commit 6b5ba62ac50a546676c158c67fbb64077a8151bd
Author: Remy Moll <me@moll.re>
Date:   Tue Dec 10 10:47:16 2024 +0100

    backwards tracking with sensible trajectories

diff --git a/Pipfile b/Pipfile
new file mode 100644
index 0000000..ee7ea66
--- /dev/null
+++ b/Pipfile
@@ -0,0 +1,17 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+numpy = "*"
+scipy = "*"
+matplotlib = "*"
+ipython = "*"
+ppigrf = "*"
+ipykernel = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.12"
diff --git a/Pipfile.lock b/Pipfile.lock
new file mode 100644
index 0000000..1576104
--- /dev/null
+++ b/Pipfile.lock
@@ -0,0 +1,953 @@
+{
+    "_meta": {
+        "hash": {
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+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.12"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
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diff --git a/tracking.ipynb b/tracking.ipynb
new file mode 100644
index 0000000..973165a
--- /dev/null
+++ b/tracking.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import scipy.integrate as spi\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ppigrf\n",
+    "\n",
+    "DEBUG = None # to be set later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Some constants\n",
+    "\n",
+    "M = 5 # nuclear mass unit u\n",
+    "# 1 u = 1.66e-27 kg\n",
+    "\n",
+    "CHARGE = 2 # charge of the nucleus in units of the elementary charge\n",
+    "# 1 e = 1.6e-19 C\n",
+    "\n",
+    "SPEED_OF_LIGHT = 3e8 # m/s\n",
+    "\n",
+    "DATE = datetime(2021, 3, 28)\n",
+    "\n",
+    "## Conversion factor when computing the Lorentz force\n",
+    "# F = gamma * q * v x B\n",
+    "CONVERSION_FACTOR = 1.66e27 * 1.6e-19 * 1e-9\n",
+    "# where we also converted from nT to T\n",
+    "R_EARTH = 6.4e6 # m\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modelling the magnetic field\n",
+    "- as a sanity check we can use a simple homogeneous magnetic field in the z direction\n",
+    "$$\n",
+    "\\mathbf{B} = B_0 \\mathbf{e}_z\n",
+    "$$\n",
+    "- In the simplest approximation, the magnetic field can be modelled as a dipole field. In cartesian coordinates, it is given by:\n",
+    "$$\n",
+    "\\mathbf{B} = ...\n",
+    "$$ \n",
+    "TODO: check\n",
+    "TODO: check reference\n",
+    "- A far better approximation is given by the IGRF model. The IGRF model is given by a sum of spherical harmonics. The coefficients are fitted to measurements and account for time variations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## helpers for IGRF, since it is given in spherical coordinates\n",
+    "def cartesian_to_spherical(v: np.ndarray) -> np.ndarray:\n",
+    "    return np.array([\n",
+    "        np.linalg.norm(v),\n",
+    "        np.degrees(np.arctan2(np.linalg.norm(v[:2]), v[2])),\n",
+    "        np.degrees(np.arctan2(v[1], v[0]))\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "#I'm lazy, so I redefine\n",
+    "cosd = lambda x: np.cos(np.deg2rad(x))\n",
+    "sind = lambda x: np.sin(np.deg2rad(x))\n",
+    "def B_to_cartesian(r_vec: np.ndarray, B: np.ndarray) -> np.ndarray:\n",
+    "    # express the spherical components of B in cartesian coordinates through the jacobian\n",
+    "    # (Bx, By, Bz) = J * (Br, Btheta, Bphi)\n",
+    "    # JACOBIAN = np.array([\n",
+    "    #     [np.sin(np.radians(r[1])) * np.cos(np.radians(r[2])), np.cos(np.radians(r[1])) * np.cos(np.radians(r[2])), -np.sin(np.radians(r[1])) * np.sin(np.radians(r[2]))],\n",
+    "    #     [np.sin(np.radians(r[1])) * np.sin(np.radians(r[2])), np.cos(np.radians(r[1])) * np.sin(np.radians(r[2])), np.sin(np.radians(r[1])) * np.cos(np.radians(r[2]))],\n",
+    "    #     [np.cos(np.radians(r[1])), -np.sin(np.radians(r[1])), 0]\n",
+    "    # ])\n",
+    "    r = r_vec[0] # though not needed\n",
+    "    theta = r_vec[1]\n",
+    "    phi = r_vec[2]\n",
+    "    JACOBIAN = np.array([\n",
+    "        [cosd(theta) * cosd(phi), - sind(theta) * cosd(phi), -cosd(theta) * sind(phi)],\n",
+    "        [cosd(theta) * sind(phi), sind(theta) * sind(phi), cosd(theta) * cosd(phi)],\n",
+    "        [sind(theta), cosd(theta), sind(theta)]\n",
+    "    ])\n",
+    "    B_cart = JACOBIAN @ B\n",
+    "    if DEBUG:\n",
+    "        print(f\"spherical B = {np.linalg.norm(B)}, cartesian B = {np.linalg.norm(B_cart)}\")\n",
+    "    return B_cart\n",
+    "\n",
+    "\n",
+    "B_INTENSITY = 1e4 # nT # to be on par with the measured values\n",
+    "## Different implementations of the magnetic field\n",
+    "def simple_field(x):\n",
+    "    B = np.array([0, 0, 1]) * B_INTENSITY\n",
+    "    return B\n",
+    "\n",
+    "\n",
+    "def dipole_field(x):\n",
+    "    intensity = B_INTENSITY * 1e-2\n",
+    "    r = np.linalg.norm(x)\n",
+    "    B =  intensity * R_EARTH**3 / r**5 * np.array([\n",
+    "        3 * x[2] * x[0],\n",
+    "        3 * x[2] * x[1],\n",
+    "        2 * x[2]**2 - x[0]**2 - x[1]**2\n",
+    "    ])\n",
+    "    return B\n",
+    "\n",
+    "\n",
+    "def IGRF_field(x):\n",
+    "    r_vec = cartesian_to_spherical(x)\n",
+    "    r = r_vec[0] * 1e-3 # convert to km\n",
+    "    B = ppigrf.igrf_gc(r, r_vec[1], r_vec[2], DATE) # returns radial, south, east\n",
+    "    B = np.array(B).flatten()\n",
+    "    B_cart = B_to_cartesian(r_vec, B)\n",
+    "    if DEBUG:\n",
+    "        print(f\"B = {B}, B_cart = {B_cart}\")\n",
+    "    return B_cart\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Magnetic field and equation of motion\n",
+    "We consider **only** the effect of the magnetic field, as given by the Lorentz force:\n",
+    "\n",
+    "$$\n",
+    "F_L = q \\cdot \\vec{v} \\times \\vec{B}(r, \\theta, \\phi)\n",
+    "$$\n",
+    "TODO: comment on synchrotron radiation\n",
+    "where:\n",
+    "- v is the velocity of the particle\n",
+    "- B is the magnetic field\n",
+    "\n",
+    "The equation of motion is still given by:\n",
+    "$$\n",
+    "m \\cdot \\frac{d^2 \\vec{r}}{dt^2} = F_L\n",
+    "$$\n",
+    "but in the relativistic case the mass is expressed as\n",
+    "$$\n",
+    "m = \\gamma m_0\n",
+    "$$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integrating the equation of motion\n",
+    "\n",
+    "As a starting point we can integrate the EOM and track the particle **forward in time**. The procedure is as follows:\n",
+    "- linearize the EOM\n",
+    "- integrate the EOM\n",
+    "- plot the trajectory\n",
+    "\n",
+    "=> Doing this for every incoming particle is not feasible!\n",
+    "\n",
+    "\n",
+    "#### Backtracking\n",
+    "We can also track the particle **backward in time**, from the detector (eg. AMS-01). The procedure is as follows:\n",
+    "- modify the EOM to track the particle backward in time\n",
+    "    - treat the velocity as negative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B_FIELD_FUNC = None # to be set later\n",
+    "\n",
+    "## Functions to be integrated\n",
+    "def gamma_inv(v: np.ndarray) -> float:\n",
+    "    v = np.linalg.norm(v)\n",
+    "    if v > SPEED_OF_LIGHT:\n",
+    "        print(f\"v = {v} > c, setting gamma = 0\")\n",
+    "        return 0\n",
+    "    return np.sqrt(1 - (v / SPEED_OF_LIGHT)**2)\n",
+    "\n",
+    "\n",
+    "def force(x: np.ndarray, v: np.ndarray) -> np.ndarray:\n",
+    "    B = B_FIELD_FUNC(x)\n",
+    "    # B is now in nT, but this is absorbed in the conversion factor\n",
+    "    F =  CHARGE * np.cross(v, B) * CONVERSION_FACTOR\n",
+    "    if DEBUG:\n",
+    "        print(f\"B = {B}\")\n",
+    "        print(f\"x = {x}\")\n",
+    "        print(f\"v = {np.linalg.norm(v)}\")\n",
+    "        print(f\"F = {F}\")\n",
+    "    return F.flatten()\n",
+    "\n",
+    "\n",
+    "# linearized equations of motion - forward\n",
+    "def y_prime(t, y):\n",
+    "    # to be compatible with the scipy integrator, we need the following signature\n",
+    "    x = y[:3]\n",
+    "    v = y[3:]\n",
+    "    a = (gamma_inv(v) / M) * force(x, v)\n",
+    "    ydot = np.concatenate((v, a))\n",
+    "    if DEBUG:\n",
+    "        print(f\"1/gamma = {gamma_inv(v)}\")\n",
+    "        print(f\"a = {a}\")\n",
+    "        print(f\"ydot: {ydot}\")\n",
+    "    return np.concatenate((v, a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Some more helpers\n",
+    "def plot_trajectory(x, v, t, show_nth: int = 1, show_earth: bool = False, fix_limits: bool = True):\n",
+    "    fig = plt.figure()\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
+    "    # show the trajectory (x)\n",
+    "    ax.plot3D(x[0, ::show_nth], x[1, ::show_nth], x[2, ::show_nth], label='trajectory')\n",
+    "    # show start and end points\n",
+    "    ax.scatter(x[0, 0], x[1, 0], x[2, 0], label='start', color='green')\n",
+    "    ax.scatter(x[0, -1], x[1, -1], x[2, -1], label='end', color='red')\n",
+    "    # show the earth\n",
+    "    if show_earth:\n",
+    "        u = np.linspace(0, 2 * np.pi, 100)\n",
+    "        v = np.linspace(0, np.pi, 100)\n",
+    "        x = R_EARTH * np.outer(np.cos(u), np.sin(v))\n",
+    "        y = R_EARTH * np.outer(np.sin(u), np.sin(v))\n",
+    "        z = R_EARTH * np.outer(np.ones(np.size(u)), np.cos(v))\n",
+    "        ax.plot_surface(x, y, z, color='b', alpha=0.1)\n",
+    "    ax.legend()\n",
+    "\n",
+    "    # set sensible view limits\n",
+    "    ax.set_box_aspect([1,1,1])\n",
+    "    if fix_limits:\n",
+    "        ax.set_xlim(-5 * R_EARTH, 5 * R_EARTH)\n",
+    "        ax.set_ylim(-5 * R_EARTH, 5 * R_EARTH)\n",
+    "        ax.set_zlim(-5 * R_EARTH, 5 * R_EARTH)\n",
+    "        # TODO: explain limit\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def energy_to_speed(E: float) -> float:\n",
+    "    # energy given in eV\n",
+    "    return np.sqrt()\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## ODE solving parameters\n",
+    "TIME_RANGE = [0, 100]\n",
+    "# TODO: cite reference\n",
+    "\n",
+    "ABS_TOL = 1e2\n",
+    "# setup of initial conditions\n",
+    "# Cartesian coordinates, SI units: m and m/s\n",
+    "X_0 = [6.5e6, 6.5e6, 0]\n",
+    "V_0 = [-1e4, -1e4, 0]\n",
+    "Y0 = np.concatenate((X_0, V_0))\n",
+    "\n",
+    "DEBUG = False\n",
+    "# CONVERSION_FACTOR = 1e-1\n",
+    "# TODO: remove this overwrite\n",
+    "B_FIELD_FUNC = IGRF_field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result has shape (6, 10000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## ODE integration\n",
+    "N_STEPS = 10000\n",
+    "t = np.linspace(TIME_RANGE[0], TIME_RANGE[1], N_STEPS)\n",
+    "\"\"\"\n",
+    "result = spi.solve_ivp(y_prime, TIME_RANGE, Y0, atol=1e5)\n",
+    "if not result.success:\n",
+    "    print(\"Integration failed\")\n",
+    "    print(result.message)\n",
+    "    exit(1)\n",
+    "\n",
+    "\n",
+    "t_int = result.t\n",
+    "y_int = result.y\n",
+    "if DEBUG:\n",
+    "    print(f\"Got result: {y_int.shape}\")\n",
+    "    print(y_int[:, 0])\n",
+    "    print(y_int[:, 1])\n",
+    "    print(f\"Took {result.nfev} function evaluations\")\n",
+    "\"\"\"\n",
+    "\n",
+    "# # saving the results\n",
+    "# TODO\n",
+    "\n",
+    "# \"\"\"\n",
+    "# ODE integration\n",
+    "sol = spi.odeint(y_prime, Y0, t, tfirst=True, atol=1e4)\n",
+    "y_int = sol.T\n",
+    "print(f\"Result has shape {y_int.shape}\")\n",
+    "# \"\"\"\n",
+    "\n",
+    "\"\"\"\n",
+    "# ODE integration\n",
+    "y_int = spi.ode(y_prime) \\\n",
+    "    .set_integrator('dopri5') \\\n",
+    "    .set_initial_value(Y0, t[0])\n",
+    "\n",
+    "t1 = 25\n",
+    "dt = 1\n",
+    "\n",
+    "while y_int.successful() and y_int.t < t1:\n",
+    "    print(y_int.t+dt, y_int.integrate(y_int.t+dt))\n",
+    "print(\"Done\")\n",
+    "\n",
+    "y_int = y_int.y\n",
+    "\"\"\"\n",
+    "\n",
+    "plot_trajectory(y_int[:3, :], y_int[3:, :], t, show_earth=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Backward integration\n",
+    "# linearized equations of motion - backward\n",
+    "def y_prime_back(t, y):\n",
+    "    # to be compatible with the scipy integrator, we need the following signature\n",
+    "    x = y[:3]\n",
+    "    v = -y[3:]\n",
+    "    a = (gamma_inv(v) / M) * force(x, v)\n",
+    "    ydot = np.concatenate((v, a))\n",
+    "    if DEBUG:\n",
+    "        print(f\"1/gamma = {gamma_inv(v)}\")\n",
+    "        print(f\"a = {a}\")\n",
+    "        print(f\"ydot: {ydot}\")\n",
+    "    return np.concatenate((v, a))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[34], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m V_0 \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5e6\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e6\u001b[39m, \u001b[38;5;241m1e6\u001b[39m]\n\u001b[1;32m      3\u001b[0m Y0 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate((X_0, V_0))\n\u001b[0;32m----> 5\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[43mspi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43modeint\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_prime_back\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e4\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m y_int \u001b[38;5;241m=\u001b[39m sol\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult has shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my_int\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/scipy/integrate/_odepack_py.py:247\u001b[0m, in \u001b[0;36modeint\u001b[0;34m(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)\u001b[0m\n\u001b[1;32m    245\u001b[0m t \u001b[38;5;241m=\u001b[39m copy(t)\n\u001b[1;32m    246\u001b[0m y0 \u001b[38;5;241m=\u001b[39m copy(y0)\n\u001b[0;32m--> 247\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43m_odepack\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43modeint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_deriv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mml\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    248\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrtol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtcrit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mixpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxhnil\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxordn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    250\u001b[0m \u001b[43m                         \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtfirst\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    252\u001b[0m     warning_msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_msgs[output[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Run with full_output = 1 to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    253\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget quantitative information.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[32], line 7\u001b[0m, in \u001b[0;36my_prime_back\u001b[0;34m(t, y)\u001b[0m\n\u001b[1;32m      5\u001b[0m x \u001b[38;5;241m=\u001b[39m y[:\u001b[38;5;241m3\u001b[39m]\n\u001b[1;32m      6\u001b[0m v \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39my[\u001b[38;5;241m3\u001b[39m:]\n\u001b[0;32m----> 7\u001b[0m a \u001b[38;5;241m=\u001b[39m (gamma_inv(v) \u001b[38;5;241m/\u001b[39m M) \u001b[38;5;241m*\u001b[39m \u001b[43mforce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m ydot \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate((v, a))\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m DEBUG:\n",
+      "Cell \u001b[0;32mIn[16], line 13\u001b[0m, in \u001b[0;36mforce\u001b[0;34m(x, v)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforce\u001b[39m(x: np\u001b[38;5;241m.\u001b[39mndarray, v: np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m---> 13\u001b[0m     B \u001b[38;5;241m=\u001b[39m \u001b[43mB_FIELD_FUNC\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;66;03m# B is now in nT, but this is absorbed in the conversion factor\u001b[39;00m\n\u001b[1;32m     15\u001b[0m     F \u001b[38;5;241m=\u001b[39m  CHARGE \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mcross(v, B) \u001b[38;5;241m*\u001b[39m CONVERSION_FACTOR\n",
+      "Cell \u001b[0;32mIn[15], line 56\u001b[0m, in \u001b[0;36mIGRF_field\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     54\u001b[0m r_vec \u001b[38;5;241m=\u001b[39m cartesian_to_spherical(x)\n\u001b[1;32m     55\u001b[0m r \u001b[38;5;241m=\u001b[39m r_vec[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1e-3\u001b[39m \u001b[38;5;66;03m# convert to km\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m B \u001b[38;5;241m=\u001b[39m \u001b[43mppigrf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43migrf_gc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mr_vec\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mr_vec\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDATE\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# returns radial, south, east\u001b[39;00m\n\u001b[1;32m     57\u001b[0m B \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(B)\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m     58\u001b[0m B_cart \u001b[38;5;241m=\u001b[39m B_to_cartesian(r_vec, B)\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/ppigrf/ppigrf.py:510\u001b[0m, in \u001b[0;36migrf_gc\u001b[0;34m(r, theta, phi, date, coeff_fn)\u001b[0m\n\u001b[1;32m    507\u001b[0m P, dP \u001b[38;5;241m=\u001b[39m get_legendre(theta, g\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m    509\u001b[0m \u001b[38;5;66;03m# Append coefficients at desired times (skip if index is already in coefficient data frame):\u001b[39;00m\n\u001b[0;32m--> 510\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43mg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    512\u001b[0m g \u001b[38;5;241m=\u001b[39m g\u001b[38;5;241m.\u001b[39mreindex(index)\u001b[38;5;241m.\u001b[39mgroupby(index)\u001b[38;5;241m.\u001b[39mfirst() \u001b[38;5;66;03m# reindex and skip duplicates\u001b[39;00m\n\u001b[1;32m    513\u001b[0m h \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mreindex(index)\u001b[38;5;241m.\u001b[39mgroupby(index)\u001b[38;5;241m.\u001b[39mfirst() \u001b[38;5;66;03m# reindex and skip duplicates\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/indexes/base.py:3323\u001b[0m, in \u001b[0;36mIndex.union\u001b[0;34m(self, other, sort)\u001b[0m\n\u001b[1;32m   3321\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_sort_keyword(sort)\n\u001b[1;32m   3322\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assert_can_do_setop(other)\n\u001b[0;32m-> 3323\u001b[0m other, result_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_can_do_setop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m other\u001b[38;5;241m.\u001b[39mdtype:\n\u001b[1;32m   3326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   3327\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCMultiIndex)\n\u001b[1;32m   3328\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_object_dtype(_unpack_nested_dtype(other))\n\u001b[1;32m   3329\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(other) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   3330\u001b[0m     ):\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/indexes/base.py:3768\u001b[0m, in \u001b[0;36mIndex._convert_can_do_setop\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   3766\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_convert_can_do_setop\u001b[39m(\u001b[38;5;28mself\u001b[39m, other) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[Index, Hashable]:\n\u001b[1;32m   3767\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, Index):\n\u001b[0;32m-> 3768\u001b[0m         other \u001b[38;5;241m=\u001b[39m \u001b[43mIndex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3769\u001b[0m         result_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m   3770\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/indexes/base.py:565\u001b[0m, in \u001b[0;36mIndex.__new__\u001b[0;34m(cls, data, dtype, copy, name, tupleize_cols)\u001b[0m\n\u001b[1;32m    562\u001b[0m         data \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39masarray_tuplesafe(data, dtype\u001b[38;5;241m=\u001b[39m_dtype_obj)\n\u001b[1;32m    564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 565\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[43msanitize_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    567\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex must be specified when data is not list-like\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(err):\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/construction.py:606\u001b[0m, in \u001b[0;36msanitize_array\u001b[0;34m(data, index, dtype, copy, allow_2d)\u001b[0m\n\u001b[1;32m    604\u001b[0m subarr \u001b[38;5;241m=\u001b[39m data\n\u001b[1;32m    605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m--> 606\u001b[0m     subarr \u001b[38;5;241m=\u001b[39m \u001b[43mmaybe_infer_to_datetimelike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    607\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    608\u001b[0m         object_index\n\u001b[1;32m    609\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m using_pyarrow_string_dtype()\n\u001b[1;32m    610\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m is_string_dtype(subarr)\n\u001b[1;32m    611\u001b[0m     ):\n\u001b[1;32m    612\u001b[0m         \u001b[38;5;66;03m# Avoid inference when string option is set\u001b[39;00m\n\u001b[1;32m    613\u001b[0m         subarr \u001b[38;5;241m=\u001b[39m data\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/dtypes/cast.py:1189\u001b[0m, in \u001b[0;36mmaybe_infer_to_datetimelike\u001b[0;34m(value)\u001b[0m\n\u001b[1;32m   1184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[1;32m   1186\u001b[0m \u001b[38;5;66;03m# error: Incompatible return value type (got \"Union[ExtensionArray,\u001b[39;00m\n\u001b[1;32m   1187\u001b[0m \u001b[38;5;66;03m# ndarray[Any, Any]]\", expected \"Union[ndarray[Any, Any], DatetimeArray,\u001b[39;00m\n\u001b[1;32m   1188\u001b[0m \u001b[38;5;66;03m# TimedeltaArray, PeriodArray, IntervalArray]\")\u001b[39;00m\n\u001b[0;32m-> 1189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaybe_convert_objects\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value]\u001b[39;49;00m\n\u001b[1;32m   1190\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1191\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Here we do not convert numeric dtypes, as if we wanted that,\u001b[39;49;00m\n\u001b[1;32m   1192\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m#  numpy would have done it for us.\u001b[39;49;00m\n\u001b[1;32m   1193\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconvert_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1194\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconvert_non_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1195\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype_if_all_nat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mM8[ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1196\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32mlib.pyx:2697\u001b[0m, in \u001b[0;36mpandas._libs.lib.maybe_convert_objects\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/indexes/datetimes.py:370\u001b[0m, in \u001b[0;36mDatetimeIndex.__new__\u001b[0;34m(cls, data, freq, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name)\u001b[0m\n\u001b[1;32m    367\u001b[0m         data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    368\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_simple_new(data, name\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 370\u001b[0m dtarr \u001b[38;5;241m=\u001b[39m \u001b[43mDatetimeArray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_from_sequence_not_strict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m    \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mambiguous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    380\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, (Index, ABCSeries)):\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/arrays/datetimes.py:362\u001b[0m, in \u001b[0;36mDatetimeArray._from_sequence_not_strict\u001b[0;34m(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous)\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    360\u001b[0m     unit \u001b[38;5;241m=\u001b[39m dtl\u001b[38;5;241m.\u001b[39mdtype_to_unit(dtype)\n\u001b[0;32m--> 362\u001b[0m data, copy \u001b[38;5;241m=\u001b[39m \u001b[43mdtl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mensure_arraylike_for_datetimelike\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    363\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcls_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDatetimeArray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m    364\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    365\u001b[0m inferred_freq \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, DatetimeArray):\n",
+      "File \u001b[0;32m~/.local/share/virtualenvs/presentation-FM-wVlUX/lib/python3.12/site-packages/pandas/core/arrays/datetimelike.py:2442\u001b[0m, in \u001b[0;36mensure_arraylike_for_datetimelike\u001b[0;34m(data, copy, cls_name)\u001b[0m\n\u001b[1;32m   2438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2439\u001b[0m     data \u001b[38;5;241m=\u001b[39m extract_array(data, extract_numpy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m   2441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, IntegerArray) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m-> 2442\u001b[0m     \u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mArrowExtensionArray\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mand\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2443\u001b[0m ):\n\u001b[1;32m   2444\u001b[0m     data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint64\u001b[39m\u001b[38;5;124m\"\u001b[39m, na_value\u001b[38;5;241m=\u001b[39miNaT)\n\u001b[1;32m   2445\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "File \u001b[0;32m<frozen abc>:117\u001b[0m, in \u001b[0;36m__instancecheck__\u001b[0;34m(cls, instance)\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "X_0 = [7e6, 7e6, 0]\n",
+    "V_0 = [-5e6, -1e6, 1e6]\n",
+    "Y0 = np.concatenate((X_0, V_0))\n",
+    "\n",
+    "sol = spi.odeint(y_prime_back, Y0, t, tfirst=True, atol=1e4)\n",
+    "y_int = sol.T\n",
+    "print(f\"Result has shape {y_int.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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O6eF8rSBLDNcoYhG/s9vt6O3tBcdx1J5wcnIy6Wvvx1RSvJDJZDAYDGAYBu3t7fB6vTT1RIgiNzdXUMwWiyhmZmZoZMg3RIrHdhUA3v2Ld+OpqacEP3tq6im86xfvwh/e+4eY14kHxKbz7W9/O6qrqwEAR48eBRBwWHO73QLPZQACieuamhrcc889+NnPfiYgBo/Hgx//+MeCzT+Uh/O1giwxXIOIJn4HAIuLixgaGsKBAwfQ3NwMiUQCh8Mh2il+rxefxQD/+cnlchQWFqKwsBBAICVEitmzs7MYHBxEbm6uIKKIJw/OJwqStsrJyaGEwX8c3141HFGMmcYEkQKBn/PjicknMG4aR2NBY8z3FyuOHz+O6667DkePHsVNN92EG2+8Ee94xzsEWlzB+K//+i9861vfwuTkJGw2G3w+H7RareAx1dXV11REEA1ZYriGEGuBmcwmHD9+XDCbIJFIwHFcyCG3eBAcMYTrytnvxACEj4zkcjmKioroZkWIwmKxUKLIy8sTRBSxEgXHcZQk+Js/eW+D35tQRDFpjhw5TpgnUkIMUqkUTz75JC5evIg//vGP+Pa3v43PfOYzeOmll0I+/tKlS3jPe96DL37xi7jpppug0+nws5/9DN/85jcFjwvluXwtI0sM1whiEb8jswkqlSrkbAJ/AxGTGMRYc78jElFMT0/DbrcjLy+PkkQkomBZNqSMc/DrH4koavW1Ee+3Ib8h3qcYMxiGwblz53Du3Dl87nOfQ3V1NR599NGQnssXL15EdXU1PvOZz9CfEX/xaIjVX3o/IksM1wB8Ph8sFgtyc3Op9AIfHMdhZmYGExMTqK+vR21tbUTxu2RP8qGuH/yzayFiSIYMg4nC4/HQ1FMwURCyIE0DsV43ElE0GBpwY92NeHr6afi57c1Tykhxfd31KYkWAOCll17C008/jRtvvBHFxcV46aWXsLa2hpaWFrhcLjzxxBMYHR1FQUEBdDodGhsbMTc3h5/97Gdob2/H7373Ozz66KMxXSuUh/O10v6bJYZ9DFJgdrlcuHjxIl7/+tfvOCm63W709fXB4XBEnU0Qkxh2W/F5r0crCoUCxcXFNPVHiMJisWBqagp2ux1qtRp6vR5OpzOhPv3g1+gnt/4E7/3Ve/HHqT/Sn11Xex0eedsjYFk27mJ2LNBqtfjzn/+MBx54AFtbW6iursY3v/lN3HLLLTh16hSeffZZnDp1CjabDc888wze8pa34P/8n/+Du+66C263G2984xvx2c9+Fl/4wheiXuuee+7B7bffjkOHDsHpdGJ6eho1NTWiPp/diqzn8z4Fv8DMsiyeeuopvO51rxN0tqytraG/vx8FBQU4fPhwVJcon8+Hp556Ctddd11SLat9fX3Iy8tDfX099XwO3kRGRkYgl8tRX1+f8HXigcPhwOXLl3H+/Pm0XA8I+PleuXIFr33ta1N+LY/HA4vFgo2NDaysrIBhGJSXl6O6uhp5eXkhI8lYMW4ex4R5AvWGejTmCyOFWIvZWYiDrOdzFiERTvyO/A4IkMbo6CgWFhZw6NAhlJeXx/SFJWG0GGeJa22OIRzStVEqFAqUlJSgpKQEXq8XKpWKvp9ut5s2I5ChuXiIojG/cQchADtrFCSKCFX4zmJ3IUsM+wjhxO/If7MsS2cTgMAEczzdGGStZCdod2MqKRPIFPFxHAeZTAaGYaBUKpGTkwOWZeH3++H3+wVEwZfXSEQTiw+PxxNSfysbUew+ZIlhnyCS+B3B8vIypqamUFlZiaampoQLaWLWGCJtBNmIITUIVXyWSCSQSCQ0RcgnCpfLRQfikiEKgkTaY7NIL7LEsMcRy2wC0YWZmZlBa2trwoM8wdLbiSLWOYb9jkxGDNHex1QRRXC0EHxfWaLYHcgSwx5GLLMJGxsbNHV07NixpKc7xSKGWLCXRfRiRSY2OpLrj+f5piOiyBLF7kGWGPYg+AXmcFECx3GYnp7G5OQkGhoaMD09nbT4HSBeUZissbKygunpadonTgazrpXicyYgxusajiiINDuZrub/L97rZokic8gSwx5DLOJ3LpcL/f39cDqdOH36NHQ6HWZnZ0WRXSayGMmAFMKHhoawtLSEmpoauFwuzMzMwGazQa1Wg2EYyOVy+Hy+qG20exWZTiWJiViIgv/YbESxu7E/v3H7FPwCM8MwIYvHRqMR/f39KCoqQltbG91UxdjQge1NPRkQyWmVSoXOzk56jwzD0MGsmZkZbG5u4vnnn4dGo6ETvDqdTqAauteRqY0r1dcNRRQOhwMAaOopOKLIEsXuQZYY9gBiKTDzZxMOHz6M8vJywe8lEokoEUOyKZ6VlRUsLS1BrVajo6ODkgEBmeC12WzweDyoqamBxWKBxWLB8PAwvF4vtFot8vPzYTAYoNFo9qxMwX6KGKKBfGblcjnkcnlMqSexiIJ4jiiVyixRxIgsMexyxFJgttls6O3thUQiCTubIFbOPtF1/H4/RkdHsbS0hOLiYigUiqhRDMdxyMnJQVlZGcrKysBxHJxOJyWK+fl5sCxLFUYNBgNNQ+0V7JZ21XSBXDdUROHz+egmLjZR+Hw+KJXKsBHFF7/4Rfz6179GT09P8k9yHyBLDLsYfr8fHo8nrN0mx3FYXFzE8PAwqqqq0NjYGPb0nMmIwW63o6enhxLX4uJiVCvLUNdhGAa5ubnIzc1FRUUFOI6DzWajRDE9PQ2JRCIgitzc3F1LFNdSxBANEolEoN8UiShkMlnYWZ1QCDczw0898aOLbESRJYZdCVJgXllZwejoKF71qleFnE0YHByE2WxGW1sbNXgJBzFqA4mss7y8jMHBQVRUVFDDH0CcTZFhGGg0Gmg0GlRVVYFlWVitVlgsFqytrWFiYgJyuZyShMFgCKsfk+kTdDqxG4khGJGIwul0xk0Usc7KZGsUAezN5Ow+BhGV8/l89NQc/GG0WCy4cOECfD4fzp07F5UUAPGKz7Gu4/f7MTg4iKGhIRw9ehQtLS2UFGKVxIj3fiUSCXQ6HWpqatDW1oZXv/rVOHToEJRKJRYXF3Hp0iVcunQJIyMjWF1dFdQ2MoFMRgyiYmwMePxxYHw86nUT3VgJUahUKuTm5uI73/kOjh49ioKCApw4cQKPPPIIHA4HnnzySchkMjz99NM4c+YMNBoNXvva12I86N7uv/9+lJeXQ6/X44Mf/CBcLhfd+PkEQFrD+XMb5N/7uZ06GzHsEoQSv5PJZILTOcdxmJqawtTUFBobG1FdXR3zF03MiCHaF4KfOurs7ERubm7ca4hxMpNKpTRSAAJ55lAuaAaDAXl5edfcgFvSMJshee97wfxxW3abu/FGsI88AkSw2kwWX/3qV/HTn/4U3/3ud9HY2Ijnn38ef/3Xf03rUADwmc98Bvfddx+Ki4vxsY99DB/+8Idx4cIFAMDPf/5zfOlLX8K3v/1tnDt3Dj/5yU/wne98B3V1dYLrXMtdT1li2AUIJ37Hrwu4XC709fXB5XLR2YR4IGa7aqR1lpaWMDg4GFGPKdZoQOyNWiaTCXyV+Z4FxNXrlVdeSVtrbCZPnGJsYJL3vhd4+mnhD59+GpL3vAfs73+f9Pqh4Ha78dWvfhVPPPEEOjs7AQB1dXW4cOECHnroIXzgAx8AANx333147WtfC7/fj49+9KP4q7/6K5jNZqjVajz44IN43/vehzvvvJM+9umnn46p7sXHfiaKLDFkGJHE7wgxkNmE4uJinDhxIqGBr1RHDH6/H8PDw1hdXd3hFR1qjWhrpmPymW9u43Q6cenSJVRUVNDWWI/HA51OR4lCq9WK3hq7Z2sMY2OCSIGA8fuBP/4xkFZqFEpxi/F+TkxMwOFw4Oabbxb83OPxoLW1lf67tbWV1igqKysBBFKwKpUKw8PDuP322+FwOKiER0dHB5577rm47mU/E0WWGDIEfn91uK4jIJD+6Ovro74JiSKVA27B7bIqlSrqGulIJcULhmHCtsYuLCzs+dZYAlGIYXIy8u8nJnYQA5D8+2qz2QAAv/nNb1BRUSH4nVKpxOTV++LLv5BrSqVSqFQqMAwDhUIBmUxGZcaJvIzH46FF7XgRiii8Xi/1vyCP2QtEkSWGDCDW2QTSU3327Nkdefp4IVbEEEwwJHUUrV02GJlIJcVzvVCtsXa7XdAayzCMoOMp3tbYTHUHiXLdaM56DQ3JrR8GpJlgfn4+pPPdZAjCCv4cHTx4EK+88gpuv/12+rOuri4AoEQBQCAImCxRkMMQ+Z/X68Xo6CiOHTu2Kwc0s8SQRoQqMIc6ZSwsLGBkZAQVFRWw2+1JWfQRiF1jiCd1FG6NaI/ZTWAYBmq1Gmq1GpWVlSFbY2UymYAookVOmYIohNvUBO7GG4Gnnw6kj8jaUilw3XUhowUxoNFocPfdd+MTn/gEWJbFuXPnsLm5iYsXL0Kr1aKqqirqGh/96Edx55134uTJkzh79ix++tOfYmhoCHV1dfQ9Ix1IPp8vKaLgkzD/M026nHbb55wgSwxpQrD4XShS8Hq9GBgYwMbGBk6cOAGNRoO5uTkqg5EMxKwxuFwuXLp0CTKZLKbUUag1dmPEEA9Iayxpj/X7/dja2oLFYsHy8jJGR0ehVCphMBiofAe/Lx/Y+wNu7COPQPKe9wRqCgTXXRfoSgpxTUAcwv/Sl76EoqIi3H///ZiamoJer0dbWxvuvffemD7jf/mXf4nJyUnce++9cLlcePvb346//uu/xpNPPkkfQzZ/hUJBD3SJEEW495ikj3crssSQBsQifmexWNDb2wuNRoNz585BoVBQEtktqqhAoCtkamoK1dXVcaWO+NiLEUM0hGqN3dzcDNkaazAYoNfrAezh4jMAGAyB7qPx8UBNoaEhZZECHwzD4KMf/Sg++tGPhvw9+d4QHDt2DHa7HUqlkv7s05/+ND796U8LHvfVr3417PWiEQX/McFEEeq1Js0muxVZYkghYhG/4zgOk5OTmJ6e3jGbQD44mZhYDobf78fQ0BBsNhudYk7mXmLBbo4YokEmk6GgoAAFBQUAAtEgqU9MTk7C4XBApVLB7/fDZDJBr9enTTVW9NpGY2NaCGG3IBxRkIwAnyjCvdZiZAFSiSwxpAixFJidTif6+vrg8Xhw5swZaLVawe/J48WKGPy8XHA8IIVwMgcQSqQvHqRq8nk3Qy6X09ZYIBB5zc7OYnV1FSMjI2lpjQW2WyrTHamImUpK5NqpvC6fKMj1CFGQeqLNZqOPIZPT8eg9pRtZYkgBSBtqpDd/dXUVAwMDKCkpwcmTJ0POJpC0k1hSFsT7OR4sLi5iaGgI1dXVaGhoQH9/f9L3sxtTSem+nlKphE6nw9bWFk6ePLmjNdbv9wtaYzUajaj3uFs3pFQhnc+XTxRkD5DJZJQoJiYmAAS+k0ajERqNBjk5OaLco8lkwtLSEt7xjndgfn4ehw8fxuc+9znccsstca2TJQYRwW9Dq6qqovrvfPj9foyMjGB5eRmHDx9GWVlZxDXFVEWNZx2fz4fh4WEYjUa0trZSr2ixTvJ7vfgsFkgTQqTWWDKVrdfraSE7UdXYa+E1DUamnzOfKJRKJY4ePYqlpSXarDA/Pw+FQoEjR44kTQ5ENPIXv/gFJBIJfvSjH+Gtb30ruru7cfjw4ZjXyRKDSOB7ME9PT6OiomLHm2y1WtHb20u7eWKZTRCLGOKJPMh9yuVynDt3TtAuKwYxhFojONzfb6mkUAj3/EK1xhJ58WRbY8k1xVS5jee6mUKmIqRQz5sIAubk5KCxsREsy1IRv2Sh0WigUqlQU1ODnJwcfPnLX8b3vvc9vPjii1liSCf4swkkbAzehDmOw/z8PEZHR1FTU4P6+vqY88fpjhgWFhYwPDxMU0fB95kqYsgiPCQSCbRaLbRaLaqrq8GyLO14Cm6NJf/jd+DwQV53kg/nT+WmGtda+oogXPGZvAcSiSTpAVYCYp8ql8vh9/vx85//HHa7nepKxYosMSSBcAVmqVRKf+bxeDAwMIDNzU2cOHGCdqnEinRFDD6fD0NDQ1hfX4/o7yDG/cSyQVwL5JFoUVQikYRtjZ2fn8fQ0NCO1lgiEcEnBqVSifX1depnkEoQ0ymXy5XS64SC2+2mHYKZuDZpbeWDGHCJ9XpwHAeHwwGj0Qi32w2dTgeXywW1Wo1HH30Uhw4dimu9LDEkiFjE78xmM/r6+qDVaulsQrxIR8RgtVrR09MDhUKBs2fPRpy0TlfEcK2eLhNBqNZYoho7NTUFu90OjUZDi9hA4HOVn5+PlZUVzM3NpfweyfclXCSTSng8nrSQXyi43W7I5fId17ZYLGAYRnRPEFKH6unpwebmJv7nf/4Ht99+O5577rm4yCFLDHEiFvE7hmEwPz+P1dVVNDU1oaqqKuGNLpURA8dtW4PGmuJKZyppv0cMQGoIUC6Xo6ioiDYMuN1uWsgmHTHd3d00msjNzU35prmxsYGZmRmBAmq60N3djbq6uril6sVAV1cXGhoadrSif//730dJSQnuvvtu0a4ll8tpeqrhqlbVyZMn8fLLL+PBBx/Ev/3bv8W8VpYY4gDfXhAIP5vgdrthNpvR0dFBT2iJQkzxO/46saaOUnU/u3WOIZ09/ul6fkqlEqWlpSgtLYXL5cLFixdRWlqKjY0NLC0twefzQafT0Y4nsVtjge1idyJRc7Lwer1QKBS76tqLi4soLCwURQctGliWjeo1EYwsMcSAWMTvAGBlZQUDAwOQyWRoampKmhSA1KSSSOpIqVRGTR2FWiebShIPmRg0YxgGFRUVtDXW4XDAYrHAbDYLWmNJjSIvLy/p+8yUkiyQ2Sljv98fcqLd4XAkPSgaCp/+9Kdxyy23oKqqClarFT/96U/x7LPP4oknnohrnSwxREEs4ndEaXRlZQVHjhyhXy4xIGYqiWVZzM/PY2RkBDU1NWhoaIj7y5pNJYmH3RARMQyDvLw85OXl4cCBA+A4jqrGmkwmTE5OCnSgSGtsvJ+bTG7O/A6gdIIcKENd2263p4QYjEYjbrvtNiwvL0On0+HYsWN44okncMMNN8S1TpYYIiAW8Tt+4fbcuXNQqVSYn58XZTMHxCMGlmXhdDoxPj6eUHeUmPfDJ4ZwG8x+n3zO1HWjndwZhtnRGksGsVZXVzE2NhZzaywfovlMxwmyOWeClMj3JNS1nU5nSojhBz/4gSjrZIkhBGIVv5ubm8PY2NiOwi2/XTVZiCGJsbW1hZGREbAsi1e/+tVJdYaIFTEQ+Hw+TE5OQiKRoKCgQJDfzkYMqblmPBu0RCKBXq+HXq9HbW0t/H4/7XgirbG5ubkCouC7p/Gvm4nNOXigL50ge0A6U0liIUsMQYhF/M7j8aC/vx9WqxUnT55Efn6+4PdinfKTXYtv+lNSUgKz2Zx0u6CYqSQizieVSiGXyzE/Pw8A9BQq1mu4m7HbIoZokEqlYVtjp6enMTAwALVaTX0odDodZDJZxiKGSKf2VIO/h/BB6jpqtTrt9xQrssTAQyzidyaTCX19fdDr9Th79mzITofdQAw+nw8DAwOwWCw4ceIEJBIJzGZz0vcjFjH4/X5cunQJVVVVqKmpoWuS/LbRaKQdNKRbJpTZTRbxQewicKTW2NHRUbjdbmi1WhpFhyvGpgqZJAZSXwj1emcjhj2A4AJzKFJgWRYTExOYnZ1Fc3MzKisrw37BxE4lxUsMW1tb6OnpgUqlwtmzZ6FUKrGxsSFad1MyxEC0pHw+H9ra2lBSUiIYFCSuaDqdDkNDQ2hubqbdMoODg1Cr1ZQo0ulhkApkSv46ldfkt8YCoKqxCwsLcDgceP755wXy4hqNJqWbNvkeZiJaiWTGY7fbRZPBSAWueWIgujPT09NoaWkJ2XXkcDjQ19cHn88X02xCpiIGviZTbW0t6uvrBaY/Ynk+J/rcXC4Xenp64PF4IJVKUVJSEvE6DMMI0hYejwdmsxkWiwUjIyPwer10k8nPz09J//1+Q7rJSKVSQaVSwePxwG63o6amhkYUc3Nz4DhOUJ8QozWWj0z6HoSLjkgqKRsx7ELwZxM8Hg+VwQ7G8vIyBgcHUVZWhoMHD8Z0Qk3GFCfRtfipo1B1DzEH5RIhmPX1dfT29qK4uBgHDx7Eyy+/LLi3YISKTBQKBT2NchwHp9NJiYLIOvA9lhNpq9yPA27B18xUrl8ikexojSWqsSaTCVNTUwIdqETfw1DXzQTCtaq6XC6wLJutMew2BBeYSXGMD74fwdGjRyOeboMhkUhE00CJxWAnVOoo1DpiRQzxrMNxHKampjA1NYWWlhYcOHAANpttx2PC/W2k+yAeBvz+e7PZvKOtkhDFbqxP7LdUUqTrhlLq1Wg00Gg0qKqqCtkaq1AoKEnk5+fH3TyR6eG2cK2qALIRw24Cy7LweDyCEJMQA/nSbG1tobe3l4rKxStLTJybxECkVBI/dVRXV4e6urqIcwHprjGQ7i2bzSawLk3F5DO//76mpkbQVrlb6xPXYsQQCaFaY4lqLNH04rfG6vX6qGSfaWIIN9wGIFtj2A2IJH5HPjg+nw+Li4sYHx/fkaOPB+moMXi9XgwMDGBjYyNk6ijUOkDyG0OsBLO5uYnu7m5otVqcPXtW0Nuejsnn4LZKj8dDZR/4Hsv5+fnIz8/PWDRxLUcM0SCVSun7AwS+n6Q+MT09DbvdTltjCVEEW+Rmauo50rUdDkdahAuTwTVBDNHE78ib19PTA7vdjlOnTlGt+0QgJjGE2og3NzfR09ODvLy8mOW8yYcw2S9KtE2dH8XU19ejtrY25EaUbq0khUKBkpISlJSU0PoEIQpSBAUC4mYFBQVJ57ZjwbUWMYTyNY8HMplM0BpLyN5isWB8fBwul4vKixsMBuh0uoxHDKGuTTqSdnOjxL4mhljF7ywWC4DAZnTu3LmQk5vxIFXtqvxp60ibbiiINU0cqVbBV2yNJLvBv5dI95+qjZNfnyBCcmQ+hVhnKhQKmnZKZURxrUQMqRhw45M9EMjdk/Th0NAQfD4fVCoVNTJKdWtsMCKlktLlmpco9i0xBBeYQ5ECy7IYHx/H3NwcGIZBc3Nz0qQAiJ9KIs+FpI4SiWj4EUMyCBcxkClmuVwek9kPEHmTSqfsNqlPAEBrays4jqMbzNzcHIaGhgTTvGLVJ66liCEdkhikNbasrIy2hM7MzMBsNqO3txccxwlUY9VqdUpfi0ipJLHbcsXGviSGSO5qBA6HA729vfD7/ejs7MRLL72U8WnlcGt5PB5cvHgxrtRRMMhrkApiWFlZQX9/P6qqqtDY2BiT2U8s18kUItUnyDRvsH9BopteNmJIDYhqrE6ng8/nw7Fjx2hrLKlRkGJ3Mu3NkRCpK2k3F56BfUYMsYjfAcDS0hKGhoZQXl6O5uZmSKVSUTuJxEolkRSH1WpFU1NTXKmjYIiVSuITA8uyGB0dxeLiIo4dOxZzS2/wvYR7TrtFRC9afQLYtlTMz8+PeYPJRgypB38fCG6N5bc3j4+PQy6XC2YokjXR8fv9IdtrUyW5LSb2DTHEIn5HcuBra2s4duwYiouL6e/EHkpLlmS8Xi/6+/thsVigUqlQV1eX1HoklSZGxMCyLJ1iJhFXPB/0WEhqt4bZoeoTZIMh9Qm5XJ6W+kQiyBTZZlJELxQh8eVXQrXGjoyMICcnR0AU8b6P0bqSdjP2BTHEIn63ubmJ3t5e5OTk4Ny5cztOA5nWN+JjY2MDvb29UKvVOHz4MMbHx0W7LzGKz36/HxcvXkRRUREOHToUd7491uhlt0QMkRBqfmJzcxNms5nKUkeqT2QilZQp+etMRgzREKo1NtQcTKTW2GBE6krKRgwpRCzidxzHYWZmBuPj46ivrw87BLYbUkkcx2F2dhbj4+NoaGigujK7pfbBcRwWFxfh9Xpx5MgRHDhwIKn7iZRK2q0RQzQEbzAejwcbGxs76hMGgwEulyvtm+W1UmPgXzeRRgGZTIbCwkLqhc5/H8O1xgZfJ922nmJizxIDKTCTjS5U15Hb7aaTt+3t7RE7eTIdMXg8HgwMDGBra0vQdST2TESip3Ayxby1tQWZTJYUKcSaStoLEUM0KBQKFBcX07QlX99pfX0dHMfB4/HQ1FOq+9uv1RpDsgh+H10uFy1kDw8P04FJvqBjNpWURpDZhNnZWRQUFECpVIb8oK+vr6Ovrw/5+fkxzSakosYQ65dwY2MDPT090Gg0OzwedoO3Axmo02g0aG1txZUrV5K6j3gK4eneyFJNRiqVChUVFaioqKAnT61WK6hPkM0lFfWJ3SyJkarrJjtYFwo5OTkoKyujrbGkIYFIjJPvmclkQm5urqA11m63Q6/Xi35PYmJPEQO/wDwyMoKTJ0/uqBXwZxNaWlpQUVER0xdB7FQSud9oA1wzMzOYmJigqaPgx6d6ijoSQk0x2+12Ua09k3nMXgfDMFAqlaiurkZ1dXXI+kReXp5A3ynZTS6TEcN+JaRQDQlkrsfhcKCrq4tGwJcvX8bS0hLKyspEv4+vfOUr+OUvf4mRkREqqHn//fejubk57rX2DDEEi9+FUkS12+10kKWzszMuWVuxU0lAZKMOvj1oe3t72BNEKoblYoHf78fg4OCOKWYx74fcC8dxO+4r1unovYzg5xxcn/B6vbQtdmxsTFCfIOmKeDe9/bxB75brktZYhmHQ2NgIjUYDq9WKy5cv4/e//z26u7tx8eJFzM7O4rrrrsN1112HysrKpK/73HPP4cMf/jDa29vh8/nw93//97jxxhvpASMe7Bli8Pv9gq6j4I18cXERQ0NDOHDgAJqamuIuOKWCGMJtoBaLBb29vVRgLlK6gGzmYnyhY40Y7HY7uru7Q04xi5H7jzeVtJ8R6T2Vy+Vh6xMkXcGXFY+lPpFtV03/tUlr7A033IAbbrgBt956Kw4fPgydTofvf//76Orqwre+9a2kr/eHP/xB8O+HH34YxcXFuHLlCl7zmtfEtdaeIQaGYQRdR2Qj588mHD9+XDCbEA/ErDGQewxej586amxsRHV1ddQvC39iOVkZhlgihpWVFQwMDFCCDaWhD4ij0roX5xgyCX59gqQrgucn+EQRarjqWis+R4ra03HtUN9Zt9uN1tZW3HnnnbjvvvtSRtabm5sAEFV5ORT2FDEEK6JarVaMjY1BpVKFnE2IB1KplLa9JgsS0fBP53xvgkipo2Dw5bLFuK9wEQN/ivnIkSPUszfUGuR+UkkMBPs5Ykhms+RP8vLrExaLJWJ9IpPdQddSxECaT8KJ6PHT3Kl4XViWxcc//nGcO3cOR44cifvv9xQxEHAcB7fbjampKTQ2NiYlFUEgZioJEObig1NH8Qj1iSV+R9YKtdHGM8WcCmmNSNdJF/Z6hMKvT9TX19P6BF+SmggFKhSKtG+Ye71dNZHrAgjbrppqddUPf/jDGBgYwAsvvJDQ3+8ZYiBwu93o6+uDy+VCZWVl0lIRBGKmkvjrTU1NYXJyMubUUah1APGIIXgdk8mE3t7emKeYxYpgYn0d9nvEkCqEqk+QKV6r1Yrnn39eYJmZ6vmJay1i4Evz8EFUX1Pp93zXXXfhsccew5///OeE5432FDGsra2hv78fBQUFKC4uFkUim0DMdlUg8IEgTmGnT5+GTqdLaB2xNI7IWvxOIOLFfPDgQRw4cCCuNtJUyXcHX2c/EwOQvkiFSFLbbDYwDIPS0lKYzWaYTCZMTk5CJpMJ9J3i9VaOBNI8kamTeyYc3Px+P62LBsPpdKZk8pnjOHzkIx/Bo48+imeffRa1tbUJr7VniIEMgR06dAjl5eUYHh4W9YQvZirJYrHA5XJBqVTGnToKBbFaRMk6Xq8XfX19sNlscZOWmBv2fpTEiAeZUleVSqU76hNbW1swm81YWFjA8PAw8vLyBPpOycxPkOd5raWSwhFSqiQxPvzhD+OnP/0pfv3rX0Oj0WBlZQUAoNPp4k5d7Rli0Ov1ePWrX00LzGIWiwFxUkkcx2F6ehqTk5NQKpWoqanZVcY/DMPA6XTi4sWLUKvVCZFWumoMBNmIQVyEej2lUilNK0WqTxCi0Gq1cW220eTVU4lMppJCXZdl2ZRFDN/73vcAAOfPnxf8/KGHHsIdd9wR11p7hhgYhhF0HclkMrjdbtHWTzaV5PF40NfXB7vdjtOnT2NkZCTjUhZ8cBwHl8sFo9GYVMFebGLw+XwYGBiA1WqlxVO9Xr+rjdLFwm71YwhXnzCbzVhcXATLsgKDm2huZOSzey1FDJFsPQGkpMYg5udpzxBDMMTuIkpmPWIdqNfr6Sl8N2gcEZApZqvVivLyclG8HcQgBqfTicHBQSgUClRVVWFzcxMjIyPwer00vWWz2aBQKPZtamkvOLiR+kR5eTmdn7BYLDHXJ67FiCGSgB6ArLqqWAj+UO0GYuAXcJuamlBVVUXvc7cQA3+KuaSkJGlXqmTvh4BlWQwODqKqqoqmL/iCZCaTCRaLBf39/XTjSZWwHB/pPMXv1oghEkI5oRF9p8XFRQwPDyM3N1cQ/fEVkNMJMkuwm1JJDocDUql0V5k3hcKeIYZgpGLuIJ71SNus0+kMWcDdDY5wwVPMw8PDonc3xQuO4zAxMQGPx4P6+no0NjYKWhn5gmQTExNob2+Hx+PZYXxDNp5QOvh7CXvdqEcikdD6BBDQdwr2LSCn483NzbjrE8kgkymsSKmk3NzcXZ8q3VPEwN+QUhExxLppktSRwWBAW1tbyI4NMdtf421XZVkWY2NjWFhYEEwxi+HgRu4nkXW8Xi96e3vhcDiobWIs1+IXRj0eD813Dw8Pw+v1CvyWo+W7dxP2YsQQDXK5HEVFRSgqKgIQqE+srKzAbrejv78/7vpEMsh0bWOvmvQAe4wY+BC7K0kqlVKvh3AfJH7qqLm5GZWVlWE/1JlKJblcLvT29sLr9e6YYk7FPESssFqt6OrqglqtRmdnJ1588cWoa4S6jkKhQElJCUpKSuiwkNlshtlsxtTUVFrTTnsR6dZKUqlUKCwsxPz8PF71qlfBbrfvmJ/g6zuJkeokyHTEEC6VtNtNeoA9Tgxip5KA8G8oP3V05swZKi8Qab10EwOZYi4sLMSpU6d2nFjESm/FSzBLS0sYHBxEbW0t6uvr6dBeLMQQ7fd5eXnIy8tDZWWlIN9N0k4ajUaQdtptIfxuaFdNxzWJAKZarYZarRbUJywWy476BIkSk5mfiOQBn2pESyXt9qh2TxEDfzORyWSip5KAwBsa3NtvMpnQ19cXMXUUar101RhinWJOd8TAF+ZrbW2l6QWyRiyIZyPj57tJ2olEE4ODg/D5fAJ3tEx/QTOhdJqJa4aTw+C/X3V1dYL6xOTkJJxOJyV24qscD7FnWnI7m0rKAFIRMQRvnBzHYXJyEtPT03HJRpD1vF6vaPcWbkOPZ4pZrBpDLOu43W709PSETGkBsZFLshuYQqFAaWkpSktLwXHcjjSGXC6nJBEtAtwvyBQZxbJBB9cnXC4X9Z/gz08Qco9lfmK3SW6TiGG3Y08TQ7SaQLzgp1rcbjd6e3vhcrliSh2FWivVzmvEiznWKWYxJ6gjbepEvkSv1+PkyZMhI6x0Tz4HpzH4Npqzs7Ow2WwAgOnpaRQWFqYl7bQfi8+hkOh3NCcnB+Xl5XR+ghC7xWKh9aRI9YlMezGEqm9lawwpQLAfAyDum086iUiuvqCgACdOnEgoz5nKVBLHcVhYWMDIyAjq6upQV1cXswBeqruS5ufnMTIyEpOabKojhkgIttF0OBx48cUX4Xa7MTg4CL/fL+h2SlXa6VpIJYnlPhipPkF8jvlEkU0lJY49RQx8RKoJJAqJRILZ2Vmsrq7GnToKtVYqis/hvJjTeU+hiIFlWQwNDcFoNMZ0X7GmktJ1qianu4MHD0IqldLT6fr6OiYnJ6FQKChJGAwGUT5zmSwEpxOp2KCD6xM+n4/qO5H6RE5ODv15uhsPInUlZYkhhSCStmK1rLpcLni9XphMJnR0dECj0SS1XiqIwW63o6enB1KpdIcXcywQs/jMX8flcqG7uxsA0NnZGZOS424W0QuVdiJF0enpaQwMDECr1QrqE8k4saUTu6n4LCZkMtmO+sTMzAzW1tYEjQex1ieSRbgaQzaVlAaIla5ZX19HX18fJBIJmpqakiYFQPzJZ5vNhkuXLoX1Yo51HbGLz2azGT09PSguLkZLS0vMU8jpKD6LBalUioKCAhoFud1u2u1Ehrb43U4qlSqme89UxJCJa6Y7SsnJyYFOp4PD4UBbW5ugPjE9PQ2JRCLQdxJzfgKITAwlJSWiXisV2FPEILZeEpFnmJmZQUtLCxYWFkTbjMSafGZZFhaLBVtbWzh+/HhYL+ZYIHbEMDMzg/HxcRw8eBCVlZVxrxELdqPstlKpRFlZGdV2stlsMJvNWFtbw/j4OJRKZcxpp2zEkNrrSqXSkPUJ4j+xtLSE0dFRQX1Cr9cnnSoMlz4L9nverdhTxBCMZIiBTAh7PB6aOlpZWcm4vlGoe3Q6nSgqKkqKFMg9ibXRzs3NweVyob29HXq9Pu6/3201BoJ4r8cXlSOmN/y00+DgIO3FLygogEajyeiQ3W5uVxUb4TZniUQCvV4PvV4fsj7hcDgE/hOJ1CfCRQxOpzPlfs9i4JokBpI6KiwsFLRT7gbhOwL+FHN+fj6V683kPQGBUHhra4u60yVqAZmJTT8dCE47kV58oj7KcRzdcMSUdIkV11rEEMuGHqo+QfS4SH2Cr++kVqujPp9IcwzZ4rPISDaVxLIsJiYmMDs7i5aWlh1G2WK2mCa6Ft8FjnRGzczMZFwVFQh4bvf29kIul6O6ujppX+DdGDGIjeBefKvVCrPZjNXVVWxubsLhcMBms9FNJxkJiFiQjRiiIycnR5AqtNvtlChIfYJfUwpVn8i2q2YQ8chihEodBUNMRdRETufhppjF9nyOF3zJjcOHD1Mv2WSwl4rPYoFhGGi1Wmi1WtTU1FBRQQC0xTK420ns1yBT7aq7OWKIBH59guhxkfrE8vIyRkdHkZOTs0PfKVy7aqpsPcXGniaGWE/la2tr6OvrQ3FxcdhJXCCzqaStrS10d3dT9VH+1GS6JpZDwev1or+/H1arlU6Ar66uZj2fRQCpT5SVlQEQpp3m5+cBYEe3U7LIRgzJgV+fAACfz7ejlZkcOq1WK5RKJb0HogacJYYUIxox8FNHhw4dQkVFRVLrxQNCMtG+iLFMMWcqYrDZbOjq6kJubq6ArMSy9ozlMekihkycaIOfW6i0k8lkwsrKCsbGxqBSqQTOaImknTJBtJn0XRZr+DUcZDIZCgsLUVhYCCDQyry2tgar1YqxsTEMDQ1Br9djc3MTCoUCNpstJV1Jf/7zn/H1r38dV65cwfLyMh599FHceuutCa+3p4ghnhpDsC9BLG+GVCqFx+MR5V5JfjESMfj9fgwNDWFtbS3itHAmIoaVlRX09/ejpqYGDQ0NgucgFjFca6mkUAj3HPlpp9raWto5w3dG0+l0lCg0Gk3MsxPXQosskBlCUiqVKCwsxNjYGM6ePQun0wmz2Yxf/epX+N73vgen04n77rsP73znO3HDDTegurpalOva7XYcP34cd955J97+9rcnvd6eIoZghCMGfuro0KFDMQ9diR0xAJH7mWOdYhZzMC0awXAch7GxMczPz+PYsWMhh3HSRQzkfvYr4nluwZ0zZMMxm82Ym5sDAIFBUbjPU6a6klJdVA933Uya9EgkEuoX8oUvfAH33nsvysrK0NTUhB/96Ef40Ic+hEcffRRvetObkr7mLbfcgltuuUWEuw9gzxOD2+2m/2ZZFuPj45ibm4spdRQMMWsMfC2n4C/F6uoq+vv7UVFRgebm5qgf3nRFDB6PhyrKdnR0hI2yxLqfbMSQ+HNUqVSoqKhARUUFWJal3U6kIMpPOxkMhpgi2FRhLxefE71uqMMowzDw+/249957UVFRAavVmvJUV6LYU8QQKZXkdDrR29sLn88Xc+ooGGL7NAMQrEe8mOfn53H06NGYB9bSUWPY3NxEd3c3dDodOjs7I57wshGDOBDruUkkEuh0Ouh0uqhpJ7E+3/FgPxWfY0G4jiS73Q4AtPgshvROqrCniCEYhBiMRiP6+/tRUlISl15PuPXEABH5I1/ERGoeBGJHDMGnxsXFRQwNDaG+vh61tbUxWWpmawziIBXPMTjt5HA4YLFYYDKZAABXrlxBQUEBjSiSnUeJhkxLYqQbkXSSAGS7klIB/oYilUphtVrR29uLw4cPo7y8PKm1xUwlATv9HYInreO5L7EiBmA7ncCyLEZGRrC8vIy2tjbaWRENqSCGcFak+zliSBdyc3ORm5uL8vJyPPPMM2hpaYHVaqU+BsRnmXQ7ib2ZXmsRQ6ThtpycnIyQVbzYc8RA4HQ6MTU1Ba/Xi7Nnz4rSAia2XSjDMJibm8PS0hKam5tRWVmZ0MlJzIgBCHxwPR4Penp6wLIsOjs745ICTkW7Klkv3M/3I9Kd7yevpVarRVFREfVZJmmn0dFRuN1ugUFRLPIP0XCt1RgipZIy7TMeK/YkMZDUkU6nA8dxovUFi1lj8Hq98Pl8WF1djerFHA1iqaKSD6vFYsHAwAAKCgpw+PDhuE8wpIiWDPjksrW1hbm5Oeh0OhQUFAjmJdKN/U5EgPB1lcvlKC4uRnFxMTiOE3Q7zczMUHnqZNJO11rEkAm/Z5vNhomJCfrv6elp9PT0ID8/H1VVVXGvt6eIgeM4jIyMYG5uDocPH4ZKpUJvb69o64uVSiJTzAzDoKWlJSlSIPclZsTQ3d2N5uZmVFVVJRzBiLGBchyHpaUlDA4OorCwEPPz8xgeHoZGo0FBQQF8Pl9GiqXpQqaUYyPNTpC004EDBwTyD4uLixgeHoZaraYkodPpYjpUZCpiyJTnc6RUUqoihldeeQWve93r6L/vvvtuAMDtt9+Ohx9+OO719hQxMAwDhUKBs2fPIi8vD1tbW6LXBJJdb2FhAcPDw6irq8PKyoooH8xoMxGxgFiCAsDx48eTMgsRK/dPNpxjx45Bp9OBYRjqomc2m2G32zE2NgaTyUSLpWIbqmQamUglxXrNYHlqknYymUwYHh6G1+sVpJ3CuaJdixFDOFvPVEUM58+fF/WgsaeIAQDq6+vpKVIqlYoqXUxSSYnkfkNNMa+trYmaAkr0g+50OtHd3U3/Ntk2uWSJwev1wmg0wuv1oqOjA7m5uXTiXKFQUGVLt9tNtfBJfz4plBYUFMR8Yt2t2G0RQzQEp50cDgdNO01PT0MqlQrSTiQlmMki8G5KJe0VnSRgDxIDH1KpFBzHifYBIG9mvG1u4aaYxe4mSmSt9fV19Pb2oqysDAcPHsRTTz2V9D0lQwxEfwkAiouLoVarw96PRCJBTk4OKioqUFtbG/LESkTmCgoKYrbU3E3YzRFDJDAMQ6d6iero5uYmFQAcGhqiaSev13tNaTTtdcltYA8SA/9DTdo+xcolkjXCMX4oRJpiFqvLKRFi4Ps68L0nxKgPJFoMNxqN6Ovro8UwEiVEynnzEXxiJT6+6+vrmJiYgFKppCY58YrMXQuFbjGJIRjEo8BgMKC+vh4ej4d2O7ndbgwODmJpaSlq2kkskHmdTM0xhJpoTmXxWWzsOWLggy87IcZoOT9iiAa+/Ea4KWaxi8axbiQ+nw/9/f3Y3Nzc0RElRodTvBED38/hyJEjKCsrw8TERNQ5BvK34e6B7+Pr9/t3TPuS/HdBQUHKN6JEsVcjhmhQKBQoKSlBSUkJzGYzamtr4ff7YTabMTU1BZlMRt8bg8EgkJkXA+QznqlUUqhaWDZiSBPIdLHY08rR1ot1illMYoh1Lbvdju7ubmq9GfyFEyNiiGcNn8+HgYEBbGxsUD8HQHzPZ6lUKpA/5ue/Z2ZmqOUmObHuVo2aVCJTKqdAYNpXp9PRtBPxMJidnRX4YifqsRyMTBJDuFTSXrH1BPYgMSRr7xkN0dYzm83o6emJaYpZbLXWaMRAUjWVlZVobGwM+aVIZ8TgcDjQ3d0NmUwWkqRSKYkR3HZJNqKZmRkMDQ3RlljilJYJZCKVlKlBM/51+bMRQCClSEh8cHAQfr9f0O2USItnpiOGcF1JqfBiSAX2HDEEQ2xiCBcx8HP2sU4xixUxRFuL4zhMTExgZmYmqjifWDWGaGuYTCb09PTQonfwFyWdInr8jaihoQFut5u2xBKnNP4mJXZaIxwyMfmcqVpKpA1aoVCgtLQUpaWlgtqRyWTC5OQk5HK5oNsplmjP7/eDYZiMzU+EKz4XFxen/X4SQZYYQqwXvAETe8utra24ppjTQQxerxe9vb1wOBxhvazFvqdIUQfHcZibm8PY2BgOHjyIysrKsGtkSkRPqVRSpzQiWb2+vg4AuHz5MtRqNY0mxEhr7BbsloghEkLVjki3Uzxpp0x1JAHhiWGv+D0D+4QYxJxlCI4Ytra20NPTg9zc3JDpkGhrieUIF2pDt1qt6OrqgkajQWdnZ0wnqVQqo7Isi6GhIRiNRpw6dQoGgyHiGrEg1ekWIlmt0WgwOzuLM2fOUG8DktYwGAyUKMTwXeYjGzFEBn82AghYZ5K008DAAFiW3eGLTQ4umSKGSO2q2VRSipDOGgN/ijmUF3Msa6UqYiBSErW1taivr49rmjXZewqVjnK73eju7qaifNE2ULGLz2JBLpcL0ho2mw0mkwmrq6sC32XSEptMO+S1UGMIJfOeDJRKJR2AJO+P2WzG2toaJiYmoFAoqKZTpgrt0UT09gL2HDEEIxXEQNo9jUZjXHLUwUhFKollWYyOjmJxcRGtra1Ucz9WpCJi2NzcRFdXF/Lz83HkyJGYNsvd6scQ3EKr0Wig0WhQU1MjMMAZHR2Fx+MRtMQmUiTd7xEDeT1TcXrnvz/V1dXw+/20yWB5eRkejwevvPIKjSa0Wm1aoohINYZsxJAmiE0MADA5OYmcnBycO3cuKW0eMYmBYRi43W68/PLLtE02kXylWDUG8oUnJj8NDQ2oqamJa+PZaw5ufAMcviSEyWTC1NQU5HI5HbAzGAxRB+yuhYiBfNbScV3Skkxe/4mJCVRUVMBsNqO/v39H2ilVp/doInp7AXuOGFKZSlpdXYXFYoFer0d7e3vSpwsx743YgiZq9kMgVsRATH4WFhZSFrnsxoE0gmBJCP5pdXJyEk6nE1qtlm5U4XwNshFDasCyLGQyWci0k9FoxPj4OJ2UJ77YiX6n+Agn0UMOEtnic5ogk8mS3nz5U8x6vR6FhYWiSWyIETHMz8/DarWipKQEx48fT+rLLcY9+f1+OJ1OrK2tJRy5pLNdNR3gn1YbGxsFvgazs7OQSCSCATuFQpGNGFJ8Xf53OFTaiaQF+URO3h+NRpPQHkD2oqxWUoaR7Knc7Xajp6eHpmdmZ2fTOpQWCfwuH2Jik+wXLNkBN6vViuHhYQBAZ2dnSiOXvWztqVKpUFFRgYqKipACcxqNBn6/HzabDWq1Oi0n6muZGIIRPCnvcrkokZPZluBup1ivS9YPRrZdNY2QSqUJt4SazWb09vYiPz+fpmfElNhIZi2Xy4Xu7m4AgQ14ZGRENKXWRDfb1dVV9PX1obS0FOvr60mF3sEbRSgSSOdmksprhRKYM5lMGBkZwcTEBCYmJgQtsanynMhUKkkikew6YghGTk4OnW3hOI62LJNutJycHEoSkdJO5PseyqI2GzGkEGLUGDiOw8zMDCYmJnZMMSdDNMFItF3VZDKht7cXxcXFaGlpgVQqFVV3Kd51OI7D5OQkpqencfToUeTk5NCBsGSwn1JJ8YB4ToyPj6OtrQ0cx8FkMoX0nNDr9aJFE5mKGPaaexvDMNBqtdBqtbQbLVT9iLxHGo2GPkfSkRT8nB0Oh6g2xKnGniOGYMRLDNGmmFM5exANHMdhdnYW4+PjO6aGxfR2iGezJa27W1tbdLJ6c3MzLbIau7n4LBZI7lur1Ub0nCDRRDJdLZmMGNINMQfcZDKZIO3kdDppfYIvqULkOsLVFwBkI4ZUgr+pxDP5HMsUc6ZSST6fD4ODgzCbzWhvb4der9+xVrojBofDga6uLigUCnR2dtLXK5XT0/E+RizY7S6srwOzs3YwjAcejxd+PyCRBE6eSqUECoUMWq0cKpUUCkXy6qyhnls4z4m1tTWMj4/TlEYinhPXUsSQyslnlUoFlUolSDuRiG9zcxMMw2BsbAz5+fn0PbLb7dR4ai9gTxIDH7FGDGSKOdqksJgtprFGH8EqpEqlcsdj0h0xEBG88vLyHQZEYs9CREIqicFqdcFiYbG15YPL5YPZDJjNfiiVkqukwIDjJAAYuFwsGMYFo9ENiQRQqSQwGOQwGHKSIolIm6bYnhPZiEF88NNOtbW1WF1dpV4j5D369a9/DbfbndJOtH/5l3/B17/+daysrOD48eP49re/jdOnTye83r4nBr/fj+HhYayursY0xZzuVNLa2hp6e3tDOsAFryUGYUWLGPjpLL7zW/Aaezli2NhwYWXFB4eDA8ACkIBhAq87x7EAfAC8YBjv1cKpCj5f4OQrlUoA+OFw+OFweLC8bIPBoERZmSZugoj3uYXznDCZTJienoZMJovoOXGtRQyZcG9jGAZKpRLNzc0AAmmnkZER/OxnP4Pb7UZpaSmuv/563HjjjXjPe94T8hAYL/7rv/4Ld999N/71X/8VZ86cwQMPPICbbroJo6OjCau57kli4G8YkeYYyElcIpHg7NmzMbWcie2hEGnghRR0Dx8+jPLy8qhreb1eUe4p3PPz+/0YHByEyWQKmc4iEIsYYoGYxOByeTA/74HVyoFhAKkU4Dg5GAbgOD9YFpBIpPD7JZBKGfj9MjAMuVei+SMBy3KQSBj4/YGIwmz2wmJZR2mpGsXFeXGdVNPhOUEKpNdaxJAJM6bgordKpcKHPvQhHDp0CHfddRceeeQRPPnkk/jP//xP3HbbbaJc85/+6Z/wwQ9+EO973/sAAP/6r/+K3/3ud/jhD3+Ie++9N6E19yQx8BFuIyemNdFO4sEQs8bAtwrlX58UwK1Wq8DVLNp9iZVKCkUwwe2xkXKhhBiS3WjSWXxeW3NiYYF8aVlwHIPA5Tn4/YF/y2RScJwfEokcHKcAw0jAMBwYhrtKGCyAADlwHHu1+4QDy3LgOGBx0YaNDSdqagzIyYm+KYlJetE8JxiGQU5ODliWhdvtFuWkGguutYghnE4ScW87d+4czp07J9r1PB4Prly5gk9/+tP0ZxKJBNdffz0uXbqU8Lr7jhj4U8zEXzje9cRMJZF7IrDZbOjq6kJubq6goBsNYjivhVtnY2MD3d3dKCgowOHDh6N+ocT4oqezxjA354LZDDCMFCzLXN3sAwQRiAhwNRLwQiKRQiplwLIyBNJM5D4YSCQyMIwfHIerUUaAFMjrIZEwcDr9GBtbQ22tHhpN9A6idHpOTE9PY2trCxcuXEib58R+rzGEum46p57X19fh9/tRUlIi+HlJSQlGRkYSXndPEgP/y0Q2co7j4PF40NvbC4/HE9GLORLETiUB28SwsrKC/v5+1NTUoKGhIa5NIVXFZ1KUb2xsRHV1dUz3xH9eiZ7K0lVjmJ72wGKRIHDLHCQSDhzHgGz6geUZkKfNsri68fsRqD0w4Djf1YiB/G2AKMjfkChTJgP8fsDvZzAxYUZ9PaDVZl40jXhO5OfnQyqVorm5eYeVZqo8J/ZjV1IkRJLc3iutqsAeJQY+yMa0vr6OgYEB5Ofn48SJEwlP5YpJDMRa0OfzYXR0FPPz8zh27NgOdo8FYrerEvnupaWluKXFyRc9mU2bv+nb7XYMDg5CqVSisLAQ+fn5kMlkSW8oMzNubGwEiNDvZ65GAn4eORK5hu172n5uzNWaQyDSIKRCIg6AQYBQfFc3Pym1kwzUIxhMTJjQ1CSBWh06LZcpraRgK81Uek7stg06HdcNFzGIbfIEAIWFhZBKpVhdXRX8fHV1NaLFbzTseWIgb353dzcdCktWZE5sD+n+/n74fD50dHQkPPkohlczWcfv9+OVV16hkVW8Q1NiEQMQIPSenh6UlpaCYRhMT09jcHAQOp0ODMPQ5x3ve7q05IHFwlwtMm/XExgGVzd87upzkIDjfFfrCYQkAhu/RBIggUCnEnM1ncReTY8QYmOu/l3gv/1+QCrlaEQxObmGlpbSsB1LmVZXDec5YTKZBJ4T/AG7eKXVs6mk1KWSFAoFTp48iaeffhq33norvYenn34ad911V8Lr7mli8Hq9GBgYAAAcO3YsKYYkkEqlohRWgYCBDTlBtLe3J6UtJFbEQKwRi4qKEo6sxCIGn8+H7u5utLS0oLi4GCzLCpRJ5+fn4XQ6cfHixbh8Dux2FkYjA4kEVzfqQOooQArbKaDA5s1djaJIWmn7/yWS7dRSgAhYWpsgZBEoRPvpWhIJeW3Yq1ERMDVlwsGDws8mee0yTQzBiOQ5MTk5GbfnxLWYSgr1mqQylXT33Xfj9ttvx6lTp3D69Gk88MADsNvttEspEexJYmAYRjDFLJVKRTPAIGwf7g2OFcTARiaToaGhIWmtdzGIYWVlBTMzM1AqlWhtbU34C5ssMbAsi+npafh8Ppw5cwZ6vV7QKUWUSb1eL2w2G8rLy+nG5HQ66Qk2nGvazIyfRwKklhBI8ZCIAcDV1BBHT/yBZdirr7X/6kaKq38f6EgKvAfba/n9gf/nrxW4LnOVWFi4XF6srFhQWhreAzsdiPewE4vnBKldhPOcuNYihnCpJKfTmTKTnr/6q7/C2toaPve5z2FlZQWtra34wx/+kFDKmmBPEsPy8jJ6enroFPOzzz4resE4UWIgBjbLy8toa2vD8PCwaEXjRNfhOA4TExOYmZlBZWUltra2kjrFkb9N5H5Ig4DT6YRMJoPBYAhLMOQ6pA2TRBMmk4m6pikUCsEJdm2NgdsNujEDEnpqJd1EJBIILC8FIFTEDNzOdtdSgAiAABkwVzfYwN8F6hLc1bWZq9GH/2r6avvxq6tWFBSoaW99JsQBk42CE/GcuBYjhnDtqolaBMeCu+66K6nUUTD2JDHk5eUJCqZimPUQhGoxjRUulws9PT1gWZbm7sXsJkpkHZ/Ph76+PthsNnR0dMBms2FzczOpeyEbYLybG2nVVavVOHr0KK5cuRL1OsFQqVQ4cOAADhw4QE+wJpMJ4+PjcDrdWF8vglZrgFargUqlBJlo5q/HcSxN+QTqCcS0Hgh0Im2TQ+D//SBpowBZMPQx/Fska0il2/WgQBoqMC29urqFAwcKoj7HVEHsAbdwnhNzc3N0wE4qldLOwXRu1JmaY4hUY9gryqrAHiUGnU4nCMvE7iRKZD2LxYKenp4dswCZJAa73Y6uri7k5OSgo6MDCoUCDodDtO6meIiBFJkrKyvR1NQEm82W9BwD/wQLADMzDng8G9jYsGBhYRYKRQ70ei00Gi10OjUkEhntLAqkhgCGYWlNgRSgA6fcwGYeKDrjapsrRx8b2OhIVAAIW17JKZmjtQmOY2A221FaqoNMJov63CfW7Ljnl8OYXA+ocv7sfW04XK6J+npFQionn4M9J0gta3Z2Fi6XCy+88EJaPCcIdls31F7yewb2KDEEQ0xiAOLrTOI4DnNzcxgbG0NTUxOqqqp2zFmIcW/xEgPRYDpw4ACamproh1Us/aF4BtTI63Po0CFUVFTQv4/lGvHA4chBaWkxiotLwLI+2Gyb2Nzcwvz8LCYnPdDptNBq1dDp8q9OJpO5BFKA9sHvZyGTMeA4P+1QIuSxPdhGNJMCdQeSagqkpwLaS4Gfb0cODMPB6/VjY8OOwsJtqfdwz/EbT01hct0BL7MIH7OMv3h4EQ/ceh1uPpSY9g2QXq0kpVKJsrIy2O12+P1+lJWV7fCcICQhpucEwW5LJWWJIQMQmxhinX7mawudOnUKBsPO4mK6Iwa+CRF/Ixb7fmIhBpZlqYBh8OvD/3v+/EC81yBwuTi4XNtryeUyGAwF0OkMAKrgcjmwsbEJi8WCubl5KJUKGAx66HQ6qNU5V2Uw+C2q/PsDACkCQ2+BDidSaOY4CSQSlhdBAAHCCfw/iUACZksczGYHCgt1UZ+X3beJVcXn4JJ20Z/d9rsTKHz079D36Vsgk8S/wWeiEEw2aL4CKd9zYmhoCD6fTzTPCYBMpF8b7aqpQpYYElzP4XCgp6cHEokkorZQulRRgQBRDQwMwGw2hzQhIuuIFTFEuh+PxyPw0g4e7hFbEmNjg0NgIw48PnBrpCOJQ05OLsrKVCgtLYHf78fW1ia2tiyYnp6G1+uCTqcHw3Bwuz1XZUo43kZPIgB697SDKfA8mKvFaH6hlbsaZQQiCvJa2e1Owech3Al+0v8VuCQ9gp+5JD1YV3wNbV9R46VPnkOuIv4ceiZE9II3ylR6TgDb9cHdlkrKEkOKEfzhTjcxrK+vo7e3F2VlZTh48GDED2C6IgaXy4Wuri6qJBtOJC0dvg78InOkWQkxRfRsNtIySrqRtvP8gToBaTMNiOUZDHoUFOjBcX7Y7S5sbQWiiZGRUeTkKKDX66HTqaFWa+msA0kZBciHo+S4PexGupBI6onlpfA4sGygMG23O5GbGz7HPm4Zx4D5+e1mKPqCsHBJu+BlFnHm6xfw/P/phD43dgXRTHQIRbtmsOcEsdEkDQXEc4JEE9E8J8g1gcwRQzARchwHp9OZJYZ0Ix4Xt1gQ7pTPcRymp6cxOTkZ1qsg1L2JuRGHyhNbLBZ0d3ejuLgYhw4diviFSHWNgdQ2qqqq0NjYGPZLzJ+FiPRFjz2VxO8S4nhtpKSLaOfEL5GyyM1VIS9Phfn5WRw+fAhOpxNbW5uYmpqC389Bq1VTolAoFLTOQFpUr94p7/mQ+gPDe35kahqw2z1QqZSC14GP6Y3piM/VxyxDzlXg1f98CU9/9AyKNbEppWbKjyGeDTrYRpM/YDc1NUU9J0h7cihp7UwSQ7hUkt1uz3YlpRvpqDEQ7+PNzc2waZpQEPOEDuz8cs/Pz2NkZCRk4TuV9xNMDHyDn1j8JWIhhlhJjGUBci4gHULbJ1UJJYurd3q1y4j+9dWNPrCBSaUSuukwTPVVf18T1tfXMTMzhdzcXGi1AY/mQDsmwLKkFkGmorfvhUQSgecauL7XG/kQU6uvjfh7JbP92l73rZfwp4+eQVEM5LAX/RjCeU4Q6RStVkvTThqNRpDizAQJhnu+2eJzGhAqlSSGiQ1/PT7R2Gw2dHd3IycnJ6xXdDiIVWPgz1eQzZ0M0p04cYK2bEZDLLWKWNchmzbLshgaGoLRaIxo8BP895H+HQ+2SWE7dURmDUghOXCfuFpcZnkbdqB7KBCR0buhfx/YmFQoLwd8Pg+s1i1sbFgwOTkJgINanQuDwQCdTgeFQobtwjMDmSxwbySNRIrVHo8vIuE1GhpxXfV1eHbuWfi57c+OlJHifNV5PPoX/ws3/8tlLG64AACv/9ZLePIjp1GqjdwCutcd3II9J1wuFx2wI54TJN0UcN5L73Ml3/PsHMMugZgDboBwMyeGP5WVlWhsbIz79CMWafGJwePxoLu7Gz6fL24RPLHE+AjBBN9LrAqSwbIaoe4p1ojB7w+c2gPqqQwCE8kSOj8gkUAQJZBJZSJdAWyf+rcL2IHuI/L/EgkDhUKGggIDDIZ8AH44ndtF09nZaahUKmi1Wuj1eqjVeVQug+gokbTW9rXCE+IP3/hD3Pm7O/H07NP0Z+erzuOHb/whAOAPHz6N6x58EUabBwBww7cvRyWHvRgxREJOTs4OzwmiEsuyLC5fvpwWzwkCcuAKJgafzwePx5ONGNIB/qaRquLz+Pg4ZmZmcPTo0YQF+sROJW1ubmJwcBB6vR4nT56Mu2NDzIjB4XBgaGgIWq027nuJRW8pPl0fDsKlWDqYtl2A5kAc1xhGwlNYJS5t/PsJ/B0ZYNtODxHxPAYqVS4OHMgDwMHnc2NrawsWywbGx8fBcSx0Og20Wh30eh3kcikC8hukMB2Z8Aw5Bjz6F49iwjKBqY0p1Onr0GBoEDzm6Y914IZvv4SVLTeAADk8+/EOFOSFjmj3esQQCcRzQqfToaCgAP39/aiqqoLZbMbAwABYlk2Z5wQB6UgKfr42mw0AshFDuiE2MTAMg6WlJTAMg46ODmg0iU+citmuCgTkxevr61FXV5fQFy5crSJe+P1+jI6Oora2Nm7TISB2Ib5YIgapFHRTJzWGwDUCBEjSOOQWSRcRUUsl/w7wpeTq/wfIg4jrbc81kJoFf/KZg0ymQH5+YNMBOFitW9jc3ILRuIrp6Smo1bnQ6bTQ63XIyxPOc0RCg6FhByHw8eRHzuCm77yEpc0AOZx/4MWI5LCfIoZwIAXgaJ4T/AE7MeQzIrWqAlliSDvEJAar1YqVlRXI5XJ0dnYmbSguRlcSx3EYHx8HABw8eBBVVVUJr5Ws+xopMjudTlRXV6OxsTHheyHrhUOsqSSZjL9xM1cHzohiamDjD/gyC0//RCU18FgJApFAwMAnILTnvxrxbSuwbt83A6K5RK5FyJZhWGg0WqjValRUlMHn82JjYwMbG5tYWRnF4qIcLlegwOz1euOqWYXCE3edwemvvQCnN/A5O//Ai3jxnrPIUwq/3vs5Ygi+Jn+DZpjwnhMjIyPwer1JeU7wrxtOWVWhUCStsJxO7J07DUIqUklLS0vUJEalUiVNCkDyqSSv14u+vj7Y7XbIZLKYCruREOtJPRRIkXltbQ1qtTqpexEzlSSRgNYR/H4WLEvaRUmLL9nUiU2nUPl0e1MPSFxs/5y5euLdHlQLpJi2p5wDpLGdXgpcb5s0gIBjWn5+AQoKCsGyPuTlMZBIAnWnCxcuQKPR0BZM0lkTL1765Dm0fuV5kPJFxzcu4pmPdaBQvU06mdikMzltHQ7RPCcC71d+zJ4TBJGUVRMlm0xhzxIDH8nOMRCby8XFRbS2tsJqtcJqtYpyb8mkkkg3lEqlQmdnJ1544YWko49E1WNJkdnv96OzsxO9vb1JFbHFTCUBgEoF2O2AUNhue7bB7w9s8PwZBFKk3vZsAE0j8YmBEIJwiI1fiyDTz9vpK34kERhsI4VuCUpLC5CTI8fi4iLOnj1LZcTn5uYglUrpppSfnx/z4YRhGPR8+tU49o/P05+97sEXceETZ6HNkdHX8lqIGOKx9WSY0J4TfP+PaJ4T0a5LiGEvYd8QQ6Kbr9vtFkg35OXlUeEvse4tkc2cDIoRNVKGYUQpZG934cS+jtVqRVdXF7RaLY4ePUr9mJO5F7FF9HJzA8RANnKGAU0BEc2iAAKOa8IpZvLfpA7BF8gLgGz821pIgZRRoMuLfFZY+jj+NUnBWyoN1EPUahV8Ph8YhoFSqRR01mxubsJkMmFmZoYW9kk0EWlTIq9X172vwomvvkB/du6bF3H5785BJZfuu66kcEhGJylYsZd4TphMJszOzlLi5ntOEEQS0NtLU8/ANU4MGxsb6O7uhsFgEHTViFmziHcz509XBw+KidFRFG8qibTrVldXC4rMyba9Bt9HuA0r1mtotcDaGnu1TZUTRAjbMwSBE39gzwj4M5MOoe3fA9tRgA9EJylAGoFWWEIcJGUUqE8QQtqeYyCubwQsy0KlkkMul4dsYeZLV5M+fRJNkE2JbFr5+fkhUxxyqQSX/+4cTn/tAv3Zm7/3Mn7/t6evmYhBTAG9WDwnyHsSqfisUqmyqaR0gP8iy2QyOnUY64tPJoYbGxtRXV0t+DuxZCyA+IjB7/ejv78fGxsbIaerxYoYYiEYvkrrkSNHUFZWtmOdZOchoq0RzxdJo5FCImHh8/E3cVJYDvz39uYf2MgDX+Jt9zWyiW9HEcLrk84lhmFB9JMCdQ3/VV9pYDtSIDMLfnodgINOt31yjPb8cnJyBJsSSXFMTU3RWhjZlPgaQiq5FJfuOYvOb1wEAKxaPXjnD7rwmRO7XxJjN18znOeE2WxGX18fdX1cWlpCQUEB1StLpd9zqrBniYGPeHya+VO64SaGxYwYYl3L6XSiu7sbUqkUnZ2dIUXw0iGABwReo8HBQayvr6dcpVWMriSCwkJgdZW8PiSyIW2ogQ0/YLjD0NQOwEEqJdPRpHgc+DmwLaO9nRZiaLcTy/oFbbCB6IT8nvzddlFaIgEKChLbIPhTv8EWp9PT05DL5QINIbVShmc+1oHXPfgiAGBq3YHPXWDw7weSf8/iwV5LJcUD4jlRVlYGjuMwOjqKzc1NgefEb37zG9hstpQbExF8+ctfxu9+9zv09PRAoVBgY2MjoXWuKWJwuVzo7u4GgIhTumLNHpC1om3mZrMZ3d3dKC0tRUtLS9gPdTqUWoOLzOE+0OmIGID4uqeKihgYjQDZ4IHAVLRwyC0QGQQIgl+MJp1FEvpzMvW87eYmgVQKOtFMNv/A+8XSaIIUt0kEy7J+cBwDjSaX5qSTfe3CWZySgilpv/zv2w/jL380CABYtHO4+7E5fPddBkG3Uiqx11NJsYJhAt1nWq0WLS0t8Hq9WF1dxczMDJ5++mm43W68+c1vxs0334ybb74Z9fX1KbkPj8eDd77znejs7MQPfvCDhNfZs8TA/7CRacNIm7nJZEJvby+Ki4vR0tISsYc/XTUGjuMwPz+P0dFRNDc3R51PSLXJDiky63Q6HD16NOJrJDYxhNo84t1Q5HIZ8vP9MJu3tYkCiqYMjRS2W07J9bdP/ABDo4DAbbFXO5VYyGTb6raBjiO/gDwC65GCN3c1KmMRcIILPM+yMmHkJdaGGVwwdTgcMJlMMJvNsFim8JlTcnz5lcDnedjoxN/+bADf/IsWVBrEn/4NRiYH3NINfvFZLpfjwIEDeOSRR/CVr3wFPT09eM1rXoNf/vKX+N3vfoff//73KbmHL37xiwCAhx9+OKl19iwxBCPcZs5X/Tx48CAqKytjWkvMGkOo++KntE6ePHl1Yjb6WqmKGIxGI3p7e1FbW4v6+vqom5aYHVKREC/5VFTIYbG4aboHCMw4kGI0Se+QNBEhgMD9EInuba+FwIBc4O8Aoqm03clEnkeg/kAiCQmILhIggd/vRVGRFirVdvQlRhouHIgiKWm/tFgsyFGv4LPPmgEAw6s2fPMPw/j8G5tg0KZ2GvdaiRjIdUNlLFwuFyoqKvDJT34Sn/zkJ1P63ouFfUUMwbMMPp8Pg4ODMJvNMat+AuKmkqRSKT1pki+I2+1Gd3c3WJaNS3guFREDv8gcjyaU2BGDxWKB0+lEQUEB/XIlcg2pVIKqKgmmp1nalrqdMuL38m8Xmon6KgEZiAvMOOBqTYJsNsLZhm01Ve5q5Br478D7FHiMSqXAgQM7a1np2DClUikKCwtxa2EhFtZewL8NBj4/T0/Z8NL3ruC+TjmqSgupW5qYJ23y3qV7k46l1piq64aqDQYXn/dCd9K+Igb+Zu5wONDV1QW5XB7R0SzcWvF2OYVDsATF5uYmbZE9cuRIXF9EsZRRCcHEUmQOB7GIgWVZzMzMYHx8HAqFAkNDQ9Dr9SgsLEx48txgUMJstmNzkz/FDGwPrhFPBu6qFDfHixT4La4MjSgC089+Oqy2rbXEXH1vSeoI4DgWUqkMLBuQ166uLtyxOWbi1NhWxODe1x/AV/+0AACw+RgMuAxwmzxYWwtIQxChuYKCgqSF5jLpi7DbbD1jyQiEw7333ov7778/4mOGh4dx8ODBhK8RjD1LDMEfNj4xkOGwiooKNDc3JySVHXzKTxR8YlhdXcXg4CAaGhpQU1MT99piRgwejweXL18Gx3ERi8yR1hBjc5uenobFYsGJEyeQm5sLt9sNkylgjEM6KsbHx+mJNtb3srZWhbExJ5xOcqongnjkNd+OGkiBmmW3/0ciA2LjSX5GahbkNdi29vSDyGsHooxAxHLgQD7y8kJvsJk4Ob7pUAFcnBwPPDMNAPjBy2s4UqbBT993ltYmjEYjxsfHqdBcvK89QaYihkwSQyoG3D7xiU/gjjvuiPiYurq6hNcPhT1LDMEgqaSJiQlMT0/H5CIWaS0gvtH6cCB/PzY2huXlZbS2tqKoqCjhtcQgBo7jMDIygvz8/KhF5nBIdtjO5/PB6/Via2uLihV6vV5BfpxIJvt8PgwNDcHv9yM/Px+FhYHURyTxOYlEgqYmFUZG7PB4hDWAwP1vy2/z/RG2W1W3/z+w4QeKzMTfIfA3/quRAZHW2E7PSSQMSkv1KCrSh7y/TEQM5KBzZ+cBbLq8eOhSIHIYWLbik4+O4C/aStFZW0W9l4nQ3PDwMHw+nyCaiOUgca1FDJFE9JIhBqLrlE7sG2JgGAYzMzPw+XyiSGUDAWJIVkiPRDHr6+tUciOZ+0qWGIxGI+x2O0pLS3H8+PGEv7TJRAxOpxNXrlwBwzBoaWmBSqUKqXUllUohkUjQ0tICjuOoEcvCwgKGh4epXERhYWFIuQiJRILm5lxMTDhgt/MtPVlwHKkXsHSOIdDFBJoOImml7UJyYPKZ1CpImypJVUkkzNXnwaCkRIOSEn3U1zCd2FZ/ZXD36+vAccDDLwbI4YnhNdg9PnTWGgDsFJqz2+0C2erc3FxKEuFMcK7FiCGcVlK6Btzm5uboZLbf70dPTw8AoKGhIS7Z7z1LDPwvlc1mw9bWFnJyctDZ2Zm0jDFpf012E7bZbOjq6gIAHD9+POkPRzL3xJfayMvLQ3FxcVIbU6IF+o2NDXR1daGkpAQ+n09wwgo+cQW3s2q1Wmi1WtTW1tKUE198jpAEXxFTKpWiqUmN+XknTCYv+FpGgQ0/MAkdqB+wtFAtkTDw+z3gOClIB1IgMtiuWZDCNdkLfL7ABHR1dT70+t2nvR+cGv3Ia2vg9bN45OUlAEBBngKff2wMbZVa3Hp8uwmBYRio1Wqo1WpUV1fD6/XSaGJwcJBGcoQoSD2PdCRdKxFDpFRSurwYPve5z+FHP/oR/XdbWxsA4JlnnsH58+djXmfPEgPBysoK+vv7oVKpUFpamjQpECQ7y0A0hqqqqqgfbbKQSCQJqcj6/X4MDg7CZDLh9OnTGB0dFaXVNN6IgbxXRIbk+ecDSqABbSM/OI6Dx+OBREI8mMOvHyw+Rwa8JiYm4HK5aNqjsLAQKpUKVVW5UKtdWFx0w++X0E2dTEAHNjAJrzjth1weuL7XS4bfAIbxX51u3u5eCgzHARqNHDU1BTFFmZlMJREoZBLce2MDzA4vHh9cw6/7VgEAT46sCYghGHK5HMXFxSguLgbfBIdM/Obl5aGgoCBjiqKZmmMId12Hw5G21+Lhhx9OeoYB2MPEQKSy5+fncezYMayvr4s2ewAkfiLmOA5TU1OYmpqiGkNLS0spn1gOB9Iayy8yi9HdFA8x8KOV48ePo7i4mK7Bsix9nfmaVz6fj5KF3x84qUeaCOfLRTgcDqyvr2N9fR0TExNQqVS0LnHwoBpGowfr6x5eBxIx5PEjMMHs56WGyM8ktFuJtKkCgfkIlUqJ8vI8gQ5SrK9hOhGumeLTNzZgy+nDhSkLAOBUtR5rVjd6FrdwvrEAcmn40zfDCE1wvF4vVSNdXFwEx3EYGBigwn/xdAcmit2USiJ+D3vJvQ3Yw8TgdrthsVjQ0dEBtVoNi8WSEt/neODz+dDf34/NzU2cOXMGWq0WQHqkLEJha2sLXV1dO1pjxVJpjWUNfkss/zUh8Hq99KTFlwR3OBwYHx+HTqcTvA8kmoj0xc/NzUVV1XYR1Ww2Y319HQMDA+A47iqJ6CCRaLG1xcLp5MCygdqAVEqc3Bh4vczV//bxUkhSBIrOEuTmylFcnAetNv62zt0QMRAYcuX413cdxYd+1o8XJi3oXdzC67/1EgDgY+dr8IFzsTsGyuVylJSUoKSkBJubm+jp6UFeXh4WFxcxPDwsUCPVarUpIUcxmkYSvW6mIwaxsGeJITc3F2fOnKEfLKlUCrfbLdr68U4/OxwOdHd307kJfkpLrIG5eIhhdXUVfX19qKur2+EPna6IwePxoKenBz6fT9ASS1qBNRoN+vv7odVqUVRURIvINpsNPT09KCwspF4UZO6CRBPkHkg9KNxGIJPJBGkPq9WK9fV1rKyswGabgEajQV6eDnK5Grm5HORy0pIamEMIdC0RuWxApVJArZZDq81JOl2RzoghlvbrT91Qj8HlXpjt25LgdYXJbWgymQy1tbWora2Fx+Oh0URvby8ACGTExUoDZyJiCHS9hU8lZdVV04hgqexkXNyCEc9mbjKZ0NPTg7KyMhw8eHDHh1IsiY1YiIGfygo3ySxGxBCNXOx2O65cuQK1Wo0TJ07QYjC/nnD48GE0NjbStM/U1BR9H8vKytDU1CQoIgOgqSfyRSTvUSwkwS9g19XVCWYmjMYlVFQooFSaUVpaCp1OR7/o0SKURJApWYRIxFBTkItH/7+T+MAjfZhYCxjYn28qwDNjJjw5soYPnK2KiyiC5TAUCgVKS0tRWloKjuOwtbUFk8mE+fn5HaZEiVqckutmohMKwA5iIKmkLDGkEfxTq5jCd2S9WDbhubk5jI2NRdRhSlcqye/3Y2BgABaLJWTahr9OKiMGohZLBgzJF5yQAvniMgyDnJwcqhI6MzODyclJ5Ofnw2w247nnnqMF5MLCQlof4Q8N8teMN+UUXMD+85//DIZhMDY2Bo/HI+oUcCikO2KI5ZoFeQr827uO4uZ/uQyvn8Ppr12A2xf4zOXIpPjcGxrjumYkktbpdNDpdJSkSTRBmjX40UQ8beOZIAby2Qu+rsvlAsuy2RpDppAKYoi0Hsmdr62t4dSpUzAYDGEfmw5icLvdtDU2nJ+DmPcTjhiWlpYwODi4Qy02FCnwf0cGAE+ePAm9Xk9759fW1rC8vIyRkRGo1WqactJqtfRLGBxNJJJyIr+vqalBbm7ujilg0rcffO1Eke6IIVZiAIBijRJfelMTPv/YGCUFAHjDkfiGrOIR0ON7G7AsS6OJ2dlZGk2Q4cZIFqckktwtxOBwBCKvbMSQIchksrQRA/F14DgOZ8+ejToFKhZphdvQwxWZw0Gs4jN/c+M4DhMTE5idnUVbWxsKCwsFvwtHCsS1zuFw4PTp07RIx++dJ/lpk8mEtbU1zM3NQSKRoLCwEEVFRdTmMjia4P8vnmiCYbYN4quqqgSdNv39/eA4jp5mCwpia08Nd510IR5iAIA3HSnByUod3vNwD9ZsHrQd0OJUlR4AcGHSDLvHjxsOFkZcL1HJbYlEAr1eD71eT53SQs2sEFMi/uufyaE6fgMFgd1uB8Mw2eJzOpHKVFK4GgPxiS4oKMDhw4djKkCm0nmNzAaEKjLHs0684JMLSWFtbGzgzJkzdOqcFDzJ5hxMCqSVViaTob29PeIGq1AoBCfKjY0NrK+vY3x8HE6nk54mi4qKoFKpwqac+PdDXotoQ1j8Tht+bnxubm7HBDbfYjMSdnPEQFCmy8E33t6C23/ci77FLTzy8iKmTQ7815VlAMC333kY55t2qsYSiCW5HZzy29zcpM51xOKUvP8kUk73HEMkAb3c3NyM6GIlgz1NDHyko8awuLiIoaGhkD7RkZCKVBK/yHzs2DGUlJTEvI6YEYPH40FXVxc4jkNHRwf9YobbgAmsVit6enqQn58f0bUuFPhzC01NTbDb7VhfX8fa2hqVayApJ75cQ7iUE/n/4Mgm3PPm58ZdLhc9zc7MzEAul9OZCYPBENXsKF1IhBgAoO2AFm85VoLf9K3iq3+cFPyuKj9y3SUVKR2JZNt3uaGhQfD6z87O0tfbZDIJJNxTjXCtqna7HSqVKksMmUIqawxkmG5paWlHmiSd90aIIdYic6R1kr0fMoV96dKlHY5v/NM5eSwf6+vr6O/vR3V1NWpra5P+0pC0D5FrMJvNVGGX4zhavCZS3qFSTqOjo5DJZJDJZLQ2EUsBOycnBxUVFaioqBBYbPIL2IQo0uX7GwqJEgPDMPjSG5twqFSNn768iDmLCwDwz39xKGqHkhjqxNHAf/1ZloXRaMTQ0BCmp6cxNDQEnU5HX/9Untx3w9SzmNjTxBDcrip2Ksnn88Hj8aC3txdutxsdHR0JFZHEjBj8fj8uX74MhmGiFpnDQSwdKKvVirq6OjQ0NETsPOJjYWEBo6OjOHToEMrKypK6h1AITvtsbm5ifX0dMzMzNO1Aoom8vDw6metwONDe3g6lUpnwzAQ/982fwOYLz5FNSswp/ViQKDEAgFTC4D3tFXj3qXKc+foFOL2swDPabPfgFz0rOFmlw4nKbU+PdBeBJRIJbQzo6OiA0+mk0cTU1FRc0Vy8iJRKijW9uJuwp4mBD0IMYp1SpFIpbDYbLl26BI1Gg46OjoTDUrEG3BwOB9xud1z1jXD3k0yOe35+HjMzM8jJyUFj43b7YrTOo4mJCSwuLuLEiRMRu7jEAsMwtIjZ0NAAp9NJZyYmJyehUCjAsizkcjlOnjy5Iz+d7MxEqEhmfX0dfX199HO6uroadztmIhDje8EwDK4/WIjf9hvxxd+P4YF3HEaRWoFbvvsyHB4/ZBIGL9zdiTzl9sxKJpVVVSoVbYXmR3Pj4+NwuVzQ6/WUyJONJvbT1DOwz4gBEM/Wj5z2gk/EiUAikcDj8SR1PysrKxgcHIREIsHRo0eTvp9ETqykrXRhYQF1dXUwGo2C30XqPBoYGIDVakV7e3vGWvdUKhUqKytRWVkJq9WKrq4uOlB34cIFFBQUoKioiCqEijkzERzJzM7OYn5+nrZj6nQ6ukml4oQp1oHpY+dr8eL0BibWHHjT914W/K5QrUCOXKiWm+6TcriTOz+aA0DbkUk0oVAoBJ1O8R66wqWS0im5LSayxBAEcrJdWVmBRqMRnIiTubdk5LInJycxPT2N5uZmjI6OinLyizdi8Pv96Ovrg9VqRUdHB+x2O1ZWVqJ2HhFZDIZhcPr0adFkD5IBsVcl09VAIDW2traGhYUF2jdPupzUarWoMxMkmsjJyUF7ezstoK6vr2N6eppuUoWFhaL5MItFDCVaJX58+3F8+Q8TeGHSIvjdv/6vI5BKhFFiJiOGSOAbQvn9fiojzq8NESnxWE780bqS9hr2NDEE6/8wDJNUysbn89HNr66uDmazWYzbTPiETnr8NzY20NHRAalUiuHh4bTfj8vloqfrjo4OKBQKOJ3OqJ1HNpsN3d3d0Ov1OHToUEakkIOxtraG/v5+1NfXo7q6mv6cKITypTLW1tYwMzMDmUwmmJkIlslIZGaCT8zBBWyySY2MBHyYyQbFb8eMF2IWgg/oVfje/zqKdZsHf/mDLqzZPPj0jfWoLxKejDMRMSRS15BKpbQ5gUhYEKKemJhATk6OwOI01Oc4UldSNmLIIBiGSaoA7XA40NXVBaVSic7OTvrBEAOJEAMZouMXmV2uQEdIskW9eCIGMjyXn5+PI0eOCK5LNsFQp2Oz2Yze3l5UVlaivr5+VxTfFhYWMDY2hsOHD0ds7w3um7dYLFhfX8fo6CjcbrdgZiKSTEe0mYlQrwl/k+K34q6srGBsbIx6HZAJ7Fhf11R0CBWqFbj1WAn+38V5XJyy4N3tFYLfr1i9+O2oHcftS/hfpxKz2Y0XYnw3+MONfItTQtShpFKyXUm7GIkSw/r6Onp7e1FeXo7m5mZIJBJRu5ziXWtzcxNdXV0oKCgQbMb8zSeZD3+sRGU0GtHb27tjeI7jOCgUCng8Hly8eJFaQBoMBkgkEiwtLWF4eBgtLS0J+26LCZKOm5+fR1tbW1yFb4lEQjeBpqYmOBwOrK2tYWVlhZrSxDszAQSi02jyDfzpb+J1QPLivb29cekJpap19C3HSvDvF+fx3IQZ/921hL88EXi/V7bc+OSTa9hys3hsfAItZWocr4ivrToRiN0JFc7ilEilqFQqFBQUwOl0hmxH3osCesAeJ4bgD3q8GzApAo6Pj6OlpQUHDhygvxOrk4isFWvEsLy8jIGBATQ0NKCmpmZHugxAQmkpPmKJGGZnZzE2NkbNhgjIaTgnJwevec1r6Gl6cHAQPp+PRjZHjx6lhjyZBMuyGBoagsViQXt7e1JiZvzTJNmoSZcTie5IJEGGq0JFE263GysrK1AqlXHNTMjlcqpOSvSESCtutJ79VBFDTUEuPnCuEv/vwjzue3wCfxo14dUN+TuG4fJzU9t1RZDKFlk+UVdXV1OvD5PJBLPZDI7j4HK5KFnn5OTA4XCkpQNPbOxpYghGPMTAt7tsb2+HXq/fsZZYveaxEAO/yMx3OQtehzw2VffDsixGRkawsrKy43UJ1XlETlONjY3o6+vD5uYmVCoV+vr66EZVVFSUkV5un8+H3t5eeL1enD59WnT3MLlcLpDpIDMTk5OT6O/vp8NtRUVFyM3Npd1p/f39kMlkOHz4MJ0pSUT0L7gVN7jLht+zn8phs7teWwMJw+D/XZjDhSkLdYIj+Nwtjag0iK9OGwrhUjqpAN/rw+/3Q6FQQKFQYGVlBc899xy++tWvQiqV4ty5c/B6vSltSZ6ZmcF9992HP/3pT1hZWUF5eTne+9734jOf+UxCDR/7jhhi8WQg+XsAAgOZ4LXEjBgirUWKzJubm+jo6KBaQ8EgX+xURQxkI3U6nejo6KC5UX7nEek0Ce48IlPG586dg0KhgMvlojIVU1NTUCqVlET0en3Ku1VcLhd6enqgUChw6tSplEsj8KUagu1FScrBYDBgfX2dToqHqtcEz0yQtaNFE8E9+ySSGxkZgc/ng1qtht/vh9vtFp0gJQyDu15bgzcfLcFj/au4PLuBrvktAMC/vLEUr2kVf5AxHDKhrEquS9qha2pqaBPDV7/6Vfzyl7/Er371K9xwww34m7/5G1x//fWiX39kZAQsy+Lf/u3f0NDQgIGBAXzwgx+E3W7HN77xjbjX23fEEG0zt1gs1B0sUqeMmKmkSNEHv+Ons7MzIruTE2SyxBBqDafTiStXrkCpVOLMmTP0dBNcQA0WnLPb7eju7oZGoxEou/J9Fvx+P+3y6e/vB8uydGaAyFSICdINZTAYcOjQoYxsFMH2oouLi5iYmAAQKMwPDAzQlJNCoYg4MxFcwI5GEsFdNjabDQsLC7Barbh48SLUajUtYCdjiBOM6nwVPvzaGrzb4cVr/vkSAKBUk54UEsFusfU0GAx43/veh9/+9rd485vfjHPnzuH3v/89leEWGzfffDNuvvlm+u+6ujqMjo7ie9/73rVHDPHWGBYWFjA8PIympiZUVVVF/EKQzVyMEDzcZk6KzIWFhTh8+HBMH2gxiCFYEoPcR3FxsUDQLprmkcViQW9vLyoqKiIOAUqlUoG95tbWFm0FHRwchF6vF8hUJANC/LupG8pqtWJqagq1tbWoqamh9qKzs7NUpoOfchN7ZkKj0aC4uBhbW1toa2ujKScyX0JIgsiXJwu9SgZDrhwWhxej6x401Yd/rM3tw/KmG3WFuYIZiESRyYghXFeSWq3GyZMncfLkybTe0+bmJvLz8xP62z1NDMEIRwwkb768vIwTJ07Q6cdoa5G/TTZnGeq+IhWZI0GsiIFs9kS2O/g+Ig2tkfsfGhpCc3OzoGgfDXx1Ur5MxdraGjXEIZskv8snFqyurmJgYCDue0oljEYj+vv7BfdEnn99fT1NuRFrU4VCQUmSTOAmOzMBbBefg+XLiYT11NQUJWlCFIm2WTIMg9c3FeAXPSv478EtvOl06MPV44NGfPH347B7/LjlUBG+9raWhK7HR6aIIVykYrfbM+LeNjExgW9/+9sJRQvANUAMZPLW4/Ggs7Mz5g87eZPDDa7EA/5mzje0CVdkjnWtREEGAaempjA5OblDtjua5tH09DS9/3iVZoPBl6nw+Xw05UTM4oO7fMJhdnaWPpeiovicxlIFMjcRqUMrOOVmsViwtraG4eFhOtxGiCKSTAepA/G7nEjajxwEgjdnfl2EX8AmBfScnBxawI63LvT+s5X4Td8Kepad+OdnpvHx19VCwrt+z8Im7v31CNirpa7nJ8UZJs0kMaRijuHee+/F/fffH/Exw8PDOHjwIP334uIibr75Zrzzne/EBz/4wYSuu6eJIVoqiQxn6XQ6gSF9LOBLbCQLspl7vV4MDAxga2tLYGiTyFrJwufzYXZ2FqdPn4ZOt62IGYkU+K2fp06dSuj+I0Emk+1QRl1bWxN0+ZACNhksCrYF5T+XTIFPnvHMTYSqDayvr2NxcRHDw8PQaDSUKDUaTcwzE7EqAvAL2PzBrqGhIfj9fjqBTbSkIqHSoML/PqzED/vdeOjSAgaXrPjAuSqcqtJBLpXgH347RkkBADprxWnpJKKI6UaozALHcXA6nUlFDJ/4xCdwxx13RHxMXV0d/e+lpSW87nWvw9mzZ/H9738/4evuaWIIhkwmg9vtBpCYsxkfYhV6ge0v7uXLlyGTyaIWmSMh2Xvyer20g4HfkRWt88jr9aK3txc+nw/t7e0p9xbgK6OSLp+1tTVqxkMmgLe2tuB0OgW2oJkEx3EYHR3F6upqUuRJagMajYZam5KUE7E2JZFEOGtTPslvbm7SdtlYCtjBg12EpJaWljAyMkJJqqCgIGwB+zUVMuh1enznkhGXZzdxebYfEgYCQgCAllI1PnNzQ0KvUzD2W8RA3oNYsLi4iNe97nU4efIkHnrooaRehz1PDMH2nj6fD2NjY5ibm0soVcOHWC2rVqsVAGjnTrIj+4kSg8PhoJ1HEolEQAqROo8cDgd6enqQm5uLtra2jGge5ebmorq6mkpYk8lTn88HmUyG6elpgf9zJsCyLI0IT58+TaMaMaBQKAQyHRsbG7Qu43K5aDRVWFhIrU2BwEFiYmICRqMRx44do2mlRArYfJIK58HMf/05jsMbWwy44XgVHrq0gKdG1mF2eAVrf/L6OvzliTKBKmsySOccAx+RRPTSUWNYXFzE+fPnUV1djW984xtYW1ujvystLY17vT1PDMEwm83Y2tpCR0dH0m+IGC2rS0tLGBgYAAA0NTUlfZpJNGKwWCzo6upCeXk5qqqq8MILLwCI3nm0sbGBnp4eqkS6G7p8SBpMr9fj8OHDsFqtITdJomWUrnviD9OlUkVWItm2Nm1ubqZ6Skajkcp0kJTU8vIyHeIkHV/JzkyEKmCT4jkpYBcWFsLn84FhGBzQq/DZWxrxmZsbYLJ58MAz0/hNvxFvO16C286I2ySQiYiBRGfBhOT3++FyudIiifHkk09iYmICExMTOxovEhmI3TfEYLfbMTs7C47j0NnZKUqeMVm5bFJkbm1tRVdXlyhpqUSIYWlpCYODg2hubkZVVZVAGZWv1xO86a+urmJwcBANDQ2oqqpK+t7FAPFRKC4uxsGDB8EwzI5Nkq9lpFar6Uk6HtG5eODxeNDd3Q2ZTJaWYbpghDIEMhqNuHLlCjiOQ3FxMaxWKxQKRUhr02gzE+S/QyHUYB8pYDscDoyNjWFjY4MWsIs0StQWBFIr/uQG+EMiU8QAYAcx2O12AEgLMdxxxx1RaxHxYM8TA8MwVOzNYDDA7XaLVnxKNJXk8/nQ398viFzEktjgt5pGA5HZmJmZQWtrK81Vki9OcAcL/+9mZ2cxNTWFo0eP7pouH5PJhL6+PtTU1IRt8eVrGZGUx9raGjWKJ5EEkc9OFk6nE11dXaKkCcUAsa9cWlpCXl4eGhoasLm5ienpaQwMDNDTPF+mAxBnZgIQ+hxcvHgRpaWlcLvdggJ2LhcoXM+anKI//0wMuJE9Ivi6ZJgtK6KXZnAch6mpKYyPj+Pw4cOQyWQYGxsTbf1EUklkowguMotVyI51HZZl0d/fD4vFsqMDimyoExMTKCkpEXTykJmPtbU1nDp1Clpt6hUxYwFRbI3HKzo45UFaQfny2YQoEpGJINFLSUkJmpubd02ajci9nDp1CnK5HEVFRTtmRiYnJ6lMCZmZCE4jJTozQcBxHPLz86HX68FxHKxWK0wmE3TmgPNf/9IWuocn0HCgBGq1OubXb87sxLzFiaMVWmhzhFtYJiKGcNLzDocDMplMdAmSdGBPEwMAmtPV6XQwmUyiyVgA8UcMGxsbNM0RLMWQTmIgqQ3SeUQ+mKTzSCKR4NixYzAajeju7qYdLgaDAUtLS/B4PDhz5kza8vORwG/9bG1tjWk4MRQkkm35bCKfvLa2JuiyISQRyyZFJqyrq6tRW1u7K0jB4/Ggq6sLCoUCx48f3xER8WdGiEzJ+vo6BgYG4Pf7BTIl4WQ6CElEmpkg4Du4MQwDrVYLrVaL2tpa/Hj8CoZW7Xh0wIzrjIu0gE1IKlQ6zu724YuPj+PxwUBhtTBPgZ9/4AQK1dv1nEylkiJ1JO2Gz0a82NPEwDAMmpub6UYpk8lEJ4ZYN3OSx29sbER1dXXSkuDhEI0YbDYbrly5QoXayAc2uPOouLgYJSUltMNleXkZg4OD4DgOhYWFMJlMKCoqyqgVJ4le1tfXRZ2b4Msn81tBiUwHSceQlFPwRmM0GjEwMICmpqZdM2FNNLfUanVMKa1gmRJSwJ+fn4/L2jRURxshCpYN7+D2gVdV4e5fDOPxKQ/e85oTKJIHvCYmJiZoAwEhCpVKBY+PxYd+NoDuhS0wADgA63YP/jxhwtt5In2ZihjCTT3vxTQSsMeJARAOuYmpiArElkriOA7j4+OYm5sT5PFDrZXqiMFkMqG7uxtVVVVobGwUyFuE6zySSCSQyWRYX19HeXk5Dhw4gPX1daorpdPp6Ek6nR9y4jFNZhRSGb3wW0H508dDQ0Pw+XyCk7TRaKQ+FbvBbwLYbkMuKChAS0tLQjM75DRfX18Pt9tNZyaItSl/ZiKUTAe/gB1MGKE26+ubC9FZq8el6Q187H+G8e/vOYrGxsYdyrQTExNQqVR4bEGO7gU7NDlSfOUtB/G5x8ZgdnhRkCc8uGSKGPaTexuwD4iBD0IMYmnPRyMa4hFts9mitsemmhjIRh5sOBRN84icfuvr66mwoFarRV1dnUA6m0gkFBcXUx2jVIXIJBUmlUrR3t6e1klW/vTxwYMHYbPZ6El6cHAQAFBRUYHc3NyUehzEClLnKCsrExwGkoFSqaQe1JFqM4WFhdTaFNiOJnw+HwYGBpCTkyMwIwouYN9/awve/VA3FjZceM/DPfjCG5rwuqaCHcq0gzMreOxPUwCAG8v9+NofRmB2+FGTn4OzdcKJ6d2USrLb7dlU0m6AmMJ3ZL1wmzkpMsvlcnR0dERNuYgxE0HW4d8TkYRYWFjYIRAYTfNobm4Ok5OTYT2Q+To+fB2jnp4eANtTmQUFBaINFRHvba1Wm/EuHzLYpVar4fF44HQ6ceDAAVitVrz00kshi7fpxMbGBrq7uyN2aSWLULWZ9fV1LC8vY2RkBGq1mqactFotbXpwu91ob2+HTCYLW8DWKCT44XuP4iP/PYRRox0f/fkg2g5o8bbWUpyu1qNMp4RMJsNjEy7a2vqbGQZe1g+dAnhXlR293V10AlutVmdkwC3ScFs2YsgQglNJgDjCd0D4zdxisaC7uxslJSUCmepIELNdldwTSbdYrVacOXNGELFE0zwaGxvD6upqzPpCfB0jMtRE5CnE6PABAjLB3d3dKC8vF+30myz408xnzpyh08x+vx9msxlra2sYGBgAy6bWYyIYxPe5sbERlZWVKb0WQSgPapLy6erqogVomUyGEydOCDrygNAzEwUqKX7w7kP4wYtL+I/LS+he2EL3QsDkR8oAMqkEbt/298bLcjhbZ8Dn39CIghyGFtBnZ2dpjXFjYwM5OTlpI4hw+w2JGPYi9jwx8MHvzxejaCqVSuHxeAQ/W1xcxNDQUEyeDsH3JgYxkKKe2+1GV1cXJBKJIGIhnUf83mr+PZIZC5K7T0S2IXioKbjDR6vVCuoSsbxGJKW1m4bpIk0z82ci+B4TfI+FVNVmiJR3PK27qQDf2tTj8eDKlSvwegOSFxcuXBDIdESamciTSPCR11Thr9pK8Ks+I16Y2sDwig1+DvDzSOG65gK8p70Cp6q205h8mRCz2Yy+vj7q404K2AUFBaLKkwQjUldStvi8C8AwjKgFaP4pn6Rs5ufn0dbWFrfctJipJLfbjUuXLiE/P1+QbommeUTsLuVyuWi5++AOH1K4NBqNAkvP4uLisP4K8/PzdBYlVEorE4hnmjnYY8LlclHBP1I8JRtksramhHwjSXmnG16vl9qonj59GlKplBaQSVQZymcjuIBdbpDhb15djf/vXCU8Pj82nD68NLOJzz8+iUp9Dh54x+Gw90AOK0BgfsPrDXQ5EU2t3Nxc2uWk1WpFTfuFSyU5nc4sMewWiE0Mfr8/riJzpLXEiBjIkFJ9fb3AoSya5pHVakV3dzftXElVPpxfuORbeobyV5BKpZiYmMDi4iJOnDgBvV6fknuKF8lOM+fk5Ag8JkjKqa+vj7YDk9cgHnKem5vDxMQEWltbE3bmEhtkdkKpVOLYsWP05BxcQCYpn76+PrAsSwv8JO0WTBJyOQeVkoVKYQMAFKrl8Hg8ESewyfdeKpVCoVAgLy8PVVVVVCbEZDKhv78fHMfRSCLe9yAUInUlZYkhQ0jVvAAAKlP84osvQqlUxlRkjrRWssQwOzuLpaUlaDQaNDRsyxRH6zwiXsvEWjJdufvgXnm+v8LAwADkcjn8fj9aW1t3DSmIPc0sk8lCvgZEoiI43RIKZMJ/fn5+13hOAKDpzNzcXBw9ejQsgQb7bIRLuxFrV37KSSoLbNpSCUMPV+T7HUwSfI0nPuRy+Y7rE2XY4eFhaLVaGk3EmvrkI1pX0l7EnieGYIhJDC6XCxsbGzhw4EDSp+xkiIHjOIyMjGBpaQnV1dVUnIv8LlyRGdhO0xw6dCgh+V2xwPdXqK2tRVdXF1wuF3Jzc3HlypW4J49TgVRPMwd7TDidToHHRG5uLn0NSDswSWGurKzg1KlTGbGJDAWXy4UrV65Aq9XG7FcOhE67Bct0kIjKYDAgTxHYcK1uPxQKRciZCQKv17sjfRrp+qQlm0iI84cbCwoKqLVqNESKGDJZA0oG+44YxJp+XlxcxMzMDJRKJQ4fDp/bjBXJCPL19vbC4XCgs7MTZrOZ+jsEDxAFt6MSZ7PdlKZxuVzo7u6GUqnE2bNnIZPJBJPH09PTtC5RVFSUdE4+VmRimlmlUtF0C8mJr6+v03bgwsJCuFwuOJ1OtLe375rTp9PpxJUrV5Cfn5/QQB0fwdamZrMZ6+vrGBoagtfrhUuhBwDMW5xgeRIboUT/LBYL/TzFKvqXk5MjSH1ubGzAZDJhbGwMHo8HBoOBEkW4IUu/3x8yk5BNJWUQYqeS+HMB9fX1WF5eTvYWAQQiBtKxEStIrluhUKCjowNyuRwbGxs7TkvBpOD3+zEwMACbzbZrnM2AgFxHd3c33VDIFzZ48pjk5Pv7+2k+mqQaUiFpvbCwgNHR0YwWdOVyOUpLS1FaWko3uZGRETidAQXSkZERgRFPpmC323HlyhUUFxeLLhwY3Olls9mwYlyDUroJh4fFL556ESfqiqm1Kdn0JRIJlpaWMD09jSNHjuxIOQGxif7xDYf4E9irq6sC18CCggLBgGc0raS9iD1PDMFIhhjI6dxut6OjowMulwsLCwui3Fe8qaTNzU10dXWhqKhIIMhH2lX5eVb+l9PtdqOnpwcSiQSnT5/OiP9tKJjNZvT29qKqqiqi1WqoNlDS4TQwMCCYl0hWJoPjOMzMzGBmZgZtbW27pqBL7ksmk+E1r3kNnRdYXV2lRjzkNUiVx0QoEB0uMaesw4HvGne61ornJ8yY8ajRfNV3RSaT0eK1x+PB2NgYjh8/Tgc8o/lMhBL9G1q24qevLOHyzAZsHj8OlarxsfM1OHFi2+eCFNCJD0hhYSG8Xm/YAbdsxLBLkCgxkIlbpVJJjX68Xq8onURAfO2qq6ur6OvrQ0NDg6BYTFyirFYrent7qTwFCWPJidxgMOxQd80kVlZWMDg4iIMHD6KioiLmv+Png/m+z2SDJCY8xcXFcdclxPJmFhter5fKgZw8eRIymYx22BAjHpJ2I3MsJJIQcwI9GFarFVeuXEFlZWVCHurJ4KZDxXh+woynpmz4xBs6AI6j1qbDw8PweDzQarX0hM63Ng3nM8Fv6Xb7OTzwzCz+q0uYHXhpZgMf+Gk/fvnBk6jQ5+woYJPBOpvNBrfbTRVqSQE7Swy7CIkQA7G9LCsrw8GDBwUfqlTMRIQDOSlOTEzg2LFjgp5+8mHW6/U4c+bMDqG7vLw8rKysoLq6Ou1f3HDgy24cO3YsacMfvu8zvy4xOztLfQdI0TISKfKnmcX2Zk4GpMtHpVIJlHH54A+VEWXcUBPoRMdIDJDotaamBrW1taKsGQ9uOVyM+5+YwLzFhT8MGvGGIyXIz8+H0+mE3+/H4cOH4fV6BREVf2aCn3IChNGE2e7Gx/5nBP3LgbbYm1sK8fbWUhhyFfjC78cwuGzDYwOr+OtXVdP74R9Y6uvrcfnyZWi1WmxsbGB6ehoTExN48cUXsby8nJaI/S1veQt6enpgNBphMBhw/fXX4/7770d5eXnCa+55YghVYyCiXbGAbK7E9pIPsYbSyFqRiIFlWQwNDWFtbY36SxAEdx7xjdldLhfGx8exuLhI3eyInSPJw2YC/G6aVLRY8usSZOp1bW0Ng4ODO7wF+F/OdHozxwNS0NXr9TFHexLJtvdzU1MTnUDn6xgRskz0s0A6tYjIYiagkkvxvs5KfOvZafzzn6ZwvqkQZuMyxsbGBDMdJKIKVcQnEupkZgIAPCzw0V+MYmDZBm2ODPe/pQlnanR0Fuh0lRaDyzas2zwh74uA4zg6l0IK0RcuXMDS0hLuuOMO/Od//ife+MY34u1vf3vcg7Gx4HWvex3+/u//HmVlZVhcXMQ999yDd7zjHbh48WLCazJcIk7Ruwxut5v+N+kmOHLkSMS/IamExcXFsAYwLpcLzz77LG688cak0zJLS0uYm5tDR0fHjt+RyVGPx4MTJ04ITrDROo8mJiawsLCA48ePQ6PR0GnP9fX1uE7RYoJf/G5ra0trAY7fJ7+2tga73U5nBfR6PYaHhyGTyXD8+PG0ezOHg81mowZPYhV0+RGVyWSi0tnksxBLyonoMe0G3wmHx483ffclrGy5cUuTFm8s3kRbWxuddg4F/tzI+vo6/SyQtNvnn5jF44NG6FVy/Oj2NjQW5wlkw9/3kz50L1hxz+ur8e5T5WG7nC5evIiWlhbBvXAch8bGRnzjG9/A0tISHnvsMXzlK1/Bq171qpS9RgS/+c1vcOuttyZlc7w7vhlJgvR7A7Glf/gtoB0dHWHzgPz8ZLKbarhUEtHSz83NxZkzZ+hmFU3zyO/3Y3BwkKZDyHPgd7YEi7zx1VBTtSkSkuM4Du3t7Wk/kQf3yZNZgZWVFYyOjkImk+HAgQNwOBwZjagItra20NXVhQMHDggm2ZNFcERFpLOHh4fh9XoFRfxQ7xHpCjt48GBSKQmxkKuQ4itvbcGd/9GDx8e20Fx6AK+PQApA6LkRQpY/uzSBx8ckkEmAL99ShfrCwGGMpJwuTJrRvWCFTMLguuZACjRcl1O4riSn04kjR47gf//v/41PfepTIr4a4WE2m/HII4/g7NmzSaWx9gUx8BGNGPhFZtICGmktIPCBSHYjDZVKIiqtpLbBLzJH0jzyeDw0TA6XDpFIJAJfAdLdQ6aOxVBDDYbT6UR3dzedhE23/HEoqFQqGAwGTE9Po6KiAgaDAWtra7hy5YqgAyqUU1uqQdI0ZCI9VeBLZzc3N1OPicXFRTr5S9JuarWaksKRI0d2jXYVAJTLbHhjNfDYLPDAnxeh16jxlydjJy1ibaovKsNdT18C4MNfHMyDxDSN556bpNPPTmkePv3rYQDAX54sR3VRoDEhuIBNUtZkv+EfIDmOS2vx+VOf+hS+853v0MPuY489ltR6+44YIg24mc1mKuvc3NwcdSMgm3EqfBSWl5fpQFV19XZhK5rmkd1uR3d3N504jWXzDe7uCVZDFUMJdGtri0qRi93fngxCTTOTwm3wKbqgoADFxcVpkc0mm29zc3NcnVrJgt8GWldXR0UP19bWMDU1RWt0tbW1STcLiIn5+XlMTEzg029phe6lNTxyeRFf+N0oZkwOfPy6OiiksZP6Q5fmsOH0oa4wF3//tlOQSRhqbXphcBYPXHHC4mFQrZfjr88UU0Om4AI2y7KYnp6mLoh8D2yv1wu/35/wpPq9996L+++/P+JjhoeHcfDgQQDAJz/5Sbz//e/H7OwsvvjFL+K2227DY489lvD3cF/UGPhtpcvLy5idnd2RyydF5oMHD8alX//kk0+is7MzaSkCYqpy/vx5TE1NYXp6GsePHxd8+UikQNQag99UMgtw4MABNDQ0iLL5ut1urK2twWg0wmw2Izc3l7bBxtojT3q76+rqQvpdZwqxTjPzPY/X1tZgs9mg1+spWYpdIyHtu7vtRD4/P4+xsTEYDAbYbDb4fD6B2F2mCvWks62trQ16vR4cx+FfnpvBd/88AwBoLlHjC29swvED0Rsc7G4fXvvPF+Hw+PHgO4/ghpbA98/jZ/HI5QV865lpuH0sKnVy3HsmF5xjQ+ABzq/PTE9PY3Z2FidOnEBeXp7A0nR2dhYnT57E/Px8QvUZUhuKhLq6upDvycLCAiorK3Hx4kV0dnbGfW1gH0YMwakkvs5QsMNZIuslCtLh1N/fD7PZjDNnzgh656NpHi0tLVFiE/OEqVQqd7i0GY1GdHV10VRLcXFx2OI1ua9MewMEY3FxESMjIzFtvsGex3zZ7PHxcVEHyhYWFugwVio6VBIF0dQig358spybm8PQ0JAgskyXZWUwKQCB9+uu87VoLlHj84+NYnTVhnf9sAuvayrAe9oPoKPOAEmYe3tsYBUOjx81BSpcf7AQDo8fv+1fwcOX5jFrDkyZv7axAF+5tQV6lZx6gK+vr9OZiYKCAjBMwCSovb2dfo8JYWxsbODOO+9EY2Njwu8xeZ0TATkk85ty4sW+ixhMJhMGBwfpxGhvby+cTidl9Xjx7LPP4tixY0lPxVosFrz00kvQ6XRoa2sT9JhH6zyamprC3Nwcjh07FjexJQqSajEajVhbW4Pf7xdIU0ilUnpiOn78+K6bGp6ZmRHlvkj7I+lsIQNlpC4RTx1lZmYG09PTaG1tjdhNk27Mzs5iampKsPkGgy92Zzab06JnRe7rxIkTYdud120ePPCnKfyqdxns1Z2sRKPEufp8tFVqUV+Yh1KdEip54H265TsvYcPpRalWiZZSNS5OWahDXEGeHB9/fR3e1loWkliIten4+DjW19cBgIo/qlQqlJSUwOFw4K1vfSu0Wi1+85vfiDZLEg4vvfQSXn75ZbzqVa+CwWDA5OQkPvvZz2J1dRWDg4MJ1w/3BTH4fD56qicpmzNnzuDKlStQqVQ4fvx4wjnj559/Hi0tLUmd7mw2G1555RW4XC5cf/31YTuPgovMZLbBYrGgra0tY8qa/BZQo9EIh8MBhUIBv9+fVrKKBv4084kTJ0SfZuYPlBmNRlqXIGQZLtVC2ooXFxdx8uTJXTNlDYAeOk6cOAGtVhvT3/B9NtbX1wV6VmL4GwCxkQIf0+sOPPLyAn7btwqrO/Y5JgCoylfh3acq8Pa2MqiVkZMoCwsLNLLKzc2lr8PXv/51/OlPf6IS40899VRa1Iz7+/vxsY99jEr5lJWV4eabb8Y//MM/JJVZ2HfEYLVacenSJUil0piLzJFw8eJF1NfXJ5wLNplM6OnpQVlZGebm5uhMRLTOI9L2ybIsWltbRescShY+nw89PT2w2+3IycmB1WqlXS3FxcUZkwBgWRaDg4PY3NzEiRMnUj47QUTeSMrJarWGLOKTVOb6+nrCUWsqwHEcJicnqUlSomQVam4k2foMiaxiJQU+3D4/rsxu4sKUGaOrNkyuOWCye+BjhdvcDQeLcLhcg9c2FqCpODYPhsXFRYyOjoacn9jY2MDNN9+MtbU1aDQazM7O4vz583j44Yd3VYo1Vuy7GoPRaATLsmhpaRHFJD2Z6WdS8G5paUFJSQnm5ubAsiwYhonYeeRwONDd3Y28vLxd0/YJbAv0SaVS2icdbOVJbCyLi4vTJvBGHPY8Hk/appmDu3uC7TyJlaXVaoXT6cSpU6d2jfQGmUonOlHJkFW4uZH19XVqqcm3NY32eSCkcPLkyZgjGD6UMinO1ufjbL0whej1s/j8Y6P4Ve8KPnC2CndfXx/XusvLyxgdHQ2ZBnS73Xj/+98PlUpFu/zGx8fx+OOP76o6UjzYN8TAsixGR0extLQEAKIN5SRiyclxHMbHx2mIXlBQQNfw+XxUITVUkXljYwM9PT0oLy9PuYJlPCBtsjqdTmDMwrfyJMXrYIG34uLilM0JxOPNnEoE23mSwjWZPp2amhJYmmYK/Ajm1KlTokdWfI8J/uchlLVr8HtFakOJkkIkyKUSjBkDekhHKuKLjlZWVjA8PByyZuXxeHD77bdjdXUVTz31FI1wGhsb0djYKM7NZwD7ghh8Ph91BDt9+jQuXLgQ1lUpXsTblUQ6jzY3NwX+0GSD9/l8tB86eNMnbYxNTU2iRDtigZBVRUVFxDZZvoUjyccbjUY6J8AvXouRh07WmzmVWFxchEqlwpkzZ+jcCBG6I3WJcFPHqQLHcbRmlY4IJtjSk2/t2t/fT2Wri4qKsLKyQls/xSYF4GrqbM0BADhYEjsxGI1GDA4OCiS9CXw+Hz7wgQ9genoaf/rTn3ZNA0YosCyHZ8eMeP3B2FLi+4IYZmZmAAAdHR2CaWUxEE8qiahjMgyDzs5OwZeeSGYPDw+jtLQURUVFdHMknTTT09OiqJCKCTIL0NDQEJeIGl/grbm5GVarFUajETMzMxgcHEzaV0Fsb2ax4PF4qLnS8ePHIZVKoVQqdwjdBU8dFxcXp7QFlCjK2mw2nDp1KuXdMsEIlqcgEupGoxGjo6MAgIqKCtqQIfbrsOH00u6jUl1s9ToyhHj06NEdKSG/348PfehDGBwcxDPPPLOrvrN8bDq9+O+X5/EfL85izuzAzFffGNPf7QtiqK+vF/gWiC2XHctaRK/eYDBQFylA2HnU3t4Oo9GI2dlZDA0N0RPTxsYGNjY2BD3RuwGkt/3IkSNJOZvx5wQaGhropkD0izQaDR2qi8WMPdXezInC5XKhq6sLarU6ZATDMAzUajXUajVqa2vpcCGZOs7JyRG0gIr1vFiWRV9fH6117AZFWSKh7vf7YbVaUVNTQ8UEiZyLmKm31a2AQmpBnjymKWkytBnqs8+yLD760Y/ipZdewnPPPZdRL/Vw2HJ58cMXpvGDF6ZhdQW6tLQ5sW/3+4IYgusA6fZRIDnUmpoagRBacOeRWq2GRqNBfX09HA4HVlZWMDk5CZ/PB61WC5PJBKlUmnE7QH57ZSr8ooN9FUJtjsXFxQL7RIJMeDPHAiKGmJ+fj0OHDsW0qfOHC/ktoPx8fHFxcVKbo9/vpzLjp06d2jWOfsB2qyzfKInfEjw+Pr4j5ZRopOPwBDbHaO2oQKCTsK+vD4cOHdrRjciyLO655x48++yzeOaZZ9IqZxILXF4/fnhhGv/67CS2rhJCY7Ea739VLd7aGvu97gtiCEa8ngzR1opEMnNzcxgdHcXhw4cFBe9omkcMw2BlZQV6vR5NTU10mGxiYgJ5eXkoLi5OyJksWfDbPtvb21PeXqlQKARm7GTyuqenBwzDCIbJSIRx+PDhXSUlQdJayVheSqVS+p5zPIcy/uYYr+ghaS3mOA4nTpzYVaQwOTmJ+fl5nDp1SjCfE5yCJKk38t4n6jFB2lVl0siPJ7IzBw8e3NFmyrIs/v7v/x6/+93v8Oyzz6ZU+DBecByHPw6t4su/G8acOVBLaSxW42PXN+INR8ogkcT3mdy3xCBmjcHj2WnUwZfaOHXq1A4t9nCTzEDAEaunpwclJSVoamqCRCJBXl4eDhw4QK0bScpJoVDQE7SY6YVQ8Hq96Ovrg9frRXt7e9pnJ/ibI//kODIyQsf7q6urd1WRjwxUipnWYhgGBoMBBoMhpAFPLKk3MgcjkUjQ2tq6a7wnyCT/wsLCDlIIhby8POTl5aGmpoZ6TBBLTeL7HMsUuv8qMTAI//6QJovm5uYdXY0cx+G+++7Dz3/+czzzzDOor4+v3TWVmDc78PeP9uP58cA0dolWiU/dfBC3tlbETQgEu+PTIjJSXWPg+zl0dnYKUj/RNI/IqDpxxAr+Pd+60e/3w2w2w2g0ore3l56gU9H+6XK50N3dDaVSmdG2TwJycjQYDOA4DsvLyygtLYXJZMLs7Cw13ykuLk57IZWAGNk0NjamtIsseHPkp95CSVN4vV50dXVBLpfTAvhuAH+o7uTJk3FP8ofzmBgZGaEaRqTrLfhQQ1JIdk/oTMLm5ia6u7vR2Ni4Iz3EcRzuv/9+/PCHP8QzzzxDFU0zDZbl8JOXZvHVx0fg8PihkEnwwVfX4m/PNyAvhpRZJOwLYghl75mqGoPL5cKVK1cgl8t3+DlE0zwiY/6xFnP5fgH89s+hoSGqXURy0Mls5KToV1BQgJaWll3T9slPa505c4YSMBmiMhqNGBsbg1qtppFGLMVrMUBqHS0tLWmdbA1OvREzpv7+frAsi/z8fGxtbSEvLw+tra275r1MlhSCEewxEdztRTSMioqKArW9q4XXLedOYiBmSfX19TsInuM4PPjgg/jOd76Dp59+OqozZLqwtOHE3f/dgxenzACA0zX5uP8dx1BbKE7qd19IYnAcJ0j3XLlyBYWFhQKfg0QxPz+PlZUVtLe3Y2trC1euXEFRUZHAlzcWzaPR0VEYjUa0trYm7X9MZAiIwJ3T6UR+fj5NL8TTdUJyqlVVVairq9s1HT78aea2trawaS2+heX6+jqUSiV9HVKVeiM+Fsl2a4kJjuOwtraGoaEhejhJtiVYzHsjpJDspHUsIFHV+vo6TCYT5HI5cnUF+N+/WgUAXP7Uq2kEQboJa2pqdtQMOI7D9773PXz5y1/GE088gdOnT6f0vmPF08Or+MTPe7Hh8EIll+JTNzfjts6ahNNGobAviaGnpwdarRZ1dXVJr720tIT5+XnU1tait7cX9fX1glxyNM0jssG53W60tramZKjIbrfDaDTCaDTCarVCr9fTE3SkDYEM1Ikt5Z0s+NPM8Xgz8zt71tbWAECQehMjpTI3N4eJiQmBCf1ugNPppO3Shw4dEkRVm5ubNKoiJ+h0HQBIhxupxaVbK4rIZq+treGOX63A6gX+8bVanG4sg0qlQm9vL60PBd/3D3/4Q/zDP/wDfv/73+PcuXNpve9Q8PhYfO0PI/j3F6YBAEcrdPj2u9pQI1KUwMe+IAZAqD0+MDAApVIpykg6GYf3+Xw4evSooGc5WucRydsrFAocO3YsLV0hLpeLksTGxoagUEnCd35a69ixY7tKz0WsaWbS2UOiKrfbTQuV/OHCeNabmprC/Pw82trako76xARplSU2rsGbPj+qIido8jqE89kQA5kmhWC856Er6J7fwifOFqAl1wa73Q6lUonKykpBIZ/jOPzkJz/BPffcg9/+9rc4f/58Ru8bAEw2Nz70ky5cngmkju48V4tP3dIMpSw19aN9SQzDw8NgGCbpIhHLsujq6oLJZEJHR4dgM4hWZN7a2kJPTw/9smYi10s2hNXVVZjNZipw53A4YLFYUiY/kCisViu6u7tRXFws6jQz0dEnhEkc2ghhRoviiOjcysqKKPlxMWG323HlyhXa4RbtNSMnaEKYqZDMBrb1wshrlmlSAID/+/gYfvryIv7XiRKc167Tw9L6+jrMZjOuXLmC8fFxFBYW4t///d/x6KOP4sYbb8z0bWN4eQsf+NErWNxwQqOU4Zt/eRw3Hk7tUN2+KD4DoEwPBIq2Xq83qfX+//bOOyyqM23j99CU3oQRUFRQQRDpWKPYRVEGE1PW2KKuSYzZL8ZYsulr1hhNwhdbzGei0cSYAJaIHRWNJSpNBUQFlTLAzNAGhoGp7/eHe87OwICUKUc8v+vKtSttXoaZc5/3eZ/7fqh2P2r2QEdEgToMNPWoS80ujubBblZWVigrK4NSqTTYoJWOYEg3s6bj2MfHhy6zUPlFVG+8Lt+IWq3GnTt3UFNTg8jISJObDzWh6uN9+vTRMla2hbm5OT2qUzMy++HDh8jJyaG7vdojmK3BRFEAgLC+jth/g48r9wV4eYYX7Tnp27cv/X4+fvw49u/fD1tbW/z444+orKzEzJkzTZZIcDq3Av/zW/Z/ps7ZYNeCSAx0N/yNSbcRBk3Mzc3R1NTU6e+XSqXIzMxEz549MXToUNqJCrTdeQT8twbNNBOWWq1GSUkJevbsiaioKEgkEgiFQty+fRuEEPpiYIr0T2O7mTUTQJv7RiwtLbVmXufm5qKhocEk+UJtQXXSUE0DnaF5ZLZUKqWfC03B7IiZrHmkN5OENMD9cQNDqQRw8+qv9fuYm5tDLpcjNzcXiYmJ8Pb2xtGjR7Fp0yYEBgYiODjY6Ovdf60YHxy+DTUBxgzsha1/C4WTjXHiTLpNKUkul9M7hkePHtFTzzpKbW0tMjMz0bt3b/j7+0MqleLq1auYNGlSm51H1PSwiooKhISE6D1GoitQdXsqw0fzwk+lXlJlFqof3N3dXW8pqG3RkdnMhkaz/VMoFEKpVMLCwgKDBw8Gl8tljB+AMtVRO1JDQAkm1d1jYWHxxPnflCgIhUKEh4czShSampqQnp6OzzMIisRK/DvOH7zg/7YZp6am4m9/+xt27dqFl19+2YQrffw8bjlXgK/P3AMAvBLVF/+KGwqLdmQ86Ytus2NoXkrqTCRGeXk5fedKveGodFXNQ+bmokBFbUulUkRFRTHqDVFXV4esrKxWU0ibp15SOwnNFFTqDlqfTmjN2czUAHpTQ/lGnJycUF9fD0IInJyc8ODBA9y5c0dLME0VRFddXY3s7GyDm+o0jZaa879zc3OhVCpbRKhTN0YikYhxoiCTyeiOrRnB1th+sQhn7lTSwnDx4kXMnTsX27dvx0svvWTStarVBJ8ezcVPV4sAACsmDMTKyU8+O9I33UYYNOmowY3qOHnw4AGCg4O1etOpO6OGhgbY2Njo7DzKzs6GhYUFIiMjGZVHQyVEtvesQ3MqGRX0JxQK6b59anQlFRHdWTQPczUD1JgAFZ1ubW1NT8+jDq9FIhFKSkqQl5cHR0dHuiXYWJPZqL+nn5+fUduLNc1k/v7+qK+vh0gkQlFREXJzc+Hk5ARCCH1jxJRJdcDjSkJGRgYcHR0REBAAK1EDtl8swp8FVaiUyHHvVjpefPFFfP3115g3b55JfTxqNcH7h27jwI0SAMAnMwOwcPSAJ3yXYeg2pSSFQkF7CYRCIe7fv9+u3mMqp766urpFlw4hBAqFAjk5ORCJRLC1tQWXy6UdthKJBNnZ2XTvuKkPcDWhSjSBgYF6iQWmIqKFQiGqq6s7HfRn7NnMHYHyAjSfUtcczTGems9FR4PdOgJ1HhQQEMCoGcKNjY3IyclBXV0dCCGwtbWlzyWMNdq1NShRsLW11Wp9fuWHDNzk1+GFwVbYtXIO1q9fj+XLlzNGFMw4wFcvBiM+1HTpwd1SGKqqqpCbm4uxY8e2+T2UkUqlUiEsLEzrcLF555FSqaQP5iorK2FpaQm5XA4vLy/4+fkxRhSo3U9xcbHOUYT6QPPAVtNt3FpUNkV73cymgGr77GirbPNaPOURoEIP9fG6oIyIQUFBjHFaAy3HhFpYWNAGw8rKSroDijqXMOYZjUKhQEZGBr3z0/w7HLlZgXVH7kBdX4nl/UVYvepdk4nCjh07sH3HdxD1m4iegRMBosaiIeb4eOF0k6yHotsIg1KppMtH1OHc+PHjW/166kJgZ2eHYcOGablr29N5dO/ePTg4OEAikdCdLFwut80Lo6GhWiurqqoQFhZmlH775m7j1oL+OutmNgZUh09H2j51oVar6dBDyiNA3T336tWrUxdGqozHNCMiIQR37txBdXU1wsPDW5SPNEPuRCIRFAqFVsidIc9oqBBBaopec3HOvHkbf/u1AGY2Tvh3/FD8bbhhDvDbwx9/HEXyIzNcKAPMOMAI3EPSV2uQlZWFwMBAk62rWwpDfX09rl27hkmTJun82urqamRlZaFPnz5apqAnZR5R/dllZWUIDg6Gs7OzVgIqdWGk7p4N6Sptjmb0RmhoqElaKzWD/oRCIR305+TkhKKiIjg4ODBuNjPlnxgwYIBe8/U1Zxx3Ns+qtLQU9+7dY1z8xpNEQdfXSyQSuhQpkUjoMxo3Nze9lhOp+e8WFhY6QwTz8/MRExODsUs+wg1Vf3g5WeP8qmhYWZjmNfnt2fv4+sw9cDjA1/8pH7m4uGDTpk1YvHixSdYEdFNhkEql+PPPPzFlypQWd398Ph95eXnw9/fX6uqgMo9UKhUtCM07j3JyclBfX4/Q0FCdph3N7g2hUEj7A6gEVENdEGUyGbKysmBpaWm06I0nQZmnSktLUV5eDgBwdXUFl8s1aVePJpQR0Rj+Ccp5LRKJUFdX98SD/OLiYhQWFiIkJERr1oep0RSFzno7mp/R2NjY0Durruy4VSoVPRo0JCSkxQ6toKAA06ZNw6uvvopP/vU5xm2+AFG9DOti/LFsnPHnK/xyrQj/PJQDAPh0ViBeHd4XiYmJWLBgAbKyshAQEGD0NVF0S2GQyWQ4f/48pkyZopWAWlBQgKKiIoSEhGhtyzXPEzgcTosLuEwmo+chBAcHt+uiRt0xCgQCCIVCKBQK+kLQ2bKCLhoaGpCVlfXEA1NToOlmdnd3h0gkgkAggEQiMfk8Bapur6/D+Y7Q/CDfxsZGy1RHtfGGhYUxKpOJEIK8vDzU1NTozfCnVCq1SpFmZmZaU/va+z5RqVT0tLrQ0NAW3/fo0SNMmzYNPB4PCQkJMDMzQ2J6Cd5LugUbK3OcfXccPByN1011Mqccb/6SCTUBXhrqgG2vx6CpqQl2dnbYv38/pk9nzxj0gkqlor0LSqUSqampmDBhAqysrGifgVgsbpF186R4C4lEgqysLDg5OSEgIKBTF3RCCOrr6yEUCiEQCNDU1EQfynXFREZNnPLy8sLAgQMZE5kNtO1m1kz+1Az6o7q9DA1VomFC3Z5qatBMhCWEYPDgwfDy8mKM0FOiUFtbi/DwcIOIuebUPir4kDqXaKv8Rs21VqlUCA0NbXF+xefzMWXKFEydOhXbt2+nn1O1muCF764gs7gWUwK42Dkv3CjvoeySWry48yrkSjVeieqLT2b4oaSkBGKxGElJSdi1axcuXLjA7hj0gaYwEEJw6tQpjBs3DmZmZsjMzASHw2nRCfMkUaBmFfTt27dLh5KaaAa6CQQCNDQ00LVnd3f3dpdYqAuvoY1OnYHP57d7NjOVnU/dPVtbW2vdPev7jfro0SM8fPiQkSWae/fuoaysDK6urqitrdXrMKaurs3QoqDrMSnvCBUnT5XfqCRU4LGY3Lx5EwqFAmFhYS2eo4qKCkydOhVjxozBrl27WtzY5ZXVIW7bJShUBF8+PwwvRhr2vVQubsSsrZchqpdhgr87vp8X3sLRPGnSJPj6+mLnzp0GXUtbdEthAIDTp08jJCQEd+7cgaOjI21WonhS5xHlAzD0rALKRCYUClFXV9euWQpUHhOTBsUA2m7mzrTKUmUFqg1WM4ahq62fVCmRz+czLlVW0zUcFhYGW1tbrYA7oVAIqVSqdfdsrFZfQgjtOzGWKOiCKr9R5xI9e/ZEr169UFdXB6VSiYiIiBY7b5FIhJiYGISEhGDv3r2tCuuOtEJsPJkPGytzpKwYAx83w3TzSeVKvLjzKnL4dRjMtUPyG6Ng37NltWDChAnw9vbGnj17DLKO9tBthEGtVmslqqampoIQgv79+2uVWdrTeVRYWIiSkhKD+QBao/ksBQcHB1okbGxstC5uTMtj0nQzh4WFddnN3Lz1U/Mgv6NDdzT77akLL1PQrNu31eFD3T2LRCKIxWL6taF592yIteXm5qKurg7h4eGM8Z1Q5bf79++jqalJa76Ei4sLLCwsUF1djenTp2PQoEE4cOBAm+ValZpg7q6/8NeDavi62eLQ8tFw0HHB7gpqNcFbv2bi+O0KuNha4cjy0ejrYoN169YhJiYG3t7eqK+vx/79+7Fx40acOnUKkydP1usaOkK3FIaSkhLk5ubCx8cHgwcPpr/mSdPWVCoVvWUODQ01ae6+XC6nRYJy1wKPe7SZFGUMGN7NrDl0hzrIb57V86S11dXVISwsjFFxDZpr68jdePO7Z2rOhru7u97Kb4QQuguPSaIAtBQsqVRKPx9r1qyBlZUV+Hw+/P39cezYsXaVZ4X1TYjbehnl4iaM93PD/82P0Gto3XcXCvHFiXxYmnPwy5IRiBrw+IZz8eLFOHv2LMrLy+Ho6Ihhw4ZhzZo1JhUFoJsJg1wux927d8Hn82FhYYGAgAC4ubkBeHLnkVwux82bN6FWqxESEsKoN4JUKkVWVhbkcjnUajVdh3d3dzdYBEN7MbabmeqJp0RC84ymeYlFpVLh1q1baGpqQlhYGKP+pmq1mg5e7MradHX1UM+FpsGwo2vLzc1lrChQN28REREtzgyvXLmCBQsWQCqVQiKRICIiAnFxcXjvvfeeeEZzu1SMF767AplSjRfC++DL54fpZY7ytQdV+Nuua1CpCT6PH4q5JjTUtZduIwwKhQLp6emQSCQIDw/HrVu3MGDAAPTu3fuJh8xUyyc1TpIp8crAf8eD9uzZE8OGDQMAeiobFcFgKtc1E9zMzc9oKOOUi4sL7t69C0IIQkJCGOHtoKAESyaTISwsTG+eDk23MWUwpBJh2zudjcoOo95HTBOFtjwUDQ0NeP7552Fubo5jx46hvr4ex44dw/Xr17Fjx452vTdO5VbgzV8yoVITLBjZD5/MCuzSe0pUL8OMb/+EsF6G+FAvfP1iMKO6B1uj2wiDWCym82SsrKxw/fp1eHp6wsvLi47Mbl46Ah732t+8eROenp70RCemQI26bG08aGuuay6Xa/CpbPqazaxPZDIZ3e1VU1MDc3NzeHt7g8vldijoz5BQ/fZUa6WhBEuzRVokEtE7K6oWr6tsRYlCQ0MDwsPDGWFCpKAO6Klcpubrb2xsxIsvvgiZTIYTJ0506YzrUFYp3vnt8XCuV6K8sZ43FOad2Dmo1ATzfriGK4VVGORuhyNvjYaNFXOiYNqi2wgDIQRyuZz+d0ZGBlxdXdGnT59Wdwrl5eXIy8uDn5+fUSaHdQSqVba9oy6N6bqWSCTIzMzU+2xmfdDU1ETHZru7u6OysrJDQX+GRKlUIisrCxwOByEhIUbdYVF1eKFQSB9eU68PW1tbxosCNQAoIiKixTmRTCbDK6+8gpqaGpw+fVovpsDfbhRj3cHH09OmBfZGwssh6GnZsUrC/6bexzep92BjZY4/3hqNge7MiZd/Et1SGAghtFN50KBBsLKyatF59PDhQxQVFSEoKMjkJqfmUII1ZMgQeHp6dvj7mx/WUoNV9OG6NuRs5q4ilUqRkZEBFxcXBAQE0Gujgv6ou2eqDm/MPCuFQqFVdjNluZLyjohEIlRVVdF334QQREZGMq58VFBQgPLycp2jQuVyOebNmwc+n4/U1FS9dhGeuF2OfxzIhlylxrA+jtjxaji8nNrXvJBVXIMXvrsKlZrgm5dMG6HdGbqNMACP7xyozqPKykoUFBRAIpHAxcUFXC4Xbm5usLCwQF5eHqqrqxEaGsqoITGUD+Dhw4d6c+VSJQUqmqMrrmtjZgt1lPr6enokq2YwYnN01eH1JZqtIZfLkZmZiR49euhM+zQllGA1NDQAQKcjKQxFYWEhSktLERER0aITT6lU4rXXXsPdu3dx7tw5utFEn1wprMSbv2SiVqqAi60Vtr4SilED235fSuVKzPj2Eh5WNmBmsCe2vNLxEcOdgSqX64NuJQxNTU0tOo8aGxshEAggEAhQX18Pc3NzmJubIyQkhJEmJ4FAgNDQUIOsjXKTUiJB1Z0p0WyrfNARN7OxEYvFyMzM7PAuhjKRUTurpqYm+rDWzc1NL/V/aqwkNW+bSaJAdUY1NjbSrmEqkqKjbcGGgJorEhER0aJ1XKVS4fXXX0dmZibOnz9v0Lyrkmoplu3LQF55HTgcYPHoAVg11Y8uLW3YsAEHDx5Efn4+rK2t0Td+Naqch8DDsSdO/mMsHG2M97zpSxy6jTAUFRWhsLAQYWFhsLS0bPHkUGUGc3NzWFhY0C5jaiKbKbfPmjOjQ0NDjdZrr8t1TYmEZnmhK25mQ1NVVYWbN29i4MCB8Pb27vTP0YwqoaKhnZ2daZHobIqo5lhJpokC1cobHh7e4qKvqy24q89HR6Bec+Hh4S129Wq1GitWrMClS5eQlpZmlDGnjXIVPvkjF7+lPx676etmiy9fGIbwfi6YNm0aXn75ZURGRuJqUT3WX6wCAPzwajAmDjX8zvrNN99EYWEhTp06pbef2W2EISkpCUuWLIGdnR1mzZqF+Ph4jBgxAubm5rh48SIaGhowYMAA+rCUchkLBAKIxWI4OjrSImFM279cLkd2djZ9IGmqtsrWXNcNDQ20Y5hJZTfgv3lR/v7+nTqLaYuuBv01NjYiPT0drq6uGDJkCKPOYp4kCrrQ9XxoHl7r8/ejIsfDw8Nb7JzVajVWrVqFU6dOIS0tDf36GdcTcPaOAGsP3oaoXgYA4IV4Yk2MPzwcrVHXpMDkry9AUCdD3fVDOPzJ/CdOkewqb731FrZv344BAwbg4MGDCA4OppttukK3EQbg8cXt7NmzSEpKwpEjR9CjRw/4+fnh6tWr+Prrr7Fo0SKd30e1OQqFQtTU1NAXRS6Xa9C7d8q4RpUZTF3PpZDL5RAIBHjw4AHkcjlsbW3Ru3dver4zE6AmmxkjL0oz6K+qqoqOyW7NYNjZMaHGgAqdk8vl9O66o8jlcnq0a1VVFd3x5ebmBicnpy79viUlJSgoKNAZOa5Wq/H+++/j0KFDSEtLg6+v8WcoAECtVI4Nx/Pxe0YJCAGsLc2xYFR/PKyU4FSuAF4Olrj68UzcysrA0KFDDbaOlStX4ueff0ZycjKWLFmCqVOn4ttvv9XLz+5WwqCJTCbDG2+8gZ9//hk9evSAjY0NZs6cidmzZ2P06NGtviGaR1HY29vTOwl9Rj2IxWJkZ2c/8bDUFGi6mYOCgiAWi+mLABNc11SIYHBwMFxdXY362Lpmf1MXRWdnZ1oUPD09GReFrg9RaE7z0a4AOp1pRcWhh4WFtcgBI4Tgs88+w969e3H+/Hn4+/t3ee1dJYcvxqdHc3HjUY3Wx71Kz4Hz6BouXbpksMd+5513sHv3bqSlpSEkJATbtm3Dl19+ieTkZERERHT553dbYXjvvfewf/9+pKSkYOjQoUhLS0NSUhIOHz4MtVqN2NhYzJ49G2PHjm1TJDTvFO3s7GiR6EpWEdXd4+vra/St8JNoy81MxS9QrmsrKyujegOoNuPi4mKEhoaafIiNWq2mL4qUd0SlUoHL5WLIkCGMmmtNua31KQrNUavV9E2ESCSCXC7XSoRt6zGpHWBoaGiLOHRCCDZu3IjvvvsO586dM+hdeEchhOD47Qos35+p8UE1hvdzxGvjBmO8n7vex4ZevnwZr7/+On788UdERkYCADIzMzFz5kysXbsWK1as0FlOKqmWoq9L+25uu60wXL16FX379m3RVqlUKnHx4kUkJibi8OHDkMvliI2NRXx8PKKjo1vtzFEoFPQEsqqqKtja2tLlpo6UV0pLS+nuHmNPDnsSHXEza7quhUIhzM3NaZEwhOtaM721+bAlJlBbW0s/d01NTXRHD9UGa0qRoAbZKJVKg7qtNdE155ma2ufm5qZVoi0vL8edO3d0zrYmhCAhIQFff/01zp49i5CQEIOvvaNsOpWPbecLAQCk8iE4vQbQn3O0tsTkAC6mB/XGKN9eHTbJ6aK2thaNjY3w8PDQEoC1a9di9+7duH79Ovr16/d4Rr1QgvP5Qhy/XY6bpWI8+mJGux6j2wpDe1CpVPjzzz+RlJSEQ4cOQSqVIjY2FjweDxMmTGi1U0mpVNIv+MrKSrq80lb0AiGEbr9jYncP5WZ2c3ODv79/h+7+De261oymNkR6a1ehTH++vr7w9vbWGUdB3Tl3ZBiTPtAUBV2DbIwFdXgtEolQU1MDOzs7uLm5wdzcHAUFBS3G7QKP/+47duzA559/jlOnTiEqKsoka2+LByIJpiZchEJFoEzbjvP7EmDp7IlfbxQjOYOPSomM/lorCzOEeztj9EBXhHo7I9DTAU42HXst6GpHpT525doNLF75IcbyXoV130BcLqiCSOPxzTjAgw2sMHQIlUqFK1eu0CJRV1eH6dOng8fjYeLEia0eQmvWnEUiEXr27EmLBFWDV6vVdPiXqeO8dUFd2Ly9veHj49OlkpC+XddUr31DQwPCwsJMNiimNah22bZMf5qTyDSD/tzd3Q3a3MAUUWiOQqFAZWUlPc7SysqKbm5wdHSEmZkZCCH48ccf8cEHH+D48eMYPXq0qZfdAkIIFu25gbS7IiiKs/HL30drnX3Y2TsgR9CIE7fLcSpXgIq6phY/w8vJGv697dHXxQZ9nK3h5WQNZ1sr2Pe0gH0PS/S0NAMBoFKpQTgc1DUqIG5UoFaqgEgiQ3FVA4qqpCiulqJAKIFSrX0572lphsj+Lpga2BtTA3vDzb59bfmsMOhArVbjr7/+QlJSEg4ePIjq6mrExMSAx+Nh8uTJrd6xqlQqLZGwsrJCr169IBaLoVKpGHlhM6SbWZeBrCOua+rCplAoEBoayqj8HuC/z92QIUPg4eHRru9pamqiRYK6c9Zsg9XXOQ0V1qdWq3XOQTY1IpEIt27dQmBgIMzNzenn5Ny5c8jPz4ebmxsSExORkpKC6OhoUy9XJ6l5AizZmw6iUqDsh+VQ1pRpfX737t1YuHAhgP9UDCobcKWwCn8VVuE2X4ziaqne1+TQwwy1D29jtL8X3n5pGsL6OaGHRcfLV6wwPAG1Wo0bN27QIiEUCjF16lTweDxMnTq11UNolUoFgUCAu3fvQqVSwcrKClwu1yTx2K1hTDezLte1psu4+UWfimowMzMzeuBcexAIBMjJycHQoUM7/dxR51YikUivQX9MFwVql9X8dUcIwdWrV/HZZ5/h0qVLsLKywrRp08Dj8RAXF8eoGd1NChWmfHMRxdVSvD7OF2tjOt4lVdekQF5ZHe4LJeDXNIJf2wh+jRTiRgXqm5Sob1JCplQ9ToUGoFapoGqSwApKqJrqYaWSISrQBxOihsHb1QZ+vR1gCxlGjRqFV199Fe+//36nfz9WGDqAWq1GZmYmLRJ8Ph9TpkwBj8fDtGnTtAxg9fX1yM7OhrOzM/z9/bVq8NRBLRWPbWyRYIKbmXJdU1Elmq5aDoeDzMxMegYFU/wdFNRhaVBQkN7yefQV9Md0UaiurkZ2dnaru6zDhw9j6dKlOHDgAHx8fHD48GEcPnwYmzdvxrhx40ywYt1sO1+ATafuguvQA+fejYZtD8M+z3v37sXatWtx6NAhDB8+HF9++SU+++wzHDlyBBMnTtT62pKSEvTt27dLj8cKQyehesKTkpKQnJyM4uJiTJo0CTweD9bW1vjggw/w888/Y9iwYVoXfuqglrpzNuYMBUD/s5n1gaYLvba2FmZmZrC1tUVQUBCjRpgC/91lGdJD0fwwX61Wax3mtyaUKpUKWVlZIIQwUhRqamqQlZXVqlP92LFjWLhwIfbt24fZs2ebYIXto1Iiw7gvz6NBrkLCSyHghRoukoM6WH7vvfdQX1+P7777DomJiViyZAkSEhKwaNEi1NXVQSAQYNCgQWxWEpOg5uP+/vvv2LNnD0pLS+Hn54d3330XM2bMaLUsoKubhxIJQ8RBG3o2c1dpaGhAeno6bGxsYG5ujurqaroGz+VyTS4SJSUluH//vs62SkNBCIFYLKZr8K0F/VGiAAChoaGM22VR7bytnWWdOXMGc+fOxa5du/Dyyy+bYIXt5+MjOfjpahGG9XHE4TdH62X8pyaaLajU/3/77bfh4+OD5557DtHR0di0aRNef/11qFQq7Nq1C0qlEkuWLNFb5huzbimeUjgcDoKCgnD+/HnU1tZi69atEIlE2LJlC9566y1MmDABcXFxiI2NhbOzMy0SZmZmcHV1haurq1a5KScnh2755HK5nZ7dq4mmm5lpmfsAUFdXh8zMTPTp0we+vr7gcDh0DV4oFOLhw4cmdV1Tcei6XLmGhMPhwMnJCU5OThg4cCAd9FdcXIy8vDw4OzujV69eqKiogLm5OSNFQSwWIysrC4MGDdIpChcuXMDcuXOxfft2vPTSSyZYYfspqmrAL9eKAQBrp/nrXRRu3bqFW7duIS4uDvb29vT7ftiwYfj73/8OMzMz7NmzB6+++iqAxzdTBw4cwKhRo/T6nmZ3DHpCIpFg8uTJ+N///V+635qK0k5MTMTBgweRm5uL6Oho8Hg8xMbGwtXVtVXPg1gspuPCVSqVlkh09I3PhNnMbUG1yw4YMAD9+/fX+TWmcl1ruq3DwsIYFdXe2NiIiooKPHz4ECqVCg4ODnQ6rql3VxR1dXXIyMiAj4+PTpf/lStXMHv2bHz11VdYsmQJI5oy2uKt/ZlIuVWOcYPd8NNr+vVV3Lp1C+Hh4VCpVNi5cyf+9re/0X9HtVqNlStXYufOnUhJSUFgYCDEYjHefvttVFdX48aNG3pdCysMeqSt+h41iYoSiZs3b2Ls2LHg8XiYNWsWevXq1apIUDVEgUAAhUJBi0Rb9WYKys1sZ2eHoKAgRkU/A0BlZSVu3brVoXZZynUtEAggEokM5rqm/mZlZWWMdFtTo0LNzMwQEBBAx3O0J+jPGNTX1yMjIwP9+/fXKfg3btxAXFwc1q9fj+XLl5tcFC5evIhNmzYhIyMD5eXlOHToEHg8Hv35W6W1mLX1Mjgc4NiK5xDgqb+bhNraWixatAienp7o0aMHvv32WyQkJGDBggX0OeCdO3ewefNm7N27Fx4eHnB1dYWDgwNSU1P17mZnhcEEUC5oqrspMzMTo0ePpkWCy+W2KhLUNDaBQAC5XI5evXqBy+XqNI91xc1sDCoqKpCbm9uleBDNw3yRSKS3Ehx1SC8QCBAeHs6YO3AKpVKJzMxMeuiU5t++taA/SjiN8TqQSCTIyMhA37594ePj0+Lz2dnZmDFjBj744AOsXLmSEa/NEydO4PLlywgPD8fs2bNbCMOCH6/jwj0R4kO98M1LIXp97KqqKuzYsQPBwcGYOXMm1q9fj48//hibNm3C4sWLtXLBrl27hsrKSri4uCAyMtIgFQBWGEwMIQRFRUW0SNy4cQMjR45EXFwc4uLi4OHh0apISCQSWiQo8xglEhKJBFlZWXpxMxsCKklTXyNMgdZd1+3dXWn+nDt37qCqqgrh4eGMO6SnRKE986M1M62ap5/qI65EF1QTgZeXFwYOHNji8zk5OZg+fTpWrlyJdevWMe61CTw+29EUhuySWvC2XYa5GQfn3h2Hfq76v1GorKzUei9s3LgR69atw7///W+8/vrrcHJyglQqRXV1tcFH67LCwCAIISgpKUFycjIOHjyIv/76C1FRUbTBx8vLq1WRoMxjAoEAUqkUhBB4enpi8ODBJhv+0xrUQW5ISIjBTEttua6p2d+6UKvVyMvLQ21tLSIiIhjnVKeMf+0RheY0F05DBP1JpVKkp6fDw8NDZ+x4fn4+YmJisGzZMnz66aeMFAWgpTC8tucGzuUL8UJ4H2yeE2zQx1YqlfTfIiEhAStXrsRnn32GBQsWYM2aNbCyssKuXbsMelbICgNDIYSgrKwMycnJSE5OxpUrVxAeHg4ejwcej4e+ffvqfFPx+Xw6UqChoYF2GFOHkqYUCUIIPdzdmAe57XVdq9Vq5OTkQCKRIDw8nHGdW10RheZoBv0JhUJIpdI2nejtgZpa5+7urnPGSEFBAaZNm4ZXX30VX3zxBePOuzTRFAbqbMGMA5x9NxoDehm+rKjZsrpjxw784x//QJ8+fVBTU4O8vLx2R7B0FlYYngIIIaioqMDBgweRnJyMP//8EyEhIYiPj0dcXBz69+8PQggSExPh7u6u5Wam2hsFAgEkEglcXFzoerMxs4cIIcjPz4dIJDJ5zV5ztjPlunZzc4NIJIJCoUBYWBjjcpkUCgUyMzNhZWVlEDc49ZyIRKJOBf01NTXRo0x1nWc9evSIjrdISEhgtCgA2sKw5Kd0pN4RYHaoF77W89lCW2g2s0RGRkIkEuHSpUsGLyMBrDA8dRBCIBQKcejQISQnJyMtLQ1Dhw4Fh8NBYWEhLl++rPOwD3h8R0fdNdfV1dExFO7u7ga9O6aMdXV1dQgLCzNoomhHaWpqols+lUolHBwc6KRPpqxTUxSCg4MNflHtaNCfTCZDeno6nJ2ddc63pqJjpk6diu3btzNeFID/CsOgqPGY8e0lmHGAMyvHwdfNuJ1pSqUSK1aswM6dO1FQUNDqe1vfsMLwFEMIQXl5OWbPno1bt25BoVAgICAA8fHx4PF4GDRoUKs13MbGRvquWSwWw8nJiX7z67OuTk0Oa2pqQlhYGOPKM0qlEtnZ2SCEIDAwkD6opVzX+pjY1xUUCgUyMjLQo0cPo4iCrsfXnGJIxcq7u7vDwcEBcrkcGRkZcHR0REBAQIvXW0VFBaZOnYoxY8Zg165djDPftQYlDOeb+uFIdhlmBntiyyuhRl+HUqnEDz/8gLFjx2LIkCFGe1xWGJ5ilEolZs6cCYFAgOPHj8PS0hJHjhxBcnIyUlNTMXjwYPpMoq12VSqrSCgUora2Fo6OjvQFsSsiQV10qUA3ph2CUzV7XS2fzS+I7RnGZIj1ZWRk0GGCpr7T1hX0RwiBnZ2dTse1SCRCTEwMQkJCsHfvXsYZK5sjkUhQUFAA4HGsyCebvsVPVT5QEyBlxRgM9TLNKFl95R91BFYYnnL27duHuLg4rYNcqvvkjz/+QHJyMk6fPg0fHx/weDzEx8frvLOjkMlktEjU1NTAwcGBviB2pLRCua0tLS27fFBqCORyOTIzM9GjR48n1uyb+wKM4bpmmig0RyaT0W5blUpFB/0RQtC/f3/I5XLMmDEDgwYNwoEDBxh3U6CLtLQ0jB8/nv630/jX4Bg1G04yAbK/ec2EKzM+rDA8A4jFYhw9ehTJyck4deoUvL29ERcXh/j4+DZnO8vlclokqqurYW9vT4tEW739TU1NyMzMpBNSmXZRo8ofNjY2HV5f87tmQ7iuKdGytrZm5PPXXLQ4HA7EYjGEQiE+/fRTHD9+HD179oSPjw9Onjxp8FkfhqCuSYFRG85BIlNi96JIjPdzN/WSjAorDM8YdXV1OHbsGJKSknDy5El4enrS5aa2athyuVyrtNJa6qlUKkVmZiZ9EMm0ixolWvb29ggMDOzS+nS5rimR6KzruiuiZQwocx21E2y+PrFYjMmTJ0MqlcLBwYHOB9uyZYvW2Eum8/3FQvz7eD4Gudvh9DtjGeu3MBSsMDzDSCQSHD9+HElJSThx4gTc3NzonURYWFirF6Xm9Xcql8fe3h537txB7969dfaxm5rGxkZkZGTA2dm5zXJaZ9Dlum7PDAVNngZRoM5kdJUHGxoa8Pzzz8Pc3BzHjh2DjY0NHj58iMOHD2PevHl6c7gbGqVKjbFfnkeZuAlfPj8ML0Z2bejN0wgrDCwAHr+pT548iaSkJBw7dgwuLi6YNWsW4uPjERkZ2epFSqlUQiQSgc/no6amBpaWlujTp49RD2nbg1QqRUZGBnr16mXw3ChN17VAIIBMJnui65oSBVtb2zbLe6aCmvfA4XBaHNQDj0V3zpw5kMvlOHHiBCMGQHWWU7kVWLYvA662VriybkKnZiY/7XQbYZg1axays7MhFArh7OyMSZMmYePGjTonRbG0TWNjI06dOoWkpCSkpKTA3t4ecXFx4PF4GD58eIuLAjXD18fHBz179qTr71RrI5fLNVnCJ/DfQDcPD482W3gNAZVpRe0kdLmunwZRoLrLwsLCWvz9ZTIZXnnlFdTU1OD06dNagW9PI/N+uIY/71fijWhfrJn29JS/9Em3EYZvvvkGI0eOhIeHB/h8PlatWgXgcd47S+dpamrCmTNnkJSUhKNHj8La2hqzZs0Cj8fDqFGj8Ouvv6KoqIiODKZQqVR0J49IJKI7ebhcLhwcHIx2caainzUHAJmS5q5rR0dHuh5vCp/Ck6BG2FKO8Oa7Hblcjnnz5oHP5yM1NdUk88P1ycPKBozfnAYOB7j43nj0dWFWgKKx6DbC0Jw//vgDPB4PMpnsqWiVexqQyWRITU1FcnIyjhw5AqVSiYaGBrz99tv45JNPWu1Tb97JY2FhQYuEIYfsUJPD+vXrhwEDBhjkMbqCWCymzXVKpbLDMRSGRq1W49atW5DJZAgLC2vxPlIqlXjttddw9+5dnDt3Dm5ubiZaqTbbtm3Dpk2bUFFRgeDgYGzZsoUenvUkPj+Wh//78yHG+7lh9yL9DuJ5muiWwlBdXY033ngDfD4fly5dMvVyuiUJCQn45z//ieeeew4ZGRngcDiYOXMm4uPj8dxzz7Uqxmq1mhYJoVBIt3tyuVy9zgqora1FVlZWq5PDTI1MJkNGRgYcHBwQGBio1fXFBNe1Wq3G7du30djYiPDw8BZ/T5VKhWXLliE7Oxvnzp3r9DwNffPbb79h/vz5+O677zB8+HAkJCQgMTERd+/ehbt72y2nTQoVRmw4i1qpAj8ujMAE/6evzVZfdCthWLNmDbZu3QqpVIoRI0YgJSUFrq6upl5Wt+Pw4cN47bXXcOLECQwfPhxKpRJpaWlISkrCoUOHoFKpEBsbi9mzZ2Ps2LGtBtJptnsKhUJwOBwtkehsWaW6uhrZ2dkdmgpnTJqLQnMxNLXrmhCC27dvo6GhAeHh4S3+fmq1Gm+99RYuX76MtLQ0eHl5GXQ9HWH48OGIjIzE1q1bATxea9++fbFixQqsXbu2ze89mFmKlb/fhJeTNS6uHg9zPc9zfppgtDCsXbsWGzdubPNr7ty5Q/dHV1ZWorq6GkVFRfj000/h6OiIlJQUk9eVuxsKhQJFRUU6h7AolUr8+eefSExMxOHDh9HU1ITY2FjEx8cjOjq61awktVqN2tpaWiQoTwCXy4Wzs3O7RYIaFerv78/IxoOmpiY6W0iXKDSnNde1oc5qCCF04GFERIROUVi1ahVOnTqFtLQ0Ru3G5HI5bGxskJSUpD15bcEC1NbW4siRI21+/yvf/4WrD6rw7uTBWDFxkIFXy2wYLQzU/Nq28PHx0XlHWlpair59++LKlSsYOXKkoZbI0gYqlQqXLl2idxISiQSxsbGIi4vDxIkTW81hIoSgpqaGbvds77hOoVCI27dvd2lUqCGhRMHJyalTPoq2XNfOzs5dFglqcl1NTQ0iIiJaiLharcb777+PQ4cOIS0tDb6+vl16PH1TVlYGLy+vFu/51atX48KFC7h27Vqr31tSLcVzX54HhwNcWjMBXk6mP+MxJYxOtXJzc+v0gZZarQbweNvOYhrMzc0xbtw4jBs3DgkJCbh69SqSkpKwatUqiMVixMTEgMfjYdKkSVqHrRwOBy4uLnBxcYGfnx/EYjEEAgHy8vKgUqm0RIJqnaTmRwcFBT2xlmwKuioKALSEQLMMd/v27S67rql5GdXV1TpFgRCCzz77DImJiYwUha5yKIsPABjl6/rMiwLA8B1De7l27Rpu3LiBMWPGwNnZGYWFhfjwww8hEAiQm5vLuKjnZx21Wo1r167Rc66rqqroIS5TpkxpNYeJMo5RI0wVCgU9lY7P5yM4OJiR7lpqiI0hHNdA113XhBDcu3cPQqEQERERLTqiCCH44osvsHPnTpw/fx6BgYF6Xb++6GwpiRCC6M1pKKqS4usXgzE7jHnnUsaGWU3TncTGxgYHDx7ExIkT4efnh8WLF2PYsGG4cOGCQUTh0aNHWLx4MQYMGABra2v4+vri448/hlwu1/tjdUfMzMwwcuRIfPXVVygsLERqair69++Pjz76CP3798e8efOQnJwMiUSi9X0cDgeOjo4YPHgwxowZg4iICMjlcpSUlAB4XEoQCARQqVSm+LV0QomCi4uLQUQBePy8ODs7w8/PD2PGjKHnXty7dw9paWm4efMmKioqoFQqW3wvIQT379+HQCBAeHi4TlFISEjA9u3bcfr0acaKAgBYWVkhPDwcZ8+epT+mVqtx9uzZNsvJ6UU1KKqSwtbKHNOGMq8EaQq6xY7B2Jw8eRK//fYbXnnlFQwcOBA5OTlYunQp5s2bh82bN5t6eU8tarUaWVlZSEpKQnJyMvh8PiZPngwej4eYmJgWMQtFRUV48OABQkJCYGFhQe8kmpqanhhBYQyobCYXFxedk80MTXPXtVQqhYuLC7hcLnr16gUrKysUFBSAz+cjIiKiRVssIQTbt2/Hv//9b5w6dardXgBT8ttvv2HBggXYuXMnoqKikJCQgN9//x35+fmtpryuTb6FAzdK8EJ4H2yeE2zkFTMTVhj0xKZNm7Bjxw48ePDA1EvpFlDmKkokHj16hEmTJoHH42H69On44osvYG5ujvfee08rgoEQgoaGBlokGhsb4erqSl8MjWV2pEShtRnIpqC567pnz56Qy+UIDQ2Fs7Oz1tcSQvDDDz/gww8/xPHjxzF69GgTrbrjbN26lTa4hYSE4Ntvv8Xw4cN1fq1cqUbE+jOoa1Ji/9LhGOXLvFKkKWCFQU988MEHOHnyJNLT0029lG4HIQQ5OTlISkpCUlIS7t69Cw6Hg5UrV+Ltt99u0xhH3TELBAI6p4jL5dJnE4agsbER6enpRgns6yz37t1DSUkJbG1tIZFI4OjoCDc3NyiVSvj6+mLfvn147733cPToUURHR5t6uQbjfL4Qi/bcgJt9D/y1buIz7V3QhNFdSU8LBQUF2LJlC1tGMhAcDgdBQUEYOnQo6uvrIRKJ8OKLL+LkyZNISEjA+PHjwePxEBsb26Jt087ODnZ2dvDx8aHvmIuLi5GXlwcXFxe6k6c1E15HoUTBzc0Nfn5+jBSFoqIilJWVISoqCvb29pDJZBCJRMjKysLcuXPh5uaGqqoqfPfdd91aFAAg5VY5AGD60N6sKGjA7hg06KihDgD4fD7GjRuH6Oho7Nq1y9BLfKZJTEzEqlWrcPbsWQwcOJDupklOTkZSUhJycnIwbtw48Hg8zJw5E66urq1emBsbG2kzXV1dHZydnWmR6GzDwtMgCsXFxSgsLER4eLjWOFiK3bt3Y+XKlQgODsbNmzcxaNAgLF26FP/4xz9MsFrDIleqEb7+DOqblPh92UhEDXi6AwD1CSsMGnTUUFdWVobo6GiMGDECe/bsYVwyZndDrVajsrJSp0+BEILCwkIkJibi4MGDyM7OxnPPPQcej4dZs2bBzc2tTZGgau9isRhOTk60SLRmwmsONe/B3d2dkUOKgMemz/v37yMsLExnNHZKSgoWLVqEn3/+GfHx8airq8Px48fR0NCAxYsXm2DFhuVcvgCv7UmH+3/KSGbsjoGGFYZOwufzMX78eISHh+Pnn39m3LD7ZxlCCB4+fEj7JDIyMjBq1CjweDzExcWBy+W2euFuamqiRaK2tpZOPOVyua2KxNMgCnw+H3fv3tV50AwAZ86cwdy5c/HDDz/gpZdeMsEKjc/K37NxMJOPhaP645NZzG3DNQWsMHQCPp+P6Oho9OvXDz/99JOWKDAxiuFZhhCC4uJiWiSuX7+OESNGIC4uDnFxcfD09Gz1Qi6TyWiRqKmpgYODAy0SVL+/VCpFeno6uFwuY0WhvLwcd+7cQUhIiM55CRcuXMCcOXOwfft2zJs3j5G/g77RLCMlvT4SEf3ZMpImrDB0gj179mDRokU6P8c+ncyFEILS0lIkJyfj4MGDuHLlCqKioujpdH369Gn1oiiXy2mRqK6uhr29PZycnFBRUWGSyXDtpaKiAnl5eQgODtaZNHz58mU8//zz+Oqrr7BkyRJG/g6G4HJBJebuuoZedj1w/X22jNQcxgiDUqmkLe0suvn8889x7NgxZGdnw8rKCrW1taZe0lMLIQRlZWU4ePAgkpOTcfnyZYSFhYHH44HH48Hb27tNkSgtLcXDhw+hVqvp2QlcLtcksxNaQygUIicnB8OGDdMZFXL9+nXExcXh888/x/LlyxkrCoZ43X96NBe7Lz/CixF98OULrKmtOYw5Lc3MzMTf//53TJs2DV988QXq6+tNvSTGIZfLMWfOHLzxxhumXspTD4fDgZeXF1asWIHz58+jpKQECxYsQGpqKoYNG4Zx48bh66+/xoMHD1rsAhUKBUpKSuDt7Y1x48ahX79+qKurw19//YWrV6+isLAQEonEpLtHkUiE27dvIygoSKcoZGdnIz4+Hh999BGjRQHQ/+ueEIKzd4QAgIlDnt1hPG3BmB3DpUuXcOnSJfD5fGzbtg379+/Hyy+/bOplMZI9e/bgf/7nf9gdgwEghKCyspLeSaSlpSEwMBDx8fHg8Xior6/HP//5T2zevLlFzIVSqaQH7FRWVhp9wA5FZWUlbt68iaFDh+qMgcjJycH06dOxcuVKrFu3jtGioIm+Xvf3BfWY/M1FWFmYIfujybCxYu1czWHMMzJmzBiMGTMGO3bsQJ8+fRAWFtbiawghT82LmOXphMPhwM3NDcuWLcPf//53VFVV4ciRI0hKSsL69euhVqsREhKi83VoYWEBDw8PeHh4QKlUoqqqCgKBANevX0ePHj3ocpO9vb3BXsdVVVW4desWAgMDdYpCfn4+Zs6ciTfffPOpEgV9kvqf3cIoX1dWFFqBMc+KQqGApaUl9u3bh+eee44eF6hWq2l/APUiVqlU4HA4rG+AxaBwOBz06tULixcvxogRIzBhwgRERkaCEILRo0dj4MCB9JlE892DhYUFLQTUgB2BQID09HRYWlrSn9PnFLbq6mrcvHkT/v7+OrvjCgoKEBsbi4ULF+KTTz55JkUBAM7eEQAAJvozb24HU2DMldXS0hJSqRR//fUXZs6cSR/iURf/GzduICUlBVKpFObm5jAzM6NruDU1NXT08tPG2rVrweFw2vwvPz/f1Mt8plEoFJg1axaWLVuGo0ePIiUlBRUVFVi9ejVu376N5557DhEREfjXv/6FnJwcekgUBTVgJygoCOPGjYOfnx/kcjkyMzNx6dIl3L17F7W1tV06k6itrUV2djb8/Px0jjR99OgRYmNjMWfOHGzYsMHkN1Wmet2LpQpkFtcAACaw5wutwogdg1KphIWFBX799Ve4urpqlZEqKyuxbt06eizfgwcPEB8fjy1btsDJyQnA4zuhRYsWobi4GPPnz8fSpUsRHPx0dBq8++67WLhwYZtf4+PjY5zFsOjE0tIS586d0+pUcnJywvz58zF//nyIxWKkpKQgOTkZ0dHR6Nu3L+Li4hAfH4+goCCti3DzKWzUqM6srCz6c1wut81gwOaIxWJkZWVh8ODB9E5bEz6fjxkzZmD69On45ptvTC4KgOle91cfVEFNAF83W3ZSWxswQhioN8Du3bsxceJEeHh4AHgsGB9++CHu3buH1NRUuLu7IyMjA6tXr8bu3bvxzjvvAAAiIyNx48YNHDt2DHv27MHo0aPh7u6O2NhYrF+/XmcmDFPoyvhSFuPR1tB7R0dHzJ07F3PnzkV9fT2OHTuG5ORkTJ48Gb1796ZFIiQkROuibGZmRv/9hwwZQo/qvHnzJjgcjpZItHYxr6urQ2ZmJnx9fdGnT8vJYxUVFZg+fTrGjx+Pbdu2MUIUANO97i8XVAIAxgxk47XbwuSvErVaDXNzc8hkMly9ehUzZ86EnZ0dgMflo9TUVKSnpyMhIQGXL19GeHg4Jk2ahOPHjwP4r6HM2toaL7zwAlJSUlBYWIjGxkbs3bvXaPn7xqC4uBjZ2dkoLi6GSqVCdnY2srOzW0w6YzEd9vb2ePnll5GYmIiKigps2LABfD4fMTExCAoKwvvvv48bN260KDeZmZnB1dUVAQEBGDt2LIKCggAAt2/fxsWLF5GXl4eqqiqt76uvr0dmZiYGDBgAb2/vFmsRCoWYMWMGoqKi8P333z+1sS36fN1TwjCaFYY2MemOQS6X4/PPP4ednR3kcjnc3NwwYsQIegdRUFCA0tJS7Nu3D7/++iv+7//+D3Z2djAzM4OPjw/Kysroeip1SF1bW4vPPvsMDg4O+PXXX2Ftba11gP0089FHH+Gnn36i/x0aGgoAOH/+fLePR34asbOzw5w5czBnzhxIpVKcPHkSycnJmDVrFpycnDBr1izEx8cjKiqqxU7CxcUFLi4u8Pf3R21tLT2/XKVSwd3dHQ4ODigoKED//v3Rv3//Fo9dVVWFWbNmITAwEHv27DHZFDt9oK/XPb+2EQ8qG2DGAUb4tnSBs2hATIhSqSTffvst8fT0JBwOh3A4HLJ69WqSn59PCCFk8+bNxN/fn/766upqkpycTJYuXUpWrVql9bPUajUhhJDVq1cTT09P8ssvvxjvF+lmbN26lfTr14/06NGDREVFkWvXrpl6Sd0KqVRKDh8+TObNm0ecnJxInz59yJtvvklOnz5N6urqSENDg87/JBIJ4fP55Nq1a+Tw4cPkjz/+INeuXSMPHz7U+j4+n0/CwsJIbGwskclkpv51GcNvN4pJvzUphLftkqmXwnhMKgyaCIVCsmnTJjJo0CAydepUUltbS86fP088PT3Jvn37Wny9QqEghBCiUqno/z1+/DixsLAgGzduNOrauxMHDhwgVlZW5McffyS5ublk6dKlxMnJiQgEAlMvrVvS2NhIjh49ShYsWECcnZ2Jh4cHWbZsGTlx4gQRi8UtxEEkEpETJ06Q7OxswufzSVZWFjl58iRJTEwkMTEx5JtvviERERFk6tSppLGx0dS/HqN4+9dM0m9NCtl8Kt/US2E8jBEGTSQSCf3/161bR8LCwsiGDRvoN8H9+/fpz1PC8Mcff5Dg4GDy8ssvG3293YmoqCiyfPly+t8qlYp4enqSDRs2mHBVzwYymYwcO3aMvPbaa8TV1ZVwuVyyZMkScuzYMSIWi0lGRgZ5/fXXSVZWFpFIJFo7iby8PDJ//nxiZWVFzMzMyPPPP09+//13rffSs4xarSZRn58h/dakkCsFlaZeDuNhZOHd1taWPlT+8MMPsWTJEvz++++Ii4vDzp078eDBA/przczMIBaL8c4772DQoEFYv349ALQ43GN5MnK5HBkZGZg0aRL9MTMzM0yaNAlXr1414cqeDaysrDB9+nT88MMPKC8vx969e8HhcLBw4UIMGDAAY8aMwZ07d9CvXz+tVlaqg6mkpASRkZG4dOkSAgIC8NFHH2HChAkm/I2YQ2lNIwR1MliacxDq7WTq5TAexmQltYf6+noUFxfD19eXHppSVFSEdevWIS0tDRkZGXSrK0vHKSsrg5eXF65cuYKRI0fSH1+9ejUuXLhAe0lYjEtRURFGjRoFBwcHVFdXQ6lUIjY2FvHx8YiOjgYhBK+88gpqampw+vRprelsNTU1OgfzPGsczCzFyt9vItTbCYfeHG3q5TAeRu4YWsPe3h6BgYFak7SodtZvvvkGHh4eUKlUJlwhC4t+kUgkmDJlCqZNm4bc3Fzw+XwkJyfDzs4Oy5cvR//+/REcHAw+n48TJ060GNnJisJjbjx67HaOZAfytIunShh0sXjxYuTm5mL27NkA0C3aUk1Fr169YG5uDoFAoPVxgUDATqYzEXZ2dti8eTO+//57mJmZwcLCAtHR0di2bRuKi4uRnJwMd3d3HD16VOd0NpbHZBRVAwAi+rFC2R66xVXU0tKSNrI9q8Fg+sDKygrh4eE4e/Ys/TG1Wo2zZ89qlZZYjMvMmTN1mtPMzc0xceJEpKen6/QymJpHjx5h8eLFGDBgAKytreHr64uPP/4YcrncqOuolcpxT/DYDBfOCkO7eHpdLywGYeXKlViwYAEiIiIQFRWFhIQENDQ0tDrKlIWlNfLz86FWq7Fz504MHDgQOTk5WLp0KRoaGrB582ajrSOj6HEZycfNFq52PYz2uE8zrDCwaPHSSy9BJBLho48+QkVFBUJCQnDy5Emd2f4sLG0xbdo0TJs2jf63j48P7t69ix07dhhVGKg0VbaM1H66RSmJRb+89dZbKCoqgkwmw7Vr1zB8+HCDP+bFixcxc+ZMeHp6gsPh4PDhwwZ/TBbjIxaLjX4WcqtUDAAY1sfJqI/7NMMKAwsjaGhoQHBwMLZt22bqpbAYiIKCAmzZsgXLli0z2mMSQpDDp4TB8QlfzULBCgMLI4iJicH69esRHx9v6qWwPIHODNnh8/mYNm0a5syZg6VLlxptraU1jaiRKmBpzoFfb3ujPe7TDnvGwMLC0iE6OmSnrKwM48ePx6hRo/D9998beHXaULsFv9726GHxdMaOmwJWGFhYWDpER4bs8Pl8jB8/HuHh4di9e7fRfUa3/iMMQV5ORn3cpx1WGFhYWAwCn89HdHQ0+vXrh82bN0MkEtGfM5Zh8nYpe77QGVhhYGFhMQhnzpxBQUEBCgoKWowdNUZEGyEEOWXUjoEVho7AHj6zsLAYhIULF4I8jvZv8Z8xENTJUCtVwNyMg0FcO6M8ZneB3TGwMAKJRIKCggL63w8fPkR2djZcXFx0zjNmYXkSdwX1AID+rjbswXMHeapit1m6L2lpaRg/fnyLjy9YsAB79uwx/oJYWJ5hWGFgeabZsGEDDh48iPz8fFhbW2PUqFHYuHEj/Pz8TL00FhaTwZ4xsDzTXLhwAcuXL8dff/2FM2fOQKFQYMqUKWhoaDD10lhYTAa7Y2Bh0UAkEsHd3R0XLlzA2LFjTb0cFhaTwO4YWFg0EIsftzeyQ29YnmXYHQMLy39Qq9WYNWsWamtrcenSJVMvh4XFZLA7BhaW/7B8+XLk5OTgwIEDpl4KY5g1axa8vb3Rs2dPeHh4YN68eSgrKzP1slgMDCsMLCx4PIMiJSUF58+fb+HSfZYZP348fv/9d9y9exfJyckoLCzECy+8YOplsRgYtpTE8kxDCMGKFStw6NAhpKWlYdCgQaZeEqP5448/wOPxIJPJ6DnrLN0P1vnM8kyzfPly7N+/H0eOHIG9vT0qKioAAI6OjrC2tjbx6phFdXU1fvnlF4waNYoVhW4Ou2NgeabhcDg6P7579+4nzhx4VlizZg22bt0KqVSKESNGICUlBa6urqZeFosBYc8YWJ5pWgt5686i0NEJbO+99x6ysrJw+vRpmJubY/78+UYLwmMxDeyOgYXFROzYsQM7duzAo0ePAACBgYH46KOPEBMTY9DHFYlEqKqqavNrfHx8YGVl1eLjpaWl6Nu3L65cuYKRI0caaoksJoY9Y2BhMRF9+vTBF198gUGDBoEQgp9++glxcXHIyspCYGCgwR63IxPYmqNWqwEAMplMn0tiYRjsjoGFhUG4uLhg06ZNWLx4samXgmvXruHGjRsYM2YMnJ2dUVhYiA8//BACgQC5ubno0aOHqZfIYiDYMwYWFgagUqlw4MABNDQ0MKZEY2Njg4MHD2LixInw8/PD4sWLMWzYMFy4cIEVhW4Ou2NgYTEht2/fxsiRI9HU1AQ7Ozvs378f06dPN/WyWJ5xWGFgYTEhcrkcxcXFEIvFSEpKwq5du3DhwgUEBASYemkszzCsMLCwMIhJkybB19cXO3fuNPVSWJ5h2DMGFhYGoVar2Y4fFpPDtquysJiIdevWISYmBt7e3qivr8f+/fuRlpaGU6dOmXppLM84rDCwsJgIoVCI+fPno7y8HI6Ojhg2bBhOnTqFyZMnm3ppLM847BkDCwsLC4sW7BkDCwsLC4sWrDCwsLCwsGjBCgMLCwsLixasMLCwsLCwaMEKAwsLCwuLFqwwsLCwsLBowQoDCwsLC4sWrDCwsLCwsGjBCgMLCwsLixasMLCwsLCwaMEKAwsLCwuLFv8PzwaQWXvhXaMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_trajectory(y_int[:3, :], y_int[3:, :], t, show_earth=True, fix_limits=True)\n",
+    "# CAUTION: start is the start of the backward integration -> so end was technically earlier\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Appendix - coordinate systems\n",
+    "The IGRF is usually computed in *geodetic* coordinates: the Earth is an ellipse and the coordinate axis follow the ellipsoid. \n",
+    "\n",
+    "To simplify the computation, we will use *geocentric* coordinates where we consider the Earth as a sphere.\n",
+    "\n",
+    "In the following we will express vectors in the following 3D coordinate system:\n",
+    "- the origin is the center of the Earth\n",
+    "- r is given in km\n",
+    "- theta is given in degrees (corresponds to colatitudes) -> -90 to 90\n",
+    "- phi is given in degrees (corresponds to longitudes) -> -180 to 180\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Example - the spherical earth\n",
+    "# generate a grid of points that correspond to the surface of the earth\n",
+    "n = 15\n",
+    "lat_range = np.linspace(-90, 90, 2*n)\n",
+    "lon_range = np.linspace(-180, 180, n)\n",
+    "lat, lon = np.meshgrid(lat_range, lon_range)\n",
+    "lat = lat.flatten()\n",
+    "lon = lon.flatten()\n",
+    "\n",
+    "# map to cartesian coordinates for plotting\n",
+    "r = 6.4e3\n",
+    "x = r * np.cos(np.radians(lat)) * np.cos(np.radians(lon))\n",
+    "y = r * np.cos(np.radians(lat)) * np.sin(np.radians(lon))\n",
+    "z = r * np.sin(np.radians(lat))\n",
+    "\n",
+    "# plot the grid\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.set_box_aspect([1,1,1])\n",
+    "ax.scatter(x, y, z)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "presentation-FM-wVlUX",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}