{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/stammler/dustpy/HEAD?labpath=examples%2F3_advanced_customization.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Advanced Customization\n",
    "\n",
    "The core principle of `DustPy` is that you can change anything easily. Not only the initial conditions as shown in the previous chapter, but also the physics behind the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Grids\n",
    "\n",
    "By default the radial and the mass grid will be created when calling `Simulation.initialize()`. But there can be situations where you need to know the grid sizes before completely initializing the Simulation object. For example if you want to create custom fields and you need to initialize them with the correct shape.\n",
    "\n",
    "In that case you can call `Simulation.makegrids()` to only create the grids without initializing the simulation objects. In fact, `Simulation.makegrids()` is by default called within `Simulation.initialize()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : NoneType\n",
       "    m            : NoneType\n",
       "    Nm           : NoneType\n",
       "    Nr           : NoneType\n",
       "    OmegaK       : NoneType\n",
       "    r            : NoneType\n",
       "    ri           : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
       "    m            : Field (Mass grid [g]), \u001b[95mconstant\u001b[0m\n",
       "    Nm           : Field (# of mass bins), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "  -----\n",
       "    OmegaK       : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Keplerian frequency has not been initialized at this point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Radial Grid\n",
    "\n",
    "By default the radial grid is a regular logarithmic grid. Meaning, the ratio of adjacent grid cells is constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.r[1:]/sim.grid.r[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained in the previous chapter, the location of the grid boundaries and the number of grid cells can be controlled via `Simulation.ini.grid.rmin`, `Simulation.ini.grid.rmax`, and `Simulation.ini.grid.Nr`.  \n",
    "`Simulation.makegrids()` will use these parameters to create the radial grid.\n",
    "\n",
    "But it is also possible to completely customize the radial grid. To do so you have to set the locations of the radial grid cell interfaces `Simulation.grid.ri` before calling either `Simulation.makegrids()` or `Simulation.initialize()`.\n",
    "\n",
    "In this example we simply want to refine the grid at a given location. We use this helper function, which takes an existing grid `ri` and  doubles the number of grid cells in a region `num` grid cells on both sides around location `r0`. We also recursively call this function with reduced `num` to even further refine the grid and to have a smooth transition between the high and low resolution regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def refinegrid(ri, r0, num=3):\n",
    "    \"\"\"Function to refine the radial grid\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ri : array\n",
    "        Radial grid\n",
    "    r0 : float\n",
    "        Radial location around which grid should be refined\n",
    "    num : int, option, default : 3\n",
    "        Number of refinement iterations\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    ri : array\n",
    "        New refined radial grid\"\"\"\n",
    "    if num == 0:\n",
    "        return ri\n",
    "    ind = np.argmin(r0 > ri) - 1\n",
    "    indl = ind-num\n",
    "    indr = ind+num+1\n",
    "    ril = ri[:indl]\n",
    "    rir = ri[indr:]\n",
    "    N = (2*num+1)*2\n",
    "    rim = np.empty(N)\n",
    "    for i in range(0, N, 2):\n",
    "        j = ind-num+int(i/2)\n",
    "        rim[i] = ri[j]\n",
    "        rim[i+1] = 0.5*(ri[j]+ri[j+1])\n",
    "    ri = np.concatenate((ril, rim, rir))\n",
    "    return refinegrid(ri, r0, num=num-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a regular logarithmic grid and feed it to our function. We want to refine the grid in a location around $4.5\\,\\mathrm{AU}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dustpy.constants as c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = np.logspace(0., 3., num=100, base=10.) * c.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = refinegrid(ri, 4.5*c.au, num=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a new empty Simulation object, assign the grid cell interfaces and initialize the grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.ri = ri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : NoneType\n",
       "    m            : NoneType\n",
       "    Nm           : NoneType\n",
       "    Nr           : NoneType\n",
       "    OmegaK       : NoneType\n",
       "    r            : NoneType\n",
       "    ri           : ndarray\n",
       "  -----"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** it is sufficient to assign a `numpy.ndarray` to `Simulation.grid.ri` and not a `simframe.Field`.\n",
    "\n",
    "We can now make the grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
       "    m            : Field (Mass grid [g]), \u001b[95mconstant\u001b[0m\n",
       "    Nm           : Field (# of mass bins), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "  -----\n",
       "    OmegaK       : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, `Simulation.grid.ri` was automatically converted to a `simframe.Field` and the other fields were created. The number of radial grid cells is greater than $100$ as we added more grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.Nr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see that we actually refined the grid at the correct location, we can plot the location of the radial grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogy(sim.grid.r/c.au)\n",
    "ax.axhline(4.5, c=\"gray\", lw=1)\n",
    "ax.set_xlabel(\"# of radial grid cell\")\n",
    "ax.set_ylabel(\"Location of radial grid cell [AU]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The position of the radial grid cells have to be exactly in the center between their grid cell interfaces and are automatically calculated by `Simulation.makegrids()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.r == 0.5 * (sim.grid.ri[1:] + sim.grid.ri[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Mass Grid\n",
    "\n",
    "You should **NEVER** set the mass grid manually! The mass grid has to be strictly logarithmic. Only customize the mass grid by setting `Simulation.ini.grid.mmin`, `Simulation.ini.grid.mmax`, and `Simulation.ini.grid.Nmbpd`.\n",
    "\n",
    "If you have to create your own non-logarithmic mass grid for some reason, be aware that you have to re-write the entire coagulation algorithm as well, since it only conserves mass on a logarithmic grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Physics of a Field\n",
    "\n",
    "In this example we want to have a fragmentation velocity that depends on the temperature in the disk. Is the temperature below 150 K, we want to have a fragmentation velocity of 10 m/s, otherwise it shall be 1 m/s. The idea behind this approach is than particles coated in water ice are stickier that pure silicate particles and can widthstand higher collision velocities. See for example [Pinilla et al. (2017)](https://doi.org/10.3847/1538-4357/aa7edb). However, keep in mind that newer experiments suggest that particles covered in water ice do not have a beneficial collisional behavior, see e.g. [Musiolik & Wurm (2019)](https://doi.org/10.3847/1538-4357/ab0428).\n",
    "\n",
    "First, we initialize our simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fragmentation velocity has the shape `(Nr,)`, meaning there is one value at every location in the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.frag.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But right now it's constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EaSE2JiZGnTt3VmJiogzDUHp6uhISEswaHgAAAAAA824nfuaZZ3T27FmNHDlSe/bs0eXLl5WVlZXvT3Ht3LlT06ZN08CBA+Xn5yebzSabzVbgfnPnzlXbtm3l5eUlHx8f9erVS5s2bcqz74EDB/TWW2+pc+fOqlatmv3q8sCBA/XLL78U+xgAAAAAADfGZpj08GrlypVVr1497d6924zhChQeHq7IyMhc7dc7nPHjx2vWrFny8PBQjx49lJqaqp9//lmGYSgiIkLh4eE5+vv5+enkyZPy8vLSHXfcIR8fH/3222/av3+/bDabZsyYofHjxxf7WIKCgiRJ0dHRxR4LAAAAAG5mxc0/pl2JdXV1VdOmTc0arkDt27fXlClT9N133+nUqVNyc3O7bv/Vq1dr1qxZqlq1qvbs2aOlS5dq5cqVWr9+vZydnTVq1ChdvHgxxz5NmjTRF198ocTERK1atUoLFizQvn379NFHH8kwDE2YMEG//fZbCR4lAAAAAOCvTAuxnTt31t69e80arkCTJk3Sq6++qr59+6pmzZoF9p8xY4YkafLkyWrUqJG9vX379hozZowuXryoOXPm5Nhn9erVGj58uNzd3XO0P/bYY+rRo4cyMzO1cOFCE44GAAAAAFAYpoXYadOm6fTp05o8ebIyMzPNGtYUV69e1Zo1ayRJgwcPzrU9u23ZsmWFHrNFixaSpN9//92EGQIAAAAACsO01Ym/+uor9enTR2+++aYWLFigsLAw1a5dO99X7EyZMsWs0gU6ePCg0tLSVL16dfn5+eXa3qpVK0m6oSvJR48elaRCXQUGAAAAAJjDtBD7yiuvyGazyTAMxcbGXvc9sKUdYrNf9ZNXgJUkT09PeXt768KFC7p8+bIqVap03fFiY2O1fPlySVK/fv0KPY/sB5jzGq9BgwaFHgcAAAAAHJVpIfbzzz83ayjTXblyRZJUsWLFfPt4enrq4sWLBYbYjIwMPfjgg0pLS9O9996r1q1bmz5fAAAAAEDeTAuxI0eONGuom9q4ceO0YcMG1a9fXx988MEN7ZvfEtL5XaEFAAAAAORk2sJONzMvLy9JUkpKSr59kpOTJem6V2Fff/11ffjhh7r11lv1448/ysfHx9yJAgAAAACuy7QQe+TIEc2ePVv79+/Pt8/+/fs1e/Zs+6JIpaVu3bqSpBMnTuS5PTk5WRcvXlSVKlXyDbEfffSRJk+erMqVK2vlypVq2LBhic0XAAAAAJA300LszJkz9cwzz+iWW27Jt0+lSpX09NNPa/bs2WaVLZTGjRvLzc1NiYmJOnnyZK7tu3btkiQ1b948z/3/97//aezYsapYsaJWrFih4ODgkpwuAAAAACAfpoXYn3/+WcHBwfarnnnx9/dXcHCwVq1aZVbZQvHw8FCXLl0kSQsXLsy1PSIiQpLUt2/fXNu+//57jRgxQi4uLlqyZIlCQkJKdrIAAAAAgHyZFmKPHz+u+vXrF9ivQYMG+d7WW5KefvppSdLUqVN1+PBhe/vmzZv18ccfy9vbWw899FCOfTZu3KjBgwfLMAwtWLBAPXr0KNU5AwAAAAByMm11YmdnZ6WlpRXYLy0tTZmZmcWut2LFCr322mv2z+np6ZKkO+64w942ZcoU9e7dW5LUrVs3Pfnkk5o1a5aCg4PVvXt3paena9WqVTIMQ59//rm8vb1z1OjTp4+uXr2qevXqaenSpVq6dGmueXTs2FEPP/xwsY8HAAAAAFAw00JsYGCgNmzYoJSUlHzfx5qSkqINGzaoUaNGxa6XmJiorVu35mr/a1tiYmKObTNnzlRwcLDef/99rVq1Sq6ururWrZumTJmiDh065Brr4sWLkqS4uDjFxcXlOxdCLAAAAACUDtNuJx48eLDOnz+vhx9+2P66mr9KSUnRI488ogsXLmjw4MHFrvfggw/KMIzr/jz44IN57rdjxw4lJyfrwoUL+uGHH/IMsJIKHN8wDM2dO7fYxwIAAAAAKBybYRiGGQOlpKSobdu2iomJUY0aNTR06FA1aNBAkhQbG6tvvvlGZ8+eVePGjbV9+3Z5enqaUbZcCAoKkiRFR0eX8UwAAAAAoGQVN/+YdjtxxYoVtXr1ag0bNkxr1qzRzJkzZbPZJP15RVOSOnfurC+//JIACwAAAAAoEtNCrCTVrFlTq1ev1vbt27V69WodP35cklSnTh1169ZNbdq0MbMcAAAAAMDBmBpis7Vp04bACgAAAAAwnWkLOwEAAAAAUNKKHGJ37dqlhISEYhVPSEjQrl27ijUGAAAAAMBxFDnE3n777frXv/5VrOIvv/wytx0DAAAAAAqtWLcTm/R2HgAAAAAACqVYCzutXLlSXbp0KfL+Bw4cKE55AAAAAICDKVaIPX36tE6fPl2sCWS/SxYAAAAAgIIUOcTGxcWZOQ8AAAAAAApU5BDr7+9v5jwAAAAAACgQ74kFAAAAAFgGIRYAAAAAYBmEWAAAAACAZRBiAQAAAACWQYgFAAAAAFgGIRYAAAAAYBmEWAAAAACAZZgWYkeNGqUtW7aYNRwAAAAAALmYFmLnzZunkJAQNWvWTLNnz9aFCxfMGhoAAAAAAEkmhtivvvpKoaGhio6O1lNPPaXatWtr+PDhWr9+vVklAAAAAAAOzrQQe//992vt2rU6fPiwnn32WVWuXFnz589X586d9Y9//EPTp0/XuXPnzCoHAAAAAHBApi/s1KBBA02bNk3Hjx9XRESE7rrrLnuw9fPz03333aeff/7Z7LIAAAAAAAdQYqsTu7i4aODAgfr+++8VFxensWPHKj09XQsXLlSPHj3UsGFDvfvuu0pJSSmpKQAAAAAAypkSf8XOmjVrNHHiRH366aeSJA8PD4WEhOjYsWOaMGGCmjZtqv3795f0NAAAAAAA5UCJhNgzZ85o2rRpatSokbp3764FCxaoYcOGmj17tn7//XetX79ecXFxGjNmjBISEjRu3LiSmAYAAAAAoJxxMWsgwzC0cuVKffLJJ1qxYoWuXbsmNzc3DR06VGPGjFHHjh1z9Pfz89N//vMfHTx4kPfLAgAAAAAKxbQQGxAQoBMnTsgwDDVs2FCPPvqoRo0apapVqxa439q1a82aBgAAAACgHDMtxP7+++8aMGCAxowZo27duhV6v4kTJ2r48OFmTQMAAAAAUI6ZFmKPHz+umjVr3vB+gYGBCgwMNGsaAAAAAIByzLSFnV544QV99tlnBfabO3euRo8ebVZZAAAAAIADMS3Ezp07Vxs2bCiw38aNGzVv3jyzygIAAAAAHEiJvyf279LT0+Xs7FzaZQEAAAAA5UCphljDMLRr1y5Vr169NMsCAAAAAMqJYi3s1KVLlxyfV65cmastW0ZGhmJjY3X69GlWIwYAAAAAFEmxQmxUVJT9zzabTadPn9bp06fz7V+hQgX16dNH77zzTnHKAgAAAAAcVLFCbFxcnKQ/bxOuX7++Bg8erH//+9959nV1dVW1atVUoUKF4pQEAAAAADiwYoVYf39/+59ffvlltWzZMkcbAAAAAABmKlaI/auXX37ZrKEAAAAAAMhTqb9iBwAAAACAoipyiHVycpKLi4sOHTokSXJ2di70j4uLaReAAQAAAAAOpMhpsm7durLZbPaFmurUqSObzWbaxAAAAAAA+Lsih9j4+PjrfgYAAAAAwGw8EwsAAAAAsAxCLAAAAADAMkwLsYsXL1arVq30888/59tn9erVatWqlSIjI80qCwAAAABwIKaF2M8//1zHjh1Tx44d8+1z5513Kj4+Xp999plZZQEAAAAADsS0ELtnzx61aNFCbm5u+fZxc3NTcHCwdu/ebVZZAAAAAIADMS3Enj17VrVq1Sqwn6+vr86ePWtWWQAAAACAAzEtxHp7eyshIaHAfsePH5eXl5dZZQEAAAAADsS0ENu2bVtt3rxZ+/bty7fPvn37tHnzZrVp08assgAAAAAAB2JaiH388ceVmZmp3r17KyIiItf2iIgI9e7dW1lZWXr88cfNKgsAAAAAcCAuZg3Us2dPPfXUU3r33Xd17733ytvbW/Xr15ckHT16VBcvXpRhGBo3bpz69OljVlkAAAAAgAMx7UqsJE2fPl1ffPGFGjdurAsXLmjnzp3auXOnLly4oCZNmmjevHmaOXOmmSUBAAAAAA7EZhiGURIDnzp1SsePH5ck1alTR76+viVRplwICgqSJEVHR5fxTAAAAACgZBU3/5h2O/Hf+fr6ElwBAAAAAKYqkRB78uRJbdy4USdPnpQk1a5dWyEhIapdu3ZJlAMAAAAAOAhTQ2xiYqLGjh2rJUuWKCsrK8c2m82mQYMG6f3331f16tXNLAsAAAAAcBCmhdikpCSFhobq4MGD8vDwUI8ePRQQECCbzab4+Hj9+OOPWrhwofbu3astW7aocuXKZpUGAAAAADgI00LstGnTdPDgQQ0ZMiTPq63nzp3TE088oW+//VZvvfWW3njjDbNKAwAAAAAchGmrEzdp0kRXr17VkSNHVKFChTz7XLt2TQ0bNpS7u7sOHjxoRtlygdWJAQAAADiK4uYf094Te+zYMYWEhOQbYCWpQoUKCgkJUUJCglllAQAAAAAOxLQQ6+HhoXPnzhXY79y5c/Lw8DCrLAAAAADAgZgWYlu3bq1169Zpx44d+fbZuXOnoqKidPvtt5tVFgAAAADgQEwLsU899ZSuXbumrl27asqUKYqJidHVq1d19epVHThwQC+//LK6deumzMxMPfXUU2aVBQAAAAA4ENMWdpKkN998U1OmTFF+Q9psNr322mt6/vnnzSpZLrCwEwAAAABHcdMs7CRJzz//vLZs2aJhw4YpICBArq6ucnV1VUBAgIYPH67NmzcTYAEAAAAARWbae2Kz3X777Zo3b57ZwwIAAAAAYO6VWAAAAAAAShIhFgAAAABgGUW+nbh+/fpFLmqz2RQbG1vk/QEAAAAAjqnIITY+Pt7EaQAAAAAAULAih9isrCwz5wEAAAAAQIF4JhYAAAAAYBmEWAAAAACAZZgeYn/77Tc99dRTCgkJUePGjTVx4kT7tk2bNmn27Nk6f/682WUBAAAAAA6gyM/E5mXGjBl67rnnlJGRIenPVYjPnTuXo89TTz0lNzc3PfbYY2aWBgAAAAA4ANOuxK5YsUITJkxQnTp1tHjxYp09e1aGYeTo06FDB1WvXl2RkZFmlQUAAAAAOBDTrsTOmDFDnp6eWrVq1XXfIRscHKyDBw+aVRYAAAAA4EBMuxK7c+dO3XHHHdcNsJJUrVo1nT592qyyAAAAAAAHYlqITU9PV6VKlQrsd/bsWbm4mPooLgAAAADAQZgWYuvVq6c9e/Zct096err27t2rwMBAs8oCAAAAAByIaSG2X79+io+P14wZM/Lt8/bbbysxMVEDBw40qywAAAAAwIGYdl/vxIkTNX/+fD377LPaunWrBgwYIEk6c+aMlixZoiVLlmj+/PmqV6+ennjiCbPKAgAAAAAciM34+3twiuHQoUMaPHiw9u/fL5vNJsMwZLPZJEmGYahp06ZaunSpGjZsaFbJciEoKEiSFB0dXcYzAQAAAICSVdz8Y+oKS4GBgdq9e7eWLVumn376SfHx8crKypKfn5+6d++uQYMGydnZ2cySAAAAAAAHYvoywU5OTurfv7/69+9v9tAAAAAAAAdn2sJOy5cvV2ZmplnDAQAAAACQi6mrE9eqVUtPPPGENm3aZNawAAAAAADYmRZiBwwYoEuXLumDDz7QnXfeqQYNGuill17SgQMHzCoBAAAAAHBwpoXYRYsW6cyZM/rkk0/UqVMnHTt2TFOnTlVQUJDatGmjmTNn6vTp02aVAwAAAAA4IFNfsfNXv//+u77++mvNnz9fe/bskSQ5Ozurc+fOGjZsmEaMGFESZS2JV+wAAAAAcBTFzT+mXYn9u1q1amnChAn69ddfFR0drRdeeEF169bV6tWrNXr06GKPv3PnTk2bNk0DBw6Un5+fbDab/Z201zN37ly1bdtWXl5e8vHxUa9evQp8hnfjxo3q1auXfHx85OXlpbZt2+qLL74o9jEAAAAAAG6M6a/YyYu/v7+aNm2qxo0bKy4uTmZc/H3ttdcUGRl5Q/uMHz9es2bNkoeHh3r06KHU1FStWrVKP/30kyIiIhQeHp5rn0WLFunee+9VVlaWQkNDVa1aNf38888aOXKk9u7dq3feeafYxwIAAAAAKJwSC7GZmZlauXKlvv76a3333XdKSUmRYRjy9/fX/fffX+zx27dvr+bNm6tNmzZq06aNAgIClJaWlm//1atXa9asWapatao2b96sRo0aSZI2b96ssLAwjRo1SmFhYfL29rbvc/78eY0ePVqZmZlatGiRBg4cKEk6c+aMOnbsqOnTp6tPnz4KCwsr9vEAAAAAAApmeojduHGj5s+fr4iICP3xxx8yDEM+Pj4aNmyYhg0bppCQEFPqTJo06Yb6z5gxQ5I0efJke4CV/gzDY8aM0ezZszVnzhw988wz9m2ffvqpLl26pP79+9sDrCTdeuutevvttzVw4EBNnz6dEAsAAAAApcS0Z2Kff/551atXT6Ghofroo4905coVDR48WJGRkTp9+rQ+/PBD0wLsjbp69arWrFkjSRo8eHCu7dlty5Yty9G+YsWKfPfp3bu33N3dtXr1aqWmppo9ZQAAAABAHky7EvvWW2/JyclJXbp00bBhwzRw4EBVqlTJrOGL5eDBg0pLS1P16tXl5+eXa3urVq0kSXv37s3Rnr2qcvb2v3J1ddVtt92mHTt26NChQ2revHkJzLzsGIahS6kZZT0NAAAAACa4xd2lUAvhWoFpIXb69Om677775Ovra9aQpklISJCkPAOsJHl6esrb21sXLlzQ5cuXValSJV26dElJSUnX3c/Pz087duzQsWPHChVis5eS/rvY2Fg1aNCgMIdSai6lZqjFv34q62kAAAAAMMGel3uoskeFsp6GKUy7nfipp566KQOsJF25ckWSVLFixXz7eHp6SpIuX76cY5/r7ff3fQAAAAAAJatUXrGDP+X3Mt/8rtACAAAAAHJyiBDr5eUlSUpJScm3T3JysiTZn+PN3id7v1tuuaXAfcqTW9xdtOflHmU9DQAAAAAmuMW9/ES/8nMk11G3bl1J0okTJ/LcnpycrIsXL6pKlSr2QHrLLbeocuXKSkpK0okTJ9S0adNc+2WP5+/vX0IzLzs2m63c3DMPAAAAoPww7ZnYm1njxo3l5uamxMREnTx5Mtf2Xbt2SVKuxZlatGiRY/tfXbt2Tfv375e7u7sCAwNLYNYAAAAAgL9ziBDr4eGhLl26SJIWLlyYa3tERIQkqW/fvjnae/funWP7Xy1fvlypqanq1q2b3N3dzZ4yAAAAACAPDhFiJenpp5+WJE2dOlWHDx+2t2/evFkff/yxvL299dBDD+XY5+GHH9Ytt9yiyMhILV682N5+9uxZTZw4UZL0zDPPlMLsAQAAAACShUPsihUrdMcdd9h/0tPTJSlH24oVK+z9u3XrpieffFJ//PGHgoODFR4erl69eik0NFQZGRn6/PPP5e3tnaOGj4+PPvvsMzk5OWnw4MHq0qWLhgwZosaNG+vIkSN6+umnFRYWVopHDQAAAACOzfSFnVJSUrRjxw6dOnVKaWlp+fYbMWJEseokJiZq69atudr/2paYmJhj28yZMxUcHKz3339fq1atkqurq7p166YpU6aoQ4cOedYZNGiQ1q9fr6lTp2rLli1KT09X06ZN9cQTT2jkyJHFOgYAAAAAwI2xGYZhmDXYSy+9pHffffe6r7IxDEM2m02ZmZlmlbW87PfE5vceWQAAAAAoL4qbf0y7Evv2229r6tSpcnZ2Vu/evRUYGFgu358KAAAAACg7poXYTz75RB4eHvrll1/UqlUrs4YFAAAAAMDOtIWdjh8/rk6dOhFgAQAAAAAlxrQQW7NmTXl6epo1HAAAAAAAuZgWYu+77z5FRUUpOTnZrCEBAAAAAMjBtBD7yiuv6B//+If69eunI0eOmDUsAAAAAAB2pi3s1KtXL2VlZSkqKkr/+Mc/5O/vLz8/Pzk55c7JNptNP//8s1mlAQAAAAAOwrQQGxUVZf9zZmamjh49qqNHj+bZ12azmVUWAAAAAOBATAuxcXFxZg0FAAAAAECeTAux/v7+Zg0FAAAAAECeTFvYCQAAAACAkmbaldhsZ86c0WeffaZffvlFJ0+elCTVrl1boaGhGjVqlG699VazSwIAAAAAHITNMAzDrMEWLVqk0aNH68qVK/r7sDabTZUqVdKcOXM0aNAgs0qWC0FBQZKk6OjoMp4JAAAAAJSs4uYf024n3rFjh4YOHark5GQNGDBAS5Ys0a+//qrdu3dr6dKlGjhwoK5cuaL7779fO3bsMKssAAAAAMCBmHY78ZtvvqnMzExFRERowIABObY1b95c/fr105IlSzRo0CBNmzZNERERZpUGAAAAADgI067EbtiwQR06dMgVYP9qwIABCgkJ0S+//GJWWQAAAACAAzEtxCYlJalu3boF9qtbt66SkpLMKgsAAAAAcCCmhdiaNWvq119/LbDf7t27VbNmTbPKAgAAAAAciGkh9q677tLBgwf1wgsvKDMzM9d2wzA0efJkHThwQD179jSrLAAAAADAgZj2ip0TJ06oZcuWOn/+vOrWrat77rlHAQEBkqRjx45p4cKFio+PV9WqVbVr1y75+fmZUbZc4BU7AAAAABxFcfOPaasT+/n5ac2aNXrggQe0f/9+/fvf/5bNZpMk+ztjmzVrpvnz5xNgAQAAAABFYlqIlf4MqXv37lVUVJR++eUX/f7775KkWrVq6c4771RYWJiZ5QAAAAAADsbUEJstLCyMwAoAAAAAMJ1pCzsBAAAAAFDSinwldv369ZKktm3byt3d3f65sEJDQ4taGgAAAADgoIocYsPCwmSz2RQTE6PAwED758LK6zU8AAAAAABcT5FD7IgRI2Sz2VS5cuUcnwEAAAAAKCmmvScWRcd7YgEAAAA4iuLmHxZ2AgAAAABYhmkhtn79+po0aVKB/Z5//nk1aNDArLIAAAAAAAdiWoiNj49XYmJigf3OnTun+Ph4s8oCAAAAABxIqd9OnJycrAoVKpR2WQAAAABAOVDk1YlvVFZWlg4ePKi1a9eqbt26pVUWAAAAAFCOFOtKrLOzs/1HkubNm5ej7a8/FSpU0G233aYzZ85o6NChpkweAAAAAOBYinUltk6dOvZ3wyYkJKhixYqqVq1ann1dXV1Vq1Yt9evXT+PGjStOWQAAAACAgypWiP3rAk1OTk4aMmSIPvvss+LOCQAAAACAPJn2TOzatWtVs2ZNs4YDAAAAACAX00Jsp06dzBoKAAAAAIA8lcjqxNHR0Tp8+LAuX74swzDy7DNixIiSKA0AAAAAKMdMDbGrV6/W448/rtjY2Hz7GIYhm81GiAUAAAAA3DDTQuyOHTvUu3dv2Ww23X///dq3b5/27dun5557TrGxsVq9erUuXLigUaNG8Z5YAAAAAECRmBZi33zzTWVkZGjlypXq3r27Ro0apX379un111+XJF28eFGPPfaYli9frh07dphVFgAAAADgQJzMGmjTpk1q2bKlunfvnud2b29vffHFF3JyctLkyZPNKgsAAAAAcCCmhdjz58+rUaNG9s+urq6SpOTkZHubm5ub7rzzTq1atcqssgAAAAAAB2JaiK1evbouXbqU47MkHT16NEe/q1evKikpyayyAAAAAAAHYlqIbdiwoeLi4uyf27ZtK8Mw9PHHH9vbjhw5ojVr1qh+/fpmlQUAAAAAOBDTQmyvXr108OBBxcTESJJ69uwpf39/ffjhh2rXrp0GDRqkNm3aKDU1VQ899JBZZQEAAAAADsS01YlHjBihypUrKysrS9Kfz8R+9913uueee7R9+3Zt375dTk5Oevjhh/Xkk0+aVRYAAAAA4EBshmEYJV3kwIEDunDhgho2bGh/Vhb/JygoSJIUHR1dxjMBAAAAgJJV3Pxj2pXY62nSpElplAEAAAAAlHOmPRPr7OxcqGddH3nkEbm4lEp2BgAAAACUM6aFWMMwVNg7k0vhDmYAAAAAQDlkWogtrKSkJLm5uZV2WQAAAABAOVCs+3oTEhJyfL5y5UqutmwZGRk6ePCgfvrpJzVo0KA4ZQEAAAAADqpYITYgIEA2m83+edGiRVq0aNF19zEMQ4888khxygIAAAAAHFSxQmxoaKg9xK5bt041atTIdyViV1dX1apVS/369dOAAQOKUxYAAAAA4KCKFWKjoqLsf3ZyctLdd9+tzz77rLhzAgAAAAAgT6a96yYuLk5eXl5mDQcAAAAAQC6mhVh/f3+zhgIAAAAAIE+mhdhsUVFRWr9+vU6dOqW0tLQ8+9hsNs2ZM8fs0gAAAACAcs60EJuUlKT+/fvrl19+kWEY1+1LiAUAAAAAFIVpIXbSpElav369GjZsqDFjxigwMFCVKlUya3gAAAAAAMwLsZGRkbr11lu1ZcsW+fj4mDUsAAAAAAB2TmYNlJSUpJCQEAIsAAAAAKDEmBZiGzVqpOTkZLOGAwAAAAAgF9NC7D//+U9FRUXpyJEjZg0JAAAAAEAOpoXYhx9+WOPGjVOnTp30+eef68SJE2YNDQAAAACAJBMXdnJ2dpYkGYahhx9++Lp9bTabMjIyzCoNAAAAAHAQpoXYOnXqyGazmTUcAAAAAAC5mBZi4+PjzRoKAAAAAIA8mfZMLAAAAAAAJa3EQmxaWppOnTql8+fPl1QJAAAAAICDMT3E/ve//1XLli3l6ekpPz8/TZgwwb5t8eLFGjhwIK/hAQAAAAAUiWkhNjMzUwMGDND/+3//TzExMfrHP/4hwzBy9GnRooWWLl2qBQsWmFUWAAAAAOBATAux77//viIjI3X33Xfr2LFj2rdvX64+DRo0UMOGDfXDDz+YVRYAAAAA4EBMC7Fz587VrbfeqgULFujWW2/Nt1/Tpk117Ngxs8oCAAAAAByIaSH24MGDateunTw9Pa/bz9PTU4mJiWaVBQAAAAA4ENNCbIUKFZSamlpgv4SEBFWqVMmssgAAAAAAB2JaiA0KCtLOnTt1+fLlfPucPXtWu3fvVnBwsFllAQAAAAAOxLQQO3z4cP3xxx8aM2aM0tPTc23PzMzU2LFjlZKSopEjR5pVFgAAAADgQFzMGujRRx/VwoUL9c0332jTpk266667JEl79uzRk08+qeXLlysuLk49evTQAw88YFZZAAAAAIADsRl/f5lrMaSmpuqZZ57Rp59+qmvXruXY5uzsrNGjR2vWrFlyd3c3q2S5EBQUJEmKjo4u45kAAAAAQMkqbv4xNcRmS0xMVFRUlOLj45WVlSU/Pz917txZtWrVMrtUuUCIBQAAAOAoipt/TLud+K+qV6+uIUOGlMTQAAAAAAAHZtrCTgAAAAAAlDTTr8SuW7dO69at06lTp5SWlpZnH5vNpjlz5phdGgAAAABQzpkWYs+fP68BAwZow4YNKugxW0IsAAAAAKAoTAuxTz31lH755RcFBQXp0UcfVf369eXl5WXW8AAAAAAAmBdily1bJj8/P23evJnwCgAAAAAoEaYt7JSZman27dsTYAEAAAAAJca0ENu6dWudPn3arOEAAAAAAMjFtBA7efJkbd68WStXrjRrSAAAAAAAcjDtmdguXbro66+/1ogRI9SrVy91795dtWvXlpNT3jk5NDTUrNIAAAAAAAdh6ntir1y5ogoVKujLL7/Ul19+ed2+mZmZZpYGAAAAADgA00Ls3Llz9dBDD8kwDLVs2fKmfcVOTEyMpk6dqjVr1uj8+fPy9fVVnz599Morr6hatWq5+iclJemtt95SZGSk4uLilJWVJX9/f/Xq1UvPP/+8atSoUQZHAQAAAACOyWYYhmHGQE2bNlV8fLy+//57hYWFmTGk6dasWaO+ffsqJSVFTZo0UdOmTbV//34dOnTI/nogPz8/e/9z586pQ4cOOnz4sGrWrKm2bdtKkrZt26bTp0/L19dXmzdvlr+/f7HmFRQUJEmKjo4u1jgAAAAAcLMrbv4xbWGn+Ph4derU6aYNsCkpKbr//vuVkpKil156STExMVq0aJEOHDigCRMm6MSJE3rooYdy7PPGG2/o8OHD6tevn+Li4hQZGWm/IjtgwACdOnVKL730UhkdEQAAAAA4HtNCbO3atVWxYkWzhjPd4sWLdebMGTVu3Fgvv/yyvd1ms+mNN95QQECAfvrpJ+3Zs8e+bf369ZKk559/Xu7u7vZ2d3d3TZkyRZK0ffv2UjoCAAAAAIBpIXbEiBFau3atzp8/b9aQptq5c6ekP1dF/vuKyRUqVFBISIgkKTIy0t7u5uZW4LhVq1Y1cZYAAAAAgOsxLcS+8MIL6ty5szp37qyoqCiZ9KitaZKTkyVJVapUyXN7dhj965XYHj16SJKmTZum1NRUe3tqaqpee+01Scp1CzIAAAAAoOSYtjpxo0aNJEnHjh1T165dVaFCBdWsWTPP98TabDbFxsaaVbpQqlevbp9fXuLi4nJtnzBhgtatW6fIyEjVq1dP7dq1kyRt3bpVqampeuedd/Tggw8Weg7ZDzD/XWxsrBo0aFDocQAAAADAUZkWYuPj43N8Tk9PV0JCglnDF1toaKjeeOMNrVixQufOncvxOp2TJ09q1apVkqTLly/b2z09PbVixQo9+uij+uqrr3Lcaty5c2d17Nix9A4AAAAAAGDe7cRZWVk39FPaevTooVatWunKlSu6++67tW3bNl25ckWbN2/W3XffrYyMDEnKceU4ISFBbdu21Q8//KAvvvhCZ8+e1dmzZzVv3jzt2bNHYWFh+uWXXwo9h+jo6Dx/uAoLAAAAAIVjWoi92dlsNi1evFhBQUHasWOH2rVrp0qVKqlDhw46e/asXnnlFUk5n5kdOXKk9u/fr08//VTDhw9X9erVVb16dY0YMUKffPKJUlNTNWHChDI6IgAAAABwPKbdTmwF/v7+2r17t5YsWaJNmzbp6tWrCgoK0gMPPKDFixdL+r/nVo8fP66oqCi5ubmpb9++ucbq37+/XF1dtX37dqWmpuZ4BQ8AAAAAoGSYFmKz36laEFdXV1WtWlUNGzaUzWYzq3yhubi4aMiQIRoyZEiO9k2bNkmSwsLCJEknTpyQ9Odzsc7OzrnGcXZ2lqenpy5cuKCLFy+qZs2aJTtxAAAAAIB5ITYsLOyGQqmXl5eGDh2q119/vczftXr69GlFRESoatWqGjhwoCTZQ+n58+cVFxenevXq5dgnNjZWFy5ckKenZ45FogAAAAAAJce0Z2JHjBihvn37yjAM2Ww2tWzZUv3791f//v3VqlUre8Dt06ePunbtKjc3N/33v/9V+/btdeHCBbOmcV379+/P8b5X6c8rrv3799fly5c1ffp0eXh4SJLq1aun5s2bS5Iee+wxJSUl2fe5ePGiHnvsMUlSeHi4XFwc6q5sAAAAACgzNsMwDDMGSkxMVLt27dS4cWPNnj3b/t7YbEeOHNG4ceN04MABbdmyRZ6ennrkkUe0YMECTZo0SW+88YYZ07iuBx98UEuWLFGrVq3k6+urs2fPasOGDUpLS9OUKVP06quv5ui/detWdevWTVeuXFG1atXs74ndsmWL/vjjDwUEBGjjxo2qVatWseaV/RxudHR0scYBAAAAgJtdcfOPaSH24Ycf1g8//KDY2Nh8Fzm6evWqGjZsqJ49e2rOnDm6dOmS6tevr5o1a2r//v1mTOO6li5dqo8++kh79uzRH3/8oSpVqqh9+/YaP368/VnYv4uNjdVbb72ln3/+WSdPnpSTk5Pq1aunfv366dlnn5WPj0+x50WIBQAAAOAobpoQ6+vrq7CwMH3zzTfX7Xffffdp3bp1OnXqlCSpe/fu2rRpk5KTk82YhiURYgEAAAA4iuLmH9OeiU1KSsrx3Gh+Ll26lKMfiyIBAAAAAArLtBDbqFEjrV27VjExMfn2iYmJ0dq1axUYGGhvO3XqFEEWAAAAAFAopoXYsWPHKi0tTaGhoXrzzTd1+PBhpaamKjU1VYcPH9a0adPUqVMnpaena+zYsZL+fEZ2586dat26tVnTAAAAAACUY6a9G+bRRx9VTEyMZs2apcmTJ2vy5Mm5+hiGofHjx+uRRx6RJB07dkzDhg3ToEGDzJoGAAAAAKAcM21hp2wbN27Uhx9+qE2bNtkXb/L19VVISIgee+wxdezY0cxy5QILOwEAAABwFMXNP6Zdic0WEhKikJAQs4cFAAAAAMC8Z2IBAAAAAChphFgAAAAAgGWYGmJTUlI0depUtWnTRt7e3nJ2ds7zx8XF9LuYAQAAAAAOwLQ0mZSUpDvvvFPR0dFydnaWq6urDMOQr6+vTp8+rez1o/z9/c0qCQAAAABwMKZdiZ02bZr279+vRx99VJcuXdLgwYNls9l08uRJJScna+7cuapZs6batWuno0ePmlUWAAAAAOBATAuxS5cuVa1atTR79my5u7vLZrPZt7m7u2vEiBFavXq1lixZounTp5tVFgAAAADgQEwLsceOHVOrVq1UoUKFPwd2+nPoa9eu2fs0bdpUnTp10ty5c80qCwAAAABwIKaFWHd3d7m7u9s/33LLLZKk06dP5+jn4+OjuLg4s8oCAAAAAByIaSG2Tp06On78uP1zkyZNJEnr1q2zt2VkZGj79u2qWrWqWWUBAAAAAA7EtBB75513au/evbp8+bIkqW/fvnJxcdG4ceP00UcfadmyZRo8eLDi4+PVqVMns8oCAAAAAByIaSH2vvvuU/PmzbVp0yZJUq1atfTmm2/q4sWLGjt2rMLDw/Xdd9/p1ltv1VtvvWVWWQAAAACAA7EZ2S9wLSFbt27VkiVLdOHCBQUGBmrUqFHy8fEpyZKWExQUJEmKjo4u45kAAAAAQMkqbv5xMWsily5dks1mU6VKlXK0t2vXTu3atTOrDAAAAADAgZl2O7G3t7d69Ohh1nAAAAAAAORiWoitXLmy6tevb9ZwAAAAAADkYlqIbdmypWJjY80aDgAAAACAXEwLsZMmTdL27dsVERFh1pAAAAAAAORg2sJOHh4eevjhh3XvvfeqT58+6tu3r+rWrSt3d/c8+4eGhppVGgAAAADgIEx7xY6Tk5NsNpuyh7PZbNftn5mZaUbZcoFX7AAAAABwFDfNK3ZGjBhRYHAFAAAAAKA4TAuxc+fONWsoAAAAAADyVOSFnbp06aK33347z20JCQk6f/58kScFAAAAAEBeihxio6KidODAgTy31atXT88++2yRJwUAAAAAQF5Me8XOXxmGIZPWiwIAAAAAwK5EQiwAAAAAACWBEAsAAAAAsAxCLAAAAADAMgixAAAAAADLsBlFXIHJyclJNputaEVtNmVkZBRp3/IoKChIkhQdHV3GMwEAAACAklXc/ONSnOJFXYGYlYsBAAAAAEVR5BCblZVl5jwAAAAAACgQz8QCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLcLgQGxMTowceeEC+vr5yc3NTQECAnnjiCZ07dy7ffQzD0Ny5cxUaGiofHx95eHiofv36uv/++xUdHV2KswcAAAAAx+ZQIXbNmjW6/fbb9fXXX8vb21t9+vSRm5ub/vOf/6hly5Y6ceJErn1SU1PVq1cvjRo1StHR0QoJCVHfvn3l4+Ojb7/9Vr/++msZHAkAAAAAOCaXsp5AaUlJSdH999+vlJQUvfTSS/rXv/4l6c+rrBMnTtQ777yjhx56SD/++GOO/caMGaOVK1fqkUce0axZs+Th4WHfdurUKV27dq1UjwMAAAAAHJnNMAyjrCdRGr766isNHz5cjRs31m+//SYnp/+7CH3t2jUFBgYqPj5eu3fvVosWLSRJ27ZtU7t27dS2bVtt2bJFNputROYWFBQkSdyaDAAAAKDcK27+cZjbiXfu3ClJCg0NzRFgJalChQoKCQmRJEVGRtrbP/nkE0nSE088UWIBFgAAAABQeA5zO3FycrIkqUqVKnlur1q1qiRpz5499rY1a9ZIkjp06KDY2Fh98803On78uKpXr66ePXuqY8eOJTxrAAAAAMBfOUyIrV69uiTp2LFjeW6Pi4vLsT01NVVHjx6V9GeY/ec//6m0tDR7/9dff1333nuvvvjiC7m6uhZqDtmXzf8uNjZWDRo0KNyBAAAAAIADc5jbiUNDQyVJK1asyPU6nZMnT2rVqlWSpMuXL0uSLl68aN/++OOPq3fv3oqJidHFixe1ePFiVatWTQsWLNCLL75YOgcAAAAAAHCchZ0Mw9Dtt9+uXbt26fbbb9d//vMfNW3aVPv27dNjjz2mmJgYZWRkqEmTJoqJidHvv/+u2rVrS5Juu+027dmzJ8eztN9//7169+4td3d3nTlzRrfcckuR58bCTgAAAAAcBQs7FZLNZtPixYsVFBSkHTt2qF27dqpUqZI6dOigs2fP6pVXXpH0f8/Menl52fcdMWJErsWgevXqpRo1aig1NVXbtm0rteMAAAAAAEfmMM/ESpK/v792796tJUuWaNOmTbp69aqCgoL0wAMPaPHixZL+7/8VuOWWW1SlShVduHBBAQEBeY4XEBCgs2fP6uzZs6V1CAAAAADg0BwqxEqSi4uLhgwZoiFDhuRo37RpkyQpLCzM3hYcHKy1a9fqwoULeY51/vx5STmv2gIAAAAASo7D3E58PadPn1ZERISqVq2qgQMH2tv79esnSYqKisq1T0JCguLj4yVJLVu2LI1pAgAAAIDDc6gQu3//fqWmpuZoO3HihPr376/Lly9r+vTp8vDwsG8bPXq0fRXi7777zt6ekpKixx9/XBkZGerVq5fq1KlTascAAAAAAI7MoW4nfuedd7RkyRK1atVKvr6+Onv2rDZs2KC0tDRNmTJFI0eOzNH/lltu0VdffaW+ffsqPDxc7dq1k6+vr7Zu3arff/9dAQEB+u9//1tGRwMAAAAAjsehrsSGh4erffv2OnDggCIiIrRv3z717NlTa9eu1auvvprnPnfddZe2b9+uAQMG6MiRI1q+fLnc3d311FNPafv27fbX8AAAAAAASp7DvCf2ZsZ7YgEAAAA4Ct4TCwAAAABwGIRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWIbNMAyjrCfh6CpVqqRr166pQYMGZT0VAAAAAChRsbGxqlChgi5fvlyk/bkSexPw9PRUhQoVrtsnKytLiYmJysrKKvS4N7pPbGysYmNjCz2+oyvK30lZK+s5l0Z9s2uYMV5xxijpc5/z/saV9XlUFGU955KuXxLjl+W5Xxr7ce7fuLI+j4qirOdstXO/vP+bL908536FChXk6elZ9AEMWEJcXJwhyYiLiyuxfZo2bWo0bdq0aBN0QEX5OylrZT3n0qhvdg0zxivOGCV97nPe37iyPo+KoqznXNL1S2L8sjz3S2M/zv0bV9bnUVGU9Zytdu6X93/zDaP8nPtciQUAAAAAWAYhFgAAAABgGYRYi/D29tbLL78sb2/vEt0HhWfF77es51wa9c2uYcZ4xRmDc//mY8Xvt6znXNL1S2L8sjz3S3s/FI4Vv9+ynrPVzn3+zbcOVieGXVBQkCQpOjq6jGcCoLRw3gOOiXMfcEzl5dznSiwAAAAAwDK4EgsAAAAAsAyuxAIAAAAALIMQCwAAAACwDEIsAAAAAMAyCLEAAAAAAMsgxAIAAAAALIMQCwAAAACwDEIsAAAAAMAyCLEokt27d+vOO++Uh4eH6tWrp/fff7+spwSghO3YsUMjRoxQw4YNZbPZNHny5LKeEoBS8O2336p3797y9fVV5cqVFRoaqg0bNpT1tACUsHnz5un222+Xt7e3PD091apVK/3vf/8r62lJklzKegKwnsTERHXv3l1t27bV8uXLtWvXLo0fP16VK1fW8OHDy3p6AErIxo0btWXLFnXs2FHnzp0r6+kAKCUzZ85Uo0aN9J///EdeXl76/PPP1bVrV23btk0tWrQo6+kBKCEXLlxQeHi4goOD5e7urqVLl2ro0KFyd3dXeHh4mc7NZhiGUaYzgOW89tpreu+99xQfH6+KFStKkh5//HGtXr1ahw4dKuPZASgpWVlZcnL68waegIAADRs2TFOnTi3jWQEoaX/88YeqVq1q/5yVlaVmzZopJCRE//3vf8twZgBKW8eOHeXr66uFCxeW6Ty4nRg37Mcff1SvXr3sAVaShgwZosOHD+vo0aNlODMAJSk7wAJwLH8NsNKf/1tw2223KS4uroxmBKCsVK1aVdeuXSvraRBiy5udO3dq2rRpGjhwoPz8/GSz2WSz2Qrc7+rVq3rppZcUGBgod3d31apVS6NHj9bJkydz9T106JCaNGmSoy3788GDB805EAA3pDTOfQA3n7I49zMzM7V9+3Y1bNjQjEMAUASlee5nZGTo0qVLWrBggVatWqXHHnvMzEMpGgPlSv/+/Q1JuX6u5+rVq8Ydd9xhSDJ8fX2Ne+65x2jbtq0hyahevboRGxubo7+Li4vx4Ycf5hpDkjF//nzTjwlAwUrj3P8rf39/48UXXzT7MADcoNI+9w3DMGbOnGk4Ozsbe/fuNfNQANyA0jr3T506ZR/b2dnZ+Pjjj0vqkG4ICzuVM+3bt1fz5s3Vpk0btWnTRgEBAUpLS7vuPlOnTtWWLVvUvn17/fTTT/Ly8pIkzZgxQ88884xGjx6tqKioUpg9gKLi3AccU2mf+1u3btVzzz2nyZMnq1mzZmYfDoBCKq1zv1q1atq+fbsuX76slStX6oknnlDVqlU1aNCgkjq0QmFhp3LO3d1daWlpyu+vOT09XTVq1FBSUpJ27dqlli1b5tjeokUL7d27Vzt27FDr1q0lSTVq1NAzzzyjSZMm2fudPn1avr6++v7773X33XeX3AEBKJSSOPf/ioWdgJtTSZ778fHxuuOOOxQaGqoFCxYU6tZFAKWjpP/dz/bII49o3bp1Zb6YK8/EOriNGzcqKSlJDRo0yPXLLEmDBw+WJC1btszeFhgYqAMHDuTol/25cePGJThbAGYpyrkPwPqKeu5fvHhRvXv3VkBAgObNm0eABSzGrH/3g4ODb4qFXAmxDm7Pnj2SpFatWuW5Pbt979699ra77rpL33//va5evWpvi4iIUKNGjVS/fv0SnC0AsxTl3AdgfUU599PT0zVw4EClpKQoMjJSHh4eJT9RAKYy69/9TZs2KSAgwNS5FQXPxDq4hIQESZKfn1+e27Pbjx07Zm8bM2aMZs+erXvuuUfjx4/Xr7/+qo8//lifffZZyU8YgCmKcu4nJiZq3bp1kqSUlBQdOHBAERER8vT05DECwCKKcu4//vjjWrdunT755BPFxcXZX63j5uaW5xUdADefopz7nTt31qBBg9SkSROlpqYqMjJSX3/99U3xfmhCrIO7cuWKJOV45+tfeXp6SpIuX75sb6tevbpWrVqlJ554Qr1799att96qGTNmaPjw4SU/YQCmKMq5Hx0drSFDhtg/L1q0SIsWLZK/v7/i4+NLbrIATFOUc3/16tXKysrSQw89lKMv5z5gHUU591u0aKH33ntPx48fl6enp5o2baply5apT58+JT/hAhBiUSTBwcHasGFDWU8DQCkKCwvLd8EIAOUXQRVwTDNnztTMmTPLehp54plYB5e9tHZKSkqe25OTkyVJlSpVKrU5ASh5nPuAY+LcBxxTeTv3CbEOrm7dupKkEydO5Lk9u93f37/U5gSg5HHuA46Jcx9wTOXt3CfEOrgWLVpIknbt2pXn9uz25s2bl9qcAJQ8zn3AMXHuA46pvJ37hFgHFxISosqVKys2Nla7d+/OtT0iIkKS1Ldv31KeGYCSxLkPOCbOfcAxlbdznxDr4FxdXfXEE09IksaOHWu/H16SZsyYob1796pTp05q3bp1WU0RQAng3AccE+c+4JjK27lvM1hqslxZsWKFXnvtNfvnbdu2yTAMtWvXzt42ZcoU9e7d2/45NTVVYWFh2rp1q3x9fXXnnXfq2LFj2rp1q6pXr64tW7aofv36pXocAG4M5z7gmDj3Acfk6Oc+r9gpZxITE7V169Zc7X9tS0xMzLHN3d1da9eu1Ztvvqmvv/5aS5culY+Pjx588EG99tpr+b4UGcDNg3MfcEyc+4BjcvRznyuxAAAAAADL4JlYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAAAAAJZBiAUAAAAAWAYhFgAAAABgGYRYAAAAAIBlEGIBAPmy2Ww5fipUqKBq1aqpWbNmevDBB7Vo0SJlZGRcd/+AgIDSm3A5lZycrHHjxqlOnTpycXGRzWbTK6+8UtbTcmhz587NcW54eXldt/+2bdvsfV999dXr9g0ICJDNZlNUVFSB9cPCwnK0L126NNd5CwDlDSEWAFCgkSNHauTIkRo6dKhCQkKUkZGhL774QoMHD9Y//vEPbdu2rUTqZv+HuqMHtueff17vvfee3N3ddc8992jkyJEKDg4u62mVqpv1d6FFixYaOXKkhg0bdt1+X375pf3P8+fPL7H51K1b136+enp6llgdAChLLmU9AQDAzW/u3Lm52mJjY/XCCy/o22+/VefOnbVx48ZcwSomJkYVKlQonUmWY0uXLpWHh4d+/fXXAq/4oXSFh4cXGKyvXbum//3vf5KkmjVr6tChQ9q6davatWtn+nxatWplP1+joqKUnJxseg0AKGtciQUAFEmDBg20YMECPfTQQ0pJSdHo0aNz9WnSpIkaNGhQBrMrX06cOKEaNWoQYC1q5cqVOnfunEJCQvT4449LynllFgBwYwixAIBimT59ujw9PfXrr79qw4YNObbl90zspk2bFB4eLn9/f7m5ualmzZpq27atnnvuOV25ckWSFBYWplGjRkmS/vWvf+V4xi/7SpNhGPrmm2903333KTAwUJ6enqpUqZLatm2rDz74QFlZWblqv/LKK/Yx9u3bp379+qlKlSry9PRUp06dtGnTpnyPdevWrbrvvvtUu3Ztubm5ydfXV127dtUnn3ySq29KSorefPNNtWzZUl5eXvLy8tIdd9yhefPmFfarVVhYmGw2mwzD0LFjx/J8zjH7O05PT9err76qJk2ayM3NTeHh4fY+x48f12OPPWb/vmvUqKGBAwdq+/btuWrGx8fbn7VMTk7W008/rTp16sjDw0OtWrXSsmXL7H0XLlyodu3aydPTU7feeqvGjRunq1evFvr4pJvnd2Hbtm3q06ePqlatKpvNpt27d9/QcVzPV199JUkaNmyY/bbjBQsW6Nq1a6bVAABHwu3EAIBiqVy5su6++25FRERo7dq16tix43X7L1u2TOHh4TIMQ23btlWHDh108eJFHT58WG+99ZbGjBkjLy8v9ezZUxkZGdq4caNatGiR41blhg0bSpLS0tJ0//33q2rVqmratKlatWqlP/74Q5s2bdLYsWO1bdu2PG+FlqQdO3Zo7NixatCgge666y4dOHBA69evV9euXbV9+3bddtttOfrPmjVLTz/9tLKystS6dWuFhobq3Llz2rt3r5599lk98sgj9r5nz55V9+7dtXfvXtWsWVOdOnWSYRjatGmTHnzwQe3YsUPvvfdegd9tz549FRAQoHnz5snT01ODBw/Os19WVpbCw8O1fv16derUSc2bN1fVqlUlSfv27VOXLl107tw5NW7cWAMHDlRCQoKWLFmiZcuW6euvv9aQIUNyjZmenq6uXbsqLi7Ofqzr16/XgAEDtHLlSu3bt08TJ05Up06ddNddd2n9+vV677339McffxT6mc+b5Xdh/fr1evTRRxUYGKgePXro999/l5OTOf8/f1JSkr777ju5urrqnnvukY+Pjzp06KBNmzZp5cqV6tu3ryl1AMChGAAA5EOSUZh/KqZOnWpIMoYOHZprf39//xxtoaGhhiQjIiIi1zjbtm0zLl26ZP/8+eefG5KMl19+Oc+6165dM5YsWWKkp6fnaD979qxx++23G5KMdevW5dj28ssv249r1qxZObaNHz/ekGQMHz48R/u6desMm81mVKpUyVi9enWuOaxYsSJHW69evQxJxpNPPmmkpqba20+fPm2f1w8//JDnMeUlr+/xr9skGQ0bNjROnDiRY1tWVpbRrFkzQ5IxceJEIysry74tIiLCcHJyMry8vIzff//d3h4XF2cfs0uXLsaVK1fs27L/Pho2bGhUqVLF2L59u33byZMnjRo1ahiSjNjY2EId1830u/DWW28Vas6FnU+2Tz/91JBk9O/f3972wQcfGJKMIUOG5LmPv7+/IclYu3ZtgfU7deqUb5/scQCgvOF2YgBAsVWrVk2SdOHChQL7JiYmSpK6deuWa1ubNm1UqVKlQtd1cXFReHh4rsWjqlevrjfffFOSFBkZmee+ISEhGjduXI62yZMnS/rzytxfTZs2TYZh6MUXX1TXrl1zzaFXr172z7t379b333+vNm3aaMaMGXJzc7Nvu/XWW/Xf//5XkvThhx8W+jgL480331Tt2rVztEVFRWnfvn2qW7eupk6dmuM25EGDBik8PFxXrlzRZ599lms8JycnffjhhzlWuB0xYoSqVaumI0eOaOzYsbr99tvt22rVqqUHHnhAUu7vLz83y+9Cs2bN9Oyzzxa61o3Ifvb1r6sX33PPPapQoYKWLVumpKSkEqkLAOUZtxMDAIrNMAxJKtQ7KVu3bq2YmBgNHz5cU6ZMUevWrYt96+bu3bv1008/6dixY0pJSZFhGLp8+bIk6fDhw3nu06NHj1xtVatWlY+Pj06dOmVvy8jIsL+v89FHHy1wLj/99JOkP1etzeu4sp+RNfO1RDabLc/bUn/55RdJ/xea/m748OFavHixvd9fBQQEKDAwMEebk5OT/P39de7cuTy/v/r160tSju/vem6W34U+ffqUyPtUExIStH79enl7e+f4+6latap69eqlyMhILVy4UA8//LDptQGgPCPEAgCK7dy5c5IkHx+fAvu+8cYb2rdvn5YtW6Zly5apSpUq6tixo/r166dhw4bJ3d290HXT09P14IMP6ptvvsm3T3aA+Ts/P7882ytVqqTz58/bP//xxx+6evWqfHx8VKVKlQLnFB8fL0l68cUX9eKLL+bbLzU1tcCxCqtGjRo5rvhm+/333yUpz8W1/tp+8uTJXNv+flU3W/YKyXltz96WlpZW4Jylm+d3oW7duoWucyPmz58vwzA0ePDgXH8/w4YNU2RkpL766qtcIbYwgfpG/o8jAChvCLEAgGL79ddfJUlNmzYtsG+dOnW0Y8cOrVmzRsuXL9e6devsIebtt9/W5s2b7YsSFWTGjBn65ptv1KxZM7399ttq1aqVqlSpogoVKujQoUNq3Lix/T/2/86shXv+LnsV3I4dO5ba64VuJOz91fUCUEHfjxnf383yu1DU768g2bcSR0VF5VrwLD09XdKft14fO3ZM/v7+9m0VK1aU9OcK1/nJ3vbX270BwFEQYgEAxZKUlKQff/xRktS5c+dC7ePi4qIePXrYb0k9duyYRo8erTVr1uitt97S22+/XahxlixZIkn65ptvFBQUlGPb0aNHC3sI11WtWjV5eHjo/Pnzunjxory9va/bP/sKb3h4uJ555hlT5lBUtWrVkvTn95uX7KvG+V11LQ1W+l24ETt37lRMTIwk6ciRIzpy5Eie/QzD0Pz58/XCCy/Y2/z8/PTbb79dd97Z2/K7owAAyjMWdgIAFMszzzyj5ORktWnTRu3bty/SGP7+/po0aZIkaf/+/fZ2V1dXSX8+l5qX7IWk8voP+W+//bZIc/k7Z2dnhYWFSZJ9Uabr6d69u6T/C1Vl6c4775T05/tcMzMzc23Pfn9pdr+bwc38u3Ajsr/bCRMmyDCMPH+yn7XO7pstNDRUkrRixYo8x87KyrJvu5n+7gCgtBBiAQBFcvToUd17772aM2eOPD09NWfOnELt9+677+r06dO52r///ntJf95imi37SuLBgwfzHCt74aGPPvooR3tERIS++OKLQs2nMCZNmiSbzabXX39da9euzbEtIyPDPndJateunbp3766NGzdq7NixunTpUq7x9uzZo5UrV5o2v/yEhYWpWbNmio+P10svvZTjdtolS5Zo8eLF8vLy0ujRo0t8Lnmx4u9CYWRmZtqfzR06dGi+/e68807Vrl1bMTEx2rlzp7199OjR8vLy0sqVK/Xxxx/nGvvFF1/UwYMH5efnp0GDBpXMQQDATYzbiQEABXrwwQcl/XkF6NKlSzp06JAOHDggwzDUqFEjff3112rWrFmhxvrXv/6lCRMmqEWLFmrUqJEMw9CePXt06NAh+fj4aMKECfa+d9xxh2rUqKGIiAiFhYWpfv36cnJy0ujRo9WhQwdNnDhRK1eu1HPPPaeFCxcqMDBQhw8f1o4dOzRhwgS98847phx/p06d9Pbbb2vixInq0qWLbr/9djVq1Ejnzp3Tnj17lJaWposXL9r7f/XVV+rZs6c++OADff311woODlatWrWUlJSkvXv36vjx43ryySfVs2dPU+aXH5vNpvnz56tz58564403tGTJEgUHByshIUEbN26Ui4uL5syZI19f3xKdR36s+LtQGD/99JPOnDmjwMBAtWrVKt9+Tk5OuvfeezVjxgx9+eWXat26tSTJ19dXX3zxhYYOHaoxY8bo3XffVXBwsDIzM7Vt2zYlJCTI29tb3377bYk9zwsANzOuxAIACjRv3jzNmzdP33zzjX755Rc5OztrxIgRWrx4sWJiYnK8L7Qg7733nu677z6lpKTohx9+0MqVK+Xi4qKnn35ae/fuVaNGjex93d3dtWLFCnXv3l27d+/W3LlzNWfOHB06dEjSn7ddbtiwQV26dNHRo0e1fPlyubq6atGiRRo7dqyp38GECRO0bt06DRgwQAkJCYqIiND+/fvVrFkzTZ8+PUffGjVqaNOmTZo9e7aaNm2qX3/9VREREdq7d6/q16+vf//73zkCWklq1qyZdu3apUceeURXrlxRRESEDh48qPDwcG3cuFH33HNPqcwjL1b9XShI9oJO17sKmy27zzfffJPjVukBAwZo165deuihh3Tt2jUtXbpUK1asUMWKFfXkk09q7969Rb59HwCszmbkt1QfAAAA8jR37lyNGjVKL7/8sl555ZWynk6eAgICdOzYsXxXZQYAq+J2YgAAgCJaunSp4uPj5e7unut53LKwa9cuzZ49W9L/vb8ZAMobQiwAAEAR7dmzR3v27JGnp+dNEWITEhI0b968sp4GAJQobicGAAAAAFgGCzsBAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsAxCLAAAAADAMgixAAAAAADLIMQCAAAAACyDEAsAAAAAsIz/D7HM/85KBlqqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(sim.grid.r/c.au, sim.dust.v.frag)\n",
    "ax.set_xlabel(\"Distance from star [AU]\")\n",
    "ax.set_ylabel(\"Fragmentation velocity [cm/s]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to write a function that takes the simulation object as input parameter and returns our desired fragmentation velocities. We can use the fact that the gas temperature has the same shape. Keep in mind that everything has to be in cgs units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def v_frag(sim):\n",
    "    return np.where(sim.gas.T<150., 1000., 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now assign this function to the updater of the dust fragmentation velocities. For details of this process, please have a look at the [Simframe documentation](https://simframe.rtfd.io)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.frag.updater = v_frag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The updater of a group/field stores a `simframe.Heatbeat` object. When calling the `update()` function the heartbeat will be executed which consists of a `systole`, the actual `updater`, and a `diastole`. The `systole` is executed before the actual update functions, the `diastole` afterwards.\n",
    "\n",
    "When assigning a function (or `None`) to the updater of a group/field a new `Heartbeat` object will be created with empty systoles and diastoles only executing the update function. If the existing updater already has systoles/diastoles, those would be overwritten with an empty function.\n",
    "\n",
    "To prevent this you can directly assign the function only to the updater leaving the systoles/diastoles as they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.updater.updater = v_frag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The systoles/diastoles can be set with the following command. Only for demonstration here, since we assign `None`. Read more about this in the section about Systoles and Diastoles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.updater.systole = None\n",
    "sim.dust.v.updater.diastole = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of now, the simulation object still holds the old data for the fragmentation velocity. We have to tell it to update itself. We can either update the whole simulation frame with `Simulation.update()`, or we just update the fragmentation velocities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.frag.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fragmentation velocities should now show our desired behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(sim.grid.r/c.au, sim.dust.v.frag)\n",
    "ax.set_xlabel(\"Distance from star [AU]\")\n",
    "ax.set_ylabel(\"Fragmentation velocity [cm/s]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** If you customized a quantity on which other quantities depend on, you also have to update these quantities. In our case this would be the sticking/fragmentation probabilites. So it is always better to update the whole simulation frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Custom Fields\n",
    "\n",
    "We can not only modify existing fields, we can also create our own fields.\n",
    "\n",
    "In this example we want to add another field `rsnow` to `Simulation.grid`, that gives us the location of the so called snowline, i.e., the location in the disk where water ice starts to sublime.\n",
    "\n",
    "First, we add the field and initialize it with zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.addfield(\"rsnow\", 0., description=\"Snowline location [cm]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grid group has now a new member."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
       "    m            : Field (Mass grid [g]), \u001b[95mconstant\u001b[0m\n",
       "    Nm           : Field (# of mass bins), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    OmegaK       : Field (Keplerian frequency [1/s])\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "    rsnow        : Field (Snowline location [cm])\n",
       "  -----"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a next step we have to write a function that returns us the location of the snowline. Here we simply use the first grid cell where the temperature is smaller than $150\\,\\mathrm{K}$ and return the value of the inner interface of that grid cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rsnow(sim):\n",
    "    isnow = np.argmax(sim.gas.T<150.)\n",
    "    return sim.grid.ri[isnow]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign this function to the updater of our snowline field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.rsnow.updater.updater = rsnow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And update the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The snowline is located at 2.15 AU.\n"
     ]
    }
   ],
   "source": [
    "print(\"The snowline is located at {:4.2f} AU.\".format(sim.grid.rsnow/c.au))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now the temperature is constant throughout the simulation, because the stellar parameters do not change. To see an effect in our snowline location, we need to have a changing temperature profile.\n",
    "\n",
    "To achieve this, we let the stellar radius decrease from a value of $3\\,M_\\odot$ to $2\\,M_\\odot$ within the first $10,000\\,\\mathrm{yrs}$. This results in decreasing disk temperature. This is only for demonstration purposes and is not necessarily physical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Rstar(sim):\n",
    "    dR = -1.*c.R_sun\n",
    "    dt = 1.e4 * c.year\n",
    "    m = dR/dt\n",
    "    R = m*sim.t + 3.*c.R_sun\n",
    "    R = np.maximum(R, c.R_sun)\n",
    "    return R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And assign this to the updater of the stellar radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.star.R.updater.updater = Rstar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modifying the Update Order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we are still not done, yet. We have given `DustPy` instructions how to update the snowline location, but we have not yet told it to actually update it regularily.\n",
    "\n",
    "`DustPy` calls `Simulation.update()`, the updater of the simulation object, once per timestep after the integration step and just before writing the data files. The updater of a group/field is basically a list of groups/fields, whose updater is called in that order.\n",
    "\n",
    "For the main simulation object this is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['star', 'grid', 'gas', 'dust']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that if you call `Simulation.update()` you basically call `Simulation.star.update()`, `Simulation.grid.update()`, `Simulation.gas.update()`, and `Simulation.dust.update()` in that order.\n",
    "\n",
    "The updaters of the sub-groups and fields look as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['M', 'R', 'T', 'L']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.star.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OmegaK']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mu',\n",
       " 'T',\n",
       " 'alpha',\n",
       " 'cs',\n",
       " 'Hp',\n",
       " 'nu',\n",
       " 'rho',\n",
       " 'n',\n",
       " 'mfp',\n",
       " 'P',\n",
       " 'eta',\n",
       " 'torque',\n",
       " 'S']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ext', 'tot']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.S.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['visc', 'rad']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.v.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['delta',\n",
       " 'rhos',\n",
       " 'fill',\n",
       " 'a',\n",
       " 'St',\n",
       " 'H',\n",
       " 'rho',\n",
       " 'backreaction',\n",
       " 'v',\n",
       " 'D',\n",
       " 'eps',\n",
       " 'kernel',\n",
       " 'p',\n",
       " 'S']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.backreaction.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rad', 'turb', 'vert']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.delta.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adv', 'diff', 'tot']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.Fi.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['frag', 'stick']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.p.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ext', 'tot']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.S.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['frag', 'driftmax', 'rel']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['azi', 'brown', 'rad', 'turb', 'vert', 'tot']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.rel.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The gas updater does not contain the updaters of `Simulation.gas.v` and `Simulation.gas.Fi` and the updater of the gas sources does not contain the updater of `Simulation.gas.S.hyd`. These are quantities that are calculated in the finalization step of the integrator, since they are derived from the result of the implicit gas integration.\n",
    "\n",
    "The same is true for `Simulation.dust.v.rad`, `Simulation.dust.Fi`, `Simulation.dust.S.coag`, and `Simulation.dust.S.hyd` in the dust updater, which are also calculated from the implicit integration in the finalization step of the integrator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the grid updater is only updating the Keplerian frequency, but not our snowline location. So we can simply adding it to the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\", \"rsnow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you assign lists to updaters, their systoles and diastoles will always be overwritten with `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OmegaK', 'rsnow']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Systoles and Diastoles\n",
    "\n",
    "However, the previous solution has a conceptional problem. As you can see from the update order previously the grid is updated before the gas. The snowline location, however, needs the gas temperature and, therefore, has to be updated after the gas. But we also cannot update the grid as a whole after the gas, because the gas updaters need the Keplerian frequency. We need another solution.\n",
    "\n",
    "But first, we revert the grid updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every updater has a systole and a diastole. That is a function that is called before respectively after the actual updater. Since no other quantity depends on our snowline location, we can simply update it at the end and put it in the diastole of the main updater. Or we could assign it to the diastole of the gas temperature updater, since it only requires the updated gas temperature.\n",
    "\n",
    "We therefore write a diastole function, that is updating the snowline location separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diastole(sim):\n",
    "    sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And assign this function to the diastole of the gas temperature updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.gas.T.updater.diastole = diastole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now every time `Simulation.gas.T.update()` is called, `Simulation.grid.rsnow.update()` will be called at the end of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Snapshots\n",
    "\n",
    "As already explained in a previous chapter, the snapshots can be customized by simply setting `Simulation.t.snapshots`. In this example we only want to run the simulation for $10,000\\,\\mathrm{yrs}$. The snapshots have to include the starting time to write out the initial conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.hstack(\n",
    "    [sim.t, np.geomspace(1.e3, 1.e4, num=21) * c.year]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now change the data directory to avoid an overwrite error and start the simulation with our modifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer.datadir = \"3_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DustPy v1.0.8\n",
      "\n",
      "Documentation: https://stammler.github.io/dustpy/\n",
      "PyPI:          https://pypi.org/project/dustpy/\n",
      "GitHub:        https://github.com/stammler/dustpy/\n",
      "\u001b[94m\n",
      "Please cite Stammler & Birnstiel (2022).\u001b[0m\n",
      "\n",
      "\u001b[93mChecking for mass conservation...\n",
      "\u001b[0m\n",
      "\u001b[93m    - Sticking:\u001b[0m\n",
      "\u001b[0m        max. rel. error: \u001b[92m 2.81e-14\u001b[0m\n",
      "        for particle collision\n",
      "            m[114] =  1.93e+04 g    with\n",
      "            m[116] =  3.73e+04 g\u001b[0m\n",
      "\u001b[93m    - Full fragmentation:\u001b[0m\n",
      "\u001b[0m        max. rel. error: \u001b[92m 6.66e-16\u001b[0m\n",
      "        for particle collision\n",
      "            m[90] =  7.20e+00 g    with\n",
      "            m[95] =  3.73e+01 g\u001b[0m\n",
      "\u001b[93m    - Erosion:\u001b[0m\n",
      "\u001b[0m        max. rel. error: \u001b[92m 1.78e-15\u001b[0m\n",
      "        for particle collision\n",
      "            m[110] =  5.18e+03 g    with\n",
      "            m[118] =  7.20e+04 g\n",
      "\u001b[0m\n",
      "Creating data directory 3_data.\n",
      "Writing file \u001b[94m3_data/data0000.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0001.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0002.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0003.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0004.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0005.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0006.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0007.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0008.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0009.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0010.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0011.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0012.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0013.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0014.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0015.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0016.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0017.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0018.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0019.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0020.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0021.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Execution time: \u001b[94m0:02:33\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now have a look at the result of our modifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1575x648.949 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.panel(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The change in fragmentation velocity has an obvious effect on the particles sizes.\n",
    "\n",
    "To check the time evolution of the snowline, we have to read the data. The gray lines are the positions of the radial grid cell interfaces and snapshots, which explains the discrete behavior of the snowline location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = sim.writer.read.sequence(\"t\") / c.year\n",
    "ri = sim.writer.read.sequence(\"grid.ri\") / c.au\n",
    "rsnow = sim.writer.read.sequence(\"grid.rsnow\") / c.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(t, rsnow)\n",
    "ax.hlines(ri, 1.e3, 1e4, lw=1, color=\"gray\", alpha=0.25)\n",
    "ax.vlines(t, 2.5, 5.5, lw=1, color=\"gray\", alpha=0.25)\n",
    "ax.set_xlim(1.e3, 1.e4)\n",
    "ax.set_ylim(2.5, 5.5)\n",
    "ax.set_xlabel(\"Time [yr]\")\n",
    "ax.set_ylabel(\"Snowline location [AU]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dustpy_develop",
   "language": "python",
   "name": "dustpy_develop"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
