6DRHZk8k2HmFnbRc/W15CtF7L+MxYfjA9nDDbMDuHUvnhhQsgPJ74gUpyEr0cpg7CnTpOagzgn0i37AdFLQl0SrHdR+CHYLaVSYKvDYfk4sCMoaXUvuYk+b7Zjzmpr1FqEmkT5djhLv9SvsMslToe0icGNk017ZU+kslXu67JH5r3QlyONIdZjdDh28BJzj8g18ucGpgxWEzy2qZcK++f13VqVBpmp8/mqoRJnN/ZREb2fKnNBNsbQEQSNATJm3HXmg6+A8AtE29hWd4yHt71MP/c909ZJnvfq3JM0x6nf2xz02buWH0HuTG5/GHBH6Tf4d1bYO0f4YtfBV4T4LWr5Ji2cu6edrc0R/nTUl66BB4uDu6bEQJzXxO/3fJbUsJTAofK2qxQs4nbJn0Ps83Mk/ufDL7HrwGDxcBvt/yWc946hx+u/eFxt1H9d+GUJrgJIT4RQowWQhQKIe6zH3tcCPG4/f83CSHihRCT7X8jt8D6mrApArVQkZcU6To43IOuv56SSbMpTI6C9MmSUDTu5ILJmfzx4gmSidgfcnX2dH509hguXblSzrebRZKiwnj4isl8q3BISsVJUiKPzJnKWFUdvz2v2MO5N1ZVhya5iD9fNRMlIh7iclE17eLelePY88uz+OnyYhQhoGkvqqypvHvbPO6/zU687ESvJD2GbT9fwqrrc9FZB6V0D2SNm0eq0sMnN47icrf4/u+VyGiob124nIVjUiRB92f+adwtTSap46U5SVH7J+bN+yWh1+rlWJvZ//nayqRGAXKP/iKr+pphoAUyJkNUspS6m/2ktTgYVNpEuX/wv7c2+xopJXJsW5l/gtO8F5JGQ0wGxGYH9qc074f0ScEZpdkgmWPaBMiYKs1cJj/29LaDkrnkzoXkMUHW3Ctf59zu+d5n3D75HU29XvpSDAHaldZvg+h0+V3Zf7cqRcUfFvyBlQUreWzvY9z0+U3sPvw+IjodhI2Wig/50/Y/8b0vv0dmVCZPnv0kMboYaZZyMJpA1wlgGnT9f9OjZEZlcufUO1lTv4Y3D7/p+qy7FmrtWlkwTav0bR5/ZjZHeo7wy1k/D9yT5MCb8Ny5ZLeUc+GoC3nz8Jv+i/kNdsCHd4dWpcAPbMLGXWvu4s3DbzI6YTQbGjZwyxe3/FdFQ33jM5+tikAt8LSX128DBOTYm6iHRUmC4216aLITSwdhiM+HsFjfh7W1VD7sjiqo6ZNkslunm4Rps0LdNsie5TqWPcsZsRMfaa/l1HVUZllnTCVcpyY6fTRoIzwkxwidhmLFXgIgzsWc+QAAIxhJREFU1b63DGmKKbZ5Onk1TTtBpWHMpHmuvbXsl1FD7mjcJe+BVi//kkYH1hjS7cluWXa+7m3LH2iDvgbXfUsdB/3NMOjlUnLY4zOm2vcWQMpvOQAodse4DE/0q1k075eEPjxOXqfV5N8B3bRXCgMg75s/v4ChTyYhpk2CxCJQh0Grn/vRXgbCKglv5jT5f3/X4CB8mVMl0wrEGJr2gjYSCpfYf2sB8j+b90kGmDcfEIF9G/XbpV8mfbL83doldq1Ky33z7+NXc37Fka7D3BBpZk5yOItysjhr/0O8XPYyFxddzEvnvkRyhD0ApL1cmhvHXwo2S+A1Hb+H8ASo/BKE4Lqx1zE3Yy4P7HiA8i77d7L3Fdec6nX+zwXsr3ifp+NiOL9/gEW6ID6OCnsV2Z1Pc8vEWwB4fL+ffuZvfkv6eQ686fuZA6ZB+Mcs+PjHPsLFM6XPsLlpM7+c/Uv+tvBh/jakprqnit9vDRzWDMgkWrMh+JhThG80Y7DZBBbFhtoGNLuFVdZukhJ+1gzXscxpkji6q7pOYmm3C6tUkni5h2haTFC7xcVkwEmkPeL32w5Jgp8713Use6Y0ifS4eg84H7ZMO7FUqeW46g2eF1e7RRKrVLsEnTZBSpDeklftFkkUdHYpK3euZFru12CzSYLkWNNxvuZ9nvejt0HuN91u9onPt5syvBiqI5Inxx4t5SDC3gykcbfcs4OBpE2U0re3JNq8DxIKICwaIhIk06rzagYjhLzfju80zc68vBlIf4tLSwE5vrtG1sNyh0MjSZ8Iao0kwv6ItOPa0ye57p8/6bdxN0QkQlyuXLu/SfpEvNG8z7Vm+kT/Tnu7Vkn6ZFeGvT9/Sn+r9B1lz5RrDnXK79AORVG4bPRlfDrtl/yhvYML0uawSBXDDy0RfHzxx9w7514Zsuu8Bvsas78nXwPlp9Rulhr4nNthsA36GlEpKu6bfx9xYXHc8sUtVPVUSd9I6gR5746u9XuqpoEm7u7fS6pQcU9Xt2R0/mAxQeVq0Ojh8Geko+aqYtnPfHuz25y2MqixP0uO36k/1G2RjHDHkx6Rbvva9/H3PX/nnLxzuGz0ZXB0HbObyrjFIMubf14TIGKuZiPcnyOj14Jh/UNSmzlJNdIc+EYzBqvFZtcYFDji1laxZpN8oBwEHyQRM/S4CLOxXxLVXK9EqYzJ0ixisVeOrN8qwzJHLXWNSSqSkuthtyJitV7EEuQDC55Ernod6KKlOceBgkXSFNHvZlev+koSecc16CKkyaZ2k2uM2SCvJ9dtTcf67uM6Dju1FCdy50opv8MtRqBqtXx1RP0oiiSs3gS/dot0sjoYSNYM+cC6J9CBi/E6mFb6RBA2TzORzSr36rhXIJlw/VbPh6enTvoiHIw3sVDmY3gTLwehdTArByNp9CKs7gQfpHbXsNP1vTtQt0WawOLzIDoNYjL9S9KNu+RvTlFcWqM3c7NZ7f6Kya61Ww/6msM6q6T/IWua1I4Si/wzBodTOnuW65x+8k4i2w6xcmCIn83/Pb/OPJsbG6vIDk/xc76d0sGeOU0y50BEunazZPYFZ7quHUgKT+Lpc55Gpai46fObONi6R15D3gL7vfXUYo/2HuXbq27AIGz8PecCYiJSAkeaNWwHU7+daUkh4fbJt5Mbk8svNrmF57oLLdUbAhPg6g3SWqCNcAYh9Jv6uWf9PaRFpvGrOb+SPhd73sxNzTWMjc7ld1t/59/p/eHd8nX3C4HDo202WP07qc1sCVKX7QTgG80YjAYTFqyobTY4/Kk82FUtf6iFiz0Hj1kuo2gO2AvqVaySNuFxF3qOy5wuTRR19j7FRz6X2keeW5SPoshonKo1rvpKZR9I4hGX4xqXOh6i0uDgu/K91QzlH0PxuVJidMDxgB21R4L0NkhpZtQSr2s4VxIbBwOp2yz3muOmpUQmSR/BUTfVvdweill0luuY4//uOQOVX0l7dYob08qZLU1m7lpP7SZpZtLYzWNavSRO7uYC44B8SN01qJy5UtJ0Z+JNe6XjuXCJ5zhDr2cOhbeWolJD/hmSmbk/iPXbPLWU9EmSAHgTuarV8j5Fy3am5C+QZhT3RDshJBHMneuqZZU5VZoM3dc09svvyyHdp02U5iJvibVprxQyHCa6rOnyN+ijBdrt8rnzXeMad/oSnPrt8jedPkkyXY1e7tcbjbvkbzMiQe7RZvZv6qrdLO+vokhG7S9x0WKUxDt3nhRUVFqPe5Ybk8tTZz+FVsB1SVH8U2NkMGOyvE67Rma2mnmt/DWu/vhqhk39PNnSStGo5a41/aF2C6DA7NvkNddvI0IbwR/m/4HWoVZ+s+U30slet00mcU69XgYxBAp9rl4vhYbCxVC/FZuw8YuNv6BlsIX7F9wvfS42qzRflZyPVqXlD/pRDJmH+M2W33g62ftb5DOSWCRNcI7KAd5wN0EGC0E+AfhGM4bB4WHMihWVzR7uWL0Bdjwlic/U6zwHh8dB0dlSAjD2S84ekwlZMz3HjV4mTQLb/iWJ0+4XJREN8yrSV7xCFsvb/4YMBazZIH+M7hmlKjVMvBwqv5CmjKrVkgiOvdDzXGkT5V522zM699mrVhad7Tmu5HxAuEpR7H5BSnjeTLBkpfzhO4j5ofcl4Y5xaxUZmyWleUepcbNBqvuFiz2vYay96kmpjHih44j8gXszrfwzpCbgMGUc+VyatMae7xoTmSiJfvnHrmNVXwEKFJ7pOpZn1+IczB4kMwmPdzmnQe6ht96l9Qj7vcmb7/q+dBHyO3YvqW4akkTQnRnlzpP7cJghQIbz9jd7amSFS6C3ztMhf3QtHj4ttUYS81ovxlD5pf1a7d9X/kL5W636ynNc7WZJ3OzBDmRNl9n23tnZ9dskU9CEyb+c2b5am9UinwuHZuxgXt4EuK9J+lzy7Mwoe7b8rXonLjbtkd9r7ly5Zvokn+ssjCvktfwrWTw4xGNtmzhz3wN8PyWJ+3b9mR+v+zFL31rKfdvuY3zieF6LncU4m0buK2euvEY3c5gTdZvldx+VIk25dkY/MXkid029i89qPuOvu/8qryt7pkuQ874fAMM90h+Tf4a8Z11H+deOh1ldv5ofT/8xk1Mmy3GtpZIGFJ8HmVMpbDrInVPvZG39Wj6ocovUdzDjxf9PvjoEPG84vufZt8vf7MDJi3T6RjOGob4BLIoNlbDJukNv3wRb/wnjLvIkgg7M/b58wP61UBKA+T+QfgV3aPUw/TtSUnjhQml+WugngbtgkZQ4Vv8e3rdLMVOu8x035VopeXz8A/js5zJpzJuQq1Qw5w4pKe55Gbb8HYrOkQ5vd6TYk9M2PSof0LKPZEim1qs8siPpbPsTUqtpOSDviTcmXCal/4adkskYepylQZxIyJda1L5X5XXsfl5K4JOu9j2XopKMGaTjLzLZ07QGUltqOwithyTR2vuyvI+Rbk7HuBxJyPa8LIn9UJdMQJtwmef35TDvHXxPvraVSULmzoxAXnt7mYuYH10jJVj37yEiQRKcsg9cUrKjXLs7Axm9TL46HKEgGW94gqdZsmCRdGZ3ueWKVH4pNY6IBNeamdOkpuaAzSq1PXctxaFRuq/Z3yqJo/ve8s+Q99bdn9K4S36vDg0xNlNqSu5MF1w5HU7GEMAc5iC0ju911BKpzQx1eQxLqNnMQ8NaXj33FVYUnkedPoJPeiso7ShlVtosHl/6OE+e/STpdTskcdaEQYHdhOmu7YL8ndRvdzHe7Jny92/3Vd047kauGHMFT5c+zVO2TkT2LIjLlj4yd0bvQO1madLMPwNy5vJ+VCSPlT3P+YXne4bKOgh+3jz5fTTt5trCi5iaMpX7t99Py6C9S17dFqkhFq+UvrJAOSBVa6TPxV4Kx8Pce4LxjWYM/d09ACg2G1z6jCQoRWfDykf9T8iZDcv+JH8U026EGTf5H7fgh1Kqby+Hs37ncmS6Q1FgxcNSK2jcDRf9S0oz3kgeA0vvlYStuwYu+JsvIQeYdoO0675/mzRPOaQP7zXPfUhKyU8sklqQw1HojrgcmHiF7C/x5rcgcZS8Xm/MvFlqR+98F9bcJx92d5OZA7O/J+/FO9+FbU9IjSQ61XNMfK6UrHY8IxlXxSfy/qq8avVMvFJG43z+C9j5tLwn8+7yXXPKtVKCPfS+1N6sRhfDc7/OorNh2+MyymTzX6Vpo3il57ixF0im5Sjit/lvUpDIP8Nz3NTrJBOt3y7twbtflFJ9Qr5rTEy6ZJT735BE3NgPFZ/azYNu7WUnXg4oLu2vo1KaYIrO8Vyz6GxJvB0MpGq1dJ67M/LEQikQOEySYNcahacp1MFAyj9yHTvymTStFbhpZGOWSz+cexb64U+lRuYwwSUVQVSqLwNxaJ8ORj5qqXye3J3LVrO8jqKzGJ88gXvn3Mv7yUvY1NDGpyvf4YGFDzAvcx7KYIdkZI7vIWWsFCa8HdWNO2USn8MsWbhEmlDt4xRF4aczf8rymDE8mhDHb43VsjZV/gL/JUWq10uzW9YMXu49xC+SE5mtjuWXs3/pWUOqZqMMJojNkkzfZkHduIvfz/89VmHlF5t+gdVqkYw9d47UFAvOlPO8/UbGAclkRy2WWpYuyr82c4LwjWYMA12yJpDKZoWcWXDTF3D1a75mH3fMuhnu2gsrHwncA0EbDpc/Dz+tg3l3+h8D0q77g4PwowqXFOAP8+6G27bC3QekJOkPukj47mpYdj/cvs3lFPVGzmy45i2p/dzwoadPwx0rH4WpN0gJ8LLn/TOjsCi49FlpF03Ihwv+4f+ejL8EJl8rJej4PMkQ/eGs38hzfvEr+ZDP/4HvmMhEOPPnUq1e9RNpPhhzrv81M6bAOzfDuj/JEEoH0XLHGf8nbclPLZVazbw7fZlWdKpkjDuegvdvlxLe3O+7fCQOTLhcMq1PfyqdhD21MN0PQ517h6ybtPsFmSVs6odp3/YcE5slv+tdz0ppeuPDUir2Pt+U66QGtuXvUlPZ/qSMBPO+J+Mukoyleb9Lc0saLaV/BzKmQMo4yQCFkObBPS9JSTw8zjVuzAoZdutgNIOddo3schcjVxSpoR3+zBWG3H5YmlfcmVbmNMlQDr3vOlazUZZVGe3GBMddJP0r7j4tx/oOzU9RJGGt/MIz7LP0bUnIHabVvPnye3IzSWpUGu4fVLjJAG/Vf8nlH17OzpQCGXThrjUIAUfX0JE9nZ9t+TX373yAJep4/tHUjF7l9nswDUkJ32HizJkj91DxCdnR2fx05k/Z1ryN+9b9BNFVJZktyPHmQV8nes0G6dspXCwFiIJFUqA4SdFJp7RRz38ahvpkGWqFEYqMHS/cJcBAUKll8lYwKIqnQzcQwqL9awDeKDrL05HsD1o9nP/Xkc9VsBDu3Cf3GIhRKgpc8HdJ+PVxno5zdyQUwM3rpBSYPUsSQn+YdYuUgtsOwaxbfc15IOde+YoM79OGw+Jf+D9X9ky46AlYd78ksgt+7H/c0nul+WHPS5Lp+Gt6FBYFFz4Gb1wnI4/GX+rrDwJ5LHu2LBEOct2sab7jzvotPLkYHl8g8z5m3+6rVcakw+SrJDEf6pQS/pJ7fZnW9G/LSrAf3S2JZ8sBuPgpz+9MUWDGd2QRv9K3JWMbaIVLnvI8V9Z0GaG24WGYfI0sk2E1Sa3VHZOvlgxrx1Ow6B67RqbxvCcqtbz+LX+XCW3xuVIji0jy9eFEJsOeF6WWI4RkmumTXXkzjjUPvCEZzaQrZJTYwfckU9Dby4eotTD6bKj4GEwPSKFqoA1V9Trumv5tJo1fxh+3/ZEbK55hbGYGy7Y/RIFWITYslua6TWwRbXyqmDBXN3HrpFu5RUlCU3mzNO3k2zXmw6skgR9/qXwfFiUZ3cF34Zw/cnHRxdT11fF06dPEx8VyR9EyWWU0b4HUWss+9Ay8KP9IRkA5THAlK+Wxpt2uYIQTiG80YzD0yQxMRZwkxvBNgT/C7A1F8fQDBEJUMkQtGvlcoTC3mAw4L4B24o5JV8i/YNDHSo1suFtKuIGYYMl5cOsmGS6aM9f/OEWB696VtYfCYvz7lkASvEufllrA+IslwfeHc/4oHa6H3peEaN7dvmPC4+HcB6UG1bhLSv0TLvUdN+U66d95+zvy/ZgVvuZBRYElv4IXL5QmybYyaeJzJBc6kDpOMoH1D0oNYM+LUtOKSfccN+tW6dv76AeS6Fd9BUt/7QpTBilMzLkdvvy1lJSHOqRgcL5X2Gb+QkgohI1/kb6i7U/KXAlvpjXju/I6tz8J8++WzMhmhhk3sShpFDPTZvLOkXd4f/c/eNhYC6tdVWMjIyNYlreMb0+6hbzYPKkdfJYoGW/+AldXx+h0T+I+/hL5HR35DIpXcNek2+ja9TRPxMfSW/4cP5v5M9ThcXLfe1+Gxb+U92C4W0ZDTrzcJSyNPsdVvPEkMAbFpzbJfxmmT58udu48vpJKbz38LKV9tYzuGuLqvz5wgnd2GqdximE1j6yldtkjpRxhpf4w1CX9NyqN1FK8tQ8H9r8hCXD6JFjxZyl5e2OwQ9Y7at4rNZUrXpSarTd2Pe/qfZEzF65925MxgAx1fXy+zNMQNkmEr3vP1w91+HN45TJpOmyvkOaXq1/3XfOlS6WJZvp3pJ9p/CVwiVf9pK6jdD4+j4bMSfTnzCR5y2MUTP0u2mV/9By35o9S67zqNZkV/+7N0qzrrsFbzfCYXeK/dQPseh7bp/fwyLzrebZpLXMz5vK7eb8jpb0Snl0uGcMZP4avfit7wN+60dMcuu91GYzgiD47RiiKsitQ2aFvNGN47b5/Um5uZUzPAFc98tAJ3tlpnMZpAFKCHmiVzuhAzAhksb/+JukzcE8udcdwt2RG2kjp7wuP9z9u13Ow/SmZ+b/sflcklzsGO+Dly6Q5Jm8BXPWqf6a18xn4+EeSGWVMhRtX+frcTIPw7LmucjgZU+E7X/iaTY98AS9fKp3SvfVSw7nuXd488hYPbH+AME0Yd025iwv3fYS2/CMYd7HUbCZdBRf9M/C9Ow6cZgwB8NK9f6VS6aJkcJArHnzwBO/sNE7jNP7jYbNJB7M+LjjTaq+Q5rq8BYE1qKEu6dQXQprHvDUeByo+hW3/lD61s3/v1LSqe6u5d/O97GnbQ3ZUJpcabCyrO0DGqGXSR2cfZ7aa2di4kQ+PfsiZ2WeysnCl/3VGQDDG8I32MdgsFtCCOuwbHZx1GqfxzYVKFVjrcEfyGN+8IG9EJPiPpPPGmGXyzwv5sfk8v+x51jes58kDT/KXgX38JSOJRFs1uV/dhlalpcfYw9Heo5htZhL0CcxMm+lnga+PbzRjsNgjvXTR3+jbcBqncRr/IVAUhYXZC1mYvZD6vnrWNqylvKucxoFGzDYzyRHJzM2Yy7TUaczNnItWFULk43HgG00RrYBOaAiL9xOjfxqncRqn8W9Edkw2140NELF2kvGNZgwWlSDcpiEiMX3kwadxGqfxPwUhBDbAKgQWATYhsAiBVchjVgIcdxtvBftnrs8tQmAT9uMEOO493u08rjEj7+PspFguSg3BFHaM+EYzBpPKis6mEJM56t+9ldM4jWOCO1GzuhMN+zGbk6jYCZgbkXF+5kbc3AmX5zz7XK95nu/thM0W6jzXftyvwXEun2sQYMOTmLoT0mMjyPJcjv//J0KtgBoFtaKgVkCjKKjsr45jahQ0isKE6AAO7q+JbzRjMCoWwq1qopM09A/IrlEK7pEJ9v8rCsNWQZMJyoZslNv/AIoj1JREqiiJUJOhUxOhVpzzXHVT3M8pf42e0WDC69XtE+HvM/s53I/5HYfnuBHWDD7Oa0zANQVCgMVOEGyA1SZnWR3v7Q+2zU6YbI4HHwXhQTzczoHAZhPYUOQcXITNcS4r9v/b3AiSY13751ahuM3FjQgpdsLl2qfvfPuecY11P24VitsxxX6tyHPjOKdjjONz16vruOv//o7ZkH//LVBhQ4Vw/SkCtc8xG2qEa6wiXz2OYUOlCFTChtr+uQ6b/XP5J4/b7Od3vaoUm30N7+NWVEKgxmr/3IpK2FDZ36sQqITVPsd+TFhRYZXvEXKs472Q35R8b3N+5vhcEY558jPXeytqLCBs9uAoAQiETTj/73i23N/nmG8GAmTrfw18YxmDxWxlWDERP76KP9YNYqzTY0KHkTDM6Jz/7yOWHuIYVlzJO2phIYNGADb0ZGJVXLdRL4aJpo8wDPazyD8NFhSE/ZEWzj+Vn2M2ewkr1wjcPpWMRx5T2Umyg1AoHuMcY3Cb7/4YOd7LV7XX+0DjnI+ofdcqO6GyH1e8ko3+C6EIT2LjeTccD7v3sWDH3QgXNrQBzq0OMt93D95/wkkUXQTX83PXGK/PFftnzvN4z3c/ZifWCp6f2wm+yv6LUzuJuxSQpJDk+nMXmhQc5VQUt/d4vgevMd7v3c+Bx3vFsa7i+9lIcz0/d9+vBtC6nRs/a/nuPaRrDTbX43OIi/sfiEpSFGUZ8CigBp4SQtzv9bli//xcYAj4lhAiQOPYr4fWymY0yd08mnEdXUoSUWrQKwK9ShBmf422Wpi3tZSoocPoFEGYAlFqK5GKDZX9O7OJcgZtCgNWFUahYLKBCZVdgnRIyNgJOjhkbg9Z210IB7usab8nbnWcPPUPB0sQHp+hgOJ2Pm+dxfH7VgBFCOdvWgk4VqAI++eK++fCNcfx2/Yap3IeE6AobmsIz3nIPXvszW0vnnvGY8+SyABCOO+F476478n9G1AQ2LfkvFce99FNE/L4foTjmHD7ztzHCK9xbudyP+Z23EMjdCps7r8QtzHu+3JbTwhh/0y4rkKAfMzUrrU8fmh+zu84H9Ls4gH38Qjw87n7CO85noTV/T1uTMPx4j1Gsf9z05IUd6Jqn+MksF5zPMYEP4drHcXtUIBzOBmNY6D3NTnW8PO59z3weu/+jDifde/xQP6UIRK9qsSfCJwyxqAoihr4B3AW0ADsUBTlAyGEe4uk5UCR/W8W8E/76wlH2eaNbMsqJr+ylv9L6SVZo6Gvpovhzj4nkRhsbsbQ7WqoYgK67H/+oABh9r/TcCGwketkwYfFjfyZEsKYQOdxJ2ahrOVF4L7+nr3Xdp/r7733MZUncQx5XrB1/M0NbBYd8f/C+7gIPt7+6sOsnG+D7cX7lzrCGJ8k4WO5Tvd9BrrOwHsZaO9k+vm+ORFfF6dSY5gJVAohjgIoivIacAHgzhguAF4QUoTZqihKnKIo6UKI5hO9mdLtn3NmrwGBjQ7MdAij33GRtiTyW33bGIacEmcLjRwqfob5fSyPgbqGOtZ7nOLzYwywnyAUf6S9+/s80H4CjvP7Xgl5PyHD53zK1zvfKcapY8jHA9edFCHf1GO7+yJgRnPo5/HSy0M+jTjmX8qx7bUm4eR8u6eSMWQC9W7vG/DVBvyNyQQ8GIOiKDcDNwPk5AToJzACrHot2j6BgoJKUdkdWRYirZ1uZgUb4cphjOlh/uWfgGWmA75xvwg/5wttXKDFhOK7mvceFT/H/J9OCXA82B49B4V8PR4mgMDHhMdnI2zM33WMeM8Vjxf/5xth3f8gKN7X+x/MIf5T76aC4kcj+LonPXFXmzN/7siDjgOnkjEE0y2PZQxCiCeAJ0DWSjqezfzo0X8dz7TTOI3TOI3/eZzKIkENQLbb+yz4/+3df+hddR3H8ecr22apOZcr5g/y65qEiG3TxnIlBFG6f2aoMBLUECJ0YYTKQhAjikwSlEgpElQiIS3byMwfKAtZuaX78R1zuTXFteV3VtqkWNre/fH5fNs593vPnd/v7rnnfLmvB1zuOZ/7ufe+7pvvvZ/vOffc82HvFPqYmVmNBjkwbAAWSBqRNBNYCazp6LMGuFLJUuDNOr5fMDOzagPblRQR70haBfyWdBzdvRGxTdJX8u33AI+SDlXdSTpc9UuDymdmZslAf8cQEY+SPvyLbfcUlgO4bpCZzMyszBMRmJlZiQcGMzMr8cBgZmYlHhjMzKxE0e9f9Q2YpP3AK1O8+8nA632MUxfn7J/pkBGcs5+mQ0YYfM6PRMTcbjdM+4HhaEjaGBHnN53jSJyzf6ZDRnDOfpoOGaFdOb0ryczMSjwwmJlZybAPDD9qOsC75Jz9Mx0ygnP203TICC3KOdTfMZiZ2UTDvsVgZmYdPDCYmVnJ0A4Mki6StEPSTkmrG87ysqStkjZJ2pjb5kh6QtJL+fqkQv9v5Nw7JH2+xlz3ShqTNFpom3QuSefl17dT0l2aMLVYLTlvlfSXXNNNkpY3mVPS6ZKelrRd0jZJ1+f2VtWzR87W1FPSsZKek7Q5Z/xmbm9bLatytqaWlSJi6C6k037vAs4EZgKbgbMbzPMycHJH2/eA1Xl5NXBbXj47550FjOTXcUxNuS4EFgOjR5MLeA74JGmGvt8AFw8g563ADV36NpITmAcszssnAH/KWVpVzx45W1PP/HjH5+UZwB+ApS2sZVXO1tSy6jKsWwxLgJ0R8eeI+A/wILCi4UydVgD35eX7gEsK7Q9GxMGI2E2au2JJHQEiYh3w96PJJWke8IGIWB/pL/z+wn3qzFmlkZwRsS8ins/LB4DtpPnMW1XPHjmrDDxnJG/l1Rn5ErSvllU5qzT2Huo0rAPDqcCrhfU99P7jr1sAj0v6o6Qv57YPR569Ll9/KLc3nX2yuU7Ny53tg7BK0pa8q2l8t0LjOSWdASwi/QfZ2np25IQW1VPSMZI2AWPAExHRylpW5IQW1bKbYR0Yuu2fa/K43WURsRi4GLhO0oU9+rYt+7iqXE3lvRuYDywE9gHfz+2N5pR0PPAw8LWI+GevrhV5msrZqnpGxH8jYiFpXvglks7p0b2xWlbkbFUtuxnWgWEPcHph/TRgb0NZiIi9+XoM+CVp19BreROSfD2WuzedfbK59uTlzvZaRcRr+U15CPgxh3e3NZZT0gzSh+1PI+IXubl19eyWs431zLneAJ4BLqKFteyWs621LBrWgWEDsEDSiKSZwEpgTRNBJB0n6YTxZeBzwGjOc1XudhXwq7y8BlgpaZakEWAB6YupQZlUrrxJf0DS0nwkxZWF+9Rm/AMi+wKppo3lzI/5E2B7RNxRuKlV9azK2aZ6SporaXZefh/wWeBF2lfLrjnbVMtKdX6z3eYLsJx0xMUu4OYGc5xJOhJhM7BtPAvwQeAp4KV8Padwn5tz7h3UeHQC8DPSpu7bpP9arplKLuB80h//LuAH5F/c15zzAWArsIX0hpvXZE7gU6TN/y3ApnxZ3rZ69sjZmnoC5wIv5CyjwC1Tfc/UXMuqnK2pZdXFp8QwM7OSYd2VZGZmFTwwmJlZiQcGMzMr8cBgZmYlHhjMzKzEA4NZgaTZkq4trJ8i6aGanusSSbdU3PZWvp4r6bE6nt+sigcGs7LZwP8HhojYGxGX1fRcNwE/7NUhIvYD+yQtqymD2QQeGMzKvgvMz+fJv13SGcrzPEi6WtIjktZK2i1plaSvS3pB0u8lzcn95kt6LJ8U8XeSPtb5JJLOAg5GxOt5fUTSekkbJH2ro/sjwBW1vmqzAg8MZmWrgV0RsTAibuxy+znAF0nnt/k28K+IWASsJ52qANKk7l+NiPOAG+i+VbAMeL6wfidwd0R8AvhrR9+NwKen+HrMJu29TQcwm2aejjRPwQFJbwJrc/tW4Nx8VtILgJ8XJtma1eVx5gH7C+vLgEvz8gPAbYXbxoBT+hPf7Mg8MJhNzsHC8qHC+iHS++k9wBuRTrXcy7+BEzvaqs5Pc2zubzYQ3pVkVnaANKXllESau2C3pMshna1U0se7dN0OfLSw/izpLL8w8fuEszh8Bk6z2nlgMCuIiL8Bz0oalXT7FB/mCuAaSeNnzO02bew6YJEO72+6njRJ0wYmbkl8Bvj1FLOYTZrPrmrWEEl3Amsj4skj9FsHrIiIfwwmmQ07bzGYNec7wPt7dZA0F7jDg4INkrcYzMysxFsMZmZW4oHBzMxKPDCYmVmJBwYzMyvxwGBmZiX/A8e1AtzWkMeHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['rmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric position differences \\n Test Particles only\")\n",
+    "legend = ax.legend()\n",
+    "legend.remove()\n",
+    "fig.savefig(\"symba_swifter_comparison-9pl-18tp-testparticles-rmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['vmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric velocity differences \\n Test Particles only\")\n",
+    "legend = ax.legend()\n",
+    "legend.remove()\n",
+    "fig.savefig(\"symba_swifter_comparison-9pl-18tp-testparticles-vmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;rmag&#x27; (time (d): 333)&gt;\n",
+       "array([0.00000000e+00, 2.81661760e-10, 1.96588167e-01, 3.41033868e-01,\n",
+       "       3.66560841e-01, 3.66301948e-01, 3.66249335e-01, 3.66396298e-01,\n",
+       "       3.66718394e-01, 3.67175496e-01, 3.67715021e-01, 3.68275152e-01,\n",
+       "       3.68790522e-01, 3.69199134e-01, 3.69448202e-01, 3.69499736e-01,\n",
+       "       3.69335058e-01, 3.68957123e-01, 3.68389904e-01, 3.67675715e-01,\n",
+       "       3.66870691e-01, 3.66038894e-01, 3.65245879e-01, 3.64552504e-01,\n",
+       "       3.64009490e-01, 3.63653178e-01, 3.63502759e-01, 3.63558964e-01,\n",
+       "       3.63804172e-01, 3.64203926e-01, 3.64709715e-01, 3.65262866e-01,\n",
+       "       3.65799451e-01, 3.66256038e-01, 3.66575758e-01, 3.66714174e-01,\n",
+       "       3.66644175e-01, 3.66359092e-01, 3.65873566e-01, 3.65221944e-01,\n",
+       "       3.64454472e-01, 3.63631943e-01, 3.62819516e-01, 3.62080376e-01,\n",
+       "       3.61469889e-01, 3.61030854e-01, 3.60790163e-01, 3.60756788e-01,\n",
+       "       3.60921178e-01, 3.61256029e-01, 3.61718521e-01, 3.62253903e-01,\n",
+       "       3.62800136e-01, 3.63293324e-01, 3.63673632e-01, 3.63891182e-01,\n",
+       "       3.63911357e-01, 3.63718679e-01, 3.63318627e-01, 3.62737123e-01,\n",
+       "       3.62017697e-01, 3.61216780e-01, 3.60397813e-01, 3.59624972e-01,\n",
+       "       3.58957128e-01, 3.58442669e-01, 3.58115556e-01, 3.57992626e-01,\n",
+       "       3.58072312e-01, 3.58335003e-01, 3.58744642e-01, 3.59251395e-01,\n",
+       "       3.59795862e-01, 3.60314438e-01, 4.12957901e-01, 5.53838132e-01,\n",
+       "       6.88620712e-01, 8.15487973e-01, 8.20298524e-01, 8.52242029e-01,\n",
+       "...\n",
+       "       1.18537517e+00, 1.18611657e+00, 1.18648245e+00, 1.18644060e+00,\n",
+       "       1.18599627e+00, 1.18519174e+00, 1.18410280e+00, 1.18283210e+00,\n",
+       "       1.18150005e+00, 1.18023365e+00, 1.17915442e+00, 1.17836658e+00,\n",
+       "       1.17794674e+00, 1.17793651e+00, 1.17833843e+00, 1.17911578e+00,\n",
+       "       1.18019630e+00, 1.18147927e+00, 1.18284532e+00, 1.18416783e+00,\n",
+       "       1.18532462e+00, 1.18620895e+00, 1.18673960e+00, 1.18686887e+00,\n",
+       "       1.18658701e+00, 1.18592279e+00, 1.18494010e+00, 1.18373170e+00,\n",
+       "       1.18241136e+00, 1.18110396e+00, 1.25299579e+00, 1.25223986e+00,\n",
+       "       1.25181471e+00, 1.25176461e+00, 1.25209741e+00, 1.25278376e+00,\n",
+       "       1.25376011e+00, 1.25493534e+00, 1.25620005e+00, 1.25743745e+00,\n",
+       "       1.25853473e+00, 1.25939339e+00, 1.25993785e+00, 1.26012176e+00,\n",
+       "       1.25993167e+00, 1.25938790e+00, 1.25854278e+00, 1.25747625e+00,\n",
+       "       1.25628915e+00, 1.25509455e+00, 1.25400756e+00, 1.25313436e+00,\n",
+       "       1.25256181e+00, 1.25234851e+00, 1.25251854e+00, 1.25305873e+00,\n",
+       "       1.25391995e+00, 1.25502210e+00, 1.25626238e+00, 1.25752571e+00,\n",
+       "       1.25869602e+00, 1.25966725e+00, 1.26035290e+00, 1.26069342e+00,\n",
+       "       1.26066111e+00, 1.26026226e+00, 1.25953643e+00, 1.25855297e+00,\n",
+       "       1.25740517e+00, 1.25620218e+00, 1.25505935e+00, 1.25408756e+00,\n",
+       "       1.25338232e+00, 1.25301396e+00, 1.25302049e+00, 1.25340406e+00,\n",
+       "       1.25413096e+00])\n",
+       "Coordinates:\n",
+       "    id        int64 2\n",
+       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 ... 3.63e+03 3.641e+03 3.652e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'rmag'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time (d)</span>: 333</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-934c4bab-67f7-4643-9182-363295c7239b' class='xr-array-in' type='checkbox' checked><label for='section-934c4bab-67f7-4643-9182-363295c7239b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 2.817e-10 0.1966 0.341 0.3666 ... 1.253 1.253 1.253 1.253 1.254</span></div><div class='xr-array-data'><pre>array([0.00000000e+00, 2.81661760e-10, 1.96588167e-01, 3.41033868e-01,\n",
+       "       3.66560841e-01, 3.66301948e-01, 3.66249335e-01, 3.66396298e-01,\n",
+       "       3.66718394e-01, 3.67175496e-01, 3.67715021e-01, 3.68275152e-01,\n",
+       "       3.68790522e-01, 3.69199134e-01, 3.69448202e-01, 3.69499736e-01,\n",
+       "       3.69335058e-01, 3.68957123e-01, 3.68389904e-01, 3.67675715e-01,\n",
+       "       3.66870691e-01, 3.66038894e-01, 3.65245879e-01, 3.64552504e-01,\n",
+       "       3.64009490e-01, 3.63653178e-01, 3.63502759e-01, 3.63558964e-01,\n",
+       "       3.63804172e-01, 3.64203926e-01, 3.64709715e-01, 3.65262866e-01,\n",
+       "       3.65799451e-01, 3.66256038e-01, 3.66575758e-01, 3.66714174e-01,\n",
+       "       3.66644175e-01, 3.66359092e-01, 3.65873566e-01, 3.65221944e-01,\n",
+       "       3.64454472e-01, 3.63631943e-01, 3.62819516e-01, 3.62080376e-01,\n",
+       "       3.61469889e-01, 3.61030854e-01, 3.60790163e-01, 3.60756788e-01,\n",
+       "       3.60921178e-01, 3.61256029e-01, 3.61718521e-01, 3.62253903e-01,\n",
+       "       3.62800136e-01, 3.63293324e-01, 3.63673632e-01, 3.63891182e-01,\n",
+       "       3.63911357e-01, 3.63718679e-01, 3.63318627e-01, 3.62737123e-01,\n",
+       "       3.62017697e-01, 3.61216780e-01, 3.60397813e-01, 3.59624972e-01,\n",
+       "       3.58957128e-01, 3.58442669e-01, 3.58115556e-01, 3.57992626e-01,\n",
+       "       3.58072312e-01, 3.58335003e-01, 3.58744642e-01, 3.59251395e-01,\n",
+       "       3.59795862e-01, 3.60314438e-01, 4.12957901e-01, 5.53838132e-01,\n",
+       "       6.88620712e-01, 8.15487973e-01, 8.20298524e-01, 8.52242029e-01,\n",
+       "...\n",
+       "       1.18537517e+00, 1.18611657e+00, 1.18648245e+00, 1.18644060e+00,\n",
+       "       1.18599627e+00, 1.18519174e+00, 1.18410280e+00, 1.18283210e+00,\n",
+       "       1.18150005e+00, 1.18023365e+00, 1.17915442e+00, 1.17836658e+00,\n",
+       "       1.17794674e+00, 1.17793651e+00, 1.17833843e+00, 1.17911578e+00,\n",
+       "       1.18019630e+00, 1.18147927e+00, 1.18284532e+00, 1.18416783e+00,\n",
+       "       1.18532462e+00, 1.18620895e+00, 1.18673960e+00, 1.18686887e+00,\n",
+       "       1.18658701e+00, 1.18592279e+00, 1.18494010e+00, 1.18373170e+00,\n",
+       "       1.18241136e+00, 1.18110396e+00, 1.25299579e+00, 1.25223986e+00,\n",
+       "       1.25181471e+00, 1.25176461e+00, 1.25209741e+00, 1.25278376e+00,\n",
+       "       1.25376011e+00, 1.25493534e+00, 1.25620005e+00, 1.25743745e+00,\n",
+       "       1.25853473e+00, 1.25939339e+00, 1.25993785e+00, 1.26012176e+00,\n",
+       "       1.25993167e+00, 1.25938790e+00, 1.25854278e+00, 1.25747625e+00,\n",
+       "       1.25628915e+00, 1.25509455e+00, 1.25400756e+00, 1.25313436e+00,\n",
+       "       1.25256181e+00, 1.25234851e+00, 1.25251854e+00, 1.25305873e+00,\n",
+       "       1.25391995e+00, 1.25502210e+00, 1.25626238e+00, 1.25752571e+00,\n",
+       "       1.25869602e+00, 1.25966725e+00, 1.26035290e+00, 1.26069342e+00,\n",
+       "       1.26066111e+00, 1.26026226e+00, 1.25953643e+00, 1.25855297e+00,\n",
+       "       1.25740517e+00, 1.25620218e+00, 1.25505935e+00, 1.25408756e+00,\n",
+       "       1.25338232e+00, 1.25301396e+00, 1.25302049e+00, 1.25340406e+00,\n",
+       "       1.25413096e+00])</pre></div></div></li><li class='xr-section-item'><input id='section-8c0d58c8-a907-42a2-908d-53c85ec339a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c0d58c8-a907-42a2-908d-53c85ec339a2' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-82f0be55-5674-40bd-86cc-ee0f43675535' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82f0be55-5674-40bd-86cc-ee0f43675535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b221e6ba-0310-46c9-88b0-d34cc6482ca6' class='xr-var-data-in' type='checkbox'><label for='data-b221e6ba-0310-46c9-88b0-d34cc6482ca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time (d)</span></div><div class='xr-var-dims'>(time (d))</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 11.0 ... 3.641e+03 3.652e+03</div><input id='attrs-58db77b4-04a6-4a79-9adf-42a7047607ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58db77b4-04a6-4a79-9adf-42a7047607ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23b94260-97d0-463c-aa88-bb24dd635e26' class='xr-var-data-in' type='checkbox'><label for='data-23b94260-97d0-463c-aa88-bb24dd635e26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0.,   11.,   22., ..., 3630., 3641., 3652.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec271324-fb01-4492-a273-5faa3bacbe74' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec271324-fb01-4492-a273-5faa3bacbe74' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'rmag' (time (d): 333)>\n",
+       "array([0.00000000e+00, 2.81661760e-10, 1.96588167e-01, 3.41033868e-01,\n",
+       "       3.66560841e-01, 3.66301948e-01, 3.66249335e-01, 3.66396298e-01,\n",
+       "       3.66718394e-01, 3.67175496e-01, 3.67715021e-01, 3.68275152e-01,\n",
+       "       3.68790522e-01, 3.69199134e-01, 3.69448202e-01, 3.69499736e-01,\n",
+       "       3.69335058e-01, 3.68957123e-01, 3.68389904e-01, 3.67675715e-01,\n",
+       "       3.66870691e-01, 3.66038894e-01, 3.65245879e-01, 3.64552504e-01,\n",
+       "       3.64009490e-01, 3.63653178e-01, 3.63502759e-01, 3.63558964e-01,\n",
+       "       3.63804172e-01, 3.64203926e-01, 3.64709715e-01, 3.65262866e-01,\n",
+       "       3.65799451e-01, 3.66256038e-01, 3.66575758e-01, 3.66714174e-01,\n",
+       "       3.66644175e-01, 3.66359092e-01, 3.65873566e-01, 3.65221944e-01,\n",
+       "       3.64454472e-01, 3.63631943e-01, 3.62819516e-01, 3.62080376e-01,\n",
+       "       3.61469889e-01, 3.61030854e-01, 3.60790163e-01, 3.60756788e-01,\n",
+       "       3.60921178e-01, 3.61256029e-01, 3.61718521e-01, 3.62253903e-01,\n",
+       "       3.62800136e-01, 3.63293324e-01, 3.63673632e-01, 3.63891182e-01,\n",
+       "       3.63911357e-01, 3.63718679e-01, 3.63318627e-01, 3.62737123e-01,\n",
+       "       3.62017697e-01, 3.61216780e-01, 3.60397813e-01, 3.59624972e-01,\n",
+       "       3.58957128e-01, 3.58442669e-01, 3.58115556e-01, 3.57992626e-01,\n",
+       "       3.58072312e-01, 3.58335003e-01, 3.58744642e-01, 3.59251395e-01,\n",
+       "       3.59795862e-01, 3.60314438e-01, 4.12957901e-01, 5.53838132e-01,\n",
+       "       6.88620712e-01, 8.15487973e-01, 8.20298524e-01, 8.52242029e-01,\n",
+       "...\n",
+       "       1.18537517e+00, 1.18611657e+00, 1.18648245e+00, 1.18644060e+00,\n",
+       "       1.18599627e+00, 1.18519174e+00, 1.18410280e+00, 1.18283210e+00,\n",
+       "       1.18150005e+00, 1.18023365e+00, 1.17915442e+00, 1.17836658e+00,\n",
+       "       1.17794674e+00, 1.17793651e+00, 1.17833843e+00, 1.17911578e+00,\n",
+       "       1.18019630e+00, 1.18147927e+00, 1.18284532e+00, 1.18416783e+00,\n",
+       "       1.18532462e+00, 1.18620895e+00, 1.18673960e+00, 1.18686887e+00,\n",
+       "       1.18658701e+00, 1.18592279e+00, 1.18494010e+00, 1.18373170e+00,\n",
+       "       1.18241136e+00, 1.18110396e+00, 1.25299579e+00, 1.25223986e+00,\n",
+       "       1.25181471e+00, 1.25176461e+00, 1.25209741e+00, 1.25278376e+00,\n",
+       "       1.25376011e+00, 1.25493534e+00, 1.25620005e+00, 1.25743745e+00,\n",
+       "       1.25853473e+00, 1.25939339e+00, 1.25993785e+00, 1.26012176e+00,\n",
+       "       1.25993167e+00, 1.25938790e+00, 1.25854278e+00, 1.25747625e+00,\n",
+       "       1.25628915e+00, 1.25509455e+00, 1.25400756e+00, 1.25313436e+00,\n",
+       "       1.25256181e+00, 1.25234851e+00, 1.25251854e+00, 1.25305873e+00,\n",
+       "       1.25391995e+00, 1.25502210e+00, 1.25626238e+00, 1.25752571e+00,\n",
+       "       1.25869602e+00, 1.25966725e+00, 1.26035290e+00, 1.26069342e+00,\n",
+       "       1.26066111e+00, 1.26026226e+00, 1.25953643e+00, 1.25855297e+00,\n",
+       "       1.25740517e+00, 1.25620218e+00, 1.25505935e+00, 1.25408756e+00,\n",
+       "       1.25338232e+00, 1.25301396e+00, 1.25302049e+00, 1.25340406e+00,\n",
+       "       1.25413096e+00])\n",
+       "Coordinates:\n",
+       "    id        int64 2\n",
+       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 ... 3.63e+03 3.641e+03 3.652e+03"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "swiftdiff['rmag'].sel(id=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "swiftestOOF",
+   "language": "python",
+   "name": "swiftestoof"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/symba_swifter_comparison/9pl_18tp_encounters/tp.in b/examples/symba_swifter_comparison/9pl_18tp_encounters/tp.in
index c7cf002d6..7c1b15bd6 100644
--- a/examples/symba_swifter_comparison/9pl_18tp_encounters/tp.in
+++ b/examples/symba_swifter_comparison/9pl_18tp_encounters/tp.in
@@ -1,49 +1,49 @@
 16
 101
-0.33208578766229190915 0.07439013071780828379 -0.02438290851908785084
--0.008988542188201206762 0.028710618792657981169 0.0034094833969203438596
+-0.0658187108448175795 0.30391138014159824188 0.030872485461978960153
+-0.030537616761930286291 -0.0049297226604189817514 0.0026371811668407158825
 102
-0.33203966624962866216 0.07434400930514498129 -0.02438290851908785084
--0.014195641132219511543 0.028710618792657981169 0.0034094833969203438596
+-0.065864832257480881994 0.3038652587289349949 0.030872485461978960153
+-0.035744715705948587603 -0.0049297226604189817514 0.0026371811668407158825
 103
--0.7187543234391324809 -0.011798260816488121555 0.041316403191083782287
-0.0065615071841567274707 -0.020313576971905909774 -0.00029114855617710840843
+-0.6526399503364792576 -0.30651935535365792962 0.033456491497379246824
+0.014807065041004032965 -0.0184014319837384685 -0.0007407193515014080928
 104
--0.71886874402007694407 -0.011912681397432518804 0.041316403191083782287
--0.006132960226534060408 -0.020313576971905909774 -0.00029114855617710840843
+-0.65275437091742372075 -0.30663377593460228177 0.033456491497379246824
+0.0021125976303132450868 -0.0184014319837384685 -0.0007407193515014080928
 105
-0.35683111163121072895 -0.9518327808922094624 4.4027442504036787155e-05
-0.022724479262608666269 0.0059737936889703449964 -3.3484113013969089573e-07
+0.58052308875528702004 -0.8331397763444912119 3.7646553415201541957e-05
+0.020730998066553867065 0.009770187318278569788 -5.1179589633921335467e-07
 106
-0.3567106558193317012 -0.95195323670408849015 4.4027442504036787155e-05
-0.008935598794060913702 0.0059737936889703449964 -3.3484113013969089573e-07
+0.58040263294340799227 -0.83326023215637023966 3.7646553415201541957e-05
+0.0069421175980061153657 0.009770187318278569788 -5.1179589633921335467e-07
 107
--1.5233391647104730371 0.6724145771476651712 0.051459143378398922164
--0.0020480822268840624331 -0.011607719813367209372 -0.000117479966462153095864
+-1.5891096979602641337 0.49388011604967890777 0.049330990309104823244
+-0.00055132825635455804184 -0.012168467501132099878 -0.00016594932370266260858
 108
--1.5234032495379807859 0.6723504923201574224 0.051459143378398922164
--0.008207040423331847523 -0.011607719813367209372 -0.000117479966462153095864
+-1.5891737827877718825 0.49381603122217127 0.049330990309104823244
+-0.0067102864528023435653 -0.012168467501132099878 -0.00016594932370266260858
 109
-4.050605826355517358 -2.9904269687677218492 -0.078187280837353656526
-0.041279424970441319642 0.006432188574295680597 -0.00012509257442073270106
+4.1155004823659924185 -2.893171407164709663 -0.080043092204059404504
+0.0411371945893665783 0.006534697671907701254 -0.00012233719535540690457
 110
-4.049284028339322994 -2.9917487667839162135 -0.078187280837353656526
--0.032485009432853539924 0.006432188574295680597 -0.00012509257442073270106
+4.114178684349798054 -2.8944932051809040274 -0.080043092204059404504
+-0.032627239813928281265 0.006534697671907701254 -0.00012233719535540690457
 111
-6.299479995832536261 -7.7058625321556393217 -0.11669919842191249504
-0.02612723553831041573 0.0035242303011843410798 -0.00022097170940726839814
+6.3594761400945154506 -7.652737529060036792 -0.12000977499446359442
+0.02609853948273724994 0.0035590677039893160206 -0.00022043610541731448703
 112
-6.2983790111222752728 -7.70696351686590031 -0.11669919842191249504
--0.01809910222875676239 0.0035242303011843410798 -0.00022097170940726839814
+6.3583751553842544624 -7.65383851377029778 -0.12000977499446359442
+-0.01812779828432992818 0.0035590677039893160206 -0.00022043610541731448703
 113
-14.856321905516212567 13.007829033301401722 -0.14417795763685259391
-0.010478935887110856981 0.0027821364817078499815 4.40781085949555924e-05
+14.817019253266252576 13.049505570448612701 -0.14351615042000470668
+0.010470241012353788054 0.002774730265364384104 4.416262654344997005e-05
 114
-14.855842389541807691 13.007349517326996846 -0.14417795763685259391
--0.015710591190212928187 0.0027821364817078499815 4.40781085949555924e-05
+14.8165397372918477 13.049026054474207825 -0.14351615042000470668
+-0.015719286064969997113 0.002774730265364384104 4.416262654344997005e-05
 115
-29.55768244045575699 -4.6291447957067299868 -0.58590957207831262377
-0.014905509815736753265 0.0031274056019462009859 -7.51415892482447254e-05
+29.564692754289236376 -4.5822270889269072214 -0.5870359532621901577
+0.014900470225798949711 0.0031282868879460171488 -7.5042704502708602616e-05
 116
-29.557216915563323312 -4.6296103205991601115 -0.58590957207831262377
--0.0139657618108195089035 0.0031274056019462009859 -7.51415892482447254e-05
+29.564227229396802699 -4.582692613819337346 -0.5870359532621901577
+-0.013970801400757312458 0.0031282868879460171488 -7.5042704502708602616e-05
diff --git a/python/swiftest/swiftest/init_cond.py b/python/swiftest/swiftest/init_cond.py
index 6e5048c0f..f9a7378c0 100644
--- a/python/swiftest/swiftest/init_cond.py
+++ b/python/swiftest/swiftest/init_cond.py
@@ -121,7 +121,6 @@ def solar_system_horizons(plname, idval, param, ephemerides_start_date, ds):
         tlab.append('vx')
         tlab.append('vy')
         tlab.append('vz')
-    plab.append('Rhill')
 
     dims = ['time', 'id', 'vec']
     t = np.array([0.0])
@@ -193,11 +192,14 @@ def solar_system_horizons(plname, idval, param, ephemerides_start_date, ds):
             p11.append(pldata[key].vectors()['vy'][0] * VCONV)
             p12.append(pldata[key].vectors()['vz'][0] * VCONV)
         if ispl:
-            Rhill.append(pldata[key].elements()['a'][0] * (3 * MSun_over_Mpl[key]) ** (-THIRDLONG))
             Rpl.append(planetradius[key] * DCONV)
             GMpl.append(GMcb[0] / MSun_over_Mpl[key])
             # Generate planet value vectors
-            pvec = np.vstack([p1, p2, p3, p4, p5, p6, GMpl, Rpl, p7, p8, p9, p10, p11, p12, Rhill])
+            if (param['RHILL_PRESENT'] == 'YES'):
+                Rhill.append(pldata[key].elements()['a'][0] * DCONV * (3 * MSun_over_Mpl[key]) ** (-THIRDLONG))
+                pvec = np.vstack([p1, p2, p3, p4, p5, p6, GMpl, Rpl, Rhill, p7, p8, p9, p10, p11, p12])
+            else:
+                pvec = np.vstack([p1, p2, p3, p4, p5, p6, GMpl, Rpl, p7, p8, p9, p10, p11, p12])
         else:
             pvec = np.vstack([p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12])
             plab = tlab.copy()
diff --git a/python/swiftest/swiftest/io.py b/python/swiftest/swiftest/io.py
index ceab9a74f..2dd4ef7b3 100644
--- a/python/swiftest/swiftest/io.py
+++ b/python/swiftest/swiftest/io.py
@@ -71,6 +71,7 @@ def read_swiftest_param(param_file_name, param):
         param['EXTRA_FORCE'] = param['EXTRA_FORCE'].upper()
         param['BIG_DISCARD'] = param['BIG_DISCARD'].upper()
         param['CHK_CLOSE'] = param['CHK_CLOSE'].upper()
+        param['RHILL_PRESENT'] = param['RHILL_PRESENT'].upper()
         param['FRAGMENTATION'] = param['FRAGMENTATION'].upper()
         param['ROTATION'] = param['ROTATION'].upper()
         param['TIDES'] = param['TIDES'].upper()
@@ -401,6 +402,8 @@ def make_swiftest_labels(param):
     plab = tlab.copy()
     plab.append('Mass')
     plab.append('Radius')
+    if param['RHILL_PRESENT'] == 'YES':
+        plab.append('Rhill')
     clab = ['Mass', 'Radius', 'J_2', 'J_4']
     if param['ROTATION'] == 'YES':
         clab.append('Ip_x')
@@ -491,6 +494,8 @@ def swiftest_stream(f, param):
             p6 = f.read_reals(np.float64)
             Mpl = f.read_reals(np.float64)
             Rpl = f.read_reals(np.float64)
+            if param['RHILL_PRESENT'] == 'YES':
+                Rhill = f.read_reals(np.float64)
             if param['ROTATION'] == 'YES':
                 Ipplx = f.read_reals(np.float64)
                 Ipply = f.read_reals(np.float64)
@@ -523,6 +528,9 @@ def swiftest_stream(f, param):
             tvec = np.empty((6, 0))
             tpid = np.empty(0)
         cvec = np.array([Mcb, Rcb, J2cb, J4cb])
+        if param['RHILL_PRESENT'] == 'YES':
+           if npl > 0:
+              pvec = np.vstack([pvec, Rhill])
         if param['ROTATION'] == 'YES':
             cvec = np.vstack([cvec, Ipcbx, Ipcby, Ipcbz, rotcbx, rotcby, rotcbz])
             if npl > 0:
@@ -670,7 +678,10 @@ def swiftest_xr2infile(ds, param, framenum=-1):
         print(pl.id.count().values, file=plfile)
         for i in pl.id:
             pli = pl.sel(id=i)
-            print(i.values, pli['Mass'].values, file=plfile)
+            if param['RHILL_PRESENT'] == 'YES':
+               print(i.values, pli['Mass'].values, pli['Rhill'].values, file=plfile)
+            else:
+               print(i.values, pli['Mass'].values, file=plfile)
             print(pli['Radius'].values, file=plfile)
             print(pli['px'].values, pli['py'].values, pli['pz'].values, file=plfile)
             print(pli['vx'].values, pli['vy'].values, pli['vz'].values, file=plfile)
@@ -689,36 +700,55 @@ def swiftest_xr2infile(ds, param, framenum=-1):
         # Now make Swiftest files
         cbfile = FortranFile(param['CB_IN'], 'w')
         cbfile.write_record(cbid)
-        MSun = np.double(1.0)
         cbfile.write_record(np.double(GMSun))
-        cbfile.write_record(np.double(rmin))
+        cbfile.write_record(np.double(RSun))
         cbfile.write_record(np.double(J2))
         cbfile.write_record(np.double(J4))
         cbfile.close()
         
         plfile = FortranFile(param['PL_IN'], 'w')
-        plfile.write_record(npl)
+        npl = pl.id.count().values
+        plid = pl.id.values
+        px = pl['px'].values  
+        py = pl['py'].values  
+        pz = pl['pz'].values  
+        vx = pl['vx'].values  
+        vy = pl['vy'].values  
+        vz = pl['vz'].values  
+        mass = pl['Mass'].values  
+        radius = pl['Radius'].values  
         
+        plfile.write_record(npl)
         plfile.write_record(plid)
-        plfile.write_record(p_pl[0])
-        plfile.write_record(p_pl[1])
-        plfile.write_record(p_pl[2])
-        plfile.write_record(v_pl[0])
-        plfile.write_record(v_pl[1])
-        plfile.write_record(v_pl[2])
+        plfile.write_record(px)
+        plfile.write_record(py)
+        plfile.write_record(pz)
+        plfile.write_record(vx)
+        plfile.write_record(vy)
+        plfile.write_record(vz)
         plfile.write_record(mass)
+        if param['RHILL_PRESENT'] == 'YES':
+            rhill = pl['Rhill'].values
+            plfile.write_record(rhill)
         plfile.write_record(radius)
         plfile.close()
         tpfile = FortranFile(param['TP_IN'], 'w')
-        ntp = 1
+        ntp = tp.id.count().values
+        tpid = tp.id.values
+        px = tp['px'].values  
+        py = tp['py'].values  
+        pz = tp['pz'].values  
+        vx = tp['vx'].values  
+        vy = tp['vy'].values  
+        vz = tp['vz'].values 
         tpfile.write_record(ntp)
         tpfile.write_record(tpid)
-        tpfile.write_record(p_tp[0])
-        tpfile.write_record(p_tp[1])
-        tpfile.write_record(p_tp[2])
-        tpfile.write_record(v_tp[0])
-        tpfile.write_record(v_tp[1])
-        tpfile.write_record(v_tp[2])
+        tpfile.write_record(px)
+        tpfile.write_record(py)
+        tpfile.write_record(pz)
+        tpfile.write_record(vx)
+        tpfile.write_record(vy)
+        tpfile.write_record(vz)
     else:
         print(f"{param['IN_TYPE']} is an unknown file type")
 
@@ -1021,9 +1051,13 @@ def swifter2swiftest(swifter_param, plname="", tpname="", cbname="", conversion_
             for n in range(1, npl):  # Loop over planets
                 line = plold.readline()
                 i_list = [i for i in line.split(" ") if i.strip()]
-                name = int(i_list[0])
+                idnum = int(i_list[0])
                 GMpl = real2float(i_list[1])
-                print(name, GMpl, file=plnew)
+                if swifter_param['RHILL_PRESENT'] == 'YES':
+                   Rhill = real2float(i_list[2])
+                   print(idnum, GMpl, Rhill, file=plnew)
+                else:
+                   print(idnum, GMpl, file=plnew)
                 if swifter_param['CHK_CLOSE'] == 'YES':
                     line = plold.readline()
                     i_list = [i for i in line.split(" ") if i.strip()]
@@ -1237,7 +1271,6 @@ def swiftest2swifter_param(swiftest_param, J2=0.0, J4=0.0):
        tmp = swifter_param.pop(key, None)
     swifter_param['J2'] = J2
     swifter_param['J4'] = J4
-    swifter_param['RHILL_PRESENT'] = "YES"
     swifter_param['CHK_CLOSE'] = "YES"
     if swifter_param['OUT_STAT'] == "REPLACE":
         swifter_param['OUT_STAT'] = "UNKNOWN"
diff --git a/python/swiftest/swiftest/simulation_class.py b/python/swiftest/swiftest/simulation_class.py
index e20ef05f7..670b72a60 100644
--- a/python/swiftest/swiftest/simulation_class.py
+++ b/python/swiftest/swiftest/simulation_class.py
@@ -39,6 +39,7 @@ def __init__(self, codename="Swiftest", param_file=""):
             'EXTRA_FORCE': "NO",
             'BIG_DISCARD': "NO",
             'CHK_CLOSE': "YES",
+            'RHILL_PRESENT': "YES",
             'FRAGMENTATION': "NO",
             'ROTATION': "NO",
             'TIDES': "NO",
diff --git a/src/io/io.f90 b/src/io/io.f90
index cf4772a8f..7c50242cb 100644
--- a/src/io/io.f90
+++ b/src/io/io.f90
@@ -259,10 +259,10 @@ module subroutine io_param_reader(self, unit, iotype, v_list, iostat, iomsg)
    
          ! Determine if the GR flag is set correctly for this integrator
          select case(integrator)
-         case(WHM, RMVS)
+         case(WHM, RMVS, SYMBA)
             write(*,*) "GR             = ", self%lgr
          case default   
-            write(iomsg, *) 'GR is not yet implemented for this integrator. This parameter will be ignored.'
+            if (self%lgr) write(iomsg, *) 'GR is not yet implemented for this integrator. This parameter will be ignored.'
          end select
       end associate
 
@@ -612,41 +612,33 @@ module subroutine io_read_body_in(self, param)
             do i = 1, nbody
                select type(self)
                class is (swiftest_pl)
-                  read(iu, *, iostat = ierr) self%id(i), val 
-                  self%Gmass(i) = real(val, kind=DP)
-                  self%mass(i) = real(val / param%GU, kind=DP)
-
-                  if (param%lclose) then
-                     if (param%lrhill_present) then
-                        read(iu, *, iostat = ierr) self%radius(i), self%rhill(i)
-                     else
-                        read(iu, *, iostat = ierr) self%radius(i)
-                     end if
-                     if (ierr /= 0 ) exit
+                  if (param%lrhill_present) then
+                     read(iu, *, iostat=ierr, err=100) self%id(i), val, self%rhill(i)
                   else
-                     self%radius(i) = 0.0_DP
+                     read(iu, *, iostat=ierr, err=100) self%id(i), val
                   end if
+                  self%Gmass(i) = real(val, kind=DP)
+                  self%mass(i) = real(val / param%GU, kind=DP)
+                  if (param%lclose) read(iu, *, iostat=ierr, err=100) self%radius(i)
                   if (param%lrotation) then
-                     read(iu, iostat = ierr) self%Ip(:, i)
-                     read(iu, iostat = ierr) self%rot(:, i)
+                     read(iu, iostat=ierr, err=100) self%Ip(:, i)
+                     read(iu, iostat=ierr, err=100) self%rot(:, i)
                   end if
                   if (param%ltides) then
-                     read(iu, iostat = ierr) self%k2(i)
-                     read(iu, iostat = ierr) self%Q(i)
+                     read(iu, iostat=ierr, err=100) self%k2(i)
+                     read(iu, iostat=ierr, err=100) self%Q(i)
                   end if
                class is (swiftest_tp)
-                  read(iu, *, iostat = ierr) self%id(i)
+                  read(iu, *, iostat=ierr, err=100) self%id(i)
                end select
-               if (ierr /= 0 ) exit
-               read(iu, *, iostat = ierr) self%xh(1, i), self%xh(2, i), self%xh(3, i)
-               read(iu, *, iostat = ierr) self%vh(1, i), self%vh(2, i), self%vh(3, i)
-               if (ierr /= 0 ) exit
+               read(iu, *, iostat=ierr, err=100) self%xh(1, i), self%xh(2, i), self%xh(3, i)
+               read(iu, *, iostat=ierr, err=100) self%vh(1, i), self%vh(2, i), self%vh(3, i)
                self%status(i) = ACTIVE
             end do
          end if
       case (REAL4_TYPE, REAL8_TYPE)  !, SWIFTER_REAL4_TYPE, SWIFTER_REAL8_TYPE)
-         open(unit = iu, file = infile, status = 'old', form = 'UNFORMATTED', iostat = ierr)
-         read(iu, iostat = ierr) nbody
+         open(unit=iu, file=infile, status='old', form='UNFORMATTED', iostat=ierr)
+         read(iu, iostat=ierr, err=100) nbody
          call self%setup(nbody)
          if (nbody > 0) then
             call self%read_frame(iu, param, XV, ierr)
@@ -658,7 +650,7 @@ module subroutine io_read_body_in(self, param)
       end select
       close(iu)
 
-      if (ierr /= 0 ) then
+      100 if (ierr /= 0 ) then
          write(*,*) 'Error reading in initial conditions from ',trim(adjustl(infile))
          call util_exit(FAILURE)
       end if
@@ -817,45 +809,46 @@ module subroutine io_read_frame_body(self, iu, param, form, ierr)
       integer(I4B),               intent(out)   :: ierr   !! Error code
 
       associate(n => self%nbody)
-         read(iu, iostat = ierr) self%id(1:n)
-         !read(iu, iostat = ierr) self%name(1:n)
+         read(iu, iostat=ierr, err=100) self%id(1:n)
+         !read(iu, iostat=ierr, err=100) self%name(1:n)
          select case (form)
          case (EL) 
-            read(iu, iostat = ierr) self%a(1:n)
-            read(iu, iostat = ierr) self%e(1:n)
-            read(iu, iostat = ierr) self%inc(1:n)
-            read(iu, iostat = ierr) self%capom(:)
-            read(iu, iostat = ierr) self%omega(:)
-            read(iu, iostat = ierr) self%capm(:)
+            read(iu, iostat=ierr, err=100) self%a(1:n)
+            read(iu, iostat=ierr, err=100) self%e(1:n)
+            read(iu, iostat=ierr, err=100) self%inc(1:n)
+            read(iu, iostat=ierr, err=100) self%capom(:)
+            read(iu, iostat=ierr, err=100) self%omega(:)
+            read(iu, iostat=ierr, err=100) self%capm(:)
          case (XV)
-            read(iu, iostat = ierr) self%xh(1, 1:n)
-            read(iu, iostat = ierr) self%xh(2, 1:n)
-            read(iu, iostat = ierr) self%xh(3, 1:n)
-            read(iu, iostat = ierr) self%vh(1, 1:n)
-            read(iu, iostat = ierr) self%vh(2, 1:n)
-            read(iu, iostat = ierr) self%vh(3, 1:n)
+            read(iu, iostat=ierr, err=100) self%xh(1, 1:n)
+            read(iu, iostat=ierr, err=100) self%xh(2, 1:n)
+            read(iu, iostat=ierr, err=100) self%xh(3, 1:n)
+            read(iu, iostat=ierr, err=100) self%vh(1, 1:n)
+            read(iu, iostat=ierr, err=100) self%vh(2, 1:n)
+            read(iu, iostat=ierr, err=100) self%vh(3, 1:n)
          end select
          select type(pl => self)  
          class is (swiftest_pl)  ! Additional output if the passed polymorphic object is a massive body
-            read(iu, iostat = ierr) pl%Gmass(1:n)
+            read(iu, iostat=ierr, err=100) pl%Gmass(1:n)
             pl%mass(1:n) = pl%Gmass / param%GU 
-            read(iu, iostat = ierr) pl%radius(1:n)
+            if (param%lrhill_present) read(iu, iostat=ierr, err=100) pl%rhill(1:n)
+            read(iu, iostat=ierr, err=100) pl%radius(1:n)
             if (param%lrotation) then
-               read(iu, iostat = ierr) pl%rot(1, 1:n)
-               read(iu, iostat = ierr) pl%rot(2, 1:n)
-               read(iu, iostat = ierr) pl%rot(3, 1:n)
-               read(iu, iostat = ierr) pl%Ip(1, 1:n)
-               read(iu, iostat = ierr) pl%Ip(2, 1:n)
-               read(iu, iostat = ierr) pl%Ip(3, 1:n)
+               read(iu, iostat=ierr, err=100) pl%rot(1, 1:n)
+               read(iu, iostat=ierr, err=100) pl%rot(2, 1:n)
+               read(iu, iostat=ierr, err=100) pl%rot(3, 1:n)
+               read(iu, iostat=ierr, err=100) pl%Ip(1, 1:n)
+               read(iu, iostat=ierr, err=100) pl%Ip(2, 1:n)
+               read(iu, iostat=ierr, err=100) pl%Ip(3, 1:n)
             end if
             if (param%ltides) then
-               read(iu, iostat = ierr) pl%k2(1:n)
-               read(iu, iostat = ierr) pl%Q(1:n)
+               read(iu, iostat=ierr, err=100) pl%k2(1:n)
+               read(iu, iostat=ierr, err=100) pl%Q(1:n)
             end if
          end select
       end associate
 
-      if (ierr /=0) then
+      100 if (ierr /=0) then
          write(*,*) 'Error reading Swiftest body data'
          call util_exit(FAILURE)
       end if
@@ -878,22 +871,22 @@ module subroutine io_read_frame_cb(self, iu, param, form, ierr)
       character(*),               intent(in)    :: form     !! Input format code ("XV" or "EL")
       integer(I4B),               intent(out)   :: ierr     !! Error cod
 
-      read(iu, iostat = ierr) self%id
-      !read(iu, iostat = ierr) self%name
-      read(iu, iostat = ierr) self%Gmass
+      read(iu, iostat=ierr, err=100) self%id
+      !read(iu, iostat=ierr, err=100) self%name
+      read(iu, iostat=ierr, err=100) self%Gmass
       self%mass = self%Gmass / param%GU
-      read(iu, iostat = ierr) self%radius
-      read(iu, iostat = ierr) self%j2rp2 
-      read(iu, iostat = ierr) self%j4rp4 
+      read(iu, iostat=ierr, err=100) self%radius
+      read(iu, iostat=ierr, err=100) self%j2rp2 
+      read(iu, iostat=ierr, err=100) self%j4rp4 
       if (param%lrotation) then
-         read(iu, iostat = ierr) self%Ip(:)
-         read(iu, iostat = ierr) self%rot(:)
+         read(iu, iostat=ierr, err=100) self%Ip(:)
+         read(iu, iostat=ierr, err=100) self%rot(:)
       end if
       if (param%ltides) then
-         read(iu, iostat = ierr) self%k2
-         read(iu, iostat = ierr) self%Q
+         read(iu, iostat=ierr, err=100) self%k2
+         read(iu, iostat=ierr, err=100) self%Q
       end if
-      if (ierr /=0) then
+      100 if (ierr /=0) then
          write(*,*) 'Error reading central body data'
          call util_exit(FAILURE)
       end if
@@ -1160,6 +1153,7 @@ module subroutine io_write_frame_body(self, iu, param)
          select type(pl => self)  
          class is (swiftest_pl)  ! Additional output if the passed polymorphic object is a massive body
             write(iu) pl%Gmass(1:n)
+            write(iu) pl%rhill(1:n)
             write(iu) pl%radius(1:n)
             if (param%lrotation) then
                write(iu) pl%rot(1, 1:n)
diff --git a/src/main/swiftest_driver.f90 b/src/main/swiftest_driver.f90
index 4ed7cf3fe..4c6bccc72 100644
--- a/src/main/swiftest_driver.f90
+++ b/src/main/swiftest_driver.f90
@@ -10,9 +10,9 @@ program swiftest_driver
    implicit none
 
    class(swiftest_nbody_system), allocatable  :: nbody_system     !! Polymorphic object containing the nbody system to be integrated
-   type(swiftest_parameters)               :: param
+   class(swiftest_parameters),   allocatable  :: param            !! Run configuration parameters
    integer(I4B)                               :: integrator       !! Integrator type code (see swiftest_globals for symbolic names)
-   character(len=:),allocatable               :: param_file_name !! Name of the file containing user-defined parameters
+   character(len=:),allocatable               :: param_file_name  !! Name of the file containing user-defined parameters
    integer(I4B)                               :: ierr             !! I/O error code 
    integer(I8B)                               :: iloop            !! Loop counter
    integer(I8B)                               :: idump            !! Dump cadence counter
@@ -31,6 +31,12 @@ program swiftest_driver
    end if
    !$ start_wall_time = omp_get_wtime()
    !> Read in the user-defined parameters file and the initial conditions of the system
+   select case(integrator)
+   case(symba)
+      allocate(symba_parameters :: param)
+   case default
+      allocate(swiftest_parameters :: param)
+   end select
    param%integrator = integrator
    call setup_construct_system(nbody_system, param)
    call param%read_from_file(param_file_name)
diff --git a/src/modules/helio_classes.f90 b/src/modules/helio_classes.f90
index 2f8a52808..d03466676 100644
--- a/src/modules/helio_classes.f90
+++ b/src/modules/helio_classes.f90
@@ -7,21 +7,22 @@ module helio_classes
    use swiftest_classes, only : swiftest_cb, swiftest_pl, swiftest_tp, swiftest_nbody_system
    use whm_classes, only : whm_nbody_system
    implicit none
+   public
 
 
    !********************************************************************************************************************************
    !  helio_nbody_system class definitions and method interfaces
    !********************************************************************************************************************************
-   type, public, extends(whm_nbody_system) :: helio_nbody_system
+   type, extends(whm_nbody_system) :: helio_nbody_system
    contains
-      procedure, public :: step => helio_step_system  !! Advance the Helio nbody system forward in time by one step
+      procedure :: step => helio_step_system  !! Advance the Helio nbody system forward in time by one step
    end type helio_nbody_system
 
    !********************************************************************************************************************************
    ! helio_cb class definitions and method interfaces
    !*******************************************************************************************************************************
    !> Helio central body particle class
-   type, public, extends(swiftest_cb) :: helio_cb
+   type, extends(swiftest_cb) :: helio_cb
       real(DP), dimension(NDIM)  :: ptbeg !! negative barycentric velocity of the central body at the beginning of time step
       real(DP), dimension(NDIM)  :: ptend !! negative barycentric velocity of the central body at the end of time step
    contains
@@ -32,15 +33,15 @@ module helio_classes
    !*******************************************************************************************************************************
 
    !! Helio massive body particle class
-   type, public, extends(swiftest_pl) :: helio_pl
+   type, extends(swiftest_pl) :: helio_pl
    contains
-      procedure, public :: vh2vb    => helio_coord_vh2vb_pl  !! Convert massive bodies from heliocentric to barycentric coordinates (velocity only)
-      procedure, public :: vb2vh    => helio_coord_vb2vh_pl  !! Convert massive bodies from barycentric to heliocentric coordinates (velocity only)
-      procedure, public :: drift    => helio_drift_pl        !! Method for Danby drift in Democratic Heliocentric coordinates 
-      procedure, public :: lindrift => helio_drift_linear_pl !! Method for linear drift of massive bodies due to barycentric momentum of Sun
-      procedure, public :: accel    => helio_kick_getacch_pl      !! Compute heliocentric accelerations of massive bodies
-      procedure, public :: kick     => helio_kick_vb_pl       !! Kicks the barycentric velocities
-      procedure, public :: step     => helio_step_pl         !! Steps the body forward one stepsize
+      procedure :: vh2vb    => helio_coord_vh2vb_pl  !! Convert massive bodies from heliocentric to barycentric coordinates (velocity only)
+      procedure :: vb2vh    => helio_coord_vb2vh_pl  !! Convert massive bodies from barycentric to heliocentric coordinates (velocity only)
+      procedure :: drift    => helio_drift_pl        !! Method for Danby drift in Democratic Heliocentric coordinates 
+      procedure :: lindrift => helio_drift_linear_pl !! Method for linear drift of massive bodies due to barycentric momentum of Sun
+      procedure :: accel    => helio_kick_getacch_pl      !! Compute heliocentric accelerations of massive bodies
+      procedure :: kick     => helio_kick_vb_pl       !! Kicks the barycentric velocities
+      procedure :: step     => helio_step_pl         !! Steps the body forward one stepsize
    end type helio_pl
 
    !********************************************************************************************************************************
@@ -48,15 +49,15 @@ module helio_classes
    !*******************************************************************************************************************************
 
    !! Helio test particle class
-   type, public, extends(swiftest_tp) :: helio_tp
+   type, extends(swiftest_tp) :: helio_tp
    contains
-      procedure, public :: vh2vb    => helio_coord_vh2vb_tp  !! Convert test particles from heliocentric to barycentric coordinates (velocity only)
-      procedure, public :: vb2vh    => helio_coord_vb2vh_tp  !! Convert test particles from barycentric to heliocentric coordinates (velocity only)
-      procedure, public :: lindrift => helio_drift_linear_tp !! Method for linear drift of massive bodies due to barycentric momentum of Sun
-      procedure, public :: drift    => helio_drift_tp        !! Method for Danby drift in Democratic Heliocentric coordinates 
-      procedure, public :: accel    => helio_kick_getacch_tp      !! Compute heliocentric accelerations of massive bodies
-      procedure, public :: kick     => helio_kick_vb_tp       !! Kicks the barycentric velocities
-      procedure, public :: step     => helio_step_tp         !! Steps the body forward one stepsize
+      procedure :: vh2vb    => helio_coord_vh2vb_tp  !! Convert test particles from heliocentric to barycentric coordinates (velocity only)
+      procedure :: vb2vh    => helio_coord_vb2vh_tp  !! Convert test particles from barycentric to heliocentric coordinates (velocity only)
+      procedure :: lindrift => helio_drift_linear_tp !! Method for linear drift of massive bodies due to barycentric momentum of Sun
+      procedure :: drift    => helio_drift_tp        !! Method for Danby drift in Democratic Heliocentric coordinates 
+      procedure :: accel    => helio_kick_getacch_tp      !! Compute heliocentric accelerations of massive bodies
+      procedure :: kick     => helio_kick_vb_tp       !! Kicks the barycentric velocities
+      procedure :: step     => helio_step_tp         !! Steps the body forward one stepsize
    end type helio_tp
 
    interface
diff --git a/src/modules/module_nrutil.f90 b/src/modules/module_nrutil.f90
deleted file mode 100644
index ce8eeabbc..000000000
--- a/src/modules/module_nrutil.f90
+++ /dev/null
@@ -1,189 +0,0 @@
-!**********************************************************************************************************************************
-!
-!  Unit Name   : module_nrutil
-!  Unit Type   : module
-!  Project     : SWIFTEST
-!  Package     : module
-!  Language    : Fortran 90/95
-!
-!  Description : Definition of data and utility functions taken from Numerical Recipes in Fortran 90
-!
-!  Input
-!    Arguments : N/A
-!    Terminal  : N/A
-!    File      : N/A
-!
-!  Output
-!    Arguments : N/A
-!    Terminal  : N/A
-!    File      : N/A
-!
-!  Invocation  : N/A
-!
-!  Notes       : Reference: Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery B. P. 1996. Numerical Recipes in
-!                           Fortran 90, The Art of Scientific Computing, 2nd Edition, Vol. 2 of Fortran Numerical Recipes,
-!                           (Cambridge University Press).
-!
-!**********************************************************************************************************************************
-MODULE module_nrutil
-
-     USE swiftest_globals
-     IMPLICIT NONE
-
-     INTEGER(I4B), PARAMETER :: NPAR_ARTH   = 16
-     INTEGER(I4B), PARAMETER :: NPAR2_ARTH  =  8
-     INTEGER(I4B), PARAMETER :: NPAR_CUMSUM = 16
-
-     INTERFACE arth
-          MODULE PROCEDURE arth_d, arth_i
-     END INTERFACE
-
-     INTERFACE cumsum
-          MODULE PROCEDURE cumsum_i
-     END INTERFACE
-
-     INTERFACE outerdiff
-          MODULE PROCEDURE outerdiff_d, outerdiff_i
-     END INTERFACE
-
-     INTERFACE outerprod
-          MODULE PROCEDURE outerprod_d
-     END INTERFACE
-
-CONTAINS
-
-     FUNCTION arth_d(first, increment, n)
-          INTEGER(I4B), INTENT(IN) :: n
-          REAL(DP), INTENT(IN)     :: first, increment
-          REAL(DP), DIMENSION(n)   :: arth_d
-          INTEGER(I4B)             :: k, k2
-          REAL(DP)                 :: temp
-          IF (n > 0) arth_d(1) = first
-          IF (n <= NPAR_ARTH) THEN
-               DO k = 2, n
-                    arth_d(k) = arth_d(k-1) + increment
-               END DO
-          ELSE
-               DO k = 2, NPAR2_ARTH
-                    arth_d(k) = arth_d(k-1) + increment
-               END DO
-               temp = increment*NPAR2_ARTH
-               k = NPAR2_ARTH
-               DO
-                    IF (k >= n) EXIT
-                    k2 = k + k
-                    arth_d(k+1:MIN(k2, n)) = temp + arth_d(1:MIN(k, n-k))
-                    temp = temp + temp
-                    k = k2
-               END DO
-          END IF
-          RETURN
-     END FUNCTION arth_d
-
-     FUNCTION arth_i(first, increment, n)
-          INTEGER(I4B), INTENT(IN)   :: first, increment, n
-          INTEGER(I4B), DIMENSION(n) :: arth_i
-          INTEGER(I4B)               :: k, k2, temp
-          IF (n > 0) arth_i(1) = first
-          IF (n <= NPAR_ARTH) THEN
-               DO k = 2, n
-                    arth_i(k) = arth_i(k-1) + increment
-               END DO
-          ELSE
-               DO k = 2, NPAR2_ARTH
-                    arth_i(k) = arth_i(k-1) + increment
-               END DO
-               temp = increment*NPAR2_ARTH
-               k = NPAR2_ARTH
-               DO
-                    IF (k >= n) EXIT
-                    k2 = k + k
-                    arth_i(k+1:MIN(k2, n)) = temp + arth_i(1:MIN(k, n-k))
-                    temp = temp + temp
-                    k = k2
-               END DO
-          END IF
-          RETURN
-     END FUNCTION arth_i
-
-     RECURSIVE FUNCTION cumsum_i(arr, seed) RESULT(ans)
-          INTEGER(I4B), DIMENSION(:), INTENT(IN) :: arr
-          INTEGER(I4B), OPTIONAL, INTENT(IN)     :: seed
-          INTEGER(I4B), DIMENSION(SIZE(arr))     :: ans
-          INTEGER(I4B)                           :: n, j, sd
-          n = SIZE(arr)
-          IF (n == 0_I4B) RETURN
-          sd = 0_I4B
-          IF (PRESENT(seed)) sd = seed
-          ans(1) = arr(1) + sd
-          IF (n < NPAR_CUMSUM) THEN
-               DO j = 2, n
-                    ans(j) = ans(j-1) + arr(j)
-               END DO
-          ELSE
-               ans(2:n:2) = cumsum_i(arr(2:n:2) + arr(1:n-1:2), sd)
-               ans(3:n:2) = ans(2:n-1:2) + arr(3:n:2)
-          END IF
-          RETURN
-     END FUNCTION cumsum_i
-
-     FUNCTION iminloc(arr)
-          REAL(DP), DIMENSION(:), INTENT(IN) :: arr
-          INTEGER(I4B), DIMENSION(1)         :: imin
-          INTEGER(I4B)                       :: iminloc
-          imin = MINLOC(arr(:))
-          iminloc = imin(1)
-          RETURN
-     END FUNCTION iminloc
-
-     FUNCTION outerdiff_d(a, b)
-          REAL(DP), DIMENSION(:), INTENT(IN)    :: a, b
-          REAL(DP), DIMENSION(SIZE(a), SIZE(b)) :: outerdiff_d
-          outerdiff_d = SPREAD(a, DIM = 2, NCOPIES = SIZE(b)) - SPREAD(b, DIM = 1, NCOPIES = SIZE(a))
-          RETURN
-     END FUNCTION outerdiff_d
-
-     FUNCTION outerdiff_i(a, b)
-          INTEGER(I4B), DIMENSION(:), INTENT(IN)    :: a, b
-          INTEGER(I4B), DIMENSION(SIZE(a), SIZE(b)) :: outerdiff_i
-          outerdiff_i = SPREAD(a, DIM = 2, NCOPIES = SIZE(b)) - SPREAD(b, DIM = 1, NCOPIES = SIZE(a))
-          RETURN
-     END FUNCTION outerdiff_i
-
-     FUNCTION outerprod_d(a, b)
-          REAL(DP), DIMENSION(:), INTENT(IN)    :: a, b
-          REAL(DP), DIMENSION(SIZE(a), SIZE(b)) :: outerprod_d
-          outerprod_d = SPREAD(a, DIM = 2, NCOPIES = SIZE(b))*SPREAD(b, DIM = 1, NCOPIES = SIZE(a))
-          RETURN
-     END FUNCTION outerprod_d
-
-     FUNCTION upper_triangle(j, k, extra)
-          INTEGER(I4B), INTENT(IN)           :: j, k
-          INTEGER(I4B), OPTIONAL, INTENT(IN) :: extra
-          LOGICAL , DIMENSION(j, k)      :: upper_triangle
-          INTEGER(I4B)                       :: n
-          n = 0
-          IF (PRESENT(extra)) n = extra
-          upper_triangle = (outerdiff(arth_i(1, 1, j), arth_i(1, 1, k)) < n)
-          RETURN
-     END FUNCTION upper_triangle
-
-END MODULE module_nrutil
-!**********************************************************************************************************************************
-!
-!  Author(s)   : David E. Kaufmann
-!
-!  Revision Control System (RCS) Information
-!
-!  Source File : $RCSfile$
-!  Full Path   : $Source$
-!  Revision    : $Revision$
-!  Date        : $Date$
-!  Programmer  : $Author$
-!  Locked By   : $Locker$
-!  State       : $State$
-!
-!  Modification History:
-!
-!  $Log$
-!**********************************************************************************************************************************
diff --git a/src/modules/rmvs_classes.f90 b/src/modules/rmvs_classes.f90
index 8b0ad2c2f..37a88993c 100644
--- a/src/modules/rmvs_classes.f90
+++ b/src/modules/rmvs_classes.f90
@@ -6,28 +6,27 @@ module rmvs_classes
    use swiftest_globals
    use whm_classes, only : whm_cb, whm_pl, whm_tp, whm_nbody_system
    implicit none
-
    public
+
    integer(I4B), private, parameter :: NTENC = 10
    integer(I4B), private, parameter :: NTPHENC = 3
    integer(I4B), private, parameter :: NTPENC = NTENC * NTPHENC
-   real(DP), private, parameter     :: RHSCALE = 3.5_DP
-   real(DP), private, parameter     :: RHPSCALE = 1.0_DP
-   real(DP), private, parameter     :: FACQDT = 2.0_DP
+   real(DP),     private, parameter :: RHSCALE = 3.5_DP
+   real(DP),     private, parameter :: RHPSCALE = 1.0_DP
+   real(DP),     private, parameter :: FACQDT = 2.0_DP
 
    !********************************************************************************************************************************
    !  rmvs_nbody_system class definitions and method interfaces
    !********************************************************************************************************************************
-   type, public, extends(whm_nbody_system) :: rmvs_nbody_system
+   type, extends(whm_nbody_system) :: rmvs_nbody_system
       !> In the RMVS integrator, only test particles are discarded
       logical                               :: lplanetocentric = .false.  !! Flag that indicates that the object is a planetocentric set of masive bodies used for close encounter calculations
       real(DP)                              :: rts                        !! fraction of Hill's sphere radius to use as radius of encounter region
       real(DP), dimension(:,:), allocatable :: vbeg                       !! Planet velocities at beginning ot step
    contains
-      private
       !> Replace the abstract procedures with concrete ones
-      procedure, public :: initialize    => rmvs_setup_system      !! Performs RMVS-specific initilization steps, including generating the close encounter planetocentric structures
-      procedure, public :: step          => rmvs_step_system       !! Advance the RMVS nbody system forward in time by one step
+      procedure :: initialize => rmvs_setup_initialize_system  !! Performs RMVS-specific initilization steps, including generating the close encounter planetocentric structures
+      procedure :: step       => rmvs_step_system              !! Advance the RMVS nbody system forward in time by one step
    end type rmvs_nbody_system
 
    type, private :: rmvs_interp
@@ -41,7 +40,7 @@ module rmvs_classes
    ! rmvs_cb class definitions and method interfaces
    !*******************************************************************************************************************************
    !> RMVS central body particle class 
-   type, public, extends(whm_cb) :: rmvs_cb
+   type, extends(whm_cb) :: rmvs_cb
       type(rmvs_interp), dimension(:), allocatable :: outer !! interpolated heliocentric central body position for outer encounters
       type(rmvs_interp), dimension(:), allocatable :: inner !! interpolated heliocentric central body position for inner encounters
       logical                                      :: lplanetocentric = .false.  !! Flag that indicates that the object is a planetocentric set of masive bodies used for close encounter calculations
@@ -52,7 +51,7 @@ module rmvs_classes
    !*******************************************************************************************************************************
 
    !! RMVS test particle class
-   type, public, extends(whm_tp) :: rmvs_tp
+   type, extends(whm_tp) :: rmvs_tp
       !! Note to developers: If you add componenets to this class, be sure to update methods and subroutines that traverse the
       !!    component list, such as rmvs_setup_tp and rmvs_util_spill_tp
       ! encounter steps)
@@ -67,14 +66,13 @@ module rmvs_classes
       integer(I4B)                              :: ipleP           !!  index value of encountering planet
       logical                                   :: lplanetocentric = .false.  !! Flag that indicates that the object is a planetocentric set of masive bodies used for close encounter calculations
    contains
-      private
-      procedure, public :: discard         => rmvs_discard_tp         !! Check to see if test particles should be discarded based on pericenter passage distances with respect to planets encountered
-      procedure, public :: encounter_check => rmvs_encounter_check_tp !! Checks if any test particles are undergoing a close encounter with a massive body
-      procedure, public :: fill            => rmvs_util_fill_tp            !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
-      procedure, public :: accel           => rmvs_kick_getacch_tp         !!  Calculates either the standard or modified version of the acceleration depending if the
-                                                                        !! if the test particle is undergoing a close encounter or not
-      procedure, public :: setup           => rmvs_setup_tp           !! Constructor method - Allocates space for number of particles
-      procedure, public :: spill           => rmvs_util_spill_tp           !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
+      procedure :: discard         => rmvs_discard_tp         !! Check to see if test particles should be discarded based on pericenter passage distances with respect to planets encountered
+      procedure :: encounter_check => rmvs_encounter_check_tp !! Checks if any test particles are undergoing a close encounter with a massive body
+      procedure :: fill            => rmvs_util_fill_tp       !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
+      procedure :: accel           => rmvs_kick_getacch_tp    !! Calculates either the standard or modified version of the acceleration depending if the
+                                                              !!    if the test particle is undergoing a close encounter or not
+      procedure :: setup           => rmvs_setup_tp           !! Constructor method - Allocates space for number of particles
+      procedure :: spill           => rmvs_util_spill_tp      !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
    end type rmvs_tp
 
    !********************************************************************************************************************************
@@ -82,19 +80,18 @@ module rmvs_classes
    !*******************************************************************************************************************************
  
    !> RMVS massive body particle class
-   type, public, extends(whm_pl) :: rmvs_pl
-      integer(I4B),             dimension(:), allocatable :: nenc    !! number of test particles encountering planet this full rmvs time step
-      integer(I4B),             dimension(:), allocatable :: tpenc1P !! index of first test particle encountering planet
-      integer(I4B),             dimension(:), allocatable :: plind ! Connects the planetocentric indices back to the heliocentric planet list
-      type(rmvs_interp),        dimension(:), allocatable :: outer !! interpolated heliocentric central body position for outer encounters
-      type(rmvs_interp),        dimension(:), allocatable :: inner !! interpolated heliocentric central body position for inner encounters
-      class(rmvs_nbody_system), dimension(:), allocatable :: planetocentric
-      logical                                            :: lplanetocentric = .false.  !! Flag that indicates that the object is a planetocentric set of masive bodies used for close encounter calculations
+   type, extends(whm_pl) :: rmvs_pl
+      integer(I4B),             dimension(:), allocatable :: nenc                      !! number of test particles encountering planet this full rmvs time step
+      integer(I4B),             dimension(:), allocatable :: tpenc1P                   !! index of first test particle encountering planet
+      integer(I4B),             dimension(:), allocatable :: plind                     !! Connects the planetocentric indices back to the heliocentric planet list
+      type(rmvs_interp),        dimension(:), allocatable :: outer                     !! interpolated heliocentric central body position for outer encounters
+      type(rmvs_interp),        dimension(:), allocatable :: inner                     !! interpolated heliocentric central body position for inner encounters
+      class(rmvs_nbody_system), dimension(:), allocatable :: planetocentric            !! Planetocentric version of the massive body objects (one for each massive body)
+      logical                                             :: lplanetocentric = .false. !! Flag that indicates that the object is a planetocentric set of masive bodies used for close encounter calculations
    contains
-      private
-      procedure, public :: fill                => rmvs_util_fill_pl    !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
-      procedure, public :: setup               => rmvs_setup_pl    !! Constructor method - Allocates space for number of particles
-      procedure, public :: spill               => rmvs_util_spill_pl    !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
+      procedure :: fill  => rmvs_util_fill_pl   !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
+      procedure :: setup => rmvs_setup_pl       !! Constructor method - Allocates space for number of particles
+      procedure :: spill => rmvs_util_spill_pl  !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
    end type rmvs_pl
 
    interface
@@ -152,12 +149,12 @@ module subroutine rmvs_setup_pl(self,n)
          integer,             intent(in)    :: n    !! Number of test particles to allocate
       end subroutine rmvs_setup_pl
 
-      module subroutine rmvs_setup_system(self, param)
+      module subroutine rmvs_setup_initialize_system(self, param)
          use swiftest_classes, only : swiftest_parameters
          implicit none
          class(rmvs_nbody_system),   intent(inout) :: self    !! RMVS system object
          class(swiftest_parameters), intent(inout) :: param  !! Current run configuration parameters 
-      end subroutine rmvs_setup_system
+      end subroutine rmvs_setup_initialize_system
 
       module subroutine rmvs_setup_tp(self,n)
          implicit none
diff --git a/src/modules/swiftest.f90 b/src/modules/swiftest.f90
index 2ab44a9d5..10578c5b6 100644
--- a/src/modules/swiftest.f90
+++ b/src/modules/swiftest.f90
@@ -10,7 +10,6 @@ module swiftest
    use rmvs_classes
    use helio_classes
    use symba_classes
-   use module_nrutil
    use lambda_function
    !use advisor_annotate
    !$ use omp_lib
diff --git a/src/modules/swiftest_classes.f90 b/src/modules/swiftest_classes.f90
index 417138122..eff9f4077 100644
--- a/src/modules/swiftest_classes.f90
+++ b/src/modules/swiftest_classes.f90
@@ -5,23 +5,7 @@ module swiftest_classes
    !! Adapted from David E. Kaufmann's Swifter routine: module_swifter.f90
    use swiftest_globals
    implicit none
-   private
-   public :: discard_pl, discard_system, discard_tp 
-   public :: drift_all, drift_body, drift_one
-   public :: eucl_dist_index_plpl
-   public :: gr_kick_getaccb_ns_body, gr_p4_pos_kick, gr_pseudovel2vel, gr_vel2pseudovel
-   public :: io_dump_param, io_dump_swiftest, io_dump_system, io_get_args, io_get_token, io_param_reader, io_param_writer, io_read_body_in, &
-             io_read_cb_in, io_read_param_in, io_read_frame_body, io_read_frame_cb, io_read_frame_system, &
-             io_toupper, io_write_discard, io_write_encounter, io_write_frame_body, io_write_frame_cb, io_write_frame_system
-   public :: kick_getacch_int_pl
-   public :: obl_acc_body, obl_acc_pl, obl_acc_tp
-   public :: orbel_el2xv_vec, orbel_xv2el_vec, orbel_scget, orbel_xv2aeq, orbel_xv2aqt
-   public :: setup_body, setup_construct_system, setup_initialize_system, setup_pl, setup_tp
-   public :: tides_kick_getacch_pl, tides_step_spin_system
-   public :: user_kick_getacch_body
-   public :: util_coord_b2h_pl, util_coord_b2h_tp, util_coord_h2b_pl, util_coord_h2b_tp, util_exit, util_fill_body, util_fill_pl, util_fill_tp, &
-             util_index, util_peri_tp, util_reverse_status, util_set_beg_end_pl, util_set_ir3h, util_set_msys, util_set_mu_pl, &
-             util_set_mu_tp, util_set_rhill, util_set_rhill_approximate, util_sort, util_spill_body, util_spill_pl, util_spill_tp, util_valid, util_version
+   public
 
    !********************************************************************************************************************************
    ! swiftest_parameters class definitions 
@@ -29,7 +13,7 @@ module swiftest_classes
 
    !> User defined parameters that are read in from the parameters input file. 
    !>    Each paramter is initialized to a default values. 
-   type, public :: swiftest_parameters
+   type :: swiftest_parameters
       integer(I4B)         :: integrator     = UNKNOWN_INTEGRATOR !! Symbolic name of the nbody integrator  used
       integer(I4B)         :: nplmax         = -1                 !! Maximum allowed number of massive bodies
       integer(I4B)         :: ntpmax         = -1                 !! Maximum allowed number of test particles
@@ -76,33 +60,31 @@ module swiftest_classes
       logical :: lyarkovsky = .false.        !! Turn on Yarkovsky effect
       logical :: lyorp = .false.             !! Turn on YORP effect
    contains
-      private
-      procedure, public :: reader         => io_param_reader
-      procedure, public :: writer         => io_param_writer
-      procedure, public :: dump           => io_dump_param
-      procedure, public :: read_from_file => io_read_param_in
+      procedure :: reader         => io_param_reader
+      procedure :: writer         => io_param_writer
+      procedure :: dump           => io_dump_param
+      procedure :: read_from_file => io_read_param_in
    end type swiftest_parameters
 
    !********************************************************************************************************************************
    ! swiftest_base class definitions and methods
    !********************************************************************************************************************************
-   type, abstract, public :: swiftest_base
+   type, abstract :: swiftest_base
       !! An superclass for a generic Swiftest object
       logical :: lintegrate = .false.  !! Flag indicating that this object should be integrated in the current step 
    contains
       !! The minimal methods that all systems must have
-      private
       procedure                                         :: dump => io_dump_swiftest 
-      procedure(abstract_initialize),  public, deferred :: initialize
-      procedure(abstract_read_frame),  public, deferred :: read_frame
-      procedure(abstract_write_frame), public, deferred :: write_frame
+      procedure(abstract_initialize),  deferred :: initialize
+      procedure(abstract_read_frame),  deferred :: read_frame
+      procedure(abstract_write_frame), deferred :: write_frame
    end type swiftest_base
 
    !********************************************************************************************************************************
    ! swiftest_cb class definitions and methods
    !********************************************************************************************************************************
    !> A concrete lass for the central body in a Swiftest simulation
-   type, abstract, public, extends(swiftest_base) :: swiftest_cb           
+   type, abstract, extends(swiftest_base) :: swiftest_cb           
       character(len=STRMAX)     :: name             !! Non-unique name
       integer(I4B)              :: id       = 0      !! External identifier (unique)
       real(DP)                  :: mass     = 0.0_DP !! Central body mass (units MU)
@@ -128,17 +110,16 @@ module swiftest_classes
       real(DP), dimension(NDIM) :: L0       = 0.0_DP !! Initial angular momentum of the central body
       real(DP), dimension(NDIM) :: dL       = 0.0_DP !! Change in angular momentum of the central body
    contains
-      private
-      procedure, public         :: initialize  => io_read_cb_in        !! I/O routine for reading in central body data
-      procedure, public         :: write_frame => io_write_frame_cb    !! I/O routine for writing out a single frame of time-series data for the central body
-      procedure, public         :: read_frame  => io_read_frame_cb     !! I/O routine for reading out a single frame of time-series data for the central body
+      procedure :: initialize  => io_read_cb_in        !! I/O routine for reading in central body data
+      procedure :: read_frame  => io_read_frame_cb     !! I/O routine for reading out a single frame of time-series data for the central body
+      procedure :: write_frame => io_write_frame_cb    !! I/O routine for writing out a single frame of time-series data for the central body
    end type swiftest_cb
 
    !********************************************************************************************************************************
    ! swiftest_body definitions and methods
    !********************************************************************************************************************************
    !> An abstract class for a generic collection of Swiftest bodies
-   type, abstract, public, extends(swiftest_base) :: swiftest_body
+   type, abstract, extends(swiftest_base) :: swiftest_body
       !! Superclass that defines the generic elements of a Swiftest particle 
       logical                                            :: lfirst = .true. !! Run the current step as a first
       integer(I4B)                                       :: nbody = 0       !! Number of bodies
@@ -165,35 +146,36 @@ module swiftest_classes
       !! Note to developers: If you add components to this class, be sure to update methods and subroutines that traverse the
       !!    component list, such as setup_body and util_spill
    contains
-      private
-      procedure(abstract_discard_body), public, deferred :: discard
-      procedure(abstract_kick_body),    public, deferred :: kick     
-      procedure(abstract_set_mu),       public, deferred :: set_mu
-      procedure(abstract_step_body),    public, deferred :: step
-      procedure(abstract_accel),        public, deferred :: accel
+      procedure(abstract_discard_body), deferred :: discard
+      procedure(abstract_kick_body),    deferred :: kick     
+      procedure(abstract_set_mu),       deferred :: set_mu
+      procedure(abstract_step_body),    deferred :: step
+      procedure(abstract_accel),        deferred :: accel
       ! These are concrete because the implementation is the same for all types of particles
-      procedure, public :: drift          => drift_body          !! Loop through bodies and call Danby drift routine on heliocentric variables
-      procedure, public :: v2pv           => gr_vh2pv_body       !! Converts from velocity to psudeovelocity for GR calculations using symplectic integrators
-      procedure, public :: pv2v           => gr_pv2vh_body       !! Converts from psudeovelocity to velocity for GR calculations using symplectic integrators
-      procedure, public :: initialize     => io_read_body_in     !! Read in body initial conditions from a file
-      procedure, public :: read_frame     => io_read_frame_body  !! I/O routine for writing out a single frame of time-series data for the central body
-      procedure, public :: write_frame    => io_write_frame_body !! I/O routine for writing out a single frame of time-series data for the central body
-      procedure, public :: accel_obl      => obl_acc_body        !! Compute the barycentric accelerations of bodies due to the oblateness of the central body
-      procedure, public :: el2xv          => orbel_el2xv_vec     !! Convert orbital elements to position and velocity vectors
-      procedure, public :: xv2el          => orbel_xv2el_vec     !! Convert position and velocity vectors to orbital  elements 
-      procedure, public :: set_ir3        => util_set_ir3h       !! Sets the inverse heliocentric radius term (1/rh**3)
-      procedure, public :: setup          => setup_body          !! A constructor that sets the number of bodies and allocates all allocatable arrays
-      procedure, public :: accel_user     => user_kick_getacch_body   !! Add user-supplied heliocentric accelerations to planets
-      procedure, public :: fill           => util_fill_body      !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
-      procedure, public :: spill          => util_spill_body     !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
-      procedure, public :: reverse_status => util_reverse_status !! Reverses the active/inactive status of all particles in a structure
+      procedure :: drift          => drift_body               !! Loop through bodies and call Danby drift routine on heliocentric variables
+      procedure :: v2pv           => gr_vh2pv_body            !! Converts from velocity to psudeovelocity for GR calculations using symplectic integrators
+      procedure :: pv2v           => gr_pv2vh_body            !! Converts from psudeovelocity to velocity for GR calculations using symplectic integrators
+      procedure :: initialize     => io_read_body_in          !! Read in body initial conditions from a file
+      procedure :: read_frame     => io_read_frame_body       !! I/O routine for writing out a single frame of time-series data for the central body
+      procedure :: write_frame    => io_write_frame_body      !! I/O routine for writing out a single frame of time-series data for the central body
+      procedure :: accel_obl      => obl_acc_body             !! Compute the barycentric accelerations of bodies due to the oblateness of the central body
+      procedure :: el2xv          => orbel_el2xv_vec          !! Convert orbital elements to position and velocity vectors
+      procedure :: xv2el          => orbel_xv2el_vec          !! Convert position and velocity vectors to orbital  elements 
+      procedure :: setup          => setup_body               !! A constructor that sets the number of bodies and allocates all allocatable arrays
+      procedure :: accel_user     => user_kick_getacch_body   !! Add user-supplied heliocentric accelerations to planets
+      procedure :: fill           => util_fill_body           !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
+      procedure :: reverse_status => util_reverse_status      !! Reverses the active/inactive status of all particles in a structure
+      procedure :: set_ir3        => util_set_ir3h            !! Sets the inverse heliocentric radius term (1/rh**3)
+      procedure :: sort           => util_sort_body           !! Sorts body arrays by a sortable componen
+      procedure :: rearrange      => util_sort_rearrange_body !! Rearranges the order of array elements of body based on an input index array. Used in sorting methods
+      procedure :: spill          => util_spill_body          !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
    end type swiftest_body
       
    !********************************************************************************************************************************
    ! swiftest_pl definitions and methods
    !********************************************************************************************************************************
    !> An abstract class for a generic collection of Swiftest massive bodies
-   type, abstract, public, extends(swiftest_body) :: swiftest_pl
+   type, abstract, extends(swiftest_body) :: swiftest_pl
       !! Superclass that defines the generic elements of a Swiftest particle 
       real(DP),     dimension(:),   allocatable :: mass    !! Body mass (units MU)
       real(DP),     dimension(:),   allocatable :: Gmass   !! Mass gravitational term G * mass (units GU * MU)
@@ -213,29 +195,30 @@ module swiftest_classes
       !! Note to developers: If you add components to this class, be sure to update methods and subroutines that traverse the
       !!    component list, such as setup_pl and util_spill_pl
    contains
-      private
       ! Massive body-specific concrete methods 
       ! These are concrete because they are the same implemenation for all integrators
-      procedure, public :: discard      => discard_pl            !! Placeholder method for discarding massive bodies 
-      procedure, public :: eucl_index   => eucl_dist_index_plpl  !! Sets up the (i, j) -> k indexing used for the single-loop blocking Euclidean distance matrix
-      procedure, public :: accel_int    => kick_getacch_int_pl   !! Compute direct cross (third) term heliocentric accelerations of massive bodies
-      procedure, public :: accel_obl    => obl_acc_pl            !! Compute the barycentric accelerations of bodies due to the oblateness of the central body
-      procedure, public :: setup        => setup_pl              !! A base constructor that sets the number of bodies and allocates and initializes all arrays  
-      procedure, public :: accel_tides  => tides_kick_getacch_pl !! Compute the accelerations of bodies due to tidal interactions with the central body
-      procedure, public :: set_mu       => util_set_mu_pl        !! Method used to construct the vectorized form of the central body mass
-      procedure, public :: set_rhill    => util_set_rhill        !! Calculates the Hill's radii for each body
-      procedure, public :: h2b          => util_coord_h2b_pl     !! Convert massive bodies from heliocentric to barycentric coordinates (position and velocity)
-      procedure, public :: b2h          => util_coord_b2h_pl     !! Convert massive bodies from barycentric to heliocentric coordinates (position and velocity)
-      procedure, public :: fill         => util_fill_pl          !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
-      procedure, public :: set_beg_end  => util_set_beg_end_pl   !! Sets the beginning and ending positions and velocities of planets.
-      procedure, public :: spill        => util_spill_pl         !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
+      procedure :: discard      => discard_pl             !! Placeholder method for discarding massive bodies 
+      procedure :: eucl_index   => eucl_dist_index_plpl   !! Sets up the (i, j) -> k indexing used for the single-loop blocking Euclidean distance matrix
+      procedure :: accel_int    => kick_getacch_int_pl    !! Compute direct cross (third) term heliocentric accelerations of massive bodies
+      procedure :: accel_obl    => obl_acc_pl             !! Compute the barycentric accelerations of bodies due to the oblateness of the central body
+      procedure :: setup        => setup_pl               !! A base constructor that sets the number of bodies and allocates and initializes all arrays  
+      procedure :: accel_tides  => tides_kick_getacch_pl  !! Compute the accelerations of bodies due to tidal interactions with the central body
+      procedure :: h2b          => util_coord_h2b_pl      !! Convert massive bodies from heliocentric to barycentric coordinates (position and velocity)
+      procedure :: b2h          => util_coord_b2h_pl      !! Convert massive bodies from barycentric to heliocentric coordinates (position and velocity)
+      procedure :: fill         => util_fill_pl           !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
+      procedure :: set_beg_end  => util_set_beg_end_pl    !! Sets the beginning and ending positions and velocities of planets.
+      procedure :: set_mu       => util_set_mu_pl         !! Method used to construct the vectorized form of the central body mass
+      procedure :: set_rhill    => util_set_rhill         !! Calculates the Hill's radii for each body
+      procedure :: sort         => util_sort_pl           !! Sorts body arrays by a sortable component
+      procedure :: rearrange    => util_sort_rearrange_pl !! Rearranges the order of array elements of body based on an input index array. Used in sorting methods
+      procedure :: spill        => util_spill_pl          !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
    end type swiftest_pl
 
    !********************************************************************************************************************************
    ! swiftest_tp definitions and methods
    !********************************************************************************************************************************
    !> An abstract class for a generic collection of Swiftest test particles
-   type, abstract, public, extends(swiftest_body) :: swiftest_tp
+   type, abstract, extends(swiftest_body) :: swiftest_tp
       !! Superclass that defines the generic elements of a Swiftest test particle 
       integer(I4B), dimension(:),    allocatable :: isperi !! Perihelion passage flag
       real(DP),     dimension(:),    allocatable :: peri   !! Perihelion distance
@@ -243,26 +226,27 @@ module swiftest_classes
       !! Note to developers: If you add components to this class, be sure to update methods and subroutines that traverse the
       !!    component list, such as setup_tp and util_spill_tp
    contains
-      private
       ! Test particle-specific concrete methods 
       ! These are concrete because they are the same implemenation for all integrators
-      procedure, public :: discard    => discard_tp           !! Check to see if test particles should be discarded based on their positions relative to the massive bodies
-      procedure, public :: accel_int  => kick_getacch_int_tp  !! Compute direct cross (third) term heliocentric accelerations of test particles by massive bodies
-      procedure, public :: accel_obl  => obl_acc_tp           !! Compute the barycentric accelerations of bodies due to the oblateness of the central body
-      procedure, public :: setup      => setup_tp             !! A base constructor that sets the number of bodies and 
-      procedure, public :: set_mu     => util_set_mu_tp       !! Method used to construct the vectorized form of the central body mass
-      procedure, public :: h2b        => util_coord_h2b_tp    !! Convert test particles from heliocentric to barycentric coordinates (position and velocity)
-      procedure, public :: b2h        => util_coord_b2h_tp    !! Convert test particles from barycentric to heliocentric coordinates (position and velocity)
-      procedure, public :: fill       => util_fill_tp         !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
-      procedure, public :: get_peri   => util_peri_tp         !! Determine system pericenter passages for test particles 
-      procedure, public :: spill      => util_spill_tp        !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
+      procedure :: discard    => discard_tp             !! Check to see if test particles should be discarded based on their positions relative to the massive bodies
+      procedure :: accel_int  => kick_getacch_int_tp    !! Compute direct cross (third) term heliocentric accelerations of test particles by massive bodies
+      procedure :: accel_obl  => obl_acc_tp             !! Compute the barycentric accelerations of bodies due to the oblateness of the central body
+      procedure :: setup      => setup_tp               !! A base constructor that sets the number of bodies and 
+      procedure :: h2b        => util_coord_h2b_tp      !! Convert test particles from heliocentric to barycentric coordinates (position and velocity)
+      procedure :: b2h        => util_coord_b2h_tp      !! Convert test particles from barycentric to heliocentric coordinates (position and velocity)
+      procedure :: fill       => util_fill_tp           !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
+      procedure :: get_peri   => util_peri_tp           !! Determine system pericenter passages for test particles 
+      procedure :: set_mu     => util_set_mu_tp         !! Method used to construct the vectorized form of the central body mass
+      procedure :: sort       => util_sort_tp           !! Sorts body arrays by a sortable component
+      procedure :: rearrange  => util_sort_rearrange_tp !! Rearranges the order of array elements of body based on an input index array. Used in sorting methods
+      procedure :: spill      => util_spill_tp          !! "Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
    end type swiftest_tp
 
    !********************************************************************************************************************************
    ! swiftest_nbody_system class definitions and methods
    !********************************************************************************************************************************
    !> An abstract class for a basic Swiftest nbody system 
-   type, abstract, public, extends(swiftest_base) :: swiftest_nbody_system
+   type, abstract, extends(swiftest_base) :: swiftest_nbody_system
       !!  This superclass contains a minimial system of a set of test particles (tp), massive bodies (pl), and a central body (cb)
       class(swiftest_cb),            allocatable :: cb                   !! Central body data structure
       class(swiftest_pl),            allocatable :: pl                   !! Massive body data structure
@@ -281,19 +265,18 @@ module swiftest_classes
                                                                          !!    separately from massive bodies.  Massive body variables are saved at half steps, and passed to 
                                                                          !!    the test particles
    contains
-      private
       !> Each integrator will have its own version of the step
-      procedure(abstract_step_system), public, deferred :: step
+      procedure(abstract_step_system), deferred :: step
 
       ! Concrete classes that are common to the basic integrator (only test particles considered for discard)
-      procedure, public :: discard       => discard_system          !! Perform a discard step on the system
-      procedure, public :: dump          => io_dump_system          !! Dump the state of the system to a file
-      procedure, public :: initialize    => setup_initialize_system !! Initialize the system from input files
-      procedure, public :: read_frame    => io_read_frame_system    !! Append a frame of output data to file
-      procedure, public :: write_discard => io_write_discard        !! Append a frame of output data to file
-      procedure, public :: write_frame   => io_write_frame_system   !! Append a frame of output data to file
-      procedure, public :: step_spin     => tides_step_spin_system  !! Steps the spins of the massive & central bodies due to tides.
-      procedure, public :: set_msys      => util_set_msys           !! Sets the value of msys from the masses of system bodies.
+      procedure :: discard       => discard_system          !! Perform a discard step on the system
+      procedure :: dump          => io_dump_system          !! Dump the state of the system to a file
+      procedure :: read_frame    => io_read_frame_system    !! Append a frame of output data to file
+      procedure :: write_discard => io_write_discard        !! Append a frame of output data to file
+      procedure :: write_frame   => io_write_frame_system   !! Append a frame of output data to file
+      procedure :: initialize    => setup_initialize_system !! Initialize the system from input files
+      procedure :: step_spin     => tides_step_spin_system  !! Steps the spins of the massive & central bodies due to tides.
+      procedure :: set_msys      => util_set_msys           !! Sets the value of msys from the masses of system bodies.
    end type swiftest_nbody_system
 
    abstract interface
@@ -661,16 +644,23 @@ end subroutine orbel_scget
 
       module pure subroutine orbel_xv2aeq(mu, x, v, a, e, q)
          implicit none
-         real(DP), intent(in)  :: mu
-         real(DP), dimension(:), intent(in)  :: x, v
-         real(DP), intent(out) :: a, e, q
+         real(DP),               intent(in)  :: mu !! Gravitational constant
+         real(DP), dimension(:), intent(in)  :: x  !! Position vector
+         real(DP), dimension(:), intent(in)  :: v  !! Velocity vector
+         real(DP),               intent(out) :: a  !! semimajor axis
+         real(DP),               intent(out) :: e  !! eccentricity
+         real(DP),               intent(out) :: q  !! periapsis
       end subroutine orbel_xv2aeq
 
       module pure subroutine orbel_xv2aqt(mu, x, v, a, q, capm, tperi)
          implicit none
-         real(DP), intent(in)  :: mu
-         real(DP), dimension(:), intent(in)  :: x, v
-         real(DP), intent(out) :: a, q, capm, tperi
+         real(DP),               intent(in)  :: mu    !! Gravitational constant
+         real(DP), dimension(:), intent(in)  :: x     !! Position vector
+         real(DP), dimension(:), intent(in)  :: v     !! Velocity vector
+         real(DP),               intent(out) :: a     !! semimajor axis
+         real(DP),               intent(out) :: q     !! periapsis
+         real(DP),               intent(out) :: capm  !! mean anomaly
+         real(DP),               intent(out) :: tperi !! time of pericenter passage
       end subroutine orbel_xv2aqt
 
       module subroutine orbel_xv2el_vec(self, cb)
@@ -782,12 +772,6 @@ module subroutine util_fill_tp(self, inserts, lfill_list)
          logical, dimension(:), intent(in)    :: lfill_list !! Logical array of bodies to merge into the keeps
       end subroutine util_fill_tp
 
-      module subroutine util_index(arr, index)
-         implicit none
-         integer(I4B), dimension(:), intent(out) :: index
-         real(DP),     dimension(:), intent(in)   :: arr
-      end subroutine util_index
-
       module subroutine util_peri_tp(self, system, param) 
          implicit none
          class(swiftest_tp),           intent(inout) :: self   !! Swiftest test particle object
@@ -849,23 +833,80 @@ module subroutine util_sort_i4b(arr)
          integer(I4B), dimension(:), intent(inout) :: arr
       end subroutine util_sort_i4b
 
+      module subroutine util_sort_index_i4b(arr,ind)
+         implicit none
+         integer(I4B), dimension(:), intent(in)  :: arr
+         integer(I4B), dimension(:), intent(out) :: ind
+      end subroutine util_sort_index_i4b
+
       module subroutine util_sort_sp(arr)
          implicit none
          real(SP), dimension(:), intent(inout) :: arr
       end subroutine util_sort_sp
 
+      module subroutine util_sort_index_sp(arr,ind)
+         implicit none
+         real(SP), dimension(:), intent(in)  :: arr
+         integer(I4B), dimension(:), intent(out) :: ind
+      end subroutine util_sort_index_sp
+
       module subroutine util_sort_dp(arr)
          implicit none
          real(DP), dimension(:), intent(inout) :: arr
       end subroutine util_sort_dp
-   end interface
+
+      module subroutine util_sort_index_dp(arr,ind)
+         implicit none
+         real(DP), dimension(:), intent(in)  :: arr
+         integer(I4B), dimension(:), intent(out) :: ind
+      end subroutine util_sort_index_dp
+   end interface util_sort
 
    interface
+      module subroutine util_sort_rearrange_body(self, ind)
+         implicit none
+         class(swiftest_body),               intent(inout) :: self !! Swiftest body object
+         integer(I4B),         dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      end subroutine util_sort_rearrange_body
+
+      module subroutine util_sort_rearrange_pl(self, ind)
+         implicit none
+         class(swiftest_pl),                 intent(inout) :: self !! Swiftest massive body object
+         integer(I4B),         dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      end subroutine util_sort_rearrange_pl
+
+      module subroutine util_sort_rearrange_tp(self, ind)
+         implicit none
+         class(swiftest_tp),                 intent(inout) :: self !! Swiftest test particle object
+         integer(I4B),         dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      end subroutine util_sort_rearrange_tp
+
+      module subroutine util_sort_body(self, sortby, ascending)
+         implicit none
+         class(swiftest_body), intent(inout) :: self    !! Swiftest body object
+         character(*),         intent(in)    :: sortby  !! Sorting attribute
+         logical,              intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      end subroutine util_sort_body
+
+      module subroutine util_sort_pl(self, sortby, ascending)
+         implicit none
+         class(swiftest_pl), intent(inout) :: self    !! Swiftest body object
+         character(*),       intent(in)    :: sortby  !! Sorting attribute
+         logical,            intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      end subroutine util_sort_pl
+
+      module subroutine util_sort_tp(self, sortby, ascending)
+         implicit none
+         class(swiftest_tp), intent(inout) :: self    !! Swiftest body object
+         character(*),       intent(in)    :: sortby  !! Sorting attribute
+         logical,            intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      end subroutine util_sort_tp
+   
       module subroutine util_spill_body(self, discards, lspill_list)
          implicit none
-         class(swiftest_body), intent(inout) :: self        !! Swiftest body object
-         class(swiftest_body), intent(inout) :: discards    !! Discarded object 
-         logical, dimension(:), intent(in)   :: lspill_list !! Logical array of bodies to spill into the discards
+         class(swiftest_body),  intent(inout) :: self        !! Swiftest body object
+         class(swiftest_body),  intent(inout) :: discards    !! Discarded object 
+         logical, dimension(:), intent(in)    :: lspill_list !! Logical array of bodies to spill into the discards
       end subroutine util_spill_body
 
       module subroutine util_spill_pl(self, discards, lspill_list)
diff --git a/src/modules/swiftest_globals.f90 b/src/modules/swiftest_globals.f90
index a1f0d7511..5ec55f6c6 100644
--- a/src/modules/swiftest_globals.f90
+++ b/src/modules/swiftest_globals.f90
@@ -86,6 +86,7 @@ module swiftest_globals
    integer(I4B), parameter :: SUPERCATASTROPHIC  = -10
    integer(I4B), parameter :: GRAZE_AND_MERGE    = -11
    integer(I4B), parameter :: HIT_AND_RUN        = -12
+   integer(I4B), parameter :: COLLISION          = -13
 
    !>Symbolic names for collisional outcomes from collresolve_resolve:
    integer(I4B), parameter :: COLLRESOLVE_REGIME_MERGE              =  1
diff --git a/src/modules/symba_classes.f90 b/src/modules/symba_classes.f90
index 72fb06ae7..fc3520079 100644
--- a/src/modules/symba_classes.f90
+++ b/src/modules/symba_classes.f90
@@ -8,6 +8,7 @@ module symba_classes
    use helio_classes,    only : helio_cb, helio_pl, helio_tp, helio_nbody_system
    use rmvs_classes,     only : rmvs_chk_ind
    implicit none
+   public
 
    integer(I4B), private, parameter :: NENMAX           = 32767
    integer(I4B), private, parameter :: NTENC            = 3
@@ -16,28 +17,26 @@ module symba_classes
    character(*),          parameter :: PARTICLE_OUTFILE = 'particle.dat'
    integer(I4B),          parameter :: PARTICLEUNIT     = 44 !! File unit number for the binary particle info output file
 
-   type, public, extends(swiftest_parameters) :: symba_parameters
+   type, extends(swiftest_parameters) :: symba_parameters
       character(STRMAX)                       :: particle_file  = PARTICLE_OUTFILE !! Name of output particle information file
       real(DP)                                :: MTINY          = -1.0_DP          !! Smallest mass that is fully gravitating
       integer(I4B), dimension(:), allocatable :: seed                              !! Random seeds
       logical                                 :: lfragmentation = .false.          !! Do fragmentation modeling instead of simple merger.
    contains
-      private
-      procedure, public :: reader => symba_io_param_reader
-      procedure, public :: writer => symba_io_param_writer
+      procedure :: reader => symba_io_param_reader
+      procedure :: writer => symba_io_param_writer
    end type symba_parameters
 
    !********************************************************************************************************************************
    ! symba_cb class definitions and method interfaces
    !*******************************************************************************************************************************
    !> SyMBA central body particle class
-   type, public, extends(helio_cb) :: symba_cb
+   type, extends(helio_cb) :: symba_cb
       real(DP) :: M0  = 0.0_DP !! Initial mass of the central body
       real(DP) :: dM  = 0.0_DP !! Change in mass of the central body
       real(DP) :: R0  = 0.0_DP !! Initial radius of the central body
       real(DP) :: dR  = 0.0_DP !! Change in the radius of the central body
    contains
-      private
    end type symba_cb
 
    !********************************************************************************************************************************
@@ -45,17 +44,16 @@ module symba_classes
    !*******************************************************************************************************************************
    !> Class definition for the particle origin information object. This object is used to track time, location, and collisional regime
    !> of fragments produced in collisional events.
-   type, public, extends(swiftest_base) :: symba_particle_info
+   type, extends(swiftest_base) :: symba_particle_info
       character(len=32)         :: origin_type !! String containing a description of the origin of the particle (e.g. Initial Conditions, Supercatastrophic, Disruption, etc.)
       real(DP)                  :: origin_time !! The time of the particle's formation
       real(DP), dimension(NDIM) :: origin_xh   !! The heliocentric distance vector at the time of the particle's formation
       real(DP), dimension(NDIM) :: origin_vh   !! The heliocentric velocity vector at the time of the particle's formation
    contains
-      private
-      procedure, public :: dump        => symba_io_dump_particle_info       !! I/O routine for dumping particle info to file
-      procedure, public :: initialize  => symba_io_initialize_particle_info !! I/O routine for reading in particle info data
-      procedure, public :: read_frame  => symba_io_read_frame_info          !! I/O routine for reading in a single frame of particle info
-      procedure, public :: write_frame => symba_io_write_frame_info         !! I/O routine for writing out a single frame of particle info
+      procedure :: dump        => symba_io_dump_particle_info       !! I/O routine for dumping particle info to file
+      procedure :: initialize  => symba_io_initialize_particle_info !! I/O routine for reading in particle info data
+      procedure :: read_frame  => symba_io_read_frame_info          !! I/O routine for reading in a single frame of particle info
+      procedure :: write_frame => symba_io_write_frame_info         !! I/O routine for writing out a single frame of particle info
    end type symba_particle_info
 
    !********************************************************************************************************************************
@@ -72,7 +70,7 @@ module symba_classes
    !                                    symba_pl class definitions and method interfaces
    !*******************************************************************************************************************************
    !> SyMBA massive body class
-   type, public, extends(helio_pl) :: symba_pl
+   type, extends(helio_pl) :: symba_pl
       logical,                   dimension(:), allocatable :: lcollision !! flag indicating whether body has merged with another this time step
       logical,                   dimension(:), allocatable :: lencounter !! flag indicating whether body is part of an encounter this time step
       logical,                   dimension(:), allocatable :: lmtiny     !! flag indicating whether this body is below the MTINY cutoff value
@@ -88,34 +86,35 @@ module symba_classes
       type(symba_kinship),       dimension(:), allocatable :: kin        !! Array of merger relationship structures that can account for multiple pairwise mergers in a single step
       type(symba_particle_info), dimension(:), allocatable :: info
    contains
-      private
-      procedure, public :: discard         => symba_discard_pl         !! Process massive body discards
-      procedure, public :: encounter_check => symba_encounter_check_pl !! Checks if massive bodies are going through close encounters with each other
-      procedure, public :: accel           => symba_kick_getacch_pl         !! Compute heliocentric accelerations of massive bodies
-      procedure, public :: setup           => symba_setup_pl           !! Constructor method - Allocates space for number of particle
+      procedure :: discard         => symba_discard_pl             !! Process massive body discards
+      procedure :: encounter_check => symba_encounter_check_pl     !! Checks if massive bodies are going through close encounters with each other
+      procedure :: accel           => symba_kick_getacch_pl        !! Compute heliocentric accelerations of massive bodies
+      procedure :: setup           => symba_setup_pl               !! Constructor method - Allocates space for number of particle
+      procedure :: sort            => symba_util_sort_pl           !! Sorts body arrays by a sortable componen
+      procedure :: rearrange       => symba_util_sort_rearrange_pl !! Rearranges the order of array elements of body based on an input index array. Used in sorting methods
    end type symba_pl
 
    !********************************************************************************************************************************
    !                                    symba_tp class definitions and method interfaces
    !*******************************************************************************************************************************
    !> SyMBA test particle class
-   type, public, extends(helio_tp) :: symba_tp
+   type, extends(helio_tp) :: symba_tp
       integer(I4B), dimension(:), allocatable :: nplenc  !! number of encounters with planets this time step
       integer(I4B), dimension(:), allocatable :: levelg  !! level at which this particle should be moved
       integer(I4B), dimension(:), allocatable :: levelm  !! deepest encounter level achieved this time step
    contains
-      private
-      procedure, public :: discard         => symba_discard_tp         !! process test particle discards
-      procedure, public :: encounter_check => symba_encounter_check_tp !! Checks if any test particles are undergoing a close encounter with a massive body
-      procedure, public :: accel           => symba_kick_getacch_tp         !! Compute heliocentric accelerations of test particles
-      procedure, public :: setup           => symba_setup_tp           !! Constructor method - Allocates space for number of particle
+      procedure :: encounter_check => symba_encounter_check_tp     !! Checks if any test particles are undergoing a close encounter with a massive body
+      procedure :: accel           => symba_kick_getacch_tp        !! Compute heliocentric accelerations of test particles
+      procedure :: setup           => symba_setup_tp               !! Constructor method - Allocates space for number of particle
+      procedure :: sort            => symba_util_sort_tp           !! Sorts body arrays by a sortable componen
+      procedure :: rearrange       => symba_util_sort_rearrange_tp !! Rearranges the order of array elements of body based on an input index array. Used in sorting methods
    end type symba_tp
 
    !********************************************************************************************************************************
    !                                    symba_pltpenc class definitions and method interfaces
    !*******************************************************************************************************************************
    !> SyMBA class for tracking pl-tp close encounters in a step
-   type, public :: symba_pltpenc
+   type :: symba_pltpenc
       integer(I4B)                              :: nenc   !! Total number of encounters
       logical,      dimension(:),   allocatable :: lvdotr !! relative vdotr flag
       integer(I4B), dimension(:),   allocatable :: status !! status of the interaction
@@ -123,46 +122,67 @@ module symba_classes
       integer(I4B), dimension(:),   allocatable :: index1 !! position of the planet in encounter
       integer(I4B), dimension(:),   allocatable :: index2 !! position of the test particle in encounter
    contains
-      procedure, public :: encounter_check => symba_encounter_check_pltpenc !! Checks if massive bodies are going through close encounters with each other
-      procedure, public :: kick            => symba_kick_pltpenc            !! Kick barycentric velocities of active test particles within SyMBA recursion
-      procedure, public :: setup           => symba_setup_pltpenc           !! A constructor that sets the number of encounters and allocates and initializes all arrays  
-      procedure, public :: copy            => symba_util_copy_pltpenc       !! Copies all elements of one pltpenc list to another
-      procedure, public :: resize          => symba_util_resize_pltpenc     !! Checks the current size of the pltpenc_list against the required size and extends it by a factor of 2 more than requested if it is too small 
+      procedure :: collision_check => symba_collision_check_pltpenc !! Checks if a test particle is going to collide with a massive body
+      procedure :: encounter_check => symba_encounter_check_pltpenc !! Checks if massive bodies are going through close encounters with each other
+      procedure :: kick            => symba_kick_pltpenc            !! Kick barycentric velocities of active test particles within SyMBA recursion
+      procedure :: setup           => symba_setup_pltpenc           !! A constructor that sets the number of encounters and allocates and initializes all arrays  
+      procedure :: copy            => symba_util_copy_pltpenc       !! Copies all elements of one pltpenc list to another
+      procedure :: resize          => symba_util_resize_pltpenc     !! Checks the current size of the pltpenc_list against the required size and extends it by a factor of 2 more than requested if it is too small 
    end type symba_pltpenc
 
    !********************************************************************************************************************************
    !                                    symba_plplenc class definitions and method interfaces
    !*******************************************************************************************************************************
    !> SyMBA class for tracking pl-pl close encounters in a step
-   type, public, extends(symba_pltpenc) :: symba_plplenc
-      real(DP),     dimension(:,:), allocatable :: xh1 !! the heliocentric position of parent 1 in encounter
-      real(DP),     dimension(:,:), allocatable :: xh2 !! the heliocentric position of parent 2 in encounter
-      real(DP),     dimension(:,:), allocatable :: vb1 !! the barycentric velocity of parent 1 in encounter
-      real(DP),     dimension(:,:), allocatable :: vb2 !! the barycentric velocity of parent 2 in encounter
+   type, extends(symba_pltpenc) :: symba_plplenc
+      real(DP), dimension(:,:), allocatable :: xh1 !! the heliocentric position of parent 1 in encounter
+      real(DP), dimension(:,:), allocatable :: xh2 !! the heliocentric position of parent 2 in encounter
+      real(DP), dimension(:,:), allocatable :: vb1 !! the barycentric velocity of parent 1 in encounter
+      real(DP), dimension(:,:), allocatable :: vb2 !! the barycentric velocity of parent 2 in encounter
    contains
-      procedure, public :: setup           => symba_setup_plplenc           !! A constructor that sets the number of encounters and allocates and initializes all arrays  
-      procedure, public :: copy            => symba_util_copy_plplenc       !! Copies all elements of one plplenc list to another
+      procedure :: collision_check => symba_collision_check_plplenc !! Checks if two massive bodies are going to collide 
+      procedure :: setup           => symba_setup_plplenc           !! A constructor that sets the number of encounters and allocates and initializes all arrays  
+      procedure :: copy            => symba_util_copy_plplenc       !! Copies all elements of one plplenc list to another
    end type symba_plplenc
 
    !********************************************************************************************************************************
    !  symba_nbody_system class definitions and method interfaces
    !********************************************************************************************************************************
-   type, public, extends(helio_nbody_system) :: symba_nbody_system
+   type, extends(helio_nbody_system) :: symba_nbody_system
       class(symba_pl),      allocatable :: mergeadd_list !! List of added bodies in mergers or collisions
       class(symba_pl),      allocatable :: mergesub_list !! List of subtracted bodies in mergers or collisions
       class(symba_pltpenc), allocatable :: pltpenc_list  !! List of massive body-test particle encounters in a single step 
       class(symba_plplenc), allocatable :: plplenc_list  !! List of massive body-massive body encounters in a single step
       class(symba_pl),      allocatable :: pl_discards   !! Discarded test particle data structure
    contains
-      private
-      procedure, public :: initialize     => symba_setup_system       !! Performs SyMBA-specific initilization steps
-      procedure, public :: step           => symba_step_system        !! Advance the SyMBA nbody system forward in time by one step
-      procedure, public :: interp         => symba_step_interp_system !! Perform an interpolation step on the SymBA nbody system 
-      procedure, public :: recursive_step => symba_step_recur_system  !! Step interacting planets and active test particles ahead in democratic heliocentric coordinates at the current recursion level, if applicable, and descend to the next deeper level if necessary
-      procedure, public :: reset          => symba_step_reset_system  !! Resets pl, tp,and encounter structures at the start of a new step 
+      procedure :: initialize     => symba_setup_initialize_system       !! Performs SyMBA-specific initilization steps
+      procedure :: step           => symba_step_system        !! Advance the SyMBA nbody system forward in time by one step
+      procedure :: interp         => symba_step_interp_system !! Perform an interpolation step on the SymBA nbody system 
+      procedure :: recursive_step => symba_step_recur_system  !! Step interacting planets and active test particles ahead in democratic heliocentric coordinates at the current recursion level, if applicable, and descend to the next deeper level if necessary
+      procedure :: reset          => symba_step_reset_system  !! Resets pl, tp,and encounter structures at the start of a new step 
    end type symba_nbody_system
 
    interface
+      module subroutine symba_collision_check_pltpenc(self, system, param, t, dt, irec)
+         implicit none
+         class(symba_pltpenc),       intent(inout) :: self   !! SyMBA pl-tp encounter list object
+         class(symba_nbody_system),  intent(inout) :: system !! SyMBA nbody system object
+         class(swiftest_parameters), intent(in)    :: param  !! Current run configuration parameters 
+         real(DP),                   intent(in)    :: t      !! current time
+         real(DP),                   intent(in)    :: dt     !! step size
+         integer(I4B),               intent(in)    :: irec   !! Current recursion level
+      end subroutine symba_collision_check_pltpenc
+
+      module subroutine symba_collision_check_plplenc(self, system, param, t, dt, irec)
+         implicit none
+         class(symba_plplenc),       intent(inout) :: self   !! SyMBA pl-tp encounter list object
+         class(symba_nbody_system),  intent(inout) :: system !! SyMBA nbody system object
+         class(swiftest_parameters), intent(in)    :: param  !! Current run configuration parameters 
+         real(DP),                   intent(in)    :: t      !! current time
+         real(DP),                   intent(in)    :: dt     !! step size
+         integer(I4B),               intent(in)    :: irec   !! Current recursion level
+      end subroutine symba_collision_check_plplenc
+
       module subroutine symba_discard_pl(self, system, param)
          use swiftest_classes, only : swiftest_nbody_system, swiftest_parameters
          implicit none
@@ -171,14 +191,6 @@ module subroutine symba_discard_pl(self, system, param)
          class(swiftest_parameters),   intent(in)    :: param  !! Current run configuration parameters 
       end subroutine symba_discard_pl
 
-      module subroutine symba_discard_tp(self, system, param)
-         use swiftest_classes, only : swiftest_nbody_system, swiftest_parameters
-         implicit none
-         class(symba_tp),              intent(inout) :: self   !! SyMBA test particle object
-         class(swiftest_nbody_system), intent(inout) :: system !! Swiftest nbody system object
-         class(swiftest_parameters),   intent(in)    :: param  !! Current run configuration parameters 
-      end subroutine symba_discard_tp
-
       module pure elemental subroutine symba_encounter_check_one(xr, yr, zr, vxr, vyr, vzr, rhill1, rhill2, dt, irec, lencounter, lvdotr)
          implicit none
          real(DP),     intent(in)  :: xr, yr, zr, vxr, vyr, vzr
@@ -314,12 +326,12 @@ module subroutine symba_setup_plplenc(self,n)
          integer,              intent(in)    :: n    !! Number of encounters to allocate space for
       end subroutine symba_setup_plplenc
 
-      module subroutine symba_setup_system(self, param)
+      module subroutine symba_setup_initialize_system(self, param)
          use swiftest_classes, only : swiftest_parameters
          implicit none
          class(symba_nbody_system),  intent(inout) :: self  !! SyMBA system object
          class(swiftest_parameters), intent(inout) :: param !! Current run configuration parameters 
-      end subroutine symba_setup_system
+      end subroutine symba_setup_initialize_system
 
       module subroutine symba_setup_tp(self,n)
          implicit none
@@ -345,11 +357,12 @@ module subroutine symba_step_interp_system(self, param, t, dt)
          real(DP),                   intent(in)    :: dt    !! Current stepsize
       end subroutine symba_step_interp_system
 
-      module recursive subroutine symba_step_recur_system(self, param, ireci)
+      module recursive subroutine symba_step_recur_system(self, param, t, ireci)
          implicit none
          class(symba_nbody_system),  intent(inout) :: self  !! SyMBA nbody system object
          class(swiftest_parameters), intent(inout) :: param !! Current run configuration parameters 
-         integer(I4B), value,        intent(in)    :: ireci !! input recursion level
+         real(DP),                   value         :: t
+         integer(I4B),               value         :: ireci !! input recursion level
       end subroutine symba_step_recur_system
 
       module subroutine symba_step_reset_system(self)
@@ -375,5 +388,32 @@ module subroutine symba_util_resize_pltpenc(self, nrequested)
          integer(I4B),         intent(in)    :: nrequested !! New size of list needed
       end subroutine symba_util_resize_pltpenc
 
+      module subroutine symba_util_sort_pl(self, sortby, ascending)
+         implicit none
+         class(symba_pl), intent(inout) :: self      !! Symba massive body object
+         character(*),    intent(in)    :: sortby    !! Sorting attribute
+         logical,         intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      end subroutine symba_util_sort_pl 
+
+      module subroutine symba_util_sort_tp(self, sortby, ascending)
+         implicit none
+         class(symba_tp), intent(inout) :: self      !! Swiftest test particle object
+         character(*),    intent(in)    :: sortby    !! Sorting attribute
+         logical,         intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      end subroutine symba_util_sort_tp
+
+      module subroutine symba_util_sort_rearrange_pl(self, ind)
+         implicit none
+         class(symba_pl),               intent(inout) :: self !! Symba massive body object
+         integer(I4B),    dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      end subroutine symba_util_sort_rearrange_pl
+
+      module subroutine symba_util_sort_rearrange_tp(self, ind)
+         implicit none
+         class(symba_tp),               intent(inout) :: self !! Symba massive body object
+         integer(I4B),    dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      end subroutine symba_util_sort_rearrange_tp
+        
+
    end interface
 end module symba_classes
\ No newline at end of file
diff --git a/src/modules/whm_classes.f90 b/src/modules/whm_classes.f90
index 46a4e3743..a6ab59958 100644
--- a/src/modules/whm_classes.f90
+++ b/src/modules/whm_classes.f90
@@ -12,7 +12,7 @@ module whm_classes
    ! whm_cb class definitions and method interfaces
    !*******************************************************************************************************************************
    !> Swiftest central body particle class
-   type, public, extends(swiftest_cb) :: whm_cb
+   type, extends(swiftest_cb) :: whm_cb
    contains
    end type whm_cb
 
@@ -21,7 +21,7 @@ module whm_classes
    !*******************************************************************************************************************************
 
    !> WHM massive body particle class
-   type, public, extends(swiftest_pl) :: whm_pl
+   type, extends(swiftest_pl) :: whm_pl
       real(DP), dimension(:),   allocatable :: eta    !! Jacobi mass
       real(DP), dimension(:,:), allocatable :: xj     !! Jacobi position
       real(DP), dimension(:,:), allocatable :: vj     !! Jacobi velocity
@@ -30,20 +30,20 @@ module whm_classes
       !! Note to developers: If you add componenets to this class, be sure to update methods and subroutines that traverse the
       !!    component list, such as whm_setup_pl and whm_util_spill_pl
    contains
-      procedure, public :: h2j         => whm_coord_h2j_pl       !! Convert position and velcoity vectors from heliocentric to Jacobi coordinates 
-      procedure, public :: j2h         => whm_coord_j2h_pl       !! Convert position and velcoity vectors from Jacobi to helliocentric coordinates 
-      procedure, public :: vh2vj       => whm_coord_vh2vj_pl     !! Convert velocity vectors from heliocentric to Jacobi coordinates 
-      procedure, public :: drift       => whm_drift_pl           !! Loop through massive bodies and call Danby drift routine to jacobi coordinates
-      procedure, public :: fill        => whm_util_fill_pl       !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
-      procedure, public :: accel       => whm_kick_getacch_pl    !! Compute heliocentric accelerations of massive bodies
-      procedure, public :: kick        => whm_kick_vh_pl         !! Kick heliocentric velocities of massive bodies
-      procedure, public :: accel_gr    => whm_gr_kick_getacch_pl !! Acceleration term arising from the post-Newtonian correction
-      procedure, public :: gr_pos_kick => whm_gr_p4_pl           !! Position kick due to p**4 term in the post-Newtonian correction
-      procedure, public :: setup       => whm_setup_pl           !! Constructor method - Allocates space for number of particles
-      procedure, public :: set_mu      => whm_util_set_mu_eta_pl !! Sets the Jacobi mass value for all massive bodies.
-      procedure, public :: set_ir3     => whm_setup_set_ir3j     !! Sets both the heliocentric and jacobi inverse radius terms (1/rj**3 and 1/rh**3)
-      procedure, public :: step        => whm_step_pl            !! Steps the body forward one stepsize
-      procedure, public :: spill       => whm_util_spill_pl      !!"Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
+      procedure :: h2j         => whm_coord_h2j_pl       !! Convert position and velcoity vectors from heliocentric to Jacobi coordinates 
+      procedure :: j2h         => whm_coord_j2h_pl       !! Convert position and velcoity vectors from Jacobi to helliocentric coordinates 
+      procedure :: vh2vj       => whm_coord_vh2vj_pl     !! Convert velocity vectors from heliocentric to Jacobi coordinates 
+      procedure :: drift       => whm_drift_pl           !! Loop through massive bodies and call Danby drift routine to jacobi coordinates
+      procedure :: fill        => whm_util_fill_pl       !! "Fills" bodies from one object into another depending on the results of a mask (uses the MERGE intrinsic)
+      procedure :: accel       => whm_kick_getacch_pl    !! Compute heliocentric accelerations of massive bodies
+      procedure :: kick        => whm_kick_vh_pl         !! Kick heliocentric velocities of massive bodies
+      procedure :: accel_gr    => whm_gr_kick_getacch_pl !! Acceleration term arising from the post-Newtonian correction
+      procedure :: gr_pos_kick => whm_gr_p4_pl           !! Position kick due to p**4 term in the post-Newtonian correction
+      procedure :: setup       => whm_setup_pl           !! Constructor method - Allocates space for number of particles
+      procedure :: set_mu      => whm_util_set_mu_eta_pl !! Sets the Jacobi mass value for all massive bodies.
+      procedure :: set_ir3     => whm_util_set_ir3j     !! Sets both the heliocentric and jacobi inverse radius terms (1/rj**3 and 1/rh**3)
+      procedure :: step        => whm_step_pl            !! Steps the body forward one stepsize
+      procedure :: spill       => whm_util_spill_pl      !!"Spills" bodies from one object to another depending on the results of a mask (uses the PACK intrinsic)
    end type whm_pl
 
    !********************************************************************************************************************************
@@ -51,29 +51,27 @@ module whm_classes
    !*******************************************************************************************************************************
 
    !! WHM test particle class
-   type, public, extends(swiftest_tp) :: whm_tp
+   type, extends(swiftest_tp) :: whm_tp
       !! Note to developers: If you add componenets to this class, be sure to update methods and subroutines that traverse the
       !!    component list, such as whm_setup_tp and whm_util_spill_tp
    contains
-      private
-      procedure, public :: accel       => whm_kick_getacch_tp    !! Compute heliocentric accelerations of test particles
-      procedure, public :: kick        => whm_kick_vh_tp         !! Kick heliocentric velocities of test particles
-      procedure, public :: accel_gr    => whm_gr_kick_getacch_tp !! Acceleration term arising from the post-Newtonian correction
-      procedure, public :: gr_pos_kick => whm_gr_p4_tp           !! Position kick due to p**4 term in the post-Newtonian correction
-      procedure, public :: setup       => whm_setup_tp           !! Allocates new components of the whm class and recursively calls parent allocations
-      procedure, public :: step        => whm_step_tp            !! Steps the particle forward one stepsize
+      procedure :: accel       => whm_kick_getacch_tp    !! Compute heliocentric accelerations of test particles
+      procedure :: kick        => whm_kick_vh_tp         !! Kick heliocentric velocities of test particles
+      procedure :: accel_gr    => whm_gr_kick_getacch_tp !! Acceleration term arising from the post-Newtonian correction
+      procedure :: gr_pos_kick => whm_gr_p4_tp           !! Position kick due to p**4 term in the post-Newtonian correction
+      procedure :: setup       => whm_setup_tp           !! Allocates new components of the whm class and recursively calls parent allocations
+      procedure :: step        => whm_step_tp            !! Steps the particle forward one stepsize
    end type whm_tp
 
    !********************************************************************************************************************************
    !  whm_nbody_system class definitions and method interfaces
    !********************************************************************************************************************************
    !> An abstract class for the WHM integrator nbody system 
-   type, public, extends(swiftest_nbody_system) :: whm_nbody_system
+   type, extends(swiftest_nbody_system) :: whm_nbody_system
    contains
-      private
       !> Replace the abstract procedures with concrete ones
-      procedure, public :: initialize   => whm_setup_system      !! Performs WHM-specific initilization steps, like calculating the Jacobi masses
-      procedure, public :: step         => whm_step_system       !! Advance the WHM nbody system forward in time by one step
+      procedure :: initialize   => whm_setup_initialize_system      !! Performs WHM-specific initilization steps, like calculating the Jacobi masses
+      procedure :: step         => whm_step_system       !! Advance the WHM nbody system forward in time by one step
    end type whm_nbody_system
 
    interface
@@ -199,10 +197,10 @@ module subroutine whm_setup_pl(self,n)
          integer(I4B),  intent(in)       :: n    !! Number of test particles to allocate
       end subroutine whm_setup_pl
 
-      module subroutine whm_setup_set_ir3j(self)
+      module subroutine whm_util_set_ir3j(self)
          implicit none
          class(whm_pl),                intent(inout) :: self    !! WHM massive body object
-      end subroutine whm_setup_set_ir3j
+      end subroutine whm_util_set_ir3j
 
       module subroutine whm_util_set_mu_eta_pl(self, cb)
          use swiftest_classes, only : swiftest_cb
@@ -211,12 +209,12 @@ module subroutine whm_util_set_mu_eta_pl(self, cb)
          class(swiftest_cb),           intent(inout) :: cb     !! Swiftest central body object
       end subroutine whm_util_set_mu_eta_pl
 
-      module subroutine whm_setup_system(self, param)
+      module subroutine whm_setup_initialize_system(self, param)
          use swiftest_classes, only : swiftest_parameters
          implicit none
          class(whm_nbody_system),    intent(inout) :: self   !! WHM nbody system object
          class(swiftest_parameters), intent(inout) :: param  !! Current run configuration parameters 
-      end subroutine whm_setup_system
+      end subroutine whm_setup_initialize_system
 
       !> Reads WHM test particle object in from file
       module subroutine whm_setup_tp(self,n)
diff --git a/src/orbel/orbel_el2xv.f90 b/src/orbel/orbel.f90
similarity index 65%
rename from src/orbel/orbel_el2xv.f90
rename to src/orbel/orbel.f90
index 3f1a81fe9..850b643f1 100644
--- a/src/orbel/orbel_el2xv.f90
+++ b/src/orbel/orbel.f90
@@ -1,11 +1,15 @@
-submodule (swiftest_classes) s_orbel_el2xv
+submodule (swiftest_classes) s_orbel
    use swiftest
 contains
-   module procedure orbel_el2xv_vec
+   module subroutine orbel_el2xv_vec(self, cb)
       !! author: David A. Minton
       !!
       !! A wrapper method that converts all of the cartesian position and velocity vectors of a Swiftest body object to orbital elements.
       implicit none
+      ! Arguments
+      class(swiftest_body),         intent(inout) :: self !! Swiftest body object
+      class(swiftest_cb),           intent(inout) :: cb   !! Swiftest central body objec
+      ! Internals
       integer(I4B) :: i
    
       if (self%nbody == 0) return
@@ -15,7 +19,7 @@
          call orbel_el2xv(self%mu(i), self%a(i), self%e(i), self%inc(i), self%capom(i), &
                            self%omega(i), self%capm(i), self%xh(:, i), self%vh(:, i))
       end do
-   end procedure orbel_el2xv_vec
+   end subroutine orbel_el2xv_vec
 
    pure subroutine orbel_el2xv(mu, a, ie, inc, capom, omega, capm, x, v)
       !! author: David A. Minton
@@ -118,6 +122,33 @@ pure subroutine orbel_el2xv(mu, a, ie, inc, capom, omega, capm, x, v)
       return
    end subroutine orbel_el2xv
 
+   module pure subroutine orbel_scget(angle, sx, cx)
+      !! author: David A. Minton
+      !!
+      !! Efficiently compute the sine and cosine of an input angle
+      !!      Input angle must be in radians
+      !!
+      !! Adapted from David E. Kaufmann's Swifter routine: orbel_scget.f90
+      !! Adapted from Hal Levison's Swift routine orbel_scget.f
+      implicit none
+      ! Arguments
+      real(DP), intent(in)  :: angle
+      real(DP), intent(out) :: sx, cx
+      ! Internals
+      integer(I4B) :: nper
+      real(DP)   :: x
+
+      nper = angle / TWOPI
+      x = angle - nper * TWOPI
+      if (x < 0.0_DP) x = x + TWOPI
+      sx = sin(x)
+      cx = sqrt(1.0_DP - sx**2)
+      if ((x > PIBY2) .and. (x < PI3BY2)) cx = -cx
+
+      return
+
+   end subroutine orbel_scget
+
    !**********************************************************************
    ! Code converted to Modern Fortran by David A. Minton
    ! Date: 2020-06-29  
@@ -207,8 +238,6 @@ real(DP) pure function orbel_flon(e,icapn)
       !  set iflag nonzero if capn < 0., in which case solve for -capn
       !  and change the sign of the final answer for f.
       !  Begin with a reasonable guess based on solving the cubic for small F
-
-
       a = 6 * ( e - 1.d0) / e
       b = -6 * capn / e
       sq = SQRT(0.25_DP * b**2 + a**3 / 27._DP)
@@ -657,5 +686,311 @@ real(DP) pure function orbel_fhybrid(e,n)
       return
    end function orbel_fhybrid
 
+   module pure subroutine orbel_xv2aeq(mu, x, v, a, e, q)
+      !! author: David A. Minton
+      !!
+      !! Compute semimajor axis, eccentricity, and pericentric distance from relative Cartesian position and velocity
+      !!
+      !! Adapted from David E. Kaufmann's Swifter routine: orbel_xv2aeq.f90
+      !! Adapted from Luke Dones' Swift routine orbel_xv2aeq.f
+      implicit none
+      !! Arguments
+      real(DP), intent(in)  :: mu
+      real(DP), dimension(:), intent(in)  :: x, v
+      real(DP), intent(out) :: a, e, q
+      integer(I4B) :: iorbit_type
+      real(DP)   :: r, v2, h2, energy, fac
+      real(DP), dimension(NDIM) :: hvec
+
+      a = 0.0_DP
+      e = 0.0_DP
+      q = 0.0_DP
+      r = sqrt(dot_product(x(:), x(:))) 
+      v2 = dot_product(v(:), v(:))
+      hvec(:) = x(:) .cross. v(:)
+      h2 = dot_product(hvec(:), hvec(:))
+      if (h2 == 0.0_DP) return
+      energy = 0.5_DP * v2 - mu / r
+      if (abs(energy * r / mu) < sqrt(VSMALL)) then
+         iorbit_type = PARABOLA
+      else
+         a = -0.5_DP * mu / energy
+         if (a < 0.0_DP) then
+            fac = -h2 / (mu * a)
+            if (fac > VSMALL) then
+               iorbit_type = HYPERBOLA
+            else
+               iorbit_type = PARABOLA
+            end if
+         else
+            iorbit_type = ELLIPSE
+         end if
+      end if
+      select case (iorbit_type)
+      case (ELLIPSE)
+         fac = 1.0_DP - h2 / (mu * a)
+         if (fac > VSMALL) e = sqrt(fac)
+         q = a * (1.0_DP - e)
+      case (PARABOLA)
+         a = 0.5_DP * h2 / mu
+         e = 1.0_DP
+         q = a
+      case (HYPERBOLA)
+         e = sqrt(1.0_DP + fac)
+         q = a * (1.0_DP - e)
+      end select
+
+      return
+
+   end subroutine orbel_xv2aeq
+
+   module pure subroutine orbel_xv2aqt(mu, x, v, a, q, capm, tperi)
+      !! author: David A. Minton
+      !!
+      !! Compute semimajor axis, pericentric distance, mean anomaly, and time to nearest pericenter passage from
+      !! relative Cartesian position and velocity
+      !!      tperi > 0 means nearest pericenter passage is in the future
+      !!      tperi < 0 means nearest pericenter passage is in the past
+      !!
+      !! Adapted from David E. Kaufmann's Swifter routine: orbel_xv2aqt.f90
+      implicit none
+      ! Arguments
+      real(DP),               intent(in)  :: mu    !! Gravitational constant
+      real(DP), dimension(:), intent(in)  :: x     !! Position vector
+      real(DP), dimension(:), intent(in)  :: v     !! Velocity vector
+      real(DP),               intent(out) :: a     !! semimajor axis
+      real(DP),               intent(out) :: q     !! periapsis
+      real(DP),               intent(out) :: capm  !! mean anomaly
+      real(DP),               intent(out) :: tperi !! time of pericenter passage
+      ! Internals
+      integer(I4B) :: iorbit_type
+      real(DP)   :: r, v2, h2, rdotv, energy, fac, w, face, cape, e, tmpf, capf, mm
+      real(DP), dimension(NDIM) :: hvec
+
+      a = 0.0_DP
+      q = 0.0_DP
+      capm = 0.0_DP
+      tperi = 0.0_DP
+      r = sqrt(dot_product(x(:), x(:)))
+      v2 = dot_product(v(:), v(:))
+      hvec(:) = x(:) .cross. v(:)
+      h2 = dot_product(hvec(:), hvec(:))
+      if (h2 == 0.0_DP) return
+      rdotv = dot_product(x(:), v(:))
+      energy = 0.5_DP * v2 - mu / r
+      if (abs(energy * r / mu) < sqrt(VSMALL)) then
+         iorbit_type = PARABOLA
+      else
+         a = -0.5_DP * mu / energy
+         if (a < 0.0_DP) then
+            fac = -h2 / (mu * a)
+            if (fac > VSMALL) then
+               iorbit_type = HYPERBOLA
+            else
+               iorbit_type = PARABOLA
+            end if
+         else
+            iorbit_type = ELLIPSE
+         end if
+      end if
+      select case (iorbit_type)
+      case (ELLIPSE)
+         fac = 1.0_DP - h2 / (mu * a)
+         if (fac > VSMALL) then
+            e = sqrt(fac)
+            cape = 0.0_DP
+            face = (a - r) / (a * e)
+            if (face < -1.0_DP) then
+               cape = PI
+            else if (face < 1.0_DP) then
+               cape = acos(face)
+            end if
+            if (rdotv < 0.0_DP) cape = TWOPI - cape
+         else
+            e = 0.0_DP
+            cape = 0.0_DP
+         end if
+         capm = cape - e * sin(cape)
+         q = a * (1.0_DP - e)
+         mm = sqrt(mu / a**3)
+         if (capm < PI) then
+            tperi = -1.0_DP * capm / mm
+         else
+            tperi = -1.0_DP * (capm - TWOPI) / mm
+         end if
+      case (PARABOLA)
+         a = 0.5_DP * h2 / mu
+         e = 1.0_DP
+         w = 0.0_DP
+         fac = 2 * a / r - 1.0_DP
+         if (fac < -1.0_DP) then
+            w = PI
+         else if (fac < 1.0_DP) then
+            w = acos(fac)
+         end if
+         if (rdotv < 0.0_DP) w = TWOPI - w
+         tmpf = tan(0.5_DP * w)
+         capm = tmpf*(1.0_DP + tmpf * tmpf / 3.0_DP)
+         q = a
+         mm = sqrt(0.5_DP * mu / q**3)
+         tperi = -1.0_DP * capm / mm
+      case (HYPERBOLA)
+         e = sqrt(1.0_DP + fac)
+         tmpf = (a - r) / (a * e)
+         if (tmpf < 1.0_DP) tmpf = 1.0_DP
+         capf = log(tmpf + sqrt(tmpf * tmpf - 1.0_DP))
+         if (rdotv < 0.0_DP) capf = -capf
+         capm = e * sinh(capf) - capf
+         q = a * (1.0_DP - e)
+         mm = sqrt(-mu / a**3)
+         tperi = -1.0_DP * capm / mm
+      end select
+
+      return
+
+   end subroutine orbel_xv2aqt
+
+
+   module subroutine orbel_xv2el_vec(self, cb)
+      !! author: David A. Minton
+      !!
+      !! A wrapper method that converts all of the cartesian position and velocity vectors of a Swiftest body object to orbital elements.
+      implicit none
+      ! Arguments
+      class(swiftest_body), intent(inout) :: self !! Swiftest body object
+      class(swiftest_cb),   intent(inout) :: cb   !! Swiftest central body object
+      ! internals
+      integer(I4B) :: i
+    
+      if (self%nbody == 0) return
+      call self%set_mu(cb)
+      !do concurrent (i = 1:self%nbody)
+      do i = 1, self%nbody
+         call orbel_xv2el(self%mu(i), self%xh(:, i), self%vh(:, i), self%a(i), self%e(i), self%inc(i),  &
+                          self%capom(i), self%omega(i), self%capm(i))
+      end do
+   end subroutine orbel_xv2el_vec 
+
+   pure subroutine orbel_xv2el(mu, x, v, a, e, inc, capom, omega, capm)
+      !! author: David A. Minton
+      !!
+      !! Compute osculating orbital elements from relative Cartesian position and velocity
+      !!  All angular measures are returned in radians
+      !!      If inclination < TINY, longitude of the ascending node is arbitrarily set to 0
+      !!
+      !!      If eccentricity < sqrt(TINY), argument of pericenter is arbitrarily set to 0
+      !!
+      !!      References: Danby, J. M. A. 1988. Fundamentals of Celestial Mechanics, (Willmann-Bell, Inc.), 201 - 206.
+      !!              Fitzpatrick, P. M. 1970. Principles of Celestial Mechanics, (Academic Press), 69 - 73.
+      !!              Roy, A. E. 1982. Orbital Motion, (Adam Hilger, Ltd.), 75 - 95
+      !!
+      !! Adapted from David E. Kaufmann's Swifter routine: orbel_xv2el.f90
+      !! Adapted from Martin Duncan's Swift routine orbel_xv2el.f
+      implicit none
+      real(DP), intent(in)  :: mu
+      real(DP), dimension(:), intent(in)  :: x, v
+      real(DP), intent(out) :: a, e, inc, capom, omega, capm
+      integer(I4B) :: iorbit_type
+      real(DP)   :: r, v2, h2, h, rdotv, energy, fac, u, w, cw, sw, face, cape, tmpf, capf
+      real(DP), dimension(NDIM) :: hvec
+
+      a = 0.0_DP
+      e = 0.0_DP
+      inc = 0.0_DP
+      capom = 0.0_DP
+      omega = 0.0_DP
+      capm = 0.0_DP
+      r = sqrt(dot_product(x(:), x(:))) 
+      v2 = dot_product(v(:), v(:))
+      hvec = x(:) .cross. v(:)
+      h2 = dot_product(hvec(:), hvec(:)) 
+      h = sqrt(h2)
+      if (h2 == 0.0_DP) return
+      rdotv = dot_product(x(:), v(:))
+      energy = 0.5_DP * v2 - mu / r
+      fac = hvec(3) / h
+      if (fac < -1.0_DP) then
+         inc = PI
+      else if (fac < 1.0_DP) then
+         inc = acos(fac)
+      end if
+      fac = sqrt(hvec(1)**2 + hvec(2)**2) / h
+      if (fac**2 < VSMALL) then
+         u = atan2(x(2), x(1))
+         if (hvec(3) < 0.0_DP) u = -u
+      else
+         capom = atan2(hvec(1), -hvec(2))
+         u = atan2(x(3) / sin(inc), x(1) * cos(capom) + x(2) * sin(capom))
+      end if
+      if (capom < 0.0_DP) capom = capom + TWOPI
+      if (u < 0.0_DP) u = u + TWOPI
+      if (abs(energy * r / mu) < sqrt(VSMALL)) then
+         iorbit_type = parabola
+      else
+         a = -0.5_DP * mu / energy
+         if (a < 0.0_DP) then
+            fac = -h2 / (mu * a)
+            if (fac > VSMALL) then
+               iorbit_type = HYPERBOLA
+            else
+               iorbit_type = PARABOLA
+            end if
+         else
+            iorbit_type = ELLIPSE
+         end if
+      end if
+      select case (iorbit_type)
+         case (ELLIPSE)
+            fac = 1.0_DP - h2 / (mu * a)
+            if (fac > VSMALL) then
+               e = sqrt(fac)
+               cape = 0.0_DP
+               face = (a - r) / (a * e)
+               if (face < -1.0_DP) then
+                  cape = PI
+               else if (face < 1.0_DP) then
+                  cape = acos(face)
+               end if
+               if (rdotv < 0.0_DP) cape = TWOPI - cape
+               fac = 1.0_DP - e * cos(cape)
+               cw = (cos(cape) - e) / fac
+               sw = sqrt(1.0_DP - e**2) * sin(cape) / fac
+               w = atan2(sw, cw)
+               if (w < 0.0_DP) w = w + TWOPI
+            else
+               cape = u
+               w = u
+            end if
+            capm = cape - e * sin(cape)
+         case (PARABOLA)
+            a = 0.5_DP * h2 / mu
+            e = 1.0_DP
+            w = 0.0_DP
+            fac = 2 * a / r - 1.0_DP
+            if (fac < -1.0_DP) then
+               w = PI
+            else if (fac < 1.0_DP) then
+               w = acos(fac)
+            end if
+            if (rdotv < 0.0_DP) w = TWOPI - w
+            tmpf = tan(0.5_DP * w)
+            capm = tmpf * (1.0_DP + tmpf * tmpf / 3.0_DP)
+         case (HYPERBOLA)
+            e = sqrt(1.0_DP + fac)
+            tmpf = max((a - r) / (a * e), 1.0_DP)
+            capf = log(tmpf + sqrt(tmpf**2 - 1.0_DP))
+            if (rdotv < 0.0_DP) capf = -capf
+            fac = e * cosh(capf) - 1.0_DP
+            cw = (e - cosh(capf)) / fac
+            sw = sqrt(e * e - 1.0_DP) * sinh(capf) / fac
+            w = atan2(sw, cw)
+            if (w < 0.0_DP) w = w + TWOPI
+            capm = e * sinh(capf) - capf
+      end select
+      omega = u - w
+      if (omega < 0.0_DP) omega = omega + TWOPI
+
+      return
+   end subroutine orbel_xv2el
 
-end submodule s_orbel_el2xv
+end submodule s_orbel
diff --git a/src/orbel/orbel_scget.f90 b/src/orbel/orbel_scget.f90
deleted file mode 100644
index 0cdb67c72..000000000
--- a/src/orbel/orbel_scget.f90
+++ /dev/null
@@ -1,27 +0,0 @@
-submodule (swiftest_classes) s_orbel_scget
-   use swiftest
-contains
-   module procedure orbel_scget
-      !! author: David A. Minton
-      !!
-      !! Efficiently compute the sine and cosine of an input angle
-      !!      Input angle must be in radians
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: orbel_scget.f90
-      !! Adapted from Hal Levison's Swift routine orbel_scget.f
-      implicit none
-      integer(I4B) :: nper
-      real(DP)   :: x
-
-   ! executable code
-      nper = angle / TWOPI
-      x = angle - nper * TWOPI
-      if (x < 0.0_DP) x = x + TWOPI
-      sx = sin(x)
-      cx = sqrt(1.0_DP - sx**2)
-      if ((x > PIBY2) .and. (x < PI3BY2)) cx = -cx
-
-      return
-
-   end procedure orbel_scget
-end submodule s_orbel_scget
diff --git a/src/orbel/orbel_xv2aeq.f90 b/src/orbel/orbel_xv2aeq.f90
deleted file mode 100644
index 8338d6559..000000000
--- a/src/orbel/orbel_xv2aeq.f90
+++ /dev/null
@@ -1,57 +0,0 @@
-submodule (swiftest_classes) s_orbel_xv2aeq
-   use swiftest
-contains
-   module procedure orbel_xv2aeq 
-      !! author: David A. Minton
-      !!
-      !! Compute semimajor axis, eccentricity, and pericentric distance from relative Cartesian position and velocity
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: orbel_xv2aeq.f90
-      !! Adapted from Luke Dones' Swift routine orbel_xv2aeq.f
-      implicit none
-      integer(I4B) :: iorbit_type
-      real(DP)   :: r, v2, h2, energy, fac
-      real(DP), dimension(NDIM) :: hvec
-
-      a = 0.0_DP
-      e = 0.0_DP
-      q = 0.0_DP
-      r = sqrt(dot_product(x(:), x(:))) 
-      v2 = dot_product(v(:), v(:))
-      hvec(:) = x(:) .cross. v(:)
-      h2 = dot_product(hvec(:), hvec(:))
-      if (h2 == 0.0_DP) return
-      energy = 0.5_DP * v2 - mu / r
-      if (abs(energy * r / mu) < sqrt(VSMALL)) then
-         iorbit_type = PARABOLA
-      else
-         a = -0.5_DP * mu / energy
-         if (a < 0.0_DP) then
-            fac = -h2 / (mu * a)
-            if (fac > VSMALL) then
-               iorbit_type = HYPERBOLA
-            else
-               iorbit_type = PARABOLA
-            end if
-         else
-            iorbit_type = ELLIPSE
-         end if
-      end if
-      select case (iorbit_type)
-      case (ELLIPSE)
-         fac = 1.0_DP - h2 / (mu * a)
-         if (fac > VSMALL) e = sqrt(fac)
-         q = a * (1.0_DP - e)
-      case (PARABOLA)
-         a = 0.5_DP * h2 / mu
-         e = 1.0_DP
-         q = a
-      case (HYPERBOLA)
-         e = sqrt(1.0_DP + fac)
-         q = a * (1.0_DP - e)
-      end select
-
-      return
-
-   end procedure orbel_xv2aeq
-end submodule s_orbel_xv2aeq
diff --git a/src/orbel/orbel_xv2aqt.f90 b/src/orbel/orbel_xv2aqt.f90
deleted file mode 100644
index 3c8bf3f3e..000000000
--- a/src/orbel/orbel_xv2aqt.f90
+++ /dev/null
@@ -1,100 +0,0 @@
-submodule (swiftest_classes) s_orbel_xv2aqt
-   use swiftest
-contains
-   module procedure orbel_xv2aqt ! (mu, px, py, pz, vx, vy, vz, a, q, capm, tperi
-      !! author: David A. Minton
-      !!
-      !! Compute semimajor axis, pericentric distance, mean anomaly, and time to nearest pericenter passage from
-      !! relative Cartesian position and velocity
-      !!      tperi > 0 means nearest pericenter passage is in the future
-      !!      tperi < 0 means nearest pericenter passage is in the past
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: orbel_xv2aqt.f90
-      implicit none
-      integer(I4B) :: iorbit_type
-      real(DP)   :: r, v2, h2, rdotv, energy, fac, w, face, cape, e, tmpf, capf, mm
-      real(DP), dimension(NDIM) :: hvec
-
-      a = 0.0_DP
-      q = 0.0_DP
-      capm = 0.0_DP
-      tperi = 0.0_DP
-      r = sqrt(dot_product(x(:), x(:)))
-      v2 = dot_product(v(:), v(:))
-      hvec(:) = x(:) .cross. v(:)
-      h2 = dot_product(hvec(:), hvec(:))
-      if (h2 == 0.0_DP) return
-      rdotv = dot_product(x(:), v(:))
-      energy = 0.5_DP * v2 - mu / r
-      if (abs(energy * r / mu) < sqrt(VSMALL)) then
-         iorbit_type = PARABOLA
-      else
-         a = -0.5_DP * mu / energy
-         if (a < 0.0_DP) then
-            fac = -h2 / (mu * a)
-            if (fac > VSMALL) then
-               iorbit_type = HYPERBOLA
-            else
-               iorbit_type = PARABOLA
-            end if
-         else
-            iorbit_type = ELLIPSE
-         end if
-      end if
-      select case (iorbit_type)
-      case (ELLIPSE)
-         fac = 1.0_DP - h2 / (mu * a)
-         if (fac > VSMALL) then
-            e = sqrt(fac)
-            cape = 0.0_DP
-            face = (a - r) / (a * e)
-            if (face < -1.0_DP) then
-               cape = PI
-            else if (face < 1.0_DP) then
-               cape = acos(face)
-            end if
-            if (rdotv < 0.0_DP) cape = TWOPI - cape
-         else
-            e = 0.0_DP
-            cape = 0.0_DP
-         end if
-         capm = cape - e * sin(cape)
-         q = a * (1.0_DP - e)
-         mm = sqrt(mu / a**3)
-         if (capm < PI) then
-            tperi = -1.0_DP * capm / mm
-         else
-            tperi = -1.0_DP * (capm - TWOPI) / mm
-         end if
-      case (PARABOLA)
-         a = 0.5_DP * h2 / mu
-         e = 1.0_DP
-         w = 0.0_DP
-         fac = 2 * a / r - 1.0_DP
-         if (fac < -1.0_DP) then
-            w = PI
-         else if (fac < 1.0_DP) then
-            w = acos(fac)
-         end if
-         if (rdotv < 0.0_DP) w = TWOPI - w
-         tmpf = tan(0.5_DP * w)
-         capm = tmpf*(1.0_DP + tmpf * tmpf / 3.0_DP)
-         q = a
-         mm = sqrt(0.5_DP * mu / q**3)
-         tperi = -1.0_DP * capm / mm
-      case (HYPERBOLA)
-         e = sqrt(1.0_DP + fac)
-         tmpf = (a - r) / (a * e)
-         if (tmpf < 1.0_DP) tmpf = 1.0_DP
-         capf = log(tmpf + sqrt(tmpf * tmpf - 1.0_DP))
-         if (rdotv < 0.0_DP) capf = -capf
-         capm = e * sinh(capf) - capf
-         q = a * (1.0_DP - e)
-         mm = sqrt(-mu / a**3)
-         tperi = -1.0_DP * capm / mm
-      end select
-
-      return
-
-   end procedure orbel_xv2aqt
-end submodule s_orbel_xv2aqt
diff --git a/src/orbel/orbel_xv2el.f90 b/src/orbel/orbel_xv2el.f90
deleted file mode 100644
index 434925c7d..000000000
--- a/src/orbel/orbel_xv2el.f90
+++ /dev/null
@@ -1,142 +0,0 @@
-submodule (swiftest_classes) s_orbel_xv2el
-   use swiftest
-contains
-
-   module procedure orbel_xv2el_vec
-      !! author: David A. Minton
-      !!
-      !! A wrapper method that converts all of the cartesian position and velocity vectors of a Swiftest body object to orbital elements.
-      implicit none
-      integer(I4B) :: i
-    
-      if (self%nbody == 0) return
-      call self%set_mu(cb)
-      !do concurrent (i = 1:self%nbody)
-      do i = 1, self%nbody
-         call orbel_xv2el(self%mu(i), self%xh(:, i), self%vh(:, i), self%a(i), self%e(i), self%inc(i),  &
-                          self%capom(i), self%omega(i), self%capm(i))
-      end do
-   end procedure orbel_xv2el_vec 
-
-   pure subroutine orbel_xv2el(mu, x, v, a, e, inc, capom, omega, capm)
-      !! author: David A. Minton
-      !!
-      !! Compute osculating orbital elements from relative Cartesian position and velocity
-      !!  All angular measures are returned in radians
-      !!      If inclination < TINY, longitude of the ascending node is arbitrarily set to 0
-      !!
-      !!      If eccentricity < sqrt(TINY), argument of pericenter is arbitrarily set to 0
-      !!
-      !!      References: Danby, J. M. A. 1988. Fundamentals of Celestial Mechanics, (Willmann-Bell, Inc.), 201 - 206.
-      !!              Fitzpatrick, P. M. 1970. Principles of Celestial Mechanics, (Academic Press), 69 - 73.
-      !!              Roy, A. E. 1982. Orbital Motion, (Adam Hilger, Ltd.), 75 - 95
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: orbel_xv2el.f90
-      !! Adapted from Martin Duncan's Swift routine orbel_xv2el.f
-      implicit none
-      real(DP), intent(in)  :: mu
-      real(DP), dimension(:), intent(in)  :: x, v
-      real(DP), intent(out) :: a, e, inc, capom, omega, capm
-      integer(I4B) :: iorbit_type
-      real(DP)   :: r, v2, h2, h, rdotv, energy, fac, u, w, cw, sw, face, cape, tmpf, capf
-      real(DP), dimension(NDIM) :: hvec
-
-      a = 0.0_DP
-      e = 0.0_DP
-      inc = 0.0_DP
-      capom = 0.0_DP
-      omega = 0.0_DP
-      capm = 0.0_DP
-      r = sqrt(dot_product(x(:), x(:))) 
-      v2 = dot_product(v(:), v(:))
-      hvec = x(:) .cross. v(:)
-      h2 = dot_product(hvec(:), hvec(:)) 
-      h = sqrt(h2)
-      if (h2 == 0.0_DP) return
-      rdotv = dot_product(x(:), v(:))
-      energy = 0.5_DP * v2 - mu / r
-      fac = hvec(3) / h
-      if (fac < -1.0_DP) then
-         inc = PI
-      else if (fac < 1.0_DP) then
-         inc = acos(fac)
-      end if
-      fac = sqrt(hvec(1)**2 + hvec(2)**2) / h
-      if (fac**2 < VSMALL) then
-         u = atan2(x(2), x(1))
-         if (hvec(3) < 0.0_DP) u = -u
-      else
-         capom = atan2(hvec(1), -hvec(2))
-         u = atan2(x(3) / sin(inc), x(1) * cos(capom) + x(2) * sin(capom))
-      end if
-      if (capom < 0.0_DP) capom = capom + TWOPI
-      if (u < 0.0_DP) u = u + TWOPI
-      if (abs(energy * r / mu) < sqrt(VSMALL)) then
-         iorbit_type = parabola
-      else
-         a = -0.5_DP * mu / energy
-         if (a < 0.0_DP) then
-            fac = -h2 / (mu * a)
-            if (fac > VSMALL) then
-               iorbit_type = HYPERBOLA
-            else
-               iorbit_type = PARABOLA
-            end if
-         else
-            iorbit_type = ELLIPSE
-         end if
-      end if
-      select case (iorbit_type)
-         case (ELLIPSE)
-            fac = 1.0_DP - h2 / (mu * a)
-            if (fac > VSMALL) then
-               e = sqrt(fac)
-               cape = 0.0_DP
-               face = (a - r) / (a * e)
-               if (face < -1.0_DP) then
-                  cape = PI
-               else if (face < 1.0_DP) then
-                  cape = acos(face)
-               end if
-               if (rdotv < 0.0_DP) cape = TWOPI - cape
-               fac = 1.0_DP - e * cos(cape)
-               cw = (cos(cape) - e) / fac
-               sw = sqrt(1.0_DP - e**2) * sin(cape) / fac
-               w = atan2(sw, cw)
-               if (w < 0.0_DP) w = w + TWOPI
-            else
-               cape = u
-               w = u
-            end if
-            capm = cape - e * sin(cape)
-         case (PARABOLA)
-            a = 0.5_DP * h2 / mu
-            e = 1.0_DP
-            w = 0.0_DP
-            fac = 2 * a / r - 1.0_DP
-            if (fac < -1.0_DP) then
-               w = PI
-            else if (fac < 1.0_DP) then
-               w = acos(fac)
-            end if
-            if (rdotv < 0.0_DP) w = TWOPI - w
-            tmpf = tan(0.5_DP * w)
-            capm = tmpf * (1.0_DP + tmpf * tmpf / 3.0_DP)
-         case (HYPERBOLA)
-            e = sqrt(1.0_DP + fac)
-            tmpf = max((a - r) / (a * e), 1.0_DP)
-            capf = log(tmpf + sqrt(tmpf**2 - 1.0_DP))
-            if (rdotv < 0.0_DP) capf = -capf
-            fac = e * cosh(capf) - 1.0_DP
-            cw = (e - cosh(capf)) / fac
-            sw = sqrt(e * e - 1.0_DP) * sinh(capf) / fac
-            w = atan2(sw, cw)
-            if (w < 0.0_DP) w = w + TWOPI
-            capm = e * sinh(capf) - capf
-      end select
-      omega = u - w
-      if (omega < 0.0_DP) omega = omega + TWOPI
-
-      return
-   end subroutine orbel_xv2el
-end submodule s_orbel_xv2el
diff --git a/src/rmvs/rmvs_setup.f90 b/src/rmvs/rmvs_setup.f90
index 4cda7bd6f..58002401e 100644
--- a/src/rmvs/rmvs_setup.f90
+++ b/src/rmvs/rmvs_setup.f90
@@ -46,7 +46,7 @@ module subroutine rmvs_setup_pl(self,n)
       return
    end subroutine rmvs_setup_pl 
 
-   module subroutine rmvs_setup_system(self, param)
+   module subroutine rmvs_setup_initialize_system(self, param)
       !! author: David A. Minton
       !!
       !! Initialize an RMVS nbody system from files and sets up the planetocentric structures.
@@ -64,7 +64,7 @@ module subroutine rmvs_setup_system(self, param)
       integer(I4B) :: i, j
 
       ! Call parent method
-      call whm_setup_system(self, param)
+      call whm_setup_initialize_system(self, param)
 
       ! Set up the pl-tp planetocentric encounter structures for pl and cb. The planetocentric tp structures are 
       ! generated as necessary during close encounter steps.
@@ -116,7 +116,7 @@ module subroutine rmvs_setup_system(self, param)
          end select
       end select
    
-   end subroutine rmvs_setup_system
+   end subroutine rmvs_setup_initialize_system
 
    module subroutine rmvs_setup_tp(self,n)
       !! author: David A. Minton
diff --git a/src/setup/setup.f90 b/src/setup/setup.f90
index e85f9754a..cbd7aabfe 100644
--- a/src/setup/setup.f90
+++ b/src/setup/setup.f90
@@ -69,7 +69,6 @@ module subroutine setup_construct_system(system, param)
       return
    end subroutine setup_construct_system
 
-
    module subroutine setup_initialize_system(self, param)
       !! author: David A. Minton
       !!
@@ -82,12 +81,13 @@ module subroutine setup_initialize_system(self, param)
   
       call self%cb%initialize(param)
       call self%pl%initialize(param)
-      if (.not.param%lrhill_present) call self%pl%set_rhill(self%cb)
       call self%tp%initialize(param)
+      call util_valid(self%pl, self%tp)
       call self%set_msys()
       call self%pl%set_mu(self%cb) 
       call self%tp%set_mu(self%cb) 
       call self%pl%eucl_index()
+      if (.not.param%lrhill_present) call self%pl%set_rhill(self%cb)
       return
    end subroutine setup_initialize_system
 
@@ -177,7 +177,7 @@ module subroutine setup_pl(self,n)
       self%Gmass(:) = 0.0_DP
       self%rhill(:) = 0.0_DP
       self%radius(:) = 0.0_DP
-      self%density(:) = 0.0_DP
+      self%density(:) = 1.0_DP
       self%rot(:,:) = 0.0_DP
       self%Ip(:,:) = 0.0_DP
       self%k2(:) = 0.0_DP
diff --git a/src/symba/symba_collision.f90 b/src/symba/symba_collision.f90
new file mode 100644
index 000000000..b8060829c
--- /dev/null
+++ b/src/symba/symba_collision.f90
@@ -0,0 +1,119 @@
+submodule (symba_classes) s_symba_collision
+   use swiftest
+contains
+   module subroutine symba_collision_check_plplenc(self, system, param, t, dt, irec)
+      !! author: Jennifer L.L. Pouplin, Carlisle A. wishard, and David A. Minton
+      !!
+      !! Check for merger between massive bodies in SyMBA. If the user has turned on the FRAGMENTATION feature, it will call the 
+      !! symba_regime subroutine to determine what kind of collision will occur.
+      !! 
+      !! Adapted from David E. Kaufmann's Swifter routine symba_merge_pl.f90
+      !!
+      !! Adapted from Hal Levison's Swift routine symba5_merge.f
+      implicit none
+      ! Arguments
+      class(symba_plplenc),       intent(inout) :: self   !! SyMBA pl-tp encounter list object
+      class(symba_nbody_system),  intent(inout) :: system !! SyMBA nbody system object
+      class(swiftest_parameters), intent(in)    :: param  !! Current run configuration parameters 
+      real(DP),                   intent(in)    :: t      !! current time
+      real(DP),                   intent(in)    :: dt     !! step size
+      integer(I4B),               intent(in)    :: irec   !! Current recursion level
+   end subroutine symba_collision_check_plplenc
+
+   module subroutine symba_collision_check_pltpenc(self, system, param, t, dt, irec)
+      !! author: David A. Minton
+      !!
+      !! Check for merger between massive bodies and test particles in SyMBA
+      !! 
+      !! Adapted from David E. Kaufmann's Swifter routine symba_merge_tp.f90
+      !!
+      !! Adapted from Hal Levison's Swift routine symba5_merge.f
+      implicit none
+      ! Arguments
+      class(symba_pltpenc),       intent(inout) :: self   !! SyMBA pl-tp encounter list object
+      class(symba_nbody_system),  intent(inout) :: system !! SyMBA nbody system object
+      class(swiftest_parameters), intent(in)    :: param  !! Current run configuration parameters 
+      real(DP),                   intent(in)    :: t      !! current time
+      real(DP),                   intent(in)    :: dt     !! step size
+      integer(I4B),               intent(in)    :: irec   !! Current recursion level
+      ! Internals
+      logical, dimension(:), allocatable        :: lcollision, mask
+      real(DP), dimension(NDIM)                 :: xr, vr
+      integer(I4B)                              :: k
+
+      select type(pl => system%pl)
+      class is (symba_pl)
+         select type(tp => system%tp)
+         class is (symba_tp)
+            associate(pltpenc_list => self, npltpenc => self%nenc, plind => self%index1(1:self%nenc), tpind => self%index2(1:self%nenc))
+               allocate(lcollision(npltpenc), mask(npltpenc))
+               mask(:) = ((pltpenc_list%status(1:npltpenc) == ACTIVE) .and. (pl%levelg(plind) >= irec) .and. (tp%levelg(tpind) >= irec))
+               lcollision(:) = .false.
+               do concurrent(k = 1:npltpenc, mask(k))
+                  associate(i => plind(k), j => tpind(k))
+                     xr(:) = pl%xh(:, i) - tp%xh(:, j) 
+                     vr(:) = pl%vb(:, i) - tp%vb(:, j)
+                     lcollision(k) = symba_collision_check_one(xr(1), xr(2), xr(3), vr(1), vr(2), vr(3), pl%Gmass(i), pl%radius(i), dt, pltpenc_list%lvdotr(k))
+                  end associate
+               end do
+
+               if (any(lcollision(:))) then
+                  where(lcollision(1:npltpenc))
+                     pltpenc_list%status(1:npltpenc) = COLLISION
+                     tp%status(tpind(1:npltpenc)) = DISCARDED_PLR
+                     tp%ldiscard(tpind(1:npltpenc)) = .true.
+                  end where
+                  do k = 1, npltpenc
+                     if (pltpenc_list%status(k) /= COLLISION) cycle
+                     write(*,*) 'Test particle ',tp%id(tpind(k)), ' collided with massive body ',pl%id(plind(k)), ' at time ',t
+                  end do
+               end if
+            end associate
+         end select
+      end select
+      return 
+   end subroutine symba_collision_check_pltpenc
+
+   pure elemental function symba_collision_check_one(xr, yr, zr, vxr, vyr, vzr, Gmtot, rlim, dt, lvdotr) result(lcollision)
+      !! author: David A. Minton
+      !! 
+      !! Check for a merger between a single pair of particles
+      !!
+      !! Adapted from David E. Kaufmann's Swifter routines symba_merge_tp.f90 and symba_merge_pl.f90
+      !!
+      !! Adapted from Hal Levison's Swift routine symba5_merge.f
+      implicit none
+      ! Arguments
+      real(DP), intent(in) :: xr, yr, zr    !! Relative position vector components
+      real(DP), intent(in) :: vxr, vyr, vzr !! Relative velocity vector components
+      real(DP), intent(in) :: Gmtot         !! Sum of G*mass of colliding bodies
+      real(DP), intent(in) :: rlim          !! Collision limit - Typically the sum of the radii of colliding bodies
+      real(DP), intent(in) :: dt            !! Step size
+      logical,  intent(in) :: lvdotr        !! Logical flag indicating that these two bodies are approaching in the current substep
+      ! Result
+      logical              :: lcollision    !! Logical flag indicating whether these two bodies will collide or not
+      ! Internals
+      real(DP) :: r2, rlim2, a, e, q, vdotr, tcr2, dt2
+
+      r2 = xr**2 + yr**2 + zr**2
+      rlim2 = rlim**2
+   
+      if (r2 <= rlim2) then ! checks if bodies are actively colliding in this time step
+         lcollision = .true.
+      else ! if they are not actively colliding in  this time step, checks if they are going to collide next time step based on velocities and q 
+         lcollision = .false.
+         vdotr = xr * vxr + yr * vyr + zr * vzr
+         if (lvdotr .and. (vdotr > 0.0_DP)) then 
+            tcr2 = r2 / (vxr**2 + vyr**2 + vzr**2)
+            dt2 = dt**2
+            if (tcr2 <= dt2) then
+               call orbel_xv2aeq(Gmtot, [xr, yr, zr], [vxr, vyr, vzr], a, e, q)
+               lcollision = (q < rlim) 
+            end if
+         end if
+      end if
+      return
+   end function symba_collision_check_one
+
+
+end submodule s_symba_collision
\ No newline at end of file
diff --git a/src/symba/symba_discard.f90 b/src/symba/symba_discard.f90
index 8bafdb2b5..3f8ada6fe 100644
--- a/src/symba/symba_discard.f90
+++ b/src/symba/symba_discard.f90
@@ -12,14 +12,4 @@ module subroutine symba_discard_pl(self, system, param)
       return
    end subroutine symba_discard_pl
 
-   module subroutine symba_discard_tp(self, system, param)
-      implicit none
-      ! Arguments
-      class(symba_tp),              intent(inout) :: self   !! SyMBA test particle object
-      class(swiftest_nbody_system), intent(inout) :: system !! Swiftest nbody system object
-      class(swiftest_parameters),   intent(in)    :: param  !! Current run configuration parameters 
-
-      return
-   end subroutine symba_discard_tp
-
 end submodule s_symba_discard
\ No newline at end of file
diff --git a/src/symba/symba_encounter_check.f90 b/src/symba/symba_encounter_check.f90
index 17383f9c0..796df5d4a 100644
--- a/src/symba/symba_encounter_check.f90
+++ b/src/symba/symba_encounter_check.f90
@@ -82,7 +82,7 @@ module function symba_encounter_check_pltpenc(self, system, dt, irec) result(lan
       class is (symba_pl)
          select type(tp => system%tp)
          class is (symba_tp)
-            do i = 1, self%nenc
+            do concurrent(i = 1:self%nenc, self%status(i) == ACTIVE .and. self%level(i) == irec - 1)
                associate(index_i => self%index1(i), index_j => self%index2(i))
                   if (isplpl) then
                      xr(:) = pl%xh(:,index_j) - pl%xh(:,index_i)
diff --git a/src/symba/symba_io.f90 b/src/symba/symba_io.f90
index bebb225b5..acc3aabf9 100644
--- a/src/symba/symba_io.f90
+++ b/src/symba/symba_io.f90
@@ -51,6 +51,7 @@ module subroutine symba_io_param_reader(self, unit, iotype, v_list, iostat, ioms
          call random_seed(size = nseeds)
          if (allocated(param%seed)) deallocate(param%seed)
          allocate(param%seed(nseeds))
+         rewind(unit)
          do
             read(unit = unit, fmt = linefmt, iostat = iostat, end = 1) line
             line_trim = trim(adjustl(line))
@@ -121,8 +122,10 @@ module subroutine symba_io_param_reader(self, unit, iotype, v_list, iostat, ioms
             return
          end if
       end associate
-      return
 
+      iostat = 0
+
+      return
    end subroutine symba_io_param_reader
 
    module subroutine symba_io_param_writer(self, unit, iotype, v_list, iostat, iomsg) 
diff --git a/src/symba/symba_kick.f90 b/src/symba/symba_kick.f90
index 4da46f5a6..140eb0072 100644
--- a/src/symba/symba_kick.f90
+++ b/src/symba/symba_kick.f90
@@ -29,14 +29,14 @@ module subroutine symba_kick_getacch_pl(self, system, param, t, lbeg)
             associate(i => plplenc_list%index1(k), j => plplenc_list%index2(k))
                dx(:) = pl%xh(:, j) - pl%xh(:, i)
                rji2 = dot_product(dx(:), dx(:))
-               rlim2 = (pl%radius(i) + pl%radius(j))**2
-               if (rji2 > rlim2) then
+               !rlim2 = (pl%radius(i) + pl%radius(j))**2
+               !if (rji2 > rlim2) then
                   irij3 = 1.0_DP / (rji2 * sqrt(rji2))
                   faci = pl%Gmass(i) * irij3
                   facj = pl%Gmass(j) * irij3
                   pl%ah(:, i) = pl%ah(:, i) - facj * dx(:)
                   pl%ah(:, j) = pl%ah(:, j) + faci * dx(:)
-               end if
+               !end if
             end associate
          end do
          call helio_kick_getacch_pl(pl, system, param, t, lbeg)
@@ -72,13 +72,17 @@ module subroutine symba_kick_getacch_tp(self, system, param, t, lbeg)
             do k = 1, npltpenc
                associate(i => pltpenc_list%index1(k), j => pltpenc_list%index2(k))
                   if (tp%status(j) == ACTIVE) THEN
-                     dx(:) = tp%xh(:,j) - pl%xh(:,i)
+                     if (lbeg) then
+                        dx(:) = tp%xh(:,j) - pl%xbeg(:,i)
+                     else
+                        dx(:) = tp%xh(:,j) - pl%xend(:,i)
+                     end if
                      rji2 = dot_product(dx(:), dx(:))
-                     rlim2 = (pl%radius(i))**2
-                     if (rji2 > rlim2) then
+                     !rlim2 = (pl%radius(i))**2
+                     !if (rji2 > rlim2) then
                         fac = pl%Gmass(i) / (rji2 * sqrt(rji2))
                         tp%ah(:,j) = tp%ah(:,j) + fac * dx(:)
-                     end if
+                     !end if
                   end IF
                end associate
             end do
@@ -160,9 +164,9 @@ module subroutine symba_kick_pltpenc(self, system, dt, irec, sgn)
                         ir3 = 1.0_DP / (r2 * sqrt(r2))
                         fac = ir3
                      end if
-                     faci = fac * pl%mass(i)
+                     faci = fac * pl%Gmass(i)
                      if (isplpl) then
-                        facj = fac * pl%mass(j)
+                        facj = fac * pl%Gmass(j)
                         pl%ah(:,i) = pl%ah(:,i) + facj*dx(:)
                         pl%ah(:,j) = pl%ah(:,j) - faci*dx(:)
                      else
diff --git a/src/symba/symba_setup.f90 b/src/symba/symba_setup.f90
index 5ac26c220..51aaf69ba 100644
--- a/src/symba/symba_setup.f90
+++ b/src/symba/symba_setup.f90
@@ -100,10 +100,11 @@ module subroutine symba_setup_plplenc(self,n)
       return
    end subroutine symba_setup_plplenc
 
-   module subroutine symba_setup_system(self, param)
+   module subroutine symba_setup_initialize_system(self, param)
       !! author: David A. Minton
       !!
       !! Initialize an SyMBA nbody system from files and sets up the planetocentric structures.
+      !! This subroutine will also sort the massive bodies in descending order by mass
       !! 
       implicit none
       ! Arguments
@@ -114,13 +115,14 @@ module subroutine symba_setup_system(self, param)
 
       ! Call parent method
       associate(system => self)
-         call whm_setup_system(system, param)
+         call whm_setup_initialize_system(system, param)
          call system%mergeadd_list%setup(1)
          call system%mergesub_list%setup(1)
          call system%pltpenc_list%setup(1)
          call system%plplenc_list%setup(1)
          select type(pl => system%pl)
          class is (symba_pl)
+            call pl%sort("mass", ascending=.false.)
             select type(param)
             class is (symba_parameters)
                pl%lmtiny(:) = pl%Gmass(:) > param%MTINY
@@ -129,7 +131,7 @@ module subroutine symba_setup_system(self, param)
          end select
       end associate
       return
-   end subroutine symba_setup_system
+   end subroutine symba_setup_initialize_system
 
    module subroutine symba_setup_tp(self,n)
       !! author: David A. Minton
diff --git a/src/symba/symba_step.f90 b/src/symba/symba_step.f90
index 896af6ab4..4e7082c4b 100644
--- a/src/symba/symba_step.f90
+++ b/src/symba/symba_step.f90
@@ -52,7 +52,6 @@ module subroutine symba_step_interp_system(self, param, t, dt)
       real(DP),                   intent(in)    :: dt    !! Current stepsize
       ! Internals
       real(DP)                                  :: dth   !! Half step size
-      integer(I4B)                              :: irec  !! Recursion level
 
       dth = 0.5_DP * dt
       associate(system => self)
@@ -62,25 +61,23 @@ module subroutine symba_step_interp_system(self, param, t, dt)
             class is (symba_tp)
                select type(cb => system%cb)
                class is (symba_cb)
-                  irec = -1
                   call pl%vh2vb(cb)
                   call pl%lindrift(cb, dth, mask=(pl%status(:) == ACTIVE), lbeg=.true.)
                   call pl%kick(system, param, t, dth, mask=(pl%status(:) == ACTIVE), lbeg=.true.)
-                  call pl%drift(system, param, dt, mask=(pl%status(:) == ACTIVE .and. pl%levelg(:) == irec))
+                  call pl%drift(system, param, dt, mask=(pl%status(:) == ACTIVE .and. pl%levelg(:) == -1))
 
                   call tp%vh2vb(vbcb = -cb%ptbeg)
                   call tp%lindrift(cb, dth, mask=(tp%status(:) == ACTIVE), lbeg=.true.)
                   call tp%kick(system, param, t, dth, mask=(tp%status(:) == ACTIVE), lbeg=.true.)
-                  call tp%drift(system, param, dt, mask=(tp%status(:) == ACTIVE .and. tp%levelg(:) == irec))
+                  call tp%drift(system, param, dt, mask=(tp%status(:) == ACTIVE .and. tp%levelg(:) == -1))
 
-                  irec = 0
-                  call system%recursive_step(param, irec)
+                  call system%recursive_step(param, t, 0)
 
                   call pl%kick(system, param, t, dth, mask=(pl%status(:) == ACTIVE), lbeg=.false.)
                   call pl%vb2vh(cb)
                   call pl%lindrift(cb, dth, mask=(pl%status(:) == ACTIVE), lbeg=.false.)
 
-                  call tp%kick(system, param, t, dth, mask=(tp%status(:) == ACTIVE), lbeg=.true.)
+                  call tp%kick(system, param, t, dth, mask=(tp%status(:) == ACTIVE), lbeg=.false.)
                   call tp%vb2vh(vbcb = -cb%ptend)
                   call tp%lindrift(cb, dth, mask=(tp%status(:) == ACTIVE), lbeg=.false.)
                end select
@@ -90,7 +87,7 @@ module subroutine symba_step_interp_system(self, param, t, dt)
       return
    end subroutine symba_step_interp_system
 
-   module recursive subroutine symba_step_recur_system(self, param, ireci)
+   module recursive subroutine symba_step_recur_system(self, param, t, ireci)
       !! author: David A. Minton
       !!
       !! Step interacting planets and active test particles ahead in democratic heliocentric coordinates at the current
@@ -102,9 +99,10 @@ module recursive subroutine symba_step_recur_system(self, param, ireci)
       ! Arguments
       class(symba_nbody_system),  intent(inout) :: self  !! SyMBA nbody system object
       class(swiftest_parameters), intent(inout) :: param !! Current run configuration parameters 
-      integer(I4B), value,        intent(in)    :: ireci !! input recursion level
+      real(DP),                   value         :: t
+      integer(I4B),               value         :: ireci !! input recursion level
       ! Internals
-      integer(I4B) :: i, j, irecp, nloops, sgn
+      integer(I4B) :: i, j, irecp, nloops
       real(DP) :: dtl, dth
       real(DP), dimension(NDIM) :: xr, vr
       logical :: lencounter
@@ -130,29 +128,37 @@ module recursive subroutine symba_step_recur_system(self, param, ireci)
                end if
                do j = 1, nloops
                   lencounter = plplenc_list%encounter_check(system, dtl, irecp) .or. pltpenc_list%encounter_check(system, dtl, irecp)
-                  sgn = 1
-                  call plplenc_list%kick(system, dth, irecp, sgn)
-                  call pltpenc_list%kick(system, dth, irecp, sgn)
+                  call plplenc_list%kick(system, dth, irecp, 1)
+                  call pltpenc_list%kick(system, dth, irecp, 1)
                   if (ireci /= 0) then
-                     sgn = -1
-                     call plplenc_list%kick(system, dth, irecp, sgn)
-                     call pltpenc_list%kick(system, dth, irecp, sgn)
+                     call plplenc_list%kick(system, dth, irecp, -1)
+                     call pltpenc_list%kick(system, dth, irecp, -1)
                   end if
 
                   call pl%drift(system, param, dtl, mask=(pl%status(:) == ACTIVE .and. pl%levelg(:) == ireci))
                   call tp%drift(system, param, dtl, mask=(tp%status(:) == ACTIVE .and. tp%levelg(:) == ireci))
 
-                  if (lencounter) call system%recursive_step(param, irecp)
+                  if (lencounter) call system%recursive_step(param, t+dth,irecp)
 
-                  sgn = 1
-                  call plplenc_list%kick(system, dth, irecp, sgn)
-                  call pltpenc_list%kick(system, dth, irecp, sgn)
+                  call plplenc_list%kick(system, dth, irecp, 1)
+                  call pltpenc_list%kick(system, dth, irecp, 1)
                   if (ireci /= 0) then
-                     sgn = -1
-                     call plplenc_list%kick(system, dth, irecp, sgn)
-                     call pltpenc_list%kick(system, dth, irecp, sgn)
+                     call plplenc_list%kick(system, dth, irecp, -1)
+                     call pltpenc_list%kick(system, dth, irecp, -1)
                   end if
-
+                  if (param%lclose) then
+                     call plplenc_list%collision_check(system, param, t+dtl, dtl, ireci) 
+                     call pltpenc_list%collision_check(system, param, t+dtl, dtl, ireci) 
+                  end if
+                  associate (plind1 => plplenc_list%index1(1:plplenc_list%nenc), &
+                             plind2 => plplenc_list%index2(1:plplenc_list%nenc), &
+                             plind3 => pltpenc_list%index1(1:pltpenc_list%nenc), &
+                             tpind  => pltpenc_list%index2(1:pltpenc_list%nenc))
+                     where(pl%levelg([plind1,plind2,plind3]) == irecp) pl%levelg(:) = ireci
+                     where(tp%levelg(tpind) == irecp) tp%levelg(:) = ireci
+                  end associate
+                  where(plplenc_list%level(1:plplenc_list%nenc) == irecp) plplenc_list%level(:) = ireci
+                  where(pltpenc_list%level(1:pltpenc_list%nenc) == irecp) pltpenc_list%level(:) = ireci
                end do
             end select
          end select
diff --git a/src/symba/symba_util.f90 b/src/symba/symba_util.f90
index 031ae4ae5..a89da7b6a 100644
--- a/src/symba/symba_util.f90
+++ b/src/symba/symba_util.f90
@@ -65,5 +65,166 @@ module subroutine symba_util_resize_pltpenc(self, nrequested)
       return
    end subroutine symba_util_resize_pltpenc
 
+   module subroutine symba_util_sort_pl(self, sortby, ascending)
+      !! author: David A. Minton
+      !!
+      !! Sort a Swiftest test particle object  in-place. 
+      !! sortby is a string indicating which array component to sort.
+      implicit none
+      ! Arguments
+      class(symba_pl), intent(inout) :: self      !! Symba massive body object
+      character(*),    intent(in)    :: sortby    !! Sorting attribute
+      logical,         intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      ! Internals
+      integer(I4B), dimension(self%nbody) :: ind
+
+      associate(pl => self, npl => self%nbody)
+         select case(sortby)
+         case("nplenc")
+            if (ascending) then
+               call util_sort(pl%nplenc(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%nplenc(1:npl), ind(1:npl))
+            end if
+         case("ntpenc")
+            if (ascending) then
+               call util_sort(pl%ntpenc(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%ntpenc(1:npl), ind(1:npl))
+            end if
+         case("levelg")
+            if (ascending) then
+               call util_sort(pl%levelg(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%levelg(1:npl), ind(1:npl))
+            end if
+         case("levelm")
+            if (ascending) then
+               call util_sort(pl%levelm(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%levelm(1:npl), ind(1:npl))
+            end if
+         case("peri")
+            if (ascending) then
+               call util_sort(pl%peri(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%peri(1:npl), ind(1:npl))
+            end if
+         case("atp")
+            if (ascending) then
+               call util_sort(pl%atp(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%atp(1:npl), ind(1:npl))
+            end if
+         case default
+            call util_sort_pl(pl, sortby, ascending)
+            return
+         end select
+         call pl%rearrange(ind)
+      end associate
+      return
+   end subroutine symba_util_sort_pl
+
+   module subroutine symba_util_sort_tp(self, sortby, ascending)
+      !! author: David A. Minton
+      !!
+      !! Sort a Swiftest test particle object  in-place. 
+      !! sortby is a string indicating which array component to sort.
+      implicit none
+      ! Arguments
+      class(symba_tp), intent(inout) :: self      !! Swiftest test particle object
+      character(*),    intent(in)    :: sortby    !! Sorting attribute
+      logical,         intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      ! Internals
+      integer(I4B), dimension(self%nbody) :: ind
+
+      associate(tp => self, ntp => self%nbody)
+         select case(sortby)
+         case("nplenc")
+            if (ascending) then
+               call util_sort(tp%nplenc(1:ntp), ind(1:ntp))
+            else
+               call util_sort(-tp%nplenc(1:ntp), ind(1:ntp))
+            end if
+         case("levelg")
+            if (ascending) then
+               call util_sort(tp%levelg(1:ntp), ind(1:ntp))
+            else
+               call util_sort(-tp%levelg(1:ntp), ind(1:ntp))
+            end if
+         case("levelm")
+            if (ascending) then
+               call util_sort(tp%levelm(1:ntp), ind(1:ntp))
+            else
+               call util_sort(-tp%levelm(1:ntp), ind(1:ntp))
+            end if
+         case default
+            call util_sort_tp(tp, sortby, ascending)
+            return
+         end select
+         call tp%rearrange(ind)
+      end associate
+      return
+   end subroutine symba_util_sort_tp
+
+   module subroutine symba_util_sort_rearrange_pl(self, ind)
+      !! author: David A. Minton
+      !!
+      !! Rearrange SyMBA massive body structure in-place from an index list.
+      !! This is a helper utility used to make polymorphic sorting work on Swiftest structures.
+      implicit none
+      ! Arguments
+      class(symba_pl),               intent(inout) :: self !! Symba massive body object
+      integer(I4B),    dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      ! Internals
+      class(symba_pl), allocatable :: pl_sorted  !! Temporary holder for sorted body
+      integer(I4B) :: i, j
+
+      associate(pl => self, npl => self%nbody)
+         call util_sort_rearrange_pl(pl,ind)
+         allocate(pl_sorted, source=self)
+         pl%lcollision(1:npl) = pl_sorted%lcollision(ind(1:npl))
+         pl%lencounter(1:npl) = pl_sorted%lencounter(ind(1:npl))
+         pl%nplenc(1:npl) = pl_sorted%nplenc(ind(1:npl))
+         pl%ntpenc(1:npl) = pl_sorted%ntpenc(ind(1:npl))
+         pl%levelg(1:npl) = pl_sorted%levelg(ind(1:npl))
+         pl%levelm(1:npl) = pl_sorted%levelm(ind(1:npl))
+         pl%isperi(1:npl) = pl_sorted%isperi(ind(1:npl))
+         pl%peri(1:npl) = pl_sorted%peri(ind(1:npl))
+         pl%atp(1:npl) = pl_sorted%atp(ind(1:npl))
+         pl%info(1:npl) = pl_sorted%info(ind(1:npl))
+         pl%kin(1:npl) = pl_sorted%kin(ind(1:npl))
+         do i = 1, npl
+            do j = 1, pl%kin(i)%nchild
+               pl%kin(i)%child(j) = ind(pl%kin(i)%child(j))
+            end do
+         end do
+         deallocate(pl_sorted)
+      end associate
+      return
+   end subroutine symba_util_sort_rearrange_pl
+
+   module subroutine symba_util_sort_rearrange_tp(self, ind)
+      !! author: David A. Minton
+      !!
+      !! Rearrange SyMBA test particle object  in-place from an index list.
+      !! This is a helper utility used to make polymorphic sorting work on Swiftest structures.
+      implicit none
+      ! Arguments
+      class(symba_tp),               intent(inout) :: self !! Symba massive body object
+      integer(I4B),    dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      ! Internals
+      class(symba_tp), allocatable :: tp_sorted  !! Temporary holder for sorted body
+
+      associate(tp => self, ntp => self%nbody)
+         call util_sort_rearrange_tp(tp,ind)
+         allocate(tp_sorted, source=self)
+         tp%nplenc(1:ntp) = tp_sorted%nplenc(ind(1:ntp))
+         tp%levelg(1:ntp) = tp_sorted%levelg(ind(1:ntp))
+         tp%levelm(1:ntp) = tp_sorted%levelm(ind(1:ntp))
+         deallocate(tp_sorted)
+      end associate
+      return
+   end subroutine symba_util_sort_rearrange_tp
 
 end submodule s_symba_util
\ No newline at end of file
diff --git a/src/util/util_index.f90 b/src/util/util_index.f90
deleted file mode 100644
index fcece8809..000000000
--- a/src/util/util_index.f90
+++ /dev/null
@@ -1,103 +0,0 @@
-submodule (swiftest_classes) s_util_index
-   use swiftest
-contains
-   module subroutine util_index(arr, index)
-      !! author: David A. Minton
-      !!
-      !! Index input real array into ascending numerical order using Quicksort algorithm
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: util_index.f90
-      !! Adapted from Numerical Recipes in Fortran 90: The Art of Parallel Scientific Computing, by Press, Teukolsky,
-      !!      Vetterling, and Flannery, 2nd ed., pp. 1173-4
-      implicit none
-      ! Arguments
-      integer(I4B),  dimension(:), intent(out) :: index
-      real(DP),      dimension(:), intent(in)   :: arr
-      ! Internals
-      integer(I4B), parameter         :: nn = 15, nstack = 50
-      integer(I4B)                    :: n, k, i, j, indext, jstack, l, r, dum
-      integer(I4B), dimension(nstack) :: istack
-      real(DP)                        :: a
-
-      n = size(arr)
-      if (n /= size(index)) then
-         write(*, *) "Swiftest Error:"
-         write(*, *) "   array size mismatch in util_index"
-         call util_exit(FAILURE)
-      end if
-      index = arth(1, 1, n)
-      jstack = 0
-      ! l is the counter ie 'the one we are at'
-      l = 1
-      ! r is the length of the array ie 'the total number of particles'
-      r = n
-      do
-         if ((r - l) < nn) then
-            do j = l + 1, r
-               indext = index(j)
-               a = arr(indext)
-               do i = j - 1, l, -1
-                  if (arr(index(i)) <= a) exit
-                  index(i+1) = index(i)
-               end do
-               index(i+1) = indext
-            end do
-            if (jstack == 0) return
-            r = istack(jstack)
-            l = istack(jstack-1)
-            jstack = jstack - 2
-         else
-            k = (l + r)/2
-            dum = index(k); index(k) = index(l+1); index(l+1) = dum
-            ! if the mass of the particle we are at in our counting is greater than the mass of the last particle then put the particle we are at above the last one
-            if (arr(index(l)) > arr(index(r))) then
-               dum = index(l); index(l) = index(r); index(r) = dum
-            end if
-            ! if the mass of the particle above the one we are at in our counting is greater than the last particle then put that particle above the last one
-            if (arr(index(l+1)) > arr(index(r))) then
-               dum = index(l+1); index(l+1) = index(r); index(r) = dum
-            end if
-            ! if the mass of teh particle we are at in our counting is greater than the one above it, then put it above the one above it
-            if (arr(index(l)) > arr(index(l+1))) then
-               dum = index(l); index(l) = index(l+1); index(l+1) = dum
-            end if
-            i = l + 1
-            j = r
-            indext = index(l+1)
-            a = arr(indext)
-            do
-               do
-                  i = i + 1
-                  if (arr(index(i)) >= a) exit
-               end do
-               do
-                  j = j - 1
-                  if (arr(index(j)) <= a) exit
-            end do
-            if (j < i) exit
-            dum = index(i); index(i) = index(j); index(j) = dum
-         end do
-         index(l+1) = index(j)
-         index(j) = indext
-         jstack = jstack + 2
-         if (jstack > nstack) then
-            write(*, *) "Swiftest Error:"
-            write(*, *) "   nstack too small in util_sort"
-            call util_exit(FAILURE)
-         end if
-         if ((r - i + 1) >= (j - l)) then
-            istack(jstack) = r
-            istack(jstack-1) = i
-            r = j - 1
-         else
-            istack(jstack) = j - 1
-            istack(jstack-1) = l
-            l = i
-         end if
-      end if
-   end do
-
-   return
-
-   end subroutine util_index
-end submodule s_util_index
diff --git a/src/util/util_sort.f90 b/src/util/util_sort.f90
index 126f4f12d..34fe600ed 100644
--- a/src/util/util_sort.f90
+++ b/src/util/util_sort.f90
@@ -1,263 +1,419 @@
 submodule (swiftest_classes) s_util_sort
    use swiftest
 contains
-   module subroutine util_sort_dp(arr)
+   module subroutine util_sort_body(self, sortby, ascending)
       !! author: David A. Minton
       !!
-      !! Sort input double precision array into ascending numerical order using Quicksort algorithm
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: util_sort_dp.f90
-      !! Adapted from Numerical Recipes in Fortran 90: The Art of Parallel Scientific Computing, by Press, Teukolsky,
-      !!        Vetterling, and Flannery, 2nd ed., pp. 1169-70
+      !! Sort a Swiftest body structure in-place. 
+      !! sortby is a string indicating which array component to sort.
       implicit none
       ! Arguments
-      real(DP), dimension(:), intent(inout) :: arr
+      class(swiftest_body), intent(inout) :: self      !! Swiftest body object
+      character(*),         intent(in)    :: sortby    !! Sorting attribute
+      logical,              intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
       ! Internals
-      integer(I4B), parameter :: NN = 15, NSTACK = 50
-      real(DP)                :: a, dum
-      integer(I4B)            :: n, k, i, j, jstack, l, r
-      integer(I4B), dimension(NSTACK) :: istack
+      integer(I4B), dimension(self%nbody) :: ind
 
-   ! executable code
-      n = size(arr)
-      jstack = 0
-      l = 1
-      r = n
-      do
-         if ((r - l) < NN) then
-            do j = l + 1, r
-               a = arr(j)
-               do i = j - 1, l, -1
-                  if (arr(i) <= a) exit
-                  arr(i+1) = arr(i)
-               end do
-               arr(i+1) = a
-            end do
-            if (jstack == 0) return
-            r = istack(jstack)
-            l = istack(jstack-1)
-            jstack = jstack - 2
-         else
-            k = (l + r)/2
-            dum = arr(k); arr(k) = arr(l+1); arr(l+1) = dum
-            if (arr(l) > arr(r)) then
-               dum = arr(l); arr(l) = arr(r); arr(r) = dum
+      associate(body => self, n => self%nbody)
+         select case(sortby)
+         case("id")
+            if (ascending) then
+               call util_sort(body%id(1:n), ind(1:n))
+            else
+               call util_sort(-body%id(1:n), ind(1:n))
             end if
-            if (arr(l+1) > arr(r)) then
-               dum = arr(l+1); arr(l+1) = arr(r); arr(r) = dum
+         case("a")
+            if (ascending) then
+               call util_sort(body%a(1:n), ind(1:n))
+            else
+               call util_sort(-body%a(1:n), ind(1:n))
             end if
-            if (arr(l) > arr(l+1)) then
-               dum = arr(l); arr(l) = arr(l+1); arr(l+1) = dum
+         case("e")
+            if (ascending) then
+               call util_sort(body%e(1:n), ind(1:n))
+            else
+               call util_sort(-body%e(1:n), ind(1:n))
             end if
-            i = l + 1
-            j = r
-            a = arr(l+1)
-            do
-               do
-                  i = i + 1
-                  if (arr(i) >= a) exit
-               end do
-               do
-                  j = j - 1
-                  if (arr(j) <= a) exit
-               end do
-               if (j < i) exit
-               dum = arr(i); arr(i) = arr(j); arr(j) = dum
-            end do
-            arr(l+1) = arr(j)
-            arr(j) = a
-            jstack = jstack + 2
-            if (jstack > NSTACK) then
-               write(*, *) "Swiftest Error:"
-               write(*, *) "   NSTACK too small in util_sort_I4B"
-               call util_exit(FAILURE)
+         case("inc")
+            if (ascending) then
+               call util_sort(body%inc(1:n), ind(1:n))
+            else
+               call util_sort(-body%inc(1:n), ind(1:n))
             end if
-            if ((r - i + 1) >= (j - l)) then
-               istack(jstack) = r
-               istack(jstack-1) = i
-               r = j - 1
+         case("capom")
+            if (ascending) then
+               call util_sort(body%capom(1:n), ind(1:n))
             else
-               istack(jstack) = j - 1
-               istack(jstack-1) = l
-               l = i
+               call util_sort(-body%capom(1:n), ind(1:n))
             end if
-         end if
-      end do
+         case("mu")
+            if (ascending) then
+               call util_sort(body%mu(1:n), ind(1:n))
+            else
+               call util_sort(-body%mu(1:n), ind(1:n))
+            end if
+         case default
+            write(*,*) 'Cannot sort structure by component '//trim(adjustl(sortby))
+            return
+         end select
+         call body%rearrange(ind)
+      end associate
 
       return
+   end subroutine util_sort_body
 
-   end subroutine util_sort_dp
-
-   module subroutine util_sort_i4b(arr)
+   module subroutine util_sort_pl(self, sortby, ascending)
       !! author: David A. Minton
       !!
-      !! Sort input double precision array into ascending numerical order using Quicksort algorithm
-      !!
-      !! Adapted from David E. Kaufmann's Swifter routine: util_sort_i4b.f90
-      !! Adapted from Numerical Recipes in Fortran 90: The Art of Parallel Scientific Computing, by Press, Teukolsky,
-      !!      Vetterling, and Flannery, 2nd ed., pp. 1169-70
+      !! Sort a Swiftest massive body object in-place. 
+      !! sortby is a string indicating which array component to sort.
       implicit none
       ! Arguments
-      integer(I4B), dimension(:), intent(inout) :: arr
+      class(swiftest_pl), intent(inout) :: self      !! Swiftest massive body object
+      character(*),       intent(in)    :: sortby    !! Sorting attribute
+      logical,            intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
       ! Internals
-      integer(I4B), parameter :: NN = 15, NSTACK = 50
-      integer(I4B)            :: a, n, k, i, j, jstack, l, r, dum
-      integer(I4B), dimension(NSTACK) :: istack
-   
-   ! executable code
-      n = size(arr)
-      jstack = 0
-      l = 1
-      r = n
-      do
-         if ((r - l) < NN) then
-            do j = l + 1, r
-               a = arr(j)
-               do i = j - 1, l, -1
-                  if (arr(i) <= a) exit
-                  arr(i+1) = arr(i)
-               end do
-               arr(i+1) = a
-            end do
-            if (jstack == 0) return
-            r = istack(jstack)
-            l = istack(jstack-1)
-            jstack = jstack - 2
-         else
-            k = (l + r)/2
-            dum = arr(k); arr(k) = arr(l+1); arr(l+1) = dum
-            if (arr(l) > arr(r)) then
-               dum = arr(l); arr(l) = arr(r); arr(r) = dum
+      integer(I4B), dimension(self%nbody) :: ind
+
+      associate(pl => self, npl => self%nbody)
+         select case(sortby)
+         case("Gmass","mass")
+            if (ascending) then
+               call util_sort(pl%Gmass(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%Gmass(1:npl), ind(1:npl))
             end if
-            if (arr(l+1) > arr(r)) then
-               dum = arr(l+1); arr(l+1) = arr(r); arr(r) = dum
+         case("rhill")
+            if (ascending) then
+               call util_sort(pl%rhill(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%rhill(1:npl), ind(1:npl))
             end if
-            if (arr(l) > arr(l+1)) then
-               dum = arr(l); arr(l) = arr(l+1); arr(l+1) = dum
+         case("radius")
+            if (ascending) then
+               call util_sort(pl%radius(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%radius(1:npl), ind(1:npl))
             end if
-            i = l + 1
-            j = r
-            a = arr(l+1)
-            do
-               do
-                  i = i + 1
-                  if (arr(i) >= a) exit
-               end do
-               do
-                  j = j - 1
-                  if (arr(j) <= a) exit
-               end do
-               if (j < i) exit
-               dum = arr(i); arr(i) = arr(j); arr(j) = dum
-            end do
-            arr(l+1) = arr(j)
-            arr(j) = a
-            jstack = jstack + 2
-            if (jstack > NSTACK) then
-               write(*, *) "Swiftest Error:"
-               write(*, *) "   NSTACK too small in util_sort_i4b"
-               call util_exit(FAILURE)
+         case("density")
+            if (ascending) then
+               call util_sort(pl%density(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%density(1:npl), ind(1:npl))
             end if
-            if ((r - i + 1) >= (j - l)) then
-               istack(jstack) = r
-               istack(jstack-1) = i
-               r = j - 1
+         case("k2")
+            if (ascending) then
+               call util_sort(pl%k2(1:npl), ind(1:npl))
             else
-               istack(jstack) = j - 1
-               istack(jstack-1) = l
-               l = i
+               call util_sort(-pl%k2(1:npl), ind(1:npl))
             end if
-         end if
-      end do
-   
+         case("Q")
+            if (ascending) then
+               call util_sort(pl%Q(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%Q(1:npl), ind(1:npl))
+            end if
+         case("tlag")
+            if (ascending) then
+               call util_sort(pl%tlag(1:npl), ind(1:npl))
+            else
+               call util_sort(-pl%tlag(1:npl), ind(1:npl))
+            end if
+         case default
+            call util_sort_body(pl, sortby, ascending)
+            return
+         end select
+         call pl%rearrange(ind)
+      end associate
+
       return
-   
-      end subroutine util_sort_i4b
+   end subroutine util_sort_pl
+
+   module subroutine util_sort_tp(self, sortby, ascending)
+      !! author: David A. Minton
+      !!
+      !! Sort a Swiftest test particle object  in-place. 
+      !! sortby is a string indicating which array component to sort.
+      implicit none
+      ! Arguments
+      class(swiftest_tp), intent(inout) :: self      !! Swiftest test particle object
+      character(*),       intent(in)    :: sortby    !! Sorting attribute
+      logical,            intent(in)    :: ascending !! Logical flag indicating whether or not the sorting should be in ascending or descending order
+      ! Internals
+      integer(I4B), dimension(self%nbody) :: ind
 
-      module subroutine util_sort_sp(arr)
-         !! author: David A. Minton
-         !!
-         !! Sort input single precision array into ascending numerical order using Quicksort algorithm
-         !!
-         !! Adapted from David E. Kaufmann's Swifter routine: util_sort_DP.f90
-         !! Adapted from Numerical Recipes in Fortran 90: The Art of Parallel Scientific Computing, by Press, Teukolsky,
-         !!      Vetterling, and Flannery, 2nd ed., pp. 1169-70
-         implicit none
-         ! Arguments
-         real(SP), dimension(:), intent(inout) :: arr
-         ! Internals
-         integer(I4B), parameter       :: NN = 15, NSTACK = 50
-         real(SP)                :: a, dum
-         integer(I4B)            :: n, k, i, j, jstack, l, r
-         integer(I4B), dimension(NSTACK) :: istack
-   
-      ! executable code
-         n = size(arr)
-         jstack = 0
-         l = 1
-         r = n
-         do
-            if ((r - l) < NN) then
-               do j = l + 1, r
-                  a = arr(j)
-                  do i = j - 1, l, -1
-                     if (arr(i) <= a) exit
-                     arr(i+1) = arr(i)
-                  end do
-                  arr(i+1) = a
-               end do
-               if (jstack == 0) return
-               r = istack(jstack)
-               l = istack(jstack-1)
-               jstack = jstack - 2
+      associate(tp => self, ntp => self%nbody)
+         select case(sortby)
+         case("peri")
+            if (ascending) then
+               call util_sort(tp%peri(1:ntp), ind(1:ntp))
             else
-               k = (l + r)/2
-               dum = arr(k); arr(k) = arr(l+1); arr(l+1) = dum
-               if (arr(l) > arr(r)) then
-                  dum = arr(l); arr(l) = arr(r); arr(r) = dum
-               end if
-               if (arr(l+1) > arr(r)) then
-                  dum = arr(l+1); arr(l+1) = arr(r); arr(r) = dum
-               end if
-               if (arr(l) > arr(l+1)) then
-                  dum = arr(l); arr(l) = arr(l+1); arr(l+1) = dum
-               end if
-               i = l + 1
-               j = r
-               a = arr(l+1)
-               do
-                  do
-                     i = i + 1
-                     if (arr(i) >= a) exit
-                  end do
-                  do
-                     j = j - 1
-                     if (arr(j) <= a) exit
-                  end do
-                  if (j < i) exit
-                  dum = arr(i); arr(i) = arr(j); arr(j) = dum
-               end do
-               arr(l+1) = arr(j)
-               arr(j) = a
-               jstack = jstack + 2
-               if (jstack > NSTACK) then
-                  write(*, *) "Swiftest Error:"
-                  write(*, *) "   NSTACK too small in util_sort_I4B"
-                  call util_exit(FAILURE)
-               end if
-               if ((r - i + 1) >= (j - l)) then
-                  istack(jstack) = r
-                  istack(jstack-1) = i
-                  r = j - 1
-               else
-                  istack(jstack) = j - 1
-                  istack(jstack-1) = l
-                  l = i
-               end if
+               call util_sort(-tp%peri(1:ntp), ind(1:ntp))
             end if
+         case("atp")
+            if (ascending) then
+               call util_sort(tp%atp(1:ntp), ind(1:ntp))
+            else
+               call util_sort(-tp%atp(1:ntp), ind(1:ntp))
+            end if
+         case default
+            call util_sort_body(tp, sortby, ascending)
+            return
+         end select
+
+         call tp%rearrange(ind)
+      end associate
+
+      return
+   end subroutine util_sort_tp
+
+   module subroutine util_sort_rearrange_body(self, ind)
+      !! author: David A. Minton
+      !!
+      !! Rearrange Swiftest body structure in-place from an index list.
+      !! This is a helper utility used to make polymorphic sorting work on Swiftest structures.
+      implicit none
+      ! Arguments
+      class(swiftest_body),               intent(inout) :: self !! Swiftest body object
+      integer(I4B),         dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      ! Internals
+      class(swiftest_body), allocatable :: body_sorted  !! Temporary holder for sorted body
+
+      associate(n => self%nbody)
+         allocate(body_sorted, source=self)
+         self%id(1:n) = body_sorted%id(ind(1:n))
+         self%name(1:n) = body_sorted%name(ind(1:n))
+         self%status(1:n) = body_sorted%status(ind(1:n))
+         self%ldiscard(1:n) = body_sorted%ldiscard(ind(1:n))
+         self%xh(:,1:n) = body_sorted%xh(:,ind(1:n))
+         self%vh(:,1:n) = body_sorted%vh(:,ind(1:n))
+         self%xb(:,1:n) = body_sorted%xb(:,ind(1:n))
+         self%vb(:,1:n) = body_sorted%vb(:,ind(1:n))
+         self%ah(:,1:n) = body_sorted%ah(:,ind(1:n))
+         self%aobl(:,1:n) = body_sorted%aobl(:,ind(1:n))
+         self%atide(:,1:n) = body_sorted%atide(:,ind(1:n))
+         self%agr(:,1:n) = body_sorted%agr(:,ind(1:n))
+         self%ir3h(1:n) = body_sorted%ir3h(ind(1:n))
+         self%a(1:n) = body_sorted%a(ind(1:n))
+         self%e(1:n) = body_sorted%e(ind(1:n))
+         self%inc(1:n) = body_sorted%inc(ind(1:n))
+         self%capom(1:n) = body_sorted%capom(ind(1:n))
+         self%omega(1:n) = body_sorted%omega(ind(1:n))
+         self%capm(1:n) = body_sorted%capm(ind(1:n))
+         self%mu(1:n) = body_sorted%mu(ind(1:n))
+         deallocate(body_sorted)
+      end associate
+      return
+   end subroutine util_sort_rearrange_body
+
+   module subroutine util_sort_rearrange_pl(self, ind)
+      !! author: David A. Minton
+      !!
+      !! Rearrange Swiftest massive body structure in-place from an index list.
+      !! This is a helper utility used to make polymorphic sorting work on Swiftest structures.
+      implicit none
+      class(swiftest_pl),               intent(inout) :: self !! Swiftest massive body object
+      integer(I4B),       dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      ! Internals
+      class(swiftest_pl), allocatable :: pl_sorted  !! Temporary holder for sorted body
+
+      associate(pl => self, npl => self%nbody)
+         call util_sort_rearrange_body(pl,ind)
+         allocate(pl_sorted, source=self)
+         pl%mass(1:npl) = pl_sorted%mass(ind(1:npl))
+         pl%Gmass(1:npl) = pl_sorted%Gmass(ind(1:npl))
+         pl%rhill(1:npl) = pl_sorted%rhill(ind(1:npl))
+         pl%radius(1:npl) = pl_sorted%radius(ind(1:npl))
+         pl%xbeg(:,1:npl) = pl_sorted%xbeg(:,ind(1:npl))
+         pl%xend(:,1:npl) = pl_sorted%xend(:,ind(1:npl))
+         pl%vbeg(:,1:npl) = pl_sorted%vbeg(:,ind(1:npl))
+         pl%density(1:npl) = pl_sorted%density(ind(1:npl))
+         pl%Ip(:,1:npl) = pl_sorted%Ip(:,ind(1:npl))
+         pl%rot(:,1:npl) = pl_sorted%rot(:,ind(1:npl))
+         pl%k2(1:npl) = pl_sorted%k2(ind(1:npl))
+         pl%Q(1:npl) = pl_sorted%Q(ind(1:npl))
+         pl%tlag(1:npl) = pl_sorted%tlag(ind(1:npl))
+         deallocate(pl_sorted)
+      end associate
+      return
+   end subroutine util_sort_rearrange_pl
+
+   module subroutine util_sort_rearrange_tp(self, ind)
+      !! author: David A. Minton
+      !!
+      !! Rearrange Swiftest massive body structure in-place from an index list.
+      !! This is a helper utility used to make polymorphic sorting work on Swiftest structures.
+      implicit none
+      ! Arguments
+      class(swiftest_tp),                 intent(inout) :: self !! Swiftest test particle object
+      integer(I4B),         dimension(:), intent(in)    :: ind  !! Index array used to restructure the body (should contain all 1:n index values in the desired order)
+      ! Internals
+      class(swiftest_tp), allocatable :: tp_sorted  !! Temporary holder for sorted body
+
+      associate(tp => self, ntp => self%nbody)
+         call util_sort_rearrange_body(tp,ind)
+         allocate(tp_sorted, source=self)
+         tp%isperi(1:ntp) = tp_sorted%isperi(ind(1:ntp))
+         tp%peri(1:ntp) = tp_sorted%peri(ind(1:ntp))
+         tp%atp(1:ntp) = tp_sorted%atp(ind(1:ntp))
+         deallocate(tp_sorted)
+      end associate
+      return
+   end subroutine util_sort_rearrange_tp
+
+   module subroutine util_sort_dp(arr)
+      !! author: David A. Minton
+      !!
+      !! Sort input double precision array in place into ascending numerical order using insertion sort.
+      !! This algorithm works well for partially sorted arrays (which is usually the case here)
+      !!
+      implicit none
+      ! Arguments
+      real(DP), dimension(:), intent(inout) :: arr
+      ! Internals
+      real(DP)                :: tmp
+      integer(I4B)            :: n, i, j
+
+      n = size(arr)
+      do i = 2, n
+         tmp = arr(i)
+         do j = i - 1, 1, -1
+            if (arr(j) <= tmp) exit
+            arr(j + 1) = arr(j)
+         end do
+         arr(j + 1) = tmp
+      end do
+      return
+   end subroutine util_sort_dp
+
+   module subroutine util_sort_index_dp(arr, ind)
+      !! author: David A. Minton
+      !!
+      !! Sort input double precision array by index in ascending numerical order using insertion sort.
+      !! This algorithm works well for partially sorted arrays (which is usually the case here)
+      !!
+      implicit none
+      ! Arguments
+      real(DP), dimension(:), intent(in)  :: arr
+      integer(I4B), dimension(:), intent(out) :: ind
+      ! Internals
+      real(DP) :: tmp
+      integer(I4B) :: n, i, j
+
+      n = size(arr)
+      ind = [(i, i=1, n)]
+      do i = 2, n
+         tmp = arr(ind(i))
+         do j = i - 1, 1, -1
+            if (arr(ind(j)) <= tmp) exit
+            ind(j + 1) = ind(j)
+         end do
+         ind(j + 1) = i
+      end do
+      return
+   end subroutine util_sort_index_dp
+
+   module subroutine util_sort_i4b(arr)
+      !! author: David A. Minton
+      !!
+      !! Sort input integer array in place into ascending numerical order using insertion sort.
+      !! This algorithm works well for partially sorted arrays (which is usually the case here)
+      !!
+      implicit none
+      ! Arguments
+      integer(I4B), dimension(:), intent(inout) :: arr
+      ! Internals
+      integer(I4B) :: tmp
+      integer(I4B) :: n, i, j
+
+      n = size(arr)
+      do i = 2, n
+         tmp = arr(i)
+         do j = i - 1, 1, -1
+            if (arr(j) <= tmp) exit
+            arr(j + 1) = arr(j)
+         end do
+         arr(j + 1) = tmp
+      end do
+      return
+   end subroutine util_sort_i4b
+
+   module subroutine util_sort_index_i4b(arr, ind)
+      !! author: David A. Minton
+      !!
+      !! Sort input integer array by index in ascending numerical order using insertion sort.
+      !! This algorithm works well for partially sorted arrays (which is usually the case here)
+      !!
+      implicit none
+      ! Arguments
+      integer(I4B), dimension(:), intent(in)  :: arr
+      integer(I4B), dimension(:), intent(out) :: ind
+      ! Internals
+      integer(I4B) :: tmp
+      integer(I4B) :: n, i, j
+
+      n = size(arr)
+      ind = [(i, i=1, n)]
+      do i = 2, n
+         tmp = arr(ind(i))
+         do j = i - 1, 1, -1
+            if (arr(ind(j)) <= tmp) exit
+            ind(j + 1) = ind(j)
+         end do
+         ind(j + 1) = i
+      end do
+      return
+   end subroutine util_sort_index_i4b
+
+   module subroutine util_sort_sp(arr)
+      !! author: David A. Minton
+      !!
+      !! Sort input single precision array in place into ascending numerical order using insertion sort.
+      !! This algorithm works well for partially sorted arrays (which is usually the case here)
+      !
+      implicit none
+      ! Arguments
+      real(SP), dimension(:), intent(inout) :: arr
+      ! Internals
+      real(SP) :: tmp
+      integer(I4B) :: n, i, j
+
+      n = size(arr)
+      do i = 2, n
+         tmp = arr(i)
+         do j = i - 1, 1, -1
+            if (arr(j) <= tmp) exit
+            arr(j + 1) = arr(j)
          end do
-   
-         return
-   
-      end subroutine util_sort_sp
+         arr(j + 1) = tmp
+      end do
+      return
+   end subroutine util_sort_sp
+
+   module subroutine util_sort_index_sp(arr, ind)
+      !! author: David A. Minton
+      !!
+      !! Sort input single precision array by index in ascending numerical order using insertion sort.
+      !! This algorithm works well for partially sorted arrays (which is usually the case here)
+      !!
+      implicit none
+      ! Arguments
+      real(SP), dimension(:), intent(in)  :: arr
+      integer(I4B), dimension(:), intent(out) :: ind
+      ! Internals
+      real(SP) :: tmp
+      integer(I4B) :: n, i, j
+
+      n = size(arr)
+      ind = [(i, i=1, n)]
+      do i = 2, n
+         tmp = arr(ind(i))
+         do j = i - 1, 1, -1
+            if (arr(ind(j)) <= tmp) exit
+            ind(j + 1) = ind(j)
+         end do
+         ind(j + 1) = i
+      end do
+      return
+   end subroutine util_sort_index_sp
 end submodule s_util_sort
diff --git a/src/util/util_valid.f90 b/src/util/util_valid.f90
index ac81673ca..ac9cc2fad 100644
--- a/src/util/util_valid.f90
+++ b/src/util/util_valid.f90
@@ -32,7 +32,6 @@ module subroutine util_valid(pl, tp)
                call util_exit(FAILURE)
             end if
          end do
-         deallocate(idarr)
       end associate
 
       return
diff --git a/src/whm/whm_setup.f90 b/src/whm/whm_setup.f90
index 1f098df26..940ba0b26 100644
--- a/src/whm/whm_setup.f90
+++ b/src/whm/whm_setup.f90
@@ -71,7 +71,7 @@ module subroutine whm_util_set_mu_eta_pl(self, cb)
 
    end subroutine whm_util_set_mu_eta_pl
 
-   module subroutine whm_setup_system(self, param)
+   module subroutine whm_setup_initialize_system(self, param)
       !! author: David A. Minton
       !!
       !! Initialize a WHM nbody system from files
@@ -91,29 +91,6 @@ module subroutine whm_setup_system(self, param)
          call self%tp%v2pv(param)
       end if
 
-   end subroutine whm_setup_system
-
-   module subroutine whm_setup_set_ir3j(self)
-      !! author: David A. Minton
-      !!
-      !! Sets the inverse Jacobi and heliocentric radii cubed (1/rj**3 and 1/rh**3)
-      implicit none
-      ! Arguments
-      class(whm_pl),                 intent(inout) :: self    !! WHM massive body object
-      ! Internals
-      integer(I4B)                                 :: i
-      real(DP)                                     :: r2, ir
-
-      if (self%nbody > 0) then
-         do i = 1, self%nbody
-            r2 = dot_product(self%xh(:, i), self%xh(:, i))
-            ir = 1.0_DP / sqrt(r2)
-            self%ir3h(i) = ir / r2
-            r2 = dot_product(self%xj(:, i), self%xj(:, i))
-            ir = 1.0_DP / sqrt(r2)
-            self%ir3j(i) = ir / r2
-         end do
-      end if
-   end subroutine whm_setup_set_ir3j
+   end subroutine whm_setup_initialize_system
 
 end submodule s_whm_setup
\ No newline at end of file
diff --git a/src/whm/whm_util.f90 b/src/whm/whm_util.f90
index 275130df9..67c7ef4a1 100644
--- a/src/whm/whm_util.f90
+++ b/src/whm/whm_util.f90
@@ -89,4 +89,27 @@ module subroutine whm_util_fill_pl(self, inserts, lfill_list)
    
       end subroutine whm_util_fill_pl
 
+      module subroutine whm_util_set_ir3j(self)
+         !! author: David A. Minton
+         !!
+         !! Sets the inverse Jacobi and heliocentric radii cubed (1/rj**3 and 1/rh**3)
+         implicit none
+         ! Arguments
+         class(whm_pl),                 intent(inout) :: self    !! WHM massive body object
+         ! Internals
+         integer(I4B)                                 :: i
+         real(DP)                                     :: r2, ir
+   
+         if (self%nbody > 0) then
+            do i = 1, self%nbody
+               r2 = dot_product(self%xh(:, i), self%xh(:, i))
+               ir = 1.0_DP / sqrt(r2)
+               self%ir3h(i) = ir / r2
+               r2 = dot_product(self%xj(:, i), self%xj(:, i))
+               ir = 1.0_DP / sqrt(r2)
+               self%ir3j(i) = ir / r2
+            end do
+         end if
+      end subroutine whm_util_set_ir3j
+
 end submodule s_whm_util