From d11230ee867c5924563b33c4be8c80ee2772f0c0 Mon Sep 17 00:00:00 2001
From: David A Minton <daminton@purdue.edu>
Date: Tue, 13 Jul 2021 06:44:45 -0400
Subject: [PATCH] Updated example runs with correct initial conditions
 generators

---
 .../helio_swifter_comparison/cb.swiftest.in   |    1 +
 .../helio_swifter_comparison/init_cond.py     |    0
 .../helio_swifter_comparison/pl.swifter.in    |   48 +-
 .../helio_swifter_comparison/pl.swiftest.in   |   32 +-
 .../helio_swifter_comparison/tp.swifter.in    |   16 +-
 .../helio_swifter_comparison/tp.swiftest.in   |   16 +-
 examples/rmvs_gr_test/cb.swiftest.in          |  Bin 64 -> 0 bytes
 examples/rmvs_gr_test/init_cond.py            |  316 ----
 .../mercury_collider_init_cond.py             |  183 --
 examples/rmvs_gr_test/param.swifter.in        |   26 -
 examples/rmvs_gr_test/param.swiftest.in       |   29 -
 examples/rmvs_gr_test/param.tpcollider.in     |   26 -
 examples/rmvs_gr_test/particle_distance.ipynb | 1513 -----------------
 examples/rmvs_gr_test/pl.swifter.in           |   40 -
 examples/rmvs_gr_test/pl.swiftest.in          |  Bin 700 -> 0 bytes
 examples/rmvs_gr_test/pl.tpcollider.in        |   36 -
 examples/rmvs_gr_test/rmvs_vs_whm.ipynb       |  120 --
 examples/rmvs_gr_test/swifter2swiftest.py     |    1 -
 .../rmvs_gr_test/swiftest_relativity.ipynb    |  199 ---
 .../swifttest_rmvs_vs_swifter_rmvs.ipynb      |  156 --
 examples/rmvs_gr_test/test_unit_change.ipynb  |  169 --
 examples/rmvs_gr_test/tp.swifter.in           |   31 -
 examples/rmvs_gr_test/tp.swiftest.in          |  Bin 592 -> 0 bytes
 examples/rmvs_gr_test/tp.tpcollider.in        |    4 -
 .../tp_collider_extract_init.ipynb            |  654 -------
 .../1pl_1tp_encounter/check_init_cond.ipynb   |  930 ----------
 .../swiftest_vs_swifter.ipynb                 |    4 +-
 .../9pl_18tp_encounters/init_cond.py          |    2 +-
 .../9pl_18tp_encounters/param.swifter.in      |    2 +-
 .../9pl_18tp_encounters/param.swiftest.in     |    2 +-
 .../swiftest_rmvs_vs_swifter_rmvs.ipynb       |  402 +++--
 .../mars_ejecta/cb.swiftest.in                |    1 +
 .../{config.swiftest.in => param.swiftest.in} |    7 -
 .../mars_ejecta/profmaker.sh                  |    2 -
 .../mars_ejecta/profswifter.sh                |    2 -
 .../mars_ejecta/start.in                      |    1 -
 .../swiftest_rmvs_vs_swifter_rmvs.ipynb       |   44 +-
 examples/whm_gr_test/cb.swiftest.in           |    1 +
 examples/whm_gr_test/init_cond.py             |    3 +-
 examples/whm_gr_test/pl.swifter.in            |   48 +-
 examples/whm_gr_test/pl.swiftest.in           |   32 +-
 .../whm_gr_test/swiftest_relativity.ipynb     |   19 +-
 42 files changed, 396 insertions(+), 4722 deletions(-)
 mode change 100644 => 100755 examples/helio_swifter_comparison/init_cond.py
 delete mode 100644 examples/rmvs_gr_test/cb.swiftest.in
 delete mode 100644 examples/rmvs_gr_test/init_cond.py
 delete mode 100755 examples/rmvs_gr_test/mercury_collider_init_cond.py
 delete mode 100644 examples/rmvs_gr_test/param.swifter.in
 delete mode 100644 examples/rmvs_gr_test/param.swiftest.in
 delete mode 100644 examples/rmvs_gr_test/param.tpcollider.in
 delete mode 100644 examples/rmvs_gr_test/particle_distance.ipynb
 delete mode 100644 examples/rmvs_gr_test/pl.swifter.in
 delete mode 100644 examples/rmvs_gr_test/pl.swiftest.in
 delete mode 100644 examples/rmvs_gr_test/pl.tpcollider.in
 delete mode 100644 examples/rmvs_gr_test/rmvs_vs_whm.ipynb
 delete mode 120000 examples/rmvs_gr_test/swifter2swiftest.py
 delete mode 100644 examples/rmvs_gr_test/swiftest_relativity.ipynb
 delete mode 100644 examples/rmvs_gr_test/swifttest_rmvs_vs_swifter_rmvs.ipynb
 delete mode 100644 examples/rmvs_gr_test/test_unit_change.ipynb
 delete mode 100644 examples/rmvs_gr_test/tp.swifter.in
 delete mode 100644 examples/rmvs_gr_test/tp.swiftest.in
 delete mode 100644 examples/rmvs_gr_test/tp.tpcollider.in
 delete mode 100644 examples/rmvs_gr_test/tp_collider_extract_init.ipynb
 delete mode 100644 examples/rmvs_swifter_comparison/1pl_1tp_encounter/check_init_cond.ipynb
 rename examples/rmvs_swifter_comparison/mars_ejecta/{config.swiftest.in => param.swiftest.in} (80%)
 delete mode 100755 examples/rmvs_swifter_comparison/mars_ejecta/profmaker.sh
 delete mode 100755 examples/rmvs_swifter_comparison/mars_ejecta/profswifter.sh
 delete mode 100644 examples/rmvs_swifter_comparison/mars_ejecta/start.in

diff --git a/examples/helio_swifter_comparison/cb.swiftest.in b/examples/helio_swifter_comparison/cb.swiftest.in
index 058975b81..e4a010b1e 100644
--- a/examples/helio_swifter_comparison/cb.swiftest.in
+++ b/examples/helio_swifter_comparison/cb.swiftest.in
@@ -1,3 +1,4 @@
+0
 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
old mode 100644
new mode 100755
diff --git a/examples/helio_swifter_comparison/pl.swifter.in b/examples/helio_swifter_comparison/pl.swifter.in
index aba56d467..e0ef4e881 100644
--- a/examples/helio_swifter_comparison/pl.swifter.in
+++ b/examples/helio_swifter_comparison/pl.swifter.in
@@ -2,35 +2,35 @@
 0 39.476926408897625196
 0.0 0.0 0.0
 0.0 0.0 0.0
-1 6.5537098095653139645e-06 0.0014751254963649625977
+1 6.5537098095653139645e-06 0.0014751243077781048702
 1.6306381826061645943e-05
-0.359124056979876094 -0.1001978128323056938 -0.041130148620746292965
-0.7664364270424182397 10.3592906410849091145 0.7762248217818495593
-2 9.663313399581537916e-05 0.0067591139064765566703
+0.33206272695596028566 0.07436707001147663254 -0.02438290851908785084
+-4.2340114788918336805 10.486553514018327622 1.2453138107251555947
+2 9.663313399581537916e-05 0.006759104275397271956
 4.0453784346544178454e-05
--0.709853246614207567 0.109615461427968005625 0.042466530791895232277
--1.166834223638398553 -7.334297883841826485 -0.033323414543104576783
-3 0.000120026935827952453094 0.010044751446422198828
+-0.7188115337296047125 -0.0118554711069603201795 0.041316403191083782287
+0.07826338813583945357 -7.419533988988633545 -0.10634201014368884618
+3 0.000120026935827952453094 0.010044787321379672528
 4.25875607065040958e-05
-0.26014404284638581455 -0.9828537227999029069 4.5807148740206238052e-05
-5.9724418390973225248 1.5843954077771575533 -9.4205748659356694786e-05
-4 1.2739802010675941456e-05 0.0072467561525263839036
+0.35677088372527121507 -0.95189300879814897627 4.4027442504036787155e-05
+5.7819217550992820422 2.18192814489641851 -0.00012230072278352209966
+4 1.2739802010675941456e-05 0.007246743835971885302
 2.265740805092889601e-05
--1.4908630412685239808 0.7412277078494349247 0.052104480532706012874
--2.084278892390818102 -4.1405652065758745757 -0.035644761583621103612
-5 0.037692251088985676735 0.35527141892920671874
+-1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
+-1.8728417739956807141 -4.239719661832373223 -0.042909557750301418264
+5 0.037692251088985676735 0.35527126534549128905
 0.00046732617030490929307
-4.0233930071159198505 -3.029555621945668964 -0.077433472926114965684
-1.626590141045528945 2.3340622087669935288 -0.046085347207395002237
-6 0.011285899820091272997 0.43765136932522125042
+4.049944927347420176 -2.9910878677758190314 -0.078187280837353656526
+1.6060801375519682711 2.349356876761497338 -0.045690062807172619064
+6 0.011285899820091272997 0.4376527512949726007
 0.00038925687730393611812
-6.274810893232299236 -7.7275164380757708216 -0.115372736553069593635
-1.4703000143673246375 1.2821134193800077011 -0.08078666716402813097
-7 0.0017236589478267730203 0.469520070575212966
+6.298929503477405767 -7.706413024510769816 -0.11669919842191249504
+1.4661378456572359413 1.2872251175075805794 -0.08070991686100478242
+7 0.0017236589478267730203 0.4695362423191493196
 0.00016953449859497231466
-14.871766666738729157 12.9908875920566391216 -0.14444232402201501175
--0.9541590491729433116 1.0172543087941671172 0.016087073469786578863
-8 0.0020336100526728302319 0.78126715446178621345
+14.856082147529010129 13.007589275314199284 -0.14417795763685259391
+-0.9554310497290159123 1.0161753499437922057 0.016099529164307530124
+8 0.0020336100526728302319 0.7812870996943599397
 0.000164587904124493665
-29.554624389819270647 -4.648140925388063671 -0.5854586034520335991
-0.1723572655485145611 1.1421549698170996955 -0.027459964210413734165
+29.55744967800954015 -4.629377558152945049 -0.58590957207831262377
+0.17162147939801157335 1.1422848961108499101 -0.027445465472921385952
diff --git a/examples/helio_swifter_comparison/pl.swiftest.in b/examples/helio_swifter_comparison/pl.swiftest.in
index 27814f389..9d49cc3da 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
 1.6306381826061645943e-05
-0.359124056979876094 -0.1001978128323056938 -0.041130148620746292965
-0.7664364270424182397 10.3592906410849091145 0.7762248217818495593
+0.33206272695596028566 0.07436707001147663254 -0.02438290851908785084
+-4.2340114788918336805 10.486553514018327622 1.2453138107251555947
 2 9.663313399581537916e-05
 4.0453784346544178454e-05
--0.709853246614207567 0.109615461427968005625 0.042466530791895232277
--1.166834223638398553 -7.334297883841826485 -0.033323414543104576783
+-0.7188115337296047125 -0.0118554711069603201795 0.041316403191083782287
+0.07826338813583945357 -7.419533988988633545 -0.10634201014368884618
 3 0.000120026935827952453094
 4.25875607065040958e-05
-0.26014404284638581455 -0.9828537227999029069 4.5807148740206238052e-05
-5.9724418390973225248 1.5843954077771575533 -9.4205748659356694786e-05
+0.35677088372527121507 -0.95189300879814897627 4.4027442504036787155e-05
+5.7819217550992820422 2.18192814489641851 -0.00012230072278352209966
 4 1.2739802010675941456e-05
 2.265740805092889601e-05
--1.4908630412685239808 0.7412277078494349247 0.052104480532706012874
--2.084278892390818102 -4.1405652065758745757 -0.035644761583621103612
+-1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
+-1.8728417739956807141 -4.239719661832373223 -0.042909557750301418264
 5 0.037692251088985676735
 0.00046732617030490929307
-4.0233930071159198505 -3.029555621945668964 -0.077433472926114965684
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+4.049944927347420176 -2.9910878677758190314 -0.078187280837353656526
+1.6060801375519682711 2.349356876761497338 -0.045690062807172619064
 6 0.011285899820091272997
 0.00038925687730393611812
-6.274810893232299236 -7.7275164380757708216 -0.115372736553069593635
-1.4703000143673246375 1.2821134193800077011 -0.08078666716402813097
+6.298929503477405767 -7.706413024510769816 -0.11669919842191249504
+1.4661378456572359413 1.2872251175075805794 -0.08070991686100478242
 7 0.0017236589478267730203
 0.00016953449859497231466
-14.871766666738729157 12.9908875920566391216 -0.14444232402201501175
--0.9541590491729433116 1.0172543087941671172 0.016087073469786578863
+14.856082147529010129 13.007589275314199284 -0.14417795763685259391
+-0.9554310497290159123 1.0161753499437922057 0.016099529164307530124
 8 0.0020336100526728302319
 0.000164587904124493665
-29.554624389819270647 -4.648140925388063671 -0.5854586034520335991
-0.1723572655485145611 1.1421549698170996955 -0.027459964210413734165
+29.55744967800954015 -4.629377558152945049 -0.58590957207831262377
+0.17162147939801157335 1.1422848961108499101 -0.027445465472921385952
diff --git a/examples/helio_swifter_comparison/tp.swifter.in b/examples/helio_swifter_comparison/tp.swifter.in
index 62acd79fc..b37f04011 100644
--- a/examples/helio_swifter_comparison/tp.swifter.in
+++ b/examples/helio_swifter_comparison/tp.swifter.in
@@ -1,13 +1,13 @@
 4
 101
-2.3133253483335658451 1.6360857008750779862 -0.37450983998533471375
--2.2458876465769251093 2.8378699270656317882 0.50346273267874514076
+2.2759060918449769417 1.6823262546111898974 -0.3661544509052930274
+-2.3097811686367798667 2.7916683305060454227 0.51377483806222698173
 102
-3.009555158239280992 -1.1130165423439479788 0.51172110509120705135
-0.70453633545041942506 2.5148434686651768256 -1.80152331908826862
+3.0206599411327550442 -1.0715345879373190385 0.4820489106686373093
+0.64736314289225124926 2.5354787229381968757 -1.8109825958052419904
 103
--0.5218824163555056961 -3.1396467647675119217 0.7342355813480357929
-3.0582031698647593751 -0.12050283730110719834 -0.096945705299882042706
+-0.47156753362343428737 -3.1411451968218520037 0.73253063903937232215
+3.067486522793096946 -0.061867034122113133084 -0.11064022385054755856
 104
--2.075368356279947868 -0.76569201199778380573 0.27541025252901979448
-1.7615515330387480359 -3.9484151677488075983 -0.096278788580453326945
+-2.0454358521790818592 -0.83017357434175576003 0.27369621627497042748
+1.8825682786003801814 -3.9015333153827542793 -0.112405737336568095776
diff --git a/examples/helio_swifter_comparison/tp.swiftest.in b/examples/helio_swifter_comparison/tp.swiftest.in
index 62acd79fc..b37f04011 100644
--- a/examples/helio_swifter_comparison/tp.swiftest.in
+++ b/examples/helio_swifter_comparison/tp.swiftest.in
@@ -1,13 +1,13 @@
 4
 101
-2.3133253483335658451 1.6360857008750779862 -0.37450983998533471375
--2.2458876465769251093 2.8378699270656317882 0.50346273267874514076
+2.2759060918449769417 1.6823262546111898974 -0.3661544509052930274
+-2.3097811686367798667 2.7916683305060454227 0.51377483806222698173
 102
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-0.70453633545041942506 2.5148434686651768256 -1.80152331908826862
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+0.64736314289225124926 2.5354787229381968757 -1.8109825958052419904
 103
--0.5218824163555056961 -3.1396467647675119217 0.7342355813480357929
-3.0582031698647593751 -0.12050283730110719834 -0.096945705299882042706
+-0.47156753362343428737 -3.1411451968218520037 0.73253063903937232215
+3.067486522793096946 -0.061867034122113133084 -0.11064022385054755856
 104
--2.075368356279947868 -0.76569201199778380573 0.27541025252901979448
-1.7615515330387480359 -3.9484151677488075983 -0.096278788580453326945
+-2.0454358521790818592 -0.83017357434175576003 0.27369621627497042748
+1.8825682786003801814 -3.9015333153827542793 -0.112405737336568095776
diff --git a/examples/rmvs_gr_test/cb.swiftest.in b/examples/rmvs_gr_test/cb.swiftest.in
deleted file mode 100644
index 2386b53c8a2bcee968968e01db63bf30bc75c07a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 64
zcmd;JU|=xH*zksXud@ROkPX6j{SWxW@f6#``25D)QG73^*uwa(zDpQecDd{U@d2$e
B4EX>6

diff --git a/examples/rmvs_gr_test/init_cond.py b/examples/rmvs_gr_test/init_cond.py
deleted file mode 100644
index 7083997bc..000000000
--- a/examples/rmvs_gr_test/init_cond.py
+++ /dev/null
@@ -1,316 +0,0 @@
-"""
-This script generates initial conditions for the solar using JPL Horizons data.
-
-For testing RMVS, the code generates clones of test particles based on one that is fated to impact Mercury.
-To use the script, modify the variables just after the  "if __name__ == '__main__':" line
-"""
-import numpy as np
-import sys
-from astroquery.jplhorizons import Horizons
-import astropy.constants as const 
-import swiftestio as swio
-from scipy.io import FortranFile
-
-from numpy.random import default_rng
-
-#Values from JPL Horizons
-AU2M = np.longdouble(const.au.value)
-GMSunSI = np.longdouble(const.GM_sun.value)
-Rsun = np.longdouble(const.R_sun.value)
-GC = np.longdouble(const.G.value)
-JD = 86400
-year = np.longdouble(365.25 * JD)
-c = np.longdouble(299792458.0)
-MSun_over_Mpl = np.array([6023600.0,
-                 408523.71,
-                 328900.56,
-                 3098708.,
-                 1047.3486,
-                 3497.898,
-                 22902.98,
-                 19412.24,
-                 1.35e8], dtype=np.longdouble)
-
-MU2KG    = np.longdouble(GMSunSI / GC) #Conversion from mass unit to kg
-DU2M     = np.longdouble(AU2M)         #Conversion from radius unit to centimeters
-TU2S     = np.longdouble(year)         #Conversion from time unit to seconds
-GU       = np.longdouble(GC / (DU2M**3 / (MU2KG * TU2S**2)))
-
-GMSun = np.longdouble(GMSunSI / (DU2M**3 / TU2S**2))
-
-# Simulation start, stop, and output cadence times
-t_0	  = 0 # simulation start time
-deltaT	= 0.25 * JD / TU2S   # simulation step size
-end_sim = 500 * year  / TU2S # simulation end time
-t_print = 0.1 * year / TU2S #output interval to print results
-
-# Solar oblatenes values: From Mecheri et al. (2004), using Corbard (b) 2002 values (Table II)
-J2 = np.longdouble(2.198e-7) * (Rsun / DU2M)**2
-J4 = np.longdouble(-4.805e-9) * (Rsun / DU2M)**4
-
-tstart = '2021-01-28'
-tend = '2021-01-29'
-tstep = '1d'
-planetid = {
-   'mercury'     : '1',
-   'venus'       : '2',
-   'earthmoon'   : '3',
-   'mars'        : '4',
-   'jupiter'     : '5',
-   'saturn'      : '6',
-   'uranus'      : '7',
-   'neptune'     : '8',
-   'plutocharon' : '9'
-}
-npl = 9
-
-#Planet Msun/M ratio
-MSun_over_Mpl = {
-   'mercury'     : np.longdouble(6023600.0),
-   'venus'       : np.longdouble(408523.71),
-   'earthmoon'   : np.longdouble(328900.56),
-   'mars'        : np.longdouble(3098708.),
-   'jupiter'     : np.longdouble(1047.3486),
-   'saturn'      : np.longdouble(3497.898),
-   'uranus'      : np.longdouble(22902.98),
-   'neptune'     : np.longdouble(19412.24),
-   'plutocharon' : np.longdouble(1.35e8)
-}
-
-#Planet radii in meters
-Rpl = {
-   'mercury'     : np.longdouble(2439.4e3),
-   'venus'       : np.longdouble(6051.8e3),
-   'earthmoon'   : np.longdouble(6371.0084e3), # Earth only for radius
-   'mars'        : np.longdouble(3389.50e3),
-   'jupiter'     : np.longdouble(69911e3),
-   'saturn'      : np.longdouble(58232.0e3),
-   'uranus'      : np.longdouble(25362.e3),
-   'neptune'     : np.longdouble(24622.e3),
-   'plutocharon' : np.longdouble(1188.3e3)
-}
-
-pdata = {}
-plvec = {}
-Rhill = {}
-THIRDLONG = np.longdouble(1.0) / np.longdouble(3.0)
-
-for key,val in planetid.items():
-   pdata[key] = Horizons(id=val, id_type='majorbody',location='@sun',
-            epochs={'start': tstart, 'stop': tend,
-            'step': tstep})
-   plvec[key] = np.array([pdata[key].vectors()['x'][0],
-         pdata[key].vectors()['y'][0], 
-         pdata[key].vectors()['z'][0], 
-         pdata[key].vectors()['vx'][0], 
-         pdata[key].vectors()['vy'][0], 
-         pdata[key].vectors()['vz'][0] 
-         ])
-   Rhill[key] = np.longdouble(pdata[key].elements()['a'][0]) * (3 * MSun_over_Mpl[key])**(-THIRDLONG)
-
-if __name__ == '__main__':
-
-   nclones = 10
-   xv_dispersion_factor = 0.01 # randomly alter x and v vectors by this fraction of the original
-
-   # Get tp data from collidor simulation to produce the parent of the clones
-   inparfile  = "param.tpcollider.in"
-   paramfile = swio.read_swifter_param(inparfile)
-   swifterdat = swio.swifter2xr(paramfile)
-   px = swifterdat.isel(time=-1).sel(id=100)['px'].values.item()
-   py = swifterdat.isel(time=-1).sel(id=100)['py'].values.item()
-   pz = swifterdat.isel(time=-1).sel(id=100)['pz'].values.item()
-
-   vx = swifterdat.isel(time=-1).sel(id=100)['vx'].values.item()
-   vy = swifterdat.isel(time=-1).sel(id=100)['vy'].values.item()
-   vz = swifterdat.isel(time=-1).sel(id=100)['vz'].values.item()
-
-   jangofett = np.array([px, py, pz, -vx, -vy, -vz])
-
-   # generate random clones
-   rng = default_rng()
-   clone_xv = (rng.standard_normal((nclones, 6)) - 0.5) * xv_dispersion_factor + 1.0
-   clonetroops = jangofett * clone_xv
-   clonenames = range(100, 100 + nclones)
-   tpvec = dict(zip(clonenames,clonetroops))
-
-   # Convert from AU-day to AU-year just because I find it easier to keep track of the sim progress
-   for plid in plvec:
-      plvec[plid][3:] *= year / JD
-
-   for tpid in tpvec:
-      tpvec[tpid][3:] *= year / JD
-
-   # Names of all output files
-   swifter_input  = "param.swifter.in"
-   swifter_pl     = "pl.swifter.in"
-   swifter_tp     = "tp.swifter.in"
-   swifter_bin    = "bin.swifter.dat"
-   swifter_enc    = "enc.swifter.dat"
-
-   swiftest_input = "param.swiftest.in"
-   swiftest_pl    = "pl.swiftest.in"
-   swiftest_tp    = "tp.swiftest.in"
-   swiftest_cb    = "cb.swiftest.in"
-   swiftest_bin   = "bin.swiftest.dat"
-   swiftest_enc   = "enc.swiftest.dat"
-
-   iout = int(np.ceil(t_print / deltaT))
-   rmin = Rsun / DU2M
-   rmax = 1000.0
-
-   #Make Swifter files
-   plfile = open(swifter_pl, 'w')
-   print(npl+1, f'! Planet input file generated using init_cond.py using JPL Horizons data for the major planets (and Pluto) for epoch {tstart}' ,file=plfile)
-   print(1,GMSun,file=plfile)
-   print('0.0 0.0 0.0',file=plfile)
-   print('0.0 0.0 0.0',file=plfile)
-   for i, plid in enumerate(plvec): 
-      print(i + 2,"{:.23g}".format(GMSun * MSun_over_Mpl[plid]**-1),Rhill[plid], file=plfile)
-      print(Rpl[plid] / DU2M, file=plfile)
-      print(plvec[plid][0],plvec[plid][1],plvec[plid][2], file=plfile)
-      print(plvec[plid][3],plvec[plid][4],plvec[plid][5], file=plfile)
-   plfile.close()
-
-   tpfile = open(swifter_tp, 'w')
-   print(nclones,file=tpfile)
-   for tpid, tp in tpvec.items():
-      print(tpid, file=tpfile)
-      print(f'{tp[0]} {tp[1]} {tp[2]}', file=tpfile)
-      print(f'{tp[3]} {tp[4]} {tp[5]}', file=tpfile)
-   tpfile.close()
-
-   sys.stdout = open(swifter_input, "w")
-   print(f'! Swifter input file generated using init_cond.py')
-   print(f'T0            {t_0} ')
-   print(f'TSTOP         {end_sim}')
-   print(f'DT            {deltaT}')
-   print(f'PL_IN         {swifter_pl}')
-   print(f'TP_IN         {swifter_tp}')
-   print(f'IN_TYPE       ASCII')
-   print(f'ISTEP_OUT     {iout:d}')
-   print(f'ISTEP_DUMP    {iout:d}')
-   print(f'BIN_OUT       {swifter_bin}')
-   print(f'OUT_TYPE      REAL8')
-   print(f'OUT_FORM      EL')
-   print(f'OUT_STAT      NEW')
-   print(f'J2            {J2}')
-   print(f'J4            {J4}')
-   print(f'CHK_CLOSE     yes')
-   print(f'CHK_RMIN      {rmin}')
-   print(f'CHK_RMAX      {rmax}')
-   print(f'CHK_EJECT     {rmax}')
-   print(f'CHK_QMIN      {rmin}')
-   print(f'CHK_QMIN_COORD HELIO')
-   print(f'CHK_QMIN_RANGE {rmin} {rmax}')
-   print(f'ENC_OUT        {swifter_enc}')
-   print(f'EXTRA_FORCE    no')
-   print(f'BIG_DISCARD    no')
-   print(f'RHILL_PRESENT  yes')
-
-   #Now make Swiftest files
-   cbfile = FortranFile(swiftest_cb, 'w')
-   Msun = np.double(1.0)
-   cbfile.write_record(np.double(GMSun))
-   cbfile.write_record(np.double(rmin))
-   cbfile.write_record(np.double(J2))
-   cbfile.write_record(np.double(J4))
-   cbfile.close()
-
-   plfile = FortranFile(swiftest_pl, 'w')
-   plfile.write_record(npl)
-
-   name = np.empty(npl, dtype=np.int32)
-   px = np.empty(npl, dtype=np.double)
-   py = np.empty(npl, dtype=np.double)
-   pz = np.empty(npl, dtype=np.double)
-   vx = np.empty(npl, dtype=np.double)
-   vy = np.empty(npl, dtype=np.double)
-   vz = np.empty(npl, dtype=np.double)
-   mass = np.empty(npl, dtype=np.double)
-   Gmass = np.empty(npl, dtype=np.double)
-   radius = np.empty(npl, dtype=np.double)
-   for i, plid in enumerate(plvec):
-      name[i] = i + 2
-      px[i] = plvec[plid][0]
-      py[i] = plvec[plid][1]
-      pz[i] = plvec[plid][2]
-      vx[i] = plvec[plid][3]
-      vy[i] = plvec[plid][4]
-      vz[i] = plvec[plid][5]
-      Gmass[i] = GMSun * MSun_over_Mpl[plid]**-1
-      radius[i] = Rpl[plid] / DU2M
-   plfile.write_record(name.T)
-   plfile.write_record(px.T)
-   plfile.write_record(py.T)
-   plfile.write_record(pz.T)
-   plfile.write_record(vx.T)
-   plfile.write_record(vy.T)
-   plfile.write_record(vz.T)
-   plfile.write_record(Gmass.T)
-   plfile.write_record(radius.T)
-   plfile.close()
-   tpfile = FortranFile(swiftest_tp, 'w')
-   ntp = nclones
-   tpfile.write_record(ntp)
-   name = np.empty(ntp, dtype=np.int32)
-   px = np.empty(ntp, dtype=np.double)
-   py = np.empty(ntp, dtype=np.double)
-   pz = np.empty(ntp, dtype=np.double)
-   vx = np.empty(ntp, dtype=np.double)
-   vy = np.empty(ntp, dtype=np.double)
-   vz = np.empty(ntp, dtype=np.double)
-   for i, tpid in enumerate(tpvec):
-      name[i] = int(tpid)
-      px[i] = tpvec[tpid][0]
-      py[i] = tpvec[tpid][1]
-      pz[i] = tpvec[tpid][2]
-      vx[i] = tpvec[tpid][3]
-      vy[i] = tpvec[tpid][4]
-      vz[i] = tpvec[tpid][5]
-   tpfile.write_record(name.T)
-   tpfile.write_record(px.T)
-   tpfile.write_record(py.T)
-   tpfile.write_record(pz.T)
-   tpfile.write_record(vx.T)
-   tpfile.write_record(vy.T)
-   tpfile.write_record(vz.T)
-
-   tpfile.close()
-
-   sys.stdout = open(swiftest_input, "w")
-   print(f'! Swiftest input file generated using init_cond.py')
-   print(f'T0             {t_0} ')
-   print(f'TSTOP          {end_sim}')
-   print(f'DT             {deltaT}')
-   print(f'CB_IN          {swiftest_cb}')
-   print(f'PL_IN          {swiftest_pl}')
-   print(f'TP_IN          {swiftest_tp}')
-   print(f'IN_TYPE        REAL8')
-   print(f'ISTEP_OUT      {iout:d}')
-   print(f'ISTEP_DUMP     {iout:d}')
-   print(f'BIN_OUT        {swiftest_bin}')
-   print(f'OUT_TYPE       REAL8')
-   print(f'OUT_FORM       EL')
-   print(f'OUT_STAT       REPLACE')
-   print(f'CHK_CLOSE      yes')
-   print(f'CHK_RMIN       {rmin}')
-   print(f'CHK_RMAX       {rmax}')
-   print(f'CHK_EJECT      {rmax}')
-   print(f'CHK_QMIN       {rmin}')
-   print(f'CHK_QMIN_COORD HELIO')
-   print(f'CHK_QMIN_RANGE {rmin} {rmax}')
-   print(f'ENC_OUT        {swiftest_enc}')
-   print(f'EXTRA_FORCE    no')
-   print(f'BIG_DISCARD    no')
-   print(f'ROTATION       no')
-   print(f'GR             no')
-   print(f'MU2KG          {MU2KG}')
-   print(f'DU2M           {DU2M}')
-   print(f'TU2S           {TU2S}')
-
-
-   sys.stdout = sys.__stdout__
-
-
-
diff --git a/examples/rmvs_gr_test/mercury_collider_init_cond.py b/examples/rmvs_gr_test/mercury_collider_init_cond.py
deleted file mode 100755
index 494669bb8..000000000
--- a/examples/rmvs_gr_test/mercury_collider_init_cond.py
+++ /dev/null
@@ -1,183 +0,0 @@
-"""
-This script generates initial conditions for the solar using JPL Horizons data. 
-Makes a test particle located at Mercury's position, 500 years into the future so that I can model an impact onto Mercury.
-
-To use the script, modify the variables just after the  "if __name__ == '__main__':" line
-"""
-import numpy as np
-import sys
-from astroquery.jplhorizons import Horizons
-import astropy.constants as const 
-
-#Values from JPL Horizons
-AU2M = const.au.value
-GMSunSI = const.GM_sun.value
-Rsun = const.R_sun.value
-GC = const.G.value
-JD = 86400
-year = 365.25 * JD
-c = 299792458.0
-
-
-
-MU2KG   = GMSunSI / GC #Conversion from mass unit (G * Msun) to kg
-DU2M   = AU2M      #Conversion from distance unit (AU) to meters
-TU2S   = JD       #Conversion from time unit (Julian Day) to seconds
-GU     = GC / (DU2M**3 / (MU2KG * TU2S**2))
-
-GMSun = GMSunSI / (DU2M**3 / TU2S**2)
-
-
-
-# Solar oblateness values: From Mecheri et al. (2004), using Corbard (b) 2002 values (Table II)
-J2 = 2.198e-7 * (Rsun / DU2M)**2
-J4 = -4.805e-9 * (Rsun / DU2M)**4
-
-npl = 9
-
-tstart = '2421-01-31'
-tend = '2421-02-01'
-tstep = '1d'
-
-# All planets except for Mercury
-planetid = {
-   'venus'      : '2',
-   'earthmoon'   : '3',
-   'mars'      : '4',
-   'jupiter'    : '5',
-   'saturn'     : '6',
-   'uranus'     : '7',
-   'neptune'    : '8',
-   'plutocharon' : '9'
-}
-
-tpid = {'mercury_impactor' : '1'}
-
-#Planet Msun/M ratio
-MSun_over_Mpl = {
-   'mercury'    : 6023600.0,
-   'venus'      : 408523.71,
-   'earthmoon'   : 328900.56,
-   'mars'      : 3098708.,
-   'jupiter'    : 1047.3486,
-   'saturn'     : 3497.898,
-   'uranus'     : 22902.98,
-   'neptune'    : 19412.24,
-   'plutocharon' :  1.35e8
-}
-
-#Planet radii in meters
-Rpl = {
-   'mercury'    : 2439.4e3,
-   'venus'      : 6051.8e3,
-   'earthmoon'   : 6371.0084e3, # Earth only for radius
-   'mars'      : 3389.50e3,
-   'jupiter'    : 69911e3,
-   'saturn'     : 58232.0e3,
-   'uranus'     : 25362.e3,
-   'neptune'    : 24622.e3,
-   'plutocharon' : 1188.3e3
-}
-
-pdata = {}
-vec = {}
-Rhill = {}
-
-for key,val in planetid.items():
-   pdata[key] = Horizons(id=val, id_type='majorbody',location='@sun',
-            epochs={'start': tstart, 'stop': tend,
-            'step': tstep})
-   vec[key] = np.array([pdata[key].vectors()['x'][0],
-               pdata[key].vectors()['y'][0], 
-               pdata[key].vectors()['z'][0], 
-               pdata[key].vectors()['vx'][0], 
-               pdata[key].vectors()['vy'][0], 
-               pdata[key].vectors()['vz'][0]
-         ])
-   Rhill[key] = pdata[key].elements()['a'][0] * (3 * MSun_over_Mpl[key])**(-1.0 / 3.0)
-
-# Make a test particle initially at Mercury's position but with 10% higher velocity
-vfactor = 1.00
-tpdata = Horizons(id='1', id_type='majorbody',location='@sun',
-            epochs={'start': tstart, 'stop': tend,
-            'step': tstep}) 
-tpvec = [tpdata.vectors()['x'][0], 
-         tpdata.vectors()['y'][0], 
-         tpdata.vectors()['z'][0],
-         vfactor * tpdata.vectors()['vx'][0], 
-         vfactor * tpdata.vectors()['vy'][0],
-         vfactor * tpdata.vectors()['vz'][0]
-      ]
-
-if __name__ == '__main__':
-   # Names of all output files
-   swifter_input  = "param.tpcollider.in"
-   swifter_pl     = "pl.tpcollider.in"
-   swifter_tp     = "tp.tpcollider.in"
-   swifter_bin    = "bin.tpcollider.dat"
-   swifter_enc    = "enc.tpcollider.dat"
-
-   # Simulation start, stop, and output cadence times
-   t_0	  = 0 * year / TU2S # simulation start time
-   deltaT	= 0.1 * JD / TU2S   # simulation step size
-   t_print = 4.e0 * year / TU2S #output interval to print results
-   end_sim = 400.0 * year / TU2S # simulation end time
-
-
-   iout = int(np.ceil(t_print / deltaT))
-   rmin = Rsun / DU2M
-   rmax = 10000.0
-
-   #Make Swifter files
-   # Reverse all the velocity signs to fake going backward in time
-   plfile = open(swifter_pl, 'w')
-   print(f'{npl} ! Planet input file generated using init_cond.py using JPL Horizons data for the major planets (and Pluto) for epoch {tstart}' ,file=plfile)
-   print(f'1 {GMSun}',file=plfile)
-   print(f'0.0 0.0 0.0',file=plfile)
-   print(f'0.0 0.0 0.0',file=plfile)
-   for i, key in enumerate(planetid): 
-      print(f'{i + 3} {GMSun / MSun_over_Mpl[key]} {Rhill[key]}', file=plfile)
-      print(f'{Rpl[key] / DU2M}', file=plfile)
-      print(f'{vec[key][0]} {vec[key][1]} {vec[key][2]}', file=plfile)
-      print(f'{-vec[key][3]} {-vec[key][4]} {-vec[key][5]}', file=plfile)
-   plfile.close()
-   tpfile = open(swifter_tp, 'w')
-   print(1,file=tpfile)
-   print(100,file=tpfile)
-   print(f'{tpvec[0]} {tpvec[1]} {tpvec[2]}', file=tpfile)
-   print(f'{-tpvec[3]} {-tpvec[4]} {-tpvec[5]}', file=tpfile)
-   tpfile.close()
-
-   sys.stdout = open(swifter_input, "w")
-   print(f'! Swifter input file generated using init_cond.py')
-   print(f'T0            {t_0} ')
-   print(f'TSTOP         {end_sim}')
-   print(f'DT            {deltaT}')
-   print(f'PL_IN         {swifter_pl}')
-   print(f'TP_IN         {swifter_tp}')
-   print(f'IN_TYPE       ASCII')
-   print(f'ISTEP_OUT     {iout:d}')
-   print(f'ISTEP_DUMP    {iout:d}')
-   print(f'BIN_OUT       {swifter_bin}')
-   print(f'OUT_TYPE      REAL8')
-   print(f'OUT_FORM      XV')
-   print(f'OUT_STAT      NEW')
-   print(f'J2            {J2}')
-   print(f'J4            {J4}')
-   print(f'CHK_CLOSE     yes')
-   print(f'CHK_RMIN      {rmin}')
-   print(f'CHK_RMAX      {rmax}')
-   print(f'CHK_EJECT     {rmax}')
-   print(f'CHK_QMIN      {rmin}')
-   print(f'CHK_QMIN_COORD HELIO')
-   print(f'CHK_QMIN_RANGE {rmin} {rmax}')
-   print(f'ENC_OUT        {swifter_enc}')
-   print(f'EXTRA_FORCE    no')
-   print(f'BIG_DISCARD    no')
-   print(f'RHILL_PRESENT  yes')
-
-
-   sys.stdout = sys.__stdout__
-
-
-
diff --git a/examples/rmvs_gr_test/param.swifter.in b/examples/rmvs_gr_test/param.swifter.in
deleted file mode 100644
index 5834d2dcc..000000000
--- a/examples/rmvs_gr_test/param.swifter.in
+++ /dev/null
@@ -1,26 +0,0 @@
-! Swifter input file generated using init_cond.py
-T0            0 
-TSTOP         1.0
-DT            0.0006844626967830253
-PL_IN         pl.swifter.in
-TP_IN         tp.swifter.in
-IN_TYPE       ASCII
-ISTEP_OUT     1
-ISTEP_DUMP    1
-BIN_OUT       bin.swifter.dat
-OUT_TYPE      REAL8
-OUT_FORM      EL
-OUT_STAT      NEW
-J2            4.7535806948127355e-12
-J4            -2.2473967953572827e-18
-CHK_CLOSE     yes
-CHK_RMIN      0.004650467260962157
-CHK_RMAX      1000.0
-CHK_EJECT     1000.0
-CHK_QMIN      0.004650467260962157
-CHK_QMIN_COORD HELIO
-CHK_QMIN_RANGE 0.004650467260962157 1000.0
-ENC_OUT        enc.swifter.dat
-EXTRA_FORCE    no
-BIG_DISCARD    no
-RHILL_PRESENT  yes
diff --git a/examples/rmvs_gr_test/param.swiftest.in b/examples/rmvs_gr_test/param.swiftest.in
deleted file mode 100644
index 6e623a5dd..000000000
--- a/examples/rmvs_gr_test/param.swiftest.in
+++ /dev/null
@@ -1,29 +0,0 @@
-! Swiftest input file generated using init_cond.py
-T0             0 
-TSTOP          1.0
-DT             0.0006844626967830253
-CB_IN          cb.swiftest.in
-PL_IN          pl.swiftest.in
-TP_IN          tp.swiftest.in
-IN_TYPE        REAL8
-ISTEP_OUT      1
-ISTEP_DUMP     1
-BIN_OUT        bin.swiftest.dat
-OUT_TYPE       REAL8
-OUT_FORM       EL
-OUT_STAT       REPLACE
-CHK_CLOSE      yes
-CHK_RMIN       0.004650467260962157
-CHK_RMAX       1000.0
-CHK_EJECT      1000.0
-CHK_QMIN       0.004650467260962157
-CHK_QMIN_COORD HELIO
-CHK_QMIN_RANGE 0.004650467260962157 1000.0
-ENC_OUT        enc.swiftest.dat
-EXTRA_FORCE    no
-BIG_DISCARD    no
-ROTATION       no
-GR             no
-MU2KG          1.988409870698051e+30
-DU2M           149597870700.0
-TU2S           31557600.0
diff --git a/examples/rmvs_gr_test/param.tpcollider.in b/examples/rmvs_gr_test/param.tpcollider.in
deleted file mode 100644
index a6066b36c..000000000
--- a/examples/rmvs_gr_test/param.tpcollider.in
+++ /dev/null
@@ -1,26 +0,0 @@
-! Swifter input file generated using init_cond.py
-T0            0.0 
-TSTOP         146100.0
-DT            0.1
-PL_IN         pl.tpcollider.in
-TP_IN         tp.tpcollider.in
-IN_TYPE       ASCII
-ISTEP_OUT     14610
-ISTEP_DUMP    14610
-BIN_OUT       bin.tpcollider.dat
-OUT_TYPE      REAL8
-OUT_FORM      XV
-OUT_STAT      NEW
-J2            4.7535806948127355e-12
-J4            -2.2473967953572827e-18
-CHK_CLOSE     yes
-CHK_RMIN      0.004650467260962157
-CHK_RMAX      10000.0
-CHK_EJECT     10000.0
-CHK_QMIN      0.004650467260962157
-CHK_QMIN_COORD HELIO
-CHK_QMIN_RANGE 0.004650467260962157 10000.0
-ENC_OUT        enc.tpcollider.dat
-EXTRA_FORCE    no
-BIG_DISCARD    no
-RHILL_PRESENT  yes
diff --git a/examples/rmvs_gr_test/particle_distance.ipynb b/examples/rmvs_gr_test/particle_distance.ipynb
deleted file mode 100644
index 5286218d8..000000000
--- a/examples/rmvs_gr_test/particle_distance.ipynb
+++ /dev/null
@@ -1,1513 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import swiftestio as swio\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.swifter.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile  = \"param.swifter.in\"\n",
-    "paramfile = swio.read_swifter_param(inparfile)\n",
-    "swifterdat = swio.swifter2xr(paramfile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "  color: var(--xr-disabled-color);\n",
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-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-summary,\n",
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-       "\n",
-       ".xr-section-details {\n",
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-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
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-       "\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",
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-       "\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",
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-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
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-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
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-       "  content: ')';\n",
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-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
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-       "\n",
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-       "\n",
-       ".xr-var-dtype {\n",
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-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
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-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
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-       "\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",
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-       "\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.Dataset&gt;\n",
-       "Dimensions:  (id: 19, time: 1462)\n",
-       "Coordinates:\n",
-       "  * id       (id) int64 2 3 4 5 6 7 8 9 10 ... 102 103 104 105 106 107 108 109\n",
-       "  * time     (time) float64 0.0 0.0006845 0.001369 ... 0.9986 0.9993 1.0\n",
-       "Data variables:\n",
-       "    a        (time, id) float64 0.3871 0.7233 1.0 1.524 ... 0.5458 0.537 0.567\n",
-       "    e        (time, id) float64 0.2056 0.006785 0.01673 ... 0.366 0.3625 0.3776\n",
-       "    inc      (time, id) float64 0.1222 0.05925 4.707e-05 ... 0.1256 0.1248\n",
-       "    capom    (time, id) float64 0.8431 1.337 3.081 ... 0.929 0.9151 0.9155\n",
-       "    omega    (time, id) float64 0.5094 0.9622 5.0 5.003 ... 5.833 5.887 5.824\n",
-       "    capm     (time, id) float64 6.205 2.489 0.4222 1.624 ... 2.416 2.764 1.578\n",
-       "    Mass     (time, id) float64 6.554e-06 9.663e-05 0.00012 ... nan nan nan\n",
-       "    Radius   (time, id) float64 1.631e-05 4.045e-05 4.259e-05 ... nan nan nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b408ef8e-f44d-402c-95bf-3008fb8be3dc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b408ef8e-f44d-402c-95bf-3008fb8be3dc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 19</li><li><span class='xr-has-index'>time</span>: 1462</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d9bf7fe9-e8ec-431b-9acc-4103bf256048' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9bf7fe9-e8ec-431b-9acc-4103bf256048' 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'>2 3 4 5 6 7 ... 105 106 107 108 109</div><input id='attrs-124fc236-b176-4119-a0c6-8c365699e6bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-124fc236-b176-4119-a0c6-8c365699e6bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ede6d46b-350b-4016-ab13-6099a402ae6b' class='xr-var-data-in' type='checkbox'><label for='data-ede6d46b-350b-4016-ab13-6099a402ae6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  2,   3,   4,   5,   6,   7,   8,   9,  10, 100, 101, 102, 103, 104,\n",
-       "       105, 106, 107, 108, 109])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0006845 ... 0.9993 1.0</div><input id='attrs-62569ec1-51f9-47a9-be21-117d71717206' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62569ec1-51f9-47a9-be21-117d71717206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6862e28f-55c7-4ae6-8491-c9d73b638434' class='xr-var-data-in' type='checkbox'><label for='data-6862e28f-55c7-4ae6-8491-c9d73b638434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 6.844627e-04, 1.368925e-03, ..., 9.986311e-01,\n",
-       "       9.993155e-01, 1.000000e+00])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83f3335d-4675-42c1-ab9a-4d6ab2c4b5d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83f3335d-4675-42c1-ab9a-4d6ab2c4b5d5' class='xr-section-summary' >Data variables: <span>(8)</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>a</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3871 0.7233 1.0 ... 0.537 0.567</div><input id='attrs-a540279c-158a-4e9b-ad79-8b12223f12e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a540279c-158a-4e9b-ad79-8b12223f12e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e686785-e198-4fee-a281-849b23bc3c7e' class='xr-var-data-in' type='checkbox'><label for='data-9e686785-e198-4fee-a281-849b23bc3c7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.38709778, 0.72333057, 1.00000316, ..., 0.54582849, 0.53695852,\n",
-       "        0.56701816],\n",
-       "       [0.3870978 , 0.72333063, 1.00000315, ..., 0.54582844, 0.53695848,\n",
-       "        0.56701811],\n",
-       "       [0.38709781, 0.72333068, 1.00000315, ..., 0.54582839, 0.53695843,\n",
-       "        0.56701805],\n",
-       "       ...,\n",
-       "       [0.3870994 , 0.72334347, 1.00000392, ..., 0.5458248 , 0.53695627,\n",
-       "        0.56704436],\n",
-       "       [0.3870994 , 0.72334339, 1.00000407, ..., 0.54582477, 0.53695625,\n",
-       "        0.56704421],\n",
-       "       [0.3870994 , 0.72334332, 1.00000422, ..., 0.54582475, 0.53695624,\n",
-       "        0.56704405]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2056 0.006785 ... 0.3625 0.3776</div><input id='attrs-b8ed6ff1-875d-4789-8c33-38b99bfbd1f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8ed6ff1-875d-4789-8c33-38b99bfbd1f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4918c24f-9ca7-4fdc-902f-c1bb2a4cfcae' class='xr-var-data-in' type='checkbox'><label for='data-4918c24f-9ca7-4fdc-902f-c1bb2a4cfcae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.20563642, 0.00678536, 0.01672608, ..., 0.36595187, 0.36245559,\n",
-       "        0.3775499 ],\n",
-       "       [0.20563645, 0.00678532, 0.01672609, ..., 0.36595182, 0.36245554,\n",
-       "        0.37754985],\n",
-       "       [0.20563647, 0.00678529, 0.01672611, ..., 0.36595177, 0.36245549,\n",
-       "        0.3775498 ],\n",
-       "       ...,\n",
-       "       [0.20562454, 0.00677425, 0.01669242, ..., 0.36595302, 0.36246182,\n",
-       "        0.37757438],\n",
-       "       [0.20562457, 0.00677416, 0.01669252, ..., 0.36595306, 0.36246185,\n",
-       "        0.37757457],\n",
-       "       [0.20562461, 0.00677408, 0.01669262, ..., 0.36595311, 0.36246188,\n",
-       "        0.37757477]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inc</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1222 0.05925 ... 0.1256 0.1248</div><input id='attrs-ea3d6b43-3236-4ea7-b59c-12c73a0ea798' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea3d6b43-3236-4ea7-b59c-12c73a0ea798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1393dd1c-0912-4469-a11c-c01ee58e09c4' class='xr-var-data-in' type='checkbox'><label for='data-1393dd1c-0912-4469-a11c-c01ee58e09c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.22237372e-01, 5.92457143e-02, 4.70747470e-05, ...,\n",
-       "        1.29511514e-01, 1.25594817e-01, 1.24763934e-01],\n",
-       "       [1.22237370e-01, 5.92457121e-02, 4.70752375e-05, ...,\n",
-       "        1.29511514e-01, 1.25594817e-01, 1.24763934e-01],\n",
-       "       [1.22237368e-01, 5.92457099e-02, 4.70757186e-05, ...,\n",
-       "        1.29511513e-01, 1.25594816e-01, 1.24763934e-01],\n",
-       "       ...,\n",
-       "       [1.22235869e-01, 5.92433945e-02, 4.92043563e-05, ...,\n",
-       "        1.29514763e-01, 1.25598489e-01, 1.24765047e-01],\n",
-       "       [1.22235870e-01, 5.92433867e-02, 4.92124537e-05, ...,\n",
-       "        1.29514769e-01, 1.25598493e-01, 1.24765061e-01],\n",
-       "       [1.22235871e-01, 5.92433790e-02, 4.92205317e-05, ...,\n",
-       "        1.29514774e-01, 1.25598497e-01, 1.24765075e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>capom</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8431 1.337 ... 0.9151 0.9155</div><input id='attrs-88f4e45b-bc4f-4377-8b8a-1e1a078411f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88f4e45b-bc4f-4377-8b8a-1e1a078411f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12f21f6f-8a18-4a3b-ad27-c07e25fbd7e5' class='xr-var-data-in' type='checkbox'><label for='data-12f21f6f-8a18-4a3b-ad27-c07e25fbd7e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.84307332, 1.3373202 , 3.08130542, ..., 0.92907829, 0.91518821,\n",
-       "        0.91554922],\n",
-       "       [0.84307332, 1.33732019, 3.08129373, ..., 0.92907825, 0.91518816,\n",
-       "        0.91554918],\n",
-       "       [0.84307331, 1.33732017, 3.08128232, ..., 0.92907821, 0.91518812,\n",
-       "        0.91554913],\n",
-       "       ...,\n",
-       "       [0.84303949, 1.33726354, 3.04284404, ..., 0.92902143, 0.91513631,\n",
-       "        0.91546522],\n",
-       "       [0.84303942, 1.33726329, 3.04266915, ..., 0.92902138, 0.91513629,\n",
-       "        0.91546474],\n",
-       "       [0.84303935, 1.33726304, 3.04249636, ..., 0.92902133, 0.91513626,\n",
-       "        0.91546427]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>omega</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5094 0.9622 5.0 ... 5.887 5.824</div><input id='attrs-4c55cca9-9f39-4881-b4a1-9b4635df3679' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c55cca9-9f39-4881-b4a1-9b4635df3679' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-442610aa-f866-443a-8536-ef8adfd346be' class='xr-var-data-in' type='checkbox'><label for='data-442610aa-f866-443a-8536-ef8adfd346be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.50941007, 0.96219574, 4.99961945, ..., 5.83252386, 5.88648565,\n",
-       "        5.8238945 ],\n",
-       "       [0.50941005, 0.96220624, 4.99962791, ..., 5.83252401, 5.88648582,\n",
-       "        5.82389465],\n",
-       "       [0.50941003, 0.96221678, 4.99963605, ..., 5.83252417, 5.88648598,\n",
-       "        5.8238948 ],\n",
-       "       ...,\n",
-       "       [0.5094769 , 0.9581504 , 5.04123893, ..., 5.83262451, 5.88658382,\n",
-       "        5.82397583],\n",
-       "       [0.50947679, 0.95813508, 5.04142223, ..., 5.83262446, 5.88658379,\n",
-       "        5.82397559],\n",
-       "       [0.50947666, 0.95811979, 5.04160339, ..., 5.83262441, 5.88658376,\n",
-       "        5.82397534]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>capm</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.205 2.489 0.4222 ... 2.764 1.578</div><input id='attrs-0a0f28ac-ca50-48d8-a0c0-d7bb083b807d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a0f28ac-ca50-48d8-a0c0-d7bb083b807d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea6c3d2e-121b-4338-85eb-70d289bee8f9' class='xr-var-data-in' type='checkbox'><label for='data-ea6c3d2e-121b-4338-85eb-70d289bee8f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6.20531946, 2.4893451 , 0.4221554 , ..., 5.68468511, 5.64509111,\n",
-       "        5.71328083],\n",
-       "       [6.22317575, 2.49632517, 0.42645906, ..., 5.69534939, 5.65602071,\n",
-       "        5.72335293],\n",
-       "       [6.24103202, 2.50330522, 0.43076273, ..., 5.70601367, 5.66695032,\n",
-       "        5.73342503],\n",
-       "       ...,\n",
-       "       [0.84154173, 0.12633672, 0.41031993, ..., 2.39433857, 2.74189406,\n",
-       "        1.55828016],\n",
-       "       [0.85939797, 0.13334255, 0.41461219, ..., 2.40500322, 2.75282396,\n",
-       "        1.56835226],\n",
-       "       [0.87725422, 0.14034836, 0.41890449, ..., 2.41566786, 2.76375386,\n",
-       "        1.57842438]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Mass</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.554e-06 9.663e-05 ... nan nan</div><input id='attrs-e229f207-62ce-42ae-8bcb-83bcf134b7c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e229f207-62ce-42ae-8bcb-83bcf134b7c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ddae73e-8a40-4c68-a724-cd17f358736b' class='xr-var-data-in' type='checkbox'><label for='data-1ddae73e-8a40-4c68-a724-cd17f358736b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6.55370981e-06, 9.66331340e-05, 1.20026936e-04, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [6.55370981e-06, 9.66331340e-05, 1.20026936e-04, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [6.55370981e-06, 9.66331340e-05, 1.20026936e-04, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       ...,\n",
-       "       [6.55370981e-06, 9.66331340e-05, 1.20026936e-04, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [6.55370981e-06, 9.66331340e-05, 1.20026936e-04, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [6.55370981e-06, 9.66331340e-05, 1.20026936e-04, ...,\n",
-       "                   nan,            nan,            nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Radius</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.631e-05 4.045e-05 ... nan nan</div><input id='attrs-6cc0ccd3-a83c-40dc-97ea-1d7ca9aa4c18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cc0ccd3-a83c-40dc-97ea-1d7ca9aa4c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d834e40a-cfbb-4734-9168-c6d47f2f8f19' class='xr-var-data-in' type='checkbox'><label for='data-d834e40a-cfbb-4734-9168-c6d47f2f8f19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.63063818e-05, 4.04537843e-05, 4.25875607e-05, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [1.63063818e-05, 4.04537843e-05, 4.25875607e-05, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [1.63063818e-05, 4.04537843e-05, 4.25875607e-05, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       ...,\n",
-       "       [1.63063818e-05, 4.04537843e-05, 4.25875607e-05, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [1.63063818e-05, 4.04537843e-05, 4.25875607e-05, ...,\n",
-       "                   nan,            nan,            nan],\n",
-       "       [1.63063818e-05, 4.04537843e-05, 4.25875607e-05, ...,\n",
-       "                   nan,            nan,            nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eeb393d4-2d3b-4aaf-995a-be1410cb6d71' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eeb393d4-2d3b-4aaf-995a-be1410cb6d71' 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.Dataset>\n",
-       "Dimensions:  (id: 19, time: 1462)\n",
-       "Coordinates:\n",
-       "  * id       (id) int64 2 3 4 5 6 7 8 9 10 ... 102 103 104 105 106 107 108 109\n",
-       "  * time     (time) float64 0.0 0.0006845 0.001369 ... 0.9986 0.9993 1.0\n",
-       "Data variables:\n",
-       "    a        (time, id) float64 0.3871 0.7233 1.0 1.524 ... 0.5458 0.537 0.567\n",
-       "    e        (time, id) float64 0.2056 0.006785 0.01673 ... 0.366 0.3625 0.3776\n",
-       "    inc      (time, id) float64 0.1222 0.05925 4.707e-05 ... 0.1256 0.1248\n",
-       "    capom    (time, id) float64 0.8431 1.337 3.081 ... 0.929 0.9151 0.9155\n",
-       "    omega    (time, id) float64 0.5094 0.9622 5.0 5.003 ... 5.833 5.887 5.824\n",
-       "    capm     (time, id) float64 6.205 2.489 0.4222 1.624 ... 2.416 2.764 1.578\n",
-       "    Mass     (time, id) float64 6.554e-06 9.663e-05 0.00012 ... nan nan nan\n",
-       "    Radius   (time, id) float64 1.631e-05 4.045e-05 4.259e-05 ... nan nan nan"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "swifterdat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swifterdat['r'] = np.sqrt(swifterdat['px']**2 + swifterdat['py']**2 + swifterdat['pz']**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f86205dcdc0>]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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dVyq72FtU+MSj/N6xCqx9Yb3PlzW+5jk3Y1IW+M+/T8ewexmigLOXZvj+N7wXt1+/CQBYCtDdKmyldQRa6zdrrZ+ptX6G1vpfNz97jdb6Nc1/P6S1/mqt9fO01s/VWv/8KucT2v2PdY4gjyzO4wjYsZyfSzyBi81yB8Si0jixMQIgE8Y5hSzLWmMyKjApizbTCM3LCIT09m2feAw3HZ8C4O+DG2FJ8EMONJQN3WXKRwHZEbjPJhVxntgY4Z33X2Cx6t152qnYjHB7OvLGDs114JyjPnN+D49enmN7UmZxBLn8Tc7ekeo8PH4tY+9ImLz/fDjozqx3QOYJuoxA3jtPuWELt12/yX5u19s79Gc+/OlLuDRb4nu/4k4AsqNeha11ZbFVDJ3cHGcRm7nQEBeJzLz0lsc53Q0o8Q3PuuUEtielSBi3ygfmoKlrjarWGJcFxmXRknahuX/PFZTNlhXecd95fMmdNwIQIsVMjuDB83soGskBN1YOdKe17ipXM9Qkj16esZ/LcQT20HrhHTfg7KWZB3G5tpMBDdnnsTkpAfAkrxu1ctCdzU4+/6nXi1xJTi8o6wiUkpx0XjbtOvBDw6r76SxrUdX4rNPHMCkLsbAsRzW0qGqMywKjQgnZWvo5Lxo+7eTmuPn3Z1BGcNTt/nO7uH5rjFPbEx6DXrgHjQwN2UNLgnPGpcKx6UjOCPby0tvpuMDzbj/JZgQ5lat2AY7LAqMybzFzGcH7H3wcs2WNF6UcgbOpOfhBa40HL+zhtiZVzsoIGBz64v4SVa2hVBoauvmEyWYeuUg76ks50FBzjS+64xQAHh7azSCL7ffeHBtHwHFG7gHD3dN3P3ABG+MCz73tJBYVr6pxW0xwn7HZ2q0nN1sVTjxOXrbmK+5kocXp41M8lJ2xcdBdje1piTtvPiYSxm17FqFwcFFpjMoCo7JgnXQONGShoK1Jk/ld4XbV6+0IHtvFU05tYyxAIlbOdnw6Si7mm45vmL8RoppJWeDmE9MENLTEqPEqXLq5qMxYn3v7dfjApy6SG3Ze1W1EKUFMADAulbkPGQcNBz2cvWTuzx03HvPGDs0eoErxZPGF3QV25xWedmq7/S6UzZd1G0Vx0J09aE4fm7LPZlnVqDXw1BvM9R5ilEPWSZeFEqAh872fe9tJlIViC5fyMoIao0K1UAYLt2XAD/c8dBHPufUkNscltAZLlM6WNQplvgf3HR+6sIfJqMCTTm5gnji8r9say6qh3TluPGYaCnDPx+7Pp96whU9L0NDeEmWzdyToblwWeM6tJ9i949a4cMGY3VuTUmFSFllOmgui7N9uTopm7MERXDG779wOnnrDFiajIpkR3HRiKkJDj16e45aTGY5gVOCWkxsiWXxpf4Ebj02964e2WBo45/h0hEWlSUjHqlKOT0fYW1TkgrcLcFQUGJcqeXgfm45YCZxdvDZ6laL4jXGBY5MRCw1Z6OGOG7cTY1VtFM85ausInnRyg9WX2+f/lFNbAHgy32YEp7YnAllsN3WJSVmwc9+dL7E5LqGUTDyPSoVxUbT/pmxvUWG7gY84DfrevMKJjRHGJT9W1bwr4lSz/rhagjMX9nDbdZuYjvlI2K7dW05sJIOoU9tTjEveuc6rGkoBt1+/mSSLT21PvOvHY5m984zTx3BuZ05mUPbAPr4xwryimwLaKH7UZtMyFzQuFeuk7frbHFsuaICGrogtqhoPXdjHU27Yag5AGe+9+cQGLghvWjq3M8et1214fxON1TiCm09s4GEmvdVa4+LeEjc1h5sY1YxMSgrQKgOrSjl1bIKq1uTiajOCUcMRJFQpN2xPWI5g6WQXEma6v6gwHZU4vjFicWjrCGxGIMFMN58w9z3lCKyjJu9DwxE99QbjCLjDxjquG49NkxzBuFQYlYrlXXbmFbanI2yOSxYyWFQ1xkWB8chEuXzU2R3e3GGzqGqMSuPw3Xm6ZuEUG4hwz+ehC3u49boN0dHZbPrmExt4fG/BZiDndxa4fnuM6ahMZtNPum5TLCq7uL/E6eN27/Bw27gs2iyL3Dsz/z5Qjrp7zgVGBZ9N27+9YXvCZ35VyAUNGcEVsYcu7KGqNZ5yymQE3KZ2o5oLu3N2AZ7bmbcHEltoVDUZwYkNPHJpRo41W9aYV3WXEQhpt4FzzKZeEKmkPRDsAUEtQrvgxoXCqChYvHfPWcxcertwNoYEM+0vKmyMCxzfGLPyUatRT2UE82WN67YmmI4KNmOz3MEtJ3hHPWsOreu2Jzi+MWLhBwtlnT4+FTFoAJgk7sPubIntaWkcgZBdjEfmoOHmDpg1d0MTCXMcwbLWGJcdzETNy653C9WIjuDkpvj93L2jNdhXVp7bneOG7UmzD+W9c+vJDSxr3b6QyDUTRC1aRyCRxZORaoMoau+0QVRzT6nns3Qc/mTEO3z7t6e2p6yTttn0ZFSgFIKoVdnaOgJ7aNywNZEXc7MwbzqxgVrTG2N/Yd6sdOOxqVnMQnYxLk1GsKzpCkmbip8+lrGYG7UCwEU1/mKmopouijeRYgrnvPHYhOUI7EYYNQ5KaguxMS5xbGPU9lUK7aELe9gYF61zlTKC6ajA9VsTliy27SVuOWmIZ8pRW0czLQvcenKTJSQv7S+wOS5xbDoSnw1gI0XFasJ35hW2JiNsjEt2rGWlDUcgZH6AeT728JZwaAMB8o7AclKn24wgfj7zZY1HLs1w63WbBlZNZEY3n5Qztgu7c1zfOPNURmCfIfV89heGE0sFUZYjGOfsnWP83lk4e0fKCLqAbCJkfs1YhWr24QANXRFzDy0pvbWZwi0CDm2hh1PbE0ylVLkli83GoJQptjIyN73toKGMjIBYhHazjhqymItqrDzx1PaUlSe6fIPEuxhoqBChoU9f3MeTTm620SubsTVwmyEkeWho2nwGoKPqDiJThsNhHcESJzZHmArRq4WZLNxGRZyAObC3JyU2xgXLEdjgYWQzP0HVZTMCDrpr+QbrCAjJtI3i7fqjMraHL+5Da7TQUE42DdCqrrrWOL+7cDICKYo35DQAMmNrg6jjCVi12Yel6Aj8vUM7ArveGwgwoRqSsul275QFxgW/d1Zl6+sInKhtMpIygo4jAEDyBLbPkF3MkiOYNmQxQOuh7WJuOQJB7TMZFQ40xEc1NlIk09tAPsrNvYWGjk2wu6hIWCtUIHHZhc0IJGjIfsZGwhJZPB0VuGF7IqqGTm2biBOg76kdf1KWOH18yrb3vri/wPGNMabjItkps+UIuIxgVmFrOsLmhIeGlpVu7yfA68v3Fya72JqU/GFTN3yDwBHsZ3AE1gFuTkby3ml+fstJG0TFz+dSI+3NyQjGZdHuC+r5XAqyaZ7D0a2TBhhoyO6dNpumgocOzplIijvPESQyglJhPCqGjOBKWZsRFGaTpQpZbhIyAtvH5tSxaRMpymTxDVsTdiy78VqSSuQIOuxYzAgEnLONXkuzmKVGceNS4eTmGFrT87J/O2qxcf7QshkBJx+tau3JJrmx7D29fmvCZgTnd+a4vnHSAH0Ato5gZAID7j5c2l/i+MYI01GZVA1Z6I4by2YEElm8rOsWtgN4ffn+wjjOrcmIfTnNsskIJgI0FGYElGrIhTFkxV1HFgNMNt387PrtsZgRWI5gWjaKNGI9PB5m08LzmThZliy04Pk1e4bYIEriCMalwomNsfcyIm9ObkA2cARXzhZOKmaiGh7PnpTmoAHoojKbEZxKZQTNYrYLkFoQVqfeLeZ4LFPIUmPSRJzm+6QXM9UzZdEe3kqUwO0tKmyMS0eiyGOmowbn5KGhJiOY8vJRo3DpyHBKq661bjiCEtdtjVmy+LGdhowUsgs3ih8X/H24uL/ECZsRJFpMjBMOcWfWcQRcRjBf6taxArS+3Eo+N8cltqd8RtBi4xJH4IgCADojsOu2bIIovgYnDQ1ZWNVkBJJqSAeHd/y5bGioEVqMCv4+dNBQE0RRsOrSgYYK/j7szqvGSZu9QxPPfmA6yEevkIU3nsegq5aMBOg2Bi1HcEx2BGEkQqX5LTQkLOaq1tAa3qamDojdYDFTUfxi2UWvKaXPZhNxAvTGiA5AAde38tHZku6Wuaw0xkUhZgT2Z/b5cKqu840qZdrUN1DYvpsRlEXBSh0v7S3ajICrSfBIRIGA3513qiG211Cddvj2bzfGBbYmI5YjsFnWWFINNfdha2IIccoRuM85pwbn+q0JxqUioSHrHKyjloKo8cjB9Ynn49Z4mOsnyOKSH2u3VQ3xHEGbETRZJMcFGdiucwSUo7ZrpCys0GLICK6IuWTxVMA550vTyuHE5hhK0TjnYztzTMoCx6YjcWO0BG8TiVRUVLMXRPGSWmGUUA01C85Gd26HSmshRCbhnJvOYqZegLKsTBuHsoEMpF5D00Y+CtByx6rWbcQJyJr36ajA9dsT1JqGMs5dNqoUmxFQztWOPx0V4ka8uL/E8Y0xNsbCWEuHIyh4At6qhiSOoNX+C9Gr/dvNSYljUkZQa7+OgCCLO6diHUF8P311mIleuSK9calQFArXNY46tBYa2krIR5cVpm7gQ6x3m02f3ByzEK1tC5ESWlwOVEOy9FpWh+01QdSmEETNGy5IKSvaGBzBFTGXJB0LJKmFhsrC4OPUYt5fmENSKZUuinGgITqqWWBUKGw3VanSoZVK83fnZgFu2wUoLeaRlY/Ki3mr6YJJHTaLhoy0c5M4go2ROWjsd6bGcvFs6p7OnCj++i26zcSiqnFptmwPGvfvXHPJYgnvvbi/aFRDNrugScRuU9NOZVGZTCjFESwa+WhRKFZfbv/WcgRcZfGymZfIESw7h8ipuuwaKQvVEvCko17U7X26YWtCvpPAzQgkfs2KI2w/r4o4KG0QcGJzjI1xKe6dyciRj5IZgal1sV1fKYh24Tr8RnFHOcTdeYXNhsi3/w5t2WQpgIGruSaYq7L1dQQONCRFryYjMA/wuk26Z4oh9MyiMoc3vamt1FFqF2AOmrFxKmN6rI6MVKJT2WkKljbGPDbZEX9FG71Si3mvwfUljsCSkYC5r/zLZOqmoMw6AvqwGZcFikKxY82dQ+t6hoBvm7ZNiu7QYg5vwMhHLTQU3ofZssJ8WeOEmxEwzrXd1AWtALGHwdZU5gisbBIA66jtGtloOAKqzqNu3v2crCNwMoLjTJ1HGEQBnKOu2rlzHM7l2RKFMlBUSnHnOVcGGho3Gf50REtyXXWOVJV/ebbEsekIG838yb3TZkYFJgnobnNctFXDpCNoYDvA7OshI7hCFuLZy1ozlb5VG0Gd3JqQ1ZHLSrfYZYosnjo4JxnV7C3bA9Li0NLcJcJr14EeAJosXjoH4FhazHOfI+BwzpFzH9ItJkwUT8E5y9q/pxTf4GYENnILX9XYRa+yI+gyAj5StA7rxEYqI9BOdEcfWvb+bU9KbE5KvrFZ1R0QnL7cQn72+VB1Hq4oQHIEbUbQQHdSRuAWulFrxsqlAQNbkRxV073TZtMpxZ25Li1Nvri3wIkNN4iSoniXq6P4taW/d0jFnaMOk7g6C6vagIzh19yMYOAIrpC5ygdJVjhrOAIAjc6Z8+bmM8miGKcamNo8l/bNYrbXoxegXw0M8NWRW5NSjGragrJCJp73FjZVFlRDdXcAytXafkZA1RJYGAMAy7t0GUHpZEb+51pJa6HEw9sli7lI0WLQxzfGXU0CRTw7m5qrp7D3b6vpNTSvavJz7gHB6ctbjqDB9amMwO0DNRnZOgKeI7BkPvlsWl6pwGTENxiceYc3nc1UtR88yAVlZTcWKbQIgyghm3YyczojMH2gNkaC0sdV3Nk9TUA6e/MKm+ORGERZlRwAsSp/Vba2jmDhEF4tDs0cNlNnMdNdCLuHmEMWF4VCoRj5aFO5CoCNalqOYJSORLanoyZ1LUTZmpsqk4t5YcniREbgLmZinGVl2gBY6AFgoCHHuXJ1HnajS602vApyKSMItP/mb/3P2Xke3xi1cBtZaLSsW6iAOwDdjMDCTNS7cX2YieYIfNVQiZ3ZMoK1uijegYaELMv2gqLqPLoqWDeLTO0dmgB1IREu0LLzstfiivQuNbCqnT9ZOOhm04IU1dZ4FE2gSD3nedUFZK26jQmiNpvMz/47tGWVF0StytbWEbSQiCtRTEU1TAsGF8aYjoSK02AsatFc3Fvg+LRZzCMaMvA4AiG72JkvW4JqY1wkJZ8TIVXea7TQNiOgCS//8KZ72bhkpPmetDLFiRTLREYw5mWFriqqcwSyfJQrNGqhoU05I7CN4gCwLSbajGAyatt2U8/HNoprxxIyAvN8Rlg2dQXenFpcPwENLSooZe75iQ1aNWSDKLeBHRWwGOm1+W4lQ8AvG2gIgFyMWTlOhdmHFhoyY/FQFGDl0nJVvoUbN5jM3G06NxKyi715ha1xKZLFYTY9VBZfIet6icsSxfmyUz5w0Z3VvANNVEOMU9e6fTcwYKoy6agmzAjSen33+7i2O6taxdDmhNaqu5nRSDggbB3Bxsj0z6fgh4VzaHGH9ywgIwFaPuoSzxzvMnOgIXsfwiyrciLhqXBouWqSLssKoKF9Cw11GQHtqH2OQMwIHDKfw6FH7QEhZwSuvJfjStziNI4jmI4MZn9sStd5WG6rLApRgWQLKAGz3slsOswIKq42o26vZfZhfL1LHjREB2QUv0ZCd/OqDXo2J7Sqa0FkFzRXt/SfDcPV2fswVBZfQXNLutvFTDXhcsjiksEmPWKTgTHcg8aORW0M+7ISIHMxM9g40GQEdjEzypSwoAxgoprGERSFwua4JAnJZeUeWnRUs+9AD+OywMa4YIqWukiR6+PiF4HR98ElSSVoyCMRmbEut9BQlxFQUafLEXDEpnWkLiHJKVPcjIB6zvtBRmDG9++p2yCtg0JpjsAGPh10F0pyqSyLcvpdFF8y6qlQYKA1rYCzsCoAVt5rxREAn114NR4Jfs0GURtjObtIte2wlfTtcyY5gm69jwWhxapsbR2BjRRLp9LSvkjDNZcsHpeKVPpUdUBsSgqXBA5Y1d2CMFpoCsZwyGJBiro7r/zFLBJefDMyrXVbKwGYw4vMCFyFy0iOXu1hc2xK49AuiTge0XUebkEZxxFUdZcRjBpuhssIlLLtAuixbBO16aho1wTnqCft4c2ohmZdRiBBQ6GahNKX279za0bCqNPti2NfcsMfWuZ6XMGf27pc7N9UBWQx0x6jLG1GIJP5nmqIDMi6fZgii913PHBN56xT5eo8vJ5SjIx7WZn3i2w1dUFlQb+lzFszQouTVdnaOoKFix23RUuyBI6NajyOIK1WMGMJqXLZpcqSfNS8XIMnvHZmTkYwKbHHwBiAjx2H33Femff5WgiD62djG6SZ8ThoqMsIzLxo+MvlGyYMNj7zcH0aGrL3qiyMBl1SIE0aGSO3qSsnqrZqEjpSDKLXVEYg1Hn494E+TG2WZQr+zFjx4e0SvBIn1mUE3AtsOmgoMdbCxfVpiGxRd7Aql12EsCoHDVXOPtzghBZL4vAO5qW19qAhkxEQRLcDt7UvDgo+5yq6lFLYGpdMHUGYTQ8ZwRUxm5Kaw0F6dV8Y1TCEl7OYax0fzHMyI6DkdC5mSkfxcxfGYAivZVVjtqw7jmBc0k3nHNK8I579ue87OnUAbD8bD9dnyWI/IxgxfX0WB8iy3IwgjO4qx+HbeXHQXYdB09ixffZloeSMYKkDaIjPCLYmJTYEaMj02LFqGfqe2mh1Oiq6jCB4Ph2cwz9nwM8IOogsdK4EWcxmBLLks6prT2gBxAS8q/QB7H2geIT03nH7QHFtO2yH0K02my7IveM2KrRnSPgd3fYf9v+5ebUcQcm/KXBVtr6OoHYPLf49oSFZTEfxvnwUiJ2Ki2cDFueMIRiTXVhoSM4I3MUcHlq7CxtxdlENF3GWTQsDjkQMF/M20/Pevl/Xzo2M4hed0gfgeRc3umOzCzcjaIv06EOrzbJYuM2v4HX/1pqrnxdhDE81pJhXIVbN+ySK1sFSVcpL757SkIFt610Uiu0FtXRUQ0o1L2NiqpRbpQ8rye3gxImYEVSe0od7AXzLBTEZgdsHCrD7kMsIGtFGcu/wmZ9t0XHMgYbItutBVb75WbB3HNgOQPO+CLnFxFBZfAVtEWwwIF0mP2JUGyFZTI1FkcUR9BBEr1ylpa9woTerjQg9nJNphzByDlz7fVxz01vAFEGxZfIOsVnVOtr8+047BPtdw81jG4O5skKpGng6Kp0Ka54jAGwLEBmDtodJpEByMoJW+8/c07aOgCHNrU4dAAsNVU1biJS+3OVvjjG9oBYOjGHG4jgcJyPgcG9KkpvBEXB7x13vQOxcXSLfzivNETzxOoK2/YcbRDHZtA2i7FjhfYgzAnrvLJxselTynXtXZWvrCFySilvMdW0OpKlzeHNRjV2kNtKNHEEIDRFqEhd6AITKYk8LTRNeNiJML2bdQSI2Eg5xzrl/eHMZgasa4uR0s0UHY9jvyh2442RG0I1ln2UYKbrVn4AcKboOH4jvqdtaId10zpEJE32L7LsIADhqkjoax50Pp8Taa5r4AXA4glA+aiFAS8DTTsXNCNouucJ9kGsS6vTeCVqJAOkgiq9SdjkCk/mF933hZBdtNh3M63JmRuDBlwy/Zg/9TmhRYm/BZNOuw2eaHq7K1tYRLILiJ0BKSbuNkZsRhAeEWw0MJA7AjEPLzptTuNiMoF3ME86p1N5BA8R8Q1ewZAnekn0xzSTYGBT+asZq7ilRHNQ6RMdRi/JRT/IZciX5HMHYcdLu31qr6hqFQtsJdFwqRhjg1hHQh419FwEAto2B/RtXP0+3oa4d2M5yBCE0FGYEXHZBcAQhNOQQ8KJ81IVVS7qhYdhKxPxdwG8sw/sQy2jbLNIJomodZ4ju3rEV/vHhbe6dvafTcUm2cF8sO6Kbq/Dfz4WGPJnwUEdwxcxdgDZqiaPXOBLhWkzEi5nOCKZeas4cgJYjGJWoah1lDi7O2eG4XEYgQ0O+WoHOCPYDaGh7MmJVQ6MAbgu/o6t5B+h72kodky0mun73SfloBszk1ovY7+Pawml7YcYq6dYDgeadmteOo3nfmNAwk30OraNmHOK+g8XbZxTKe8PsgldixRwB9XxaoQUTRGmtI2iIGysmi8Mgqmq/v/0O4dztsC1H0GZstFPpxoorv6160I7BvThoWXdckP1+ofJwL+DqOCmqEa90611rugXNqmx9HUGAZwPxYp5VPozBlbZ7JBWX3kZkcRzVtBK/ossIgLgHTasaaipAKa36XpCSWkcQRmT2FYDufQjnFY61NaUzAlc1xL0Fyx6cLmTARZyjjIwgIjYZ+ajPu9D1IiFZTDkVex37Hbimc1ZF0ipTonvaFQ5OStNnPzwgFg4pa8biyWL7bIomU4lgxyAzkjiCacARRIq02leHme8c1Fws4+ds/9a1ymmtwAVRYQ0OHTzEECA1lqv0AegK/3CsjbGQTQewVrh3djMzAp8j4FVdq7K1dQQLxwNzemkqI+Aqi8MFkVINUQegS0YCTlQTRooOR2DmxfMNdl4bkxJax/NyFU9dQZlMFk9HJdkKYBHUEbjf29qMIosjXD+Gc7heNvZ+KqVINUl4TzkpqtfCgKlJcFtC2+9ANp2r0hnBotKts1RKkRlb95xdjoCAhpoW4dYo2LHrwipDQzPPudIcgSu0yFX6cK+FXFaxfDQcy30lKUDLaEOhxQZHPFeBUyHuacjVbY5LLGsdXXPuyYTp55xPFnfrT6pSXpWtrSNw9fr8oRVHNdQLS9wyee7wDgmvMaF86Ko/u0jEnYc1F+cE6FS5CqIae1DsE4RkeHhHEjgCzgG6dLydv3NQcovZHpwbrkPkcH0nUkx1t2zHCg/c4J6KBWVBmk8VUtn7CQgFf0u/Gth8p/jgcp0K9brKLkPsxiLbQiyr9tnYz0vaf/P/dJXyrGk3bsYRMqMgeOCCKLcYE+gq+tvvWPu4PsBn09065fdO61SYFweFe4fKpqsge+J6QbkqJa5fGSUfpVpMuFmW1EhyVba2jsDrZdNG8SFs4pPF9qGTmGlCgRQuwJKI4rvoNcQ547EK1S16qgdNGFVzEsWwkMWdq7X9IKphMXS3XwpDks6WFcqia3BHpfkhnMMRvLPAEdgXDLnmNkgDZFlhqBqiDpvS4Qgmo4J5Q5nTSbItdOOxcaDJLiJc38ezOX35gTICOy9WNeQSvHxthuucCpVW+nQZQQxZjRLrPc6m45qEEPpqe0EtZKciZdOtAokp+KODKDkj2JqU2CUgWhehsM/7SlYXr9QRKKVeopT6sFLqXqXUq4jf/xOl1Lub/71fKVUppW5Y5Zysha+XBHgYI4xqqAPC1anTY/kp6ZiKhKM6AppEdBUu9vO85r0hEZnFvKz8DpEAT/BuBhkBddiMQ5gpIp7rNhsAaJ19R/B2jq4myLPZopu7+a5pbJzLCEw1sB+RUZlKBA1xJGnpO9fY6dd+RkBIFLuqb1lfbpuaWaPgtkV4HwiOoKrN3FOVxa44AqBhpk4mLHM4y7qOZdwcnOOs0/B69vuWZehUwr3jZ0ZU64tOvecT8GEA4QY+XPNHCwNZqGpjbCDa0NkZCbrPK3HvNVmFrcwRKKVKAK8G8FIAzwbwCqXUs93PaK1/VGv9Aq31CwD8IIDf11qfW9WcXPNgDIYjmAfQkLQxklrocCxiAbqyPIAnvBYOwQvQVbzhAdi+t5iIOkOILKosbuAkOwa7qV1JLksWV+07oO38Uni2hEPbDW/HYp1rRqHRJNDPU5FiRBYzkk8XegDoKmV3LKrVsdvczf4/pS/fd+AcgMkIqLGY9d49Z5orCUlz6s1ilPbffm9vXrVuHR3XYmLWRvGK/X5h7QmnQFpU3buP2/vArD+3bxFAZwST8PAmFHcbYyNVBZx6AyIL7t5ER0Nyq7RVZgQvBHCv1vrjWus5gNcDeJnw+VcA+MUVzsczNxUrGz0xG8UHkAGFc4ZS1FR6S2mhI46AJYs72ZqdV3xohYs5nd5yxObeovJaPbcZAaHRDrOs8ACcLf2MoBSi11R24ZLFdv6cFNUjizmOIHEfQo7AFC0xEKDznM08ZI6AagHSvkY0oS+3LcKtjQh1UaiEGRN8Q9cZVuZK3Jeo2M+H9zTkCFjnWnVwW3aLCSHw6YIovkrZDaKoLDIknjc5jsCFQrleQ45M2J2fu3dsa5kOLv3MIotvA/CA8+8zzc8iU0ptAXgJgF9mfv+dSqm7lVJ3nz17tpfJVQ6uD9CEpPviE8DZGMQBnruY/V5DMpzDyUfdA9fOi8Kg3Tlzi5nCOcPvtx8cNCUT1VCSXCojiGEMLpvpcH2AVmK5HIEp+KOzrFblUpZ80znb3E2EAP2MINb++5E351TcAxCw8t7wkAzgREJfHrYIB0whHtdzyesFxQQ+YeYXZxc+rEWNRa13gJaPhgIDrsWE7Qk2JriSEE7kYNUoiCKdZsARMNn03NmHnKObLSt/rxIQ0jLIZsZMELVKW6UjUMTPuG/2FwH8MQcLaa1fq7W+S2t91+nTp3uZ3MLxwIC5+WwRWNsgjU6VyYKyMIoiSaoUR8ArkMbB3HniOSCLiT710eEdFsXM44jTvQZgDqTK4Uq4dxvsR7g+dR8s3isrMlztv/2urHzUq9Zmms4l8F73+5mxYqfSvftYVtXEGUHc4TLsD0Tpy8MW4QCtGoprM+LsIsoIRNLcD6L4jCBsVxE/a7fHTlnE757oqvIdaIhbM07DRoCqI9Dx3mE71tqxeH4tBwJ01wzlXEOVHLdmfu/Dj+BjZy9jFbZKR3AGwJOdf98O4CHmsy/HFYSFgOYhBtFdnBGYBx8fEN1DrGvTGKxNSZuohVrM9sUnAKOfDzgCXj7qcwQ03+Av5s0JjXO6VbAWIosKyggM2tyH7nOUPBGIq5RngdRxLMA5oVY9ijrDjECS5AbvNqiDz3ndR5kGdosqUPoQ2Hgk7RWyi9KJFDfHJXaDHjSLAM6hJLn7AX8D0A4x5EpIgjds/yEGPiFHFa73uKdUOHc7rzDLigjeUGhBVAPnBlGuXh9g9k6UEdjsIt6H9j7YOhbS4QfZu3sNgChyYwo7v/Nn34E3vOMMVmGrdARvB3CnUuoOpdQE5rB/U/ghpdRJAF8G4NdWOJfI3CpYgG5jEGYEna46TuuigjJiLPviE0BegOFiploP+KqhmPCilDfmGvEG8lPXOLpzXzjjzs+LakJ5IscROL1sALqOIGqHwEBDJiMIYKZMAp6S97YOsZVNhps6qCMgKk6pGg/3595YQVQd1UAQFbVmrO5zVmkUZmxxAZsPkVECgzAj4JU+AaxK7B2qGNN8b0Jg4KwtquAvVA2RAoNQPirU4HiKJ4Krc19ABKRgVR/2SWVPFKqwZNZMWOexDNZMn7YyR6C1XgL4HgC/DeCDAH5Ja32PUuqVSqlXOh/9RgC/o7XeWdVcKFs4fXEAOUKKe9B0DyjsZcM1zgphDAobp/riuPOw5r6sxMydIov9qIbrLb8IorsJIedcVuFijg+kUJ7IFho5vWzs5zk4J1XwR3EEKUkuhUNTb8AKv58dy1cNpcliTl9OHRApxROlL7eHU5I0r3TbMM/OK4bt6MJBiuD1OIJRTDyHxZgjFjoJHCIB0Xaqoe75LCq/sDPmCPganHFuRtAWdvKOwIOXiaaUVXCvqIwgfM4TImgLkYe+bZT+yBM3rfWbAbw5+Nlrgn+/DsDrVjkPyiiyOI44mwhpbEmqeFN3UsfuwB0xOKcPY2S0oeaUD1EdAaXF9+fFp/k+hkm9cyFOb9NRDYfr70cZAe14zFwCAp54PuEBSBUsuQcgVb0aFz/Rh3dVd90mAboHzbztlNlBgACRiREHBFdYF3ZFde9DqDQDaKeyqP1Di6ojyO0PtAyCqGlZYM7BOYFqiJJ9hllWqoW72wIkhGvDnl/x8wkdQRE16OMCEUqQ4cm4CXg5LELsOAIXVvWzNaqws9L+nPq2VUJDR9rCA5AquQ+1/xQ2HqakAJPeRguQKG0PDm/21X3kWJkZQYiNO2oZc21CecMsZkr5EMI5ESEZtkMg0mmqshgg+IZF3GKC7pTpHFpEoVFH8JrP2UdJ4exhRrCs/e6wcQsDGiKriHsad5kNlD6EvjwUBQA8dOJyYpTSJ8wIxiV9eLsN0gB6vXfFmH6VsrtmbOtor1q7LGKhRaNSKoS1HDVsZLNp7amGxsR9D/dOJ5eO93TozEnJcSIjCN+iRxHPoaKwb1tbRxDie5x8NCR4gRAbt2lkgHNG5FkMDXESv7AJV0xSEa0VyIOmK5zhMVo/uuMqTqnFXHmLOcaggSd2eIdpPpsRVH5B2ZjMLuro0AKCjCCIXrmOruFYGwTfwHEElDIqlRG0WVYga3WfDxWIULUZVaiSG8UOP8wI7JCS5NN811iCGdXgENCkHXYUONew4M8l8u31wnmFDlEpRRLPpo4gwPUTQouSuB7Q1FME+zAFJ1IQLZdNe8+5jp9zn7a2jiAmvAg4p8GgO4I3ju7ad8GGygdqMQdwTtjALixtV0o1VZuxrDAkb+mU1FmAnBQwHIuErGo/4iTGWjJRTXi9MCMYE4fWovY3IrUxtNbRAUEdgFQ1MOAf3iH0YMdKVdR2ZH4MM8WqoRjvdddfKWZGfHZBRYoczBS2hQg7yIYZAdfRNcTG6YyAzqap4CEF0cZBVMyVUBAZtQ9jjiBWINnvax81VxW9CJSHVMFfFESV1H3w505Bk1UQJPZta+sIFmEkzGQEk+DABWgNcEpXHR5aEmkUSRTJxexv6lTU1s2dIJ7LYDEnxyI4AkY1ROnLfamj6SNU11SE5ENDFK6fko+6/e4Bh8xfEI7AgwxovDeUOgI+zNS2jh75DtE7vAm8V8oQwwprP1L0IRGA7+gaZn72O1kLD292rEBpJrWriN91QRC8URDFFz0CdCBCOcTpOH73xCJwKvT7CLTXhoIvrCOCqEQgQvUrC1uq2PsxJ57z4Ah6tji9pcniaaDPBmRsHKDldFFUQ3l9YlNTizlqOkephjKUPnb+kSY8bIiVkd5afiWqjnR4F6019pdVdHjba7jXc69DvbCEOrS4vkWhkwZkOMfOi4Lb3MPUOjTXqcQvWo+jV+rQsgSvG6FzfIO7/nI5gkUgfaWyrFY+mpCihvwG1WvIBlERrk/CHTKsGgdk8d4JeSWAycwzMuAw86OuZ8byMyOqSpmrI6AyghAKTXFBfdraOoJIv0xpoZchFk+Rdb43B4AJISsMF3Mb3RFFWXFjszgjCDcG9T6CVBRf1zpaqFnZBZHehhlBWZjXaC4q//tpHUec0VhBgzSqb1FI5Jux6B40PmwXH94hnm3HShWBtRyOlxH4Y1FtqCm81/63e8kwyKD05dRYnBIrdPiA76hJ51rGLZ9DSIQqxoylvQLBW/rPh+rySY8lO0TqfRHh3iG5oMDhc+IBt+kcQKMKOUKL6GU5bdA2cAQrt0UdQCJcJBIcDgDjzRPQUJiSUs2nQpIUYBqbLcNNTePsJbGYPbWC5TdCBRKZXaQWczz3EDMN6yQAelOHcAdF8FKHt6lSJjYiRRZXhGooGCvOsuogWyNgpogsJjICwuFT97TtnR+0OKcOwPCekq0cCn+9u/N1rxceuqT0leAbXIulvXnZDNXAbr6saGioih3iONo7iSIw5r0g7pwsVxJCUVrH9z1XNSS2mCAUd0NGsAJrH2KghaajmrjHjlRQBtA4Z0QWt9pkOVUmG5sFBWUkjBHo1CniL8SgzX/TrwFML2b/0LJjUThn9sYI+hYtyEPLh+4ovDe8n+7fA3EFL0DzDZR8FPAhq+g1ouShFUfCXLV2WXSySUpfTvFKJRUYRBlwfE9DpZkdl5LkhpLPdLFffB/at8cFz4eSS+c7lXCsMIjyYVV6zcQVvOF9oOFEIiNgIFoqIGuFFkSLEwqC7tPW0hFQG5FqMRFVA7ebWk7Z6KiGJovdxRWWtgO8iiJqMZFQDQEx8Re+ChGgpYDuy0PsOO53d/875EpSUQ3VyTR8PnRGQFTUkiRp3MrB/H0cxYfKFJJ4LqjDjecbqJfXk/eBhE5CUpYgnhnnSt2H0OG787XXDtcM2baDyKZJoQWxd/z1Ht8HusWEJvcOuf4CRx2/1CmUfMY1OGFGYMddko6Az4DtWCREW/nPGXCl1/G6Ct+017etpyNg4Jy4L071hHDOLLKYiJDC0nb7OQqjDXHO+NCqo+hhVPhdG7uujsEBmLuYExtjVCoPg5awcYk8oxRIJIxB4Nnh4Ua1mKDko+YAlGEmiugO74MEY5D3IXg+YYblXsMdN3QqdGYkO4IQxgBoWWusQIob+YWtRFqxghAJA00QRWQXYdt197ub/7YHZbD+QoJ3WUWZOcWnhPchXFtdNu0/H5JfCzIQgFkzTndV9+fu5weOoEcLMTmAzgiithCkbDIvqgmhodyiEbopG6V8kA9AaqwuEgnxXnksLno1c/e/I50RxBuD5BICbDzFEVD1FKF8lGpGxqf5cnZBrYcW148KqRL3gcqMoui1aH8ejhXCTFTwEK53M19/LceRMEWa00V6i4DfoLT/lfCc7ViR0KLymwtSUlRq74xJojvIspo9EdbzlEQQ5XcUoOA9uqAsXUfgZ+ZKKeNcCQh64Ah6tI4klSEYqgoWoCOy8I1NZH+ghFqG2hi28Mz9THi4jQs6vU1FNdTcKZI0pyaBgtsmEUeQlxEsK1PRbe9RUago7Q773QM0nr2ofPko1SaccyqpFhN0dOdzBG174sShxVVrU59J3VNS8RQcgLbOIXTA0Zqh7kMQMXOOOikTJlVy8TseqLeKmb+XSXOus23o8N2/t3McBRBMuLZCUYD5bzoQSYkCun0YZtOU0xwcQW9GRa+2stiNDKJ34gqyyVRGwMpHCawwyggoDHrkb/zwzVXcYl4SizklH2UlcFV8AEp96mW1jE8ijoO5hxmbVf1MgoiZLiiLo1eqxUROcRod3aWyCwUa75UPiDDzk/Xl/tqKpI7BeuA5AmLNONcLX6voziuU96ZxfTogWzbBTjtWFfTDovYOETGHTizsMuuOFQpAUlwJdXhTL4iiWonYa4T3IczYUsWmfdp6OgKCYGs3WYBzkqXtJCQiZxehioKSola1iYSLYOFQeGKocHF/Z8clOQIiig/HCp3YgeoI3NR8pILNSjue+D7EGzG8p+E7cQG6cVuo2qDahFNOjCZJ/QOQOtwWVY1C+Rt2HEAGB8kIKGz8icCJIVls1/I8cNRxFqmiZxNej2oTHmUEB4BVw7Go9izhWBRpHt4HSi7dKXR8Z05xJWnVEF1Qlgp8LBQbtquYUxnPQBb3Z2FfHIBZzAtOAkdtRDe78NNbrTVRWWzJM2cBknBOkJIuicXMVOemOAJL5IaFddSb03IhESkjIKWOZHYRE93hWLktJsKWyWURtwmfkwok+hWGIQRjf+7Oy70H3bzSBK+Zr+84w0ZxQEzwhmORdQQhwdtCQ/5BGa+Zgoa1MrIsKiPIgVWB0FFn7B3inob3IYTt3LHCLCvFlZDrPaMtSU4Ld/vfS+JeDRlBj0alYlTb5PBAohq3UVFu+F5cQ0b5C9BGIuECDD1+zgKkmnBRCpBoY1B6diaqoRYztVBD55rqlEkWlAWQCBC/sCR8A5Ydi1Zt+PchVKaQ8tHgPth3MocHjfu9AONcJ5Ej8Ml86jCllVghFxRDMLkcAdtiIsChU1lk2GXWjBVzFyab7mDVolBQKg9WBWKnQt0HkjQP74P3bGIoiqvnGQXPkK8j8DPgaP2xxZgEqiDsQ0oU0KetpSNYkKlYvABDnH1MQEPUAgzldOFbqwCeNMovZPHTSPd72bHSGUEMDVEFZaFskote7d+H98GapBpKZTOhvLerJPXhNlMs6B+m4T0NlSlkpBjcdw6Ccb+XGcsXBQANmU9o/9O8S9wQ0HyGUiClMwKyxcQB6wi4amAgKNILyGkghsiovUMV6VFdZs3fy9lmnBHE+3BM3HeONKeq33PqecjCQTfgpCCrKJuOnWaftpaOgJKPUs3IQqJR6osTSuDcsSidOte3KCVbo6NX66BCBUicXVSJaPKJVhaHL1Gx35HslJmIhJeV38sGsLUZbpYVb4wxMS/qPoRk/oyJFFPSV44jCA/AsCslJdvNyYwk1VoIf4UOMZS+UrUZVRWT9CE0yVWjh/MKi++6eVFZpAxzzom6GXcu5nrm+3lV0SW9dyjVUFj5TQVRPkQb3/cs1ZCgnvKeT0Gvv0E11KORZDGTEZAbP0GAhj3vqTbHpEqEiETKwi8CI8licl7pjIAuKKNVIun0liaC50t5MXMFV2FqHhHPlMKKKvAinasPnVgy0j1EwiK9LgOhMiP/sCE5gkRUTc19Hhzeor6cijq9+xA3WQRimIniCGgoVA6iwizSziul/Q+loRS/RgZkXDZD8Gup5o98RhDfh8nIX+85LyAK574I5NJAAzMlVFF92po6Ap7wClNX9yEWhUKhgqiNgoaCVJlukEbgnAScExKgluBNp/kxRxDKCsPSdjuWW2BjL52WwBEcwShNFrP6+eAQCau1Kd04FaHT9RRBpBhAD2aOQf0GSUbGz3BRxRxBGN3RHAGVbdJOZbGU1x8ddYZtIeLsgq1GT0Sv1KtEqfUXOkS6KMufO8mvUeudUjwFGbDMr6WDKKkIzHyPYO/U8QvnOaHFuPADkVFRDKqhVRv1EEOOgCIH7d+kimJCuINTuAAxNp5dyJJRrJPba2jsRTV+2k1BMGKZPNF6wBq1mLvWA3JEFspHaQlmc9+DzCFVT0HBOQbXj3Fc6sD1DoglpxrK5EqCgyuaV6Av51RD4bzC+5DLEZRFCO/RDh+A965hKqiJswvhGQr8Wrb2PwyiCH6NlV5T+z5VN1P4e4d+ARENhVKBz5ARrNioYpAwvaWgACA+mLuNEUemdnGRmmMyeqUiMj+aJBczoYWmFSAF6VSoA8I6AArOoTqZLiuNQsU1EOmMID68F8ThHcJMXG8ZIFSAEJlREClSGQEP52RwBKPwQGIygsQBEbaFAGJ9eXtPvY6hsaghartOQKHUATguw4wgXjMcrBo6xDAjJR1icMhzrxENv59phPcEgigS5qQL69KqIXrvpNrBhxJTwKqG/GzNnW/ftp6OgKzkMzfYLjzqIZq/CQ63Kn5AYcEV3dSs8P7ezCs/JU0Tz0yVKJHNkO2xl11qbv9WGmtRE4fWKCS8iKia3dT+9ULJZ66Kh3KIFFdCQUPp6NVeT+YIwtch5lcW1zHMFOjLq5p2wPZ37fyr8B3d+RxBiiympNdUhB5WKVNvFQtrdbjXiLqf4a4XCi0sdDUlyeJERhDAiVJw12XTeUEUtf7GARc01BGswCiccxosZuoh2n+nsPHwcKMWM0mSkjgnl97KqiEuyiBxTu+A8KPqirhX9t8hBEMrfWJOgoriU5XFVAO7svBVInSVaAZHQB3ewWfa+5CQ0Ya9bKjr5VYWh9JDO1Z4eFPQF+ATriHhT73jgVaapWEtKiNYMHUspPRa2juUOILYO9RzLgvlvQ+7dWKEfDSsgA+J7jggIyCywKlwL5ynINo4A2Z6DQ11BP0ZiXMGi5mCHsy/aY4gbBTn/o50BFS7CmojRpXFaWwSECIyL4qPxwpfftJmBETaHS1m4jBNFcVQskkKEqE4Aur7udex/x2OFTrzsIWBHasKDgf3GoCJ7qgDgori6XqKRGZEFNZxDjH8fu5YLcwZXE+pPI4gVYQYVjzXtSF4Y8zel9Ha9UdBk1E2ndg7VU2rlIAOq5eCqFhynBeQ0SS2ve/xvbJjRTwWyQXJsGqftpaOgCKLw/SW4wjC7py2PxAV5doHSeuX4wOQ4gjCl5q3C9BrwkUpH7jK4jhVprBjOxanX476s9ccsXlwbDzslGnHWgQcARXpu9cBGqeSIP7CPlDt9ci5H5x4joqRiOCB6rk0J0nsuFqbioTdsSi9vlIK4RvkuGp0si2EkBFQxX7tffBUcmmosC32y3hBVFy3QIs2SLI4aFeRcojUPQ2JZ8rh23+HAVmcRaaFFn3aWjoCrnMg0C0WjiOgHiK1edwxyDazbfouY7T2c/aSXLMrM5eDYbQiV9I6RC4zyklvzWK2TkwkB6P7cPCMgJIC8pmRn4GkDi0Oo6WyC2pTk8RzqlqbgqxGMd8QRcKBQ6TargONqmvpf8fkASi1hajC5yxDk5KMNuQIcoQW3N4JRRt5rV6I4IGEaN0zhM7EkkHUkpZ6p4QWfdp6OgJqMTNRTYojoHDVrgLUXxB+1kDg+kJ0Zz9HOQJqMVOqjVA1RBF/4WHKZgRFrBqKJHBBbYaEjUdkcYJvqIjvF94rqmUyYDH7BK5f+C8s4e4DRYBGLSaYTZ0inin1VKgvJ7mgNhK2MCc9d2peNEQRO0SPLA72DiW1BUwAQL+XgYd9qCjeQnLh3klBhVIQFWYX6V5DBEQbwKq5wSSleIoriwfVUO9Gpa6RfJThCKIGYsThHab5dF8SGtenyDp3rDmxAKnFTOu4GQmcQCLmq4ZouaA7lqSWyXEqqYzAXs9+R9mJ+Zs6JmXpZ0jhveE7BKKmc0Va+0/XEcTQUKgvz+IIWlFADt8gH4BiZbFV3InZU0yASuIBKoq3Y8XV6PR6CEUb4TuLAQTV2hzRnVINHURw4q+ZCAIcKotXbznVkVTaav8mbD5FKQzMdfzIIK0SoTiCIM0Xy+SDjZFBeJWF8qSHXFRDOaioKIZxiNLGoBr5UaR5SDxT1+MyMYp3CR0wpfRxx+A5ghgyiHsN0Ztakhx3Y8XziqJ4VjVkoaE4ijf/Dp1r+gDkXp6kVOckqNoa+zeLwNFRY7m/Y2GmKKqmnFjIEUgZcAZpHqy/QvnzCmXclMO3/w4zgvheBfwNk9X1ZWvpCBZEhDQOMgLuAIxxdvrQsr8z14vHCp2FvSYb3dlN3RbFyIuZw8bjalP68O6iycyMoCL6A7G8i7PxiQOQqrS0sJYL1VCfccfKj8h4fDl1H0IFCHV4cw3EJNWQbU9AvpchgCYpLN67D4Q4AmjeBuaRtzkcAe1c3c6ivMAghlULJddAUHvV/jsUbYRwYvgMqSCKex8BLTnuPjMX1vtBJehch9whI1ixUYqg7I0fYaZUZBocSIRe3xaWpCoMu5eaC+QZSbgyvV5SMIbNLpZ8NmP+HXYypWEMex33/31MOIbIaGw8jtApqS3gZGLE9eznwvseK31858rpuKMDIqPFBH0f/DVDtSY2/457DXHrLwpECOgkzi7iQ8sl/CnFXfsdA1I2Jb2mgocRE4iQ6r0gqGE5giCIonp+hQqxJERGFPt1qqGDBlFEZXsRVhbHdTN92no6AmKhhpF3Lr5MFv0E6S2vHkgvZjZCouCcZjF3za5ShBf9JjAzlo+NU/BXiuhuG5u1TiV2iBRHQEdkMYmdiuIpzbv9XIrwz+m51I0VOFdKikoQf1JGQBH5dqxQcpxaMxRHRc+L50rs7RJJ8wQ3Yyqs/bknFXdMJBxm5vTc7T3lhRaUei9XNcTtnag9C5XdZvCMrliBgqv6tPV0BDYjEKCaBXMAUrpqTs8eZwQyZEBhhSHeO2+ieL9Tof8Znt+IWwqzBG8qIyCUN7mpsncAKv8A7O5DHAEC3XMh6wMCgpfXcYcNxNJZXUWsGTt2Vh1BiiMIHCL1+kL7HXNeQGSuk1INFS1ubufF8g3hWIKD4jgCCmZi5x7IlykFXPiaVyrwAWKOwOdm/DXKNZuMC+voYj93rG69p9cMVYxpftfxDaviB4AVOwKl1EuUUh9WSt2rlHoV85kXK6XerZS6Ryn1+6ucjzWqQZopsFFtJMxtfArnpEgxICYao8VFjRVt1jgjoF58Yn/nfjZFeM2X/GION3UOdszBKy1HQGxE29o7xEwpOSfgZwS5mVj4DMP2EZRKKSwOot6Ja7+jnXtda5Cy3SiKj9dfmBHMueChjCED7j6EUTX1+s+wjUEyI2UPZpXMpqlXl1J8ijtnzonF+5DPLpbO3qHeO+F+RkICwr5FHCeRx5X4UBSV+ZnfdWt5lRnBaFUDK6VKAK8G8FUAzgB4u1LqTVrrDzifuQ7ATwJ4idb6fqXUTauaj2tUgzSgiZgrP/KhcU7/xdo8RhscSMRBmdP0yx2DwhPjQhZacxxzEnzkHRYHkT1oQhXP1F9O4QvSOacS8i40AUptDDkC5NuEUPCeLNvl0nw3uhNx/QZnV0rR/EaAZ1NvMTP/jjuZcvchlo/G0J1997P5PK/ECg/KWLrbrWWeI8jICMJsOnv9CVyJndcyDqLiDER785DmHteL+NfLLcak1WFWxaiBCR0k9mmrzAheCOBerfXHtdZzAK8H8LLgM98E4I1a6/sBQGv9yArn0xrVIA0A3IZe3MYfB5FBjtJHlmAmYCaCI6DSZPP3GRlBagEGY+VmBFzvfHsdOy+K8BoF2DGFv5IZAZM9RZJPEt4LHGLS8TAcgROZUu8+NteLYSYezvEP03RlsVBQJpD0duzsvkXBWNJrKNtsmpRe+8FDKqpmnVgOvxHAWmSxX6v9z8kInGyalEv7WWQ2z0iII8Lmj5TD79NW6QhuA/CA8+8zzc9ceyaA65VSv6eUeodS6m9QAymlvlMpdbdS6u6zZ88eemJLQi4I+A9oIXnzqJCFVg9IBK/9XGqsEKOl4Rw68k4XxVAEb3h4c9lF2EeIbjHhjsWlt7Gumnp3bhDdEc8wysRY1VCR3IjRS0aYe+plBETvJnO9MEKP73vYwI5T+sSVxfT7db3rMS0maI4g4RCZg3nkwKocNk6944GrwbHrrm1MlxGhU9ez1wEayWfwmfCtgzyuX0Brp5MpoQ7L4cTsv9O1J8E+JNZMn7ZKR0DNWgf/HgH4AgBfB+BrAPwzpdQzoz/S+rVa67u01nedPn360BOjcFzAx3IrJvKJ+6UIhU3Oxg8xYYDShKdVFNSiCUvuK2Ex1+5ipmCmKCLj4Jx0dW7YjZEjvFwHzJF1IVlckZyEH71KSp/QiXGQQQRRkNh4oEphoDvXuYYHoL1mmBGE2UWk/ScPbz94oBqk2bHjjqEyRMYdbmMPVqWDB+odDymZsG1MRwUGi8x96HMEFBJQOE6aD9rcsWguKJw7hwQUkRNjFUhOcHetqobOAHiy8+/bATxEfOa3tNY7WutHAfwBgOevcE4AaHIQQPN6wgSeHS5mKqpuU/MuMqC6BrrptP0cF925MkbSiTmZiiRXBdC25RULykLpayLNp2AmqlqbzggKb4OZedBwh78x5I3IadCjrI5Q3kTRHUNauof3nDu0iLnzDlE+vCPVmrBmUhnpuFRxby0WQw/vKR/t8zxC7IBTmRGH2YewKhUxRwWGhDoHsLJWGRIOM3MqIDtQRuBCoUv6fQSAW4wZw6V92iodwdsB3KmUukMpNQHwcgBvCj7zawBepJQaKaW2AHwRgA+ucE4AGrKYOJjdakVeAxy/ZCRaNNGBRD/EEKsW8V572Cxj+AjwyTrphRjuWFwLA/M72anEBTZCHYFzT9kDMDhoUhAZNVYMiXDOvGsoJzWmc8fg2gW40V334pN0NpOCyLiOoaG+nF4zYWbEHd5OFC9wIPY69nNh23V7zZQzD1VrlAO2nwuLEGPnSryghz287T2lA0D/PvCwqjcWJfnk+IZUEFXTbygDrlxGkFQNKcPs3a61fiD1Wde01kul1PcA+G0AJYCf1lrfo5R6ZfP712itP6iU+i0A7wVQA/gprfX7D/wtDmhUv3sgxDkF5UNCe03JR8kDMISZKkkB0m1Ybu4hscmTZ91i3pr4SyAsTusOwDjKjRVI9GJ23wNNEV5uTQKneAr7uFAcQZiJVcIBCJgiKXughqRlFAkLWdZyFsIKMr/BZYgeR0VUkJt/d4560hDHKfUU9SY6O/Y86fBjtRa9lh1YleMIKC6IyW7Degpq7rvzZftvWfraRPHEC4jMWLFsnL2njoPaHJf+9do1KjvXnCAqlEKvWjWUdARaa62U+lUYLP9AprV+M4A3Bz97TfDvHwXwowcd+zDGRWQ+zslxBOG7ROkXn9jrAPQhaT8XFpRxRGOX3nKbh+r1Ii9mSb/cvrNYgESSqqEwQmIILzfL4nXj/oFEcQRUJuZ+b3fuZl519Lft3JkqUeqwsQcN9QIi97tIVdFmbAoiYyLFusYEBaOeKrwxuLXsQjVcFmkdpJfdMk4slI9SzzDMpjnRRqT8Sq6/eF6UM6cz84NnBMuqxmiDCaJSqiEn8OEy0lC0cVRUQ3+mlPrClc3iChsFiQA+dtxufBKbDOCcFGnJHIBjB1ft2kLIC5CSrXVjydhkKKebL+lK5kLlHYB+URaBc47CCCkfEonTbh+yqiiOgIGGOFK5qnVH8HJjJeAVd83wfXH8LEvkCCqfb+BaPrSOmiCeQ+UNlyG6b32TDlz3u1GiADtWSr5MyUe57MINoijJMfUa1BSsRTkL+7l0x9AAoiUCMk69R40VFfsx2a273q9qRtDYlwP4O0qp+wDswCiCtNb6c1c2sxUaFxm4jaykjRFp3jMkadRDdKMaS+CmNOFLAs4B/CiDlzqGmUrcdM6MVXhwDj1W0HWTiNDD/uwc4eX2XuE06JSKJycTc7+3+/3sGBYa4jqZprKLUVFE12PVJM5hk3KIXBQf6stFjiDgLiTJJwvnBIcpdyBRBC/1DK0Es2i+K7UeXI6AC6Ji+bJQae7cUxoaKiLpK39PO8gqHItT75HFcGH/I65INBFE9WW5juClK5vBVTA+MnBxTnrjh10PuTTfxdBZtUJZ4PJy2XxGxiZ92RoNa4Wv5ONe5u0TXtRYaQVI3DAvHqt9c1WC8KIOQO7w7lQ86VcTsjCT41Sk9N1ex84dQNsbyZ27S+S7f9tdL1YgkWum7GAmqt24+QwFGSScJpP1TAiymOcIZJjTjdA5p+JmRtOiRFVrTAKc3czT4Qg4x0PsQz7w6e5pSMra75zuKEBkF0wwmc4IFFHsRz9nTzV0tR2B1vq+lc3gKhhLFhdptQKpheY04VU6Iwg17ymOgIJz7OdSvV7iA4ImLcejAqEEjsJf7TgW1qKqYM11ZMJrTB2AEYwRQzXhZ4rCvCAl1v5zcEfdZWIcru9EwoWKa0F8PFsmeN2x2DXjHDRmfFlNIinNouyWcFC1DhyiAKOZeTFV+U6EznEElIMKcXbAP+RzJJ8WZ2ehQruna40tJgNOKn2CZ0j1wzLz6vZOxdwHb80wvNIkeM5c8NCXrVI+emSNJYspnJOSfrkEL5e6ln6Uy5JUUWrOHFpOhEQ7sbj7I4eZunzDZESP5RaBAdR96ObONyLzo1fqPQN2njEkR4/lpt0c4Z+SvroHcy45zSl9/GpgDhqymzqhniriyuIQfgj15Yss1ZCFamhHvahqMXp1505VA5vvmJFFBjwPz5UU3uFNrXcXZ09lda7joZyYS5p39yGtxGLVe2FGQMloU0667J6Nve61WkdwZG3BFWVlcASjUnnVuRTjb//OUw1RkTdB8KY04RzRTaXmrGrIwTmpeU0y5uUtZuYwJVvpsqqNEBriD2/7/6kiva49RpzVmd9rIVoO585p0JXn6Ny/beceHYD0geSphgTZpPm9c08Ta4arlh07jppbM/Hb9uooS7ZjpzOQOCPloML2kGc+43ZhTTqxWt47lEgkpzaD3oeFd3hzY7XBGCsTtjCaC6tefdXQZ5TxBJSKI2EOX9bdpqZhHz8y5aCAMKpJd9RkNOhlRkZAkIhcqw23xwk3VoTFE+SZ9zmBI4gJVy4y7Q43DmcPSVKJS+C1/3EEmJo79QYsM1aQXXAcgTtWTUfxEQ9CrK0OIqu9sVjnWmnWAcdv20sXZbGBSJRlcbJqh0xliz9jcQQnVkjN3YVzeO2/PxZVDWzmEGekWaohBgp13wuySo5gLR0BVRUIWAWIHN2FrW0rDmcv3aIYDhvvXq7BFf2EeO+ChXNiJ5aaO5feugU2OSQpVwXbfsdUZXHpqihop0KV73PV2mF0R0k+7e95iakP5/BV0W4UL0MU0rsU7LyWzkFDzStuCijVZjiQSEl0fXXuaTISTkSm7prh2lCPC+I+MOvBr5vhssg8OMeFMFNIQPo+dDUjIZHfjZVQDblQVIILSgVRfdlaOgIWZw9wTk6/bMZwIpbEpuagIerQ4gm2zmFwkUhK+UBtaja9dSIRiSTV2sXZZcgqJyNIqnhq+RCh4BUJZuJUSpQmPIXrcwVlLgSTHsuPTDkScb6UxwpxaBoC7OAH6ZWkgF+TkDqYc8UKPGfkRtV8p+DcwzsVkPlN5+hAhOIIpszeCTmC8JJ+FsllBKHDv4bfUHZUjSpGAvwIXTq07BjdWHJkyqfTMa6aPLwzxuKUD+6mrmvNRqZuFM9j8eZnteazmXCsihnLJafZ7o9OZsSpRMz1fOkhNZabGXHvNY6eMxOZll50R2cgrXoq6cSUk63RzzDu6JrOVDipY0tILtMZQVLXT/FrycONyZSLMCMg1l+gLAIyOQJCPuq+SzldR6CxrGpSJWc/F0o+yfdvBNlT5PCbec4SDRv7srV0BAtGPeBH6HzLZDOGOZBYOZ1THWmaXdEHYApXjbDqmu6X4h2mGYSXXayUrtonnnks3lyrU5xw0FCqcRaFjbNyTgHGABjij62n6MaK2y/YaLnL/FJz53s85aX5LiTSkoih9r95XvOqYlt2d/ehc1Bc5mfnzfIpZYCzHyi7TfBdYjYtQ4BeO5iM9Q4I0tfS3/fuXKm5y3vHnxf3bGxhXbdm6MzvSlUWr6UjkAgvt46A3qzdxmjWH5vmu8Vp7KGVwFVjjkCoio5K/PmNaCMNyqlMRoUHPXBzt2NxOCcAT1XD3ofSv1dmLB5n5yJO+3chWSw5V/6+5+HZLhTFK0DCsXiBgSvtHZcqguRcaEhyiGF9A1e/YcbSfCCSnZE6YoXmnoaXpKBJNrtweCxuXdXNYdo5fJkj4GoSRgES4M7V/Yz9vW3vwan32sxPEKW0Y7X1SsFzHnXP2cx9UA31bjlqhYrBz11dvwSJeHpiliymiE0+ErERIK8aSmQEDjZuF9iUiGomoxKzlG6cWszE5yZuu4octUwKEqkTByBRHS61J5beWWA/Y/+fm3sX3dFj0e9blu/DnOmUaYUCc8chci+58YrAEhmB1CkT8BVIXIGXzZLtAc+R067QguY3/Pqa1GHKrffu3Qbd3OkisPwaiKquMavMO8vJKuWyCPgb2uHba0mcRFkozJtrDRnBCkxKEd3OnJx+GTCLWYYo/IM5GUWlOIKqO3DJlLTwI2/7fcI5md/L6e2kTGcE7caonPSWJbGdTZ0i1hmc3b0PnErEfi7C2bksy3mG4X230tc2y2KJTcchchlBgLOLHIHrCMhnU7a/l9bfuPRJc279mXnlq4ZYyLTsOCO25sIJogCr4klzBNLcl3UnAU6RynwRWCy0kDqZSvtwOgo4MSbgtHPnOCog3oeUw+/L1tIR5JDFUvoOBJFIqqCMS28LguANPmc14V4Gkkinc4hnu8C4xTxfNpEIV/TjpspMFG9/5r+8nnJiceM2ifjjCGV7vZDIjyPTOKvjZIWe8kY43Kpad/wGByt4mZGsGmIdgQMZSA7Rl/fyGSlgHFSqKMuvY+GhQlulnHKadkyW32jnzr+RL5w7F7BUjcPXmgtWiOZ7UVbncAQSrFoWSYLXvacLYayQqxsygp6NjQwK/zDlIgwgxMbpz/mtFajPmBdiS1h1Oy8hjbQ/S7203U3zu8UcN/2ajPzuo5wTM2N194GVoiYw4bL0u00CMUnqRugcCWy+Y3c9ieBt5y7yDb6MUTwAG6dC6/XjAzBFdJv2H5IjqPI5ArYancgIGFjLfdcAh43b7ygV+5n5dEILvm+WGzzIz5DjCIDOqYgw7kGI51QQNe6ieD57ImAtKiMYlYNqaJUmQzWdNl6OROQDKYxMJfmep14R8F6brVAv4M5733LeYnZTUqllsp27FJGNS+XVJPDYuK+flySyyQjQZnXJ6DUxVlAcJB5IlWYzv7C3PJfmu4c3xxH4UTwfPLjYOMfNuO0qUgdglbwPXdbDw4mx0ILLulO1Bm6WJT/DwnP4dNv1g72PQMoiD5oRzJrMe2MUB2TTkbsPB46gV5MIV/dtTOyCdxUn2RwBT04DIe7NRSxuVMNkBCFHIKT5loQiCd4M1ZAHiQjz8msS0uop0ak0rQC4IjD7d94bqRIHkqh4CrgLEu4IIDLR4afUU84BOFvWmBCHQycfzZfR8vJlN6ih70OsxedVa/ZzqcBn4UBypIOKshl+/ZnryQ7R5W/YWpeUasi53kyEVcv2cM8humeLRrQxZtR7iSLEvmztHIGkcClL92BOcwSVcCCZxZw4TJ2IrGLgHDO+ie64FgZmLKItRLSYu+tJi3kSRCJyRsArb8zPCg9/TWrxRfhLNdFdgitJOWCiBiIFGXBzt8GDdYh0jYc9ANOwo9vCgMvWAOMouApy+31cjooSR0zadhV8ryE34zFzp5vOuUV6STjRIem5DDilxXf3DicKsD+rPFJWDh448YALq7Y9pTKk16JqyNmHU8Lph9n0kBH0aJzGHuhSeAvVSBt/WXWLi1/McjTpqgekjMBqk7mmZnbursLFHb+dE0F40fLRMBJJRNWiakh5r0OkJbl+FSwg4P8V3zs/HCsZkTkHYOoVhlLxk/09p3CJFEgCV9LJRyuyhYFSCpMmy8q9D3wb9BialKS2du5cG2qga+SXDB6EfRg6c4mTcOXEfGGdE6wIfIqVvpqfMao1D1aNx5qOOmiIU5q5991mD9I+lAoH+7K1cwTS4e2SWRyR5cIr0gKMI1Pheu5hKnEEiUOr1r6ePYsjoMjiprBOakNByenYymIHs2cdXaJPjfs5GT7qxuJIeq8YTuIkAshKIv5sRkAdWvZzfk2CDEVxqiHA3IeUasirzWAPU0qB5H/H8PWLnPTaI4uZe+XVLVTCPnRVZFnPUFbvuUEU1223nTsTiHiwakpocRCOYCEEZA1XJ+2JvmztHIEMrzSpchNlSBt/4WLjTP+c1GL2cU4pvS2Syoew4IqSTXqqIamOwMGhpUPLjMWXyduftYoTgSMInZjUeljiCFzZJBe9jr2NT6uU7Nz91tECJNJwRpwj8FodM7h3Wfgvr+ccgT1skhxBotmaex84+bId369JoNc7YNZWKnhIBVF+xT3/Uifze5ffyAiiEgFg6xCj/kAuFNU4FTIjKNv7yQaAAd9QForl1+ZO4DPUEfRonccXyOKGI5AhkZwS/7qrtBQ2z7KqRYjCLmZR+VD4i1lSuKRgpqlHSPJtIcxYtVNyT0eKKb6hdSrada5UtF/42RNzuHkFRKKe3amoZTZsurmbkxEs6QMe6GAm+1rPVPETpxoCOkcgqYa8jIBtreDAOVJ2W/iFltJ9WDQZogS9LipZaOHDaPxLnQAf3uOUUe5z5l4vab6b2dOFirvterCqwBFY0tc66lRGsL+oyGwAyHP4fdnaOQJxAbrkLYfjBgeu+TsGGkpUH3cb0YmQ2AMp1dLCnbuseffSW6ZxFmC6UkptIexYewuDc25N4vfPToLqaYmAt5EitRHt/NN1BPka9CTR6BWU1WwrBzsW15AN6NRT7TuSmftQuY6AhYYCjiARVVc13TnV7WSaiqo9iOyJrr/2MK1lcYQLozEZAcU3SLAqV7Hu/l0O0Z1SDXVkfsUjAaU/FucIpq3D5zPgvmztHEG7AEV1Ry0WuwA+rMC1aU4tUpdwXWSlt3xUs9FEIvvLKknw+hyBDA2lHOKi6hzB5jjGTEdNsVhdm8pOCWbqJJgczl6IVbB2LP/Q4jFhF6Kga0b8dgEc0Wh/P6802eYYADYnJfYWXRFYqlGh5AgmowKznCjeOpVEEZgJMiS+yzgouddVt3dyMlKJm3ErzTk4sauBSNQRNOthnhFEubBqaLmqobyMoEMVZsuKVAwBHVk8ZAQrsByy2B4QkgbdXczSRkwVLAFyrxc7fkqmaQ/hvXmVPLyrzKjGRiMp1dDenFc+2Og1lT0BFiKjpY5mLNWqc9w5hGO5sskUN8N1yrTXcwubOE4CaA5AYe6b4xJ78yq5HixXInIEEYlIByL2cN+dV9iaUPLE7jDN4RtSNR5At3dE7X8lc0F2vbeOJyna4Lm6KCOQ6ocamIm8B6pbM6liTKCR9zIZonsfZsuarCEA3L3DnzN9WZzLf4abeIg4BFQqqnF1/ZxTkd6J64+VljF6hVvEZ+xG353bjEBegErJ8lGgi2pEiKyuW5yTgnNCGCMdKfIyOav3litq894VDVgJsCbbQtjPpdpQe1kd09wNADbGTUYgRd4OVzITOIIWMkiMZQ/I/XmFzXG81e315s043Fi2O6dU49FliBKcGJP0KdgxpVJaJMcqmrnzGcFGE0TtLypUDARYFAqFSlcWT5uxZpkcwWxRk1XFwKAaWqnJypvuoEzh7B6uL2QEslrBJ6kAmiOwMFMn06SgoSYjaA4byjm5LzXPgYZmyzzV0N6iwiYRcZq5Kq8dQooj4HTj5m8L715xUWfy1YSO4+GgDjN+8Ma6DBktN/etSYn9RZWQCXf3YS5gxxFHwBRH2qh6d0FnBEqpVpJrW4mQDrH0W5xwogB7H7hIuHQzP2k9NJ+zWWtKaJHijMzc+b2zGQRR3IFr1Xt5GQE/ls83VGxGMMlw+H3Z2jkCOTXvDuYlg1WPSz8CdH/mmj28pfTdbU+cwjkrT/kQf8YStfuLim22Zseyi3lUxC8+AUKOQHaIywYaovgBc73Cc4jJjIAhB4GOgBex8dKP4qnPdH3qmyieuVe+FFV+uUvKiW2OS+zOq2TwAJhgJUc+KmVGtpWzxZg5R215EDkTa9ZygkcAuqAmJZuUoEIL31hHkCpOWyTWw6KunRbh8We2m71jnw93H+x6sI6AWg8HVQ1JZHGkGhrko/1Z16tfIItb9YC8AKVNbSMtuVLWhYaEtglNai719LEH8a7AEdhr2sXMHTTTgCOQcP02I2Acgb3GfkMoi9yFLeRjIvRR065ChvfSLYztNRdCNbAd339XryxFXSzpTAwANhqyWHL4dqzZsobWdLYG5JGIZQOJ7M/NmuGej83YuAZ9dl6eoCFDvpyS2qbk0kC3ZiTFk2nPksERCDxClxEsWdWQnf+yMqKAyaggs6epxxHINRCdakiAhqpBNbQS414NB/iLOS09rJ10k97UtXagKCGKSuq4Sx9mIsniiRnLHjap6C4VcQIyR+BGd/uLuoWmQrNztZuaaylgxmoyEOYwtVXDIklfFk4UL/MNlT20uCIwp+I5tantQcmNtTUusT9PqIaa8S35LslHpWrg9vvVGruLpbk+C93lZAQ+Sc+91AnosjoxinehISHI2GuDB9kBy8o81cJ25vtS2bQfRLEZQalaWJVq/wF0GUHHEUhEt1UNyQ5fgtH6srUji7uqQF5WaAtQJKWPq1aQMHQZ53THoquB7fjLuhK10JtNers3X7I9TsxYTkYgRJwAWnyS/n6dBG5f4Ajs5/aEjMCNkLiXqACdQ0xxBC2uL2UXhVVi5UJD6eZ7XNM5wESdu5kZwW7CEcQZAY3HL2vdjsVzOCZzKJR836W3ublzsCoXak5tz6U6XUkPOFlkks+Tx3IzAmrvuNCQzBGYude6ZmXC7hvkUmfIsiGLWY6gNO8rsW0oBtVQj7YzMxHSNlH8FC6uXOWDFO1LkIhbUJYTkUnZhSsfzRkrpVMH8uoIlpUMDY0DaEiOFDW4l6iYuRfJ7Kks3HcI84e8bR/BRfpASBanAwOOnAY6+ajUMnnUOgKzRiXoLi8jqNvsguVwGnVbUdBtDsz4fosT8cU0thiTuaehFFq6D20QxbT/ANING8tGcmyr36l5WSe5N1+KGWkLM2nNK7rajKDi3+4XcQTy3pGCqL5spdCQUuolSqkPK6XuVUq9ivj9i5VSjyul3t3875+vcj4AcGnfbLLjG5ScrltcnALET2/llBQA9hdyOwR7vVRk6vc44eWjewsZXrGLeZbQqQNOHUGKI5hXPDQU3gfhXrmZEWVhryHuHcmA0/wstalrTWrL7bxSbSHCjIA7TDfG5m1T86WUETQb32YEiRYTOdXAUtW3vcai5lVydnyvJkZQwNnPcc/QZiB2LGmPSRlBKDAwP6Ozbl/xxO+dnXnF1ovY8S2sSvUZApz2LJYjSEDQIjRU+uvhmswIlFIlgFcD+CoAZwC8XSn1Jq31B4KP/qHW+utXNY/QJEfgvv0pyRHUCfWKXcxLS3hJWCFPsNmx3I1IHVzTUQGlTFSTo8XPhYZyVEMSNDQOFrMoKxQquu3fWmmvOwd6XrUYoXfaeEFhZR2PcGi5dSWLSpNvjwO6w+Zyk5HKGUGaI/Bf0CNwBC00xBHwDe9SKlEt4xfy8XNfJO57Wdh3a0g8T+AIEodpVddQil8PfjHm4YQWlhtM7Z3sOoKEasjOy3zna5MsfiGAe7XWH9dazwG8HsDLVni9LLMbcXsaOwKfgKIPQFtY4hapkI3NrHpAgETcJlxcBaX926qW37KklDLwwyKxmMu0aqhdzGJhXRcBGmiIObSC9DZVQGRefMJFk36Fa/rNVXx012rjk44n3e8esM3W+Ixgs3UECzO2oCLbXfCV2kCerNDCOXsNzEQVlAF+TUI6EEkXR3ZND3kCfuE5V35tzRYCvxYILVJzl9R7RdHsnZwgqt07dOBjYR45I3BUQ4u6LUILrXMEfPDQl63SEdwG4AHn32ean4X255VS71FK/aZS6jnUQEqp71RK3a2Uuvvs2bOHmtSl/QU2xoXYn13KCACLL6cbxQEJSMQrsEkvQKlhHtBp1bNUQ1LlqkN4pSuLExxBmOYLG2NRaVzaX+IYka3Za6ZJS/dAErqBFh0kInYM9Q5A2SEuhYIyC53ZjFQ6TPcSHEGnJklnBB00xHEERZKjKgsjMZWIZ/dglu5pWSjvDWWSqqbNphPBg6z9D4oxGQe1NSmx03A4SRltVbOZX5cRVGwg4gacOdCQBJH1Zat0BNSsdfDvdwJ4qtb6+QB+HMCvUgNprV+rtb5La33X6dOnDzWpy7Mljm+Myd/lvJgG6Ig4aXGNogNQiKISGK3tSmn72FPKIsBpY1BnqIYy5aPpymJDSG4koKGWLE7wDed357h+a8KO5TZIk98hzLc6AByIIoFnp9pjuJzRQjgAQ2goSzVEvPikm5f8vmX7nFOqoYnTCkWqsK5qjQu7cwDAdVvx/vEL69KBiCirbh1iGj6yh7ys/XdavXDPZ9r0gkqsmS4jYIIob+/I7yPYX1SotZz5Ad16uFYzgjMAnuz8+3YAD7kf0Fpf1Fpfbv77zQDGSqkbVzgnXNpf4jgBCwHdhpovZZbeRkiiAiTEOaUWE5Vc2GQhEekNWIDbxqAn1VBGRrBoGmdJqhRAVj64EdKFnQV50HRzT9z3gHfhFUgdAc9FiSc3x9hf1OLhbQ9AWwQmVRYDXUYgrZkURzBt4bb0fbDXk+S9kkrOjr+sNc7tGFjrhu3YUbsZosQRjJq1LMFao/ae8vzauChQFgq7jdInNfdUNr01HjVjyZXtFlaV1pVSCY6gGX9nZiFAvqAMcDmCa9MRvB3AnUqpO5RSEwAvB/Am9wNKqVtUE94qpV7YzOexFc7JOAIGemiLn4TSdvM5v22CpADZX6YlnzutfjnBEQgbDGi06pnprQQNlYUhDucVX2Bjx7eHJKca6pQP6UNrf1Hh0mzJZgSjQHGSIhFziGBp4994zMzj4Yuz9vqh2fFnAn8DuI5A4ggC1VBSTSJkF834l60jEOS9FmbiK6xN9nS+yQio59PxXXwhFRAfzBL/JMGqRaFww/YEj16aNw4/zRFI2bTdOxK/4cpouWejlPLeIyCtUYv9bwi9hgBZet2XrUw1pLVeKqW+B8BvAygB/LTW+h6l1Cub378GwF8F8F1KqSWAPQAv11qH8FGvdnnGY9A52n+gW8xVzb9EJcbGCbXCpMSx6QhnL82SVbDLdjHzvrttdSykty3hJSxmwBw29vCWovicgwbIqxJ99LI9aKSMgH+LFOBDBhKJ6GLHG2P6M6e2pwCARy7ue/P05+Qf3lx2YaGzy21GwMNtuxnyUfdzckawwLhUwis0u3uaiqrP78yxOS5Jp++JKASIbGwJeEloEewdbl6nj03x6OUZTh+fJvk8CfoCgO1pmVQNWYcodYYFmr2zqJr3b/B7Z2cuZwSt4u5adgRAC/e8OfjZa5z//gkAP7HKOYR2eX+JG49tkb+LCV4Zd1wwFZSAu5j5ZlcAcNPxKc5emonFT7ZvjERGAsaxnNuZy9Fdk81I0BBgDpu9pj0Bhevbxm02I+CbzqU3tb2Hj142kfd1bEZg2naYhnm80gdAi/9L7SpsIz/unp5qM4J977u4Fh5aKY6gI4v5sex9l+Sj5nMS3GY+c3F/yT4bO9ZimYBzmqj63O6chIXazzWcSs0cgGZevhRa4oy6e0rfhxuPG0dw/fZE5Ag6dZiQTY9HOLez186Rm3sLJwp7Z9qINuz14zk1jrzZO1L3UeDah4aOpF3aX/BkcRTFy1hhleiUCXQ4JxvVHJ/ikUv76a6bdeN4hMW8NSmdF9MIqXllMgKOpALMIkwtwLJQ7cGWSxZL1blnLxlHwB027lhSxgO4yi/+Piwq+QC88ZjJCB5u5iVF3u2hnOIIDlBHkCIRpUIjlyPgisnMfJX4Zi7zGROIXNjl+Rt7zZkAhdrrebUZOe1Z2OczMUGUAAH6Sh+ZX7N8Q8ohziu+1xBgMoJO8hl/zg6/k3jO0+g5r+64Xj9HMFviGEMWd3ivfHhb5UNKHQG4GQF9q286sYFHLs0S2GQX1UgZwUYrH81YzBnp7W5iAY4K1eri+e6WaZzTztU6AoksNmPJldNAd4hwNQnWmUtZQ5wREJu6IQftc2Z7DY19aChLNZShJikLGve23+nS/oIliu18pf5Adq7Lusa5nURGUKjkoWVJ+py+RRKsClhoaC73p8pYM0AHDaVktFY1JO3D6VgOomzPJesspFdVAh2XMGQEPZnWGpdnS5xgOAL7og6pLQTgVFqKL1HJwzlvOj7FIxdlaMji+lY+ytnm2KiGuB4n7tyzoKGMjCAFDdn57gnqqY4jMI5AIosB46jZiuEMuSrgOHMBO96ajLA5LjtHIBCSKa23PYwvNY4zh29gOQLnnqYyo0sJaMhtKJeKqi8I0l7A3Puukl6673LfrBBu48n8KeZVjQu7cz4waAUglcgRbI5HbTYt7WkbkEl7ZzoqsTuT931ZKEc1lAcBXqvy0SNnO3ND4HBkMWAW6n4izXejGimdBrrsgluoNx2fYm9R4fG9hbhodAIbBxpoqJGPygqQPLI4VdE4KlRHFjMtDLrFnC7K6shixhE40Z2UPZnPyOStJf6kewWYrKBVDQnPJ4Vnb0QZAU+SpprOudCQxCsBRhzBFZPZ+eZU5xr56Jwl8gGzxlOBj1sUWCiZ8E/xa6ePG+ju04/vixkIYCr8pfVuCsqWiToCpxgzEUTt2Che2IdtRpDgCFIBWR+2Vo7AbsJj07zFnOIIuPepAgRZzCzUm06YxfypC/vJ9HZvUSUzgtT7CMqiaDXOXMESkMsRFEn5aAwNSaqhGaajgte8O5FwDhTgjk1dU3pZubVTx6ZtRiDBgHsJsnhcFhiXXQYlkaRJaKh5bruLZUZGIENDtjhN7hhaYLaocXF/ietFaKhIZtOWI0gVgQFdZTH3uZbDuThLOkRpzQCmoExrYGe+TKqGktCQk03nZQR5dQRDRtCTWQ03V0cANDhnMqox7Qnedf953H59SoEkQxQ3Hd8AADy2MxfbANuxZNXQyCzm2VLcZPb7pVVDKa6kI4tT0JDkXMfOAShCD859kFRRQDoTG1tZofAyGQC4cXsitoUAzP2R5LHWNsdlpmqoglL8YepyBFKWAqShobGtLBYEBuOy+37S8/EzAhlekTrkhhwBt/5uPG7mcnmWdoh780p8zlvNPbooZeZlJ7SQoaEuI2AzUifjTnYfzVhbh7X1cgRNNCZCQ2U6qhkXCu89cwEfO7uDv/z5VPskQgLHZQRNeitez0mVZQmcPQQFFUWpkhEn4JPFEjFrSVk+ik9vavdnoirFqeJNHYDvfuCCmRdzCLoyRi6rAzrCmJs7AI9XkmSFm5NSfA+vqxqalPSrEO31AGB/XiUhkWWtRWjIKoJSHIE1OSNQXQGl4KhTb0QrI2hIzgjMZ9KZuZRNW2VVrYVsxoEAuV5DgDnYU1H8qFDJOoJIHXaN9ho6cmahIa7FBGAOeemtYoB5uBd2F9gcl/i6z30S+ZlQNcSTxRveuNz1zFhyRuDKBKXFnKpctb9LKUDcjcW+sziMaoRukwAvHTXz6O6DdCgDwM/88SfxWTcdw0ufxz0f22OHV2sBBhoKx6bm1Tl8Gbpzr0+NA5iNn3o2gOlSmnKIAO+k7Xznzbu1U3AbANyQIItnKVi1UHjw/B7e+M4Hcdq5t+GcgDRZfP3WpP2eqfWwv0zsnWl3jyT+KTebTgVRo0Jh3pwzQx3BFbbuXQRS1JmxmJuH+9Ln3cKO1R5aib5FJzZH7QNP497yYna1/JJTaVNSST46KpKyNfcaqXcWd1XKPEcAyNCDyzdIL6UHDPn3mm/5fFEq7LYe4OzUdjojcAUGUkbg3iNJNbQ7X4o1HlMXGkrUsQB8C2qgu6dzKYt0nlkqY0vBOZvjEpdmSzz7SSfw2r/xBcz1uswPkNefDRx42K5ZMwKMBvjdWWWCV1Z0ASbCl+SxgB/dc8+6LJTnfK7ZyuKjZlbzLkNDKkn02EX3177gyeTvAacZ2aJm1RGAkazedHyKM+f3khxBivBKRZz2580aTUY19nOp+6CUvJgB+b0M7lxTBUuAybKo90kAwC0nNnDziSn+xV98Dj7rpuP8WE4rcQluc+EHiSPYEbB/a95hIzjEWssHjXt4p0jS8LqhjZyMLcepyBmbC6vS8/++r3wm/tLn3YYX3XkjC33F7ywWOJxjpjI/xRHsJwhe11mmhBaA7PDdZ8fvw+4zHDQEmHOkdSrM/erD1soRSG8nszYuCpxfGBkj9xBPbo5xx43b+KI7bmDHcWEMaSED6BxBivBKQkM56W33c5HwyljM9jtujkt2Uyul2t4r3Fi2KEvrzIxAqCM4dWyKt/3QV7JjWDMSYL7NdjdeTkaQlo8CPkQjtScG0k46Z07UdUNzyfycsWQyX3UZMONUnnJqC085RQssrJXOnMLrh2YbA6bWqCQwAExBmbUciEzMCMbu85EDJIAPoux19hPBZB+2lo6AenG9NYP3yrj+D3/Dc9oXfnPmRjUSbgx0PEGaI5BhDBd6kNJba9KhlXXYlJ0jkGxUKuwK3UcB44DnVS1mBDkcQa65z1kki7dzOQKZ2AT8+0RFdy5ckCLyraV6XYXXDc19GVNqrI0xL+0F8qTXORZKgKWxLM+QWu9pfi0PVrWWUg2F1+fmNSkL8Rwxb0LjVYB92Vo5gsuzJbYnpXiIjEs3qqFvvqScsNaqhpY1toXNA3S1BClOQpoT0O9inozyDxuOH7BmNmC6ShlVWp4INKqhRJaVspznDHQRZztHwtyfi6quJgDhorurkxHkjyURxXYsSRWVa2EdgTSWLSpLrdFa844c6J6Ne31uXoCcEeQ8H/tzKRtwf79KfgBYO7KYbzhnbVQWsI2w+4hqpJJ1a1ZCmnPQ5Each17MBzgguH7q1g5y2Fy/zT8fO44WJH65VhZ5z9l1+mxtRpl3T628NyeKz+EIwmtzY8kcgePEWGLTXI/rCutes7unT/xosXPXzeHNwY5Ax+GkstbUnLYzYFU3YEi1mOiuKa93TjFkzTqvVSqGgDVzBNK7CKy5N/xQUU3G4WfNQkOpvkXmM/xYWRlBLvwwyj9spIgT6OZcFvymtvOSDpu+nk04lhTFj8uihas4Hbd7cMh1HmXzee5wyDto8px09xkJGjqIw5eI4vCah3m/rm3KJs3Jmi0qyyHNc/kb6SU3OWMdLCOQ944da5U1BMCaOQLp7WTWcqKtHMtdNABw+kQiqnE3mBDVbHhkZNqppNpQW0vNK8UR2O8vbeoc+MF7Nod1BJlwG9BJSHOyrBx5LzeO++OJcEAUhUpGirl1BFkZQWGdtJxNjzPGyjV7Ta4Q01oyI8gMoiZlkaxJeEIcQaJ6OgUNpaTlfdnaOQJOV24tN5VMWS5uDHTQEJ+aO4t5lI44xbFyo84MwiuXIxhlpLf2XkscgX9PD7d0vYwgsclsUVmWI5CaAjYSRS66cyNhCRoC0s7VPfRSTeespQoHkxnBAbLglNn7kNo7LUeQEfhIYyml2vt0WIfvB1EyDCjtQfc6A0fQo12epTOCgxwQuePkQkNZUY1w0NjGZrlj9cURpDKCScZiLgvzqkNR2ttrxJmfXXQSxTRHIDrqSTq6yyURu0gxRzWUeU8T0JfkpM1c+gmizDXtOs7LCLKEFomxrCNgD++MIjDzuwyOoLQcQd7eWbVqaL0cwf4Sx4XOo4B/ww/jhe2rHIF0entqe4Ibj01w84kN8vcHyS5sdJ4DGeRL4OTDJs0RpA/AUalw3dYkS5LrXvuJ2kGgOyshzeIIEk3nzOfTEFkqUkw5V48j6Ek1JLWgdj8HHA5WdeclwTlmThMUSi4Cs5a6p7ZFS5bQ4pCw6kEd/qozgrWSj17aX6TJ4owIKddsG4PUQywKhbf+4xcL7/3NI7wAE9Vc2k93YwR6kCgeoI7AjMNfryxUEoPOwbNzzR0rSUgeBBrKkChKzqKFDBLPOYUdl5nQ0CgjM7JzSsmmRz1yOPaaqT1YFgrf8aKn40vuvJGe0wEy/C4jSDuVVBvq1DVHmY5geoU4grVxBFWtsTOvkhyBT0geLmEaFwpzpKMaQO5/VGYeNEB3KGdJFA/NEZjP5NURpDgClZb2eqqU/jiC1D19xQufjDtOb7Pfsz20ClnqmJURNN/r8BmBAw1Jr6ocpQ/KLiNI1RHkO9eU2WumsmkA+MGv/Rz2dwfhlVqOIEM11F9GcHihRR+2No7A9ge/UhwB4OKc/cEYKedko87DcwRpKWrLERxAPsrZqe0pnnSShsba662MI5Dv6U0nNvANz7+Vn1dm9HoQjiDpCGykmFFHIL6q0oXbmPVw503H8cybj+FZT+J7N4XXPDRHkLFmcszn11LPR947uQFZDqzaqoYSdQQDNNSz5fQZAgLlwyGhITvWYaNXbwEmDoiuaImDDBzMNFP5kKwjyMwIpM3zmm/5guT9HvfJEbjRaw8QIJCG7Tb65AjaA0JWy4xLJc4rR5L7lFNb+J1/8GXifKKxDssRFFd+72wfQDU0Fd7u50b5PK+UyRGUssPvy9bGEVzOaEEN9JsRuJvxUOOUB4lqmsMmkd6OSyUSszlpfq5qKCe9PZngB4CeOQIvUuwHZkrzN3LE6f4uVz6awvXTz6Z/OEeaV66Vfe2dA6jDNlOqoScktJD3TgpWTTn8vmxtVEP2NZVXqo7A/L1Nb/vDs1MRkpUKpg6IXDLSjCVvjI1MaKgvSSHQb6R4eFFAnsKl5W+Ez/WtGspVdLnXfqLWZx1BX7CqV4OTyxEkxBFmrDxo6LCqoRx+rQ9bH0eQ8ZpKYDUbIxXFJ8fx1Aq5UY0ciaQOmpzFbO9VHxlBjvX5bHp9zkWeo0sR+cATqSOQD5otodMucDD1VMoOErCkrIUTexBshGNytp3kCMzfF0r+fjlCi7aOIEEWD03nejYLDZ3IJIuVOnz/79x+KSnLbToHdC/hTkXx6Ygzp0o5D34YtXjv4e+DFeX0eU8PrUAq82CMzUTEacbIVA0lDoiDFvuZv+nnPrjXf6KWKx/NHSdnrM1M1VBqD3ocwSEzgivVYmJtOIKvevbN+MPv/3K2aMvaqMdULJdETNnoAOltOiM42EEjj2VxztRY/ThEoHtvQZ8cQX9c0OGejfu7JHSXWKf2ZT8paKhPOKdXMr+nIOog2XQHDR0yiOpRNTTIR3u2jXGJJ98gvxkJyE/zc6xTDV2FqKZXjuCwhGR/znVUKsyro8kRpPmbHjmCDBJxVCixmAzot23HKjKCwwZR5QE4vxSZf9AiMGms3DqCoencVbKcBmnZY60iqsnFoQ+Z3h4oI0hFnUV/UU1fJGKfhYP2Xk8yqmAnoyJZYQ3kk8WSUykLle2kgR7k0s4z6ev59KoOOyRZnOucsjiCXGioTDv8PmxtMoJcswdtH/2/c3sN5Y4D9FBZXB50AQrvEMhtQz3qL8vq64A4yD3NHSsnS9kcl4kK6wa6S8lHM+C2USG/WhJYjTgiVWGdY6vgCJJ9i5oWGu77i13LhVVHhWmgqMHzjEeNIxgygsD6zAjaitoeI60k/NCTaiinojG319C4x4ygvyyrv+j1IPUim2P5VakHJfNTjfxS0FCfBHxfz8Yd69CSY5W/d770ztP4b9/xRfism+gK6tyMQCmT+aVaqgD53UdX/WKaISMIbNQjOdNlBFcuqjm5aQqzuEgw96DJaXbVcgQ9dB/Nta5K+bBwjksi9pOx5YyzNSnFZ9jCTD046u/58s/C8247mZzTuCwwX9aHPnT7fM59ZQRFE53XOr0Py0Lhi59BN69z55LK1gCD/c+ady7T12rI4iOSEQyOILBxT5GIO0afBWWpw+arn3MzXvftX4jbrtsU55SrSpEOmmPTEUaFwnaqkV+PqqHV9G/qZ6yccV710me1L7qhrE/I4G+/6OnJ+QBdc8S+YJjDEvlAzwKDRmmWajGRstwgCjDPr6o1P6dciPYzoY5AKfUSpdSHlVL3KqVeJXzuC5VSlVLqr65yPjnWVyTijtEXBp0z1nRU4sWffVNyrNRiLgrztixpI/6Vz78db/zuL053dO2pjsAdo0+O4PAVz/lZylc/5xZ8wVOvZ3/fQndCLxvze+sQe1S3HZrg7Q9W7dOp9LVmumeTnpMRBaSz6aRq6FqvLFZKlQBeDeClAJ4N4BVKqWczn/sRAL+9qrkcxPrU7eZ2pUyZ+wrDvtpeSO/EtZZSuGxOSnzu7dclxxn3eGj1xTfkvJkr13KVWDl24O6jPcJtfanb+tw7h4VVgR6lqAfMCPI4gpQo4MqohlY5+gsB3Ku1/rjWeg7g9QBeRnzu7wH4ZQCPrHAu2davfLS/h9gu5r7S28yoph/tf791BEAfm9pRy/TWfbS/NXPYXkMHsb56QfW6d+xYfWQEPTmCUZv5pb/fZJQnCtj4TM8IANwG4AHn32ean7WmlLoNwDcCeI00kFLqO5VSdyul7j579mzvE3WtbYfQx+Fd9hfV9BUh5ZKRgFmEfRw0fR5aq1ANHZ4k7e/Qyo06bUDQB9zWd0bQz33ok3ju515djYzgM4EjoGYesic/BuAHtNY8vQ5Aa/1arfVdWuu7Tp8+3df8SOs2dY+HVo81CX29mStFUgFNRtDHQTPqMWLuuY5AqT76FvWjZALycehenWtfGPoKijH73Id9tRvPhlXFYr+DiTauZdXQGQBPdv59O4CHgs/cBeD1TfHJjQC+Vim11Fr/6grnJVqf8tE2leyR0Ds88ZwHPeR+JsdWgR0fOnot+zkc3Dn14eiuBkfQl1PJeRNdrrWBT4+w6pWEAE1GwM/9yz77NP7Olz6dVfdZazOCa7iO4O0A7lRK3QHgQQAvB/BN7ge01nfY/1ZKvQ7Ab1xNJwAcTAqYPVafVco9ZQRZHEFZoNa8BC7XOilgf7LCvvDeXutFelS45Paz6Uc11M867fOe9qlA6otXyn029jPSfbjtuk3xXcvWrvk6Aq31Uin1PTBqoBLAT2ut71FKvbL5vcgLXC3rs+lcuYpUuSdIJBfnnFd9OIL+IsVRT2OtQibcp8M/7BvKDmJ9cQR9OsTcRn55Y/WTsR3k+335s27Cg+f3DnU94Mq9oWylBWVa6zcDeHPwM9IBaK2/bZVzybW+OoYCTnFaT4u5Dzw7t8WE/Uyt60NdD+i7OKgfhzjuKUoE+uspBZgAZFTIrxEF+iURxz0JJFYhve5HidUXv5aH6wPAN3/RUw91LWt9ZkaSDZXFgfWLc/a7McZFcehmXnazbmRlBCWWQnXkQa/ZJ+/SV/Ta76F1eEewPS1xYjP9/uY+ScS+Kr9XIR/tc81cyWy6L5uW6XdY9GGDIwisr0UDuNhkP2qZPsa5fnuC/+svPw9f9eybk5/9nr/wWZgv+8gI+j8g+oru+ilY6i+L/Ntf8nR83fNuTX5u3GNG0NeaX0VzwT4ri/vi1/pw+Lm2NS1xanuSJJUPa4MjCKyv4hozho1q+hmrj00BAK944VOyPvfnnn6ql+t1GUF/ZHEf0Z1S/RYs5UAGKbt+e9K2Q5bsWbccxzc8/1Z83lOuP/Q1e6sj6BVu69NRm8Zzh/1+k1GBzXGJGzKeT182Lgv82Q99xQANXWlrIYNe9cv9LOY+MoKrYasgU/uKOo9Sp8yD2NZkhP/4is/rZay+MrZVtKHu7Tn31Ajvzd/7IjzppPy6277tSmQggyMIrN/XKvanfBgV6oqmpH3aStoh9ORU+m0ueG0+n/4yglXAqv2Q+X1kawBwx43bvYxz1OzaXLkrtD5Jqq4o5ugcWlfD+m4p7P7/Ycfqs934tZyx9fFWsX4J3h7lvUU/FfKfyTY4gsB6rQbueTH3MaerYb0eED0Tz33CVX04lathffWUGrcO8aj1GlLX7LO5UjbcncD6er0k0KXKfWUX1yr0sNG8jq8P2V3v2HGf8tErKCvs02xGcFhbhSS3L9HGtZqtXSkbOILAesU5e249cK2mtzcem+I/vPwFePEz+Rfm5FqfBX9lTyRi9wrDa/P53HrdJm7pgQBdjUx4vYOoK2WDIwisz4izzwjpubedxIXdxaHHuVr2shfclv5QhvX9KtE+Dq1n3XICP/CSZ+FFd662M+6q7Dtf9HT8zf/tjkOPo5RCWagj179pc1JiM/GS+HW3wREE1mfTubaNQQ+H1g+85FmHHuMzwfpWpvTV/uO7XvyMQ49ztWxUFsjorJw3Vl9ZVo/Fad/3lc/Exb1rN4i6EjY4gsD6fFdqnxWngxkb9cjhlIXqpcZjsM564116rMr/TJV89mkDcBaYUgp/+fNv66Wq9ko1jFon+5wnncDzbjuJrR5S/WPTEbamQyzUpz3ntpN45s3HDj3O8ea5HN9I910a7PCmdA/95q+k3XXXXfruu+++2tPIsnM7c/zUH34c/+irP3vlTaMGO7h95OFL2ByXePINW1d7KoMFVtca77z/PO562g1XeyqfMaaUeofW+i7yd4MjGGywwQb7zDfJEQzQ0GCDDTbYmtvgCAYbbLDB1twGRzDYYIMNtuY2OILBBhtssDW3wREMNthgg625DY5gsMEGG2zNbXAEgw022GBrboMjGGywwQZbc7vmCsqUUmcB3PcE//xGAI/2OJ1V2bUwz2thjsC1Mc9hjv3ZtTDPqzXHp2qtyRa515wjOIwppe7mKuuOkl0L87wW5ghcG/Mc5tifXQvzPIpzHKChwQYbbLA1t8ERDDbYYIOtua2bI3jt1Z5Apl0L87wW5ghcG/Mc5tifXQvzPHJzXCuOYLDBBhtssNjWLSMYbLDBBhsssMERDDbYYIOtua2NI1BKvUQp9WGl1L1KqVdd7fkAgFLqyUqptyqlPqiUukcp9b3Nz29QSv1PpdRHm/+//gjMtVRKvUsp9RtHeI7XKaXeoJT6UHNP//xRm6dS6h80z/r9SqlfVEptHIU5KqV+Win1iFLq/c7P2HkppX6w2UsfVkp9zVWc4482z/u9SqlfUUpddzXnyM3T+d0/VkpppdSNV3uerq2FI1BKlQBeDeClAJ4N4BVKqWdf3VkBAJYA/pHW+nMA/DkAf7eZ16sAvEVrfSeAtzT/vtr2vQA+6Pz7KM7xPwD4La31swA8H2a+R2aeSqnbAPx9AHdprZ8LoATw8iMyx9cBeEnwM3JezRp9OYDnNH/zk80euxpz/J8Anqu1/lwAHwHwg1d5jtw8oZR6MoCvAnC/87OrOc/W1sIRAHghgHu11h/XWs8BvB7Ay67ynKC1/pTW+p3Nf1+CObhug5nbf20+9l8B/KWrMsHGlFK3A/g6AD/l/PiozfEEgC8F8F8AQGs911pfwBGbJ4ARgE2l1AjAFoCHcATmqLX+AwDngh9z83oZgNdrrWda608AuBdmj13xOWqtf0drvWz++WcAbr+ac+Tm2di/B/D9AFyFzlWbp2vr4ghuA/CA8+8zzc+OjCmlngbg8wC8DcDNWutPAcZZALjpKk4NAH4MZgHXzs+O2hyfDuAsgJ9pIKyfUkpt4wjNU2v9IIB/CxMRfgrA41rr3zlKcwyMm9dR3U9/E8BvNv99pOaolPoGAA9qrd8T/OpIzHNdHIEifnZkdLNKqWMAfhnA92mtL17t+bimlPp6AI9ord9xteeSsBGAzwfwn7TWnwdgB0cDrmqtwdhfBuAOALcC2FZKfcvVndUTsiO3n5RS/xQGav0F+yPiY1dljkqpLQD/FMA/p35N/OyKz3NdHMEZAE92/n07TEp+1U0pNYZxAr+gtX5j8+OHlVJPan7/JACPXK35AfjfAHyDUuqTMJDaX1BK/TyO1hwB84zPaK3f1vz7DTCO4SjN8ysBfEJrfVZrvQDwRgBffMTm6Bo3ryO1n5RS3wrg6wF8s+4Ko47SHJ8B4/zf0+yj2wG8Uyl1C47IPNfFEbwdwJ1KqTuUUhMYcuZNV3lOUEopGEz7g1rr/9f51ZsAfGvz398K4Neu9Nysaa1/UGt9u9b6aTD37X9prb8FR2iOAKC1/jSAB5RSn9386CsAfABHa573A/hzSqmt5tl/BQwvdJTm6Bo3rzcBeLlSaqqUugPAnQD+v6swPyilXgLgBwB8g9Z61/nVkZmj1vp9WuubtNZPa/bRGQCf36zZozFPrfVa/A/A18KoCj4G4J9e7fk0c/oSmDTwvQDe3fzvawGcglFpfLT5/xuu9lyb+b4YwG80/33k5gjgBQDubu7nrwK4/qjNE8APA/gQgPcD+DkA06MwRwC/CMNbLGAOqr8lzQsG6vgYgA8DeOlVnOO9MBi73T+vuZpz5OYZ/P6TAG682vN0/ze0mBhssMEGW3NbF2hosMEGG2wwxgZHMNhggw225jY4gsEGG2ywNbfBEQw22GCDrbkNjmCwwQYbbM1tcASDDZawpqvpdzf/fatS6g1Xe06DDdanDfLRwQZLWNMH6je06Rg62GCfcTa62hMYbLBrwP5vAM9QSr0bprjqc7TWz1VKfRtMR84SwHMB/DsAEwB/HcAMwNdqrc8ppZ4B0wb9NIBdAN+htf7Qlf4Sgw3G2QANDTZY2l4F4GNa6xcA+CfB754L4JtgWgf/awC72jS9+1MAf6P5zGsB/D2t9RcA+McAfvJKTHqwwXJtyAgGG+xw9lZt3iVxSSn1OIBfb37+PgCf23SW/WIA/8O0FwJg2koMNtiRscERDDbY4Wzm/Hft/LuG2V8FgAtNNjHYYEfSBmhosMHSdgnA8Sfyh9q8X+ITSqm/BpiOs0qp5/c5ucEGO6wNjmCwwRKmtX4MwB83LyP/0ScwxDcD+FtKqfcAuAdH4DWpgw3m2iAfHWywwQZbcxsygsEGG2ywNbfBEQw22GCDrbkNjmCwwQYbbM1tcASDDTbYYGtugyMYbLDBBltzGxzBYIMNNtia2+AIBhtssMHW3P5/ZoYOrnUX9dEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "swifterdat['r'].sel(id=100).plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swiftest file param.swiftest.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile  = \"param.swiftest.in\"\n",
-    "paramfile = swio.read_swiftest_config(inparfile)\n",
-    "swiftestdat = swio.swiftest2xr(paramfile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
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-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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-       ".xr-var-attrs,\n",
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-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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-       "  float: right;\n",
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-       "\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",
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-       "  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.Dataset&gt;\n",
-       "Dimensions:  (id: 12, time: 49)\n",
-       "Coordinates:\n",
-       "  * id       (id) int64 0 2 3 4 5 6 7 8 9 10 100 433\n",
-       "  * time     (time) float64 0.0 121.8 243.5 ... 5.6e+03 5.722e+03 5.844e+03\n",
-       "Data variables:\n",
-       "    Mass     (time, id) float64 1.0 1.66e-07 2.448e-06 ... 7.407e-09 nan nan\n",
-       "    Radius   (time, id) float64 0.00465 1.631e-05 4.045e-05 ... nan nan\n",
-       "    J_2      (time, id) float64 4.754e-12 nan nan nan nan ... nan nan nan nan\n",
-       "    J_4      (time, id) float64 -2.247e-18 nan nan nan nan ... nan nan nan nan\n",
-       "    px       (time, id) float64 nan 0.103 0.0611 -0.6062 ... 29.09 nan -0.9879\n",
-       "    py       (time, id) float64 nan 0.2898 -0.7245 0.7761 ... -24.15 nan -1.04\n",
-       "    pz       (time, id) float64 nan 0.01423 -0.01347 ... -5.83 nan -0.2681\n",
-       "    vx       (time, id) float64 nan -0.03214 0.02002 ... 0.002132 nan 0.007837\n",
-       "    vy       (time, id) float64 nan 0.0105 0.001627 ... 0.001664 nan -0.01188\n",
-       "    vz       (time, id) float64 nan 0.003806 -0.001133 ... nan -3.825e-05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e1d11449-c617-4c16-913b-6e4169ae0e0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1d11449-c617-4c16-913b-6e4169ae0e0a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 12</li><li><span class='xr-has-index'>time</span>: 49</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e4ef7aed-1e99-4c13-b9d5-7c9d2aa56fba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4ef7aed-1e99-4c13-b9d5-7c9d2aa56fba' 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'>0 2 3 4 5 6 7 8 9 10 100 433</div><input id='attrs-56aa7b31-4135-4df8-a8fb-9510574c805d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56aa7b31-4135-4df8-a8fb-9510574c805d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6915743e-e0ab-4ba7-bbcd-eaddb264936a' class='xr-var-data-in' type='checkbox'><label for='data-6915743e-e0ab-4ba7-bbcd-eaddb264936a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   2,   3,   4,   5,   6,   7,   8,   9,  10, 100, 433])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 121.8 ... 5.722e+03 5.844e+03</div><input id='attrs-b8c8017d-b268-42f5-b72d-4979cc3b4158' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8c8017d-b268-42f5-b72d-4979cc3b4158' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-633332c9-eb52-47ce-89ce-4ffb4478a479' class='xr-var-data-in' type='checkbox'><label for='data-633332c9-eb52-47ce-89ce-4ffb4478a479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0.  ,  121.75,  243.5 ,  365.25,  487.  ,  608.75,  730.5 ,  852.25,\n",
-       "        974.  , 1095.75, 1217.5 , 1339.25, 1461.  , 1582.75, 1704.5 , 1826.25,\n",
-       "       1948.  , 2069.75, 2191.5 , 2313.25, 2435.  , 2556.75, 2678.5 , 2800.25,\n",
-       "       2922.  , 3043.75, 3165.5 , 3287.25, 3409.  , 3530.75, 3652.5 , 3774.25,\n",
-       "       3896.  , 4017.75, 4139.5 , 4261.25, 4383.  , 4504.75, 4626.5 , 4748.25,\n",
-       "       4870.  , 4991.75, 5113.5 , 5235.25, 5357.  , 5478.75, 5600.5 , 5722.25,\n",
-       "       5844.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb127476-e33b-4faf-b860-97d1cc470093' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb127476-e33b-4faf-b860-97d1cc470093' class='xr-section-summary' >Data variables: <span>(10)</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>Mass</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.66e-07 2.448e-06 ... nan nan</div><input id='attrs-dcfa03b8-2c28-4387-979d-847d037e4d07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcfa03b8-2c28-4387-979d-847d037e4d07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5559a684-3421-43cf-b43d-5293f61c2521' class='xr-var-data-in' type='checkbox'><label for='data-5559a684-3421-43cf-b43d-5293f61c2521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "...\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan],\n",
-       "       [1.00000000e+00, 1.66013680e-07, 2.44783834e-06, 3.04043265e-06,\n",
-       "        3.22715145e-07, 9.54791938e-04, 2.85885981e-04, 4.36624404e-05,\n",
-       "        5.15138902e-05, 7.40740741e-09,            nan,            nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Radius</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00465 1.631e-05 ... nan nan</div><input id='attrs-37b7d0be-10a9-4ab3-b5de-d269eb0b6cab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37b7d0be-10a9-4ab3-b5de-d269eb0b6cab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65ea6a70-85a7-40d3-83ce-83e8b900ee67' class='xr-var-data-in' type='checkbox'><label for='data-65ea6a70-85a7-40d3-83ce-83e8b900ee67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "...\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan],\n",
-       "       [4.65046726e-03, 1.63063818e-05, 4.04537843e-05, 4.25875607e-05,\n",
-       "        2.26574081e-05, 4.67326170e-04, 3.89256877e-04, 1.69534499e-04,\n",
-       "        1.64587904e-04, 7.94329488e-06,            nan,            nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>J_2</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.754e-12 nan nan ... nan nan nan</div><input id='attrs-0211c4cc-531c-41ca-9542-44f0f57a8b5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0211c4cc-531c-41ca-9542-44f0f57a8b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32833d5a-e3b1-4502-b165-cd1157abf534' class='xr-var-data-in' type='checkbox'><label for='data-32833d5a-e3b1-4502-b165-cd1157abf534' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "...\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [4.75358069e-12,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>J_4</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.247e-18 nan nan ... nan nan nan</div><input id='attrs-39f6323a-3ccb-48a0-9818-2e71794d8c0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39f6323a-3ccb-48a0-9818-2e71794d8c0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8b79981-19fc-45da-a1d7-f86b83df154e' class='xr-var-data-in' type='checkbox'><label for='data-f8b79981-19fc-45da-a1d7-f86b83df154e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "...\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan],\n",
-       "       [-2.2473968e-18,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan,\n",
-       "                   nan,            nan,            nan,            nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>px</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.103 0.0611 ... nan -0.9879</div><input id='attrs-45a81b1b-9447-4de5-a7e8-26963a5744f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45a81b1b-9447-4de5-a7e8-26963a5744f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49d9d250-aae0-410d-86f5-7d76009118ed' class='xr-var-data-in' type='checkbox'><label for='data-49d9d250-aae0-410d-86f5-7d76009118ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[            nan,  1.03025686e-01,  6.11021803e-02,\n",
-       "        -6.06179634e-01,  2.75194418e-01,  3.20013544e+00,\n",
-       "         5.60738217e+00,  1.52822542e+01,  2.94748307e+01,\n",
-       "         1.41400092e+01, -3.58609928e-01, -1.23689280e+00],\n",
-       "       [            nan, -3.14325328e-01, -2.35034679e-01,\n",
-       "        -3.74966833e-01, -1.20648355e+00,  3.84634467e+00,\n",
-       "         6.11946302e+00,  1.49712633e+01,  2.95363258e+01,\n",
-       "         1.44969683e+01,  1.94578081e-01, -3.18288875e-01],\n",
-       "       [            nan,  3.31246723e-01,  4.12566718e-01,\n",
-       "         9.97712640e-01, -1.60946357e+00,  4.36163206e+00,\n",
-       "         6.60420160e+00,  1.46516177e+01,  2.95928503e+01,\n",
-       "         1.48522211e+01,  1.46357787e-01,  1.02315741e+00],\n",
-       "       [            nan, -2.96028341e-01, -5.46607338e-01,\n",
-       "        -6.06025661e-01, -5.14303678e-01,  4.72548683e+00,\n",
-       "         7.05921796e+00,  1.43234388e+01,  2.96443543e+01,\n",
-       "         1.52057012e+01, -4.28478750e-01,  1.58961925e+00],\n",
-       "       [            nan, -5.64771875e-02,  6.54680737e-01,\n",
-       "        -3.74969168e-01,  1.08963412e+00,  4.92262335e+00,\n",
-       "         7.48226010e+00,  1.39869012e+01,  2.96908368e+01,\n",
-       "         1.55573907e+01, -1.37682613e-02,  5.78467939e-01],\n",
-       "...\n",
-       "       [            nan,  3.21831143e-01, -5.70787259e-01,\n",
-       "         9.97909012e-01,  1.39147524e+00,  3.72759665e+00,\n",
-       "        -5.93391382e+00, -3.70883757e+00,  2.75338218e+01,\n",
-       "         2.80351552e+01,             nan,  7.07427903e-01],\n",
-       "       [            nan, -3.93827531e-01,  4.51252331e-01,\n",
-       "        -6.04787192e-01,  4.97085706e-01,  3.04524781e+00,\n",
-       "        -6.46920283e+00, -4.18141034e+00,  2.73815348e+01,\n",
-       "         2.83028698e+01,             nan,  1.56512796e+00],\n",
-       "       [            nan,  1.89337955e-01, -2.73820029e-01,\n",
-       "        -3.76450717e-01, -1.04097350e+00,  2.25630860e+00,\n",
-       "        -6.96744266e+00, -4.65131642e+00,  2.72246271e+01,\n",
-       "         2.85682301e+01,             nan,  1.02136088e+00],\n",
-       "       [            nan, -3.86841453e-02,  1.05604080e-01,\n",
-       "         9.97894095e-01, -1.64585165e+00,  1.38976515e+00,\n",
-       "        -7.42608594e+00, -5.11820241e+00,  2.70631576e+01,\n",
-       "         2.88312638e+01,             nan, -8.45039495e-01],\n",
-       "       [            nan, -2.44782310e-01,  9.66249750e-02,\n",
-       "        -6.04745910e-01, -7.40620631e-01,  4.76349179e-01,\n",
-       "        -7.84289969e+00, -5.58173439e+00,  2.68971665e+01,\n",
-       "         2.90919791e+01,             nan, -9.87858153e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>py</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.2898 -0.7245 ... nan -1.04</div><input id='attrs-7fda551b-eedd-4c78-8f10-241906a82123' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fda551b-eedd-4c78-8f10-241906a82123' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b766f9dc-4fb5-4ba9-ba19-5e90db236728' class='xr-var-data-in' type='checkbox'><label for='data-b766f9dc-4fb5-4ba9-ba19-5e90db236728' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[            nan,  2.89779605e-01, -7.24546690e-01,\n",
-       "         7.76121455e-01,  1.51937689e+00, -3.95349821e+00,\n",
-       "        -8.25864911e+00,  1.25390531e+01, -5.14768653e+00,\n",
-       "        -3.11414181e+01, -3.14614714e-01, -2.55357395e-01],\n",
-       "       [            nan, -3.20767294e-01,  6.78896897e-01,\n",
-       "        -9.41751297e-01,  1.13349928e+00, -3.26872488e+00,\n",
-       "        -7.86006607e+00,  1.28839979e+01, -4.76770583e+00,\n",
-       "        -3.10629712e+01, -3.99564247e-01, -1.59878340e+00],\n",
-       "       [            nan, -2.19617929e-01, -5.99188709e-01,\n",
-       "         9.23151438e-02, -2.95973255e-01, -2.47285596e+00,\n",
-       "        -7.42636206e+00,  1.32217154e+01, -4.38682707e+00,\n",
-       "        -3.09810209e+01,  2.82557651e-01, -1.46008979e+00],\n",
-       "       [            nan,  1.77992777e-01,  4.64772508e-01,\n",
-       "         7.76252505e-01, -1.41021696e+00, -1.59127310e+00,\n",
-       "        -6.95926817e+00,  1.35519719e+01, -4.00513381e+00,\n",
-       "        -3.08956218e+01, -1.01344145e-01, -2.22103027e-01],\n",
-       "       [            nan, -4.61512530e-01, -3.15189583e-01,\n",
-       "        -9.41706290e-01, -8.53723777e-01, -6.53664485e-01,\n",
-       "        -6.46069799e+00,  1.38745127e+01, -3.62273337e+00,\n",
-       "        -3.08068516e+01, -4.78437700e-01,  1.09156155e+00],\n",
-       "...\n",
-       "       [            nan,  9.75130788e-02, -4.42906679e-01,\n",
-       "         9.07967656e-02,  1.42816684e-02,  3.29756321e+00,\n",
-       "         6.92307262e+00,  1.84733946e+01,  1.14557834e+01,\n",
-       "        -2.49447489e+01,             nan, -1.62726809e+00],\n",
-       "       [            nan, -1.15345682e-01,  5.63918448e-01,\n",
-       "         7.77219649e-01,  1.43849588e+00,  3.96670583e+00,\n",
-       "         6.46005219e+00,  1.83510182e+01,  1.18105324e+01,\n",
-       "        -2.47484629e+01,             nan, -6.15628024e-01],\n",
-       "       [            nan, -3.96539229e-01, -6.71763548e-01,\n",
-       "        -9.41097240e-01,  1.27600875e+00,  4.49713436e+00,\n",
-       "         5.95981311e+00,  1.82163906e+01,  1.21631713e+01,\n",
-       "        -2.45503530e+01,             nan,  8.49153726e-01],\n",
-       "       [            nan,  3.06897601e-01,  7.12376415e-01,\n",
-       "         9.07192087e-02, -7.89222822e-02,  4.87312702e+00,\n",
-       "         5.42553931e+00,  1.80695742e+01,  1.25136555e+01,\n",
-       "        -2.43504346e+01,             nan,  7.63969490e-01],\n",
-       "       [            nan, -3.89109270e-01, -7.20760209e-01,\n",
-       "         7.77275251e-01, -1.33123586e+00,  5.08522878e+00,\n",
-       "         4.86052936e+00,  1.79105951e+01,  1.28618960e+01,\n",
-       "        -2.41487672e+01,             nan, -1.04013683e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pz</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.01423 ... nan -0.2681</div><input id='attrs-4a1c0926-86a8-4dfa-baf6-7551e0fb22cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a1c0926-86a8-4dfa-baf6-7551e0fb22cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c174cf2f-d627-41ce-9d2c-c42cf273e3f3' class='xr-var-data-in' type='checkbox'><label for='data-c174cf2f-d627-41ce-9d2c-c42cf273e3f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[            nan,  1.42290460e-02, -1.34690430e-02,\n",
-       "        -3.47500478e-05,  2.50892459e-02, -5.51773729e-02,\n",
-       "        -7.95844523e-02, -1.51414358e-01, -5.73344182e-01,\n",
-       "        -7.56572259e-01,  7.22004165e-03, -2.23013201e-01],\n",
-       "       [            nan,  2.62106482e-03,  2.28797775e-02,\n",
-       "         4.57758248e-05,  5.33492285e-02, -7.24790407e-02,\n",
-       "        -1.06887704e-01, -1.46121972e-01, -5.82576983e-01,\n",
-       "        -8.67942509e-01, -5.05082226e-02, -2.22662516e-01],\n",
-       "       [            nan, -4.83310594e-02, -3.20305985e-02,\n",
-       "        -8.15987128e-06,  3.32770012e-02, -8.73130964e-02,\n",
-       "        -1.33713421e-01, -1.40744132e-01, -5.91711992e-01,\n",
-       "        -9.79215904e-01,  9.64855473e-03,  4.29323619e-03],\n",
-       "       [            nan,  4.16990436e-02,  3.79201788e-02,\n",
-       "        -3.50690161e-05, -1.69391020e-02, -9.91151665e-02,\n",
-       "        -1.59937226e-01, -1.35282571e-01, -6.00746547e-01,\n",
-       "        -1.09037994e+00,  3.10569609e-02,  2.27251814e-01],\n",
-       "       [            nan, -3.25339188e-02, -4.21034259e-02,\n",
-       "         4.91794894e-05, -4.46206711e-02, -1.07420241e-01,\n",
-       "        -1.85436759e-01, -1.29740444e-01, -6.09679369e-01,\n",
-       "        -1.20142365e+00, -3.78254502e-02,  2.09036617e-01],\n",
-       "...\n",
-       "       [            nan, -2.15394614e-02,  2.68330608e-02,\n",
-       "        -1.46407594e-05, -3.37954143e-02, -9.70992669e-02,\n",
-       "         1.16009666e-01,  1.16511804e-01, -8.70352087e-01,\n",
-       "        -5.43868380e+00,             nan, -6.31386175e-02],\n",
-       "       [            nan,  2.66832151e-02, -1.82707331e-02,\n",
-       "        -6.09773578e-05,  1.79730633e-02, -8.46150614e-02,\n",
-       "         1.45368687e-01,  1.22183330e-01, -8.74136224e-01,\n",
-       "        -5.53729106e+00,             nan,  1.81309197e-01],\n",
-       "       [            nan, -4.97726582e-02,  6.54872783e-03,\n",
-       "         8.17746528e-05,  5.22495929e-02, -6.91695689e-02,\n",
-       "         1.73895964e-01,  1.27777018e-01, -8.77772983e-01,\n",
-       "        -5.63545254e+00,             nan,  2.52849985e-01],\n",
-       "       [            nan,  2.86318758e-02,  3.71580338e-03,\n",
-       "        -1.38671461e-05,  3.86674939e-02, -5.13458904e-02,\n",
-       "         2.01435569e-01,  1.33288103e-01, -8.81262548e-01,\n",
-       "        -5.73316423e+00,             nan, -5.14988845e-02],\n",
-       "       [            nan, -9.36022395e-03, -1.54985358e-02,\n",
-       "        -6.25217263e-05, -9.75893944e-03, -3.17927020e-02,\n",
-       "         2.27840691e-01,  1.38711986e-01, -8.84604999e-01,\n",
-       "        -5.83042206e+00,             nan, -2.68094026e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan -0.03214 ... nan 0.007837</div><input id='attrs-345a6934-eff4-4bd0-95ad-2c37597ea4e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-345a6934-eff4-4bd0-95ad-2c37597ea4e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb08aa78-8f88-4728-989e-8d907e8240fa' class='xr-var-data-in' type='checkbox'><label for='data-eb08aa78-8f88-4728-989e-8d907e8240fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[            nan, -3.21424835e-02,  2.00191525e-02,\n",
-       "        -1.38393547e-02, -1.32402015e-02,  5.78068104e-03,\n",
-       "         4.31169572e-03, -2.51840443e-03,  5.25439500e-04,\n",
-       "         2.93879838e-03,  1.27351496e-02,  4.47112321e-04],\n",
-       "       [            nan,  1.43712310e-02, -1.91813933e-02,\n",
-       "         1.57037996e-02, -9.05554760e-03,  4.80107908e-03,\n",
-       "         4.09697961e-03, -2.59008983e-03,  4.84704634e-04,\n",
-       "         2.92494190e-03,  1.94530805e-02,  1.14774912e-02],\n",
-       "       [            nan,  1.00153752e-02,  1.65242362e-02,\n",
-       "        -1.86466719e-03,  3.05118561e-03,  3.63531620e-03,\n",
-       "         3.86258208e-03, -2.66062623e-03,  4.43730168e-04,\n",
-       "         2.91071106e-03, -2.98268721e-02,  9.07257676e-03],\n",
-       "       [            nan, -2.02632848e-02, -1.31780580e-02,\n",
-       "        -1.38407444e-02,  1.36727736e-02,  2.32073547e-03,\n",
-       "         3.60898104e-03, -2.73011728e-03,  4.02376759e-04,\n",
-       "         2.89596497e-03,  7.94697147e-04, -9.25984764e-04],\n",
-       "       [            nan,  2.22825657e-02,  8.65750876e-03,\n",
-       "         1.57040923e-02,  9.16079997e-03,  9.05083471e-04,\n",
-       "         3.33732354e-03, -2.79806801e-03,  3.61104223e-04,\n",
-       "         2.88116164e-03,  2.22122090e-02, -1.54367215e-02],\n",
-       "...\n",
-       "       [            nan, -1.36124278e-02,  1.22577299e-02,\n",
-       "        -1.83911723e-03,  3.89783403e-04, -5.09348277e-03,\n",
-       "        -4.54118206e-03, -3.89145458e-03, -1.23165123e-03,\n",
-       "         2.20866059e-03,             nan,  1.05336058e-02],\n",
-       "       [            nan,  2.07246761e-03, -1.58547154e-02,\n",
-       "        -1.38577237e-02, -1.26982627e-02, -6.08016201e-03,\n",
-       "        -4.24821554e-03, -3.87109199e-03, -1.26993197e-03,\n",
-       "         2.18912336e-03,             nan,  2.38174613e-03],\n",
-       "       [            nan,  1.97577751e-02,  1.85925275e-02,\n",
-       "         1.56943465e-02, -1.03160564e-02, -6.83990965e-03,\n",
-       "        -3.93290770e-03, -3.84759444e-03, -1.30750020e-03,\n",
-       "         2.17004285e-03,             nan, -1.19787803e-02],\n",
-       "       [            nan, -3.35588445e-02, -2.00761789e-02,\n",
-       "        -1.83744893e-03,  1.19158558e-03, -7.35264789e-03,\n",
-       "        -3.59824076e-03, -3.82155838e-03, -1.34494621e-03,\n",
-       "         2.15082487e-03,             nan, -1.22218199e-02],\n",
-       "       [            nan,  1.81329106e-02,  1.99116005e-02,\n",
-       "        -1.38584268e-02,  1.27557913e-02, -7.60938096e-03,\n",
-       "        -3.24610604e-03, -3.79242133e-03, -1.38169862e-03,\n",
-       "         2.13203699e-03,             nan,  7.83724129e-03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0105 ... nan -0.01188</div><input id='attrs-2f02cbe4-fffd-46b4-97fe-69adcdeac5ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f02cbe4-fffd-46b4-97fe-69adcdeac5ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6686affc-9bdd-4f76-9fad-2a2c95bdb05c' class='xr-var-data-in' type='checkbox'><label for='data-6686affc-9bdd-4f76-9fad-2a2c95bdb05c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[            nan,  1.04977753e-02,  1.62659157e-03,\n",
-       "        -1.06548048e-02,  3.68201258e-03,  5.10890264e-03,\n",
-       "         3.12514022e-03,  2.86225626e-03,  3.11707334e-03,\n",
-       "         6.29791189e-04, -1.83960609e-02, -1.59870159e-02],\n",
-       "       [            nan, -1.84075474e-02, -6.73097180e-03,\n",
-       "        -6.42858019e-03, -9.00649978e-03,  6.11206636e-03,\n",
-       "         3.42024531e-03,  2.80383234e-03,  3.12476310e-03,\n",
-       "         6.58758840e-04,  1.45494107e-02, -4.83300782e-03],\n",
-       "       [            nan,  2.47437354e-02,  1.13994847e-02,\n",
-       "         1.70667530e-02, -1.25675049e-02,  6.92694099e-03,\n",
-       "         3.70192592e-03,  2.74360893e-03,  3.13189818e-03,\n",
-       "         6.87412101e-04,  1.48669582e-02,  6.49307052e-03],\n",
-       "       [            nan, -2.29339808e-02, -1.55130221e-02,\n",
-       "        -1.06527702e-02, -3.59288496e-03,  7.51406290e-03,\n",
-       "         3.96840875e-03,  2.68115482e-03,  3.13802456e-03,\n",
-       "         7.15297032e-04, -2.45593114e-02,  1.26421959e-02],\n",
-       "       [            nan, -1.98517449e-03,  1.81393048e-02,\n",
-       "        -6.42990690e-03,  1.22146108e-02,  7.84312985e-03,\n",
-       "         4.21891011e-03,  2.61699903e-03,  3.14364739e-03,\n",
-       "         7.42919045e-04,  1.71611701e-03,  5.25446996e-03],\n",
-       "...\n",
-       "       [            nan,  2.81774895e-02, -1.60746169e-02,\n",
-       "         1.70684200e-02,  1.51893916e-02,  6.00993551e-03,\n",
-       "        -3.64201637e-03, -9.54841963e-04,  2.92212991e-03,\n",
-       "         1.60451206e-03,             nan,  3.88590176e-03],\n",
-       "       [            nan, -2.57898531e-02,  1.25504514e-02,\n",
-       "        -1.06305295e-02,  5.76219664e-03,  4.95219469e-03,\n",
-       "        -3.96007606e-03, -1.05548500e-03,  2.90519743e-03,\n",
-       "         1.61976883e-03,             nan,  1.17710267e-02],\n",
-       "       [            nan,  1.35262193e-02, -7.72245410e-03,\n",
-       "        -6.45476956e-03, -7.65231168e-03,  3.73970239e-03,\n",
-       "        -4.25298474e-03, -1.15591444e-03,  2.88763442e-03,\n",
-       "         1.63463775e-03,             nan,  9.51506954e-03],\n",
-       "       [            nan, -2.49776969e-03,  2.86349913e-03,\n",
-       "         1.70689546e-02, -1.27807932e-02,  2.42378339e-03,\n",
-       "        -4.51912231e-03, -1.25587413e-03,  2.86962900e-03,\n",
-       "         1.64930609e-03,             nan, -1.24882820e-02],\n",
-       "       [            nan, -1.36451325e-02,  2.61559296e-03,\n",
-       "        -1.06290261e-02, -5.60233872e-03,  1.05616596e-03,\n",
-       "        -4.75756466e-03, -1.35554788e-03,  2.85092830e-03,\n",
-       "         1.66352012e-03,             nan, -1.18830536e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vz</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.003806 ... nan -3.825e-05</div><input id='attrs-c70b3560-85fd-4880-8b90-bddde141297c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c70b3560-85fd-4880-8b90-bddde141297c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee4f01ff-9f55-4838-8525-e83da7bd1e50' class='xr-var-data-in' type='checkbox'><label for='data-ee4f01ff-9f55-4838-8525-e83da7bd1e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[            nan,  3.80629614e-03, -1.13289907e-03,\n",
-       "         5.39913433e-07,  4.01955181e-04, -1.50552801e-04,\n",
-       "        -2.25881761e-04,  4.31085247e-05, -7.62336075e-05,\n",
-       "        -9.15113746e-04, -2.67215070e-03, -1.65284886e-03],\n",
-       "       [            nan, -2.82248221e-03,  1.01449330e-03,\n",
-       "         2.59020187e-07,  3.33920989e-05, -1.32798031e-04,\n",
-       "        -2.22463155e-04,  4.38255268e-05, -7.54331728e-05,\n",
-       "        -9.14361896e-04, -5.97262879e-04,  1.29294232e-03],\n",
-       "       [            nan,  1.10330781e-03, -7.97082509e-04,\n",
-       "        -8.13668819e-07, -3.38228860e-04, -1.10103738e-04,\n",
-       "        -2.18033563e-04,  4.45162702e-05, -7.46230985e-05,\n",
-       "        -9.13518866e-04,  3.95295068e-03,  2.13373474e-03],\n",
-       "       [            nan, -1.54360122e-05,  5.47478987e-04,\n",
-       "         5.89161417e-07, -4.10690772e-04, -8.31320010e-05,\n",
-       "        -2.12582566e-04,  4.51945545e-05, -7.37892278e-05,\n",
-       "        -9.12571841e-04, -2.07949542e-03,  1.21620815e-03],\n",
-       "       [            nan, -2.20613465e-03, -2.50548451e-04,\n",
-       "         2.40112133e-07,  3.12798384e-05, -5.28257404e-05,\n",
-       "        -2.06130167e-04,  4.58467552e-05, -7.29448401e-05,\n",
-       "        -9.11535431e-04, -1.89900062e-03, -1.87313591e-03],\n",
-       "...\n",
-       "       [            nan,  3.55129222e-03, -9.28488775e-04,\n",
-       "        -1.40399159e-06,  3.08855936e-04,  8.89711927e-05,\n",
-       "         2.44114247e-04,  4.68903795e-05, -3.16864637e-05,\n",
-       "        -8.11734784e-04,             nan,  2.08376412e-03],\n",
-       "       [            nan, -2.29800558e-03,  1.08749830e-03,\n",
-       "         9.80901200e-07,  4.31902840e-04,  1.15442900e-04,\n",
-       "         2.37947506e-04,  4.62724514e-05, -3.04735235e-05,\n",
-       "        -8.08088581e-04,             nan,  1.64270019e-03],\n",
-       "       [            nan, -7.06045827e-04, -1.17900186e-03,\n",
-       "         4.53384390e-07,  9.23326491e-05,  1.37477837e-04,\n",
-       "         2.30458226e-04,  4.56078704e-05, -2.92693999e-05,\n",
-       "        -8.04416190e-04,             nan, -8.71206463e-04],\n",
-       "       [            nan,  2.87290636e-03,  1.19769307e-03,\n",
-       "        -1.43180632e-06, -2.97090747e-04,  1.54421446e-04,\n",
-       "         2.21736988e-04,  4.49173372e-05, -2.80538412e-05,\n",
-       "        -8.00698048e-04,             nan, -3.27600362e-03],\n",
-       "       [            nan, -2.77793472e-03, -1.11275370e-03,\n",
-       "         9.80348787e-07, -4.29924335e-04,  1.65844210e-04,\n",
-       "         2.11836142e-04,  4.41762028e-05, -2.68519968e-05,\n",
-       "        -7.96959991e-04,             nan, -3.82505884e-05]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82e9f8a0-f79c-4a72-8558-c67c26ba2cc2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-82e9f8a0-f79c-4a72-8558-c67c26ba2cc2' 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.Dataset>\n",
-       "Dimensions:  (id: 12, time: 49)\n",
-       "Coordinates:\n",
-       "  * id       (id) int64 0 2 3 4 5 6 7 8 9 10 100 433\n",
-       "  * time     (time) float64 0.0 121.8 243.5 ... 5.6e+03 5.722e+03 5.844e+03\n",
-       "Data variables:\n",
-       "    Mass     (time, id) float64 1.0 1.66e-07 2.448e-06 ... 7.407e-09 nan nan\n",
-       "    Radius   (time, id) float64 0.00465 1.631e-05 4.045e-05 ... nan nan\n",
-       "    J_2      (time, id) float64 4.754e-12 nan nan nan nan ... nan nan nan nan\n",
-       "    J_4      (time, id) float64 -2.247e-18 nan nan nan nan ... nan nan nan nan\n",
-       "    px       (time, id) float64 nan 0.103 0.0611 -0.6062 ... 29.09 nan -0.9879\n",
-       "    py       (time, id) float64 nan 0.2898 -0.7245 0.7761 ... -24.15 nan -1.04\n",
-       "    pz       (time, id) float64 nan 0.01423 -0.01347 ... -5.83 nan -0.2681\n",
-       "    vx       (time, id) float64 nan -0.03214 0.02002 ... 0.002132 nan 0.007837\n",
-       "    vy       (time, id) float64 nan 0.0105 0.001627 ... 0.001664 nan -0.01188\n",
-       "    vz       (time, id) float64 nan 0.003806 -0.001133 ... nan -3.825e-05"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "swiftestdat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftestdat['r'] = np.sqrt(swiftestdat['px']**2 + swiftestdat['py']**2 + swiftestdat['pz']**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f85e0a8e430>]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p100swiftest = swiftestdat['r'].sel(id=100)\n",
-    "p100swiftest.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dr = p100swiftest - p100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f85f071c820>]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dr.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/rmvs_gr_test/pl.swifter.in b/examples/rmvs_gr_test/pl.swifter.in
deleted file mode 100644
index d0d4e7ff9..000000000
--- a/examples/rmvs_gr_test/pl.swifter.in
+++ /dev/null
@@ -1,40 +0,0 @@
-10 ! Planet input file generated using init_cond.py using JPL Horizons data for the major planets (and Pluto) for epoch 2021-01-28
-1 39.47692640889762629
-0.0 0.0 0.0
-0.0 0.0 0.0
-2 6.553709809565313959502e-06 0.0014751229680863789154
-1.6306381826061645943e-05
-0.1030256860922895 0.2897796047098886 0.01422904600374035
--11.74004209950937 3.8343124110162736 1.3902496665973592
-3 9.6633133995815387361564e-05 0.006759127649782299051
-4.0453784346544178454e-05
-0.06110218027254217 -0.7245466901305982 -0.01346904300924688
-7.311995449678243 0.5941125721336201 -0.4137913843379075
-4 0.00012002693582795246295385 0.0100447565675466429165
-4.25875607065040958e-05
--0.6061796342297583 0.7761214554702035 -3.4750047790977e-05
--5.054824314301841 -3.891667468503358 0.00019720338148272726
-5 1.2739802010675942316241e-05 0.0072464490746299085006
-2.265740805092889601e-05
-0.2751944175855944 1.51937688993241 0.02508924593104206
--4.835983593209577 1.344855094041679 0.14681413000004515
-6 0.037692251088985682938581 0.3552852357486060849
-0.00046732617030490929307
-3.200135438345358 -3.953498213518368 -0.05517737289975112
-2.111393749129838 1.8660266890185446 -0.05498941067210089
-7 0.011285899820091272946487 0.43763064566943408597
-0.00038925687730393611812
-5.607382165725712 -8.258649105608766 -0.07958445228024298
-1.5748468603228847 1.1414574661825514 -0.08250331331320372
-8 0.0017236589478267728883093 0.4690969274244374022
-0.00016953449859497231466
-15.28225422201768 12.53905314208462 -0.1514143582550325
--0.9198472198098231 1.0454390993472462 0.01574538863031621
-9 0.0020336100526728304385693 0.7807192056765467829
-0.000164587904124493665
-29.47483071169769 -5.147686530859088 -0.5733441819169969
-0.19191677740340274 1.1385110364087574 -0.027844325148353527
-10 2.9242167710294538257026e-07 0.05383468172776979939
-7.943294877391593783e-06
-14.14000920780611 -31.14141812522779 -0.7565722591093476
-1.073396108697069 0.23003123192799815 -0.33424529561177047
diff --git a/examples/rmvs_gr_test/pl.swiftest.in b/examples/rmvs_gr_test/pl.swiftest.in
deleted file mode 100644
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diff --git a/examples/rmvs_gr_test/pl.tpcollider.in b/examples/rmvs_gr_test/pl.tpcollider.in
deleted file mode 100644
index 95f1a048c..000000000
--- a/examples/rmvs_gr_test/pl.tpcollider.in
+++ /dev/null
@@ -1,36 +0,0 @@
-9 ! Planet input file generated using init_cond.py using JPL Horizons data for the major planets (and Pluto) for epoch 2421-01-31
-1 0.0002959122081920778
-0.0 0.0 0.0
-0.0 0.0 0.0
-3 7.243452483873648e-10 0.006759130149202631
-4.0453784346544176e-05
-0.7019627011222136 -0.1852496375751218 -0.04300948405789237
--0.005060711738168192 -0.01946939433513342 1.612614183356907e-06
-4 8.99701138216602e-10 0.010044559796067993
-4.25875607065041e-05
--0.5697193205332233 0.8029811715067949 -0.0007045655351608364
-0.01430660935370767 0.01002546009563165 -1.100857588556753e-05
-5 9.549535102761467e-11 0.00724618889821577
-2.2657408050928896e-05
-0.7993560103471902 -1.148987408482768 -0.04313430212910231
--0.01198004310618487 -0.009212920234676632 8.927002293279674e-05
-6 2.825345908631355e-07 0.3551533878488701
-0.0004673261703049093
--4.634097296304171 -2.863163670218678 0.1152887094960275
--0.003870142070986203 0.006066584891983585 5.910227945707925e-05
-7 8.459715183006417e-08 0.4376906786688663
-0.00038925687730393614
--8.865265590469349 2.837147222179521 0.3099061219991513
-0.001998614714254602 0.005352670386838502 -0.0001703654134002045
-8 1.2920249163736676e-08 0.46813372268314535
-0.00016953449859497232
-15.47151038891844 -12.61881964551067 -0.2448817967119467
--0.002462937202468351 -0.002853377332777701 2.133002929086309e-05
-9 1.5243589003230837e-08 0.7791996556565636
-0.00016458790412449367
--24.87809567256067 17.00453520872124 0.2234899970988031
-0.001786881544518242 0.002576864647985829 -9.421562352086046e-05
-10 2.19194228290428e-12 0.05348153138291406
-7.943294877391593e-06
--9.921482526550596 41.28101896209601 -1.54188968720156
-0.002154849797934256 0.001120743083473351 -0.0007433230817862677
diff --git a/examples/rmvs_gr_test/rmvs_vs_whm.ipynb b/examples/rmvs_gr_test/rmvs_vs_whm.ipynb
deleted file mode 100644
index 4c91f2f4a..000000000
--- a/examples/rmvs_gr_test/rmvs_vs_whm.ipynb
+++ /dev/null
@@ -1,120 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import swiftestio as swio\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.swifter.in\n",
-      "Reading Swifter file param.swifter.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile  = \"param.swifter.in\"\n",
-    "rmvsfile = swio.read_swifter_param(inparfile)\n",
-    "rmvsfile['BIN_OUT'] = 'bin.swifter.rmvs.dat'\n",
-    "whmfile = swio.read_swifter_param(inparfile)\n",
-    "whmfile['BIN_OUT'] = 'bin.swifter.whm.dat'\n",
-    "\n",
-    "rmvsdat = swio.swifter2xr(rmvsfile)\n",
-    "whmdat = swio.swifter2xr(whmfile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rmvsdat['r'] = np.sqrt(rmvsdat['px']**2 + rmvsdat['py']**2 + rmvsdat['pz']**2)\n",
-    "whmdat['r'] = np.sqrt(whmdat['px']**2 + whmdat['py']**2 + whmdat['pz']**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb0c8a36bb0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb0784450d0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb078445190>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb078445250>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb078445310>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb0784453d0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb078445490>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb078445550>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb078445610>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb0784456d0>]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dr = rmvsdat['r'] - whmdat['r']\n",
-    "dr.sel(id=slice(100,109)).plot.line(x=\"time\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/rmvs_gr_test/swifter2swiftest.py b/examples/rmvs_gr_test/swifter2swiftest.py
deleted file mode 120000
index 3539718d1..000000000
--- a/examples/rmvs_gr_test/swifter2swiftest.py
+++ /dev/null
@@ -1 +0,0 @@
-/Users/daminton/git/swiftest/python/swifter2swiftest.py
\ No newline at end of file
diff --git a/examples/rmvs_gr_test/swiftest_relativity.ipynb b/examples/rmvs_gr_test/swiftest_relativity.ipynb
deleted file mode 100644
index 302934bc0..000000000
--- a/examples/rmvs_gr_test/swiftest_relativity.ipynb
+++ /dev/null
@@ -1,199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import swiftestio as swio\n",
-    "from astroquery.jplhorizons import Horizons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.gr.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile = 'param.gr.in'\n",
-    "paramgr = swio.read_swifter_param(inparfile)\n",
-    "swiftergr = swio.swifter2xr(paramgr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.nogr.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile = 'param.nogr.in'\n",
-    "paramnogr = swio.read_swifter_param(inparfile)\n",
-    "swifternogr = swio.swifter2xr(paramnogr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swiftest file param.gr.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "param_file_name = 'param.gr.in'\n",
-    "config = swio.read_swiftest_config(param_file_name)\n",
-    "swiftestdat = swio.swiftest2xr(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tsim = swiftergr['time'] / 365.25\n",
-    "varpigrsim = (swiftergr.sel(id=2)['omega'].data + swiftergr.sel(id=2)['capom'].data) * 180.0 / np.pi\n",
-    "varpinogrsim = (swifternogr.sel(id=2)['omega'].data + swifternogr.sel(id=2)['capom'].data) * 180.0 / np.pi\n",
-    "varpiswiftest = (swiftestdat.sel(id=2)['omega'].data + swiftestdat.sel(id=2)['capom'].data) * 180.0 / np.pi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "obj = Horizons(id='1', id_type='majorbody',location='@sun',\n",
-    "               epochs={'start':'2020-07-13', 'stop':'3024-07-13',\n",
-    "                       'step':'1461d'})\n",
-    "el = obj.elements()\n",
-    "t = (el['datetime_jd']-el['datetime_jd'][0]) / 365.25\n",
-    "varpi = el['w'] + el['Omega']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dvarpigrsim = np.diff(varpigrsim) / np.diff(tsim) * 3600 * 100 \n",
-    "dvarpinogrsim = np.diff(varpinogrsim) / np.diff(tsim) * 3600 * 100 \n",
-    "dvarpiswiftest = np.diff(varpiswiftest) / np.diff(tsim) * 3600 * 100 \n",
-    "dvarpi= np.diff(varpi) / np.diff(t) * 3600 * 100 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean precession rate for Mercury long. peri. (arcsec/100 y)\n",
-      "JPL Horizons  : 570.3933741520308\n",
-      "Swifter no GR : 527.4373381337329\n",
-      "Swifter GR    : 570.37497883185\n",
-      "Swiftest GR   : 570.3749787753405\n",
-      "GR - no GR    : 42.93764069811709\n",
-      "Obs - Swifter : -0.018395320180843555\n",
-      "Obs - Swiftest: -0.018395376690250487\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "ax.plot(t, varpi, label=\"JPL Horizons\")\n",
-    "ax.plot(tsim, varpinogrsim, label=\"Swifter no GR\")\n",
-    "ax.plot(tsim, varpigrsim, label=\"Swifter GR\")\n",
-    "ax.plot(tsim, varpiswiftest, label=\"Swiftest GR\")\n",
-    "ax.set_xlabel('Time (y)')\n",
-    "ax.set_ylabel('Mercury $\\\\varpi$ (deg)')\n",
-    "ax.legend()\n",
-    "print('Mean precession rate for Mercury long. peri. (arcsec/100 y)')\n",
-    "print(f'JPL Horizons  : {np.mean(dvarpi)}')\n",
-    "print(f'Swifter no GR : {np.mean(dvarpinogrsim)}')\n",
-    "print(f'Swifter GR    : {np.mean(dvarpigrsim)}')\n",
-    "print(f'Swiftest GR   : {np.mean(dvarpiswiftest)}')\n",
-    "print(f'GR - no GR    : {np.mean(dvarpigrsim) - np.mean(dvarpinogrsim)}')\n",
-    "print(f'Obs - Swifter : {np.mean(dvarpigrsim) - np.mean(dvarpi)}')\n",
-    "print(f'Obs - Swiftest: {np.mean(dvarpiswiftest) - np.mean(dvarpi)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/rmvs_gr_test/swifttest_rmvs_vs_swifter_rmvs.ipynb b/examples/rmvs_gr_test/swifttest_rmvs_vs_swifter_rmvs.ipynb
deleted file mode 100644
index 4dbc0754d..000000000
--- a/examples/rmvs_gr_test/swifttest_rmvs_vs_swifter_rmvs.ipynb
+++ /dev/null
@@ -1,156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import swiftestio as swio\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.swifter.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile = 'param.swifter.in'\n",
-    "paramgr = swio.read_swifter_param(inparfile)\n",
-    "swifterdat = swio.swifter2xr(paramgr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swiftest file param.swiftest.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "param_file_name = 'param.swiftest.in'\n",
-    "config = swio.read_swiftest_config(param_file_name)\n",
-    "swiftestdat = swio.swiftest2xr(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftdiff = swiftestdat - swifterdat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swiftdiff = swiftdiff.rename({'time' : 'time (y)'})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff0608c8f40>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c8520>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c8df0>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c81f0>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c8220>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c8160>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c8280>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7220>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7640>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7e80>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff021368eb0>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7400>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7bb0>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c79d0>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7f40>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c76a0>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7940>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7190>,\n",
-       " <matplotlib.lines.Line2D at 0x7ff0608c7ca0>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "swiftdiff['a'].plot.line(x=\"time (y)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/rmvs_gr_test/test_unit_change.ipynb b/examples/rmvs_gr_test/test_unit_change.ipynb
deleted file mode 100644
index 272e8dda0..000000000
--- a/examples/rmvs_gr_test/test_unit_change.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import sys\n",
-    "from astroquery.jplhorizons import Horizons\n",
-    "import astropy.constants as const \n",
-    "import swiftestio as swio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.01746575523103058"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Values from JPL Horizons\n",
-    "AU2M = const.au.value\n",
-    "GMSunSI = const.GM_sun.value\n",
-    "Rsun = const.R_sun.value\n",
-    "GC = const.G.value\n",
-    "JD = 86400\n",
-    "year = 365.25 * JD\n",
-    "c = 299792458.0\n",
-    "\n",
-    "MU2KG   = GMSunSI / GC #Conversion from mass unit (G * Msun) to kg\n",
-    "DU2M   = AU2M      #Conversion from distance unit (AU) to meters\n",
-    "TU2S   = year       #Conversion from time unit (Julian Day) to seconds\n",
-    "GU     = GC / (DU2M**3 / (MU2KG * TU2S**2))\n",
-    "\n",
-    "GMSun = GMSunSI / (DU2M**3 / TU2S**2)\n",
-    "\n",
-    "# Solar oblateness values: From Mecheri et al. (2004), using Corbard (b) 2002 values (Table II)\n",
-    "J2 = 2.198e-7 * (Rsun / DU2M)**2\n",
-    "J4 = -4.805e-9 * (Rsun / DU2M)**4\n",
-    "\n",
-    "npl = 10\n",
-    "\n",
-    "tstart = '2021-01-28'\n",
-    "tend = '2021-01-29'\n",
-    "tstep = '1d'\n",
-    "planetid = {\n",
-    "   'mercury'     : '1',\n",
-    "   'venus'       : '2',\n",
-    "   'earthmoon'   : '3',\n",
-    "   'mars'        : '4',\n",
-    "   'jupiter'     : '5',\n",
-    "   'saturn'      : '6',\n",
-    "   'uranus'      : '7',\n",
-    "   'neptune'     : '8',\n",
-    "   'plutocharon' : '9'\n",
-    "}\n",
-    "\n",
-    "#Planet Msun/M ratio\n",
-    "MSun_over_Mpl = {\n",
-    "   'mercury'     : 6023600.0,\n",
-    "   'venus'       : 408523.71,\n",
-    "   'earthmoon'   : 328900.56,\n",
-    "   'mars'        : 3098708.,\n",
-    "   'jupiter'     : 1047.3486,\n",
-    "   'saturn'      : 3497.898,\n",
-    "   'uranus'      : 22902.98,\n",
-    "   'neptune'     : 19412.24,\n",
-    "   'plutocharon' :  1.35e8\n",
-    "}\n",
-    "\n",
-    "#Planet radii in meters\n",
-    "Rpl = {\n",
-    "   'mercury'     : 2439.4e3,\n",
-    "   'venus'       : 6051.8e3,\n",
-    "   'earthmoon'   : 6371.0084e3, # Earth only for radius\n",
-    "   'mars'        : 3389.50e3,\n",
-    "   'jupiter'     : 69911e3,\n",
-    "   'saturn'      : 58232.0e3,\n",
-    "   'uranus'      : 25362.e3,\n",
-    "   'neptune'     : 24622.e3,\n",
-    "   'plutocharon' : 1188.3e3\n",
-    "}\n",
-    "\n",
-    "pdata = {}\n",
-    "plvec = {}\n",
-    "Rhill = {}\n",
-    "\n",
-    "for key,val in planetid.items():\n",
-    "   pdata[key] = Horizons(id=val, id_type='majorbody',location='@sun',\n",
-    "            epochs={'start': tstart, 'stop': tend,\n",
-    "            'step': tstep})\n",
-    "   plvec[key] = np.array([pdata[key].vectors()['x'][0],\n",
-    "         pdata[key].vectors()['y'][0], \n",
-    "         pdata[key].vectors()['z'][0], \n",
-    "         pdata[key].vectors()['vx'][0], \n",
-    "         pdata[key].vectors()['vy'][0], \n",
-    "         pdata[key].vectors()['vz'][0] \n",
-    "         ])\n",
-    "   Rhill[key] = pdata[key].elements()['a'][0] * (3 * MSun_over_Mpl[key])**(-1.0 / 3.0)\n",
-    "ver = np.sqrt(plvec['earthmoon'][3]**2 + plvec['earthmoon'][4]**2 + plvec['earthmoon'][5]**2)\n",
-    "ver"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.37936709813392"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "for plid in plvec:\n",
-    "    plvec[plid][3:] *= year / JD\n",
-    "ver = np.sqrt(plvec['earthmoon'][3]**2 + plvec['earthmoon'][4]**2 + plvec['earthmoon'][5]**2)\n",
-    "ver"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
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diff --git a/examples/rmvs_gr_test/tp.tpcollider.in b/examples/rmvs_gr_test/tp.tpcollider.in
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deleted file mode 100644
index 2f651edb5..000000000
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- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import swiftestio as swio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.tpcollider.in\n"
-     ]
-    }
-   ],
-   "source": [
-    "inparfile  = \"param.tpcollider.in\"\n",
-    "paramfile = swio.read_swifter_param(inparfile)\n",
-    "swifterdat = swio.swifter2xr(paramfile)"
-   ]
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-       "Coordinates:\n",
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-       "    time     float64 1.461e+04</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2f61c88b-98e1-4d64-a451-2cd7df055a8a' class='xr-array-in' type='checkbox' checked><label for='section-2f61c88b-98e1-4d64-a451-2cd7df055a8a' 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>1.461e+04</span></div><div class='xr-array-data'><pre>array(14610.)</pre></div></div></li><li class='xr-section-item'><input id='section-1595a11f-6b26-4e50-a21a-3cf5440c88b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1595a11f-6b26-4e50-a21a-3cf5440c88b1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-9bcf2d16-3c33-4d9d-8ef2-029f4a63d168' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bcf2d16-3c33-4d9d-8ef2-029f4a63d168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07dc5811-28ea-464d-a6a4-3dbc6d74e281' class='xr-var-data-in' type='checkbox'><label for='data-07dc5811-28ea-464d-a6a4-3dbc6d74e281' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(100)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.461e+04</div><input id='attrs-e044c824-3859-4a87-b444-9958f503eafd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e044c824-3859-4a87-b444-9958f503eafd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a93aead8-d9c0-4f58-910e-fcf4c0d04b29' class='xr-var-data-in' type='checkbox'><label for='data-a93aead8-d9c0-4f58-910e-fcf4c0d04b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(14610.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84358176-10bc-45a7-879f-bef15a60eb2e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-84358176-10bc-45a7-879f-bef15a60eb2e' 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 'time' ()>\n",
-       "array(14610.)\n",
-       "Coordinates:\n",
-       "    id       int64 100\n",
-       "    time     float64 1.461e+04"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "swifterdat.isel(time=-1).sel(id=100)['time']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "px = swifterdat.isel(time=-1).sel(id=100)['px'].values.item()\n",
-    "py = swifterdat.isel(time=-1).sel(id=100)['py'].values.item()\n",
-    "pz = swifterdat.isel(time=-1).sel(id=100)['pz'].values.item()\n",
-    "\n",
-    "vx = swifterdat.isel(time=-1).sel(id=100)['vx'].values.item()\n",
-    "vy = swifterdat.isel(time=-1).sel(id=100)['vy'].values.item()\n",
-    "vz = swifterdat.isel(time=-1).sel(id=100)['vz'].values.item()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'tpfile' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m-------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-4b034bea19f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../tp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{px} {py} {pz}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{vx} {vy} {vz}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'tpfile' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "pfile = open(\"../tp\", 'w')\n",
-    "print(1,file=tpfile)\n",
-    "print(100,file=tpfile)\n",
-    "print(f'{px} {py} {pz}', file=tpfile)\n",
-    "print(f'{vx} {vy} {vz}', file=tpfile)\n",
-    "tpfile.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy.random import default_rng"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nclones = 10\n",
-    "xv_dispersion_factor = 0.01 \n",
-    "rng = default_rng()\n",
-    "clone_xv = (rng.standard_normal((nclones, 6)) - 0.5) * xv_dispersion_factor + 1.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "px = 1.0\n",
-    "py = 2.0\n",
-    "pz = 3.0\n",
-    "vx = 4.0\n",
-    "vy = 5.0\n",
-    "vz = 6.0\n",
-    "jangofett = np.array([px, py, pz, -vx, -vy, -vz])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  2.,  3., -4., -5., -6.])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jangofett"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clones = jangofett * clone_xv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 6)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clones.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clonenames = range(100, 100 + nclones)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clonedat = dict(zip(clonenames,clones))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{100: array([ 1.00930105,  1.9948283 ,  2.99208025, -4.05525929, -5.09831281,\n",
-       "        -5.92580054]),\n",
-       " 101: array([ 0.99941117,  1.97090351,  3.03529928, -3.97161239, -4.96155703,\n",
-       "        -5.94367707]),\n",
-       " 102: array([ 0.98148793,  1.99203431,  2.9652801 , -4.04205948, -4.96976478,\n",
-       "        -5.90277054]),\n",
-       " 103: array([ 0.98976919,  2.00294464,  2.97587626, -4.01195673, -5.04441437,\n",
-       "        -5.96164778]),\n",
-       " 104: array([ 0.99530072,  1.99825316,  3.00016025, -3.95468703, -4.94515931,\n",
-       "        -5.96765703]),\n",
-       " 105: array([ 1.00614369,  1.98935313,  2.95064276, -4.01105523, -5.00312835,\n",
-       "        -6.06301916]),\n",
-       " 106: array([ 0.98931165,  1.98780538,  2.95666409, -3.98514127, -4.89578071,\n",
-       "        -5.98396813]),\n",
-       " 107: array([ 0.99959316,  2.00748056,  3.04892677, -3.97927546, -4.89214677,\n",
-       "        -5.97922326]),\n",
-       " 108: array([ 0.99488235,  1.99323474,  3.00338001, -3.97767383, -5.04020251,\n",
-       "        -5.9724389 ]),\n",
-       " 109: array([ 0.99568866,  1.97598485,  3.00296884, -4.0810708 , -4.92477352,\n",
-       "        -5.87353764])}"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clonedat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "101",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-37e89c8b68d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m101\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 101"
-     ]
-    }
-   ],
-   "source": [
-    "clones[101]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/check_init_cond.ipynb b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/check_init_cond.ipynb
deleted file mode 100644
index 33b054177..000000000
--- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/check_init_cond.ipynb
+++ /dev/null
@@ -1,930 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import swiftest\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swifter file param.swifter.in\n",
-      "Reading in time 1.355e-01\n",
-      "Creating Dataset\n",
-      "Successfully converted 397 output frames.\n",
-      "Swifter simulation data stored as xarray DataSet .ds\n"
-     ]
-    }
-   ],
-   "source": [
-    "swiftersim = swiftest.Simulation(param_file=\"param.swifter.in\", codename=\"Swifter\")\n",
-    "swiftersim.bin2xr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.collections.PathCollection at 0x2b959fecacd0>,\n",
-       " <matplotlib.collections.PathCollection at 0x2b959f384710>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "swiftersim.ds.plot.scatter(x='px', y='py', hue='id', hue_style=\"discrete\", marker='.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading Swiftest file param.swiftest.in\n",
-      "Reading in time 2.002e-01\n",
-      "Creating Dataset\n",
-      "Successfully converted 586 output frames.\n",
-      "Swiftest simulation data stored as xarray DataSet .ds\n"
-     ]
-    }
-   ],
-   "source": [
-    "swiftestsim = swiftest.Simulation(param_file=\"param.swiftest.in\")\n",
-    "swiftestsim.bin2xr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.collections.PathCollection at 0x2b959ffbd3d0>,\n",
-       " <matplotlib.collections.PathCollection at 0x2b959ffbde90>,\n",
-       " <matplotlib.collections.PathCollection at 0x2b95a00336d0>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "swiftestsim.ds.plot.scatter(x='px', y='py', hue='id', hue_style=\"discrete\", marker='.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  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.Dataset&gt;\n",
-       "Dimensions:   (id: 2, time: 397)\n",
-       "Coordinates:\n",
-       "  * id        (id) int64 2 100\n",
-       "    time (y)  (time) float64 0.0 0.0003422 0.0006845 ... 0.1348 0.1352 0.1355\n",
-       "Dimensions without coordinates: time\n",
-       "Data variables:\n",
-       "    Mass      (time, id) float64 0.0 nan 0.0 nan 0.0 nan ... nan 0.0 nan 0.0 nan\n",
-       "    Radius    (time, id) float64 0.0 nan 0.0 nan 0.0 nan ... nan 0.0 nan 0.0 nan\n",
-       "    delta x   (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    delta y   (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    pz        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    vx        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    vy        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    vz        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3d2978cd-04bf-4bc5-b977-c56b8dc7e29b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d2978cd-04bf-4bc5-b977-c56b8dc7e29b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 2</li><li><span>time</span>: 397</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-359ffbac-441c-4e08-b9bf-c419ee671d44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-359ffbac-441c-4e08-b9bf-c419ee671d44' 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'>2 100</div><input id='attrs-ba703e96-29c8-41e3-8d7b-1b36043c93fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba703e96-29c8-41e3-8d7b-1b36043c93fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-425c21ed-af0e-456c-9e1e-27bf84afd53e' class='xr-var-data-in' type='checkbox'><label for='data-425c21ed-af0e-456c-9e1e-27bf84afd53e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  2, 100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time (y)</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0003422 ... 0.1352 0.1355</div><input id='attrs-34041ddc-0976-4457-af19-eafaf17493c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34041ddc-0976-4457-af19-eafaf17493c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b915ca33-9338-4706-9d6a-165353aa220a' class='xr-var-data-in' type='checkbox'><label for='data-b915ca33-9338-4706-9d6a-165353aa220a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.000342, 0.000684, ..., 0.134839, 0.135181, 0.135524])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4985a7f-28ad-415a-ae3b-0c747f4dfa54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4985a7f-28ad-415a-ae3b-0c747f4dfa54' class='xr-section-summary' >Data variables: <span>(8)</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>Mass</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 nan 0.0 nan ... 0.0 nan 0.0 nan</div><input id='attrs-d505aa02-a94d-4dd4-923d-5d011eee7b7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d505aa02-a94d-4dd4-923d-5d011eee7b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-599f20bc-c36a-4e17-8e40-4c38f3777b06' class='xr-var-data-in' type='checkbox'><label for='data-599f20bc-c36a-4e17-8e40-4c38f3777b06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "...\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Radius</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 nan 0.0 nan ... 0.0 nan 0.0 nan</div><input id='attrs-4f74bb1b-e63e-4631-b34a-73554c52a1e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f74bb1b-e63e-4631-b34a-73554c52a1e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72d937e9-a4e3-40a6-bffa-49a935b38496' class='xr-var-data-in' type='checkbox'><label for='data-72d937e9-a4e3-40a6-bffa-49a935b38496' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "...\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta x</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 nan</div><input id='attrs-85a37aae-509e-4793-8493-001ac609e570' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85a37aae-509e-4793-8493-001ac609e570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-589dca86-a933-463a-8250-0a6e25420d8d' class='xr-var-data-in' type='checkbox'><label for='data-589dca86-a933-463a-8250-0a6e25420d8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "...\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta y</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 nan</div><input id='attrs-01d99fbd-0bb3-4afd-8f32-f603e6e924e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01d99fbd-0bb3-4afd-8f32-f603e6e924e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64f994b0-4ded-4ed7-b238-fc4a502bbf1d' class='xr-var-data-in' type='checkbox'><label for='data-64f994b0-4ded-4ed7-b238-fc4a502bbf1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "...\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pz</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 nan</div><input id='attrs-38cdc34e-2b6e-4fb6-8192-aefefdab64d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38cdc34e-2b6e-4fb6-8192-aefefdab64d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8ed9a8a-da58-42aa-bbcd-1c39075ffbf6' class='xr-var-data-in' type='checkbox'><label for='data-b8ed9a8a-da58-42aa-bbcd-1c39075ffbf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "...\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 nan</div><input id='attrs-d346702c-9c5d-468e-b860-7429735ab912' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d346702c-9c5d-468e-b860-7429735ab912' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5b608c8-af28-419c-9ac6-b832b8eadcac' class='xr-var-data-in' type='checkbox'><label for='data-b5b608c8-af28-419c-9ac6-b832b8eadcac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "...\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 nan</div><input id='attrs-312cc549-6593-47bd-8484-90e0e474ebbf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-312cc549-6593-47bd-8484-90e0e474ebbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e12a3a21-6335-4335-9656-a01e8406b213' class='xr-var-data-in' type='checkbox'><label for='data-e12a3a21-6335-4335-9656-a01e8406b213' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "...\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0., nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vz</span></div><div class='xr-var-dims'>(time, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 nan</div><input id='attrs-18f48aec-ef3c-417e-b08a-1556281929eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18f48aec-ef3c-417e-b08a-1556281929eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92a676a8-7ac0-4947-8de7-1c75e566f974' class='xr-var-data-in' type='checkbox'><label for='data-92a676a8-7ac0-4947-8de7-1c75e566f974' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "...\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0.,  0.],\n",
-       "       [ 0., nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4f6e8c3-b952-472f-8358-3f2c5992eec5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4f6e8c3-b952-472f-8358-3f2c5992eec5' 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.Dataset>\n",
-       "Dimensions:   (id: 2, time: 397)\n",
-       "Coordinates:\n",
-       "  * id        (id) int64 2 100\n",
-       "    time (y)  (time) float64 0.0 0.0003422 0.0006845 ... 0.1348 0.1352 0.1355\n",
-       "Dimensions without coordinates: time\n",
-       "Data variables:\n",
-       "    Mass      (time, id) float64 0.0 nan 0.0 nan 0.0 nan ... nan 0.0 nan 0.0 nan\n",
-       "    Radius    (time, id) float64 0.0 nan 0.0 nan 0.0 nan ... nan 0.0 nan 0.0 nan\n",
-       "    delta x   (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    delta y   (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    pz        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    vx        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    vy        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
-       "    vz        (time, id) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "swiftdiff = swiftestsim.ds - swiftersim.ds\n",
-    "swiftdiff = swiftdiff.rename_vars({'time' : 'time (y)','px' : 'delta x','py' : 'delta y'})\n",
-    "swiftdiff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.collections.PathCollection at 0x2b95a006dfd0>,\n",
-       " <matplotlib.collections.PathCollection at 0x2b95a009ecd0>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "swiftdiff.plot.scatter(x='delta x', y='delta y', hue='id', hue_style=\"discrete\", marker='.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.savefig('rmvsdiff.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "swiftestOOF",
-   "language": "python",
-   "name": "swiftestoof"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
index b62f2f3d1..52568fd84 100644
--- a/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
+++ b/examples/rmvs_swifter_comparison/1pl_1tp_encounter/swiftest_vs_swifter.ipynb
@@ -81,8 +81,8 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2b5bcd10b290>,\n",
-       " <matplotlib.lines.Line2D at 0x2b5bcd10b510>]"
+       "[<matplotlib.lines.Line2D at 0x2acdc3dd7450>,\n",
+       " <matplotlib.lines.Line2D at 0x2acdc3db7c10>]"
       ]
      },
      "execution_count": 6,
diff --git a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/init_cond.py b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/init_cond.py
index 11e04e0f3..321c79932 100755
--- a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/init_cond.py
+++ b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/init_cond.py
@@ -24,7 +24,7 @@
 
 sim.param['T0'] = 0.0
 sim.param['DT'] = 1.0 
-sim.param['TSTOP'] = 365.25
+sim.param['TSTOP'] = 365.25e1
 sim.param['ISTEP_OUT']  = 11
 sim.param['ISTEP_DUMP'] = 1
 sim.param['CHK_QMIN_COORD'] = "HELIO"
diff --git a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swifter.in b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swifter.in
index ab8bf65ca..aa33eeaa4 100644
--- a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swifter.in
+++ b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swifter.in
@@ -1,6 +1,6 @@
 ! VERSION        Swifter parameter file converted from Swiftest
 T0               0.0
-TSTOP            365.25
+TSTOP            3652.5
 DT               1.0
 ISTEP_OUT        11
 ISTEP_DUMP       1
diff --git a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swiftest.in b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swiftest.in
index 385ac46bb..6504c9637 100644
--- a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swiftest.in
+++ b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/param.swiftest.in
@@ -1,6 +1,6 @@
 ! VERSION        Swiftest parameter input
 T0               0.0
-TSTOP            365.25
+TSTOP            3652.5
 DT               1.0
 ISTEP_OUT        11
 ISTEP_DUMP       1
diff --git a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb
index e8d0dbc26..181cd0237 100644
--- a/examples/rmvs_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb
+++ b/examples/rmvs_swifter_comparison/9pl_18tp_encounters/swiftest_rmvs_vs_swifter_rmvs.ipynb
@@ -6,9 +6,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
     "import swiftest\n",
+    "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
@@ -22,9 +21,9 @@
      "output_type": "stream",
      "text": [
       "Reading Swifter file param.swifter.in\n",
-      "Reading in time 3.630e+02\n",
+      "Reading in time 3.652e+03\n",
       "Creating Dataset\n",
-      "Successfully converted 34 output frames.\n",
+      "Successfully converted 333 output frames.\n",
       "Swifter simulation data stored as xarray DataSet .ds\n"
      ]
     }
@@ -46,9 +45,9 @@
      "output_type": "stream",
      "text": [
       "Reading Swiftest file param.swiftest.in\n",
-      "Reading in time 3.630e+02\n",
+      "Reading in time 3.652e+03\n",
       "Creating Dataset\n",
-      "Successfully converted 34 output frames.\n",
+      "Successfully converted 333 output frames.\n",
       "Swiftest simulation data stored as xarray DataSet .ds\n"
      ]
     }
@@ -100,7 +99,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['rmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric position differences \\n Planets only\")\n",
+    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-planets-rmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['vmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric velocity differences \\n Planets only\")\n",
+    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-planets-vmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['rmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric position differences \\n Test Particles only\")\n",
+    "legend = ax.legend()\n",
+    "legend.remove()\n",
+    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-testparticles-rmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "swiftdiff['vmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
+    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
+    "ax.set_title(\"Heliocentric velocity differences \\n Test Particles only\")\n",
+    "legend = ax.legend()\n",
+    "legend.remove()\n",
+    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-testparticles-vmag.png\", facecolor='white', transparent=False, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -457,154 +578,147 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;px&#x27; (time (d): 34)&gt;\n",
-       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;rmag&#x27; (time (d): 333)&gt;\n",
+       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "...\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13180114e-12,\n",
+       "       6.30252092e-12, 1.12657932e-11, 1.70947866e-11, 2.35410127e-11,\n",
+       "       3.01486367e-11, 3.63634702e-11, 4.16224366e-11, 4.54289913e-11,\n",
+       "       4.74142910e-11, 4.73824194e-11, 4.53327404e-11, 4.14594589e-11,\n",
+       "       3.61300773e-11, 2.98446324e-11, 2.31845539e-11, 1.67548923e-11,\n",
+       "       1.11262399e-11, 6.78147816e-12, 4.07218435e-12, 3.25977426e-12,\n",
+       "       4.52137637e-12, 7.66342713e-12, 1.23344633e-11, 1.81013732e-11,\n",
+       "       2.44264806e-11, 3.07065663e-11, 3.63320360e-11, 4.07478190e-11,\n",
+       "       4.35128453e-11, 4.43475549e-11, 4.31649567e-11, 4.00801554e-11,\n",
+       "       3.53984592e-11, 2.95862328e-11, 2.32329074e-11, 1.70175537e-11,\n",
+       "       1.17040422e-11])\n",
        "Coordinates:\n",
-       "    id        int64 101\n",
-       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 33.0 ... 330.0 341.0 352.0 363.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'px'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time (d)</span>: 34</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-477b2956-f9c8-4255-9d02-219b00ab7bfc' class='xr-array-in' type='checkbox' checked><label for='section-477b2956-f9c8-4255-9d02-219b00ab7bfc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></div></li><li class='xr-section-item'><input id='section-531cbf50-4b03-447f-8d82-0524083d6824' class='xr-section-summary-in' type='checkbox'  checked><label for='section-531cbf50-4b03-447f-8d82-0524083d6824' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>101</div><input id='attrs-d4ee194c-833f-447d-bced-8112a479edf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4ee194c-833f-447d-bced-8112a479edf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b88472a-c82b-4a2f-bc56-ee139cf621c8' class='xr-var-data-in' type='checkbox'><label for='data-8b88472a-c82b-4a2f-bc56-ee139cf621c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(101)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time (d)</span></div><div class='xr-var-dims'>(time (d))</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 11.0 22.0 ... 341.0 352.0 363.0</div><input id='attrs-5a82cb1d-72e7-4aa4-bbae-beeb4e6f980d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a82cb1d-72e7-4aa4-bbae-beeb4e6f980d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-311312f4-66eb-4b1e-9113-2e0229038068' class='xr-var-data-in' type='checkbox'><label for='data-311312f4-66eb-4b1e-9113-2e0229038068' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,  11.,  22.,  33.,  44.,  55.,  66.,  77.,  88.,  99., 110., 121.,\n",
-       "       132., 143., 154., 165., 176., 187., 198., 209., 220., 231., 242., 253.,\n",
-       "       264., 275., 286., 297., 308., 319., 330., 341., 352., 363.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01639bff-6bb9-4227-b738-41759ca9ac88' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-01639bff-6bb9-4227-b738-41759ca9ac88' 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>"
+       "    id        int64 2\n",
+       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 ... 3.63e+03 3.641e+03 3.652e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'rmag'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time (d)</span>: 333</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-73a914d5-59cb-4d91-af80-1dd90f1d58ac' class='xr-array-in' type='checkbox' checked><label for='section-73a914d5-59cb-4d91-af80-1dd90f1d58ac' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 ... 2.959e-11 2.323e-11 1.702e-11 1.17e-11</span></div><div class='xr-array-data'><pre>array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "...\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13180114e-12,\n",
+       "       6.30252092e-12, 1.12657932e-11, 1.70947866e-11, 2.35410127e-11,\n",
+       "       3.01486367e-11, 3.63634702e-11, 4.16224366e-11, 4.54289913e-11,\n",
+       "       4.74142910e-11, 4.73824194e-11, 4.53327404e-11, 4.14594589e-11,\n",
+       "       3.61300773e-11, 2.98446324e-11, 2.31845539e-11, 1.67548923e-11,\n",
+       "       1.11262399e-11, 6.78147816e-12, 4.07218435e-12, 3.25977426e-12,\n",
+       "       4.52137637e-12, 7.66342713e-12, 1.23344633e-11, 1.81013732e-11,\n",
+       "       2.44264806e-11, 3.07065663e-11, 3.63320360e-11, 4.07478190e-11,\n",
+       "       4.35128453e-11, 4.43475549e-11, 4.31649567e-11, 4.00801554e-11,\n",
+       "       3.53984592e-11, 2.95862328e-11, 2.32329074e-11, 1.70175537e-11,\n",
+       "       1.17040422e-11])</pre></div></div></li><li class='xr-section-item'><input id='section-a8a0d6f2-bdd2-456f-801a-52d60f714dc0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8a0d6f2-bdd2-456f-801a-52d60f714dc0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-f070fef3-fc1b-4824-8b97-261439e8eed7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f070fef3-fc1b-4824-8b97-261439e8eed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-906e8a5c-ff37-4f76-9ff5-a425aaaec90b' class='xr-var-data-in' type='checkbox'><label for='data-906e8a5c-ff37-4f76-9ff5-a425aaaec90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time (d)</span></div><div class='xr-var-dims'>(time (d))</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 11.0 ... 3.641e+03 3.652e+03</div><input id='attrs-d17ea53a-4be8-4488-b880-ac8e6aa4a39e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d17ea53a-4be8-4488-b880-ac8e6aa4a39e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa01b33-72b0-48d2-b84a-d945ee80d254' class='xr-var-data-in' type='checkbox'><label for='data-9fa01b33-72b0-48d2-b84a-d945ee80d254' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0.,   11.,   22., ..., 3630., 3641., 3652.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74abe0d2-ce40-4016-be93-cd11612a197f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74abe0d2-ce40-4016-be93-cd11612a197f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'px' (time (d): 34)>\n",
-       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+       "<xarray.DataArray 'rmag' (time (d): 333)>\n",
+       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "...\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.13180114e-12,\n",
+       "       6.30252092e-12, 1.12657932e-11, 1.70947866e-11, 2.35410127e-11,\n",
+       "       3.01486367e-11, 3.63634702e-11, 4.16224366e-11, 4.54289913e-11,\n",
+       "       4.74142910e-11, 4.73824194e-11, 4.53327404e-11, 4.14594589e-11,\n",
+       "       3.61300773e-11, 2.98446324e-11, 2.31845539e-11, 1.67548923e-11,\n",
+       "       1.11262399e-11, 6.78147816e-12, 4.07218435e-12, 3.25977426e-12,\n",
+       "       4.52137637e-12, 7.66342713e-12, 1.23344633e-11, 1.81013732e-11,\n",
+       "       2.44264806e-11, 3.07065663e-11, 3.63320360e-11, 4.07478190e-11,\n",
+       "       4.35128453e-11, 4.43475549e-11, 4.31649567e-11, 4.00801554e-11,\n",
+       "       3.53984592e-11, 2.95862328e-11, 2.32329074e-11, 1.70175537e-11,\n",
+       "       1.17040422e-11])\n",
        "Coordinates:\n",
-       "    id        int64 101\n",
-       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 33.0 ... 330.0 341.0 352.0 363.0"
+       "    id        int64 2\n",
+       "  * time (d)  (time (d)) float64 0.0 11.0 22.0 ... 3.63e+03 3.641e+03 3.652e+03"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "swiftdiff['px'].sel(id=102)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['rmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric position differences \\n Planets only\")\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-planets-rmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['vmag'].sel(id=plidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric velocity differences \\n Planets only\")\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-planets-vmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['rmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric position differences \\n Test Particles only\")\n",
-    "legend = ax.legend()\n",
-    "legend.remove()\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-testparticles-rmag.png\", facecolor='white', transparent=False, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "swiftdiff['vmag'].sel(id=tpidx).plot.line(ax=ax, x=\"time (d)\")\n",
-    "ax.set_ylabel(\"$|\\mathbf{v}_{swiftest} - \\mathbf{v}_{swifter}|$\")\n",
-    "ax.set_title(\"Heliocentric velocity differences \\n Test Particles only\")\n",
-    "legend = ax.legend()\n",
-    "legend.remove()\n",
-    "fig.savefig(\"rmvs_swifter_comparison-mars_ejecta-testparticles-vmag.png\", facecolor='white', transparent=False, dpi=300)"
+    "swiftdiff['rmag'].sel(id=2)"
    ]
   },
   {
diff --git a/examples/rmvs_swifter_comparison/mars_ejecta/cb.swiftest.in b/examples/rmvs_swifter_comparison/mars_ejecta/cb.swiftest.in
index 2aafc8408..40cf41bad 100644
--- a/examples/rmvs_swifter_comparison/mars_ejecta/cb.swiftest.in
+++ b/examples/rmvs_swifter_comparison/mars_ejecta/cb.swiftest.in
@@ -1,3 +1,4 @@
+0
 0.00029591220828563
 0.004650467260962157
 0.0
diff --git a/examples/rmvs_swifter_comparison/mars_ejecta/config.swiftest.in b/examples/rmvs_swifter_comparison/mars_ejecta/param.swiftest.in
similarity index 80%
rename from examples/rmvs_swifter_comparison/mars_ejecta/config.swiftest.in
rename to examples/rmvs_swifter_comparison/mars_ejecta/param.swiftest.in
index 3f459514f..7df10c4d0 100644
--- a/examples/rmvs_swifter_comparison/mars_ejecta/config.swiftest.in
+++ b/examples/rmvs_swifter_comparison/mars_ejecta/param.swiftest.in
@@ -1,8 +1,3 @@
-!
-! Parameter file for the CHO run of the 4 giant planets and Pluto.
-!
-!NPLMAX         -1                 ! not used
-!NTPMAX         -1                 ! not used
 T0             0.0e0
 TSTOP          6000.000           ! simulation length in days
 DT             1e0            ! stepsize in days 
@@ -21,8 +16,6 @@ CHK_RMIN       0.005e0               ! check for close solar encounters
 CHK_RMAX       10000.0e0              ! discard outside of
 CHK_EJECT      -1.0                ! ignore this check
 CHK_QMIN       -1.0                ! ignore this check
-!CHK_QMIN_COORD HELIO               ! commented out here
-!CHK_QMIN_RANGE 1.0 1000.0          ! commented out here
 ENC_OUT        enc.swiftest.dat
 EXTRA_FORCE    no                  ! no extra user-defined forces
 BIG_DISCARD    yes                 ! output all planets if anything discarded
diff --git a/examples/rmvs_swifter_comparison/mars_ejecta/profmaker.sh b/examples/rmvs_swifter_comparison/mars_ejecta/profmaker.sh
deleted file mode 100755
index 9b1adcd8c..000000000
--- a/examples/rmvs_swifter_comparison/mars_ejecta/profmaker.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/bash
-gprof ./swiftest_driver | /home/daminton/git/gprof2dot/gprof2dot.py | dot -Tpng -o swiftest_profile.png
diff --git a/examples/rmvs_swifter_comparison/mars_ejecta/profswifter.sh b/examples/rmvs_swifter_comparison/mars_ejecta/profswifter.sh
deleted file mode 100755
index a03493f54..000000000
--- a/examples/rmvs_swifter_comparison/mars_ejecta/profswifter.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/bash
-gprof ./swifter_rmvs | /home/daminton/git/gprof2dot/gprof2dot.py | dot -Tpng -o swifter_profile.png
diff --git a/examples/rmvs_swifter_comparison/mars_ejecta/start.in b/examples/rmvs_swifter_comparison/mars_ejecta/start.in
deleted file mode 100644
index d6d7c3850..000000000
--- a/examples/rmvs_swifter_comparison/mars_ejecta/start.in
+++ /dev/null
@@ -1 +0,0 @@
-param.in
diff --git a/examples/rmvs_swifter_comparison/mars_ejecta/swiftest_rmvs_vs_swifter_rmvs.ipynb b/examples/rmvs_swifter_comparison/mars_ejecta/swiftest_rmvs_vs_swifter_rmvs.ipynb
index 2ef9e28a5..c0ae7eec3 100644
--- a/examples/rmvs_swifter_comparison/mars_ejecta/swiftest_rmvs_vs_swifter_rmvs.ipynb
+++ b/examples/rmvs_swifter_comparison/mars_ejecta/swiftest_rmvs_vs_swifter_rmvs.ipynb
@@ -6,8 +6,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import swiftest\n",
     "import numpy as np\n",
-    "import swiftestio as swio\n",
     "import matplotlib.pyplot as plt"
    ]
   },
@@ -20,14 +20,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reading Swifter file param.swifter.in\n"
+      "Reading Swifter file param.swifter.in\n",
+      "Reading in time 6.000e+03\n",
+      "Creating Dataset\n",
+      "Successfully converted 6001 output frames.\n",
+      "Swifter simulation data stored as xarray DataSet .ds\n"
      ]
     }
    ],
    "source": [
     "inparfile = 'param.swifter.in'\n",
-    "param = swio.read_swifter_param(inparfile)\n",
-    "swifterdat = swio.swifter2xr(param)"
+    "swiftersim = swiftest.Simulation(param_file=inparfile, codename=\"Swifter\")\n",
+    "swiftersim.bin2xr()\n",
+    "swifterdat = swiftersim.ds"
    ]
   },
   {
@@ -39,14 +44,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reading Swiftest file param.swiftest.in\n"
+      "Reading Swiftest file param.swiftest.in\n",
+      "Reading in time 6.001e+03\n",
+      "Creating Dataset\n",
+      "Successfully converted 6002 output frames.\n",
+      "Swiftest simulation data stored as xarray DataSet .ds\n"
      ]
     }
    ],
    "source": [
-    "param_file_name = 'param.swiftest.in'\n",
-    "config = swio.read_swiftest_config(param_file_name)\n",
-    "swiftestdat = swio.swiftest2xr(config)"
+    "inparfile = 'param.swiftest.in'\n",
+    "swiftestsim = swiftest.Simulation(param_file=inparfile)\n",
+    "swiftestsim.bin2xr()\n",
+    "swiftestdat = swiftestsim.ds"
    ]
   },
   {
@@ -94,7 +104,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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xFPBUPYRnZmbVaErnUMzMrIw5oZiZWSacUMzMLBNOKGZmlgknFDOzZmjz5s0MHTqUgw8+mP79+/P973+/5Dqb1FVeZmbWMHbeeWdmzZpF27Zt2bp1K4cffjijR4/m0EMPLbpO91DMzJohSbRt2xZI5vTaunUrUqHZrWrPPRQzs8b0yCRY9XK2de7zeRg9pcZi27dvZ/DgwSxdupTzzjvvMzV9vZmZNaAWLVowf/58KisrmTNnDgsXLiypPvdQzMwaUy16EvWtffv2DB8+nN///vcMGDCg6HrcQzEza4bWrFnDunXrAPjwww954okn6NOnT0l1uodiZtYMrVy5kjPPPJPt27fz0Ucfccopp3D88ceXVKcTiplZM3TQQQcxb968TOv0kJeZmWXCCcXMzDLhhGJmZplwQjEzs0w4oZiZWSacUMzMLBNOKGZmzdj27ds55JBDSv4OCjihmJk1a1OnTqVv376Z1OWEYmbWTFVWVvLwww9zzjnnZFKfvylvZtaIfjLnJ7z63quZ1tlnjz5cNvSyGstdeOGF/PSnP2XDhg2ZHNc9FDOzZuihhx5ir732YvDgwZnV6R6KmVkjqk1Poj7Mnj2b6dOnM2PGDDZv3sz69es544wzuPvuu4uu0z0UM7Nm6Oqrr6ayspJly5Yxbdo0jjnmmJKSCTihmJlZRjzkZWbWzA0fPpzhw4eXXI97KGZmlgknFDMzy0STSiiSRklaImmppEkFtkvS9en2P0kalK7vJulJSYslLZJ0QcNHb2bWvDWZhCKpBXAjMBroB4yX1C+v2Gigd/qYCPwyXb8NuDgi+gKHAucV2NfMzOpRk0kowFBgaUS8ERFbgGnAmLwyY4A7I/E80F5Sp4hYGREvAUTEBmAx0KUhgzcza+6aUkLpAizPeV7Jp5NCjWUk9QAOAf6YfYhmZlaVpnTZsAqsi7qUkdQW+B/gwohYX/Ag0kSS4TK6d+9eXKRmZp8BPXr0oF27drRo0YKKigrmzp1bUn01JhRJtf3UXVfVh3gtVQLdcp53BVbUtoykliTJ5NcRcV9VB4mIm4GbAYYMGZKfsMzMmpUnn3ySDh06ZFJXbXood5D0Agr1DnYI4HbgzhJieQHoLakn8DZwKnBaXpnpwPmSpgHDgL9GxEpJAv4TWBwR15QQg5mZFanGhBIRR+evk7RPRKzKMpCI2CbpfOBRoAXwq4hYJOncdPtNwAzgOGApsAk4O939MOBrwMuS5qfrroiIGVnGaGaWtVU//jF/W5zt9PU79+3DPldcUWM5SYwcORJJfOtb32LixIklHbfYcyhfB35a0pELSBPAjLx1N+UsB3Begf3+QPU9KDMzyzN79mw6d+7M6tWrGTFiBH369OHII48sur5iE8oYSZuAxyNiSdFHNzNr5mrTk6gvnTt3BmCvvfbixBNPZM6cOSUllGIvGx5LMux0oqRbiz66mZk1ig8++ODjOzV+8MEHPPbYYwwYMKCkOovqoUTEO8Dv04eZmZWZd955hxNPPBGAbdu2cdpppzFq1KiS6iwqoUi6EWgTEWdJGhkRj5UUhZmZNaj99tuPBQsWZFpnsUNeW4A30uVjMorFzMzKWLEJZRPwufTLhP66uZmZFX2V13vAhySzA8/OLhwzMytXdeqhSGov6TZgXLrqTmBI5lGZmVnZqVMPJSLWSZoC9ADeBQ4Cqpw3y8zMmo9ihrwmAG9GxKPAixnHY2ZmZaqYk/LvA+dKuk7S2ZIOyTooMzOrf+vWreOkk06iT58+9O3bl+eee66k+urcQ4mIqyXNBP4MDASOBOaVFIWZmTW4Cy64gFGjRnHvvfeyZcsWNm3aVFJ9dU4okn5AMhvwfGB+RDxVUgRmZtbg1q9fz9NPP83tt98OQKtWrWjVqlVJdRbTQ/mepL1JbrM7TlKviPhmSVGYmTVTz/z2z7y7fGOmdXbo1pYjTjmg2jJvvPEGHTt25Oyzz2bBggUMHjyYqVOn0qZNm6KPW+wXG78FzIuIKU4mZmblZ9u2bbz00kt8+9vfZt68ebRp04YpU6aUVGexX2z8FfBtSW1Ibrk7v6QozMyaqZp6EvWla9eudO3alWHDhgFw0kknlZxQiu2hfJckGVUA15cUgZmZNbh99tmHbt26sWRJckurmTNn0q9fv5LqLLaH8jrQG3ggIv6lpAjMzKxR/Pu//zunn346W7ZsYb/99uO2224rqb5iE8oiYDkwQdLPIuILJUVhZmYNbuDAgcydOzez+opNKAcAa4CbSb7oaGZmzVyx51D6kHyZ8RJgYnbhmJlZuSo2obQHLgMuBTZnFo2ZmZWtYoe8fgD0iYglkj7KMiAzMytPteqhSGohaaWkcwAiojIinkiXJ9VngGZmVh5qlVAiYjuwEOhVv+GYmVm5qss5lF2BSyXNlTQ9fTxQX4GZmVn9WbJkCQMHDvz4sdtuu3HdddeVVGddzqF8Mf05KH0ARElHNzOzRnHggQcyf/58ALZv306XLl048cQTS6qzLgmlZ0lHMjOzJmnmzJn06tWLfffdt6R6ap1QIuIvJR3JzMw+5cnbb2b1X97ItM699t2Po8+q/VcEp02bxvjx40s+brHfQzEzs8+ALVu2MH36dE4++eSS6yr2eyhmZpaBuvQk6sMjjzzCoEGD2HvvvUuuq849FElfKfmoVdc9StISSUslfer7LUpcn27/k6RBtd3XzMw+7Z577slkuAuKG/L6USZHziOpBXAjMBroB4yXlD85/2iSafN7k8wh9ss67GtmZjk2bdrE448/ztixYzOpr5ghL2Vy5E8bCiyNiDcAJE0DxgCv5JQZA9wZEQE8L6m9pE5Aj1rsm5nbL/4xH7ZqWR9Vm1kzMPgrR7C6clWjxtCyhdi9096sXbs2szqLSSj19d2TLiT3WNmhEhhWizJdarkvAJImks6Q3L1796IC/Ugt+LBie1H7mpmF4CM17tf44qPsj9+UTsoX6vnkt7iqMrXZN1kZcTPJfVwYMmRIUa/oN/7fZcXsZmYGwOLFi9mnS6fGDiNzTSmhVALdcp53BVbUskyrWuxrZmb1qJiT8u9kHkXiBaC3pJ6SWgGnAtPzykwHvp5e7XUo8NeIWFnLfc3MrB7VuYcSESPqI5CI2CbpfOBRoAXwq4hYJOncdPtNwAzgOGApsAk4u7p96yNOMzMrrCkNeRERM0iSRu66m3KWAzivtvuamVnD8dQrZmbN1LXXXkv//v0ZMGAA48ePZ/Pm0u7oXlRCkXRRzvKBJUVgZmYN7u233+b6669n7ty5LFy4kO3btzNt2rSS6qzTkJek9sC1QB9Jm4E/ARNIz2WYmVn52LZtGx9++CEtW7Zk06ZNdO7cuaT66pRQImIdcLakfwRWASOB+0qKwMysGVv34OtsWfFBpnW26tyG9l+p/o7tXbp04ZJLLqF79+7ssssujBw5kpEjR5Z03GLPoRxFcvnwoUC9XPVlZmb15/333+eBBx7gzTffZMWKFXzwwQfcfffdJdVZ7FVe7YHLgEtJhrzMzKwINfUk6ssTTzxBz5496dixIwBjx47l2Wef5Ywzzii6zmJ7KD8AHoiIJcBHRR/dzMwaRffu3Xn++efZtGkTEcHMmTPp27dvSXUW1UOJiEqSaVCICN97xMyszAwbNoyTTjqJQYMGUVFRwSGHHMLEiaXd7KuohCLpRqBNRJwlaWREPFZSFGZm1uCuuuoqrrrqqszqK3bIawvwRrp8TEaxmJlZGSs2oWwCPiepJVDcTUXMzOwzpdirvN4DPiS57e7s7MIxM7NyVaceSnrL3duAcemqO4EhmUdlZmZlp87flJc0heQe7u8CB+FvypuZGcUNeU0A3oyIR4EXM47HzMzKVDEn5d8HzpV0naSzJR2SdVBmZlb/pk6dyoABA+jfvz/XXXddyfXVOaFExNXAN4HJwJvAkSVHYWZmDWrhwoXccsstzJkzhwULFvDQQw/x2muvlVRnnROKpB8AY0gmhXw7IqaWFIGZmTW4xYsXc+ihh7LrrrtSUVHBUUcdxf33319SncXcU/57kr5HkozGSeoVEd8sKQozs2bqkUceYdWqVZnWuc8++zB69OhqywwYMIArr7yStWvXsssuuzBjxgyGDCntot1iv4fyK+AcoA3wi5IiMDOzBte3b18uu+wyRowYQdu2bTn44IOpqCg2JSSK3fu7JNOvVABT8XkUM7Oi1NSTqE8TJkxgwoTkDiRXXHEFXbt2Lam+YqdeeR1oTTKFvZOJmVkZWr16NQBvvfUW9913H+PHjy+pvmJ7KIuA5cAEST+LiC+UFIWZmTW4cePGsXbtWlq2bMmNN97I7rvvXlJ9xSaUXiTfR7k5/WlmZmXmmWeeybS+YhPK8oiYJakTsDrLgMzMrDwVew5llKSuwE3AtRnGY2ZmZarYhNIeuAy4FPhbZtGYmTUTEdHYIdSorjEWm1B+QHKF1xJge5F1mJk1S61bt2bt2rVNOqlEBGvXrqV169a13qdW51AktQAqgf8TEbdGRGX6nIiYVEywZmbNVdeuXamsrGTNmjWNHUq1WrduXafvptQqoUTEdkkLSa7uMjOzErRs2ZKePXs2dhiZq8uQ167ApZLmSpqePh7IIghJe0h6XNJr6c+CF0NLGiVpiaSlkiblrP+ZpFcl/UnS/ZLaZxGXmZnVXl0SyhcBAYOA43MeWZgEzIyI3sDM9PknpMNuNwKjgX7AeEn90s2PAwMi4iDgz8DlGcVlZma1VJfvodRn/2wMMDxdvgN4iuQqslxDgaUR8QaApGnpfq9ExGM55Z4HTqrHWM3MrIAaE4qk7uliwcsRcravi4j1Rcaxd0SsBIiIlZL2KlCmC8l0LztUAsMKlPsG8Jsi4zAzsyLVpodyB0kyUTVlArgduLOqApKeAPYpsOnKWsRAFcf/RJKTdCWwDfh1NXFMBCYCdO/evapiZmZWRzUmlIg4OosDRcSXq9om6R1JndLeSVXTuVQC3XKedwVW5NRxJsk5nWOjmou7I+JmkjnIGDJkSNO9CNzMrMwU+8XGrE0HzkyXzwQKXT32AtBbUk9JrYBT0/2QNIrknMtXI2JTA8RrZmZ5mkpCmQKMkPQayb3qpwBI6ixpBkBEbAPOBx4FFgO/jYhF6f43AO2AxyXNl3RTQzfAzKy5K+1+jxmJiLXAsQXWrwCOy3k+A5hRoNz+9RqgmZnVqKn0UMzMrMw5oZiZWSacUMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxczMMuGEYmZmmXBCMTOzTDihmJlZJpxQzMwsE04oZmaWCScUMzPLhBOKmZllwgnFzMwy4YRiZmaZcEIxM7NMOKGYmVkmnFDMzCwTTihmZpYJJxQzM8uEE4qZmWXCCcXMzDLhhGJmZplwQjEzs0w4oZiZWSacUMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxczMMtEkEoqkPSQ9Lum19OfuVZQbJWmJpKWSJhXYfomkkNSh/qM2M7NcTSKhAJOAmRHRG5iZPv8ESS2AG4HRQD9gvKR+Odu7ASOAtxokYjMz+4SmklDGAHeky3cAJxQoMxRYGhFvRMQWYFq63w7XApcCUY9xmplZFZpKQtk7IlYCpD/3KlCmC7A853llug5JXwXejogFNR1I0kRJcyXNXbNmTemRm5kZABUNdSBJTwD7FNh0ZW2rKLAuJO2a1jGyNpVExM3AzQBDhgxxb8bMLCMNllAi4stVbZP0jqROEbFSUidgdYFilUC3nOddgRVAL6AnsEDSjvUvSRoaEasya4CZmVWrqQx5TQfOTJfPBB4oUOYFoLeknpJaAacC0yPi5YjYKyJ6REQPksQzyMnEzKxhNZWEMgUYIek1kiu1pgBI6ixpBkBEbAPOBx4FFgO/jYhFjRSvmZnlabAhr+pExFrg2ALrVwDH5TyfAcyooa4eWcdnZmY1ayo9FDMzK3NOKGZmlgknFDMzy4QTipmZZcIJxczMMuGEYmZmmXBCMTOzTDihmJlZJpxQzMwsE04oZmaWCScUMzPLhBOKmZllwgnFzMwy4YRiZmaZcEIxM7NMOKGYmVkmnFDMzCwTTihmZpYJJxQzM8uEE4qZmWXCCcXMzDLhhGJmZplwQjEzs0w4oZiZWSYUEY0dQ6ORtAb4S5G7dwDezTCcxuS2ND2flXaA29JUldKWfSOiY/7KZp1QSiFpbkQMaew4suC2ND2flXaA29JU1UdbPORlZmaZcEIxM7NMOKEU7+bGDiBDbkvT81lpB7gtTVXmbfE5FDMzy4R7KGZmlgknFDMzy4QTShEkjZK0RNJSSZMaO558kn4labWkhTnr9pD0uKTX0p+752y7PG3LEkn/kLN+sKSX023XS1IjtKWbpCclLZa0SNIF5dgeSa0lzZG0IG3HVeXYjrw2tZA0T9JD5dwWScvSGOZLmlvmbWkv6V5Jr6Z/M19s0LZEhB91eAAtgNeB/YBWwAKgX2PHlRfjkcAgYGHOup8Ck9LlScBP0uV+aRt2BnqmbWuRbpsDfBEQ8AgwuhHa0gkYlC63A/6cxlxW7UmP2TZdbgn8ETi03NqR16aLgP8CHirz99gyoEPeunJtyx3AOelyK6B9Q7alwd+E5f5IX+RHc55fDlze2HEViLMHn0woS4BO6XInYEmh+IFH0zZ2Al7NWT8e+I8m0K4HgBHl3B5gV+AlYFi5tgPoCswEjuHvCaVc27KMTyeUsmsLsBvwJunFVo3RFg951V0XYHnO88p0XVO3d0SsBEh/7pWur6o9XdLl/PWNRlIP4BCS/+7Lrj3pENF8YDXweESUZTtS1wGXAh/lrCvXtgTwmKQXJU1M15VjW/YD1gC3pUORt0pqQwO2xQml7gqNJZbztddVtadJtVNSW+B/gAsjYn11RQusaxLtiYjtETGQ5L/7oZIGVFO8ybZD0vHA6oh4sba7FFjXJNqSOiwiBgGjgfMkHVlN2abclgqSoe5fRsQhwAckQ1xVybwtTih1Vwl0y3neFVjRSLHUxTuSOgGkP1en66tqT2W6nL++wUlqSZJMfh0R96Wry7Y9EbEOeAoYRXm24zDgq5KWAdOAYyTdTXm2hYhYkf5cDdwPDKU821IJVKY9X4B7SRJMg7XFCaXuXgB6S+opqRVwKjC9kWOqjenAmenymSTnInasP1XSzpJ6Ar2BOWnXeIOkQ9MrPL6es0+DSY/9n8DiiLgmZ1NZtUdSR0nt0+VdgC8Dr5ZbOwAi4vKI6BoRPUje/7Mi4oxybIukNpLa7VgGRgILKcO2RMQqYLmkA9NVxwKv0JBtaegTYJ+FB3AcydVGrwNXNnY8BeK7B1gJbCX5b2MCsCfJSdTX0p975JS/Mm3LEnKu5gCGkPxxvQ7cQN7JvgZqy+Ek3e0/AfPTx3Hl1h7gIGBe2o6FwPfS9WXVjgLtGs7fT8qXXVtIzjssSB+Ldvw9l2Nb0hgGAnPT99nvgN0bsi2eesXMzDLhIS8zM8uEE4qZmWXCCcXMzDLhhGJmZplwQjEzs0w4oZhlIJ3l9Z9znneWdG89HesESd+rYtvG9GdHSb+vj+ObVcUJxSwb7YGPE0pErIiIk+rpWJcCv6iuQESsAVZKOqyeYjD7FCcUs2xMAXql99T4maQeSu9HI+ksSb+T9KCkNyWdL+midAK/5yXtkZbrJen36SSFz0jqk38QSQcAf4uId9PnPSU9J+kFST/MK/474PR6bbVZDicUs2xMAl6PiIER8a8Ftg8ATiOZJ+pHwKZIJvB7jmRqC4Cbge9ExGDgEgr3Qg4jmfp+h6kkkwF+AViVV3YucESR7TGrs4rGDsCsmXgyIjaQzJH0V+DBdP3LwEHpbMpfAv475+Z4OxeopxPJFOU7HAaMS5fvAn6Ss2010Dmb8M1q5oRi1jD+lrP8Uc7zj0j+DncC1kUyvX11PgQ+l7euqvmTWqflzRqEh7zMsrGB5BbFRYnkHi9vSjoZklmWJR1coOhiYP+c57NJZvyFT58vOYBkgj+zBuGEYpaBiFgLzJa0UNLPiqzmdGCCpB0z344pUOZp4BD9fVzsApKbQr3Ap3suRwMPFxmLWZ15tmGzMiNpKvBgRDxRQ7mngTER8X7DRGbNnXsoZuXnx8Cu1RWQ1BG4xsnEGpJ7KGZmlgn3UMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxczMMvH/AVCw/kc/JJStAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -120,7 +130,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -141,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -153,7 +163,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -176,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -188,7 +198,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -219,9 +229,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "swiftestOOF",
    "language": "python",
-   "name": "python3"
+   "name": "swiftestoof"
   },
   "language_info": {
    "codemirror_mode": {
@@ -233,7 +243,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/whm_gr_test/cb.swiftest.in b/examples/whm_gr_test/cb.swiftest.in
index 058975b81..e4a010b1e 100644
--- a/examples/whm_gr_test/cb.swiftest.in
+++ b/examples/whm_gr_test/cb.swiftest.in
@@ -1,3 +1,4 @@
+0
 39.476926408897626
 0.004650467260962157
 4.7535806948127355e-12
diff --git a/examples/whm_gr_test/init_cond.py b/examples/whm_gr_test/init_cond.py
index ddd4a315c..57b0fb534 100755
--- a/examples/whm_gr_test/init_cond.py
+++ b/examples/whm_gr_test/init_cond.py
@@ -6,7 +6,7 @@
 sim.param['TP_IN'] = "tp.swiftest.in"
 sim.param['CB_IN'] = "cb.swiftest.in"
 sim.param['BIN_OUT'] = "bin.swiftest.dat"
-sim.param['ENC_OUT'] = "enc.swiftest.dat
+sim.param['ENC_OUT'] = "enc.swiftest.dat"
 
 sim.param['MU2KG'] = swiftest.MSun
 sim.param['TU2S'] = swiftest.YR2S
@@ -41,7 +41,6 @@
 for name, id in bodyid.items():
    sim.add(name, idval=id)
    
-"
 sim.save("param.swiftest.in")
 sim.param['PL_IN'] = "pl.swifter.in"
 sim.param['TP_IN'] = "tp.swifter.in"
diff --git a/examples/whm_gr_test/pl.swifter.in b/examples/whm_gr_test/pl.swifter.in
index fb614bcbd..e0ef4e881 100644
--- a/examples/whm_gr_test/pl.swifter.in
+++ b/examples/whm_gr_test/pl.swifter.in
@@ -2,35 +2,35 @@
 0 39.476926408897625196
 0.0 0.0 0.0
 0.0 0.0 0.0
-1 6.5537098095653139645e-06 0.0014751244996981091688
+1 6.5537098095653139645e-06 0.0014751243077781048702
 1.6306381826061645943e-05
-0.3424231950105547928 0.045418598038753463242 -0.027698852186471431547
--3.3367255059737357791 10.64622790896598922 1.1760542104884461008
-2 9.663313399581537916e-05 0.006759105927487850036
+0.33206272695596028566 0.07436707001147663254 -0.02438290851908785084
+-4.2340114788918336805 10.486553514018327622 1.2453138107251555947
+2 9.663313399581537916e-05 0.006759104275397271956
 4.0453784346544178454e-05
--0.7187409324847692238 0.008460121607685697903 0.041591140610292232083
--0.12984901004910088259 -7.4200262222437848615 -0.09433956287915627552
-3 0.000120026935827952453094 0.010044781409779763954
+-0.7188115337296047125 -0.0118554711069603201795 0.041316403191083782287
+0.07826338813583945357 -7.419533988988633545 -0.10634201014368884618
+3 0.000120026935827952453094 0.010044787321379672528
 4.25875607065040958e-05
-0.34089134446377761245 -0.95773246142964407746 4.435598231277717939e-05
-5.817808761117269037 2.0836935934869021533 -0.00011769228482089048983
-4 1.2739802010675941456e-05 0.007246745952808948377
+0.35677088372527121507 -0.95189300879814897627 4.4027442504036787155e-05
+5.7819217550992820422 2.18192814489641851 -0.00012230072278352209966
+4 1.2739802010675941456e-05 0.007246743835971885302
 2.265740805092889601e-05
--1.5181949547849429294 0.68396861668094566244 0.051574975426518870902
--1.908390450061588966 -4.22386803675231535 -0.041705300362175825686
-5 0.037692251088985676735 0.35527129445679039879
+-1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
+-1.8728417739956807141 -4.239719661832373223 -0.042909557750301418264
+5 0.037692251088985676735 0.35527126534549128905
 0.00046732617030490929307
-4.0455430243320495975 -2.9975165850765859155 -0.07806209765007877943
-1.6095092959604531543 2.3468202746428659788 -0.045756236082127820444
-6 0.011285899820091272997 0.43765252895139074356
+4.049944927347420176 -2.9910878677758190314 -0.078187280837353656526
+1.6060801375519682711 2.349356876761497338 -0.045690062807172619064
+6 0.011285899820091272997 0.4376527512949726007
 0.00038925687730393611812
-6.29491448542079457 -7.7099360898166890976 -0.11647820911165690516
-1.466832763395646828 1.2863740305106504463 -0.0807227702193550598
-7 0.0017236589478267730203 0.4695335374930126262
+6.298929503477405767 -7.706413024510769816 -0.11669919842191249504
+1.4661378456572359413 1.2872251175075805794 -0.08070991686100478242
+7 0.0017236589478267730203 0.4695362423191493196
 0.00016953449859497231466
-14.8586976850394894 13.004806892491039605 -0.14422203290693380584
--0.9552190982402187873 1.0163552941629989916 0.016097455433874226457
-8 0.0020336100526728302319 0.78128379442879379807
+14.856082147529010129 13.007589275314199284 -0.14417795763685259391
+-0.9554310497290159123 1.0161753499437922057 0.016099529164307530124
+8 0.0020336100526728302319 0.7812870996943599397
 0.000164587904124493665
-29.55697963607957135 -4.6325049341728004038 -0.5858344271811812831
-0.17174414812321077623 1.1422632832628163399 -0.02744788192962509712
+29.55744967800954015 -4.629377558152945049 -0.58590957207831262377
+0.17162147939801157335 1.1422848961108499101 -0.027445465472921385952
diff --git a/examples/whm_gr_test/pl.swiftest.in b/examples/whm_gr_test/pl.swiftest.in
index 874012fc6..9d49cc3da 100644
--- a/examples/whm_gr_test/pl.swiftest.in
+++ b/examples/whm_gr_test/pl.swiftest.in
@@ -1,33 +1,33 @@
 8
 1 6.5537098095653139645e-06
 1.6306381826061645943e-05
-0.3424231950105547928 0.045418598038753463242 -0.027698852186471431547
--3.3367255059737357791 10.64622790896598922 1.1760542104884461008
+0.33206272695596028566 0.07436707001147663254 -0.02438290851908785084
+-4.2340114788918336805 10.486553514018327622 1.2453138107251555947
 2 9.663313399581537916e-05
 4.0453784346544178454e-05
--0.7187409324847692238 0.008460121607685697903 0.041591140610292232083
--0.12984901004910088259 -7.4200262222437848615 -0.09433956287915627552
+-0.7188115337296047125 -0.0118554711069603201795 0.041316403191083782287
+0.07826338813583945357 -7.419533988988633545 -0.10634201014368884618
 3 0.000120026935827952453094
 4.25875607065040958e-05
-0.34089134446377761245 -0.95773246142964407746 4.435598231277717939e-05
-5.817808761117269037 2.0836935934869021533 -0.00011769228482089048983
+0.35677088372527121507 -0.95189300879814897627 4.4027442504036787155e-05
+5.7819217550992820422 2.18192814489641851 -0.00012230072278352209966
 4 1.2739802010675941456e-05
 2.265740805092889601e-05
--1.5181949547849429294 0.68396861668094566244 0.051574975426518870902
--1.908390450061588966 -4.22386803675231535 -0.041705300362175825686
+-1.5233712071242269115 0.6723825347339112968 0.051459143378398922164
+-1.8728417739956807141 -4.239719661832373223 -0.042909557750301418264
 5 0.037692251088985676735
 0.00046732617030490929307
-4.0455430243320495975 -2.9975165850765859155 -0.07806209765007877943
-1.6095092959604531543 2.3468202746428659788 -0.045756236082127820444
+4.049944927347420176 -2.9910878677758190314 -0.078187280837353656526
+1.6060801375519682711 2.349356876761497338 -0.045690062807172619064
 6 0.011285899820091272997
 0.00038925687730393611812
-6.29491448542079457 -7.7099360898166890976 -0.11647820911165690516
-1.466832763395646828 1.2863740305106504463 -0.0807227702193550598
+6.298929503477405767 -7.706413024510769816 -0.11669919842191249504
+1.4661378456572359413 1.2872251175075805794 -0.08070991686100478242
 7 0.0017236589478267730203
 0.00016953449859497231466
-14.8586976850394894 13.004806892491039605 -0.14422203290693380584
--0.9552190982402187873 1.0163552941629989916 0.016097455433874226457
+14.856082147529010129 13.007589275314199284 -0.14417795763685259391
+-0.9554310497290159123 1.0161753499437922057 0.016099529164307530124
 8 0.0020336100526728302319
 0.000164587904124493665
-29.55697963607957135 -4.6325049341728004038 -0.5858344271811812831
-0.17174414812321077623 1.1422632832628163399 -0.02744788192962509712
+29.55744967800954015 -4.629377558152945049 -0.58590957207831262377
+0.17162147939801157335 1.1422848961108499101 -0.027445465472921385952
diff --git a/examples/whm_gr_test/swiftest_relativity.ipynb b/examples/whm_gr_test/swiftest_relativity.ipynb
index 90f48b315..0d8111fea 100644
--- a/examples/whm_gr_test/swiftest_relativity.ipynb
+++ b/examples/whm_gr_test/swiftest_relativity.ipynb
@@ -115,16 +115,16 @@
      "text": [
       "Mean precession rate for Mercury long. peri. (arcsec/100 y)\n",
       "JPL Horizons      : 573.8351991142854\n",
-      "Swifter GR        : 579.6804682138315\n",
-      "Swiftest GR       : 579.6804681921402\n",
-      "Obs - Swifter     : -5.845269099546055\n",
-      "Obs - Swiftest    : -5.845269077854606\n",
-      "Swiftest - Swifter: -2.169144863728434e-08\n"
+      "Swifter GR        : 579.5897815748845\n",
+      "Swiftest GR       : 579.5897815748845\n",
+      "Obs - Swifter     : -5.754582460598964\n",
+      "Obs - Swiftest    : -5.754582460598964\n",
+      "Swiftest - Swifter: 0.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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rWIOH21VD5Dr6pjzO1N8H8s6enzle+RfgF8qnONns4+Dl8p2IqHLRNIN5hiUSY4zJhJTkcwxe9AohCo93/hSAD36LoVRhf+5rGXGhL9Ph40vPDkNpH/8wKzZ+y44jm9hxcg+lA0pwU3vPRmDmNpZIjDEmEyb++gTrHcm8FX4zxYqFsWr3MRbEHGZg51oE+l88ZUmxkHDaXf2MFyLNPjb81xhjrtDpU/v5aN88mhPIdS1dc8d+8NtWQoL8uKNZzixzmxtYIjHGmCv07dwXOOEQHr3qacThYP3e4/y2+SD3tqhC4UIFp8HHEkkukpumkY+Pj2fEiBGX3G5Ty5uCLvHMMcYdXEJzAomscwsA782JoUiAL3e1CPducDnMEkkukVumkT/vconEppY3BqbOG8QRH+H++q45aBfEHGL2xgP0bxVB0QA/L0eXsyyR5BLenEZ+w4YNNGnShKioKOrXr09MTAwDBw5k27ZtREVF8fTTT/8jVpta3hQEKU5l5vp9LNp6mKQU5z+2JSUlMCZuLlHqR3SDuzmX7OTl6RuoXDKI+1pGeCli7yk4jXgeeHPpm2w+ujlLz1mrRC2ebfLsJbd7cxr5jz/+mMcee4zbb7+dc+fOkZKSwpAhQ1i/fj2rV6++KFabWt7kd4u2Hea16avwOf0ThxOrcNq3Jm1rlaZF1VI0DAth/frXiPOBF2r9B3E4+Gz+NrYfOs2Yu6/y2uJS3mSJJJfw5jTyzZs35/XXXyc2NpaePXtSvXp1j85rU8ub/MLpVJ7/8hPijn3NiWJHOF7CgY8qzc+FsG3HDWzeEk/pkPnEFD1KRIoPZ4NvY/eRBN6fE0PHOmVoW7O0t6vgFZZI0nG5O4fs5K1p5GvXrk3Tpk358ccfufbaaxk9evSFZrP02NTyJr+a/MdsfnV+iDMEWvsUp0uVLqzc9yeTdTtnyn8BwBGn0jCpKPsO38z941fiEPDzcTCoWx3vBu9FOZZI3Mvkpl6TPQIYBDQHzn+yhQDxqhqVzvEhwGggElDgHlVdLCJvA9cD54BtwN2qGp8tlchGW7ZsweFwXLgbSDuN/J133smdd955YRr5/fv3X/E08h988AEiwqpVq2jYsCHbt28nIiKCRx99lO3bt7N27VoaNGjwj2NTa9euHc8//zwjR47kgQceADI2tfyECWlXWDYm90hxKjPXvkZKMExt9ymVw5oDriVd+x3dxpQ/XicksAQdmzxBkaIVSEpxMv+vQ/ywJo6rq5WiUokg71bAi3Kss11Vt6hqlDtJNAYSgCmqemuq8snA95c4xXvATFWtBTQANrnLZwORqlof+AvIe5P545pG/q677qJOnTrUr1+fjRs38sorrwDpTyNfv379DE0jv3Hjxgud7S+99BJJSUnUr1+fyMhIXnrJtQrbxIkTiYyMJCoqis2bN3PnnXdSsmRJWrRoQWRk5EWd7eenlp83bx5VqlShSZMm3HXXXZedWn7+/Pns2LEjk98lY7LPdwtnszL4KB2l7IUkcl7xElW55/rP6dlhKEWKVgBcdyHta5dh+G0N6RWdsRGW+ZVXppEXkU7Ay6raIlWZALuBdqoak2b/osAaIEIvE7CI9ABuVtXbL3d9m0Y+d7L/A+MtKU6l38jWrAw+yoyOY6lQ4bKrehdYl5pG3lvDf28D0rZztAQOpE0ibhHAIWCMiKwSkdEikt7asPcAP6d3QRHpJyLLRWT5oUOHMhO7MSaf+W7BrAt3I5ZEPJfjiURE/IHuwKQ0m3pzcXI5zxdoBIxU1YbAaWBgmvO+ACQDX6V3AlUdparRqhodGhqaiRoYY/KTFKcya/3rADzWYYiXo8mbvHFH0hlYqaoHzheIiC/Qk392xqcWC8Sq6vk5Q77DlVjOH38X0A24/XJNX/+mIK4WmVvY9954y8S501gZfJROdjdyxbyRSNK78+gAbFbV2HT2R1X3A3vcI78A2gMbAUTkOuBZoLuqpj90KAMCAgI4cuSIfaB5gapy5MgRAgICvB2KKWBSnMpPW17DX5Unrxvu7XDyrBx9jkREgnCNpuufZtNFfSYiUh4Yrapd3EWPAF+5m8a2A3e7yz8ECgGz3aOY/lTVAXioYsWKxMbGYv0n3hEQEEDFihW9HYYpYMb8+CZrgs5yu18DypSJ9HY4eZZXRm15W3qjtowxeVdyUiJ/rBhBpTJRhIe1wuHz738jJ549Tc8vmpIsMP32RQQEFs2BSPO2S43asifbjTF53rtTb+WLhO2wBYo6lfqOwrQs05j2DftTpkx91Olk954/+GvPAkoUqUB4heZ8PvsN9vgLj4T0tCSSSZZIjDF52sz5/+WLhO3c5F+OBqENWHVwDcvP7GPhgfn8b+Z8qqc4OCApnHCkeoB3tetL3URf7u06yCtx5yeWSIwxedbWbbMYtG0SUeLPCz2ncCBBGDFmGTEHT1G50DrCis7nTFAc9R0luLpSIxpVacmWuO38sn4JZ/QQPaOfwce34M3Wm9UskRhj8qRTJ/fxxLz/IwgY1uULthxO5u6xy0hMSuHr+5pSqURbth66h8XbjjB20U7mxgrdTpdj2pogigc1ZPitDWletaS3q5EvWCIxxuRJX855ip0+8HmDJzmYXIlenyymeJA/X93XlBpligBQqUQQbWuW5j/NKvPGT5uYtCKWtjVDGXpLA0oWLuTlGuQflkiMMXlOQsJhvj62htY+Rahd+066vb+AYoF+fP/g1ZQpevHzSJVKBDHyjsbExZ+hbNEAHI7LT3hqPGOJxBiT50yZN4h4h3Bv1EO8PG0Du48mMOH+ZukmkdTKhwTmUIQFi63ZbozJU5KSEhi3bz4N1Z/dzrZMXhnLw+2q0zTC+ju8xRKJMSbXyMgD0jMXvsE+H6Fz2Z68MGU90ZWL82i7ajkQnbkUa9oyxuQKI+du491f/6JRWAhXVy1F6xqhNKgU8o99nCnJfLZjOuFOeH5uJOVD/Bh+WxS+PvY3sTdZIjHGeN3uIwm8++tf1ChTmJOJybz761+8M/svrgovzsPtqtOqeik27jvBlDkD2eaj1D3YgHuuqc6THWsQXMg+xrzN/geMMV6lqrzywwaqBaykVZkV1CpVi8odW7PycDijFsZy1+dLKRrgS0XHHPaHLaD2OV+eue1tGoWX83boxs0SiTHGq2ZvPMD2HbNJrvw1X55xwJ4Y2PMDxZ3KvXWvIrDEY6zZtppFKT9S3ung015TKRZiSSQ3sURijPGaM+dS+HDGFCTsK/wRfmw7kqSkM2zY9Ts/7ZnDe8eWU+LIHQSoICKMaD+SYiHh3g7bpGGJxBjjNaNnzyax5Eecc8DYlkMJC7sGgKpVO9IdWL3uKz5cOZy1zjN80njghe0md/E4kYhIMJCoqikeHleTfy6lGwEMApoD51c+DAHiVTUqneNDgNFAJKDAPaq6WERKuM8bDuwEeqnqMU9iM8bkvHNJySzc8xzHCsFnTQZRvdp1F+0TVe92Rte7nXNnT+JfqIgXojQZ8a9j5kTEISJ9RORHETkIbAb2icgGEXlbRKpn5EKqukVVo9xJojGQAExR1VtTlU8Gvr/EKd4DZqpqLaABsMldPhCYo6rVgTnu98aYXG709GfZGJhMn+Cm1K9762X3tSSSu2Vk8PXvQFXgOaCsqlZS1dJAS+BPYIiI3OHhddsD21R11/kCca2T24uL13NHRIoCrYDPAFT1nKrGuzffAIxzvx4H3OhhLMaYHHY8ficTjs+k2lnhoRtHeDsck0kZadrqoKpJaQtV9SiuO4jJIuLn4XUvWqMdV2I6oKox6ewfARwCxohIA2AF8JiqngbKqOo+d0z7RKR0ehcUkX5AP4CwsDAPwzXGZKX//XAPpxzCLaUfx9/fZuHN6/71jiS9JHIl+5wnIv5Ad2BSmk29SeduxM0XaASMVNWGwGk8bMJS1VGqGq2q0aGhoZ4caozJQvOXvMuPzkPUjy/D7R1u93Y4JgtkuLNdRJ5Mp/g4sEJVV3twzc7ASlU9kOrcvkBPXH0n6YkFYlV1ifv9d/ydSA6ISDn33Ug54KAHsRhjcsjx47sZ/vP9fHc2jornlFKlB9maIPmEJxPURAMDgAruf/2ANsCnIvKMB+dJ786jA7BZVWPTO0BV9wN73CO/wNXHstH9ejpwl/v1XcA0D2IxxmTSjp1z2fLXjMvuM2vBa9wwuQvfJ+6lq1Zk1/bn6dOifg5FaLKbJ8N/SwKNVPUUgIi8jOvOoBWuPou3/u0EIhIEdAT6p9l0UZ+JiJQHRqtqF3fRI8BX7qax7cDd7vIhwLcici+wG7jFgzoZYzJhycpRPLLmfc44hGsWv0b/xo8SFdnnwvbjx3fzxow7+Sn5CHXElzejXqL/9CLUrVyYRmEh3gvcZCnJyLTNACKyCWigqufc7wsBq1W1toiscvdd5AnR0dG6fPlyb4dhTJ7226K3ePqvLwhzOri2ZH2+PrqaYw4hIkWo6BtMGf9izEuI5agD+oU04P6un/HYtxuZvfEAPz12DdVK25DevEZEVqhqdNpyT+5Ivgb+FJHzTUfXAxPcDyhuvPRhxpj85sufnmfYwenUSPHl0WZj8QmoRLEDsWz660326TYOJiewNuUkZcSH95sOom7tm5i5fh8/rtvH09fWtCSSz2T4jgRARBoD1wACLFTVPPlnvd2RGHPlduz9i96zelDurA9/7X6W086QC9scAk4Ffx8H10aW5bq6ZSkXEkDRAF9uG7WEMkULMfWhFvjZ+iF5UqbvSNwPDNYGiqnqYBEJE5Emqro0KwM1xuRuH/z4BAn+wr31XiSsWyeOnD6Hn8NBpRKBlA8JZPuh00xYupvvV8byw5q4C8f5OoRx91xlSSQf8qRpawTgBNoBg4GTuB5IvCob4jLG5EIL1q9god9OmiUVo1uLXunuU7NsEV7pXpeBnWux7dApDpxIZP/xs5QPCaBu+WI5HLHJCZ4kkqaq2khEVgGo6jH3CCpjTAGQ4lTGz3uWs8HCo61f+df9A/x8qFu+mCWPAsCTe8wkEfHBNfMuIhKK6w7FGFMAfPH7b6wI2k8bLUFkjU7eDsfkIp4kkveBKUAZEXkdWAi8kS1RGWNylQMnEpm34VWSBZ5o95q3wzG5TIabtlT1KxFZgeupcoAbVXXT5Y4xxuQNCeeSCfTzwTWm5mJDJn3FmqKH6exbhvDKrXI4OpPb/WsiucQcWwCdRaSzqr6TxTEZY3LI8TNJfDxvG58v3EHDsBA+7NOIUmnmv/p53T72JY7AP0h5stP7XorU5GYZadoq4v4XDTzA33NtDQDqZF9oxpjs9MW8P+ny9neMnBvDNdVKsWp3PN3eX8jK3X8vMHo8IYkvZw5lQ+Fz3FcymtDSdb0YscmtPJkiZRZwk6qedL8vAkxS1YvXx8zl7IFEU1AtWTmKOTHTWHJ6D9t9XL/7vqoUd0LrQpVZsPdedh0PoEW1UgT5+xB37ARnCz2C+gjT+iykUICNwCrIsmKKlDDgXKr353Ctk26MyQPWbZjEfes+INCp1EguROP4slxTrRynkuLZc+YAk5N2EVpqELeU7MCWk+04k7yHUN8f+dNfGF79Dksi5pI8SSRfAktFZAquIcA9+HuJW2NMLqZOJ8OXvUkJVT7r9APXfbSJu64O575uf7dOr9swicFLX+MHnzlQbM6F8mYE0q7Z094I2+QRnozael1Efsa1JC7A3aq6KnvCMsZkpcUrP2apnGVgmZZ8uyYRBe5uEf6PferVvYUJNa7nh3kvEZ94lJJBZShRuBzR9e5AHDatibm0jIzaEnV3pKjqSmDl5fYxxuQuzpRkhq8bRQWFa69+nTbvLKNrvXJULB500b6+fgH06PC2F6I0eVlG/sz4XUQeEZGw1IUi4i8i7URkHH+vUGiMyWVm/fEGmxwpPFSlO9+vOcaps8nc3zLC22GZfCQjTVvXAffgWnukChAPBOJKQrOAdzOyZrt7mdyJqYoigEFAc+D8ErohQLyqRqVz/E5cE0WmAMnnRw6ISBTwMRAAJAMP2ozExrgkJSXw4dZJVBMH7Zu/SPt3F9E8oiT1KlrHuck6/5pIVDUR18y/I0TEDygFnFHVeE8upKpbgCgA95xde4Epqjr8/D4iMgw4fpnTtFXVw2nK3gL+q6o/i0gX9/s2nsRmTH71zewn2OUDH9Xqy4Tl+9l3PJG3bra10k3W8qgHTVWTVHWfp0kkHe2Bbaq663yBe72TXqRZuz0jYQFF3a+LAXGX2deYAuPI4b8YceAPWhBE/cgH+eC3GFpWL0XL6qHeDs3kM54M/81Kt3FxwmgJHFDVmEsco8AsEVHgE1Ud5S5/HPhFRIbiSoxXp3ewiPQD+gGEhYWlt4sx+coHsx8mUeDZ1m8ycv52jp9JYmDnWt4Oy+RDOT6mz72GSXdgUppNvbn83UgLVW0EdAYeEpHzM8c9ADyhqpWAJ4DP0jtYVUeparSqRoeG2l9kJm86fiaJT+Zt4+DJxMvut2Hz93x/No7bg6vhH9KUMX/spEfDCrY2iMkWniy1e6+qpvsh7aHOwEpVPZDq3L5AT6DxpQ5S1Tj314PuhyKbAPNxjRh7zL3bJGB0FsRoTK6T4lSe/Go2SSeH8tvGeOILneOoQNeA8gxo8yZlyzUE4MCBtbyx+DVKKAy4biSDft4CwP91qnm50xtzxTxp2homIrfjGhm1FJigqhuu4Jrp3Xl0ADaramx6B4hIMOBQ1ZPu151wLfcLrj6R1sBcXMsAX6ppzJg8bdisLZw98yrrip8kPEkolRBEuL8/0ySOH375D10LlWXH2aOsliRwwDOhXXn8+1h+3XSQ/q0jqBAS6O0qmHzKk0RyBHgN8Mc1+upbEXlfVT/J6AlEJAjoCPRPs+miPhMRKQ+MVtUuQBlginutBF/ga1Wd6d71fuA9911NIu5+EGPyk5/W7WPR8g/ZVukU9xepzYDuE/ngtxhGzN1GGd8YIkK/YTr7qZwktHdWJcXRmUELKlKk0FGevramPTdispUns/+udPdRnH8fCCxR1Tw3ltBm/zV5yY7Dp7nlg2kUC3udwuLDt30WXJhA8djpc6yOjWfNnng2xB7m2Bk4mZjMmaQUutUvR/9WVSkW5OflGpj8Iitm/z1/omdxPUtSDNcDgsaYbPTx3G3UKTmC1T7CO9HP/2MW3uLB/rStWZq2NUsDNbwXpCnQrmTU1mRgK1ARW7PdmGx1+NRZNm8ez6pix/lPcFUaRN7m7ZCMuYgndyTFRaSSqm4FtorIp8Aq4MfsCc0YM37Rdgj9mdIpykNds2LQpDFZz5NEUhSYKyKHgY245sVKyY6gjDGQmJTCqrVvsz0UXq3YlaCgUt4OyZh0eZJI2gLrgaa4JllU7G7EmAxTp9OjdT2mLo9hb8gSqiX7cH3rV7MxMmMyJ8M/1aq6VlWdqrpYVceq6rh0JlA0xqRj6KQb6Ti2Ppu3TM/Q/qrKvOUvcMDPwVP1++Pj65/NERpz5WzZM2Oy2dQ5zzIuYRvHHHDPoudZtXb8vx4zafES1gRvJjo5gBZXPZQDURpz5SyRGJONNmyazKt7fqQpAUzrMJqSKvRbMYTpc4eSmJR+F+M38+fwxfr7OeMQnmrxcg5HbIznPHkg8WHgK1U9lr0hZT97INHkhKNHt3Lr1BsRYOKN0yheoiqHD2/h3qm3sN1PqXcyAD3bm5Ll2hBduThNqpRk0ZofmbD3NU76wJu1+tO2+aPeroYxF2TFA4llgWUishL4HPjF1mk35tKG/zKAow74stlrFC9RFYDvN/qybfszdI74hvnBOzhT+HPqHB/PL6sd/LJa2RWYiJ8DPm3yKg3q9vRyDYzJGE86218EquOapr0vECMib4hI1WyKzZg868TxPfycuJ/uARWoU+tGABZtO8yQnzfTqm5t3rr3B2beMJ2+RWpwplAyCYHnOBOQRA0NZGz70ZZETJ7i0RQpqqoish/Yj2sW4OLAdyIyW1WfyY4AjcmLflz8JokO4eYo1/yk+46f4ZGvVxERWpi3bm6AiFC8RFWevOl7nvRyrMZklifrkTyKa+2Pw7jW/HhaVZNExIFr6nZLJMbgel5k0r4F1MGHurV64nQqT05cQ2JSCh/f0ZjChby1MKkx2SNDP9Hu9dQbAD1Tr7MOoKpOEemWHcEZkxet2zSJGIeTQeU6APD10t0s3n6E//WsR7XShb0cnTFZL0N9JO5O9YZpk0iq7ZuyNCpj8rBJaz4l0Kl0ufpZ9hxN4H8/baJl9VLcdlUlb4dmTLbw5B57sYhcparLruRCIlITmJiqKAIYBDTHNeUKuObvilfVqHSO34lr2voUIDn1EDQReQR4GFe/zY/WX2O85eSJvcw8u5+uAeUJCi5Dv2+WICIMuak+7oXZjMl3PJ1ra4D7A/00ILhuVjK0sJWqbsG1siIi4gPsBaao6vDz+4jIMOD45WJIOy2LiLQFbgDqq+pZESmd0QoZk9V+WPQGiQ6hZ4P+DJv1F39sPcIbPerZMrcmX/MkkXTOwuu2B7albipz98P0wrXuuiceAIao6lkAVT2YZVEa44GdO+fxwb55NFB/XphThnV7t3JjVHl6N7EmLZO/eZJI7rpE+eAruO5Fa7QDLYEDqhpziWMUmCUiCnyiqqPc5TWAliLyOq41259Kr/lNRPrhXs89LCzsCkI2BdnJxCTemrmFE4lJ3HtNFepXDPnH9oSEwzzx2yP4oOzYeQ9n/c/wUZ9GdKlX1pq0TL7nSSI5nep1ANAN8LiTXUT8ge7Ac2k29ebi5JJaC1WNczddzRaRzao6H1cdigPNgKuAb0UkIu1T9+7EMwpcU6R4GrcpuNbGxvPIhFXsOXqKIv4wbXUcLaqV5I6mlWkWUZKQQF9e+u4mtjmcVIntQHidFrxyfV2KB9uMvaZgyHAiUdVhqd+LyFAgY3Ni/1NnYKWqHkh1Ll+gJ9D4MtePc389KCJTgCbAfCAW+N6dOJaKiBPXmvKHriA2Y/5hwtLdDJq2njpFt1Gi+qfsdDhpmOzA92wwE34N5Gufs6T4J7Iu8BzRxypzU/dn6Va/vLfDNiZHZebJqCBcI688ld6dRwdgs6rGpneAiAQDDlU96X7dib+b1Kbi6leZKyI1AH9cD00akym7jyTw8rQNdKm4gOUBPyBA7+CqbE/YzxbHSU4GnSRYlSCn0CqxNC/0nUj54vaciCl4PHmyfR2ufgoAHyAUD/tHRCQI6Aj0T7Ppoj4TESkPjFbVLkAZYIq7rdkX+FpVZ7p3/Rz4XETWA+eAu2wySZMVhszcRPMSo5gbGENV9eH9jp9QsWIzb4dlTK7jyR1J6qfXk3F1jCd7cjFVTQBKplPeN52yOKCL+/V2XE/Wp3fOc8AdnsRhzL9Zsm0/pw49yapSR2nnKMb/ekwhqLCNLDcmPZ70kaT7VLsx+U3C6aN8NLs7q4qf4fbAKjxz0/c4fGx+LGMuJcPTyIvIOBEJSfW+uIh8ni1RGeMlGzZ/z3++acvqgATu8GvIwF7TLYkY8y88+Q2pr6rx59+o6jERaZj1IRmT804c38OHMwcw8cwuiqG0ONmOpx9639thGZMneJJIHCJS/PxSuyJSwsPjjcmVThzfQ+/JXYh1KC3PlWbB7rt56IHuOBz2IKExGeFJIhgGLBKR73CN3uoFvJ4tURmTQ9TpZND024hzKE+VvZeX5tbg0fbViaxQzNuhGZNneLIeye/AclzPbAiutUk2ZmNsxlwxdTrZ+NdUflg7hnmndtIyOIynbvwG/0JF/rHf1788xBznCR4t0ZT3V9SjdrlCPNy2mpeiNiZvylAicS+xO1VVGwOWPEyulpBwmPsndmStIxk/Veo7ApiQuJt1X7di2LWfUr68awWCdRsmMfTAAlpJEf482o9jp48w9u6r8PfN8BgUYwyeNW39mZn1SIzJKV/9+iRrHck8HdqcG1q8SLFiYcz5Ywgv/jWeXr/0pY4jiLiUM+x1KKVSYG3cg+xNOMwLXetQt7w1aRnjqRxbj8SYnHA8fidjjqykjU9R7uwy6kJ5iyZP8/DZGszY+iqHSKREciClk4uw9fB1lK9QjU+ur0ud8kW9GLkxeZe31iMxJlt8NudJTgk8evUgAJJSnHzw21bG/7mLo6cLEV7yHepWKIa/jwM/H+GWtqW5LtKmejcmMzxJJLuB24EIVR0sImFAWcCeeDe5woEDa/n6xF908y9N9WrXcSIxiYe+WsmCmMN0rFOGu5qHc3XVkjas15gs5kkiGQE4cY3aGoxr/fTJuNYAMcbrPv79aVIEHmz9BnuOJnDP2GXsOHyat26uT69oW6XQmOziSSJpqqqNRGQVXHiy3VbuMbnClpgfmZK4l1sCKvPjtlA+nreQFKfyxb1NuLpqKW+HZ0y+5kkiSRIRH9xTyYtIKK47FGO8KiX5HK8sfJGiqvwScxu7V2+mZfVSvNK9LlVDbX0QY7KbJ4nkfWAKUMa9PvrNwEvZEpUxHhj/y8OsdyRTO+4qAivUZHiH6jQKK+7tsIwpMDyZRv4rEVkBtHcX3aCqm7MnLGMyZs+exXx4cBF1zvhTLvxR3r2toY3AMiaH/WsiEZG067Kf/y29VkRQ1e4ZuZCI1AQmpiqKAAYBzYGa7rIQIF5Vo9I5fieuDv4UIFlVo9Nsfwp4GwhVVVtqtwBIST7HK78+igPFN/kxhtzcwJKIMV6QkTuS5sAeXEvhLuHvROIRVd0CRAG4+1r2AlNUdfj5fURkGHD8Mqdpm16SEJFKuJbw3X0lsZnc7WxyCst2HCMiNJjyIYEA7D+0leen92aZbyJXHYnktXt6EeDn4+VIjSmYMpJIyuL6kO4N9AF+BCao6oZMXLc9sC31qovuiSF74Rpe7Kl3gWeAaZmIyeQyZ5NT+HZ5LCN+38q+44kAhJUIIrrEStbwOcd8hSZH6/PgTR9eSDDGmJz3r4lEVVOAmcBMESmEK6HMFZHBqvrBFV73Nlx3OKm1xLUOfMylQgFmiYgCn6jqKAAR6Q7sVdU1l2vWEJF+QD+AsLCwKwzb5JSNcSe4/4vl7I0/Q+PKxXmpWx12bJvK6gPjmet7nOIpwouV/48b7uyLjz1gaIxXZXQa+UJAV1xJJBzXCK7vr+SC7mdPugPPpdnUm4uTS2otVDVOREoDs0VkM65p7V8AOv3bdd2JZxRAdHS0XknsJmfsO36Ge8a65gYde1cUZw9/zrjl37POkUyQv3JTYBgPdHyfEiVsundjcoOMdLaPAyKBn4H/qur6TF6zM7BSVQ+kuoYv0BNofKmDVDXO/fWgiEwBmgDHgCrA+buRisBKEWmiqvszGafJAefOnmTJmjHUrdaFEiWqcTIxibvHLMORFMt9DVcwbPHj7PSBMIXnyrSie4sXKVyknLfDNsakkpE7kv/gmu23BvBoquaj87P/ejplanp3Hh2Azaoam94BIhIMOFT1pPt1J2Cwqq4DSqfabycQbaO28gZ1OnlxUjd+TjmKbBxFbfWl2Nki+AbFc7I4vHsMaosPwyJupn3zZ/DxtYkUjMmNMtJHkmWr/IhIEK6O+/5pNl3UZyIi5YHRqtoFKANMcScxX+BrVZ2ZVXEZ7/ji5/78nHKUO4MiKOpflN8PrWdtoaPUcAbQvXgdmkZ0pX6dXojDFpoyJjfz5Mn2TFPVBKBkOuV90ymLA7q4X28HGmTg/OGZDtLkiEXLR/DOocV09AnhqZum8Ptfh/jf4uXc3jSM13vU83Z4xhgP2J96Jsft2rWAZ9aNIMLp4LUe37P/5Fn+79s11C5XlJe61fF2eMYYD1kiMTlq4+ap3PnbAwjwXocR+AeW4tEJqziX7OSjPg3toUJj8iBLJCbH/LniE+5e/CKFFMa1eZ+Q0k3p/+UKlu08xhs96xFhM/UakyflaB+JKVgOH97MH2vHsuPYVnYm7GNeynHC8eHjrl+S4FOVHh/9wa4jCbx6Q11uiKrg7XCNMVfIEonJcjFbZ/LlsqHMOLufJBF8VanodNAloDTPdB3L2oOBPPTVQnx9HHx5b1OaV71o/IUxJg+xRGKyjNOpPDf2en7y2UWAU+kZFMYtjR4iIrwtfn5BAExYupsXpy6jeunCfHpnNJVKBHk5amNMZlkiMVni9NlkXhrTm9mBu2h4oghbjz1As5s7UbN6GQCSU5wMnfUXH8/bRqsaoXzUpyFFAvy8HLUxJitYIjGZFnssgde+fJg/im3mmuQiDOw9k4cmrOG+L5bTpV5Z9h9PZOO+EyQmOenTNIzB3evi62PjPIzJLyyRmExJcSqvjv8vS4supVFKAO/d8Qv+hYrw3YCrGTxjIz+t20f10oXp06QyzSJK0LFOGVt8yph8xhKJyZQxv81lU9AMwlJ8+KjXD/gXKgJAgJ8Pb/Soxxv2lLox+Z4lEnPFYo+eZs5fT3E6UBjd8i2bldeYAsoaqs0VUVXe/fYB1gefo1/xZtSodp23QzLGeIklEnNFvp07hfmFVtIwyZ/7r//Y2+EYY7zImraMx2Ys/IZPtr+Kn8CQLp/h8LEfI2MKMvsEMB6Z8MurvBv3DcHAu43/S/nyUd4OyRjjZTmWSESkJjAxVVEEMAhoDtR0l4UA8aoalc7xO4GTQAqQrKrR7vK3geuBc8A24G5Vjc+OOhRkJxOTGDXtPsadWUFEkg9DOo6jVpWG3g7LGJML5FgiUdUtQBSAiPgAe4Epqjr8/D4iMgw4fpnTtE1nGd3ZwHOqmiwibwLPAc9mYegFVmJSCh/9vpUZa/dSyec1VpU4RFRCEK/0mErVcuW9HZ4xJpfwVtNWe2Cbqu46XyCup9R6Ae08OZGqzkr19k/g5iyJsIBbtPUwz09Zx+4jJ+gYPoxFgcfoKmUZeNdUQoKDvR2eMSYX8VYiuWiNdqAlcEBVYy5xjAKzRESBT1R1VDr73MM/m88uEJF+QD+AsLCwKwo6v5mz6E0W75lHbOIR9qacIdy3MPc1foqJf1Xn6yW7uTp0FZVrTGWRz1nuL1KLR26caOunG2MuIqqasxcU8QfigLqqeiBV+Uhgq6oOu8Rx5VU1TkRK42rOekRV56fa/gIQDfTUf6lUdHS0Ll++PAtqkzelJJ9j+NRbGXt6K0WcSkV8KecbzPLk45xwCJGnChHs78MS/wSKOZVHK3SgV6fh3g7bGONlIrLifP90at64I+kMrEyTRHyBnkDjSx2kqnHurwdFZArQBJjvPv4uoBvQ/t+SSEF3+tR+nv2+B/P0FH0CK/N0j+9w+BTi+1V7+fOHBVwVMpYNxfYiwIPF6vOf9sPsiXVjzGV5I5H05uJmrQ7AZlWNTe8AEQkGHKp60v26EzDYve06XJ3rrVU1IfvCzvvij+3gvqk3slVSeLFcO3p1eo85mw4ydNYWNu8/SePKlXmt91RCfE8CEFS4tJcjNsbkBTmaSEQkCOgI9E+z6aI+ExEpD4xW1S5AGWCKe9ZYX+BrVZ3p3vVDoBAw2739T1UdkG2VyKOOH99Nv6k92CEpfFinP0XL/4ebP17Mil3HCC8ZxHu3RXF9/fI4HAIEejtcY0wekuN9JLlBQesjOXliL/0md2OLJPFG1Xv47UB7Jq2IpWRwIf6vUw1ublwRP1sfxBjzL3JTH4nJQWs3TOS1pf8jRpJ5Oex2Xppfn/iEvfRrGcHD7arZKoXGmEyzRJJP7dq1gPfmP89sZzwlUN6IuINhy5tzNimRaQ9dQ53yRb0dojEmn7BEkg+cOZfCD2vjmLZ6L7dFV+T0/jd5e//v+Cg8EFKP29oOY8DEXew+Es+X9zaxJGKMyVKWSPIwp1MZNnsL4//czfEzSZQJOMqEsw+zrvBZrpHCDO7yGUWK1+LJb1ezdMdR3u/dkKYRJb0dtjEmn7Ee1jxszKKdfPT7NppFlGB454OEhL3JpuBEoo9E8MS1MznjU4WbRi7ip3X7ebFrbbo3sPmxjDFZz+5I8qiYAyd5c+Zm2tcqRfsy4xm841dKAcPrDOT/ZpWh75gVnDqbDMBnd0XTvnYZ7wZsjMm3LJHkQUkpTp74djUlCiUS6vscr+47RAsJZkiPbwgpXoXR5eK5bdRiqpUuzMjbG1OpRJC3QzbG5GOWSPKgD37bytFDKylf+XN+THEyoGgkA64fh4+vPwBRlUL449l2FA30s+dDjDHZzhJJHvPH1sPMX/oREv4r+0X4sOY9tG725EX7lSxcyAvRGWMKIkskecj2gyf57Ie+7KiwkwinD++1H0FY2DXeDssYU8BZIskj9h2K5eXJPVhdIpEOUpzXb55skyoaY3IFSyR5QNz+ddw/ow97A5V7gxrz2E1jbIEpY0yuYZ9GudzR+D0MmNGHQz5OHih+J4/fMs6SiDEmV7FPpFzsdMIxHpzUnT2+yu2F+9D/hme8HZIxxlzEEkkudeZsAg+Pv5YN/sn08e/AY7e84O2QjDEmXZZIvESdTlasGccz41vT8fNIvv/1qQvbjp44woCxbVhe6Ay9HA15us9w7wVqjDH/Isc620WkJjAxVVEEMAhoDtR0l4UA8aoalc7xO4GTQAqQfH5xFREp4T5vOLAT6KWqx7KhCllm4+apvLj4ZWIcToo4lYriy8t7f2HDN+u5o+X7PDfjVjYEJHO7X2MG9hnr7XCNMeayvLJCooj4AHuBpqq6K1X5MOC4qg5O55idQLSqHk5T/hZwVFWHiMhAoLiqPnu563tzhcTV67/mwWVvEKzwYFhnrrv6Ofz8gnh/eh/GnIqhkFNJFuhftCMP9HzXKzEaY0x6ctsKie2BbWmSiAC9gHYenusGoI379ThgLnDZROItS1eN5uHVwwlVYfR1YylXvvGFbb3ajWf9mMfZV2QR/6nUiz7XDfJipMYYk3HeSiS3ARPSlLUEDqhqzCWOUWCWiCjwiaqOcpeXUdV9AKq6T0TSfUpPRPoB/QDCwsIyG79H1Olk6u8DeX33T1RUB592m0Bo6boXtsfFn6H3p38Sn3ATX/d6m3oVi+VofMYYkxk53rQlIv5AHFBXVQ+kKh8JbFXVYZc4rryqxrkTxWzgEVWdLyLxqhqSar9jqlr8cjHkZNPW/v2r+e8vA1jIaWomOpDE5wgsEkHxID98REBg6Y6jHE9IYvx9TWlQKSRH4jLGGE/lpqatzsDKNEnEF+gJNL7UQaoa5/56UESmAE2A+cABESnnvhspBxzM1ug9MPfPYTy/aQxJQIPDNTgV+BjFiwZy9PQ5dh4+jVMVVSgS4MsHvRtaEjHG5EneSCS9ubhZqwOwWVVj0ztARIIBh6qedL/uBJzvkJ8O3AUMcX+dli1Re2j6b88zaPd0wpMdHNrTlwYtuvBkx5r4OMTboRljTJbK0UQiIkFAR6B/mk0X9ZmISHlgtKp2AcoAU1z98fgCX6vqTPeuQ4BvReReYDdwS/bVIGO+/Kk/bx1aRO1EX7btG8hrt7Smc71y3g7LGGOyRY4mElVNAEqmU943nbI4oIv79XagwSXOeQTXKLBc4dPpd/L+sVXUO12I3cdeYly/NkRWsM5zY0z+ZbP/XiFVZffRBPbGn2FffCJx8WeI3fEiP/ltpMHJII4kvc6kh1tQrligt0M1xphsZYnkChw9fY5HJ6xi4da/n41sUfIz1paOodGZICqW+4iRXRtQJMDPi1EaY0zOsETiofV7j9P/yxUcOnWWgZ1rUb9CMbZs+i/vHI2hrRRl2L2z8fML8naYxhiTYyyReGDKqlgGTl5HyWB/vhvQnPoVQ/hm5iO8c3QuraQwQ2+daUnEGFPg2Oy/Hti7ZzYdyi1mbJ+S1CtflK9+fpDXD8yljRTh3Vtn4V+oiLdDNMaYHGd3JB44cvob5heOY/7cKQQ5lQSH0N5RlLd7zcKvULC3wzPGGK+wROKBB9oNo+Pueew8spFdJ3ZR1K8I/bp9Zs1ZxpgCzRKJB0qXiaR0mUiu9nYgxhiTi1gfiTHGmEyxRGKMMSZTLJEYY4zJFEskxhhjMsUSiTHGmEyxRGKMMSZTLJEYY4zJFEskxhhjMkVU1dsx5DgROQTsusLDSwGH/3Wv/MXqXDBYnQuGzNS5sqqGpi0skIkkM0RkuapGezuOnGR1LhiszgVDdtTZmraMMcZkiiUSY4wxmWKJxHOjvB2AF1idCwarc8GQ5XW2PhJjjDGZYnckxhhjMsUSiTHGmEyxROIBEblORLaIyFYRGejteLKaiFQSkd9FZJOIbBCRx9zlJURktojEuL8W93asWU1EfERklYjMcL/P13UWkRAR+U5ENrv/v5sXgDo/4f65Xi8iE0QkIL/VWUQ+F5GDIrI+Vdkl6ygiz7k/z7aIyLVXel1LJBkkIj7AR0BnoA7QW0TqeDeqLJcM/J+q1gaaAQ+56zgQmKOq1YE57vf5zWPAplTv83ud3wNmqmotoAGuuufbOotIBeBRIFpVIwEf4DbyX53HAtelKUu3ju7f7duAuu5jRrg/5zxmiSTjmgBbVXW7qp4DvgFu8HJMWUpV96nqSvfrk7g+XCrgquc4927jgBu9EmA2EZGKQFdgdKrifFtnESkKtAI+A1DVc6oaTz6us5svECgivkAQEEc+q7OqzgeOpim+VB1vAL5R1bOqugPYiutzzmOWSDKuArAn1ftYd1m+JCLhQENgCVBGVfeBK9kApb0YWnYYDjwDOFOV5ec6RwCHgDHu5rzRIhJMPq6zqu4FhgK7gX3AcVWdRT6ucyqXqmOWfaZZIsk4SacsX46dFpHCwGTgcVU94e14spOIdAMOquoKb8eSg3yBRsBIVW0InCbvN+lclrtf4AagClAeCBaRO7wblddl2WeaJZKMiwUqpXpfEdetcb4iIn64kshXqvq9u/iAiJRzby8HHPRWfNmgBdBdRHbiaq5sJyLjyd91jgViVXWJ+/13uBJLfq5zB2CHqh5S1STge+Bq8nedz7tUHbPsM80SScYtA6qLSBUR8cfVSTXdyzFlKRERXO3mm1T1nVSbpgN3uV/fBUzL6diyi6o+p6oVVTUc1//pb6p6B/m7zvuBPSJS013UHthIPq4zriatZiIS5P45b4+rDzA/1/m8S9VxOnCbiBQSkSpAdWDplVzAnmz3gIh0wdWe7gN8rqqvezeirCUi1wALgHX83V/wPK5+km+BMFy/kLeoatoOvTxPRNoAT6lqNxEpST6us4hE4Rpc4A9sB+7G9Ydlfq7zf4FbcY1OXAXcBxQmH9VZRCYAbXBNFX8AeBmYyiXqKCIvAPfg+p48rqo/X9F1LZEYY4zJDGvaMsYYkymWSIwxxmSKJRJjjDGZYonEGGNMplgiMcYYkymWSIzJBBEpKSKr3f/2i8he9+tTIjIim675uIjc+S/7fCMi1bPj+sakZcN/jckiIvIKcEpVh2bjNXyBlUAjVU2+zH6tgTtU9f7sisWY8+yOxJhsICJtUq1t8oqIjBORWSKyU0R6ishbIrJORGa6p6VBRBqLyDwRWSEiv5yf1iKNdsBKVU0WkaoisjLVNauLyPk5wxYAHdyJx5hsZYnEmJxRFddU9TcA44HfVbUecAbo6k4mHwA3q2pj4HMgvZkTWgArAFR1G3Dc/ZQ6uJ5OH+ve5sQ1LXiDbKqPMRfYXyvG5IyfVTVJRNbhmmJnprt8HRAO1AQigdmuqaDwwTXdeVrl+OcCXKOBu0XkSVzTf6ReT+IgrpluC9LMxsYLLJEYkzPOgutOQUSS9O/OSSeu30MBNqhq8385zxkgINX7ybjmU/oNWKGqR1JtC3Dvb0y2sqYtY3KHLUCoiDQH13T+IlI3nf02AdXOv1HVROAXYCQwJs2+NYAN2ROuMX+zRGJMLuBevvlm4E0RWQOsxrVeRlo/41omN7WvcC1INOt8gYiUAc6cXxnPmOxkw3+NyWNEZArwjKrGuN8/BRRT1ZdS7fMEcEJVP/NSmKYAsT4SY/Kegbg63WPcSaUqrmHBqcUDX+ZwXKaAsjsSY4wxmWJ9JMYYYzLFEokxxphMsURijDEmUyyRGGOMyRRLJMYYYzLl/wFeaAdkiqK5SQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -153,13 +153,6 @@
     "print(f'Swiftest - Swifter: {np.mean(dvarpi_swiftest - dvarpi_swifter)}')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,