{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Simple Customization\n",
    "\n",
    "If you do not want to run the standard simulation you can easily modify the initial conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.011774Z",
     "iopub.status.busy": "2021-12-15T09:46:58.010469Z",
     "iopub.status.idle": "2021-12-15T09:46:58.851889Z",
     "shell.execute_reply": "2021-12-15T09:46:58.850705Z"
    }
   },
   "outputs": [],
   "source": [
    "from dustpy import Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.857689Z",
     "iopub.status.busy": "2021-12-15T09:46:58.856440Z",
     "iopub.status.idle": "2021-12-15T09:46:58.859741Z",
     "shell.execute_reply": "2021-12-15T09:46:58.858761Z"
    }
   },
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation object has an attribute to set the initial conditions `Simulation.ini`. The attribute is structured in initial conditions for dust, gas, the grid, and the central star. If you call `Simulatio.initialize()`, the simulation object will be filled according the parameters set here. We'll go through all options one by one.\n",
    "\n",
    "**--> All quantities are in cgs units! <--**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.867681Z",
     "iopub.status.busy": "2021-12-15T09:46:58.866555Z",
     "iopub.status.idle": "2021-12-15T09:46:58.874364Z",
     "shell.execute_reply": "2021-12-15T09:46:58.873320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(dust=namespace(aIniMax=0.0001,\n",
       "                         allowDriftingParticles=False,\n",
       "                         erosionMassRatio=10.0,\n",
       "                         d2gRatio=0.01,\n",
       "                         distExp=-3.5,\n",
       "                         excavatedMass=1.0,\n",
       "                         fragmentDistribution=-1.8333333333333333,\n",
       "                         rhoMonomer=1.67,\n",
       "                         vfrag=100.0),\n",
       "          gas=namespace(alpha=0.001,\n",
       "                        gamma=1.4,\n",
       "                        Mdisk=9.942049353490256e+31,\n",
       "                        mu=3.847030424486999e-24,\n",
       "                        SigmaExp=-1.0,\n",
       "                        SigmaRc=897587224200000.0),\n",
       "          grid=namespace(Nmbpd=7,\n",
       "                         mmin=1e-12,\n",
       "                         mmax=100000.0,\n",
       "                         Nr=100,\n",
       "                         rmin=14959787070000.0,\n",
       "                         rmax=1.495978707e+16),\n",
       "          star=namespace(M=1.988409870698051e+33, R=139140000000.0, T=5772.0))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.ini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stellar parameters\n",
    "\n",
    "The stellar parameters mainly influence the dynamical time scales and the temperature profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.880530Z",
     "iopub.status.busy": "2021-12-15T09:46:58.879570Z",
     "iopub.status.idle": "2021-12-15T09:46:58.883199Z",
     "shell.execute_reply": "2021-12-15T09:46:58.883979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(M=1.988409870698051e+33, R=139140000000.0, T=5772.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.ini.star"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* M\n",
    "\n",
    "Stellar mass, default: $1\\ M_\\odot$  \n",
    "Has influence on the dynamical timescales in the disk.\n",
    "\n",
    "* R\n",
    "\n",
    "Stellar radius, default: $2\\ M_\\odot$  \n",
    "Has influence on the temperature profile in the disk. The default temperature profile is a passively irradiated disk with a constant irradtion angle of 0.05.\n",
    "\n",
    "* T\n",
    "\n",
    "Effective temperature, default: 5772 K  \n",
    "Has influence on the temperature profile in the disk. The default temperature profile is a passively irradiated disk with a constant irradtion angle of 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid parameters\n",
    "\n",
    "The grid parameters determine the shape of the radial and mass grids. While you can set your own non-standard radial grid (see later), you should under no circumstances meddle with the mass grid, unless you know exactly what you are doing. Please always use the `Simulation.ini.grid` namespace to customize your mass grid. The mass grid has to be strickly logarithmic. Otherwise, the coagulation algorithm will produce wrong results.  \n",
    "**Attentian:** If you changed the mass grid for any reason during the simulation, you have to re-calculate the coagulation parameters in `Simulation.dust.coagulation`.\n",
    "\n",
    "**Note:** The performance of `DustPy` is very sensitive to the mass grid. A finer mass grid slows down the simulation drastically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.890712Z",
     "iopub.status.busy": "2021-12-15T09:46:58.889819Z",
     "iopub.status.idle": "2021-12-15T09:46:58.893874Z",
     "shell.execute_reply": "2021-12-15T09:46:58.894575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(Nmbpd=7,\n",
       "          mmin=1e-12,\n",
       "          mmax=100000.0,\n",
       "          Nr=100,\n",
       "          rmin=14959787070000.0,\n",
       "          rmax=1.495978707e+16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.ini.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Nmbpd\n",
    "\n",
    "Number of mass bins per mass decade, default: 7  \n",
    "Should be at least 7, see [Drążkowska et al. (2014)](https://doi.org/10.1051/0004-6361/201423708)  \n",
    "This will have a large impact on the accuracy and the computational costs of the simulation.\n",
    "\n",
    "* mmin\n",
    "\n",
    "Minimum particle mass, default: $10^{-12}$ g\n",
    "\n",
    "* mmax\n",
    "\n",
    "Maximum particle mass, default: $10^{5}$ g\n",
    "\n",
    "* Nr\n",
    "\n",
    "Number of radial grid cells, default: 100\n",
    "\n",
    "* rmin\n",
    "\n",
    "Location of inner radial grid boundary, default 1 AU\n",
    "\n",
    "* rmax\n",
    "\n",
    "Location of outer radial grid boundary, default 1000 AU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gas Parameters\n",
    "\n",
    "The gas parameters define the initial conditions of the gas disk. The standard surface density profile is the self-similar solution of [Lynden-Bell & Pringle (1974)](https://doi.org/10.1093/mnras/168.3.603)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.902617Z",
     "iopub.status.busy": "2021-12-15T09:46:58.901656Z",
     "iopub.status.idle": "2021-12-15T09:46:58.905394Z",
     "shell.execute_reply": "2021-12-15T09:46:58.906186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(alpha=0.001,\n",
       "          gamma=1.4,\n",
       "          Mdisk=9.942049353490256e+31,\n",
       "          mu=3.847030424486999e-24,\n",
       "          SigmaExp=-1.0,\n",
       "          SigmaRc=897587224200000.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.ini.gas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* alpha\n",
    "\n",
    "alpha viscosity parameter, default: $10^{-3}$  \n",
    "see [Shakura & Sunyaev (1973)](http://adsabs.harvard.edu/abs/1973A%26A....24..337S) for details\n",
    "\n",
    "* gamma\n",
    "\n",
    "heat capacity ratio, default: 7/5\n",
    "\n",
    "* Mdisk\n",
    "\n",
    "Inital gas disk mass, default: $0.05\\ M_\\odot$\n",
    "\n",
    "* mu\n",
    "\n",
    "Mean molecular weight of the gas, default: $2.3\\ m_\\mathrm{proton}$\n",
    "\n",
    "* SigmaExp\n",
    "\n",
    "Power law exponent of surface density profile, default: -1  \n",
    "see [Lynden-Bell & Pringle (1974)](https://doi.org/10.1093/mnras/168.3.603)\n",
    "\n",
    "* SigmaRc\n",
    "\n",
    "Critical cut-off radius of surface density, default: 30 AU  \n",
    "see [Lynden-Bell & Pringle (1974)](https://doi.org/10.1093/mnras/168.3.603)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dust Parameters\n",
    "\n",
    "The dust parameters define the initial conditions of the dust disk and the basic collisional behavior of the dust particles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.914372Z",
     "iopub.status.busy": "2021-12-15T09:46:58.913406Z",
     "iopub.status.idle": "2021-12-15T09:46:58.917122Z",
     "shell.execute_reply": "2021-12-15T09:46:58.917896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(aIniMax=0.0001,\n",
       "          allowDriftingParticles=False,\n",
       "          erosionMassRatio=10.0,\n",
       "          d2gRatio=0.01,\n",
       "          distExp=-3.5,\n",
       "          excavatedMass=1.0,\n",
       "          fragmentDistribution=-1.8333333333333333,\n",
       "          rhoMonomer=1.67,\n",
       "          vfrag=100.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.ini.dust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* aIniMax\n",
    "\n",
    "Maximum particle size that will be filled initially, default: 1 µm\n",
    "\n",
    "* allowDriftingParticles\n",
    "\n",
    "If `True` initially drifting particles will not be removed, default: `False`  \n",
    "Dust particles in the disk drift inwards more rapidly with increasing Stokes number. In the outer disk the gas surface density is low and therefore even the small initial particles can have large Stokes numbers causing them to drift already from the beginning on. This causes a visible particle wave in the simulation. By setting this parameter to `False`, these drifting particles will be removed.\n",
    "\n",
    "* erosionMassRatio\n",
    "\n",
    "If the mass ratio of colliding particles is below this ratio, both particles fully fragment, default: 10  \n",
    "In the standard model three things can happen when particles collide: they can stick together forming a larger particle, they can both fully fragment, or the smaller collision partner can fully fragment, chipping off some mass of the larger collision partner. The latter is called erosion. This parameter defines the threshold between full fragmentation and erosion.\n",
    "\n",
    "* d2gRatio\n",
    "\n",
    "Initial dust-to-gas ratio, default: $10^{-2}$\n",
    "\n",
    "* distExp\n",
    "\n",
    "Inital size distribution of particles, default: -7/2  \n",
    "The particles will initially have a size distribution of $n\\left(a\\right) \\propto a^\\mathrm{distExp}$ up to a maximum particle size of `Simulation.ini.dust.aIniMax`. The standard model uses the so-called MRN distribution of interstellar grains as initial condition. See [Mathis et al. (1977)](https://doi.org/10.1086/155591).\n",
    "\n",
    "* excavatedMass\n",
    "\n",
    "Excavated mass from larger particle in units of smaller particle mass, default: 1  \n",
    "In a erosive collision event the smaller particle will chip off mass from the larger particle. This parameter determines how much mass will be chipped off.\n",
    "\n",
    "* fragmentDistribution\n",
    "\n",
    "mass distribution of fragments, default: -11/6  \n",
    "Whenever a particle collision produces fragments the fragments will have a mass distribution of $n \\left(m\\right) \\mathrm{d}m \\propto m^\\mathrm{fragmentDistribution} \\mathrm{d}m$. The value used by the standard model is taken from [Dohnanyi (1969)](https://doi.org/10.1029/JB074i010p02531).\n",
    "\n",
    "* rhoMonomer\n",
    "\n",
    "Monomer bulk mass density, default: 1.67 g/cm³  \n",
    "In the standard model all particles will have a filling factor of unity and share therefore this bulk mass density.\n",
    "\n",
    "* vFrag\n",
    "\n",
    "Fragmentation velocity, default 1 m/s.  \n",
    "Particle collisions with a collision velocity that exceeds the fragmentation velocity will result in a fragmenting collision. Either full fragmentation or erosion depending on the mass ratio of the collision partners. Collisions below the fragmentation velocity lead to sticking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the Initial Conditions\n",
    "\n",
    "In this example we want to run a simulation with a more massive disk mass. We can use the constants module of `DustPy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.925087Z",
     "iopub.status.busy": "2021-12-15T09:46:58.924055Z",
     "iopub.status.idle": "2021-12-15T09:46:58.927531Z",
     "shell.execute_reply": "2021-12-15T09:46:58.926657Z"
    }
   },
   "outputs": [],
   "source": [
    "from dustpy import constants as c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:58.934961Z",
     "iopub.status.busy": "2021-12-15T09:46:58.933817Z",
     "iopub.status.idle": "2021-12-15T09:46:58.936483Z",
     "shell.execute_reply": "2021-12-15T09:46:58.937396Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.ini.gas.Mdisk = 0.1 * c.M_sun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now initialize the simulation as described in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:59.007190Z",
     "iopub.status.busy": "2021-12-15T09:46:59.006019Z",
     "iopub.status.idle": "2021-12-15T09:46:59.328841Z",
     "shell.execute_reply": "2021-12-15T09:46:59.327779Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the Snapshots\n",
    "\n",
    "The standard model runs for 100,000 years and starts writing data files at 1,000 years with 10 files per time decade. In this example we only want to run the simulation for 10,000 years with only 5 snapshots per decade. This can easily be set by modifying `Simulation.t.snapshots`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:59.334494Z",
     "iopub.status.busy": "2021-12-15T09:46:59.333263Z",
     "iopub.status.idle": "2021-12-15T09:46:59.336554Z",
     "shell.execute_reply": "2021-12-15T09:46:59.335612Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:59.342377Z",
     "iopub.status.busy": "2021-12-15T09:46:59.341227Z",
     "iopub.status.idle": "2021-12-15T09:46:59.343589Z",
     "shell.execute_reply": "2021-12-15T09:46:59.344465Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.logspace(3, 4, num=6, base=10) * c.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the Output Directory\n",
    "\n",
    "By default `DustPy` will protect already existing data files. If we ran the simulation now, an error would be raised, because we have already existing data files in the output directory from the previous chapter.\n",
    "\n",
    "We could either set `Simulation.writer.overwrite` to `True` to overwrite our existing data files or we could change the name of the output directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:59.350039Z",
     "iopub.status.busy": "2021-12-15T09:46:59.349073Z",
     "iopub.status.idle": "2021-12-15T09:46:59.351054Z",
     "shell.execute_reply": "2021-12-15T09:46:59.351810Z"
    }
   },
   "outputs": [],
   "source": [
    "sim.writer.datadir = \"2_data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation is now ready to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:46:59.358955Z",
     "iopub.status.busy": "2021-12-15T09:46:59.356600Z",
     "iopub.status.idle": "2021-12-15T09:50:41.077774Z",
     "shell.execute_reply": "2021-12-15T09:50:41.076816Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DustPy v0.5.8\n",
      "\n",
      "Documentation: https://stammler.github.io/dustpy/\n",
      "PyPI:          https://pypi.org/project/dustpy/\n",
      "GitHub:        https://github.com/stammler/dustpy/\n",
      "\u001b[91m\n",
      "Please read README.md on the GitHub repository for\n",
      "information about the Terms of Usage.\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 4.44e-16\u001b[0m\n",
      "        for particle collision\n",
      "            m[47] =  5.18e-06 g    with\n",
      "            m[54] =  5.18e-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 '2_data'.\n",
      "Writing file \u001b[94m2_data/data0000.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m2_data/data0001.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m2_data/data0002.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m2_data/data0003.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m2_data/data0004.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m2_data/data0005.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m2_data/data0006.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m2_data/frame.dmp\u001b[0m\n",
      "Execution time: \u001b[94m0:03:41\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation has now run for 10,000 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:50:41.089472Z",
     "iopub.status.busy": "2021-12-15T09:50:41.088332Z",
     "iopub.status.idle": "2021-12-15T09:50:41.092360Z",
     "shell.execute_reply": "2021-12-15T09:50:41.093292Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.t / c.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can have a look at the current state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:50:41.100403Z",
     "iopub.status.busy": "2021-12-15T09:50:41.099373Z",
     "iopub.status.idle": "2021-12-15T09:50:41.102132Z",
     "shell.execute_reply": "2021-12-15T09:50:41.102972Z"
    }
   },
   "outputs": [],
   "source": [
    "from dustpy import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-15T09:50:41.134856Z",
     "iopub.status.busy": "2021-12-15T09:50:41.114897Z",
     "iopub.status.idle": "2021-12-15T09:50:42.650952Z",
     "shell.execute_reply": "2021-12-15T09:50:42.651891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1575x648.949 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.panel(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When calling `Simulation.run()`, `DustPy` will perform a small mass conservation check, by going through all possible collisions and calculating their relative mass error. The limitation here is machine precision. Everything smaller than $10^{-13}$ is still acceptable. If you created your own collision model, that is not compatible with the default sticking-erosion-fragmentation model, you can either ignore the output, or you can overwrite the `Simulation.checkmassconservation()` function that is called by default when calling `Simulation.run()`.\n",
    "\n",
    "See a later chapter for details on coagulation."
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}