diff --git a/Makefile b/Makefile
index b5fb81071..50e5f1d89 100644
--- a/Makefile
+++ b/Makefile
@@ -59,13 +59,17 @@ SWIFTEST_MODULES =   swiftest_globals.f90 \
 
 include Makefile.Defines
 
+MKL_ROOT = /apps/spack/bell/apps/intel-parallel-studio/cluster.2019.5-intel-19.0.5-4brgqlf/mkl/lib
+IMKL = -I$(MKLROOT)/include
+LMKL = -L$(MKLROOT)/lib/intel64 -qopt-matmul 
+
 MODULES         = $(SWIFTEST_MODULES) $(USER_MODULES) 
 
-.PHONY : all mod lib libdir drivers bin clean force 
+.PHONY : all mod lib libdir fast drivers bin clean force 
 
 % : %.f90 force
-	$(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include  $< -o $@ \
-	  -L$(SWIFTEST_HOME)/lib -lswiftest -L$(NETCDF_FORTRAN_HOME)/lib -lnetcdf -lnetcdff
+	$(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) $< -o $@ \
+	  -L$(SWIFTEST_HOME)/lib -lswiftest -L$(NETCDF_FORTRAN_HOME)/lib -lnetcdf -lnetcdff $(LMKL)
 	$(INSTALL_PROGRAM) $@ $(SWIFTEST_HOME)/bin
 	rm -f $@
 
@@ -73,11 +77,12 @@ all:
 	cd $(SWIFTEST_HOME); \
 	  make mod; \
 	  make lib; \
+	  make fast; \
 	  make drivers; \
 
 mod:
 	cd $(SWIFTEST_HOME)/src/modules/; \
-	  $(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include -c $(MODULES); \
+	  $(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c $(MODULES); \
 	  $(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o; \
 	  $(INSTALL_DATA) *.mod *.smod $(SWIFTEST_HOME)/include; \
 	  rm -f *.o *.mod  *.smod
@@ -93,11 +98,6 @@ lib:
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
 	  make libdir
-	cd $(SWIFTEST_HOME)/src/fraggle; \
-	  rm -f Makefile.Defines Makefile; \
-	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
-	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
 	cd $(SWIFTEST_HOME)/src/gr; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
@@ -143,11 +143,6 @@ lib:
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
 	  make libdir
-	cd $(SWIFTEST_HOME)/src/util; \
-	  rm -f Makefile.Defines Makefile; \
-	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
-	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
 	cd $(SWIFTEST_HOME)/src/whm; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
@@ -174,8 +169,39 @@ lib:
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
 	  make libdir
 
+fast:
+	cd $(SWIFTEST_HOME)/src/fraggle; \
+	  rm -f Makefile.Defines Makefile; \
+	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
+	  ln -s $(SWIFTEST_HOME)/Makefile .; \
+	  make fastdir
+
+	cd $(SWIFTEST_HOME)/src/util; \
+	  rm -f Makefile.Defines Makefile; \
+	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
+	  ln -s $(SWIFTEST_HOME)/Makefile .; \
+	  make fastdir
+
+	cd $(SWIFTEST_HOME)/src/rmvs; \
+		$(FORTRAN) $(FFASTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c rmvs_encounter_check.f90; \
+		$(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o *.smod; \
+		$(INSTALL_DATA) *.smod $(SWIFTEST_HOME)/include; \
+		rm -f *.o *.smod	
+
+	cd $(SWIFTEST_HOME)/src/symba; \
+		$(FORTRAN) $(FFASTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c symba_encounter_check.f90; \
+		$(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o *.smod; \
+		$(INSTALL_DATA) *.smod $(SWIFTEST_HOME)/include; \
+		rm -f *.o *.smod
+
 libdir:
-	$(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include -c *.f90; \
+	$(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c *.f90; \
+	$(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o *.smod; \
+	$(INSTALL_DATA) *.smod $(SWIFTEST_HOME)/include; \
+	rm -f *.o *.smod
+
+fastdir:
+	$(FORTRAN) $(FFASTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c *.f90; \
 	$(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o *.smod; \
 	$(INSTALL_DATA) *.smod $(SWIFTEST_HOME)/include; \
 	rm -f *.o *.smod
diff --git a/Makefile.Defines b/Makefile.Defines
index 36de2cdb7..6effd7332 100644
--- a/Makefile.Defines
+++ b/Makefile.Defines
@@ -46,37 +46,36 @@ COLLRESOLVE_HOME = $(ROOT_DIR)/collresolve/
 # DO NOT include in FFLAGS the "-c" option to compile object only
 # this is done explicitly as needed in the Makefile
 ADVIXE_DIR = /apps/cent7/intel/advisor_2019
-ADVIXE_FLAGS = -g -O2 -qopt-report=5 -vec -vecabi=cmdtarget -simd -shared-intel -debug inline-debug-info -DTBB_DEBUG -DTBB_USE_THREADING_TOOLS -fp-model no-except -mp1 -xhost -traceback 
+ADVIXE_FLAGS = -g -O2 -qopt-report=5 -vecabi=cmdtarget -simd -shared-intel -debug inline-debug-info -DTBB_DEBUG -DTBB_USE_THREADING_TOOLS -xhost -traceback 
 
-VTUNE_FLAGS = -g -O2 -vec -simd -shared-intel -qopenmp -debug inline-debug-info -parallel-source-info=2 -parallel -DTBB_DEBUG -DTBB_USE_THREADING_TOOLS -qopenmp -fp-model no-except -mp1 -xhost -traceback 
+VTUNE_FLAGS = -g -O2 -qopt-report=5 -simd -shared-intel -qopenmp -debug inline-debug-info -parallel-source-info=2 -parallel -DTBB_DEBUG -DTBB_USE_THREADING_TOOLS -qopenmp -fp-model no-except -mp1 -xhost -traceback 
 #Be sure to set the environment variable KMP_FORKJOIN_FRAMES=1 for OpenMP debuging in vtune
 
 IDEBUG = -O0 -init=snan,arrays  -nogen-interfaces -no-pie -no-ftz -fpe-all=0 -g -traceback -mp1 -fp-model strict -fpe0 -debug all -align all -pad -ip -prec-div -prec-sqrt -assume protect-parens -CB -no-wrap-margin
-STRICTREAL = -fp-model strict -fp-model no-except -prec-div -prec-sqrt -assume protect-parens 
-SIMDVEC = -simd -xhost -align all -assume contiguous_assumed_shape -vecabi=cmdtarget -prec-div -prec-sqrt -assume protect-parens
-PAR = -qopenmp #-parallel  #Something goes wrong in SyMBA at the moment with auto-paralellization enabled
-HEAPARR = -heap-arrays 1048576 
+STRICTREAL = -fp-model strict -prec-div -prec-sqrt -assume protect-parens 
+SIMDVEC = -simd -xhost -align all -assume contiguous_assumed_shape -vecabi=cmdtarget -fp-model no-except 
+PAR = -qopenmp -parallel  
+HEAPARR = -heap-arrays 4194304 
 OPTREPORT = -qopt-report=5 
-IPRODUCTION = -init=snan,arrays  -no-wrap-margin -O3 $(STRICTREAL) $(PAR) $(SIMDVEC) $(HEAPARR) 
+IPRODUCTION = -no-wrap-margin -O3 $(PAR) $(SIMDVEC) #$(HEAPARR)
 
 #gfortran flags
 GDEBUG = -g -Og -fbacktrace -fbounds-check -ffree-line-length-none
 GPAR = -fopenmp #-ftree-parallelize-loops=4
 GMEM =  -fsanitize-address-use-after-scope -fstack-check  -fsanitize=bounds-strict -fsanitize=undefined -fsanitize=signed-integer-overflow -fsanitize=object-size -fstack-protector-all
 GWARNINGS = -Wall -Warray-bounds  -Wimplicit-interface -Wextra -Warray-temporaries
-GPRODUCTION = -O3 -ffree-line-length-none $(GPAR)
+GPRODUCTION = -O2 -ffree-line-length-none $(GPAR)
 
-#FFLAGS  = $(IDEBUG) $(HEAPARR) $(SIMDVEC) $(PAR)
-FFLAGS  = $(IPRODUCTION) $(OPTREPORT)
+
+#FFLAGS  = $(IDEBUG) $(SIMDVEC) $(PAR)
+FFLAGS = $(IPRODUCTION) $(STRICTREAL) $(OPTREPORT) 
+FFASTFLAGS = $(IPRODUCTION) -fp-model fast $(OPTREPORT)
 FORTRAN = ifort
 #AR     = xiar
 
 #FORTRAN = gfortran
 #FFLAGS =  $(GDEBUG) $(GMEM) $(GPAR)
-#FFLAGS =  $(GPRODUCTION) -g -fbacktrace #-fcheck=all #-Wall 
-AR     = ar
-
-# DO NOT include in CFLAGS the "-c" option to compile object only
+#FFLAGS =  $(GPRODUCTION) -g -fbacktrace #-fcheck=all #-Wall AR     = ar # DO NOT include in CFLAGS the "-c" option to compile object only
 # this is done explicitly as needed in the Makefile
 
 CC              = icc
diff --git a/docs/src/rmvs_encounter_check.f90 b/docs/src/rmvs_encounter_check.f90
index 4cb119e48..43e748b41 100644
--- a/docs/src/rmvs_encounter_check.f90
+++ b/docs/src/rmvs_encounter_check.f90
@@ -36,10 +36,7 @@ module function rmvs_encounter_check_tp(self, system, dt) result(lencounter)
                   if ((.not.tp%lmask(i)).or.(tp%plencP(i) /= 0)) cycle
                   xr(:) = tp%xh(:, i) - pl%xbeg(:, j)
                   vr(:) = tp%vh(:, i) - pl%vbeg(:, j)
-                  r2 = dot_product(xr(:), xr(:))
-                  v2 = dot_product(vr(:), vr(:))
-                  vdotr = dot_product(vr(:), xr(:))
-                  lflag = rmvs_chk_ind(r2, v2, vdotr, dt, r2crit(j))
+                  lflag = rmvs_chk_ind(xr(1), xr(2), xr(3), vr(1), vr(2), vr(3), dt, r2crit(j))
                   if (lflag) tp%plencP(i) = j
                end do
                pl%nenc(j) = count(tp%plencP(1:ntp) == j)
diff --git a/docs/src/symba_encounter_check.f90 b/docs/src/symba_encounter_check.f90
index 6f6010047..3445bcfda 100644
--- a/docs/src/symba_encounter_check.f90
+++ b/docs/src/symba_encounter_check.f90
@@ -261,20 +261,20 @@ module pure elemental subroutine symba_encounter_check_one(xr, yr, zr, vxr, vyr,
       !! Adapted from Hal Levison's Swift routine symba5_chk.f
       implicit none
       ! Arguments
-      real(DP),     intent(in)  :: xr, yr, zr, vxr, vyr, vzr
-      real(DP),     intent(in)  :: rhill1, rhill2, dt
-      integer(I4B), intent(in)  :: irec
-      logical,      intent(out) :: lencounter, lvdotr
+      real(DP),     intent(in)  :: xr, yr, zr     !! Relative distance vector components
+      real(DP),     intent(in)  :: vxr, vyr, vzr  !! Relative velocity vector components
+      real(DP),     intent(in)  :: rhill1, rhill2 !! Hill spheres of the two bodies
+      real(DP),     intent(in)  :: dt             !! Step size
+      integer(I4B), intent(in)  :: irec           !! Current SyMBA recursion level
+      real(DP),     intent(in)  :: r2crit         !! Square of the critical encounter distance
+      logical,      intent(out) :: lencounter     !! Flag indicating that an encounter has occurred
+      logical,      intent(out) :: lvdotr         !! Logical flag indicating the direction of the v .dot. r vector
       ! Internals
-      real(DP)     :: r2, v2, rcrit, r2crit, vdotr
+      real(DP)     :: r2crit
 
-      rcrit = (rhill1 + rhill2)*RHSCALE*(RSHELL**(irec))
-      r2crit = rcrit**2
-      r2 = xr**2 + yr**2 + zr**2
-      v2 = vxr**2 + vyr**2 + vzr**2
-      vdotr = xr * vxr + yr * vyr + zr * vzr
-      lencounter = rmvs_chk_ind(r2, v2, vdotr, dt, r2crit)
-      lvdotr = (vdotr < 0.0_DP)
+      r2crit = (rhill1 + rhill2)*RHSCALE*(RSHELL**(irec))
+      r2crit = r2crit**2
+      call rmvs_chk_ind(xr, yr, zr, vxr, vyr, vzr, dt, r2crit, lencounter, lvdotr)
 
       return
    end subroutine symba_encounter_check_one
diff --git a/examples/symba_mars_disk/aescattermovie.py b/examples/symba_mars_disk/aescattermovie.py
index 85b99c0fa..50dd53064 100755
--- a/examples/symba_mars_disk/aescattermovie.py
+++ b/examples/symba_mars_disk/aescattermovie.py
@@ -79,8 +79,7 @@ def setup_plot(self):
     def data_stream(self, frame=0):
         while True:
             d = self.ds.isel(time=frame)
-
-            d = d.where(d['radius'] < self.Rcb, drop=True)
+            d = d.where(np.invert(np.isnan(d['a'])), drop=True)
             d['radmarker'] = (d['radius'] / self.Rcb) * radscale
             radius = d['radmarker'].values
 
diff --git a/examples/symba_mars_disk/param.in b/examples/symba_mars_disk/param.in
index 1fac92462..1769c6c74 100644
--- a/examples/symba_mars_disk/param.in
+++ b/examples/symba_mars_disk/param.in
@@ -1,17 +1,17 @@
 !Parameter file for the SyMBA-RINGMOONS test
 T0             0.0 
-TSTOP          1.0e12 
+TSTOP          60000.0
 DT             600.0
 CB_IN          cb.in
 PL_IN          mars.in
 TP_IN          tp.in
 IN_TYPE        ASCII
-ISTEP_OUT      1
-ISTEP_DUMP     1
+ISTEP_OUT      100
+ISTEP_DUMP     100
 BIN_OUT        bin.nc 
 PARTICLE_OUT   particle.dat
-OUT_TYPE       NETCDF_DOUBLE
-OUT_FORM       XVEL
+OUT_TYPE       REAL8
+OUT_FORM       XV
 OUT_STAT       REPLACE
 CHK_CLOSE      yes
 CHK_RMIN       3389500.0
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.in
index 01c5fb3dd..2df47f957 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.in
@@ -1,5 +1,5 @@
 Sun
-0.0002959122081920778
+0.00029591220819207774
 0.004650467260962157
 4.7535806948127355e-12
 -2.2473967953572827e-18
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.swiftest.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.swiftest.in
index 01c5fb3dd..2df47f957 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.swiftest.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/cb.swiftest.in
@@ -1,5 +1,5 @@
 Sun
-0.0002959122081920778
+0.00029591220819207774
 0.004650467260962157
 4.7535806948127355e-12
 -2.2473967953572827e-18
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/init_cond.py b/examples/symba_swifter_comparison/8pl_16tp_encounters/init_cond.py
index 425ea1b73..23b65080f 100755
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/init_cond.py
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/init_cond.py
@@ -19,7 +19,7 @@
 swiftest_pl    = "pl.swiftest.in"
 swiftest_tp    = "tp.swiftest.in"
 swiftest_cb    = "cb.swiftest.in"
-swiftest_bin   = "bin.swiftest.dat"
+swiftest_bin   = "bin.swiftest.nc"
 swiftest_enc   = "enc.swiftest.dat"
 swiftest_dis  = "discard.swiftest.dat"
 
@@ -28,8 +28,8 @@
 sim.param['T0'] = 0.0
 sim.param['DT'] = 1.0 
 sim.param['TSTOP'] = 365.25e1
-sim.param['ISTEP_OUT']  = 11
-sim.param['ISTEP_DUMP'] = 1
+sim.param['ISTEP_OUT']  = 10
+sim.param['ISTEP_DUMP'] = 10
 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"
@@ -141,4 +141,5 @@
 sim.param['TP_IN'] = swifter_tp
 sim.param['BIN_OUT'] = swifter_bin
 sim.param['ENC_OUT'] = swifter_enc
+sim.param['OUT_TYPE'] = "REAL8"
 sim.save(swifter_input, codename="Swifter")
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swifter.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swifter.in
index b8731386b..15fdfcbe3 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swifter.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swifter.in
@@ -2,10 +2,10 @@
 T0               0.0
 TSTOP            3652.5
 DT               1.0
-ISTEP_OUT        11
-ISTEP_DUMP       1
+ISTEP_OUT        10
+ISTEP_DUMP       10
 OUT_FORM         XV
-OUT_TYPE         NETCDF_DOUBLE
+OUT_TYPE         REAL8
 OUT_STAT         UNKNOWN
 IN_TYPE          ASCII
 PL_IN            pl.swifter.in
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swiftest.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swiftest.in
index bca11da6d..ad787b5bf 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swiftest.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swiftest.in
@@ -2,8 +2,8 @@
 T0               0.0
 TSTOP            3652.5
 DT               1.0
-ISTEP_OUT        11
-ISTEP_DUMP       1
+ISTEP_OUT        10
+ISTEP_DUMP       10
 OUT_FORM         XV
 OUT_TYPE         NETCDF_DOUBLE
 OUT_STAT         UNKNOWN
@@ -11,7 +11,7 @@ IN_TYPE          ASCII
 PL_IN            pl.swiftest.in
 TP_IN            tp.swiftest.in
 CB_IN            cb.swiftest.in
-BIN_OUT          bin.swiftest.dat
+BIN_OUT          bin.swiftest.nc
 CHK_QMIN         0.004650467260962157
 CHK_RMIN         0.004650467260962157
 CHK_RMAX         1000.0
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.in
index 72b1baa3b..93c1187e2 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.in
@@ -1,33 +1,33 @@
 8
-Mercury 4.9125474498983625056e-11 0.0014751323154597007903
+Mercury 4.9125474498983623693e-11 0.0014751323154597003982
 1.6306381826061645943e-05
 0.053584775529809842987 -0.4548355025417368247 -0.04208301187261995896
 0.022298358665237189014 0.0047355207618514265702 -0.0016584224113858070382
-Venus 7.243452483873647106e-10 0.0067590814914530454873
+Venus 7.243452483873646905e-10 0.006759081491453044288
 4.0453784346544178454e-05
 0.12681182092868958922 -0.7161485778943049718 -0.017146261752773749032
 0.01978070713081106144 0.0034557070729633850362 -0.00109402215681010293
-Earth 8.997011382166018993e-10 0.010044922299157372164
+Earth 8.9970113821660187435e-10 0.010044922299157369357
 4.25875607065040958e-05
 0.9913796310092216624 -0.17236385208280941006 4.574442303609438109e-06
 0.0026673818939059660942 0.016885702625202340249 -8.2074388361713082097e-07
-Mars 9.549535102761465872e-11 0.007246507286611460043
+Mars 9.549535102761465607e-11 0.0072465072866114584993
 2.265740805092889601e-05
 -1.6436878725691590475 -0.09931688681832298582 0.038237939117251117105
 0.0013642455487206960919 -0.0127728951275482699446 -0.00030115173687901287654
-Jupiter 2.8253459086313549713e-07 0.3552710784524730732
+Jupiter 2.825345908631354893e-07 0.35527107845247299128
 0.00046732617030490929307
 4.304060110247122317 -2.579516473452256875 -0.08558202993848706974
 0.003792902202341501966 0.0068350794283332117623 -0.00011324814038141340017
-Saturn 8.45971518300641563e-08 0.4376659135625219704
+Saturn 8.459715183006415395e-08 0.43766591356252188504
 0.00038925687730393611812
 6.54409134618183419 -7.483470470167333133 -0.1303290586096018111
 0.003893524262024787054 0.0036668581511023591937 -0.00021865564058601801348
-Uranus 1.2920249163736673984e-08 0.46971473227488383167
+Uranus 1.2920249163736673626e-08 0.46971473227488373932
 0.00016953449859497231466
 14.692788408572690528 13.179130291284799625 -0.14143429698462339772
 -0.0026516826407085368304 0.0027513763836455209892 4.4427867883713361775e-05
-Neptune 1.5243589003230834746e-08 0.78149679568494038567
+Neptune 1.5243589003230834323e-08 0.7814967956849401736
 0.000164587904124493665
 29.58593540166936009 -4.435365846939811618 -0.5905556302070252839
 0.0004489890080502080224 0.003131021601122137201 -7.4728898269552307757e-05
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swifter.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swifter.in
index c0567724d..611be7721 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swifter.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swifter.in
@@ -1,36 +1,36 @@
 9
-0 0.0002959122081920778
+0 0.00029591220819207774
 0.0 0.0 0.0
 0.0 0.0 0.0
-1 4.9125474498983625056e-11 0.0014751323154597007903
+1 4.9125474498983623693e-11 0.0014751323154597003982
 1.6306381826061645943e-05
 0.053584775529809842987 -0.4548355025417368247 -0.04208301187261995896
 0.022298358665237189014 0.0047355207618514265702 -0.0016584224113858070382
-2 7.243452483873647106e-10 0.0067590814914530454873
+2 7.243452483873646905e-10 0.006759081491453044288
 4.0453784346544178454e-05
 0.12681182092868958922 -0.7161485778943049718 -0.017146261752773749032
 0.01978070713081106144 0.0034557070729633850362 -0.00109402215681010293
-3 8.997011382166018993e-10 0.010044922299157372164
+3 8.9970113821660187435e-10 0.010044922299157369357
 4.25875607065040958e-05
 0.9913796310092216624 -0.17236385208280941006 4.574442303609438109e-06
 0.0026673818939059660942 0.016885702625202340249 -8.2074388361713082097e-07
-4 9.549535102761465872e-11 0.007246507286611460043
+4 9.549535102761465607e-11 0.0072465072866114584993
 2.265740805092889601e-05
 -1.6436878725691590475 -0.09931688681832298582 0.038237939117251117105
 0.0013642455487206960919 -0.0127728951275482699446 -0.00030115173687901287654
-5 2.8253459086313549713e-07 0.3552710784524730732
+5 2.825345908631354893e-07 0.35527107845247299128
 0.00046732617030490929307
 4.304060110247122317 -2.579516473452256875 -0.08558202993848706974
 0.003792902202341501966 0.0068350794283332117623 -0.00011324814038141340017
-6 8.45971518300641563e-08 0.4376659135625219704
+6 8.459715183006415395e-08 0.43766591356252188504
 0.00038925687730393611812
 6.54409134618183419 -7.483470470167333133 -0.1303290586096018111
 0.003893524262024787054 0.0036668581511023591937 -0.00021865564058601801348
-7 1.2920249163736673984e-08 0.46971473227488383167
+7 1.2920249163736673626e-08 0.46971473227488373932
 0.00016953449859497231466
 14.692788408572690528 13.179130291284799625 -0.14143429698462339772
 -0.0026516826407085368304 0.0027513763836455209892 4.4427867883713361775e-05
-8 1.5243589003230834746e-08 0.78149679568494038567
+8 1.5243589003230834323e-08 0.7814967956849401736
 0.000164587904124493665
 29.58593540166936009 -4.435365846939811618 -0.5905556302070252839
 0.0004489890080502080224 0.003131021601122137201 -7.4728898269552307757e-05
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swiftest.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swiftest.in
index 72b1baa3b..93c1187e2 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swiftest.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swiftest.in
@@ -1,33 +1,33 @@
 8
-Mercury 4.9125474498983625056e-11 0.0014751323154597007903
+Mercury 4.9125474498983623693e-11 0.0014751323154597003982
 1.6306381826061645943e-05
 0.053584775529809842987 -0.4548355025417368247 -0.04208301187261995896
 0.022298358665237189014 0.0047355207618514265702 -0.0016584224113858070382
-Venus 7.243452483873647106e-10 0.0067590814914530454873
+Venus 7.243452483873646905e-10 0.006759081491453044288
 4.0453784346544178454e-05
 0.12681182092868958922 -0.7161485778943049718 -0.017146261752773749032
 0.01978070713081106144 0.0034557070729633850362 -0.00109402215681010293
-Earth 8.997011382166018993e-10 0.010044922299157372164
+Earth 8.9970113821660187435e-10 0.010044922299157369357
 4.25875607065040958e-05
 0.9913796310092216624 -0.17236385208280941006 4.574442303609438109e-06
 0.0026673818939059660942 0.016885702625202340249 -8.2074388361713082097e-07
-Mars 9.549535102761465872e-11 0.007246507286611460043
+Mars 9.549535102761465607e-11 0.0072465072866114584993
 2.265740805092889601e-05
 -1.6436878725691590475 -0.09931688681832298582 0.038237939117251117105
 0.0013642455487206960919 -0.0127728951275482699446 -0.00030115173687901287654
-Jupiter 2.8253459086313549713e-07 0.3552710784524730732
+Jupiter 2.825345908631354893e-07 0.35527107845247299128
 0.00046732617030490929307
 4.304060110247122317 -2.579516473452256875 -0.08558202993848706974
 0.003792902202341501966 0.0068350794283332117623 -0.00011324814038141340017
-Saturn 8.45971518300641563e-08 0.4376659135625219704
+Saturn 8.459715183006415395e-08 0.43766591356252188504
 0.00038925687730393611812
 6.54409134618183419 -7.483470470167333133 -0.1303290586096018111
 0.003893524262024787054 0.0036668581511023591937 -0.00021865564058601801348
-Uranus 1.2920249163736673984e-08 0.46971473227488383167
+Uranus 1.2920249163736673626e-08 0.46971473227488373932
 0.00016953449859497231466
 14.692788408572690528 13.179130291284799625 -0.14143429698462339772
 -0.0026516826407085368304 0.0027513763836455209892 4.4427867883713361775e-05
-Neptune 1.5243589003230834746e-08 0.78149679568494038567
+Neptune 1.5243589003230834323e-08 0.7814967956849401736
 0.000164587904124493665
 29.58593540166936009 -4.435365846939811618 -0.5905556302070252839
 0.0004489890080502080224 0.003131021601122137201 -7.4728898269552307757e-05
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/swiftest_symba_vs_swifter_symba.ipynb b/examples/symba_swifter_comparison/8pl_16tp_encounters/swiftest_symba_vs_swifter_symba.ipynb
index ca5040a17..4f99d59cc 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/swiftest_symba_vs_swifter_symba.ipynb
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/swiftest_symba_vs_swifter_symba.ipynb
@@ -21,9 +21,9 @@
      "output_type": "stream",
      "text": [
       "Reading Swifter file param.swifter.in\n",
-      "Reading in time 3.652e+03\n",
+      "Reading in time 3.650e+03\n",
       "Creating Dataset\n",
-      "Successfully converted 333 output frames.\n",
+      "Successfully converted 366 output frames.\n",
       "Swifter simulation data stored as xarray DataSet .ds\n"
      ]
     }
@@ -45,9 +45,9 @@
      "output_type": "stream",
      "text": [
       "Reading Swiftest file param.swiftest.in\n",
-      "Reading in time 3.652e+03\n",
+      "\n",
       "Creating Dataset\n",
-      "Successfully converted 333 output frames.\n",
+      "Successfully converted 366 output frames.\n",
       "Swiftest simulation data stored as xarray DataSet .ds\n"
      ]
     }
@@ -83,8 +83,8 @@
    "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)"
+    "swiftdiff['rmag'] = np.sqrt(swiftdiff['xhx']**2 + swiftdiff['xhy']**2 + swiftdiff['xhz']**2)\n",
+    "swiftdiff['vmag'] = np.sqrt(swiftdiff['vhx']**2 + swiftdiff['vhy']**2 + swiftdiff['vhz']**2)"
    ]
   },
   {
@@ -93,8 +93,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plidx = swiftdiff.id.values[swiftdiff.id.values < 10]\n",
-    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 10]"
+    "plidx = swiftdiff.id.values[swiftdiff.id.values < 9]\n",
+    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 9]"
    ]
   },
   {
@@ -104,7 +104,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -130,7 +130,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -163,7 +163,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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HAZ0k5QPXm9mDwHlsf9H+CKJ7hZcAceByM0sefVxB9A2zlsCL4QHwIPCYpHlERyrn1d7WOOdc9jIznstbxvjJM9hYUMxPj+vH/x3Vl2ZN6uayeqO/0Vdubq55dWPnXEOxYmMB1z49nVdnrmC/7u245ZxhDNyrbdpfR9JUM8utaFo2nApzzjm3i8yM/0zJ58bnZ1JUkuDqkwdyyWG9aZJT91/+9cTinHP13JK1W7h60jTembuaEb07cMvZw+jdqXXG4vHE4pxz9VQiYTzywSJue3kOMYnfnjGEb4/oSSyW2d+Ge2Jxzrl6aN7Kr7nyqTymLl7HUQM68/szh9K1fctMhwV4YnHOuXqlOJ7g/rcXcMfrc2nVLIc/fXM/zjygG9lUwcoTi3PO1RMzlm5g7IQ8ZizdyMlD9+I3pw+hc5vmmQ5rO55YnHMuyxUUx7nrjXn89b/z2b11M/763QMZNaRLpsOqlCcW55zLYlMXr2PshM+Zv2oz5wzvzq9PGUS7Vk0zHVaVPLE451wW2lJUwm0vz+Hh9xfRtV1LHrlkBEf275zpsHaKJxbnnMsy785dzbiJeeSv28qFh+zN2FED2a15/emu60+kzjnXwG3YWszvn5/Fv6csoXen1jz5g0MY0btDpsOqNk8szjmXBV6duYJrn57G6q+LuPzIPvz0uH60aFp7pe1rkycW55zLoDVfFzL+2Zk8+/lSBu7Vhr9deBBDu7fLdFi7xBOLc85lgJkx+fOl/ObZmWwqKObnx/fn8iP71Flp+9rkicU55+rY8g0FXDNpGq/PXsn+Pdpz6znD6L9nm0yHlTaeWJxzro6YGU98soTfPz+L4kSCa0/Zl+8d1pucDBeNTDdPLM45Vwe+XLOFcRPzeH/+Gg7ZpyM3nz2UvTtmrrR9bfLE4pxztSieMB5+fxF/eHkOOTHx+zOHcv6IHllVNDLdPLE451wtmbtiE2OfyuPTL9dzzMA9+N2ZQ+jSLjtK29emOvv6gaSHJK2UND2lbbykryR9Fh4np0y7StI8SXMknZjSPlzStDDtToW0L6m5pH+H9o8k9aqrbXPOuVTF8QR/eX0up9z5LotWb+aO8/bnwYtyG0VSgbo9YnkYuAt4tFz77Wb2h9QGSYOA84DBQFfgNUn9zSwO3AuMAT4EXgBGAS8ClwLrzKyvpPOAW4Bv1d7mOOfc9qZ/tYFfTchj1rKNnDqsC+NPH0yn3bKvtH1tqrPEYmZvV+MoYjTwhJkVAgslzQNGSFoEtDWzDwAkPQqcQZRYRgPjw/ITgLskycwsbRvhnHOVKCiOc8frc7n/7QV0bN2M+y8YzgmD98p0WBmRDddYfiTpQmAK8AszWwd0IzoiScoPbcVhuHw74XkJgJmVSNoAdARWl39BSWOIjnro2bNnWjfGOdf4fLJoLVdOyGPB6s18M7c715yc/aXta1Omf+J5L9AH2B9YBvwxtFf0dQmror2qZbZvNLvfzHLNLLdz5/pRhto5l302F5Zw/TPT+eZ9H1AUT/DYpSO49Zz9GnVSgQwfsZjZiuSwpAeA58JoPtAjZdbuwNLQ3r2C9tRl8iU1AdoBa2sncudcY/fO3FWMe2oaSzds5aJDevGrEwfQuh6Vtq9NGT1ikZR6b80zgeQ3xiYD54VvevUG+gEfm9kyYJOkkeHbYBcCz6Qsc1EYPgd4w6+vOOfSbcOWYn71n8+54MGPad40xn9+cAjjTx/sSSVFne0JSY8DRwGdJOUD1wNHSdqf6JTVIuAHAGY2Q9KTwEygBPhh+EYYwBVE3zBrSXTR/sXQ/iDwWLjQv5boW2XOOZc2L89YzrVPT2ft5iL+76g+/PjY+lvavjapsX+oz83NtSlTpmQ6DOdcFlu1qZDxk2fw/LRlDOrSllvPGcaQbvW7tP2ukjTVzHIrmubHbs45Vwkz4+nPvuI3z85kS2GcX504gDFH7EPTnEx/7ym7eWJxzrkKLF2/lWsmTePNOas4sGdU2r7vHg2ntH1t8sTinHMpEgnj8U++5KYXZhNPGNedOoiLDu3V4Erb1yZPLM45FyxavZlxE/P4cMFaDuvbkZvOHEbPjq0yHVa944nFOdfoxRPGQ+8u5I+vzqFpLMbNZw3lWwc17NL2tckTi3OuUZuzPCpt//mS9Ry375789owh7NWuRabDqtc8sTjnGqWikgT3vjWfu96cS5sWTbnz/AM4bVgXP0pJA08szrlGJy9/PWMn5DF7+SZO368r1582iI6NrLR9bfLE4pxrNAqK49z+6hc88M4COrdpzt8uzOW4QXtmOqwGxxOLc65R+GjBGsZNnMbC1Zs5f0QPxp20L+1aNu4qxLXFE4tzrkH7urCEW16czWMfLqZHh5b86/sHc2jfTpkOq0HzxOKca7D++8Uqrp4Ylba/5LDe/PLE/rRq5t1ebfM97JxrcNZvKeLG52bx1P/y6bvHbky4/FCG7717psNqNDyxOOcalBenLePXz8xg/ZYi/t8xffnRMX1p3sRL29clTyzOuQZh5aYCrn9mBi9OX86Qbm155JKDGNy1cZe2zxRPLM65es3MmPi/r7jhuZlsLY4zdtQAxhy+D028tH3GeGJxztVbX63fytUTp/HfL1aRu/fu3HLOMPp03i3TYTV6nlicc/VOImH886PF3PzibAwYf9ogLjykFzEvbZ8VPLE45+qVhas3c+VTeXy8cC2H9+vE788cSo8OXto+m9TZSUhJD0laKWl6StttkmZLypM0SVL70N5L0lZJn4XHX1OWGS5pmqR5ku5UqBgnqbmkf4f2jyT1qqttc87VvpJ4gvv+O59Rf36b2cs2cus5w3j0khGeVLJQXV7dehgYVa7tVWCImQ0DvgCuSpk238z2D4/LU9rvBcYA/cIjuc5LgXVm1he4Hbgl/ZvgnMuE2cs3cta973PTi7M5sn9nXvv5kXwz1++Xkq3q7FSYmb1d/ijCzF5JGf0QOKeqdUjqArQ1sw/C+KPAGcCLwGhgfJh1AnCXJJmZpSN+51zdKypJcNeb87jnzXm0a9mUu799ICcP3csTSpbLpmsslwD/ThnvLelTYCNwrZm9A3QD8lPmyQ9thOclAGZWImkD0BFYXf6FJI0hOuqhZ8+ead4M51w6fLZkPWMnfM4XK77mzAO68etTB9GhdbNMh+V2QlYkFknXACXAP0PTMqCnma2RNBx4WtJgoKKPKckjkqqmbdtodj9wP0Bubq4f0TiXRbYWxfnTq3N48N2F7Nm2BQ9dnMsxA720fX2S8cQi6SLgVODY5GkrMysECsPwVEnzgf5ERyjdUxbvDiwNw/lADyBfUhOgHbC2TjbCOZcWH8xfw7iJeSxes4VvH9yTq04aSJsWXtq+vsloYpE0CrgSONLMtqS0dwbWmllc0j5EF+kXmNlaSZskjQQ+Ai4E/hIWmwxcBHxAdK3mDb++4lz9sKmgmJtenM2/PvqSvTu24vHLRnJIn46ZDsvVUJ0lFkmPA0cBnSTlA9cTfQusOfBquBj3YfgG2BHADZJKgDhwuZkljz6uIPqGWUuii/YvhvYHgcckzSM6UjmvDjbLObeL3py9kqsnTWPFxgIuO7w3Pz9+AC2bedHI+kw7+lAvaWevbq83s427HlLdys3NtSlTpmQ6DOcanXWbi7jhuZlM+vQr+u+5G7ecPYwDenpp+/pC0lQzy61o2s4csTxCdBG8qu/3GdFRxKPVjs4516iYGc9PW8b1z8xgw9ZifnxsP354dB8vbd+A7DCxmNnR5dsk7WVmy2snJOdcQ7VyYwHXPj2dV2auYGi3dvzj+wezb5e2mQ7LpVlNr7FcCNyazkCccw2XmfGfqfn89rmZFJYkuOqkgVz6jd5e2r6BqmliGS1pC/Cqmc1JZ0DOuYZlydotXD1pGu/MXc2IXh24+eyh7OOl7Ru0miaWs4ADgDMl9TWz76cxJudcA5BIGI99uJhbXpqNgBtHD+Y7B+/tpe0bgRolFjNbAbwUHs45t435q77mygl5TFm8jiP6d+b3Zw6h++5ehbixqFFikXQ30NrMLpZ0Qrliks65RqoknuD+dxbw59fm0rJpDn88dz/OOrCbF41sZGp6KqwIWBGGjwE8sTjXyM1cupGxT33O9K82ctKQvfjN6MHs0aZFpsNyGVDTxLIFaCepKeDlgZ1rxApL4tz1xjzufWs+7Vs1497vHMhJQ7tkOiyXQTVNLGuBrcDdwHvpC8c5V5/878t1jJ2Qx7yVX3PWgd247tRBtG/lpe0bu2ollnDr4NuBAcA/iH5pf2n6w3LOZbMtRSX84eUv+Pv7C+nStgV//95BHD1gj0yH5bJEtRKLma2XdDPQi+gGWsOAibUQl3MuS70/bzXjJk7jy7VbuGDk3lx50kB2a57xO3C4LFKTd8OlwEIzexmYmuZ4nHNZamNBMTe9MIvHP15Cr46t+PeYkRy8j5e2d9urSWJZB1wuaQDwOfCZmX2a3rCcc9nk9VkruGbSdFZuKuAHR+zDz47vT4umXjTSVazaicXMbpL0OvAFsD/RvVM8sTjXAK35upDfPDuTyZ8vZeBebbjvguHs16N9psNyWa7aiUXSDUAO8BnR0cpbaY7JOZdhZsazecsYP3kGmwqK+dlx/bniqD40a+JFI92O1eSI5TpJexLVCjtbUh8zuyz9oTnnMmHFxgKumTSd12atYL/u7bj1nJEM2KtNpsNy9UhNv8rxA+A+M/NaYc41EGbGk1OW8NvnZ1FUkuCak/flkm/0JseLRrpqqmlieQi4QlJr4J9m9ln6QnLO1bUla7cwbmIe781bw8G9O3DL2cPo1al1psNy9VRNT5j+mCgpNQHu3JkFJD0kaaWk6SltHSS9KmlueN49ZdpVkuZJmiPpxJT24ZKmhWl3KlS3k9Rc0r9D+0eSetVw25xrNOIJ46F3F3LC7W/z+ZIN/O7MITx+2UhPKm6X1DSxzAdaAM+Y2RE7uczDwKhybeOA182sH/B6GEfSIOA8YHBY5h5Jye823guMAfqFR3KdlwLrzKwvUXWAW6q/Wc41HvNWbuLcv77PDc/NZOQ+HXjlZ0f4/VJcWtQ0scwA3gAulfTJzixgZm8T1RhLNRp4JAw/ApyR0v6EmRWa2UJgHjBCUhegrZl9YGZGVFLmjArWNQE4Nnk045wrUxxPcPeb8zj5jndZsHozt39rPx66+CC6tm+Z6dBcA1HTayz9gVXA/UQ/mKypPc1sGYCZLZOULDbUDfgwZb780FYchsu3J5dZEtZVImkD0JGo9IxzDpj+1QbGTshj5rKNnDK0C+NPH0znNs0zHZZrYGqaWAYS3YPlbmAx0TWXdKroSMOqaK9qme1XLo0hOp1Gz55e9d81fAXFce58fS73vb2ADq2b8dfvDmfUkL0yHZZroGqaWNoDVwJj2bXqxiskdQlHK12AlaE9H+iRMl93YGlo715Be+oy+ZKaAO3Y/tQbAGZ2P9HRFrm5uRUmH+caiqmL1zJ2Qh7zV23m3OHdufaUQbRr1TTTYbkGrKbXWG4gunA/B0jswutPBi4KwxcBz6S0nxe+6dWb6CL9x+G02SZJI8P1kwvLLZNc1znAG+E6jHON0ubCEsZPnsE5f/2AguIEj14ygtvO3c+Tiqt1O3XEEr6RlQ/82sz+Zmb5YRwzG7eT63gcOAroJCkfuB64GXhS0qXAl8C5YZ0zJD0JzARKgB+aWTys6gqib5i1BF4MD4AHgcckzSM6UjlvZ+JyriF6d+5qxk3MI3/dVi46ZG/GjhpIay9t7+qIdvZDvaRXgSlmdlXthlS3cnNzbcqUKZkOw7m02LC1mN89P5Mnp+SzT6fW3HLOMA7q1SHTYbkGSNJUM8utaFp1PsK0AsZKOp6y6xpmZqN3NUDn3K57ZcZyrn16Oms2F3HFUX34ybH9vLS9y4jqJJZDwvOB4QGVfOvKOVd3Vn9dyPjJM3gubxn7dmnLgxcdxNDu7TIdlmvEqpNYetdaFM65ajMzJn++lPGTZ7C5MM4vju/P5Uf1oWmOl7Z3mbXTicXMFtdmIM65nbdsw1aunTSd12evZP8e7bntnGH029NL27vs4F8Tca4eMTMe/3gJN70wi+JEgmtP2ZfvHeal7V128cTiXD2xeM1mxj01jQ8WrOHQPh25+axh9OzYKtNhObedmtya+DQze7Y2gnHObS+eMP7+3kL+8MocmsZi3HTWUM47qAdeY9Vlq5ocsfwO8MTiXB34YsUmxk7I47Ml6zl24B789swhdGnnVYhddqtJYvGPSc7VsuJ4gnvfms9db8yjdfMc7jhvf07fr6sfpbh6oSaJxX+74lwtmpa/gV9N+JzZyzdx2n5duf60QXTazUvbu/rDL947lyUKiuP8+bW5PPDOAjq2bsYDF+Zy/KA9Mx2Wc9XmicW5LPDJorVcOSGPBas3863cHlx9yr60a+lViF39VJPEsiLtUTjXSH1dWMJtL83m0Q8X0619S/5x6cF8o1+nTIfl3C6pdmIxs+NrIxDnGpu3v1jFVROnsXTDVi4+tBe/PGGAl7Z3DYK/i52rYxu2FHPj8zOZMDWfPp1bM+HyQxi+t5e2dw2HJxbn6tBL05fx62dmsHZzET88ug//7xgvbe8anholFkk/N7M/heEB4RbFzrlKrNpUyPWTp/PCtOUM6tKWv198EEO6eWl71zBVK7FIag/cDgyUVADkAZcC30t/aM7Vf2bGpE+/4obnZrKlMM6vThzAmCP28dL2rkGrVmIxs/XA9ySdAiwHTgAm1kJcztV7S9dv5epJ03hrziqG7707t5w9jL577JbpsJyrdTW9xnIkcCcwEuiC1w5zrlQiYfzr4y+5+cXZxBPG9acN4sJDenlpe9do1PR4vD1wJTAWKNiVACQNkPRZymOjpJ9KGi/pq5T2k1OWuUrSPElzJJ2Y0j5c0rQw7U55YSVXxxau3sz5D3zItU9PZ/8e7XnlZ0f4/VJco1PTI5YbgIFmNkdSYlcCCBf+9weQlAN8BUwium5zu5n9IXV+SYOA84DBQFfgNUn9zSwO3AuMAT4EXgBGAS/uSnzO7Yx4wnjw3QX88ZUvaNYkxi1nD+WbuV7a3jVONUosZpYP5IfhcWmM51hgvpktruI/5GjgCTMrBBZKmgeMkLQIaGtmHwBIehQ4A08srpbNWb6JsRM+5/P8DRy375787swh7Nm2RabDci5janQqTNLdkh4OwyekMZ7zgMdTxn8kKU/SQ5J2D23dgCUp8+SHtm5huHz7diSNkTRF0pRVq1alL3rXqBSVJPjza19w6l/eIX/dVv5y/gE8cOFwTyqu0avpNZYiYEEYPiYdgUhqBpwO/Cc03Qv0ITpNtgz4Y3LWCha3Ktq3bzS738xyzSy3c+fOuxK2a6Q+X7Ke0/7yLn9+bS4nD+3Cqz8/ktP8finOATW/xrIFaCepKdAzTbGcBPzPzFYAJJ8BJD0APBdG84EeKct1B5aG9u4VtDuXNluL4tz+2hf87Z0F7NGmBQ9elMux+3ppe+dS1TSxrAW2AncD76UplvNJOQ0mqYuZLQujZwLTw/Bk4F+S/kR08b4f8LGZxSVtkjQS+Ai4EPhLmmJzjo8WrOHKp/JYtGYL54/oyVUnD6RtCy9t71x5Nf3l/QDgH8CjRL+83yWSWgHHAz9Iab5V0v5Ep7MWJaeZ2QxJTwIzgRLgh+EbYQBXAA8DLYku2vuFe7fLNhUUc8tLs/nHh1/Ss0Mr/vX9gzm0r5e2d64yMqvenYYlDQB6AauBYcBqM6u3P5DMzc21KVOmZDoMl6XenLOSayZOY9nGAi45rDe/OKE/rZp57VbnJE01s9yKptXkf8ilwEIzexmYukuROZel1m0u4sbnZzLxf1/Rb4/deOqKQzmw5+47XtA5V6PEsg64PBy5fA58Zmafpjcs5zLnhWnLuO6Z6azfUsz/O6YvPzqmL82beGl753ZWTe4geZOk14EviL4KfATgicXVeys3FnDdMzN4acZyhnRry6OXHMygrm0zHZZz9U61E4ukG4Ac4DOio5W30hyTc3XKzJgwNZ8bn5tJQUmCK0cN5LLDe9PES9s7VyM1OWK5TtJ1RD+uPFtSHzO7LP2hOVf78tdt4epJ03n7i1Uc1Gt3bj57GH06e2l753ZFTb/e8hDwfaA1cE/6wnGubiQSxj8+WswtL87GgBtGD+a7B+9NzKsQO7fLappYfkxU1qUJcAfRdRbn6oUFq77myqfy+GTROg7v14mbzhpK991bZTos5xqMmiaW+US/eH/GzH6WxnicqzUl8QQPvLOQ21/7ghZNYtx2zjDOGd7d63s5l2Y1TSwziCoMXyrpNjM7KI0xOZd2M5du5Mqn8pj21QZGDd6LG84YzB5tvAqxc7WhpomlD9HvWe4Pz85lpcKSOHe9MY9735pP+1ZNuec7B3Ly0C6ZDsu5Bq2miWWJmb0hqQuwMp0BOZcun365jrET8pi78mvOOqAbvz51ELu3bpbpsJxr8GqaWEZJ+oKouvFioov5zmWFrUVx/vDKHB56byF7tW3B3y8+iKMH7pHpsJxrNGqaWNoDVwJjib527FxWeH/+asY9NY0v127huyN7cuWogbTx0vbO1amaJpYbgIFmNkdSfIdzO1fLNhYUc9MLs3n84y/p1bEVT4wZych9OmY6LOcapZ1KLJJyiO7Q+Gsz+5uZ5YdxzGxcLcbn3A69PmsF10yazspNBYw5Yh9+dlx/WjbzopHOZcpOJZZwd8bpRN8Gcy4rrN1cxG+encEzny1lwJ5tuO+C4ezXo32mw3Ku0avOqbBWwFhJx1N2L3kzs9HpD8u5ypkZz+YtY/zkGWwqKOanx/Xj/47qS7MmXjTSuWxQncRySHg+MDwgum2wc3VmxcYCrpk0nddmrWC/7u249ZyRDNirTabDcs6lqE5i6V1rUTi3A2bGk1OW8NvnZ1FUkuCak/flkm/0JseLRjqXdXaYWCT1DIMVHp2kTF9vZhtrEoSkRcAmIA6UmFmupA7Av4FewCLgm2a2Lsx/FdEtkuPAj8NtkpE0HHgYaAm8APzEzPyoqp5bsnYLV02cxrvzVnNw7w7ccvYwenVqnemwnHOV2JkjlkeIkkpVHw2NqEN/dBdiOdrMVqeMjwNeN7ObJY0L41dKGgScBwwGugKvSepvZnHgXmAM8CFRYhkFvLgLMbkMiieMRz9YxK0vzSEnJn57xhC+PaKnl7Z3LsvtMLGY2dF1EUgFRgNHheFHgLeIfpQ5GnjCzAqBhZLmASPCUU9bM/sAQNKjwBl4YqmX5q3cxJVPTWPq4nUcNaAzvz9zKF3bt8x0WM65nVDTH0immwGvSDLgPjO7H9jTzJYBmNkyScmaHN2IjkiS8kNbcRgu374dSWOIjmzo2bNnRbO4DCmOJ7j/7QXc8dpcWjXP4fZv7ccZ+3fz0vbO1SPZklgOM7OlIXm8Kml2FfNW1MNUdqquwusrIXHdD5Cbm+vXYLLE9K82MHZCHjOXbeSUoV0Yf/pgOrdpnumwnHPVlBWJxcyWhueVkiYBI4AVkrqEo5XUKsr5QI+UxbsT/a4mPwyXb3dZrqA4zp2vz+W+txfQoXUz/vrd4Ywaslemw3LO1VDGf1EmqbWkNslh4ARgOjAZuCjMdhHwTBieDJwnqbmk3kR3svw4nDbbJGmkovMmF6Ys47LU1MVrOeXOd7jnrfmcdUA3XvvZkZ5UnKvnsuGIZU9gUjiH3gT4l5m9JOkT4ElJlwJfAucCmNkMSU8CM4ES4IfhG2EAV1D2deMX8Qv3WWtzYQm3vTyHRz5YRNd2LXn0khEc0b9zpsNyzqWBGvvPPHJzc23KlCmZDqNReXfuasZNzOOr9Vu56JBe/OrEAbRung2fcZxzO0vSVDPLrWia/292dWbD1mJ+9/xMnpySzz6dWvPkDw7hoF4dMh2Wcy7NPLG4OvHKjOVc+/R01mwu4oqj+vCTY/vRoqmXtneuIfLE4mrV6q8LGT95Bs/lLWPfLm158KKDGNq9XabDcs7VIk8srlaYGc98tpTfPDuDzYVxfnF8fy4/qg9NczL+RUTnXC3zxOLSbtmGrVwzaTpvzF7JAT3bc+vZw+i3p5e2d66x8MTi0iaRMJ74ZAk3vTCLkoTx61MHcfGhvby0vXONjCcWlxaL12zmyqfy+HDBWg7t05GbzxpGz46tMh2Wcy4DPLG4XRJPGH9/byF/eGUOTWMxbj5rKN86qIcXjXSuEfPE4mrsixWbGDshj8+WrOfYgXvwuzOHsle7FpkOyzmXYZ5YXLUVlST463/n85c35tKmRVPuOG9/Tt+vqx+lOOcATyyumvLy1zN2Qh6zl2/itP26Mv60QXTczUvbO+fKeGJxO6WgOM7tr33BA28voHOb5jxwYS7HD9oz02E557KQJxa3Qx8vXMu4p/JYsHoz5x3Ug6tO3pd2LZtmOiznXJbyxOIq9XVhCbe+NJtHP1hMjw4t+ef3D+awvp0yHZZzLst5YnEV+u8Xq7h64jSWbtjK9w6LStu3auZvF+fcjnlP4baxfksRNz43i6f+l0+fzq2ZcPkhDN/bS9s753aeJxZX6qXpy7j26Rms21LEj47uy4+O6eul7Z1z1eaJxbFqUyHXT57OC9OWM6hLWx7+3kEM6eal7Z1zNeOJpREzMyZ9+hU3PDeTLUVxfnXiAMYcsY+XtnfO7ZKM9yCSekh6U9IsSTMk/SS0j5f0laTPwuPklGWukjRP0hxJJ6a0D5c0LUy7U/5T8Ep9tX4r33v4E37+5Of06bwbL/z4cH54dF9PKs65XZYNRywlwC/M7H+S2gBTJb0apt1uZn9InVnSIOA8YDDQFXhNUn8ziwP3AmOAD4EXgFHAi3W0HfVCImH88+MvufmFWSQMrj9tEBce4qXtnXPpk/HEYmbLgGVheJOkWUC3KhYZDTxhZoXAQknzgBGSFgFtzewDAEmPAmfgiaXUwtVRafuPF67lG307cdNZQ+nRwUvbO+fSK+OJJZWkXsABwEfAYcCPJF0ITCE6qllHlHQ+TFksP7QVh+Hy7Y1eSTzBQ+8t5I+vfEGzJjFuPXsY5+Z296KRzrlakTUn1CXtBjwF/NTMNhKd1uoD7E90RPPH5KwVLG5VtFf0WmMkTZE0ZdWqVbsaelabvXwjZ9/7Pr9/YTZH9O/Maz8/km/6/VKcc7UoK45YJDUlSir/NLOJAGa2ImX6A8BzYTQf6JGyeHdgaWjvXkH7dszsfuB+gNzc3AqTT31XVJLg7jfncc9b82jboil/Of8ATh3WxROKc67WZTyxhG9uPQjMMrM/pbR3CddfAM4EpofhycC/JP2J6OJ9P+BjM4tL2iRpJNGptAuBv9TVdmSTz5dEpe3nrNjE6P27cv1pg+nQulmmw3LONRIZTyxE11IuAKZJ+iy0XQ2cL2l/otNZi4AfAJjZDElPAjOJvlH2w/CNMIArgIeBlkQX7RvVhfutRVFp+7+9s4A92rTgwYtyOXZfL23vnKtbMmuQZ4J2Wm5urk2ZMiXTYeyyDxesYdxTeSxas4XzR/TkqpMH0raFl7Z3ztUOSVPNLLeiadlwxOJ2waaCYm5+cTb//OhLenZoxb8uO5hD+3hpe+dc5nhiqcfenLOSayZOY9nGAi79Rm9+cUJ/L23vnMs474XqoXWbi7jxuZlM/PQr+u2xG09dcSgH9tw902E55xzgiaXeeWHaMq57ZjrrtxTz42P68sNj+tK8iZe2d85lD08s9cTKjQVc98wMXpqxnCHd2vLoJQczqGvbTIflnHPb8cSS5cyMCVPzufG5mRSUJLhy1EAuO7w3TbwKsXMuS3liyWL567Zw9aTpvP3FKg7qtTs3nz2MPp13y3RYzjlXJU8sWSiRMB77cDG3vDQbgBtGD+a7B+9NzEvbO+fqAU8sWWbBqq+58qk8Plm0jsP7RaXtu+/upe2dc/WHJ5YsURJP8MA7C7n9tS9o0STGbecM45zhXtreOVf/eGLJArOWbWTshDymfbWBEwfvyY2jh7BH2xaZDss552rEE0sGFZbEufuNedzz1nzat2rK3d8+kJOH7uVHKc65es0TS4Z8+uU6xk7IY+7KrznzgG5cd+ogdvfS9s65BsATSx3bWhTnj6/M4aH3FrJn2xb8/eKDOHrgHpkOyznXCJlZrZwh8cRShz6Yv4ZxE/NYvGYL3z64J1edNJA2XtreuQbPzDArwayYRKJo24cVYaXjhSQSRdvPZ9GzbdNWvG2blVtHaVtxufGyx8ABN9Ct2/lp315PLHVgU0ExN704m3999CV7d2zF45eN5JA+HTMdlnMNUrITjzrooko68uLSDrjiTjvZVlyu066oI6+40y6dFtqjexamaxtziLrvJpg1AXIwa4JZDmY5JBI5mMVIJGJYIodEogWJRCviiRiJeIx4QsTjMdq2yaFbt7SFVcoTSy17c/ZKrp40jRUbC/j+N3rzixMG0LKZF410DUPUiReX67STnWpxxZ12onDb9pTlK+q0t5+vkES8iHgyMaR03mbFmBWT7k48tdOuqPNOJHKi57iIJ3JIxFsTj+9GwqL2aL6c0MnHSFgOllzOYqXtFuYvW3fF7Tk5TcjJySEnJ4dYLFY6vDOPZs3KhvfYY0ja9lMqTyy1ZN3mIm54biaTQmn7e644lAO8tL3bBVEnXv7T8I5OrVTSVu7TdjxRSDy+fYed2KbzLt6m84468OK0bmPUcVbRcac8LJETOu7dwnBqe7Ijj4V5yjry0nWmdu6J6AhAsabE1AypKbFYM2Kxptt04lU9muTkkNNs5zv4mj5isVjWf3PUE0uamRkvTFvO9ZO9tH19ZmYVn0qppNPe/rRJ1FFHj4LS4US8kHhyuXj5T+/RJ/zyHXf0XBIe6do+Sjvv1E/UlXfMMSzRnIS13OGn7tR2aAJqgmiK1BSpWWnnHYtF41EH3pycnMo78WZNd70z3tE82dZZmxnxhBE3I5GAIjPiRQkSiXhoi6bFE9H00uHkcinDCTMSRpjXKEkkSMQT9N2zDd07tE577J5Y0mjlxgJ+/cx0Xp6xgqHd2vHYpQezbxcvbb8jZgkSieLQiRdW2ZHH44WUlBRQEi8kXlKwTacdjxdGn7bjYd5E4TbnvrftuItKL6ZCMVgJRlkHLsXTuH3avvNOPbWxXUcewxKttm0Ln84tEcPCuXXRBEKHjZId9/afuBVrTk4stQPfthNv2qx6HfB2HXIsBoqhWE70rBiEtoRRYUcXTxjxkhLi8QTxkjjxkhKKE0ZxcZzieIKSeJzikjjxeILi4mK2lsQpKS4hXlhCvKQ4Gi4uoKS4hES8mERJnHhxnERYp5XESSQSWDzqQBPxBMQNSySiR9wwS2BmkBDEE8lsiyVAiehUmiUMASQMGWAgDBJE7aVtIIseJIdJaQ/zxoAYBiZi2HbzRDXLLTyLZA3zaD5F60FIlA2j0rlSh1E0bsl2lb4aIExiRtt8fnjvb9L2Xk9qcIlF0ijgDiAH+JuZ3Vzbr1m+tP24kwby/W9kZ2n7qBPfttMuCR10SUnKI15AvCT6tF2S+mk72XlXcpEyeS7crBisGKMYsxKSnTcUg5KfvuNIJUiJNG6ftjtHXRLPIZFoQjyRQ0m8CXFrQjzehHiiGfFEK4oTTUhYM0oSTYnTFEs0JZFoQiKeQ8KaYIkmWKIpJJpEn8ITMUg0Cc9RwiAeizooC88JIRNKENqjjsnMojazqPMyI5YALIEsgULnJjOUiPaLkuPRBkbLhO1N7jphpR0XJkQCKEQUgm0u7U6i9SW7pOhfWbITSi5bvqPa9lHapu2nlXaNipV2ahDbZl5TLEQsCMPbtJc+R9Ot/DQJIxamNwOahQ67FsTKPdeRhMWRGXESQHhPEJJfaCsbL2srHTbDtmlPVDCeINZqda3E36ASi6Qc4G7geCAf+ETSZDObWVuvuWTtFq55uqy0/S1nD6N3p9bE44UUFhZSXJzsrLeG50JK4gWUFG+lsLCQgq2bKSgsoKhwC0UFhRQWFlFSXEhxcTHFJcWUlJREn8biceIl0aetRIlF59sTRiJumAmFT1pW+r6JRW2m6FNUQtF7iljojELnlyDqDC10OKZyz1FXQhgu7XiS02mOrEUYDvMRXlPadv7UDsiiDqesiyvrYKKOLoqJ0k6o7H/4Nh1WaQdXdWdFsiNKeU52XjGic9Y5pZ1YynzK0IcDEX00qiEr97ztxNAxlT6XdUZK6chSOyuVvbFKn227Tq18JxYPzwZKlA5bmM9S25TYblo0nrJsBc9gJEJ2NYXXDs/R2yw5HpYrHQ6vIYvaFL2WkuNEy0Zv4Wh66bRwiKHwmgIUMyx5FBGz6A8YC+/I8BaStpmEEDFFHzhiUtlRi0TMSudAFr1HY9uNx0rnixFDVjZX6nCMGEokx5PLROMFHQfW8B1WtQaVWIARwDwzWwAg6QlgNJD2xHL35T+lWcEIkDicGIcjbMVWXvt4GaUdobbt6FI/iZlygNagNjV6/W0/H9YuS31BKumstlkgUdpZKdn5WCJ8qq7809Z2ndd2nVn5TqyEZCcmtv+EFnVQKcMVTbfQQVlyOHSqSpSOly6X7GRInvNIOR9S2iGlnA+JJY/EQqdTeq5EKLbtOZNY2MdKPpLjEJ37TxmOKTyT7JyihWKKhfli0Zkoi5FM36XPJmLkhD9nDjELnVXojKJlQlsiWirZaZW+oiU7ruh9HrOc0uUJ06IPEznho4OQ5YCldmyEWAhbEf7kpcmn7J1m4WiMlCmp80QJrqJpyemlK992/Tu5XEWvWbauSuKqdN3VWbbcPthmnnLrLD+PVb5sIkyLA3v160JtaGiJpRuwJGU8Hzi4/EySxgBjAHr27FmjFyppWkLzLUtJ7WSifjf1k5ZVMJ78VAVlHV/Kp6iU+UrfNKXzUjZNhM6Usg4vdFql/wvDx6Pkp6pkclAYUVi3JCRL/veOPlmFC5mxcJ42Fos6MCmGYobIIQdQLBZ1hLHwVrIYObFkNxZD5JQeUcRMEIsRIwdT6IiIzsnHFEMxpcRWFm8yxmibk71uTji7EuYtPTeksouwKZ2xUbbdyfVRuki5tpRnKbmfVLpsrGzFZa+RkhWi9SR/0azS9SdjVQi47OxQMpbktllKUzL2snhLXzelKTlS+lrJ/bftwDb7pmxque0u/3rbLavyq9hmn233qhWuN7WtgvnYPs6y19X2bbuyrRXEWfFrld+GssYK/xY13obU9Vbw+hUuu/3fq6q/Z3J8tw6183u6hpZYKti7KR8Nkg1m9wP3A+Tm5u7wA3hFfvKXu2qymHPONXjZd3V51+QDPVLGuwNLMxSLc841Sg0tsXwC9JPUW1Iz4DxgcoZjcs65RqVBnQozsxJJPwJeJvpOzUNmNiPDYTnnXKPSoBILgJm9ALyQ6Ticc66xaminwpxzzmWYJxbnnHNp5YnFOedcWnlicc45l1ZKlkNorCStAhbXcPFOQO1UcUsvjzN96kOM4HGmU32IEeo+zr3NrHNFExp9YtkVkqaYWW6m49gRjzN96kOM4HGmU32IEbIrTj8V5pxzLq08sTjnnEsrTyy75v5MB7CTPM70qQ8xgseZTvUhRsiiOP0ai3POubTyIxbnnHNp5YnFOedcWnliqSFJoyTNkTRP0rgMx7JI0jRJn0maEto6SHpV0tzwvHvK/FeFuOdIOrEW43pI0kpJ01Paqh2XpOFh++ZJulMV3i4v7XGOl/RV2KefSTo5k3FK6iHpTUmzJM2Q9JPQnlX7s4o4s2Z/Smoh6WNJn4cYfxPas21fVhZn1uzLSlm417c/dv5BVJJ/PrAP0Az4HBiUwXgWAZ3Ktd0KjAvD44BbwvCgEG9zoHfYjpxaiusI4EBg+q7EBXwMHEJ0h9AXgZPqIM7xwC8rmDcjcQJdgAPDcBvgixBLVu3PKuLMmv0Z1rdbGG4KfASMzMJ9WVmcWbMvK3v4EUvNjADmmdkCMysCngBGZzim8kYDj4ThR4AzUtqfMLNCM1sIzCPanrQzs7eBtbsSl6QuQFsz+8Ci/yGPpixTm3FWJiNxmtkyM/tfGN4EzAK6kWX7s4o4K1PncVrk6zDaNDyM7NuXlcVZmYz9HyrPE0vNdAOWpIznU/V/ntpmwCuSpkoaE9r2NLNlEP1nB/YI7ZmOvbpxdQvD5dvrwo8k5YVTZcnTIhmPU1Iv4ACiT7BZuz/LxQlZtD8l5Uj6DFgJvGpmWbkvK4kTsmhfVsQTS81UdH4yk9/bPszMDgROAn4o6Ygq5s222JMqiytT8d4L9AH2B5YBfwztGY1T0m7AU8BPzWxjVbNWEk+m4syq/WlmcTPbH+hO9Kl+SBWzZ2xfVhJnVu3LinhiqZl8oEfKeHdgaYZiwcyWhueVwCSiU1srwiEw4XllmD3TsVc3rvwwXL69VpnZivCfOgE8QNnpwozFKakpUWf9TzObGJqzbn9WFGc27s8Q13rgLWAUWbgvK4ozW/dlKk8sNfMJ0E9Sb0nNgPOAyZkIRFJrSW2Sw8AJwPQQz0VhtouAZ8LwZOA8Sc0l9Qb6EV3YqyvViiucktgkaWT4JsuFKcvUmmQHE5xJtE8zFmdY54PALDP7U8qkrNqflcWZTftTUmdJ7cNwS+A4YDbZty8rjDOb9mWlavObAQ35AZxM9I2X+cA1GYxjH6JvgnwOzEjGAnQEXgfmhucOKctcE+KeQy1+OwR4nOhQvZjoU9OlNYkLyCX6zzMfuItQMaKW43wMmAbkEf2H7ZLJOIFvEJ2+yAM+C4+Ts21/VhFn1uxPYBjwaYhlOnBdTf/P1PK+rCzOrNmXlT28pItzzrm08lNhzjnn0soTi3POubTyxOKccy6tPLE455xLK08szjnn0soTi3NpJKm9pP9LGe8qaUItvdYZkq6rZNrX4bmzpJdq4/Wdq4wnFufSqz1QmljMbKmZnVNLrzUWuKeqGcxsFbBM0mG1FINz2/HE4lx63Qz0CffJuE1SL4X7vEi6WNLTkp6VtFDSjyT9XNKnkj6U1CHM10fSS6Go6DuSBpZ/EUn9gUIzWx3Ge0v6QNInkm4sN/vTwHdqdaudS+GJxbn0GgfMN7P9zexXFUwfAnybqL7T74AtZnYA8AFRqQ2A+4H/Z2bDgV9S8VHJYcD/UsbvAO41s4OA5eXmnQIcXsPtca7ammQ6AOcamTctuk/JJkkbgGdD+zRgWKgKfCjwn5Sb/DWvYD1dgFUp44cBZ4fhx4BbUqatBLqmJ3zndswTi3N1qzBlOJEyniD6/xgD1ltUKr0qW4F25doqq8/UIszvXJ3wU2HOpdcmolvy1ohF9y5ZKOlciKoFS9qvgllnAX1Txt8jqrIN219P6U9ZBVznap0nFufSyMzWAO9Jmi7pthqu5jvApZKSFasruu3128ABKjtf9hOim7x9wvZHMkcDz9cwFueqzasbO1dPSboDeNbMXtvBfG8Do81sXd1E5ho7P2Jxrv76PdCqqhkkdQb+5EnF1SU/YnHOOZdWfsTinHMurTyxOOecSytPLM4559LKE4tzzrm08sTinHMurf4/W/9XmSZ1ttYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -198,7 +198,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -218,404 +218,6 @@
     "legend.remove()\n",
     "fig.savefig(\"symba_swifter_comparison-8pl-16tp-testparticles-vmag.png\", facecolor='white', transparent=False, dpi=300)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftdiff = swiftdiff.rename({'time (d)' : 'time'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --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",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --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",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\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",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\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",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\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",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\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",
-       "}\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;px&#x27; (id: 8)&gt;\n",
-       "array([0., 0., 0., 0., 0., 0., 0., 0.])\n",
-       "Coordinates:\n",
-       "  * id       (id) int64 1 2 3 4 5 6 7 8\n",
-       "    time     float64 11.0</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'>id</span>: 8</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5a0b3e5f-3765-4c62-99cf-d7824baab663' class='xr-array-in' type='checkbox' checked><label for='section-5a0b3e5f-3765-4c62-99cf-d7824baab663' 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</span></div><div class='xr-array-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></div></li><li class='xr-section-item'><input id='section-127b94a9-4391-4761-a1e2-d5a2c139e81e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-127b94a9-4391-4761-a1e2-d5a2c139e81e' 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 class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8</div><input id='attrs-c5ae617e-19d0-42fa-b996-a5a8f0f6458f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5ae617e-19d0-42fa-b996-a5a8f0f6458f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ab3f0c-79ef-43ee-a84d-399fb5f175d9' class='xr-var-data-in' type='checkbox'><label for='data-79ab3f0c-79ef-43ee-a84d-399fb5f175d9' 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([1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.0</div><input id='attrs-ddf3dda4-8057-472d-84ed-4dcb26b2b3c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddf3dda4-8057-472d-84ed-4dcb26b2b3c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-343f7939-c529-4907-8694-922a6b084f43' class='xr-var-data-in' type='checkbox'><label for='data-343f7939-c529-4907-8694-922a6b084f43' 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(11.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7ab9f03-cf2e-41f7-9ce1-c2e2c57f15cc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7ab9f03-cf2e-41f7-9ce1-c2e2c57f15cc' 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' (id: 8)>\n",
-       "array([0., 0., 0., 0., 0., 0., 0., 0.])\n",
-       "Coordinates:\n",
-       "  * id       (id) int64 1 2 3 4 5 6 7 8\n",
-       "    time     float64 11.0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "swiftdiff['px'].sel(id=plidx).isel(time=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/python/swiftest/swiftest/io.py b/python/swiftest/swiftest/io.py
index da1c49f17..540f9cf30 100644
--- a/python/swiftest/swiftest/io.py
+++ b/python/swiftest/swiftest/io.py
@@ -694,8 +694,8 @@ def swiftest2xr(param):
         ds = xr.combine_by_coords([cbds, plds, tpds])
 
     elif ((param['OUT_TYPE'] == 'NETCDF_DOUBLE') or (param['OUT_TYPE'] == 'NETCDF_FLOAT')):
-        print('\nCreating Dataset')
-        ds = xr.open_dataset(param['BIN_OUT'])
+        print('\nCreating Dataset from NetCDF file')
+        ds = xr.open_dataset(param['BIN_OUT'], mask_and_scale=False)
         ds = clean_string_values(param, ds)
     else:
         print(f"Error encountered. OUT_TYPE {param['OUT_TYPE']} not recognized.")
diff --git a/src/fraggle/fraggle_generate.f90 b/src/fraggle/fraggle_generate.f90
index 6f7ccb7a3..1c189c5ca 100644
--- a/src/fraggle/fraggle_generate.f90
+++ b/src/fraggle/fraggle_generate.f90
@@ -545,14 +545,18 @@ function radial_objective_function(v_r_mag_input) result(fval)
             integer(I4B)                          :: i
             real(DP), dimension(:,:), allocatable :: v_shift
             real(DP), dimension(frag%nbody)       :: kearr
-            real(DP)                              :: keo, ke_radial
+            real(DP)                              :: keo, ke_radial, rotmag2, vmag2
       
             associate(nfrag => frag%nbody)
                allocate(v_shift, mold=frag%vb)
                v_shift(:,:) = fraggle_util_vmag_to_vb(v_r_mag_input, frag%v_r_unit, frag%v_t_mag, frag%v_t_unit, frag%mass, frag%vbcom) 
-               do concurrent(i = 1:nfrag)
-                  kearr(i) = frag%mass(i) * (frag%Ip(3, i) * frag%radius(i)**2 * dot_product(frag%rot(:, i), frag%rot(:, i)) + dot_product(v_shift(:, i), v_shift(:, i)))
+               !$omp do simd 
+               do i = 1,nfrag
+                  rotmag2 = frag%rot(1,i)**2 + frag%rot(2,i)**2 + frag%rot(3,i)**2
+                  vmag2 = v_shift(1,i)**2 + v_shift(2,i)**2 + v_shift(3,i)**2
+                  kearr(i) = frag%mass(i) * (frag%Ip(3, i) * frag%radius(i)**2 * rotmag2 + vmag2) 
                end do
+               !$omp end do simd
                keo = 2 * frag%ke_budget - sum(kearr(:))
                ke_radial = frag%ke_budget - frag%ke_orbit - frag%ke_spin
                ! The following ensures that fval = 0 is a local minimum, which is what the BFGS method is searching for
diff --git a/src/io/io.f90 b/src/io/io.f90
index eacaacf48..f04c07e4f 100644
--- a/src/io/io.f90
+++ b/src/io/io.f90
@@ -1202,13 +1202,13 @@ module function io_read_frame_body(self, iu, param) result(ierr)
                   else
                      read(iu, *, err = 667, iomsg = errmsg) name(i), val
                   end if
-                  call self%info(i)%set_value(name=name(i))
                   self%Gmass(i) = real(val, kind=DP)
                   self%mass(i) = real(val / param%GU, kind=DP)
                   read(iu, *, err = 667, iomsg = errmsg) self%radius(i)
                class is (swiftest_tp)
-                  read(iu, *, err = 667, iomsg = errmsg) self%id(i)
+                  read(iu, *, err = 667, iomsg = errmsg) name(i)
                end select
+               call self%info(i)%set_value(name=name(i))
 
                select case(param%in_form)
                case (XV)
diff --git a/src/kick/kick.f90 b/src/kick/kick.f90
index 9f678dcae..45da5cb31 100644
--- a/src/kick/kick.f90
+++ b/src/kick/kick.f90
@@ -63,24 +63,25 @@ module subroutine kick_getacch_int_all_pl(npl, nplpl, k_plpl, x, Gmass, radius,
       real(DP)     :: rji2, rlim2
       real(DP)     :: xr, yr, zr
 
-
       ahi(:,:) = 0.0_DP
       ahj(:,:) = 0.0_DP
-      !$omp parallel do default(private)&
-      !$omp shared(nplpl, k_plpl, x, Gmass, radius)  &
+
+      !$omp parallel do default(private) schedule(static)&
+      !$omp shared(nplpl, k_plpl, x, Gmass, radius) &
+      !$omp lastprivate(rji2, rlim2, xr, yr, zr) &
       !$omp reduction(+:ahi) &
       !$omp reduction(-:ahj) 
       do k = 1_I8B, nplpl
-         i = k_plpl(1,k)
-         j = k_plpl(2,k)
-         xr = x(1, j) - x(1, i)
-         yr = x(2, j) - x(2, i)
-         zr = x(3, j) - x(3, i)
+         i = k_plpl(1, k)
+         j = k_plpl(2, k)
+         xr = x(1, j) - x(1, i) 
+         yr = x(2, j) - x(2, i) 
+         zr = x(3, j) - x(3, i) 
          rji2 = xr**2 + yr**2 + zr**2
          rlim2 = (radius(i) + radius(j))**2
          if (rji2 > rlim2) call kick_getacch_int_one_pl(rji2, xr, yr, zr, Gmass(i), Gmass(j), ahi(1,i), ahi(2,i), ahi(3,i), ahj(1,j), ahj(2,j), ahj(3,j))
       end do
-      !$omp end parallel do
+      !$omp end parallel do 
      
       do concurrent(i = 1:npl)
          acc(:,i) = acc(:,i) + ahi(:,i) + ahj(:,i)
@@ -110,8 +111,9 @@ module subroutine kick_getacch_int_all_tp(ntp, npl, xtp, xpl, GMpl, lmask, acc)
       real(DP)     :: xr, yr, zr
       integer(I4B) :: i, j
 
-      !$omp parallel do default(private)&
-      !$omp shared(npl, ntp, lmask, xtp, xpl, acc)
+      !$omp parallel do default(private) schedule(static)&
+      !$omp shared(npl, ntp, lmask, xtp, xpl) &
+      !$omp reduction(-:acc)
       do i = 1, ntp
          if (lmask(i)) then
             do j = 1, npl
@@ -119,7 +121,7 @@ module subroutine kick_getacch_int_all_tp(ntp, npl, xtp, xpl, GMpl, lmask, acc)
                yr = xtp(2, i) - xpl(2, j)
                zr = xtp(3, i) - xpl(3, j)
                rji2 = xr**2 + yr**2 + zr**2
-               call kick_getacch_int_one_tp(rji2, xr, yr, zr, GMpl(i), acc(1,i), acc(2,i), acc(3,i))
+               call kick_getacch_int_one_tp(rji2, xr, yr, zr, GMpl(j), acc(1,i), acc(2,i), acc(3,i))
             end do
          end if
       end do
@@ -130,6 +132,7 @@ end subroutine kick_getacch_int_all_tp
 
 
    module pure subroutine kick_getacch_int_one_pl(rji2, xr, yr, zr, Gmi, Gmj, axi, ayi, azi, axj, ayj, azj)
+      !$omp declare simd(kick_getacch_int_one_pl)
       !! author: David A. Minton
       !!
       !! Compute direct cross (third) term heliocentric accelerations for a single pair of massive bodies
@@ -161,6 +164,7 @@ end subroutine kick_getacch_int_one_pl
 
 
    module pure subroutine kick_getacch_int_one_tp(rji2, xr, yr, zr, GMpl, ax, ay, az)
+      !$omp declare simd(kick_getacch_int_one_tp)
       !! author: David A. Minton
       !!
       !! Compute direct cross (third) term heliocentric accelerations of a single test particle massive body pair.
diff --git a/src/modules/rmvs_classes.f90 b/src/modules/rmvs_classes.f90
index 9ec0c8b86..b29cd02c4 100644
--- a/src/modules/rmvs_classes.f90
+++ b/src/modules/rmvs_classes.f90
@@ -104,6 +104,7 @@ module rmvs_classes
 
    interface
       module pure subroutine rmvs_chk_ind(xr, yr, zr, vxr, vyr, vzr, dt, r2crit, lencounter, lvdotr)
+         !$omp declare simd(rmvs_chk_ind)
          implicit none
          real(DP), intent(in)  :: xr, yr, zr    !! Relative distance vector components
          real(DP), intent(in)  :: vxr, vyr, vzr !! Relative velocity vector components
diff --git a/src/modules/swiftest_classes.f90 b/src/modules/swiftest_classes.f90
index 8c7376c88..598cb4811 100644
--- a/src/modules/swiftest_classes.f90
+++ b/src/modules/swiftest_classes.f90
@@ -561,6 +561,23 @@ module pure elemental subroutine drift_one(mu, px, py, pz, vx, vy, vz, dt, iflag
          integer(I4B), intent(out)      :: iflag !! iflag : error status flag for Danby drift (0 = OK, nonzero = ERROR)
       end subroutine drift_one
 
+      module pure subroutine util_index_eucl_ij_to_k(n, i, j, k)
+         !$omp declare simd(util_index_eucl_ij_to_k)
+         implicit none
+         integer(I4B), intent(in)  :: n !! Number of bodies
+         integer(I4B), intent(in)  :: i !! Index of the ith body
+         integer(I4B), intent(in)  :: j !! Index of the jth body
+         integer(I8B), intent(out) :: k !! Index of the flattened matrix
+      end subroutine util_index_eucl_ij_to_k
+
+      module pure subroutine util_index_eucl_k_to_ij(n, k, i, j)
+         implicit none
+         integer(I4B), intent(in)  :: n !! Number of bodies
+         integer(I8B), intent(in)  :: k !! Index of the flattened matrix
+         integer(I4B), intent(out) :: i !! Index of the ith body
+         integer(I4B), intent(out) :: j !! Index of the jth body
+      end subroutine util_index_eucl_k_to_ij
+
       module subroutine util_index_eucl_plpl(self, param)
          implicit none
          class(swiftest_pl),         intent(inout) :: self  !! Swiftest massive body object
@@ -859,6 +876,7 @@ module subroutine kick_getacch_int_all_tp(ntp, npl, xtp, xpl, GMpl, lmask, acc)
       end subroutine kick_getacch_int_all_tp
 
       module pure subroutine kick_getacch_int_one_pl(rji2, xr, yr, zr, Gmi, Gmj, axi, ayi, azi, axj, ayj, azj)
+         !$omp declare simd(kick_getacch_int_one_pl)
          implicit none
          real(DP), intent(in)  :: rji2            !! Square of distance between the two bodies
          real(DP), intent(in)  :: xr, yr, zr      !! Distances between the two bodies in x, y, and z directions
@@ -869,6 +887,7 @@ module pure subroutine kick_getacch_int_one_pl(rji2, xr, yr, zr, Gmi, Gmj, axi,
       end subroutine kick_getacch_int_one_pl
 
       module pure subroutine kick_getacch_int_one_tp(rji2, xr, yr, zr, Gmpl, ax, ay, az)
+         !$omp declare simd(kick_getacch_int_one_tp)
          implicit none
          real(DP), intent(in)  :: rji2         !! Square of distance between the test particle and massive body
          real(DP), intent(in)  :: xr, yr, zr   !! Distances between the two bodies in x, y, and z directions
diff --git a/src/modules/symba_classes.f90 b/src/modules/symba_classes.f90
index bd4b14486..40267e298 100644
--- a/src/modules/symba_classes.f90
+++ b/src/modules/symba_classes.f90
@@ -257,6 +257,7 @@ module subroutine symba_drift_tp(self, system, param, dt)
       end subroutine symba_drift_tp
 
       module pure subroutine symba_encounter_check_one(xr, yr, zr, vxr, vyr, vzr, rhill1, rhill2, dt, irec, lencounter, lvdotr)
+         !$omp declare simd(symba_encounter_check_one)
          implicit none
          real(DP),     intent(in)  :: xr, yr, zr, vxr, vyr, vzr
          real(DP),     intent(in)  :: rhill1, rhill2, dt
diff --git a/src/netcdf/netcdf.f90 b/src/netcdf/netcdf.f90
index 80bcdcb5d..b26a1ba63 100644
--- a/src/netcdf/netcdf.f90
+++ b/src/netcdf/netcdf.f90
@@ -38,17 +38,22 @@ module subroutine netcdf_initialize_output(self, param)
       !! author: Carlisle A. Wishard, Dana Singh, and David A. Minton
       !!
       !! Initialize a NetCDF file system and defines all variables.
+      use, intrinsic :: ieee_arithmetic
       implicit none
       ! Arguments
       class(netcdf_parameters),   intent(inout) :: self    !! Parameters used to identify a particular NetCDF dataset
       class(swiftest_parameters), intent(in)    :: param           !! Current run configuration parameters 
       ! Internals
       logical :: fileExists
-      integer(I4B) :: old_mode
+      integer(I4B) :: old_mode, nvar, varid, vartype
+      real(DP) :: dfill
+      real(SP) :: sfill
+
+      dfill = ieee_value(dfill, IEEE_QUIET_NAN)
+      sfill = ieee_value(sfill, IEEE_QUIET_NAN)
 
       !! Create the new output file, deleting any previously existing output file of the same name
       call check( nf90_create(param%outfile, NF90_NETCDF4, self%ncid) )
-      call check( nf90_set_fill(self%ncid, nf90_nofill, old_mode) )
 
       ! Define the NetCDF dimensions with particle name as the record dimension
       call check( nf90_def_dim(self%ncid, ID_DIMNAME, NF90_UNLIMITED, self%id_dimid) )     ! 'x' dimension
@@ -138,6 +143,22 @@ module subroutine netcdf_initialize_output(self, param)
       call check( nf90_def_var(self%ncid, DISCARD_VHZ_VARNAME, self%out_type, self%id_dimid,  self%discard_vhz_varid) )
       call check( nf90_def_var(self%ncid, DISCARD_BODY_ID_VARNAME, NF90_INT, self%id_dimid, self%discard_body_id_varid) )
 
+      ! Set fill mode to NaN for all variables
+      call check( nf90_inquire(self%ncid, nVariables=nvar) )
+      do varid = 1, nvar
+         call check( nf90_inquire_variable(self%ncid, varid, xtype=vartype) )
+         select case(vartype)
+         case(NF90_INT)
+            call check( nf90_def_var_fill(self%ncid, varid, 0, NF90_FILL_INT) )
+         case(NF90_FLOAT)
+            call check( nf90_def_var_fill(self%ncid, varid, 0, sfill) )
+         case(NF90_DOUBLE)
+            call check( nf90_def_var_fill(self%ncid, varid, 0, dfill) )
+         case(NF90_CHAR)
+            call check( nf90_def_var_fill(self%ncid, varid, 0, 0) )
+         end select
+      end do
+
       return
    end subroutine netcdf_initialize_output
 
@@ -150,11 +171,8 @@ module subroutine netcdf_open(self, param)
       ! Arguments
       class(netcdf_parameters),   intent(inout) :: self   !! Parameters used to identify a particular NetCDF dataset
       class(swiftest_parameters), intent(in)    :: param  !! Current run configuration parameters
-      ! Internals
-      integer(I4B) :: old_mode
 
       call check( nf90_open(param%outfile, nf90_write, self%ncid) )
-      call check( nf90_set_fill(self%ncid, nf90_nofill, old_mode) )
 
       call check( nf90_inq_varid(self%ncid, TIME_DIMNAME, self%time_varid))
       call check( nf90_inq_varid(self%ncid, ID_DIMNAME, self%id_varid))
@@ -249,7 +267,7 @@ module subroutine netcdf_write_frame_base(self, iu, param)
       class(netcdf_parameters),   intent(inout) :: iu     !! Parameters used to identify a particular NetCDF dataset
       class(swiftest_parameters), intent(in)    :: param  !! Current run configuration parameters
       ! Internals
-      integer(I4B)                              :: i, j, tslot, strlen, idslot
+      integer(I4B)                              :: i, j, tslot, strlen, idslot, old_mode
       integer(I4B), dimension(:), allocatable   :: ind
       character(len=:), allocatable             :: charstring
 
@@ -257,6 +275,7 @@ module subroutine netcdf_write_frame_base(self, iu, param)
 
       tslot = int(param%ioutput, kind=I4B) + 1
 
+      call check( nf90_set_fill(iu%ncid, nf90_nofill, old_mode) )
       select type(self)
          class is (swiftest_body)
          associate(n => self%nbody)
@@ -332,6 +351,7 @@ module subroutine netcdf_write_frame_base(self, iu, param)
          end if
 
       end select
+      call check( nf90_set_fill(iu%ncid, old_mode, old_mode) )
 
       return
    end subroutine netcdf_write_frame_base
@@ -346,13 +366,14 @@ module subroutine netcdf_write_particle_info_base(self, iu)
       class(swiftest_base),       intent(in)    :: self   !! Swiftest particle object
       class(netcdf_parameters),   intent(inout) :: iu     !! Parameters used to identify a particular NetCDF dataset
       ! Internals
-      integer(I4B)                              :: i, j, tslot, strlen, idslot
+      integer(I4B)                              :: i, j, tslot, strlen, idslot, old_mode
       integer(I4B), dimension(:), allocatable   :: ind
       character(len=:), allocatable             :: charstring
       character(len=NAMELEN)                    :: emptystr, lenstr
       character(len=:), allocatable :: fmtlabel
 
       ! This string of spaces of length NAMELEN is used to clear out any old data left behind inside the string variables
+      call check( nf90_set_fill(iu%ncid, nf90_nofill, old_mode) )
       write(lenstr, *) NAMELEN
       fmtlabel = "(A" // trim(adjustl(lenstr)) // ")"
       write(emptystr, fmtlabel) " "
@@ -448,6 +469,7 @@ module subroutine netcdf_write_particle_info_base(self, iu)
          call check( nf90_put_var(iu%ncid, iu%discard_vhz_varid, self%info%discard_vh(3), start=[idslot]) )
       end select
 
+      call check( nf90_set_fill(iu%ncid, old_mode, old_mode) )
       return
    end subroutine netcdf_write_particle_info_base
 
@@ -464,12 +486,11 @@ module subroutine netcdf_write_hdr_system(self, iu, param)
       class(netcdf_parameters),     intent(inout) :: iu    !! Parameters used to for writing a NetCDF dataset to file
       class(swiftest_parameters),   intent(in)    :: param !! Current run configuration parameters
       ! Internals
-      integer(I4B) :: tslot, old_mode
+      integer(I4B) :: tslot
 
       tslot = int(param%ioutput, kind=I4B) + 1
 
       call check( nf90_open(param%outfile, nf90_write, iu%ncid) )
-      call check( nf90_set_fill(iu%ncid, nf90_nofill, old_mode) )
 
       call check( nf90_put_var(iu%ncid, iu%time_varid, param%t, start=[tslot]) )
       call check( nf90_put_var(iu%ncid, iu%npl_varid, self%pl%nbody, start=[tslot]) )
diff --git a/src/rmvs/rmvs_encounter_check.f90 b/src/rmvs/rmvs_encounter_check.f90
index 71465870d..4c59f0a15 100644
--- a/src/rmvs/rmvs_encounter_check.f90
+++ b/src/rmvs/rmvs_encounter_check.f90
@@ -59,6 +59,7 @@ end function rmvs_encounter_check_tp
 
 
    module pure subroutine rmvs_chk_ind(xr, yr, zr, vxr, vyr, vzr, dt, r2crit, lencounter, lvdotr)
+      !$omp declare simd(rmvs_chk_ind)
       !! author: David A. Minton
       !!
       !! Determine whether a test particle and planet are having or will have an encounter within the next time step
diff --git a/src/symba/symba_encounter_check.f90 b/src/symba/symba_encounter_check.f90
index c7e56ad27..e20e51ea9 100644
--- a/src/symba/symba_encounter_check.f90
+++ b/src/symba/symba_encounter_check.f90
@@ -18,9 +18,10 @@ subroutine symba_encounter_check_all(nplplm, k_plpl, x, v, rhill,  dt, irec, len
       integer(I8B) :: k
       integer(I4B) :: i, j
       real(DP) :: xr, yr, zr, vxr, vyr, vzr, rhill1, rhill2
-
-      !$omp parallel do default(private)&
-      !$omp shared(nplplm, k_plpl, x, v, rhill, dt, irec, lencounter, loc_lvdotr)
+      
+      !$omp parallel do simd default(private) schedule(static)&
+      !$omp shared(nplplm, k_plpl, x, v, rhill,  dt, irec, lencounter, loc_lvdotr) &
+      !$omp lastprivate(xr, yr, zr, vxr, vyr, vzr, rhill1, rhill2)
       do k = 1_I8B, nplplm
          i = k_plpl(1, k)
          j = k_plpl(2, k)
@@ -34,7 +35,7 @@ subroutine symba_encounter_check_all(nplplm, k_plpl, x, v, rhill,  dt, irec, len
          rhill2 = rhill(j)
          call symba_encounter_check_one(xr, yr, zr, vxr, vyr, vzr, rhill1, rhill2, dt, irec, lencounter(k), loc_lvdotr(k))
       end do
-      !$omp end parallel do
+      !$omp end parallel do simd
 
       return
    end subroutine symba_encounter_check_all
@@ -54,42 +55,44 @@ module function symba_encounter_check_pl(self, system, dt, irec) result(lany_enc
       ! Result
       logical                                   :: lany_encounter !! Returns true if there is at least one close encounter      
       ! Internals
-      integer(I8B) :: k, nplplm
-      integer(I4B) :: i, j, nenc
+      integer(I8B) :: k, nplplm, kenc
+      integer(I4B) :: i, j, nenc, npl
       logical, dimension(:), allocatable :: lencounter, loc_lvdotr, lvdotr
-      integer(I8B), dimension(:), allocatable :: kidx 
+      integer(I4B), dimension(:), allocatable :: index1, index2
   
       if (self%nbody == 0) return
 
       associate(pl => self)
          nplplm = pl%nplplm
+         npl = pl%nbody
          allocate(lencounter(nplplm))
          allocate(loc_lvdotr(nplplm))
   
          call symba_encounter_check_all(nplplm, pl%k_plpl, pl%xh, pl%vh, pl%rhill, dt, irec, lencounter, loc_lvdotr)
 
-         !$omp parallel workshare
          nenc = count(lencounter(:))
-         !$omp end parallel workshare
 
          lany_encounter = nenc > 0
          if (lany_encounter) then 
             associate(plplenc_list => system%plplenc_list)
                call plplenc_list%resize(nenc)
-               allocate(lvdotr(nenc))
-               allocate(kidx(nenc))
-               lvdotr(:) = pack(loc_lvdotr(:), lencounter(:))
-               kidx(:) = pack([(k, k = 1_I8B, nplplm)], lencounter(:))
-               call move_alloc(lvdotr, plplenc_list%lvdotr)
-               call move_alloc(kidx, plplenc_list%kidx)
-               deallocate(lencounter, loc_lvdotr)
-               plplenc_list%index1(1:nenc) = pl%k_plpl(1,plplenc_list%kidx(1:nenc))
-               plplenc_list%index2(1:nenc) = pl%k_plpl(2,plplenc_list%kidx(1:nenc))
-               plplenc_list%id1(1:nenc) = pl%id(plplenc_list%index1(1:nenc))
-               plplenc_list%id2(1:nenc) = pl%id(plplenc_list%index2(1:nenc))
+               allocate(lvdotr(nenc))					
+               allocate(index1(nenc))					
+               allocate(index2(nenc))					
+               lvdotr(:) = pack(loc_lvdotr(:), lencounter(:))					
+               index1(:) = pack(pl%k_plpl(1,1:nplplm), lencounter(:)) 					
+               index2(:) = pack(pl%k_plpl(2,1:nplplm), lencounter(:)) 					
+               deallocate(lencounter, loc_lvdotr)					
+               call move_alloc(lvdotr, plplenc_list%lvdotr)					
+               call move_alloc(index1, plplenc_list%index1) 					
+               call move_alloc(index2, plplenc_list%index2) 					
                do k = 1, nenc
                   i = plplenc_list%index1(k)
                   j = plplenc_list%index2(k)
+                  call util_index_eucl_ij_to_k(npl, i, j, kenc)
+                  plplenc_list%kidx(k) = kenc
+                  plplenc_list%id1(k) = pl%id(plplenc_list%index1(k))					
+                  plplenc_list%id2(k) = pl%id(plplenc_list%index2(k))					
                   plplenc_list%status(k) = ACTIVE
                   plplenc_list%level(k) = irec
                   pl%lencounter(i) = .true.
@@ -267,6 +270,7 @@ end function symba_encounter_check_tp
 
 
    module pure subroutine symba_encounter_check_one(xr, yr, zr, vxr, vyr, vzr, rhill1, rhill2, dt, irec, lencounter, lvdotr)
+      !$omp declare simd(symba_encounter_check_one)
       !! author: David A. Minton
       !!
       !! Check for an encounter.
@@ -280,9 +284,14 @@ module pure subroutine symba_encounter_check_one(xr, yr, zr, vxr, vyr, vzr, rhil
       integer(I4B), intent(in)  :: irec
       logical,      intent(out) :: lencounter, lvdotr
       ! Internals
-      real(DP)     :: r2crit
+      real(DP)     :: r2crit, rshell_irec
+      integer(I4B) :: i
 
-      r2crit = (rhill1 + rhill2)*RHSCALE*(RSHELL**(irec))
+      rshell_irec = 1._DP
+      do i = 1, irec
+         rshell_irec = rshell_irec * RSHELL
+      end do
+      r2crit = (rhill1 + rhill2) * RHSCALE * rshell_irec
       r2crit = r2crit**2
       call rmvs_chk_ind(xr, yr, zr, vxr, vyr, vzr, dt, r2crit, lencounter, lvdotr)
 
diff --git a/src/symba/symba_kick.f90 b/src/symba/symba_kick.f90
index 8a70e3c21..325ef5a45 100644
--- a/src/symba/symba_kick.f90
+++ b/src/symba/symba_kick.f90
@@ -46,14 +46,15 @@ module subroutine symba_kick_getacch_pl(self, system, param, t, lbeg)
          associate(pl => self, npl => self%nbody, plplenc_list => system%plplenc_list, radius => self%radius)
             ! Apply kicks to all bodies (including those in the encounter list)
             call helio_kick_getacch_pl(pl, system, param, t, lbeg)
-
-            ! Remove kicks from bodies involved currently in the encounter list, as these are dealt with separately.
-            nplplenc = int(plplenc_list%nenc, kind=I8B)
-            allocate(k_plpl_enc(2,nplplenc))
-            k_plpl_enc(:,1:nplplenc) = pl%k_plpl(:,plplenc_list%kidx(1:nplplenc))
-            ah_enc(:,:) = 0.0_DP
-            call kick_getacch_int_all_pl(npl, nplplenc, k_plpl_enc, pl%xh, pl%Gmass, pl%radius, ah_enc)
-            pl%ah(:,1:npl) = pl%ah(:,1:npl) - ah_enc(:,1:npl)
+            if (plplenc_list%nenc > 0) then 
+               ! Remove kicks from bodies involved currently in the encounter list, as these are dealt with separately.
+               nplplenc = int(plplenc_list%nenc, kind=I8B)
+               allocate(k_plpl_enc(2,nplplenc))
+               k_plpl_enc(:,1:nplplenc) = pl%k_plpl(:,plplenc_list%kidx(1:nplplenc))
+               ah_enc(:,:) = 0.0_DP
+               call kick_getacch_int_all_pl(npl, nplplenc, k_plpl_enc, pl%xh, pl%Gmass, pl%radius, ah_enc)
+               pl%ah(:,1:npl) = pl%ah(:,1:npl) - ah_enc(:,1:npl)
+            end if
 
          end associate
       end select
diff --git a/src/symba/symba_util.f90 b/src/symba/symba_util.f90
index 8b8e48f47..a59d1f0b8 100644
--- a/src/symba/symba_util.f90
+++ b/src/symba/symba_util.f90
@@ -285,7 +285,8 @@ module subroutine symba_util_index_eucl_plpl(self, param)
       class(symba_pl),            intent(inout) :: self  !! SyMBA massive body object
       class(swiftest_parameters), intent(in)    :: param !! Current run configuration parameters
       ! Internals
-      integer(I8B) :: i, j, counter, npl, nplm, nplpl, nplplm
+      integer(I8B) :: k, nplpl, nplplm
+      integer(I4B) :: i, j, npl, nplm, ip, jp
 
       associate(pl => self)
          npl = int(self%nbody, kind=I8B)
@@ -296,12 +297,11 @@ module subroutine symba_util_index_eucl_plpl(self, param)
          pl%nplplm = nplm * npl - nplm * (nplm + 1) / 2 ! number of entries in a strict lower triangle, npl x npl, minus first column including only mutually interacting bodies
          if (allocated(self%k_plpl)) deallocate(self%k_plpl) ! Reset the index array if it's been set previously
          allocate(self%k_plpl(2, pl%nplpl))
-         do i = 1, npl
-            counter = (i - 1_I8B) * npl - i * (i - 1_I8B) / 2_I8B + 1_I8B
-            do j = i + 1_I8B, npl
-               self%k_plpl(1, counter) = i
-               self%k_plpl(2, counter) = j
-               counter = counter + 1_I8B
+         do concurrent (i = 1:npl)
+            do concurrent (j = i+1:npl)
+               call util_index_eucl_ij_to_k(npl, i, j, k)
+               self%k_plpl(1, k) = i
+               self%k_plpl(2, k) = j
             end do
          end do
       end associate
diff --git a/src/util/util_get_energy_momentum.f90 b/src/util/util_get_energy_momentum.f90
index 803b7bac1..677de5646 100644
--- a/src/util/util_get_energy_momentum.f90
+++ b/src/util/util_get_energy_momentum.f90
@@ -16,12 +16,10 @@ module subroutine util_get_energy_momentum_system(self, param)
       integer(I4B) :: i, j
       integer(I8B) :: k, nplpl
       real(DP) :: oblpot, kecb, kespincb
-      real(DP), dimension(self%pl%nbody) :: irh, kepl, kespinpl, pecb
+      real(DP), dimension(self%pl%nbody) :: irh, kepl, kespinpl
       real(DP), dimension(self%pl%nbody) :: Lplorbitx, Lplorbity, Lplorbitz
       real(DP), dimension(self%pl%nbody) :: Lplspinx, Lplspiny, Lplspinz
       real(DP), dimension(NDIM) :: Lcborbit, Lcbspin
-      real(DP), dimension(:), allocatable :: pepl 
-      logical, dimension(:), allocatable :: lstatpl
       real(DP) :: hx, hy, hz
 
       associate(system => self, pl => self%pl, npl => self%pl%nbody, cb => self%cb)
@@ -81,45 +79,16 @@ module subroutine util_get_energy_momentum_system(self, param)
             kespinpl(:) = 0.0_DP
          end if
    
-         ! Do the central body potential energy component first
-         where(.not. pl%lmask(1:npl))
-            pecb(1:npl) = 0.0_DP
-         end where
+         call util_get_energy_potential(npl, nplpl, pl%k_plpl, pl%lmask, cb%Gmass, pl%Gmass, pl%mass, pl%xb, system%pe)
 
-         do concurrent(i = 1:npl, pl%lmask(i))
-            pecb(i) = -cb%Gmass * pl%mass(i) / norm2(pl%xb(:,i)) 
-         end do
-   
-         ! Do the potential energy between pairs of massive bodies
-         allocate(lstatpl(nplpl))
-         allocate(pepl(nplpl))
-         do concurrent (k = 1:nplpl)
-            i = pl%k_plpl(1,k)
-            j = pl%k_plpl(2,k)
-            lstatpl(k) = (pl%lmask(i) .and. pl%lmask(j))
-         end do
-
-         where(.not.lstatpl(1:nplpl)) 
-            pepl(1:nplpl) = 0.0_DP
-         end where
-
-         do concurrent (k = 1:nplpl, lstatpl(k))
-            i = pl%k_plpl(1,k)
-            j = pl%k_plpl(2,k)
-            pepl(k) = -(pl%Gmass(i) * pl%mass(j)) / norm2(pl%xb(:, i) - pl%xb(:, j))
-         end do
-   
-         system%pe = sum(pepl(:), lstatpl(:)) + sum(pecb(1:npl), pl%lmask(1:npl))
-         deallocate(lstatpl, pepl)
-
-         system%ke_orbit = 0.5_DP * (kecb + sum(kepl(1:npl), pl%lmask(1:npl)))
-         if (param%lrotation) system%ke_spin = 0.5_DP * (kespincb + sum(kespinpl(1:npl), pl%lmask(1:npl)))
-   
          ! Potential energy from the oblateness term
          if (param%loblatecb) then
             call system%obl_pot()
             system%pe = system%pe + system%oblpot
          end if
+
+         system%ke_orbit = 0.5_DP * (kecb + sum(kepl(1:npl), pl%lmask(1:npl)))
+         if (param%lrotation) system%ke_spin = 0.5_DP * (kespincb + sum(kespinpl(1:npl), pl%lmask(1:npl)))
    
          system%Lorbit(1) = Lcborbit(1) + sum(Lplorbitx(1:npl), pl%lmask(1:npl)) 
          system%Lorbit(2) = Lcborbit(2) + sum(Lplorbity(1:npl), pl%lmask(1:npl)) 
@@ -138,4 +107,55 @@ module subroutine util_get_energy_momentum_system(self, param)
       return
    end subroutine util_get_energy_momentum_system
 
+
+   subroutine util_get_energy_potential(npl, nplpl, k_plpl, lmask, GMcb, Gmass, mass, xb, pe)
+      !! author: David A. Minton
+      !!
+      !! Compute total system potential energy
+      implicit none
+      ! Arguments
+      integer(I4B),                 intent(in)  :: npl
+      integer(I8B),                 intent(in)  :: nplpl
+      integer(I4B), dimension(:,:), intent(in)  :: k_plpl
+      logical,      dimension(:),   intent(in)  :: lmask
+      real(DP),                     intent(in)  :: GMcb
+      real(DP),     dimension(:),   intent(in)  :: Gmass
+      real(DP),     dimension(:),   intent(in)  :: mass
+      real(DP),     dimension(:,:), intent(in)  :: xb
+      real(DP),                     intent(out) :: pe
+      ! Internals
+      integer(I4B) :: i, j
+      integer(I8B) :: k
+      real(DP), dimension(npl) :: pecb
+      real(DP), dimension(nplpl) :: pepl 
+      logical, dimension(nplpl) :: lstatpl
+
+      ! Do the central body potential energy component first
+      where(.not. lmask(1:npl))
+         pecb(1:npl) = 0.0_DP
+      end where
+
+      do concurrent(i = 1:npl, lmask(i))
+         pecb(i) = -GMcb * mass(i) / norm2(xb(:,i)) 
+      end do
+
+      !$omp parallel do default(private) schedule(static)&
+      !$omp shared(nplpl, k_plpl, xb, mass, Gmass, pepl, lstatpl, lmask)
+      do k = 1, nplpl
+         i = k_plpl(1,k)
+         j = k_plpl(2,k)
+         lstatpl(k) = (lmask(i) .and. lmask(j))
+         if (lstatpl(k)) then
+            pepl(k) = -(Gmass(i) * mass(j)) / norm2(xb(:, i) - xb(:, j))
+         else
+            pepl(k) = 0.0_DP
+         end if
+      end do
+      !$omp end parallel do 
+
+      pe = sum(pepl(:), lstatpl(:)) + sum(pecb(1:npl), lmask(1:npl))
+
+      return
+   end subroutine util_get_energy_potential
+
 end submodule s_util_get_energy_momentum
diff --git a/src/util/util_index.f90 b/src/util/util_index.f90
index 0e42ec7c7..1ee60e400 100644
--- a/src/util/util_index.f90
+++ b/src/util/util_index.f90
@@ -2,7 +2,8 @@
    use swiftest
 contains
 
-   module subroutine util_index_eucl_plpl(self, param)
+   module pure subroutine util_index_eucl_ij_to_k(n, i, j, k)
+      !$omp declare simd(util_index_eucl_ij_to_k)
       !! author: Jacob R. Elliott and David A. Minton
       !!
       !! Turns i,j indices into k index for use in the Euclidean distance matrix for pl-pl interactions.
@@ -13,23 +14,80 @@ module subroutine util_index_eucl_plpl(self, param)
       !!       2019. hal-0204751
       implicit none
       ! Arguments
+      integer(I4B), intent(in)  :: n !! Number of bodies
+      integer(I4B), intent(in)  :: i !! Index of the ith body
+      integer(I4B), intent(in)  :: j !! Index of the jth body
+      integer(I8B), intent(out) :: k !! Index of the flattened matrix
+      ! Internals
+      integer(I8B) :: i8, j8, n8
+     
+      i8 = int(i, kind=I8B)
+      j8 = int(j, kind=I8B)
+      n8 = int(n, kind=I8B)
+      k = (i8 - 1_I8B) * n8 - i8 * (i8 - 1_I8B) / 2_I8B + (j8 - i8)
+
+      return
+   end subroutine util_index_eucl_ij_to_k
+
+
+   module pure subroutine util_index_eucl_k_to_ij(n, k, i, j)
+      !! author: Jacob R. Elliott and David A. Minton
+      !!
+      !! Turns k index into i,j indices for use in the Euclidean distance matrix for pl-pl interactions.
+      !!
+      !! Reference:
+      !!
+      !!    Mélodie Angeletti, Jean-Marie Bonny, Jonas Koko. Parallel Euclidean distance matrix computation on big datasets *. 
+      !!       2019. hal-0204751
+      implicit none
+      ! Arguments
+      integer(I4B), intent(in)  :: n !! Number of bodies
+      integer(I8B), intent(in)  :: k !! Index of the flattened matrix
+      integer(I4B), intent(out) :: i !! Index of the ith body
+      integer(I4B), intent(out) :: j !! Index of the jth body
+      ! Internals
+      integer(I8B) :: kp, p, i8, j8, n8
+
+      n8 = int(n, kind=I8B)
+    
+      kp = n8 * (n8 - 1_I8B) / 2_I8B - k
+      p = floor((sqrt(1._DP + 8_I8B * kp) - 1_I8B) / 2_I8B)
+      i8 = n8 - 1_I8B - p
+      j8 = k - (n8 - 1_I8B) * (n8 - 2_I8B) / 2_I8B + p * (p + 1_I8B) / 2_I8B + 1_I8B
+
+      i = int(i8, kind=I4B)
+      j = int(j8, kind=I4B)
+
+      return
+   end subroutine util_index_eucl_k_to_ij
+
+
+   module subroutine util_index_eucl_plpl(self, param)
+      !! author: Jacob R. Elliott and David A. Minton
+      !!
+      !! Turns i,j indices into k index for use in the Euclidean distance matrix for pl-pl interactions for a Swiftest massive body object
+      !!
+      !! Reference:
+      !!
+      !!    Mélodie Angeletti, Jean-Marie Bonny, Jonas Koko. Parallel Euclidean distance matrix computation on big datasets *. 
+      !!       2019. hal-0204751
+      implicit none
+      ! Arguments
       class(swiftest_pl),         intent(inout) :: self  !! Swiftest massive body object
       class(swiftest_parameters), intent(in)    :: param !! Current run configuration parameters
       ! Internals
-      integer(I8B) :: i, j, counter, npl
+      integer(I4B) :: i, j, npl
+      integer(I8B) :: k
 
       npl = int(self%nbody, kind=I8B)
       associate(nplpl => self%nplpl)
-         nplpl = (npl * (npl - 1) / 2) ! number of entries in a strict lower triangle, npl x npl, minus first column
+         nplpl = (npl * (npl - 1) / 2) ! number of entries in a strict lower triangle, npl x npl
          if (allocated(self%k_plpl)) deallocate(self%k_plpl) ! Reset the index array if it's been set previously
          allocate(self%k_plpl(2, nplpl))
-         do i = 1_I8B, npl
-            counter = (i - 1_I8B) * npl - i * (i - 1_I8B) / 2_I8B + 1_I8B
-            do j = i + 1_I8B, npl
-               self%k_plpl(1, counter) = i
-               self%k_plpl(2, counter) = j
-               counter = counter + 1_I8B
-            end do
+         do concurrent (i=1:npl, j=1:npl, j>i)
+            call util_index_eucl_ij_to_k(npl, i, j, k)
+            self%k_plpl(1, k) = i
+            self%k_plpl(2, k) = j
          end do
       end associate
 
diff --git a/src/util/util_minimize_bfgs.f90 b/src/util/util_minimize_bfgs.f90
index 01b57d868..3278b6de4 100644
--- a/src/util/util_minimize_bfgs.f90
+++ b/src/util/util_minimize_bfgs.f90
@@ -60,22 +60,10 @@ module function util_minimize_bfgs(f, N, x0, eps, maxloop, lerr) result(x1)
       do i = 1, maxloop 
          !check for convergence
          conv = count(abs(grad1(:)) > eps)
-         ! write(*,*) 'loop: ', i
-         ! write(*,*) 'conv: ', conv
-         ! write(*,*) 'grad1 / eps'
-         ! do j = 1, N
-         !    write(*,*) j, abs(grad1(j)) / eps
-         ! end do
-         if (conv == 0) then
-            ! write(*,*) "BFGS converged on gradient after ",i," iterations" 
-            exit 
-         end if
+         if (conv == 0) exit 
          S(:) = -matmul(H(:,:), grad1(:))
          astar = minimize1D(f, x1, S, N, graddelta, lerr)
-         if (lerr) then
-            ! write(*,*) "Exiting BFGS with error in minimize1D step"
-            exit
-         end if
+         if (lerr) exit
          ! Get new x values 
          P(:) = astar * S(:) 
          x1(:) = x1(:) + P(:)
@@ -86,19 +74,23 @@ module function util_minimize_bfgs(f, N, x0, eps, maxloop, lerr) result(x1)
          Py = sum(P(:) * y(:))
          ! set up factors for H matrix update 
          yHy = 0._DP
+         !$omp do simd schedule(static)&
+         !$omp firstprivate(N, y, H) &
+         !$omp reduction(+:yHy)
          do k = 1, N 
             do j = 1, N
                yHy = yHy + y(j) * H(j,k) * y(k)
             end do
          end do
+         !$omp end do simd
          ! prevent divide by zero (convergence) 
-         if (abs(Py) < tiny(Py)) then
-            ! write(*,*) "BFGS Converged on tiny Py after ",i," iterations"
-            exit
-         end if
+         if (abs(Py) < tiny(Py)) exit
          ! set up update 
          PyH(:,:) = 0._DP
          HyP(:,:) = 0._DP
+         !$omp parallel do default(private) schedule(static)&
+         !$omp shared(N, PP, P, y, H) &
+         !$omp reduction(+:PyH, HyP)
          do k = 1, N 
             do j = 1, N
                PP(j, k) = P(j) * P(k)
@@ -108,6 +100,7 @@ module function util_minimize_bfgs(f, N, x0, eps, maxloop, lerr) result(x1)
                end do
             end do
          end do
+         !$omp end parallel do 
          ! update H matrix 
          H(:,:) = H(:,:) + ((1._DP - yHy / Py) * PP(:,:) - PyH(:,:) - HyP(:,:)) / Py
          ! Normalize to prevent it from blowing up if it takes many iterations to find a solution
@@ -117,13 +110,10 @@ module function util_minimize_bfgs(f, N, x0, eps, maxloop, lerr) result(x1)
          if (any(fpe_flag)) exit 
          if (i == maxloop) then
             lerr = .true.
-            ! write(*,*) "BFGS ran out of loops!"
          end if
       end do
       call ieee_get_flag(ieee_usual, fpe_flag)
       lerr = lerr .or. any(fpe_flag)  
-      ! if (any(fpe_flag)) write(*,*) 'BFGS did not converge due to fpe'
-      ! if (lerr) write(*,*) "BFGS did not converge!"
       call ieee_set_status(original_fpe_status)
 
       return