L8_least_squares.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93d356e1",
   "metadata": {},
   "source": [
    "## Least squares, Richter's law and Python implementation\n",
    "\n",
    "In this noteboook we will implement the possibility to apply the Richter's law, which relates the number of earthquake events, in a given area, as a function of their magnitude, to the seismic datasets we already saw in the last notebook. \n",
    "\n",
    "The Richter's law has the *linear* form:\n",
    "\n",
    "$$ \\log_{10} N(M) = m\\cdot M + q$$\n",
    "\n",
    "where $N(M)$ is the number of seismic events of magnitude $M$; $m$ is the *slope* and $q$ is the *intercept* of the line expressing the law. \n",
    "\n",
    "Having obtained the *number* $N(M)$, for $M$ ranging in a given interval, the coefficients $m$ and $q$ will be estimated by the *least squares* method. For that, we will make use of the *numpy* function ***polyfit***. \n",
    "\n",
    "\n",
    "### Least squares\n",
    "\n",
    "Imagine to have two lists of variables (*x* and *y*) that are supposed to be related by some law, let's say, a quadratic one; for instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e6779b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x=[-3, -1, 1.5, 2, 4]\n",
    "y=[9.9, 2.2, 3.32, 5.1, 16.8]\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(x,y, \"k*\")\n",
    "plt.xlim(-5, 5)\n",
    "plt.ylim(0, 18)\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5338f6f",
   "metadata": {},
   "source": [
    "In the cell above we defined the two variables and gave their graphical representation.\n",
    "\n",
    "Now, by looking at the plot, it is reasonable to suppose that a suitable model for $y(x)$ could be of the type: \n",
    "\n",
    "$$y = ax^2 + bx +c$$\n",
    "\n",
    "where the coefficients $a,\\ b$ and $c$ have some values that could be estimated by the *least squares* method. The task is very simple if the *numpy* function *polyfit* is used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f71b63d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of the fit:  [0.97374075 0.006954   1.17850434]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "fit = np.polyfit(x, y, 2)  # x: independent variable\n",
    "                           # y: dependent variable\n",
    "                           # 2: degree of the polynomium\n",
    "\n",
    "print(\"Coefficients of the fit: \", fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676279c0",
   "metadata": {},
   "source": [
    "The *polyfit* function accepts the two lists as input, plus the *degree* of the polynomial fit (2, in this example). It returns the coefficients of the fit (from the highest to the lowest power term), which have been stored in the variable *fit*. Other arguments or returned values can be optionally set or requested (see [here]( https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html) for details). \n",
    "\n",
    "We can use the numpy function *polyval* to compute *y* from the *fit* and a list of *x* values, as we do below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08cae238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U3nrrLdt+MrezsLp+PXMGRldsRgKDE8Ply5epV68eM2fOzHafRx55xDb7VVJSEqutDXfZ2Lx5M48++iiDBg1i165dDBo0iH79+mVbz98VeHtDjx56XZqTCt7N/waTkpIIDQ3F39/f6NCEE4qNhXPnoHRpaNbM6GjujKGJoVOnTvznP/+xzZGbFbPZjMVisS0lS5bM8T2nTZtG+/btCQ8Pp0aNGoSHh9O2bVuXP5uz/idavhxuquIsHOzmf4MWi4W2bdvampKym9tZeCbryVv37vqkzhU5fR/Dhg0bKFOmDNWqVePJJ5/k1KlTOe6/efNmOnToYLetY8eObNq0KdtjUlNTSUlJsVucTatWena3U6cgh6/iEpTSHXNGLI4oGXk3czsL95KenjkpjyvVRrqZU8/g1qlTJ/r27UtQUBAJCQm8/vrrtGnThu3bt2c7+9GJEycoe1Mp0rJly3LixIlsP2fy5MlMmjQpX2PPb76+EBYGn38O33wDzZsbHdGdu3IFihUz5rMvXYJ/5ovJte+++45iNwTcqVMnu9dLliyJt7c3/v7+WCyW/AhTuKiff9bVkAMCMqfodUVOfcXw6KOP0qVLF2rXrk1YWBjff/89f/zxB6tuUx/i5kt5pVSOl/fh4eEkJyfbliNHjuRL/PmtXz/9uGSJNCcVpNatW9vN4jZ9+nSjQxJO6quv9GPPnuDEM3fellNfMdwsMDCQoKAgDhw4kO0+FovllquDU6dO3XIVcSOz2ezU869atW+vz0SSkmDjRmjZ0uiI7kyRIvrM3ajPzquiRYvywAMP5H8wwq2kp+urecg8iXNVTn3FcLOzZ89y5MgRAgMDs92nSZMmREdH222LioqiadOmjg7P4czmzHZL65mJKzKZdHOOEYuj+oWzmttZeJaYGN0HWLIktGtndDR3x9DEcOnSJdvlOUBCQgLx8fEkJiZy6dIlxo0bx+bNmzl06BAbNmwgLCyMUqVK0fOGXp3BgwcTHh5uez5mzBiioqKYMmUK+/btY8qUKaxbt85tbkZ69FH9GBkJaWnGxiIyVapUidjYWI4dO8aZM2eMDkcYwHqy1qsXFCpkbCx3y9DEEBcXR0hICCEhIQCMHTuWkJAQ3njjDby9vfntt9/o3r071apVY8iQIVSrVo3NmzfbjR9PTEy0GwXStGlTvvzyS+bPn0/dunVZsGABX331FY0aNSrw7+cIbdvCvffqM5OYGKOjEVZvvvkmhw4dokqVKpQuXdrocEQBS0vTJ2vg+s1IADLncxacYc7nnDz1FHzyCTz5JMyZY3Q0QoioKOjYUd/Udvw4+Dhh763M+ezmrGckkZH69nshhLG+/lo/9u7tnEkhryQxuKBWrfSZyblzetpPIYRxrl3LvNvZHZqRQBKDS/LxgT599Lr1TEUIYYx16+D8ebBYoEULo6PJH5IYXJR1dNKyZVKKWwgjWU/O+vRx3dpIN5PE4KKaNYPAQLhwQXd8CSEKXmqqLmwJ7tOMBJIYXJa3d2Zzkivf7CaEK4uK0vOk3HcfPPyw0dHkH0kMLszanPTtt3D1qrGxCOGJrCdlffuClxv9mrrRV/E8TZroOWUvXoTvvzc6GiE8y99/65MycK9mJJDE4NK8vDL/QcroJCEK1po1uhhkxYqQixmHXYokBhdnbU5auVLPcyCEKBjWZqR+/RxXnNEokhhcXIMGUKmSnp3sNtNUCCHyyZUr+mQM3K8ZCSQxuDyTKfMfpoxOEqJgrFqlk0PlylC/vtHR5D9JDG7A2py0apXuiBZCONbixfrRHZuRQBKDWwgJgapV9ZBV6802QgjHOHcus9l24EBjY3EUSQxuwGSCxx7T6xERxsYihLv75htdhqZuXahTx+hoHEMSg5uwnrmsW6fnhBZCOIb15Mt6MuaOJDG4iSpV9A1vGRmZ7Z9CiPx16BD89JO+Sh8wwOhoHEcSgxuR5iQhHOuLL/Rj69a66oC7ksTgRh59VM/VsHMn7N5tdDRCuBel4PPP9bo7NyOBJAa3cu+90LmzXl+0yNhYhHA3O3fCvn1QuDD06mV0NI4licHNWM9kFi3S/Q1CiPxhbaLt1g0CAoyNxdEMTQyxsbGEhYVRrlw5TCYTy28YhH/9+nVeeeUV6tSpQ9GiRSlXrhyDBw/m+PHjOb7nggULMJlMtyxXPaQuddeuULw4JCbqTjIhxN1LS8sc1DFokLGxFARDE8Ply5epV68eM2fOvOW1K1eusGPHDl5//XV27NjB0qVL+eOPP+jWrdtt37d48eIkJSXZLYULF3bEV3A6fn66NjxIJ7QQ+eXHH+HECd1c27Gj0dE4no+RH96pUyc6deqU5WsBAQFER0fbbZsxYwYNGzYkMTGRihUrZvu+JpMJi8WSr7G6ksceg7lzYckSmDFDt4kKIe6c9SSrf38oVMjYWAqCS/UxJCcnYzKZKFGiRI77Xbp0iaCgIMqXL0/Xrl3ZuXNnjvunpqaSkpJit7iyFi30ULrkZKm4KsTdunwZli7V6+4+GsnKZRLD1atXGT9+PP/6178oXrx4tvvVqFGDBQsWsGLFChYvXkzhwoV5+OGHOXDgQLbHTJ48mYCAANtSoUIFR3yFAuPllXkntHV4nRDiznz7rU4OVapAo0ZGR1MwTEopZXQQoJt/li1bRo8ePW557fr16/Tt25fExEQ2bNiQY2K4WUZGBg899BAtWrRg+vTpWe6TmppKamqq7XlKSgoVKlQgOTk5T5/lTHbvhtq19WVvUpJuGxVC5F2nTnq2tgkTYOJEo6O5cykpKQQEBOTqd83prxiuX79Ov379SEhIIDo6Os8/1F5eXjRo0CDHKwaz2Uzx4sXtFlf34IMQHAzXr+u+BiFE3p08CVFRet1dK6lmxakTgzUpHDhwgHXr1nHvHZz2KqWIj48nMDDQARE6NymRIcTd+fJLfT9Qo0a6tL2nMDQxXLp0ifj4eOLj4wFISEggPj6exMRE0tLS6NOnD3FxcSxatIj09HROnDjBiRMnuHbtmu09Bg8eTHh4uO35pEmTWLt2LX/99Rfx8fEMHz6c+Ph4RowYUdBfz3ADBuhiXz//DH/9ZXQ0QrgeT6ikmhVDE0NcXBwhISGEhIQAMHbsWEJCQnjjjTc4evQoK1as4OjRowQHBxMYGGhbNm3aZHuPxMREkm6oM33hwgWeeuopatasSYcOHTh27BixsbE0bNiwwL+f0cqVg7Zt9bqUyBAib/btg7g48PbOnCXRUzhN57MzyUsnjbNbuBCGDIEHHoA//nDPaQiFcITx42HKFOjSBb77zuho7p5bdT6Lu9OrFxQtCgcPwsaNRkcjhGtIS4PPPtPrw4cbG4sRJDG4uWLFMi+D580zNhYhXMWaNboERunS+orB00hi8ADWM56vvwYXv6lbiAJhPYkaNAh8fY2NxQiSGDxAkyZQvTpcuaKTgxAieydPwsqVev3xx42NxSiSGDyAyZR51TB3rrGxCOHsIiJ0H0PDhrp6gCeSxOAhBg3Sw+62bIE9e4yORgjnpFRmM9KwYcbGYiRJDB7CYtGT+ADMn29sLEI4q19+0SdOfn66xLanksTgQaxnQAsX6hpKQgh71quFPn3cf/rOnEhi8CCdOkHZsnDqlMzTIMTNrlzJnL7Tk5uRQBKDRylUSN8FDdIJLcTNvvkGLl6E++/Xk115MkkMHsY6/G71ajh+3NhYhHAm1makxx/Xk115Mg//+p6nRg14+GFdSlhmdxNCO3gQYmL00G7rVbUnk8Tggaztp/Pm6eF5Qni6BQv0Y4cO4OIz++YLSQweqF8/XVjvjz/0XA1CeLL09MzE4IkF87IiicED3VhYTzqhhaeLjoZjx6BkSejWzehonIMkBg9lbU76+ms9EkMIT2U9OXrsMTCbjY3FWUhi8FBNm2YW1vvqK6OjEcIYJ0/Ct9/qdU8tmJcVSQwe6sbCerNmSSe08Ezz5ukqAI0aQXCw0dE4D0kMHuzxx/Wl844dsG2b0dEIUbDS02H2bL3+zDPGxuJsJDF4sFKl9Agl0FcNQniS77+HxES4557MvwOhSWLwcNYzpS+/hHPnjI1FiIJkPRl6/HFdTVVkMjQxxMbGEhYWRrly5TCZTCxfvtzudaUUEydOpFy5cvj5+dGqVSt279592/eNjIykVq1amM1matWqxbJlyxz0DVxf48a6bfXq1cyx3EK4u4QEfcUAMGKEsbE4I0MTw+XLl6lXrx4zZ87M8vWpU6fywQcfMHPmTLZt24bFYqF9+/ZczGF85ebNm3n00UcZNGgQu3btYtCgQfTr14+tW7c66mu4NJMp86ph9mxdKkMIdzdnjh5w0b49VK1qdDROSDkJQC1btsz2PCMjQ1ksFvXuu+/atl29elUFBASo2bNnZ/s+/fr1U4888ojdto4dO6r+/fvnOpbk5GQFqOTk5Nx/ARd28aJS/v5KgVL16o1T27ZtMzokIRzm6lWlSpXS/96XLjU6moKTl981p+1jSEhI4MSJE3To0MG2zWw207JlSzZt2pTtcZs3b7Y7BqBjx445HpOamkpKSord4kmKFcssHLZrVxM+l+p6wo1FRsKZM3DffRAWZnQ0zslpE8OJEycAKFu2rN32smXL2l7L7ri8HjN58mQCAgJsSwUPqqJ1+PBhtm/fTosW1omgu7NoUQw7duxg+/btHD582ND4hMhv1k7nJ58EHx9jY3FWTv+fxWQy2T1XSt2y7W6PCQ8PZ+zYsbbnKSkpHpMcKlWqdMOz9UArzp7tRWhoqG2rkrvfhJv47TfYuBG8veGJJ4yOxnk57RWDxWIBuOVM/9SpU7dcEdx8XF6PMZvNFC9e3G7xFBEREfjYTpusNzM8Afjg4+NDRESEQZEJkf+sN7T16KGbkkTWnDYxVK5cGYvFQnR0tG3btWvXiImJoWnTptke16RJE7tjAKKionI8xpMNHDjwhhFby4ATQDmgO1u3bmXgwIHGBSdEPrp4ERYu1Otyp3PODG1KunTpEgcPHrQ9T0hIID4+npIlS1KxYkWef/553nnnHapWrUrVqlV55513KFKkCP/6179sxwwePJj77ruPyZMnAzBmzBhatGjBlClT6N69O99++y3r1q1j48aNBf79XI2XVzoZGZ8C/wbkL0e4l0WL4NIlqFYN2rQxOhon5+ghUjlZv369Am5ZhgwZopTSQ1YnTJigLBaLMpvNqkWLFuq3336ze4+WLVva9rdasmSJql69uipUqJCqUaOGioyMzFNcnjZc9ciRI8pisagGDRqod96JUJCmQKn165OMDk2IfJGRoVTdunqI6gcfGB2NMfLyu2ZSSnoWb5aSkkJAQADJycke09+QmpqKr68vJpOJbt0UK1eaGDMGpk0zOjIh7t6mTXqu88KFMyfl8TR5+V1z2j4GUbDMZrNt5Nazz+rHBQvg8mUDgxIin/zvf/pxwADPTAp5JYlB3KJDB6hSBZKTMzvrhHBVR4/qmQoBnnvO2FhcRa4Tw9GjRx0Zh3AiXl4werRenzZN6icJ1zZzJqSlQYsW8NBDRkfjGnKdGGrXri2lEjzI449DQAD88QesXm10NELcmcuXdcE8gBvuYRW3kevE8M477zBy5Eh69+7N2bNnHRmTcAL+/rpkAMCHHxobixB36rPP4Px53TTatavR0biOXCeGZ599ll27dnH+/HkefPBBVqxY4ci4hBMYNUqXDvjxR4iPNzoaIfImIyNzVN2YMfrfssidPN3gVrlyZX788UdmzpxJ7969qVmz5g3lFLQdO3bka4DCOBUrQp8+8NVX+g9MJvIRrmT1ajhwQDeJPv640dG4ljzf+Xz48GEiIyMpWbIk3bt3vyUxCPcydqxODF98AZMnQ2Cg0REJkTsffKAfn3pKl5YXuZenX/VPPvmEF198kXbt2vH7779TunRpR8UlnETDhtC0qb5B6KOP4K23jI5IiNuLj4f163Xz0ahRRkfjenLdx/DII4/wyiuvMHPmTJYuXSpJwYNYR3PMmgVXrhgbixC5Ye1b6NMHPKSCfr7KdWJIT0/n119/ZfDgwY6MRzihHj2gcmU4exZkxLJwdklJuukTZIjqncp1YoiOjqZ8+fKOjEU4KW9vueFNuI6PPoLr13UTaMOGRkfjmqQkhsiVYcOgeHHYtw/WrDE6GiGy9vffmZPxvPCCsbG4MkkMIleKF8+cClFueBPOKiICzpyBSpV0E6i4M5IYRK6NHq3rKK1bB7/+anQ0QthTKvOkZfRokJH0d04Sg8i1oCA9ygPkqkE4n++/h717dTmX4cONjsa1SWIQeWId5bFoERw5YmwsQlgpBe+8o9efeko3fYo7J4lB5EmjRtCqlR718d//Gh2NEFpsLPz8M5jN8OKLRkfj+iQxiDx77TX9+MkncPKksbEIAZlXC8OGSdmW/CCJQeRZ27Z6fPjVq9LXIIy3bRtERen7bV56yeho3IMkBpFnJlPmVcNHH+l690IYxXq1MHCgvkNf3D2nTwyVKlXCZDLdsowcOTLL/Tds2JDl/vv27SvgyN1b165Qpw5cvAgzZhgdjfBUv/8Oy5frk5Xx442Oxn04fWLYtm0bSUlJtiU6OhqAvn375njc/v377Y6rWrVqQYTrMby84NVX9fr//R9cumRsPMIzvfuufuzVC2rWNDYWd+L0iaF06dJYLBbb8t1331GlShVatmyZ43FlypSxO85bpm/Kd337QtWqcO5cZhkCIQrKn3/C4sV63dq0KfKH0yeGG127do2IiAiGDRuGyWTKcd+QkBACAwNp27Yt69evz3Hf1NRUUlJS7BZxe97emZfv77+vO6OFKChTpuiCjp06QUiI0dG4F5dKDMuXL+fChQsMHTo0230CAwOZM2cOkZGRLF26lOrVq9O2bVtiY2OzPWby5MkEBATYlgpSwD3XHntM17s/cQLmzTM6GuEpjh7NnGrW2qQp8o9JKaWMDiK3OnbsiK+vLytXrszTcWFhYZhMJlasWJHl66mpqaSmptqep6SkUKFCBZKTkykut1De1syZepasoCA9x26hQkZHJNzdCy/oEvAtWkBMjNHRuIaUlBQCAgJy9bvmMlcMhw8fZt26dTxhLfGZB40bN+bAgQPZvm42mylevLjdInJv+HAoWxYOH9alMoRwpNOn4eOP9br0LTiGyySG+fPnU6ZMGbp06ZLnY3fu3Emg3A7pMH5+mTWUJk+G9HRj4xHubdo0Pe9C/frQvr3R0bgnl0gMGRkZzJ8/nyFDhuBzUy3d8PBwu+lGp02bxvLlyzlw4AC7d+8mPDycyMhInnvuuYIO26M88wzccw/88Qd8843R0Qh3de6cbroE3bdwmzEo4g65RGJYt24diYmJDBs27JbXkpKSSExMtD2/du0a48aNo27dujRv3pyNGzeyatUqevXqVZAhexx/fxgzRq9PnAhpaYaGI9zU1KmQkgJ160L37kZH475cqvO5oOSlk0ZkSk6G++/XZ3Xz50MOg8eEyLOkJKhSRTcjrVgBYWFGR+Ra3LLzWTi/gAAID9frEybADQO9hLhr77yjk0Ljxroki3AcSQwiX40cCeXKQWIizJljdDTCXRw6lDkS6Z13pG/B0SQxiHzl5wevv67X//MfuHzZ2HiEe5g0SU8O1a4dtG5tdDTuTxKDyHfDhum+hlOnYPp0o6MRrm7vXli4UK+//baxsXgKSQwi3/n66jM80KNIZL4GcTcmTNA1kbp31xNECceTxCAcYsAAqF0bLlyA994zOhrhqnbsgCVLdJ/CW28ZHY3nkMQgHMLbW/cxgL5T9cQJQ8MRLurf/9aP//qXnhhKFAxJDMJhunXTl/5XrmROvyhEbv30E3z/vT7JmDjR6Gg8iyQG4TAmU2ZCmD1bF9kTIjeUyiyQN3w4PPCAsfF4GkkMwqHattXL9euZHdJC3E5UlL5iMJszhz+LgiOJQTicdYjhZ5/B7t3GxiKcX1oavPyyXn/2WShf3th4PJEkBuFwjRpBz556yOELL+hmAiGy8+mn8OuvulqvzLdgDEkMokD897/6/oboaPjuO6OjEc7qwoXMpqOJE+Hee42MxnNJYhAFokqVzMl8xo6VAnsia2++CWfOQM2aeo4PYQxJDKLAvPoqWCxw8KCUyhC32r8fZszQ6x9+KHOHG0kSgygw/v566k/Qd7GePGlsPMK5vPii7nju0gU6djQ6Gs8miUEUqMGD9Vy9Fy9Kx6LItGYNrFoFPj7w/vtGRyMkMYgC5eWV2Yw0b56uhSM82/XrerQawOjRUL26sfEISQzCAE2a6No3Sul5omX4qmebNQv27YNSpeRmNmchiUEYYsoUKFIENm6Er782OhphlDNndFlt0EUXS5QwNBzxD0kMwhDly8P48Xr9pZd0oT3hOeLi4mjTpg3PPHOKCxegbl144gmjoxJWTp0YJk6ciMlkslssFkuOx8TExBAaGkrhwoW5//77mT17dgFFK/Jq3DioWBGOHNE3wAnPsXDhQtavP01kZClAl2b39jY2JpHJqRMDwIMPPkhSUpJt+e2337LdNyEhgc6dO9O8eXN27tzJq6++yujRo4mMjCzAiEVu+fllJoR334UDB4yNRzjW4cOH2b59Ozt27ODLL5cAn6CUF23bnqd48e0clvK7TsPH6ABux8fH57ZXCVazZ8+mYsWKTJs2DYCaNWsSFxfHe++9R+/evR0YpbhTffvq2jjR0fDUU/Djj7pct3A/lSpVuuHZaKAxkMwPP9Smfv3jACgZieAUnP6K4cCBA5QrV47KlSvTv39//vrrr2z33bx5Mx06dLDb1rFjR+Li4rh+/bqjQxV3wGSCjz/WHdEbNsDcuUZHJBwlIiICHx8foALwT8ldXgGO4+PjQ0REhHHBCTtOnRgaNWrEwoULWbt2LZ988gknTpygadOmnD17Nsv9T5w4QdmyZe22lS1blrS0NM6cOZPt56SmppKSkmK3iIJTuXLmfL7jxkFSkrHxCMcYOHAgW7ZsBWYBxYCfgDkAbN26lYEDBxoYnbiRUyeGTp060bt3b+rUqUO7du1YtWoVAJ999lm2x5huaoewXprevP1GkydPJiAgwLZUqFAhH6IXeTF6tL4jOjkZRo0yOhrhKFFR9wBdgFTgKby8pN3QGTl1YrhZ0aJFqVOnDgey6aW0WCycuGnW+VOnTuHj48O9OdTvDQ8PJzk52bYcOXIkX+MWt+fjo/safHwgMhKWLTM6IpHfzp6F99+vCEC5cguYPft5QkNDsVgslClTxuDoxI1cKjGkpqayd+9eAgMDs3y9SZMmREdH222Lioqifv36FMqhVKPZbKZ48eJ2iyh49eplztw1cqSuzS/cx7hxcPasNzVrZvDXX0/x9NNPs3XrVg4dOkR5mabNqTh1Yhg3bhwxMTEkJCSwdetW+vTpQ0pKCkOGDAH0mf7gwYNt+48YMYLDhw8zduxY9u7dy7x585g7dy7jxo0z6iuIPHr9dahWTfczWG+AE65v3TpYsEAPNpg71wuzWTchmUwmzGazscGJWzh1Yjh69CgDBgygevXq9OrVC19fX7Zs2UJQUBAASUlJJCYm2vavXLkyq1evZsOGDQQHB/PWW28xffp0GarqQgoXhjm6P5KPP4bYWGPjEXfvyhV4+mm9/uyzulaWcG4mJQOHb5GSkkJAQADJycnSrGSQp5/WCaJaNdi1SycM4ZpeflnfyFi+POzeDfInZYy8/K459RWD8FxTpkBgIPzxB7zxhtHRiDu1cWPm/AoffSRJwVVIYhBOqUQJXY4Z9NnmunWGhiPuwIULMHAgZGTAoEEQFmZ0RCK3JDEIp9W9e2bb9KBBcPq0sfGI3FNK/79LTIT774eZM42OSOSFJAbh1D74AGrVghMnYNgwmdTHVXz2mZ5nw9sbvvhCmpBcjSQG4dSKFIHFi8Fshu++g//9z+iIxO0cPAjPPafX33wTGjUyNh6Rd5IYhNOrWxemTtXr48bBr78aG4/I3rVrMGAAXL4MLVvCK68YHZG4E5IYhEsYNQq6dIHUVP3D8/ffRkcksjJhAsTFwT33wOefy+Q7rkoSg3AJJhPMnw8WC+zZAy++aHRE4mY//qiHGQN88glILUrXJYlBuIzSpXWnJuihrMuXGxqOuMHZs3rkmFLw5JMgxQZcmyQG4VI6dND9DADDh4PMBmm89HQYPBiOH4fq1eHDD42OSNwtSQzC5bz9NoSGwrlz+l6HS5eMjsizvfYarF6ty5YsXgxFixodkbhbkhiEy/H1haVLoUwZXUdpyBB9d60oWHFxcdSq9batX2H+fAgJMTYmkT8kMQiXVLGiTg6FCunHSZOMjsjz/Pe/G9i7V48CePVV6N/f4IBEvpHqqlmQ6qquY/58fUc06Dtt+/Y1Nh53d/jwYc6cOcOZM7507lyajAwLvr5r+fnn0phMilKlStnK4gvnkpffNUkMWZDE4FpefFGXzvDz09U8H3rI6Ijcl547vTAQAzQEfgceBlJs+8hPinOSstvCo0ydCo88om96695d11USjvH55xGYTJ+ik8JZoDvWpODj40NERISB0Yn84mN0AELcLW9vPRqmcWPYvx969oT162VyH0dIShr4TyHDNKAv8Jftta1bt/KQXK65BbliEG6hRAlYuVI/btkCTzwhI5XyW2TkjbWPxuDlFQOAl5f8jLgb+T8q3EbVqpmlnhct0vWVpLk7f6xerWtUKQWPPXaJsmWXEhoayuzZswkNDcVisVCmTBmjwxT5RDqfsyCdz64tIkLfiauU7pj+7391rSVxZ378ETp31gUM+/fX/33T0lLx9fXFZDKhlOLatWuYzWajQxU5kM5n4dEeewzmzNHr77+vK36KO7NpE3TrppNC9+6wcKG+IjObzf+MUNIjlSQpuBenTgyTJ0+mQYMG+Pv7U6ZMGXr06MH+/ftzPGbDhg2YTKZbln379hVQ1MIZPPEETJ+u1996CyZPNjYeV7R9O3TqpOdW6NABvvpK31Ao3J9TJ4aYmBhGjhzJli1biI6OJi0tjQ4dOnD58uXbHrt//36SkpJsS9WqVQsgYuFMRo3KLAP96qswbZqh4biU33/XySAlBVq0gGXL9Cx6wjM49XDVNWvW2D2fP38+ZcqUYfv27bRo0SLHY8uUKUOJEiUcGJ1wBS+/DFeu6JIZL7ygb4J7+mmjo3Juf/wB7drpIoUNG+opVYsUMToqUZCc+orhZsnJyQCULFnytvuGhIQQGBhI27ZtWb9+vaNDE05swgSdIACeeQY+/tjYeJzZr79CmzZw8iTUqwdr1oC/v9FRiYLmMolBKcXYsWNp1qwZtWvXzna/wMBA5syZQ2RkJEuXLqV69eq0bduW2NjYbI9JTU0lJSXFbhHuw2SCd9/NHL46YgSMH595n0NcXBxt2rQhLi7O2EANFhUFzZrBsWNQq5Z+fs89RkclDKFcxLPPPquCgoLUkSNH8nxs165dVVhYWLavT5gwQQG3LMnJyXcTsnAyGRlKTZiglE4PSvXrp9Tffys1atQoBajRo0cbHaJh5s5VysdH/3dp2VKpc+eMjkjkt+Tk5Fz/rrnEfQyjRo1i+fLlxMbGUrly5Twf//bbbxMREcHevXuzfD01NZXU1FTb85SUFCpUqCD3Mbipzz6DJ59UXL9uol69Sxw71oAzZ/ZRpkwZvv/+e5TynCqhSsEbb8B//qOfDxwIc+dKR7M7cpvqqkopRo0axbJly9iwYcMdjyzq06cP586d48cff8zV/nKDm/szmVoDy4ASwAGgMybTn3aVQZ34TyNfXLumh/V+/rl+/u9/w5tvys2A7iovv2tOPSpp5MiRfPHFF3z77bf4+/tz4p+ymQEBAfj5+QEQHh7OsWPHWLhwIQDTpk2jUqVKPPjgg1y7do2IiAgiIyOJjIw07HsI5xMR8QRDhrQgPX0FUBXYjFI9gJ/x8fFhwYIFhsbnaBcuQK9eutigt7fukB8+3OiohNNwYJPWXSOLdn9AzZ8/37bPkCFDVMuWLW3Pp0yZoqpUqaIKFy6s7rnnHtWsWTO1atWqPH1uXtrihOvavn27grIKtv7T73BVwQtq27btRofmULGxSgUF6f6EYsWUWrPG6IhEQXC7PoaCJk1JnmHHjh2EhoZiMhVFqc+A3gCEhl4kMtIfd+tiSE3VQ3enTtVpsHJlfeNavXpGRyYKgtRKEiIXypQpg8VioX79WsyadYagoHcwmS6zfbs/devqTmp3OW3avRsaNdJ3giulp0PdtUuSgsiaXDFkQa4YPEdqqn2V0L17r/Hkk2Y2bdKv9+yp299LlzY2zjuVkaFrRo0fr68YSpWCTz6BHj2MjkwUNLliECKXbq4SWquWmdhYeOcdXTBu2TKoU0dPUuNqp1B79+p6Ry+8oJNC587w22+SFMTtSWIQ4ibe3hAeDlu36juAT56EPn301KHR0c6fIA4dgqFDoXZt+OEHXedo1ixd88hiMTo64QokMQiRjZAQXXr6tdf0j+svv+gz8FatYONGo6O7VVISPPccVKum+0cyMnRTWHy8LgMi9yeI3JLEIEQOChfWdwX/9ReMGQO+vhAbC82b67kKnKG80rlz+gqnShX43//g+nVo314nsqVL9ZSnQuSFJAYhcqFsWT2fw8GD8NRT4OOjK482aKALz82cqZucCsrVq7r/o18/uO8+XSTw77+hSRM9FWdUlI5NiDsho5KyIKOSxO38+SdMnAiLFmX2OXh5QevWMGCAbsLJRXX4PElP13cqf/GFvhL4pwo9oIed/uc/0KWLNBmJrLlNrSSjSGIQuXXsGCxZAosX66Ybq0KF9LwGISG6E7h2bahRI/fF6ZTSnci//Za5xMTAP1VhAChfHvr3h3/9C4KDJSGInEliuEuSGMSd+OsvPS/yl1/qCW9u5u2t2/sffFA3TWUWANevK6UL2+3fr6fWvHjx1vcoWRL69tXJoFkzfZUiRG5IYrhLkhjE3dqzRzf77N6tf+R//x3On8/bexQqpIfL1qmjl5AQaNlSd4ALkVduU11VCFdVq5ZerJTSw0mticLaP2AyZS6gryruv18ngmrVdHIQoqBJYhCiAJhMUK6cXtq3NzoaIXImLZRCCCHsSGIQQghhRxKDEEIIO5IYhBBC2JHEIIQQwo4kBiGEEHYkMQghhLAjiUEIIYQdSQxCCCHsuERi+Oijj6hcuTKFCxcmNDSUn376Kcf9Y2JiCA0NpXDhwtx///3Mnj27gCIVQgjX5/SJ4auvvuL555/ntddeY+fOnTRv3pxOnTqRmJiY5f4JCQl07tyZ5s2bs3PnTl599VVGjx5NZGRkAUcuhBCuyemrqzZq1IiHHnqIWbNm2bbVrFmTHj16MHny5Fv2f+WVV1ixYgV79+61bRsxYgS7du1i8+bNufpMqa4qhHA3blNd9dq1a2zfvp3x48fbbe/QoQObNm3K8pjNmzfToUMHu20dO3Zk7ty5XL9+nUJZlKtMTU0lNTXV9jz5n9KXKSkpd/sVhBDCKVh/z3JzLeDUieHMmTOkp6dTtmxZu+1ly5blxI1TWd3gxIkTWe6flpbGmTNnCAwMvOWYyZMnM2nSpFu2V6hQ4S6iF0II53Px4kUCAgJy3MepE4OV6aY5C5VSt2y73f5ZbbcKDw9n7NixtucZGRmcO3eOe++9N8fPcYSUlBQqVKjAkSNHPKoZS763fG9PYOT3Vkpx8eJFypUrd9t9nToxlCpVCm9v71uuDk6dOnXLVYGVxWLJcn8fHx/uvffeLI8xm82Yb5qMt0SJEnceeD4oXry4R/3BWMn39izyvQvW7a4UrJx6VJKvry+hoaFER0fbbY+OjqZp06ZZHtOkSZNb9o+KiqJ+/fpZ9i8IIYSw59SJAWDs2LF8+umnzJs3j7179/LCCy+QmJjIiBEjAN0MNHjwYNv+I0aM4PDhw4wdO5a9e/cyb9485s6dy7hx44z6CkII4VKcuikJ4NFHH+Xs2bO8+eabJCUlUbt2bVavXk1QUBAASUlJdvc0VK5cmdWrV/PCCy/wv//9j3LlyjF9+nR69+5t1FfIE7PZzIQJE25p2nJ38r3le3sCV/neTn8fgxBCiILl9E1JQgghCpYkBiGEEHYkMQghhLAjiUEIIYQdSQwuIjU1leDgYEwmE/Hx8UaH41CHDh1i+PDhVK5cGT8/P6pUqcKECRO4du2a0aHlu7yWlHd1kydPpkGDBvj7+1OmTBl69OjB/v37jQ6rwE2ePBmTycTzzz9vdChZksTgIl5++eVc3cruDvbt20dGRgYff/wxu3fv5sMPP2T27Nm8+uqrRoeWr/JaUt4dxMTEMHLkSLZs2UJ0dDRpaWl06NCBy5cvGx1agdm2bRtz5syhbt26RoeSPSWc3urVq1WNGjXU7t27FaB27txpdEgFburUqapy5cpGh5GvGjZsqEaMGGG3rUaNGmr8+PEGRVTwTp06pQAVExNjdCgF4uLFi6pq1aoqOjpatWzZUo0ZM8bokLIkVwxO7uTJkzz55JN8/vnnFClSxOhwDJOcnEzJkiWNDiPfWEvK31wiPqeS8u7IWuLenf7f5mTkyJF06dKFdu3aGR1Kjpz+zmdPppRi6NChjBgxgvr163Po0CGjQzLEn3/+yYwZM3j//feNDiXf3ElJeXejlGLs2LE0a9aM2rVrGx2Ow3355Zfs2LGDbdu2GR3KbckVgwEmTpyIyWTKcYmLi2PGjBmkpKQQHh5udMj5Irff+0bHjx/nkUceoW/fvjzxxBMGRe44eS0p706ee+45fv31VxYvXmx0KA535MgRxowZQ0REBIULFzY6nNuSkhgGOHPmDGfOnMlxn0qVKtG/f39Wrlxp90ORnp6Ot7c3AwcO5LPPPnN0qPkqt9/b+odz/PhxWrduTaNGjViwYAFeXu5zHnPt2jWKFCnCkiVL6Nmzp237mDFjiI+PJyYmxsDoHG/UqFEsX76c2NhYKleubHQ4Drd8+XJ69uyJt7e3bVt6ejomkwkvLy9SU1PtXjOaJAYnlpiYaDe96PHjx+nYsSPffPMNjRo1onz58gZG51jHjh2jdevWhIaGEhER4VR/NPmlUaNGhIaG8tFHH9m21apVi+7du2c5n7k7UEoxatQoli1bxoYNG6hatarRIRWIixcvcvjwYbttjz/+ODVq1OCVV15xuqY06WNwYhUrVrR7XqxYMQCqVKni1knh+PHjtGrViooVK/Lee+9x+vRp22sWi8XAyPLX2LFjGTRoEPXr16dJkybMmTPHrqS8Oxo5ciRffPEF3377Lf7+/rb+lICAAPz8/AyOznH8/f1v+fEvWrQo9957r9MlBZDEIJxQVFQUBw8e5ODBg7ckQHe6wL1dSXl3NGvWLABatWplt33+/PkMHTq04AMSWZKmJCGEEHbcpzdPCCFEvpDEIIQQwo4kBiGEEHYkMQghhLAjiUEIIYQdSQxCCCHsSGIQQghhRxKDEEIIO5IYhChg6enpNG3alN69e9ttT05OpkKFCvz73/82KDIhNLnzWQgDHDhwgODgYObMmcPAgQMBGDx4MLt27WLbtm34+voaHKHwZJIYhDDI9OnTmThxIr///jvbtm2jb9++/PLLLwQHBxsdmvBwkhiEMIhSijZt2uDt7c1vv/3GqFGjpBlJOAVJDEIYaN++fdSsWZM6deqwY8cOfHyk4LEwnnQ+C2GgefPmUaRIERISEjh69KjR4QgByBWDEIbZvHkzLVq04Pvvv2fq1Kmkp6ezbt06j5nzWTgvuWIQwgB///03Q4YM4emnn6Zdu3Z8+umnbNu2jY8//tjo0ISQxCCEEcaPH09GRgZTpkwB9DSu77//Pi+99BKHDh0yNjjh8aQpSYgCFhMTQ9u2bdmwYQPNmjWze61jx46kpaVJk5IwlCQGIYQQdqQpSQghhB1JDEIIIexIYhBCCGFHEoMQQgg7khiEEELYkcQghBDCjiQGIYQQdiQxCCGEsCOJQQghhB1JDEIIIexIYhBCCGFHEoMQQgg7/w84Hx45BgD+twAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plot=np.linspace(-4.2, 4.2, 40)\n",
    "y_plot=np.polyval(fit, x_plot)\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.plot(x,y, \"k*\", label=\"Actual values\")\n",
    "plt.plot(x_plot, y_plot, \"b-\", label=\"Fit\")\n",
    "plt.xlim(-5, 5)\n",
    "plt.ylim(0, 18)\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.legend(frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d1700a",
   "metadata": {},
   "source": [
    "We can also easily estimate the error on fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44e571e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated squared error of fit:  0.007\n",
      "Estimated error of fit:          0.038\n"
     ]
    }
   ],
   "source": [
    "sum2=np.sum((y-np.polyval(fit, x))**2)\n",
    "std=np.sqrt(sum2/len(x))\n",
    "\n",
    "print(\"Estimated squared error of fit: \", sum2.round(3)) \n",
    "print(\"Estimated error of fit:         \", std.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71966e14-1a71-4094-8607-4c742f29ebd1",
   "metadata": {},
   "source": [
    "The error of fit can be also obtained by setting the keyword variable *full* to ***True*** in the arguments of the *np.polyfit* function; this provides a list of data which contains, in position 1, a *one value list* containing the *squared error* of the fit (in position 0):   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "566bd9cc-a5bd-438e-9ed8-8e8bef96c159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated squared error of fit:  0.007\n"
     ]
    }
   ],
   "source": [
    "result=np.polyfit(x,y,2, full=True)\n",
    "print(\"Estimated squared error of fit: \", result[1][0].round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a626b211-91cb-4e25-baf7-776db184b5b4",
   "metadata": {},
   "source": [
    "Errors on the single parameters can be estimated starting from the covariance matrix from the fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b57e1221-5450-47b8-a7fc-e061431d9b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance matrix: \n",
      "\n",
      "array([[ 2.82951185e-05, -2.35476154e-05, -1.66020183e-04],\n",
      "       [-2.35476154e-05,  1.43422069e-04,  5.14866711e-05],\n",
      "       [-1.66020183e-04,  5.14866711e-05,  1.77278887e-03]])\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "fit, cov=np.polyfit(x, y, 2,cov=True)\n",
    "print(\"Covariance matrix: \\n\")\n",
    "pprint.pprint(cov)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8912e5-b386-4766-9e3b-6bbc8e88c035",
   "metadata": {},
   "source": [
    "Then the (squared) errors on parameters can be calculated as the *eigenvalues* of such *covariance matrix*; the *numpy* function *linalg.eigh* (*eigh* is a method of the more general *linalg* class) provides the *eigenvalues* and the corresponding *eigenvectors* of a matrix:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33648d1d-f3ee-4248-b1ca-41446153a244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:            [0.97374075 0.006954   1.17850434]\n",
      "Errors on parameters:  [0.00316364 0.01201303 0.04231057]\n",
      "Eigenvectors:\n",
      "\n",
      "array([[ 0.98619082, -0.13621768, -0.09419346],\n",
      "       [ 0.13979527,  0.98964834,  0.03245674],\n",
      "       [ 0.08879722, -0.04517634,  0.9950247 ]])\n"
     ]
    }
   ],
   "source": [
    "dcov=np.linalg.eigh(cov)\n",
    "eigvals=np.sqrt(dcov[0])\n",
    "print(\"Parameters:           \", fit)\n",
    "print(\"Errors on parameters: \", eigvals)\n",
    "print(\"Eigenvectors:\\n\")\n",
    "pprint.pprint(dcov[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f951c5af",
   "metadata": {},
   "source": [
    "## Exercise: the Richter's law\n",
    "\n",
    "In the class *Data* previoulsy illustrated, try to implement a new method for the application of the Richter's law to the objects of the class. \n",
    "\n",
    "In doing that, you might need to *group* the events whose magnitude is in a given range, in a limited number of *channels* (*bins*): you might want to use the *numpy* function ***histogram***     \n",
    "\n",
    "In the example below, \n",
    "\n",
    "- we generate a list *x* of 10000 random numbers following a Poisson distribution centered on the value of 20; \n",
    "- we make a graphic histogram representation of *x* by using the *matplotlib* function *hist*, with 40 *bins*;\n",
    "- we use *histogram* from the *numpy library* to group all the *x* entries in 10 *bins*, according to their value; *histogram* returns two lists (of size 10, in this case): *count*, which contains the number of entries in each bin, and *values* which contains the values associated to each bins.    \n",
    "- Then we print the values of *count* and the associated *values* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b38fd33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical histogram\n",
      "\n",
      "   Count     Bin value\n",
      "     24  -->  5.00\n",
      "    200  -->  8.30\n",
      "    833  -->  11.60\n",
      "   2844  -->  14.90\n",
      "   2638  -->  18.20\n",
      "   1902  -->  21.50\n",
      "   1235  -->  24.80\n",
      "    259  -->  28.10\n",
      "     45  -->  31.40\n",
      "     20  -->  34.70\n"
     ]
    }
   ],
   "source": [
    "import random \n",
    "\n",
    "x=np.random.poisson(20, 10000)\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.hist(x, 40)\n",
    "plt.xlabel(\"Bins\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.show()\n",
    "\n",
    "count, values=np.histogram(x, 10)\n",
    "\n",
    "print(\"Numerical histogram\\n\")\n",
    "print(\"   Count     Bin value\")\n",
    "for ic, iv in zip(count, values):\n",
    "    print(\" %6i  -->  %4.2f\" %  (ic, iv))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca74584e",
   "metadata": {},
   "source": [
    "### The implementation of the Richter's fit\n",
    "\n",
    "Here is the proposal.\n",
    "\n",
    "The possibility has been added to select only those events whose magnitude is within a given range; the optional selection of events occurred in a specific depth interval is implemented as well.\n",
    "\n",
    "Moreover, a graphical representation of the Richter's law is implemented (*richter_plot* method).\n",
    "\n",
    "\n",
    "```\n",
    "    def richter(self, mgm=0, mgx=0, d_min=0, d_max=0, bins=0, plot=False): \n",
    " \n",
    "        \"\"\"\n",
    "        Computes Richter's law fit\n",
    "        \n",
    "        Input:\n",
    "             mgm: if not 0, minimum magnitude (default 0)\n",
    "             mgx: if not 0, maximum magnitude (default 0)\n",
    "             d_min: if not 0, minimum depth (default 0)\n",
    "             d_max: if not 0, maximum depth (default 0)\n",
    "             bins: if not 0, sets the number of bins of\n",
    "                   the histogram of the magnitude distribution\n",
    "                   (default 0)\n",
    "             plot: if True, a plot of the Richter's law is produced\n",
    "                   (default False)\n",
    "        \"\"\"\n",
    "        self.richter_flag=False\n",
    "        self.describe()\n",
    "        \n",
    "        if bins !=0:\n",
    "           self.bins=bins\n",
    "        \n",
    "        minimum=np.min(self.magnitude)\n",
    "        maximum=np.max(self.magnitude)     \n",
    "        minimum_d=np.min(self.depth)\n",
    "        maximum_d=np.max(self.depth)\n",
    "        \n",
    "        if mgm != 0:\n",
    "           minimum=mgm\n",
    "        if mgx != 0:\n",
    "           maximum=mgx           \n",
    "        if d_min != 0:\n",
    "           minimum_d=d_min\n",
    "        if d_max != 0:\n",
    "           maximum_d=d_max \n",
    "        \n",
    "        mag=self.magnitude \n",
    "        depth=self.depth\n",
    "        \n",
    "        cases_mag=np.where((mag >= minimum) & (mag <= maximum))\n",
    "        cases_depth=np.where((depth >= minimum_d) & (depth <= maximum_d))\n",
    "        cases=np.intersect1d(cases_mag, cases_depth)\n",
    "        \n",
    "        count, mag=np.histogram(mag[cases], bins=self.bins)          \n",
    "        nz_counts=np.array([])\n",
    "        nz_mag=np.array([])\n",
    "        for ic in range(len(count)):\n",
    "            if count[ic] != 0:\n",
    "               nz_counts=np.append(nz_counts, count[ic])\n",
    "               nz_mag=np.append(nz_mag, mag[ic])                    \n",
    "        nz_log_counts=np.log10(nz_counts)         \n",
    "        fit=np.polyfit(nz_mag, nz_log_counts, 1)\n",
    "        \n",
    "        self.richter_fit=fit\n",
    "        self.nz_log_counts=nz_log_counts\n",
    "        self.nz_mag=nz_mag        \n",
    "        self.richter_flag=True\n",
    "        \n",
    "        print(\"Fit results:\")\n",
    "        print(f\"Number of events: {len(cases)}\")\n",
    "        print(\"Intercept: %4.2f,   Slope: %6.4f\" % (fit[1], fit[0]))\n",
    "        \n",
    "        if plot:\n",
    "           self.richter_plot(mgm, mgx)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ce3b1c",
   "metadata": {},
   "source": [
    "Below, the new method is incorporated within the *Data* class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35079a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Stat():\n",
    "          \n",
    "      def set_size(self):\n",
    "          self.size=len(self.magnitude)\n",
    "          return self.size\n",
    "        \n",
    "      def average(self):\n",
    "          ave = 0.\n",
    "          size = self.set_size()\n",
    "          self.size=size\n",
    "          for ix in self.magnitude:\n",
    "              ave=ave+ix\n",
    "          ave=ave/size\n",
    "          self.ave=ave\n",
    "          self.flag=True\n",
    "          return ave\n",
    "        \n",
    "      def standard_deviation(self, force=True):\n",
    "          if (not self.flag) or (self.flag and force):\n",
    "             ave=self.average() \n",
    "            \n",
    "          ave=self.ave\n",
    "          size=self.size\n",
    "          std=0.\n",
    "          for ix in self.magnitude:\n",
    "              std=std+(ix-ave)**2\n",
    "                \n",
    "          std=(std/(size-1))**0.5\n",
    "          self.std=std\n",
    "          return std\n",
    "            \n",
    "      def describe(self):   \n",
    "              \n",
    "          self.average()\n",
    "          self.standard_deviation()\n",
    "          self.min_mag=np.min(self.magnitude)\n",
    "          self.max_mag=np.max(self.magnitude)\n",
    "          self.depth_min=np.min(self.depth)\n",
    "          self.depth_max=np.max(self.depth)\n",
    "                    \n",
    "          print(\"data-set: %s\" % self.name)\n",
    "          print(\"Size: %4i\" % self.size)\n",
    "          print(\"Minimum magnitude:  %5.2f\" % self.min_mag)\n",
    "          print(\"Maximum magnitude:  %5.2f\" % self.max_mag)\n",
    "          print(\"Average magnitude:  %5.2f\" % self.ave)\n",
    "          print(\"Stand. dev:         %5.2f\\n\" % self.std)\n",
    "          print(\"Depths (km):\")\n",
    "          print(\"Minimum depth: %6.1f, maximum depth %6.1f\\n\" %\n",
    "                (self.depth_min, self.depth_max))\n",
    "            \n",
    "          if self.richter_flag:\n",
    "             inter=self.richter_fit[1]\n",
    "             slope=self.richter_fit[0]\n",
    "             print(\"Richter fit:\")\n",
    "             print(\"Intercept: %4.2f,   Slope: %6.4f\\n\" % (inter, slope))\n",
    "                \n",
    "class Data(Stat):\n",
    "    number_of_obj=0  \n",
    "    obj_files=[]\n",
    "    obj_names=[]\n",
    "    flag_array=False\n",
    "    bins=10\n",
    "    \n",
    "    def __init__(self, name):\n",
    "        self.name=name       \n",
    "        self.flag=True\n",
    "        self.size=None\n",
    "        self.richter_flag=False\n",
    "        \n",
    "    def set_data(self, type_of_data, val):\n",
    "        \n",
    "        match type_of_data:\n",
    "            case 'magnitude':\n",
    "               self.magnitude=np.array(val)\n",
    "            \n",
    "            case 'depth':\n",
    "               self.depth=np.array(val)\n",
    "               \n",
    "            case other:\n",
    "                print(f\"{type_of_data} not implemented\")\n",
    "                \n",
    "    def richter(self, mgm=0, mgx=0, d_min=0, d_max=0, bins=0, plot=False): \n",
    " \n",
    "        \"\"\"\n",
    "        Computes Richter's law fit\n",
    "        \n",
    "        Input:\n",
    "             mgm: if not 0, minimun magnitude (default 0)\n",
    "             mgx: if not 0, maximum magnitude (default 0)\n",
    "             d_min: if not 0, minimum depth (default 0)\n",
    "             d_max: if not 0, maximim depth (default 0)\n",
    "             bins: if not 0, sets the number of bins of\n",
    "                   the histogram of the magnitude distribution\n",
    "                   (default 0)\n",
    "             plot: if True, a plot of the Richter's law is produced\n",
    "                   (default False)\n",
    "        \"\"\"\n",
    "        self.richter_flag=False\n",
    "        self.describe()\n",
    "        \n",
    "        if bins !=0:\n",
    "           self.bins=bins\n",
    "        \n",
    "        minimum=np.min(self.magnitude)\n",
    "        maximum=np.max(self.magnitude)     \n",
    "        minimum_d=np.min(self.depth)\n",
    "        maximum_d=np.max(self.depth)\n",
    "        \n",
    "        if mgm != 0:\n",
    "           minimum=mgm\n",
    "        if mgx != 0:\n",
    "           maximum=mgx           \n",
    "        if d_min != 0:\n",
    "           minimum_d=d_min\n",
    "        if d_max != 0:\n",
    "           maximum_d=d_max \n",
    "        \n",
    "        mag=self.magnitude \n",
    "        depth=self.depth\n",
    "        \n",
    "        cases_mag=np.where((mag >= minimum) & (mag <= maximum))\n",
    "        cases_depth=np.where((depth >= minimum_d) & (depth <= maximum_d))\n",
    "        cases=np.intersect1d(cases_mag, cases_depth)\n",
    "        \n",
    "        count, mag=np.histogram(mag[cases], bins=self.bins)          \n",
    "        nz_counts=np.array([])\n",
    "        nz_mag=np.array([])\n",
    "        for ic in range(len(count)):\n",
    "            if count[ic] != 0:\n",
    "               nz_counts=np.append(nz_counts, count[ic])\n",
    "               nz_mag=np.append(nz_mag, mag[ic])                    \n",
    "        nz_log_counts=np.log10(nz_counts)         \n",
    "        fit=np.polyfit(nz_mag, nz_log_counts, 1)\n",
    "        \n",
    "        self.richter_fit=fit\n",
    "        self.nz_log_counts=nz_log_counts\n",
    "        self.nz_mag=nz_mag        \n",
    "        self.richter_flag=True\n",
    "        \n",
    "        print(\"Fit results:\")\n",
    "        print(f\"Number of events: {len(cases)}\")\n",
    "        print(\"Intercept: %4.2f,   Slope: %6.4f\" % (fit[1], fit[0]))\n",
    "        \n",
    "        if plot:\n",
    "           self.richter_plot(mgm=mgm, mgx=mgx)\n",
    "        \n",
    "    def richter_plot(self, mgm=0, mgx=0, force=False):\n",
    "        \"\"\"\n",
    "        Richter's law plot\n",
    "        \n",
    "        Input:\n",
    "            mgm: if not 0, sets the minimim magnitude considered\n",
    "            mgx: if not 0, sets the maximum magnitude considered\n",
    "            force: if True, a Richter's law fit is requested \n",
    "                   (default False)\n",
    "        \"\"\"\n",
    "        if force or (not self.richter_flag):\n",
    "           self.richter(mgm, mgx, d_min=0, d_max=0)   \n",
    "           fit=self.richter_fit\n",
    "        \n",
    "        mag_min=self.min_mag\n",
    "        mag_max=self.max_mag\n",
    "    \n",
    "        if mgm != 0:\n",
    "           mag_min=mgm\n",
    "        if mgx != 0:\n",
    "           mag_max=mgx\n",
    "           \n",
    "        p1x=mag_min\n",
    "        p2x=mag_max    \n",
    "        p1y=np.polyval(self.richter_fit, p1x)\n",
    "        p2y=np.polyval(self.richter_fit, p2x)\n",
    "    \n",
    "        nz_mag=self.nz_mag\n",
    "        nz_log_counts=self.nz_log_counts\n",
    "       \n",
    "        plt.figure(figsize=(4,3))\n",
    "        plt.plot(nz_mag, nz_log_counts, \"k*\", label=\"Actual values\")\n",
    "        plt.plot((p1x, p2x), (p1y, p2y), \"k-\", label=\"Linear regression\")        \n",
    "        plt.xlabel('Magnitude')\n",
    "        plt.ylabel('Log count')\n",
    "        plt.title('Richter Law')\n",
    "        plt.legend(frameon=False)\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"\")\n",
    "        \n",
    "    def depth_distribution(self, bins=30):\n",
    "        depth=self.depth\n",
    "\n",
    "        plt.figure(figsize=(4,3))\n",
    "        plt.hist(depth, bins)\n",
    "        plt.xlabel(\"Depth (km)\")\n",
    "        plt.ylabel(\"Number\")\n",
    "        plt.title(f\"Region: {self.name.capitalize()}\")\n",
    "        plt.show()\n",
    "        \n",
    "    @classmethod\n",
    "    def set_bins(cls, new_bin):\n",
    "        if new_bin <=2:\n",
    "           new_bin=2\n",
    "        \n",
    "        cls.bins=new_bin\n",
    "                  \n",
    "    @classmethod\n",
    "    def read_info(cls, path, info_file):         \n",
    "        file=path+'/'+info_file\n",
    "        fi=open(file)\n",
    "        text=fi.read()\n",
    "        fi.close()\n",
    "        text=text.rstrip().splitlines()\n",
    "        num_lines=len(text)\n",
    "        cls.obj_names=[]\n",
    "        cls.obj_files=[]\n",
    "        cls.number_of_obj=num_lines\n",
    "             \n",
    "        for line in text:\n",
    "            line=line.split()\n",
    "            cls.obj_files.append(path+'/'+line[0])\n",
    "            cls.obj_names.append(line[1])\n",
    "            \n",
    "    @classmethod\n",
    "    def setup(cls):\n",
    "        for obj, file in zip(cls.obj_names, cls.obj_files):\n",
    "            idata=pd.read_csv(file, sep='|')\n",
    "            idata.rename(columns={\"Depth/Km\": \"Depth\"}, inplace=True)\n",
    "           \n",
    "            if not cls.flag_array:\n",
    "               try:\n",
    "                  eval(obj).set_data('magnitude', idata.Magnitude)\n",
    "                  eval(obj).set_data('depth', idata.Depth)\n",
    "               except NameError:\n",
    "                  print(\"Array method should be set as 'region variables'\")\n",
    "                  print(\"are not defined. Use the command:\")\n",
    "                  print(\">>> Data.set_array()\")\n",
    "                  break\n",
    "            else:\n",
    "               ipos=cls.obj_dictionary[obj]               \n",
    "               cls.obj_array[ipos].set_data('magnitude', idata.Magnitude)\n",
    "               cls.obj_array[ipos].set_data('depth', idata.Depth)\n",
    "               cls.obj_array[ipos].flag_ready=True           \n",
    "                    \n",
    "            \n",
    "    @classmethod\n",
    "    def set_array(cls, out=False): \n",
    "        \n",
    "        cls.flag_array=True\n",
    "        number_list=list(range(len(cls.obj_names)))\n",
    "        l_set=list(iset for iset in cls.obj_names)        \n",
    "        cls.obj_dictionary=dict(zip(cls.obj_names, number_list))\n",
    "        cls.obj_array=np.array(number_list, dtype='object')\n",
    "        \n",
    "        for name in cls.obj_dictionary:\n",
    "            ipos=cls.obj_dictionary[name]\n",
    "            cls.obj_array[ipos]=cls(cls.obj_names[ipos])\n",
    "            \n",
    "        cls.setup()\n",
    "        \n",
    "        if out:\n",
    "            return cls.obj_dictionary, cls.obj_array \n",
    "        \n",
    "    @classmethod\n",
    "    def describe_all(cls):\n",
    "        print(f\"Number of datasets: {cls.number_of_obj}\")\n",
    "        print(f\"Regions: {cls.obj_names}\\n\")\n",
    "        \n",
    "        for obj in cls.obj_names:\n",
    "            if not cls.flag_array:\n",
    "                eval(obj).describe()\n",
    "                print(\"\")\n",
    "            else:\n",
    "                ipos=cls.obj_dictionary[obj]\n",
    "                cls.obj_array[ipos].describe()\n",
    "                print(\"\")\n",
    "                   \n",
    "        print(\"-\"*35)\n",
    "            \n",
    "    @classmethod\n",
    "    def richter_all(cls, mgm=0, mgx=0, d_min=0, d_max=0, plot=False):\n",
    "        for obj in cls.obj_names:\n",
    "            if not cls.flag_array:\n",
    "               eval(obj).richter(mgm, mgx, d_min, d_max, plot=plot)\n",
    "               print(\"\\n\")\n",
    "            else:\n",
    "                ipos=cls.obj_dictionary[obj]\n",
    "                cls.obj_array[ipos].richter(mgm, mgx, d_min, d_max, plot=plot)\n",
    "                print(\"\\n\")\n",
    "                \n",
    "        print(\"\")\n",
    "        print(\"-\"*35)        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf86958",
   "metadata": {},
   "source": [
    "Lets's try it by using the dataset in the array mode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26a6a2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start(path='L7_data_files', info='info.dat'):\n",
    "    global rd, ra\n",
    "    Data.read_info(path, info)\n",
    "    for obj in Data.obj_names:\n",
    "        exec(obj + ' = Data(obj)')\n",
    "    \n",
    "    rd, ra=Data.set_array(out=True)\n",
    "    \n",
    "start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2407eb50",
   "metadata": {},
   "source": [
    "Have a look at whole dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b9c8ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of datasets: 5\n",
      "Regions: ['sicilia', 'north', 'central', 'emilia', 'marche']\n",
      "\n",
      "data-set: sicilia\n",
      "Size:  559\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.30\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.35\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.0, maximum depth  208.2\n",
      "\n",
      "\n",
      "data-set: north\n",
      "Size:  131\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   3.90\n",
      "Average magnitude:   2.36\n",
      "Stand. dev:          0.39\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    1.5, maximum depth   62.4\n",
      "\n",
      "\n",
      "data-set: central\n",
      "Size:  367\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   3.90\n",
      "Average magnitude:   2.29\n",
      "Stand. dev:          0.34\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    4.9, maximum depth   69.5\n",
      "\n",
      "\n",
      "data-set: emilia\n",
      "Size:  144\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.10\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.37\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    1.1, maximum depth   71.1\n",
      "\n",
      "\n",
      "data-set: marche\n",
      "Size:  528\n",
      "Minimum magnitude:   1.00\n",
      "Maximum magnitude:   4.20\n",
      "Average magnitude:   2.08\n",
      "Stand. dev:          0.47\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.6, maximum depth   34.8\n",
      "\n",
      "\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "Data.describe_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a08317",
   "metadata": {},
   "source": [
    "With reference to the case of *sicilia*, let's see the depth distribution: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82b1a1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ra[rd['sicilia']].depth_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afff547a",
   "metadata": {},
   "source": [
    "Then, do select only the events occurring at depths shallower than 50 km and make the Richter's fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbc0a2ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-set: sicilia\n",
      "Size:  559\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.30\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.35\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.0, maximum depth  208.2\n",
      "\n",
      "Fit results:\n",
      "Number of events: 476\n",
      "Intercept: 4.48,   Slope: -1.1113\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ra[rd['sicilia']].richter(d_max=50, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5779107c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-set: sicilia\n",
      "Size:  559\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.30\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.35\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.0, maximum depth  208.2\n",
      "\n",
      "Fit results:\n",
      "Number of events: 472\n",
      "Intercept: 4.51,   Slope: -1.0592\n"
     ]
    },
    {
     "data": {
      "image/png": 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BcHJyQmxsrNjRWDkquCfG2dkZmzdvhrOzMywsLFC7du1y2V5ubi527NiBNWvWQCaTyacrV67AxsYGu3btAgC0atUKJ0+eLHI9Ojo6yMvLU5hnbm6OtLQ0hZ3sm528AHDu3DlMmzYNPXv2RPPmzSGRSBQ6dUtCT08PAwcOxK5duxAUFIQmTZooPLvs3Llz8PX1xYABA9CyZUtYWFjgzp07Ra7P3NwcABT6Tt7O7eTkhGvXrsHW1haNGjVSmGrUqAHgv6sSP/zwQyxevBjx8fHQ0dFBWFiYSp9NrZXfGa7Kq7z6HIpy8eJFsrGxIQCkra1N69evp/z8/ArZNqt4r1+/lv/3zc/Pp9evX5fbtsLCwkhHR4eePXum9N5XX31FUqmUiIgiIiJIQ0ODFixYQAkJCfTHH3/QypUr5W09PT2pb9++9Pfff9PDhw+JiCghIYEEQaAVK1bQzZs3aePGjWRiYqLQ5yCVSsnT05MSEhIoKiqKOnToQHp6egr9F3hHnwMR0fHjx0kikVDTpk1p6dKlCu/179+fpFIpxcfHk0wmoz59+pChoSFNnz5d3ubtPhNXV1fq0KEDXbt2jc6cOUNt2rRR6HO4f/8+mZub06BBgyg6Oppu3bpFx44do9GjR1Nubi5FRUXRt99+S5cvX6a7d+/Snj17SEdHhw4fPlzs5xCTqvs1Lg6FqOjiQET05MkTGjBggLyDsl+/fvT48eMK2z6rmnr37k09e/Ys9L2CTtXY2FgiIgoJCSGpVEo6OjpUq1YtGjhwoLztxYsXqVWrViSRSOjNvyl/+uknsrKyoho1atCoUaPo22+/VSgOcXFx5OLiQhKJhBo3bkx79+5V2lGXpDjk5uaSpaUlAaBbt24pvJecnEweHh6kp6dHVlZWtHHjRnJ3dy+2OCQkJJCrqyvp6emRVCql48ePKxQHIqLr16/TgAEDqGbNmqSnp0d2dnY0Y8YMys/Pp4SEBPLy8iJzc3OSSCTUpEkThQ5wdaTqfk0g4p7Qtz1//hzGxsZIT0+v0D4AIsKPP/6IWbNmITs7G9bW1ggKCkK7du0qLANjrGpSdb/GfQ5qRBAETJ48GVFRUWjUqBHu3buHjh07YuXKleXeackYY28StTgsX74crVu3hqGhIWrXro3+/fsjKSmp2GVCQ0Ph6ekJc3NzGBkZwc3NDceOHVNo4+/vL7+T883p9evX5flxyoyjoyPi4uIwbNgw5OXlYc6cOejZsycePHggdjTGWDUhanE4c+aM/C/l8PBw5Obmolu3bsXegn727Fl4enri8OHDiI2NhYeHB/r06YP4+HiFdkZGRvK7OQsmXV3d8v5IZcbQ0BC7du3CL7/8Aj09PRw7dgxSqVTh9n7GGCs35dkBoqoHDx4QADpz5oxKy9nb29PixYvlr/38/MjY2LjEy79+/ZrS09PlU8GdkBXZIV2cq1evkr29PQEgDQ0NWrRoUbF3hzLG2Nsq9R3S6enpAABTU9MSL5Ofn4+MjAylZTIzM2FjY4P69eujd+/eSkcWb1q+fLn87k1jY2NYWVmV7gOUkxYtWuDSpUsYM2YM8vPzsWjRInTt2hX//POP2NEYY1WU2lytRETo168fnj59inPnzpV4uVWrVmHFihVITEyU30gUFRWFmzdvomXLlnj+/DnWr1+Pw4cP48qVK2jcuLHSOrKysuS35gP/9epbWVlV+NVKJREQEICJEyfixYsXMDc3x86dO+Hl5SV2LMaYmlP5KszyPIxRxaRJk8jGxoZSUlJKvExgYCDp6+srPECsMHl5eeTg4EBTp04t0XrFuM9BFUlJSeTg4CC/J+LLL7+k7OxssWMxxtRYpTytNHXqVBw4cAARERGoX79+iZYJDg7G2LFjsWfPHnTt2rXYthoaGmjdujVu3LhRFnFF16RJE0RFRWHSpEkAgJUrV8Ld3Z0HcGGMlRlRiwMRYcqUKQgNDcWpU6dK/MCqoKAg+Pr6IjAwEL169SrRdmQyGSwtLd83strQ1dXFpk2bsHfvXhgZGeHixYuQSqX47bffxI7GGKsCRC0OkydPRkBAAAIDA2FoaIi0tDSkpaUpjLU8d+5cjBo1Sv46KCgIo0aNwpo1a+Dq6ipfpqAzGwAWL16MY8eO4fbt25DJZBg7dixkMhkmTpxYoZ+vIgwaNAjx8fFo3bo1nj59iv79+2PGjBkKfSiMFUcQBOzfv1/sGNVCpfquy/Uk1zugkEFPAJCfn5+8jY+PD7m7u8tfu7u7F7qMj4+PvM2MGTPI2tqadHR0yNzcnLp160aRkZElzqXufQ6FycrKos8++0z+fTg7O9PNmzfFjsXUgI+PD/Xr16/I91NTU8v14X/s/4n5XfOzlcqAWM9WKgsHDx6Ej48Pnjx5AkNDQ2zduhVDhgwROxYTka+vL549e6bWf7ESEfLy8qCl9e4hZnJycqCtrS1qhsqIn61UzfXu3RtXrlxB+/btkZGRgaFDh2LChAkKp+pY2SAivHjxQpSpLP+me/NUx507dyAIAkJDQ+Hh4QF9fX04ODjg4sWLCstERkaiY8eO0NPTg5WVFaZNm6bwZIOAgAC4uLjA0NAQFhYW+PjjjxUe/3L69GkIgoBjx47BxcUFEomk0EvYC/Ls2bMHnTp1gq6uLgICAgAAfn5+aNasGXR1dWFnZ4cff/xRKaNUKoWuri5cXFywf/9+CIIgH7uhqAxEhO+++w4NGzaEnp4eHBwcsG/fPvl6nz59iuHDh8Pc3Bx6enpo3Lgx/Pz8AADZ2dmYMmUKLC0toaurC1tbWyxfvrzQ7xoArl69is6dO0NPTw9mZmb45JNPFMaW9/X1Rf/+/bF69WpYWlrCzMwMkydPVhqcqFyU1yFMZVYZTyu9LScnh+bNm0eCIBAAatmyJSUkJIgdq0rJzMws8tRoeU+ZmZklzvmu00p445HZBeMr29nZ0cGDBykpKYkGDRpENjY2lJOTQ0REf/zxBxkYGNDatWvp+vXrdOHCBXJ0dCRfX1/5Ordt20aHDx+mW7du0cWLF8nV1ZV69Oghfz8iIoIAUKtWrej48eN08+ZNevTokVK2gjy2trYUEhJCt2/fpvv379OWLVvI0tJSPi8kJIRMTU3J39+fiIieP39OpqamNGLECLp27RodPnyYmjRpojB2dFEZvvrqK7Kzs6OjR4/SrVu3yM/PjyQSCZ0+fZqIiCZPnkxSqZQuX75MycnJFB4eTgcOHCAiolWrVpGVlRWdPXuW7ty5Q+fOnaPAwMBCv+sXL15Q3bp1aeDAgXT16lU6efIkNWjQQOEUuY+PDxkZGdHEiRMpMTGRfv/9d9LX16ctW7aU7D/+G3g8hzJQFYpDgePHj1Pt2rUJAOnr68v/52HvryoXh19++UX+/rVr1wgAJSYmEhHRyJEj6ZNPPlFYx7lz50hDQ4NevXpV6DYuXbpEACgjI4OI/n/HvH///mKzF+RZt26dwnwrKyuFnS4R0dKlS8nNzY2I/htnwszMTCHP1q1bCy0Ob2bIzMwkXV1dpT7KsWPH0rBhw4iIqE+fPjR69OhC806dOpU6d+5c5GBdb37XW7ZsIRMTE4X/locOHSINDQ1KS0sjov/+29nY2Cg8Lmfw4ME0ZMiQQtdfHFX3a1Xz5BqT8/T0xJUrVzBixAicPHkSvr6+OHXqFDZt2gQDAwOV1xcTE4MvvvgC3333HVxcXMohceWhr6+vcAqgorddnlq1aiX/94JLwB88eAA7OzvExsbi5s2b8iFGgf9OseXn5yM5ORnNmjVDfHw8Fi1aBJlMhidPnsgfOX/v3j3Y29vLlyvpb+jNdg8fPkRKSgrGjh2L8ePHy+fn5ubC2NgYAJCUlIRWrVopPGyzTZs271x3QkICXr9+DU9PT4U22dnZcHR0BAB8+umn+OijjxAXF4du3bqhf//+8jFXfH194enpiaZNm6J79+7o3bs3unXrVuh2ExMT4eDgIB92FAA+/PBD5OfnIykpCXXq1AEANG/eHJqamvI2lpaWuHr1ajHfVtng4lANWFhY4NixY1ixYgUWLFiAHTt2IDo6Gnv27FHYCZTEjh07EBERgZ07d1b74iAIgsL/2FXJmx2+giAAgHwHn5+fjwkTJmDatGlKy1lbW+PFixfo1q0bunXrhoCAAJibm+PevXvw8vJCdna2QvuSfn9vtivIsXXrVrRt21ahXcFOlIjkuQtQEf00ha370KFDqFevnkI7iUQCAOjRowfu3r2LQ4cO4cSJE+jSpQsmT56M1atXw8nJCcnJyThy5AhOnDgBb29vdO3aVaHP4s08b2cs8Ob8tzvfBUGokPFduDhUE5qampg3bx46dOiAjz/+GElJSWjTpg3Wr1+PTz75pMgfKQDcvXsXjx49giAICA4OBgDs3r0bPj4+ICLUqlULNjY2FfVRmMicnJxw7do1NGrUqND3r169ikePHmHFihXyh1jGxMSU2fbr1KmDevXq4fbt2xg+fHihbezs7LBr1y5kZWXJd+olyWBvbw+JRIJ79+7B3d29yHbm5ubw9fWFr68vOnTogNmzZ2P16tUA/hsuYMiQIRgyZAgGDRqE7t2748mTJ0oPB7W3t8evv/6KFy9eyAvUhQsXoKGhgSZNmpTouyhPXByqmY4dO0Imk8HHxweHDx/GxIkTcerUKWzZskV+SP42W1tb+b8XFJGHDx/C2dlZPr+ov8qYekhPT5dfpVPA1NQU1tbWKq/ryy+/hKurKyZPnozx48ejRo0aSExMRHh4ODZs2ABra2vo6Ohgw4YNmDhxIv78808sXbq0jD7JfxYtWoRp06bByMgIPXr0QFZWFmJiYvD06VPMmjULH3/8MebNm4dPPvkEc+bMwb179+Q77+L+EDI0NMTnn3+OmTNnIj8/H+3bt8fz588RGRkJAwMD+Pj4YMGCBXB2dkbz5s2RlZWFgwcPolmzZgCAtWvXwtLSElKpFBoaGti7dy8sLCxQs2ZNpW0NHz4cCxcuhI+PDxYtWoSHDx9i6tSpGDlypPyUkqhU7tWoBqpSh3RR8vLyaPXq1aSlpUUAqGHDhnT58uVC2wYEBMjbvT1paWlRQEBABadnqvDx8Sn2xlEU0iFd0GlLRPT06VMCQBEREfJ5ly5dIk9PTzIwMKAaNWpQq1at6Ntvv5W/HxgYSLa2tiSRSMjNzY0OHDhQaGfw06dPi81eWJ4Cu3btIqlUSjo6OmRiYkIdO3ak0NBQ+fsXLlygVq1akY6ODjk7O1NgYCABoL/++qvYDPn5+bR+/Xpq2rQpaWtrk7m5OXl5ecnHmVm6dCk1a9aM9PT0yNTUlPr160e3b98mov86maVSKdWoUYOMjIyoS5cuFBcXJ1/3m9810X9Xfnl4eJCuri6ZmprS+PHj5Z32RIVfTDB9+nSFG4NLim+CKwOV+SY4VUVHR2PIkCG4e/cutLW1sWrVKkybNk3pr6u4uDiFI4UCsbGxcHJyqqi4jJXarl27MHr0aKSnp0NPT0/sOBWOb4JjKmnbti3i4+MxcOBA5OTkYMaMGejfvz+ePHlSaHsNDQ2FfzKmrnbs2IHz588jOTkZ+/fvx5dffglvb+9qWRhKg/8PZzAxMcG+ffuwceNG6Ojo4MCBA5BKpYiMjJS3qV27NiwsLODs7IzNmzfD2dkZFhYW8gGWGFM3aWlpGDFiBJo1a4aZM2di8ODB2LJli9ixKg0+rVSI6nRa6W3x8fEYMmQIbty4AU1NTXzzzTf44osvoKGhgaysLOjo6EAQBBARsrOz5VeCMMbUG59WYu/F0dERsbGx+Pjjj5GXl4e5c+eiR48eePDgASQSibwvQhAELgyMVWFcHJgSQ0NDBAQEYNu2bdDT08Px48fh4OCAiIgIsaMxxioIFwdWKEEQMGbMGFy+fBn29vZIS0tDly5dsGjRIuTl5YkdjzFWzrg4sGI1b94cly9fxtixY0FEWLx4Mbp27Yp//vlH7GiMsXLExYG9k76+Pn755RcEBATAwMAAp0+fhoODA44ePSp2NMZYOeHiwEps+PDhiI2NhVQqxaNHj9CjRw/MmTOnYgYeYYxVKC4OTCVNmjTBxYsXMXnyZADAypUr4e7ujrt374qcjDFWlrg4MJXp6upi48aN2LdvH4yNjXHx4kU4Ojrit99+EzsaY6yMcHFgpfbRRx8hPj4ebdq0wdOnT9G/f3/MmDEDWVlZYkdjjL0nUYvD8uXL0bp1axgaGqJ27dro378/kpKS3rncmTNn4OzsDF1dXTRs2BCbN29WahMSEiJ/Nru9vT3CwsLK4yNUew0aNMC5c+fw2WefAQDWr1+Pdu3a4ebNmyInY4y9D1GLw5kzZzB58mRERUUhPDwcubm56NatG168eFHkMsnJyejZsyc6dOiA+Ph4fPXVV5g2bRpCQkLkbS5evIghQ4Zg5MiRuHLlCkaOHAlvb29ER0dXxMeqdnR0dLB69WocPHgQZmZmiIuLg5OTk3xgIMZYJaTyQ8HL0YMHDwiA/Lnphfniiy/Izs5OYd6ECRPI1dVV/trb25u6d++u0MbLy4uGDh1aohzVYTyH8pKSkkIdOnSQjxnwySef0MuXL8WOxVi1p+p+Ta36HNLT0wFAaTi9N128eFFpwG4vLy/ExMTIL6ksqs2bTxl9U1ZWFp4/f64wsdKpX78+Tp06hfnz50MQBGzZsgVt2rRBYmKi2NEYYypQm+JARJg1axbat2+PFi1aFNkuLS1NaQi9OnXqIDc3F48ePSq2TVpaWqHrXL58OYyNjeVTwbi3rHS0tLSwdOlSHD9+HHXq1MGff/4JFxcX/Prrr2JHY4yVkNoUhylTpuCPP/5AUFDQO9u+PUoZ/e+p42/OL6xNUWPHzp07F+np6fIpJSVF1fisEF27doVMJkOXLl3w8uVL+Pr6YtSoUcjMzBQ7GmPsHdSiOEydOhUHDhxAREQE6tevX2xbCwsLpSOABw8eQEtLC2ZmZsW2KWrQbolEAiMjI4WJlQ0LCwscO3YM33zzDTQ0NLBz5064uLjgypUrYkdjjBVD1OJARJgyZQpCQ0Nx6tQpNGjQ4J3LuLm5ITw8XGHe8ePH4eLiAm1t7WLbtGvXruzCsxLT1NTEvHnzcPr0adSrVw9JSUlo27YtNm/eLD/qY4ypmfLrG3+3Tz/9lIyNjen06dOUmpoqn968umXOnDk0cuRI+evbt2+Tvr4+zZw5kxISEmjbtm2kra1N+/btk7e5cOECaWpq0ooVKygxMZFWrFhBWlpaFBUVVaJcfLVS+Xn48CH16tVLfjXT4MGD6dmzZ2LHYqzKU3W/JmpxKNhBvD35+fnJ2/j4+JC7u7vCcqdPnyZHR0fS0dEhW1tb+umnn5TWvXfvXmratClpa2uTnZ0dhYSElDgXF4fylZeXR6tXryYtLS0CQA0bNqRLly6JHYuxKk3V/RqPIV2I6jyGdEWKjo7G0KFDcefOHWhra+O7777D9OnTi7xwgDFWejyGNKs02rZti/j4eAwcOBA5OTmYOXMm+vfvjydPnogdjbFqj4sDE1XNmjWxb98+bNq0CTo6Ojhw4ACkUikuXLggdjTGqjUuDkx0giBg0qRJiIqKQuPGjZGSkgJ3d3esWLEC+fn5YsdjrFri4sDUhqOjI2JjYzF8+HDk5eVh7ty56NGjB/7991+xozFW7XBxYGrF0NAQO3fuxPbt26Gnp4fjx49DKpXi1KlTYkdjrFrh4sDUjiAIGD16NC5fvozmzZsjLS0NXbt2xcKFC5GXlyd2PMaqBS4OTG01b94cly5dwrhx40BEWLJkCbp06YL79++LHY2xKo+LA1Nr+vr62Lp1K3bt2gUDAwOcOXMGUqkUR44cETsaY1UaFwdWKXz88ceIi4uDVCrFo0eP0LNnT3z55ZfyMTwYY2WLiwOrNBo3boyLFy9iypQpAIDvvvsOHTt2xN27d0VOxljVw8WBVSq6urrYsGEDQkJCYGxsjKioKEilUuzfv1/saIxVKVwcWKU0cOBAxMfHo02bNnj27BkGDBiA6dOnIysrS+xojFUJXBxYpdWgQQOcO3cOn3/+OQDghx9+QLt27XDz5k2RkzFW+XFxYJWajo4OVq1ahYMHD8LMzAxxcXFwcnLC7t27xY7GWKWmcnHQ1NTEgwcPlOY/fvwYmpqaZRKKMVX16tULMpkMHTp0QEZGBoYNG4YJEybg1atXYkdjrFJSuTgUNfxDVlYWdHR03jsQY6VVv359nDp1Cl9//TUEQcCWLVvQpk0bJCQkiB2NsUpHq6QNf/jhBwD/Pdrgl19+gYGBgfy9vLw8nD17FnZ2dmWfkDEVaGlpYcmSJXB3d8fw4cPx559/onXr1ti0aRN8fHx4ICHGSqjEI8E1aNAAAHD37l3Ur19f4RSSjo4ObG1tsWTJErRt27Z8klYgHgmuavj3338xYsQInDhxAgAwcuRI/Pjjjwp/2DBWXai6X1N5mFAPDw+EhobCxMSk1CHVHReHqiM/Px8rVqzA119/jfz8fDRp0gR79uyBg4OD2NEYq1DlPkxoRERElS4MrGrR0NDAV199hdOnT6NevXq4fv062rZti82bNxfZf8YYK8WRQ15eHvz9/XHy5Ek8ePBAaaSuqvDcfT5yqJoePXoEX19fHDp0CAAwePBgbN26FcbGxiInY6z8lfuRw/Tp0zF9+nTk5eWhRYsWcHBwUJgYU1e1atXC77//jjVr1kBLSwt79+6Fo6MjLl++LHY0xtQPqcjMzIwOHTqk6mKFOnPmDPXu3ZssLS0JAIWFhRXb3sfHhwAoTfb29vI2fn5+hbZ59epViXOlp6cTAEpPTy/tR2NqLjo6mmxtbQkAaWtr0/fff0/5+flix2Ks3Ki6X1P5yEFHRweNGjUqk8L04sULODg4YOPGjSVqv379eqSmpsqnlJQUmJqaYvDgwQrtjIyMFNqlpqZCV1e3TDKzqqFNmzaIj4/HRx99hJycHMyaNQv9+vXD48ePxY7GmFpQuTh89tlnWL9+fZl05vXo0QPffPMNBg4cWKL2xsbGsLCwkE8xMTF4+vQpRo8erdBOEASFdhYWFu+dlVU9NWvWxN69e7Fp0ybo6Ojg999/h1QqxYULF8SOxpjoSnwTXIHz588jIiICR44cQfPmzaGtra3wfmhoaJmFe5dt27aha9eusLGxUZifmZkJGxsb5OXlQSqVYunSpXB0dCxyPVlZWQpP83z+/Hm5ZWbqRRAETJo0Ce3atYO3tzdu3LgBd3d3LF26FF9++SU0NPjxY6x6UvmXX7NmTQwYMADu7u6oVasWjI2NFaaKkpqaiiNHjmDcuHEK8+3s7ODv748DBw4gKCgIurq6+PDDD3Hjxo0i17V8+XKFz2BlZVXe8ZmakUqliI2NxfDhw5GXl4evvvoKPXr0wL///it2NMZEofKlrOVFEASEhYWhf//+JWq/fPlyrFmzBv/880+xz3TKz8+Hk5MTOnbsKH8EyNsKO3KwsrLiS1mrISKCv78/Jk+ejFevXsHCwgK7du1C586dxY7G2Hsp90tZ1QERYfv27Rg5cuQ7H/anoaGB1q1bF3vkIJFIYGRkpDCx6kkQBIwePRoxMTFo3rw50tLS0LVrVyxYsAC5ublix2OswqhcHBo0aICGDRsWOVWEM2fO4ObNmxg7duw72xIRZDIZLC0tKyAZqyrs7e1x6dIljBs3DkSEpUuXokuXLrh//77Y0RirECp3SM+YMUPhdU5ODuLj43H06FHMnj1bpXVlZmYqjNqVnJwMmUwGU1NTWFtbY+7cubh//z527NihsNy2bdvQtm1btGjRQmmdixcvhqurKxo3boznz5/jhx9+gEwmw6ZNm1TKxpi+vj62bt0KDw8PTJgwAWfPnoVUKsWvv/6Knj17ih2PsfJVVjdYbNy4kXx9fVVaJiIiotAb1nx8fIjov5ve3N3dFZZ59uwZ6enp0ZYtWwpd54wZM8ja2pp0dHTI3NycunXrRpGRkSrl4pvg2NuuX79Ojo6O8t/o7NmzKTs7W+xYjJWYqvu1MuuQvn37NqRSaZW4DJSfrcQKk5WVhdmzZ2PDhg0AAFdXVwQFBcHW1lbcYIyVgGgd0vv27YOpqWlZrY4xtSORSPDDDz8gJCQENWvWRFRUFBwdHREWFiZ2NMbKnMp9Do6OjgqjaRER0tLS8PDhQ/z4449lGo4xdTRw4EA4OTlh6NChiI6OxsCBAzF16lSsWrUKEolE7HiMlQmVTystXrxY4bWGhgbMzc3RqVOnKjNMKJ9WYiWRk5ODefPmYdWqVQAAJycnBAcHl9mzxxgrS+U+Elx1wMWBqeLw4cMYNWoUHj9+DENDQ2zZsgVDhw4VOxZjCiqkOOTl5WH//v1ITEyEIAiwt7dH3759FcaVrsy4ODBV/f333/j4449x7tw5AMD48eOxbt066Ovri5yMsf+Ue3G4efMmevbsifv376Np06YgIly/fh1WVlY4dOgQPvjgg1KHVxdcHFhp5ObmYsmSJfjmm29ARGjRogWCg4Nhb28vdjTGyv9qpWnTpuGDDz5ASkoK4uLiEB8fj3v37qFBgwaYNm1aqUIzpi5iYmLQuXNnxMTEqLyslpYWlixZgvDwcFhYWODPP/9E69at4efnx+NVs0pH5eJw5swZfPfddwqXrZqZmWHFihU4c+ZMmYZjrKLt2LEDERER2LlzZ6nX0aVLF8hkMnh6euLly5cYM2YMRo0ahYyMjDJMylj5Urk4SCSSQn/kmZmZ73wIHmPq6O7du4iNjUVcXByCg4MBALt370ZcXBxiY2Nx9+5dlddZp04dHD16FMuWLYOmpiYCAgLg4uICmUxWxukZKx8q9zmMGjUKcXFx2LZtG9q0aQMAiI6Oxvjx4+Hs7Ax/f//yyFmhuM+hennzvh1BEEBE8n8WeJ/TQufPn8ewYcPw999/QyKRYO3atZg4caLCdhkrb+Xe5/DDDz/ggw8+gJubG3R1deWD6TRq1Ajr168vVWjGxBQQEAAtrf/uBy0oAgX/1NLSQkBAwHutv3379pDJZOjduzeysrIwadIkeHt749mzZ++1XsbKU6nvc7h58yYSExNBRLC3t69SN/7wkUP1ExcXB2dnZ6X5sbGxcHJyKpNtEBHWrVuHL7/8Ejk5OWjQoAGCg4PRunXrMlk/Y8WpsGcrNWrUCH369EHfvn2rVGFg1VvBmNHlMXa0IAiYOXMmzp8/jwYNGiA5ORkffvgh1q5dy1czMbWj8v8BgwYNwooVK5Tmr1q1CoMHDy6TUIxVtNq1a8PCwgLOzs7YvHkznJ2dYWFhgdq1a5f5ttq0aYO4uDgMGjQIOTk5mDVrFvr27YvHjx+X+bYYKy2VTyuZm5vj1KlTaNmypcL8q1evomvXrlViQHY+rVQ9ZWVlQUdHR94ZnZ2dXa4P0iMibN68GTNnzkRWVhbq16+PoKAgtG/fvty2yaqvcj+tVNQlq9ra2lViLAdWfUkkEvkVRIIglPsTVgVBwKeffoqoqCg0adIEf//9Nzp16oTly5cjPz+/XLfN2LuoXBwKHgnwtt27d/NjAhgrBalUipiYGAwfPhx5eXn46quv0L179ypxFM4qL5XHc/j666/x0Ucf4datW+jcuTMA4OTJkwgKCsLevXvLPCBj1YGhoSF27tyJLl26YMqUKQgPD4dUKkVAQAC6dOkidjxWDal85NC3b1/s378fN2/exKRJk/DZZ5/h77//xokTJ9C/f/9yiMhY9SAIAkaPHo3Lly+jRYsWSEtLg6enJxYsWIDc3Fyx47FqhsdzKAR3SDOxvXz5EjNmzMDWrVsBAB07dkRgYCDq1asncjJWWYk2hjRjrOzo6+tjy5YtCAwMhIGBAc6ePQsHBwccPnxY7GismuDiwJgaGzZsGOLi4uDo6IjHjx+jV69emD17NnJycsSOxqo4UYvD2bNn0adPH9StWxeCIGD//v3Ftj99+jQEQVCa/vrrL4V2ISEhsLe3h0Qigb29PcLCwsrxUzBWvho3boyLFy9i6tSpAIDVq1ejQ4cOuHPnjrjBWJUmanF48eIFHBwcsHHjRpWWS0pKQmpqqnxq3Lix/L2LFy9iyJAhGDlyJK5cuYKRI0fC29sb0dHRZR2fsQojkUjwww8/IDQ0FDVr1kR0dDQcHR0RGhoqdjRWRalNh7QgCAgLCyv2iqfTp0/Dw8MDT58+Rc2aNQttM2TIEDx//hxHjhyRz+vevTtMTEwQFBRUoizcIc3U2Z07dzB06FD5HzxTpkzBqlWroKurK3Iyps5U3a+pfJ/DrFmzCp0vCAJ0dXXRqFEj9OvXT2GkuLLm6OiI169fw97eHvPnz4eHh4f8vYsXL2LmzJkK7b28vLBu3boi15eVlYWsrCz5a77Tm6kzW1tbnDt3DvPmzcOqVauwceNGXLhwAcHBwQpH0Yy9F1JRp06dyMjIiGrUqEFOTk7k6OhIBgYGZGxsTG3btqWaNWuSiYkJXbt2TaX1AqCwsLBi2/z111+0ZcsWio2NpcjISPr0009JEAQ6c+aMvI22tjbt2rVLYbldu3aRjo5OketduHAhAVCa0tPTVfoMjFW0w4cPU61atQgAGRgYUGBgoNiRmJpKT09Xab+mcp9Dv3790LVrV/zzzz/yoRXv378PT09PDBs2DPfv30fHjh2V/novC02bNsX48ePh5OQENzc3/Pjjj+jVqxdWr16t0O7tEbbofyN7FWXu3LlIT0+XTykpKWWenbHy0KNHD8hkMnTs2BGZmZn4+OOPMX78eLx8+bLQ9jExMejcuTNiYmIqOCmrbFQuDqtWrcLSpUsVzlkZGRlh0aJF+O6776Cvr48FCxYgNja2TIMWxdXVFTdu3JC/trCwQFpamkKbBw8eoE6dOkWuQyKRwMjISGFirLKoV68eTp48iQULFkAQBPzyyy9o06YNEhISlNru2LEDERER2LlzpwhJWWWicnFIT0/HgwcPlOY/fPhQfq6+Zs2ayM7Ofv90JRAfHw9LS0v5azc3N4SHhyu0OX78ONq1a1cheRgTg5aWFhYvXozw8HBYWFjg2rVrcHFxgZ+fH+7cuSM/yi94aObu3bsRFxeH2NhY3L17V+T0TB2p3CHdr18/jBkzBmvWrEHr1q0hCAIuXbqEzz//XH6l0aVLl9CkSZN3riszMxM3b96Uv05OToZMJoOpqSmsra0xd+5c3L9/Hzt27AAArFu3Dra2tmjevDmys7MREBCAkJAQhISEyNcxffp0dOzYEStXrkS/fv3w22+/4cSJEzh//ryqH5WxSqdLly6QyWQYOXIkwsPDMWbMGIX3C06vPnz4UGFYVFKPixaZOlG1UyMjI4PGjRtHOjo6pKGhQRoaGqSjo0Pjx4+nzMxMIiKKj4+n+Pj4d64rIiKi0I5gHx8fIiLy8fEhd3d3efuVK1fSBx98QLq6umRiYkLt27enQ4cOKa1379691LRpU9LW1iY7OzsKCQlR6TOq2nHDmLrJy8uj5cuXk6amZqH/jxVMWlpaFBAQIHZcVgFU3a+V+j6HzMxM3L59G0SEDz74AAYGBu9fqdQE3+fAqooLFy5g6NCh+Pvvvwt9PzY2Fk5OThWciomhwh68Z2BgAFNTU9SqVatKFQbGqpIPP/xQfjXTm4q7eo8xoBTFIT8/H0uWLIGxsTFsbGxgbW2NmjVrYunSpTy0IWNqyMzMDAEBATA0NJQXBW1tbZiamqJ27doip2PqSuXiMG/ePGzcuBErVqxAfHw84uLisGzZMmzYsAFff/11eWRkjL0nKysrPHz4EFFRUWjQoAGys7Px/Plz7NmzhzujWaFU7nOoW7cuNm/ejL59+yrM/+233zBp0iTcv3+/TAOKgfscWFWWnp6OcePGYd++fQCA3r17w9/fH2ZmZiInY+Wp3Pscnjx5Ajs7O6X5dnZ2ePLkiaqrY4xVMGNjY+zZswc//fQTJBIJDh48CKlUypd7MwUqF4eiHrG9ceNGODg4lEkoxlj5EgQBEydORHR0NJo0aYK///4bnTp1wrJly7jvkAEoxWmlM2fOoFevXrC2toabmxsEQUBkZCRSUlJw+PBhdOjQobyyVhg+rcSqk8zMTHz66acICAgAAHh6emLnzp3FPnKGVT7lflrJ3d0d169fx4ABA/Ds2TM8efIEAwcORFJSUpUoDIxVNwYGBtixYwf8/Pygr6+P8PBwODg44OTJk2JHYyIqs8F+UlJSsHDhQmzfvr0sVicqPnJg1VVCQgKGDBmCP//8E4IgYP78+ViwYAG0tFR+0g5TMxV2E9zbnjx5gl9//bWsVscYE4G9vT0uXbqETz75BESEpUuXonPnzkXeYc2qLlHHkGaMqR89PT38/PPPCAoKgqGhIc6dOwepVIpDhw6JHY1VIC4OjLFCDR06FHFxcXBycsLjx4/Ru3dvzJ49u8Iex8/ExcWBMVakRo0aITIyEtOmTQMArF69Gh07dsSdO3fEDcbKXYl7mQYOHFjs+8+ePXvfLIwxNSSRSLB+/Xp4eHhg9OjRiI6OhlQqxfbt29+5X2CVV4mPHIyNjYudbGxsMGrUqPLMyhgTUf/+/SGTyeDq6or09HR89NFHmDp1Kl6/fi12NFYOyuxS1qqEL2VlrGg5OTmYP38+vvvuOwCAo6MjgoOD0bhxY5GTseKIdikrY6x60NbWxsqVK3H48GHUqlUL8fHxcHJyQmBgoNjRWBni4sAYK5UePXrIBxLKzMzE8OHDMW7cOLx8+VLsaKwMcHFgjJVavXr1cPLkSSxYsACCIGDbtm1o06YNrl27JnY09p64ODDG3ouWlhYWL16MEydOwMLCAteuXUPr1q2xfft2HkioEuPiwBgrE507d4ZMJkO3bt3w6tUrjB07FiNHjkRGRobY0VgpcHFgjJWZOnXq4MiRI1i+fDk0NTWxa9cuODs7QyaTiR2NqUjU4nD27Fn06dMHdevWhSAI2L9/f7HtQ0ND4enpCXNzcxgZGcHNzQ3Hjh1TaOPv7w9BEJQmvhabsYqhoaGBOXPm4MyZM7CyssKNGzfQtm1bbNq0iU8zVSKiFocXL14UObJcYc6ePQtPT08cPnwYsbGx8PDwQJ8+fRAfH6/QzsjICKmpqQqTrq5ueXwExlgRPvzwQ8THx6NPnz7Izs7GlClTMGjQIH6aQmVBagIAhYWFqbycvb09LV68WP7az8+PjI2N3ytLeno6AaD09PT3Wg9jjCg/P5/Wrl1L2traBIBsbW0pKipK7FjVjqr7tUrd55Cfn4+MjAyYmpoqzM/MzISNjQ3q16+P3r17Kx1ZvC0rKwvPnz9XmBhjZUMQBMyYMQORkZFo2LAh7ty5g/bt22PNmjU8XrUaq9TFYc2aNXjx4gW8vb3l8+zs7ODv748DBw4gKCgIurq6+PDDD3Hjxo0i17N8+XKF50RZWVlVRHzGqhUXFxfExcVh8ODByM3Nxeeff46+ffvi0aNHYkdjhSnnI5kSg4qnlQIDA0lfX5/Cw8OLbZeXl0cODg40derUItu8fv2a0tPT5VNKSgqfVmKsnOTn59PmzZtJIpEQAKpXrx6dPXtW7FhVXrU4rRQcHIyxY8diz5496Nq1a7FtNTQ00Lp162KPHCQSCYyMjBQmxlj5EAQBEyZMQHR0NJo2bYr79++jU6dO+Pbbb5GXlyd2PPY/la44BAUFwdfXF4GBgejVq9c72xMRZDIZLC0tKyAdY6ykHBwcEBMTg5EjRyI/Px/z589H9+7dkZaWJnY0BpGLQ2ZmJmQymfwGmeTkZMhkMty7dw8AMHfuXIUxIoKCgjBq1CisWbMGrq6uSEtLQ1paGtLT0+VtFi9ejGPHjuH27duQyWQYO3YsZDIZJk6cWKGfjTH2bgYGBtixYwf8/f2hr6+PEydOQCqV4sSJE2JHY+V7lqt4ERERBEBp8vHxISIiHx8fcnd3l7d3d3cvtj0R0YwZM8ja2pp0dHTI3NycunXrRpGRkSrl4ktZGat4CQkJ1KJFCwJAgiDQ/PnzKScnR+xYVYaq+zUe7KcQPNgPY+J49eoVZsyYgS1btgAAOnTogMDAQNSvX1/kZJUfD/bDGKu09PT08PPPP2P37t0wNDTEuXPnIJVKcejQIbGjVTtcHBhjamfIkCGIi4uDs7MzHj9+jN69e+Pzzz9Hdna22NGqDS4OjDG11KhRI1y4cAHTp08H8N9Nrx06dEBycrLIyaoHLg6MMbUlkUiwbt06hIWFoWbNmrh06RIcHR0REhJS5DIxMTHo3LkzYmJiKjBp1cPFgTGm9vr37w+ZTAY3Nzekp6dj0KBBmDJlSqGP4t+xYwciIiKwc+dOEZJWHXy1UiH4aiXG1FNOTg6+/vprrFy5EgAglUoRHBwMiUSCR48eQRAE9OjRAw8ePEDt2rVx5MgREBFq1aoFGxsbkdOLS9X9GheHQnBxYEy9HT16FCNHjsSjR49gYGCAzMxM+XuCIICI5P8sUN13dXwpK2OsyuvevTuuXLkCd3d3eWEQBAHA/xeBgn9qaWkhICBAnKCVGBcHxlilVLduXZw8eRILFy5UOkp4U3R0NIYPH17B6So/Lg6MsUpLU1MTixYtwokTJ2BmZqbwXsGRBCsdLg6MsUqvc+fOOHHiBHR0dOTzTExMULt2bdSuXVvEZJUXFwfGWJUglUrx7NkzLFu2DJqamnjy5AmMjIzw8OFDsaNVSlwcGGNVhp6eHubOnYuzZ8/CysoKN2/ehKurKzZt2lTtr1ZSFRcHxliV065dO8hkMvTt2xfZ2dmYMmUKBg0ahGfPnokdrdLg4sAYq5JMTU2xf/9+rFu3Dtra2ggNDYWjoyOio6PFjlYpcHFgjFVZgiBg+vTpiIyMRMOGDXHnzh20b98eq1evRn5+vtjx1BoXB8ZYlefi4oK4uDh4e3sjNzcXs2fPRp8+ffDo0SOxo6ktLg6MsWrB2NgYu3fvxubNmyGRSHD48GFIpVKcPXtW7GhqiYsDY6zaEAQBEyZMwKVLl9C0aVPcv38fHh4e+Oabb5CXlyd2PLXCxYExVu20atUKMTExGDVqFPLz8/H111/Dy8sLaWlpYkdTG1wcGGPVkoGBAX799Vf4+/tDX18fJ0+ehIODA06cOCF2NLXAxYExVq35+PggJiYGLVu2xIMHD9CtWzfMnz8fubm5YkcTlajF4ezZs+jTpw/q1q0LQRCwf//+dy5z5swZODs7Q1dXFw0bNsTmzZuV2oSEhMDe3h4SiQT29vYICwsrh/SMsaqiWbNmiI6OxoQJE0BE+Pbbb+Hh4YG///5b7GiiEbU4vHjxAg4ODti4cWOJ2icnJ6Nnz57o0KED4uPj8dVXX2HatGkK48levHgRQ4YMwciRI3HlyhWMHDkS3t7efOMLY6xYenp62Lx5M3bv3g1DQ0OcP38eUqkUhw4dEjuaOEhNAKCwsLBi23zxxRdkZ2enMG/ChAnk6uoqf+3t7U3du3dXaOPl5UVDhw4tcZb09HQCQOnp6SVehjFWddy8eZOcnZ0JAAGgWbNmUVZWltix3ouq+7VK1edw8eJFdOvWTWGel5cXYmJikJOTU2ybyMjIIteblZWF58+fK0yMserrgw8+wIULFzBjxgwAwPfff4/27dsjOTlZ3GAVqFIVh7S0NNSpU0dhXp06dZCbmyu/07GoNsVdorZ8+XIYGxvLJysrq7IPzxirVCQSCdauXYv9+/fDxMQEly9fhqOjI/bt2yd2tApRqYoDoDy6E/3vMbxvzi+sTXGjQs2dOxfp6enyKSUlpQwTM8Yqs379+kEmk8HNzQ3p6ekYPHgwJk+ejNevX4sdrVxVquJgYWGhdATw4MEDaGlpyYcILKrN20cTb5JIJDAyMlKYGGOsgLW1Nc6cOYM5c+YAAH788Ue4urri+vXrIicrP5WqOLi5uSE8PFxh3vHjx+Hi4gJtbe1i27Rr167CcjLGqh5tbW0sX74cR44cgbm5Oa5cuQInJyfs2rVL7Gjlo1y7x98hIyOD4uPjKT4+ngDQ999/T/Hx8XT37l0iIpozZw6NHDlS3v727dukr69PM2fOpISEBNq2bRtpa2vTvn375G0uXLhAmpqatGLFCkpMTKQVK1aQlpYWRUVFlTgXX63EGCvO/fv3qVOnTvKrmcaMGUOZmZlixyqWqvs1UYtDRESE/Mt9c/Lx8SEiIh8fH3J3d1dY5vTp0+To6Eg6Ojpka2tLP/30k9J69+7dS02bNiVtbW2ys7OjkJAQlXJxcWCMvUtubi4tWrSIBEEgAGRvb09//vmn2LGKpOp+TSDigVXf9vz5cxgbGyM9PZ37HxhjxYqIiMDw4cORmpoKPT09bNiwAWPGjCn2IhgxqLpfq1R9Dowxpm48PDwgk8ng5eWFV69eYdy4cRgxYgQyMjLEjvZeuDgwxth7ql27Ng4fPowVK1ZAU1MTgYGBcHJyQnx8vNjRSo2LA2OMlQENDQ18+eWXOHv2LKytrXHz5k24urpi06ZNqIxn77k4MMZYGWrXrh3i4+PRr18/ZGdnY8qUKfjoo4/w9OlTsaOphIsDY4yVMVNTU4SFhWH9+vXQ1tZGWFgYHB0dERUVJXa0EuPiwBhj5UAQBEybNg2RkZFo2LAh7t69iw4dOmD16tXIz88XO947cXFgjLFy5OLigri4OHh7eyM3NxezZ89Gnz595A8LVVdcHBhjrJwZGxtj9+7d+Pnnn6Grq4vDhw/DwcEBZ8+eFTtakbg4MMZYBRAEAZ988gmio6NhZ2eHf/75Bx4eHvjmm2+Ql5cndjwlXBwYY6wCtWrVCpcvX4aPjw/y8/Px9ddfw8vLq9gxZ8TAxYExxiqYgYEB/P394e/vD319fZw8eRIODg5KT5QWExcHxhgTiY+PD2JjY9GyZUs8ePAAXl5emDdvHnJzc8WOxsWBMcbEZGdnh+joaEyYMAFEhGXLlsHDw0P0ESm5ODDGmMj09PSwefNm7N69G4aGhjh//jykUikOHjwoWiYuDowxpiaGDBmC+Ph4ODs748mTJ+jTpw8+++wzZGdnV3gWLg6MMaZGPvjgA1y4cAEzZswAAHz//fdo3749bt++XaE5uDgwxpiakUgkWLt2LX777TeYmJjg8uXLcHR0xL59+yosAxcHxhhTU3379oVMJkO7du3w/PlzDB48GJMmTcLr16/LfdtcHBhjTI1ZW1vj9OnTmDNnDgDgp59+gqurK5KSksp1u1wcGGNMzWlra2P58uU4evQozM3NceXKlXLvg9Aq17UzxhgrM15eXpDJZDhy5Ah69OhRrtsSqDKOX1fOnj9/DmNjY6Snp8PIyEjsOIwx9t5U3a+Jflrpxx9/RIMGDaCrqwtnZ2ecO3euyLa+vr4QBEFpat68ubyNv79/oW0qogOHMcaqClGLQ3BwMGbMmIF58+YhPj4eHTp0QI8ePXDv3r1C269fvx6pqanyKSUlBaamphg8eLBCOyMjI4V2qamp0NXVrYiPxBhjVYKoxeH777/H2LFjMW7cODRr1gzr1q2DlZUVfvrpp0LbGxsbw8LCQj7FxMTg6dOnGD16tEI7QRAU2llYWFTEx2GMsSpDtOKQnZ2N2NhYdOvWTWF+t27dEBkZWaJ1bNu2DV27doWNjY3C/MzMTNjY2KB+/fro3bs34uPji11PVlYWnj9/rjAxxlh1JlpxePToEfLy8lCnTh2F+XXq1CnRoBepqak4cuQIxo0bpzDfzs4O/v7+OHDgAIKCgqCrq4sPP/wQN27cKHJdy5cvh7GxsXyysrIq3YdijLEqQvQOaUEQFF4TkdK8wvj7+6NmzZro37+/wnxXV1eMGDECDg4O6NChA/bs2YMmTZpgw4YNRa5r7ty5SE9Pl09iPyqXMcbEJtp9DrVq1YKmpqbSUcKDBw+UjibeRkTYvn07Ro4cCR0dnWLbamhooHXr1sUeOUgkEkgkkpKHZ4yxKk604qCjowNnZ2eEh4djwIAB8vnh4eHo169fscueOXMGN2/exNixY9+5HSKCTCZDy5YtS5yt4NYP7ntgjFUVBfuzEt/aRiLavXs3aWtr07Zt2yghIYFmzJhBNWrUoDt37hAR0Zw5c2jkyJFKy40YMYLatm1b6DoXLVpER48epVu3blF8fDyNHj2atLS0KDo6usS5UlJSCABPPPHEU5WbUlJSSrQfFPXxGUOGDMHjx4+xZMkSpKamokWLFjh8+LD86qPU1FSlex7S09MREhKC9evXF7rOZ8+e4ZNPPkFaWhqMjY3h6OiIs2fPok2bNiXOVbduXaSkpMDQ0LBE/R8Fnj9/DisrK6SkpFS6O6s5uzg4uziqY3YiQkZGBurWrVui9vz4jDJUmR+7wdnFwdnFwdnfTfSrlRhjjKkfLg6MMcaUcHEoQxKJBAsXLqyUl8VydnFwdnFw9nfjPgfGGGNK+MiBMcaYEi4OjDHGlHBxYIwxpoSLA2OMMSVcHIqwfPlytG7dGoaGhqhduzb69++PpKSkdy535swZODs7Q1dXFw0bNsTmzZuV2oSEhMDe3h4SiQT29vYICwsTPXtoaCg8PT1hbm4OIyMjuLm54dixYwptKmII1tJkP336dKG5/vrrL4V26vi9q8vQtz/99BNatWoFIyMj+X//I0eOFLuMOvzWS5NdXX7rpcleob/1Ej9wqJrx8vIiPz8/+vPPP0kmk1GvXr3I2tqaMjMzi1zm9u3bpK+vT9OnT6eEhATaunUraWtr0759++RtIiMjSVNTk5YtW0aJiYm0bNky0tLSoqioKFGzT58+nVauXEmXLl2i69ev09y5c0lbW5vi4uLkbfz8/MjIyIhSU1MVprJUmuwREREEgJKSkhRy5ebmytuo6/f+7NkzhcwpKSlkampKCxculLepiO/9wIEDdOjQIUpKSqKkpCT66quvSFtbm/78889C26vLb7002dXlt16a7BX5W+fiUEIPHjwgAHTmzJki23zxxRdkZ2enMG/ChAnk6uoqf+3t7U3du3dXaOPl5UVDhw4t28BvKEn2wtjb29PixYvlr/38/MjY2LiM0xWvJNkL/od5+vRpkW0qy/ceFhZGgiDIHz5JJM73TkRkYmJCv/zyS6HvqetvvUBx2QujDr/1AsVlr8jfOp9WKqH09HQAgKmpaZFtLl68qDTsqZeXF2JiYpCTk1Nsm5IOjVoaJcn+tvz8fGRkZCgto+oQrO9LleyOjo6wtLREly5dEBERofBeZfney2ro2/eRl5eH3bt348WLF3Bzcyu0jbr+1kuS/W3q8ltXJXtF/Na5OJQAEWHWrFlo3749WrRoUWS7tLS0Qoc9zc3NxaNHj4ptU5KhUUujpNnftmbNGrx48QLe3t7yeaUZgvV9lDS7paUltmzZgpCQEISGhqJp06bo0qULzp49K29TGb73shz6tjSuXr0KAwMDSCQSTJw4EWFhYbC3ty+0rbr91lXJ/jaxf+uqZK/Q37pKxxnV1KRJk8jGxuadz0Fv3LgxLVu2TGHe+fPnCYD8fKW2tjYFBgYqtAkICCCJRFK2of+npNnfFBgYSPr6+hQeHl5su7y8PHJwcKCpU6e+b8xClSZ7gd69e1OfPn3kryvD975s2TIyMzOjrKysYtuV1/eelZVFN27coMuXL9OcOXOoVq1adO3atULbqttvXZXsb1KH33ppsxcor986Hzm8w9SpU3HgwAFERESgfv36xba1sLAodNhTLS0tmJmZFdvmXUOjloYq2QsEBwdj7Nix2LNnD7p27Vps25IMwVpapcn+JldXV4Vc6v69UxkPfVsaOjo6aNSoEVxcXLB8+XI4ODgUOW6Kuv3WVcleQF1+66XJ/qby+q1zcSgCEWHKlCkIDQ3FqVOn0KBBg3cu4+bmhvDwcIV5x48fh4uLC7S1tYtt065dO1GzA0BQUBB8fX0RGBiIXr16lWg7MpkMlpaW7xtZYZ2lyf62+Ph4hVzq/L0DpRv6tiy/96K2k5WVVeh76vJbL0px2QH1+K0Xt63isr+t3H7rKh1nVCOffvopGRsb0+nTpxUuGXv58qW8zdvDmBZc3jdz5kxKSEigbdu2KV3ed+HCBdLU1KQVK1ZQYmIirVixoswv7ytN9sDAQNLS0qJNmzYpLPPs2TN5m7IYgrU8sq9du5bCwsLo+vXr9Oeff9KcOXMIAIWEhMjbqOv3XqC8h759l7lz59LZs2cpOTmZ/vjjD/rqq69IQ0ODjh8/Xmhudfmtlya7uvzWS5O9In/rXByKgCLGX/Xz85O38fHxIXd3d4XlTp8+TY6OjqSjo0O2trb0008/Ka1779691LRpU9LW1iY7OzuF/7BiZXd3dy90GR8fH3mbGTNmkLW1Neno6JC5uTl169aNIiMjRc++cuVK+uCDD0hXV5dMTEyoffv2dOjQIaV1q+P3TvTfvQ56enq0ZcuWQtdbEd/7mDFjyMbGRr6NLl26yHdQReVWh996abKry2+9NNkr8rfOj+xmjDGmhPscGGOMKeHiwBhjTAkXB8YYY0q4ODDGGFPCxYExxpgSLg6MMcaUcHFgjDGmhIsDY4wxJVwcGFMTixYtglQqLZd1+/v7o2bNmuWyblY1cXFg1VLB2M0TJ05Uem/SpEkQBAG+vr4Vmunzzz/HyZMn5a99fX3Rv3//Cs3AWAEuDqzasrKywu7du/Hq1Sv5vNevXyMoKAjW1tYVnsfAwED+uGvGxMbFgVVbTk5OsLa2RmhoqHxeaGgorKys4OjoKJ939OhRtG/fHjVr1oSZmRl69+6NW7duKawrMjISUqkUurq6cHFxwf79+yEIAmQyGQDg9OnTEAQBJ0+ehIuLC/T19dGuXTskJSXJ1/HmaaVFixbh119/xW+//QZBECAIAk6fPi1fz7Nnz+TLyWQyCIKAO3fuyOf5+/vD2toa+vr6GDBgAB4/fqz0+X///Xc4OztDV1cXDRs2xOLFi5Gbm/se3yirSrg4sGpt9OjR8PPzk7/evn07xowZo9DmxYsXmDVrFi5fvoyTJ09CQ0MDAwYMQH5+PgAgIyMDffr0QcuWLREXF4elS5fiyy+/LHR78+bNw5o1axATEwMtLS2lbRX4/PPP4e3tje7duyM1NRWpqaklfh5/dHQ0xowZg0mTJkEmk8HDwwPffPONQptjx45hxIgRmDZtGhISEvDzzz/D398f3377bYm2waqBUjxllrFKz8fHh/r160cPHz4kiURCycnJdOfOHdLV1aWHDx9Sv379FB7h/KYHDx4QALp69SoREf30009kZmZGr169krfZunUrAaD4+HgiIoqIiCAAdOLECXmbQ4cOEQD5cgsXLiQHBweljG8qWM/Tp0/l8+Lj4wkAJScnExHRsGHDqHv37grLDRkyhIyNjeWvO3TooDTM586dO8nS0rKor4xVM3zkwKq1WrVqoVevXvj111/h5+eHXr16oVatWgptbt26hY8//hgNGzaEkZGRfIS3e/fuAQCSkpLQqlUr6Orqypdp06ZNodtr1aqV/N8LRu968OBBmX6mxMREuLm5Kcx7+3VsbCyWLFkCAwMD+TR+/Hikpqbi5cuXZZqHVU5aYgdgTGxjxozBlClTAACbNm1Ser9Pnz6wsrLC1q1bUbduXeTn56NFixbIzs4G8N+wjoIgKCxDRQyTUjCEJgD5MgWnp0pCQ0NDaf05OTkl2vab8vPzsXjxYgwcOFDpvTeLHKu+uDiwaq979+7yHb2Xl5fCe48fP0ZiYiJ+/vlndOjQAQBw/vx5hTZ2dnbYtWsXsrKyIJFIAAAxMTHvnUtHRwd5eXkK88zNzQEAqampMDExAQB5p3cBe3t7REVFKcx7+7WTkxOSkpLQqFGj987JqiY+rcSqPU1NTSQmJiIxMRGampoK75mYmMDMzAxbtmzBzZs3cerUKcyaNUuhzccff4z8/Hx88sknSExMxLFjx7B69WoAUDqiUIWtrS3++OMPJCUl4dGjR8jJyUGjRo1gZWWFRYsW4fr16zh06BDWrFmjsNy0adNw9OhRfPfdd7h+/To2btyIo0ePKrRZsGABduzYgUWLFuHatWtITExEcHAw5s+fX+q8rGrh4sAYACMjIxgZGSnN19DQwO7duxEbG4sWLVpg5syZWLVqldKyv//+O2QyGaRSKebNm4cFCxYAeL9TNOPHj0fTpk3h4uICc3NzXLhwAdra2ggKCsJff/0FBwcHrFy5UulKJFdXV/zyyy/YsGEDpFIpjh8/rrTT9/LywsGDBxEeHo7WrVvD1dUV33//PWxsbEqdl1UtPIY0Y+Vg165dGD16NNLT06Gnpyd2HMZUxn0OjJWBHTt2oGHDhqhXrx6uXLmCL7/8Et7e3lwYWKXFxYGxMpCWloYFCxYgLS0NlpaWGDx4MN9Qxio1Pq3EGGNMCXdIM8YYU8LFgTHGmBIuDowxxpRwcWCMMaaEiwNjjDElXBwYY4wp4eLAGGNMCRcHxhhjSv4PWd3y8XHo7xsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ra[rd['sicilia']].richter(mgx=3.5, d_max=50, bins=5, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c94993e0",
   "metadata": {},
   "source": [
    "Below, the Richter's fit for the events at higher depths in Sicilia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9ab97a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-set: sicilia\n",
      "Size:  559\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.30\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.35\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.0, maximum depth  208.2\n",
      "\n",
      "Fit results:\n",
      "Number of events: 83\n",
      "Intercept: 4.47,   Slope: -1.3724\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ra[rd['sicilia']].richter(mgx=3.5, d_min=50, bins=5, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99d02c1",
   "metadata": {},
   "source": [
    "The fit can be done for all the created datasets, by using the class method *richter_all* \n",
    "\n",
    "Note below, that the class method *set_bins* is used to modify the value of the class attribute *bins*: this becomes the object attribute of every instance of the class *Data*, and determines the number of *channels* in the definition of the histogram of magnitude values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b2e3ec04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-set: sicilia\n",
      "Size:  559\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.30\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.35\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.0, maximum depth  208.2\n",
      "\n",
      "Fit results:\n",
      "Number of events: 559\n",
      "Intercept: 5.02,   Slope: -1.2126\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "data-set: north\n",
      "Size:  131\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   3.90\n",
      "Average magnitude:   2.36\n",
      "Stand. dev:          0.39\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    1.5, maximum depth   62.4\n",
      "\n",
      "Fit results:\n",
      "Number of events: 131\n",
      "Intercept: 3.76,   Slope: -0.9745\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "data-set: central\n",
      "Size:  367\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   3.90\n",
      "Average magnitude:   2.29\n",
      "Stand. dev:          0.34\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    4.9, maximum depth   69.5\n",
      "\n",
      "Fit results:\n",
      "Number of events: 367\n",
      "Intercept: 5.14,   Slope: -1.3588\n"
     ]
    },
    {
     "data": {
      "image/png": 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27Ik3b95gwoQJGD16dKWvfkjN9KFB/5Ir16qWlpaG+Ph4DBs2DMC7saahQ4fiwIED0jYl3Y9VLScnB5MnT0azZs2kjy5/+fKlXFfhI0eORGBgoPRJuIcPH8awYcOgrq4O4F3X1fz589GiRQvUqVMHenp6uHXr1idd6ScmJiIsLAx6enrSxd7eHsC77iQHBwd4eHigdevWGDx4MPbt24dnz5599PGUjcpNjF4dzM3NcfbsWWzYsAHffPMNDh8+jOvXr+PEiRNwcHDgOx5RMDU1NUgkEumfirR//34UFRWhXr160nWMMWhqauLZs2cwMjKSPsdKHmpqaqW6MwoLC2VejxkzBo8fP8bWrVthY2MDgUAANzc3FBQUVPo4/fr1g0QiQUhICNq2bYvIyEhs3rxZuv3rr7/GhQsXsGnTJjRp0gTa2toYNGhQucco+aLF+9n/m1sikaBfv35lPlLFysoK6urqCA0NRXR0NC5evIjt27djyZIliIuL++iHQioTutJXEDU1NSxcuBARERGoX78+bt++jc8++wy7d++uPX2DRMaHBv2rWlFREfz9/fH9998jOTlZuvz++++wsbHB4cOHAQBt2rTB5cuXy30fLS0tFBcXy6wzMzNDdna2zGf1/cFRAIiMjISvry+8vLzQsmVLCAQCmcHQytDW1sbAgQNx+PBhHD16FM2aNYOzs7PMMcaMGYPPP/8crVu3hqWlJe7fv1/u+5XMlf3+2MR/czs5OeHmzZuwtbVFkyZNZJaSubk5jkOHDh2wYsUKiMViaGlp4dSpU3L9bEpLsT1NykfRffplefLkCevbt6+0j3fIkCHVenxSfd6+fSsdw5FIJOzt27cKO9apU6eYlpYWe/78ealtixcvZiKRiDHGWFhYGFNTU2NLly5lKSkp7MaNG2z9+vXStj169GDe3t7s77//Zo8fP2aMMZaSksI4jmPr1q1jd+7cYTt27GBGRkYyffoikYj16NGDpaSksNjYWObu7s60tbVlxgfwgT59xhi7ePEiEwgEzM7Ojq1atUpm24ABA5hIJGJisZglJyezfv36MX19fTZz5kxpm/+OSbi6ujJ3d3d28+ZNFhERwdq1ayfTp//w4UNmZmbGBg0axOLi4tjdu3fZhQsX2NixY1lRURGLjY1lq1evZtevX2cPHjxgJ06cYFpaWuzs2bMV/hx8kqeuUdGvJhKJhG3atEk62Ne4cWOWkJBQrRlI7dK3b1/m5eVV5raSwcjExETGGGOBgYFMJBIxLS0tZmpqygYOHChtGxMTw9q0acMEAgF7/zpw165dzNramunq6rIvv/ySrV69WqboJyUlMRcXFyYQCFjTpk3ZL7/8UqoAV6boFxUVSb/8cPfuXZlt6enprGvXrkxbW5tZW1uzHTt2sM6dO1dY9FNSUpirqyvT1tZmIpGIXbx4UaboM8bY7du32eeff87q1KnDtLW1mb29PZs1axaTSCQsJSWF9ezZk5mZmTGBQMCaNWsmM3CsjOSpa/TsnWoWGxuLYcOG4cGDB9DS0sKmTZswffr0Ss1iTwghZaFn7ygxV1dXiMViDBgwAAUFBfD19cWgQYPw/PlzvqMRQlQAFX0eGBkZISgoCNu2bYOmpiaCgoLg6OiI+Ph4vqMRQmo5Kvo84TgOvr6+iI6ORqNGjXD//n106NABmzdvpm/3EEIUhoo+z1xcXJCUlIRBgwahqKgIc+fORf/+/fH06VO+oxFCaiEq+krA0NAQJ06cwM6dOyEQCHDmzBmIRCJER0fzHY0QUstQ0VcSHMdhypQp0gdXZWZmolOnTli/fr3C7+okhKgOKvpKRiQSITExESNGjEBxcTEWLlyIvn37Sp/cSAghn4KKvhLS19dHQEAA9u3bB6FQiHPnzkEkEuHq1at8RyOE1HBU9JUUx3GYMGEC4uPjYW9vj0ePHqFr16747rvvSj0nhRBCKouKvpJr3bo1EhIS4OPjA4lEgm+//Ra9evXCP//8w3c0UgNUx8Tk5J2acq6p6NcAurq6OHjwIA4ePAgdHR1cunQJIpEIV65c4Tsa4dmYMWMwYMCAcrdnZWWhd+/e1RdIhdWUc01Fvwbx8fHB9evX0bJlS2RnZ6N79+5YtmwZdfeQcllaWkIgEPCagTGGoqKiSrX977Pv+cjwsZThXFcGFf0apkWLFoiPj8eECRPAGMPKlSvRvXt3PHr0iO9otQpjDK9eveJlqco7st/vciiZNDwoKAhdu3aFjo4OHBwcEBMTI7NPdHQ0OnXqBG1tbVhbW8PX11dmUvCAgAC4uLhAX18flpaWGDFiBHJycqTbw8PDwXEcLly4ABcXFwgEAkRGRpbKVpLnxIkT6NKlC4RCIQICAgAAfn5+aN68OYRCIezt7bFz585SGUUiEYRCIVxcXBAcHCwzIXp5GRhj2LBhAxo1agRtbW04ODjg5MmT0vd99uwZRo4cCTMzM2hra6Np06bw8/MDABQUFGD69OmwsrKCUCiEra0t1q5dW+a5BoA//vgD3bp1k06uPnHiRLx8+VK6veS3tE2bNsHKygomJiaYNm2awv7HJ6W4h30qJ74erawIhw8fZnp6egwAMzMzYxcuXOA7Uq3x8uXLMue7rY7l5cuXlc7p4+PD+vfvX+52vPdo45L5Y+3t7dlvv/3G0tLS2KBBg5iNjQ0rLCxkjDF248YNpqenx7Zs2cJu377Nrl27xhwdHdmYMWOk77l//3529uxZdvfuXRYTE8NcXV1Z7969pdvDwsIYANamTRt28eJFdufOHfbkyZNS2Ury2NrassDAQHbv3j328OFDtnfvXmZlZSVdFxgYyIyNjdnBgwcZY4zl5eUxY2NjNmrUKHbz5k129uxZ1qxZM5m5ccvLsHjxYmZvb8/Onz/P7t69y/z8/JhAIGDh4eGMMcamTZvGRCIRu379OktPT2ehoaHs9OnTjDHGNm7cyKytrdnVq1fZ/fv3WWRkJDty5EiZ5/rVq1esbt26bODAgeyPP/5gly9fZg0bNmQ+Pj4yf3cGBgZs8uTJLDU1lZ05c4bp6OiwvXv3Vu4v/z30PP0K1KaizxhjaWlpzMHBQVowFi1aJP0HTD5ebS76P/30k3T7zZs3GQCWmprKGGNs9OjRbOLEiTLvERkZydTU1NibN2/KPEZ8fDwDwF68eMEY+/+CGxwcXGH2kjwlk7qXsLa2limmjDG2atUq5ubmxhh795x/ExMTmTz79u0rs+i/n+Hly5dMKBSy6OhomfceP348Gz58OGOMsX79+rGxY8eWmXfGjBmsW7du0kly/uv9c713715mZGQk83cZEhLC1NTUWHZ2NmPs3d+djY2NzCTwgwcPZkOHDi3z/SsiT12jOXJruGbNmiE2NhZz5szBrl27sHbtWkRGRuLo0aOoX78+3/FqLB0dHZlfxav72IrUpk0b6X9bWVkBeDfJub29PRITE3Hnzh3pVIvAu64uiUSC9PR0NG/eHGKxGMuXL0dycjKePn0qvWM8IyMDLVq0kO7n4uJSqTzvt3v8+DEyMzMxfvx4fPXVV9L1RUVFMDQ0BPBuMvg2bdpAKBRKt7dr1+6D752SkoK3b9+iR48eMm0KCgrg6OgIAJgyZQq++OILJCUlwdPTEwMGDED79u0BvOuO6dGjB+zs7NCrVy/07dsXnp6eZR43NTUVDg4O0ukXAaBDhw6QSCRIS0uDhYUFAKBly5bSSeCBd38ff/zxRwVn69PxWvTXrl2LoKAg3Lp1C9ra2mjfvj3Wr18POzu7CveLiIjAnDlzcPPmTdStWxfz58/H5MmTqym18hEKhdi5cye6dOmCCRMmICoqCiKRCP7+/vDy8uI7Xo3EcZzMP9jaRFNTU/rfJZP3lBRuiUSCSZMmwdfXt9R+DRo0wKtXr+Dp6QlPT08EBATAzMwMGRkZ6NmzZ6nJyit7/t5vV5Jj3759+Oyzz2TalRRHxlipSYdYOeMgZb13SEiIzETyAKQDsL1798aDBw8QEhKCS5cuwcPDA9OmTcOmTZvg5OSE9PR0nDt3DpcuXcKQIUPQvXt3mTGB9/OUNzHS++vf/7so2abox67wOpAbERGBadOmITY2FqGhoSgqKoKnp6fMoNF/paenw8vLC+7u7hCLxVi8eDF8fX0RGBhYjcmV05AhQyAWi+Hs7Ix///0Xffr0wfz58xU/MERqjZJJw/87YXiTJk2gpaWFW7du4cmTJ1i3bh3c3d1hb28vM4j7qSwsLFCvXj3cu3ev1PEbNmwIALC3t8eNGzeQn58v3S8hIeGD792iRQsIBAJkZGSUem9ra2tpOzMzM4wZMwYBAQHYunUr9u7dK91mYGCAoUOHYt++fTh+/DgCAwPLfCJuixYtkJycLFPLrl27BjU1NTRr1uyjzk1V4fVK//z58zKv/fz8YG5ujsTERHTq1KnMfXbv3o0GDRpg69atAIDmzZsjISEBmzZtwhdffKHoyEqvcePGuHbtGr7++mts374dGzduRGRkJI4dOwYbGxu+4xEFyM3NlX5rpYSxsTEaNGgg93stWLAArq6umDZtGr766ivo6uoiNTUVoaGh2L59Oxo0aAAtLS1s374dkydPxp9//olVq1ZV0U/yzvLly+Hr6wsDAwP07t0b+fn5SEhIwLNnzzBnzhyMGDECS5YswcSJE7Fw4UJkZGRg06ZNAFDhtKP6+vqYN28eZs+eDYlEgo4dOyIvLw/R0dHQ09ODj48Pli5dCmdnZ7Rs2RL5+fn47bff0Lx5cwDAli1bYGVlBZFIBDU1Nfzyyy+wtLREnTp1Sh1r5MiRWLZsGXx8fLB8+XI8fvwYM2bMwOjRo6VdO7yRe8RAgf766y8GgP3xxx/ltnF3d2e+vr4y64KCgpiGhgYrKCgo1f7t27csNzdXumRmZtaqgdyKBAUFsTp16jAAzMjI6IMDa6Tm8fHxKXMwuORbIihjILdksJMxxp49e1Zq0vD4+HjWo0cPpqenx3R1dVmbNm3Y6tWrpduPHDnCbG1tmUAgYG5ubuz06dNlDqI+e/aswuxl5Slx+PBh6UTuRkZGrFOnTiwoKEi6/dq1a6xNmzZMS0uLOTs7syNHjjAA7NatWxVmkEgkbNu2bczOzo5pamoyMzMz1rNnTxYREcEYezdg3Lx5c6atrc2MjY1Z//792b179xhj7wZnRSIR09XVZQYGBszDw4MlJSVJ3/v9c83Yu29Cde3alQmFQmZsbMy++uor6WA3Y2UPws+cOZN17ty5wvNWlhr57R2JRML69evHOnbsWGG7pk2bynwAGXv3AQDAHj16VKr9smXLyvxHoQpFn7F3/7DatWsn/blnzpzJ8vPz+Y5FSJUKCAhgmpqa7PXr13xH4YU8RV9pbs6aPn06bty4gaNHj36wbXmDOGX9ardo0SLk5uZKl8zMzKoJXEPY2toiMjISc+fOBQBs27YNHTp0wL1790q1TUhIQLdu3SrVP0oIn/z9/REVFYX09HQEBwdjwYIFGDJkCLS1tfmOpvSUoujPmDEDp0+fRlhY2Ae/ZmhpaYns7GyZdTk5OdDQ0ICJiUmp9gKBAAYGBjKLqtHS0sKmTZtw+vRpGBsbIyEhAY6OjqW+deDv74+wsDAcOnSIp6SEVE52djZGjRqF5s2bY/bs2Rg8eLDMgCspH8dYFd7zLSfGGGbMmIFTp04hPDwcTZs2/eA+CxYswJkzZ5CSkiJdN2XKFCQnJ5e6nbwseXl5MDQ0RG5urkr+DyAjIwPDhw+XTsU4evRoTJ48GUKhEL1790ZOTg7Mzc1x7tw5MMZgampKA8CEKDm56ppie5oqNmXKFGZoaMjCw8NZVlaWdHm/X27hwoVs9OjR0tf37t1jOjo6bPbs2SwlJYXt37+faWpqspMnT1bqmLXtjtyPUVBQwBYsWFBqnIPjOJk/SxZCiHKrMQO5/y06JYufn5+0jY+PT6nR7PDwcObo6Mi0tLSYra0t27VrV6WPSUX//507d47p6+uX+/egoaHBAgIC+I5JCPkAeeoar907fFD17p3/evjwIby9vZGUlFRqW2JiIpycnHhIRQiRhzx1TSkGcgl/6tWrh127dvEdgxBSTajoE9StWxeWlpZo1qyZ9CqB47gyn4FOCKnZqOgT1K9fH/fv38etW7eQlpaGbt26gTGGWbNmYdy4cRU+C4kQUrNQ0ScA3t3PwHEcLC0tcfHiRaxYsQJqamrw8/NDu3btcPPmTb4jEkKqABV9Uoq6ujqWLl2Ky5cvw8rKCikpKWjbti0OHDhQpVP5EUKqHxV9Uq4uXbogOTkZnp6eePPmDcaPH4/Ro0fzNrkIIeTTUdEnFSq5O3fNmjVQV1fH4cOH4ezsjN9//53vaISQj0BFn3yQmpoaFi1ahPDwcNSrVw+3b9/GZ599hj179lB3DyE1DBV9UmkdO3ZEcnIy+vTpg/z8fEyePBnDhw9HXl4e39EIIZVERZ/IxdTUFKdPn8bGjRuhoaGB48ePw8nJqcw7egkhyoeKPpGbmpoa5s2bh8jISNjY2ODu3btwc3PDjh07qLuHECVHRZ98NFdXV4jFYvTv3x8FBQWYMWMGBg0ahOfPn/MdjRBSDir65JMYGRnh1KlT2Lp1KzQ1NREUFARHR0fEx8fzHY0QUgYq+uSTcRyHmTNn4tq1a2jYsCHu37+Pjh07YsuWLdTdQ4iSoaJPqkzbtm0hFosxaNAgFBYWYs6cOejfvz+ePn3KdzRCyP9Q0SdVytDQECdOnMCPP/4ILS0tnDlzBiKRSDo9IyGEX1T0SZXjOA5Tp05FbGwsmjRpgszMTHTq1AkbNmyARCLhOx4hKo2KPlEYR0dHJCUlYfjw4SguLsaCBQvQt29fPH78mO9ohKgsKvpEofT19XH48GHs3bsXQqEQ586dg0gkoglaCOEJFX2icBzH4auvvkJcXBzs7Ozw6NEjdOnSBatXr6buHkKqGRV9Um3atGmDhIQEjB49GhKJBN988w169eqFf/75h+9ohKgMKvqkWunp6cHf3x9+fn7Q0dFBaGgoRCIRrly5wnc0QlQCFX3CizFjxuD69eto2bIlsrOz0b17dyxfvhzFxcV8RyOkVqOiT3jTokULxMfHY/z48WCMYcWKFejevTuysrL4jkZIrUVFn/BKR0cHP/30EwICAqCrq4vw8HA4ODjg4sWLfEcjpFaiok+UwsiRI5GYmIg2bdrg8ePH6NWrF5YsWYKioiK+oxFSq1DRJ0rDzs4OsbGxmDx5MhhjWLNmDbp164a///6b72iE1BpU9IlS0dbWxq5du3Ds2DHo6+sjMjISIpEIZ8+e5TsaIbUCFX2ilIYOHYqkpCQ4OTnh33//RZ8+fTB//nwUFhbyHY2QGo3Xon/16lX069cPdevWBcdxCA4OrrB9eHg4OI4rtdy6dat6ApNq1aRJE0RHR2PGjBkAgI0bN6Jz587IyMjgORkhNRevRf/Vq1dwcHDAjh075NovLS0NWVlZ0qVp06YKSkj4JhAI8MMPPyAwMBCGhoaIiYmBSCTC6dOn+Y5GSI0kd9FXV1dHTk5OqfX//vsv1NXV5Xqv3r1747vvvsPAgQPl2s/c3ByWlpbSRd7jkppn4MCBEIvFaNu2LZ49e4b+/ftj9uzZKCgo4DsaITWK3EW/vOnv8vPzoaWl9cmBKsPR0RFWVlbw8PBAWFhYhW3z8/ORl5cns5CaqWHDhoiKisKcOXMAAFu3bkXHjh2Rnp7OczJCag6Nyjb84YcfALx7YuJPP/0EPT096bbi4mJcvXoV9vb2VZ/wPVZWVti7dy+cnZ2Rn5+PQ4cOwcPDA+Hh4ejUqVOZ+6xduxYrVqxQaC5SfbS0tPD999+jS5cu8PHxwfXr1+Ho6Ij9+/fjiy++4DseIUqPY5Wcubphw4YAgAcPHqB+/foyXSpaWlqwtbXFypUr8dlnn31cEI7DqVOnMGDAALn269evHziOK7ePNz8/H/n5+dLXeXl5sLa2Rm5uLgwMDD4qK1EOGRkZGDZsGGJiYgAA06ZNw6ZNmyAUCnlORkj1ysvLg6GhYaXqWqWv9Et+he7atSuCgoJgZGT0aSmriKurKwICAsrdLhAIIBAIqjERqS4NGjRAREQEvvnmG2zYsAE//vgjoqOjceLECTRp0oTveIQoJbn79MPCwpSm4AOAWCyGlZUV3zEITzQ1NbF+/XqcPXsWpqamEIvFcHJywrFjx/iORohSqvSVfoni4mIcPHgQly9fRk5OTqmZj+R5LvrLly9x584d6ev09HQkJyfD2NgYDRo0wKJFi/Dw4UP4+/sDeDdwZ2tri5YtW6KgoAABAQEIDAxEYGCgvD8GqWV69+6N5ORkDB8+HJGRkRg+fDjCwsKwdetWaGtr8x2PEKUhd9GfOXMmDh48iD59+qBVq1bgOO6jD56QkICuXbtKX5d8K8PHxwcHDx5EVlaWzI04BQUFmDdvHh4+fAhtbW20bNkSISEh8PLy+ugMpPaoV68erly5ghUrVmD16tXYu3cvYmNjceLECdjZ2fEdjxClUOmB3BKmpqbw9/evsYVWngEPUnOFhoZi1KhRyMnJga6uLnbv3o1Ro0bxHYsQhZCnrsndp6+lpUWDZETp9ejRA8nJyejatStevXqF0aNHw9vbG507d0ZCQgLf8QjhjdxFf+7cudi2bVu5N2kRoiysrKwQGhqKFStWQE1NDWfOnMHVq1exdetWvqMRwhu5+/SjoqIQFhaGc+fOoWXLltDU1JTZHhQUVGXhCPlUf//9N/r06QNLS0tMmTIFEokEhw8fRpMmTdC3b1+YmZnBxsaG75iEVBu5+/THjh1b4XY/P79PCqRo1KevWirzRQP6rZXUdAq5OauEshd1Qt4XEBCAMWPGlDvtopWVFW7cuIE2bdpUczJC+EGTqJBabeTIkYiLiytzm7m5ObKystCuXTvs3buXrviJSpD7Sr9hw4YV/sp87969TwpEiKKoqalBIpFI/zxy5Ag2b96Ms2fPYtKkSQgLC8OePXuo24/UanIX/VmzZsm8LiwshFgsxvnz5/H1119XVS5CqkzJ/AvW1tYYP3489u/fj8zMTNjZ2eHMmTP4/vvvsXjxYhw7dgwJCQk4ceIEHB0d+Y5NiELIPZBbnh9//BEJCQlK3+dPA7mqqWS+B47jwBhDQUGBzIP4YmJiMGzYMGRkZEBLSwubN2/G1KlTP+mOc0Kqi0JvzipP79696Rk4RGkJBAJpAec4rtSTV93c3CAWi+Ht7Y2CggJMnz4dgwcPxvPnz3lIS4jiVFnRP3nyJIyNjavq7QipdsbGxggODsaWLVugqamJwMBAODk54fr163xHI6TKyN2n7+joKPMrL2MM2dnZePz4MXbu3Fml4QipbhzHYdasWejQoQOGDh2K9PR0dOjQARs2bMDMmTOpu4fUeHL36f936kE1NTWYmZmhS5cuCp8usSpQnz6prOfPn2PChAnSbktvb2/4+fnRb7RE6chT16psILemoKJP5MEYw86dOzFnzhwUFBSgQYMGOHbsGNzc3PiORoiUwot+cXExgoODkZqaCo7j0KJFC3h7e8vMm6usqOiTjyEWizFkyBDcuXMHGhoaWLNmDebOnQs1Nbq/kfBPoUX/zp078PLywsOHD2FnZwfGGG7fvg1ra2uEhISgcePGnxRe0ajok4+Vl5eHSZMmSadi9PLyws8//wxTU1OekxFVp9CvbPr6+qJx48bIzMxEUlISxGIxMjIy0LBhQ/j6+n50aEKUnYGBAY4cOYI9e/ZAKBTi7NmzEIlEiIyM5DsaIZUm95W+rq4uYmNj0bp1a5n1v//+Ozp06ICXL19WacCqRlf6pCrcuHEDQ4YMQVpaGtTV1bFy5UosXLiQunsILxR6pS8QCPDixYtS61++fAktLS15346QGqlNmzZISEjA6NGjUVxcjCVLlqBXr17IycnhOxohFZK76Pft2xcTJ05EXFwcGGNgjCE2NhaTJ0+Gt7e3IjISopT09PTw888/48CBA9DW1kZoaCgcHBwQFhbGdzRCyiV30f/hhx/QuHFjuLm5QSgUQigUokOHDmjSpAm2bdumiIyEKC2O4zB27Fhcv34dLVq0QHZ2Nrp3744VK1aguLiY73iElPLR39O/c+cOUlNTwRhDixYtasxk6dSnTxTl9evXmDFjBg4cOAAA6Nq1Kw4fPgwrKyuek5Hajm7OqgAVfaJohw4dwpQpU/Dq1SuYm5sjICAAPXr04DsWqcUUOpA7aNAgrFu3rtT6jRs3YvDgwfK+HSG1zujRo5GQkIA2bdogJycHPXv2xDfffFPulI2EVCe5i35ERAT69OlTan2vXr1w9erVKglFSE1nb2+P2NhYTJo0CYwxrF69Gt26dcPff//NdzSi4uQu+uV9NVNTUxN5eXlVEoqQ2kBbWxu7d+/G0aNHoa+vj8jISIhEIpw7d47vaESFyV30W7VqhePHj5daf+zYMbRo0aJKQhFSmwwbNgxJSUlwdHTEv//+Cy8vLyxYsACFhYV8RyMqSO6B3NOnT+OLL77AiBEj0K1bNwDA5cuXcfToUfzyyy8YMGCAInJWGRrIJXx5+/Ytvv76a+zYsQPAu9m6jh07hgYNGvCcjNR0Ch3I9fb2RnBwMO7cuYOpU6di7ty5+Pvvv3Hp0iWlL/iE8EkoFGL79u04efIkDA0NERMTA5FIhNOnT/MdjagQ+somITy4d+8ehg0bJp2Kcfbs2Vi3bh09yoR8FF4mRv8YV69eRb9+/VC3bl1wHIfg4OAP7hMREQFnZ2cIhUI0atQIu3fvVnxQQqpYo0aNEBUVhVmzZgEAtmzZgo4dOyI9PZ3fYKTW47Xov3r1Cg4ODtI+zg9JT0+Hl5cX3N3dIRaLsXjxYvj6+kqnsyOkJtHS0sKWLVvw66+/wsjICNevX4ejoyOCgoL4jkZqMaXp3uE4DqdOnapwXGDBggU4ffo0UlNTpesmT56M33//HTExMZU6DnXvEGX04MEDDBs2DLGxsQCA6dOnY9OmTRAIBDwnIzVBjenekVdMTAw8PT1l1vXs2RMJCQnlfv0tPz8feXl5MgshysbGxgZXr17F/PnzAQA7duxA+/btcefOHZ6TkdqmRhX97OxsWFhYyKyzsLBAUVERnjx5UuY+a9euhaGhoXSxtraujqiEyE1TUxPr169HSEgITExMkJSUBCcnpzLviylPQkICunXrhoSEBAUmJTWZhrw7zJkzp8z1HMdBKBSiSZMm6N+/P4yNjT85XHnHeV9J79R/15dYtGiRTOa8vDwq/ESpeXl5ITk5GcOHD0dUVBSGDRuGsLAwbNmyBdra2hXu6+/vj7CwMBw6dAguLi7VlJjUJHIXfbFYjKSkJBQXF0snRv/rr7+grq4Oe3t77Ny5E3PnzkVUVFSV36FraWmJ7OxsmXU5OTnQ0NCAiYlJmfsIBALqFyU1Tv369REWFobly5djzZo12LNnD2JiYnDixAnY2dnJtH3w4AGePHkCjuOkvxUcO3YMPj4+YIzB1NQUNjY2fPwYRAnJ3b3Tv39/dO/eHY8ePUJiYiKSkpLw8OFD9OjRA8OHD8fDhw/RqVMnzJ49u8rDurm5ITQ0VGbdxYsX4eLiAk1NzSo/HiF80tDQwHfffYcLFy7AzMwMN27cgLOzMwICAmTa2drawsXFBc7Oznj8+DEA4PHjx3B2doaLiwtsbW15SE+UFpNT3bp12c2bN0ut//PPP1ndunUZY4wlJiYyExOTD77XixcvmFgsZmKxmAFgmzdvZmKxmD148IAxxtjChQvZ6NGjpe3v3bvHdHR02OzZs1lKSgrbv38/09TUZCdPnqx0/tzcXAaA5ebmVnofQvj26NEj1rVrVwaAAWDjxo1jr169YowxFhAQwDQ0NKTb3l80NDRYQEAAz+mJoslT1+Qu+rq6uiwsLKzU+rCwMKanp8cYY+zu3btMX1//g+8VFhZW5gfVx8eHMcaYj48P69y5s8w+4eHhzNHRkWlpaTFbW1u2a9cuufJT0Sc1VVFREVu2bBnjOI4BYC1atJBegCUmJpb5bykxMZHn1KQ6KLTojxgxgjVs2JAFBQWxzMxM9vfff7OgoCDWqFEjNmrUKMYYY0ePHmXOzs7yJ68GVPRJTXf58mVmaWnJADBtbW124MABlpCQwAAwNTU1mT+p6KsGeeqa3DdnvXz5ErNnz4a/v790JiANDQ34+Phgy5Yt0NXVRXJyMgBAJBJ9RIeTYtHNWaQ2+OeffzB69GjpGNcXX3yByMhI2NjYYPz48di/fz8yMzNx/fp11K9fn+e0RNGqZY7cly9f4t69e2CMoXHjxtDT0/uosNWNij6pLSQSCdauXYulS5dCIpHAzs4Ox48fh4ODAxhjKCgooG+uqYhquSNXT08PxsbGMDU1rTEFn5DaRE1NDUuWLEFYWBjq1q2LtLQ0uLq6Yu/evQBABZ+USe6iL5FIsHLlShgaGsLGxgYNGjRAnTp1sGrVKkgkEkVkJIRUoFOnTkhOTkbv3r3x9u1bTJo0CSNGjKBHjpAyyV30lyxZgh07dmDdunXSG7XWrFmD7du349tvv1VERkLIB5iZmeG3337Dhg0boK6ujmPHjsHZ2RlisZjvaETJyN2nX7duXezevRve3t4y63/99VdMnToVDx8+rNKAVY369EltFxMTg6FDhyIzM1P6+OYpU6aU+6gSUvMptE//6dOnsLe3L7Xe3t4eT58+lfftCCFVzM3NDcnJyfD29kZBQQGmTZuGIUOG4Pnz53xHI0pA7qJf3qQnO3bsgIODQ5WEIoR8GmNjYwQHB2Pz5s3Q1NTEyZMn4eTkJJ2ekaguubt3IiIi0KdPHzRo0ABubm7gOA7R0dHIzMzE2bNn4e7urqisVYK6d4iqiY+Px9ChQ3H//n1oampiw4YNmDlzJnX31CIK7d7p3Lkzbt++jc8//xzPnz/H06dPMXDgQKSlpSl9wSdEFbVr1w5isRgDBw5EYWEhZs+ejc8//5y6Y1VUlU2XmJmZiWXLluHAgQNV8XYKQ1f6RFUxxvDjjz9i7ty5KCgoQIMGDXD8+HG4urryHY18Il6mS3z69Cl+/vnnqno7QkgV4zgO06dPR0xMDBo3boyMjAy4u7tj48aNdI+NCqlR0yUSQj6dk5MTkpKSMHToUBQVFWH+/Pnw9vYud8pRUrtQ0SdEBRkYGODo0aPYs2cPBAIBQkJCIBKJEBUVxXc0omBU9AlRURzHYeLEiYiPj0ezZs3w8OFDdOnSBWvXrqXunlqs0gO5AwcOrHD78+fPERERgeLi4ioJpig0kEtIaS9fvsSUKVOkUzF6enri0KFDMDc35zkZqQyFDOQaGhpWuNjY2ODLL7/85PCEkOqnp6cHf39/7N+/H9ra2rh48SJEIhHCw8P5jkaqWJV9ZbOmoCt9Qip28+ZNDBkyBCkpKVBTU8PSpUvxzTffQF1dne9opBy8fGWTEFI7tGzZEvHx8Rg7diwkEgmWL18OT09PZGdn8x2NVAEq+oSQUnR1dXHgwAH4+/tDV1cXV65cgYODAy5dusR3NPKJqOgTQso1evRoJCQkoHXr1sjJyYGnpye++eYb6fzYpOahok8IqZC9vT3i4uIwceJEMMawevVqeHh4KP3cGaRsVPQJIR+kra2NPXv24OjRo9DT08PVq1chEolw/vx5vqMROVHRJ4RU2rBhw5CUlASRSIQnT56gd+/eWLhwIQoLC/mORiqJij4hRC5NmzZFTEwMpk2bBgBYv349unTpgoyMDJ6Tkcqgok8IkZtQKMSOHTvwyy+/wMDAANHR0XB0dMSZM2f4jkY+gIo+IeSjDRo0CGKxGC4uLnj69Cm8vb2lz+snyomKPiHkkzRq1AjXrl3DrFmzAACbN2+Gu7s70tPT+Q1GykRFnxDyybS0tLBlyxYEBwejTp06iI+Ph6OjI4KCgviORv6Dij4hpMr0798fycnJcHV1RW5uLr744gvMmDED+fn5fEcj/8N70d+5cycaNmwIoVAIZ2dnREZGlts2PDwcHMeVWm7dulWNiQkhFbGxscHVq1fx9ddfAwB27NiB9u3b486dOzwnIwDPRf/48eOYNWsWlixZArFYDHd3d/Tu3fuDX/1KS0tDVlaWdGnatGk1JSaEVIampiY2bNiA3377DSYmJkhKSoKTkxNOnDjBdzSVx2vR37x5M8aPH48JEyagefPm2Lp1K6ytrbFr164K9zM3N4elpaV0oUe+EqKc+vTpg+TkZHTs2BEvXrzA0KFDMWXKFLx584bvaCqLt6JfUFCAxMREeHp6yqz39PREdHR0hfs6OjrCysoKHh4eCAsLq7Btfn4+8vLyZBZCSPWpX78+wsLCsGjRIgDA7t274erqirS0NJ6TqSbeiv6TJ09QXFwMCwsLmfUWFhblPrfbysoKe/fuRWBgIIKCgmBnZwcPDw9cvXq13OOsXbtWZoYva2vrKv05CCEfpqGhgTVr1uD8+fMwMzPDjRs34OzsjMOHD/MdTeXwNnPWo0ePUK9ePURHR8PNzU26fvXq1Th06FClB2f79esHjuNw+vTpMrfn5+fLfHMgLy8P1tbWNHMWITx59OgRRo4cKZ2Kcfz48fjhhx+go6PDb7AarEbMnGVqagp1dfVSV/U5OTmlrv4r4urqir/++qvc7QKBAAYGBjILIYQ/devWxaVLl7Bs2TJwHIf9+/ejXbt2SElJ4TuaSuCt6GtpacHZ2RmhoaEy60NDQ9G+fftKv49YLIaVlVVVxyOEKJC6ujqWL1+OS5cuwdLSEjdv3kTbtm1x8OBBvqPVerx+e2fOnDn46aefcODAAaSmpmL27NnIyMjA5MmTAQCLFi3Cl19+KW2/detWBAcH46+//sLNmzexaNEiBAYGYvr06Xz9CISQT9CtWzckJyeje/fueP36NcaOHQsfHx+8fPmS72i1lgafBx86dCj+/fdfrFy5EllZWWjVqhXOnj0LGxsbAEBWVpbMd/YLCgowb948PHz4ENra2mjZsiVCQkLg5eXF149ACPlEFhYWOH/+PNatW4elS5fC398f8fHxOHHiBFq3bs13vFqHt4Fcvsgz4EEIqV5Xr17F8OHD8ejRIwiFQvzwww+YMGECOI7jO5pSqxEDuYQQ8l+dOnVCcnIyevXqhbdv32LixIkYOXIkXrx4wXe0WoOKPiFEqZiZmSEkJATr1q2Duro6jh49CicnJ4jFYr6j1QpU9AkhSkdNTQ0LFizA1atXYW1tjTt37sDNzQ07d+6EivVIVzkq+oQQpdW+fXuIxWL069cP+fn5mDZtGoYMGYLc3Fy+o9VYVPQJIUrNxMQEv/76K77//ntoaGjg5MmTcHR0REJCAt/RaiQq+oQQpcdxnPS+HqFQiPT0dLRv3x7btm2j7h45UdEnhNQYiYmJePv2LRo3bozCwkLMmjULAwcOxLNnz/iOVmNQ0SeEKLUHDx4gMTERSUlJOH78OIB330ufP38+NDU1ERwcDEdHR8TGxvKctGagm7MIIUrt/RuzOI4DY0z65/s0NDSwdu1azJkzB2pqqnU9SzdnEUJqjYCAAGhovHtiTEmhL/lTQ0MDe/fuxZAhQ1BUVISvv/4a3t7e+Pfff3nLq+yo6BNClNrIkSMRFxdX5ra4uDh89dVXOHbsGHbt2gWBQICQkBCIRCJERUVVc9KagYo+IaTGKOm2+W/3DcdxmDx5MuLi4tCsWTP8/fff6NKlC9auXQuJRMJHVKVFRZ8QovTMzc1haWkJZ2dn7N69G87OzrC0tIS5ublMOwcHByQkJGDkyJEoLi7G4sWL0bt3b+Tk5PCUXPnQQC4hpEbIz8+HlpaWdBC3oKAAAoGgzLaMMRw4cAAzZszAmzdvYGVlhSNHjqBLly7VG7qa0EAuIaTWEQgE0m/ycBxXbsEv2T5+/HjEx8ejefPmyMrKgoeHB1auXIni4uLqiqyUqOgTQmqtVq1a4fr16xgzZgwkEgmWLVsGT0/PUnNzqxIq+oSQWk1XVxd+fn74+eefoaOjgytXrkAkEuHSpUt8R+MFFX1CiEr48ssvkZiYiFatWuGff/6Bp6cnvv32WxQVFfEdrVpR0SeEqAx7e3vEx8fjq6++AmMM3333HTw8PPDw4UO+o1UbKvqEEJWira2NvXv34siRI9DT08PVq1chEolw/vx5vqNVCyr6hBCVNHz4cCQmJkIkEuHJkyfo3bs3Fi1ahMLCQr6jKRQVfUKIymrWrBliYmIwdepUAMC6devQpUsXZGZm8pxMcajoE0JUmlAoxI8//ogTJ07AwMAA0dHREIlEOHPmDN/RFIKKPiGEABg8eDCSkpLg7OyMp0+fwtvbG3PnzkVBQQHf0aoUFX1CCPmfxo0b49q1a5g5cyYAYPPmzXB3d8f9+/f5DVaFqOgTQsh7BAIBtm7dilOnTqFOnTqIj4+Ho6MjTp06xXe0KkFFnxBCyjBgwAAkJyfjs88+w/PnzzFw4ED4+voiPz+f72ifhIo+IYSUw8bGBpGRkZg3bx4AYPv27ejQoQPu3r3Lc7KPR0WfEEIqoKmpiY0bN+K3336DsbExEhMT4eTkhF9++YXvaB+F96K/c+dONGzYEEKhEM7OzoiMjKywfUREBJydnSEUCtGoUSPs3r27mpISQlRZnz59kJycjA4dOiAvLw9DhgzB1KlT8fbtW76jyYXXon/8+HHMmjULS5YsgVgshru7O3r37o2MjIwy26enp8PLywvu7u4Qi8VYvHgxfH19ERgYWM3JCSGqyNraGuHh4Vi0aBEAYNeuXXB1dcXt27d5TiYHxqN27dqxyZMny6yzt7dnCxcuLLP9/Pnzmb29vcy6SZMmMVdX10ofMzc3lwFgubm58gcmhJD/OX/+PDMzM2MAmK6uLgsICOAtizx1jbcr/YKCAiQmJsLT01NmvaenJ6Kjo8vcJyYmplT7nj17IiEhodznZeTn5yMvL09mIYSQT9WzZ08kJyejc+fOePXqFUaNGoUJEybg9evXfEerEG9F/8mTJyguLoaFhYXMegsLi3JntcnOzi6zfVFREZ48eVLmPmvXroWhoaF0sba2rpofgBCi8urWrYtLly5h6dKl4DgO+/fvx2effYbU1FS+o5WL94HckjkvSzDGSq37UPuy1pdYtGgRcnNzpUttfpASIaT6aWhoYMWKFQgNDYWFhQX+/PNPuLi44Oeff+Y7Wpl4K/qmpqZQV1cvdVWfk5NT6mq+hKWlZZntNTQ0YGJiUuY+AoEABgYGMgshhFQ1Dw8PJCcnw8PDA69fv8aYMWPg4+ODV69e8R1NBm9FX0tLC87OzggNDZVZHxoaivbt25e5j5ubW6n2Fy9ehIuLCzQ1NRWWlRBCKsPS0hIXLlzAqlWroKamBn9/f7i4uOCPP/7gO9r/U/iwcgWOHTvGNDU12f79+1lKSgqbNWsW09XVZffv32eMMbZw4UI2evRoaft79+4xHR0dNnv2bJaSksL279/PNDU12cmTJyt9TPr2DiGkOoSHh7O6desyAEwoFLJ9+/YxiUSikGPJU9d4LfqMMfbjjz8yGxsbpqWlxZycnFhERIR0m4+PD+vcubNM+/DwcObo6Mi0tLSYra0t27Vrl1zHo6JPCKkuOTk5rFevXgwAA8BGjBjB8vLyqvw48tQ1jrH/jYSqiLy8PBgaGiI3N5f69wkhCieRSLBx40YsWbIExcXFaNq0KU6cOAGRSFRlx5CnrvH+7R1CCKnN1NTUsGDBAkRERKB+/fr466+/4Orqil27doGPa24q+oQQUg06dOiA5ORk9O3bF/n5+Zg6dSqGDh2K3Nzcas1BRZ8QQqqJiYkJTp8+je+//x4aGhr45Zdf4OTkhISEhGrLQEWfEEKqEcdxmDNnDqKiomBjY4N79+6hffv2+OGHH6qlu4eKPiGE8OCzzz6DWCzGgAEDUFhYiJkzZ2LgwIF49uyZQo9LRZ8QQnhiZGSEoKAgbNu2DZqamggODoajoyPi4uIUdkwq+oQQwiOO4+Dr64vo6Gg0atQIDx48QHJyssKOp6GwdyaEEFJpLi4uSEpKgr+/PyZOnKiw49DNWYQQUsPRzVmEEELKREWfEEJUCBV9QghRIVT0CSFEhVDRJ4QQFUJFnxBCVAgVfUIIUSEqd3NWyW0JeXl5PCchhJCqUVLPKnPblcoV/RcvXgAArK2teU5CCCFV68WLFzA0NKywjcrdkSuRSPDo0SPo6+uD47hK75eXlwdra2tkZmbSnbwfQOeqcug8VQ6dpw9jjOHFixeoW7cu1NQq7rVXuSt9NTU11K9f/6P3NzAwoA9eJdG5qhw6T5VD56liH7rCL0EDuYQQokKo6BNCiAqhol9JAoEAy5Ytg0Ag4DuK0qNzVTl0niqHzlPVUrmBXEIIUWV0pU8IISqEij4hhKgQKvqEEKJCqOgTQogKoaIPYO3atWjbti309fVhbm6OAQMGIC0t7YP7RUREwNnZGUKhEI0aNcLu3burIS2/PuZchYeHg+O4UsutW7eqKXX127VrF9q0aSO9ocjNzQ3nzp2rcB9V/DzJe55U8bNU1ajo490/tmnTpiE2NhahoaEoKiqCp6cnXr16Ve4+6enp8PLygru7O8RiMRYvXgxfX18EBgZWY/Lq9zHnqkRaWhqysrKkS9OmTashMT/q16+PdevWISEhAQkJCejWrRv69++PmzdvltleVT9P8p6nEqr0WapyjJSSk5PDALCIiIhy28yfP5/Z29vLrJs0aRJzdXVVdDylUplzFRYWxgCwZ8+eVV8wJWRkZMR++umnMrfR5+n/VXSe6LP06ehKvwy5ubkAAGNj43LbxMTEwNPTU2Zdz549kZCQgMLCQoXmUyaVOVclHB0dYWVlBQ8PD4SFhSk6mtIoLi7GsWPH8OrVK7i5uZXZhj5PlTtPJVT1s1QVVO6Bax/CGMOcOXPQsWNHtGrVqtx22dnZsLCwkFlnYWGBoqIiPHnyBFZWVoqOyrvKnisrKyvs3bsXzs7OyM/Px6FDh+Dh4YHw8HB06tSpGhNXrz/++ANubm54+/Yt9PT0cOrUKbRo0aLMtqr8eZLnPKnqZ6kqUdH/j+nTp+PGjRuIior6YNv/PpqZ/e/mZnke2VyTVfZc2dnZwc7OTvrazc0NmZmZ2LRpU63+h2pnZ4fk5GQ8f/4cgYGB8PHxQURERLkFTVU/T/KcJ1X9LFUl6t55z4wZM3D69GmEhYV98PHLlpaWyM7OllmXk5MDDQ0NmJiYKDKmUpDnXJXF1dUVf/31lwKSKQ8tLS00adIELi4uWLt2LRwcHLBt27Yy26ry50me81QWVfgsVSUq+nh3RTV9+nQEBQXhypUraNiw4Qf3cXNzQ2hoqMy6ixcvwsXFBZqamoqKyruPOVdlEYvFtbrLoiyMMeTn55e5TVU/T2Wp6DyVRRU/S5+EvzFk5TFlyhRmaGjIwsPDWVZWlnR5/fq1tM3ChQvZ6NGjpa/v3bvHdHR02OzZs1lKSgrbv38/09TUZCdPnuTjR6g2H3OutmzZwk6dOsVu377N/vzzT7Zw4UIGgAUGBvLxI1SLRYsWsatXr7L09HR248YNtnjxYqampsYuXrzIGKPPUwl5z5MqfpaqGhV9xhiAMhc/Pz9pGx8fH9a5c2eZ/cLDw5mjoyPT0tJitra2bNeuXdUbnAcfc67Wr1/PGjduzIRCITMyMmIdO3ZkISEh1R++Go0bN47Z2NgwLS0tZmZmxjw8PKSFjDH6PJWQ9zyp4mepqtGjlQkhRIVQnz4hhKgQKvqEEKJCqOgTQogKoaJPCCEqhIo+IYSoECr6hBCiQqjoE0KICqGiTwghKoSKPiHVYPny5RCJRAp574MHD6JOnToKeW9S+1DRJ7XOmDFjwHEcJk+eXGrb1KlTwXEcxowZU62Z5s2bh8uXL0tfjxkzBgMGDKjWDIQAVPRJLWVtbY1jx47hzZs30nVv377F0aNH0aBBg2rPo6enV+sfkUxqBir6pFZycnJCgwYNEBQUJF0XFBQEa2trODo6StedP38eHTt2RJ06dWBiYoK+ffvi7t27Mu8VHR0NkUgEoVAIFxcXBAcHg+M4JCcnAwDCw8PBcRwuX74MFxcX6OjooH379khLS5O+x/vdO8uXL8fPP/+MX3/9FRzHgeM4hIeHS9/n+fPn0v2Sk5PBcRzu378vXXfw4EE0aNAAOjo6+Pzzz/Hvv/+W+vnPnDkDZ2dnCIVCNGrUCCtWrEBRUdEnnFFSW1DRJ7XW2LFj4efnJ3194MABjBs3TqbNq1evMGfOHFy/fh2XL1+GmpoaPv/8c0gkEgDAixcv0K9fP7Ru3RpJSUlYtWoVFixYUObxlixZgu+//x4JCQnQ0NAodawS8+bNw5AhQ9CrVy9kZWUhKysL7du3r9TPFBcXh3HjxmHq1KlITk5G165d8d1338m0uXDhAkaNGgVfX1+kpKRgz549OHjwIFavXl2pY5Baju/HfBJS1Xx8fFj//v3Z48ePmUAgYOnp6ez+/ftMKBSyx48fs/79+zMfH58y983JyWEA2B9//MEYY2zXrl3MxMSEvXnzRtpm3759DAATi8WMMcbCwsIYAHbp0iVpm5CQEAZAut+yZcuYg4NDqYzvK3mfZ8+eSdeJxWIGgKWnpzPGGBs+fDjr1auXzH5Dhw5lhoaG0tfu7u5szZo1Mm0OHTrErKysyjtlRIXQlT6ptUxNTdGnTx/8/PPP8PPzQ58+fWBqairT5u7duxgxYgQaNWoEAwMD6UxgGRkZAIC0tDS0adMGQqFQuk+7du3KPF6bNm2k/10yk1NOTk6V/kypqalwc3OTWfff14mJiVi5ciX09PSky1dffYWsrCy8fv26SvOQmocmRie12rhx4zB9+nQAwI8//lhqe79+/WBtbY19+/ahbt26kEgkaNWqFQoKCgC8m7qvvAnL/+v9aQ1L9inpJqoMNTW1Uu9fWFhYqWO/TyKRYMWKFRg4cGCpbe//z4uoJir6pFbr1auXtID37NlTZtu///6L1NRU7NmzB+7u7gCAqKgomTb29vY4fPgw8vPzIRAIAAAJCQmfnEtLSwvFxcUy68zMzAAAWVlZMDIyAgDpYHGJFi1aIDY2Vmbdf187OTkhLS0NTZo0+eScpPah7h1Sq6mrqyM1NRWpqalQV1eX2WZkZAQTExPs3bsXd+7cwZUrVzBnzhyZNiNGjIBEIsHEiRORmpqKCxcuYNOmTQBQ6jcAedja2uLGjRtIS0vDkydPUFhYiCZNmsDa2hrLly/H7du3ERISgu+//15mP19fX5w/fx4bNmzA7du3sWPHDpw/f16mzdKlS+Hv74/ly5fj5s2bSE1NxfHjx/HNN998dF5Se1DRJ7WegYEBDAwMSq1XU1PDsWPHkJiYiFatWmH27NnYuHFjqX3PnDmD5ORkiEQiLFmyBEuXLgXwaV0lX331Fezs7ODi4gIzMzNcu3YNmpqaOHr0KG7dugUHBwesX7++1DdzXF1d8dNPP2H79u0QiUS4ePFiqWLes2dP/PbbbwgNDUXbtm3h6uqKzZs3w8bG5qPzktqD5sglRE6HDx/G2LFjkZubC21tbb7jECIX6tMn5AP8/f3RqFEj1KtXD7///jsWLFiAIUOGUMEnNRIVfUI+IDs7G0uXLkV2djasrKwwePBgutGJ1FjUvUMIISqEBnIJIUSFUNEnhBAVQkWfEEJUCBV9QghRIVT0CSFEhVDRJ4QQFUJFnxBCVAgVfUIIUSH/B6TIkimv/xomAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "data-set: emilia\n",
      "Size:  144\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.10\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.37\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    1.1, maximum depth   71.1\n",
      "\n",
      "Fit results:\n",
      "Number of events: 144\n",
      "Intercept: 4.08,   Slope: -1.0902\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "data-set: marche\n",
      "Size:  528\n",
      "Minimum magnitude:   1.00\n",
      "Maximum magnitude:   4.20\n",
      "Average magnitude:   2.08\n",
      "Stand. dev:          0.47\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.6, maximum depth   34.8\n",
      "\n",
      "Fit results:\n",
      "Number of events: 528\n",
      "Intercept: 2.85,   Slope: -0.4997\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "Data.set_bins(5)\n",
    "Data.richter_all(plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054e5f45",
   "metadata": {},
   "source": [
    "Now, the class method *describe_all* also reports the data from the fits just perfomed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b9b26e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of datasets: 5\n",
      "Regions: ['sicilia', 'north', 'central', 'emilia', 'marche']\n",
      "\n",
      "data-set: sicilia\n",
      "Size:  559\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.30\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.35\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.0, maximum depth  208.2\n",
      "\n",
      "Richter fit:\n",
      "Intercept: 5.02,   Slope: -1.2126\n",
      "\n",
      "\n",
      "data-set: north\n",
      "Size:  131\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   3.90\n",
      "Average magnitude:   2.36\n",
      "Stand. dev:          0.39\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    1.5, maximum depth   62.4\n",
      "\n",
      "Richter fit:\n",
      "Intercept: 3.76,   Slope: -0.9745\n",
      "\n",
      "\n",
      "data-set: central\n",
      "Size:  367\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   3.90\n",
      "Average magnitude:   2.29\n",
      "Stand. dev:          0.34\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    4.9, maximum depth   69.5\n",
      "\n",
      "Richter fit:\n",
      "Intercept: 5.14,   Slope: -1.3588\n",
      "\n",
      "\n",
      "data-set: emilia\n",
      "Size:  144\n",
      "Minimum magnitude:   2.00\n",
      "Maximum magnitude:   4.10\n",
      "Average magnitude:   2.33\n",
      "Stand. dev:          0.37\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    1.1, maximum depth   71.1\n",
      "\n",
      "Richter fit:\n",
      "Intercept: 4.08,   Slope: -1.0902\n",
      "\n",
      "\n",
      "data-set: marche\n",
      "Size:  528\n",
      "Minimum magnitude:   1.00\n",
      "Maximum magnitude:   4.20\n",
      "Average magnitude:   2.08\n",
      "Stand. dev:          0.47\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.6, maximum depth   34.8\n",
      "\n",
      "Richter fit:\n",
      "Intercept: 2.85,   Slope: -0.4997\n",
      "\n",
      "\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "Data.describe_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3af1116",
   "metadata": {},
   "source": [
    "Let's have a more detailed look to the case of the *marche* dataset. It refers to the seismic sequence of November/December 2022 in that area (sea).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4bb843bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-set: marche\n",
      "Size:  528\n",
      "Minimum magnitude:   1.00\n",
      "Maximum magnitude:   4.20\n",
      "Average magnitude:   2.08\n",
      "Stand. dev:          0.47\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.6, maximum depth   34.8\n",
      "\n",
      "Richter fit:\n",
      "Intercept: 2.85,   Slope: -0.4997\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ra[rd[\"marche\"]].describe()\n",
    "ra[rd[\"marche\"]].depth_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e30db790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data-set: marche\n",
      "Size:  528\n",
      "Minimum magnitude:   1.00\n",
      "Maximum magnitude:   4.20\n",
      "Average magnitude:   2.08\n",
      "Stand. dev:          0.47\n",
      "\n",
      "Depths (km):\n",
      "Minimum depth:    0.6, maximum depth   34.8\n",
      "\n",
      "Fit results:\n",
      "Number of events: 301\n",
      "Intercept: 4.03,   Slope: -0.9672\n"
     ]
    },
    {
     "data": {
      "image/png": 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BAgMDZTq3eku+LU31T03b9OfPny9po7WysmL//e9/5RQhacjev3/PxGIxY4wxsVjM3r9/L7djBQYGMoFAwF6/fl1m3ffff8+sra0ZY4yFhYUxPp/PVq9ezZKSklhCQgLbunWrpG7fvn3ZkCFD2P/+9z+WnZ3NGGMsKSmJ8Xg8tmXLFvbgwQO2Z88epqWlJdWmb21tzfr27cuSkpLYjRs3mKOjI1NVVZV6PoAq2vQZYyw4OJgJhUJmZmbG1q9fL7Vu2LBhzNramsXFxbH4+Hg2ePBg1qxZM7ZgwQJJnU+fSdjb2zNHR0d2584dFhERwbp16ybVpv/06VPWokULNmrUKBYVFcUePnzILl68yKZMmcKKi4vZjRs32MaNG9mtW7dYWloa++uvv5hAIGDnzp2r9Dy4JEteo6Qvg1OnTjFdXV0GgAmFQrZ7925WUlIihygJqdqgQYPYgAEDyl1X+jAyJiaGMcaYv78/s7a2ZgKBgOnq6rIRI0ZI6kZGRjJLS0smFArZx/eB+/btY0ZGRqxp06bsm2++YRs3bpRK+rGxsczOzo4JhULWvn17duLEiTIJuDpJv7i4mBkaGjIA7OHDh1LrUlNTmbOzM1NVVWVGRkZsz549zMnJqdKkn5SUxOzt7ZmqqiqztrZmwcHBUkmfMcbu3bvHhg8fzpo3b85UVVWZubk5W7hwIROLxSwpKYm5ubmxFi1aMKFQyDp06CD14Lg+kiWv8RhrLP2Qqic3NxeamprIycmBhoaGzNtnZmZi6tSpOH/+PADAzc0Nnp6eaNmyZW2HSggh1SJLXqM2fRkZGBggKCgIe/fuhYqKCi5evAhLS0sEBARwHRohhFSJkn4N8Hg8zJ49G7GxsbC1tcWLFy8wcuRITJs2rdovphBCCBc4TfqbN29G165d0axZM+jp6WHYsGFISUmpcruIiAiIRCKoqKigbdu22L9/fx1EW1bHjh0RGRmJFStWgMfjwdPTE9bW1oiMjOQkHkIIqQqnST8iIgJz5szBjRs3EBISguLiYri6ulY6xkVqaioGDBgAR0dHxMXF4fvvv8f8+fPh7+9fh5H/H4FAgM2bNyM8PBxt2rTBo0eP0LNnT6xZs6ZM/2BCCOFavXqQm52dDT09PURERKBXr17l1lm+fDnOnDmD5ORkSdmsWbNw+/btat1hf+6D3Mrk5ORg7ty5kmkUu3XrBl9fX7Rv375Wj0MIIR9rsA9yc3JyAADa2toV1omMjISrq6tUmZubG6Kjo8u9sy4oKEBubq7UIi+amprw8fHB0aNH0bx5c9y8eRPW1tY4ePBg4xmsiRDSoNWbpM8Yw6JFi9CzZ0906dKlwnqZmZnQ19eXKtPX10dxcXG5bwNu3rxZMi6IpqYmjIyMaj32T40bNw4JCQlwdnbG27dvMWPGDAwbNkwy+iIhhHCl3iT9uXPnIiEhAUePHq2ybumY36VK76I/LQeAlStXIicnR7Kkp6fXTsBVMDIyQmhoKHbs2AGBQIAzZ87AwsIC586dq5PjE0JIeepF0p83bx7OnDmDsLCwKqchNDAwQGZmplRZVlYWlJSUoKOjU6a+UCiEhoaG1FJX+Hw+Fi9ejJs3b6Jz58549uwZBg4ciDlz5uDt27d1FgchhJTiNOkzxjB37lwEBATgn3/+qdYIdg4ODggJCZEqCw4Ohp2dHZSVleUV6mexsrJCdHQ0Fi5cCAD47bffYGtri5iYGG4DI41eXUxMTj5oMN+1HIeDqNJ3333HNDU1WXh4OMvIyJAsb9++ldRZsWIFmzRpkuTzo0ePmJqaGvPw8GBJSUns8OHDTFlZmZ08ebJax+R6EpXg4GDWsmVLyeQamzZtqnTCCEIq4+7uzoYOHVrh+oyMDLkO+kb+D5ffdYOZRGXfvn3IyclB7969YWhoKFlK5xgFPgyR+vGECaampjh37hzCw8NhbW2N9evX45dffsHIkSO5OAWZ9e3bFwkJCRg5ciSKi4vx/fffo3fv3pUOF0tITRkYGEAoFHIaA2MMxcXF1aorr3dbZImhpurDd10tcv8VVM9wfadfSiwWMy8vL6aurs4AsGbNmjFvb2/JsLyEW2KxmL1584aTRZZ/A1Xd6eOjUS5LpxL09/dnvXv3ZqqqqszS0pJdv35daptr164xR0dHpqKiwlq3bs3mzZvH3rx5I1nv4+PDRCIRU1dXZ/r6+mz8+PHs2bNnkvVhYWEMALtw4QITiURMWVmZ/fPPP2ViK43n+PHjzMnJiQmFQubp6ckYY8zT05OZm5tLhlzeu3dvmRitrKyYUChkIpGIBQYGSk2TWFEMYrGYbd26lZmamjIVFRVmaWnJTpw4Idnvy5cv2ddff810dXWZiooKa9eunSSmgoICNmfOHGZgYMCEQiEzNjZmmzZtKve7ZoyxhIQE5uzszFRUVJi2tjb79ttvWV5eXplrt337dmZgYMC0tbXZ7NmzWWFhYYXXsyI0tHIl6kvSL/Xw4UPWo0cPyVj9Y8aMYS9evOA6LIX35s2bcue7rYvl4wRblZokfXNzc/b333+zlJQUNmrUKGZsbMyKiooYYx8Slbq6Otu5cye7d+8eu3btGrOxsWGTJ0+W7PPw4cPs3Llz7OHDhywyMpLZ29uz/v37S9aXJlxLS0sWHBzMHjx4wJ4/f14mttJ4TExMmL+/P3v06BF7+vQpO3DgADM0NJSU+fv7M21tbebl5cUYYyw3N5dpa2uziRMnsjt37rBz586xDh06lJv0P43h+++/Z+bm5uzChQvs4cOH7MiRI0woFLLw8HDGGGNz5sxh1tbW7NatWyw1NZWFhISwM2fOMMYY2759OzMyMmKXL19mjx8/ZleuXGF//vlnud91fn4+a9myJRsxYgRLTExkly5dYqampszd3V3q2mloaLBZs2ax5ORkdvbsWaampsYOHDhQvYv/EUr6lahvSZ+xD5NZb9iwQTKxdqtWrVhoaCjXYSm0xpz0Dx06JFl/584dBoAlJyczxhibNGkSmzFjhtQ+rly5wvh8Pnv37l25x7h58yYDILmLLU24p06dqjT20nhKJ3UvZWRkJJVMGWNs/fr1zMHBgTH2YZx/HR0dqXgOHjxYbtL/OIY3b94wFRWVMn/ZTJs2jY0fP54xxtjgwYPZlClTyo133rx5rE+fPhX+Jfbxd33gwAGmpaUldS2DgoIYn89nmZmZjLEP187Y2Fjqmd7o0aPZ2LFjy91/ZWTJazRHbj2gpKSEVatWwdXVFRMnTsS9e/fw1VdfYfHixdi4cWPDaCdsZNTU1PDmzRvOji1PlpaWkv82NDQE8KHbs7m5OWJiYvDgwQPJVIvAh/ZwsViM1NRUdOzYEXFxcVi7di3i4+Px8uVLyTzAT548QadOnSTb2dnZVSuej+tlZ2cjPT0d06ZNw7fffispLy4uhqamJoAPk8FbWlpCRUVFsr5bt25V7jspKQnv379H3759peoUFhZK5r7+7rvvMHLkSMTGxsLV1RXDhg1Djx49AHyYE7hv374wMzNDv379MGjQoDKjA5RKTk6GlZWVZPpFAPjyyy8hFouRkpIiecG0c+fOkknggQ/XIzExsZJv6/NR0q9HunbtitjYWCxZsgT79+/HTz/9hODgYPj5+cHCwoLr8BQKj8eT+h+2Mfm4a3PpC42liVssFmPmzJmYP39+me3atGmD/Px8uLq6wtXVFb6+vmjRogWePHkCNze3MpOVV/f7+7heaRwHDx5E9+7dpeqVJkfGWIUvaFZn30FBQVITyQOQ3Fj1798faWlpCAoKQmhoKFxcXDBnzhzs2LEDtra2SE1Nxfnz5xEaGooxY8bgq6++wsmTJ8sct7wYS31c/mk3cx6PJ4lTXijp1zNNmzbFvn37MHDgQEydOhWJiYno2rUrtmzZgvnz54PPrxfv05FGqnTS8Hbt2pW7PjExEc+fP8eWLVskQ5pER0fX2vH19fXRqlUrPHr0CBMmTCi3jrm5Ofz8/FBQUCBJ1tWJoVOnThAKhXjy5AmcnJwqrNeiRQtMnjwZkydPhqOjI5YuXYodO3YAADQ0NDB27FiMHTsWo0aNQr9+/fDy5csy44V16tQJf/zxB/Lz8yW/eK5duwY+n48OHTpU67uQF8og9dSgQYOQmJiIQYMGoaCgAB4eHnBzc8PTp0+5Do3UMzk5OYiPj5daPu7mLIvly5cjMjISc+bMQXx8PO7fv48zZ85g3rx5AD7c7QsEAvz666949OgRzpw5g/Xr19fm6WDt2rXYvHkzdu/ejXv37iExMRFHjhzBzz//DAD4+uuvIRaLMWPGDCQnJ+PixYuSpFzR3TUANGvWDEuWLIGHhwf++OMPPHz4EHFxcdi7dy/++OMPAMDq1atx+vRpPHjwAHfu3MHff/+Njh07AgB27tyJY8eO4e7du7h37x5OnDgBAwMDNG/evMyxJkyYABUVFbi7u+O///0vwsLCMG/ePEyaNKnM2GF1TuYnBg1cfXyQWxmxWMz279/PVFVVGQCmpaUl1cWMKDZ3d/dyHwaX9hJBOQ9ySx92MsbYq1evykwafvPmTda3b1+mrq7OmjZtyiwtLdnGjRsl6//8809mYmLChEIhc3BwYGfOnCn3IeqrV68qjb28eEr5+flJJnLX0tJivXr1YgEBAZL1165dY5aWlkwgEDCRSMT+/PNPBoDdvXu30hjEYjHbvXs3MzMzY8rKyqxFixbMzc2NRUREMMY+PDDu2LEjU1VVZdra2mzo0KHs0aNHjLEPD2etra1Z06ZNmYaGBnNxcWGxsbGSfX/8XTNW/S6bH1uwYAFzcnKq9HsrD02MXgl5jqcvTykpKZg4caLkz9iBAwciNzcXP//8c7UfmBHSWPn5+WHKlCnIycmBqqoq1+HUuQY7nj6pmJmZGa5fv47//Oc/4PP5CAoKwpUrV7B161auQyOkznl7e+Pq1atITU3FqVOnsHz5cowZM0YhE76sKOk3IP/++y+GDRuGgwcPSh7onjx5EtOnT0dUVBTS0tI4jpCQupGZmYmJEyeiY8eO8PDwwOjRo3HgwAGuw2oQqHmnAansIVUpBbuchBBQ806j5evrCyWlinvZCgQC7N+/nxI/IaRClPQbkAkTJiAqKqrcdd27d0dhYSG+++47DB48GM+ePavj6AghDQEl/QaqtE2/9OeePXuwa9cuCIVCBAUFwcLCAmfPnuUyREJIPURJv4HR09ODgYEBRCIR9u/fD5FIBAMDAxgYGGDBggWIjo6GpaUlsrOzMWTIEMyaNQv5+flch00IqSfoQW4DVFBQAIFAAB6PB8YYCgsLpQZlKygowH/+8x/JW4rt27eHn58funbtylXIhBA5oge5jZxQKJT05OHxeGVG4RQKhdi+fTsuXbqEVq1a4f79+3BwcMCGDRvkPnsQIaR+o6TfiPXp0weJiYkYM2YMSkpK8MMPP8DJyQmPHj3iOjRCCEco6TdyWlpaOHbsGHx8fKChoYHr16/DysoKXl5e1LWTEAVESV8B8Hg8TJw4Ebdv34ajoyPevHmDKVOmYPTo0Xjx4gXX4RFC6hAlfQViYmKCsLAwbN68GUpKSvD394eFhQWCg4O5Do0QUkco6SuYJk2aYMWKFYiKioK5uTkyMjLg5uaGhQsX4t27d1yHRwiRM0r6CsrW1hYxMTGYM2cOAGD37t3o2rUrbt++zXFkhBB5oqSvwNTU1LBnzx4EBQVBX18fd+7cQbdu3bBjxw65z9NJCOEGJX2CAQMGIDExEUOHDkVhYSGWLl2Kr776Cunp6VyHRgipZZT0CYAPk0EHBgbi4MGDUFNTQ1hYGCwtLXHs2DGuQyOE1CJK+kSCx+Nh+vTpiI+PR7du3fD69WuMHz8eEydOxOvXr7kOjxBSCyjpkzLat2+Pq1evYs2aNeDz+fDz84OVlRUiIiK4Do0Q8pko6ZNyKSsrY+3atbh69Sratm2LJ0+ewNnZGStWrEBhYSHX4RFCaoiSPqmUg4MD4uPjMW3aNDDGsHXrVtjb2yM5OVlSJzo6Gn369EF0dDSHkRJCqoOSPqlSs2bNcOjQIQQEBEBHRwdxcXGwtbXF3r17wRiDt7c3wsLC4OPjw3WohJAq0Hj6RCYZGRmYMmUKLl68CADo0aMHUlJS8OLFC+jp6eH8+fNgjEFXVxfGxsYcR0uIYpAlr1HSJzJjjEmmafxY6aQuH9cjhMgfTaJC5IrH48HX1xdNmjSRKi9N8kpKSvD19eUiNEJIFSjpkxqZMGECbt68We66Q4cOYcKECXUcESGkOjhN+pcvX8bgwYPRsmVL8Hg8nDp1qtL64eHh4PF4ZZa7d+/WTcCkXJ829UybNg0//vgjTc1ISD3EadLPz8+HlZUV9uzZI9N2KSkpyMjIkCzt27eXU4SkMnp6ejAwMIBIJML+/fthY2MDFRUVlJSUYO3atejZsycePHjAdZiEkI8ocXnw/v37o3///jJvp6enh+bNm9d+QEQmrVu3xuPHjyEQCMDj8TBjxgwUFhbC398fs2fPRlRUFKytrbF7925MnTpVMpk7IYQ7DbJN38bGBoaGhnBxcUFYWFildQsKCpCbmyu1kNojFAolyZzH40EoFOLrr79GQkICnJyckJ+fj+nTp2PEiBF4/vw5x9ESQhpU0jc0NMSBAwfg7++PgIAAmJmZwcXFBZcvX65wm82bN0NTU1OyGBkZ1WHEiqtNmza4dOkStm3bBmVlZZw6dQoWFha4cOEC16ERotDqTT99Ho+HwMBADBs2TKbtBg8eDB6PhzNnzpS7vqCgAAUFBZLPubm5MDIyon76dSg+Ph4TJkxAUlISAGDu3LnYtm0bVFVVOY6MkMZBofrp29vb4/79+xWuFwqF0NDQkFpI3bK2tkZ0dDTmz58PANizZw9EIhHi4uI4jowQxdPgk35cXBwMDQ25DoNUQVVVFbt378aFCxdgYGCA5ORkdO/eHVu3bkVJSQnX4RGiMDhN+m/evEF8fDzi4+MBAKmpqYiPj8eTJ08AACtXrsQ333wjqb9r1y6cOnUK9+/fx507d7By5Ur4+/tj7ty5XIRPasDNzQ2JiYkYPnw4ioqKsGLFCvTp0wdpaWlch0aIQuA06UdHR8PGxgY2NjYAgEWLFsHGxgarV68G8GFwr9JfAABQWFiIJUuWwNLSEo6Ojrh69SqCgoIwYsQITuInNaOrqwt/f394enpCXV0dly9fhqWlJfz8/LgOjZBGT+YHuU2aNEFGRgb09PSkyktHWazvf6rTgGv1y8OHDzFp0iRERkYCAMaPH4+9e/dCS0uL48gIaTjk+iC3ot8RBQUFEAgEsu6OKLgvvvgCly9fxrp169CkSRMcPXoUVlZWVb5/QQipmWq/kfvLL78A+NC18tChQ1BXV5esKykpweXLl2Fubl77EZJGT0lJCT/88ANcXV0xceJEPHjwAC4uLliyZAnWr18PoVDIdYiENBrVbt4xNTUFAKSlpaF169ZSw+oKBAKYmJhg3bp16N69u3wirSXUvFO/vXnzBosWLcLBgwcBAFZWVvDz80Pnzp05joyQ+kuuk6g4OzsjICCgwba5UtJvGE6fPo3p06fj+fPnEAqF2LZtG+bOnVvu5C2EKDq5tumHhYU12IRPGo6hQ4ciMTER/fv3R0FBARYsWID+/fvj33//5To0Qho0me/0S0pK4OXlhUuXLiErKwtisVhq/T///FOrAdY2utNvWBhj2LdvHxYvXoz3799DW1sbBw8epG66hHxErnf6CxYswIIFC1BSUoIuXbrAyspKaiGkNvF4PMyePRuxsbGwtbXFy5cvMXLkSEydOhV5eXlch0dIgyPznb6uri68vb0xYMAAecUkV3Sn33AVFhZi7dq12LJlCxhjaNu2LXx8fNCjRw+uQyOEU3K90xcIBGjXrl2NgyOkpgQCATZt2oSIiAgYGxvj0aNHcHR0xOrVq1FUVMR1eIQ0CDIn/cWLF2P37t0VvqRFiLw5Ojri9u3bmDRpEsRiMdavX48vv/wS9+7d4zo0Quo9mZt3hg8fjrCwMGhra6Nz585QVlaWWh8QEFCrAdY2at5pXI4fP45Zs2bh9evXUFNTw86dO/Htt9/S1IxEoci1ead58+YYPnw4nJycoKurKzUrlaamZo2DJqQmxo4di8TERLi4uODt27eYOXMmhg4diqysLK5DI6ReqjczZ9UVutNvnMRiMXbv3o0VK1agsLAQenp68PT0xMCBA7kOjRC5U6iZswgBAD6fDw8PD9y6dQtdunRBVlYWBg0ahNmzZ+Pt27dch0dIvSHznb6pqWml7aWPHj367KDkie70G7/379/j+++/x86dOwEAZmZm8PPzg0gk4jgyQuRDlrxW7VE2Sy1cuFDqc1FREeLi4nDhwgUsXbpU1t0RUutUVFTw888/Y8CAAXB3d0dKSgrs7e2xbt06LFu2TGqwQEIUTa216e/duxfR0dE4cuRIbexObuhOX7G8fPkSM2fOxMmTJwEAPXv2hI+PD0xMTLgNjJBaxEmbfv/+/eHv719buyOkVmhra+Ovv/7CH3/8gWbNmuHq1auwtLSEj48PvWtCFFKtJf2TJ09CW1u7tnZHSK3h8Xj45ptvcPv2bXz55ZfIy8vDN998g3HjxuHly5dch0dInZK5Td/GxkbqQS5jDJmZmcjOzsZvv/1Wq8ERUptMTU0RERGBrVu3Ys2aNfjrr79w7do1/PHHH3BxceE6PELqhMxt+j/++KPUZz6fjxYtWqB3794NYrpEatMnABAdHY0JEyZIhm5YtGgRNm7cCBUVFY4jI0R2cp05q6GjpE9K5efnY+nSpdi3bx8AwMLCAn5+frCwsOA4MkJkI/ekX1JSglOnTiE5ORk8Hg+dOnXCkCFDGkRXOEr65FNBQUGYOnUqsrKyIBAIsGXLFixYsICmZiQNhlyT/oMHDzBgwAA8ffoUZmZmYIzh3r17MDIyQlBQEL744ovPCl7eKOmT8mRlZWH69Ok4e/YsAMDFxQVeXl5o3bo1x5ERUjW5dtmcP38+vvjiC6SnpyM2NhZxcXF48uQJTE1NMX/+/BoHTQiX9PT0cPr0afz+++9QU1PDpUuXYGlpiRMnTnAdGiG1SuakHxERgW3btkl1z9TR0cGWLVsQERFRq8ERUpd4PB5mzJiBuLg4dO3aFa9evcKYMWPg7u6O3NxcmfcXHR2NPn36IDo6Wg7RElIzMid9oVBY7tykb968gUAgqJWgCOFShw4dcO3aNfzwww/g8/nw9vaGlZUVrl69KtN+vL29ERYWBh8fHzlFSojsZE76gwYNwowZMxAVFQXGGBhjuHHjBmbNmoUhQ4bII0ZC6pyysjLWrVuHK1euwNTUFI8fP4aTkxNWrVqFwsLCCrdLS0tDTEwMYmNjcfz4cQDAsWPHEBsbi5iYGKSlpdXVKRBSLpkf5L5+/Rru7u44e/asZNas4uJiDBkyBF5eXvV+IhV6kEtklZubi4ULF0rGlRKJRPD19S33vZSPX1zk8XhgjEl+llKwXtKkDsh95qzTp0/j3r17OHnyJE6cOIGUlBQEBgbW+4RPSE1oaGjA09MTJ06cgLa2NmJiYmBra4t9+/aVSeC+vr5QUvrwonvputKfSkpK8PX1rdvgCfkEvZxFiAyePn2KKVOmICQkBAAwcOBAHD58GPr6+pI6sbGx5Y7dX/rLgpDaJtc7/VGjRmHLli1lyrdv347Ro0fLujtCGpRWrVrhwoUL2LVrF4RCIYKCgmBhYSHp3/+x0pe76CUvUp/UqMtmefOO9uvXD5cvX66VoAipqbroJsnn87FgwQJER0fD0tIS2dnZGDJkCGbNmoX8/Hzo6enBwMAAIpEI+/fvh0gkgoGBAfT09OQWEyHVJXPSr6hrprKyco36MhNSm+qym2SXLl1w8+ZNLFmyBDweD7///jtsbGyQkZGBx48fIyoqCjNnzkRUVBQeP35Mb/eSekHmpN+lSxdJV7SPHTt2DJ06daqVoAiRBZfdJIVCIbZv347Q0FC0bt0a9+/fh4ODA7Zv346SkhIAH3rxCIVCucVAiCxkfpB75swZjBw5El9//TX69OkDALh06RKOHj2KEydOYNiwYfKIs9bQg9zGp750k3z16hW+++47yS+eHj16wMfHB23btpX7sYlik+uD3CFDhuDUqVN48OABZs+ejcWLF+N///sfQkNDZU74ly9fxuDBg9GyZUvweDycOnWqym0iIiIgEomgoqKCtm3bYv/+/bKeAmlk6ks3SS0tLRw9ehS+vr7Q0NDA9evXYWVlBS8vL+qbT+oPxqFz586xVatWMX9/fwaABQYGVlr/0aNHTE1NjS1YsIAlJSWxgwcPMmVlZXby5MlqHzMnJ4cBYDk5OZ8ZPalPYmJiGIAyS0xMDCfxPH78mPXq1UsSx8iRI9nz5885iYU0frLkNU6T/seqk/SXLVvGzM3NpcpmzpzJ7O3tq30cSvqNU2nS5/P5Uj+5SvqMMVZcXMy2bNnClJWVGQBmaGjILl68yFk8pPGSJa81qA7EkZGRcHV1lSpzc3NDdHQ0ioqKyt2moKAAubm5UgtpfOpjN8kmTZpg+fLluHHjBszNzZGRkQE3NzcsWLAA79694ywuotgaVNLPzMyUevMRAPT19VFcXIznz5+Xu83mzZuhqakpWYyMjOoiVFLHWrduXW+7Sdra2iImJgZz584FAPzyyy+ws7NDfHw8t4ERhdSgkj4g3VMD+L8Hdp+Wl1q5ciVycnIkS3p6utxjJNwQCoWSfwf1rZukmpoafv31V5w7dw76+vpISkpCt27dsH37dojFYq7DIwqkQSV9AwMDZGZmSpVlZWVBSUkJOjo65W4jFAqhoaEhtRDClf79+yMxMRFDhw5FUVERli1bBhcXF7oZIXVGSdYNFi1aVG45j8eDiooK2rVrh6FDh0rNrFVbHBwcyoxxEhwcDDs7O8kwz4TUdy1atEBgYCA8PT2xYMEChIeHw8LCAvv378e4ceO4Do80drI+Je7duzfT0NBgTZs2Zba2tszGxoapq6szTU1N1r17d9a8eXOmpaXF7ty5U+W+8vLyWFxcHIuLi2MA2M8//8zi4uJYWloaY4yxFStWsEmTJknql3bZ9PDwYElJSezw4cPUZZM0aPfv32fdu3eXdO2cMGECe/XqFddhkQZGrl02d+7cyUaMGCG185ycHDZq1Ci2a9culp+fz4YOHcpcXV2r3FdYWFi5favd3d0ZY4y5u7szJycnqW3Cw8OZjY0NEwgEzMTEhO3bt0+m+Cnpk/qmqKiIrVmzhjVp0oQBYG3atGHh4eFch0UaEFnymszDMLRq1QohISFlxtm5c+cOXF1d8fTpU8TGxsLV1bXCHjVcomEYSH0VGRmJSZMm4eHDh+DxeFi2bBnWrVtHc0+TKsl1GIacnBxkZWWVKc/Ozpb0gW/evHml84gSQspycHBAXFwcpk2bBsYYtm7diu7duyMpKYnr0EgjInPSHzp0KKZOnYrAwED873//w9OnTxEYGIhp06ZJxt65efMmOnToUNuxEtLoNWvWDIcOHUJAQAB0dHQQHx8PkUiEPXv20Pg9pFbI3Lzz5s0beHh4wNvbG8XFxQA+DGrl7u6OnTt3omnTppKXTqytrWs73s9GzTukocjIyMCUKVNw8eJFAB8mKvL09IShoSHHkZH6Rpa8VuM5ct+8eYNHjx6BMYYvvvgC6urqNQq2rlHSJw0JYwx79+7F0qVL8f79e+jo6ODgwYMYPnw416GRekSubfql1NXVoa2tDV1d3QaT8AlpaHg8HubOnYuYmBhYW1vjxYsXGDFiBKZPn443b95wHR5pgGRO+mKxGOvWrYOmpiaMjY3Rpk0bNG/eHOvXr6fXyQmRk06dOiEqKgrLly8Hj8fD4cOHYW1tjRs3bnAdGmlgZE76q1atwp49e7BlyxbExcUhNjYWmzZtwq+//ooffvhBHjESQgAIBAJs2bIFYWFhaNOmDR4+fIiePXti7dq1kudrhFRF5jb9li1bYv/+/RgyZIhU+enTpzF79mw8ffq0VgOsbdSmTxqD169fY+7cufDz8wMAdO/eHb6+vmjXrh3HkREuyLVN/+XLlzA3Ny9Tbm5ujpcvX8q6O0JIDTRv3hy+vr74888/oampiaioKFhbW+PQoUPUtZNUSuakb2VlhT179pQp37NnD6ysrGolKEJI9YwfPx4JCQno3bs38vPz8e2332L48OHIzs7mOjRST8mc9Ldt2wZPT0906tQJ06ZNw/Tp09GpUyd4eXlh+/bt8oiREFKJNm3a4NKlS9i+fTuUlZVx+vRpWFhY4Pz581L1oqOj0adPH0RHR3MUKakPZE76Tk5OuHfvHoYPH47Xr1/j5cuXGDFiBFJSUuDo6CiPGAkhVeDz+ViyZAlu3ryJTp064dmzZxgwYADmzp2Lt2/fAgC8vb0RFhYGHx8fjqMlXKrxy1mfSk9Px5o1a+Dp6Vkbu5MbepBLGrt3795h5cqV2L17NwDA1NQU69evx6JFi5CVlQU9PT2cP38ejDHo6urC2NiY44jJ56qTN3I/dfv2bdja2qKkpKQ2dic3lPSJoggODoabm1uZch6PJ/Wwlx78Nnx18kYuIaR+c3V1xb59+yqcV1pJSQm+vr5chEY4REmfkEZs1qxZuHXrVrnrbty4gQkTJtRxRIRrlPQJaeRK7/Q/veNftWoVXr16xUVIhEPVnhh9xIgRla5//fr158ZCCJEDPT09GBgYwMjICJMnT8amTZvw9OlTXLx4EZaWlvD29oazszPXYZI6Uu0HuVOmTKnWDo8cOfJZAckbPcgliqigoAACgUDyEPfatWuYOnUq7t+/Dx6Ph8WLF2PDhg0QCoVch0pqgJPeOw0FJX1CPsjPz8fixYvx+++/AwAsLS3h5+eHLl26cBwZkRX13iGEVKlp06bYv38/zpw5gxYtWiAhIQF2dnbYvXs3DZPeiFHSJ0TBDR48GImJiRg4cCAKCgqwcOFC9OvXD//++y/XoRE5oKRPCIG+vj7Onj2Lffv2QVVVFSEhIbCwsIC/vz/XoZFaRkmfEALgQ5fOWbNmIS4uDiKRCC9fvsSoUaMwZcoU5Obmch0eqSWU9AkhUszMzHD9+nWsWrUKfD4fXl5esLa2xrVr17gOjdQCSvqEkDIEAgE2bNiAiIgImJiYIDU1Fb169cIPP/yAoqIirsMjn4GSPiGkQj179kR8fDy++eYbiMVibNiwAV9++SXu3bvHdWikhijpE0IqpampiT/++APHjx+HlpYWbt26BRsbGxw4cIBG6GyAKOkTQqplzJgxSEhIgIuLC96+fYuZM2di6NChyMrK4jo0IgNK+oSQamvdujWCg4Px888/QyAQ4OzZs7CwsEBQUBDXoZFqoqRPCJEJn8+Hh4cHoqOjYWFhgaysLAwaNAizZ8+WTM1I6i9K+oSQGrGwsMDNmzexaNEiAMC+fftga2tLE6/Xc5T0CSE1pqKigp9++gmhoaFo1aoVUlJS4ODggE2bNtX7qVMVFSV9Qshnc3FxQUJCAkaPHo3i4mKsWrUKTk5OSE1N5To08glK+oSQWqGtrY3jx4/D29sbzZo1w7Vr12BlZQVvb2/q2lmPUNInhNQaHo+HSZMmISEhAT179kReXh7c3d0xduxYvHz5kuvwCOpB0v/tt99gamoKFRUViEQiXLlypcK64eHh4PF4ZZa7d+/WYcSEkKqYmJggPDwcmzZtgpKSEk6cOAELCwuEhoZyHZrC4zTpHz9+HAsXLsSqVasQFxcHR0dH9O/fH0+ePKl0u5SUFGRkZEiW9u3b11HEhJDqatKkCVauXIkbN27AzMwM//77L/r27YtFixbh/fv3XIensDidLrF79+6wtbXFvn37JGUdO3bEsGHDsHnz5jL1w8PD4ezsjFevXqF58+bVOkZBQQEKCgokn3Nzc2FkZETTJRJSh96+fYulS5fit99+AwB88cUX0NbWxm+//QY7OzuOo2v4GsR0iYWFhYiJiYGrq6tUuaurK65fv17ptjY2NjA0NISLiwvCwsIqrbt582ZoampKFiMjo8+OnRAiGzU1Nezduxd///039PT08PDhQ9y6dQseHh40NWMd4yzpP3/+HCUlJdDX15cq19fXR2ZmZrnbGBoa4sCBA/D390dAQADMzMzg4uKCy5cvV3iclStXIicnR7Kkp6fX6nkQQqonLS0NBgYG8PPzg0AgAABcvXoV9vb2OHfuHNLS0jiOUDEocR0Aj8eT+swYK1NWyszMDGZmZpLPDg4OSE9Px44dO9CrV69ytxEKhRAKhbUXMCGkRkxMTMotv3XrFgYOHAgA1LWzDnB2p6+rq4smTZqUuavPysoqc/dfGXt7e9y/f7+2wyOE1DJfX18oKVV+n/nNN98gJyenjiJSTJwlfYFAAJFIhJCQEKnykJAQ9OjRo9r7iYuLg6GhYW2HRwipZRMmTEBUVFS566ZPnw4+nw8fHx9YWVlV2nWbfB5Ou2wuWrQIhw4dgqenJ5KTk+Hh4YEnT55g1qxZAD60x3/zzTeS+rt27cKpU6dw//593LlzBytXroS/vz/mzp3L1SkQQmqAz+dL/fzuu+9w5coVmJqaIi0tDb1798aqVatQWFjIZZiNEqdt+mPHjsWLFy+wbt06ZGRkoEuXLjh37hyMjY0BABkZGVJ99gsLC7FkyRI8ffoUqqqq6Ny5M4KCgjBgwACuToEQIgM9PT0YGBjAyMgI06ZNw+HDh5Geng49PT20bt0at2/fxoIFC3DkyBFs2rQJFy9ehK+vL8zNzbkOvdHgtJ8+F2Tpz0oIqX0FBQUQCATg8XhgjKGwsLBMZwt/f3/MmDEDL1++hKqqKn766SfMmjWrwk4eiq5B9NMnhCgmoVAoSd48Hq/c3nUjR45EYmIi+vbti3fv3mH27NkYNGgQnj17VtfhNjqU9Akh9VLLli1x4cIF7N69G0KhEOfOnYOFhQXOnDnDdWgNGiV9Qki9xefzMX/+fMTExMDKygrZ2dkYOnQoZs6cifz8fK7Da5Ao6RNC6r3OnTsjKioKS5cuBY/Hw4EDB2BjY4ObN29yHVqDQ0mfENIgCIVCbNu2DZcuXULr1q1x//599OjRA+vXr0dxcTHX4TUYlPQJIQ2Ks7MzEhISMG7cOJSUlGD16tXo1asXHj58yHVoDQIlfUJIg6OlpYWjR4/Cz88PGhoaiIyMhLW1NY4cOULj91SBkj4hpMH6+uuvkZCQgF69euHNmzeYOnUqRo0ahRcvXlR7H9HR0ejTpw+io6PlGGn9QUmfENKgGRsb459//sHWrVuhrKyMgIAAWFhYIDg4uFrbe3t7IywsDD4+PnKOtH6gN3IJIY1GXFwcJkyYgOTkZADA/PnzsWXLFqiqqkrVS0tLw/Pnz8Hj8dC/f39kZWVBT08P58+fB2MMurq6kuFgGgJZ8holfUJIo/Lu3TssW7YMe/bsAQB06tQJfn5+sLa2ltT5eDiH0uEgSn+WakipkYZhIIQoLFVVVfz66684d+4cDAwMkJSUhG7dumH79u2SqRk/Htu/NLmX/lRSUoKvry83wdcButMnhDRa2dnZmDFjBk6dOgUA6N27N7y9vWFkZITY2FiIRKIy28TExMDW1raOI/08dKdPCCEAWrRogYCAABw6dAhNmzZFeHg4LCwscOzYMUmdT8f2b+wU4ywJIQqLx+Nh2rRpiI+PR/fu3ZGTk4Px48djw4YN0NPTg0gkwv79+yESiWBgYAA9PT2uQ5Yrat4hhCiM4uJibNy4EevXr0dJSQlat24Nb29vODs7Vzi2f0NAzTuEEFIOJSUlrFmzBlevXsUXX3yB//3vf3BxccHy5csbbMKXFSV9QojCsbe3R3x8PKZPnw7GGLZt2wZ7e3skJSVxHZrcUdInhCgkdXV1HDx4EIGBgdDR0UF8fDxEIhF+/fXXBtVHX1aU9AkhCm3YsGFITExEv3798P79e8yfPx/9+/dHRkYG16HJBSV9QojCMzQ0xLlz57Bnzx6oqKjg4sWLsLCwQGBgINeh1TpK+oQQgg9dO+fMmYPY2FjY2trixYsXGDFiBKZNm4a8vDyuw6s1lPQJIeQjHTt2RGRkJFasWAEejwdPT09YW1sjMjKS69BqBSV9Qgj5hEAgwObNmxEeHo42bdrg0aNH6NmzJ9asWYOioiKuw/sslPQJIaQCvXr1QkJCAiZOnAixWIx169bB0dER9+/f5zq0GqOkTwghldDU1ISPjw+OHj2K5s2bIyoqCjY2Njh06FCD7NpJSZ8QQqph3LhxSEhIgLOzM/Lz8/Htt99i+PDhyM7O5jo0mVDSJ4SQajIyMkJoaCh27NgBgUCA06dPw8LCAufPn+c6tGqjpE8IITLg8/lYvHgxbt68ic6dO+PZs2cYMGAA5s6di7dv33IdXpUo6RNCSA1YWVkhOjoaCxcuBADs3bsXIpEIsbGx3AZWBUr6hBBSQyoqKti5cyeCg4PRsmVL3L17F927d8eWLVtQUlLCdXjloqRPCCGfqW/fvkhISMDIkSNRXFyMlStXwtnZGY8fP+Y6tDIo6RNCSC3Q0dHBiRMn4OXlBXV1dVy5cgVWVlbw9fWtV107KekTQkgt4fF4cHd3x+3bt9GjRw/k5uZi0qRJGD9+PF69esV1eAAo6RNCSK1r27YtIiIisGHDBigpKeH48eOwtLTEP//8w3Vo3Cf93377DaamplBRUYFIJMKVK1cqrR8REQGRSAQVFRW0bdsW+/fvr6NICSGk+pSUlLBq1Spcv34dHTp0kEzNuGTJEhQUFHAWF6dJ//jx41i4cCFWrVqFuLg4ODo6on///njy5Em59VNTUzFgwAA4OjoiLi4O33//PebPnw9/f/86jpwQQqqna9euiI2NxaxZswAAP/30E7p164b//ve/3ATEONStWzc2a9YsqTJzc3O2YsWKcusvW7aMmZubS5XNnDmT2dvbV/uYOTk5DADLycmRPWBCCPkMZ86cYS1atGAAmFAoZDt37mQlJSWfvV9Z8hpnd/qFhYWIiYmBq6urVLmrqyuuX79e7jaRkZFl6ru5uSE6OrrC4U4LCgqQm5srtRBCCBcGDx6MxMREDBw4EAUFBfDw8ICbmxuePn1aZzFwlvSfP3+OkpIS6OvrS5Xr6+sjMzOz3G0yMzPLrV9cXIznz5+Xu83mzZuhqakpWYyMjGrnBAghpAb09fVx9uxZ7Nu3D6qqqggNDYWFhQVOnjxZJ8fn/EEuj8eT+swYK1NWVf3yykutXLkSOTk5kiU9Pf0zIyaEkM/D4/Ewa9YsxMXFQSQS4dWrVxg9ejSmTJki99YIzpK+rq4umjRpUuauPisrq8zdfCkDA4Ny6yspKUFHR6fcbYRCITQ0NKQWQgipD8zMzBAZGYlVq1aBz+fDy8sL1tbWuHbtmtyOyVnSFwgEEIlECAkJkSoPCQlBjx49yt3GwcGhTP3g4GDY2dlBWVlZbrESQoi8KCsrY8OGDYiIiICJiQlSU1Nx9+5d+R3wsx8bf4Zjx44xZWVldvjwYZaUlMQWLlzImjZtyh4/fswYY2zFihVs0qRJkvqPHj1iampqzMPDgyUlJbHDhw8zZWVldvLkyWofk3rvEELqq5ycHPbLL78wsVgs83bVzWtK8vt1UrWxY8fixYsXWLduHTIyMtClSxecO3cOxsbGAICMjAypPvumpqY4d+4cPDw8sHfvXrRs2RK//PILRo4cydUpEEJIrdHQ0MC8efPkegweY/VoJKA6kJubC01NTeTk5FD7PiGkUZAlr3Hee4cQQkjdoaRPCCEKhJI+IYQoEEr6hBCiQCjpE0KIAqGkTwghCoTTfvpcKO2hSqNtEkIai9J8Vp0e+AqX9PPy8gCARtskhDQ6eXl50NTUrLSOwr2cJRaL8e+//6JZs2aVjub5qdzcXBgZGSE9PV1hX+pS9O+Azp/Ov76eP2MMeXl5aNmyJfj8ylvtFe5On8/no3Xr1jXenkbqpO+Azp/Ovz6ef1V3+KXoQS4hhCgQSvqEEKJAKOlXk1AoxJo1ayAUCrkOhTOK/h3Q+dP5N4bzV7gHuYQQosjoTp8QQhQIJX1CCFEglPQJIUSBUNInhBAFQkkfwObNm9G1a1c0a9YMenp6GDZsGFJSUqrcLiIiAiKRCCoqKmjbti32799fB9HWvpqcf3h4OHg8Xpnl7t27dRR17dq3bx8sLS0lL944ODjg/PnzlW7TWK4/IPv5N7br/7HNmzeDx+Nh4cKFldZrqNefkj4+XLw5c+bgxo0bCAkJQXFxMVxdXZGfn1/hNqmpqRgwYAAcHR0RFxeH77//HvPnz4e/v38dRl47anL+pVJSUpCRkSFZ2rdvXwcR177WrVtjy5YtiI6ORnR0NPr06YOhQ4fizp075dZvTNcfkP38SzWW61/q1q1bOHDgACwtLSut16CvPyNlZGVlMQAsIiKiwjrLli1j5ubmUmUzZ85k9vb28g5P7qpz/mFhYQwAe/XqVd0FVse0tLTYoUOHyl3XmK9/qcrOvzFe/7y8PNa+fXsWEhLCnJyc2IIFCyqs25CvP93plyMnJwcAoK2tXWGdyMhIuLq6SpW5ubkhOjoaRUVFco1P3qpz/qVsbGxgaGgIFxcXhIWFyTu0OlFSUoJjx44hPz8fDg4O5dZpzNe/OudfqjFd/zlz5mDgwIH46quvqqzbkK+/wg24VhXGGBYtWoSePXuiS5cuFdbLzMyEvr6+VJm+vj6Ki4vx/PlzGBoayjtUuaju+RsaGuLAgQMQiUQoKCiAj48PXFxcEB4ejl69etVhxLUnMTERDg4OeP/+PdTV1REYGIhOnTqVW7cxXn9Zzr+xXf9jx44hNjYWt27dqlb9hnz9Kel/Yu7cuUhISMDVq1errPvp0Mzs/7/cLMuQzfVNdc/fzMwMZmZmks8ODg5IT0/Hjh07GuT/9MCHc4qPj8fr16/h7+8Pd3d3REREVJj4Gtv1l+X8G9P1T09Px4IFCxAcHAwVFZVqb9dQrz8173xk3rx5OHPmDMLCwqocftnAwACZmZlSZVlZWVBSUoKOjo48w5QbWc6/PPb29rh//74cIqsbAoEA7dq1g52dHTZv3gwrKyvs3r273LqN8frLcv7laajXPyYmBllZWRCJRFBSUoKSkhIiIiLwyy+/QElJCSUlJWW2acjXn+708eE39Lx58xAYGIjw8HCYmppWuY2DgwPOnj0rVRYcHAw7OzsoKyvLK1S5qMn5lycuLq5e/1krK8YYCgoKyl3XmK5/RSo7//I01Ovv4uKCxMREqbIpU6bA3Nwcy5cvR5MmTcps06CvP2ePkOuR7777jmlqarLw8HCWkZEhWd6+fSups2LFCjZp0iTJ50ePHjE1NTXm4eHBkpKS2OHDh5mysjI7efIkF6fwWWpy/jt37mSBgYHs3r177L///S9bsWIFA8D8/f25OIXPtnLlSnb58mWWmprKEhIS2Pfff8/4fD4LDg5mjDXu68+Y7Off2K7/pz7tvdOYrj8lfcYYgHKXI0eOSOq4u7szJycnqe3Cw8OZjY0NEwgEzMTEhO3bt69uA68lNTn/rVu3si+++IKpqKgwLS0t1rNnTxYUFFT3wdeSqVOnMmNjYyYQCFiLFi2Yi4uLJOEx1rivP2Oyn39ju/6f+jTpN6brT0MrE0KIAqEHuYQQokAo6RNCiAKhpE8IIQqEkj4hhCgQSvqEEKJAKOkTQogCoaRPCCEKhJI+IYQoEEr6hNSBtWvXwtraWi779vLyQvPmzeWyb9L4UNInjc7kyZPB4/Ewa9asMutmz54NHo+HyZMn12lMS5YswaVLlySfJ0+ejGHDhtVpDIQAlPRJI2VkZIRjx47h3bt3krL379/j6NGjaNOmTZ3Ho66uXu+H3CWKgZI+aZRsbW3Rpk0bBAQESMoCAgJgZGQEGxsbSdmFCxfQs2dPNG/eHDo6Ohg0aBAePnwota/r16/D2toaKioqsLOzw6lTp8Dj8RAfHw8ACA8PB4/Hw6VLl2BnZwc1NTX06NEDKSkpkn183Lyzdu1a/PHHHzh9+jR4PB54PB7Cw8Ml+3n9+rVku/j4ePB4PDx+/FhS5uXlhTZt2kBNTQ3Dhw/Hixcvypz/2bNnIRKJoKKigrZt2+LHH39EcXHxZ3yjpLGgpE8arSlTpuDIkSOSz56enpg6dapUnfz8fCxatAi3bt3CpUuXwOfzMXz4cIjFYgBAXl4eBg8eDAsLC8TGxmL9+vVYvnx5ucdbtWoVfvrpJ0RHR0NJSanMsUotWbIEY8aMQb9+/ZCRkYGMjAz06NGjWucUFRWFqVOnYvbs2YiPj4ezszM2bNggVefixYuYOHEi5s+fj6SkJPz+++/w8vLCxo0bq3UM0shxPcwnIbXN3d2dDR06lGVnZzOhUMhSU1PZ48ePmYqKCsvOzmZDhw5l7u7u5W6blZXFALDExETGGGP79u1jOjo67N27d5I6Bw8eZABYXFwcY4yxsLAwBoCFhoZK6gQFBTEAku3WrFnDrKysysT4sdL9vHr1SlIWFxfHALDU1FTGGGPjx49n/fr1k9pu7NixTFNTU/LZ0dGRbdq0SaqOj48PMzQ0rOgrIwqE7vRJo6Wrq4uBAwfijz/+wJEjRzBw4EDo6upK1Xn48CG+/vprtG3bFhoaGpJZw548eQIASElJgaWlpdTcqd26dSv3eJaWlpL/Lp1BKisrq1bPKTk5GQ4ODlJln36OiYnBunXroK6uLlm+/fZbZGRk4O3bt7UaD2l4aLpE0qhNnToVc+fOBQDs3bu3zPrBgwfDyMgIBw8eRMuWLSEWi9GlSxcUFhYC+DBlYEUTYH/q42nySrcpbSaqDj6fX2b/RUVF1Tr2x8RiMX788UeMGDGizDpZJv4mjRMlfdKo9evXT5LA3dzcpNa9ePECycnJ+P333+Ho6AgAuHr1qlQdc3Nz+Pn5oaCgAEKhEAAQHR392XEJBIIyE263aNECAJCRkQEtLS0AkDwsLtWpUyfcuHFDquzTz7a2tkhJSUG7du0+O07S+FDzDmnUmjRpguTkZCQnJ5eZ4FpLSws6Ojo4cOAAHjx4gH/++QeLFi2SqvP1119DLBZjxowZSE5OxsWLF7Fjxw4AKPMXgCxMTEyQkJCAlJQUPH/+HEVFRWjXrh2MjIywdu1a3Lt3D0FBQfjpp5+ktps/fz4uXLiAbdu24d69e9izZw8uXLggVWf16tXw9vbG2rVrcefOHSQnJ+P48eP4z3/+U+N4SeNBSZ80ehoaGtDQ0ChTzufzcezYMcTExKBLly7w8PDA9u3by2x79uxZxMfHw9raGqtWrcLq1asBfF5TybfffgszMzPY2dmhRYsWuHbtGpSVlXH06FHcvXsXVlZW2Lp1a5meOfb29jh06BB+/fVXWFtbIzg4uEwyd3Nzw99//42QkBB07doV9vb2+Pnnn2FsbFzjeEnjQXPkEiIjPz8/TJkyBTk5OVBVVeU6HEJkQm36hFTB29sbbdu2RatWrXD79m0sX74cY8aMoYRPGiRK+oRUITMzE6tXr0ZmZiYMDQ0xevRoetGJNFjUvEMIIQqEHuQSQogCoaRPCCEKhJI+IYQoEEr6hBCiQCjpE0KIAqGkTwghCoSSPiGEKBBK+oQQokD+HwhPc4g6kn/yAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ra[rd[\"marche\"]].richter(mgm=2., bins=8, plot=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
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  "varInspector": {
   "cols": {
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    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
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    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}