diff --git a/Makefile b/Makefile
index c3cce41c2..34df2b87a 100644
--- a/Makefile
+++ b/Makefile
@@ -16,7 +16,7 @@
 #                              library, Swifter drivers and tools
 #                (2) mod     : builds modules
 #                (3) lib     : builds entire Swifter library
-#                (4) libdir  : compiles local directory source and adds the
+#                (4) fastdir  : compiles local directory source and adds the
 #                              resulting objects to the Swifter library
 #                (5) drivers : builds Swifter drivers
 #                (6) tools   : builds Swifter tools
@@ -36,7 +36,7 @@
 #    Terminal  : status messages
 #    File      : none
 #
-#  Invocation  : make [all|mod|lib|libdir|drivers|tools|bin|clean]
+#  Invocation  : make [all|mod|lib|fastdir|drivers|tools|bin|clean]
 #
 #  Notes       : The use of the above arguments as phony targets inside the
 #                makefile precludes their use as base names of Swifter drivers
@@ -65,7 +65,7 @@ LMKL = -L$(MKLROOT)/lib/intel64 -qopt-matmul
 
 MODULES         = $(SWIFTEST_MODULES) $(USER_MODULES) 
 
-.PHONY : all mod lib libdir fast drivers bin clean force 
+.PHONY : all mod fast strict drivers bin clean force 
 
 % : %.f90 force
 	$(FORTRAN) $(FFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) $< -o $@ \
@@ -76,8 +76,8 @@ MODULES         = $(SWIFTEST_MODULES) $(USER_MODULES)
 all:
 	cd $(SWIFTEST_HOME); \
 	  make mod; \
-	  make lib; \
 	  make fast; \
+	  make strict; \
 	  make drivers; \
 
 mod:
@@ -87,137 +87,136 @@ mod:
 	  $(INSTALL_DATA) *.mod *.smod $(SWIFTEST_HOME)/include; \
 	  rm -f *.o *.mod  *.smod
 
-lib:
+fast:
 	cd $(SWIFTEST_HOME)/src/discard; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/gr; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/drift; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/io; \
+	  make fastdir
+	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/kick; \
+	  make fastdir	  
+	cd $(SWIFTEST_HOME)/src/gr; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/netcdf; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/helio; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/obl; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/io; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/operators; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/netcdf; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/setup; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/obl; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/tides; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/operators; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/whm; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/orbel; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
+	  make fastdir
 	cd $(SWIFTEST_HOME)/src/rmvs; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/helio; \
+	  make fastdir	
+	cd $(SWIFTEST_HOME)/src/setup; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
+	  make fastdir
 	cd $(SWIFTEST_HOME)/src/symba; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/user; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/tides; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-	cd $(SWIFTEST_HOME)/src/walltime; \
+	  make fastdir
+	cd $(SWIFTEST_HOME)/src/user; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make libdir
-
-fast:
-	cd $(SWIFTEST_HOME)/src/fraggle; \
+	  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/util; \
+	  make fastdir	
+	cd $(SWIFTEST_HOME)/src/walltime; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
 	  make fastdir
-
-	cd $(SWIFTEST_HOME)/src/orbel; \
+	cd $(SWIFTEST_HOME)/src/whm; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
 	  make fastdir
 
-	cd $(SWIFTEST_HOME)/src/drift; \
+strict:
+	cd $(SWIFTEST_HOME)/src/kick; \
 	  rm -f Makefile.Defines Makefile; \
 	  ln -s $(SWIFTEST_HOME)/Makefile.Defines .; \
 	  ln -s $(SWIFTEST_HOME)/Makefile .; \
-	  make fastdir
-
+	  make strictdir
 	cd $(SWIFTEST_HOME)/src/helio; \
-		$(FORTRAN) $(FFASTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c helio_drift.f90; \
+		$(FORTRAN) $(FSTRICTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c helio_kick.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) $(FSTRICTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c symba_kick.f90; \
+		$(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o *.smod; \
+		$(INSTALL_DATA) *.smod $(SWIFTEST_HOME)/include; \
+		rm -f *.o *.smod
 	cd $(SWIFTEST_HOME)/src/rmvs; \
-		$(FORTRAN) $(FFASTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c rmvs_encounter_check.f90; \
+		$(FORTRAN) $(FSTRICTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c rmvs_kick.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; \
+	cd $(SWIFTEST_HOME)/src/whm; \
+		$(FORTRAN) $(FSTRICTFLAGS) -I$(SWIFTEST_HOME)/include -I$(NETCDF_FORTRAN_HOME)/include $(IMKL) -c whm_kick.f90; \
 		$(AR) rv $(SWIFTEST_HOME)/lib/libswiftest.a *.o *.smod; \
 		$(INSTALL_DATA) *.smod $(SWIFTEST_HOME)/include; \
 		rm -f *.o *.smod
 
-libdir:
+fastdir:
 	$(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; \
+strictdir:
+	$(FORTRAN) $(FSTRICTFLAGS) -I$(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
+	rm -f *.o *.smo	
 
 drivers:
 	cd $(SWIFTEST_HOME)/src/main; \
diff --git a/Makefile.Defines b/Makefile.Defines
index 16ce3afc3..278388cb0 100644
--- a/Makefile.Defines
+++ b/Makefile.Defines
@@ -52,12 +52,12 @@ VTUNE_FLAGS = -g -O2 -qopt-report=5 -simd -shared-intel -qopenmp -debug inline-d
 #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 -prec-div -prec-sqrt -assume protect-parens 
+STRICTREAL = -fp-model=precise -prec-div -prec-sqrt -assume protect-parens 
 SIMDVEC = -simd -xhost -align all -assume contiguous_assumed_shape -vecabi=cmdtarget -fp-model no-except -fma
 PAR = -qopenmp -parallel  
 HEAPARR = -heap-arrays 4194304 
 OPTREPORT = -qopt-report=5 
-IPRODUCTION = -no-wrap-margin -O3 -ipo -qopt-prefetch=0 -sox $(PAR) $(SIMDVEC) #$(HEAPARR)
+IPRODUCTION = -no-wrap-margin -O3 -qopt-prefetch=0 -sox $(PAR) $(SIMDVEC) #$(HEAPARR)
 
 #gfortran flags
 GDEBUG = -g -Og -fbacktrace -fbounds-check -ffree-line-length-none
@@ -69,8 +69,8 @@ GPRODUCTION = -O2 -ffree-line-length-none $(GPAR)
 
 #FFLAGS  = $(IDEBUG)  #$(SIMDVEC) $(PAR)
 #FFASTFLAGS  = $(IDEBUG) #$(SIMDVEC) $(PAR)
-FFLAGS = $(IPRODUCTION) $(STRICTREAL) #$(ADVIXE_FLAGS)
-FFASTFLAGS = $(IPRODUCTION) -fp-model fast #$(ADVIXE_FLAGS)
+FSTRICTFLAGS = $(IPRODUCTION) $(STRICTREAL) $(OPTREPORT) #$(ADVIXE_FLAGS)
+FFLAGS = $(IPRODUCTION) -fp-model=fast $(OPTREPORT) #$(ADVIXE_FLAGS)
 FORTRAN = ifort
 AR     = xiar
 
diff --git a/examples/helio_swifter_comparison/cb.swiftest.in b/examples/helio_swifter_comparison/cb.swiftest.in
index e4a010b1e..64406c4cf 100644
--- a/examples/helio_swifter_comparison/cb.swiftest.in
+++ b/examples/helio_swifter_comparison/cb.swiftest.in
@@ -1,4 +1,4 @@
-0
+Sun
 39.476926408897626
 0.004650467260962157
 4.7535806948127355e-12
diff --git a/examples/helio_swifter_comparison/init_cond.py b/examples/helio_swifter_comparison/init_cond.py
index b8b9b4369..a71bc7b1c 100755
--- a/examples/helio_swifter_comparison/init_cond.py
+++ b/examples/helio_swifter_comparison/init_cond.py
@@ -17,6 +17,7 @@
 sim.param['CHK_EJECT'] = 1000.0
 sim.param['ISTEP_OUT']  = 1
 sim.param['ISTEP_DUMP'] = 1
+sim.param['IN_FORM'] = "XV"
 sim.param['OUT_FORM'] = "XV"
 sim.param['OUT_STAT'] = "UNKNOWN"
 sim.param['GR'] = 'NO'
@@ -32,10 +33,10 @@
    "Saturn": 6,
    "Uranus": 7,
    "Neptune": 8,
-   "Ceres":  101,
-   "Pallas": 102,
-   "Juno":  103,
-   "Vesta": 104
+   "Ceres":  9,
+   "Pallas": 10,
+   "Juno":  11,
+   "Vesta": 12
 }
 
 for name, id in bodyid.items():
@@ -44,7 +45,7 @@
 sim.param['PL_IN'] = "pl.swiftest.in"
 sim.param['TP_IN'] = "tp.swiftest.in"
 sim.param['CB_IN'] = "cb.swiftest.in"
-sim.param['BIN_OUT'] = "bin.swiftest.dat"
+sim.param['BIN_OUT'] = "bin.swiftest.nc"
 sim.param['ENC_OUT'] = "enc.swiftest.dat"
 sim.save("param.swiftest.in")
 sim.param['PL_IN'] = "pl.swifter.in"
diff --git a/examples/helio_swifter_comparison/param.swifter.in b/examples/helio_swifter_comparison/param.swifter.in
index 417c3ab04..fa104fd7e 100644
--- a/examples/helio_swifter_comparison/param.swifter.in
+++ b/examples/helio_swifter_comparison/param.swifter.in
@@ -11,13 +11,13 @@ IN_TYPE          ASCII
 PL_IN            pl.swifter.in
 TP_IN            tp.swifter.in
 BIN_OUT          bin.swifter.dat
-ENC_OUT          enc.swifter.dat
 CHK_QMIN         0.004650467260962157
 CHK_RMIN         0.004650467260962157
 CHK_RMAX         1000.0
 CHK_EJECT        1000.0
 CHK_QMIN_COORD   HELIO
 CHK_QMIN_RANGE   0.004650467260962157 1000.0
+ENC_OUT          enc.swifter.dat
 EXTRA_FORCE      NO
 BIG_DISCARD      NO
 CHK_CLOSE        YES
diff --git a/examples/helio_swifter_comparison/param.swiftest.in b/examples/helio_swifter_comparison/param.swiftest.in
index df058ad4c..fc031dc72 100644
--- a/examples/helio_swifter_comparison/param.swiftest.in
+++ b/examples/helio_swifter_comparison/param.swiftest.in
@@ -4,15 +4,14 @@ TSTOP            1.0
 DT               0.0006844626967830253
 ISTEP_OUT        1
 ISTEP_DUMP       1
-OUT_FORM         XV
-OUT_TYPE         REAL8
+OUT_FORM         XVEL
+OUT_TYPE         NETCDF_DOUBLE
 OUT_STAT         UNKNOWN
 IN_TYPE          ASCII
 PL_IN            pl.swiftest.in
 TP_IN            tp.swiftest.in
 CB_IN            cb.swiftest.in
-BIN_OUT          bin.swiftest.dat
-ENC_OUT          enc.swiftest.dat
+BIN_OUT          bin.swiftest.nc
 CHK_QMIN         0.004650467260962157
 CHK_RMIN         0.004650467260962157
 CHK_RMAX         1000.0
@@ -22,7 +21,10 @@ CHK_QMIN_RANGE   0.004650467260962157 1000.0
 MU2KG            1.988409870698051e+30
 TU2S             31557600.0
 DU2M             149597870700.0
+IN_FORM          XV
+ENC_OUT          enc.swiftest.dat
 EXTRA_FORCE      NO
+DISCARD_OUT      discard.out
 BIG_DISCARD      NO
 CHK_CLOSE        YES
 RHILL_PRESENT    YES
diff --git a/examples/helio_swifter_comparison/pl.swifter.in b/examples/helio_swifter_comparison/pl.swifter.in
index 0b02f19c8..181595803 100644
--- a/examples/helio_swifter_comparison/pl.swifter.in
+++ b/examples/helio_swifter_comparison/pl.swifter.in
@@ -1,36 +1,36 @@
 9
-0 39.476926408897625196
+0 39.476926408897626
 0.0 0.0 0.0
 0.0 0.0 0.0
-1 6.5537098095653139645e-06 0.001475124456355905224
+1 6.5537098095653139645e-06 0.0014751320469864830743
 1.6306381826061645943e-05
--0.30949970210807342674 0.1619004125820537876 0.041620272188990829754
--6.8742992150644793847 -8.672423996611946485 -0.078109307586001638286
-2 9.663313399581537916e-05 0.006759069616556246028
+0.25597748680933402055 -0.33873157013416782535 -0.051160436706398457196
+6.1515614442706225157 6.693373063190126291 -0.017305148628664950593
+2 9.663313399581537916e-05 0.0067591015124708249373
 4.0453784346544178454e-05
--0.5567137338251560985 -0.46074173273652380134 0.02580196630219121906
-4.6580776303108450487 -5.726444072926637749 -0.3473859047161406309
-3 0.000120026935827952453094 0.010044908171483009529
+0.31726034651636542128 -0.654711054374790713 -0.027292938884777531716
+6.598488376677801111 3.1963353072519729466 -0.33689924099817045804
+3 0.000120026935827952453094 0.010044886970936247304
 4.25875607065040958e-05
-0.6978790186886838498 -0.73607603319120218366 3.261671020506711323e-05
-4.4579240279134950613 4.300011122687349501 -0.00022055769049333364448
-4 1.2739802010675941456e-05 0.0072466797341124641736
+1.0035242101099290934 -0.0018228334577166870837 -3.6653532112110000198e-06
+-0.09070203147464428398 6.2603556827487729817 -0.00030066016029169661568
+4 1.2739802010675941456e-05 0.0072464547040638876134
 2.265740805092889601e-05
--1.617661473167097963 0.38314370807747849534 0.04771055403546069218
--0.98751874613118001086 -4.5371239937302254657 -0.07086074102213555221
-5 0.037692251088985676735 0.35527079166215922855
+-1.6246010829214110327 -0.22657397469775839016 0.035102757644925722258
+0.8960028670481912773 -4.6255927366612961593 -0.118919639419818187306
+5 0.037692251088985676735 0.355270418186049151
 0.00046732617030490929307
-4.1527454588897487753 -2.8347492039446908763 -0.081136554176388195336
-1.5225069137843642898 2.4087104911325327961 -0.044067446366273183833
-6 0.011285899820091272997 0.43765832419088212185
+4.3414830724724824407 -2.51086598242009007 -0.086704432177356224876
+1.3483266539369778604 2.5183130150315082463 -0.04062579121197158367
+6 0.011285899820091272997 0.43766612292716386504
 0.00038925687730393611812
-6.39471595410062843 -7.621162747287802297 -0.121992225877669294154
-1.4493167787574136286 1.3075474785896286071 -0.08039429377859412155
-7 0.0017236589478267730203 0.46960112247450473807
+6.5829270711489096257 -7.4466885388333317053 -0.1325136240669045895
+1.4148318095568700728 1.3475938908840546106 -0.079718098761849415056
+7 0.0017236589478267730203 0.46974626380654876733
 0.00016953449859497231466
-14.793135356927480828 13.074218343364380601 -0.14311846037737518955
--0.9605086875596024784 1.0118431725941020164 0.016148779866732710198
-8 0.0020336100526728302319 0.78136567314580814177
+14.666242725889420129 13.206619035005820351 -0.14098973299186590147
+-0.97063485833896719253 1.0031115306266739172 0.016248131244499269076
+8 0.0020336100526728302319 0.7815278251043273693
 0.000164587904124493665
-29.568629894896030663 -4.5543028991960081697 -0.58771107137394917874
-0.16867624969736024011 1.1427992197933557251 -0.027387722828706092838
+29.590408344240941574 -4.4040527861079086236 -0.5913025789369640295
+0.16275481312443448273 1.1438129826052378228 -0.027269849433711815306
diff --git a/examples/helio_swifter_comparison/pl.swiftest.in b/examples/helio_swifter_comparison/pl.swiftest.in
index 84cae57a2..c227e04f1 100644
--- a/examples/helio_swifter_comparison/pl.swiftest.in
+++ b/examples/helio_swifter_comparison/pl.swiftest.in
@@ -1,33 +1,33 @@
 8
-1 6.5537098095653139645e-06 0.001475124456355905224
+Mercury 6.5537098095653139645e-06 0.0014751320469864830743
 1.6306381826061645943e-05
--0.30949970210807342674 0.1619004125820537876 0.041620272188990829754
--6.8742992150644793847 -8.672423996611946485 -0.078109307586001638286
-2 9.663313399581537916e-05 0.006759069616556246028
+0.25597748680933402055 -0.33873157013416782535 -0.051160436706398457196
+6.1515614442706225157 6.693373063190126291 -0.017305148628664950593
+Venus 9.663313399581537916e-05 0.0067591015124708249373
 4.0453784346544178454e-05
--0.5567137338251560985 -0.46074173273652380134 0.02580196630219121906
-4.6580776303108450487 -5.726444072926637749 -0.3473859047161406309
-3 0.000120026935827952453094 0.010044908171483009529
+0.31726034651636542128 -0.654711054374790713 -0.027292938884777531716
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diff --git a/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb b/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb
index 709b6cd44..07c9a252b 100644
--- a/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb
+++ b/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb
@@ -43,8 +43,8 @@
      "output_type": "stream",
      "text": [
       "Reading Swiftest file param.swiftest.in\n",
-      "Reading in time 1.000e+00\n",
-      "Creating Dataset\n",
+      "\n",
+      "Creating Dataset from NetCDF file\n",
       "Successfully converted 1462 output frames.\n",
       "Swiftest simulation data stored as xarray DataSet .ds\n"
      ]
@@ -68,9 +68,382 @@
    "cell_type": "code",
    "execution_count": 5,
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+       "Coordinates:\n",
+       "  * id       (id) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "    time     float64 0.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vhx'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2b1bf72f-4970-4330-acf0-554a70e4eb07' class='xr-array-in' type='checkbox' checked><label for='section-2b1bf72f-4970-4330-acf0-554a70e4eb07' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></div></li><li class='xr-section-item'><input id='section-db3837c0-5962-4109-a413-752200a95b7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3837c0-5962-4109-a413-752200a95b7a' 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 9 10 11 12</div><input id='attrs-d38c062b-a42d-4f00-b38b-103f3da85174' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d38c062b-a42d-4f00-b38b-103f3da85174' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f825313b-27dd-4787-b443-d8845f19868b' class='xr-var-data-in' type='checkbox'><label for='data-f825313b-27dd-4787-b443-d8845f19868b' 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'><dt><span>_FillValue :</span></dt><dd>-2147483647</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</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'>0.0</div><input id='attrs-1b5cef86-656a-4d9b-8e18-28ac0c598e1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b5cef86-656a-4d9b-8e18-28ac0c598e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f97f1953-5d1c-46f1-921f-9e0197bcdebd' class='xr-var-data-in' type='checkbox'><label for='data-f97f1953-5d1c-46f1-921f-9e0197bcdebd' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1df42f29-136d-405a-ab3d-72cb0c6d6bea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1df42f29-136d-405a-ab3d-72cb0c6d6bea' 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 'vhx' (id: 12)>\n",
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+       "Coordinates:\n",
+       "  * id       (id) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "    time     float64 0.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "swiftdiff = swiftdiff.rename({'time' : 'time (y)'})"
+    "swiftdiff.isel(time=0)['vhx']"
    ]
   },
   {
@@ -79,8 +452,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "swiftdiff['dr'] = np.sqrt(swiftdiff['px']**2 + swiftdiff['py']**2 + swiftdiff['pz']**2)\n",
-    "swiftdiff['dv'] = np.sqrt(swiftdiff['vx']**2 + swiftdiff['vy']**2 + swiftdiff['vz']**2)"
+    "swiftdiff = swiftdiff.rename({'time' : 'time (y)'})"
    ]
   },
   {
@@ -89,18 +461,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plidx = swiftdiff.id.values[swiftdiff.id.values < 10]\n",
-    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 10]"
+    "swiftdiff['dr'] = np.sqrt(swiftdiff['xhx']**2 + swiftdiff['xhy']**2 + swiftdiff['xhz']**2)\n",
+    "swiftdiff['dv'] = np.sqrt(swiftdiff['vhx']**2 + swiftdiff['vhy']**2 + swiftdiff['vhz']**2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "plidx = swiftdiff.id.values[swiftdiff.id.values < 9]\n",
+    "tpidx = swiftdiff.id.values[swiftdiff.id.values >= 9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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w0AVD2Jddxll/W0VJVZ0To7TNvqxSwgL9SOwS6pLj6cSgaZpPem9dBnVmxdUTkjr83tnDevLE3OEUVNTywYYMJ0TXMXuzyhjcK9Km5jBH0IlB0zSf9N3eHEYlRDEmqYtd779uUm9GJUbz/MoDHC2sdHB0tlNKsTfb+YvzNKYTg6ZpPie/vIZtR4s5Z0iP09rPU5eNoKbewuznVnM4v8JB0XVMZlEVZdX1Tl3juTmdGJzk6NGjzJw5kyFDhjBs2DAWLlzo7pA0rdNYtS8XpeDswbGntZ+hcZFcNTaBylozb6857KDoOmZfdhnguo5n0InBafz9/Xn22WfZu3cv69ev58UXX2TPnj3uDkvTOoXv9+XSIzKIYXGn/2H69ytH0T82nHfWZVBvtjgguo7ZZx2RNMgFpTAa6MTgJL169WLMmDEAREREMGTIEI4d85xx0ZrmqywWxdpDBUwf2N1hE9T6dw8HjPkErrYvxyiFEd6BkVWny/dnPn/1IGTvdOw+e46A85+2efP09HS2bt3KxIkTHRuHpmmnSMsvp6SqjnHJXR22zz9dNIQVu7N57rsDvHb9OIft1xZ7s0oZ1MN1zUig7xicrry8nCuuuILnnnuOyEjX/uNqWme02brmwtje9o1GakmCdf7At3tyXDpCqay6jsP5FYxMiHLZMaEz3DF04Mre0erq6rjiiiu49tprufzyy90Wh6Z1JlsyiokODaBvN8eui/z2DeNZ8NYmPtx4hPvnDHbovluz+3gpSsEIFycGfcfgJEopbrrpJoYMGcI999zj7nA0rdPYfKSIMUldHF4Ab8agWEYmRLH1SLFD99uWdYcKEIGR8Tox+IQ1a9bw3nvv8f333zN69GhGjx7N8uXL3R2Wpvm04spaDuaWO7QZqbEzEqPZcqSIoopap+y/MaUUX+w4zqQ+McSEBzn9eI35flOSm0ydOhWlPG+xD03zZQ1X8/bOdm7PRaPieGddBq//nMbvZzu3OSm9oJK0vApumJLs1OO0RN8xaJrmMzZnFOFnEkYlOqfpZUxSF6YP7M5LPxyioLzGKcdosOZgPgBTB7S/joSj6cSgaZrP2HKkiKG9IgkNdE5jiJ9JuGNmf5SCHZklTjlGg3WHCugVFUxyjGsqqjamE4OmaT6h3mxh29Fip/UvNBhsLWa3xzoj2RmMSXr5TOnXzS2ryOnEoGmaTziYV05lrZnRidFOPU5kcAB9uoWxKb3QacfYl11GUWUdU/rFOO0YbdGJQdM0n5DqwmJz5wyOZe3BAsqqnbOIz9pDRv/ClP46MWiaptltX3YZAX5CHwdPbGvJnOE9qTVb+POSXU7Z//q0Avp0C6NXVIhT9t8enRicpLq6mgkTJjBq1CiGDRvGI4884u6QNM2npWaX0bdbOIH+zv9YG5PUhbBAP77alU1FjWPXhVZKsT2zhDOSoh26345waWIQkTkikioiB0XkwRZejxKRL0Rku4jsFpEbXBmfIwUFBfH999+zfft2tm3bxooVK1i/fr27w9I0n5WaXeay0tQmk/DOjROoqbfw6VbHVk3OKqkmr6yGES6e7dyYyxKDiPgBLwLnA0OBeSIytNlmdwB7lFKjgBnAsyIS6KoYHUlECA83SvXW1dVRV1fnltEFmtYZlFXXcay4yqVrFozt3YXRidG8tjrNoZNZr3nNuIA8s383h+2zo1w583kCcFAplQYgIouAS4HGq9coIEKMT9BwoBA4rfu0v238G/sK953OLk4xuOtgHpjwQLvbmc1mxo4dy8GDB7njjjt02W1Nc5L91nUSBrswMYgIV49P5MFPd7LxcCET+55+R7HZokgvMKq3DogNP+392cuVTUnxwNFGjzOtzzX2AjAEOA7sBH6rlDplySQRuUVEUkQkJS8vz1nxnjY/Pz+2bdtGZmYmGzduZNcu53RUaVpn17D85cAerksMAJeOjic00I931qU7ZH/rDhUAsPDq0W5tYXDlHUNLZ9n8/ms2sA04G+gHfCsiPymlmswkUUq9CrwKMG7cuDbv4Wy5sne26OhoZsyYwYoVKxg+fLi7w9E0n5OaXUZ4kD8JXVw7iick0I95E5J4d106JZV1RIUGnNb+Fm/JpEtoALOH9XRQhPZx5R1DJpDY6HECxp1BYzcAnyrDQeAw4JrC5w6Wl5dHcXExAFVVVXz33XcMHuyVp6JpHi81u4yBPcLdcpV96eg46syKFbuzTms/x4qr+GpXFlP6dyM4wM9B0dnHlYlhEzBARPpYO5SvBpY22+YIcA6AiPQABgFpLozRYbKyspg5cyYjR45k/PjxnHfeeVx00UXuDkvTfNKhvHIGxLq2GanBiPgokmNC+de3B6ipN9u9n293Z1NdZ+HWaf0cGJ19XNaUpJSqF5E7ga8BP+BNpdRuEbnV+vrLwBPA2yKyE6Pp6QGlVL6rYnSkkSNHsnXrVneHoWk+r6SyjvzyWvp2d/7EtpaICPfOGsRdH25lydZj/HJ8Uof3YbYo3l2XwfD4SIbHu38JYJeux6CUWg4sb/bcy41+Pg7McmVMmqZ5t0P55QD07e6+UTwXjezFK6sP8fzKg1w8Kq7D1V2/3ZNNWn4FL107xiOGteuZz5qmebW0vAoAt90xgHHX8PBFwzhWXMXDn+/GYrF9XkNOaTVPLt9HfHSI2zudG+jEoGmaV0vLK8ffJCR1df26BY1N6NOVu88ZwCebM/nt/7bZ9B6zRXHh8z+TWVTJwqtH42dy/90C6MSgaZqXS8urICkmlAA/93+c/d+5A7jr7P58sf04K3a1PUpJKcXzKw+QX17D1ROSGJfc1UVRts/9v0lN07TTkJZfTt9u7utfaExEuPucAQztFcnDn++mpKr1stxPLNvLwpUHuGRUHH+d61nzm3Ri0DTNa5ktivT8Svq5sX+huQA/E3+7YiT55TX8btHWFtds+O+GI7y55jBnD47lX7907yznlujE4GRms5kzzjhDz2HQNCfILKqk1myhnxtHJLVkREIUj186nFWpecz8x4/8b9MRymvqKa6s5cHFO3jos510Cw/i+XlneEy/QmPtjqkSEVsH5RY3L12hwcKFCxkyZAilpfpXo2mO5gkjklozf1JvhsZF8uclu3hg8U4eWLzzxGuDe0bw1g3jCQ9y6YwBm9kS1TsYNY3aSmsKeBt41wEx+YzMzEy+/PJL/vjHP/LPf/7T3eFoms85lOf+OQxtGZPUhaV3TmVzRhE/pOZSZ7YwJqkLs4f1xOSBdwoN2k0MSqmZzZ8TkZ5KqWznhORY2U8+Sc1ex5bdDhoymJ4PPdTudr/73e/4+9//TllZmUOPr2maIS2/gujQALqGee6yLX4mYUKfrkzo4zmjjtpjbx/D9Q6NwgctW7aM2NhYxo4d6+5QNM1nHcotp68L1njubOxt4LpURCqBb5VSqY4MyNFsubJ3hjVr1rB06VKWL19OdXU1paWlzJ8/n/fff98t8WiaL0rLr2DGwO7uDsPn2HvHcDlwELhMRF53YDw+46mnniIzM5P09HQWLVrE2WefrZOCpjlQWXUdeWU1Htu/4M3sumNQSuUAK6xfmqZpLpdhXQIzOca9pTB8kV13DCLyooi8bf1ZV0Ntx4wZM1i2bJm7w9A0n3K00EgMiW6ukeSL7G1KquXkAjpnOygWTdM0mx2xJoYkfcfgcPYmhkogSkQCgI6vSqFpmnaajhRWEh0aQGTw6a2zrJ3K3lFJhUAV8CKwxnHhaJqm2eZIYaXbS237qg7dMYhItIi8BVxhfepdYJzDo9I0TWvH0cJK3b/gJB26Y1BKFYvI00AykA+MBD51QlyapmmtMlsUmUVVnD+il7tD8Un2NCXdBBxWSn0NbHZwPJrWadXWWwj01wWPbXG8uIp6iyKxi75jcAZ7EkMRcKuIDAK2A9uUUlsdG5ZvSE5OJiIiAj8/P/z9/UlJSXF3SJqHemLZHt74+TALpiRzzpBYyqrruUBfDbdqX7ZRf2xQTz25zRk6nBiUUk+JyEpgPzAamAboxNCKVatW0a1bN3eHoXmwnw/k88bPhwF4e206b69NB+CdGycwXZd7aNHeLKOM/aCekW6OxDd1ODGIyOOAH7AN427hBwfHpGmdRlWtmUeW7iIi2J9ld01l8ZZjZBVX8fHmTH715kbGJEXz8vyxxEYGuztUj7I3q5TeMaEeu56Bt7PnjuFhEekBnAFcISL9lFI3Oz40x/jpo/3kHy136D67JYZz1i8GtrudiDBr1ixEhN/85jfccsstDo1D82619RZ+/8l20vIrePfGCfSOCeOe84y/q/mTenPpi2vYcqSYez/ezrs3TvC45R/daW9WKUN76bsFZ7E33f4GeEUppWsltWHNmjXExcWRm5vLeeedx+DBg5k2bZq7w9I8QEVNPb94ZR27j5fywJzBnDWgaZPRqMRoDv71fN5dl8Hjy/bwzZ4cZg/r6aZoPUtNvZmMwkouHR3v7lB8lr2J4U3gNhEJAz5QSm1zXEiOZcuVvbPExcUBEBsby2WXXcbGjRt1YtAA+Ovyvew+Xso/fzGKy8cktLiNv5+J6yb35n+bjvL4F3s4a0A3QgN108nRwiqUguRuekSSs9g7Nu5ujKTiDzzvuHB8R0VFxYmV2yoqKvjmm28YPny4m6PSPMGXO7L474Yj/GZa31aTQoMAPxN/uWw4x4qrePnHtDa37SyOFBrrPCd11Qv0OIu9ieEQEAx8rpTSl8AtyMnJYerUqYwaNYoJEyZw4YUXMmfOHHeHpblZfnkNf1qyk1EJUdw3e5BN7xmf3JXZw3rw3rp0quvMTo7Q86XnG8XzeuvieU5j733pbuAocJOIPKOUGm/Lm0RkDrAQY1TT60qpp1vYZgbwHBAA5CulptsZo1v17duX7du3uzsMzcM8+eVeKmrMPHPVKAL8bL8u+9WUZL7encPSbcf5xfhEJ0bo+Y4UVhIe5E+MB6/z7O3svWMYiPHh/ipwgy1vEBE/jKJ75wNDgXkiMrTZNtHAS8AlSqlhwFV2xqdpHmfXsRI+3XqMG6f2YWCPiA69d3LfGPrHhrN4S6aTovMeGQUVJHUN1aO0nMjexDAYY1LbfYCtYzAnAAeVUmlKqVpgEXBps22uAT5VSh0BUErl2hmfpnmcv63YR5fQAG6f2a/D7xURZg3tweaMIoora50QnffIKKjUzUhOZm9iiAYeAO4Hqm18TzxG81ODTOtzjQ0EuojIDyKyWUSub2lHInKLiKSISEpeXl7HItc0N1i+M4ufDuRz19kD7F4/4MKRvai3KJZsPebg6LyH2aI4WlSpF+dxMnsTw+MYHc+pgMXG97R036eaPfYHxgIXArOBP4vIKeNNlVKvKqXGKaXGde+uSwZonq2ytp6HP9/NiPgorp/c2+79DIuLYkR8FIs2HUWp5v91OoeskirqzIrkGD0iyZlsSgwi4iciWSLyawClVKZS6jvrzw/aeKxMoHGvWQJwvIVtViilKpRS+cBqYJSN+9c0j/TO2gzyy2t49JKh+Hegw7klV09IZF92GdszSxwUnXfJKLCOSNLrMDiVTX+lSikzsAvoeOPoSZuAASLSR0QCgauBpc22+Rw4S0T8RSQUmAjsPY1jappbFVfW8p8fDjJzUHfG9u562vu7ZFQcIQF+vLcuwwHReZ+GxKCbkpyrI5cvocD91rb9pdavz219s1KqHrgT+Brjw/4jpdRuEblVRG61brMXWAHsADZiDGnd1YEYPUpxcTFXXnklgwcPZsiQIaxbt87dIWku9pcv91JRa+bB84c4ZH8RwQFcOTaBJduOdcpO6IzCCgL8hF5RIe4Oxad1ZB7DZOv3MdYvOLWPoE1KqeXA8mbPvdzs8TPAMx3Zr6f67W9/y5w5c/jkk0+ora2lsrLS3SFpLvTfDUf4ZHMmd53dn0E9OzY8tS1Xjk3gvfUZvPnzYe6ZZdskOV9xOM8Yqupn0kNVnakjiaGP06LwQaWlpaxevZq3334bgMDAQAID9YSczqKkso6HPtvJpL5d+d25jq3XNTIhisE9I/j3qoNcODLOoUnH0x3ILWdwJzpfd7E5MSilvLJRc9Xbr5Kb4dgaM7G9+zJzQdvTN9LS0ujevTs33HAD27dvZ+zYsSxcuJCwMD2aojNYttMYV7FgSrLDr25FhPdumsjMf/zAs9+k8ur14xy6f09VXWcmvaCCi0fFuTsUn6cXmHWS+vp6tmzZwm233cbWrVsJCwvj6adPqQCi+ahV+3KJjw5xWqns7hFB3DKtL9/syWHLkSKnHMPTHMwtRykY2EMv5+lsPl/Dt70re2dJSEggISGBiRMnAnDllVfqxNBJ/HbRVr7bm8s1E5OcWrbhpql9eG99Bg8u3sHyu8867aGwni4t36iq2j9WJwZn6/Bfkohc7IxAfE3Pnj1JTEwkNTUVgJUrVzJ06NB23qV5u6Xbj/P5tuN0CQ1gwZRkpx4rLMifW6f3Y39OOUcKfX9gQ7o1MfTW5badzp47hr8CXzg6EF/073//m2uvvZba2lr69u3LW2+95e6QNCeqN1u4+8OtAHz9f9OIjXD+Os0jE6IA+HF/Hn27+/aVdHp+Bb2iggkJ9HN3KD7PnsSgx4nZaPTo0aSkpLg7DM1Fvt2TA8C8CUkuSQoAY5O6kNg1hK93Z3PDmb49cDC9oEIXz2ugFOxdClGJED+m/e07yJ5Gyc5ZpEXT2vH+hgwSuoTwl7muW6nPZBIuHhnH+rRCPk452v4bvFh6QSV9unXyZqTyXNjwKqx9Hj66HnZ85JTD+HZvlaa5SGFFLesOFTB3dLzLJ19dMzEJgN9/soPS6jqXHttViipqKayo7dzF8+qq4e0L4avfw7cPG3cL0+93yqF0YtA0B3hnbToWhVvG2Cd0CeXPFxkDG1bsynb58V1h29FiAEYnRrs1DrfJ3gkrHoD8/eAfAnP/A79aCqGnX3+rJfYkhhyHR6FpXkwpxfKdWUxI7uq2Wcg3TEkmsWsIb69Jd8vxnS23zFj2Jb5LJ62RtOQ22Pw2BEfDg0dg9DXQta/TDtfhxKCUOs8ZgWiatzqUV8GB3HIuHNnLbTGYTMJ1k3qzJ6uUtQfz3RaHs+SXGwUDY8KC3ByJG9RWQs4emHwn3JsK/s4vraObkjTtNKWkFwIwdUA3t8Zx4UijGeu2D7awI7PYrbE42p7jpZ13qOrXfwBlhqTJEOCa0W46MThJamoqo0ePPvEVGRnJc8895+6wNCdYl1ZATFggfd08YiY+OoSnLh9BZW09l7ywhqIK3ynLfSivnGFxke4Ow/WydxlNSAAJ4112WLsSg4jc0+jnzlX310aDBg1i27ZtbNu2jc2bNxMaGspll13m7rA0ByuprGPFrmxmDevp1PIXtpo3IYlXrhsLwMebfWf46rGiKuKjO2H/wgrrApmJkyCih8sO26HEICLRIvIWcJWI3C4iUwFbl/bstFauXEm/fv3o3dv+9X41z/TJlkxq6i3Mn5Tk7lBOOHtwD8b17sJ/Nxyh3mzrkuyeq6SqjrKa+s7X8Zz6FaT/ZPx84wqXHrpDM5+VUsXADSJyIZANzAI+dUJcDlP8xSFqj1c4dJ+BcWFEX2z7KqeLFi1i3rx5Do1Bcz+lFB9syOCMpGiGxUW5O5wmbprah9s+2MK9H29n4dVnuDuc07LdOlR1YI9OtA6DxQyLrjF+vvCf4OK7UXv7GKZjDFudBOhRSm2ora1l6dKlXHXVVe4ORXOw9WmFpOVVMH+i590Jnj+iF7fN6Mfn2457/SilA7nlAIxKiHZvIK6U/hMoizESafxNLj+8vWW3o4EHgPsB10fdAR25sneGr776ijFjxtCjh+vaBzXX+HLncUIC/LhghPuGqbbl7rMH8PnWYzz2xR4+vX0KYUHeWWU/s6iSsEA/okMD3B2Kc1ks8N+roPgomKz/VlPudkso9t4xPA58rpRKBby/EdOJPvzwQ92M5IMqaur5fNtxzh3aw2OHUIYE+vHk5SM4kFvGM1+nujscu2UWVZHQJdQjOvedquAgHPwO8lMhdzcMvsilHc6N2XUJoZTKBDKtP+vO51ZUVlby7bff8sorr7g7FM3BfkjNo6y6nvkTPafTuSUzBsUyb0IS763PYP6kJPrHel87fWp2GUN7dYKhqutfMr7PfRki4yB+rNtCsXe46osi8rb151kOjciHhIaGUlBQQFSUZ3VMaqfvu705dA0LZFyyc2rVONI95w0kNNCPv3y5192hdFhBeQ1HCis5Iyna3aE4V30tbH4LAsNh1NXQdzoEuW99DXubkmqBNOvPZzsoFk3zClW1Zr7fl8uMQd1dXknVHjHhQfz2nAH8kJrHyr3eVersUJ4xonCwr98xvH6O8X3K3S4fgdQSexNDJRAlIgGAZ99La5qDfbL5KCVVdVw93nv+9K+fnEyfbmEsXHkApbxnSZXMImPJ0gRfncOw7UP48BrI3mE8PtM9nc3N2ZsYCoFDwIvAGseFo2meb/nObPrHhjOhj+c3IzUI9Ddx09Q+7MgsYat1XoA3OFZUBeC7s57XvQjpPxs/z/wTBHjGedo78/kK61PvAuMcHpWmeaiiilo2phcyZ1hPd4fSYRdZq7/e/8kON0diu2PFVXQLDyI4wDNHfp220mMw4gp4tASm/97d0ZzQ4ZnPIvI0kAzkAyPx8JnPmuZIn287htmimO2FiSE6NJAJyV3ZmF7I/pwyr5hJnFlU5bulMOqqoarQGIHkYexpSroJ6KuU2qyUeksp9YWjg9I0T1RZW8/TK/aR0CWE4fHe2Rn6ynVjCQv0469f7sVi8fy+hmPFVST4YjNSeR68Ncf4OcI3EkMRcKuIPCciN4iIzYVYRGSOiKSKyEERaXX+g4iMFxGziFxpR3we41//+hfDhg1j+PDhzJs3j+rqaneHpJ2GDWmFVNdZuOe8gV472apLWCAPnD+YH/fn0feh5by46iAF5TU8991+cks96+/TYlFGYvDFO4b9X8HxrTDoAuh/jrujOYU9K7g9BdwMPAocBqbZ8j4R8cPorD4fGArME5GhrWz3N+DrjsbmSY4dO8bzzz9PSkoKu3btwmw2s2jRIneHpZ2G1QfyCPI3eWwJDFtdN6k3980aCMAzX6cy9i/f8dx3B/j1uylujqyp/PIaaustvtmUtN/68Tb3PxDhec2SHU4MIvI4cClG8bxjSqmFNr51AnBQKZWmlKoFFln309xdwGIgt6OxeZr6+nqqqqqor6+nsrKSuDjPu2XUbKOUYtmOLCb1jfH6jlAR4c6zB7DuD02nIO3ILOHTLZluiupUR315qGplIcQOg5Bou3dRVF1EQVWB42JqpMMlMZRSD4vIwxhJ5QoR6aeUutmGt8YDjVcOyQQmNt5AROKByzAmzbW6XJGI3ALcApCU1PZY8q+++ors7GwbwrNdz549Of/889vcJj4+nvvuu4+kpCRCQkKYNWsWs2bpSeLe6sudWeSV1TB9end3h+IwvaJC2P3YbJ5YtoeLR8Vx7esbuOej7Vw6Ot4jJu6lZhtVVQd4YRmPdpUeg8SJ7W/Xgs05m3nwpwfJrsjm5hE3c/cYx899sHcew5vAECAGeMnG97T0l9a89+s54AGllLmtHSmlXlVKjVNKjeve3TP/oxYVFfH5559z+PBhjh8/TkVFBe+//767w9LsoJTi4c93A3CNh9dG6qiwIH+evmIkZ/Y/uV71Le+meMQkuNTsUsIC/XxrDsOR9fDdo1B63O7RSBuyNpBdkc2do+9kdvJsx8ZnZW8d3rsxymL4AwuxrZ8hE0hs9DgBON5sm3HAImvHXjfgAhGpV0otsTPOdq/sneW7776jT58+NCSuyy+/nLVr1zJ//ny3xKPZb+exEgorarn5rD5e34zUluV3n8UFz//Eyn25vLM2nesmJ7v1zmFfdhmDekZg8oC7F4dZ+ThkrDUmsnXwjqGyrpK/b/o7iw8sJjYklt+M+o2TgrQ/MRwCBmCU3v4/G9+zCRggIn2AY8DVwDWNN1BK9Wn42Vqkb9npJAV3SkpKYv369VRWVhISEsLKlSsZN07PBfRGP6TmIQK3Tnfv2h7ONjQuku0Pz+KC53/i0S/2kF1aQ3JMKJeOjnd5aXGlFKk5ZZw/3Ls7+psw10PGGhhxFVzxuk1vKa8t58kNT3K45DC7CnYBMDNxJpf2a6l71nHsTQy7MfoLbhKRZ5RSrfYHNFBK1YvInRijjfyAN5VSu0XkVuvrL9sZi0eaOHEiV155JWPGjMHf358zzjiDW265xd1haXb4ITWXkfFRxIQHuTsUp4sKDeC7e6Yz+emVvPzjIcCYS3DvrEEujSOntIbiyjoG9/Sh/oU8a3XbiLaTXb2lnuKaYj7Y+wGv7zQSSLeQblzY90Iu6HMB0xJsGgh6WuxNDP0w5jO8av1uE6XUcmB5s+daTAhKqQV2xuYxHnvsMR577DF3h6GdhuLKWrYdLebOswe4OxSXCQn0Y8ntZ7LgrY2kF1Ty7+8P8uupfYly4Qpq+7JLARjkK4mhpgzetV7lD53b6mZKKS77/DLSS9NPPHfD8Bv43ZjfYRJ7u4Q7zt7EcFQp9b2I9MIHhpVqWmuW78zGomDGIM8c5OAsyd3C+OH3M1m5N4eb3klh1OPfsOq+GfTpFuaS46dmlwH4zh1D9i6oLIAufaDHsBY3qbfU8/but0kvTSc5Mpnrhl7H1PipxIW7fpi7vYlhjojsx5iwloHRGa1pPsVsUby6+hB9u4cxujMtRN/IOUN6cO95A3n22/1c/eo6Njx0rkuOm5pdRo/IIKJDA11yPKcryzK+X/1fCAhu8lJuZS4vbXuJdcfXcbziOMF+wXxyyScE+bmv6dLee5No4AHgfqDGYdE4kCcMt2uPN8TYme3ILCa9oJJfTU72rZExHXTHzP7MHNSdnNIavtnt2DlBrdmXXcbgnt5Zj6pFZdbfWwuznK/64ioWH1hMraWWMbFjWHzJYrcmBbA/MTyOMSIpFWhzzoE7BAcHU1BQ4NEfvEopCgoKCA4Obn9jzS1W789HBC4e1blnrJtMwnO/PINAfxO3vr+Z8pp6px6v3mzhYG657zQjAaz/D4gfhHQ58VRZbRn/2PQPCqsLOTP+TFb9YhXvnP8OSZHunytjU1OStX5RJvBnpdTrSqlM62OUUq0Ww3OXhIQEMjMzycvLc3cobQoODiYhIcHdYWgtUErxfWouI+Kj6BrmI80ZpyEqNIDHLxnGg5/uZPa/VrP6/plOm+NwOL+CWrPFdzqeq0ug5IhRRVWEZ1OeZVP2JnYX7D6xye2jbndjgKeyKTEopcwisgtjNJLHCwgIoE+fPu1vqGmtWLr9ONuPFvPYJS13FHZGvxyfyKJNR9l2tJgnl+/lzxedUgPTIfZZO559JjGkvEWZCAuHTOLAV79ie952EiMSOTfpXKbET2Fu/7kEmFw34ssWHel8DgXuF5HzODljWSmlnDvTQtPc4KOUoyR0CeG6Sb3dHYrHEBE+vW0KV7+6njd+PkxcdAg3TXX8BVhqdhl+JqF/bLjD9+1qKdkppB5exvZuXfkqL4WE8AQm9ZrEPePuYWCXge4Or1UdSQyTrd/HWL/g1FpHmub1CitqWXuogDtn9u/Unc4tMZmEl+aP4ZrX1vPEsj1M7NOV4fFRDj3G1qNF9OkWRpC/d5cfqa6v5oavbzAehIfRJ6oPS+cudW9QNupI53OfFr76OiMoTXOntYfyUQpmDo51dygeqVt4EM9cOQqAuz7c6tCV4GrrLWxIK2TGQC+fN1KaxTubjRUJnsgr4OeznmfxJYvdHJTt2k0MIpIkIkkYdwenfDW8LiI+NLZM68zWHMwnItifkQ6+EvYloxKjeWLucA7nV/DuunSH7Te9oIJ6i3L4XYhL7V0G/xzMih1v0qe2jrn9LyOq70yP60doiy1NSe9gJIG27qkV8DbwrgNi0jS3+vlgPpP7xuDv57oSBN5o/sQkPtmcydMr9nH24B4kxYSe9j5T0o0KO17d8bz9QwpMJg4GBnJ3z2kw+Y/ujqjD2k0MSqmZrghE0zxBen4FRwuruPks3UraHhHhH1eO5KJ//8xVr6zli7umEhtxevNy1qUV0Csq2HvnMOxeAvuW8WScMRdh9KgFdq+74E76kkjTGlm5zyj9Nd3b27hdZECPCN6+YQK5ZTX8e+XB097f/uwyhvSKxLomi9eoNddyw4obuGjb37gooRffB/sT6h/K6NjR7g7NLjoxaJqVUorFmzMZ0iuS3jGuKRbnCyb3i+GaCUl8uPEIW47YXGz5FLX1Fg7llXtlM1JmWSYpOSnEVJYw1Gxidp/ZvDH7Da/qV2jM3iJ6muZzNmcUsSerlCcvG+HuULzO/XMGs3JvLk8s28Ont02x64p/9/ES6i3KK5qRtuZu5dmUZ7EoCwAVdRUA3FlSyfiYoXDW0+4M77TpOwZNs3pnXQYRwf7MPcP72oTdLSokgLvO6c/WI8Ws3GtfJf61hwoA72jG+/Hoj+zM30lkUCSRQZH0MgUzp7qeYRXFMORid4d32nRi0DSgpLKOr3ZmcdXYREID9Y20Pa4am0j/2HDu+Wgb3+3J6fD7fzqQR//YcI8vtV1vqeeNXW8QGxrLy+e+bHwVVvBMbj6hM/4IY653d4inTScGTQNWpeZSb1FcPMqH1hh2sUB/E2/8ahzVdRZufX8zOaXVNr+33mxh29Fipg3w/LuFzLJMAJIjk40nzHWQ/hMMvRSm/x4Cvb9/Sl8aaRrw3voMEruGMKqTLsjjKL1jwlh291Rm/Ws1izYe5bfntr4kakluNnt//hGlLOSX1zAiN4OEjCOsW7zdhRF33Lrj6xiZE8UsNZB1iz801lrIS4RDJlj8oUtjiRs4hN4jRjt8vzoxaJ3e9qPFbM4o4tGLh+raSA4wsEcE45O78PXu7DYTw7ZvlpPyxacnHk8Cin6CtS6I8XSNIZojB37kyIlnkiH/OGz+wKVxjL/0Sp0YNM0ZftyfhwjMPSPe3aH4hqpizhvUlaPf/YeU519hXHQZhMZASFdjBbPgKIgfi7m+jqDQMG5/47889vluFm/JZOsjs5y2zoO9lFK8vvMNssqNotLfHPmWafFn8eRZTxobvDIDsnfAn3LAz7XDU6XNghT204lB6/Q2HC5gUI8Ij+/09HhF6bDuJdj4CrcABACF1q8WqPIpSL0fps/voDxtDPNjKgk4ZIKeIyDKc5J0flU+L2x/gYiACIL9gwnxD2FKwpmYTH5QcgxydsDYX52ylrM304lB69Rq6y1sziji6vHuX07RK1Xkw94vYOOrkLvn5PNiovSClxj3eTRXjU3grxcNAEsdlFlHK+3+FPX5SkxKYPuHPIu1bb6hiT5+rPHdPwQufwWi3LPS4eaczSw5uASAJ858gnN6n9N0g71fGN8TJ7o2MCfTiUHr1HYeK6a6zsLEPl3dHYr3UQrevRRydhmPYwbA4AvgzN9BaFcigbkZ2/k4JZN7zhtITHgEBFknr02/H8u+YKQyhf2T/85XP63nnDmXM9w/Ew6uBBRY6iHtB/j3WLh9PXR1/aqML2x9gc05m+ka3JVBXQedusG+ZcZazqOvcXlszqQTg9aprU8z2jkm6MTQcRtfM5JC3BiYtwgiepyyyU1T+/JRSiazn1vNhofObdJ/YLFYMJn8WB16Hv+qT2DeyHMgMhgm3WZsoBQs/jXs+gRenAC3rYNu/V1yarXmWr449AVpJWnMTp7NM9OfOXWjn/5pDFPtPRW8rLZTe3Ri0Dq1nw/kM7BHODHhQe4OxfPl7IHMjTBglrHA/Ve/N55vJSmAUT77pql9eOPnw7zw/cEmo5SUxQIm4b8bjjC0VyTdI5r9G4jAlW+AfxBs+wD+dy1EJTZ9fdJt0O9sR58pyw8v59F1jwIwolsLJVIqCmDlYzBwDlz5psOP7246MWidVml1HesPF3DHDNdchXqFonQICIXwWDi6CfYuhfMeN67eX50B5hrwD4Yuycb2F/2r1aTQ4A/nD+aTzZn867v91JrN/H72YMBIDGXVZtLyK3j3xgmt11ea+xIEhMCxLVBZcPL5vFQjWZ35W5h4y2mddkZpBsfLj594nJKdAsCmazcR7N9Cp3LWNuP7GfN9YkJbczoxaJ3W1iPFKAWT+sa4OxT3UMr4oPULMIaQ1pTDwlEQ2g3uSoE3zjW2KzkKuz9r+r6idJh2P4y7sd3D+PuZ+PLuqUz92ypeXHWI2cN6MrRXJEUVNRRXm7lwZC+mtVcf6cJnT31u/X/gp2fhu0fgyDqY+jvoNcrm0z95Oop5y+ZRVlfW5Pm4sLiWk8LG1+Cr+yGky8lOch/j0sQgInOAhYAf8LpS6ulmr18LPGB9WA7cppTy7GmQmtdavDmT8CB/xvSOdncorlNfY4ykMdfChldOXvle8m8oOGT8XJkPf0s++Z5jW07+/EAGhER3+LAJXUL54NcTufb1DVzywhoA5uTk0MNk4tGLh9l1Kky6DWKHwooHjXPK3WP0d0QlQPJU6Dvdpt3kVeVRVlfG/CHzOa/3eSeejwtvpZjitv9C7DD41VII9c2+KZclBhHxA14EzgMygU0islQp1WiMG4eB6UqpIhE5H3gV8K1xYJpHyCqpYvnOLH41JblzFc3b8zl8evPJx8HRUF0MS+86+dy4GyGil/Ha+F8bz310HYxdYFdSaDCxT1eun9ybd9dlAGBCEdc17NS+hY7oOx1uXwff/NkYIbTvS6gpgd2fwl2bbdrFS9teAmBCzwmM6TGm7Y33fgF5+2DEVT6bFMC1dwwTgINKqTQAEVkEXAqcSAxKqcaz4dcD7hm8rPm899ZlYFGKBVOS3R2K65Rln0wK130GX/zOuOoO6QKf/cZ4ftZfYMpdp7736tMv9eDvZ+LxS4dz58z+LN5yjOAf1uNXVXLa+wVg1hPGl1Lw9UOQ8iZseNUYPtvOHIiC6gL8xI+zEs5q+xjfPgxrFkKPEUa/hg9zZWKIB442epxJ23cDNwFfOTUirVOyWBQfb87knCE9SOx6+gvYe7Rjm+Hnf53sTwCYcrcxkud3O05ulzQJdn4CI692ekixkcHcNqMfn23wp6LGwQWeRaDXaKivNkZNpa2CeW0XtsurzGNS3CT8Te18HO7+zPi9XfORy0tfuJory263NORAtbihyEyMxPBAK6/fIiIpIpKSl5fnwBC1zmDzkSLyymq4YERPd4fiXPu/MZpYUlcYncU1ZdB3Bsx86NRtuyTDtPsg3HVlry1mM2JywkfQqF/CH44Z8wty90J1aYubKaUoqSkhtzKX2JDYtvd54FsoPW6U6/DxpACuvWPIBBoNQiYBON58IxEZCbwOnK+UKmj+OoBS6lWM/gfGjRvXYnLRtJYopXjh+4NEBPtz3lAfTgzmemPcv7kWBl/kkKYgR2uY4OYUQeEQPwYyfoanE2H+p9C/aTmLpzY+xYf7jLuJHmGtDLktyYTlv4fU5RDTH87w/kV4bOHKxLAJGCAifYBjwNVAk3nkIpIEfApcp5Ta78LYtE5AKcWjS3fz4/48bpnWl/AgH+h0Vgq+/4sxpHTsAvALgsM/wJb3jKRwwT9g3E3ujrJFymJxzh1DgxkPGndCX94DObtPSQz7CveRHJnMvMHzmJ08u+l7y3Jg/Uuw6XVQFjj3MZh0O/h3jkKLopTrLrhF5ALgOYzhqm8qpf4qIrcCKKVeFpHXgSuADOtb6pVS49ra57hx41RKSooTo9Z8xbd7crj5XeNvZddjs70iMez5aRWr3nqFVv+f1lcbQ1CbEwGTvzEZTTxjocZJ0RcRE3ByhTyFwmTyIyDYyVVJq0sxWq3F+L0EhmEBKuoqCDAFNJ2rYDEbv09LvfEevwC3/g5XsZMjtN5cPiZxOHNummvXvkVkc2ufry79n6GUWg4sb/bcy41+/jXwa1fGpHm2Q3nl7DpWwrlDehB2mh/kaw7mA7Dy3ulekRQActIOUltdzehZFzR9QSnI2QnpxpwA+p9rlI6ozDdKQccOMZo+PEj3/YnUB9RRGVp54rmo2B6EdXNyv0ZhJZRnGx/4uXsh+VwO+ptYf3w9M3tOIEbVQUUeFGVAVREEhUGXPtBrpDHxz43ydq4lzBRK78iW51TE93bOwE3v+N+hdUp/W7GP//xw6MTjHY/OIjLYvo6/nw7k8eP+PCb17Uq/7uGOCtHplMVCQHAQM8d0gc1vGR3IpcetI4wU9MSoPBo7xN2htuvYo+uIGptIv4v7ufjI1uPVlMFTCfyt8nM+Dguipqdwc/o/CVIYdwS9z4Tks4zhuoGeMVrNsl/o278fl156qUuPqxOD5pGySqqaJAWAkY9+wxd3TiUpJpSoENsSRE29mRdXHeL5lQcAuGqcF02NqSzEUpyJmGvgU+uN9MA5RtmH8B5GU9HYBRDZq83deAwXNlu3KCgCzv4z6zI+ppcI10UNJ2j4fcZIo9ghRj0mD2M2m/Hzc1IHfRt0YtA8TmFFLRcs/IkAP2HlPTNI6BLCVa+sY3NGERe/8DMmgcvHJPDMlSNbL7xm9d2eXJ5feQA/k/DGr8Yxvb2aPJ4i/wC8OAF1vC+muhijBMOVb0LsYHdHZj+FW8tTK6X4TfV+DqtqfjHgF/xi0h/dFoutdGLQNKs7PthCUWUd980aSFKMcUv/ya2TWXOwgOPFVfwv5SifbM4ks6iSxy4ZzqCeEa3ua1N6IcEBJrY/Mosgf9f/B+swc72xfvDHC0BZsCRNwZSRD7d97QM1/5VrZ041U1xTzLqsdYzuPporBl7hvkA6QCcGTQN+3J/HurQCJvXtyp1nn6zdLyJMHdANgCvGJvDhxiP845tU5r64hlevH8tZA069EzhSUMnba9OZ3DfG85NCwSEjGRQchDpr5+y0+1H7wpBj5T6QFLBOZ3XPeeRW5nL393cDMH/ofAZ39Y47L7PZjL+/6z+mdWLQPEZJVR2/enMjAH+ZO7zV7fxMwvxJvZk1rAfXv7GRX725kfHJXYmLDiGxSwgLzuzDlzuz+PMSY8nJayZ68HrOGevgmz/BMeuQ637nQO/JRido0iTUnmedO9bfhZRSbstvO/J2sLtgN9MTpjO+53j3BNFBFosFpZS+Y9A6L7NFcb01Kbx07Rj6x7bePNQgNiKYj26dzLWvbWDD4cITz7/x82Eqas0APHLxUC4e1Ur5ZHcpPGys/lVTBnn7oSLXWBWt3zkw6dYmmxqzg30jMTRMJXC1eks9D699GIBHpzxK12DXVkVNSUnh8OHDHX6fxWIB0IlB67zeWnOY7UeLuWJMAucPt71URWRwAP/7zSTe/PkweWU1xEYG88zXqQA8dMFgbjjT9QvIt8pigWW/g8wUyN19cu2A8TfC1P9r5S0WxA0fDE7hps7nzLJMymrLiAuLIybY9YsyrV27lvLyciIjIzv83tjYWBITE9vf0MF0YtDc7pvd2fzly73MGNSdf1zV/kij5kID/Zv0R4yIj+KFVQf55TgPaEKqrYRl/2eseVBfY1T7jEqEkb+Ey19t9+3KYvahOwblkjuGv2/6+4mlOQEOFBtDlR8/8/EO/205Qn19PUOHDmXu3LkuP7a9dGLQ3GrXsRJ+876xoMpjlwxzyH/caQO7t79UpCtset1YGOfwaug2CAKCIWkKXPV2u+skN3B6PSEf9OmBT+ka3JV+UcbEttAAY2Sbuzqc3TWy6HToxKC51QcbMgj0M/HzA2ef3kpensJiNkpdV+Ybq30FhBgdydd8ZNdsWosvJQY7m5LqLHX8df1fKaguQCmFQmFRlhZ/NiszFXUV3DLyFm4c3v561K6gE4OmdcDRwkqWbD3O3NHxvpEUAPJSYf2LEBYLkXHGIvZ9Z9i9O2U2O680tQs1FAG054bwaOlRFh9YTHx4PJGBkYgIJkyIiPGFYBITgvF4cq/JnBXfzmpsLqQTg6bZqKrWzG0fbMbPJNx5tmcVezst6T8b33/xrjHs9DT5zKikhmoYdmSGiroKAP4w4Q9MT5zuwKBcw11zEU6Hd0Wr+Yynv9rLrmOlvDx/jG8tr5m31/jefZBDduczfQwNdZLsuGOoqDcSQ0NfgTexWCxYLBZ9x6Bp7Vm6/TjvrMvgxjP7MHNAV7Zt23ZizLbXytltzF7OzoCIWbAvHUg/rV3WVFRw8OgxuvSKY8uWLY6I0m2UWVHsd5zg7BpCtuTb9J6fj/1McU0xRdVFJJclk7M/hy3Hvev3YOtcBEtNDYVvvY2lqqpD+w8dN47ws6baHV9rdGLQXKqsuo4/frqTsb278IcLBrN96xaWLVvm7rAcaKTxbelSx+wuLplsYKmj9udOAcB+61cHRBHFWMaS8r33LsjV3hyGinXryHvuOfDz61hzm1I6MWje75PNmZTV1PPwRUMJ8DNRV1cHwG233Uaws1fychal4PkzYMx1cNa9Dtvtj++/yf71P3PTwtcweVlTRHOWOjM5/9hMxMwEwie1PxM9pyKH+V/N596x9zKnzxwXROg8JpOJiIimM/nNJSUcv/8BzBXlxuN8Y3n7AT+swr+7+4da68SguYzFonhnbTpjkqIZlRgNnBytEh0dTVCQl45MqiwESyF06wVRjlvxK1CEyIhIunR1bQkHZ7DUmqkgmMjgCCJs+B3lWnKp8q8iOjqaKAf+Tj1F1Y4dlP/4I8HDh2MKC8O/Rw9Cxo3Fr1s3d4cG6MSgudB9H28nvaCSe2ad7JhtaIN1x4xUu312G6QuN9YCjjsDSo4az0fYXsrDFspi9o2OZzjR+WzGzJKDS6ioq8BsMZ+Ye2BRliZfOZU5AIQFhLkzaocrXb6c7L/8FVVdDUDC8wsJiPOwWl7oxKC5yIa0Aj7deowBseFNaiE13DF41ZDMA18bZS0CQiBvH5RlG6updXfs8po+M1QVTgxXPV5xnD+v+XO7mwtCREAEvSN7Ozkw58lYcAPVO3c2ec5SW4tfeDhRc+fi37Mn/r08c/U9nRg0pyqtriMlvZDfLdoGwDs3TiDA7+SHncfdMXx6C+z8uO1tlAUm3gbTf299rKCuyuHrBCtfK6AHVNUbV8pvzX6LgV0H4id+CIKfyQ8TJkxifHnM34ONlNmMpaqKw5ddTl1WlvFkfT1hU6YQNGBAk23DzpxC+LRpbojSdjoxaE713LcHeHONUXL4qctHEBfddF3dhsTg9ivjmjKoKYfDP0GP4TBwduvbmvzhjPknH4s4ZfF45VN3DEZmqLHUANAjrAeRgR2vNuqJatLSODz3MlRtLQCRF19MQFwcEhBA1/nX4hcd7d4A7aATg+YUtfUWNmcUsfZQPiMTonjq8hEMizu1E9FYvEXce4VYXQr/HAK1xggRxlwHMx9yXzxWFh/oY6isq+THzB9RlWZGEkVGWQbgG30HdTm5FH/8MfkvvABAxJw5hI4ZQ5d5VyMBAW6O7vToxKA5XG29hfs/2c6SbccBuH1GvxaTAliLxLm72SBjrZEUxt8MvUbC4IvcG4+VLxTQW3JwCU9tfIqo+nAW8Xd25O8gokcEEQHtL8Tkyaq2bSP96nknHsc9+w8iL7jA/X/LDqITg+Zw//gmlSXbjhMfHcK/rzmDob1abzJQSp3aXJK1Hb7+I/SbCeGNylNH9IKkSS3vyBQA/oH2BbzmOeP7hJsdVsrCEbyhKelwyWG2521v9fWfj/1MkF8Q/z3vv3Agk9tH384fzkwkwM8zr6jrsrOp2r6D2oyMVrepz8mh6IMPAOh6041EXXopwQMHuipEl9CJQXMopRSvrk4D4ONbJ5/Sp4BSJ+vmABaz2bjKalwSY9+XkP6T8WWrgDC4c6ORPE7s/+RxMPmfOqO0ugT+ezUcWQdJkz0qKUBDYvDszudH1z7Klty2y1QMixlGfEQ8WWTSJaQr4R7at1C8ZAlZD/7Bpm0lMJD45xcSMWOGc4NyE50YNMf59hGy9m/itYAq+sQEE7fszZOvhXSFbgNg9T/AXHPiaQvTMTEUHu/SdF8BoXDHhpOPzXVw+Eejk7i5ygJYsxD+Naz12MK6Q8J4GDgHCg/BkfVwfCuYa41jTb7TzpN2HovFgkUU7+15jzpLnbvDadGhkkOc1/s87h3X+ozvmOAYqLS/iJ6rVG02Elzv994lsH9/TKGtDygQf3/fGTHWAp0YtNNnsUDRYVjzHBbVjfGmKqJKayBoMPgHGUM5D60CZTa2H34ldDNuvdWBWkw5ZpjarLM3fgxEN1uaM6Zfy8dXyti2srDRk3LyW3Up5OyCQ98bE9Ma9BoNfoFw3WcQFG7z6aaVpLH66OoTj0trSymsLqTaXE1NfQ3V5mpU47sVIK8yj7SSNMQaV2RgJL8a9iv8Tf5c0u8SooKa9sGszlzNpqyNKLOFFZu+tTk2d5jQcwLx4fFtblOvrBcDHpAYVH09dceOUXMojaqdO6jes4f6nFxq09IInTCB0PHj3R2i2+nEoNmsvKaewsyDRKc8R0DxIUy15fjXlWEqz0EsxlC9J+uuocu4K/nrJUOatvnX14KlHkx+RrKwspQvQ4r2wIz77Q9MBMb/uv3tasqgIt9YZS08FoKNJo2vDn/FX9b/5cRku/aU1TW9azGJiW7B3Qj0CyTIL4hg/2BMcrJvwKzMpJWkYbaYuW7odRwqPsSa42v45+Z/AsYaxc07Y2vMNZyrYugR3otHJ9/GBX0vsCk2VxOEYH8balw13DA4uXO2Yt06ag6ltRxCbS0ln31GbXo6ylqjCz8/gvr1IyAhgbCJE+i6YIFT4/MWLk0MIjIHWAj4Aa8rpZ5u9rpYX78AqAQWKKW8q86ur0n7gaq8w6TnV7Bk3R7u9vuEMKlho2UQJSqcMrqSo0ZzRMVSExDFkBlXcce5Q8BkfADc+t2tpBamtrr7AccHEFMbw8yPZtocUlRgFO+c/84pV9ktefCnB9mQtaHNbcprywn2D+aifraPRpoaP5UzYs8AwM/kR5Bf23We6sx1iAj+JuO/XHV9NRZlYUX6Cg4UHWjxPRE7D9A9ogdXDLzC5rg8lo1Jt8O7ra0l/dr5xqQyiwVzYWGb20toKFFXXkHw4CEEDRhA8JDBmEJC2nyPJ8k7WsaR3QWERQUxaFJPpyValyUGEfEDXgTOAzKBTSKyVCm1p9Fm5wMDrF8Tgf9Yv2s22Hi4kAcW76C23lgD16Kwronb0OersCijkcNiUVTVmQkO8CM61Bghcnvt20w3rz+xP3/qiVUFhABDgCH+UI8/6b2vJOuMx1EKDuSUUVlTz8SkLsw9o2lzQlV9FWuOrWFkt5EM7NryqA1VoqAaZiTOsOkc8yvz+SHzBw4VH2JMjzHtbr/qyCoSIxIZ0X1Em9udGXcm5/Y+16YY7NF8FE6wfzCW6mrG/OkjRublnXjeFBVF77fexC86mv9+fq/XV1U9wY4V3LIeeZSKn9oegKDMZupzcgibPo2Anr0QPxPRv/wl/rGxLW5fa/FH+QeCMkKqqgFV3dDndTJ5tZTHGu4o66rN5B8tw2xWKIsy/m9ZlLHutMX4v2WutxjPNbyujO8ohcVi3ZcFinIqObq30Oa71cYtlCvf2cuY2b2ZfFkrTaynQWwO6HQPJDIZeFQpNdv6+A8ASqmnGm3zCvCDUupD6+NUYIZSKqu1/Y4bN06lpHS8Tvv7f3iSXH/PHgrYGVSZ6ghQflxY3kbHcadlQfDHJN4/GcwkEOofwO6iArKqKm17k1IgYApvZ86DyYR/TAy0M7S3tqqeiuKaNrdxCsE6ifPkd0yCSUD8hKQhXYmKtX3mfOLQrmQdLKa+1kKv/lEkDY2xLyyRzUqpcS295sqmpHjgaKPHmZx6N9DSNvFAk8QgIrcAtwAkJTXroLRRQJA/ITUe0BPmDB52WtJGQKFmf7rWh1BhLm9nJyf3YVEWwPYLGhMm+1ahdxFpvB5wfX2TMwvwC8Pfz+zymJyhymLGHK7oEm5jeXURApISMTlwnY7w6GCie4Y27L6FQzZ6ssXXje9R3UMJ7xqEySSIyZi5LybrB79J8Pc3IX4nk4GjxfWPdvg+G3NlYmjpt9P8f7ct26CUehV4FYw7BnuC+eWjp9HZqWmaXUa5OwDNJq5sS8kEEhs9TgCO27GNpmma5kSuTAybgAEi0kdEAoGrgeYL2S4FrhfDJKCkrf4FTdM0zfFc1pSklKoXkTuBrzGGq76plNotIrdaX38ZWI4xVPUgxnDVG1wVn6ZpmmZw6TwGpdRyjA//xs+93OhnBdzhypg0TdO0pvR4TU3TNK0JnRg0TdO0JnRi0DRN05rQiUHTNE1rwmUlMZxFRPKA1pdbals3IN+B4XgDfc6dgz7nzuF0zrm3Uqp7Sy94fWI4HSKS0lqtEF+lz7lz0OfcOTjrnHVTkqZpmtaETgyapmlaE509Mbzq7gDcQJ9z56DPuXNwyjl36j4GTdM07VSd/Y5B0zRNa0YnBk3TNK2JTpEYRGSOiKSKyEERebCF10VEnre+vkNE2l9M2MPZcM7XWs91h4isFRGvX0OlvXNutN14ETGLyJWujM8ZbDlnEZkhIttEZLeI/OjqGB3Nhr/tKBH5QkS2W8/Zq6s0i8ibIpIrIrtaed3xn1/GItW++4VR4vsQ0BcIBLYDQ5ttcwHwFcYKcpOADe6O2wXnPAXoYv35/M5wzo22+x6jyu+V7o7bBf/O0cAeIMn6ONbdcbvgnB8C/mb9uTtQCAS6O/bTOOdpwBhgVyuvO/zzqzPcMUwADiql0pRStcAi4NJm21wKvKsM64FoEenl6kAdqN1zVkqtVUoVWR+ux1gtz5vZ8u8McBewGMh1ZXBOYss5XwN8qpQ6AqCU8vbztuWcFRAhxmLL4RiJod61YTqOUmo1xjm0xuGfX50hMcQDRxs9zrQ+19FtvElHz+cmjCsOb9buOYtIPHAZ8DK+wZZ/54FAFxH5QUQ2i8j1LovOOWw55xeAIRjLAu8EfquUsrgmPLdw+OeXSxfqcRNp4bnmY3Rt2cab2Hw+IjITIzFMdWpEzmfLOT8HPKCUMhsXk17PlnP2B8YC5wAhwDoRWa+U2u/s4JzElnOeDWwDzgb6Ad+KyE9KqVInx+YuDv/86gyJIRNIbPQ4AeNKoqPbeBObzkdERgKvA+crpQpcFJuz2HLO44BF1qTQDbhAROqVUktcEqHj2fq3na+UqgAqRGQ1MArw1sRgyznfADytjAb4gyJyGBgMbHRNiC7n8M+vztCUtAkYICJ9RCQQuBpY2mybpcD11t79SUCJUirL1YE6ULvnLCJJwKfAdV589dhYu+eslOqjlEpWSiUDnwC3e3FSANv+tj8HzhIRfxEJBSYCe10cpyPZcs5HMO6QEJEewCAgzaVRupbDP798/o5BKVUvIncCX2OMaHhTKbVbRG61vv4yxgiVC4CDQCXGFYfXsvGcHwZigJesV9D1yosrU9p4zj7FlnNWSu0VkRXADsACvK6UanHYozew8d/5CeBtEdmJ0czygFLKa8txi8iHwAygm4hkAo8AAeC8zy9dEkPTNE1rojM0JWmapmkdoBODpmma1oRODJqmaVoTOjFomqZpTejEoGmapjWhE4OmNSIi0SJye6PHcSLyiZOONVdEHm5nm3+IyNnOOL6mtUYPV9W0RkQkGVimlBrugmOtBS5pa4y9iPQGXlNKzXJ2PJrWQN8xaFpTTwP9rOsXPCMiyQ118EVkgYgssdb6Pywid4rIPSKyVUTWi0hX63b9RGSFtWjdTyIyuPlBRGQgUKOUyheRCOv+AqyvRYpIuogEKKUygBgR6enC34HWyenEoGlNPQgcUkqNVkr9voXXh2OUsp4A/BWoVEqdAawDGiqXvgrcpZQaC9wHvNTCfs4EtgAopcqAH4ALra9dDSxWStVZH2+xbq9pLuHzJTE0zcFWWT/Iy0SkBPjC+vxOYKSIhGMsgvRxowquQS3spxeQ1+jx68D9wBKMkgY3N3otF4hz1AloWnt0YtC0jqlp9LOl0WMLxv8nE1CslBrdzn6qgKiGB0qpNdZmq+mAX7N6RsHW7TXNJXRTkqY1VQZE2Ptma83/wyJyFZxYj7el9bT3Av2bPfcu8CHwVrPnBwJeW/hO8z46MWhaI9Z1KdaIyC4RecbO3VwL3CQi24HdtLzE6GrgDGm6YtAHQBeM5ACAtUO6P5BiZyya1mF6uKqmuYmILAS+UEp9Z318JXCpUuq6RttcBoxRSv3ZTWFqnZDuY9A093kSY+EcROTfwPkYdfUb8weedXFcWien7xg0TdO0JnQfg6ZpmtaETgyapmlaEzoxaJqmaU3oxKBpmqY1oRODpmma1sT/A8a9454XQ9zhAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -122,12 +504,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -148,12 +530,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -174,12 +556,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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fsKunRY+xz5jBXHP6vl0eM336dH70ox9RXV1NcXEx8+fPp7Ky0tdyGGP6Fg2HIdjPAoOqRoADRaQMeExEpqvqsjaHvAlM8JqbTgHmAR3aXlR1LjAXoLKyslfWOvbee2+uvPJKZsyYwcCBAznggAPI7wWfBIwxvZeGwkh+MNvFyM6oJFXdISILgZOAZW2272rz/XwR+aOIlKvqtlSv1d0n+3SaPXs2s2fPBuDqq6+moqIia2UxxuSAcBgJZj8wZKyPQUSGezUFRKQYOBF4r90xo8Rb7EBEDvPKV52pMvptizchZd26dTz66KOcc845WS6RMaY3Etw1XjQU6nd9DKOBe7x+hgDwoKo+JSIXA6jqHOBzwDdEJAw0AmdrDi+qfNZZZ1FdXU0wGOS2225jyJAh2S6SMSYDVJVIdXWHGcydGVQXgYiizc3IwLL0Fi4BGQsMqvoOcFCc7XPafH8r0GdyWr/00kvZLoIxJgtq/nI3W371q4SP/533r7KKwIyJ6ShSUrJfZzHGmD4mtGkjUljIyB9eldDxv/3frwiFm5ld+Q1GfCL707gsMBhjjN8iDlJUxJCzz07o8H87t1AXCnHRzFMoKJuY3rIlwNJuG2OM39RBAsk/XrtbqCdTLDAYY4zPNOJAKoGhl4y1scBgjDF+c5KrMUSHqzp0vlBPJllgMMYYn6kTSarGEG1CshpDH3fBBRcwYsQIpk+f3rqtpqaGGTNmMGXKFGbMmMH27duzWEJjTNo4Cnm5+3jN3ZL3crNmzWLBggUx22688UY++clP8sEHH/DJT36SG2+8MUulM8aklRNBJIWmpC7WfM4kCwxpcuyxxzJ06NCYbY8//jhf/epXAfjqV7/KvHnzslAyY0y6qaOQl5f8+3rJqKS+P4/h6atg01J/zzlqPzg5+U/7mzdvZvTo0QCMHj26NZeSMaaPiUTw0r4lpbcEBqsxGGOMz1Sd1GoMCXY+N4Qa+OFLP2TeqnlJXyMRfb/GkMIn+3QZOXIkGzduZPTo0WzcuJERI0Zku0jGmHSIOEgKnc9dBYballruW3EfO5t38vSap6luqmZ48fCelLJTVmPIoDPOOIN77rkHgHvuuYeZM2dmuUTGmHRQJwJJdD63vq+TpqQNdRv4/JOf57Ylt/HYqsdw1OGsKWdx2SGX9bCk8fX9GkOWnHPOOSxcuJBt27ZRUVHBtddey1VXXcUXvvAF7rzzTsaPH89DDz2U7WIaY9Ihxc7n9qOSVJXHVj3Gr9/4NYLw5xP/zFFjj/KrlJ2ywJAm//jHP+Juf+655zJcEmNMxqXY+dzWqu2ruHXJrTy37jkOHXUo1x55LeMGj/OpgF2zwGCMMT5LtfP5vZr3eHrN06zcvpLFmxcjCN85+DtcMP0CAik0TaUqY4FBRIqAF4FC77oPq+o17Y4R4BbgFKABmKWqb2aqjMYY44uIA4HuawxbGrawtXErtaFaAK5/7XoAhhQO4aSJJ/HdQ77LmIFj0lrUeDJZY2gGTlDVOhEJAv8VkadV9dU2x5wMTPG+Dgf+5P1rjDG5Qx0k0HWNYVP9JmY8PCNmmyDMmzmPPcr2SGfpupXJpT0VqPNeBr2v9l3wM4F7vWNfFZEyERmtqhszVU5jjOmp7tJuX//q9dy/8n4ALtzvQqaXT6c+VM+MCTMoyi/KVDE7ldE+BhHJAxYDewK3qepr7Q4ZC6xv87rK22aBwRiTNeHqasLbqkEdcBw35UXr9w6otn4fqdlOePNm8ofHzjFw1GHuO3PZ0byDeavmsWfZnnx2ymc5d59zs3RXnctoYFDVCHCgiJQBj4nIdFVd1uaQeI1yHQb2ishFwEUA48ePT0dRjTEGAKe5mVUnzkAbG5N6X9E++7CtcRtvb3kbgI31G7ltyW0U5xdTnF/Mdw/5LsdWHJuOIvdYVkYlqeoOEVkInAS0DQxVQNvxWBXAhjjvnwvMBaisrOwdyUXaueCCC3jqqacYMWIEy5a5t/jQQw/xs5/9jBUrVvD6669TWVmZ5VIaY7rjNDSgjY0M/OxMOLLS7VQOBEB2/yuBgNfZLFAQJDByOIHycq555RperHqx9VyC8OgZj1IxqCJ7N5SATI5KGg6EvKBQDJwI3NTusCeAS0TkftxO55252r8wa9YsLrnkEs4777zWbdOnT+fRRx/l61//ehZLZoxJhoZCAPy55Xker/lnYm9atfvb4yuO55KDLgFgUMGgrIwySlYmawyjgXu8foYA8KCqPiUiFwOo6hxgPu5Q1VW4w1XPz2D5fHXsscfy0UcfxWzbe++9s1MYY0zqvMCwU+s5bY8zOGTkIUm9/ZixxzBqwKh0lCxtMjkq6R3goDjb57T5XoFv+Xndm16/ifdq3vPzlEwbOo0rD7vS13MaY3qnpz94iilAOACfGPcJPjXxU9kuUtpZEj1jjOnCm1WvA3DAmEoOHXVolkuTGX0+JYZ9sjfG9EQgEgHgy/ufx6CiIVkuTWZYjcEYY7ogYTfjqQSDWS5J5vT5GkO2xEu7PXToUC699FK2bt3KqaeeyoEHHsgzzzyT7aIak7Nqn3+BsI9L5BYfdCBFe+0Vs23MB9sBCwzGB52l3T7zzDMzXBJj+qbw9u1UffObvp6z+KCDmPiPv8dsO+jZtQDk96MVFy0wGGNykra0ADDiissZfPrpPT7fxp/8hPCGjtOmAhGHtw8pY+/Jk3t8jVxhgcEYk5sct+0/r7SUoA+f5vNKS2lZvabj9rBD3aCCHp8/l1jnszEmJ2nEWwbTpwVsAoVFOE2x+ZDUccgPK+GC/vWo7F93a4zpO6LrI+f58xiT4iK0sSn2Ek3u61BB8qux5TJrSjLG5CZvfoF0se5BMgJFxTh1dXxwwgm7Nzpujs5IsH99hrbAYIzJSeo9tOlmpbREDT7tVMI11e6ynG28tPkVPti3zJdr5AoLDGkSL+32FVdcwZNPPklBQQGTJ0/mL3/5C2VlZdktqDG5yonWGLpfWzkRRVOnMuYXv+iw/dlnvkaL0+LLNXJFt/UjERmf4NfgTBQ4V8yaNYsFCxbEbJsxYwbLli3jnXfeYerUqdxwww1ZKp0xuU+9UUl+1Rg6vQ6KxF1DrO9KpMZwD+4qal39zyhwN3CvD2XqE+Kl3f7Up3ZnZTziiCN4+OGHM1wqY/qQ1sCQ3oe2ow4iFhhiqOon2m8TkVGquik9RfLXpl/+kuYV/qbdLtx7GqOuvrpH57jrrrv44he/6FOJjOmHvMAgef7VGKrrmolo7KKQLZEIgrCltqmTd2VPSUE+Awv97xFI9YznAb/ysyD9yfXXX09+fj5f/vKXs10UY3KW3/MY/vbqWn48b1mH7cUTtoMGOOz653y5jp8uPm4yV508zffzphoYZopIA/Csqq70s0B+6+kne7/dc889PPXUUzz33HP9rnpqjK80WmPwJzB8vKORvIBw7Rn7xmy/b10JeZLP2QdP9+U6ftpnTHq6dlMNDJ/FXY3tTBHZU1W/1t0bRGQcbh/EKMAB5qrqLe2OOR54HIjOS39UVa9LsYy9zoIFC7jpppv4z3/+Q0lJSbaLY0xOU28eAz7NYwiFHQrzA3zliAkx25+pKaAwv7DD9r4spcCgqpuBBd5XosLA91X1TREZBCwWkWdVdXm7415S1dNSKVdvEi/t9g033EBzczMzZswA3A7oOXPmdHMmY0xc0b4AnwJD2FHy43Rk26ikBInIbcAAVZ0lIp9S1X919x5V3Qhs9L6vFZEVwFigfWDoE+Kl3Z49e3YWSmJMH+XzzOdQxCEYp1lKUQI+9WPkilTvtgVY7X1/QlcHxiMiE3Gbol6Ls/tIEXlbRJ4WkX3j7EdELhKRRSKyaOvWrcle3hjTB/g98zkUccjPi1Nj0P5XY0g1MDQApSISBMYn80YRGQg8Alymqrva7X4TmKCqBwB/AObFO4eqzlXVSlWtHD58eNKFN8b0AT7PfA5HlPw4tQ9V7XoWVx+UamCoAT4EbgNeTvRNXiB5BLhPVR9tv19Vd6lqnff9fCAoIuWpFFDbjUXOJblcdmMypXXms0/zGEKOUpDfSVNSP0tEndTdikiZiPwFOMvbdC9QmeB7BbgTWKGqv+3kmFHecYjIYV75qpMpI0BRURHV1dU5+YBVVaqrqykqKsp2UYzp3aKBwadh3+GIE7fz2WY+d0NVd4jIjcBEYBuwP9Dhk38njgbOBZaKyBJv29V4TVGqOgf4HPANEQkDjcDZmsLTvaKigqqqKnK1/6GoqIiKiopsF8OY3s3nmc9uH0P8z8r9rY8hlVFJs4E1qvoMsDjRN6nqf+mmpU5VbwVuTaFMMYLBIJMmTerpaYwxWaSqnD33VT7cWg9AwIlw3dO/pryuBoB8J0IhcO5fFvHRsC09vt6uxhB7jx7UsRyo1RgSsB24WET2At4GlqjqW/4WyxjT34UiymtrajhgXBn7jhlMsLGeCds/ZvPEvakZ437waykuYa+jD2Jqnj/5go6f2nEwi6OO1Ri6o6o3iMhzwPvAgcCxgAUGY4yvwl5T0SnTR/H14yYT2bGD96+D/b/0GYaed17rcWenuRw2wS0BInIdkAcswa0tLPS5TMYYQyjidi9G2/1bRyFleLKZqjUldUtVfyoiI3EnqJ0lIpNV9UL/i2aM6c/CXvbUYHTSWevw1MwPHe1vM59TbZj7OvBnVU0mV5IxxiQs7M1sjk46i6bZ9isFRqIcdbo/qI9JNTDchTusdADuZLUl/hXJGGOgJew+kFvTVEQf0BkODP2xjyHV/+Fv4waVfOD3/hXHGGNc0RpDa1NSNGmejyu2JULVkugl6kOgCHhcVY/1sTzGGAPs7mNobUqKznXNdOez1RgS9i7wPDBbRN7wsTzGGAPsHpXUscaQ+VFJ/SwupNzHMBXYCszFnfBmjDG+iTjKk+9sANrUGJzs9TFYU1JipuFOarscuMi/4hhjDCxeu50/LfwQgBGDC92NWQoM/XHmc6r/w2XAlcAPgCbfSmOMMUB9SxiA28+rZP+KMnejk53hqmBJ9BJ1HTBNVVeKSP8b5GuMSauw178wavDu9PO7m5IyPyrJZj7HISJ5QBXwE1W9Q1WrvNeo6lVpLJ8xph9qHZHUdqnN1jTb6asx7GrZxQfbP4jZ1hRpshpDPKoaEZFlwOQ0l8cYYwi1n8PA7pnPfvYxLK9ezo2v30jYcZuulm5bGve4AcEBvl0zFyTTlFQC/EBEZgAbvG2qqjP9L5Yxpj9rP4cBaF3j2c/A8ObmN3lry1scPvpw8gP5HD32aPYv35+DRx4cc9x+5fv5ds1ckExgONL792DvCyD31s40xvR64dbMqnGaknwMDBF1g83Nx9/MoIKOi/T0V8kEhh4tiSYi43DXiB4FOMBcVb2l3TEC3AKcAjQAs1T1zZ5c1xiTe0JeECho05+Qjs7naIK8PMlsh3Zvl3BgUNW1PbxWGPi+qr4pIoOAxSLyrKoub3PMycAU7+tw4E/ev8aYfiTcbi0GIC2dz9EaQ3+bwNYdf9bDS4CqbgQ2et/XisgKYCzQNjDMBO5VNynKqyJSJiKjvfcaY7IoFHFYuakW9bsBWRWamgAFVUYOLqBlx04GtjQQqNtFuMX9NB/ZudM93seHuNUY4stYYGhLRCbiLvTzWrtdY4H1bV5XedtiAoOIXIQ343r8+PFpK6cxZrc/LfyQ3z77vu/nvWDZU3x+1cLW19XAMd7Xxvkdj5fCAt+ubTWG+FJZ2vN0VX0y1QuKyEDgEeAyVd3Vfnect3T4fKKqc3HzNFFZWWkd4MZkQE19CyUFefz+7IN8Pe/QG+cRKRtK3RlfIKywozEEQFlJAfuMLQURQECEwMCBFO/n3wihaI3BAkOsVGoM1wMpBQYRCeIGhftU9dE4h1QB49q8rmD30FhjTBaFIg4lBXmcuM9IX8+7fkCQ0OiRTL/6O76eNxERJ0JAAv1uZnN3UgmTKf0PeiOO7gRWqOpvOznsCeA8cR0B7LT+BWN6h1DEiZ1X4BfHyUr+I/Ayp6acMq7vSqXGkGrTzdHAucBSEVnibbsaGA+gqnOA+bhDVVfhDlc9P8VrGdOv1NS3UN8c7vKY8oGFFBek3skajmjsvAKfqONkPGNqVEQj1owURyZHJf2Xbmob3mikb2WmRMb0DRt3NnLMTS8Qcbr+zHZARSmPX3JMytcJORozr8A3kUjWagyO45CX4aR8uSAro5KMMf7ZvKuZiKN87ZhJTBs9OO4xjyyu4oMtdT26TijspK/GkOF1nKOsxhBfKoFhs++lMMakrMFbu+CTe4/kyMnD4h6zfMMuln68s0fXCTt9r4/BUccCQxxJ/4+o6ox0FMQYk5qmkDsWv6SL/oOSgjwaWsLu+sUpaokowXz/H6LqRLLax2CT2zqypiRjeqEttU00tkQSOnZ9TSNAlx3LxQV5OAqrttRRkOLDva4pRDCQhmGdEQeCQf/PmwCrMcRngcGYXmb5hl2c8vuXOu5Q5ZF//pg79z2V+ZOO6rC7tLjzh2tZibtvxs0v9qhsx04d3qP3x6NOhECWOoAtMMSXUmAQke9F5yKIyF6qutLfYhnTf23c6dYAvnviVMYNLd69Ixym5PFmLnlnHide+Y2Y9wwbWMjINstgtnfmQWMZVBRsXecgVYdMGNKj98flKKRxVbauL22BIZ6kAoOIlAE3A9NEpAl4B5iNzTcwxjcNXhPSyfuNYurI3WsEOE1NrMTNEPHZgyuSOmdJQT5nHDDGz2L6JxJBUnw4f7D9A6qbqlO+9OaGzdbHEEdSgUFVdwDni8ipwCbgU0C81BbGmBRF+xaKg7EPLA13PYEtV6U6XHVXyy4+9+TnWvMdpWrqkKk9en9flGofw3HA74EjgNGkmDvJGOMuY7n0452EvQlqKzfXAnFGGfXRwIDjpLTGQmOoEUcdzt/3fI4bd1zKlx8/yDI0t5dqYCgDrgR+gNuUZIxJ0WNvfcwVD78Ts60gP8DAotg/T40kNkop16gTSWmNhWjK7Emlkzhk5CF+F6tfSzUwXAdMU9WVItKzepwx/dzWumYA/jLrUILeJ+eRgwspzO8fTUlEnJQ6n6OBwVJa+C+lwKCqVbgpslHVq3wtkTH9QFMowux73uDwScMIRRxE4Pi9hned/rmPBgZ1IkgKD3dbSyF9UvofFZHbRORu7/tP+VoiY/qB1VvreXlVNb999n0aWiKUBPO6XROgz9YYUhyuGnG8GoONKvJdqk1JLezOmXQC8C9/imNM3/JO1Q4WfbS9w/aPqutbv397/Q6KC7r/U8yVPobmDz4gsqv94oydc5oaUxqu2tqUZIHBd6kGhgag1FuRzbr0jenEDx9dyrsbun5ILlq7ncMmDu32XLlQY2ip+pjVp5+R9PsCgwZ1f1A70aYkCwz+SzUw1ACNwG3Ay/4Vx5jcVd8cpjkcOxZj864mzjq4gp+etk+H4wuDAVoiDurAgMIEHm45EBgiO3cAUH7pJZQclPja0EX7H5D0tcLq/n9YH4P/Up35vBfwN+BebLiqMaytrufE3/6HUKRj9tKKIcWUlsTPY1QU7DogNK9ew4Yrr0Sbm3GamtyNjsPqM2b2uMydyR89inG33Ybkp/C50Qtexfvtx4CjOuZz8pPjeDUGG5Xku6RnPovIjcBEYBuwPwnOfBaRu4DTgC2qOj3O/uOBx4E13qZHVfW6ZMpnTLZ8uLWOUET5+nF7MKZ0d36jQEA4efqolM/b9O67NC1dyoCjjiIwoITQunUUVx5C/pA05CwCWtZXUf+fF4ns2EF+eXnS79dQCCC1oJIk62NIn1R+erOBNar6DLA4iffdDdyKW8vozEuqeloKZTImayKOcuUjSwH4yuETGDe0xLdzR/sVRl37MwrGjfPtvJ3Z/sCDbLrmmpQ7ulv7QTIYGKwpyX+p/PS2AxeLyF7A28ASVX2ruzep6osiMjGF6xnT6yxZv4P7X1+HKjSGImytbaZiSDFjyoq7f3MSNJy5T+Btr6Oh1Pozou+TDKyvEO18zg/Y6gF+S/p/VFVvEJHngPeBA4FjgW4DQ4KOFJG3gQ3A5ar6bryDROQi4CKA8eNtUJTJjOdWbGbl5lrW1zSy9OMdrNxUy7ABhQDsUT6Ae2cfRp7fC9lEP7lnaE1kic629gJSsnY3JaU/MFiNIX2SDgwich2QByzBrS0s9KksbwITVLVORE4B5gFT4h2oqnOBuQCVlZWpr1VoTAJUlYv+uphnl8cud/6VI8bzi8/sl95rZ/ATONDaBJR6U5IXGIIZaEqyCW5pk0qN4aci8lPcWdNnichkVb2wpwVR1V1tvp8vIn8UkXJV3dbTcxuTjIaWMEurduIoPPDGOl5fU8OGnU2UDyzgZ2fsyyV/f4sfnjyNi47dI+1l0YgXGDLWlOQGoJTnTISTD2QNoQbe3/5+0pdatWMVYIEhHVL9bbsL+BowAPijHwURkVHAZlVVETkMN/CkvgKHMUm455WPeP69LXxUXc+6mga0TT10YGE+n953JDd/8UBKCvI5bOJQhg8q7DaFhS+iD9oMNyXF62PQcJhwddd/kuFt1d55En+0/PK1X/L4h48nUcpYAwoGpPxeE1+qgeHbuGkx8oFbcPsZuiQi/wCOB8pFpAq4BggCqOoc4HPAN0QkjDt57mxVtWYikxHXPOF2Zx03dThnHjSWYF6AycMHMLAwSOXEITHzDUZ0sYSm36Kf3DPd+UykY2D4+PIrqF2wILHzFCfeCb+zZSfjBo3jR4f/KOH3RA0qGMQepemvufU3qf62fYjb/v+4qn43kTeo6jnd7L8VdzirMRk3bEABx0wp55azE5+tmwka9tr6MxQYWvsY4jQltaxbS+G0aQz5Upd/yuSXlxMcMSLhS4adMKUFpRw99ujkymrSJtXftneB9cBsEfm1qh7qY5mMybiIKqXFGergTYKGQxAIIIHMjLyRvM6Hqzq1dRQfeCBDvvAFX68ZdsI2e7mXSTUwTMadzzDX+9eYnBaJKPkZevgCNLzxBs1r1nR7XNO7yzPWjAS7RxPVvfA8LevWxuyL1NQQGOh/e35EIzYXoZdJ9aexXlWfF5HRwBY/C2RMNoQch/y8DHQme9Zf/A2c+vruDwSCGZyrkz98OOTlUXNP/AQFBeMn+H7NsBOmIK/A9/Oa1KUaGE4Skfdxs6uuxe2MNiZnRRwl3+/JaV1wmpoYcu65DPta9zko80pLM1AiV8H48Ux95eXdCfvaEnEDh88iToT8DNaKTPdS/WmUAVcCP8AdtmpMzlJVQpHMBQZVhUiEvNJSgiNHZuSaycgrLc1oMAo5IWtK6mVS/WlcB0xT1ZUikhvLShnTCccbFJ2fwvKSKWlNG2EdruCuq2CT1HqXhP4SRCRPRDaKyNcAVLVKVf/tfX9VOgtoTLqFItG8/hmqMXjpJjLZqdybRRzrfO5tEgoMqhoBluGORjKmTwl7VYZghjqfM5maOheEnbAFhl4mmZ9GCfADEZmBm/0UQFU1fUtJGZMBEW/VtbwMDVdtnc2c1/sfhnUtdfzuzd/REGpI2zW2Nm61wNDLJPPTONL792DvC8BSVpicVNsU4rhfL6SuKUyL15RUkKnhqq2J5nr/w3DptqU8sPIBhhcPT9uQ0qFFQ6kcWZmWc5vUJPObOSltpTAmQ1SVHQ0hTrrlRWrqW9hzxEBWbamjfGABx++VeBqHHpUhw/mPeiLkuB3lN3/iZg4YfkCWS2MypdvfTBGJzq6JWztos39H29TZxvQ2OxpaOOE3/6GmvgWAY6cO557zD2VbXQvlAwsyky2VNn0MOdCUFHbcsgYDvS9diEmfRH4z78ENCl391Sjums5dredsTMapKtc+uZyq7Q3sagxTU9/CFyvHUTlxCJ+vdNdQHj6oMLNlyqEaQzQwWB9A/9LtT1tVP5GJgpjc0hSKMH/pRprDTtqvddD4MqaNGtzp/g8217JobfyUXbVNIe5+5SMqhhRTWhzk6D2H8ZPT92FgYWYfdE5zM6tPO53w5s1Es8lLQe9PA2GBoX+yn7ZJyQvvbeF7D76dkWsdMK6Mx7/VeUrmH89bxmtrajrdLwJzz61knzGdB5d0i1RXE1q/ngHH/h9Fe+2FFBYx4Kgju39jloXVa0oSa0rqTywwmJTsbHQ7JR//1tGMTOPCNT//53Le6qQ2ELWltpkZ+4zk5zOnx91fFAxQVpLdT+dOUzMApaefQenpp2W1LMmwGkP/lLGftojcBZwGbFHVDn/B4vb83QKcAjQAs1T1zUyVzySnocWdvTt+aAlDBqTvoTu2rJinl27klFte6vSY9TUNHDulnFGlmVtZLVna1AhAoLj3ljGeUMT9AGCBoX/J5E/7btwV2jrroD4Zd1W4KcDhwJ+8f00v1BhyA0NxQXpz3Jy2/2jWVtcT6aIro2JIMWccODat5eipaI1BCnMrMLQ2JdmopH4lY4FBVV8UkYldHDITuNdb5/lVESkTkdGqujEzJTRdUVXm/Gc1m3e56ZjfWredgEBhfnpnC+9fUcafz83e5KeQE2Lp1qWtTSoAA4ID2GfYPt0Ob21evZrwlq0oDmveeJ58YGXDGiIbe3+nc9Sane5iQlZj6F960097LO5yoVFV3rYOgUFELgIuAhifwUVM+rOttc3ctOA9ioN5FHjB4KjJ5Rkb+58t/1z9T37y8k86bL//tPvZd9i+nb7PaWpizczPoF4m1egf2tXv3sSGTbn1f1aYV0hhXmaH9Jrs6k2BId5fS9xJdao6F3dZUSorKy0tRwZEm45+8ZnpnHVIRZZLkzm1LbUA3HrCrZQES3h/+/vc+PqN1LXUdfk+p74eDYUY+tXzWLbPAP78zlxmH34pv9j/4C7f1xuNKBlBMM+akvqT3hQYqoBxbV5XsDtZn8myTPUp9DaOup0bh4w8hIEFA1vXDYho18uQOI1uk1vh1L1omB5kxQ5hnyNOYdzgcV2+z5jeIHOrn3fvCeA8cR0B7LT+hd6jKeQ+IIuCvelXJv2iASAggZh/owGjM9rsBgYpKtx9jgxlbzWmpzI5XPUfwPFAuYhUAdcAQQBVnQPMxx2qugp3uOr5mSqb6V6TV2Mo6merjkUDQLKBIbpmcqCoCEfdEUmBXvU5zJjOZXJU0jnd7FfgWxkqjunEb/61kgfeWN9hezQ1dVE/bUqKNiFF/20fGLSlheo77yRS6/Y9hLdsAdzhqRF1Z2VHg4oxvV1v6mMwvcD/PqxGBE6Y1jEF9aCiIPtmMa1ENnTWlNS+j6Fp+XK23vJ7N/9RnhdEysspmDgBrf/YfR3oX0HV5C4LDCZGyFH2GjWYGz67f7aL0isk2pTkNLozm8ffeQclhx4asy/yXmxwMaa3s99UEyPiOAQDuTXOPp0iToSABFrna3Q2Kik6CkmKijuco31zlDG9ndUYTIxwRMlPcInLUCTU7bDNZIhIr5tI5agT80k/OrLIcdr1MTRHO5s7lj/iWI3B5BYLDCZGKOKQn9f9A2zJliWcv+D81lw6frm88nK+uu9XfT1nTzjqxHzS77bGUGw1BpP7LDCYGGFHyU+gKWl97XrCGub8fc+ntLDUl2vfvvR2Ptr1kS/n8kuHGoPX+qrtJuXX/+9/7v7CODUGL4j09fQhpu+wwGBihCNKfgITsVoi7rrJX9r7S4waMMqXaz+w8oHWNM+9RUQjcZuSos1Duw90X+eVl3c4h9UYTK6xRk8TI+w4BBPoY2iOuJO2CvL8yxQaDAQJOb0rMLSvMXQ2j8FpaqJw2rS4tYL2Q16N6e3sN9XECEeUvASakqI1Bj87i3tjYIhoJOaTfmfzGLSpKW4zEliNweQeCwwmRijiEEyg87nFcQODrzWGvN4XGDr0MXQ2j6G5GSmKvwhPRCMIYn0MJmdYH0M/sbW2mVDEQYH7Xl3Lrqb4D+DGUAstWs2m+k1dnq+mqQZByBf/foWyWWMI19Sgzc0dthdW1zFsF4Q2evkcm3cybJcS2FpDqHR3jkentpb80fH7WtqPbDKmt7PA0A+88N4Wzr/7jQ7bh8VZq7mk4kGe3LGEJx/u/ryDgoN8/RQcDAQJR/wd/pqIxiVL+Ojs+Km8zvC+Vv3mhNZtfwLgD6ziDzHHFk6ZEvccjjpWWzA5xQJDP7DJW47z6lOmUVocpCiYx2n7j4nbl3Du/HvZ1bJHQnMJJg6e6Gs5g4Egu1p2tS4n6RdF+fuKv7OzeWfc/ZNfreI4YNGZ02gcGBssP66rQlX5XuX3AGgKN/HL129gVMkoyotjRyBtnNZI3X+u6HD+92reS3+NwXFg+xroJutrzgkWQ2n/WRiqt7DA0A+EvcyoZx5UwfBBXXcWh50woweO5rNTPpuJosUYEBzAa5te44x5Z6Tl/MFAkLEDx8ZsE0c5ZNFmAJ6c3kxjSfsay1COHHMkZYd/DnA//beU/5f/1X6Mmx2+rXVQE//aJ4w/If4Ov/z3t/D8z9N7jWz52nNQkb11v/sjCwz9QNhxJ2MlMgw1ohGCkp1lHK867Co+PfHTaTl3YV4hx487vkOG013/+hcfL/0OBAI8cPaTSF7Xn+wDEmDOiXPSUsYeqdsCwRI44w/dH5srdqyD566F2q77u4z/LDD0A+GIGxgSSXURckLkB7LzazF64GhGDxydtvNrSwvhXdvJbzMJbdO11wGwxxOPdxsUejUnBAUDYL/PZbsk/tm60g0M4aZsl6TfscDQD4S8hG+JpLoIO+E+t27Arvnz2fnUP2l47TWc+noGzTiRAUcfg4bDRKqrKZw2jcI998x2MXsm0gKB7NT00ibfa/b05sz0W407YPnjcPB5kKFBDBkNDCJyEnALkAfcoao3ttt/PPA4EO19fFRVr8tkGfuiSLTGkEBgiGgkazUGvzUtX862ubdTu2BBzPbaZ/9N7bP/bn098qqrMl00/0VCkNfXAoM3L6S/1xieuBRWPAFjDoTRB2Tkkplc8zkPuA2YAVQBb4jIE6q6vN2hL6nqaZkqV38Q8voYEpnRHHbCvs5NyIZdCxaw+cabCG9y26alsJCxN99M8X7TCQwaRGTXrtZjAwUF5JWVZamkPoqEwMfJhr1CtMYQ7ji/pF+J9rGEMhcgM/kEOAxYpaqrAUTkfmAm0D4wGJ+FIw75gcRm3oadcE7WGFrWrWPHgw/StPJ96l96CYDySy6h7MzPEBwbOxIp0MkM5ZwWael7NYZoupW1r8DoA2HCkVktTn+SySfAWKDtKvNVwOFxjjtSRN4GNgCXq+q77Q8QkYuAiwDGjx+fhqL2LREn8cV3cq0pKbRlC4TDVN9xJzsefBApLiZQWsqkBx+gYMKEbBcvc/pqU1JJuduM8uELcHVVtkuUXT4uitWdTD4B4j2ZtN3rN4EJqlonIqcA84AO00lVdS4wF6CysrL9OUw7oYgSTCCVNmR3VFKydj75FBuu2D2hrHCfvdnj0UezWKIsckJ9r/M5EIDvvA0v/cadp9HSAAUlXb9n2wduDSMV44+A4Xul9t5MCDVm7FKZfAJUAePavK7ArRW0UtVdbb6fLyJ/FJFyVd2WoTJmxPylG3nsrY99O1+9fsTHPIUSf9ZrbVMYRjl8+/l/dnuufZbVcuKfHmVVwXMEBgxg3Nw/Exw5MrkCNe6Ap38AzXXJva8b9WtqqXl9a+vHiZZtTUi+MOoU99eqaKzCP77k6zVzxsa3obwXP9RSVTgQhkx0v3/gK7s7pDuzsvvf8S7tdWr87YE8+MSPYMS0xM7z9gPuSKJE1G6EmtVuX8rAEe62g86F466AxXdD1evutgx2wmcyMLwBTBGRScDHwNlAzF+xiIwCNquqishhuNlfqzNYxoy4/431vL6mmknlA305386i16ktWkwwEj91gARhcDCPDXXd5yE6ZUspJdXbKThif+pffpmW1auTDwwbl8A7D8CQSVDgzz0C7Hyjjrr3mykc6g6nFYEh0wspm1TvHVEPO3y7XG4ZNAamdfJQy3Xjj4Sxle4kvu6M2g8O/DLsneTs+ZXz4c173El17WkEtiyHikMTDwyL7oTN77p/A93Jy3fPjbpNZ6tfgA+fcwPD6oW7j+uLNQZVDYvIJcAzuMNV71LVd0XkYm//HOBzwDdEJAw0Amerap9rKgpHHKaPKeXhbxzly/luXryIvy4P8uZXn+7xuTYt+Tk7B/6T8m99i/qXX0bDKSS1iybC++xcGHdYj8vUetp3vkmhbmSPeY/5dk6TA4ZPhQufS+81DrvQ/YqnpQF+OTq5PFSRkNs09ZVHki/LfV+AOjdNC6EmKB4KjTV9MzCA2zwEzG+3bU6b728Fbs1kmbIhnERncCIijn8dxhqJIPn5SNA9X0qBwfHe42NfhapS98ILlBxxhG/nNCYh0fU4kgkMTjj1Pp9g8e4gEGqA4iFuYMhgU5It1JMF4QQXw0n4fOrfEFMNh9zAkN+TwOCtqeDjKBmn3k1Yl1da6ts5jUlINBNAMqOCnLDbRJSKYDGEvcAQboKSoe73ofZJG9MnN4af9DFhJ7HlMxM/n4+T0sLhmMBASk1JXmDwscbg7NwBwMBj/8+3cxqTkNYaQxKt2pFQ6r//+UXQtAuqFkNDNQzdw91esxqqFkHVG+4AD3CbqyZ/IrXrdFUE389ouhWOKPkJDh9N6Hw+TkrTUBjy81o/7aRWY/A+WSVQla57+WWqLv02hLpeuS3a1dQnZimb3JLppqSSodC0A+7wUrWPOxwKS90RSovvjj326MssMPQVYcdJKAV24ufzL/Gd28cQbNPHkMKkmtampO5/vZpXvo82NDB01iwk2PUfkhQXMeBIm/1qMkwEkN0feBLhhFNvSj3muzD+qN2BqKISjv4ObF/rvi4uc0cxpTGhngWGLAhHfG5KUv+akrRdU5KGU1iDOYmmJG12O9RGfP973QYGY7JGAsmPSkr1w1rhIJhyYuy2kqEZnXxnnc9ZEHbU185nX0cltet8TqmPoXVUUvcPeqexCfLzLSiY3k0CyXc+5/BMdAsMWRBNaufb+fxMfBftY4jWGEI9CAwJVKW1uYlAYdfLjRqTdYG8JPsYcjt3lTUldaO6rpn3NtX6es76yC526GZe3ejPhBXno3V8duFmNrz+o+TfrEDDttaHefN77xIcXY5UvQrAzof/TtOihcmdc8daAtsHU7LwJfKGjOjy0Ja165Di4uTLbUwmddeUtGmpO4IoKtzs66i8TMvdkmfI5Q+9zQsrt/p6zqKxD/Fq41Je/Zc/5/vCfyNUvqbUp5I8LNwM9bGpqAZQReChsykZPoyW9R8SXv9hUqdUhUjTQLZ/L7EFcAqndMiTaEzvIgFwOgkMtZtgzjEdtxeXpbVI6WSBoRs7G0PsN7aUn5y2j2/n/M07D9Gkk/npkT/x5XzBj+6HRf9iysIXkn/zu4/BQ7PgzD9DWWwK8wmzUy9TqE4INSZWlQ6Os9TpppeTLpqSmrzcn8ddCXsc7x0fgDEHZaRo6WCBoRsRRxk6oIDDJg317ZwD3g9QrGUcMvIQX863ueBf7AimuHpXdOr9uMNhaAIJvxIU9L6M6RNEOg8M0T61EfvABH/yn2WbdT53IxRRX+ccgP+rpGkolPqonmhgCFo7vzGdCuR1PiopDSlgss0CQzfCjuPrnAP3nP6uq9yjwBBNzNVdnntj+rOuOp8j/ieNzLa+cydp4mZC9Td++l5j2LIKCdfBMymMSvr4TfdfqzEY07muAkMasglnW9+5kzQJR5Sg3zUGH7OhAui6RUgIWPSX1E4w5iDIS7GPwpj+QPI6T4nRB5uSLDB0IxxxyPMx4R14uY3En9xG4OYzkoHD4Ucf+HZOY0wbEug8u2oasglnW9+5kzRx01eI+0ux4IewY23M/rrVdWxfXQaFgxM+57kfr2Nw3mbWP/itnhdQlcat+eQPtx+lMWnTZVNS4tmEc0VGnyYichJwC+7Snneo6o3t9ou3/xSgAZilqm+mvWBNO2HDW3F3HRx5m6kNQ2H5h/Dan6B0PBTvXixmx8Iq6jcOoGBK4gmuyrY3U+I0EtKNPS46GiG/yGHwIZN7fi5jTHyBLnIlJZFNOFdk7E5EJA+4DZgBVAFviMgTqrq8zWEnA1O8r8OBP3n/ptczP4K3/hp31xyAD70vgLPugPG7ixR5YApFFaVMfOzRhC934Z17c0xjC9dekvh7OlVfDb/eA04+uufnMsbE1+WoJGtK6onDgFWquhpARO4HZgJtA8NM4F51V2V5VUTKRGS0qh8frWM9cfO3GfTwswAIoIwhQsd2fwUCIuQJ7iSX5y+K2T+0Jp9V47dz2V37J3zt6rw88lXhNh9iXvSX0oabGpM+EoD3n4n/Nxud+WxNSSkZC6xv87qKjrWBeMeMBWICg4hcBFwEMH58aukUCkvL2Tl09+03B4oIS/yROQMK8ztNk10/op61+wp7JDHzeHLTTk51imGkT/nVxx4Ck0/w51zGmI6O/BasXtj5/uKhMKzvNOdmMjDEG/PZvps/kWNQ1bnAXIDKysokFmLd7dMX/BQu+Gkqb+3gVF/OYozptSovcL/6iUzOfK4CxrV5XQFsSOEYY4wxaZTJwPAGMEVEJolIAXA28ES7Y54AzhPXEcDOdPQvGGOM6VzGmpJUNSwilwDP4A5XvUtV3xWRi739c4D5uENVV+EOVz0/U+Uzxhjjyuj4KlWdj/vwb7ttTpvvFfBh1pcxxphUWXZVY4wxMSwwGGOMiWGBwRhjTAwLDMYYY2KIdpZKNkeIyFZgbbcHxlcObPOxOLnA7rl/sHvuH3pyzxNUdXi8HTkfGHpCRBapamW2y5FJds/9g91z/5Cue7amJGOMMTEsMBhjjInR3wPD3GwXIAvsnvsHu+f+IS333K/7GIwxxnTU32sMxhhj2rHAYIwxJka/CAwicpKIrBSRVSJyVZz9IiK/9/a/IyIHZ6Ocfkrgnr/s3es7IvKKiByQjXL6qbt7bnPcoSISEZHPZbJ86ZDIPYvI8SKyRETeFZH/ZLqMfkvgd7tURJ4Ukbe9e87pLM0icpeIbBGRZZ3s9//5pap9+gs3xfeHwB5AAfA2sE+7Y04BnsZdQe4I4LVslzsD93wUMMT7/uT+cM9tjnseN8vv57Jd7gz8nMtw11Uf770eke1yZ+CerwZu8r4fDtQABdkuew/u+VjgYGBZJ/t9f371hxrDYcAqVV2tqi3A/cDMdsfMBO5V16tAmYiMznRBfdTtPavqK6q63Xv5Ku5qebkskZ8zwKXAI8CWTBYuTRK55y8Bj6rqOgBVzfX7TuSeFRgkIgIMxA0M4cwW0z+q+iLuPXTG9+dXfwgMY4H1bV5XeduSPSaXJHs/s3E/ceSybu9ZRMYCZwJz6BsS+TlPBYaIyEIRWSwi52WsdOmRyD3fCuyNuyzwUuA7qupkpnhZ4fvzK6ML9WSJxNnWfoxuIsfkkoTvR0Q+gRsYjklridIvkXv+HXClqkbcD5M5L5F7zgcOAT4JFAP/E5FXVfX9dBcuTRK5508DS4ATgMnAsyLykqruSnPZssX351d/CAxVwLg2rytwP0kke0wuSeh+RGR/4A7gZFWtzlDZ0iWRe64E7veCQjlwioiEVXVeRkrov0R/t7epaj1QLyIvAgcAuRoYErnn84Eb1W2AXyUia4BpwOuZKWLG+f786g9NSW8AU0RkkogUAGcDT7Q75gngPK93/whgp6puzHRBfdTtPYvIeOBR4Nwc/vTYVrf3rKqTVHWiqk4EHga+mcNBARL73X4c+D8RyReREuBwYEWGy+mnRO55HW4NCREZCewFrM5oKTPL9+dXn68xqGpYRC4BnsEd0XCXqr4rIhd7++fgjlA5BVgFNOB+4shZCd7zT4FhwB+9T9BhzeHMlAnec5+SyD2r6goRWQC8AzjAHaoad9hjLkjw5/xz4G4RWYrbzHKlquZsOm4R+QdwPFAuIlXANUAQ0vf8spQYxhhjYvSHpiRjjDFJsMBgjDEmhgUGY4wxMSwwGGOMiWGBwRhjTAwLDMa0ISJlIvLNNq/HiMjDabrWZ0Tkp90c8/9E5IR0XN+YzthwVWPaEJGJwFOqOj0D13oFOKOrMfYiMgG4XVU/le7yGBNlNQZjYt0ITPbWL/i1iEyM5sEXkVkiMs/L9b9GRC4Rke+JyFsi8qqIDPWOmywiC7ykdS+JyLT2FxGRqUCzqm4TkUHe+YLevsEi8pGIBFV1LTBMREZl8P/A9HMWGIyJdRXwoaoeqKpXxNk/HTeV9WHA9UCDqh4E/A+IZi6dC1yqqocAlwN/jHOeo4E3AVS1FlgInOrtOxt4RFVD3us3veONyYg+nxLDGJ+94D3Ia0VkJ/Ckt30psL+IDMRdBOmhNhlcC+OcZzSwtc3rO4AfAPNwUxpc2GbfFmCMXzdgTHcsMBiTnOY23zttXju4f08BYIeqHtjNeRqB0ugLVX3Za7Y6Dshrl8+oyDvemIywpiRjYtUCg1J9s5fzf42IfB5a1+ONt572CmDPdtvuBf4B/KXd9qlAzia+M7nHAoMxbXjrUrwsIstE5NcpnubLwGwReRt4l/hLjL4IHCSxKwbdBwzBDQ4AeB3SewKLUiyLMUmz4arGZImI3AI8qar/9l5/Dpipque2OeZM4GBV/UmWimn6IetjMCZ7fom7cA4i8gfgZNy8+m3lA7/JcLlMP2c1BmOMMTGsj8EYY0wMCwzGGGNiWGAwxhgTwwKDMcaYGBYYjDHGxPj/PCp+3orkpnkAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -198,6 +580,13 @@
     "fig.savefig(\"helio_swifter_comparison-tp-vmag.png\", facecolor='white', transparent=False, dpi=300)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/helio_swifter_comparison/tp.swifter.in b/examples/helio_swifter_comparison/tp.swifter.in
index 8a66912f4..180f3efdd 100644
--- a/examples/helio_swifter_comparison/tp.swifter.in
+++ b/examples/helio_swifter_comparison/tp.swifter.in
@@ -1,13 +1,13 @@
 4
-101
-2.1159283340247889704 1.8593322968487970837 -0.33108647801775120678
--2.557303042640355446 2.5920133227445458545 0.5530693963730075664
-102
-3.055528708824450046 -0.9023759798915096386 0.36193041623852567623
-0.4122422441588732561 2.6115158464246720372 -1.8437451126910543971
-103
--0.26900389298636068203 -3.1374127668516589296 0.7234488489303841918
-3.0956076496295565968 0.17648254651685860603 -0.16591700615421532186
-104
--1.9061083760262669262 -1.0793924233562111059 0.26419511130887440853
-2.3545884478521155142 -3.673223720899393644 -0.17666743480430943436
+9
+1.7496059999633410964 2.170163391141847864 -0.2537726760879844834
+-3.0064589998644978604 2.1233488530690124423 0.6210068204130407379
+10
+3.0772345391474851262 -0.5509101822792066283 0.11666058691376969547
+-0.08868603569822111026 2.7292630488987525612 -1.882742859645719835
+11
+0.13917497353384339354 -3.081984978409241016 0.69426813140927812196
+3.105664664763373206 0.67090307556352112164 -0.2786153399455880027
+12
+-1.5389664718057010084 -1.5223401530194009545 0.23276670506845731357
+3.2083305098906780644 -3.027143636331455024 -0.2998683055841925141
diff --git a/examples/helio_swifter_comparison/tp.swiftest.in b/examples/helio_swifter_comparison/tp.swiftest.in
index 8a66912f4..c9c243562 100644
--- a/examples/helio_swifter_comparison/tp.swiftest.in
+++ b/examples/helio_swifter_comparison/tp.swiftest.in
@@ -1,13 +1,13 @@
 4
-101
-2.1159283340247889704 1.8593322968487970837 -0.33108647801775120678
--2.557303042640355446 2.5920133227445458545 0.5530693963730075664
-102
-3.055528708824450046 -0.9023759798915096386 0.36193041623852567623
-0.4122422441588732561 2.6115158464246720372 -1.8437451126910543971
-103
--0.26900389298636068203 -3.1374127668516589296 0.7234488489303841918
-3.0956076496295565968 0.17648254651685860603 -0.16591700615421532186
-104
--1.9061083760262669262 -1.0793924233562111059 0.26419511130887440853
-2.3545884478521155142 -3.673223720899393644 -0.17666743480430943436
+Ceres
+1.7496059999633410964 2.170163391141847864 -0.2537726760879844834
+-3.0064589998644978604 2.1233488530690124423 0.6210068204130407379
+Pallas
+3.0772345391474851262 -0.5509101822792066283 0.11666058691376969547
+-0.08868603569822111026 2.7292630488987525612 -1.882742859645719835
+Juno
+0.13917497353384339354 -3.081984978409241016 0.69426813140927812196
+3.105664664763373206 0.67090307556352112164 -0.2786153399455880027
+Vesta
+-1.5389664718057010084 -1.5223401530194009545 0.23276670506845731357
+3.2083305098906780644 -3.027143636331455024 -0.2998683055841925141
diff --git a/examples/symba_mars_disk/param.in b/examples/symba_mars_disk/param.in
index 023f31647..1cf7598a5 100644
--- a/examples/symba_mars_disk/param.in
+++ b/examples/symba_mars_disk/param.in
@@ -1,13 +1,13 @@
 !Parameter file for the SyMBA-RINGMOONS test
 T0             0.0 
-TSTOP          1200.0
+TSTOP          12000.0
 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      20
+ISTEP_DUMP     20
 BIN_OUT        bin.nc
 PARTICLE_OUT   particle.dat
 OUT_TYPE       NETCDF_DOUBLE
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 15fdfcbe3..177630caf 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swifter.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swifter.in
@@ -1,9 +1,9 @@
 ! VERSION        Swifter parameter file converted from Swiftest
 T0               0.0
-TSTOP            3652.5
+TSTOP            365
 DT               1.0
-ISTEP_OUT        10
-ISTEP_DUMP       10
+ISTEP_OUT        1
+ISTEP_DUMP       1
 OUT_FORM         XV
 OUT_TYPE         REAL8
 OUT_STAT         UNKNOWN
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 ad787b5bf..e3e020d8a 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swiftest.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/param.swiftest.in
@@ -1,9 +1,9 @@
 ! VERSION        Swiftest parameter input
 T0               0.0
-TSTOP            3652.5
+TSTOP            365
 DT               1.0
-ISTEP_OUT        10
-ISTEP_DUMP       10
+ISTEP_OUT        1
+ISTEP_DUMP       1
 OUT_FORM         XV
 OUT_TYPE         NETCDF_DOUBLE
 OUT_STAT         UNKNOWN
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.in
index 93c1187e2..574290966 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 @@
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 0.00016953449859497231466
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 0.000164587904124493665
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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 611be7721..b4b8dfe4d 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swifter.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/pl.swifter.in
@@ -2,35 +2,35 @@
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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 93c1187e2..574290966 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 @@
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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 4f99d59cc..beb9f15c6 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
@@ -21,7 +21,7 @@
      "output_type": "stream",
      "text": [
       "Reading Swifter file param.swifter.in\n",
-      "Reading in time 3.650e+03\n",
+      "Reading in time 3.650e+02\n",
       "Creating Dataset\n",
       "Successfully converted 366 output frames.\n",
       "Swifter simulation data stored as xarray DataSet .ds\n"
@@ -46,7 +46,7 @@
      "text": [
       "Reading Swiftest file param.swiftest.in\n",
       "\n",
-      "Creating Dataset\n",
+      "Creating Dataset from NetCDF file\n",
       "Successfully converted 366 output frames.\n",
       "Swiftest simulation data stored as xarray DataSet .ds\n"
      ]
@@ -74,7 +74,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "swiftdiff = swiftdiff.rename({'time' : 'time (d)'})"
+    "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)"
    ]
   },
   {
@@ -83,18 +84,399 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "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)"
+    "plidx = swiftdiff.id.values[swiftdiff.id.values <= 8]\n",
+    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 8]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "\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",
+       "}\n",
+       "\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;xhx&#x27; (id: 16)&gt;\n",
+       "array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.00484658, -0.00484491,  0.        , -0.00211506,  0.        ,\n",
+       "        0.        ])\n",
+       "Coordinates:\n",
+       "  * id       (id) int64 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
+       "    time     float64 1.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'xhx'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 16</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8b2d50ea-35b2-4344-844a-cea5ad0ea5a8' class='xr-array-in' type='checkbox' checked><label for='section-8b2d50ea-35b2-4344-844a-cea5ad0ea5a8' 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 ... 0.004847 -0.004845 0.0 -0.002115 0.0 0.0</span></div><div class='xr-array-data'><pre>array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.00484658, -0.00484491,  0.        , -0.00211506,  0.        ,\n",
+       "        0.        ])</pre></div></div></li><li class='xr-section-item'><input id='section-9113c076-69af-4c15-bae4-4fe0f3649c76' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9113c076-69af-4c15-bae4-4fe0f3649c76' 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'>9 10 11 12 13 14 ... 20 21 22 23 24</div><input id='attrs-c6d5079c-df71-48b9-9191-3432bb0b2ae9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6d5079c-df71-48b9-9191-3432bb0b2ae9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff5e1713-228b-4435-a104-4f6c4af1368a' class='xr-var-data-in' type='checkbox'><label for='data-ff5e1713-228b-4435-a104-4f6c4af1368a' 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'><dt><span>_FillValue :</span></dt><dd>-2147483647</dd></dl></div><div class='xr-var-data'><pre>array([ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])</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'>1.0</div><input id='attrs-1e858417-79f5-4fb4-becf-9525d0d1e808' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e858417-79f5-4fb4-becf-9525d0d1e808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30118e58-7755-4104-8931-1f6d236b8097' class='xr-var-data-in' type='checkbox'><label for='data-30118e58-7755-4104-8931-1f6d236b8097' 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'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array(1.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f927866a-7c07-4e05-b978-287f9f46883f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f927866a-7c07-4e05-b978-287f9f46883f' 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 'xhx' (id: 16)>\n",
+       "array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.00484658, -0.00484491,  0.        , -0.00211506,  0.        ,\n",
+       "        0.        ])\n",
+       "Coordinates:\n",
+       "  * id       (id) int64 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
+       "    time     float64 1.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "plidx = swiftdiff.id.values[swiftdiff.id.values < 9]\n",
-    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 9]"
+    "swiftdiff.sel(id=tpidx)['xhx'].isel(time=1)"
    ]
   },
   {
@@ -104,7 +486,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -117,7 +499,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "swiftdiff['rmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "swiftdiff['rmag'].sel(id=plidx).plot.line(ax=ax, x=\"time\")\n",
     "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
     "ax.set_title(\"Heliocentric position differences \\n Planets only\")\n",
     "fig.savefig(\"symba_swifter_comparison-8pl-16tp-planets-rmag.png\", facecolor='white', transparent=False, dpi=300)"
@@ -130,7 +512,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -143,7 +525,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "swiftdiff['vmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "swiftdiff['vmag'].sel(id=plidx).plot.line(ax=ax, x=\"time\")\n",
     "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
     "ax.set_title(\"Heliocentric velocity differences \\n Planets only\")\n",
     "fig.savefig(\"symba_swifter_comparison-8pl-16tp-planets-vmag.png\", facecolor='white', transparent=False, dpi=300)"
@@ -163,7 +545,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -176,7 +558,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "swiftdiff['rmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "swiftdiff['rmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time\")\n",
     "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
     "ax.set_title(\"Heliocentric position differences \\n Test Particles only\")\n",
     "legend = ax.legend()\n",
@@ -198,7 +580,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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kNwALJO0H3Ed6f3b9i3+WAWdIupJ0sf2xiFgraV2Lut8A/gK4TtJzSEnnobH+Mhuf3sRvl1/HhpW/oGfDfUzb+ABTtz7B1MompsYm+ipP08dmpsRWeqjUht7aeNBLhSlUmJanezS+/Z5VQlQQgdIvJz35c2B+BUFeXsnLq59QrF8YDw1ZTzQqV9hmBdX2TH8eKtFDf55fm9dgvEIPW6N3UNmmdaL18i1MYWv0soVetuZhC1MGxqOXrUypLd9SHY8peXpwnX56aHxCbTYxDttn58mbSCJiq6QzgGuAXuDiiLhd0ul5+VLgauBo0guJNgInt6qbV30xcLGk24DNwEkxxj1R/vxbl3DA8n9mkR4D4Gn6eLRnV56aMoutU6fT37szT/XO4MneaURvH6FekEC9RE8vqBf19BDqyeO9eXzggNNwrOnxSINLqrhkaKU0Jx3SIVBUD+ugGJhH9XAflQbLK3ldxeWFevmzt7Z8oO7gcsW6FRT9A58Up7fWLa8rWxsK8+lP287TPdHsxYBjq6KpVHqmED35U1Nq0/0906j09lHpmUalpy+N9/bl8TyvWKa3L9eZWisTdcuiUL+/dwb9U6ZT6Z1BpacPJBq0/AKt012z372m85utrcXsalzK6xXKnwOF6udJA9OpumpxVdc5UHagfjH22jo1EHf9OgfKDqyzvn6tXpPvUYuu2fdgcAyove9RP6/hvmx+8BgV22Xvv4sWLYpOu0i56w+3MveKl7Kq71lsOeLd7LvoVUzfaY9WR3nrJpUKRD9U+us+K1DZCpUt0L8ljfdvydPF+cXpzS2W1a9jy+DxytZUf+umPDw9eLp/U2FZnu7fPMIvL5i6A/TtAFNnpPFBn8PNm5nq9u2YhmnFz1nQu132uLRdkXRjRCyqn++ffEkPfvvj7KEp7Pq2rzN7j70nOhwrqye/fbZ36kRHUl6lkpNXgySz9WnYunlwQtryFGx9Kn1u2Zg/i+OFeRsfaVDuSchnnm2ZMn1wYplWl3Dqk8+0nWB6g2HqDP9jNsk4kZQ06+k13N33HBY6idh46+mBnukwdfr4bC8inT0VE8/mJ2HzE7DpCdi8IX8Wpjc/WZi3ATY+BI/eXSjzBK1vgQB6pjZOMEOG2QPjO+wCM3aBGbOhp3fs940N4kRSVkS65mG2rZNgSl8aZswenXVGpIS06QnY9Dg8/Tg8vR6efqz18PiagfGtT7UKOsU6YxfYYdeBBLPDLoXxXYeOT8Yz1C7iRFKSCCo+7TbrjAR9M9Mwa4/O1rHl6ZyEcmJ5aj089Uhqnqt+bnw4jT++Bu6/LY1v2dh8nX2zYOauMHN32HF3mDknDdXx4rzpO7nprY4TSWnb380JZl1lam7e27Fkt3rVa0H1yWbjo2l840PwxIPwyF2w6pfw5EM0/HvvnZaTS04sM3fP47un5DhrL5j1zDRMnTEqX7nbOZGUJNJNsWY2yUydATvNTUM7Kv0pwTzxIDy5Lg1PPAhPPghPVKcfSGc8T65Ld+TVmz4bnlFNLHsWPvccmN5xj0l/x9vkjn4iROAHzMy2Az296aynnTOfCHjq0ZRYHl8DG+6HDWsLn2th3Yo0PeR5JqVtVJPMTvPyMB+eMTeNz9qzq5NN90bWpUQQbh81syJp4IL+7gc2L1fpT01mtSRTl3QeWw33Xp9uQBi0/p7BSeYZc1Oi2WlePsuaDzN2nrBrN04kpfmMxMw61NObr6MMc6PBpg3w2H3w+OqUXIrDfb+G331r6AOqU3cYOIOZPR9m7w2z98mfe8OOz8zPUY0+J5KS5ERiZmNt2izY/blpaKRSSTcHPLaqkGTuG5heka/bFPX2pSRzzHmw34tHNVwnkpLku7bMbKL19Axcv5n7J43LbN6YEsv6VbD+Hlh/bxpm7jbq4TiRlBWROlg0M+tmfTvAnAPSMMZ8RCxJhG//NTMrcCLphO/aMjOrcSIpyWckZmaDOZGU5ovtZmZFTiQlyU+2m5kN4kRSUnpVphOJmVmVE0lpvkZiZlbkRFKSqOCmLTOzAU4kJblpy8xsMCeS0ty0ZWZW5ERSkgifkZiZFTiRlOTbf83MBnMi6YCbtszMBoxrIpF0lKQVklZKOrvBckk6Ly+/VdJhw9WVdI6k+yTdnIejx/Q7uGnLzGyQcUskknqB84HFwELgeEkL64otBhbk4TTggjbrfjYiDs3D1WP7TdxFiplZ0XiekRwOrIyIOyNiM3AlsKSuzBLgskiuB2ZL2rPNuuPCb0g0MxtsPBPJXGBVYXp1ntdOmeHqnpGbwi6WtHOjjUs6TdJyScvXrVvXqEhbBISbtszMasYzkTQ6+ta3EzUr06ruBcD+wKHAWuDTjTYeERdGxKKIWDRnzpy2Am5E4SfbzcyKxvNVu6uB+YXpecCaNsv0NasbEQ9UZ0r6AvDt0Qt5KD/ZbmY22HiekdwALJC0n6Q+4DhgWV2ZZcCJ+e6tI4HHImJtq7r5GkrV64DbxvZrBOG7ps3MasbtjCQitko6A7gG6AUujojbJZ2ely8FrgaOBlYCG4GTW9XNq/5XSYeSmrruBt4+lt9DvmvLzGyQ8WzaIt+ae3XdvKWF8QDe1W7dPP+EUQ6zJT9HYmY2mNtoOuJEYmZW5URSks9IzMwGcyIpSe5G3sxsECeSTviMxMysxomkpB6/atfMbBAnkpKcQszMBnMiKS1A3m1mZlU+Ipbk3n/NzAZzIinJt/+amQ3mRFKS8Kt2zcyKnEhK8xmJmVmRE0lJCl8jMTMrciIpye8jMTMbzImkJN+1ZWY2mBNJSX4fiZnZYE4kJckPJJqZDeIjYkk+IzEzG8yJpCQBod6JDsPMrGs4kZSUmrYmOgozs+7hRFKa79oyMytyIinJz5GYmQ3mRFKSnyMxMxtsynAFJO3d5rrWR8TjI4yn67n3XzOzwYZNJMAXGf7CQACXApeNQkxdTYB8RmJmVjNsIomIl9XPk/TMiLi/7MYkHQX8G9ALXBQR59YtV15+NLAReEtE/LrNuu8FPgXMiYiHysbWrh4F4TMSM7OaTq+RnFi2gqRe4HxgMbAQOF7Swrpii4EFeTgNuKCdupLmA68A7i39TUqISqW6xbHcjJnZpNJpIlki6QxJB5SocziwMiLujIjNwJXAkvr1ApdFcj0wW9KebdT9LHAWjO1j5xF59T4jMTOr6TSR/DWwEnidpIvarDMXWFWYXp3ntVOmaV1JrwHui4hbWm1c0mmSlktavm7dujZDHmwgkfhmNzOzqnYutg8REQ8A381Duxr9G19/BtGsTMP5knYAPgi8criNR8SFwIUAixYt6ujMJaIyfCEzs+1MR/9aSzpf0qV5fNiDeLYamF+YngesabNMs/n7A/sBt0i6O8//taRnthlTKW7aMjMbqtM2ms3AnXn8L9qscwOwQNJ+kvqA44BldWWWAScqORJ4LCLWNqsbEb+JiN0jYt+I2JeUcA7r5I6ydlQTifwcp5lZTUdNW6Rbc3eSNBVo64HFiNgq6QzgGtItvBdHxO2STs/LlwJXk279XZm3cXKruh3G3rFq01b4hMTMrKbTRPII8BTpltyft1spIq4mJYvivKWF8QDe1W7dBmX2bTeWTtSatnz7r5lZTak2GkmzJV0CvD7PugxYNOpRdanacyS+a8vMrKbUGUlErJd0LrAv8BBwMPD1MYirK1Wbtnw+YmY2oJOmrVOAuyLiGuDGUY6nq/muLTOzoTpJJI8Cp+en2m8Bbo6Im0Y3rO7kRGJmNlTpRBIRn5T0A+APwKHAS4DtLJH4GomZWVXpRCLpo6RbcG8mnY1cN8oxdS3ftWVmNlTpf60j4sPAplz39ZK+MOpRdSk3bZmZDdVpG83FwIHArsDnRy+cLuczEjOzITpNJO8hNYtNIb1savtQ7bTRZyRmZjWdJpI7gOnANyPiJaMYT1fzNRIzs6E6TSS3Az8ETpF0wyjG09UqlX4A5DMSM7OaTvva2p/0PMmF+XO74IvtZmZDdZpIVkXED/NrcB8czYC6mZu2zMyG6rRp6yhJ84ClpPelbyd8RmJmVq/TRDIbeD9wFumZku1DxU+2m5nV67Rp66PAcyNihaT+0QyomwVu2jIzq9f2v9aSDqmOR8TqiPh+Hj97LALrRrVX7bppy8yspkwbzU2SbpV0lqT5YxZRFws/kGhmNkSZRPJpYCZwLnCXpB9JeuvYhNWdfNeWmdlQbSeSiHhfROxPerXuRaTu4y8cq8C6UfVVu27aMjMb0PbFdkm7Aq8D/gZ4Genf8nvHKK6uVD0jCScSM7OaMndt3U86g3kUuAS4PCJ+NiZRdSs3bZmZDVEmkVwFXA58JyK2jFE8Xc13bZmZDdV2IomIN4xlIJNDvmvLZyRmZjXj+oi2pKMkrZC0UtKQ50+UnJeX3yrpsOHqSvpYLnuzpO9J2mus4o/8ZLt6/GS7mVlV6SOipGM62ZCkXuB8YDGwEDhe0sK6YouBBXk4DbigjbqfioiDI+JQ4NvAhzuJrx1+st3MbKhO/rX+RIfbOhxYGRF3RsRm4EpgSV2ZJcBlkVwPzM49DDetGxGPF+rPpNaz4hjwA4lmZkN00tdWp0fRucCqwvRq4Ig2yswdrq6kTwAnAo+Rbk0eE34fiZnZUJ2ckXT6H3+jo2/9upqVaVk3Ij4YEfOBK4AzGm5cOk3ScknL161b12bIdRustWw5kZiZVY3nVePVQLGPrnnAmjbLtFMX4MvA6xttPCIujIhFEbFozpw5JUPPqq/a9TUSM7Oa8UwkNwALJO0nqQ84DlhWV2YZcGK+e+tI4LGIWNuqrqQFhfqvAX4/Vl8g/GIrM7MhOrlG8kAnG4qIrZLOAK4BeoGLI+J2Safn5UuBq4GjgZXARuDkVnXzqs+VdADpIY97gNM7ia/N75DHfPuvmVlV6UQSEa/odGMRcTUpWRTnLS2MB/Cuduvm+Q2bssZEVDttHLctmpl1Pf9rXYIvtpuZDeVEUsLA7b+9ExuImVkX6SiRSPrHwvgBoxdOlwu/j8TMrF6paySSZgOfBZ4r6WngVuAU8kXxbd3AxXYzM6sqlUgiYj1wsqRXAQ8BBwNfH4O4ulS1G3m3CJqZVXVy+y/Aloi4UdIa4MHRDKibuYsUM7OhOv3X+ihJ84ClpKau7UL1ne1OJGZmAzpNJLOB9wNnAZtGLZquV23amuAwzMy6SKdNWx8FDoiIFZL6RzOgblY7I/Fd02ZmNZ0mkg+Q3v3xA+BHoxdOd3NfW2ZmQ3WaSDYz0OfWy0h9YG378sV2ISpPP83mu+6i/7HHqWx8ksoTTxBbthD9/VCppM/+ClEpfOZX9Q48Ij94unYxv/ZJ3XRdvU7q1N3C3PCW5vpZ9WUa1hlSaeTbaWvbQ+sM2daQ6aGb6ZrtNLzFfBL9zLppOw3KRP2KJ+pnNlrbqf97HvL9hlbZ46z3MeOQQxoE0LlOE8lGYCdJU4G9RzGe7hbB5id66f3cJaw44xzon8BWveJZUXW8yaealWu0rmbz6qYbnpMNt94OttNwW6Ox3oZ16icn0XYalhlmO+2st5PtNDxjH4PfjYa7dhL9zEZjO9Kgeep0uyPUaSL5Z9I71c8nvUxq+xDBAzc9Az18H7u+7W1MP/C59M7emZ4dd6Rn5g709PVBby/09KBBn72otwd6eoY/uDdLBm5OM7Mu1WkieU9EfAa2ry5SgqB/cw+xz1x2/4e/n+hwzMy6QiddpFwA7JO7SLkFeBvbUxcpIdTju7bMzKpKd5EiaTXwE+CXwCFsR12kRES6ttXjZiYzs6pOmrYeJr2F8ADSGcnqUY2om1Uq6S4In5GYmdV08obEcyX9EPgDcCjwYuCmUY6rKwU5kbjTRjOzmtKJRNJHSe9Nvxm4OSKuG+WYulcEEfIZiZlZQSdnJB+WtAfwfOD1kvaPiFNHP7QuFJGbtnyNxMysqtPbf98O/EdEfHc0g+l2EflBUp+RmJnVdJpILgbeIWkmcEVE3Dx6IXWz/KpdJxIzs5pOj4jvISWhKcB5oxdOl8vPkfiMxMxsQKdHxDuA6cA3I+IloxhPVxt4jsSJxMysqtMj4u3AD4FTJN0wivF0t9rFdicSM7OqTo+I+5OatS6kRPcoko6StELSSklnN1guSefl5bdKOmy4upI+Jen3ufxVuRuXMRFRSWckfo7EzKym0yPiqohYBqwEftdOBUm9pN6CFwMLgeMlLawrthhYkIfTSP16DVf3WuCgiDiY9JDkBzr8Tm2opL62ep1IzMyqOj0iHiVpHrAU+GybdQ4HVkbEnRGxGbgSWFJXZglwWSTXA7Ml7dmqbkR8LyK25vrXA/M6/E7Dqt3+6zMSM7OaTo+Is4H3A2cBm9qsMxdYVZhenee1U6adugBvBb7TaOOSTpO0XNLydevWtRlyneo1Ep+RmJnVtH1ElFR8N+NHSXdsrQDafU1go8fB618E2azMsHUlfRDYSpMXbUXEhRGxKCIWzZkzp41wG67EF9vNzOqUOSLelC9onwUoIr4PEBFDLpo3sRqYX5ieB6xps0zLupJOAl4NvDkavgB6dKSL7X4fiZlZUZkj4qeBmcC5wF2SfiTprSXq3wAskLSfpD7gOGBZXZllwIn57q0jgcciYm2rupKOIjWzvSYiNpaIp3NOJGZmNW0fESPifRGxP7AIuAh4Cen233brbwXOAK4h3en11Yi4XdLpkk7Pxa4G7iTdDfYF4J2t6uY6/w7MAq6VdLOkpe3GVNbAA4m9Y7UJM7NJp+2+tiTtCrwO+BvgZaTrFveW2VhEXE1KFsV5SwvjAbyr3bp5/rPLxDASyu8j8e2/ZmYDynTaeD/pDOZR4BLg8oj42ZhE1aWq72xH7kbezKyqTCK5Crgc+E5EbBmjeLpaVHLTVq+btszMqoZNJJL2zqPvzZ97qvF/5Osj4vHRCqw75aYtXyMxM6tp54zkiww8s9GsTSeAS4HLRiGm7lWpAPIbEs3MCoZNJBHxsvEIZDKIqL7YymckZmZVvv2ojP6USPwciZnZAB8Ry6jkROLbf83ManxELCMnEvmuLTOzGieSMiqpf8r0ehQzMwMnklKikm9em+LdZmZW5SNiCdGfe8z3GYmZWY0TSQmqNm35YruZWY2PiGVUX3Xii+1mZjVOJCUoN23JT7abmdU4kZRQvdjuJ9vNzAY4kZQR1SfbnUjMzKqcSMrorz6Q6N1mZlblI2IZ1U4be8u8xsXMbNvmRFJGtYsUd9poZlbjI2IZ1U4bp/gaiZlZlRNJGdUzEnm3mZlV+YhYhnv/NTMbwomkjIpfbGVmVs9HxDIqvmvLzKzeuCYSSUdJWiFppaSzGyyXpPPy8lslHTZcXUnHSrpdUkXSojH9ArmvLTdtmZkNGLdEovQ2qPOBxcBC4HhJC+uKLQYW5OE04II26t4G/DXwk7H+DgNnJD6RMzOrGs8j4uHAyoi4MyI2A1cCS+rKLAEui+R6YLakPVvVjYjfRcSKcfkGbtoyMxtiPBPJXGBVYXp1ntdOmXbqtiTpNEnLJS1ft25dmaoDqt3I+2K7mVnNeB4RG/W9Hm2WaaduSxFxYUQsiohFc+bMKVN1QD4j6ZniMxIzs6rxPCKuBuYXpucBa9os09dG3bHn50jMzIYYzzOSG4AFkvaT1AccByyrK7MMODHfvXUk8FhErG2z7piT+9oyMxti3M5IImKrpDOAa4Be4OKIuF3S6Xn5UuBq4GhgJbAROLlVXQBJrwP+HzAH+B9JN0fEq8bmS+RE4r62zMxqxrWxPyKuJiWL4rylhfEA3tVu3Tz/KuCq0Y20sYE3JPoaiZlZldtoyvA1EjOzIZxISlD19l8nEjOzGieSMnLTVo+fbDczq/ERsQy/atfMbAgnkjJ8jcTMbAgnkjLyNZKeKVMnOBAzs+7hRFJGxd3Im5nVcyIpQW7aMjMbwomkjFrTli+2m5lVOZGU4Xe2m5kN4SNiGdVX7TqRmJnV+IhYRsVPtpuZ1XMiKSM/kIgavWfLzGz75ERSQrWvLd+1ZWY2wImkjIrf2W5mVs9HxDIqvthuZlbPR8QSVL1G4qYtM7MaJ5IyqtdIfLHdzKzGiaQM3/5rZjaEE0kZ4YvtZmb1fEQsIwIIN22ZmRU4kZRRCXAOMTMbxImkjHAiMTOr50RSgiLcO4qZWR0nkjIqFZ+RmJnVGddEIukoSSskrZR0doPlknReXn6rpMOGqytpF0nXSvpj/tx5zL6Am7bMzIYYt0QiqRc4H1gMLASOl7SwrthiYEEeTgMuaKPu2cAPImIB8IM8PTYCJxIzszrj+c7Yw4GVEXEngKQrgSXAbwtllgCXRUQA10uaLWlPYN8WdZcAL831vwhcB7x/LL7AT6fsRxw4n2+cePJYrN7MbMw9teMMPvL5z4/qOsezaWsusKowvTrPa6dMq7p7RMRagPy5e6ONSzpN0nJJy9etW9fRF9g8pYf+Hp+SmJkVjecZSaMjcLRZpp26LUXEhcCFAIsWLSpVt+rMyy7qpJqZ2TZtPM9IVgPzC9PzgDVtlmlV94Hc/EX+fHAUYzYzs2GMZyK5AVggaT9JfcBxwLK6MsuAE/PdW0cCj+XmqlZ1lwEn5fGTgG+O9RcxM7MB49a0FRFbJZ0BXAP0AhdHxO2STs/LlwJXA0cDK4GNwMmt6uZVnwt8VdIpwL3AseP1nczMDBTR0eWCSW3RokWxfPnyiQ7DzGxSkXRjRCyqn+8n283MbEScSMzMbEScSMzMbEScSMzMbES2y4vtktYB93RYfTfgoVEMZ6w4ztEzGWIExzmaJkOMMP5x7hMRc+pnbpeJZCQkLW9010K3cZyjZzLECI5zNE2GGKF74nTTlpmZjYgTiZmZjYgTSXkXTnQAbXKco2cyxAiOczRNhhihS+L0NRIzMxsRn5GYmdmIOJGYmdmIOJGUIOkoSSskrZQ0du+GL0nS3ZJ+I+lmScvzvF0kXSvpj/lz5wmI62JJD0q6rTCvaVySPpD37QpJr5rgOM+RdF/epzdLOnoi45Q0X9KPJP1O0u2S/i7P76r92SLObtuf0yX9StItOc6P5Pldsz9bxNhV+xKAiPDQxkDqvv4O4FlAH3ALsHCi48qx3Q3sVjfvX4Gz8/jZwL9MQFwvAQ4DbhsuLmBh3qfTgP3yvu6dwDjPAd7boOyExAnsCRyWx2cBf8ixdNX+bBFnt+1PATvm8anAL4Eju2l/toixq/ZlRPiMpITDgZURcWdEbAauBJZMcEytLAG+mMe/CLx2vAOIiJ8Aj9TNbhbXEuDKiNgUEXeR3klz+ATG2cyExBkRayPi13l8A/A7YC5dtj9bxNnMRMUZEfFEnpyah6CL9meLGJuZsL8hJ5L2zQVWFaZX0/oPZDwF8D1JN0o6Lc/bI9LbJcmfu09YdIM1i6sb9+8Zkm7NTV/VJo4Jj1PSvsDzSf+hdu3+rIsTumx/SuqVdDPp9dzXRkTX7c8mMUKX7UsnkvapwbxuuXf6zyLiMGAx8C5JL5nogDrQbfv3AmB/4FBgLfDpPH9C45S0I/A14O8j4vFWRRvMm8g4u25/RkR/RBwKzAMOl3RQi+ITEmeTGLtuXzqRtG81ML8wPQ9YM0GxDBIRa/Lng8BVpNPZByTtCZA/H5y4CAdpFldX7d+IeCD/EVeALzDQRDBhcUqaSjo4XxERX8+zu25/NoqzG/dnVUSsB64DjqIL92d9jN24L51I2ncDsEDSfpL6gOOAZRMcE5JmSppVHQdeCdxGiu2kXOwk4JsTE+EQzeJaBhwnaZqk/YAFwK8mID6gdhCpeh1pn8IExSlJwH8Cv4uIzxQWddX+bBZnF+7POZJm5/EZwMuB39NF+7NZjN22LwHftVVmAI4m3YVyB/DBiY4nx/Qs0p0atwC3V+MCdgV+APwxf+4yAbF9hXTqvYX039IpreICPpj37Qpg8QTH+SXgN8CtpD/QPScyTuBFpGaKW4Gb83B0t+3PFnF22/48GLgpx3Mb8OE8v2v2Z4sYu2pfRoS7SDEzs5Fx05aZmY2IE4mZmY2IE4mZmY2IE4mZmY2IE4mZmY2IE4nZGJM0W9I78/hekv57omMyG02+/ddsjOU+p74dEa264DCbtKZMdABm24Fzgf1z53t/BA6MiIMkvYXUu2wvcBCpz6Q+4ARgE3B0RDwiaX/gfGAOsBE4NSJ+P95fwqwZN22Zjb2zgTsidb73vrplBwFvIvWX9AlgY0Q8H/gFcGIucyHw7oj4E+C9wOfHI2izdvmMxGxi/SjSezs2SHoM+Fae/xvg4NyL7p8C/5W6sQLSi4vMuoYTidnE2lQYrxSmK6S/zx5gfT6bMetKbtoyG3sbSK+dLS3SuzzuknQspN51JR0ymsGZjZQTidkYi4iHgZ9Lug34VAereDNwiqRqD8/d/Ipn2w759l8zMxsRn5GYmdmIOJGYmdmIOJGYmdmIOJGYmdmIOJGYmdmIOJGYmdmIOJGYmdmI/H8nec9tCtt8ZQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -211,7 +593,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "swiftdiff['vmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "swiftdiff['vmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time\")\n",
     "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
     "ax.set_title(\"Heliocentric velocity differences \\n Test Particles only\")\n",
     "legend = ax.legend()\n",
diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.in
index 25ae7ab30..296a9cb43 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.in
@@ -1,49 +1,49 @@
 16
 SmallBody10
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 SmallBody11
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 SmallBody12
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 SmallBody15
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 SmallBody16
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 SmallBody17
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 SmallBody19
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 SmallBody20
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 SmallBody22
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 SmallBody23
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 SmallBody24
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 SmallBody25
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diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swifter.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swifter.in
index 672ab5bf6..b72496189 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swifter.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swifter.in
@@ -1,49 +1,49 @@
 16
 9
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diff --git a/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swiftest.in b/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swiftest.in
index 25ae7ab30..296a9cb43 100644
--- a/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swiftest.in
+++ b/examples/symba_swifter_comparison/8pl_16tp_encounters/tp.swiftest.in
@@ -1,49 +1,49 @@
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 SmallBody15
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 SmallBody16
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 SmallBody19
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 SmallBody20
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 SmallBody21
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 SmallBody22
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 SmallBody23
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 SmallBody24
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 SmallBody25
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diff --git a/python/swiftest/swiftest/io.py b/python/swiftest/swiftest/io.py
index 540f9cf30..b060d13ef 100644
--- a/python/swiftest/swiftest/io.py
+++ b/python/swiftest/swiftest/io.py
@@ -1501,6 +1501,12 @@ def swiftest2swifter_param(swiftest_param, J2=0.0, J4=0.0):
     swifter_param['J4'] = J4
     if swifter_param['OUT_STAT'] == "REPLACE":
         swifter_param['OUT_STAT'] = "UNKNOWN"
+    if swifter_param['OUT_TYPE'] == 'NETCDF_DOUBLE':
+       swifter_param['OUT_TYPE'] = 'REAL8'
+    elif swifter_param['OUT_TYPE'] == 'NETCDF_FLOAT':
+       swifter_param['OUT_TYPE'] = 'REAL4'
+    if swifter_param['OUT_FORM'] == 'XVEL':
+       swifter_param['OUT_FORM'] = 'XV'
     swifter_param['! VERSION'] = "Swifter parameter file converted from Swiftest"
 
     return swifter_param
diff --git a/src/drift/drift.f90 b/src/drift/drift.f90
index 296ceb553..0437a18d1 100644
--- a/src/drift/drift.f90
+++ b/src/drift/drift.f90
@@ -127,9 +127,13 @@ pure subroutine drift_dan(mu, px0, py0, pz0, vx0, vy0, vz0, dt0, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_dan.f90
       !! Adapted from Hal Levison and Martin Duncan's Swift routine drift_dan.f
       implicit none
-      integer(I4B), intent(out)                :: iflag
-      real(DP), intent(in)                     :: mu, dt0
-      real(DP), intent(inout)    :: px0, py0, pz0, vx0, vy0, vz0      
+      ! Arguments
+      real(DP),     intent(in)    :: mu            !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP),     intent(inout) :: px0, py0, pz0 !! position of body to drift
+      real(DP),     intent(inout) :: vx0, vy0, vz0 !! velocity of body to drift     
+      real(DP),     intent(in)    :: dt0           !! time step
+      integer(I4B), intent(out)   :: iflag         !! error status flag for Kepler drift (0 = OK, nonzero = NO CONVERGENCE)
+      ! Internals
       real(DP)        :: dt, f, g, fdot, gdot, c1, c2, c3, u, alpha, fp, r0
       real(DP)        :: v0s, a, asq, en, dm, ec, es, esq, xkep, fchk, s, c
       real(DP), dimension(NDIM) :: x, v, x0, v0
@@ -203,8 +207,14 @@ pure subroutine drift_kepmd(dm, es, ec, x, s, c)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepmd.f90
       !! Adapted from Martin Duncan's Swift routine drift_kepmd.f
       implicit none
-      real(DP), intent(in)  :: dm, es, ec
-      real(DP), intent(out) :: x, s, c      
+      ! Arguments
+      real(DP), intent(in)  :: dm !! increment in mean anomaly
+      real(DP), intent(in)  :: es !! eccentricity times the sine of eccentric anomaly
+      real(DP), intent(in)  :: ec !! eccentricity times the cosine of eccentric anomaly
+      real(DP), intent(out) :: x  !! solution to Kepler's equation in difference form (x = dE)
+      real(DP), intent(out) :: s  !! sine of x
+      real(DP), intent(out) :: c  !! cosine of x
+      ! Internals
       real(DP), parameter :: a0 = 39916800.0_DP, a1 = 6652800.0_DP, a2 = 332640.0_DP, a3 = 7920.0_DP, a4 = 110.0_DP
       real(DP)      :: dx, fac1, fac2, q, y, f, fp, fpp, fppp
       
@@ -221,8 +231,8 @@ pure subroutine drift_kepmd(dm, es, ec, x, s, c)
       fpp = ec * s + es * c
       fppp = ec * c - es * s
       dx = -f / fp
-      dx = -f / (fp + dx * fpp / 2.0_DP)
-      dx = -f / (fp + dx * fpp / 2.0_DP + dx**2* fppp / 6.0_DP)
+      dx = -f / (fp + dx * fpp * 0.5_DP)
+      dx = -f / (fp + dx * fpp * 0.5_DP + dx**2* fppp * SIXTH)
       x = x + dx
       y = x**2
       s = x * (a0 - y * (a1 - y * (a2 - y * (a3 - y * (a4 - y))))) / a0
@@ -270,8 +280,15 @@ pure subroutine drift_kepu_fchk(dt, r0, mu, alpha, u, s, f)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_fchk.f90
       !! Adapted from Martin Duncan's Swift routine drift_kepu_fchk.f
       implicit none
-      real(DP), intent(in)  :: dt, r0, mu, alpha, u, s
-      real(DP), intent(out) :: f
+      ! Internals
+      real(DP), intent(in)  :: dt    !! time step
+      real(DP), intent(in)  :: r0    !! distance between two bodies
+      real(DP), intent(in)  :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP), intent(in)  :: alpha !! twice the binding energy
+      real(DP), intent(in)  :: u     !! dot product of position and velocity vectors
+      real(DP), intent(in)  :: s     !! universal variable (approximate root of f)
+      real(DP), intent(out) :: f     !! function value
+      ! Arguments
       real(DP) :: x, c0, c1, c2, c3
 
       x = s**2 * alpha
@@ -294,9 +311,15 @@ pure subroutine drift_kepu_guess(dt, r0, mu, alpha, u, s)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_guess.f90
       !! Adapted from Hal Levison and Martin Duncan's Swift routine drift_kepu_guess.f
       implicit none
-      real(DP), intent(in)  :: dt, r0, mu, alpha, u
-      real(DP), intent(out) :: s      
-      integer(I4B)      :: iflag
+      ! Arguments
+      real(DP), intent(in)  :: dt    !! time ste4p
+      real(DP), intent(in)  :: r0    !! distance between two bodies
+      real(DP), intent(in)  :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP), intent(in)  :: alpha !! twice the binding energy
+      real(DP), intent(in)  :: u     !! dot product of position and velocity vectors
+      real(DP), intent(out) :: s     !! initial guess for the value of the universal variable
+      ! Internals
+      integer(I4B) :: iflag
       real(DP), parameter :: thresh = 0.4_DP, danbyk = 0.85_DP
       real(DP)        :: y, sy, cy, sigma, es, x, a, en, ec, e
 
@@ -334,19 +357,28 @@ pure subroutine drift_kepu_lag(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_lag.f90
       !! Adapted from Hal Levison's Swift routine drift_kepu_lag.f
       implicit none
-      integer(I4B), intent(out) :: iflag
-      real(DP), intent(in)      :: dt, r0, mu, alpha, u
-      real(DP), intent(inout)   :: s
-      real(DP), intent(out)     :: fp, c1, c2, c3      
-      integer( I4B) :: nc, ncmax
-      real(DP)   :: ln, x, fpp, ds, c0, f, fdt
+      ! Arguments
+      real(DP),     intent(inout) :: s     !! universal variable 
+      real(DP),     intent(in)    :: dt    !! time step
+      real(DP),     intent(in)    :: r0    !! distance between two bodies 
+      real(DP),     intent(in)    :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift 
+      real(DP),     intent(in)    :: alpha !! twice the binding energy 
+      real(DP),     intent(in)    :: u     !! dot product of position and velocity vectors
+      real(DP),     intent(out)   :: fp    !! first derivative of Kepler's equation in universal variables with respect to s (see Danby, p. 175)
+      real(DP),     intent(out)   :: c1    !! Stumpff function c1 times s
+      real(DP),     intent(out)   :: c2    !! Stumpff function c2 times s**2
+      real(DP),     intent(out)   :: c3    !! Stumpff function c3 times s**3
+      integer(I4B), intent(out)   :: iflag !! error status flag for convergence (0 = CONVERGED, nonzero = NOT CONVERGED)
+      ! Internals
+      integer(I4B) :: nc, ncmax
+      real(DP)   :: x, fpp, ds, c0, f, fdt
+      integer(I4B), parameter :: ln = 5
    
       if (alpha < 0.0_DP) then
          ncmax = NLAG2
       else
          ncmax = NLAG1
       end if
-      ln = 5.0_DP
       do nc = 0, ncmax
          x = s * s * alpha
          call drift_kepu_stumpff(x, c0, c1, c2, c3)
@@ -356,7 +388,7 @@ pure subroutine drift_kepu_lag(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
          f = r0 * c1 + u * c2 + mu * c3 - dt
          fp = r0 * c0 + u * c1 + mu * c2
          fpp = (-r0 * alpha + mu) * c1 + u * c0
-         ds = -ln * f / (fp + sign(1.0_DP, fp) * sqrt(abs((ln - 1.0_DP)**2 * fp**2 - (ln - 1.0_DP) * ln * f * fpp)))
+         ds = -ln * f / (fp + sign(1.0_DP, fp) * sqrt(abs((ln - 1)**2 * fp**2 - (ln - 1) * ln * f * fpp)))
          s = s + ds
          fdt = f / dt
          if (fdt**2 < DANBYB**2) then
@@ -365,7 +397,7 @@ pure subroutine drift_kepu_lag(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
          end if
       end do
       iflag = 2
-   
+
       return
    end subroutine drift_kepu_lag
 
@@ -380,10 +412,19 @@ pure subroutine drift_kepu_new(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_new.f90
       !! Adapted from Hal Levison's Swift routine drift_kepu_new.f
       implicit none
-      integer(I4B), intent(out) :: iflag
-      real(DP), intent(in)      :: dt, r0, mu, alpha, u
-      real(DP), intent(inout)   :: s
-      real(DP), intent(out)     :: fp, c1, c2, c3      
+      ! Arguments
+      real(DP),     intent(inout) :: s     !! universal variable
+      real(DP),     intent(in)    :: dt    !! time step
+      real(DP),     intent(in)    :: r0    !! distance between two bodies
+      real(DP),     intent(in)    :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP),     intent(in)    :: alpha !! twice the binding energy
+      real(DP),     intent(in)    :: u     !! dot product of position and velocity vectors
+      real(DP),     intent(out)   :: fp    !! first derivative of Kepler's equation in universal variables with respect to s (see Danby, p. 175)
+      real(DP),     intent(out)   :: c1    !! Stumpff function c1 times s
+      real(DP),     intent(out)   :: c2    !! Stumpff function c2 times s**2
+      real(DP),     intent(out)   :: c3    !! Stumpff function c3 times s**3
+      integer(I4B), intent(out)   :: iflag !! error status flag for convergence (0 = CONVERGED, nonzero = NOT CONVERGED)
+      ! Internals
       integer( I4B) :: nc
       real(DP)   :: x, c0, ds, f, fpp, fppp, fdt
    
@@ -398,8 +439,8 @@ pure subroutine drift_kepu_new(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
          fpp = (-r0 * alpha + mu) * c1 + u * c0
          fppp = (-r0 * alpha + mu) * c0 - u * alpha * c1
          ds = -f / fp
-         ds = -f / (fp + ds * fpp / 2.0_DP)
-         ds = -f / (fp + ds * fpp / 2.0_DP + ds**2 * fppp / 6.0_DP)
+         ds = -f / (fp + ds * fpp * 0.5_DP)
+         ds = -f / (fp + ds * fpp * 0.5_DP + ds**2 * fppp * SIXTH)
          s = s + ds
          fdt = f / dt
          if (fdt**2 < DANBYB**2) then
@@ -423,32 +464,38 @@ pure subroutine drift_kepu_p3solve(dt, r0, mu, alpha, u, s, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_p3solve.f90
       !! Adapted from Martin Duncan's Swift routine drift_kepu_p3solve.f
       implicit none
-      integer(I4B), intent(out) :: iflag
-      real(DP), intent(in)      :: dt, r0, mu, alpha, u
-      real(DP), intent(out)     :: s
+      ! Arguments
+      real(DP), intent(in)      :: dt    !! time step
+      real(DP), intent(in)      :: r0    !! distance between two bodies
+      real(DP), intent(in)      :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP), intent(in)      :: alpha !! twice the binding energy
+      real(DP), intent(in)      :: u     !! dot product of position and velocity vectors
+      real(DP), intent(out)     :: s     !! s     : real solution of cubic equation
+      integer(I4B), intent(out) :: iflag !! error status flag for solution (0 = OK, nonzero = ERROR)
+      ! Internals
       real(DP) :: denom, a0, a1, a2, q, r, sq2, sq, p1, p2
 
-      denom = (mu - alpha * r0) / 6.0_DP
+      denom = (mu - alpha * r0) * SIXTH
       a2 = 0.5_DP * u / denom
       a1 = r0 / denom
       a0 = -dt / denom
-      q = (a1 - a2**2 / 3.0_DP) / 3.0_DP
-      r = (a1 * a2 - 3 * a0) / 6.0_DP - a2**3 / 27.0_DP
+      q = (a1 - a2**2 * THIRD) * THIRD
+      r = (a1 * a2 - 3 * a0) * SIXTH - (a2 * THIRD)**3 
       sq2 = q**3 + r**2
       if (sq2 >= 0.0_DP) then
          sq = sqrt(sq2)
          if ((r + sq) <= 0.0_DP) then
-            p1 = -(-(r + sq))**(1.0_DP / 3.0_DP)
+            p1 = -(-(r + sq))**(THIRD)
          else
-            p1 = (r + sq)**(1.0_DP / 3.0_DP)
+            p1 = (r + sq)**(THIRD)
          end if
          if ((r - sq) <= 0.0_DP) then
-            p2 = -(-(r - sq))**(1.0_DP / 3.0_DP)
+            p2 = -(-(r - sq))**(THIRD)
          else
-            p2 = (r - sq)**(1.0_DP / 3.0_DP)
+            p2 = (r - sq)**(THIRD)
          end if
          iflag = 0
-         s = p1 + p2 - a2 / 3.0_DP
+         s = p1 + p2 - a2 * THIRD
       else
          iflag = 1
          s = 0.0_DP
@@ -468,8 +515,13 @@ pure subroutine drift_kepu_stumpff(x, c0, c1, c2, c3)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_stumpff.f90
       !! Adapted from Hal Levison's Swift routine drift_kepu_stumpff.f
       implicit none
-      real(DP), intent(inout) :: x
-      real(DP), intent(out)   :: c0, c1, c2, c3
+      ! Arguments
+      real(DP), intent(inout) :: x  !! argument of Stumpff functions
+      real(DP), intent(out)   :: c0 !! zeroth Stumpff function
+      real(DP), intent(out)   :: c1 !! first Stumpff function
+      real(DP), intent(out)   :: c2 !! second Stumpff function
+      real(DP), intent(out)   :: c3 !! third Stumpff function
+      ! Internals
       integer(I4B) :: i, n
       real(DP)   :: xm
 
diff --git a/src/gr/gr.f90 b/src/gr/gr.f90
index 71d50f448..8b6bb1120 100644
--- a/src/gr/gr.f90
+++ b/src/gr/gr.f90
@@ -33,8 +33,9 @@ module pure subroutine gr_kick_getaccb_ns_body(self, system, param)
 
          select type(self)
          class is (swiftest_pl)
-            do i = 1, NDIM
-               cb%agr(i) = -sum(self%Gmass(1:n) * self%agr(1:n, i) / cb%Gmass)
+            cb%agr(:) = 0.0_DP
+            do i = n, 1, -1
+               cb%agr(:) = cb%agr(:) - self%Gmass(i) * self%agr(:, i) / cb%Gmass
             end do
          end select
       end associate 
@@ -226,9 +227,9 @@ module pure subroutine gr_vel2pseudovel(param, mu, xh, vh, pv)
 
             Jinv = Jinv * det
 
-            do i = 1, NDIM
-               pv(i) = pv(i) - dot_product(Jinv(i,:), F(:))
-            end do
+            pv(1) = pv(1) - dot_product(Jinv(1,:), F(:))
+            pv(2) = pv(2) - dot_product(Jinv(2,:), F(:))
+            pv(3) = pv(3) - dot_product(Jinv(3,:), F(:))
          end do 
       end associate
    
diff --git a/src/kick/kick.f90 b/src/kick/kick.f90
index 637b38720..9c940190b 100644
--- a/src/kick/kick.f90
+++ b/src/kick/kick.f90
@@ -256,7 +256,7 @@ module pure subroutine kick_getacch_int_one_tp(rji2, xr, yr, zr, GMpl, ax, ay, a
       ! Internals
       real(DP) :: fac
 
-      fac = GMpl * sqrt(rji2**(-3))
+      fac = GMpl * sqrt(1.0_DP / (rji2*rji2*rji2))
       ax = ax - fac * xr
       ay = ay - fac * yr
       az = az - fac * zr
diff --git a/src/modules/swiftest_globals.f90 b/src/modules/swiftest_globals.f90
index b7fe1a0db..169cdcd2f 100644
--- a/src/modules/swiftest_globals.f90
+++ b/src/modules/swiftest_globals.f90
@@ -22,6 +22,7 @@ module swiftest_globals
    real(DP), parameter :: PI3BY2 = 4.712388980384689857693965074919254326296_DP !! Definition of /(3 \pi / 2\)
    real(DP), parameter :: TWOPI  = 6.283185307179586476925286766559005768394_DP !! Definition of 2 \pi
    real(DP), parameter :: THIRD  = 0.333333333333333333333333333333333333333_DP !! Definition of 1 / 3
+   real(DP), parameter :: SIXTH  = 0.166666666666666666666666666666666666667_DP !! Definition of 1 / 3
    real(DP), parameter :: DEG2RAD = PI / 180.0_DP !! Definition of conversion factor from degrees to radians
    real(DP), parameter :: RAD2DEG = 180.0_DP / PI !! Definition of conversion factor from degrees to radians
    real(DP), parameter :: GC        = 6.6743E-11_DP   !! Universal gravitational constant in SI units
@@ -31,7 +32,7 @@ module swiftest_globals
    integer(I4B), parameter :: LOWERCASE_END    = iachar('z') !! ASCII character set parameter for lower to upper conversion - end of lowercase
    integer(I4B), parameter :: UPPERCASE_OFFSET = iachar('A') - iachar('a') !! ASCII character set parameter for lower to upper conversion - offset between upper and lower
 
-   real(SP), parameter :: VERSION_NUMBER = 0.1_SP !! swiftest version
+   real(SP), parameter :: VERSION_NUMBER = 1.0_SP !! swiftest version
 
    !> Symbolic name for integrator types
    integer(I4B), parameter :: UNKNOWN_INTEGRATOR = 1
diff --git a/src/modules/swiftest_operators.f90 b/src/modules/swiftest_operators.f90
index 2c982f09c..13cb57839 100644
--- a/src/modules/swiftest_operators.f90
+++ b/src/modules/swiftest_operators.f90
@@ -15,42 +15,49 @@ module swiftest_operators
 
    interface operator(.cross.)
       module pure function operator_cross_sp(A, B) result(C)
+         !$omp declare simd(operator_cross_sp)
          implicit none
          real(SP), dimension(:), intent(in) :: A, B
          real(SP), dimension(3) :: C
       end function operator_cross_sp
 
       module pure function operator_cross_dp(A, B) result(C)
+         !$omp declare simd(operator_cross_dp)
          implicit none
          real(DP), dimension(:), intent(in) :: A, B
          real(DP), dimension(3) :: C
       end function operator_cross_dp
 
       module pure function operator_cross_qp(A, B) result(C)
+         !$omp declare simd(operator_cross_qp)
          implicit none
          real(QP), dimension(:), intent(in) :: A, B
          real(QP), dimension(3) :: C
       end function operator_cross_qp
 
       module pure function operator_cross_i1b(A, B) result(C)
+         !$omp declare simd(operator_cross_i1b)
          implicit none
          integer(I1B), dimension(:), intent(in) :: A, B
          integer(I1B), dimension(3) :: C
       end function operator_cross_i1b
 
       module pure function operator_cross_i2b(A, B) result(C)
+         !$omp declare simd(operator_cross_i2b)
          implicit none
          integer(I2B), dimension(:), intent(in) :: A, B
          integer(I2B), dimension(3) :: C
       end function operator_cross_i2b
 
       module pure function operator_cross_i4b(A, B) result(C)
+         !$omp declare simd(operator_cross_i4b)
          implicit none
          integer(I4B), dimension(:), intent(in) :: A, B
          integer(I4B), dimension(3) :: C
       end function operator_cross_i4b
 
       module pure function operator_cross_i8b(A, B) result(C)
+         !$omp declare simd(operator_cross_i8b)
          implicit none
          integer(I8B), dimension(:), intent(in) :: A, B
          integer(I8B), dimension(3) :: C
@@ -105,18 +112,21 @@ end function operator_cross_el_i8b
 
    interface operator(.mag.)
       module pure function operator_mag_sp(A) result(B)
+         !$omp declare simd(operator_mag_sp)
          implicit none
          real(SP), dimension(:), intent(in) :: A
          real(SP)                           :: B
       end function operator_mag_sp
 
       module pure function operator_mag_dp(A) result(B)
+         !$omp declare simd(operator_mag_dp)
          implicit none
          real(DP), dimension(:), intent(in) :: A
          real(DP)                           :: B
       end function operator_mag_dp
 
       module pure function operator_mag_qp(A) result(B)
+         !$omp declare simd(operator_mag_qp)
          implicit none
          real(QP), dimension(:), intent(in) :: A
          real(QP)                           :: B
diff --git a/src/obl/obl.f90 b/src/obl/obl.f90
index 977fc620c..bf369c615 100644
--- a/src/obl/obl.f90
+++ b/src/obl/obl.f90
@@ -58,8 +58,9 @@ module subroutine obl_acc_pl(self, system)
 
       associate(pl => self, npl => self%nbody, cb => system%cb)
          call obl_acc_body(pl, system)
-         do i = 1, NDIM
-            cb%aobl(i) = -sum(pl%Gmass(1:npl) * pl%aobl(i, 1:npl), pl%lmask(1:npl)) / cb%Gmass
+         cb%aobl(:) = 0.0_DP
+         do i = npl, 1, -1
+            if (pl%lmask(i)) cb%aobl(:) = cb%aobl(:) - pl%Gmass(i) * pl%aobl(:, i) / cb%Gmass
          end do
 
          do concurrent(i = 1:npl, pl%lmask(i))
diff --git a/src/operators/operator_cross.f90 b/src/operators/operator_cross.f90
index 736dc2696..99de730fe 100644
--- a/src/operators/operator_cross.f90
+++ b/src/operators/operator_cross.f90
@@ -8,6 +8,7 @@
 contains
 
    module pure function operator_cross_sp(A, B) result(C)
+      !$omp declare simd(operator_cross_sp)
       implicit none
       real(SP), dimension(:), intent(in) :: A, B
       real(SP), dimension(3) :: C
@@ -18,6 +19,7 @@ module pure function operator_cross_sp(A, B) result(C)
    end function operator_cross_sp
 
    module pure function operator_cross_dp(A, B) result(C)
+      !$omp declare simd(operator_cross_dp)
       implicit none
       real(DP), dimension(:), intent(in) :: A, B
       real(DP), dimension(3) :: C
@@ -28,6 +30,7 @@ module pure function operator_cross_dp(A, B) result(C)
    end function operator_cross_dp
 
    module pure function operator_cross_qp(A, B) result(C)
+      !$omp declare simd(operator_cross_qp)
       implicit none
       real(QP), dimension(:), intent(in) :: A, B
       real(QP), dimension(3) :: C
@@ -38,6 +41,7 @@ module pure function operator_cross_qp(A, B) result(C)
    end function operator_cross_qp
 
    module pure function operator_cross_i1b(A, B) result(C)
+      !$omp declare simd(operator_cross_i1b)
       implicit none
       integer(I1B), dimension(:), intent(in) :: A, B
       integer(I1B), dimension(3) :: C
@@ -48,6 +52,7 @@ module pure function operator_cross_i1b(A, B) result(C)
    end function operator_cross_i1b
 
    module pure function operator_cross_i2b(A, B) result(C)
+      !$omp declare simd(operator_cross_i2b)
       implicit none
       integer(I2B), dimension(:), intent(in) :: A, B
       integer(I2B), dimension(3) :: C
@@ -58,6 +63,7 @@ module pure function operator_cross_i2b(A, B) result(C)
    end function operator_cross_i2b
 
    module pure function operator_cross_i4b(A, B) result(C)
+      !$omp declare simd(operator_cross_i4b)
       implicit none
       integer(I4B), dimension(:), intent(in) :: A, B
       integer(I4B), dimension(3) :: C
@@ -68,6 +74,7 @@ module pure function operator_cross_i4b(A, B) result(C)
    end function operator_cross_i4b
 
    module pure function operator_cross_i8b(A, B) result(C)     
+      !$omp declare simd(operator_cross_i8b)
       implicit none
       integer(I8B), dimension(:), intent(in) :: A, B
       integer(I8B), dimension(3) :: C
@@ -86,9 +93,7 @@ module pure function operator_cross_el_sp(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_sp(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_sp
@@ -102,9 +107,7 @@ module pure function operator_cross_el_dp(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_dp(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_dp
@@ -118,9 +121,7 @@ module pure function operator_cross_el_qp(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_qp(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_qp
@@ -134,9 +135,7 @@ module pure function operator_cross_el_i1b(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_i1b(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_i1b
@@ -150,9 +149,7 @@ module pure function operator_cross_el_i2b(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_i2b(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_i2b
@@ -166,9 +163,7 @@ module pure function operator_cross_el_i4b(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_i4b(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_i4b
@@ -182,9 +177,7 @@ module pure function operator_cross_el_i8b(A, B) result(C)
       if (allocated(C)) deallocate(C)
       allocate(C, mold = A)
       do concurrent (i = 1:n) 
-         C(1, i) = A(2, i) * B(3, i) - A(3, i) * B(2, i)
-         C(2, i) = A(3, i) * B(1, i) - A(1, i) * B(3, i)
-         C(3, i) = A(1, i) * B(2, i) - A(2, i) * B(1, i)
+         C(:,i) = operator_cross_i8b(A(:,i), B(:,i))
       end do
       return
    end function operator_cross_el_i8b
diff --git a/src/operators/operator_mag.f90 b/src/operators/operator_mag.f90
index 5a054d5ce..f74555775 100644
--- a/src/operators/operator_mag.f90
+++ b/src/operators/operator_mag.f90
@@ -7,6 +7,7 @@
    contains
 
    module pure function operator_mag_sp(A) result(B)
+      !$omp declare simd(operator_mag_sp)
       implicit none
       real(SP), dimension(:), intent(in) :: A
       real(SP)                           :: B
@@ -15,6 +16,7 @@ module pure function operator_mag_sp(A) result(B)
    end function operator_mag_sp
 
    module pure function operator_mag_dp(A) result(B)
+      !$omp declare simd(operator_mag_dp)
       implicit none
       real(DP), dimension(:), intent(in) :: A
       real(DP)                           :: B
diff --git a/src/orbel/orbel.f90 b/src/orbel/orbel.f90
index a815b92c7..5de45e296 100644
--- a/src/orbel/orbel.f90
+++ b/src/orbel/orbel.f90
@@ -254,40 +254,24 @@ real(DP) pure function orbel_flon(e,icapn)
       biga =  (-0.5_DP * b + sq)**(1.0_DP / 3.0_DP)
       bigb = -(+0.5_DP * b + sq)**(1.0_DP / 3.0_DP) 
       x = biga + bigb
-      ! write(6,*) 'cubic = ',x**3 +a*x +b
       orbel_flon = x
       ! If capn is VSMALL (or zero) no need to go further than cubic even for
       ! e =1.
-      if( capn < VSMALL) go to 100
-
-      do i = 1,IMAX
-         x2 = x * x
-         f = a0 + x * (a1 + x2 * (a3 + x2 * (a5 + x2 * (a7 + x2 * (a9 + x2 * (a11 + x2))))))
-         fp = b1 + x2 * (b3 + x2 * (b5 + x2 * (b7 + x2 * (b9 + x2 * (b11 + 13 * x2)))))
-         dx = -f / fp
-         !   write(6,*) 'i,dx,x,f : '
-         !   write(6,432) i,dx,x,f
-         432   format(1x,i3,3(2x,1p1e22.15))
-         orbel_flon = x + dx
-         !   if we have converged here there's no point in going on
-            if(abs(dx) <= VSMALL) go to 100
+      if( capn >= VSMALL) then
+         do i = 1,IMAX
+            x2 = x**2
+            f = a0 + x * (a1 + x2 * (a3 + x2 * (a5 + x2 * (a7 + x2 * (a9 + x2 * (a11 + x2))))))
+            fp = b1 + x2 * (b3 + x2 * (b5 + x2 * (b7 + x2 * (b9 + x2 * (b11 + 13 * x2)))))
+            dx = -f / fp
+            orbel_flon = x + dx
+            !   if we have converged here there's no point in going on
+            if(abs(dx) <= VSMALL) exit
             x = orbel_flon
-      end do
-
-      ! abnormal return here - we've gone thru the loop
-      ! imax times without convergence
-      if(iflag == 1) then
-         orbel_flon = -orbel_flon
-         capn = -capn
+         end do
       end if
-      !write(*,*) 'flon : returning without complete convergence'
-      diff = e * sinh(orbel_flon) - orbel_flon - capn
-      !write(*,*) 'n, f, ecc*sinh(f) - f - n : '
-      !write(*,*) capn,orbel_flon,diff
-      return
 
       !  normal return here, but check if capn was originally negative
-      100 if(iflag == 1) then
+      if(iflag == 1) then
          orbel_flon = -orbel_flon
          capn = -capn
       end if
diff --git a/src/symba/symba_encounter_check.f90 b/src/symba/symba_encounter_check.f90
index 4896721d3..10ea00144 100644
--- a/src/symba/symba_encounter_check.f90
+++ b/src/symba/symba_encounter_check.f90
@@ -244,9 +244,9 @@ module function symba_encounter_check(self, param, system, dt, irec) result(lany
       ! Internals
       integer(I4B)              :: i, j, k, lidx, nenc_enc
       real(DP), dimension(NDIM) :: xr, vr
-      logical                   :: lencounter, isplpl
+      logical                   :: isplpl
       real(DP)                  :: rlim2, rji2
-      logical, dimension(:), allocatable :: lencmask
+      logical, dimension(:), allocatable :: lencmask, lencounter
       integer(I4B), dimension(:), allocatable :: encidx
 
       lany_encounter = .false.
@@ -269,43 +269,58 @@ module function symba_encounter_check(self, param, system, dt, irec) result(lany
             if (nenc_enc == 0) return
 
             allocate(encidx(nenc_enc))
+            allocate(lencounter(nenc_enc))
             encidx(:) = pack([(k, k = 1, self%nenc)], lencmask(:))
-
-            do lidx = 1, nenc_enc
-               k = encidx(lidx)
-               i = self%index1(k)
-               j = self%index2(k)
-               if (isplpl) then
+            lencounter(:) = .false.
+            if (isplpl) then
+               do concurrent(lidx = 1:nenc_enc)
+                  k = encidx(lidx)
+                  i = self%index1(k)
+                  j = self%index2(k)
                   xr(:) = pl%xh(:,j) - pl%xh(:,i)
                   vr(:) = pl%vb(:,j) - pl%vb(:,i)
-                  call symba_encounter_check_one(xr(1), xr(2), xr(3), vr(1), vr(2), vr(3), pl%rhill(i), pl%rhill(j), dt, irec, lencounter, self%lvdotr(k))
-               else
+                  call symba_encounter_check_one(xr(1), xr(2), xr(3), vr(1), vr(2), vr(3), pl%rhill(i), pl%rhill(j), dt, irec, lencounter(lidx), self%lvdotr(k))
+                  if (lencounter(lidx)) then
+                     rlim2 = (pl%radius(i) + pl%radius(j))**2
+                     rji2 = dot_product(xr(:), xr(:))! Check to see if these are physically overlapping bodies first, which we should ignore
+                     lencounter(lidx) = rji2 > rlim2
+                  end if
+               end do
+            else
+               do concurrent(lidx = 1:nenc_enc)
+                  k = encidx(lidx)
+                  i = self%index1(k)
+                  j = self%index2(k)
                   xr(:) = tp%xh(:,j) - pl%xh(:,i)
                   vr(:) = tp%vb(:,j) - pl%vb(:,i)
-                  call symba_encounter_check_one(xr(1), xr(2), xr(3), vr(1), vr(2), vr(3), pl%rhill(i), 0.0_DP, dt, irec, lencounter, self%lvdotr(k))
-               end if
-               if (lencounter) then
-                  if (isplpl) then
-                     rlim2 = (pl%radius(i) + pl%radius(j))**2
-                  else
+                  call symba_encounter_check_one(xr(1), xr(2), xr(3), vr(1), vr(2), vr(3), pl%rhill(i), 0.0_DP, dt, irec, lencounter(lidx), self%lvdotr(k))
+                  if (lencounter(lidx)) then
                      rlim2 = (pl%radius(i))**2
+                     rji2 = dot_product(xr(:), xr(:))! Check to see if these are physically overlapping bodies first, which we should ignore
+                     lencounter(lidx) = rji2 > rlim2
                   end if
-                  rji2 = dot_product(xr(:), xr(:))! Check to see if these are physically overlapping bodies first, which we should ignore
-                  if (rji2 > rlim2) then
-                     lany_encounter = .true.
-                     pl%levelg(i) = irec
-                     pl%levelm(i) = MAX(irec, pl%levelm(i))
-                     if (isplpl) then
-                        pl%levelg(j) = irec
-                        pl%levelm(j) = MAX(irec, pl%levelm(j))
-                     else
-                        tp%levelg(j) = irec
-                        tp%levelm(j) = MAX(irec, tp%levelm(j))
-                     end if
-                     self%level(k) = irec
+               end do
+            end if
+            lany_encounter = any(lencounter(:))
+            if (lany_encounter) then
+               nenc_enc = count(lencounter(:))
+               encidx(1:nenc_enc) = pack(encidx(:), lencounter(:))
+               do lidx = 1, nenc_enc
+                  k = encidx(lidx)
+                  i = self%index1(k)
+                  j = self%index2(k)
+                  pl%levelg(i) = irec
+                  pl%levelm(i) = MAX(irec, pl%levelm(i))
+                  if (isplpl) then
+                     pl%levelg(j) = irec
+                     pl%levelm(j) = MAX(irec, pl%levelm(j))
+                  else
+                     tp%levelg(j) = irec
+                     tp%levelm(j) = MAX(irec, tp%levelm(j))
                   end if
-               end if   
-            end do
+                  self%level(k) = irec
+               end do
+            end if   
          end select
       end select
 
diff --git a/src/util/util_coord.f90 b/src/util/util_coord.f90
index b4a77231c..41d230878 100644
--- a/src/util/util_coord.f90
+++ b/src/util/util_coord.f90
@@ -139,9 +139,12 @@ module subroutine util_coord_vb2vh_pl(self, cb)
       if (self%nbody == 0) return
 
       associate(pl => self, npl => self%nbody)
-         do i = 1, NDIM
-            cb%vb(i) = -sum(pl%Gmass(1:npl) * pl%vb(i, 1:npl)) / cb%Gmass
-            pl%vh(i, 1:npl) = pl%vb(i, 1:npl) - cb%vb(i)
+         cb%vb(:) = 0.0_DP
+         do i = npl, 1, -1
+            cb%vb(:) = cb%vb(:) - pl%Gmass(i) * pl%vb(:, i) / cb%Gmass
+         end do
+         do concurrent(i = 1:npl)
+            pl%vh(:, i) = pl%vb(:, i) - cb%vb(:)
          end do
       end associate
 
@@ -194,9 +197,12 @@ module subroutine util_coord_vh2vb_pl(self, cb)
 
       associate(pl => self, npl => self%nbody)
          Gmtot = cb%Gmass + sum(pl%Gmass(1:npl))
-         do i = 1, NDIM
-            cb%vb(i) = -sum(pl%Gmass(1:npl) * pl%vh(i, 1:npl)) / Gmtot
-            pl%vb(i, 1:npl) = pl%vh(i, 1:npl) + cb%vb(i)
+         cb%vb(:) = 0.0_DP
+         do i = npl, 1, -1
+            cb%vb(:) = cb%vb(:) - pl%Gmass(i) * pl%vh(:, i) / Gmtot
+         end do
+         do concurrent(i = 1:npl)
+            pl%vb(:, i) = pl%vh(:, i) + cb%vb(:)
          end do
       end associate