From 88d3b63e437827a634fe6c14df18a8e595991814 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Fri, 10 Dec 2021 16:43:04 -0500
Subject: [PATCH 1/6] Set the firstkick flag in SyMBA to be equal to pl%lfirst
 if a Helio step is performed

---
 src/symba/symba_step.f90 | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/symba/symba_step.f90 b/src/symba/symba_step.f90
index f10850e7f..25616b3d7 100644
--- a/src/symba/symba_step.f90
+++ b/src/symba/symba_step.f90
@@ -33,6 +33,7 @@ module subroutine symba_step_system(self, param, t, dt)
                else
                   self%irec = -1
                   call helio_step_system(self, param, t, dt)
+                  param%lfirstkick = pl%lfirst
                end if
             end select
          end select

From 917580061c7fa100b5006412c42852aad4d48c91 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Sat, 11 Dec 2021 11:04:16 -0500
Subject: [PATCH 2/6] Fixed wrong order of gr_pos_kick operator during Helio
 steps

---
 src/helio/helio_step.f90 |  4 ++--
 src/symba/symba_gr.f90   |  4 ++--
 src/symba/symba_step.f90 | 15 +++++++++------
 3 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/src/helio/helio_step.f90 b/src/helio/helio_step.f90
index fb14c9e75..9f68ce37c 100644
--- a/src/helio/helio_step.f90
+++ b/src/helio/helio_step.f90
@@ -56,8 +56,8 @@ module subroutine helio_step_pl(self, system, param, t, dt)
             call pl%kick(system, param, t, dth, lbeg=.true.)
             if (param%lgr) call pl%gr_pos_kick(system, param, dth)
             call pl%drift(system, param, dt)
-            call pl%kick(system, param, t + dt, dth, lbeg=.false.)
             if (param%lgr) call pl%gr_pos_kick(system, param, dth)
+            call pl%kick(system, param, t + dt, dth, lbeg=.false.)
             call pl%lindrift(cb, dth, lbeg=.false.)
             call pl%vb2vh(cb)
          end select
@@ -99,8 +99,8 @@ module subroutine helio_step_tp(self, system, param, t, dt)
             call tp%kick(system, param, t, dth, lbeg=.true.)
             if (param%lgr) call tp%gr_pos_kick(system, param, dth)
             call tp%drift(system, param, dt)
-            call tp%kick(system, param, t + dt, dth, lbeg=.false.)
             if (param%lgr) call tp%gr_pos_kick(system, param, dth)
+            call tp%kick(system, param, t + dt, dth, lbeg=.false.)
             call tp%lindrift(cb, dth, lbeg=.false.)
             call tp%vb2vh(vbcb = -cb%ptend)
          end select
diff --git a/src/symba/symba_gr.f90 b/src/symba/symba_gr.f90
index 1b0246aa8..5f8b9c5c1 100644
--- a/src/symba/symba_gr.f90
+++ b/src/symba/symba_gr.f90
@@ -23,7 +23,7 @@ module pure subroutine symba_gr_p4_pl(self, system, param, dt)
       associate(pl => self, npl => self%nbody)
          select type(system)
          class is (symba_nbody_system)
-            do concurrent(i = 1:npl, pl%lmask(i) .and. pl%levelg(i) == system%irec )
+            do concurrent(i = 1:npl, pl%lmask(i) .and. (pl%levelg(i) == system%irec) )
                call gr_p4_pos_kick(param, pl%xh(:, i), pl%vb(:, i), dt)
             end do
          end select
@@ -54,7 +54,7 @@ module pure subroutine symba_gr_p4_tp(self, system, param, dt)
       associate(tp => self, ntp => self%nbody)
          select type(system)
          class is (symba_nbody_system)
-            do concurrent(i = 1:ntp, tp%lmask(i) .and. tp%levelg(i) == system%irec)
+            do concurrent(i = 1:ntp, tp%lmask(i) .and. (tp%levelg(i) == system%irec))
                call gr_p4_pos_kick(param, tp%xh(:, i), tp%vb(:, i), dt)
             end do
          end select
diff --git a/src/symba/symba_step.f90 b/src/symba/symba_step.f90
index 25616b3d7..983db28f5 100644
--- a/src/symba/symba_step.f90
+++ b/src/symba/symba_step.f90
@@ -83,13 +83,13 @@ module subroutine symba_step_interp_system(self, param, t, dt)
 
                   call system%recursive_step(param, t, 0)
 
-                  call pl%kick(system, param, t, dth, lbeg=.false.)
                   if (param%lgr) call pl%gr_pos_kick(system, param, dth)
+                  call pl%kick(system, param, t, dth, lbeg=.false.)
                   call pl%lindrift(cb, dth, lbeg=.false.)
                   call pl%vb2vh(cb)
 
-                  call tp%kick(system, param, t, dth, lbeg=.false.)
                   if (param%lgr) call tp%gr_pos_kick(system, param, dth)
+                  call tp%kick(system, param, t, dth, lbeg=.false.)
                   call tp%lindrift(cb, dth, lbeg=.false.)
                   call tp%vb2vh(vbcb = -cb%ptend)
                end select
@@ -186,26 +186,29 @@ module recursive subroutine symba_step_recur_system(self, param, t, ireci)
                      call plplenc_list%kick(system, dth, irecp, -1)
                      call pltpenc_list%kick(system, dth, irecp, -1)
                   end if
+
                   if (param%lgr) then
                      call pl%gr_pos_kick(system, param, dth)
                      call tp%gr_pos_kick(system, param, dth)
                   end if
+
                   call pl%drift(system, param, dtl)
                   call tp%drift(system, param, dtl)
 
                   if (lencounter) call system%recursive_step(param, t+dth,irecp)
                   system%irec = ireci
 
+                  if (param%lgr) then
+                     call pl%gr_pos_kick(system, param, dth)
+                     call tp%gr_pos_kick(system, param, dth)
+                  end if
+
                   call plplenc_list%kick(system, dth, irecp, 1)
                   call pltpenc_list%kick(system, dth, irecp, 1)
                   if (ireci /= 0) then
                      call plplenc_list%kick(system, dth, irecp, -1)
                      call pltpenc_list%kick(system, dth, irecp, -1)
                   end if
-                  if (param%lgr) then
-                     call pl%gr_pos_kick(system, param, dth)
-                     call tp%gr_pos_kick(system, param, dth)
-                  end if
 
                   if (param%lclose) then
                      lplpl_collision = plplenc_list%collision_check(system, param, t+dtl, dtl, ireci) 

From 0c59aadff3f5c4f6e77e442da95aec8c2d016260 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Mon, 13 Dec 2021 11:31:19 -0500
Subject: [PATCH 3/6] Made calls to gr_pos_kick more consistent with the drift
 step, though it did not fix the issue with the computation of GR during close
 encounters not matching similar runs in Helio

---
 src/modules/swiftest_classes.f90 | 14 ++++++++------
 src/symba/symba_gr.f90           | 14 ++++++--------
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/src/modules/swiftest_classes.f90 b/src/modules/swiftest_classes.f90
index eeabc092b..a1489f82c 100644
--- a/src/modules/swiftest_classes.f90
+++ b/src/modules/swiftest_classes.f90
@@ -476,12 +476,6 @@ module swiftest_classes
    end type swiftest_nbody_system
 
    abstract interface
-      subroutine abstract_discard_body(self, system, param) 
-         import swiftest_body, swiftest_nbody_system, swiftest_parameters
-         class(swiftest_body),         intent(inout) :: self   !! Swiftest body object
-         class(swiftest_nbody_system), intent(inout) :: system !! Swiftest nbody system object
-         class(swiftest_parameters),   intent(inout) :: param  !! Current run configuration parameters 
-      end subroutine abstract_discard_body
 
       subroutine abstract_accel(self, system, param, t, lbeg)
          import swiftest_body, swiftest_nbody_system, swiftest_parameters, DP
@@ -492,6 +486,14 @@ subroutine abstract_accel(self, system, param, t, lbeg)
          logical,                      intent(in)    :: lbeg   !! Optional argument that determines whether or not this is the beginning or end of the step
       end subroutine abstract_accel
 
+      subroutine abstract_discard_body(self, system, param) 
+         import swiftest_body, swiftest_nbody_system, swiftest_parameters
+         class(swiftest_body),         intent(inout) :: self   !! Swiftest body object
+         class(swiftest_nbody_system), intent(inout) :: system !! Swiftest nbody system object
+         class(swiftest_parameters),   intent(inout) :: param  !! Current run configuration parameters 
+      end subroutine abstract_discard_body
+
+
       subroutine abstract_kick_body(self, system, param, t, dt, lbeg)
          import swiftest_body, swiftest_nbody_system, swiftest_parameters, DP
          implicit none
diff --git a/src/symba/symba_gr.f90 b/src/symba/symba_gr.f90
index 5f8b9c5c1..f743b124a 100644
--- a/src/symba/symba_gr.f90
+++ b/src/symba/symba_gr.f90
@@ -15,17 +15,15 @@ module pure subroutine symba_gr_p4_pl(self, system, param, dt)
       class(swiftest_nbody_system), intent(inout) :: system !! Swiftest nbody system object
       class(swiftest_parameters),   intent(in)    :: param  !! Current run configuration parameters 
       real(DP),                     intent(in)    :: dt     !! Step size
-      ! Internals
-      integer(I4B)                                 :: i
 
       if (self%nbody == 0) return
 
       associate(pl => self, npl => self%nbody)
          select type(system)
          class is (symba_nbody_system)
-            do concurrent(i = 1:npl, pl%lmask(i) .and. (pl%levelg(i) == system%irec) )
-               call gr_p4_pos_kick(param, pl%xh(:, i), pl%vb(:, i), dt)
-            end do
+            pl%lmask(1:npl) = pl%status(1:npl) /= INACTIVE .and. pl%levelg(1:npl) == system%irec
+            call helio_gr_p4_pl(pl, system, param, dt)
+            pl%lmask(1:npl) = pl%status(1:npl) /= INACTIVE 
          end select
       end associate
 
@@ -54,9 +52,9 @@ module pure subroutine symba_gr_p4_tp(self, system, param, dt)
       associate(tp => self, ntp => self%nbody)
          select type(system)
          class is (symba_nbody_system)
-            do concurrent(i = 1:ntp, tp%lmask(i) .and. (tp%levelg(i) == system%irec))
-               call gr_p4_pos_kick(param, tp%xh(:, i), tp%vb(:, i), dt)
-            end do
+            tp%lmask(1:ntp) = tp%status(1:ntp) /= INACTIVE .and. tp%levelg(1:ntp) == system%irec
+            call helio_gr_p4_tp(tp, system, param, dt)
+            tp%lmask(1:ntp) = tp%status(1:ntp) /= INACTIVE 
          end select
       end associate
 

From 2ced8fd9c1edc26d7abcac08a10854e28a27f1b7 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Mon, 13 Dec 2021 16:51:36 -0500
Subject: [PATCH 4/6] Fixed problem where the system recursion level variable
 was not set properly after returning from the recursive step.

---
 src/symba/symba_step.f90 | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/symba/symba_step.f90 b/src/symba/symba_step.f90
index 983db28f5..2bd6aca36 100644
--- a/src/symba/symba_step.f90
+++ b/src/symba/symba_step.f90
@@ -82,6 +82,7 @@ module subroutine symba_step_interp_system(self, param, t, dt)
                   call tp%drift(system, param, dt)
 
                   call system%recursive_step(param, t, 0)
+                  system%irec = -1
 
                   if (param%lgr) call pl%gr_pos_kick(system, param, dth)
                   call pl%kick(system, param, t, dth, lbeg=.false.)

From 232055804dfc8267daaf2942f65614b1e23922e9 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Tue, 14 Dec 2021 10:09:54 -0500
Subject: [PATCH 5/6] Updated Helio and GR tests to set the initial conditions
 generator to the Minton Brimley/Cocoon Line solar system date

---
 .../helio_gr_test/helio_vs_symba_GR.ipynb     | 147 +++++++++++++
 examples/helio_gr_test/init_cond.py           |   2 +-
 examples/helio_gr_test/param.swifter.in       |   4 +-
 examples/helio_gr_test/param.swiftest.in      |   4 +-
 examples/helio_gr_test/pl.swifter.in          |  48 ++---
 examples/helio_gr_test/pl.swiftest.in         |  48 ++---
 .../helio_gr_test/swiftest_relativity.ipynb   |  56 ++++-
 examples/symba_gr_test/cb.swiftest.in         |   5 +
 examples/symba_gr_test/init_cond.py           |  54 +++++
 examples/symba_gr_test/param.swifter.in       |  27 +++
 examples/symba_gr_test/param.swiftest.in      |  36 ++++
 examples/symba_gr_test/pl.swifter.in          |  36 ++++
 examples/symba_gr_test/pl.swiftest.in         |  33 +++
 .../symba_gr_test/swiftest_relativity.ipynb   | 199 ++++++++++++++++++
 examples/symba_gr_test/tp.swifter.in          |   1 +
 examples/symba_gr_test/tp.swiftest.in         |   1 +
 16 files changed, 639 insertions(+), 62 deletions(-)
 create mode 100644 examples/helio_gr_test/helio_vs_symba_GR.ipynb
 create mode 100644 examples/symba_gr_test/cb.swiftest.in
 create mode 100755 examples/symba_gr_test/init_cond.py
 create mode 100644 examples/symba_gr_test/param.swifter.in
 create mode 100644 examples/symba_gr_test/param.swiftest.in
 create mode 100644 examples/symba_gr_test/pl.swifter.in
 create mode 100644 examples/symba_gr_test/pl.swiftest.in
 create mode 100644 examples/symba_gr_test/swiftest_relativity.ipynb
 create mode 100644 examples/symba_gr_test/tp.swifter.in
 create mode 100644 examples/symba_gr_test/tp.swiftest.in

diff --git a/examples/helio_gr_test/helio_vs_symba_GR.ipynb b/examples/helio_gr_test/helio_vs_symba_GR.ipynb
new file mode 100644
index 000000000..60a80e27b
--- /dev/null
+++ b/examples/helio_gr_test/helio_vs_symba_GR.ipynb
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import swiftest\n",
+    "from astroquery.jplhorizons import Horizons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: HDF5_USE_FILE_LOCKING=FALSE\n",
+      "Reading Swiftest file param.swiftest.in\n",
+      "\n",
+      "Creating Dataset from NetCDF file\n",
+      "Successfully converted 1001 output frames.\n",
+      "Swiftest simulation data stored as xarray DataSet .ds\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env HDF5_USE_FILE_LOCKING=FALSE\n",
+    "heliosim = swiftest.Simulation(param_file=\"param.swiftest.in\")\n",
+    "heliosim.ds['varpi'] = heliosim.ds['omega'] + heliosim.ds['capom']\n",
+    "heliosim.ds = heliosim.ds.swap_dims({\"id\" : \"name\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading Swiftest file ../symba_gr_test/param.swiftest.in\n",
+      "\n",
+      "Creating Dataset from NetCDF file\n",
+      "Successfully converted 1001 output frames.\n",
+      "Swiftest simulation data stored as xarray DataSet .ds\n"
+     ]
+    }
+   ],
+   "source": [
+    "symbasim = swiftest.Simulation(param_file=\"../symba_gr_test/param.swiftest.in\")\n",
+    "symbasim.ds['varpi'] = symbasim.ds['omega'] + symbasim.ds['capom']\n",
+    "symbasim.ds = symbasim.ds.swap_dims({\"id\" : \"name\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff_varpi = symbasim.ds['varpi'] - heliosim.ds['varpi']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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",
+    "\n",
+    "diff_varpi.sel(name='Mercury').plot.line(x='time', ax=ax)\n",
+    "ax.set_xlabel('Time (y)')\n",
+    "ax.set_ylabel('Mercury $\\\\varpi_{SyMBA}-\\\\varpi_{Helio}$ (deg)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.9681148084346205e-11"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff_varpi.sel(name='Mercury')[-1].values[()]"
+   ]
+  },
+  {
+   "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/helio_gr_test/init_cond.py b/examples/helio_gr_test/init_cond.py
index e4351de42..31f5a9eda 100755
--- a/examples/helio_gr_test/init_cond.py
+++ b/examples/helio_gr_test/init_cond.py
@@ -41,7 +41,7 @@
 }
 
 for name, id in bodyid.items():
-   sim.add(name, idval=id)
+   sim.add(name, idval=id, date="2027-04-30")
    
 sim.save("param.swiftest.in")
 sim.param['PL_IN'] = "pl.swifter.in"
diff --git a/examples/helio_gr_test/param.swifter.in b/examples/helio_gr_test/param.swifter.in
index 14a2e67ab..5e59a5a8d 100644
--- a/examples/helio_gr_test/param.swifter.in
+++ b/examples/helio_gr_test/param.swifter.in
@@ -4,8 +4,8 @@ TSTOP            1000.0
 DT               0.0006844626967830253
 ISTEP_OUT        1461
 ISTEP_DUMP       1461
-OUT_FORM         XVEL
-OUT_TYPE         NETCDF_DOUBLE
+OUT_FORM         XV
+OUT_TYPE         REAL8
 OUT_STAT         UNKNOWN
 IN_TYPE          ASCII
 PL_IN            pl.swifter.in
diff --git a/examples/helio_gr_test/param.swiftest.in b/examples/helio_gr_test/param.swiftest.in
index 06cd551b5..8a634b84e 100644
--- a/examples/helio_gr_test/param.swiftest.in
+++ b/examples/helio_gr_test/param.swiftest.in
@@ -22,9 +22,7 @@ MU2KG            1.988409870698051e+30
 TU2S             31557600.0
 DU2M             149597870700.0
 IN_FORM          EL
-ENC_OUT          enc.dat
 EXTRA_FORCE      NO
-DISCARD_OUT      discard.out
 BIG_DISCARD      NO
 CHK_CLOSE        YES
 RHILL_PRESENT    YES
@@ -33,3 +31,5 @@ ROTATION         NO
 TIDES            NO
 ENERGY           NO
 GR               YES
+INTERACTION_LOOPS ADAPTIVE
+ENCOUNTER_CHECK  ADAPTIVE
diff --git a/examples/helio_gr_test/pl.swifter.in b/examples/helio_gr_test/pl.swifter.in
index 9add1dc1a..dffe532c6 100644
--- a/examples/helio_gr_test/pl.swifter.in
+++ b/examples/helio_gr_test/pl.swifter.in
@@ -2,35 +2,35 @@
 0 39.476926408897626
 0.0 0.0 0.0
 0.0 0.0 0.0
-1 6.5537098095653139645e-06 0.001475131156288637831
+1 6.5537098095653139645e-06 0.0014751260185578720416
 1.6306381826061645943e-05
--0.0361820239646683528 -0.46276010580341980782 -0.034496398006472611675
-8.183688458560222766 -0.27616682338432374386 -0.7732476400340904169
-2 9.663313399581537916e-05 0.0067590747715647875607
+0.22527006614756858727 0.22185515636479818946 -0.0025292250509892700606
+-9.2410583833491193135 7.7546860001024244665 1.481285384055779404
+2 9.663313399581537916e-05 0.006759120024335718617
 4.0453784346544178454e-05
-0.04707194870345993154 -0.7255425078625500346 -0.012673782840571969424
-7.3226765098927520106 0.45143529808423807744 -0.4163607714267330732
-3 0.000120026935827952453094 0.01004493295891520948
+0.6401338616632904488 -0.3439628247287493945 -0.041662537354174501714
+3.4536908505004217623 6.4771489080253446934 -0.110257596056190005656
+3 0.000120026935827952453094 0.0100446292823340959596
 4.25875607065040958e-05
-0.9784182446151709067 -0.2394545623617302943 7.8432240502479141865e-06
-1.3916805835822199726 6.0802455505175572043 -0.00029695052429473289775
-4 1.2739802010675941456e-05 0.007246527815634877893
+-0.7819504386201725499 -0.6346854491951327004 4.4451463454996458186e-05
+3.8578491980751480153 -4.9026737310919641898 0.0002888700847309900442
+4 1.2739802010675941456e-05 0.0072466212625671651507
 2.265740805092889601e-05
--1.64827450584581503 -0.04818173529735803734 0.039422108862210397673
-0.3393812176064170994 -4.672429323734897043 -0.1062469093563351878
-5 0.037692251088985676735 0.3552712221482522291
+-1.6500171831673979828 -0.023341429362091121319 0.039964339661466272147
+0.2624112744618213919 -4.673688782463607376 -0.10439239110136837694
+5 0.037692251088985676735 0.35521688466465032753
 0.00046732617030490929307
-4.2888079225575648223 -2.6068082746690541818 -0.08512743586621877856
-1.4000629754656241179 2.4876377693334669565 -0.04165606656604725836
-6 0.011285899820091272997 0.4376655756331854547
+-4.540785007788180394 2.8642873711036669349 0.08969619001756239107
+-1.5037012644183387769 -2.204631616790602934 0.042803317383791750652
+6 0.011285899820091272997 0.43538665458575465117
 0.00038925687730393611812
-6.528501376442308768 -7.4981197287393284157 -0.12945412016904539465
-1.4250071771025915456 1.33599992135721594 -0.07992159012428249671
-7 0.0017236589478267730203 0.46970222329693796102
+8.9010680046292307566 2.902848867584423953 -0.40487415052930197934
+-0.7439396408265333407 1.9316851317225221362 -0.004029214768814220126
+7 0.0017236589478267730203 0.46987236554736915505
 0.00016953449859497231466
-14.703390521074780395 13.168120788311910019 -0.14161196287363458923
--0.96768368440931795183 1.0056701102995595347 0.01621895610074016891
-8 0.0020336100526728302319 0.78148373992374883156
+8.1785533453299539275 17.594789418581910923 -0.04069518248189726156
+-1.3149276792509819067 0.53722869521767089375 0.019051745273498437594
+8 0.0020336100526728302319 0.7749718408665498732
 0.000164587904124493665
-29.584136736556288838 -4.4478894754775319953 -0.5902566603324214123
-0.16448791459483679965 1.1435219564135608914 -0.027304639012640681262
+29.794198627119580891 2.0446309861539178065 -0.7287194786837880578
+-0.08754730406665780868 1.1492225523523839664 -0.021678684633280679721
diff --git a/examples/helio_gr_test/pl.swiftest.in b/examples/helio_gr_test/pl.swiftest.in
index cd6a82534..37ebc69a0 100644
--- a/examples/helio_gr_test/pl.swiftest.in
+++ b/examples/helio_gr_test/pl.swiftest.in
@@ -1,33 +1,33 @@
 8
-Mercury 6.5537098095653139645e-06 0.001475131156288637831
+Mercury 6.5537098095653139645e-06 0.0014751260185578720416
 1.6306381826061645943e-05
-0.38709993253896590737 0.20562614632859010921 7.0036272314159866426
-48.303648911363737284 29.187750438580689405 192.22398026813181104
-Venus 9.663313399581537916e-05 0.0067590747715647875607
+0.38709858430953941744 0.20562340108970009189 7.0033025080013837638
+48.296118373786072198 29.20442403952453958 338.33948746828792764
+Venus 9.663313399581537916e-05 0.006759120024335718617
 4.0453784346544178454e-05
-0.7233249152313999675 0.006782779674600277614 3.3945084679511872139
-76.62172192899298295 55.11871576732421829 141.51944714681908977
-Earth 0.000120026935827952453094 0.01004493295891520948
+0.72332975797361009906 0.006717605698865438367 3.3944392733422819042
+76.60235891771118588 54.960379460829607012 200.47893395506480374
+Earth 0.000120026935827952453094 0.0100446292823340959596
 4.25875607065040958e-05
-1.0000207198983319667 0.01667731166077134064 0.0027576432734417160447
-175.55813761562768605 287.40166532678227895 245.00485341438161413
-Mars 1.2739802010675941456e-05 0.007246527815634877893
+0.99999048745432228547 0.016714003765458580048 0.0036378626088630029375
+175.0251726002310022 287.96196288125747742 114.34829340424269617
+Mars 1.2739802010675941456e-05 0.0072466212625671651507
 2.265740805092889601e-05
-1.5236922769560579116 0.093361659829406723476 1.8479133359077690724
-49.490611477105659333 286.70751815915190264 210.36775515565921069
-Jupiter 0.037692251088985676735 0.3552712221482522291
+1.5237119255895350545 0.09344151133508207807 1.8474416735579008986
+49.47285721247470036 286.7379771285890797 209.33967734771380265
+Jupiter 0.037692251088985676735 0.35521688466465032753
 0.00046732617030490929307
-5.203523661489898977 0.04851833897394460665 1.3035686835324369337
-100.51671881401649955 273.38319313278452682 318.65986979714938343
-Saturn 0.011285899820091272997 0.4376655756331854547
+5.202727800851599582 0.048244977116379678117 1.3036311345700750675
+100.51925884330809424 273.58984028825142332 129.5536700659941971
+Saturn 0.011285899820091272997 0.43538665458575465117
 0.00038925687730393611812
-9.581904852310625387 0.052236260603057811658 2.486258128114103183
-113.595257336893098454 335.64742317800698856 225.92836894588160135
-Uranus 0.0017236589478267730203 0.46970222329693796102
+9.532011952667287957 0.054863298704333408884 2.4879063632803011252
+113.63057816762059815 339.54673564023909194 290.89958065689040723
+Uranus 0.0017236589478267730203 0.46987236554736915505
 0.00016953449859497231466
-19.238019712529130345 0.0442880601027018303 0.770350326736000679
-74.09520891350530292 95.7524345508490029 236.07863258800219342
-Neptune 0.0020336100526728302319 0.78148373992374883156
+19.244988382902359803 0.0479617494230129629 0.7730102596086204647
+74.012580980165793676 93.595549122802268016 262.86586372775150267
+Neptune 0.0020336100526728302319 0.7749718408665498732
 0.000164587904124493665
-30.291369954344219195 0.013531244039650480379 1.7689741418447819665
-131.74449502957048708 245.89296040993210113 334.5035290478779757
+30.038959912561129073 0.008955570159296157365 1.7711193542961420899
+131.82211597488270627 284.47484279411258967 308.45137222693909962
diff --git a/examples/helio_gr_test/swiftest_relativity.ipynb b/examples/helio_gr_test/swiftest_relativity.ipynb
index db2b358ed..d4280e094 100644
--- a/examples/helio_gr_test/swiftest_relativity.ipynb
+++ b/examples/helio_gr_test/swiftest_relativity.ipynb
@@ -74,6 +74,15 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "diff_varpi = varpiswiftest - varpi_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "dvarpi_swiftest = np.diff(varpiswiftest)  * 3600 * 100 \n",
     "dvarpi_obs = np.diff(varpi_obs) / np.diff(t) * 3600 * 100 "
@@ -81,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -90,13 +99,13 @@
      "text": [
       "Mean precession rate for Mercury long. peri. (arcsec/100 y)\n",
       "JPL Horizons      : 571.3210506300043\n",
-      "Swiftest GR       : 571.519655556524\n",
-      "Obs - Swiftest    : -0.1986049265197031\n"
+      "Swiftest-Helio GR : 570.6677380786499\n",
+      "Obs - Swiftest    : 0.6533125513543501\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -111,23 +120,52 @@
     "fig, ax = plt.subplots()\n",
     "\n",
     "ax.plot(t, varpi_obs, label=\"JPL Horizons\")\n",
-    "ax.plot(tsim, varpiswiftest, label=\"Swiftest GR\")\n",
+    "ax.plot(tsim, varpiswiftest, label=\"Swiftest-Helio GR\")\n",
     "ax.set_xlabel('Time (y)')\n",
     "ax.set_ylabel('Mercury $\\\\varpi$ (deg)')\n",
     "ax.legend()\n",
     "print('Mean precession rate for Mercury long. peri. (arcsec/100 y)')\n",
     "print(f'JPL Horizons      : {np.mean(dvarpi_obs)}')\n",
-    "print(f'Swiftest GR       : {np.mean(dvarpi_swiftest)}')\n",
+    "print(f'Swiftest-Helio GR : {np.mean(dvarpi_swiftest)}')\n",
     "print(f'Obs - Swiftest    : {np.mean(dvarpi_obs - dvarpi_swiftest)}')\n",
     "plt.savefig(f'helio_gr_test.png', dpi=300,facecolor='white', transparent=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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",
+    "\n",
+    "ax.plot(tsim, diff_varpi)\n",
+    "ax.set_xlabel('Time (y)')\n",
+    "ax.set_ylabel('Mercury $\\\\varpi_{obs} - \\\\varpi_{sim}$ (deg)')\n",
+    "ax.set_title(\"Mercury Long. Peri. error (Swiftest-Helio)\")\n",
+    "ax.legend()\n",
+    "plt.savefig(f'helio_gr_diffvarpi.png', dpi=300,facecolor='white', transparent=False)"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/examples/symba_gr_test/cb.swiftest.in b/examples/symba_gr_test/cb.swiftest.in
new file mode 100644
index 000000000..64406c4cf
--- /dev/null
+++ b/examples/symba_gr_test/cb.swiftest.in
@@ -0,0 +1,5 @@
+Sun
+39.476926408897626
+0.004650467260962157
+4.7535806948127355e-12
+-2.2473967953572827e-18
diff --git a/examples/symba_gr_test/init_cond.py b/examples/symba_gr_test/init_cond.py
new file mode 100755
index 000000000..2639272e7
--- /dev/null
+++ b/examples/symba_gr_test/init_cond.py
@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+import swiftest
+
+sim = swiftest.Simulation()
+sim.param['PL_IN'] = "pl.swiftest.in"
+sim.param['TP_IN'] = "tp.swiftest.in"
+sim.param['CB_IN'] = "cb.swiftest.in"
+sim.param['BIN_OUT'] = "bin.swiftest.nc"
+
+sim.param['MU2KG'] = swiftest.MSun
+sim.param['TU2S'] = swiftest.YR2S
+sim.param['DU2M'] = swiftest.AU2M
+sim.param['T0'] = 0.0
+sim.param['DT'] = 0.125 * swiftest.JD2S / swiftest.YR2S
+sim.param['TSTOP'] = 1000.0
+sim.param['ISTEP_OUT']  = 2922
+sim.param['ISTEP_DUMP'] = 2922
+sim.param['CHK_QMIN_COORD'] = "HELIO"
+sim.param['CHK_QMIN'] = swiftest.RSun / swiftest.AU2M
+sim.param['CHK_QMIN_RANGE'] = f"{swiftest.RSun / swiftest.AU2M} 1000.0"
+sim.param['CHK_RMIN'] = swiftest.RSun / swiftest.AU2M
+sim.param['CHK_RMAX'] = 1000.0
+sim.param['CHK_EJECT'] = 1000.0
+sim.param['OUT_STAT'] = "UNKNOWN"
+sim.param['IN_FORM'] = "EL"
+sim.param['OUT_FORM'] = "XVEL"
+sim.param['OUT_TYPE'] = "NETCDF_DOUBLE"
+sim.param['RHILL_PRESENT'] = "YES"
+sim.param['GR'] = 'YES'
+sim.param['GMTINY'] = '1e-7'
+
+bodyid = {
+   "Sun": 0,
+   "Mercury": 1,
+   "Venus": 2,
+   "Earth": 3,
+   "Mars": 4,
+   "Jupiter": 5,
+   "Saturn": 6,
+   "Uranus": 7,
+   "Neptune": 8,
+}
+
+for name, id in bodyid.items():
+   sim.add(name, idval=id, date="2027-04-30")
+   
+sim.save("param.swiftest.in")
+sim.param['PL_IN'] = "pl.swifter.in"
+sim.param['TP_IN'] = "tp.swifter.in"
+sim.param['BIN_OUT'] = "bin.swifter.dat"
+sim.param['ENC_OUT'] = "enc.swifter.dat"
+sim.save("param.swifter.in", codename="Swifter")
+
+
diff --git a/examples/symba_gr_test/param.swifter.in b/examples/symba_gr_test/param.swifter.in
new file mode 100644
index 000000000..3f58c079b
--- /dev/null
+++ b/examples/symba_gr_test/param.swifter.in
@@ -0,0 +1,27 @@
+! VERSION        Swifter parameter file converted from Swiftest
+T0               0.0
+TSTOP            1000.0
+DT               0.00034223134839151266
+ISTEP_OUT        2922
+ISTEP_DUMP       2922
+OUT_FORM         XV
+OUT_TYPE         REAL8
+OUT_STAT         UNKNOWN
+IN_TYPE          ASCII
+PL_IN            pl.swifter.in
+TP_IN            tp.swifter.in
+BIN_OUT          bin.swifter.dat
+CHK_QMIN         0.004650467260962157
+CHK_RMIN         0.004650467260962157
+CHK_RMAX         1000.0
+CHK_EJECT        1000.0
+CHK_QMIN_COORD   HELIO
+CHK_QMIN_RANGE   0.004650467260962157 1000.0
+ENC_OUT          enc.swifter.dat
+EXTRA_FORCE      NO
+BIG_DISCARD      NO
+CHK_CLOSE        YES
+RHILL_PRESENT    YES
+C                63241.07708426628
+J2               4.7535806948127355e-12
+J4               -2.2473967953572827e-18
diff --git a/examples/symba_gr_test/param.swiftest.in b/examples/symba_gr_test/param.swiftest.in
new file mode 100644
index 000000000..86f00adb5
--- /dev/null
+++ b/examples/symba_gr_test/param.swiftest.in
@@ -0,0 +1,36 @@
+! VERSION        Swiftest parameter input
+T0               0.0
+TSTOP            1000.0
+DT               0.00034223134839151266
+ISTEP_OUT        2922
+ISTEP_DUMP       2922
+OUT_FORM         XVEL
+OUT_TYPE         NETCDF_DOUBLE
+OUT_STAT         UNKNOWN
+IN_TYPE          ASCII
+PL_IN            pl.swiftest.in
+TP_IN            tp.swiftest.in
+CB_IN            cb.swiftest.in
+BIN_OUT          bin.swiftest.nc
+CHK_QMIN         0.004650467260962157
+CHK_RMIN         0.004650467260962157
+CHK_RMAX         1000.0
+CHK_EJECT        1000.0
+CHK_QMIN_COORD   HELIO
+CHK_QMIN_RANGE   0.004650467260962157 1000.0
+MU2KG            1.988409870698051e+30
+TU2S             31557600.0
+DU2M             149597870700.0
+IN_FORM          EL
+EXTRA_FORCE      NO
+BIG_DISCARD      NO
+CHK_CLOSE        YES
+RHILL_PRESENT    YES
+FRAGMENTATION    NO
+ROTATION         NO
+TIDES            NO
+ENERGY           NO
+GR               YES
+INTERACTION_LOOPS ADAPTIVE
+ENCOUNTER_CHECK  ADAPTIVE
+GMTINY           1e-7
diff --git a/examples/symba_gr_test/pl.swifter.in b/examples/symba_gr_test/pl.swifter.in
new file mode 100644
index 000000000..dffe532c6
--- /dev/null
+++ b/examples/symba_gr_test/pl.swifter.in
@@ -0,0 +1,36 @@
+9
+0 39.476926408897626
+0.0 0.0 0.0
+0.0 0.0 0.0
+1 6.5537098095653139645e-06 0.0014751260185578720416
+1.6306381826061645943e-05
+0.22527006614756858727 0.22185515636479818946 -0.0025292250509892700606
+-9.2410583833491193135 7.7546860001024244665 1.481285384055779404
+2 9.663313399581537916e-05 0.006759120024335718617
+4.0453784346544178454e-05
+0.6401338616632904488 -0.3439628247287493945 -0.041662537354174501714
+3.4536908505004217623 6.4771489080253446934 -0.110257596056190005656
+3 0.000120026935827952453094 0.0100446292823340959596
+4.25875607065040958e-05
+-0.7819504386201725499 -0.6346854491951327004 4.4451463454996458186e-05
+3.8578491980751480153 -4.9026737310919641898 0.0002888700847309900442
+4 1.2739802010675941456e-05 0.0072466212625671651507
+2.265740805092889601e-05
+-1.6500171831673979828 -0.023341429362091121319 0.039964339661466272147
+0.2624112744618213919 -4.673688782463607376 -0.10439239110136837694
+5 0.037692251088985676735 0.35521688466465032753
+0.00046732617030490929307
+-4.540785007788180394 2.8642873711036669349 0.08969619001756239107
+-1.5037012644183387769 -2.204631616790602934 0.042803317383791750652
+6 0.011285899820091272997 0.43538665458575465117
+0.00038925687730393611812
+8.9010680046292307566 2.902848867584423953 -0.40487415052930197934
+-0.7439396408265333407 1.9316851317225221362 -0.004029214768814220126
+7 0.0017236589478267730203 0.46987236554736915505
+0.00016953449859497231466
+8.1785533453299539275 17.594789418581910923 -0.04069518248189726156
+-1.3149276792509819067 0.53722869521767089375 0.019051745273498437594
+8 0.0020336100526728302319 0.7749718408665498732
+0.000164587904124493665
+29.794198627119580891 2.0446309861539178065 -0.7287194786837880578
+-0.08754730406665780868 1.1492225523523839664 -0.021678684633280679721
diff --git a/examples/symba_gr_test/pl.swiftest.in b/examples/symba_gr_test/pl.swiftest.in
new file mode 100644
index 000000000..37ebc69a0
--- /dev/null
+++ b/examples/symba_gr_test/pl.swiftest.in
@@ -0,0 +1,33 @@
+8
+Mercury 6.5537098095653139645e-06 0.0014751260185578720416
+1.6306381826061645943e-05
+0.38709858430953941744 0.20562340108970009189 7.0033025080013837638
+48.296118373786072198 29.20442403952453958 338.33948746828792764
+Venus 9.663313399581537916e-05 0.006759120024335718617
+4.0453784346544178454e-05
+0.72332975797361009906 0.006717605698865438367 3.3944392733422819042
+76.60235891771118588 54.960379460829607012 200.47893395506480374
+Earth 0.000120026935827952453094 0.0100446292823340959596
+4.25875607065040958e-05
+0.99999048745432228547 0.016714003765458580048 0.0036378626088630029375
+175.0251726002310022 287.96196288125747742 114.34829340424269617
+Mars 1.2739802010675941456e-05 0.0072466212625671651507
+2.265740805092889601e-05
+1.5237119255895350545 0.09344151133508207807 1.8474416735579008986
+49.47285721247470036 286.7379771285890797 209.33967734771380265
+Jupiter 0.037692251088985676735 0.35521688466465032753
+0.00046732617030490929307
+5.202727800851599582 0.048244977116379678117 1.3036311345700750675
+100.51925884330809424 273.58984028825142332 129.5536700659941971
+Saturn 0.011285899820091272997 0.43538665458575465117
+0.00038925687730393611812
+9.532011952667287957 0.054863298704333408884 2.4879063632803011252
+113.63057816762059815 339.54673564023909194 290.89958065689040723
+Uranus 0.0017236589478267730203 0.46987236554736915505
+0.00016953449859497231466
+19.244988382902359803 0.0479617494230129629 0.7730102596086204647
+74.012580980165793676 93.595549122802268016 262.86586372775150267
+Neptune 0.0020336100526728302319 0.7749718408665498732
+0.000164587904124493665
+30.038959912561129073 0.008955570159296157365 1.7711193542961420899
+131.82211597488270627 284.47484279411258967 308.45137222693909962
diff --git a/examples/symba_gr_test/swiftest_relativity.ipynb b/examples/symba_gr_test/swiftest_relativity.ipynb
new file mode 100644
index 000000000..372bd257b
--- /dev/null
+++ b/examples/symba_gr_test/swiftest_relativity.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import swiftest\n",
+    "from astroquery.jplhorizons import Horizons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: HDF5_USE_FILE_LOCKING=FALSE\n",
+      "Reading Swiftest file param.swiftest.in\n",
+      "\n",
+      "Creating Dataset from NetCDF file\n",
+      "Successfully converted 1001 output frames.\n",
+      "Swiftest simulation data stored as xarray DataSet .ds\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env HDF5_USE_FILE_LOCKING=FALSE\n",
+    "sim = swiftest.Simulation(param_file=\"param.swiftest.in\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.ds = sim.ds.swap_dims({\"id\" : \"name\"})\n",
+    "sim.ds['varpi'] = sim.ds['omega'] + sim.ds['capom']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obj = Horizons(id='1', id_type='majorbody',location='@sun',\n",
+    "               epochs={'start':'2021-01-28', 'stop':'3021-02-05',\n",
+    "                       'step':'1y'})\n",
+    "el = obj.elements()\n",
+    "t = (el['datetime_jd']-el['datetime_jd'][0]) / 365.25\n",
+    "varpi_obs = el['w'] + el['Omega']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsim = sim.ds['time']\n",
+    "varpiswiftest = sim.ds.sel(name='Mercury')['varpi']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dvarpi_swiftest = np.diff(varpiswiftest)  * 3600 * 100 \n",
+    "dvarpi_obs = np.diff(varpi_obs) / np.diff(t) * 3600 * 100 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff_varpi = varpiswiftest - varpi_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean precession rate for Mercury long. peri. (arcsec/100 y)\n",
+      "JPL Horizons      : 571.3210506300043\n",
+      "Swiftest-SyMBA GR : 570.664406128945\n",
+      "Obs - Swiftest    : 0.6566445010592952\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",
+    "\n",
+    "ax.plot(t, varpi_obs, label=\"JPL Horizons\")\n",
+    "ax.plot(tsim, varpiswiftest, label=\"Swiftest-SyMBA GR\")\n",
+    "ax.set_xlabel('Time (y)')\n",
+    "ax.set_ylabel('Mercury $\\\\varpi$ (deg)')\n",
+    "ax.legend()\n",
+    "print('Mean precession rate for Mercury long. peri. (arcsec/100 y)')\n",
+    "print(f'JPL Horizons      : {np.mean(dvarpi_obs)}')\n",
+    "print(f'Swiftest-SyMBA GR : {np.mean(dvarpi_swiftest)}')\n",
+    "print(f'Obs - Swiftest    : {np.mean(dvarpi_obs - dvarpi_swiftest)}')\n",
+    "plt.savefig(f'symba_gr_test.png', dpi=300,facecolor='white', transparent=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+    "\n",
+    "ax.plot(tsim, diff_varpi)\n",
+    "ax.set_xlabel('Time (y)')\n",
+    "ax.set_ylabel('Mercury $\\\\varpi_{obs} - \\\\varpi_{sim}$ (deg)')\n",
+    "ax.set_title(\"Mercury Long. Peri. error (Swiftest-SyMBA)\")\n",
+    "ax.legend()\n",
+    "plt.savefig(f'symba_gr_diffvarpi.png', dpi=300,facecolor='white', transparent=False)"
+   ]
+  },
+  {
+   "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_gr_test/tp.swifter.in b/examples/symba_gr_test/tp.swifter.in
new file mode 100644
index 000000000..573541ac9
--- /dev/null
+++ b/examples/symba_gr_test/tp.swifter.in
@@ -0,0 +1 @@
+0
diff --git a/examples/symba_gr_test/tp.swiftest.in b/examples/symba_gr_test/tp.swiftest.in
new file mode 100644
index 000000000..573541ac9
--- /dev/null
+++ b/examples/symba_gr_test/tp.swiftest.in
@@ -0,0 +1 @@
+0

From f6e1a9e5e1229d05170dc81574df200b5ed97129 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Tue, 14 Dec 2021 10:59:02 -0500
Subject: [PATCH 6/6] Fixed bad array index range value (should be ntp not npl)

---
 examples/symba_gr_test/init_cond.py | 27 ++++++++++++++++++++++++++-
 src/symba/symba_step.f90            |  2 +-
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/examples/symba_gr_test/init_cond.py b/examples/symba_gr_test/init_cond.py
index 2639272e7..a5dbe9118 100755
--- a/examples/symba_gr_test/init_cond.py
+++ b/examples/symba_gr_test/init_cond.py
@@ -1,5 +1,7 @@
 #!/usr/bin/env python3
 import swiftest
+from numpy.random import default_rng
+import numpy as np
 
 sim = swiftest.Simulation()
 sim.param['PL_IN'] = "pl.swiftest.in"
@@ -43,7 +45,30 @@
 
 for name, id in bodyid.items():
    sim.add(name, idval=id, date="2027-04-30")
-   
+
+Me_a = sim.ds.isel(id=1)['a'].values
+Me_e = sim.ds.sel(id=1)['e'].values
+Me_i = sim.ds.sel(id=1)['inc'].values
+
+capom_pl = default_rng().uniform(0.0, 360.0, 1)
+omega_pl = default_rng().uniform(0.0, 360.0, 1)
+capm_pl = default_rng().uniform(0.0, 360.0, 1)
+
+capom_tp = default_rng().uniform(0.0, 360.0, 1)
+omega_tp = default_rng().uniform(0.0, 360.0, 1)
+capm_tp = default_rng().uniform(0.0, 360.0, 1)
+
+GMcb = sim.ds.isel(id=0)['Gmass'].values
+GU = swiftest.GC / (sim.param['DU2M']**3 / (sim.param['MU2KG'] * sim.param['TU2S']**2))
+dens = 3000.0 / (sim.param['MU2KG'] / sim.param['DU2M']**3) # Assume a bulk density of 3 g/cm^3
+GM_pl = 2e-7
+M_pl = GM_pl / GU
+R_pl = (3 * M_pl / (4 * np.pi * dens))**(1.0 / 3.0)
+Rh_pl = Me_a * (GM_pl / (3 * GMcb))**(1.0/3.0)
+
+sim.addp(np.full(1,9), np.full(1,'Planetesimal'), Me_a, Me_e, Me_i, capom_pl, omega_pl, capm_pl, GMpl=np.full(1, GM_pl), Rpl=np.full(1, R_pl), rhill=Rh_pl)
+sim.addp(np.full(1,10), np.full(1,'TestParticle'), Me_a, Me_e, Me_i, capom_tp, omega_tp, capm_tp)
+
 sim.save("param.swiftest.in")
 sim.param['PL_IN'] = "pl.swifter.in"
 sim.param['TP_IN'] = "tp.swifter.in"
diff --git a/src/symba/symba_step.f90 b/src/symba/symba_step.f90
index 2bd6aca36..cd48667af 100644
--- a/src/symba/symba_step.f90
+++ b/src/symba/symba_step.f90
@@ -274,7 +274,7 @@ module subroutine symba_step_reset_system(self, param)
                      tp%levelg(1:ntp) = -1
                      tp%levelm(1:ntp) = -1
                      tp%lmask(1:ntp) = .true.
-                     tp%ldiscard(1:npl) = .false.
+                     tp%ldiscard(1:ntp) = .false.
                      nenc_old = system%pltpenc_list%nenc
                      call system%pltpenc_list%setup(0)
                      call system%pltpenc_list%setup(nenc_old)