{
 "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:01.916960Z",
     "iopub.status.busy": "2023-11-30T11:26:01.916361Z",
     "iopub.status.idle": "2023-11-30T11:26:02.841337Z",
     "shell.execute_reply": "2023-11-30T11:26:02.839991Z"
    }
   },
   "outputs": [],
   "source": [
    "from dustpy import Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.845576Z",
     "iopub.status.busy": "2023-11-30T11:26:02.845072Z",
     "iopub.status.idle": "2023-11-30T11:26:02.851256Z",
     "shell.execute_reply": "2023-11-30T11:26:02.849848Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.854960Z",
     "iopub.status.busy": "2023-11-30T11:26:02.854337Z",
     "iopub.status.idle": "2023-11-30T11:26:02.868753Z",
     "shell.execute_reply": "2023-11-30T11:26:02.867574Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.915267Z",
     "iopub.status.busy": "2023-11-30T11:26:02.914552Z",
     "iopub.status.idle": "2023-11-30T11:26:02.922315Z",
     "shell.execute_reply": "2023-11-30T11:26:02.921095Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.926688Z",
     "iopub.status.busy": "2023-11-30T11:26:02.926033Z",
     "iopub.status.idle": "2023-11-30T11:26:02.934335Z",
     "shell.execute_reply": "2023-11-30T11:26:02.933240Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.939078Z",
     "iopub.status.busy": "2023-11-30T11:26:02.938479Z",
     "iopub.status.idle": "2023-11-30T11:26:02.950019Z",
     "shell.execute_reply": "2023-11-30T11:26:02.948636Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.956091Z",
     "iopub.status.busy": "2023-11-30T11:26:02.955455Z",
     "iopub.status.idle": "2023-11-30T11:26:02.967634Z",
     "shell.execute_reply": "2023-11-30T11:26:02.966242Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.973641Z",
     "iopub.status.busy": "2023-11-30T11:26:02.972982Z",
     "iopub.status.idle": "2023-11-30T11:26:02.978975Z",
     "shell.execute_reply": "2023-11-30T11:26:02.977601Z"
    }
   },
   "outputs": [],
   "source": [
    "import dustpy.constants as c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:02.992527Z",
     "iopub.status.busy": "2023-11-30T11:26:02.991869Z",
     "iopub.status.idle": "2023-11-30T11:26:02.998617Z",
     "shell.execute_reply": "2023-11-30T11:26:02.997219Z"
    }
   },
   "outputs": [],
   "source": [
    "ri = np.logspace(0., 3., num=100, base=10.) * c.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.003028Z",
     "iopub.status.busy": "2023-11-30T11:26:03.002392Z",
     "iopub.status.idle": "2023-11-30T11:26:03.009125Z",
     "shell.execute_reply": "2023-11-30T11:26:03.007916Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.014285Z",
     "iopub.status.busy": "2023-11-30T11:26:03.013654Z",
     "iopub.status.idle": "2023-11-30T11:26:03.020858Z",
     "shell.execute_reply": "2023-11-30T11:26:03.019449Z"
    }
   },
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.027223Z",
     "iopub.status.busy": "2023-11-30T11:26:03.026576Z",
     "iopub.status.idle": "2023-11-30T11:26:03.032371Z",
     "shell.execute_reply": "2023-11-30T11:26:03.031196Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.grid.ri = ri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.039336Z",
     "iopub.status.busy": "2023-11-30T11:26:03.037933Z",
     "iopub.status.idle": "2023-11-30T11:26:03.046474Z",
     "shell.execute_reply": "2023-11-30T11:26:03.045136Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.054851Z",
     "iopub.status.busy": "2023-11-30T11:26:03.054209Z",
     "iopub.status.idle": "2023-11-30T11:26:03.061044Z",
     "shell.execute_reply": "2023-11-30T11:26:03.059605Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.067123Z",
     "iopub.status.busy": "2023-11-30T11:26:03.065917Z",
     "iopub.status.idle": "2023-11-30T11:26:03.074413Z",
     "shell.execute_reply": "2023-11-30T11:26:03.073039Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.081168Z",
     "iopub.status.busy": "2023-11-30T11:26:03.079968Z",
     "iopub.status.idle": "2023-11-30T11:26:03.088268Z",
     "shell.execute_reply": "2023-11-30T11:26:03.086878Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.094208Z",
     "iopub.status.busy": "2023-11-30T11:26:03.093562Z",
     "iopub.status.idle": "2023-11-30T11:26:03.099499Z",
     "shell.execute_reply": "2023-11-30T11:26:03.098345Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.105341Z",
     "iopub.status.busy": "2023-11-30T11:26:03.104703Z",
     "iopub.status.idle": "2023-11-30T11:26:03.598916Z",
     "shell.execute_reply": "2023-11-30T11:26:03.597985Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.606686Z",
     "iopub.status.busy": "2023-11-30T11:26:03.606231Z",
     "iopub.status.idle": "2023-11-30T11:26:03.613444Z",
     "shell.execute_reply": "2023-11-30T11:26:03.612550Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:03.618356Z",
     "iopub.status.busy": "2023-11-30T11:26:03.618067Z",
     "iopub.status.idle": "2023-11-30T11:26:04.026411Z",
     "shell.execute_reply": "2023-11-30T11:26:04.025359Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.032148Z",
     "iopub.status.busy": "2023-11-30T11:26:04.031838Z",
     "iopub.status.idle": "2023-11-30T11:26:04.038207Z",
     "shell.execute_reply": "2023-11-30T11:26:04.037330Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.043470Z",
     "iopub.status.busy": "2023-11-30T11:26:04.043172Z",
     "iopub.status.idle": "2023-11-30T11:26:04.439078Z",
     "shell.execute_reply": "2023-11-30T11:26:04.437207Z"
    }
   },
   "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": [
    "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.444862Z",
     "iopub.status.busy": "2023-11-30T11:26:04.444524Z",
     "iopub.status.idle": "2023-11-30T11:26:04.456505Z",
     "shell.execute_reply": "2023-11-30T11:26:04.454136Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.462701Z",
     "iopub.status.busy": "2023-11-30T11:26:04.462353Z",
     "iopub.status.idle": "2023-11-30T11:26:04.468027Z",
     "shell.execute_reply": "2023-11-30T11:26:04.466866Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.473684Z",
     "iopub.status.busy": "2023-11-30T11:26:04.473115Z",
     "iopub.status.idle": "2023-11-30T11:26:04.479286Z",
     "shell.execute_reply": "2023-11-30T11:26:04.477860Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.485743Z",
     "iopub.status.busy": "2023-11-30T11:26:04.485069Z",
     "iopub.status.idle": "2023-11-30T11:26:04.491696Z",
     "shell.execute_reply": "2023-11-30T11:26:04.490240Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.497746Z",
     "iopub.status.busy": "2023-11-30T11:26:04.497075Z",
     "iopub.status.idle": "2023-11-30T11:26:04.503872Z",
     "shell.execute_reply": "2023-11-30T11:26:04.502406Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.510341Z",
     "iopub.status.busy": "2023-11-30T11:26:04.509666Z",
     "iopub.status.idle": "2023-11-30T11:26:04.516596Z",
     "shell.execute_reply": "2023-11-30T11:26:04.515079Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.522874Z",
     "iopub.status.busy": "2023-11-30T11:26:04.522211Z",
     "iopub.status.idle": "2023-11-30T11:26:04.800051Z",
     "shell.execute_reply": "2023-11-30T11:26:04.799081Z"
    }
   },
   "outputs": [
    {
     "data": {
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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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.806196Z",
     "iopub.status.busy": "2023-11-30T11:26:04.805943Z",
     "iopub.status.idle": "2023-11-30T11:26:04.852353Z",
     "shell.execute_reply": "2023-11-30T11:26:04.851258Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.857787Z",
     "iopub.status.busy": "2023-11-30T11:26:04.857543Z",
     "iopub.status.idle": "2023-11-30T11:26:04.862349Z",
     "shell.execute_reply": "2023-11-30T11:26:04.861409Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.867301Z",
     "iopub.status.busy": "2023-11-30T11:26:04.867059Z",
     "iopub.status.idle": "2023-11-30T11:26:04.873210Z",
     "shell.execute_reply": "2023-11-30T11:26:04.872238Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.878943Z",
     "iopub.status.busy": "2023-11-30T11:26:04.878627Z",
     "iopub.status.idle": "2023-11-30T11:26:04.884264Z",
     "shell.execute_reply": "2023-11-30T11:26:04.883134Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.889735Z",
     "iopub.status.busy": "2023-11-30T11:26:04.889181Z",
     "iopub.status.idle": "2023-11-30T11:26:04.895307Z",
     "shell.execute_reply": "2023-11-30T11:26:04.894043Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.grid.rsnow.updater.updater = rsnow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And update the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.901368Z",
     "iopub.status.busy": "2023-11-30T11:26:04.900722Z",
     "iopub.status.idle": "2023-11-30T11:26:04.907117Z",
     "shell.execute_reply": "2023-11-30T11:26:04.905688Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.913409Z",
     "iopub.status.busy": "2023-11-30T11:26:04.912760Z",
     "iopub.status.idle": "2023-11-30T11:26:04.920681Z",
     "shell.execute_reply": "2023-11-30T11:26:04.919181Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.926533Z",
     "iopub.status.busy": "2023-11-30T11:26:04.925884Z",
     "iopub.status.idle": "2023-11-30T11:26:04.933221Z",
     "shell.execute_reply": "2023-11-30T11:26:04.931954Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.939062Z",
     "iopub.status.busy": "2023-11-30T11:26:04.938402Z",
     "iopub.status.idle": "2023-11-30T11:26:04.944917Z",
     "shell.execute_reply": "2023-11-30T11:26:04.943437Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.951307Z",
     "iopub.status.busy": "2023-11-30T11:26:04.950638Z",
     "iopub.status.idle": "2023-11-30T11:26:04.959496Z",
     "shell.execute_reply": "2023-11-30T11:26:04.958281Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.965655Z",
     "iopub.status.busy": "2023-11-30T11:26:04.965018Z",
     "iopub.status.idle": "2023-11-30T11:26:04.973537Z",
     "shell.execute_reply": "2023-11-30T11:26:04.972318Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.979654Z",
     "iopub.status.busy": "2023-11-30T11:26:04.978981Z",
     "iopub.status.idle": "2023-11-30T11:26:04.987423Z",
     "shell.execute_reply": "2023-11-30T11:26:04.986216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OmegaK']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:04.993457Z",
     "iopub.status.busy": "2023-11-30T11:26:04.992750Z",
     "iopub.status.idle": "2023-11-30T11:26:05.001818Z",
     "shell.execute_reply": "2023-11-30T11:26:05.000376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gamma',\n",
       " 'mu',\n",
       " 'T',\n",
       " 'alpha',\n",
       " 'cs',\n",
       " 'Hp',\n",
       " 'nu',\n",
       " 'rho',\n",
       " 'n',\n",
       " 'mfp',\n",
       " 'P',\n",
       " 'eta',\n",
       " 'S']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.008318Z",
     "iopub.status.busy": "2023-11-30T11:26:05.007395Z",
     "iopub.status.idle": "2023-11-30T11:26:05.016137Z",
     "shell.execute_reply": "2023-11-30T11:26:05.014927Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.022458Z",
     "iopub.status.busy": "2023-11-30T11:26:05.021645Z",
     "iopub.status.idle": "2023-11-30T11:26:05.030112Z",
     "shell.execute_reply": "2023-11-30T11:26:05.028682Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.036082Z",
     "iopub.status.busy": "2023-11-30T11:26:05.035312Z",
     "iopub.status.idle": "2023-11-30T11:26:05.043855Z",
     "shell.execute_reply": "2023-11-30T11:26:05.042481Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.050126Z",
     "iopub.status.busy": "2023-11-30T11:26:05.049170Z",
     "iopub.status.idle": "2023-11-30T11:26:05.068110Z",
     "shell.execute_reply": "2023-11-30T11:26:05.066222Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.075861Z",
     "iopub.status.busy": "2023-11-30T11:26:05.074475Z",
     "iopub.status.idle": "2023-11-30T11:26:05.084660Z",
     "shell.execute_reply": "2023-11-30T11:26:05.083215Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.090693Z",
     "iopub.status.busy": "2023-11-30T11:26:05.089962Z",
     "iopub.status.idle": "2023-11-30T11:26:05.098624Z",
     "shell.execute_reply": "2023-11-30T11:26:05.097184Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.105014Z",
     "iopub.status.busy": "2023-11-30T11:26:05.103950Z",
     "iopub.status.idle": "2023-11-30T11:26:05.112380Z",
     "shell.execute_reply": "2023-11-30T11:26:05.111156Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.118564Z",
     "iopub.status.busy": "2023-11-30T11:26:05.117827Z",
     "iopub.status.idle": "2023-11-30T11:26:05.126024Z",
     "shell.execute_reply": "2023-11-30T11:26:05.124559Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.132182Z",
     "iopub.status.busy": "2023-11-30T11:26:05.131130Z",
     "iopub.status.idle": "2023-11-30T11:26:05.139676Z",
     "shell.execute_reply": "2023-11-30T11:26:05.138447Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.145473Z",
     "iopub.status.busy": "2023-11-30T11:26:05.144668Z",
     "iopub.status.idle": "2023-11-30T11:26:05.152820Z",
     "shell.execute_reply": "2023-11-30T11:26:05.151362Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.159157Z",
     "iopub.status.busy": "2023-11-30T11:26:05.158174Z",
     "iopub.status.idle": "2023-11-30T11:26:05.164792Z",
     "shell.execute_reply": "2023-11-30T11:26:05.163354Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.171316Z",
     "iopub.status.busy": "2023-11-30T11:26:05.170376Z",
     "iopub.status.idle": "2023-11-30T11:26:05.180441Z",
     "shell.execute_reply": "2023-11-30T11:26:05.178635Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.187143Z",
     "iopub.status.busy": "2023-11-30T11:26:05.186157Z",
     "iopub.status.idle": "2023-11-30T11:26:05.192899Z",
     "shell.execute_reply": "2023-11-30T11:26:05.191389Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.199030Z",
     "iopub.status.busy": "2023-11-30T11:26:05.197998Z",
     "iopub.status.idle": "2023-11-30T11:26:05.204522Z",
     "shell.execute_reply": "2023-11-30T11:26:05.203047Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.210865Z",
     "iopub.status.busy": "2023-11-30T11:26:05.210138Z",
     "iopub.status.idle": "2023-11-30T11:26:05.216310Z",
     "shell.execute_reply": "2023-11-30T11:26:05.214799Z"
    }
   },
   "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 $10,000\\,\\mathrm{yrs}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.222321Z",
     "iopub.status.busy": "2023-11-30T11:26:05.221634Z",
     "iopub.status.idle": "2023-11-30T11:26:05.228803Z",
     "shell.execute_reply": "2023-11-30T11:26:05.227346Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.logspace(3., 4., num=21, base=10.) * c.year"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.234860Z",
     "iopub.status.busy": "2023-11-30T11:26:05.234120Z",
     "iopub.status.idle": "2023-11-30T11:26:05.240389Z",
     "shell.execute_reply": "2023-11-30T11:26:05.238912Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.writer.datadir = \"3_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:26:05.246527Z",
     "iopub.status.busy": "2023-11-30T11:26:05.245741Z",
     "iopub.status.idle": "2023-11-30T11:31:59.731246Z",
     "shell.execute_reply": "2023-11-30T11:31:59.730004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DustPy v1.0.5\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 2.75e-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 5.55e-16\u001b[0m\n",
      "        for particle collision\n",
      "            m[55] =  7.20e-05 g    with\n",
      "            m[55] =  7.20e-05 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[94m0:05:54\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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:31:59.736899Z",
     "iopub.status.busy": "2023-11-30T11:31:59.736576Z",
     "iopub.status.idle": "2023-11-30T11:31:59.742287Z",
     "shell.execute_reply": "2023-11-30T11:31:59.741442Z"
    }
   },
   "outputs": [],
   "source": [
    "from dustpy import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:31:59.746959Z",
     "iopub.status.busy": "2023-11-30T11:31:59.746649Z",
     "iopub.status.idle": "2023-11-30T11:32:00.926880Z",
     "shell.execute_reply": "2023-11-30T11:32:00.925872Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:32:00.937002Z",
     "iopub.status.busy": "2023-11-30T11:32:00.936680Z",
     "iopub.status.idle": "2023-11-30T11:32:00.976318Z",
     "shell.execute_reply": "2023-11-30T11:32:00.975428Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-30T11:32:00.981711Z",
     "iopub.status.busy": "2023-11-30T11:32:00.981454Z",
     "iopub.status.idle": "2023-11-30T11:32:01.379549Z",
     "shell.execute_reply": "2023-11-30T11:32:01.378744Z"
    }
   },
   "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": "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}