diff --git a/Makefile.Defines b/Makefile.Defines
index d308c7110..278388cb0 100644
--- a/Makefile.Defines
+++ b/Makefile.Defines
@@ -70,7 +70,7 @@ GPRODUCTION = -O2 -ffree-line-length-none $(GPAR)
 #FFLAGS  = $(IDEBUG)  #$(SIMDVEC) $(PAR)
 #FFASTFLAGS  = $(IDEBUG) #$(SIMDVEC) $(PAR)
 FSTRICTFLAGS = $(IPRODUCTION) $(STRICTREAL) $(OPTREPORT) #$(ADVIXE_FLAGS)
-FFLAGS = $(IPRODUCTION) -fp-model fast $(OPTREPORT) #$(ADVIXE_FLAGS)
+FFLAGS = $(IPRODUCTION) -fp-model=fast $(OPTREPORT) #$(ADVIXE_FLAGS)
 FORTRAN = ifort
 AR     = xiar
 
diff --git a/examples/helio_swifter_comparison/init_cond.py b/examples/helio_swifter_comparison/init_cond.py
index c3f4be742..a71bc7b1c 100755
--- a/examples/helio_swifter_comparison/init_cond.py
+++ b/examples/helio_swifter_comparison/init_cond.py
@@ -17,6 +17,7 @@
 sim.param['CHK_EJECT'] = 1000.0
 sim.param['ISTEP_OUT']  = 1
 sim.param['ISTEP_DUMP'] = 1
+sim.param['IN_FORM'] = "XV"
 sim.param['OUT_FORM'] = "XV"
 sim.param['OUT_STAT'] = "UNKNOWN"
 sim.param['GR'] = 'NO'
diff --git a/examples/helio_swifter_comparison/param.swiftest.in b/examples/helio_swifter_comparison/param.swiftest.in
index 3d20ed4e3..fc031dc72 100644
--- a/examples/helio_swifter_comparison/param.swiftest.in
+++ b/examples/helio_swifter_comparison/param.swiftest.in
@@ -21,7 +21,7 @@ CHK_QMIN_RANGE   0.004650467260962157 1000.0
 MU2KG            1.988409870698051e+30
 TU2S             31557600.0
 DU2M             149597870700.0
-IN_FORM          EL
+IN_FORM          XV
 ENC_OUT          enc.swiftest.dat
 EXTRA_FORCE      NO
 DISCARD_OUT      discard.out
diff --git a/examples/helio_swifter_comparison/pl.swiftest.in b/examples/helio_swifter_comparison/pl.swiftest.in
index af6367df8..c227e04f1 100644
--- a/examples/helio_swifter_comparison/pl.swiftest.in
+++ b/examples/helio_swifter_comparison/pl.swiftest.in
@@ -1,33 +1,33 @@
 8
 Mercury 6.5537098095653139645e-06 0.0014751320469864830743
 1.6306381826061645943e-05
-0.3871001662734879778 0.20562290678137279398 7.0035924891789802516
-48.303217155090528934 29.188931936657478872 249.51513505827190897
+0.25597748680933402055 -0.33873157013416782535 -0.051160436706398457196
+6.1515614442706225157 6.693373063190126291 -0.017305148628664950593
 Venus 9.663313399581537916e-05 0.0067591015124708249373
 4.0453784346544178454e-05
-0.72332777691946326115 0.006778976236186400224 3.3945045285598101081
-76.62168299033216101 55.13031240212004036 163.93804780753200134
+0.31726034651636542128 -0.654711054374790713 -0.027292938884777531716
+6.598488376677801111 3.1963353072519729466 -0.33689924099817045804
 Earth 0.000120026935827952453094 0.010044886970936247304
 4.25875607065040958e-05
-1.0000161415769019957 0.016676412290744600103 0.0027631154255367738025
-175.55232875760239608 287.42522734499760872 258.78495415394627344
+1.0035242101099290934 -0.0018228334577166870837 -3.6653532112110000198e-06
+-0.09070203147464428398 6.2603556827487729817 -0.00030066016029169661568
 Mars 1.2739802010675941456e-05 0.0072464547040638876134
 2.265740805092889601e-05
-1.523676904140427002 0.09336889523077140929 1.8479174535355760156
-49.49048416488570723 286.71297337264127236 217.69847566206189526
+-1.6246010829214110327 -0.22657397469775839016 0.035102757644925722258
+0.8960028670481912773 -4.6255927366612961593 -0.118919639419818187306
 Jupiter 0.037692251088985676735 0.355270418186049151
 0.00046732617030490929307
-5.203511886158586286 0.04851730533676239243 1.3035664078742539296
-100.51660414853159864 273.38583632465582696 319.82047460791568483
+4.3414830724724824407 -2.51086598242009007 -0.086704432177356224876
+1.3483266539369778604 2.5183130150315082463 -0.04062579121197158367
 Saturn 0.011285899820091272997 0.43766612292716386504
 0.00038925687730393611812
-9.581916834333245703 0.052275407242262622587 2.4862549750808580207
-113.59523415390539469 335.6043567212101948 226.4432833007888064
+6.5829270711489096257 -7.4466885388333317053 -0.1325136240669045895
+1.4148318095568700728 1.3475938908840546106 -0.079718098761849415056
 Uranus 0.0017236589478267730203 0.46974626380654876733
 0.00016953449859497231466
-19.23982351960097148 0.044242611285930592835 0.77036564121123352056
-74.09449253346330977 95.642912088788392566 236.35401257802050168
+14.666242725889420129 13.206619035005820351 -0.14098973299186590147
+-0.97063485833896719253 1.0031115306266739172 0.016248131244499269076
 Neptune 0.0020336100526728302319 0.7815278251043273693
 0.000164587904124493665
-30.293078755751320585 0.013606718417359980194 1.7689422408080119897
-131.74363516600720914 246.0153226740773107 334.47147806471997455
+29.590408344240941574 -4.4040527861079086236 -0.5913025789369640295
+0.16275481312443448273 1.1438129826052378228 -0.027269849433711815306
diff --git a/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb b/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb
index 26e1b3f79..07c9a252b 100644
--- a/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb
+++ b/examples/helio_swifter_comparison/swiftest_vs_swifter.ipynb
@@ -68,6 +68,388 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<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",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vhx&#x27; (id: 12)&gt;\n",
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+       "Coordinates:\n",
+       "  * id       (id) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "    time     float64 0.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vhx'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2b1bf72f-4970-4330-acf0-554a70e4eb07' class='xr-array-in' type='checkbox' checked><label for='section-2b1bf72f-4970-4330-acf0-554a70e4eb07' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></div></li><li class='xr-section-item'><input id='section-db3837c0-5962-4109-a413-752200a95b7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3837c0-5962-4109-a413-752200a95b7a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-d38c062b-a42d-4f00-b38b-103f3da85174' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d38c062b-a42d-4f00-b38b-103f3da85174' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f825313b-27dd-4787-b443-d8845f19868b' class='xr-var-data-in' type='checkbox'><label for='data-f825313b-27dd-4787-b443-d8845f19868b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>-2147483647</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1b5cef86-656a-4d9b-8e18-28ac0c598e1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b5cef86-656a-4d9b-8e18-28ac0c598e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f97f1953-5d1c-46f1-921f-9e0197bcdebd' class='xr-var-data-in' type='checkbox'><label for='data-f97f1953-5d1c-46f1-921f-9e0197bcdebd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1df42f29-136d-405a-ab3d-72cb0c6d6bea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1df42f29-136d-405a-ab3d-72cb0c6d6bea' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'vhx' (id: 12)>\n",
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+       "Coordinates:\n",
+       "  * id       (id) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "    time     float64 0.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "swiftdiff.isel(time=0)['vhx']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "swiftdiff = swiftdiff.rename({'time' : 'time (y)'})"
@@ -75,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,22 +467,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "plidx = swiftdiff.id.values[swiftdiff.id.values < 10]\n",
-    "tpidx = swiftdiff.id.values[swiftdiff.id.values > 10]"
+    "plidx = swiftdiff.id.values[swiftdiff.id.values < 9]\n",
+    "tpidx = swiftdiff.id.values[swiftdiff.id.values >= 9]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -122,28 +504,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'numpy.ndarray' object has no attribute 'mid'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-ced59f426cb4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\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[0mswiftdiff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpidx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"time (y)\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\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[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"$|\\mathbf{r}_{swiftest} - \\mathbf{r}_{swifter}|$\"\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[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Helio integrator \\n Test Particles only\"\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[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"helio_swifter_comparison-tp-rmag.png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.conda/envs/cent7/2020.02-py37/swiftestOOF/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.conda/envs/cent7/2020.02-py37/swiftestOOF/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(darray, row, col, figsize, aspect, size, ax, hue, x, y, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim, add_legend, _labels, *args, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0;31m# Remove pd.Intervals if contained in xplt.values and/or yplt.values.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     xplt_val, yplt_val, x_suffix, y_suffix, kwargs = _resolve_intervals_1dplot(\n\u001b[0;32m--> 298\u001b[0;31m         \u001b[0mxplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m     )\n\u001b[1;32m    300\u001b[0m     \u001b[0mxlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_from_attrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_suffix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.conda/envs/cent7/2020.02-py37/swiftestOOF/lib/python3.7/site-packages/xarray/plot/utils.py\u001b[0m in \u001b[0;36m_resolve_intervals_1dplot\u001b[0;34m(xval, yval, kwargs)\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0mx_suffix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"_center\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_valid_other_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m             \u001b[0myval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_interval_to_mid_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myval\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    547\u001b[0m             \u001b[0my_suffix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"_center\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.conda/envs/cent7/2020.02-py37/swiftestOOF/lib/python3.7/site-packages/xarray/plot/utils.py\u001b[0m in \u001b[0;36m_interval_to_mid_points\u001b[0;34m(array)\u001b[0m\n\u001b[1;32m    478\u001b[0m     \"\"\"\n\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmid\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m]\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    481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.conda/envs/cent7/2020.02-py37/swiftestOOF/lib/python3.7/site-packages/xarray/plot/utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    478\u001b[0m     \"\"\"\n\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmid\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m]\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    481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'mid'"
-     ]
-    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -164,9 +530,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "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['dv'].sel(id=plidx).plot.line(x=\"time (y)\", ax=ax)\n",
@@ -177,9 +556,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "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['dv'].sel(id=tpidx).plot.line(x=\"time (y)\", ax=ax)\n",
@@ -188,6 +580,13 @@
     "fig.savefig(\"helio_swifter_comparison-tp-vmag.png\", facecolor='white', transparent=False, dpi=300)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/helio_swifter_comparison/tp.swiftest.in b/examples/helio_swifter_comparison/tp.swiftest.in
index ec3e48a99..c9c243562 100644
--- a/examples/helio_swifter_comparison/tp.swiftest.in
+++ b/examples/helio_swifter_comparison/tp.swiftest.in
@@ -1,13 +1,13 @@
 4
 Ceres
-2.765747739047986986 0.07842442286391371198 10.58812680413526941
-80.267870472138739046 73.70681387423925912 265.5864733899878729
+1.7496059999633410964 2.170163391141847864 -0.2537726760879844834
+-3.0064589998644978604 2.1233488530690124423 0.6210068204130407379
 Pallas
-2.7728732614124069755 0.22985557009005991302 34.91360509225025055
-172.91485570077549028 310.5371254957265137 247.03377107355748876
+3.0772345391474851262 -0.5509101822792066283 0.11666058691376969547
+-0.08868603569822111026 2.7292630488987525612 -1.882742859645719835
 Juno
-2.6683517962774989662 0.2569403610937920912 12.991505775900760611
-169.85134752220179166 247.97718553048909484 234.12051491952391302
+0.13917497353384339354 -3.081984978409241016 0.69426813140927812196
+3.105664664763373206 0.67090307556352112164 -0.2786153399455880027
 Vesta
-2.3614342874418641216 0.08829242551000027195 7.1415846765059312062
-103.80497351959969876 151.06131421642410828 334.46222015317840714
+-1.5389664718057010084 -1.5223401530194009545 0.23276670506845731357
+3.2083305098906780644 -3.027143636331455024 -0.2998683055841925141
diff --git a/src/drift/drift.f90 b/src/drift/drift.f90
index 296ceb553..0437a18d1 100644
--- a/src/drift/drift.f90
+++ b/src/drift/drift.f90
@@ -127,9 +127,13 @@ pure subroutine drift_dan(mu, px0, py0, pz0, vx0, vy0, vz0, dt0, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_dan.f90
       !! Adapted from Hal Levison and Martin Duncan's Swift routine drift_dan.f
       implicit none
-      integer(I4B), intent(out)                :: iflag
-      real(DP), intent(in)                     :: mu, dt0
-      real(DP), intent(inout)    :: px0, py0, pz0, vx0, vy0, vz0      
+      ! Arguments
+      real(DP),     intent(in)    :: mu            !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP),     intent(inout) :: px0, py0, pz0 !! position of body to drift
+      real(DP),     intent(inout) :: vx0, vy0, vz0 !! velocity of body to drift     
+      real(DP),     intent(in)    :: dt0           !! time step
+      integer(I4B), intent(out)   :: iflag         !! error status flag for Kepler drift (0 = OK, nonzero = NO CONVERGENCE)
+      ! Internals
       real(DP)        :: dt, f, g, fdot, gdot, c1, c2, c3, u, alpha, fp, r0
       real(DP)        :: v0s, a, asq, en, dm, ec, es, esq, xkep, fchk, s, c
       real(DP), dimension(NDIM) :: x, v, x0, v0
@@ -203,8 +207,14 @@ pure subroutine drift_kepmd(dm, es, ec, x, s, c)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepmd.f90
       !! Adapted from Martin Duncan's Swift routine drift_kepmd.f
       implicit none
-      real(DP), intent(in)  :: dm, es, ec
-      real(DP), intent(out) :: x, s, c      
+      ! Arguments
+      real(DP), intent(in)  :: dm !! increment in mean anomaly
+      real(DP), intent(in)  :: es !! eccentricity times the sine of eccentric anomaly
+      real(DP), intent(in)  :: ec !! eccentricity times the cosine of eccentric anomaly
+      real(DP), intent(out) :: x  !! solution to Kepler's equation in difference form (x = dE)
+      real(DP), intent(out) :: s  !! sine of x
+      real(DP), intent(out) :: c  !! cosine of x
+      ! Internals
       real(DP), parameter :: a0 = 39916800.0_DP, a1 = 6652800.0_DP, a2 = 332640.0_DP, a3 = 7920.0_DP, a4 = 110.0_DP
       real(DP)      :: dx, fac1, fac2, q, y, f, fp, fpp, fppp
       
@@ -221,8 +231,8 @@ pure subroutine drift_kepmd(dm, es, ec, x, s, c)
       fpp = ec * s + es * c
       fppp = ec * c - es * s
       dx = -f / fp
-      dx = -f / (fp + dx * fpp / 2.0_DP)
-      dx = -f / (fp + dx * fpp / 2.0_DP + dx**2* fppp / 6.0_DP)
+      dx = -f / (fp + dx * fpp * 0.5_DP)
+      dx = -f / (fp + dx * fpp * 0.5_DP + dx**2* fppp * SIXTH)
       x = x + dx
       y = x**2
       s = x * (a0 - y * (a1 - y * (a2 - y * (a3 - y * (a4 - y))))) / a0
@@ -270,8 +280,15 @@ pure subroutine drift_kepu_fchk(dt, r0, mu, alpha, u, s, f)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_fchk.f90
       !! Adapted from Martin Duncan's Swift routine drift_kepu_fchk.f
       implicit none
-      real(DP), intent(in)  :: dt, r0, mu, alpha, u, s
-      real(DP), intent(out) :: f
+      ! Internals
+      real(DP), intent(in)  :: dt    !! time step
+      real(DP), intent(in)  :: r0    !! distance between two bodies
+      real(DP), intent(in)  :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP), intent(in)  :: alpha !! twice the binding energy
+      real(DP), intent(in)  :: u     !! dot product of position and velocity vectors
+      real(DP), intent(in)  :: s     !! universal variable (approximate root of f)
+      real(DP), intent(out) :: f     !! function value
+      ! Arguments
       real(DP) :: x, c0, c1, c2, c3
 
       x = s**2 * alpha
@@ -294,9 +311,15 @@ pure subroutine drift_kepu_guess(dt, r0, mu, alpha, u, s)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_guess.f90
       !! Adapted from Hal Levison and Martin Duncan's Swift routine drift_kepu_guess.f
       implicit none
-      real(DP), intent(in)  :: dt, r0, mu, alpha, u
-      real(DP), intent(out) :: s      
-      integer(I4B)      :: iflag
+      ! Arguments
+      real(DP), intent(in)  :: dt    !! time ste4p
+      real(DP), intent(in)  :: r0    !! distance between two bodies
+      real(DP), intent(in)  :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP), intent(in)  :: alpha !! twice the binding energy
+      real(DP), intent(in)  :: u     !! dot product of position and velocity vectors
+      real(DP), intent(out) :: s     !! initial guess for the value of the universal variable
+      ! Internals
+      integer(I4B) :: iflag
       real(DP), parameter :: thresh = 0.4_DP, danbyk = 0.85_DP
       real(DP)        :: y, sy, cy, sigma, es, x, a, en, ec, e
 
@@ -334,19 +357,28 @@ pure subroutine drift_kepu_lag(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_lag.f90
       !! Adapted from Hal Levison's Swift routine drift_kepu_lag.f
       implicit none
-      integer(I4B), intent(out) :: iflag
-      real(DP), intent(in)      :: dt, r0, mu, alpha, u
-      real(DP), intent(inout)   :: s
-      real(DP), intent(out)     :: fp, c1, c2, c3      
-      integer( I4B) :: nc, ncmax
-      real(DP)   :: ln, x, fpp, ds, c0, f, fdt
+      ! Arguments
+      real(DP),     intent(inout) :: s     !! universal variable 
+      real(DP),     intent(in)    :: dt    !! time step
+      real(DP),     intent(in)    :: r0    !! distance between two bodies 
+      real(DP),     intent(in)    :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift 
+      real(DP),     intent(in)    :: alpha !! twice the binding energy 
+      real(DP),     intent(in)    :: u     !! dot product of position and velocity vectors
+      real(DP),     intent(out)   :: fp    !! first derivative of Kepler's equation in universal variables with respect to s (see Danby, p. 175)
+      real(DP),     intent(out)   :: c1    !! Stumpff function c1 times s
+      real(DP),     intent(out)   :: c2    !! Stumpff function c2 times s**2
+      real(DP),     intent(out)   :: c3    !! Stumpff function c3 times s**3
+      integer(I4B), intent(out)   :: iflag !! error status flag for convergence (0 = CONVERGED, nonzero = NOT CONVERGED)
+      ! Internals
+      integer(I4B) :: nc, ncmax
+      real(DP)   :: x, fpp, ds, c0, f, fdt
+      integer(I4B), parameter :: ln = 5
    
       if (alpha < 0.0_DP) then
          ncmax = NLAG2
       else
          ncmax = NLAG1
       end if
-      ln = 5.0_DP
       do nc = 0, ncmax
          x = s * s * alpha
          call drift_kepu_stumpff(x, c0, c1, c2, c3)
@@ -356,7 +388,7 @@ pure subroutine drift_kepu_lag(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
          f = r0 * c1 + u * c2 + mu * c3 - dt
          fp = r0 * c0 + u * c1 + mu * c2
          fpp = (-r0 * alpha + mu) * c1 + u * c0
-         ds = -ln * f / (fp + sign(1.0_DP, fp) * sqrt(abs((ln - 1.0_DP)**2 * fp**2 - (ln - 1.0_DP) * ln * f * fpp)))
+         ds = -ln * f / (fp + sign(1.0_DP, fp) * sqrt(abs((ln - 1)**2 * fp**2 - (ln - 1) * ln * f * fpp)))
          s = s + ds
          fdt = f / dt
          if (fdt**2 < DANBYB**2) then
@@ -365,7 +397,7 @@ pure subroutine drift_kepu_lag(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
          end if
       end do
       iflag = 2
-   
+
       return
    end subroutine drift_kepu_lag
 
@@ -380,10 +412,19 @@ pure subroutine drift_kepu_new(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_new.f90
       !! Adapted from Hal Levison's Swift routine drift_kepu_new.f
       implicit none
-      integer(I4B), intent(out) :: iflag
-      real(DP), intent(in)      :: dt, r0, mu, alpha, u
-      real(DP), intent(inout)   :: s
-      real(DP), intent(out)     :: fp, c1, c2, c3      
+      ! Arguments
+      real(DP),     intent(inout) :: s     !! universal variable
+      real(DP),     intent(in)    :: dt    !! time step
+      real(DP),     intent(in)    :: r0    !! distance between two bodies
+      real(DP),     intent(in)    :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP),     intent(in)    :: alpha !! twice the binding energy
+      real(DP),     intent(in)    :: u     !! dot product of position and velocity vectors
+      real(DP),     intent(out)   :: fp    !! first derivative of Kepler's equation in universal variables with respect to s (see Danby, p. 175)
+      real(DP),     intent(out)   :: c1    !! Stumpff function c1 times s
+      real(DP),     intent(out)   :: c2    !! Stumpff function c2 times s**2
+      real(DP),     intent(out)   :: c3    !! Stumpff function c3 times s**3
+      integer(I4B), intent(out)   :: iflag !! error status flag for convergence (0 = CONVERGED, nonzero = NOT CONVERGED)
+      ! Internals
       integer( I4B) :: nc
       real(DP)   :: x, c0, ds, f, fpp, fppp, fdt
    
@@ -398,8 +439,8 @@ pure subroutine drift_kepu_new(s, dt, r0, mu, alpha, u, fp, c1, c2, c3, iflag)
          fpp = (-r0 * alpha + mu) * c1 + u * c0
          fppp = (-r0 * alpha + mu) * c0 - u * alpha * c1
          ds = -f / fp
-         ds = -f / (fp + ds * fpp / 2.0_DP)
-         ds = -f / (fp + ds * fpp / 2.0_DP + ds**2 * fppp / 6.0_DP)
+         ds = -f / (fp + ds * fpp * 0.5_DP)
+         ds = -f / (fp + ds * fpp * 0.5_DP + ds**2 * fppp * SIXTH)
          s = s + ds
          fdt = f / dt
          if (fdt**2 < DANBYB**2) then
@@ -423,32 +464,38 @@ pure subroutine drift_kepu_p3solve(dt, r0, mu, alpha, u, s, iflag)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_p3solve.f90
       !! Adapted from Martin Duncan's Swift routine drift_kepu_p3solve.f
       implicit none
-      integer(I4B), intent(out) :: iflag
-      real(DP), intent(in)      :: dt, r0, mu, alpha, u
-      real(DP), intent(out)     :: s
+      ! Arguments
+      real(DP), intent(in)      :: dt    !! time step
+      real(DP), intent(in)      :: r0    !! distance between two bodies
+      real(DP), intent(in)      :: mu    !! G * (m1 + m2), G = gravitational constant, m1 = mass of central body, m2 = mass of body to drift
+      real(DP), intent(in)      :: alpha !! twice the binding energy
+      real(DP), intent(in)      :: u     !! dot product of position and velocity vectors
+      real(DP), intent(out)     :: s     !! s     : real solution of cubic equation
+      integer(I4B), intent(out) :: iflag !! error status flag for solution (0 = OK, nonzero = ERROR)
+      ! Internals
       real(DP) :: denom, a0, a1, a2, q, r, sq2, sq, p1, p2
 
-      denom = (mu - alpha * r0) / 6.0_DP
+      denom = (mu - alpha * r0) * SIXTH
       a2 = 0.5_DP * u / denom
       a1 = r0 / denom
       a0 = -dt / denom
-      q = (a1 - a2**2 / 3.0_DP) / 3.0_DP
-      r = (a1 * a2 - 3 * a0) / 6.0_DP - a2**3 / 27.0_DP
+      q = (a1 - a2**2 * THIRD) * THIRD
+      r = (a1 * a2 - 3 * a0) * SIXTH - (a2 * THIRD)**3 
       sq2 = q**3 + r**2
       if (sq2 >= 0.0_DP) then
          sq = sqrt(sq2)
          if ((r + sq) <= 0.0_DP) then
-            p1 = -(-(r + sq))**(1.0_DP / 3.0_DP)
+            p1 = -(-(r + sq))**(THIRD)
          else
-            p1 = (r + sq)**(1.0_DP / 3.0_DP)
+            p1 = (r + sq)**(THIRD)
          end if
          if ((r - sq) <= 0.0_DP) then
-            p2 = -(-(r - sq))**(1.0_DP / 3.0_DP)
+            p2 = -(-(r - sq))**(THIRD)
          else
-            p2 = (r - sq)**(1.0_DP / 3.0_DP)
+            p2 = (r - sq)**(THIRD)
          end if
          iflag = 0
-         s = p1 + p2 - a2 / 3.0_DP
+         s = p1 + p2 - a2 * THIRD
       else
          iflag = 1
          s = 0.0_DP
@@ -468,8 +515,13 @@ pure subroutine drift_kepu_stumpff(x, c0, c1, c2, c3)
       !! Adapted from David E. Kaufmann's Swifter routine: drift_kepu_stumpff.f90
       !! Adapted from Hal Levison's Swift routine drift_kepu_stumpff.f
       implicit none
-      real(DP), intent(inout) :: x
-      real(DP), intent(out)   :: c0, c1, c2, c3
+      ! Arguments
+      real(DP), intent(inout) :: x  !! argument of Stumpff functions
+      real(DP), intent(out)   :: c0 !! zeroth Stumpff function
+      real(DP), intent(out)   :: c1 !! first Stumpff function
+      real(DP), intent(out)   :: c2 !! second Stumpff function
+      real(DP), intent(out)   :: c3 !! third Stumpff function
+      ! Internals
       integer(I4B) :: i, n
       real(DP)   :: xm
 
diff --git a/src/kick/kick.f90 b/src/kick/kick.f90
index 637b38720..9c940190b 100644
--- a/src/kick/kick.f90
+++ b/src/kick/kick.f90
@@ -256,7 +256,7 @@ module pure subroutine kick_getacch_int_one_tp(rji2, xr, yr, zr, GMpl, ax, ay, a
       ! Internals
       real(DP) :: fac
 
-      fac = GMpl * sqrt(rji2**(-3))
+      fac = GMpl * sqrt(1.0_DP / (rji2*rji2*rji2))
       ax = ax - fac * xr
       ay = ay - fac * yr
       az = az - fac * zr
diff --git a/src/modules/swiftest_globals.f90 b/src/modules/swiftest_globals.f90
index b7fe1a0db..169cdcd2f 100644
--- a/src/modules/swiftest_globals.f90
+++ b/src/modules/swiftest_globals.f90
@@ -22,6 +22,7 @@ module swiftest_globals
    real(DP), parameter :: PI3BY2 = 4.712388980384689857693965074919254326296_DP !! Definition of /(3 \pi / 2\)
    real(DP), parameter :: TWOPI  = 6.283185307179586476925286766559005768394_DP !! Definition of 2 \pi
    real(DP), parameter :: THIRD  = 0.333333333333333333333333333333333333333_DP !! Definition of 1 / 3
+   real(DP), parameter :: SIXTH  = 0.166666666666666666666666666666666666667_DP !! Definition of 1 / 3
    real(DP), parameter :: DEG2RAD = PI / 180.0_DP !! Definition of conversion factor from degrees to radians
    real(DP), parameter :: RAD2DEG = 180.0_DP / PI !! Definition of conversion factor from degrees to radians
    real(DP), parameter :: GC        = 6.6743E-11_DP   !! Universal gravitational constant in SI units
@@ -31,7 +32,7 @@ module swiftest_globals
    integer(I4B), parameter :: LOWERCASE_END    = iachar('z') !! ASCII character set parameter for lower to upper conversion - end of lowercase
    integer(I4B), parameter :: UPPERCASE_OFFSET = iachar('A') - iachar('a') !! ASCII character set parameter for lower to upper conversion - offset between upper and lower
 
-   real(SP), parameter :: VERSION_NUMBER = 0.1_SP !! swiftest version
+   real(SP), parameter :: VERSION_NUMBER = 1.0_SP !! swiftest version
 
    !> Symbolic name for integrator types
    integer(I4B), parameter :: UNKNOWN_INTEGRATOR = 1