diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/init_cond.py b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/init_cond.py
index b292ed42f..a700466b1 100755
--- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/init_cond.py
+++ b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/init_cond.py
@@ -43,7 +43,7 @@
 tpid = 100
 
 radius = np.double(4.25875607065041e-05)
-mass = np.double(0.00012002693582795244940133) 
+Gmass = np.double(0.00012002693582795244940133) 
 apl = np.longdouble(1.0)
 atp = np.longdouble(1.01)
 vpl = np.longdouble(2 * np.pi)
@@ -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')
@@ -63,7 +63,7 @@
 print(1,GMSun,file=plfile)
 print('0.0 0.0 0.0',file=plfile)
 print('0.0 0.0 0.0',file=plfile)
-print(plid,"{:.23g}".format(mass),Rhill, file=plfile)
+print(plid,"{:.23g}".format(Gmass),Rhill, file=plfile)
 print(radius, file=plfile)
 print(*p_pl, file=plfile)
 print(*v_pl, file=plfile)
@@ -125,7 +125,8 @@
 plfile.write_record(v_pl[0])
 plfile.write_record(v_pl[1])
 plfile.write_record(v_pl[2])
-plfile.write_record(mass)
+plfile.write_record(Gmass)
+plfile.write_record(Rhill)
 plfile.write_record(radius)
 plfile.close()
 tpfile = FortranFile(swiftest_tp, 'w')
@@ -156,6 +157,7 @@
 print(f'OUT_TYPE       REAL8')
 print(f'OUT_FORM       XV')
 print(f'OUT_STAT       REPLACE')
+print(f'RHILL_PRESENT  yes')
 print(f'CHK_CLOSE      yes')
 print(f'CHK_RMIN       {rmin}')
 print(f'CHK_RMAX       {rmax}')
diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/param.swiftest.in b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/param.swiftest.in
index 36937896f..d9c20a3be 100644
--- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/param.swiftest.in
+++ b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/param.swiftest.in
@@ -12,6 +12,7 @@ BIN_OUT        bin.swiftest.dat
 OUT_TYPE       REAL8
 OUT_FORM       XV
 OUT_STAT       REPLACE
+RHILL_PRESENT  yes
 CHK_CLOSE      yes
 CHK_RMIN       0.004650467260962157
 CHK_RMAX       1000.0
diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swifter.in b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swifter.in
index 95513c9fd..17d461561 100644
--- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swifter.in
+++ b/examples/rmvs_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/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in
index 6f4bc1337..c94c6ae61 100644
Binary files a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in and b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/pl.swiftest.in differ
diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
index 29dcf43aa..20122244c 100644
--- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
+++ b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
@@ -81,8 +81,8 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2b47a0e74650>,\n",
-       " <matplotlib.lines.Line2D at 0x2b47a0e71250>]"
+       "[<matplotlib.lines.Line2D at 0x2b8ff43aafd0>,\n",
+       " <matplotlib.lines.Line2D at 0x2b8ff485d650>]"
       ]
      },
      "execution_count": 6,
@@ -91,7 +91,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -103,7 +103,7 @@
     }
    ],
    "source": [
-    "swiftdiff['px'].plot.line(x=\"time (y)\")"
+    "swiftdiff['vx'].plot.line(x=\"time (y)\")"
    ]
   },
   {