diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index 989b1e9d..5c74b6e6 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["2.7", "3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
       - uses: actions/checkout@v3
diff --git a/doc/effects.rst b/doc/effects.rst
index 37febb66..c980f1ea 100644
--- a/doc/effects.rst
+++ b/doc/effects.rst
@@ -199,6 +199,39 @@ em_afin (double)               Yes          Object's final semimajor axis
 General Relativity
 ^^^^^^^^^^^^^^^^^^
 
+.. _lense_thirring:
+
+lense_thirring
+**************
+
+======================= ===============================================
+Authors                 A. Akmal
+Implementation Paper    None
+Based on                `Park et al. <https://iopscience.iop.org/article/10.3847/1538-3881/abd414/>`_.
+C Example               :ref:`c_example_lense_thirring`
+Python Example          `LenseThirring.ipynb <https://github.com/dtamayo/reboundx/blob/master/ipython_examples/LenseThirring.ipynb>`_.
+======================= ===============================================
+
+Adds Lense-Thirring effect due to rotating central body in the simulation. Assumes the source body is particles[0]
+
+**Effect Parameters**
+
+============================ =========== ==================================================================
+Field (C type)               Required    Description
+============================ =========== ==================================================================
+lt_c (double)                Yes         Speed of light in the units used for the simulation.
+============================ =========== ==================================================================
+
+**Particle Parameters**
+
+============================ =========== ==================================================================
+Field (C type)               Required    Description
+============================ =========== ==================================================================
+I (double)                   Yes         Moment of Inertia of source body 
+Omega (reb_vec3d)            Yes         Angular rotation frequency (Omega_x, Omega_y, Omega_z) 
+============================ =========== ==================================================================
+
+
 .. _gr_potential:
 
 gr_potential
@@ -223,7 +256,7 @@ Nice if you have a single-star system, don't need to get GR exactly right, and w
 ============================ =========== ==================================================================
 Field (C type)               Required    Description
 ============================ =========== ==================================================================
-c (double)                   Yes         Speed of light in the units used for the simulation.
+c (double)                   Yes         Speed of light, needs to be specified in the units used for the simulation.
 ============================ =========== ==================================================================
 
 
@@ -251,7 +284,7 @@ Adding this effect to several bodies is NOT equivalent to using gr_full.
 ============================ =========== ==================================================================
 Field (C type)               Required    Description
 ============================ =========== ==================================================================
-c (double)                   Yes         Speed of light in the units used for the simulation.
+c (double)                   Yes         Speed of light, needs to be specified in the units used for the simulation.
 ============================ =========== ==================================================================
 
 
@@ -276,7 +309,7 @@ This algorithm incorporates the first-order post-newtonian effects from all bodi
 ============================ =========== ==================================================================
 Field (C type)               Required    Description
 ============================ =========== ==================================================================
-c (double)                   Yes         Speed of light in the units used for the simulation.
+c (double)                   Yes         Speed of light, needs to be specified in the units used for the simulation.
 ============================ =========== ==================================================================
 
 **Particle Parameters**
@@ -458,7 +491,7 @@ This effect consistently tracks both the spin and orbital evolution of bodies un
 In all cases, we need to set masses for all the particles that will feel these tidal forces. Particles with only mass are point particles.
 
 Particles are assumed to have structure (i.e - physical extent & distortion from spin) if the following parameters are set: physical radius particles[i].r, potential Love number of degree 2 k2 (Q/(1-Q) in Eggleton 1998), and the spin angular rotation frequency vector Omega.
-If we wish to evolve a body's spin components, the fully dimensional moment of inertia I must be set as well. If this parameter is not set, the spin components will be stationary.
+If we wish to evolve a body's spin components, the fully dimensional moment of inertia I must be set as well. If this parameter is not set, the spin components will be stationary. Note that if the body is a test particle, this is assumed to be the specific moment of inertia.
 Finally, if we wish to consider the effects of tides raised on a specific body, we must set the constant time lag tau as well.
 
 For spins that are synchronized with a circular orbit, the constant time lag can be related to the tidal quality factor Q as tau = 1/(2*n*tau), with n the orbital mean motion.
@@ -476,8 +509,8 @@ Field (C type)               Required    Description
 ============================ =========== ==================================================================
 particles[i].r (float)       Yes         Physical radius (required for contribution from tides raised on the body).
 k2 (float)                   Yes         Potential Love number of degree 2.
-Omega (reb_vec3d)            Yes         Angular rotation frequency
-I (float)                    No          Moment of inertia
+Omega (reb_vec3d)            Yes         Angular rotation frequency (Omega_x, Omega_y, Omega_z)
+I (float)                    No          Moment of inertia (for test particles, assumed to be the specific MoI I/m)
 tau (float)                  No          Constant time lag. If not set, defaults to 0
 ============================ =========== ==================================================================
 
diff --git a/examples/lense_thirring/problem.c b/examples/lense_thirring/problem.c
index 62c05d94..9a76f30f 100644
--- a/examples/lense_thirring/problem.c
+++ b/examples/lense_thirring/problem.c
@@ -1,7 +1,7 @@
 /**
- * Adding gravitational harmonics (J2, J4) to particles
+ * Adding Lense-Thirring effect
  * 
- * This example shows how to add a J2 and J4 harmonic to particles.
+ * This example shows how to add the Lense-Thirring effect to a simulation.
  * If you have GLUT installed for the visualization, press 'w' and/or 'c' for a clearer view of
  * the whole orbit.
  */
@@ -14,38 +14,31 @@
 
 int main(int argc, char* argv[]){
     struct reb_simulation* sim = reb_create_simulation();
+    sim->G = 4*M_PI*M_PI; // units of AU, yr, Msun
     struct reb_particle star = {0};
     star.m     = 1.;
     reb_add(sim, star);
     double omega = 90.361036076; //solar rotation rate in rad/year
     double C_I = 0.06884; //solar moment of inertia prefactor
     double R_eq = 0.00465247264; //solar equatorial radius in AU
-    double p_x = 0.0; //x-comp. of unit spin-pole direction
-    double p_y = 0.0; //y-comp. of unit spin-pole direction
-    double p_z = 1.0; //z-comp. of unit spin-pole direction
-
 
     struct reb_particle planet = {0};  // add a planet on a circular orbit (with default units where G=1)
-    planet.x = 1.;
-    planet.vy = 1.;
-    reb_add(sim, planet);
+    const double mp = 1.7e-7;   // approximate values for mercury in units of Msun and AU
+    const double a = 0.39;
+    const double e = 0.21;
+    reb_add_fmt(sim, "m a e", mp, a, e);
 
     struct rebx_extras* rebx = rebx_attach(sim);  // first initialize rebx
     struct rebx_force* lense  = rebx_load_force(rebx, "lense_thirring"); // add our new force
     rebx_add_force(rebx, lense);
-
-    rebx_set_param_double(rebx, &sim->particles[0].ap, "lt_rot_rate", omega);
-    rebx_set_param_double(rebx, &sim->particles[0].ap, "lt_Mom_I_fac", C_I);
-    rebx_set_param_double(rebx, &sim->particles[0].ap, "lt_R_eq", R_eq);
-    rebx_set_param_double(rebx, &sim->particles[0].ap, "lt_p_hatx", p_x);
-    rebx_set_param_double(rebx, &sim->particles[0].ap, "lt_p_haty", p_y);
-    rebx_set_param_double(rebx, &sim->particles[0].ap, "lt_p_hatz", p_z);
+    rebx_set_param_vec3d(rebx, &sim->particles[0].ap, "Omega", (struct reb_vec3d){.x=0, .y=0, .z=omega});
+    rebx_set_param_double(rebx, &sim->particles[0].ap, "I", C_I*star.m*R_eq*R_eq);
 
     // Have to set speed of light in right units (set by G & initial conditions).  Here we use default units of AU/(yr/2pi)
-    rebx_set_param_double(rebx, &lense->ap, "lt_c", 10065.32);
-
-
+    rebx_set_param_double(rebx, &lense->ap, "lt_c", 63241.077); // speed of light in AU/yr
 
-    double tmax = 100000.;
+    double tmax = 1000.;
     reb_integrate(sim, tmax);
+    struct reb_orbit orb = reb_tools_particle_to_orbit(sim->G, sim->particles[1], sim->particles[0]);
+    printf("Pericenter precession rate = %.3f arcsec/century\n", orb.pomega*206265/10.);
 }
diff --git a/ipython_examples/LT.ipynb b/ipython_examples/LT.ipynb
deleted file mode 100644
index 8441f68e..00000000
--- a/ipython_examples/LT.ipynb
+++ /dev/null
@@ -1,187 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Adding Lense-Thirring (LT) effect"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pomega = 0.0000000000000000\n"
-     ]
-    }
-   ],
-   "source": [
-    "import rebound\n",
-    "sim = rebound.Simulation()\n",
-    "sim.add(m=1., hash=\"star\") # Sun\n",
-    "sim.add(m=1.e-5,a=1,e=1.e-2, hash=\"planet\")\n",
-    "sim.move_to_com() # Moves to the center of momentum frame\n",
-    "ps = sim.particles\n",
-    "sim.integrate(1.)\n",
-    "print(\"pomega = %.16f\"%sim.particles[1].pomega)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As expected, the pericenter did not move at all.  Now let's add rotation to star"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import reboundx\n",
-    "rebx = reboundx.Extras(sim)\n",
-    "lt = rebx.load_force(\"lense_thirring\")\n",
-    "rebx.add_force(lt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The GR effects need you to set the speed of light in the right units. The constants module has a set of constants in REBOUND's default units of AU, solar masses and yr/ 2𝜋\n",
-    "  (such that G=1). If you want to use other units, you'd need to calculate c."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from reboundx import constants\n",
-    "lt.params[\"lt_c\"] = constants.C"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ps[\"star\"].params[\"lt_rot_rate\"] =  9000.0 #solar rotation rate in rad/year\n",
-    "ps[\"star\"].params[\"lt_Mom_I_fac\"] = 0.07; #solar moment of inertia prefactor\n",
-    "ps[\"star\"].params[\"lt_R_eq\"] = 1/200; #solar equatorial radius in AU\n",
-    "ps[\"star\"].params[\"lt_p_hatx\"] = 0.0\n",
-    "ps[\"star\"].params[\"lt_p_haty\"] = 0.0\n",
-    "ps[\"star\"].params[\"lt_p_hatz\"] = 1.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we integrate as normal:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pomega = -6.188432688925133e-06\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "tmax = 1.e4\n",
-    "Nout = 1000\n",
-    "times = np.logspace(0, np.log10(tmax), Nout)\n",
-    "pomegas = np.zeros(Nout)\n",
-    "\n",
-    "for i, time in enumerate(times):\n",
-    "    sim.integrate(time)\n",
-    "    pomegas[i] = ps[\"planet\"].pomega\n",
-    "print(\"pomega = {0}\".format(ps[1].pomega))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we plot the calculated precession rate. \n",
-    "Note that this is a small effect, about 7% of the solar oblateness precession rate for Mercury"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7febe2680190>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(12,8))\n",
-    "ax.plot(times, pomegas, '.', label='Integration')\n",
-    "ax.set_xlabel('Time', fontsize=24)\n",
-    "ax.set_ylabel('Longitude of Pericenter', fontsize=24)\n",
-    "ax.legend(fontsize=24)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/ipython_examples/LenseThirring.ipynb b/ipython_examples/LenseThirring.ipynb
new file mode 100644
index 00000000..431d3fda
--- /dev/null
+++ b/ipython_examples/LenseThirring.ipynb
@@ -0,0 +1,231 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adding the Lense-Thirring (LT) effect\n",
+    "\n",
+    "Without any additional effects, the pericenter of Mercury doesn't precess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pomega = 0.0000000000000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "import rebound\n",
+    "import numpy as np\n",
+    "\n",
+    "sim = rebound.Simulation()\n",
+    "sim.units = ('AU', 'yr', 'Msun')\n",
+    "sim.add(m=1., hash=\"star\") # Sun\n",
+    "sim.add(m=1.7e-7,a=0.39,e=0.21, hash=\"planet\") # Mercury like\n",
+    "sim.move_to_com() # Moves to the center of momentum frame\n",
+    "ps = sim.particles\n",
+    "sim.integrate(1.)\n",
+    "print(\"pomega = %.16f\"%sim.particles[1].pomega)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's add the Lense-Thirring effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import reboundx\n",
+    "rebx = reboundx.Extras(sim)\n",
+    "lt = rebx.load_force(\"lense_thirring\")\n",
+    "rebx.add_force(lt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to set the angular spin frequency vector of the Sun and its moment of inertia using the units we chose for our simulation above (AU, yr, Msun). We use the standard values given by Park et al. (2021)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lt.params[\"lt_c\"] = 63241.077 # AU/yr\n",
+    "Omega = 90.42                 # rad/yr\n",
+    "Omega_RA = np.radians(286.13) # spin pole RA\n",
+    "Omega_Dec = np.radians(63.87) # spin pole Dec\n",
+    "Rsun_eq = 0.00465             # AU\n",
+    "Csun = 0.06884                # dimensionless moment of inertia I = C * MR^2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "REBOUNDx needs the angular spin frequency vector components (Omega_x, Omega_y, Omega_z), so we convert from spherical coordinates (with polar angle $\\theta = \\pi/2 - $ dec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps[\"star\"].params['Omega'] = rebound.spherical_to_xyz(magnitude=Omega, theta=np.pi/2 - Omega_Dec, phi=Omega_RA)\n",
+    "ps[\"star\"].params[\"I\"] = Csun*ps[0].m*Rsun_eq**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we integrate as normal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pomega = -4.301820322183403e-07\n",
+      "CPU times: user 6.3 s, sys: 673 ms, total: 6.97 s\n",
+      "Wall time: 6.06 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tmax = 5e3\n",
+    "Nout = 1000\n",
+    "times = np.linspace(0, tmax, Nout)\n",
+    "pomegas = np.zeros(Nout)\n",
+    "\n",
+    "for i, time in enumerate(times):\n",
+    "    sim.integrate(time)\n",
+    "    pomegas[i] = ps[\"planet\"].pomega\n",
+    "print(\"pomega = {0}\".format(ps[1].pomega))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Over 10 centuries, the pericenter precession rate is "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pericenter precession rate = -0.002\"/century\n"
+     ]
+    }
+   ],
+   "source": [
+    "arcsecPerRad = 206265\n",
+    "century = 100\n",
+    "print('Pericenter precession rate = {0:.3f}\"/century'.format(ps['planet'].pomega*arcsecPerRad/(sim.t/century)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "as expected. Note that this is a small effect, about 7% of the solar oblateness precession rate for Mercury. We can also plot the pericenter vs time. Note that the positions and velocities are calculated to machine precision--the discrete steps early on are due to rounding errors in the calculation of orbital elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Longitude of Pericenter')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,8))\n",
+    "ax.plot(times, pomegas, '.')\n",
+    "ax.set_xlabel('Time', fontsize=24)\n",
+    "ax.set_ylabel('Longitude of Pericenter', fontsize=24)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/ipython_examples/TypeIMigration.ipynb b/ipython_examples/TypeIMigration.ipynb
index 06524342..181eeb79 100644
--- a/ipython_examples/TypeIMigration.ipynb
+++ b/ipython_examples/TypeIMigration.ipynb
@@ -19,8 +19,6 @@
    "source": [
     "import rebound\n",
     "import reboundx\n",
-    "import astropy.units as u\n",
-    "import astropy.constants as constants\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
@@ -63,7 +61,7 @@
     "rebx.add_force(mig)\n",
     "\n",
     "mig.params[\"tIm_scale_height_1\"] = 0.03\n",
-    "mig.params[\"tIm_surface_density_1\"] = ((1000* u.g /u.cm**2).to(u.Msun/u.AU**2)).value             #transformed from g/cm^2 to code units\n",
+    "mig.params[\"tIm_surface_density_1\"] = 1.1e-4 # 1000 g/cm^2 surface density in Msun/AU^2\n",
     "mig.params[\"tIm_surface_density_exponent\"] = 1\n",
     "mig.params[\"tIm_flaring_index\"] = 0.25"
    ]
@@ -184,7 +182,7 @@
     {
      "data": {
       "text/plain": [
-       "(0, 1)"
+       "(0.0, 1.0)"
       ]
      },
      "execution_count": 9,
@@ -193,14 +191,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -217,7 +213,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -231,7 +227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/ipython_examples/YarkovskyEffect.ipynb b/ipython_examples/YarkovskyEffect.ipynb
index de9be51a..5dfb6441 100644
--- a/ipython_examples/YarkovskyEffect.ipynb
+++ b/ipython_examples/YarkovskyEffect.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "This example shows how to add the Yarkovsky effect in a Rebound simulation. There are two versions, which we call the 'Full Version' and the 'Simple Version.' A special parameter called 'ye_flag' is used to switch between the two. The difference between the versions and what situations they're better suited for is discussed in more detail below. \n",
     "\n",
-    "For more information on this effect, please visit: (implementation paper in progress) \n",
+    "For more information on this effect, please see https://ui.adsabs.harvard.edu/abs/2022ApJS..262...41F/abstract\n",
     "\n",
     "We'll start with the Full Version."
    ]
@@ -55,25 +55,20 @@
     "import rebound\n",
     "import reboundx\n",
     "import numpy as np\n",
-    "import astropy.units as u\n",
-    "import astropy.constants as constants\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "\n",
     "#Simulation begins here\n",
     "sim = rebound.Simulation()\n",
     "\n",
-    "sim.units = ('yr', 'AU', 'Msun') #changes simulation and G to units of solar masses, years, and AU  \n",
-    "sim.integrator = \"whfast\" #integrator for sim\n",
-    "sim.dt = .05 #timestep for sim\n",
+    "sim.units = ('yr', 'AU', 'Msun') \n",
+    "sim.integrator = \"whfast\" \n",
+    "sim.dt = .05\n",
     "\n",
-    "sim.add(m=1) #Adds Sun \n",
-    "sim.add(a=.5, f=0, Omega=0, omega=0, e=0, inc=0, m=0) #adds test particle \n",
-    "\n",
-    "#Moves all particles to center of momentum frame\n",
+    "sim.add(m=1)\n",
+    "sim.add(a=.5, f=0, Omega=0, omega=0, e=0, inc=0, m=0)\n",
     "sim.move_to_com()\n",
     "\n",
-    "#Gives orbital information before the simulation begins\n",
     "print(\"\\n***INITIAL ORBITS:***\")\n",
     "for orbit in sim.calculate_orbits():\n",
     "    print(orbit)"
@@ -83,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As with all REBOUNDx effects, the parameters must be inputed with the same units as the simulation (in this case it's AU/Msun/yr). We'll use the astropy units module to help avoid errors"
+    "As with all REBOUNDx effects, the parameters must be inputed with the same units as the simulation. In our case, we chose to use AU, Msun and yr. This is an easy place to make mistake with complicated quantities like these. Check out reboundx/ipython_examples/Units.ipynb for useful utilities for making these conversions."
    ]
   },
   {
@@ -92,16 +87,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "density = (3000.0*u.kg/u.m**3).to(u.Msun/u.AU**3)\n",
-    "c = (constants.c).to(u.AU/u.yr) #speed of light\n",
-    "lstar = (3.828e26*u.kg*u.m**2/u.s**3).to(u.Msun*u.AU**2/u.yr**3) #luminosity of star\n",
-    "radius = (1000*u.m).to(u.AU) #radius of object\n",
-    "albedo = .017 #albedo of object\n",
-    "stef_boltz = constants.sigma_sb.to(u.Msun/u.yr**3/u.K**4) #Stefan-Boltzmann constant\n",
-    "emissivity = .9 #emissivity of object\n",
-    "k = .25 #constant between\n",
-    "Gamma = (310*u.kg/u.s**(5./2)).to(u.Msun/u.yr**(5./2)) #thermal inertia of object\n",
-    "rotation_period = (15470.9*u.s).to(u.yr) #rotation period of object"
+    "# effect properties\n",
+    "c = 63241.077           # speed of light in AU / yr\n",
+    "stef_boltz = 8.96e-16   # Stefan-Boltzmann constant in Msun / (yr^3 K^4)\n",
+    "lstar = 2.7e-4          # solar luminosity (Energy / time) in (Msun * AU^2 / yr^2 )\n",
+    "\n",
+    "# object properties\n",
+    "density = 5.05e6        # 3g/cc in Msun/AU^3\n",
+    "radius = 6.68e-09       # 1 km radius of object in AU\n",
+    "Gamma = 8.72e-10        # thermal inertia of 310 kg/s^(5/2) in Msun / yr^(5/2)\n",
+    "rotation_period = 4.9e-4# 4.3 hr rotation period for object in years\n",
+    "albedo = .017           # albedo of object\n",
+    "emissivity = .9         # emissivity of object\n",
+    "k = .25                 # constant between 0 and 1/4 based on object's rotation. See Veras et al. (2015) for details"
    ]
   },
   {
@@ -121,10 +119,10 @@
     "rebx = reboundx.Extras(sim)\n",
     "yark = rebx.load_force(\"yarkovsky_effect\")\n",
     "\n",
-    "#Sets the parameters for the effect\n",
-    "yark.params[\"ye_c\"] = c.value #set on the sim and not a particular particle\n",
-    "yark.params[\"ye_lstar\"] = lstar.value #set on the sim and not a particular particle\n",
-    "yark.params[\"ye_stef_boltz\"] = stef_boltz.value #set on the sim and not a particular particle"
+    "# We set the effect properties on the effect object:\n",
+    "yark.params[\"ye_c\"] = c \n",
+    "yark.params[\"ye_lstar\"] = lstar \n",
+    "yark.params[\"ye_stef_boltz\"] = stef_boltz"
    ]
   },
   {
@@ -142,14 +140,14 @@
    "source": [
     "# Sets parameters for the particle\n",
     "ps = sim.particles\n",
-    "ps[1].r = radius.value #remember radius is not inputed as a Rebx parameter - it's inputed on the particle in the Rebound sim\n",
+    "ps[1].r = radius #remember radius is not inputed as a Rebx parameter - it's inputed on the particle in the Rebound sim\n",
     "ps[1].params[\"ye_flag\"] = 0 #setting this flag to 0 will give us the full version of the effect\n",
-    "ps[1].params[\"ye_body_density\"] = density.value\n",
+    "ps[1].params[\"ye_body_density\"] = density\n",
     "ps[1].params[\"ye_albedo\"] = albedo\n",
     "ps[1].params[\"ye_emissivity\"] = emissivity\n",
     "ps[1].params[\"ye_k\"] = k\n",
-    "ps[1].params[\"ye_thermal_inertia\"] = Gamma.value\n",
-    "ps[1].params[\"ye_rotation_period\"] = rotation_period.value\n",
+    "ps[1].params[\"ye_thermal_inertia\"] = Gamma\n",
+    "ps[1].params[\"ye_rotation_period\"] = rotation_period\n",
     "\n",
     "# For this example we assume the object has a spin axis perpendicular to the orbital plane: unit vector = (0,0,1)\n",
     "ps[1].params[\"ye_spin_axis_x\"] = 0\n",
@@ -175,10 +173,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CHANGE IN SEMI-MAJOR AXIS: 1.921943330540632e-05 AU\n",
+      "CHANGE IN SEMI-MAJOR AXIS: 1.922647066021632e-05 AU\n",
       "\n",
-      "CPU times: user 1.13 s, sys: 2.98 ms, total: 1.14 s\n",
-      "Wall time: 1.14 s\n"
+      "CPU times: user 1.15 s, sys: 3.08 ms, total: 1.15 s\n",
+      "Wall time: 1.15 s\n"
      ]
     }
    ],
@@ -214,7 +212,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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SNOT3W1RB1LdvXyxevBijR4+GjY1NjXGFhYVYtWoVPDw88Ic//EFSIuaEBRERkXVZn3YF8fsuiopli8x8GbwgsjYsiIiIrMeSPblIUubVGccWmflryO83Z5kREZFVYouMnia6IHrjjTeqPS6Xy9G9e3e89tprcHR01FtiREREhiJ24DTAFpm1qHlA0DNu375d7SMnJwcffPABunfvjqtXr0r68LS0NIwdOxZeXl6QyWTYvXt3rfFHjhyBTCar8rh4Ubfvm5KSAj8/P9jb28PPzw+7du2SlBcREVmuiRuUooqhbu4toIwbwmLISoi+Q1RbUVFWVoYpU6bg/fffx44dO0R/+N27d9GzZ0+8/vrriIqKEv2+X375Rac3+PSMNqVSiejoaHz44Yd46aWXsGvXLkyYMAHp6ekICQkR/RlERGRZVOoyTEk8gUvFd+uM5UKL1kdvg6pPnz6N8ePHIz8/v36JyGTYtWsXxo0bV2PMkSNHEBYWhtu3b6Nly5bVxkRHR0Oj0WD//v3aYyNHjoSLiwuSk5NF5cJB1UREloUtMuvQkN9v0S2zuri6uqKkpERfp6tVr1690KZNGwwdOhSHDx/WeU2pVCI8PFzn2IgRI5CRkWGU3IiIyLyIbZH5eTmxRWbF9DbLLCMjAz4+Pvo6XbXatGmDDRs2IDg4GOXl5fj6668xdOhQHDlyBAMHDgQAFBUVwcPDQ+d9Hh4eKCoqqvG85eXlKC//3/LsGo3GMF+AiIiMRkqLLEbhjaWRAUbIisyV6ILo7Nmz1R5Xq9U4deoUli9fjo8++khviVWne/fu6N69u/a5QqFAQUEBVq5cqS2IgMftt6cJglDl2NPi4+OxdOlS/SdMREQmIaVFFhfhi7cGGfZ/6Mn8iS6Inn/+echkMlQ35Kh169ZYsGAB3nrrLb0mJ0ZoaCi2bNmife7p6VnlblBxcXGVu0ZPi4uLQ2xsrPa5RqNB+/bt9Z8sEREZnNgd6v28nJAY04cLLRIACQXRtWvXqj0ul8trHOBsDNnZ2WjTpo32uUKhQGpqKubNm6c9duDAAfTr16/Gc9jb28Pe3t6geRIRkWFJ2aGeLTJ6luiCyNvbu9bXHz16hO+//77WWWLPunPnDi5fvqx9fu3aNeTk5MDV1RUdOnRAXFwcCgsLsXnzZgDA559/jo4dO8Lf3x8VFRXYsmULUlJSkJKSoj3HnDlzMHDgQKxYsQKRkZHYs2cPDh48iPT0dNF5ERFR48IWGTVUgwdVX7x4ERs3bsRXX32F27dvo6KiQvR7T58+jbCwMO3zJ22rmJgYJCUlQaVS6Uzjr6iowHvvvYfCwkI0a9YM/v7++OGHHzBq1ChtTL9+/bBt2zYsWrQIixcvho+PD7Zv3841iIiILBRbZKQP9VqH6O7du9i+fTsSExPx008/ISwsDK+88grGjRuHVq1aGSJPo+I6RERE5k9Ki4xrC1kHo23uqlQq8Y9//AM7duxA165dMWnSJJw4cQIJCQnw8/OT9MFERET1JbZFxh3qSSzRBZGfnx/u3buHiRMn4sSJE9oC6P333zdYckRERE/jDvVkKKILosuXL+OVV15BWFgYevToYciciIiIqlifdgXx+y7WHQi2yEg6SdPuk5KSMHPmTJSVleHVV1/FpEmTal3wkIiISB+W7MlFkjKvzji2yKi+RO9l1rZtWyxcuBCXL1/G119/jaKiIvTv3x8PHz5EUlISfv31V0PmSUREVkilLsOoVWmiiqEg75Y4sSicxRDVS4N2u1er1fjmm2+wceNGZGVlISAgoMYtPhoTzjIjIjI97lBPUhltltmz5HI53n77bbz99tvIycnBxo0bG3I6IiIiAI93qM+4eqvOuG7uLfDVtBDeFaIGa9AdIkvFO0RERKYhZYd6hY8rkqcrjJAVNRYN+f0WNYZo5MiRyMjIqDOutLQUK1aswJo1ayQlQUREtPLHi1DEHxJVDM0a7MNiiPRKVMvs5ZdfxoQJE+Dk5IQXX3wRvXv3hpeXFxwcHHD79m2cP38e6enp2LdvH8aMGYNPP/3U0HkTEZEFeT3pJA5f/L3OOG6/QYYiumVWUVGB7777Dtu3b8exY8dQUlLy+AQyGfz8/DBixAhMnz4d3bt3N2S+RsGWGRGR8czfkYNvswrrjOMO9VSXhvx+13sMkVqtRllZGdzc3GBnZ1efU5gtFkRERIYnZdVp7lBPYphklplcLodcLq/v24mIyIqJnVLPFhkZS4Om3RMREUkldko9W2RkTCyIiIjIKKRMqZ8V5oP5I7jQIhkPCyIiIjI4KatOc7wQmQILIiIiMqiodceRmVdSZxzHC5EpSS6ICgoKIJPJ0K5dOwDAyZMnsXXrVvj5+eHNN9/Ue4JERNQ4qdRliFx9HMWl5XXGcrwQmZro3e6fmDhxIg4fPgwAKCoqwvDhw3Hy5En86U9/wrJly/SeIBERNT5PVp0WUwzFRfiyGCKTk1wQ5ebmom/fvgCAHTt2ICAgABkZGdi6dSuSkpL0nR8RETUyUeuOi55Sr4wbwvFCZBYkt8wePHgAe3t7AMDBgwfx4osvAgB8fX2hUqn0mx0RETUaUlpkswb7YP5IziIj8yG5IPL398ff//53jB49Gqmpqfjwww8BAL/99hvc3Nz0niAREZk/sbPIPBztsPudARw4TWZHckG0YsUKvPTSS/j0008RExODnj17AgD27t2rbaUREZF1kLL9RpB3S+yc2d8IWRFJV6+9zB49egSNRgMXFxftsevXr6N58+Zwd3fXa4KmwL3MiIjqtj7tCuL3XRQVyxYZGYPR9zKztbXVKYYAoGPHjvU5FRERNUJL9uQiSZlXZxxbZNRYiCqIgoKC8O9//xsuLi7o1asXZDJZjbFZWVl6S46IiMwLW2RkqUQVRJGRkdqZZePGjTNkPkREZKakbL/BFhk1NvUaQ2TpOIaIiEiX2B3qu7m3wFfTQtgiI5NoyO+35IUZDx48WONr69evl3o6IiIyYyp1GYb/9YioYkjh44oDsYNZDFGjJLkgGj16NP7whz+goqJCe+z333/H2LFjERcXp9fkiIjIdJ5sv3Gp+G6dsbMG+yB5usIIWREZhuSCKC0tDd9//z369OmDn3/+GT/88AMCAgJw584dnDlzxhA5EhGRkU3coJS0/QbHC1FjJ3nafUhICLKzszFjxgwEBwejsrISH330EebPn1/r7DMiIjJ/KnUZpiSeEHVXiDvUkyWp1zpEv/zyC06dOoV27drht99+w8WLF3Hv3j20aNFC3/kREZGRSJlFFhfhy01ZyaJIbpl98sknUCgUGD58OHJzc3Hq1ClkZ2fjueeeg1KplHSutLQ0jB07Fl5eXpDJZNi9e3et8Tt37sTw4cPRunVrODs7Q6FQ4Mcff9SJSUpKgkwmq/K4f/++1K9KRGQ1uEM9WTvJBdGqVauwe/dufPHFF3BwcIC/vz9OnjyJ8ePHY/DgwZLOdffuXfTs2ROrV68WFZ+Wlobhw4dj3759yMzMRFhYGMaOHYvs7GydOGdnZ6hUKp2Hg4ODpNyIiKyBSl2Gvh8fRGZeSZ2xMQpv7Ht3IGeRkUWS3DI7d+4cWrVqpXPMzs4On376KcaMGSPpXBEREYiIiBAd//nnn+s8X758Ofbs2YPvv/8evXr10h6XyWTw9PSUlAsRkbVhi4zofyQXRM8WQ08bNGhQg5KRqrKyEqWlpXB1ddU5fufOHXh7e+PRo0d4/vnn8eGHH+oUTM8qLy9HeXm59rlGozFYzkREpiZl+w0/LyckxvThXSGyePUaVH3q1Cl8++23yM/P11mPCHg8zsdYPvvsM9y9excTJkzQHvP19UVSUhICAwOh0WiwatUq9O/fH2fOnEHXrl2rPU98fDyWLl1qrLSJiEyGO9QTVU/yGKJt27ahf//+OH/+PHbt2oUHDx7g/PnzOHToEORyuSFyrFZycjKWLFmC7du3w93dXXs8NDQUr732Gnr27IkBAwZgx44d6NatG7744osazxUXFwe1Wq19FBQUGOMrEBEZ1ZI9uaKKIQ9HO64tRFZH8h2i5cuX429/+xtmzZoFJycnrFq1Cp06dcJbb72FNm3aGCLHKrZv345p06bh22+/xbBhw2qNtbGxQZ8+fXDp0qUaY+zt7bWb1xIRWRruUE9UN8l3iK5cuYLRo0cDeFxI3L17FzKZDPPmzcOGDRv0nuCzkpOTMXXqVGzdulWbR20EQUBOTo7RijUiInOyPu0KFPGHRBVDswb7sBgiqyX5DpGrqytKSx//xWrbti1yc3MRGBiIkpIS3Lt3T9K57ty5g8uXL2ufX7t2DTk5OXB1dUWHDh0QFxeHwsJCbN68GcDjYmjKlClYtWoVQkNDUVRUBABo1qyZtl23dOlShIaGomvXrtBoNEhISEBOTg7WrFkj9asSETVqS/bkIkmZV2ccd6gnqkdBNGDAAKSmpiIwMBATJkzAnDlzcOjQIaSmpmLo0KGSznX69GmEhYVpn8fGxgIAYmJikJSUBJVKhfz8fO3r69evx8OHDzFr1izMmjVLe/xJPACUlJTgzTffRFFREeRyOXr16oW0tDT07dtX6lclImq0Jm5Qit6hnpuyEgEyQRAEKW+4desW7t+/Dy8vL1RWVmLlypVIT09Hly5dsHjxYri4uBgqV6PRaDSQy+VQq9VwdnY2dTpERKJJ2YuMs8jI0jTk91tyQWQNWBARUWMkdko9W2RkqRry+12vdYiIiMi8iB0vxBYZUfVYEBERNWJSptSzRUZUMxZERESNFFtkRPrDgoiIqBES2yIL7tASKW9zbSGiukhamPHhw4do0qQJcnNzDZUPERHVQqUuw6hVaaLHC7EYIhJH0h2iJk2aaHeRJyIi41r540WsPnxFVCzHCxFJI3nrjkWLFiEuLg63btW94BcREenHxA1KUcVQN/cW3JiVqB4kjyFKSEjA5cuX4eXlBW9vb7Ro0ULn9aysLL0lR0Rk7aQstMgp9UT1J7kgGjdunAHSICKiZ7FFRmQ8XKm6GlypmohMLWrdcWTmldQZ5+flhMSYPpxSTwQTrVSdmZmJCxcuQCaTwc/PD7169arvqYiI6P+o1GWIXH0cxaXldcbGKLyxNDLACFkRWT7JBVFxcTFeeeUVHDlyBC1btoQgCFCr1QgLC8O2bdvQunVrQ+RJRGTxpLTI4iJ88dYgHwNnRGQ9JM8ye+edd6DRaPDzzz/j1q1buH37NnJzc6HRaPDuu+8aIkciIosXte64qGLIz8sJyrghLIaI9EzyGCK5XI6DBw+iT58+OsdPnjyJ8PBwlJSU6DM/k+AYIiIyFiktMg6cJqqdUccQVVZWws7OrspxOzs7VFZWSj0dEZHVEtsi83C0w+53BnDgNJEBSW6ZDRkyBHPmzMFvv/2mPVZYWIh58+Zh6NChek2OiMgSPdl+Q0wxFOTdEicWhbMYIjIwyQXR6tWrUVpaio4dO8LHxwddunRBp06dUFpaii+++MIQORIRWYz1aVegiD+E86rSOmNnDfbBzpnci4zIGCS3zNq3b4+srCykpqbi4sWLEAQBfn5+GDZsmCHyIyKyGGJ3qGeLjMj4uDBjNTiomoj0SaUuw7SkU6LuCgV5t+RdIaJ6Mvig6oSEBLz55ptwcHBAQkJCrbGOjo7w9/dHSEiIpESIiCwRt98gahxE3SHq1KkTTp8+DTc3N3Tq1KnW2PLychQXF2PevHn49NNP9ZaoMfEOERHpw8QNSmRcvVVnXDf3FvhqWghbZEQN1JDfb4O0zFJTUzFx4kT8/vvv+j61UbAgIqKG4A71RKZhkr3MavPCCy9g0aJFhjg1EZFZY4uMqHGq1x2iu3fv4ujRo8jPz0dFRYXOa5awfQfvEBFRfYhtkXGHeiLDMOodouzsbIwaNQr37t3D3bt34erqihs3bqB58+Zwd3e3iIKIiEgKKS0y7lBPZJ4kL8w4b948jB07Frdu3UKzZs3w008/IS8vD8HBwVi5cqUhciQiMlsrf7wIRfwhUcVQXIQviyEiMyX5DlFOTg7Wr18PW1tb2Nraory8HJ07d8Zf/vIXxMTEYPz48YbIk4jI7EStO47MvJI649giIzJ/ku8Q2dnZQSaTAQA8PDyQn58PAJDL5do/ExFZMpW6DH0/PiiqGIpReGPfuwNZDBGZOcl3iHr16oXTp0+jW7duCAsLwwcffIAbN27g66+/RmBgoCFyJCIyG1JmkcVF+OKtQT4GzoiI9EFyQbR8+XKUlj5efv7DDz9ETEwMZs6ciS5dumDTpk16T5CIyBxI2X6DLTKixod7mVWD0+6J6Gnr064gft9FUbFcW4jIdMxuYUYiIkvx6Y8XsUZEi4w71BM1bpIHVd+8eROzZs2Cn58fWrVqBVdXV52HFGlpaRg7diy8vLwgk8mwe/fuOt9z9OhRBAcHw8HBAZ07d8bf//73KjEpKSnw8/ODvb09/Pz8sGvXLkl5EREBwJI9uaKKoSDvljixKJzFEFEjJvkO0WuvvYYrV65g2rRp8PDw0M44q4+7d++iZ8+eeP311xEVFVVn/LVr1zBq1ChMnz4dW7ZswfHjx/H222+jdevW2vcrlUpER0fjww8/xEsvvYRdu3ZhwoQJSE9PR0hISL1zJSLrInZKPVtkRJZB8hgiJycnpKeno2fPnvpNRCbDrl27MG7cuBpjFixYgL179+LChQvaYzNmzMCZM2egVCoBANHR0dBoNNi/f782ZuTIkXBxcUFycrKoXDiGiMh6qdRliFx9HMWl5bXGcYd6IvPTkN9vyS0zX19flJWVSX2bXiiVSoSHh+scGzFiBE6fPo0HDx7UGpORkVHjecvLy6HRaHQeRGR9nqw6XVcxpPBxxYHYwSyGiCyI5IJo7dq1WLhwIY4ePYqbN28atZAoKiqCh4eHzjEPDw88fPgQN27cqDWmqKioxvPGx8dDLpdrH+3bt9d/8kRktlTqMoxalSZqfaEYhTeSpyuMkBURGZPkMUQtW7aEWq3GkCFDdI4LggCZTIZHjx7pLbnqPDtm6UnH7+nj1cXUNtYpLi4OsbGx2ucajYZFEZGVkDKlngstElkuyQXRpEmT0LRpU2zdurXBg6ql8vT0rHKnp7i4GE2aNIGbm1utMc/eNXqavb097O3t9Z8wEZm1JXtykaTMqzOOU+qJLJ/kgig3NxfZ2dno3r27IfKplUKhwPfff69z7MCBA+jduzfs7Oy0MampqZg3b55OTL9+/YyaKxGZLymrTgd5t8TOmf2NkBURmZLkgqh3794oKCjQS0F0584dXL58Wfv82rVryMnJgaurKzp06IC4uDgUFhZi8+bNAB7PKFu9ejViY2Mxffp0KJVKJCYm6swemzNnDgYOHIgVK1YgMjISe/bswcGDB5Gent7gfImo8eOq00RUHcnT7r/99lssWbIE8+fPR2BgoPbOzBPPPfec6HMdOXIEYWFhVY7HxMQgKSkJU6dOxfXr13HkyBHta0ePHsW8efPw888/w8vLCwsWLMCMGTN03v/dd99h0aJFuHr1Knx8fPDxxx9j/PjxovPitHsiyyS2RcYp9USNU0N+vyUXRDY2VSemyWQyow2qNgYWRESWRUqLTOHjyllkRI2UUfcyu3btmtS3EBGZzMofL4qaTg+wRUZkzSQXRN7e3obIg4hI7yZuUCLj6q0649giIyJRBdHevXsREREBOzs77N27t9bYF198US+JERHVl0pdhimJJ3Cp+G6dsWyREREgcgyRjY0NioqK4O7uXu0YIu3JOIaIiEyMLTIi62XwMUSVlZXV/pmIyJyI3aHez8sJiTF92CIjIi3JY4iIiMyN2B3qgcd7kS2NDDBCVkTUmNSrIDp58iSOHDmC4uLiKneM/vrXv+olMSIiMaS0yLgXGRHVRHJBtHz5cixatAjdu3evspeZMfc1IyJii4yI9EVyQbRq1Sps3LgRU6dONUA6RER1k9Ii48BpIhJDckFkY2OD/v250SERmYbYFhl3qCciKWqeQ1+DefPmYc2aNYbIhYioRip1GUatShNVDAV5t8SJReEshohINMl3iN577z2MHj0aPj4+8PPzq7K5686dO/WWHBERwB3qicjwJBdE77zzDg4fPoywsDC4ublxIDURGZTYHerZIiOihpBcEG3evBkpKSkYPXq0IfIhIgIgbYf6IO+W2DmTYxuJqP4kF0Surq7w8eE6HkRkONx+g4iMTfKg6iVLluDPf/4z7t27Z4h8iMjKTdygFFUMdXNvAWXcEBZDRKQXku8QJSQk4MqVK/Dw8EDHjh2rDKrOysrSW3JEZD24Qz0RmZLkgmjcuHEGSIOIrBlbZERkajJBEARTJ2FuNBoN5HI51Go1nJ2dTZ0OkUWbuEGJjKu36ozj9htEVJeG/H5LHkMEACUlJfjHP/6BuLg43Lr1+D9kWVlZKCwsrM/piMgKqdRlGP7XI6KKoRiFN/a9O5DFEBEZjOSW2dmzZzFs2DDI5XJcv34d06dPh6urK3bt2oW8vDxs3rzZEHkSkQXhDvVEZG4k3yGKjY3F1KlTcenSJTg4OGiPR0REIC0tTa/JEZHlETuLzM/LCcq4ISyGiMgoJN8hOnXqFNavX1/leNu2bVFUVKSXpIjIMkWtPY7M/JI642IU3lgaGWD4hIiI/o/kgsjBwQEajabK8V9++QWtW7fWS1JEZFmkTKlni4yITEFyQRQZGYlly5Zhx44dAACZTIb8/Hy8//77iIqK0nuCRNS4iR0vxFlkRGRKkqfdazQajBo1Cj///DNKS0vh5eWFoqIiKBQK7Nu3Dy1atDBUrkbDafdE+hG17jgy80rqjOPaQkSkDw35/ZZ8h8jZ2Rnp6ek4dOgQsrKyUFlZiaCgIAwbNkzqqYjIQqnUZYhcfRzFpeV1xu6Z1Q8927sYISsioppJLoieGDJkCIYMGQLg8bpERESAtCn1K6ICWQwRkVmQPO1+xYoV2L59u/b5hAkT4ObmhrZt2+LMmTN6TY6IGpeodcclTamP7tPBCFkREdVNckG0fv16tG/fHgCQmpqK1NRU7N+/HxEREZg/f77eEyQi86dSl6HvxwdFjxfiqtNEZG4kt8xUKpW2IPrnP/+JCRMmIDw8HB07dkRISIjeEyQi8ya2RebhaIfd7wxgIUREZknyHSIXFxcUFBQAAP71r39pB1MLgoBHjx7pNzsiMlsqdRlGrUoTVQwFebfEiUXhLIaIyGxJLojGjx+PiRMnYvjw4bh58yYiIiIAADk5OejSpYvkBNauXYtOnTrBwcEBwcHBOHbsWI2xU6dOhUwmq/Lw9/fXxiQlJVUbc//+fcm5EVH11qddgSL+EM6rSuuMnTXYBztn9jdCVkRE9Se5Zfa3v/0NHTt2REFBAf7yl7/A0dERwONW2ttvvy3pXNu3b8fcuXOxdu1a9O/fH+vXr0dERATOnz+PDh2qDrZctWoVPvnkE+3zhw8fomfPnnj55Zd14pydnfHLL7/oHHt63zUiqr8le3KRpMyrM44tMiJqTCQvzKhPISEhCAoKwrp167THevTogXHjxiE+Pr7O9+/evRvjx4/HtWvX4O3tDeDxHaK5c+c2aCkALsxIVJVKXYZpSadE3RUK8m7Ju0JEZHQN+f2W3DLTl4qKCmRmZiI8PFzneHh4ODIyMkSdIzExEcOGDdMWQ0/cuXMH3t7eaNeuHcaMGYPs7Gy95U1kjVb+eJEtMiKyaPVemLGhbty4gUePHsHDw0PnuIeHB4qKiup8v0qlwv79+7F161ad476+vkhKSkJgYCA0Gg1WrVqF/v3748yZM+jatWu15yovL0d5+f9W1K1u81oiazVxgxIZV2/VGdfNvQW+mhbCFhkRNUomK4iekMlkOs8FQahyrDpJSUlo2bIlxo0bp3M8NDQUoaGh2uf9+/dHUFAQvvjiCyQkJFR7rvj4eCxdulR68kQWTMoO9QofVyRPVxghKyIiwzBZy6xVq1awtbWtcjeouLi4yl2jZwmCgI0bN2Ly5Mlo2rRprbE2Njbo06cPLl26VGNMXFwc1Gq19vFkWQEia/WkRSamGJo12IfFEBE1evW+Q1RRUYHi4mJUVlbqHK9udlh1mjZtiuDgYKSmpuKll17SHk9NTUVkZGSt7z169CguX76MadOm1fk5giAgJycHgYGBNcbY29vD3t5eVN5Elk5si8zPywmJMX3YIiMiiyC5ILp06RLeeOONKgOfn7S6pCzOGBsbi8mTJ6N3795QKBTYsGED8vPzMWPGDACP79wUFhZi8+bNOu9LTExESEgIAgICqpxz6dKlCA0NRdeuXaHRaJCQkICcnBysWbNG6lclsipSWmQxCm8sjaz694+IqLGSXBBNnToVTZo0wT//+U+0adNG1HifmkRHR+PmzZtYtmwZVCoVAgICsG/fPu2sMZVKhfz8fJ33qNVqpKSkYNWqVdWes6SkBG+++SaKioogl8vRq1cvpKWloW/fvvXOk8jSSdmhPi7CF28N8jFwRkRExiV5HaIWLVogMzMTvr6+hsrJ5LgOEVmTqHXHRW3KyhYZEZm7hvx+S75D5Ofnhxs3bkh9GxGZGZW6DJGrj6O4tLzOWLbIiMjSSZ5ltmLFCvzxj3/EkSNHcPPmTWg0Gp0HEZm/J7PIxBRDcRG+LIaIyOJJbpnZ2DyuoWpaP8gSdrxny4wsGVtkRGSpjNoyO3z4sNS3EJEZkNIimzXYB/NHWu44QSKiZ0kuiAYNGmSIPIjIgMTOIuMO9URkrUQVRGfPnkVAQABsbGxw9uzZWmOfe+45vSRGRA3HHeqJiMQRVRA9//zzKCoqgru7O55//nnIZDJUN/TIUsYQEVmC9WlXEL/voqhYtsiIyNqJKoiuXbuG1q1ba/9MROZtyZ5cJCnz6oxji4yI6DFRBdGTlaOf/TMRmRe2yIiI6qfem7sSkXmRsv0GW2RERLpYEBFZALE71Hdzb4GvpoWwRUZE9AwWRESNmJQd6hU+rkierjBCVkREjQ8LIqJGirPIiIj0p14FUUlJCb777jtcuXIF8+fPh6urK7KysuDh4YG2bdvqO0ciesanP17EGhHjhbj9BhGROJILorNnz2LYsGGQy+W4fv06pk+fDldXV+zatQt5eXnYvHmzIfIkov8jdko9d6gnIhJP8m73sbGxmDp1Ki5dugQHBwft8YiICKSlpek1OSL6H5W6DKNWpYkqhrhDPRGRNJLvEJ06dQrr16+vcrxt27YoKirSS1JEpEvslHq2yIiI6kdyQeTg4ACNRlPl+C+//KJdzZqI9EfslPqXg9ri0wnPGz4hIiILJLllFhkZiWXLluHBgwcAHu9flp+fj/fffx9RUVF6T5DIWqnUZRj+1yOiiqEw39YshoiIGkByQbRy5Ur8/vvvcHd3R1lZGQYNGoQuXbrAyckJH3/8sSFyJLI6K3+8CEX8IVHrC80a7INNU/saISsiIssluWXm7OyM9PR0HDp0CFlZWaisrERQUBCGDRtmiPyIrE7UuuPIzCupM47jhYiI9EcmCIJg6iTMjUajgVwuh1qthrOzs6nTISuhUpchcvVxFJeW1xnLKfVERFU15Pdb8h2ihISEao/LZDI4ODigS5cuGDhwIGxtbaWemshqSdmYNS7CF28N8jFwRkRE1kVyQfS3v/0Nv//+O+7duwcXFxcIgoCSkhI0b94cjo6OKC4uRufOnXH48GG0b9/eEDkTWRS2yIiITE/yoOrly5ejT58+uHTpEm7evIlbt27h119/RUhICFatWoX8/Hx4enpi3rx5hsiXyGKo1GXo+/FBUcXQrME+2PfuQBZDREQGInkMkY+PD1JSUvD888/rHM/OzkZUVBSuXr2KjIwMREVFQaVS6TNXo+EYIjI0sS0yD0c77H5nAAshIiIRjDqGSKVS4eHDh1WOP3z4ULtStZeXF0pLS6WemsjiqdRlmJZ0CudVdf/9CPJuiZ0z+xshKyIiktwyCwsLw1tvvYXs7GztsezsbMycORNDhgwBAJw7dw6dOnXSX5ZEFmB92hUo4g+JKoZmDfZhMUREZESS7xAlJiZi8uTJCA4Ohp2dHYDHd4eGDh2KxMREAICjoyM+++wz/WZK1IiJ3aGeLTIiItOo9zpEFy9exK+//gpBEODr64vu3bvrOzeT4Rgi0he2yIiIjMeoY4ie8PX1ha+vb33fTmTxpKwtNGuwD+aP5N8nIiJTkVwQPXr0CElJSfj3v/+N4uJiVFZW6rx+6NAhvSVH1FiJ3aG+m3sLfDUthC0yIiITk1wQzZkzB0lJSRg9ejQCAgIgk8kMkRdRo6RSl2FK4glRm7IqfFyRPF1hhKyIiKgukguibdu2YceOHRg1apReEli7di0+/fRTqFQq+Pv74/PPP8eAAQOqjT1y5AjCwsKqHL9w4YJO+y4lJQWLFy/GlStX4OPjg48//hgvvfSSXvIlqglbZEREjZfkafdNmzZFly5d9PLh27dvx9y5c7Fw4UJkZ2djwIABiIiIQH5+fq3v++WXX6BSqbSPrl27al9TKpWIjo7G5MmTcebMGUyePBkTJkzAiRMn9JIzUXUmblCKKob8vJygjBvCYoiIyMxInmX22Wef4erVq1i9enWD22UhISEICgrCunXrtMd69OiBcePGIT4+vkr8kztEt2/fRsuWLas9Z3R0NDQaDfbv3689NnLkSLi4uCA5OVlUXpxlRmJJaZFxh3oiIsMy6iyz9PR0HD58GPv374e/v792LaIndu7cKeo8FRUVyMzMxPvvv69zPDw8HBkZGbW+t1evXrh//z78/PywaNEinTaaUqmsso/aiBEj8Pnnn9d4vvLycpSXl2ufazQaUd+BrBt3qCcishySC6KWLVvqZTzOjRs38OjRI3h4eOgc9/Dw0G4B8qw2bdpgw4YNCA4ORnl5Ob7++msMHToUR44cwcCBAwEARUVFks4JAPHx8Vi6dGkDvxFZE+5QT0RkWSQXRJs2bdJrAs+23QRBqLEV1717d50FIBUKBQoKCrBy5UptQST1nAAQFxeH2NhY7XONRoP27dtL+h5kHVTqMkSuPo7i0vI6Y9kiIyJqPOq9MGNDtWrVCra2tlXu3BQXF1e5w1Ob0NBQbNmyRfvc09NT8jnt7e1hb28v+jPJOrFFRkRkuepVEH333XfYsWMH8vPzUVFRofNaVlaWqHM0bdoUwcHBSE1N1WnBpaamIjIyUnQu2dnZaNOmjfa5QqFAamqqzjiiAwcOoF+/fqLPSfQstsiIiCyb5IIoISEBCxcuRExMDPbs2YPXX38dV65cwalTpzBr1ixJ54qNjcXkyZPRu3dvKBQKbNiwAfn5+ZgxYwaAx62swsJCbN68GQDw+eefo2PHjvD390dFRQW2bNmClJQUpKSkaM85Z84cDBw4ECtWrEBkZCT27NmDgwcPIj09XepXJZLUIuPaQkREjZfkgmjt2rXYsGEDXn31VXz11Vf44x//iM6dO+ODDz7ArVt1b1XwtOjoaNy8eRPLli2DSqVCQEAA9u3bB29vbwCASqXSWZOooqIC7733HgoLC9GsWTP4+/vjhx9+0Fkksl+/fti2bRsWLVqExYsXw8fHB9u3b0dISIjUr0pWTmyLjDvUExE1fpLXIWrevDkuXLgAb29vuLu7IzU1FT179sSlS5cQGhqKmzdvGipXo+E6RNaNO9QTETVODfn9lrxStaenp7bo8fb2xk8//QQAuHbtGiTWVkRmZ33aFSjiD4kqhmYN9mExRERkISS3zIYMGYLvv/8eQUFBmDZtGubNm4fvvvsOp0+fxvjx4w2RI5FRLNmTiyRlXp1xbJEREVkeyS2zyspKVFZWokmTx7XUjh07kJ6eji5dumDGjBlo2rSpQRI1JrbMrM/EDUpkXK17DBxbZERE5qshv9+SCyJrwILIekjZi4yzyIiIzJtR9zIDgJKSEpw8eRLFxcWorKzUeW3KlCn1OSWR0a1Pu4L4fRfrjOvm3gJfTQthi4yIyIJJLoi+//57TJo0CXfv3oWTk5POlhgymYwFETUKYscLKXxckTxdYYSMiIjIlCQXRH/4wx/wxhtvYPny5WjevLkhciIyGClT6tkiIyKyHpILosLCQrz77rsshqjRYYuMiIhqInkdohEjRuD06dOGyIXIYJbsyRVVDAV3aIkDsYNZDBERWRlRd4j27t2r/fPo0aMxf/58nD9/HoGBgbCzs9OJffHFF/WbIVEDSGmRcbwQEZH1EjXt3sZG3I0kmUyGR48eNTgpU+O0e8sgdi8ygOOFiIgsgcGn3T87tZ7I3IldaJHjhYiICKjnOkRE5krKQotskRER0ROiB1UfOnQIfn5+0Gg0VV5Tq9Xw9/dHWlqaXpMjkmLljxehiD8ketVpFkNERPSE6DtEn3/+OaZPn15tT04ul+Ott97C3/72NwwcOFCvCRKJEbXuODLzSuqM8/NyQmJMH7bIiIhIh+g7RGfOnMHIkSNrfD08PByZmZl6SYpILJW6DH0/PiiqGIpReGPfuwNZDBERURWi7xD997//rTLFXudETZrg999/10tSRGJImUUWF+GLtwb5GDgjIiJqrEQXRG3btsW5c+fQpUuXal8/e/Ys2rRpo7fEiGrDFhkREemT6JbZqFGj8MEHH+D+/ftVXisrK8Of//xnjBkzRq/JET1LSots1mAftsiIiEgUUQszAo9bZkFBQbC1tcXs2bPRvXt3yGQyXLhwAWvWrMGjR4+QlZUFDw8PQ+dscFyY0TyJbZF5ONph9zsDWAgREVkZgy/MCAAeHh7IyMjAzJkzERcXhyd1lEwmw4gRI7B27VqLKIbI/EjZfiPIuyV2zuxvhKyIiMiSSFqY0dvbG/v27cPt27dx+fJlCIKArl27wsXFxVD5kZUTu0M9wO03iIio/uq1UrWLiwv69Omj71yIdCzZk4skZV6dcWyRERFRQ3HrDjI7bJEREZGxsSAis8Id6omIyBRYEJHZ4A71RERkKiyIyOS4Qz0REZkaCyIyKbbIiIjIHLAgIpMR2yLj9htERGRoLIjI6KS0yGIU3lgaGWCErIiIyJqxICKj4g71RERkjlgQkdFwh3oiIjJXLIjI4FTqMkSuPo7i0vI6Y9kiIyIiU7AxdQJr165Fp06d4ODggODgYBw7dqzG2J07d2L48OFo3bo1nJ2doVAo8OOPP+rEJCUlQSaTVXncv3/f0F+FqrHyx4tQxB8SVQzFRfiyGCIiIpMwaUG0fft2zJ07FwsXLkR2djYGDBiAiIgI5OfnVxuflpaG4cOHY9++fcjMzERYWBjGjh2L7OxsnThnZ2eoVCqdh4ODgzG+Ej0lat1xUeOF/LycoIwbwvFCRERkMjJBEARTfXhISAiCgoKwbt067bEePXpg3LhxiI+PF3UOf39/REdH44MPPgDw+A7R3LlzUVJSUu+8NBoN5HI51Go1nJ2d630eazZoxb+Rd7vuu3JcW4iIiPSlIb/fJrtDVFFRgczMTISHh+scDw8PR0ZGhqhzVFZWorS0FK6urjrH79y5A29vb7Rr1w5jxoypcgfpWeXl5dBoNDoPqh+VugyDPz1UZzHk4WgHZdwQFkNERGQWTFYQ3bhxA48ePYKHh4fOcQ8PDxQVFYk6x2effYa7d+9iwoQJ2mO+vr5ISkrC3r17kZycDAcHB/Tv3x+XLl2q8Tzx8fGQy+XaR/v27ev3pazck/FC12+W1RoX5N0SJxaFcxYZERGZDZPPMpPJZDrPBUGocqw6ycnJWLJkCfbs2QN3d3ft8dDQUISGhmqf9+/fH0FBQfjiiy+QkJBQ7bni4uIQGxurfa7RaFgUSSR2Sj1bZEREZI5MVhC1atUKtra2Ve4GFRcXV7lr9Kzt27dj2rRp+PbbbzFs2LBaY21sbNCnT59a7xDZ29vD3t5efPKkJXZKvYejHXa/M4B3hYiIyCyZrGXWtGlTBAcHIzU1Ved4amoq+vXrV+P7kpOTMXXqVGzduhWjR4+u83MEQUBOTg7atGnT4JxJl9gp9YFezmyRERGRWTNpyyw2NhaTJ09G7969oVAosGHDBuTn52PGjBkAHreyCgsLsXnzZgCPi6EpU6Zg1apVCA0N1d5datasGeRyOQBg6dKlCA0NRdeuXaHRaJCQkICcnBysWbPGNF/SAqnUZZiWdArnVaV1xip8XJE8XWGErIiIiOrPpAVRdHQ0bt68iWXLlkGlUiEgIAD79u2Dt7c3AEClUumsSbR+/Xo8fPgQs2bNwqxZs7THY2JikJSUBAAoKSnBm2++iaKiIsjlcvTq1QtpaWno27evUb+bpVqfdgXx+y6KiuV4ISIiaixMug6RueI6RNVbsicXScq8OuM4XoiIiEyhIb/fJp9lRuZPSossyLslds7sb4SsiIiI9IcFEdWKLTIiIrIGLIioRmJbZN3cW+CraSFskRERUaPFgoiq4CwyIiKyNiyISMfKHy+K2qEeYIuMiIgsBwsi0pq4QYmMq7fqjGOLjIiILA0LIoJKXYYpiSdwqfhunbFskRERkSViQWTl2CIjIiJiQWTVxO5Q7+flhMSYPmyRERGRxWJBZIXE7lAPADEKbyyNDDBCVkRERKbDgsjKSGmRxUX44q1BPgbOiIiIyPRYEFkRtsiIiIiqx4LICkhpkXHgNBERWSMWRBZObIuMO9QTEZE1Y0FkobhDPRERkXgsiCwQd6gnIiKShgWRhRG7Qz1bZERERP/DgshCsEVGRERUfyyILAC33yAiImoYFkSNHHeoJyIiajgWRI0Ud6gnIiLSHxZEjRBbZERERPrFgqiREdsi4/YbRERE4rEgaiSktMi4Qz0REZE0LIgaAe5QT0REZFgsiMwcW2RERESGx4LIjEWtPY7M/JI649giIyIiahgWRGZIynghtsiIiIgajgWRmRE7XogtMiIiIv1hQWRGotYdR2ZeSZ1xXFuIiIhIv1gQmQGVugyRq4+juLS8ztg9s/qhZ3sXI2RFRERkPVgQmZiUKfUrogJZDBERERmAjakTWLt2LTp16gQHBwcEBwfj2LFjtcYfPXoUwcHBcHBwQOfOnfH3v/+9SkxKSgr8/Pxgb28PPz8/7Nq1y1DpN0jUuuOixwsp44Yguk8HI2RFRERkfUxaEG3fvh1z587FwoULkZ2djQEDBiAiIgL5+fnVxl+7dg2jRo3CgAEDkJ2djT/96U949913kZKSoo1RKpWIjo7G5MmTcebMGUyePBkTJkzAiRMnjPW16qRSl6HvxwdFjxfa9+5ADp4mIiIyIJkgCIKpPjwkJARBQUFYt26d9liPHj0wbtw4xMfHV4lfsGAB9u7diwsXLmiPzZgxA2fOnIFSqQQAREdHQ6PRYP/+/dqYkSNHwsXFBcnJyaLy0mg0kMvlUKvVcHZ2ru/Xq5bYFpmHox12vzOAhRAREZFIDfn9NtkdooqKCmRmZiI8PFzneHh4ODIyMqp9j1KprBI/YsQInD59Gg8ePKg1pqZzGtPMLZmiiqEg75Y4sSicxRAREZGRmGxQ9Y0bN/Do0SN4eHjoHPfw8EBRUVG17ykqKqo2/uHDh7hx4wbatGlTY0xN5wSA8vJylJf/b4aXRqOR+nXqdKbgNvbn1pzDE5xST0REZHwmn2Umk8l0nguCUOVYXfHPHpd6zvj4eCxdulR0zvVx8nrt+5GxRUZERGQ6JmuZtWrVCra2tlXu3BQXF1e5w/OEp6dntfFNmjSBm5tbrTE1nRMA4uLioFartY+CgoL6fKVa9e3oWuNrbJERERGZlskKoqZNmyI4OBipqak6x1NTU9GvX79q36NQKKrEHzhwAL1794adnV2tMTWdEwDs7e3h7Oys89C3nu1dEBXUtsrxWYN9sHNmf71/HhEREYln0pZZbGwsJk+ejN69e0OhUGDDhg3Iz8/HjBkzADy+c1NYWIjNmzcDeDyjbPXq1YiNjcX06dOhVCqRmJioM3tszpw5GDhwIFasWIHIyEjs2bMHBw8eRHp6ukm+49M+m/A8pii8cehCMVo722NoDw/eFSIiIjIDJi2IoqOjcfPmTSxbtgwqlQoBAQHYt28fvL29AQAqlUpnTaJOnTph3759mDdvHtasWQMvLy8kJCQgKipKG9OvXz9s27YNixYtwuLFi+Hj44Pt27cjJCTE6N+vOj3bu3C1aSIiIjNj0nWIzJUh1yEiIiIiw2iU6xARERERmQsWRERERGT1WBARERGR1WNBRERERFaPBRERERFZPRZEREREZPVYEBEREZHVY0FEREREVo8FEREREVk9FkRERERk9Uy6l5m5erKbiUajMXEmREREJNaT3+367ErGgqgapaWlAID27dubOBMiIiKSqrS0FHK5XNJ7uLlrNSorK/Hbb7/ByckJMplMr+fWaDRo3749CgoKuHGsAfE6Gwevs3HwOhsPr7VxGOo6C4KA0tJSeHl5wcZG2qgg3iGqho2NDdq1a2fQz3B2duZfNiPgdTYOXmfj4HU2Hl5r4zDEdZZ6Z+gJDqomIiIiq8eCiIiIiKweCyIjs7e3x5///GfY29ubOhWLxutsHLzOxsHrbDy81sZhjteZg6qJiIjI6vEOEREREVk9FkRERERk9VgQERERkdVjQWREa9euRadOneDg4IDg4GAcO3bM1CmZjfj4ePTp0wdOTk5wd3fHuHHj8Msvv+jECIKAJUuWwMvLC82aNcPgwYPx888/68SUl5fjnXfeQatWrdCiRQu8+OKL+M9//qMTc/v2bUyePBlyuRxyuRyTJ09GSUmJTkx+fj7Gjh2LFi1aoFWrVnj33XdRUVFhkO9uSvHx8ZDJZJg7d672GK+zfhQWFuK1116Dm5sbmjdvjueffx6ZmZna13mdG+7hw4dYtGgROnXqhGbNmqFz585YtmwZKisrtTG8zvWTlpaGsWPHwsvLCzKZDLt379Z53dyu67lz5zBo0CA0a9YMbdu2xbJly6Rv3yGQUWzbtk2ws7MTvvzyS+H8+fPCnDlzhBYtWgh5eXmmTs0sjBgxQti0aZOQm5sr5OTkCKNHjxY6dOgg3LlzRxvzySefCE5OTkJKSopw7tw5ITo6WmjTpo2g0Wi0MTNmzBDatm0rpKamCllZWUJYWJjQs2dP4eHDh9qYkSNHCgEBAUJGRoaQkZEhBAQECGPGjNG+/vDhQyEgIEAICwsTsrKyhNTUVMHLy0uYPXu2cS6GkZw8eVLo2LGj8Nxzzwlz5szRHud1brhbt24J3t7ewtSpU4UTJ04I165dEw4ePChcvnxZG8Pr3HAfffSR4ObmJvzzn/8Url27Jnz77beCo6Oj8Pnnn2tjeJ3rZ9++fcLChQuFlJQUAYCwa9cundfN6bqq1WrBw8NDeOWVV4Rz584JKSkpgpOTk7By5UpJ35kFkZH07dtXmDFjhs4xX19f4f333zdRRuatuLhYACAcPXpUEARBqKysFDw9PYVPPvlEG3P//n1BLpcLf//73wVBEISSkhLBzs5O2LZtmzamsLBQsLGxEf71r38JgiAI58+fFwAIP/30kzZGqVQKAISLFy8KgvD4PwQ2NjZCYWGhNiY5OVmwt7cX1Gq14b60EZWWlgpdu3YVUlNThUGDBmkLIl5n/ViwYIHwwgsv1Pg6r7N+jB49WnjjjTd0jo0fP1547bXXBEHgddaXZwsic7uua9euFeRyuXD//n1tTHx8vODl5SVUVlaK/p5smRlBRUUFMjMzER4ernM8PDwcGRkZJsrKvKnVagCAq6srAODatWsoKirSuYb29vYYNGiQ9hpmZmbiwYMHOjFeXl4ICAjQxiiVSsjlcoSEhGhjQkNDIZfLdWICAgLg5eWljRkxYgTKy8t1Wh6N2axZszB69GgMGzZM5zivs37s3bsXvXv3xssvvwx3d3f06tULX375pfZ1Xmf9eOGFF/Dvf/8bv/76KwDgzJkzSE9Px6hRowDwOhuKuV1XpVKJQYMG6axpNGLECPz222+4fv266O/FvcyM4MaNG3j06BE8PDx0jnt4eKCoqMhEWZkvQRAQGxuLF154AQEBAQCgvU7VXcO8vDxtTNOmTeHi4lIl5sn7i4qK4O7uXuUz3d3ddWKe/RwXFxc0bdrUIv55bdu2DVlZWTh16lSV13id9ePq1atYt24dYmNj8ac//QknT57Eu+++C3t7e0yZMoXXWU8WLFgAtVoNX19f2Nra4tGjR/j444/x6quvAuC/z4Zibte1qKgIHTt2rPI5T17r1KmTqO/FgsiIZDKZznNBEKocI2D27Nk4e/Ys0tPTq7xWn2v4bEx18fWJaYwKCgowZ84cHDhwAA4ODjXG8To3TGVlJXr37o3ly5cDAHr16oWff/4Z69atw5QpU7RxvM4Ns337dmzZsgVbt26Fv78/cnJyMHfuXHh5eSEmJkYbx+tsGOZ0XavLpab31oQtMyNo1aoVbG1tq/xfQnFxcZXK19q988472Lt3Lw4fPox27dppj3t6egJArdfQ09MTFRUVuH37dq0x//3vf6t87u+//64T8+zn3L59Gw8ePGj0/7wyMzNRXFyM4OBgNGnSBE2aNMHRo0eRkJCAJk2a6Pxf1dN4naVp06YN/Pz8dI716NED+fn5APjvs77Mnz8f77//Pl555RUEBgZi8uTJmDdvHuLj4wHwOhuKuV3X6mKKi4sBVL2LVRsWREbQtGlTBAcHIzU1Ved4amoq+vXrZ6KszIsgCJg9ezZ27tyJQ4cOVbnF2alTJ3h6eupcw4qKChw9elR7DYODg2FnZ6cTo1KpkJubq41RKBRQq9U4efKkNubEiRNQq9U6Mbm5uVCpVNqYAwcOwN7eHsHBwfr/8kY0dOhQnDt3Djk5OdpH7969MWnSJOTk5KBz5868znrQv3//KstG/Prrr/D29gbAf5/15d69e7Cx0f0Zs7W11U6753U2DHO7rgqFAmlpaTpT8Q8cOAAvL68qrbRaiR5+TQ3yZNp9YmKicP78eWHu3LlCixYthOvXr5s6NbMwc+ZMQS6XC0eOHBFUKpX2ce/ePW3MJ598IsjlcmHnzp3CuXPnhFdffbXaaZ7t2rUTDh48KGRlZQlDhgypdprnc889JyiVSkGpVAqBgYHVTvMcOnSokJWVJRw8eFBo165do50+W5enZ5kJAq+zPpw8eVJo0qSJ8PHHHwuXLl0SvvnmG6F58+bCli1btDG8zg0XExMjtG3bVjvtfufOnUKrVq2EP/7xj9oYXuf6KS0tFbKzs4Xs7GwBgPDXv/5VyM7O1i4VY07XtaSkRPDw8BBeffVV4dy5c8LOnTsFZ2dnTrs3Z2vWrBG8vb2Fpk2bCkFBQdop5fR4Wmd1j02bNmljKisrhT//+c+Cp6enYG9vLwwcOFA4d+6cznnKysqE2bNnC66urkKzZs2EMWPGCPn5+ToxN2/eFCZNmiQ4OTkJTk5OwqRJk4Tbt2/rxOTl5QmjR48WmjVrJri6ugqzZ8/WmdJpSZ4tiHid9eP7778XAgICBHt7e8HX11fYsGGDzuu8zg2n0WiEOXPmCB06dBAcHByEzp07CwsXLhTKy8u1MbzO9XP48OFq/5scExMjCIL5XdezZ88KAwYMEOzt7QVPT09hyZIlkqbcC4IgcLd7IiIisnocQ0RERERWjwURERERWT0WRERERGT1WBARERGR1WNBRERERFaPBRERERFZPRZEREREZPVYEBEREZHVY0FERGZjyZIleP755032+YsXL8abb75p0M9477338O677xr0M4hIOq5UTURGIZPJan09JiYGq1evRnl5Odzc3IyU1f/897//RdeuXXH27FlpG0JKVFxcDB8fH5w9e7bKJsZEZDosiIjIKIqKirR/3r59Oz744AOdHeGbNWsGuVxuitQAAMuXL8fRo0fx448/1vscDx48gJ2dXZ1xUVFR6NKlC1asWFHvzyIi/WLLjIiMwtPTU/uQy+WQyWRVjj3bMps6dSrGjRuH5cuXw8PDAy1btsTSpUvx8OFDzJ8/H66urmjXrh02btyo81mFhYWIjo6Gi4sL3NzcEBkZievXr9ea37Zt2/Diiy9qn2/evBlubm4oLy/XiYuKisKUKVMA/K/Ft3HjRnTu3Bn29vYQBAHfffcdAgMD0axZM7i5uWHYsGG4e/eu9hwvvvgikpOT63klicgQWBARkVk7dOgQfvvtN6SlpeGvf/0rlixZgjFjxsDFxQUnTpzAjBkzMGPGDBQUFAAA7t27h7CwMDg6OiItLQ3p6elwdHTEyJEjUVFRUe1n3L59G7m5uejdu7f22Msvv4xHjx5h79692mM3btzAP//5T7z++uvaY5cvX8aOHTuQkpKCnJwcFBUV4dVXX8Ubb7yBCxcu4MiRIxg/fjyevhnft29fFBQUIC8vT9+Xi4jqiQUREZk1V1dXJCQkoHv37njjjTfQvXt33Lt3D3/605/QtWtXxMXFoWnTpjh+/DiAx3d6bGxs8I9//AOBgYHo0aMHNm3ahPz8fBw5cqTaz8jLy4MgCPDy8tIea9asGSZOnIhNmzZpj33zzTdo164dBg8erD1WUVGBr7/+Gr169cJzzz0HlUqFhw8fYvz48ejYsSMCAwPx9ttvw9HRUfuetm3bAkCdd62IyHhYEBGRWfP394eNzf/+U+Xh4YHAwEDtc1tbW7i5uaG4uBgAkJmZicuXL8PJyQmOjo5wdHSEq6sr7t+/jytXrlT7GWVlZQAABwcHnePTp0/HgQMHUFhYCADYtGkTpk6dqjNA3NvbG61bt9Y+79mzJ4YOHYrAwEC8/PLL+PLLL3H79m2d8zZr1gzA47tZRGQempg6ASKi2jw7SFkmk1V7rLKyEgBQWVmJ4OBgfPPNN1XO9XTh8rRWrVoBeNw6ezqmV69e6NmzJzZv3owRI0bg3Llz+P7773Xe26JFC53ntra2SE1NRUZGBg4cOIAvvvgCCxcuxIkTJ7Szym7dulVrPkRkfLxDREQWJSgoCJcuXYK7uzu6dOmi86hpFpuPjw+cnZ1x/vz5Kq/9v//3/7Bp0yZs3LgRw4YNQ/v27evMQSaToX///li6dCmys7PRtGlT7Nq1S/t6bm4u7Ozs4O/vX/8vSkR6xYKIiCzKpEmT0KpVK0RGRuLYsWO4du0ajh49ijlz5uA///lPte+xsbHBsGHDkJ6eXu35CgsL8eWXX+KNN96o8/NPnDiB5cuX4/Tp08jPz8fOnTvx+++/o0ePHtqYY8eOYcCAAdrWGRGZHgsiIrIozZs3R1paGjp06IDx48ejR48eeOONN1BWVgZnZ+ca3/fmm29i27Zt2tbbE87OzoiKioKjoyPGjRtX5+c7OzsjLS0No0aNQrdu3bBo0SJ89tlniIiI0MYkJydj+vTp9f6ORKR/XJiRiAiAIAgIDQ3F3Llz8eqrr+q8Nnz4cPTo0QMJCQkN/pwffvgB8+fPx9mzZ9GkCYdxEpkL3iEiIsLjcT8bNmzAw4cPtcdu3bqFbdu24dChQ5g1a5ZePufu3bvYtGkTiyEiM8M7RERENejYsSNu376NxYsX47333jN1OkRkQCyIiIiIyOqxZUZERERWjwURERERWT0WRERERGT1WBARERGR1WNBRERERFaPBRERERFZPRZEREREZPVYEBEREZHVY0FEREREVu//AzCHJNxGQLTvAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -269,18 +267,15 @@
    "source": [
     "sim = rebound.Simulation()\n",
     "\n",
-    "sim.units = ('yr', 'AU', 'Msun') #changes simulation and G to units of solar masses, years, and AU  \n",
-    "sim.integrator = \"whfast\" #integrator for sim\n",
-    "sim.dt = .05 #timestep for sim\n",
+    "sim.units = ('yr', 'AU', 'Msun')\n",
+    "sim.integrator = \"whfast\"\n",
+    "sim.dt = .05\n",
     "\n",
-    "sim.add(m=1) #Adds Sun \n",
-    "sim.add(a=.5, f=0, Omega=0, omega=0, e=0, inc=0, m=0) #adds test particle \n",
-    "sim.add(a=.75, f=0, Omega=0, omega=0, e=0, inc=0, m=0) #adds a second test particle\n",
-    "\n",
-    "#Moves all particles to center of momentum frame\n",
+    "sim.add(m=1) \n",
+    "sim.add(a=.5, f=0, Omega=0, omega=0, e=0, inc=0, m=0)\n",
+    "sim.add(a=.75, f=0, Omega=0, omega=0, e=0, inc=0, m=0)\n",
     "sim.move_to_com()\n",
     "\n",
-    "#Gives orbital information before the simulation begins\n",
     "print(\"\\n***INITIAL ORBITS:***\")\n",
     "for orbit in sim.calculate_orbits():\n",
     "    print(orbit)"
@@ -303,20 +298,20 @@
     "rebx = reboundx.Extras(sim)\n",
     "yark = rebx.load_force(\"yarkovsky_effect\")\n",
     "\n",
-    "#Sets the parameters for the effect\n",
-    "yark.params[\"ye_c\"] = c.value\n",
-    "yark.params[\"ye_lstar\"] = lstar.value\n",
+    "# We set the effect properties on the effect object:\n",
+    "yark.params[\"ye_c\"] = c\n",
+    "yark.params[\"ye_lstar\"] = lstar\n",
     "\n",
-    "ps = sim.particles #simplifies way to access particles parameters \n",
-    "ps[1].params[\"ye_flag\"] = 1 #setting this flag to 1 will give us the outward version of the effect \n",
-    "ps[1].params[\"ye_body_density\"] = density.value\n",
+    "ps = sim.particles\n",
+    "ps[1].params[\"ye_flag\"] = 1 # setting this flag to 1 will give us the outward version of the simple effect \n",
+    "ps[1].params[\"ye_body_density\"] = density\n",
     "ps[1].params[\"ye_albedo\"] = albedo\n",
-    "ps[1].r = radius.value #remember radius is not inputed as a Rebx parameter - it's inputed on the particle in the Rebound sim\n",
+    "ps[1].r = radius            # remember radius is not inputed as a Rebx parameter\n",
     "\n",
-    "ps[2].params[\"ye_flag\"] = -1 #setting this flag to -1 will give us the inward version of the effect \n",
-    "ps[2].params[\"ye_body_density\"] = density.value\n",
+    "ps[2].params[\"ye_flag\"] = -1 # setting this flag to -1 will give us the inward version of the simple effect \n",
+    "ps[2].params[\"ye_body_density\"] = density\n",
     "ps[2].params[\"ye_albedo\"] = albedo\n",
-    "ps[2].r = radius.value \n",
+    "ps[2].r = radius\n",
     "\n",
     "rebx.add_force(yark) #adds the force to the simulation"
    ]
@@ -337,12 +332,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CHANGE IN SEMI-MAJOR AXIS(Asteroid 1): 4.179660319481027e-05 AU\n",
+      "CHANGE IN SEMI-MAJOR AXIS(Asteroid 1): 4.1780726372842025e-05 AU\n",
       "\n",
-      "CHANGE IN SEMI-MAJOR AXIS(Asteroid 2): -3.41278886567542e-05 AU\n",
+      "CHANGE IN SEMI-MAJOR AXIS(Asteroid 2): -3.4114924686634573e-05 AU\n",
       "\n",
-      "CPU times: user 1.3 s, sys: 3.65 ms, total: 1.31 s\n",
-      "Wall time: 881 ms\n"
+      "CPU times: user 1.32 s, sys: 3.54 ms, total: 1.32 s\n",
+      "Wall time: 892 ms\n"
      ]
     }
    ],
@@ -374,7 +369,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x110e74c70>"
+       "<matplotlib.legend.Legend at 0x103f73e20>"
       ]
      },
      "execution_count": 10,
@@ -383,7 +378,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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cjiFDhsDFxQXW1tZo1qwZoqOjAUDxVvGwsDBIJBJ07dpVcVx0dDQCAgJgZWWFFi1aYPXq1YrPqjpAO3fuRNeuXWFlZYWtW7eioqICH330ETw8PGBpaYlWrVrhwIEDNY57tHP0008/oXnz5rC2tka3bt2Qmpr61J83ODgYu3fvxssvvww/Pz90794dn3zyCX744QeUlZWpdxGJiDQoMUOOBfuS8cKnh9H/y1NqBRxvJ2tF12bt8DYMOEpgJ0dscVuAH6YAQgUgMQNe/gJoPUyjp9yxYwf8/f3h7++PoUOHYtKkSZgzZw4kEgkAYM6cOUhJScHPP/+Mhg0b4tq1aygurvwX7Ny5c2jbti0OHTqEoKAg1K9fHwCwfv16zJs3D6tWrUJYWBji4+MxZswY2NraYvjw4Ypzz5o1C8uWLUN0dDQsLS3xxRdfYNmyZVi7di3CwsKwadMm9OvXD5cuXUKzZs1q1J6RkYEBAwZg3LhxGD9+PC5cuIAZM2aofA2q3mdSrx7/kSYi/SFG1+Y5rwaY/XKAyd3+LQb+RhBTQeY/AQeo/N8fpgJ+PQCpu8ZOu3HjRgwdOhQA0Lt3b/z11184fPgwIiIiAADp6ekICwtDmzZtAABNmjRRHOvi4gIAcHZ2rjaNtHDhQixbtgwDBgwAUNnxSUlJwdq1a6uFnKlTpyr2AYDPPvsMs2bNwqBBgwAAS5YswdGjR7FixQp8+eWXNWpfs2YNfH19sXz5ckgkEvj7+yMpKQlLlixR+uf/888/sXDhQowdO1bpY4iINCUxQ4698ZmISbmNW/kP1BrDmF+1oE0MOWLKu/5PwKkilAN5NzQWcq5evYpz585hz549AIB69erh9ddfx6ZNmxQhZ/z48Rg4cCDi4uLQs2dPREVFoUOHDk8c886dO8jIyMDo0aMxZswYxfaysjJIpdJq+1YFJ6DyLbFZWVno2LFjtX06duyIxMTEWs91+fJltGvXTtF1AoD27dsr+dNXnrNv374IDAzEvHnzlD6OiEhs7NroH4YcMTn5VU5RPRp0JOaAk6/GTrlx40aUlZXB3f2fECUIAiwsLCCXy9GgQQNERkYiLS0NP/74Iw4dOoQePXpgwoQJ+Oyzz2ods6Kisv7169fj+eefr/bZ4297t7W1rXH8o4Glqp7Htz36mbru3buH3r17w87ODt99990zFz0TEYlNjPdIOdnUw/Se/uzaaABDjpik7pVrcH6YWtnBkZgDL6/QWBenrKwMW7ZswbJly9CzZ89qnw0cOBBff/01Jk6cCKByWmrEiBEYMWIEOnXqhJkzZ+Kzzz5TrMEpLy9XHOvq6gp3d3fcuHEDQ4YMUboeBwcHyGQynDx5Ep07d1ZsP3XqFNq2bVvrMYGBgdi7d2+1bWfOnHnmuQoLC9GrVy9YWlpi3759sLKyUrpOIqK6EuOhfTKpFRZGBaFHQN3uOKUnY8gRW+thlWtw8m5UdnA0uBZn//79kMvlGD16dI1ppP/7v//Dxo0bMXHiRMydOxfh4eEICgpCSUkJ9u/fj4CAAABAo0aNYG1tjQMHDsDDwwNWVlaQSqWYP38+Jk+eDAcHB0RGRqKkpAQXLlyAXC7H9OnTn1jTzJkzMW/ePPj5+aFVq1aIjo5GQkICvv7661r3HzduHJYtW4bp06dj7NixiI2NrXF32OPu3buHnj174v79+9i6dSsKCwtRWFgIoDLMPd5tIiISgxhdG0crcwwI90T/VjJOSWkBQ44mSN01Gm6qbNy4ERERETUCDlDZyVm0aBHi4uJQv359vP/++0hNTYW1tTU6deqE7du3A6hcw7Ny5Up89NFHmDt3Ljp16oRjx47hrbfego2NDZYuXYp//etfsLW1RUhICKZOnfrUmiZPnozCwkLMmDEDubm5CAwMxL59+2q9swoAvLy8sHv3bkybNg2rV69G27ZtsWjRIowaNeqJ54iNjcXZs2cBAE2bNq322c2bN6strCYiqqvsgmJ8+vNlfJ+QrfYY7NrohkSoy6IIA1NYWAipVKq43fhRDx48wM2bN+Hj48OpDyPEP18iUtXhyzlYfvB3JGffU+t4dm3E87Tf30/DTg4REdHfqqakVhy6hryiUrXGYNdGfzDkEBGRyatr1wYA/Fxs8flroeza6BGGHCIiMklidG3cHa3QK8iNU1J6iiGHiIhMihi3f7NrYxgYch5jQuuwTQr/XIlMmxi3fwNARz9n/Ku3P8ONgWDI+VvV03Lv378Pa2s+cdLYlJZWtqL5DB0i05JdUIx53yfjYEqu2mPIHK3wTlc/PpHYADHk/M3c3ByOjo7Iza38F8HGxuaJryIgw1JRUYE7d+7AxsaGbyknMgFVXZt9Cdm4kCZXexx2bQwf/4v/iKq3cFcFHTIeZmZm8PLyYnAlMmLs2tDjGHIeIZFI0LhxYzRq1AgPHz7UdTkkovr168PMzEzXZRCRBoixkJhdG+PEkFMLc3Nzrt0gItJj2QXF2BWbga1n0nG7sETtcaJCZZjVpwW7NkaKIYeIiAyGGF2bNt6OiApz55SUCWDIISIivVa1kHjdiRu4JX+g9ji9AlwxPyqIwcaEMOQQEZFeYteG6oohh4iI9EpihhyTt8UjLa9Y7THYtSGAIYeIiPRAYoYce+MzEZNyG7fy1ZuS8nayxpjOvuzakAJDDhER6YwYXZvnvBpg9ssBvP2bamDIISIirRKja+NkUw/Te/qza0NPxZBDRERakZghx4ydibh2p0jtMYJlDpj2YjP0CHATsTIyVgYTctasWYM1a9YgNTUVABAUFIS5c+ciMjJSt4UREdETifH2b3ZtSF0GE3I8PDzw6aefomnTpgCA//73v+jfvz/i4+MRFBSk4+qIiOhRYtz+LZNaYWFUELs2pDaJIAiCrotQl5OTE5YuXYrRo0crtX9hYSGkUikKCgrg4OCg4eqIiEyLGF0bRytzDAj3RP9WMi4kJgV1f38bTCfnUeXl5fj2229RVFSE9u3b67ocIiKTxq4N6SuDCjlJSUlo3749Hjx4ADs7O3z33XcIDAx84v4lJSUoKfnnxW2FhYXaKJOIyCQcvpyDOXuTkVWg/gsy+fZv0iSDCjn+/v5ISEhAfn4+du/ejeHDh+P48eNPDDqLFy/GggULtFwlEZHxqpqSWnHoGvKKStUagw/tI20x6DU5ERER8PPzw9q1a2v9vLZOjqenJ9fkEBGp6PDlHCw/+DuSs++pPYafiy0+fy2UXRtSmUmtyakiCEK1EPM4S0tLWFpaarEiIiLjIUbXxt3RCr2C3LiQmHTCYELOBx98gMjISHh6euLevXvYvn07jh07hgMHDui6NCIio5KYIcfC/Sm4kJav9hjs2pA+MJiQc/v2bbz55pvIzs6GVCpFy5YtceDAAbz44ou6Lo2IyOCJcfs3uzakbwwm5GzcuFHXJRARGZ3sgmLM+z4ZB1Ny1R7Dy8ka/xkcxmBDesdgQg4REYmjqmuzLyEbF9Lkao/D279J3zHkEBGZCDG6NjJHK7zT1Y+3f5NBYMghIjJi7NqQKWPIISIyQtkFxfj058v4PiFb7TFc7S0xrIM3BrT2YNeGDFKdQk5JSQmfQ0NEpEfEeI8UuzZkLFQKOb/88gu2bduGX3/9Fenp6aioqICNjQ1at26Nnj17YuTIkZDJZJqqlYiIalE1JbXuxA3ckj9Qe5xeAa6YHxXErg0ZDaVe67B3717MmjULBQUF6NOnD9q2bQt3d3dYW1sjLy8PycnJ+PXXX3H69GmMGDECCxcuhIuLizbqV4m6j4UmItJHYnRt2ng7IirMnQuJSa+p+/tbqZDTtm1bzJkzB3379oWZmdkT98vMzMQXX3wBV1dXzJgxQ+kitIUhh4gMHbs2ZIo0GnKMBUMOERkqMbo2fPs3GSqTfEEnEZGxS8yQY8bORFy7U6T2GFxITKZK6ZAzatSoWrdLpVL4+/tj6NChsLOzE60wIiJTlZghx974TMSk3MatfPWmpJxs6mF6T392bcikKT1d9corr9S6PT8/H5cuXYKFhQV+/fVX+Pr6ilqgmDhdRUT6TIyuTbDMAdNebIYeAW4iVkakWzpdk1NcXIxhw4ZBIpFg586ddR1OYxhyiEjfiPH2b3ZtyNjpdE2OtbU1Zs2ahQEDBogxHBGR0RNjITG7NkRPJ9rCYycnJ+Tn54s1HBGRUTp8OQdz9iYjq6BE7TGiQmWY1acFuzZEzyBayDl16hT8/PzEGo6IyGhULSTeG58JeXGZWmPw9m8i1Skdci5evFjr9oKCApw/fx6LFi3Cxx9/LFphRESGToyuDW//JlKf0iGnVatWkEgkqG2dsouLC2bNmoWxY8eKWhwRkaGpWki84tA15BWVqjUGuzZE4lA65Ny8ebPW7VKpFI6OjmLVQ0RkkBIz5Fi4PwUX0vLVHsPPxRafvxbKrg2RSJQOOd7e3k/9vLy8HD/88AOioqLqWhMRkUEQ4/Zvd0cr9ApyQ/9WMoYbIpHVeeHxlStXsGnTJvz3v/+FXC5Haal67VkiIkMhRtfGy8ka/xkcxmBDpEFqhZyioiLs2LEDGzduxJkzZ9CtWzd88skn7OIQkdFi14bI8KgUck6fPo0NGzZg586daNasGYYMGYKzZ89i5cqVCAwM1FSNREQ6k11QjHnfJ+NgSq7aY7BrQ6QbSoecwMBA3L9/H2+88QbOnj2rCDXvvfeexoojItKFqq7NvoRsXEiTqz0Ob/8m0i2lQ861a9cwaNAgdOvWDQEBAZqsiYhIJ7ILivHpz5fxfUK22mO42ltiWAdvDGjtwdu/iXRMpVvIN2/ejPHjx6O4uBiDBw/GkCFDIJFINFkfEZFGsWtDZLzUegv5kSNHsGnTJuzZswcPHjzAu+++i7feegvNmzfXRI2i4VvIiagKuzZEhkPd399qhZwqBQUF+Prrr7Fp0ybExcUhODj4ia9/0AcMOUQkxtu/2bUh0i51f3/X6Tk5UqkU77zzDt555x0kJCRg06ZNdRmOiEgjqqak1p24gVvyB2qP0yvAFfOjgti1ITIQderkGBp2cohMixhdG75Hikj3NNrJ6d27N+bOnYsOHTo8db979+5h9erVsLOzw4QJE5QugohILGI8tA/glBSRMVAq5Lz66qt47bXXYG9vj379+qFNmzaQyWSwsrKCXC5HSkoKTp48iZ9++gkvvfQSli5dqum6iYiqYdeGiB6n9HRVaWkpdu3ahR07duDXX39Ffn5+5QASCQIDA9GrVy+MGTMG/v7+mqy3TjhdRWR8EjPkmLEzEdfuFKk9Brs2RPpN63dXFRQUoLi4GM7OzrCwsFBnCK1jyCEyDokZcuyNz0RMym3cyldvIbGTTT1M7+nPrg2RAdD63VVSqRRSqVTdw1W2ePFi7NmzB1euXIG1tTU6dOiAJUuW6HXniIjEJUbXJljmgGkvNkOPADcRKyMifVSnW8i16fjx45gwYQKee+45lJWV4cMPP0TPnj2RkpICW1tbXZdHRBqSXVCMXbEZ2HomHbcLS9QeJypUhll9WrBrQ2RCDPYW8jt37qBRo0Y4fvw4OnfurNQxnK4iMhxiLCRu4+2IqDB3TkkRGTidPAxQlwoKCgAATk5OT9ynpKQEJSX//M2vsLBQ43URUd0cvpyDOXuTkVXArg0R1Y1BhhxBEDB9+nS88MILCA4OfuJ+ixcvxoIFC7RYGRGpo2oh8d74TMiLy9Qag7d/E9HjVJ6uysjIgEQigYeHBwDg3Llz+OabbxAYGIi3335bI0U+bsKECfjxxx9x8uRJRR21qa2T4+npyekqIj0hRteGt38TGT+tTVe98cYbePvtt/Hmm28iJycHL774IoKCgrB161bk5ORg7ty5qg6pkkmTJmHfvn04ceLEUwMOAFhaWsLS0lKj9RCRati1ISJtUTnkJCcno23btgCAnTt3Ijg4GL/99hsOHjyIcePGaSzkCIKASZMm4bvvvsOxY8fg4+OjkfMQkWYkZsgxeVs80vKK1R7jOa8GmP1yALs2RKQUlUPOw4cPFd2RQ4cOoV+/fgCAFi1aIDs7W9zqHjFhwgR88803+P7772Fvb4+cnBwAlc/rsbbm3+SI9JEY75Fi14aI1KVyyAkKCsJXX32Fvn37IiYmBgsXLgQAZGVlwdnZWfQCq6xZswYA0LVr12rbo6OjMWLECI2dl4hUl5ghx8L9KbiQlq/2GF5O1vjP4DB2bYhIbSqHnCVLluCVV17B0qVLMXz4cISGhgIA9u3bp5jG0gQDfZwPkckQo2vj7miFXkFu6N9KxnBDRHWm1sMAy8vLUVhYiAYN/vmPUGpqKmxsbNCoUSNRCxQTHwZIJD4xHtrHrg0RPY1WHwZobm5eLeAAQJMmTdQZiogMkBhdG0crcwwI92TXhog0RqmQ07p1axw+fBgNGjRAWFgYJBLJE/eNi4sTrTgi0i/ZBcX49OfL+D5B/ZsMZFIrLIwK4gsyiUjjlAo5/fv3V9xRFRUVpcl6iEjPVHVt9iVk40KaXO1x+NA+ItI2g31Bpzq4JodIeWJ0bVztLTGsgzcGtPbg7d9EpDatrck5dOgQIiIiav1s7dq1GDt2rKpDEpEeOXw5B8sP/o7k7Htqj+HnYovPXwtl14aIdErlTo6lpSUmTpyIxYsXo379+gCAO3fuYNSoUfjtt9+Ql6f+HRaaxk4OUe2qpqRWHLqGvKJStcbg7d9EpCla6+ScOHECb775Jg4dOoRvvvkGqampGDVqFAIDA5GYmKjqcESkQ2Lc/s2uDRHpK5VDzvPPP4/4+HiMGzcO4eHhqKiowMcff4yZM2c+9a4rItIPYtz+DXAhMRHpP7Wek3P16lWcP38eHh4eyMrKwpUrV3D//n3Y2tqKXR8RiUSMrg3fI0VEhsRM1QM+/fRTtG/fHi+++CKSk5Nx/vx5xMfHo2XLljh9+rQmaiQiNWUXFGPL6Zvo8u+j6P/lKbUDTkc/Z3w/oQOO/6s7hrZrwoBDRAZB5U7OF198gb179yIyMhJA5Qs7z507hw8++ABdu3ZFSUmJ6EUSkWqyC4ox7/tkHEzJVXsMmaMV3unqx64NERkslUNOUlISGjZsWG2bhYUFli5dipdeekm0wohIdWJMSXGtDREZC5VDzuMB51FdunSpUzFEpLrsgmLsis3A1jPpuF2ofic1KlSGWX1asGtDREZDrYXH58+fx7fffov09HSUllZ/psaePXtEKYyInk6Mrk0bb0dEhblzSoqIjJLKIWf79u0YNmwYevbsiZiYGPTs2RN//PEHcnJy8Morr2iiRiJ6xOHLOZizNxlZBezaEBE9jcohZ9GiRVi+fDkmTJgAe3t7fPHFF/Dx8cHYsWPRuHFjTdRIZPISM+TYG5+JvfGZkBeXqTUGuzZEZGpUDjnXr19H3759AVS+4qGoqAgSiQTTpk1D9+7dsWDBAtGLJDJViRlyTN4Wj7S8YrXH6BXgivlRQQw2RGRyVA45Tk5OuHev8sV97u7uSE5ORkhICPLz83H/vvpPTyWiSlVdm5iU27iV/0CtMfjQPiIiNUJOp06dEBMTg5CQELz22muYMmUKjhw5gpiYGPTo0UMTNRKZBDG6Ns95NcDslwN4+zcREdQIOatWrcKDB5V/u3z//fdhYWGBkydPYsCAAZgzZ47oBRIZMzHeI8WuDRFR7SSCIAi6LkJb1H1VO5HYxLj928vJGv8ZHMauDREZPXV/f6v1nBwiUp0YXRtHK3MMCPdE/1YyhhsiomdgyCHSMDG6NjKpFRZGBaFHgJuIlRERGTeGHCINYNeGiEj3GHKIRJRdUIxPf76M7xOy1R6DXRsiInGoFHLKyspgZWWFhIQEBAcHa6omIoNz+HIOlh/8HcnZ99Qew8/FFp+/FsquDRGRSFQKOfXq1YO3tzfKy8s1VQ+Rwaiaklpx6BryikqffUAt3B2t0CvIjVNSREQaoPJ01ezZs/H+++9j69atcHJy0kRNRHqNXRsiIsOgcshZuXIlrl27BplMBm9vb9ja2lb7PC4uTrTiiPQFuzZERIZH5ZATFRWlgTKI9JMYt3+za0NEpBt84jHRY8S4/RsAOvo541+9/RluiIjqSOtPPI6NjcXly5chkUgQGBiIsLAwdYci0gvZBcWY930yDqbkqj2GzNEK73T143ukiIj0gMohJzc3F4MGDcKxY8fg6OgIQRBQUFCAbt26Yfv27XBxcdFEnQCAEydOYOnSpYiNjUV2dja+++47Tp9RnVR1bfYlZONCmlztcdi1ISLSPyqHnEmTJqGwsBCXLl1CQEAAACAlJQXDhw/H5MmTsW3bNtGLrFJUVITQ0FCMHDkSAwcO1Nh5yPixa0NEZPxUDjkHDhzAoUOHFAEHAAIDA/Hll1+iZ8+eohb3uMjISERGRmr0HGTcxFhIzK4NEZFhUDnkVFRUwMLCosZ2CwsLVFRUiFIUkZiyC4qxKzYDW8+k43ZhidrjRIXKMKtPC3ZtiIgMhMohp3v37pgyZQq2bdsGmUwGAMjMzMS0adPQo0cP0Qusi5KSEpSU/PNLrbCwUIfVkLaJ0bVp4+2IqDB3TkkRERkglUPOqlWr0L9/fzRp0gSenp6QSCRIT09HSEgItm7dqoka1bZ48WIsWLBA12WQFlUtJF534gZuyR+oPU6vAFfMjwpisCEiMmBqPycnJiYGV65cgSAICAwMREREhNi1PZVEInnm3VW1dXI8PT35nBwjxK4NEZHx0vpzcl588UW8+OKL6h6uFZaWlrC0tNR1GaRBiRlyTN4Wj7S8YrXHYNeGiMg4KRVyVq5cibfffhtWVlZYuXLlU/e1s7NDUFAQnn/+eVEKfNRff/2Fa9euKb6/efMmEhIS4OTkBC8vL9HPR/opMUOOvfGZiEm5jVv56k1JeTtZY0xnX3ZtiIiMmFLTVT4+Prhw4QKcnZ3h4+Pz1H1LSkqQm5uLadOmYenSpaIVCgDHjh1Dt27damwfPnw4Nm/e/Mzj+VoHwyZG1+Y5rwaY/XIAb/8mIjIg6v7+1si7q2JiYvDGG2/gzp07Yg9dJww5hkeMro2TTT1M7+nPrg0RkYHS+pqcp3nhhRcwe/ZsTQxNJiIxQ44ZOxNx7U6R2mMEyxww7cVm6BHgJmJlRERkKNTq5BQVFeH48eNIT09HaWlptc8mT54sWnFiYydHv4nx9m92bYiIjI/WOjnx8fHo06cP7t+/j6KiIjg5OeHu3buwsbFBo0aN9DrkkH4S4/ZvmdQKC6OC2LUhIiIFlUPOtGnT8PLLL2PNmjVwdHTEmTNnYGFhgaFDh2LKlCmaqJGM1OHLOZizNxlZBeq9asHRyhwDwj3Rv5WMC4mJiKgGlUNOQkIC1q5dC3Nzc5ibm6OkpAS+vr7497//jeHDh2PAgAGaqJOMRNVC4r3xmZAXl6k1Brs2RESkDJVDjoWFBSQSCQDA1dUV6enpCAgIgFQqRXp6uugFknGoa9cG4Nu/iYhINSqHnLCwMFy4cAHNmzdHt27dMHfuXNy9exf/+9//EBISookayUBVLSRecega8opKn31ALfjQPiIiUpfKIWfRokW4d+8eAGDhwoUYPnw4xo8fj6ZNmyI6Olr0AsnwHL6cg+UHf0dy9j21x/BzscXnr4Wya0NERGrTyMMA9RVvIdccMbo27o5W6BXkxoXERERUjV49DJBMR2KGHAv3p+BCWr7aY3g5WeM/g8MYbIiISFQqh5w///wTc+fOxdGjR5Gbm4uKiopqn+flqf+sEzIMYjy0j10bIiLSNJVDztChQ3H9+nWMHj0arq6uijutyPhlFxRj3vfJOJiSq/YY7NoQEZG2qBxyTp48iZMnTyI0NFQT9ZCeqera7EvIxoU0udrj8PZvIiLSNpVDTosWLVBcXKyJWkiPiNG1kTla4Z2ufrz9m4iIdELlkLN69Wq89957mDt3LoKDg2FhYVHtc961ZNjEeI8UuzZERKQPVA45jo6OKCgoQPfu3attFwQBEokE5eXlohVH2lE1JbXuxA3ckj9QawxXe0sM6+CNAa092LUhIiK9oHLIGTJkCOrXr49vvvmGC48NHLs2RERkzFQOOcnJyYiPj4e/v78m6iENE6NrAwC9AlwxPyqIXRsiItJbKoecNm3aICMjgyHHwIjRtWnj7YioMHcuJCYiIoOgcsiZNGkSpkyZgpkzZyIkJKTGwuOWLVuKVhzVXWKGHJO3xSMtT/074ti1ISIiQ6Tyu6vMzMxqDiKRGMTCY1N5d1Vihhx74zMRk3Ibt/LVm5JysqmH6T392bUhIiKd09q7q27evKnqIaQliRlyzNiZiGt3itQeI1jmgGkvNkOPADcRKyMiItI+lUOOt7e3JuogNbFrQ0REVDulQs6+ffsQGRkJCwsL7Nu376n79uvXT5TC6OnYtSEiIno6pdbkmJmZIScnB40aNap1TY5iMK7J0bjDl3MwZ28ysgpK1DqeXRsiIjI0Gl2TU1FRUev/J+2ompLaG58JeXGZWmPIpFZYGBXErg0REZkMldfkkPbUtWsD8InERERkutQKOefOncOxY8eQm5tbo7Pz+eefi1KYqRKja+PtZI0xnX05JUVERCZN5ZCzaNEizJ49G/7+/jXeXcX3WKmPXRsiIiJxqRxyvvjiC2zatAkjRozQQDmmpeo9UisOXUNeUalaY7BrQ0REVDuVQ46ZmRk6duyoiVpMRmKGHAv3p+BCWr7aY3g5WeM/g8PYtSEiInoClUPOtGnT8OWXX2LFihUaKMd4VXVtNv6airS8+2qN4e5ohV5BbujfSsZwQ0RE9Awqh5x3330Xffv2hZ+fHwIDA2u8oHPPnj2iFWcM2LUhIiLSDbXeQn706FF069YNzs7OWl9svHr1aixduhTZ2dkICgrCihUr0KlTJ63W8Czs2hAREemeyiFny5Yt2L17N/r27auJep5qx44dmDp1KlavXo2OHTti7dq1iIyMREpKCry8vLRez+OyC4ox7/tkHEzJVXsMdm2IiIjEodRrHR7l7e2NX375BS1atNBUTU/0/PPPo3Xr1lizZo1iW0BAAKKiorB48eJnHq+p1zrcvnUdR/dtQUVmAgAByYIPjlSEIwfOSo/B27+JiIhqp9HXOjxq/vz5mDdvHqKjo2FjY6Pq4WorLS1FbGws3nvvvWrbe/bsiVOnTmmtjsed270CbS7OwyAJAMXypOMQhM34tSIISx++jiQ0rfVYV3tLDOvgjQGtPXj7NxERkchUDjkrV67E9evX4erqiiZNmtRYeBwXFydacY+6e/cuysvL4erqWm27q6srcnJyaj2mpKQEJSX/PFyvsLBQ1Jpu37qO8IvzYFbLsiSJBOhsfgmdzObiVkUDXBT88G1ZFxxDOLs2REREWqByyImKitJAGcp7fKGzIAhPXPy8ePFiLFiwQGO13ElLgesz1l1LJICnuRyeuIA+9S6gzNIRFh6DAcmrAMI1VhsREZGpU3lNjq6UlpbCxsYG3377LV555RXF9ilTpiAhIQHHjx+vcUxtnRxPT0/R1uTcvnUdDde3hrm6N5jZuAD+vYHwkYAHAw8REVFt1F2TY6bOyfLz87Fhwwa8//77yMvLA1A5TZWZmanOcEqpX78+wsPDERMTU217TEwMOnToUOsxlpaWcHBwqPYlJlcPP8S2XIAKdWPi/TtA/P+ADd2Bz1oA298Erh4QtUYiIiJTpXIn5+LFi4iIiIBUKkVqaiquXr0KX19fzJkzB2lpadiyZYumasWOHTvw5ptv4quvvkL79u2xbt06rF+/HpcuXYK3t/czj9fk3VU3ftuFZte3wLn0Fur85CCrBkDoICDkVXZ4iIjI5Kn7+1vlkBMREYHWrVvj3//+N+zt7ZGYmAhfX1+cOnUKb7zxBlJTU1WtXSWrV6/Gv//9b2RnZyM4OBjLly9H586dlTpWUyGnmluxQPK3wOUfgYL0uo/HKS0iIjJxWgs5UqkUcXFx8PPzqxZy0tLS4O/vjwcPHqhcvLZoJeQ8qirwXPoeuJdV9/EcPIGAl9jhISIik6K15+RYWVnVeiv21atX4eLioupwxs0jvPKr96fidHgKM4Czayq/7GVAYH8GHiIioidQuZPz9ttv486dO9i5cyecnJxw8eJFmJubIyoqCp07d9brt5NrvZPzJLdigdho4PdfgCL1XwGhwCktIiIyYlqbriosLESfPn1w6dIl3Lt3DzKZDDk5OWjfvj1++ukn2Nraqly8tuhNyHkUp7SIiIieSmshp8qRI0cQFxeHiooKtG7dGhEREeoMo1V6GXIeJfaiZU5pERGREdB6yHlUfn4+HB0d6zqMxul9yHmU2FNadm5A2zFA6GBA6l738YiIiLREayFnyZIlaNKkCV5//XUAwGuvvYbdu3fDzc0NP/30E0JDQ1WrXIsMKuQ8qqrDk7gDKM6r+3iNwwCvduzwEBGRQdBayPH19cXWrVvRoUMHxMTE4LXXXsOOHTuwc+dOpKen4+DBgyoXry0GG3IedfUAkPANkHkeKBRhDQ+ntIiISM9pLeRYW1vj999/h6enJ6ZMmYIHDx5g7dq1+P333/H8889DLperXLy2GEXIeZTYU1pctExERHpIa8/JadCgATIyMuDp6YkDBw7g448/BlD5NvDy8nJVh6O6qHoODyDOlNajz+Fh4CEiIgOncsgZMGAA3njjDTRr1gx//vknIiMjAQAJCQlo2rSp6AWSkh598KAYU1qPBh6pF9BxCuAfyUXLRERkMFSernr48CG++OILZGRkYMSIEQgLCwMArFixAnZ2dnjrrbc0UqgYjG66Shm3YoGDHwDpZ8QZj4uWiYhIy3R6C7mhMMmQU6Ugs7LDkxUPXP1RnLu0OKVFRERawJCjBJMOOY8T+y4tPoeHiIg0hCFHCQw5T3ArFjjyEXDjmDjjcUqLiIhExJCjBIacZ+CUFhER6SGGHCUw5Kjo6gHg2GIgO0Gc8TilRUREatB6yCktLUVubi4qKiqqbffy8lJnOK1gyFFTVYcnaSeQwbu0iIhIu7QWcv744w+MGjUKp06dqrZdEARIJBK9fiAgQ44IHp3SunlcnLel8zk8RET0FFoLOR07dkS9evXw3nvvoXHjxpBIJNU+5ws6TYzYz+FxaVHZ3eGUFhER/U1rIcfW1haxsbFo0aKFykXqGkOOBnFKi4iINERr764KDAzE3bt3VT2MjJ3UHWg7uvJLrCmt7PjKL75agoiI1KByJ+fIkSOYPXs2Fi1ahJCQEFhYWFT7XJ87JOzk6AintIiIqA60Nl1lZmZWeeBja3G48JieSRPP4fH8ezqLHR4iIqOltZBz/Pjxp37epUsXVYbTKoYcPSP2c3gahwFhQxl4iIiMDB8GqASGHD1V1eGJ3wpkx4kzJhctExEZDY2GnIsXLyI4OBhmZma4ePHiU/dt2bKl0ifXNoYcA6CJKS1HH6DDRHZ4iIgMlEZDjpmZGXJyctCoUSOYmZlBIpGgtsO4JodExyktIiKTp9GQk5aWBi8vL0gkEqSlpT11X29vb6VPrm0MOQZME1NaXLRMRGQQuCZHCQw5RkITDx5k4CEi0lsMOUpgyDFCBZlAwnYgeRdwJ0WcMfkcHiIivcKQowSGHCPHDg8RkVFiyFECQ44JYeAhIjIaDDlKYMgxUZqY0nJrCXT7EPDvLc54RET0RFoNOfn5+di1axeuX7+OmTNnwsnJCXFxcXB1dYW7u/7+DZchhxQdntOrAPmNuo9n1QBo8RIga8UODxGRhmgt5Fy8eBERERGQSqVITU3F1atX4evrizlz5iAtLQ1btmxRuXhlfPLJJ/jxxx+RkJCA+vXrIz8/X+UxGHKomluxQPK3QNpZcZ+0zOfwEBGJSt3f32aqnmj69OkYMWIE/vjjD1hZWSm2R0ZG4sSJE6oOp7TS0lK8+uqrGD9+vMbOQSbGIxzo/Skw9igwLQXo8znQuHXdxsyOB36aASwPBL7qVNk1IiIinVC5kyOVShEXFwc/Pz/Y29sjMTERvr6+SEtLg7+/Px48eKCpWgEAmzdvxtSpU9nJIc3hlBYRkV5R9/d3PVVPZGVlhcLCwhrbr169ChcXF1WH06iSkhKUlJQovq+tbqIapO5A29GVX2JMaT2QAwn/q/z6aUblXVq9FvHFoUREGqZyyOnfvz8++ugj7Ny5E0Dl+6rS09Px3nvvYeDAgaIXWBeLFy/GggULdF0GGTKP8H/CSFWH57cVQEG6+mNmnAE2dAccPAHfruzwEBFpiMrTVYWFhejTpw8uXbqEe/fuQSaTIScnB+3bt8dPP/0EW1tbpceaP3/+M0PI+fPn0aZNG8X3qkxX1dbJ8fT05HQV1Z0mFi3zOTxERLXS+nNyjhw5gri4OFRUVKB169aIiIhQeYy7d+/i7t27T92nSZMm1RY4c00O6R1NPIeHU1pERAom8zBAhhzSa2JNaVXhlBYRkfYWHq9cubLW7RKJBFZWVmjatCk6d+4Mc3NzVYd+qvT0dOTl5SE9PR3l5eVISEgAADRt2hR2dnainotIbWIvWi7MqLlomVNaRERKUbmT4+Pjgzt37uD+/fto0KABBEFAfn4+bGxsYGdnh9zcXPj6+uLo0aPw9PQUrdARI0bgv//9b43tR48eRdeuXZUag50c0pmqKa3z64G/ssUZk1NaRGQitDZdtW3bNqxbtw4bNmyAn58fAODatWsYO3Ys3n77bXTs2BGDBg2Cm5sbdu3apdpPoWEMOaQXqjo8l3/klBYRkRK0FnL8/Pywe/dutGrVqtr2+Ph4DBw4EDdu3MCpU6cwcOBAZGeL9DdWkTDkkN7RxF1aPl2AHvPY4SEio6G1NTnZ2dkoKyursb2srAw5OTkAAJlMhnv37qk6NJHpefw5PGJMad08XvkcHrvGgMdzQNgQvi2diEySyu+u6tatG8aOHYv4+HjFtvj4eIwfPx7du3cHACQlJcHHx0e8KolMgdQd6DIDePcK8NYRoN14QOql/nh/ZQNX9gHbXgc+bQLsnQic21AZpoiITIDK01U5OTl48803cfjwYVhYWACo7OL06NED//vf/+Dq6oqjR4/i4cOH6Nmzp0aKVhenq8ggcUqLiEyc1p+Tc+XKFfz+++8QBAEtWrSAv7+/OsNoFUMOGTyx79LilBYRGQCTeRhgXTDkkFER+y4tqwZA6KDK5/Cww0NEekRrIae8vBybN2/G4cOHkZubi4qKimqfHzlyRJXhtIohh4yW2IHHxqWysxM+koGHiHROayFn4sSJ2Lx5M/r27YvGjRtDIpFU+3z58uWqDKdVDDlkEqoCz6XvgXtZdR+PU1pEpGNaCzkNGzbEli1b0KdPH5WL1DWGHDI5nNIiIiOgtZAjk8lw7NgxNG/eXOUidY0hh0wap7SIyEBpLeQsW7YMN27cwKpVq2pMVek7hhyiv4k9peXgCQS8xA4PEWmE1kLOK6+8gqNHj8LJyQlBQUGKZ+VU2bNnjyrDaRVDDlEtxO7w2MuAwP4MPEQkGq2FnJEjRz718+joaFWG0yqGHKJnuBULxEYDv/8CFOXWfTxOaRGRCPicHCUw5BCpgFNaRKQnGHKUwJBDpCZOaRGRDmk15OzatQs7d+5Eeno6SktLq30WFyfSu3U0gCGHSARiT2nZuQFtxwChgytfUkpE9Bh1f3+r/BbylStXYuTIkWjUqBHi4+PRtm1bODs748aNG4iMjFR1OCIyNB7hQP9VwMw//nlburWT+uP9lQMcWQgsDwTWdgV+fq8ySBER1ZHKnZwWLVpg3rx5GDx4MOzt7ZGYmAhfX1/MnTsXeXl5WLVqlaZqrTN2cog06OoBIOEbIPM8UCjCGh5OaRHR37Q2XWVjY4PLly/D29sbjRo1QkxMDEJDQ/HHH3+gXbt2+PPPP1UuXlsYcoi0ROwpLS5aJjJp6v7+rqfqidzc3PDnn3/C29sb3t7eOHPmDEJDQ3Hz5k2Y0BpmInoaj/B/wkjVouXEHUBxnnrjFWYAZ9dUfjHwEJGSVA453bt3xw8//IDWrVtj9OjRmDZtGnbt2oULFy5gwIABmqiRiAxZVeDp/ak4U1qPBh5OaRHRU6g8XVVRUYGKigrUq1eZj3bu3ImTJ0+iadOmGDduHOrXr6+RQsXA6SoiPXIrFjj4AZB+Rpzx2OEhMlp8To4SGHKI9FBBZmWHJyseuPqj+lNaj2LgITIqWg05+fn5OHfuHHJzc1FRUVHts2HDhqk6nNYw5BAZALHv0uJzeIgMntZCzg8//IAhQ4agqKgI9vb21d5ELpFIkJcnwt/CNIQhh8jA3IoFjnwE3DgmzniNwwCvduzwEBkYrYWc5s2bo0+fPli0aBFsbGxULlSXGHKIDBSntIhMmtZCjq2tLZKSkuDr66tykbrGkENkJDilRWRStPacnF69euHChQsGGXKIyEj49678AsSZ0qp6tcSRhZzSIjIiSnVy9u3bp/j/d+7cwUcffYSRI0ciJCQEFhYW1fbt16+f+FWKhJ0cIiP26JTWzePivC1d6gV0nAL4R7LDQ6RDGp2uMjNT7j2eEokE5eXlSp9c2xhyiEyI2M/hcWlR2d3hlBaR1vE5OUpgyCEyQVUdnqSdQIZIgYdTWkRaxZCjBIYcIhPHKS0ig6Tu72/l5qEAHDlyBIGBgSgsLKzxWUFBAYKCgnDixAmlT0xEpHVSd6DtaCBqFTAtCXjrSGVHpi4K0oGfZgDLA4G1XYGf36ucKiMinVM65KxYsQJjxoypNUFJpVKMHTsWy5cvF7W4KqmpqRg9ejR8fHxgbW0NPz8/zJs3D6WlpRo5HxGZCI9wYNQvwLQUoM/nQKs3AWsn9cfLjq98ceiG7sCKVsC5DZXdIyLSCaVDTmJiInr37v3Ez3v27InYWM387eXKlSuoqKjA2rVrcenSJSxfvhxfffUVPvjgA42cj4hMzKMdnlk3gcE7gMat6jZm/s3qHR4GHiKtU3pNjpWVFZKTk9G0adNaP7927RpCQkJQXFwsaoFPsnTpUqxZswY3btxQ+hiuySEilVSt4YnfCmTHiTMmFy0TqUzjDwN0d3dHUlLSE0POxYsX0bhxY6VPXFcFBQVwcqpDW5mI6FmqOjxtR4v3aons+H+mtRx9gA4TuWiZSEOU7uRMmjQJx44dw/nz52FlZVXts+LiYrRt2xbdunXDypUrNVLoo65fv47WrVtj2bJleOutt564X0lJCUpKShTfFxYWwtPTk50cIqq7qweAY4uB7ARxxmscBoQNZeAhqoXGbyG/ffs2WrduDXNzc0ycOBH+/v6QSCS4fPkyvvzyS5SXlyMuLg6urq5Kn3z+/PlYsGDBU/c5f/482rRpo/g+KysLXbp0QZcuXbBhwwa1xmfIISLRaOI5PJ5/T2cx8BAB0NJzctLS0jB+/Hj88ssvqDpMIpGgV69eWL16NZo0aaJS0Xfv3sXdu3efuk+TJk0UnaOsrCx069YNzz//PDZv3vzMJzGzk0NEWsXAQ6QRWn0YoFwux7Vr1yAIApo1a4YGDRqoOoTKMjMz0a1bN4SHh2Pr1q0wNzdXeQwuPCYirSnIBBK2A8m7gDsp4ozJV0uQiTLqJx5XTVF5eXlhy5Yt1QKOm5ub0uMw5BCRTrDDQ1QnRh1yNm/ejJEjR9b6mSrlM+QQkc4x8BCpzKhDjlgYcohIr2hiSsutJdDtQ8D/yQ9vJTI0DDlKYMghIr1V1eE5vQqQK/+Q0yeyagC0eAmQtWKHhwweQ44SGHKIyCDcigWSvwXSzor7pGU+h4cMFEOOEhhyiMjgaOLVEpzSIgPDkKMEhhwiMmic0iITxZCjBIYcIjIanNIiE8KQowSGHCIySlUdnt9WAAXp4ozJ29JJjzDkKIEhh4iMniY6PAw8pGMMOUpgyCEik6KJ5/B4tgN6LQI8wsUZj0gJDDlKYMghIpMl9pSWgyfg25WLlkkrGHKUwJBDRAROaZHBYchRAkMOEdFjOKVFBoAhRwkMOURETyH2c3g4pUUiYchRAkMOEZGSNDGl5dMF6DGPHR5SGUOOEhhyiIjUUDWldX498Fd23cezawx4PAeEDeGrJUgpDDlKYMghIqqjqg7P5R/FuUuLr5YgJTDkKIEhh4hIRJzSIi1hyFECQw4RkYZwSos0iCFHCQw5RERaoIkprdBBlc/hYYfHJDHkKIEhh4hIy8QOPDYulZ2d8JEMPCaEIUcJDDlERDpUFXgufQ/cy6r7eJzSMhkMOUpgyCEi0hOc0iIVMOQogSGHiEgPcUqLnoEhRwkMOUREek7sKS0HTyDgJXZ4DBxDjhIYcoiIDIjYHR57GRDYn4HHADHkKIEhh4jIQN2KBWKjgd9/AYpy6z4ep7QMCkOOEhhyiIiMAKe0TA5DjhIYcoiIjAyntEwCQ44SGHKIiIyY2FNafA6P3mDIUQJDDhGRiajq8CTuAIrz6j4en8OjUww5SmDIISIyQVcPAAnfAJnngUIR1vBwSkvrGHKUwJBDRGTixJ7S4qJlrWDIUQJDDhERKYg9pcXAozEMOUpgyCEiolpxSkuvGX3I6devHxISEpCbm4sGDRogIiICS5YsgUwmU3oMhhwiInomPodH7xh9yFm+fDnat2+Pxo0bIzMzE++++y4A4NSpU0qPwZBDREQq4ZSWXjD6kPO4ffv2ISoqCiUlJbCwsFDqGIYcIiJSm9hTWnZuQNsxQOhgQOpe9/GMmEmFnLy8PIwfPx6ZmZk4efKk0scx5BARkShuxQJHPgJuHBNnvMZhgFc7dniewCRCzqxZs7Bq1Srcv38f7dq1w/79++Hs7PzE/UtKSlBSUqL4vrCwEJ6engw5REQkjoLMyg5PVjxw9UdOaWmIQYac+fPnY8GCBU/d5/z582jTpg0A4O7du8jLy0NaWhoWLFgAqVSK/fv3QyKRqDQ+Qw4REWkEp7Q0wiBDzt27d3H37t2n7tOkSRNYWVnV2H7r1i14enri1KlTaN++fa3HspNDREQ6wykt0agbcuppsKZnatiwIRo2bKjWsVXZ7NEQ8zhLS0tYWlqqNT4REVGdeIQDw76vPqV187j6b0vPjq/8OrsGkHoBHacA/pEm3eF5FoNYk3Pu3DmcO3cOL7zwAho0aIAbN25g7ty5yM7OxqVLl5QOMlx4TEREOncrFjj4AZB+RpzxXFpUdneMeErLIKerlJWUlIQpU6YgMTERRUVFaNy4MXr37o3Zs2fD3V35P1CGHCIi0htVHZ6knUCGSIHHSKe0jDrkiIUhh4iI9JJYU1qPMqIpLYYcJTDkEBGRQRB7SsvAOzwMOUpgyCEiIoOiiefwOPoAHSYaVIeHIUcJDDlERGTQrh4Aji0GshPEGa9xGBA2VO8DD0OOEhhyiIjIKFR1eOK3Atlx4oypx1NaDDlKYMghIiKjYwJTWgw5SmDIISIio2eEU1oMOUpgyCEiIpOhiefweP49naXlwMOQowSGHCIiMkkGHngYcpTAkENERCavIBNI2A4k7wLupIgzpoZfLcGQowSGHCIiokdoosPTaQbQY644Y/2NIUcJDDlERERPIGbgCYwCXvuvKGUBDDlKYcghIiJSghhTWm8dEe15O+r+/jYT5exERERkPKTuQJcZwITTwLQUoM/nQANf1cYQa/qrDurpugAiIiLSY1J3oO3oyq9bsUDyt0Da2Wc/admznXbqewqGHCIiIlKOR/g/U1BPe7VE6Bt68WoIhhwiIiJS3aMdnqrA89dtoHkvvQg4AEMOERER1VVV4NEzXHhMRERERokhh4iIiIwSQw4REREZJYYcIiIiMkoMOURERGSUGHKIiIjIKDHkEBERkVFiyCEiIiKjxJBDRERERokhh4iIiIwSQw4REREZJZN6d5UgCACAwsJCHVdCREREyqr6vV31e1xZJhVy7t27BwDw9PTUcSVERESkqnv37kEqlSq9v0RQNRYZsIqKCmRlZcHe3h4SiUS0cQsLC+Hp6YmMjAw4ODiINi7VxGutHbzO2sHrrB28ztqhyessCALu3bsHmUwGMzPlV9qYVCfHzMwMHh4eGhvfwcGB/wJpCa+1dvA6awevs3bwOmuHpq6zKh2cKlx4TEREREaJIYeIiIiMEkOOCCwtLTFv3jxYWlrquhSjx2utHbzO2sHrrB28ztqhj9fZpBYeExERkelgJ4eIiIiMEkMOERERGSWGHCIiIjJKDDkiWL16NXx8fGBlZYXw8HD8+uuvui5JLyxevBjPPfcc7O3t0ahRI0RFReHq1avV9hEEAfPnz4dMJoO1tTW6du2KS5cuVdunpKQEkyZNQsOGDWFra4t+/frh1q1b1faRy+V48803IZVKIZVK8eabbyI/P7/aPunp6Xj55Zdha2uLhg0bYvLkySgtLdXIz65LixcvhkQiwdSpUxXbeJ3Fk5mZiaFDh8LZ2Rk2NjZo1aoVYmNjFZ/zWtddWVkZZs+eDR8fH1hbW8PX1xcfffQRKioqFPvwOqvuxIkTePnllyGTySCRSLB3795qn+vbNU1KSkKXLl1gbW0Nd3d3fPTRRyq/1gEC1cn27dsFCwsLYf369UJKSoowZcoUwdbWVkhLS9N1aTrXq1cvITo6WkhOThYSEhKEvn37Cl5eXsJff/2l2OfTTz8V7O3thd27dwtJSUnC66+/LjRu3FgoLCxU7DNu3DjB3d1diImJEeLi4oRu3boJoaGhQllZmWKf3r17C8HBwcKpU6eEU6dOCcHBwcJLL72k+LysrEwIDg4WunXrJsTFxQkxMTGCTCYTJk6cqJ2LoSXnzp0TmjRpIrRs2VKYMmWKYjuvszjy8vIEb29vYcSIEcLZs2eFmzdvCocOHRKuXbum2IfXuu4+/vhjwdnZWdi/f79w8+ZN4dtvvxXs7OyEFStWKPbhdVbdTz/9JHz44YfC7t27BQDCd999V+1zfbqmBQUFgqurqzBo0CAhKSlJ2L17t2Bvby989tlnKv3MDDl11LZtW2HcuHHVtrVo0UJ47733dFSR/srNzRUACMePHxcEQRAqKioENzc34dNPP1Xs8+DBA0EqlQpfffWVIAiCkJ+fL1hYWAjbt29X7JOZmSmYmZkJBw4cEARBEFJSUgQAwpkzZxT7nD59WgAgXLlyRRCEyn+5zczMhMzMTMU+27ZtEywtLYWCggLN/dBadO/ePaFZs2ZCTEyM0KVLF0XI4XUWz6xZs4QXXnjhiZ/zWoujb9++wqhRo6ptGzBggDB06FBBEHidxfB4yNG3a7p69WpBKpUKDx48UOyzePFiQSaTCRUVFUr/nJyuqoPS0lLExsaiZ8+e1bb37NkTp06d0lFV+qugoAAA4OTkBAC4efMmcnJyql0/S0tLdOnSRXH9YmNj8fDhw2r7yGQyBAcHK/Y5ffo0pFIpnn/+ecU+7dq1g1QqrbZPcHAwZDKZYp9evXqhpKSk2lSDIZswYQL69u2LiIiIatt5ncWzb98+tGnTBq+++ioaNWqEsLAwrF+/XvE5r7U4XnjhBRw+fBi///47ACAxMREnT55Enz59APA6a4K+XdPTp0+jS5cu1Z6506tXL2RlZSE1NVXpn8uk3l0ltrt376K8vByurq7Vtru6uiInJ0dHVeknQRAwffp0vPDCCwgODgYAxTWq7fqlpaUp9qlfvz4aNGhQY5+q43NyctCoUaMa52zUqFG1fR4/T4MGDVC/fn2j+LPavn074uLicP78+Rqf8TqL58aNG1izZg2mT5+ODz74AOfOncPkyZNhaWmJYcOG8VqLZNasWSgoKECLFi1gbm6O8vJyfPLJJxg8eDAA/jOtCfp2TXNyctCkSZMa56n6zMfHR6mfiyFHBI+/0VwQBFHfcm4MJk6ciIsXL+LkyZM1PlPn+j2+T237q7OPIcrIyMCUKVNw8OBBWFlZPXE/Xue6q6ioQJs2bbBo0SIAQFhYGC5duoQ1a9Zg2LBhiv14retmx44d2Lp1K7755hsEBQUhISEBU6dOhUwmw/DhwxX78TqLT5+uaW21POnYJ+F0VR00bNgQ5ubmNdJ8bm5ujZRqyiZNmoR9+/bh6NGj1d4C7+bmBgBPvX5ubm4oLS2FXC5/6j63b9+ucd47d+5U2+fx88jlcjx8+NDg/6xiY2ORm5uL8PBw1KtXD/Xq1cPx48excuVK1KtXr9rffh7F66y6xo0bIzAwsNq2gIAApKenA+A/02KZOXMm3nvvPQwaNAghISF48803MW3aNCxevBgAr7Mm6Ns1rW2f3NxcADW7TU/DkFMH9evXR3h4OGJiYqptj4mJQYcOHXRUlf4QBAETJ07Enj17cOTIkRrtRR8fH7i5uVW7fqWlpTh+/Lji+oWHh8PCwqLaPtnZ2UhOTlbs0759exQUFODcuXOKfc6ePYuCgoJq+yQnJyM7O1uxz8GDB2FpaYnw8HDxf3gt6tGjB5KSkpCQkKD4atOmDYYMGYKEhAT4+vryOoukY8eONR6D8Pvvv8Pb2xsA/5kWy/3792FmVv3Xk7m5ueIWcl5n8enbNW3fvj1OnDhR7bbygwcPQiaT1ZjGeiqllyhTrapuId+4caOQkpIiTJ06VbC1tRVSU1N1XZrOjR8/XpBKpcKxY8eE7Oxsxdf9+/cV+3z66aeCVCoV9uzZIyQlJQmDBw+u9ZZFDw8P4dChQ0JcXJzQvXv3Wm9ZbNmypXD69Gnh9OnTQkhISK23LPbo0UOIi4sTDh06JHh4eBjkbaDKePTuKkHgdRbLuXPnhHr16gmffPKJ8Mcffwhff/21YGNjI2zdulWxD6913Q0fPlxwd3dX3EK+Z88eoWHDhsK//vUvxT68zqq7d++eEB8fL8THxwsAhM8//1yIj49XPPJEn65pfn6+4OrqKgwePFhISkoS9uzZIzg4OPAWcl348ssvBW9vb6F+/fpC69atFbdImzoAtX5FR0cr9qmoqBDmzZsnuLm5CZaWlkLnzp2FpKSkauMUFxcLEydOFJycnARra2vhpZdeEtLT06vt8+effwpDhgwR7O3tBXt7e2HIkCGCXC6vtk9aWprQt29fwdraWnBychImTpxY7fZEY/J4yOF1Fs8PP/wgBAcHC5aWlkKLFi2EdevWVfuc17ruCgsLhSlTpgheXl6ClZWV4OvrK3z44YdCSUmJYh9eZ9UdPXq01v8mDx8+XBAE/bumFy9eFDp16iRYWloKbm5uwvz581W6fVwQBIFvISciIiKjxDU5REREZJQYcoiIiMgoMeQQERGRUWLIISIiIqPEkENERERGiSGHiIiIjBJDDhERERklhhwiIiIySgw5RKRx8+fPR6tWrXR2/jlz5uDtt9/W6DneffddTJ48WaPnICLV8InHRFQnEonkqZ8PHz4cq1atQklJCZydnbVU1T9u376NZs2a4eLFi6q92E9Fubm58PPzw8WLF2u8jJaIdIMhh4jqJCcnR/H/d+zYgblz51Z7U7e1tTWkUqkuSgMALFq0CMePH8cvv/yi9hgPHz6EhYXFM/cbOHAgmjZtiiVLlqh9LiISD6eriKhO3NzcFF9SqRQSiaTGtsenq0aMGIGoqCgsWrQIrq6ucHR0xIIFC1BWVoaZM2fCyckJHh4e2LRpU7VzZWZm4vXXX0eDBg3g7OyM/v37IzU19an1bd++Hf369VN8v2XLFjg7O6OkpKTafgMHDsSwYcMA/DO9tmnTJvj6+sLS0hKCIGDXrl0ICQmBtbU1nJ2dERERgaKiIsUY/fr1w7Zt29S8kkQkNoYcItKJI0eOICsrCydOnMDnn3+O+fPn46WXXkKDBg1w9uxZjBs3DuPGjUNGRgYA4P79++jWrRvs7Oxw4sQJnDx5EnZ2dujduzdKS0trPYdcLkdycjLatGmj2Pbqq6+ivLwc+/btU2y7e/cu9u/fj5EjRyq2Xbt2DTt37sTu3buRkJCAnJwcDB48GKNGjcLly5dx7NgxDBgwAI82w9u2bYuMjAykpaWJfbmISA0MOUSkE05OTli5ciX8/f0xatQo+Pv74/79+/jggw/QrFkzvP/++6hfvz5+++03AJUdGTMzM2zYsAEhISEICAhAdHQ00tPTcezYsVrPkZaWBkEQIJPJFNusra3xxhtvIDo6WrHt66+/hoeHB7p27arYVlpaiv/9738ICwtDy5YtkZ2djbKyMgwYMABNmjRBSEgI3nnnHdjZ2SmOcXd3B4BndpeISDsYcohIJ4KCgmBm9s9/glxdXRESEqL43tzcHM7OzsjNzQUAxMbG4tq1a7C3t4ednR3s7Ozg5OSEBw8e4Pr167Weo7i4GABgZWVVbfuYMWNw8OBBZGZmAgCio6MxYsSIaouovb294eLiovg+NDQUPXr0QEhICF599VWsX78ecrm82rjW1tYAKrtORKR79XRdABGZpscX8kokklq3VVRUAAAqKioQHh6Or7/+usZYj4aRRzVs2BBA5bTVo/uEhYUhNDQUW7ZsQa9evZCUlIQffvih2rG2trbVvjc3N0dMTAxOnTqFgwcP4j//+Q8+/PBDnD17VnE3VV5e3lPrISLtYieHiAxC69at8ccff6BRo0Zo2rRpta8n3b3l5+cHBwcHpKSk1PjsrbfeQnR0NDZt2oSIiAh4eno+swaJRIKOHTtiwYIFiI+PR/369fHdd98pPk9OToaFhQWCgoLU/0GJSDQMOURkEIYMGYKGDRuif//++PXXX3Hz5k0cP34cU6ZMwa1bt2o9xszMDBERETh58mSt42VmZmL9+vUYNWrUM89/9uxZLFq0CBcuXEB6ejr27NmDO3fuICAgQLHPr7/+ik6dOimmrYhItxhyiMgg2NjY4MSJE/Dy8sKAAQMQEBCAUaNGobi4GA4ODk887u2338b27dsV015VHBwcMHDgQNjZ2SEqKuqZ53dwcMCJEyfQp08fNG/eHLNnz8ayZcsQGRmp2Gfbtm0YM2aM2j8jEYmLDwMkIqMmCALatWuHqVOnYvDgwdU+e/HFFxEQEICVK1fW+Tw//vgjZs6ciYsXL6JePS53JNIH7OQQkVGTSCRYt24dysrKFNvy8vKwfft2HDlyBBMmTBDlPEVFRYiOjmbAIdIj7OQQkclp0qQJ5HI55syZg3fffVfX5RCRhjDkEBERkVHidBUREREZJYYcIiIiMkoMOURERGSUGHKIiIjIKDHkEBERkVFiyCEiIiKjxJBDRERERokhh4iIiIwSQw4REREZpf8H8H/ra7TGm/EAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/src/lense_thirring.c b/src/lense_thirring.c
index 56181036..3fcbbed0 100644
--- a/src/lense_thirring.c
+++ b/src/lense_thirring.c
@@ -31,17 +31,17 @@
  * Implementation Paper    None
  * Based on                `Park et al. <https://iopscience.iop.org/article/10.3847/1538-3881/abd414/>`_.
  * C Example               :ref:`c_example_lense_thirring`
- * Python Example          `LT.ipynb <https://github.com/dtamayo/reboundx/blob/master/ipython_examples/LT.ipynb>`_.
+ * Python Example          `LenseThirring.ipynb <https://github.com/dtamayo/reboundx/blob/master/ipython_examples/LenseThirring.ipynb>`_.
  * ======================= ===============================================
  * 
- * Adds Lense-Thirring effect due to massive rotating bodies in the simulation.
+ * Adds Lense-Thirring effect due to rotating central body in the simulation. Assumes the source body is particles[0]
  *
  * **Effect Parameters**
  * 
  * ============================ =========== ==================================================================
  * Field (C type)               Required    Description
  * ============================ =========== ==================================================================
- * lt_c (double)                   Yes         Speed of light in the units used for the simulation.
+ * lt_c (double)                Yes         Speed of light in the units used for the simulation.
  * ============================ =========== ==================================================================
  *
  * **Particle Parameters**
@@ -49,12 +49,8 @@
  * ============================ =========== ==================================================================
  * Field (C type)               Required    Description
  * ============================ =========== ==================================================================
- * lt_rot_rate (double)         No          rotation rate, omega`
- * lt_R_eq (double)             No          Equatorial radius of source body
- * lt_Mom_I_fac (double)        No          Moment of Inertia of source body over MR^2
- * lt_p_hatx (double)           No          x-component of spin-pole unit vector
- * lt_p_haty (double)           No          y-component of spin-pole unit vector
- * lt_p_hatz (double)           No          z-component of spin-pole unit vector
+ * I (double)                   Yes         Moment of Inertia of source body 
+ * Omega (reb_vec3d)            Yes         Angular rotation frequency (Omega_x, Omega_y, Omega_z) 
  * ============================ =========== ==================================================================
  * 
  */
@@ -66,14 +62,11 @@
 #include "rebound.h"
 #include "reboundx.h"
 
-static void rebx_calculate_LT_force(struct reb_simulation* const sim, struct reb_particle* const particles, const int N, const double omega, const double R_eq, const double C_fac, const double p_hat_x, const double p_hat_y, const double p_hat_z, const int source_index, const double C2){
-    const struct reb_particle source = particles[source_index];
+static void rebx_calculate_LT_force(struct reb_simulation* const sim, struct reb_particle* const particles, const int N, const struct reb_vec3d Omega, const double I, const double C2){
     const double G = sim->G;
     const double gamma = 1.000021;   //hard-coded Eddington-Robertson-Shiff parameter for now
-    for (int i=0; i<N; i++){
-        if(i == source_index){
-            continue;
-        }
+    const struct reb_particle source = particles[0]; // hard-code particles[0] as source particle
+    for (int i=1; i<N; i++){
         const struct reb_particle p = particles[i];
         const double dx = p.x - source.x;
         const double dy = p.y - source.y;
@@ -84,20 +77,25 @@ static void rebx_calculate_LT_force(struct reb_simulation* const sim, struct reb
         const double dvx = p.vx - source.vx;
         const double dvy = p.vy - source.vy;
         const double dvz = p.vz - source.vz;
-        const double Jx = C_fac*source.m * R_eq*R_eq*omega*p_hat_x ;
-        const double Jy = C_fac*source.m * R_eq*R_eq*omega*p_hat_y ;
-        const double Jz = C_fac*source.m * R_eq*R_eq*omega*p_hat_z ;
-        const double Omega_fac = (1.+gamma)*G/2/C2;
-        const double Omega_x = Omega_fac*(-Jx +3.*(Jx*dx+Jy*dy+Jz*dz)*dx/r2)/r3;
-        const double Omega_y = Omega_fac*(-Jy +3.*(Jx*dx+Jy*dy+Jz*dz)*dy/r2)/r3;
-        const double Omega_z = Omega_fac*(-Jz +3.*(Jx*dx+Jy*dy+Jz*dz)*dz/r2)/r3;
+        const double Jx = I*Omega.x; //C_fac*source.m * R_eq*R_eq*omega*p_hat_x ;
+        const double Jy = I*Omega.y; //C_fac*source.m * R_eq*R_eq*omega*p_hat_y ;
+        const double Jz = I*Omega.z; //C_fac*source.m * R_eq*R_eq*omega*p_hat_z ;
+        const double ms = source.m;
+        const double mt = p.m;
+        const double mtot = ms + mt;
+        const double mratio = mt/ms;
+        const double Omega_fac = ms/mtot*(1.+gamma)*G/2/C2/r3;
+        const double Jdotr = Jx*dx+Jy*dy+Jz*dz;
+        const double Omega_x = Omega_fac*(-Jx +3.*Jdotr*dx/r2);
+        const double Omega_y = Omega_fac*(-Jy +3.*Jdotr*dy/r2);
+        const double Omega_z = Omega_fac*(-Jz +3.*Jdotr*dz/r2);
 
-        particles[i].ax += 2.*Omega_y*dvz - Omega_z*dvy;
-        particles[i].ay += 2.*Omega_z*dvx - Omega_x*dvz;
-        particles[i].az += 2.*Omega_x*dvy - Omega_y*dvx;
-        particles[source_index].ax -= 2.*Omega_y*dvz - Omega_z*dvy; 
-        particles[source_index].ay -= 2.*Omega_z*dvx - Omega_x*dvz;
-        particles[source_index].az -= 2.*Omega_x*dvy - Omega_y*dvx;
+        particles[i].ax += 2.*(Omega_y*dvz - Omega_z*dvy);
+        particles[i].ay += 2.*(Omega_z*dvx - Omega_x*dvz);
+        particles[i].az += 2.*(Omega_x*dvy - Omega_y*dvx);
+        particles[0].ax -= mratio * 2. * (Omega_y*dvz - Omega_z*dvy); 
+        particles[0].ay -= mratio * 2. * (Omega_z*dvx - Omega_x*dvz);
+        particles[0].az -= mratio * 2. * (Omega_x*dvy - Omega_y*dvx);
     }
 }
 
@@ -109,25 +107,11 @@ void rebx_lense_thirring(struct reb_simulation* const sim, struct rebx_force* co
     }
     const double C2 = (*c)*(*c);
 
-    for (int i=0; i<N; i++){
-        const double* omega = rebx_get_param(rebx, particles[i].ap, "lt_rot_rate");
-        if(omega != NULL){
-           const double* R_eq = rebx_get_param(rebx, particles[i].ap, "lt_R_eq");
-           if (R_eq != NULL){
-               const double* C_fac = rebx_get_param(rebx, particles[i].ap, "lt_Mom_I_fac");
-               if(C_fac != NULL){
-                  const double* p_hat_x = rebx_get_param(rebx, particles[i].ap, "lt_p_hatx");
-                  if(p_hat_x != NULL){
-                     const double* p_hat_y = rebx_get_param(rebx, particles[i].ap, "lt_p_haty");
-                     if(p_hat_y != NULL){
-                        const double* p_hat_z = rebx_get_param(rebx, particles[i].ap, "lt_p_hatz");
-                        if(p_hat_z != NULL){
-                           rebx_calculate_LT_force(sim, particles, N, *omega, *R_eq, *C_fac, *p_hat_x, *p_hat_y, *p_hat_z, i, C2);
-                        }
-                     }
-                  }
-               }
-           }
+    const double* I = rebx_get_param(rebx, particles[0].ap, "I");
+    if (I != NULL){
+        const struct reb_vec3d* Omega  = rebx_get_param(rebx, particles[0].ap, "Omega");
+        if(Omega != NULL){
+            rebx_calculate_LT_force(sim, particles, N, *Omega, *I, C2);
         }
     }
 }
diff --git a/src/tides_spin.c b/src/tides_spin.c
index 10a2b597..2bd2f22d 100644
--- a/src/tides_spin.c
+++ b/src/tides_spin.c
@@ -57,7 +57,7 @@
  * ============================ =========== ==================================================================
  * particles[i].r (float)       Yes         Physical radius (required for contribution from tides raised on the body).
  * k2 (float)                   Yes         Potential Love number of degree 2.
- * Omega (reb_vec3d)            Yes         Angular rotation frequency
+ * Omega (reb_vec3d)            Yes         Angular rotation frequency (Omega_x, Omega_y, Omega_z)
  * I (float)                    No          Moment of inertia (for test particles, assumed to be the specific MoI I/m)
  * tau (float)                  No          Constant time lag. If not set, defaults to 0
  * ============================ =========== ==================================================================