diff --git a/quadrupole/.ipynb_checkpoints/Model_of_infinite_lnegth_Quadrupole-checkpoint.ipynb b/quadrupole/.ipynb_checkpoints/Model_of_infinite_lnegth_Quadrupole-checkpoint.ipynb
index 61216bd..9415bd8 100644
--- a/quadrupole/.ipynb_checkpoints/Model_of_infinite_lnegth_Quadrupole-checkpoint.ipynb
+++ b/quadrupole/.ipynb_checkpoints/Model_of_infinite_lnegth_Quadrupole-checkpoint.ipynb
@@ -2,18 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import scipy.constants as sc\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 22,
    "metadata": {
     "scrolled": true
    },
@@ -40,33 +29,22 @@
       "  [ 0.        ]]]\n",
       "pseudo charges\n",
       "[0. 0. 0. 0.]\n",
-      "simulation time =  9.999999999999934e-07\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "simulation time =  0.00010000000000013679\n",
+      "[[0.e+00]\n",
+      " [0.e+00]\n",
+      " [1.e-06]]\n",
       "done!\n"
      ]
     }
    ],
    "source": [
+    "import numpy as np\n",
+    "import scipy.constants as sc\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "class Ion:\n",
     "    def __init__(self,position, charge, mass, v_z):\n",
-    "        self.position = np.array(position)\n",
+    "        self.position = position\n",
     "        self.charge = charge\n",
     "        self.m = mass\n",
     "        self.v_z = v_z\n",
@@ -129,14 +107,14 @@
     "\n",
     "            \n",
     "            # calculate the pseudo charge points of the quadrupole\n",
-    "            Q = signLUT[index] * self.phi_0(U,V,f,t)/2 * self.K * self.R\n",
+    "            eg = signLUT[index] * self.phi_0(U,V,f,t)/2 * self.K * self.R\n",
     "            \n",
     "            # match pseudo charge with ion in z direction\n",
     "            self.pPole[index][2] = ion.position[2]\n",
     "        \n",
     "            # calculate force on ion\n",
     "            ion.F = ion.F + self.coulombForceOnQ1(ion.charge,\n",
-    "                                                  Q,\n",
+    "                                                  eg,\n",
     "                                                 self.pPole[index],\n",
     "                                                 ion.position)\n",
     "            \n",
@@ -151,7 +129,7 @@
     "    \n",
     "t_step = 1e-9\n",
     "t_sim = 0\n",
-    "N = 1000\n",
+    "N = 100000\n",
     "\n",
     "# coffein atom 524.50002164 dalton (8.709527e-25 kg)\n",
     "ion = Ion(np.array([[0],[0],[0]]), 1000 * sc.e, 8.709527e-25,0.01)\n",
@@ -167,21 +145,28 @@
     "\n",
     "for n in range(0,N,1):\n",
     "    \n",
-    "    newPos = quad.calcNewIonPos(100,500000,1.1e6,ion,t_step,t_sim)\n",
+    "    newPos = quad.calcNewIonPos(1000,5000,1.1e6,ion,t_step,t_sim)\n",
     "    \n",
     "    if(newPos is None):\n",
     "        print('ion has left the boundary')\n",
     "        exit\n",
     "        \n",
+    "    \n",
     "    ion.position = newPos\n",
-    "    positions.append(newPos)\n",
+    "    if n % 100 is 0:\n",
+    "        positions.append(newPos)\n",
+    "        \n",
     "    t_sim += t_step\n",
     "\n",
     "\n",
     "print('simulation time = ', t_sim)\n",
-    "ax = plt.axes(projection='3d')\n",
-    "ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
-    "plt.show()\n",
+    "print(ion.position)\n",
+    "\n",
+    "if 0:\n",
+    "    ax = plt.axes(projection='3d')\n",
+    "    #ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
+    "    ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], cmap='Greens')\n",
+    "    plt.show()\n",
     "\n",
     "print('done!')\n",
     "# dummy check\n",
@@ -191,6 +176,2047 @@
     "    print(quad.coulombForceOnQ1(ion_1.charge,ion_2.charge,ion_1.position,ion_2.position))\n",
     "                 "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[0],\n",
+      "       [0],\n",
+      "       [0]]), array([[0.e+00],\n",
+      "       [0.e+00],\n",
+      "       [1.e-11]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [1.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [2.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [3.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [4.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [5.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [6.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [7.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [8.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [9.01e-09]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.001e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.101e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.201e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.301e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.401e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.501e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.601e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.701e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.801e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
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+      "       [9.4701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.4801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.4901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9901e-07]])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html\n",
+    "ax = plt.axes(projection='3d')\n",
+    "#ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
+    "ax.scatter3D(positions[:][0], positions[:][2], positions[:][1], cmap='Greens')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('z')\n",
+    "ax.set_zlabel('y')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "print(positions)"
+   ]
   }
  ],
  "metadata": {
diff --git a/quadrupole/Model_of_infinite_lnegth_Quadrupole.ipynb b/quadrupole/Model_of_infinite_lnegth_Quadrupole.ipynb
index 61216bd..9415bd8 100644
--- a/quadrupole/Model_of_infinite_lnegth_Quadrupole.ipynb
+++ b/quadrupole/Model_of_infinite_lnegth_Quadrupole.ipynb
@@ -2,18 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import scipy.constants as sc\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 22,
    "metadata": {
     "scrolled": true
    },
@@ -40,33 +29,22 @@
       "  [ 0.        ]]]\n",
       "pseudo charges\n",
       "[0. 0. 0. 0.]\n",
-      "simulation time =  9.999999999999934e-07\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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mX0+2WCwwGo0s25yRkcEy9Py96HwguMfjkeyYTqeTvf8IymxpAF4FcDchZDLac4lZgoezUiKEoK+vD1qtlum7DQaDT2+3lKCTS1asWAG1Wo3e3t6ojifFRX8u9/z88hLNNk9OTsJoNLLJKVlZWWxbJSXkStxJJQqanp6OqAbOcVwCZsj9/xFC9gR4yDCAJbyfi8/+LihijuC0/DU6Oor8/PyA5KYtl2lpaT4zwPzLZFKdT09PDwwGA5tcMj09veANHMTCf1QS7ZAbHx9HU1PTLMFNNKulHHtwKVfw6elpUdtEgGXI/wignRDyyyAPex3ANziOexkzybWJUPtvIMYIztf19vb2BjRdCDUDLJKMeCg4nU40NTUhPT0d69evZxeA1K8TDWLF8MEftEOuv78fa9asgdvt9mkASUpKYoQX2yEn1x5cqhXcarWKJjiACwF8EUAzx3Gnzv7uewCWAgAh5H8BHMBMiawLM2Wy/wx30JghOL+2HUiRJmQGmJTEo8KGyspKlmSiiES/LgfkyE5LDXrT4DeA0BIj7X/nd8hpNJqwarFYTrIBMyo2sSIXQsi/AIT8Qs9mz+8Qc9xzTvBgVkp8+HeABbuwpQjRCSHo7+/H2NgY6uvrAyZJIimt+WNqagoWiwUajeacN4TwMRdKNr4evLi4GIQQH7UYHXVMM/T+ghE59uBiBi6GQyQhulw4pwQPV9sGAneABQNVskUKl8uFlpYWJCYmhnR3EVta44Pv2Jqeno6+vj62P9VoNCH7lecrwr0fjuN8ZpvzxSP9/f3gOI4JbjIzM2UTz5zLPbhcOGcED2elRAhBe3t7wA6wYIgmRJ+YmEBraytKS0uxaNGisK8TyQrudrvR1tYGjuOwbt06dlHRhpCBgQF2cVDCC21ukAJylLQiQSDxCN+xxWazYWBgQNIbopRJNqvVGhOdZMA5ILiQ2rbFYoHVakVRUVHADrBg8LdAFno+VChTV1cn6IuJZAUnhOCjjz5inWbUjx2Y3RBC68s0XKWrVyBDxljIBcgN/w65I0eOQK1Wsxsi7eeOpkNOyiQbFQHFAuaU4OFq28DHHWDJyclYunSpqC9LbPKLrqgejwcbNmwQPGFD7OvodDpYLBY0NDSwvu1Qx+bXl2m4ShNSdIwvNSCUEvPlZqFQKMJ2yNEMvVAxktQhergocK4wJwT379sORG7/DrDjx4+L/tDF3Aymp6fR1NSEpUuXwuFwiH4dIWSgNXSj0Yj09HQxjQcM/uEqDecHBwdhNBqRlJQEQgg0Go2oEUDBEAshuhgE65AzGo1siAJ//x6sw8vj8UimTIywTCYLZCe4f0ge6AIKNAOMhttyOKLQKGH16tVIT0/H+Pi4aBvkcHC5XGhubkZqairWrVuHjz76yOemECmR+OH8wMAAa4/t6Ohgvcp09Yql7Pxcgd8hR7u9zGYzTCYTent7fbrB+B1yUpfJPhF7cI/HA5PJxDqPAiXSgs0Ai2Q/HQ5erxft7e1wuVw+feJSC1foDausrIyJdaQorfmD4zg2xtc/nO/t7WXhPPVXD3dTiZUkm5RQKpU+nupOpxMmk8mnQ06j0cBms0m26i74PTgNyamkdOPGjbMeE24GmNQEt1qtaGpqYmTgX8hSEpw6uwQaWiiXkIQiUDhvMpmYXVNqair7uxTh/HyEWq1GQUEB8+2jghu6yo+Pj7ObYqSf0YImOL+2HSzkMZvNaGtr81nh/CGlrnx8fBxdXV1Be7elIDg1drTb7QFdZORawUPBXz1Gs/P+4Tx1b5kPK7jUn2FycjKKioowPT2NwsJCKBQKmEwmNvWUL7gR6tZDlXmxAEkJLqS2TTvA1qxZE1LOF41ohb6+1+vFmTNnWAY7WBIlWoI7HA40NjYiNzc3ZFnvXGapQ2XnqXuLSqVCampqTBNdLrsmqmRLS0vz8WOjghvaIUfJHmomWqTdZHJAMoLTlRtAwC8gWAdYMEQaolOyulwuNDY2Ii8vD8uXLw95wUazulJ55YoVK0KWruRYwaOBfzjvdDrR1dUFk8mEo0ePxmw4L5ddU6Abh78lNe2QozPRguU4xDqqchz3DIBrAWgJIasC/H0TgL0AaI/yHkLID4UcWzKCU9FKoItYr9fj9OnTATvAgiEagut0OnR3dwuSt9Jzj0S40t/fj9HR0aCa9UDPkRpSHVOtVrOGj8LCwrDh/LmCXI6qQrTo/jPk/HMcKSkpOHXqFLxer9ib4nMAdmHGdDEY3iOEXCvmoIDEIbp/fZiG7D09PUE7wIIhUlWa3W5Hf38/1q9fL1jmKTZE93g8sNvtmJqa8pk3FgpydKDJFUYLCefp6h4qOy/HDU2uED2S4wbqkHvnnXcwPDyMdevWoaGhAb///e/DHpcQ8u5ZmybJIdutmHaAKRQK1NbWig7zxBKc9m5zHIdVq1aJ0nCLIbjVakVjYyOUSiVWrZoVTQVFrLSYhkKw8wsUzvOHKQQL5+eLXRMQvRaddsjdeeed2L17N44dO4aOjg4pz/UCjuMaAYwAuJcQ0irkSbIQnN8BNjAwEFECS6FQ+IzYDQW6D66qqsLIyEhELqlCzlGn0+HMmTNYtWoVWltbRV3A84HggLCowH/6KT+cdzqdPlbLchB8rvbg0UClUolaAMLgBIBlhJBpjuOuBvA3zFgnhz8Pqc4ACNwBNjw8zIapi4FSqYTdbg/7ejQrT/fB4+PjokP7cAQnhKC7uxsmk4m9L0rYaAgeq5lqMQgVzvf29sJms6Gvr0+w2CYc5NqDB1NZioVMNtGTvP8/wHHcbziOyyWE6MM9V1KCd3d3Izk52adUFGmyLNzzqBQ0OTnZJysv9TBBl8vFnFTXrVvHXkfsiizXCh5rlk38cJ5+dklJSZKJbeZyqkkkcDgckrf4chxXCGCcEEI4jjsPgAKAoKH2khK8oqJiFlHkIPjExARaWlpQXl4+SygjJcEDSU79nyNU7y1Xki2Ww35ydnwPP5ynnV+nT5+Gw+HwaYUVkp2PdYJHUgPnOO4lAJsA5HIcNwTgBwASAObFdgOAr3Mc5wZgA7CdCPziJSV4IKKoVKqIQ3T/Y/G162vWrAn4QUaigAtUJqMNKYEkp/Q50azgBoMB4+PjMVF6opA6vPQ/XqDOLyoRpdl52gobLJyXaw8uFSJxcyGE3Bzm77swU0YTDdmvqmjq2fznud1utLa2QqlUBtSuB3ue0NfiDxOkq0uowYViIwVKcH7eoLi4mM0Bo6FtTk6O4LFAsY5wZOSX24CPG0GGh4dZXZkKSajOQI49uJRRUCy5uQAxTHD+82jv9rJly1BUVBTyedGE6LS0l5eXF9ZJJpIV3OPxoLm5GUqlEuvWrYPH45k1x7uvr49pmenFP1cTS+VewcOB3wjCD+fp5JSsrCwoFIqYHmYYS62igAxCl1kvoFKFzYYHAiX48PAw+vv7We92OERKcKvViuPHj4eVnFKIJbjH40FHRwdKSkqwZMkS1nFH4W/bRKeEDA0NAQBb3f1D11jfg0dKnEDh/MTEBLNpMpvNbHWPthwn9VSTWOkkA+ZoBY9kDw4Ak5OTrJ1U6B5VbMRACIFOp4PBYMD5558veJaUmBsJbUMsLS3FkiVLwj6e4zgf0wKXy8UcSiYnJ5GamoqcnBxmohGrkDIioPtzm80GjUaDgoICNkjh9OnTSE5OZhGP2HlgUps9fOIILjZEp2oxqoITc5GIEch4PB60tLTA4/EgPz9f1IUhdAUfGBjAyMgIFi1aFHHolpCQ4BO6WiwWGAwGjI6OwuPxwOFwICcnB5mZmVGPBDqXIbrQYyoUirDhPN/ZJtziIKUn+icyRBdD8LGxMXR3dzO1mNgLROjKSm8iS5YsQXp6OguFpXod6h7j8XjQ0NCA3t5eScJpvrBErVbD6XQiNTWVqQeTkpKQk5MT0UomNeSSqvoTNlg4bzQa0d/fz1b/YOH8QvVEB2JoBafZa5vNhvPOOy/ixJKQMhklw6pVq5CZmYnJycmISmvBCEv7w/Pz87Fs2TKmkpJjv6xQKHw6nAIlpnJycgJaLvtjPqzgQsjo3+ZJs/PBwnmpV3Ah27C5QkzswW02G5qampCfny/KBz0QQpXJCCHo6urCxMSEjwFEJIm5YISlIhz/ZN1cCV34I4FonZlaLickJLBkndiBf5FALoKLPWa4cJ4q6txud9R6BJvNtnBD9EAIt4LTBo6VK1eG9QwXgmBkpbLJ9PR0rFu3LmpPtkDPGRkZQX9/P5tXzse5UJ3515ntdjsju81mQ0ZGBvs7tWySEnLuwSNFoHB+cHAQOp0Op06dAsdxPq2wYl9rQYfoYvbgdDU1m82ierfDIVCIPjk5yaSt1GyPj2hXcGoNRZtsAq0CsSArTUpKwuLFi7F48WLmH24wGDAwMMDMOqjPeqw2XkgtVVUoFEhOTkZubi5KSkpYxYKOORabnV/wWXT/CzmQDNThcKCpqQnZ2dkhp4XS54r5Qv1D9HCSU/qcSFdw2oeenZ0d0hoqFgjOB98/vKysDE6nE+3t7dDpdBgeHkZaWhpL1kU6EGA+ENz/mP4VC7HZ+VgaegDMQYju/wUbjUa0t7dj+fLlLDEUDDS8F0twOveL9ieHq6NH4pfGcRysViu6urpQUVExa4Z4oMfHcjeZWq1GSkoKcnNzkZWVhenpaRgMBrS0tMDr9TKNOH9YgJBzkyNEl/qYwZJsobLzAwMDADArnBe7gn/5y1/Gs88+q0VwPzYOwFMArgZgBbCDEHJC6PHnrMOBEILe3l7odDrB9k2U4GIy6rQO/tFHHyE/Px/V1dVhL4hIPNksFgvMZjPWrVsn6Audb5ZN6enpSE9PR0lJCdxut8+wABq25uTkhPwe59MKLiS55p+dd7lcLDs/NTWF1157DUajEQaDARUVFYJee8eOHXj22WevRHA/tqswY+5QCWADgP85+19BkD1EB2a+6BMnTiA1NVWQoypFJKHz5OQkJiYmUF9fL3hAn5jXobmDyclJlJWVCb5bR3ITmWsEuwGpVCo23ZMftlJDxmDTT+cLwT0eT0Q5oISEBOTn5yM/P5/lL/71r3/h0UcfxdjYGP7973+H3bdffPHFAGAM8ZCtAP50tj30Q47jsjiOW0QIGRVyjrKv4BMTE7BYLKioqAiY4AoFMSo46nI6NjbGpJxCIfQidLvdzPyhuLhYloGF5xpCoh1+2Epnf9HsvFqtZmGrlDO3KeTeg0cKjuOY6vLvf/8764WXAEUABnk/D5393bklOCEEAwMDGB0dZQkbsRBKcNpKqlKp0NDQgCNHjkRyyiFhsVjQ2NiI0tJSNvgvknZRqXGubxr+s7/oKKCenh5MTk4iKSkJycnJgiSjQhDrUQEt48VKj4AsBHe73WhpaUFCQgIaGhpw6tSpiCaFCiG4xWJhY4DDtZJGClqrX716NRsBHAuWTbGYwKKjgIqKijA8PAyLxYLJyUkmGaV797S0tIheS64QXQolm0w322EAfGlc8dnfCYLkBJ+amkJjYyNKSkqwePFiAB+r2cTuc8LJTqnklE88KUETgwaDYdboI9rfLRT+BKdNIwvF3CEYUlNT2Y3X6XSyuvv09LRPz7vQUlyshuh8SPx9vg7gGxzHvYyZ5NqE0P03IBPBpRoFHEx2GkxyKiWoOUNiYqKP2SL/3CJdwWkXm91uh9PpZFuYnJycOTN3CAQ5tOj8z02tVvv0vFOhTXNzMwghPj3vwQgX6yu42M/v5ptvBoAPENyP7QBmSmRdmCmT/aeY40tO8KKiolmklNJ4kQpLMjMzZ0lOpQK/06y4uDjgYyIN0e12OxobG7Fo0SKWdKQ156amJgBgZI80jI0VhLrgOY5DRkYGMjIyWM87LTnRnndKeH7kF8t7cJvNJrqD76WXXsJLL720KNjfz2bP74j0nGQpk816kSiMF/kEn5ycRHNzMyorK8MKSyKFwWBAR0dH0FHDFJF4stntduYao9Fo4HQ6Z9WcXS6XTxibkZHBFGWB2iSltk2WEmLI6F9yosMU2tra4Ha7WbunXEIXKQgeS1NFKeZE6BLNCu5wOAAAw8PDGBgYCOqm6g+xFwI1QxwfHxckxBFLLrPZDJ1Ohw0bNrARvYGQkJDgYzM8NTUFvV7P9OJ0dZfrQoqFdlF+zzsdpmAymaDX61l0RW96oUZQC4VUlk2xpkMHYpzgdA/e1tYGp9MZtJHDH5R8Qi8ur9cLh8OByclJwUIcocIVcnYqil6vR2FhoQ8xw50jP4ylenGDwcCMGdVqNYuOYsF22R9SrbZKpZL1vE9MTKCqqgoGgwFdXV2w2+3IzMxkPe+RfA5ShegWi0WSG46UmLMQPRKCezweDA0NoaSkRJDklEKMhp3uiVUqFaqrq0Wp7MKt4PxEXVVVFbRaLfsb1cvTz4WuIKFen5+k8nq9GBgYgMFgwKlTp3zq0ZH2es8HwwdgphRXXFzMet4nJibYjS9S+2kpzvMTvYIL9UmjMBqN6O7uRmZmJkpKSkQ9V+j+2Gw2o7W1FdXV1eju7pa0rm2323Hq1CkUFRVhyZIlMJlMzBedkjshIWEW0emNieO4kGRXKBRITU2F1+tFWVkZHA4HDAYD6/Wmq1p2drZkbiViIRfB+fDXh/Ptpy0Wi0/Pu9wViljrBQfmkOBCk2z8wQDV1dUYHx+P6PXCEXxoaAiDg4NsaGFvb6+opFmomwh1damurmZmC/SGQMnsT2Kv1+vzd2CG7PQxgcjOJ09iYqJPrzdd1Xp7e5GQkOCzugfDfFnBQyFS+2kpEGutosAchehiJKctLS1Qq9VoaGiA1WqNePRwsNejbaQul8tnQkqkk0r8MTo6it7e3oCuLjabDU6nEwkJCbM+J0pgpVLJVnaPx8P80+n7Ebq681c1u93us2cV49MWDc4FwfngBNpPS9UE9InNogvZgweSnEYyhog+L9CX5nQ60djYiNzc3Fl7erEE9388TaZNTEzM6j8nhCA5ORlpaWlsv0yTRsH2y/xVm67uHo+HvSYlvZBtRVJSEpOPUp82g8GA7u5uHxdWqSFHRBANgtlP2+12HDt2jJXiIrWf/kTvwUOF6OPj4+jq6polOY0m+x7Itqm5uRlVVVVsXFC454SCvzKtubkZSUlJqK+v97moKSmVSiUqK2dmttvtduj1enR2drIVNS8vD9nZ2QEvLP7qDoA5yYyPjyMnJwculwuEECiVSkGrO9+nje9YMjExAYVCgcLCwqg91gHp54hJqWKjpbiUlBTodDqsWbMGZrM5Kvtpq9Ua8No6lzinITohBJ2dnZiamgooOZVK4jo6Ooq+vr6QNfRIpac0mUazuhT8ZBq1TaZISkryyQLTGi+9sOjqHqwWT2ejL168mO01+au72+1mEUA4QvBdWJubm5GRkQGdTofOzk4kJyezvXsk/dJSr+By6tBVKhX73AkhrCtOjP30J6JMFgjhJKf+q16o5wl9PRrWnjlzBhaLJWwNXawhg0KhgMPhwPHjx32SaUBocgc6Dr/d0mKxQK/Xo7W1FW63Gzk5OcjNzUVmZiY4jsP09DRaWlpQWVnp04LLX90p2cXu3YGZJFRRUREzdjAYDExNRhNUQm2bpB71O1d2TRzH+dz4PB4PS1qGsp+mDTRicPDgQVx11VWnASgB/IEQ8rjfuewA8HN83EG2ixDyB6HHl4Xg/gkof6kqzTKHk5xG+mUqFAo4nU6cOHECGRkZWLt2bdhjiQ3RtVotpqamsHHjRp+7Nl1N6cUo9j1QM4Vly5bB7XbDYDBgeHgY7e3tUKvVsFqtWLVqVVCLaf+9O/8fILwMxzd2WLp06SzbppSUFHZjCtbsM59W8FCgtfVQ9tNTU1OYmpoStQf3eDy44447gBlbpiEAH3Ec9zohpM3voX8hhHxDzPuimJMVnB/+0vKUUMlpJHC73ejq6sKKFSsEu8gIJTi/ky0jI0NScvtDpVKxpBCV6hYUFKCrqwsAWEgZrCnFn+zAxzkBf5FNuC2Kv20TTVBRU0b+6k7PZaEQ3B+B7KffeustHD58GC0tLbj++uvxjW98A5mZmSGPc/ToUVRUVKC7u7sHAM62hG4F4E/wiDGnpos07BQqOY0EWq0Wo6OjWLJkiSiLKCEEd7vdaG5uRkpKCmpra3Hq1Cn2N0ocIWGwGBBC0NPTg+npaZ+yntPphF6vR29vLywWC7KyspCbmwuNRhNwj0jPyX91p2E8n/jhLnq+VpxGGtRLvKOjg7W/0jq+VIjFVlFqP/3Nb34TH3zwAX7wgx+gs7NTkKhmeHjYf8zREAIbKn6G47iLAZwB8C1CyGCAxwTEnITodrsdVqsVRUVFbFaX1KBlKrPZjJKSEtFfWjiC02TakiVLUFRUBLfbPUuZJsWqzYfX60VraysSExNnTVlVq9U+q4jZbIZer0d3dzfUajXy8vKQm5sbNANMV3eVSsVkr9RHjBI+lMiGD5VK5dMJRttfJyYm0NraitzcXEnEJbHcKgrM5E+WLl2K9evXS3K8s/g7gJcIIQ6O424D8DyAS4U+WfYVnLZfJiYmipacUoT7YunKmpycjPr6eoyNjcHpdIp6jVAEp5JW/nglvv+6HOR2uVxobGxEQUFB2GF2/qUvm80GvV6P9vZ2OJ1OaDQa5nfufzHTLYfT6UR9fT17X/4iG6FlOH776+TkJEpLS2G1WjE0NISpqSmkp6cjNzcX2dnZoqWjsbiC8yE2yVZUVITBQZ/FeJYdEyHEwPvxDwCeEHNOspouUsnpunXrcOLEiYjuwOE6w2j74LJly5hFVCQCmWB7UDpvjEpa+XA6nZiYmJBc9mi1WtHU1ITy8vKI6qrJyclYsmQJcz01Go0YHx/H6dOnkZqayvbuKpUKra2tSEpKQk1NDXsPQkQ2QlZ3QgjUajXS09OZuIS2vw4ODoLjOHbzEdIYEit78GBwuVyi3IUaGhrQ2dkJjuNKMUPs7QA+x38M52uRvAVAu5hzkoXgHo8HjY2NSExMZO2XtOQVqfFioC9Br9fj9OnTbAwwRSR+6v5lMlqjn56enpUzoGF5ZWUlM2bIzMxEXl5e0D2wUJjNZrS3t2PVqlWiSy6BoFQqZyXH6KA9i8WCzMxMLFu2LOjzA4lshEpo/W/M/PZXAD7tr7QxhK7uga6TWF/BxQp7VCoVdu3ahWuuueYNzJTJniGEtHIc90MAxwghrwO4k+O4LQDcmPFP3yHmnGQheHd3N/Lz89mKCiDivuVA001odKDT6QIOLoyE4AqFgnW88ZNp/iU2fvaZZrhpc4dOp0N3dzcSExPZHljIBBeKsbExDAwMYO3ataKeJxQ0OaZSqaDT6VBVVQWlUomBgQFMTU0xguXk5AT9nsKt7m63m4Xy4SI2//ZXurr39/cHbH+N5T14pDLaq6++GoSQKr9jPcz7/+8C+G6k5yULwZcvXy6bL5vH40FrayuUSiXWr18f8MuJ5LXoTcFms+HUqVM+IT8QWrzi39xhtVqh0+nQ2toKj8fDxCr8EhIf9IZlNptRX18vq3kDFcosX76cnS91j5mYmIBer0dfX5+PsiuUXh4ILrJxOp2i2l9pYwiAgO2vtJwnJaSWv8aah96clcmiVaUBM8mjxsZG1mMdDJGu4BaLBSdOnJjlxyY2U56SkoJly5Zh2bJlrINpcHCQrZJ5eXnIyclh7629vR0KhQJ1dXWSX8B8mEwmtqXxF2RwHIesrCxkZWWhoqJCtF4e+Hh193q9aG5uRn5+PuuMo6uvUEVdoPZXuh2anJwU1P4qBGJn3wWD1Ko9qSBbmWzWC0Xo6kITZiaTCW1tbT6Z7FDPEUtwqgc///zzfZJp0ZbB/DuYaCjf29sLlUoFh8OB/Px8lJeXy3qBaLVapscXEv5Hqpen453y8vLYTZjf607/idHL0wjJZrOxaEiq9lepVnCr1RpzOnRgjlfwSJ1VR0dHMTExIXoqqRBQvfrExAQKCgpmkVtKZRp/laRbgczMTExOTuLo0aOzdOdSYXBwEFqtFmvXro1otRKql09OTkZTUxOKi4uxaNEin+cD0evl6fcQqv01MTGRnauQLjCpkmyx2AsOxHiI7vV6YTQa2cwxoV+E0BWcP0ywsrLSxz2GEMJuSFKHzRMTEywaoXtOqgajuvP09HQWyke6J6c1bpvNhrVr10r2PgLp5QcGBqDT6dj7cblcQW8mgRJ1QpxsvN7ZY34DaQAMBgPOnDkDp9Pps7oHev9SruCx1gsOzGGILpbgDoeDldoWL14s6i4rhOD+yTSz2TwrlJQjaaLVatHb24s1a9b4rDD+arDJyUnodDqW8KJZeaFhoNfrRVtbG9RqNVavXi1b+K9SqZCWlobe3l7U19dDqVRCp9Ph5MmT4DhOkF4eCO1kQ0U2Ho8nbJ2Zb8hIJ5/q9Xp0dXWxHm/+XPP4Ci7VC4nYg9NusxUrVmBqaiqihFmo59D9PD+ZJrcyjZCZaat6vR719fUhQ2W+1RBNeOl0Opw+fRoOh8MnlA+0+tDIJCcnJ2SNWwpMTk6yz5LW7TMyMlBeXi5aLw+ELsNZrVYkJSX57N9DgV9qo+2vRqMR7e3tbJiC3W6X5HP4xBNcqVQKko9S5Rj1NLNaraJD+1DRwvDwMAYHB2ft5zmOg8Ph8KnjSgWv14szZ87A4/FEFConJSXNUqaNjo6yxg4ayickJLDIZ+nSpSgsLJTsPQQCNUSoq6sLuN+VQi9P/9vT0wNCCHJzcwFA1N4dmD3XnLa/arVatLa2sgYZjUYTkblFPEQPE6JTEthsNh/lGH+6STSvT5NpVqsV69evn6VMS0xMRHJyMj766COkpqayiy/aEgoVzVDjv2hvHP7KtKmpKeh0OtYs4nA4UFFRITu5aVZ+7dq1gggRqV6eKgpdLpfPViOYhFZoGY5ue4aHh1FdXc1GRrW2tgZtfw0Fi8UiifJQasREko26u2RnZ2P58uU+H2ik9XM+3G43GhsbkZGRgTVr1vgcnybTOI7DihUrWDcU3UdSg8S8vDzRZRC73Y6mpiYsWbLEJ6ssFfjSz9zcXLS0tKCoqAharRYDAwPIzs5GXl5e0ARTpBgZGcHIyEjEWXlAmF4+JycHvb29AICVK1fOEhcBgTPz/r3u/Mf7g/rlJSYmhm1/DTXmOBbtmoA53oMHKpNNTU2hubkZFRUVAd1doiU4bUYpKSmZRTJ+Dze9ePjdUGVlZUzwwd//5uXlhS1lTU1NsTxCuLp9tNDpdOjp6fFpiKHaAT5paCgfzbjlvr4+mEwmrF27VjINt39UMj09Db1ejw8//BAcx6GoqIh1ogX7zIM52YQbKBGozyFY+2tzczOAwN7qkQw9OHjwIO666y6cOXOmC4HtmhIB/AnAOgAGAJ8lhPSJeY1zGqKPjY2hp6dn1jxxPiIRrVDQhIp/M4qYTDlf8OHxeHwslPxVaRQ0a1tbWyt74mVoaAhjY2OzEnd8a2Z+VNLY2OiT3RY63oeW3BwOh6yKO7pX7u3tZSs8Hb4YjV7en/R8ogttf6XTX+kgBXrTodl6MVEatWt66623UF5evhKB7Zq+AsBECKngOG47gJ8B+KzgF8E5CtHpxUKH/YUK8yJdwZ1OJ86cOTMrmRZNGUypVPrc2akqraenhzWYuN1ulimPZqUMB77TS7jV1D8qoTrv7u5uWK1WFsoHk6ESQtDe3g6lUunTVioHqAV1dnY2qwDwJ5VIqZenSVWXyyV47+6vTJyamsLevXvxl7/8BWq1GgMDA/jKV74Sdrw1tWsqKysDIcQZxK5pK4BHzv7/bgC7OI7jiIjOFtkI7u/qQstkfHFJMDdVPsQSnBCC06dPM2so/oUvpTKNr0qrrKyExWJBe3s7LBYLkpOTMTw8jLy8PFED8ISC6teVSuUspxch8Nd5m0wm6HQ6ZqZIE4xqtZrpytPT0yVJEoYCbTPOz8/3saCmkFIv73K50NbWhoqKCp8oUYyTDc2BfPGLX0RbWxs2bdoEu90uSLEp0K6pCMAgABBC3BzHTQDIAaAP+wJnMacruMPhwNGjR1FaWio4nBEyZ4yCJtMyMzNn3dHlVKZ5PB50dXUhKysL69atY6t4T08PLBYLNBqNZMkuj8fDEpJS2F/xZaj8fvHGxkYQQuB0OlFQUCA7ud1uNxvWKPTaCKSXpzeq5OTkoHp5l8uFU6dOoaSkxGeljcbJZnp6GsXFxTj//PMj+wBkwpwR3GAwwGq14vzzz/eZXhIOQldwmkwrLS1FYWEhDAYDkyEGSqZJBYfDgaamJixevJiNXEpISPDpc6b11tOnTyMtLY1deGIz0HT0UlFRkU8rq1Sg/eJpaWkoKirCyZMnkZOTA5vNhg8//NBnhZRyphkl3NKlS0UZZfLhf6OyWq0B9fIpKSkByU2PEa7XPViDjNg6uBC7prM/LwEwxHGcCkAmZpJtgiF7iE4IQW9vLwwGA1JSUkSRGxBmvxQomUafR51a5CB3sCEE/ufPv/D8S3A0exyuMYLaOIV6LalAZ6aXl5czYQkVquh0Oib7pKF8JMIQCofDgVOnTqGsrEyysT98UQtfL0+bbjIzM+H1esPq5QHhDTJi55JRu6be3l6UlZWpEcCuCcDrAG4B8AGAGwC8I2b/Dci8gtOESWJiItatW4cPP/xQ9DHCreCDg4MYHh6elUyj3Wv8HmQpQVVcgXqrgyFYCa6jowNOp5OV4PzFFbQ5paamRvQNUiwsFguam5uxYsUKn554f6EKDeWbm5vh9XrZuYvxp6NOtVVVVbIMP6RQqVTQaDTo7+/H6tWrkZSUxMRBQvTyQPjVnbb/ijmnXbt24YorrgBmfNYC2TX9EcCfOY7rwoxd03ax750Lc0OIeJzjxMQETp48iSVLlrCEyfvvv4+NGzeKPlag53m9Xpw+fRpOpxOrVq2alUxraWmB2+1GYWHhrDJWtBgZGcHw8DBqa2ujWr34oOIKnU6HyclJ5vFGs+XB5KBSYnJyEq2trVi9erWo1YiqwHQ6nWB/Omre4X8jkQNOpxOnTp1CaWnprCiB6uX1er1gvTwfXq8Xu3btwuuvv45Dhw5FKnaRLbkhG8Hb29uh0Wh8vrz3338fF1xwgejV1J/gLpcLTU1NyMrKQllZ2axkGr2zTk5OQq/Xw2AwICkpCfn5+Sw7HAmo97rFYpl1U5EStBzU09MDs9mMrKwsFBQURB0Oh0I4XblQ8P3pjEZjQH86Oip6LiISSu6ysjK23Qh17lQvbzQaw+rlCSH43e9+h3feeQevvvpqNGXR+Udwt9s9K7Q+evRoRJ5jfIJbLBY0NjairKxslt46VDLNYrFAq9VCr9eD4zi29xXTfkmHEFRWVsqaUaYebRMTE1i9ejXrJtPr9azhQsoSHNWV19XVSX4DockunU4Hj8eDtLQ0GI1G1NXVya7ddjqdOHnypE8uQQxsNhv73P318hzH4dlnn8X+/fvx2muvRWuSuTAIfvz4cdTU1Ij+MCjB6RAF/zniYsUrDocDOp0OOp2O7X3z8/OD7h+pVl7IEIJoQQhBR0cHCCFYsWLFrGwttRrW6XSSlOCGh4cxOjqKuro6SbzJQsFoNKK1tRXp6elsaF8gJaAUoOSuqKiQJClJ9fJ6vR7PPfccjh07hsnJSfzjH//A0qVLoz38/CM4FbXwcerUKVRWVoqWb77//vsoLi7G6Ogo1qxZ47PKRGvQQDOsWq0W09PTs1Rd0Q4hEAOalMzIyBBUd+bXfk0m06zW0XCguvLa2lrZthsU1PCRbgH4SkCj0YiEhISw7aNCITW5/fHyyy/j+eefx2WXXYa3334bv/vd77B8+fJoDrkwCN7S0oKlS5eK2nd5vV4cPnwYOTk5AZNpUnqm+RNGrVbDYrFg9erVsjeM0Chh0aJFrJ4uBrQEp9VqYTAYQpbg+LrylStXyurkCoAZJNbV1QWN3mj7qE6ng8vlitifjpbd5CL3nj178Lvf/Q779++XcouxMAje3t6OwsJCwWSh87moQIafxJCa3P4YHR1FX18fNBoNzGYzEhISkJ+fj7y8PMn3qTabjUUJkewVA4Hu2/mEoWWsjo4OKJVKVFVVyW71Sx1k16xZIzgJ5V9REOpPR8ldWVkpS9lt3759eOqpp7B//36pM/8Lg+BnzpxhIXA40GRaeXk5hoaGfPbucirT+EMIVq9ezS4om80GrVYLnU4naaKLtpXyDRilBt2G6HQ6aLVapKSkoKysTJa9Lx9jY2NsFnyk+3u+P53RaGSRib8/ncPhwMmTJ2Wrqb/xxhv42c9+hv3798sRGcw/glOlEB/d3d1ITU0N6zbin0yjd+WUlBRZDRH5QwiWL18eNHSltVOdTgebzQaNRoP8/HzR4aTBYEBnZydqa2tlNwugOn26ilPCSKVI88fIyAhL3kk5qYWKg3Q6HevPz8zMRHd3N5YvXy4Lud9++2386Ec/wv79++XKwywMgvf19SEhISHkHnNgYGBWMq25uRlLly5FWlqabOR2uVxobm5GTk4Oli5dKvj4NLuq1Wp9BCrhhBKjo6MYGhpCXV2drG2lwMe14EA+bVSRJmUJbnBwEDqdDnV1dbJGCB6PB2NjY+js7IRKpWKfvdAkoxC8++67ePDBB7Fv3z45bbAWBsGHhobg8XgCOn16vV50dHTA7XZj1apVPqtna2srCgoK2AopNbnpHri0tDRsH28oEEKYXttoNCI5OZkluugFRwhBf38/TCaTzxZALlA5aEVFRdj9PT8ysVqtEZXg+PV7uZN39L0tX74cWVlZzJ/OYDBAoVCwyCRS041///vf+M53voN9+/bJ0tzDw/wjOG015GN0dBQ2mw1lZWU+v6fdRDk5ObPKQ4QQDA4OYmBgALm5ucjPzxdshCcEgYYQSAF+66VOp2MOK9PT0wCA6upq2QkQTFcuBHTohE6ng9lsDluCo5Jaq9WKmpqaOSN3sPfmcDjYzcput4v2pzt69Cjuvvtu/P3vf5dd+4CFQnCtVouJiQlUVlay301PT6OpqSmgJxu/h5sQwurVU1NTyM7ORn5+flQ91nQIQW1trew6b1pPd7vdSEhIYDerUA0O0YDeuMTqygOB794aqARHnU/dbjeqq6tlz8yHI7c/qD8dvVmF86c7ceIE7rjjDuzduxclJSXSv4HZWBgEp86Z1dXVAMDMDGtra31qiuHEK/wea7PZjIyMDOTn5wtuEOAPIaitrZVdwUXLfYWFhSguLmbNGVqtFhaLRZKbFR9S6cqDwb8ERwhBcnLynITl0Tap8E0d9foZYxRK9tTUVLS0tGDnzp149dVXfRYimTH/CA5glp/5xMQEBgcHUVNTg4GBAYyNjUWtTKOKKK1Wy/a9tKkkEHH5QwjmIkymvdXB9vf+N6toZ5LJqSv3h9frRUtLC5sZNjU1JTjJGAnk6ECjeYempibce++9cLlceOSRR/CFL3xB9s+Ph4VB8OnpaXR1dbEZVP57tWhlp3TfOz4+DoPBwOxvqThF6iEE4UANIYRekPyar8FgYN1MQsU1c6krp+ROS0tjORX/TjJagsvLy4u6UkDJXV1dLYte4PTp09ixYwfuuOMOtLa2YsmSJbj33nslf50gmJ8EdzqdPsaLU1NTOHr0KMrKylBSUhKwzVNKZRpfnOLxeOB0OrF06VLZ53UBH4fJq1evjjiLa7VaWQccIYSRJdDx+OIcuXXlgZxPA4GfZAQQcQlObnJ3dXXhC1/4Av785z+jrq5O8uMLwPwn+PT0NDPyu+iii3xfREZDRGDmxkKH0k9PT8Plcsma5BobG8PAwICkYbLT6WRksdvtPh1w3d3dc6YrD+d8Ggz+4iChWW25yd3f34/t27fjmWeewbp166I+3pe//GXs27cP+fn5aGlpmfV3QgjuuusuHDhwACkpKXjuuedQX18/vwmu0+nQ2dmJVatWobW1FRdccMHMwWUe1Qt8PISAv5K6XK5Z9d5IlGiBwE/eyVXjpgMYaHSSlJTEmivkJHgkzqeB4J/VpiW43Nxcn8+MVh5WrlwpizHE0NAQbrrpJvz2t7/Fhg3+jsWR4d1330VaWhq+9KUvBST4gQMH8PTTT+PAgQM4cuQI7rrrLhw5ckQ2gsvuqtrX1wetVov169dDrVazFX0uyM2f+sHfA/JdT6kSjU4rycrKQn5+fkhv7UCgpSKn04k1a9bISjQ6Fnd0dBTLli1DVlYWM0OUcnAiH1I4n1L4T13hD1CkJbi0tDScPn1aNteX0dFRbN++HU8//bRk5AaAiy++GH19fUH/vnfvXnzpS18Cx3E4//zzYTabwXHcIkLIqGQnwYOsBO/o6IDL5cL69eslTaaFAyWbw+EIO/WDX9Ollj1arRZnzpxBWloay8iHOgZ1e0lKSpJ98gfwsa68oKCAhckajSagaytNMkbjOCKH8ykFf4BieXk5bDYbRkZGcOrUKSQlJUGr1YIQIqm4aXx8HDfddBOefPJJfOpTn5LkmELhP/CguLgYZ86cKQIw/wi+bNkyJCYmzvpi5Gzz9Hg8aGlpQWpqKlatWiXq+HznUH5Gu7e3l3m68WWngG8ThwTOHmERSlfu79pKLYdaW1vh8XhYkktM3mGunE8pvF4vtFotGhoakJyczOyOpSrB6XQ63HjjjXjsscdwySWXSHz2sQdZCZ6amupj20SnRLS1taGgoAAajUZSggcaQhApOI5DZmYmMjMzUVFRwTzd+CtjRkYGTp8+jWXLlkUdtgoBTTgJ9UZPTk7G0qVLsXTpUpZ36O3tZfPIqLgm2HdA98Bz4XwKfGzGuGrVKiZ8onPA+CW47u5upvMXY6JpNBpx44034tFHH8Xll18u51sJCv+BB0NDQ8DsgQeSYc4mm9Ae7vr6ekxMTGB8fBydnZ1IT09Hfn5+1L3JQoYQRIPU1FSUlpaitLQUdrsdg4OD6OzsRFJSEqxWKywWi6yTROnFX11dHRHZ/KetGI1GjI6OoqOjI6A32lw6n/Jfj09uPhQKBbKzs5Gdnc0ml9ARS0D4EpzZbMaNN96I733ve7jmmmtkfz/BsGXLFuzatQvbt2/HkSNHkJmZCbn234DMWXS32w232x10v03DYCpMSU1NZXteMRnoSIYQRAOz2YyOjg6sWrUKarUaer0eWq0Wdrudld/EDAAIByl15f7ge6NRe+n09HSMj4+HHOssJSi5I31/4Upwk5OTuOGGG3DXXXfhxhtvlOEdfIybb74Zhw8fhl6vR0FBAR599FHWVfm1r30NhBB84xvfwMGDB5GSkoJnn30W69evn59lMrfbDafTKSiZRhNE4+Pj0Ov1Qfe8/pBjCEEo0AaVQP5i/gaOtN0yOzs7YrJTU4i5GHwAzNTwT58+jaSkJJ8EpFyGFNPT02hubpbs5sUvwfX29uIPf/gDDAYDbrvtNtx6660SnLEsmJ8E/9rXvoaCggJs27ZNtP8XNRDU6/VMcpqfn8/2W3M1hIAPOttKSIMKDYNpB53YhhhgJtvb398/J7pyYLbzaSB76UCjlSKF1OQOdPwvfOELSElJwdjYGK644go8+uijkr+OBJifBNfpdNi7dy/27NkDrVaLK6+8Etu2bRPdUkglmzqdDgqFArm5uTCZTEhJSZF9CAHw8c3EarXOMqMQ+nx+Q0xKSkrYrcjQ0BALk+XWlQPhnU/5vm5TU1MR6wUoKLlra2tlyV3YbDbcfPPNuOGGG7Bz504AM0nYOWwgEQOfC5jjuH8COEQI+UnUB5aT4HyYTCa8/vrr2LNnDwYGBrB582Zcf/31olsMp6am0NjYCI7joFar2couV/jq9XrR1taGhIQESVxI+fbGer2eNZTwo5O51JUDH3egCXU+5esF+H7sQnMn1GhSLnI7HA58/vOfxzXXXIPbb79dkgXg4MGDuOuuu+DxeHDrrbfigQce8Pn7wMAAbrnlFpjNZng8Hjz++OO4+uqrhR6enSDHcQ8D2AZADeAFQsjj0Zz3nBGcj8nJSezbtw979uxBZ2cnLrvsMmzbtg319fUhye4/hMDpdEKr1UKr1cLtdjOiSHXRuN1uNDU1IScnR7YGFX50Qi9ElUqF2tpa2XXlQPTOp/43LDrAIJi4Rm5yO51O3HLLLdi0aRPuvvtuScjt8XhQVVWFt956C8XFxWhoaMBLL72ElStXssfs3LkTa9euxde//nW0tbXh6quvDqlo8wMHABzHKQBcA+AwAA2AAwD+Sgh5lOM4TuzoYGAOy2R8ZGRk4HOf+xw+97nPwWq14sCBA/if//kftLS0YNOmTdi6dSs2bNjgs3qZzWY2A5yWUdRqNYqLi5mJgk6nw5kzZ+BwOBjZI20mcTgcaGxsDCgokRIpKSkoKSnB0qVL0dLSwpKSx44dYxl5qWaQ+WN4eBhjY2NYu3ZtxLp5vriGKtH44hp+BxwtZcpFbpfLhVtvvRUbN26UjNzAjH1TRUUFa4vdvn079u7d60NwjuMwOTkJYKbqEYmHGyHEy3HcPgCJhJB+juOuAfB3juPUhJDvcxyXCKCMENIu9JjnZAUPBrvdjjfffBOvvPIKTp48iYsuugjbtm1DZ2cncnJycOWVVwqSXLrdbuj1eoyPj8Nms7HOK6HJIeplNlfqLaq+y8jIYG20VJii1WrZe8jLy5OkIQaYSRjSphi5tgH0pkvHCrtcLqxYsQIFBQWS37Dcbjduu+02VFdX46GHHpL0+Lt378bBgwfxhz/8AQDw5z//GUeOHMGuXbvYY0ZHR3H55ZfDZDLBYrHg0KFDgrvTOI77fwD2EUJcHMcpzhJdSQjxcBxXAuAvAP4NYD1mwvbfCT33c7KCB0NSUhK2bNmCLVu2wOl04tChQ3jwwQeh0+lwySWXID09HRdffHHYUFKlUqGwsBCFhYWs82pgYICVrkIpuGjNOZjgQmoE0pUDsxtiDAZD1A0xFNT5tK6uTtZtQEJCAhYvXoy0tDS0tLSgoqICRqMRvb29EVUVgsHj8eCb3/wmysvLJSe3ULz00kvYsWMH7rnnHnzwwQf44he/iJaWlrCf7y233AIAuwBcwXHctwghtrMk93AcpyKE9HEcdwOAfgBPiSE3EGME50OtVsPhcOC8887Dk08+iX//+9/YvXs3HnjgAaxbtw7btm3DJZdcEjYrSmWl+fn58Hq9MBgMGBkZQUdHxyyi6HQ69PT0YM2aNdEn7VQApwBAAOIK/BCqKw8ndfV/D/yGGDFKQL7z6Vz4pwEz+Za2tjasWbOG1dL5VYVIZacUXq8X3/rWt5Cfn48f/vCHspA7kLzUXwr9xz/+EQcPHgQAXHDBBWxAQygb7ra2NnrcLwC4GsB/nSX59NkV3M1xnArAHQB+Twj5lthzj6kQfdaLnz03/pfm8Xjw3nvv4dVXX8U///lPrF69Gtu2bcOnP/1pUaSkRBkfH4fZbIZSqYTH40F9fX3UpRQuAQAHcBxAP17i6z8pWlceCFQJSAcOBvJh5z92Lp1PgY/JHWpyC99eWq/XMz/zQEMT/eH1enH//fdDpVLh17/+tWw3LLfbjaqqKrz99tsoKipCQ0MDXnzxRdTU1LDHXHXVVfjsZz+LHTt2oL29HZdddhmGh4fDfs4jIyMoKipKBlAC4GsAMgDcRwgx8ML1lYSQtkjOPaYJHg4ejwcffvghXn31VRw6dAhVVVW4/vrrsXnzZsHCCVrjNplMSE9PZ2WfgoKCiPXxnHqG3B+/BkA8AM723dAasNRe7LRVlPqw8/3oOjo6oFAo5mTgIDCz1WlvbxetwKMrn1arZc47dNwS/7y9Xi8efPBB2O12/OY3v5E9Gjlw4ADuvvtueDwefPnLX8b3v/99PPzww1i/fj22bNmCtrY2fPWrX8X09DQ4jsMTTzwRsqGFdlOeBcfN/FAB4HYAqQDuA/CfAF4ihIxHet7zmuB8eL1enDhxAq+88goOHjyI0tJSbNmyBVdffXXQZgk6TYXOIuM4LuCqWFBQIEofH5DgXgBueXXlfNBsNpXNpqWlobq6WtaGGIpIye0PmiylSbrs7Gz2fTz++OPQ6/X4/e9/PydaATnAIzktk6kA5AC4G8C3AfydEHJDNK+xYAjOh9frRXNzM1555RX84x//QGFhIbZs2YJrr72WjS72eDxoampCVlbWLANIikCilIKCgrD6+GAh+lzryqnzaUpKCpKTk6HVapnkVOqGGAqpyO0Pai/9yiuv4Ne//jVUKhUTk8xFQ4zU8Hg8UCqVsNvtSE5OTiCEsFG8HMe9B2CAEPL5aF9nQRKcD0II2tvbsXv3buzbtw9ZWVm47LLL8MYbb2DXrl2iJlfQnnCdTjfLknkW+Ek2NzA+NqMrFzMnOxrQG5hGo/ER6QRqiAnXFy4UtMtOrhsYIQRPPvkkWltb8e1vfxv79u3DZZddhk2bNkn+WnKCkttms+HKK6/Eu+++ezMh5OWzYfpyAN8ihNwmxWsteILzQQjBO++8gy996UsoLS2FWq1mZTmxtVlqyazVasFxHMtyB6rTz7Wu3OPx4NSpU7NKb/7wb4jJzMxkpSuxe1pK7jVr1kRlDxUMhBA8/fTT+Oijj/Dyyy9L8jmGk58CwF//+lc88sgj4DgOdXV1ePHFF6N6TUpup9OJ6667Dg0NDfjxj388P5tNYhFPPPEEPv3pT2Pt2rXo6+vDq6++itdeew1KpRLXXXcdtm3bhsWLF4siOx3lo9Vq4fV6mYouOTnZZ9rmXOwV6Zgksc6ndDIqbYgR05s/F+T+7W9/i8OHD2P37t2SREBC5KednZ246aab8M477yA7OxtarTaq6bNerxcKhQJerxfXXXcdampq8MQTTwAzSTYFIcQb9RvzwyeO4IFACMHw8DAjO727bt26FcuWLRNFdupfPj4+jqmpKajV6jkTzQitq4cDvzefTlih2xF/ctEWUznJ/cwzz+DAgQN47bXXJHuNDz74AI888gjeeOMNAMBjjz0GAPjud7/LHnP//fejqqoq4j5ySmj/323evBm1tbX41a9+BQA4W/OWnNxADAtd5hIcx6G4uBh33XUX7rzzToyPj2PPnj248847MTk5iWuuuQbbtm1DRUVFWLKr1WosWrQIZrMZycnJyMzMRHd3N3N7KSgokGXYAnU+LS8vDzsHPBz4+vKKigrWEEO7+CjZ7Xa7rOQGZmShr7/+Ol5//XVJXyOQu+mRI0d8HnPmzBkAwIUXXgiPx4NHHnkEV155paDj88k9PDwMQgiKi4vR2NiIK6+8Evfddx97nFzkBuIEnwWO41BYWIjbb78dt99+O3Q6Hf72t7/hgQcegE6nw1VXXYWtW7cGFYvQsT6ZmZksO7948WKW3KKmhxqNBgUFBZKYJ8jtfEobYkpKSth2pKmpCdPT01iyZAmbSiM1Xn75ZfzlL3/Bvn375qTq4A+3243Ozk4cPnwYQ0NDuPjii9Hc3CzIE4+S+7777sPg4CCsVivWrFmDH/7wh1i7di2Aj/fjciJO8DDIy8vDV7/6VXz1q1+FyWTC3r178cMf/hCDg4O4/PLLcf311zMTCJfLhaampoDJLZVKxRxCqbac2gFTh9NIrJ3m2vk0KSkJqampIIRgw4YNmJycRHd3d0RNPaGwZ88ePP/889i3b58stXsh8tPi4mJs2LABCQkJKC0tRVVVFTo7O9HQ0BDy2JS4Tz/9NPr7+/HCCy9g+/bt0Gq1Po+bi5xMfA8eISYmJlhPe1dXFy688EK8//77ePbZZ7F8+XLBxwmUyS4oKBDUSDLXzqfAjMFlZ2fnrLHP/HFK/JtWJDPP//73v+Ppp59mZU05IER+evDgQbz00kt4/vnnodfrsXbtWpw6dSqotLinp4e1lAIzEUh1dTVefPFFDA4O4sUXX4TVasXJkydx4YUX8p8az6LHMtra2nDttddi+fLlGBoawiWXXIKtW7fivPPOE3WXJoSwWeFUOkv94/2PQ40T5FbE8UFtnfzJ7Y9AM8+FNsQcPHgQP//5z7F//37ZW3XDyU8JIbjnnntw8OBBKJVKfP/738f27dsDHuvVV1/FLbfcgttuuw1PPvkkAODpp5/Gd7/7XWzbtg0vvPACAOAHP/gBpqam8Itf/IJ/44sTPJbx17/+FSUlJTjvvPNgs9nw5ptvYvfu3Th58iQ+9alPYevWrdi4caMoUwV+xxW1lKaS2enpabS3t4ds4pAalNxr164VVaYKJP2l5Tf/Wvbbb7+NH/3oRzhw4EDUicK5hNFoxLZt27B9+3b84x//QFlZGZ566ikAwF133YV3330XTz31FPbv34/33nsP+/fvZ4rKs4gTfD7C4XDg7bffxu7du3HkyBFccMEF2LZtGz71qU+JEmrQAX1arRbj4+NwOp0oKyvD4sWL59SQUSy5/UE7x/huuZTIfX19ePDBB7F///45mRIjNU6fPo2SkhKMjo7itttuQ2lpKf73f/8XAPCTn/wEk5OTsFgseOSRRwLdvBYGwY1GIz772c+ir68PJSUl+Otf/+p/JwMAPP/88/jxj38MAHjwwQdpUzzDli1b0NPTE3A8a6zC5XLh8OHDePXVV/Hee+9h/fr12LZtGzZt2iS4PZUSrbKyEmazGTqdLmSNWgro9Xp0d3dHTe5AsNls6OjowNe+9jWMjo7i9ttvx44dO3z2sfMRQ0ND+OpXv4ri4mL8/ve/R2NjI7KyskL5+i0Mgt9///3QaDR44IEH8Pjjj8NkMuFnP/uZz2OMRiPWr1+PY8eOgeM4rFu3DsePH2c3gj179mD37t1oamqaVwTnw+1241//+hd2796Nw4cPo7a2Ftu2bcNll10WtBwUzPmU1qi1Wq2PMYQU9sB6vZ4ZYMilnz9y5Ai+/e1v45lnnsHx48dhNBpx//33y/Jac4mRkRHcc889OHbsGBISEvDee++F6vuXr3+XEBLqn6SoqqoiIyMjhBBCRkZGSFVV1azHvPjii2Tnzp3s5507d5IXX3yREELI1NQUufDCC0lrayupqamR+vTOCdxuN3nvvffI3XffTVatWkVuuOEG8sILLxCtVkssFguxWCykq6uLHD58mJjNZva7QP8MBgNpb28n//d//0cOHz5M2traiF6vD/mcYP/6+vrIP//5T2IymSJ6vpB/7733HqmtrSW9vb2SfZ7/+Mc/SFVVFSkvLyePPfZY0Mft3r2bACAfffSRZK/tj8cee4wUFRWRrq6ucA8Nx8OI/81pHXx8fJzpowsLCzE+PruPPZDCaHh4ZvjiQw89hHvuuWfOEktzAaVSiYsuuggXXXQRvF4vjh8/jldeeQVPPPEEm8dNznZRhUvSJSUlsWmiDocDWq0WbW1tzN1UqKU0Hfsj58rd2NiI22+/HXv27BHV0RcKHo8Hd9xxh4++fMuWLT76cmCmAvHUU09hw4YNkrxuIBgMBhw6dAiHDh1CeXm5bK8TDpLbYHz605/GqlWrZv3bu3evz+PEzgY/deoUuru7cf3110t9yjEDhUKBhoYGPPHEEzh58iRWrFiBw4cP4+TJk/jc5z6HF154ASaTSdCxEhMTsWTJEqxbt44R9cyZMzhy5Ai6u7sxPT3NLLH4oOSWY89N0draittuuw2vvPIKKioqJDsu395YrVYze2N/PPTQQ/jOd74jm7wWAHJycrBv3z6sWLFCttcQAslX8EOHDgX9W0FBAUZHR7Fo0SKMjo4G7MwpKirC4cOH2c9DQ0PYtGkTPvjgAxw7dgwlJSVwu93QarXYtGmTz2MXElwuF9xuN5qbm6FWq9HW1obdu3dj27Zt0Gg02Lp1K6699lpB5SS1Wo2ioiIUFRXB7XazGdtUfVZQUID09HTodDr09fVh7dq1smXnOzo68JWvfAUvvfSSKEGQEAjRl584cQKDg4O45ppr8POf/1zS1/eHnDcQoZjTEH3Lli14/vnn8cADD+D555/H1q1bZz3miiuuwPe+9z22Ur355pt47LHHoNFo8PWvfx3ATEnl2muvXbDkBmZWYP4FWFNTg5qaGjz88MPo7OzE7t27cdNNNyElJQVbt27FddddJ6inXaVS+dgx6/V69Pf3w2w2gxCClStXRjwEIRw6OzuxY8cO/PnPf/ZRjM0VvF4vvv3tb+O5556b89c+V5DfN5eHBx54AG+99RYqKytx6NAh1mB/7Ngx1pKn0Wjw0EMPoaGhAQ0NDXj44YeDKpqMRiM2b96MyspKbN68OWj4+vzzz6OyshKVlZV4/vnnAcxkn6+55hqsWLECNTU1AZv9YxEcx6Gqqgrf+9738MEHH+APf/gDnE4nvvjFL+Kqq67Cb37zG4yMjAQMv/2hVCqZPj4xMRFVVVUYHx/Hhx9+iI6ODhiNRni90jQ69fX14Utf+hKeffZZ1NXVSXJMf4TTl1P136ZNm1BSUoIPP/wQW7ZswbFjx2Q5n5hAmCxcTOO+++5jmdLHHnuM3H///bMeYzAYSGlpKTEYDMRoNJLS0lJiNBqJxWIh77zzDiGEEIfDQS666CJy4MCBOT1/KeH1esnAwAD51a9+RS6++GKyceNG8thjj5HW1lYyPT0dNJPd09MzK0M/NTVFBgYGyLFjx8ihQ4fI0aNHSX9/P5mamoooW97R0UHq6urIhx9+KOtn4HK5SGlpKenp6SEOh4PU1taSlpaWoI//j//4D1mz6CIgWxZ9TldwqbF3714mgrnlllvwt7/9bdZj3njjDWzevBkajQbZ2dnYvHkzDh48iJSUFFxyySUAZvao9fX1GBoamsvTlxQcx2HJkiW4++67mfNJeno6vvnNb+LSSy/FL37xC3R2dvqs7OPj4xgYGJi151YoFMjJyUF1dTXOP/98LF68GAaDAUeOHEFLSwu0Wi08Ho+g8xoZGcH27duxa9cuWbPWwMz2Y9euXbjiiitQXV2Nm266iW1rXn/9dVlfO1Yxr6WqWVlZMJvNAGYikezsbPYzxS9+8QvY7XY8+OCDAIAf/ehHSE5Oxr333sseYzabUV9fj0OHDs17FVUg6HQ6vPbaa9izZw8MBgOuuuoqqFQquN1u3HvvvYL33ISnK9fr9WFtncbGxnDjjTfiySefnHfGiHMM2YQuMd8P/ulPfxpjY2Ozfv+Tn/jORhdbdqNwu924+eabceeddy5IcgMzPe07d+7Ezp07YTQa8f3vfx979+5FUVERnE4nrr/+etTU1IRt6+Q4DpmZmcjMzERFRQWzderr60NSUhKTzCYkJECn0+Gmm27C448/Hif3OUTME1yushvFzp07UVlZibvvvlvCs45dKJVKjI2N4fTp0/B6vdi3bx9+9rOfobu7G5s3b8a2bduwZs0aQWTn2zrRJpL9+/fj17/+NaxWK77//e9j8+bNc/TO4giEeR2i33fffcjJyWHadqPRSF0qGYxGI9atW4cTJ04AAOrr63H8+HFoNBo8+OCDaG9vxyuvvDIng/hiGdPT0zhw4AB2796N9vZ2XHrppdi6dSsaGhpE9bSbzWZ89rOfRUVFBbq6utDQ0IBf/vKXMp75gsDC0KJLDb1eTy699FJSUVFBLrvsMmIwGAghhHz00UfkK1/5CnvcH//4R1JeXk7Ky8vJM888QwghZHBwkAAgK1asIHV1daS0tJQUFBQE1TDb7XZy0003kfLycnLeeef56Kd/+tOfkvLyclJVVUUOHjwo75ueA1itVvLaa6+Rz3/+86SmpoZ8/etfJwcPHiQTExMhs+Wjo6Nk48aN5JVXXmHHcjgcUZ9POH35k08+Saqrq8nq1avJpZdeSvr6+qJ+zTmGbFn0eU1wqeB2u0lZWRnp7u5m5ZXW1lafx/z3f/83ue222wghhLz00kvkpptuIoQQ0traSmpra4ndbic9PT2krKyMuN3uOX8PcsFut5N9+/aRHTt2kJqaGnLrrbeSffv2zWp8GR8fJxdffDFrDJIKQr6bd955h1gsFkIIIb/5zW/YdzOPECe4nHj//ffJ5Zdfzn7+6U9/Sn7605/6PObyyy8n77//PiFkpt6ak5NDvF7vrMfyH7fQ4HQ6yRtvvEF27txJVq5cSW655Rby2muvkZGREXLJJZeQ5557TvLXFPLd8HHixAmyceNGyc9DZsTr4HIiVAdboMeoVCpkZmbCYDAIeu5CQUJCAi6//HL89re/RWNjI3bs2IG33noLq1evxuWXXz7LmEMKiP18//jHP+Kqq66S/DzmK2I+ix5HbEKlUmHTpk3YtGkTfvnLX87JQMVweOGFF3Ds2DH83//937k+lZhBfAWHMI9s/mPcbjcmJiaQk5Mj6LkLHYmJiZJPaqEQ+vkeOnQIP/nJT/D6669L4mazYBAmhv9EQIiGedeuXT5JthtvvJEQQkhLS4tPkq20tHRBJdnONYR8NydOnCBlZWXkzJkz5+gso0Y8ySY39u/fTyorK0lZWRn58Y9/TAgh5KGHHiJ79+4lhBBis9nIDTfcQMrLy0lDQwPp7u5mz/3xj39MysrKSFVV1ayGlXAlnmDltzfffJPU19eTVatWkfr6evL222/L9M5jH+G+m8suu4zk5+eTuro6UldXR6677rpzebqRIE7w+Yhoym8nTpwgw8PDhBBCmpubyeLFi+f25OOYS8Sz6PMRQiyE+B1xN9xwA95++20QQrB27VosXrwYwIzZg81mg8PhmPP3EMf8RpzgMiKa8hsfr776Kurr6+PJozhEI14mi3G0trbiO9/5Dt58881zfSpxzEPEV3AZEU35jT7++uuvx5/+9Kdzar0bx/xFnOAyoqGhAZ2dnejt7YXT6cTLL7+MLVu2+DyGGlECwO7du3HppZeC4ziYzWZcc801ePzxx/1Hzc57HDx4EMuXL0dFRQUef/zxWX93OBysI23Dhg3o6+ub+5NcKAiThYsjSkRafvvRj35EUlJSWOmnrq6OjI+Ps+NGWn6j6O/vJ6mpqeTnP/+5TO88MKKpLCxgxMtkcXwMKUjymc98htxwww1zTvBoGnsWMOJlsjg+RjTlNwD429/+htLS0nPiTS5VZSEOYQjn6BJHDILjuBsAXEkIufXsz18EsIEQ8g3eY1rOPmbo7M/dADYAsAN4C8BmAPcCmCaE/GI+nDshRD9X57lQEF/BP3l4BMCvCCHT5+j1hwEs4f1cfPZ3AR/DcZwKQCaA+BIeAeJ18PkJMSQZ8iPJBgA3cBz3BIAsAF6O4+yEkF2yn/UMPgJQyXFc6dlz3A7gc36PeR3ALQA+AHADgHdIPNSMCHGCz09EQ5JP0QdwHPcIZkL0uSI3CCFujuO+AeANAEoAzxBCWjmO+yGAY4SQ1wH8EcCfOY7rAmDEzPuLIwLE9+DzFBzHXQ3g1/iYJD/hk4TjuCQAfwawFmdJQgjp8TvGI5jjPXgcc4s4weOIYwEjnmSLI44FjDjB44hjASNO8DjiWMCIEzyOOBYw4gSPI44FjDjB44hjASNO8DjiWMCIEzyOOBYw/n/8h7FtidqN0AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "simulation time =  0.00010000000000013679\n",
+      "[[0.e+00]\n",
+      " [0.e+00]\n",
+      " [1.e-06]]\n",
       "done!\n"
      ]
     }
    ],
    "source": [
+    "import numpy as np\n",
+    "import scipy.constants as sc\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "class Ion:\n",
     "    def __init__(self,position, charge, mass, v_z):\n",
-    "        self.position = np.array(position)\n",
+    "        self.position = position\n",
     "        self.charge = charge\n",
     "        self.m = mass\n",
     "        self.v_z = v_z\n",
@@ -129,14 +107,14 @@
     "\n",
     "            \n",
     "            # calculate the pseudo charge points of the quadrupole\n",
-    "            Q = signLUT[index] * self.phi_0(U,V,f,t)/2 * self.K * self.R\n",
+    "            eg = signLUT[index] * self.phi_0(U,V,f,t)/2 * self.K * self.R\n",
     "            \n",
     "            # match pseudo charge with ion in z direction\n",
     "            self.pPole[index][2] = ion.position[2]\n",
     "        \n",
     "            # calculate force on ion\n",
     "            ion.F = ion.F + self.coulombForceOnQ1(ion.charge,\n",
-    "                                                  Q,\n",
+    "                                                  eg,\n",
     "                                                 self.pPole[index],\n",
     "                                                 ion.position)\n",
     "            \n",
@@ -151,7 +129,7 @@
     "    \n",
     "t_step = 1e-9\n",
     "t_sim = 0\n",
-    "N = 1000\n",
+    "N = 100000\n",
     "\n",
     "# coffein atom 524.50002164 dalton (8.709527e-25 kg)\n",
     "ion = Ion(np.array([[0],[0],[0]]), 1000 * sc.e, 8.709527e-25,0.01)\n",
@@ -167,21 +145,28 @@
     "\n",
     "for n in range(0,N,1):\n",
     "    \n",
-    "    newPos = quad.calcNewIonPos(100,500000,1.1e6,ion,t_step,t_sim)\n",
+    "    newPos = quad.calcNewIonPos(1000,5000,1.1e6,ion,t_step,t_sim)\n",
     "    \n",
     "    if(newPos is None):\n",
     "        print('ion has left the boundary')\n",
     "        exit\n",
     "        \n",
+    "    \n",
     "    ion.position = newPos\n",
-    "    positions.append(newPos)\n",
+    "    if n % 100 is 0:\n",
+    "        positions.append(newPos)\n",
+    "        \n",
     "    t_sim += t_step\n",
     "\n",
     "\n",
     "print('simulation time = ', t_sim)\n",
-    "ax = plt.axes(projection='3d')\n",
-    "ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
-    "plt.show()\n",
+    "print(ion.position)\n",
+    "\n",
+    "if 0:\n",
+    "    ax = plt.axes(projection='3d')\n",
+    "    #ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
+    "    ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], cmap='Greens')\n",
+    "    plt.show()\n",
     "\n",
     "print('done!')\n",
     "# dummy check\n",
@@ -191,6 +176,2047 @@
     "    print(quad.coulombForceOnQ1(ion_1.charge,ion_2.charge,ion_1.position,ion_2.position))\n",
     "                 "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[0],\n",
+      "       [0],\n",
+      "       [0]]), array([[0.e+00],\n",
+      "       [0.e+00],\n",
+      "       [1.e-11]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [1.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [2.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [3.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [4.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [5.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [6.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [7.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [8.01e-09]]), array([[0.00e+00],\n",
+      "       [0.00e+00],\n",
+      "       [9.01e-09]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.001e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.101e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.201e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.301e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.401e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.501e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.601e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.701e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
+      "       [1.801e-08]]), array([[0.000e+00],\n",
+      "       [0.000e+00],\n",
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+      "       [9.4701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.4801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.4901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.5901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.6901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.7901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.8901e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9001e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9101e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9201e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9301e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9401e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9501e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9601e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9701e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9801e-07]]), array([[0.0000e+00],\n",
+      "       [0.0000e+00],\n",
+      "       [9.9901e-07]])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html\n",
+    "ax = plt.axes(projection='3d')\n",
+    "#ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
+    "ax.scatter3D(positions[:][0], positions[:][2], positions[:][1], cmap='Greens')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('z')\n",
+    "ax.set_zlabel('y')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "print(positions)"
+   ]
   }
  ],
  "metadata": {