{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Basic Usage\n",
    "\n",
    "`TriPoDPy` employs the `simframe` framework for running scientific simulations. For a detailed description of the usage of `simframe` please have a look at the [Simframe Documentation](https://simframe.rtfd.io/). The `tripod` Simulation class inherits many of its atributes from `DustPy`, so for the basic structure looking at the [Dustpy Documentation](https://stammler.github.io/dustpy/) can be helpful.\n",
    "\n",
    "This notebook demonstrates the basic steps of running a `TriPoDPy` simulation:\n",
    "- Initializing and running the most simple (default) `TriPoDPy` model\n",
    "- Plotting the data\n",
    "- Resuming simulations from dump files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Simulation Frame\n",
    "\n",
    "To set up a model we have to import the `Simulation` class from the `tripodpy` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tripodpy import Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version\n",
      "----------------------------- -----------\n",
      "alabaster                     1.0.0\n",
      "appnope                       0.1.4\n",
      "asttokens                     3.0.1\n",
      "attrs                         25.4.0\n",
      "babel                         2.17.0\n",
      "beautifulsoup4                4.14.2\n",
      "bleach                        6.3.0\n",
      "certifi                       2025.11.12\n",
      "charset-normalizer            3.4.4\n",
      "comm                          0.2.3\n",
      "contourpy                     1.3.3\n",
      "cycler                        0.12.1\n",
      "debugpy                       1.8.17\n",
      "decorator                     5.2.1\n",
      "defusedxml                    0.7.1\n",
      "dill                          0.4.0\n",
      "docutils                      0.21.2\n",
      "dustpy                        1.0.8\n",
      "executing                     2.2.1\n",
      "fastjsonschema                2.21.2\n",
      "fonttools                     4.60.1\n",
      "h5py                          3.15.1\n",
      "idna                          3.11\n",
      "imagesize                     1.4.1\n",
      "ipykernel                     7.1.0\n",
      "ipython                       9.7.0\n",
      "ipython_pygments_lexers       1.1.1\n",
      "jedi                          0.19.2\n",
      "Jinja2                        3.1.6\n",
      "jsonschema                    4.25.1\n",
      "jsonschema-specifications     2025.9.1\n",
      "jupyter_client                8.6.3\n",
      "jupyter_core                  5.9.1\n",
      "jupyterlab_pygments           0.3.0\n",
      "kiwisolver                    1.4.9\n",
      "MarkupSafe                    3.0.3\n",
      "matplotlib                    3.10.7\n",
      "matplotlib-inline             0.2.1\n",
      "mistune                       3.1.4\n",
      "nbclient                      0.10.2\n",
      "nbconvert                     7.16.6\n",
      "nbformat                      5.10.4\n",
      "nbsphinx                      0.9.7\n",
      "nest-asyncio                  1.6.0\n",
      "numpy                         2.3.5\n",
      "numpydoc                      1.9.0\n",
      "packaging                     25.0\n",
      "pandocfilters                 1.5.1\n",
      "parso                         0.8.5\n",
      "pexpect                       4.9.0\n",
      "pillow                        12.0.0\n",
      "pip                           25.3\n",
      "platformdirs                  4.5.0\n",
      "prompt_toolkit                3.0.52\n",
      "psutil                        7.1.3\n",
      "ptyprocess                    0.7.0\n",
      "pure_eval                     0.2.3\n",
      "Pygments                      2.19.2\n",
      "pyparsing                     3.2.5\n",
      "python-dateutil               2.9.0.post0\n",
      "pyzmq                         27.1.0\n",
      "referencing                   0.37.0\n",
      "requests                      2.32.5\n",
      "roman-numerals-py             3.1.0\n",
      "rpds-py                       0.29.0\n",
      "scipy                         1.16.3\n",
      "simframe                      1.0.5\n",
      "six                           1.17.0\n",
      "snowballstemmer               3.0.1\n",
      "soupsieve                     2.8\n",
      "Sphinx                        8.1.3\n",
      "sphinx-automodapi             0.20.0\n",
      "sphinx-rtd-theme              3.0.2\n",
      "sphinxcontrib-applehelp       2.0.0\n",
      "sphinxcontrib-devhelp         2.0.0\n",
      "sphinxcontrib-htmlhelp        2.1.0\n",
      "sphinxcontrib-jquery          4.1\n",
      "sphinxcontrib-jsmath          1.0.1\n",
      "sphinxcontrib-qthelp          2.0.0\n",
      "sphinxcontrib-serializinghtml 2.0.0\n",
      "stack-data                    0.6.3\n",
      "tinycss2                      1.4.0\n",
      "tornado                       6.5.2\n",
      "traitlets                     5.14.3\n",
      "TriPoD                        0.0.1\n",
      "tripodpy                      1.0.0\n",
      "typing_extensions             4.15.0\n",
      "urllib3                       2.5.0\n",
      "wcwidth                       0.2.14\n",
      "webencodings                  0.5.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip list "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create an instance of this class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()\n",
    "sim.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage `sim` is an empty simulation object that controls our simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripodpy\n",
       "--------\n",
       "    components   : Group (components)\n",
       "    dust         : Group (Dust quantities)\n",
       "    gas          : Group (Gas quantities)\n",
       "    grid         : Group (Grid quantities)\n",
       "    star         : Group (Stellar quantities)\n",
       "  -----\n",
       "    t            : IntVar (Time [s]), \u001b[95mIntegration variable\u001b[0m\n",
       "  -----\n",
       "    Integrator   : Integrator (Default integrator)\n",
       "    Writer       : Writer (HDF5 file format using h5py)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the fields are initialized with `None`. All attributes can be easiliy addressed via e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Gas quantities)\n",
       "----------------------\n",
       "    boundary     : Group (Boundary conditions)\n",
       "    S            : Group (Source terms)\n",
       "    torque       : Group (Torque parameters)\n",
       "    v            : Group (Velocities)\n",
       "  -----\n",
       "    alpha        : Field (Turbulent alpha parameter)\n",
       "    cs           : Field (Isothermal sound speed [cm/s])\n",
       "    eta          : Field (Pressure gradient parameter)\n",
       "    Fi           : Field (Gas flux interfaces [g/cm/s])\n",
       "    Hp           : Field (Pressure scale height [cm])\n",
       "    mfp          : Field (Midplane mean free path [cm])\n",
       "    mu           : Field (Mean molecular weight [g])\n",
       "    n            : Field (Miplane number density [1/cm³])\n",
       "    nu           : Field (Kinematic viscosity [cm²/s])\n",
       "    P            : Field (Midplane pressure [g/cm/s²])\n",
       "    rho          : Field (Miplane mass density [g/cm³])\n",
       "    Sigma        : Field (Surface density [g/cm²])\n",
       "    SigmaFloor   : Field (Floor value of surface density [g/cm²])\n",
       "    T            : Field (Temperature [K])\n",
       "  -----"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing\n",
    "\n",
    "We can now initialize the `sim` object with `Simulation.initialize()`. `TriPoDPy` will then fill all the fields with default values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the `sim` object has now values assigned to its fields.  \n",
    "**All quantities are in cgs units.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Gas quantities)\n",
       "----------------------\n",
       "    boundary     : Group (Boundary conditions)\n",
       "    S            : Group (Source terms)\n",
       "    torque       : Group (Torque parameters)\n",
       "    v            : Group (Velocities)\n",
       "  -----\n",
       "    alpha        : Field (Turbulent alpha parameter)\n",
       "    cs           : Field (Isothermal sound speed [cm/s])\n",
       "    eta          : Field (Pressure gradient parameter)\n",
       "    Fi           : Field (Gas flux interfaces [g/cm/s])\n",
       "    Hp           : Field (Pressure scale height [cm])\n",
       "    mfp          : Field (Midplane mean free path [cm])\n",
       "    mu           : Field (Mean molecular weight [g])\n",
       "    n            : Field (Miplane number density [1/cm³])\n",
       "    nu           : Field (Kinematic viscosity [cm²/s])\n",
       "    P            : Field (Midplane pressure [g/cm/s²])\n",
       "    rho          : Field (Miplane mass density [g/cm³])\n",
       "    Sigma        : Field (Surface density [g/cm²])\n",
       "    SigmaFloor   : Field (Floor value of surface density [g/cm²])\n",
       "    T            : Field (Temperature [K])\n",
       "  -----"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display the full table of contents of the `sim` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[94mtripodpy\u001b[0m\n",
      "    - \u001b[94mcomponents\u001b[0m: \u001b[94mGroup (components)\u001b[0m\n",
      "        - \u001b[94mDefault\u001b[0m: \u001b[94mGroup (Default gas componentDefault (dust_tracer=False, gas_active=True, gas_tracer=False))\u001b[0m\n",
      "            - \u001b[94mboundary\u001b[0m: \u001b[94mGroup (Boundary conditions for component)\u001b[0m\n",
      "            - \u001b[94mdust\u001b[0m: \u001b[94mGroup (Dust related fields)\u001b[0m\n",
      "                - \u001b[94mFi\u001b[0m: Field (Gas flux [g/cm²/s])\n",
      "                - \u001b[94mpars\u001b[0m: \u001b[94mGroup (Dust parameters)\u001b[0m\n",
      "                    - \u001b[94mrhos\u001b[0m: Field (Material density of dust grains [g/cm³])\n",
      "                - \u001b[94mS\u001b[0m: \u001b[94mGroup (Gas source terms)\u001b[0m\n",
      "                    - \u001b[94mcoag\u001b[0m: Field (Hydrodynamical source term [g/cm²/s])\n",
      "                    - \u001b[94mext\u001b[0m: Field (External source term [g/cm²/s])\n",
      "                    - \u001b[94mhyd\u001b[0m: Field (Hydrodynamical source term [g/cm²/s])\n",
      "                    - \u001b[94mtot\u001b[0m: Field (Total source term [g/cm²/s])\n",
      "                - \u001b[94mS_Sigma\u001b[0m: Field (Source term for dust surface density [g/cm²])\n",
      "                - \u001b[94mSigma\u001b[0m: Field (Dust surface density [g/cm²])\n",
      "                - \u001b[94mSigma_dot\u001b[0m: Field (Gas surface density source term [g/cm²/s])\n",
      "                - \u001b[94mvalue\u001b[0m: Field (tracer value [???])\n",
      "                - \u001b[94mvalue_dot\u001b[0m: Field (Gas parameter source term [???/s])\n",
      "            - \u001b[94mgas\u001b[0m: \u001b[94mGroup (Gas related fields)\u001b[0m\n",
      "                - \u001b[94mboundary\u001b[0m: \u001b[94mGroup\u001b[0m\n",
      "                    - \u001b[94minner\u001b[0m: Constant gradient\n",
      "                    - \u001b[94mouter\u001b[0m: Value\n",
      "                - \u001b[94mFi\u001b[0m: Field (Gas flux [g/cm²/s])\n",
      "                - \u001b[94mpars\u001b[0m: \u001b[94mGroup (Gas parameters)\u001b[0m\n",
      "                    - \u001b[94mmu\u001b[0m: Field (Mean molecular weight [g])\n",
      "                    - \u001b[94mnu\u001b[0m: Field (trial frequency [1/s])\n",
      "                    - \u001b[94mTsub\u001b[0m: Field (evaporation temperatue [K])\n",
      "                - \u001b[94mS\u001b[0m: \u001b[94mGroup (Gas source terms)\u001b[0m\n",
      "                    - \u001b[94mext\u001b[0m: Field (External source term [g/cm²/s])\n",
      "                    - \u001b[94mhyd\u001b[0m: Field (Hydrodynamical source term [g/cm²/s])\n",
      "                    - \u001b[94mtot\u001b[0m: Field (Total source term [g/cm²/s])\n",
      "                - \u001b[94mSigma\u001b[0m: Field (Gas surface density [g/cm²])\n",
      "                - \u001b[94mSigma_dot\u001b[0m: Field (Gas surface density source term [g/cm²/s])\n",
      "                - \u001b[94mvalue\u001b[0m: Field (Gas parameter [???])\n",
      "                - \u001b[94mvalue_dot\u001b[0m: Field (Gas parameter source term [???/s])\n",
      "    - \u001b[94mdust\u001b[0m: \u001b[94mGroup (Dust quantities)\u001b[0m\n",
      "        - \u001b[94ma\u001b[0m: Field (Particle sizes in cm: [a0, fudge * a1, a1, 0.5 * amax, amax])\n",
      "        - \u001b[94mbackreaction\u001b[0m: \u001b[94mGroup (Backreaction coefficients)\u001b[0m\n",
      "            - \u001b[94mA\u001b[0m: Field (Pull factor)\n",
      "            - \u001b[94mB\u001b[0m: Field (Push factor)\n",
      "        - \u001b[94mboundary\u001b[0m: \u001b[94mGroup (Boundary conditions)\u001b[0m\n",
      "            - \u001b[94minner\u001b[0m: Constant gradient\n",
      "            - \u001b[94mouter\u001b[0m: Value\n",
      "        - \u001b[94mD\u001b[0m: Field (Diffusivity [cm²/s])\n",
      "        - \u001b[94mdelta\u001b[0m: \u001b[94mGroup (Mixing parameters)\u001b[0m\n",
      "            - \u001b[94mrad\u001b[0m: Field (Radial mixing parameter)\n",
      "            - \u001b[94mturb\u001b[0m: Field (Turbulent mixing parameter)\n",
      "            - \u001b[94mvert\u001b[0m: Field (Vertical mixing parameter)\n",
      "        - \u001b[94meps\u001b[0m: Field (Dust-to-gas ratio)\n",
      "        - \u001b[94mf\u001b[0m: \u001b[94mGroup (Fudge factors)\u001b[0m\n",
      "            - \u001b[94mcrit\u001b[0m: Field (Critical mass depletion coefficient for shrinking)\n",
      "            - \u001b[94mdrift\u001b[0m: Field (Drift velocity calibration factor)\n",
      "            - \u001b[94mdv\u001b[0m: Field (effective collision speed parameter)\n",
      "        - \u001b[94mFi\u001b[0m: \u001b[94mGroup (Fluxes)\u001b[0m\n",
      "            - \u001b[94madv\u001b[0m: Field (Advective flux [g/cm/s])\n",
      "            - \u001b[94mdiff\u001b[0m: Field (Diffusive flux [g/cm/s])\n",
      "            - \u001b[94mtot\u001b[0m: Field (Total flux [g/cm/s])\n",
      "        - \u001b[94mfill\u001b[0m: Field (Filling factor)\n",
      "        - \u001b[94mH\u001b[0m: Field (Scale heights [cm])\n",
      "        - \u001b[94mm\u001b[0m: Field (Particle mass [g])\n",
      "        - \u001b[94mp\u001b[0m: \u001b[94mGroup (Probabilities)\u001b[0m\n",
      "            - \u001b[94mdriftfrag\u001b[0m: Field (Transition function from drift to turbulence)\n",
      "            - \u001b[94mfrag\u001b[0m: Field (Fragmentation probability)\n",
      "            - \u001b[94mfragtrans\u001b[0m: Field (transition probability between fragmentation regimes)\n",
      "            - \u001b[94mstick\u001b[0m: Field (Sticking probability)\n",
      "        - \u001b[94mq\u001b[0m: \u001b[94mGroup (Distribution exponents)\u001b[0m\n",
      "            - \u001b[94mdrfrag\u001b[0m: Field (Size distribution exponent in drift-induced fragmentation regime)\n",
      "            - \u001b[94meff\u001b[0m: Field (Calculated distribution exponent)\n",
      "            - \u001b[94mfrag\u001b[0m: Field (Fragmentation distribution exponent)\n",
      "            - \u001b[94msweep\u001b[0m: Field (Size distribution exponent in the sweep-up regime)\n",
      "            - \u001b[94mturb1\u001b[0m: Field (Size distribution exponent in first turbulence regime)\n",
      "            - \u001b[94mturb2\u001b[0m: Field (Size distribution exponent in second turbulence regime)\n",
      "        - \u001b[94mqrec\u001b[0m: Field (reconstructed distribution exponent)\n",
      "        - \u001b[94mrho\u001b[0m: Field (Midplane mass density per mass bin [g/cm³])\n",
      "        - \u001b[94mrhos\u001b[0m: Field (Solid state density [g/cm³])\n",
      "        - \u001b[94mS\u001b[0m: \u001b[94mGroup (Sources)\u001b[0m\n",
      "            - \u001b[94mcoag\u001b[0m: Field (Coagulation sources [g/cm²/s])\n",
      "            - \u001b[94mcompo\u001b[0m: Field (Sources due to composition changes [g/cm²/s])\n",
      "            - \u001b[94mext\u001b[0m: Field (External sources [g/cm²/s])\n",
      "            - \u001b[94mhyd\u001b[0m: Field (Hydrodynamic sources [g/cm²/s])\n",
      "            - \u001b[94msmax_hyd\u001b[0m: Field (Total sources [g/cm²/s])\n",
      "            - \u001b[94mtot\u001b[0m: Field (Total sources [g/cm²/s])\n",
      "        - \u001b[94ms\u001b[0m: \u001b[94mGroup (Characteristic particle sizes)\u001b[0m\n",
      "            - \u001b[94mboundary\u001b[0m: \u001b[94mGroup (boundary conditions of smax)\u001b[0m\n",
      "                - \u001b[94minner\u001b[0m: Constant gradient\n",
      "                - \u001b[94mouter\u001b[0m: Value\n",
      "            - \u001b[94mlim\u001b[0m: Field (Limiting size for shrinking)\n",
      "            - \u001b[94mmax\u001b[0m: Field (Maximum particle size)\n",
      "            - \u001b[94mmin\u001b[0m: Field (Minimum particle size)\n",
      "            - \u001b[94msdot_coag\u001b[0m: Field (coagulation source term for amax [cm/s])\n",
      "            - \u001b[94msdot_shrink\u001b[0m: Field (shrinkage source term for amax [cm²/s])\n",
      "        - \u001b[94mSigma\u001b[0m: Field (Surface density per mass bin [g/cm²])\n",
      "        - \u001b[94mSigmaFloor\u001b[0m: Field (Floor value of surface density [g/cm²])\n",
      "        - \u001b[94mSt\u001b[0m: Field (Stokes number)\n",
      "        - \u001b[94mv\u001b[0m: \u001b[94mGroup (Velocities)\u001b[0m\n",
      "            - \u001b[94mdriftmax\u001b[0m: Field (Maximum drift velocity [cm/s])\n",
      "            - \u001b[94mfrag\u001b[0m: Field (Fragmentation velocity [cm/s])\n",
      "            - \u001b[94mrad\u001b[0m: Field (Radial velocity [cm/s])\n",
      "            - \u001b[94mrad_flux\u001b[0m: Field (Radial velocity modified to claulate the proper flux[cm/s])\n",
      "            - \u001b[94mrel\u001b[0m: \u001b[94mGroup (Relative velocities)\u001b[0m\n",
      "                - \u001b[94mazi\u001b[0m: Field (Relative azimuthal velocity [cm/s])\n",
      "                - \u001b[94mbrown\u001b[0m: Field (Relative Brownian motion velocity [cm/s])\n",
      "                - \u001b[94mrad\u001b[0m: Field (Relative radial velocity [cm/s])\n",
      "                - \u001b[94mtot\u001b[0m: Field (Total relative velocity [cm/s])\n",
      "                - \u001b[94mturb\u001b[0m: Field (Relative turbulent velocity [cm/s])\n",
      "                - \u001b[94mvert\u001b[0m: Field (Relative vertical settling velocity [cm/s])\n",
      "    - \u001b[94mgas\u001b[0m: \u001b[94mGroup (Gas quantities)\u001b[0m\n",
      "        - \u001b[94malpha\u001b[0m: Field (Turbulent alpha parameter)\n",
      "        - \u001b[94mboundary\u001b[0m: \u001b[94mGroup (Boundary conditions)\u001b[0m\n",
      "            - \u001b[94minner\u001b[0m: Constant gradient\n",
      "            - \u001b[94mouter\u001b[0m: Value\n",
      "        - \u001b[94mcs\u001b[0m: Field (Isothermal sound speed [cm/s])\n",
      "        - \u001b[94meta\u001b[0m: Field (Pressure gradient parameter)\n",
      "        - \u001b[94mFi\u001b[0m: Field (Gas flux interfaces [g/cm/s])\n",
      "        - \u001b[94mHp\u001b[0m: Field (Pressure scale height [cm])\n",
      "        - \u001b[94mmfp\u001b[0m: Field (Midplane mean free path [cm])\n",
      "        - \u001b[94mmu\u001b[0m: Field (Mean molecular weight [g])\n",
      "        - \u001b[94mn\u001b[0m: Field (Miplane number density [1/cm³])\n",
      "        - \u001b[94mnu\u001b[0m: Field (Kinematic viscosity [cm²/s])\n",
      "        - \u001b[94mP\u001b[0m: Field (Midplane pressure [g/cm/s²])\n",
      "        - \u001b[94mrho\u001b[0m: Field (Miplane mass density [g/cm³])\n",
      "        - \u001b[94mS\u001b[0m: \u001b[94mGroup (Source terms)\u001b[0m\n",
      "            - \u001b[94mext\u001b[0m: Field (External sources [g/cm²/s])\n",
      "            - \u001b[94mhyd\u001b[0m: Field (Hydrodynamic sources [g/cm²/s])\n",
      "            - \u001b[94mtot\u001b[0m: Field (Total sources [g/cm²/s])\n",
      "        - \u001b[94mSigma\u001b[0m: Field (Surface density [g/cm²])\n",
      "        - \u001b[94mSigmaFloor\u001b[0m: Field (Floor value of surface density [g/cm²])\n",
      "        - \u001b[94mT\u001b[0m: Field (Temperature [K])\n",
      "        - \u001b[94mtorque\u001b[0m: \u001b[94mGroup (Torque parameters)\u001b[0m\n",
      "            - \u001b[94mLambda\u001b[0m: Field (Specific angular momentum injection rate by torque [cm²/s²])\n",
      "            - \u001b[94mv\u001b[0m: Field (Effective velocity imposed by torque [cm/s])\n",
      "        - \u001b[94mv\u001b[0m: \u001b[94mGroup (Velocities)\u001b[0m\n",
      "            - \u001b[94mrad\u001b[0m: Field (Radial velocity [cm/s])\n",
      "            - \u001b[94mvisc\u001b[0m: Field (Viscous accretion velocity [cm/s])\n",
      "    - \u001b[94mgrid\u001b[0m: \u001b[94mGroup (Grid quantities)\u001b[0m\n",
      "        - \u001b[94mA\u001b[0m: Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
      "        - \u001b[94mNr\u001b[0m: Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
      "        - \u001b[94mOmegaK\u001b[0m: Field (Keplerian frequency [1/s])\n",
      "        - \u001b[94mr\u001b[0m: Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
      "        - \u001b[94mri\u001b[0m: Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
      "    - \u001b[94mstar\u001b[0m: \u001b[94mGroup (Stellar quantities)\u001b[0m\n",
      "        - \u001b[94mL\u001b[0m: Field (Luminosity [erg/s])\n",
      "        - \u001b[94mM\u001b[0m: Field (Mass [g])\n",
      "        - \u001b[94mR\u001b[0m: Field (Radius [cm])\n",
      "        - \u001b[94mT\u001b[0m: Field (Effective temperature [K])\n",
      "    - \u001b[94mt\u001b[0m: IntVar (Time [s]), \u001b[95mIntegration variable\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.toc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial condition of the default simulation is printed below. For more details about the varibles see Section 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(dust=namespace(aIniMax=0.0001,\n",
       "                         allowDriftingParticles=False,\n",
       "                         d2gRatio=0.01,\n",
       "                         distExp=-3.5,\n",
       "                         rhoMonomer=1.67,\n",
       "                         vFrag=100.0),\n",
       "          gas=namespace(alpha=0.001,\n",
       "                        Mdisk=9.942049353490256e+31,\n",
       "                        mu=3.847030424486999e-24,\n",
       "                        SigmaExp=-1.0,\n",
       "                        SigmaRc=897587224200000.0),\n",
       "          grid=namespace(mmin=1e-12,\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.ini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a Simulation\n",
    "\n",
    "The simulation is now ready to go. We can start it with ``Simulation.run()``. As the default simulation runs for 100,000 years, this will merely take a few moments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "tripodpy v1.0.0\n",
      "\n",
      "Creating data directory data.\n",
      "Writing file \u001b[94mdata/data0000.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0001.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0002.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0003.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0004.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0005.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0006.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0007.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0008.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0009.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0010.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0011.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0012.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0013.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0014.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0015.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0016.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0017.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0018.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0019.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94mdata/data0020.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Execution time: \u001b[94m0:00:08\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `TriPoDPy` has used the hdf5 writer to save output files to the `data/` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "`TriPoDPy` is coming with a simple plotting script that can be used to check the status of a simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tripodpy import plot\n",
    "import dustpy.constants as c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotting script does either take the simulation object as argument or a data directory.\n",
    "\n",
    "If the argument is a simulation object the script is only plotting the current state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x889.969 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.panel(sim,show_limits=True,ir=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue and the green lines in the top left panel are analytical estimates for the fragmentation and drift barrier taken from [Birnstiel et al. (2012)](https://doi.org/10.1051/0004-6361/201118136). The middle panel on the top shows the size distribution at the radial index `ir`. The top right shows the mass evolution of dust and gas (if multiple snapshots are provided). The bottom left shows the midplane density of the dust (red) and the power law exponent q aka. $\\xi$ (blue). The bottom middle pannel shows the total dust and gas surface desity along with the dust do gas ratio (gray) as a function of radius. If we pass the data directory as argument, we also have access to the time evolution. Furthermore, some plots can be adjusted by specifying the time `it`, radial `ir`, or size `ia` index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x889.969 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.panel(\"data\", it=10, ir=10, ia=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dust distribution with TriPoD\n",
    "`TriPoDPy`simulates the evolution of the dust surface density in terms of the two population surface densities $\\Sigma_0$ and $\\Sigma_1$.\n",
    "\n",
    "Together, they constitute the total dust density $\\Sigma_{tot} = \\int \\limits_{m\\left(s_{min}\\right)}^{m\\left(s_{max}\\right)} \\sigma\\left(m\\right) \\mathrm{d}m = \\int \\limits_{m\\left(s_{min}\\right)}^{m\\left(s_{int}\\right)} \\sigma\\left(m\\right) \\mathrm{d}m + \\int \\limits_{m\\left(s_{int}\\right)}^{m\\left(s_{max}\\right)} \\sigma\\left(m\\right) \\mathrm{d}m = \\Sigma_0 + \\Sigma_1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.11816465e+01 1.04183445e+01 9.70477551e+00 9.03822292e+00\n",
       " 8.41623126e+00 7.83595545e+00 7.29466546e+00 6.78979477e+00\n",
       " 6.31893572e+00 5.87983130e+00 5.47036721e+00 5.08856402e+00\n",
       " 4.73256977e+00 4.40065277e+00 4.09119469e+00 3.80268401e+00\n",
       " 3.53370963e+00 3.28295483e+00 3.04919145e+00 2.83127432e+00\n",
       " 2.62813594e+00 2.43878143e+00 2.26228369e+00 2.09777883e+00\n",
       " 1.94446187e+00 1.80158268e+00 1.66844213e+00 1.54438864e+00\n",
       " 1.42881484e+00 1.32115458e+00 1.22088020e+00 1.12750002e+00\n",
       " 1.04055614e+00 9.59622497e-01 8.84303228e-01 8.14231318e-01\n",
       " 7.49067575e-01 6.88499863e-01 6.32242471e-01 5.80034566e-01\n",
       " 5.31629104e-01 4.86701769e-01 4.44833737e-01 4.05750353e-01\n",
       " 3.69280669e-01 3.35434352e-01 3.04187869e-01 2.75371203e-01\n",
       " 2.48806414e-01 2.24327155e-01 2.01785224e-01 1.81051909e-01\n",
       " 1.62016500e-01 1.44574390e-01 1.28627518e-01 1.14080000e-01\n",
       " 1.00834994e-01 8.87948903e-02 7.78657906e-02 6.79631535e-02\n",
       " 5.90140081e-02 5.09564151e-02 4.37432311e-02 3.73549731e-02\n",
       " 3.17840475e-02 2.69552356e-02 2.27335733e-02 1.90178477e-02\n",
       " 1.57507053e-02 1.29151878e-02 1.04876296e-02 8.43199312e-03\n",
       " 6.70878990e-03 5.27902614e-03 4.10241710e-03 3.12740682e-03\n",
       " 2.26042366e-03 1.35092232e-03 4.32536686e-04 7.90011850e-05\n",
       " 9.09758036e-06 7.01977289e-07 2.00000000e-11 2.00000000e-11\n",
       " 2.00000000e-11 2.00000000e-11 2.00000000e-11 2.00000000e-11\n",
       " 2.00000000e-11 2.00000000e-11 2.00000000e-11 2.00000000e-11\n",
       " 2.00000000e-11 2.00000000e-11 2.00000000e-11 2.00000000e-11\n",
       " 2.00000000e-11 2.00000000e-11 2.00000000e-11 2.00000000e-11]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SigmaTot = sim.dust.Sigma.sum(-1)\n",
    "SigmaTot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to display the data in a more granular way and compare it to `Dustpy` outputs, we can use the distribution exponents $\\xi$ to interpolate the dust surface density over a logarithmic mass grid. The density in the mass interval $\\left[m_0, m_1\\right]$ is then given by\n",
    "\n",
    "$\\Sigma_{[m_0, m_1]} = \\int\\limits_{m_0}^{m_1} \\sigma\\left(m\\right) \\mathrm{d}m = \\Sigma_{tot} \\frac{m_1^{\\frac{\\xi+4}{3}}-m_0^{\\frac{\\xi+4}{3}}}{m_{max}^{\\frac{\\xi+4}{3}}-m_{min}^{\\frac{\\xi+4}{3}}}$ for $\\xi_{calc} \\neq -4$ and $\\Sigma_{tot} \\frac{\\log(m_1/m_0)}{\\log(m_{max}/m_{min})}$ for $\\xi_{calc} = -4$.\n",
    "\n",
    "This calculation is automatically done in the data readout scheme of the plotting routine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tripodpy.utils import read_data\n",
    "data = read_data(\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotted dust density distribution is then given by\n",
    "\n",
    "$\\Sigma_\\mathrm{d} = \\int\\limits_0^\\infty \\sigma \\left(m \\right) \\mathrm{d} \\log m$.\n",
    "\n",
    "In this way the distribution is independent of the mass grid.\n",
    "\n",
    "The internal dust distribution `TriPoDPy` uses after interpolation of the dust density are $\\Sigma_{\\mathrm{d},\\,i} \\equiv \\Sigma_\\mathrm{d} \\left(m_i \\right)$ with\n",
    "\n",
    "$\\Sigma_\\mathrm{d} = \\sum\\limits_i \\Sigma_{\\mathrm{d},\\,i}$,\n",
    "\n",
    "meaning the numerical sum over the mass dimension is the dust surface density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.11816465e+01, 1.04183445e+01, 9.70477551e+00, 9.03822292e+00,\n",
       "       8.41623126e+00, 7.83595545e+00, 7.29466546e+00, 6.78979477e+00,\n",
       "       6.31893572e+00, 5.87983130e+00, 5.47036721e+00, 5.08856402e+00,\n",
       "       4.73256977e+00, 4.40065277e+00, 4.09119469e+00, 3.80268401e+00,\n",
       "       3.53370963e+00, 3.28295483e+00, 3.04919145e+00, 2.83127432e+00,\n",
       "       2.62813594e+00, 2.43878143e+00, 2.26228369e+00, 2.09777883e+00,\n",
       "       1.94446187e+00, 1.80158268e+00, 1.66844213e+00, 1.54438864e+00,\n",
       "       1.42881484e+00, 1.32115458e+00, 1.22088020e+00, 1.12750002e+00,\n",
       "       1.04055614e+00, 9.59622497e-01, 8.84303228e-01, 8.14231318e-01,\n",
       "       7.49067575e-01, 6.88499863e-01, 6.32242471e-01, 5.80034566e-01,\n",
       "       5.31629104e-01, 4.86701769e-01, 4.44833737e-01, 4.05750353e-01,\n",
       "       3.69280669e-01, 3.35434352e-01, 3.04187869e-01, 2.75371203e-01,\n",
       "       2.48806414e-01, 2.24327155e-01, 2.01785224e-01, 1.81051909e-01,\n",
       "       1.62016500e-01, 1.44574390e-01, 1.28627518e-01, 1.14080000e-01,\n",
       "       1.00834994e-01, 8.87948903e-02, 7.78657906e-02, 6.79631535e-02,\n",
       "       5.90140081e-02, 5.09564151e-02, 4.37432311e-02, 3.73549731e-02,\n",
       "       3.17840475e-02, 2.69552356e-02, 2.27335733e-02, 1.90178477e-02,\n",
       "       1.57507053e-02, 1.29151878e-02, 1.04876296e-02, 8.43199312e-03,\n",
       "       6.70878990e-03, 5.27902614e-03, 4.10241710e-03, 3.12740682e-03,\n",
       "       2.26042366e-03, 1.35092232e-03, 4.32536686e-04, 7.90011850e-05,\n",
       "       9.09758036e-06, 7.01977289e-07, 2.00000000e-11, 2.00000000e-11,\n",
       "       2.00000000e-11, 2.00000000e-11, 2.00000000e-11, 2.00000000e-11,\n",
       "       2.00000000e-11, 2.00000000e-11, 2.00000000e-11, 2.00000000e-11,\n",
       "       2.00000000e-11, 2.00000000e-11, 2.00000000e-11, 2.00000000e-11,\n",
       "       2.00000000e-11, 2.00000000e-11, 2.00000000e-11, 2.00000000e-11])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dust.Sigma_recon.sum(-1)[-1,...]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more in depth analysis the internally used sizes and power law exponents can be found in the simulation Object/data under:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "power law exponent of the particle size distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-3.57885522 -3.58436399 -3.58985937 -3.59026723 -3.59045478]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.qrec[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the minimum and maximum sizes of the distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.22875516e-05 5.22875516e-05 5.22875516e-05 5.22875516e-05\n",
       " 5.22875516e-05]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.s.min[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.13647524 0.13476168 0.13092258 0.12635921 0.12193889]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.s.max[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the size array with all the sizes used for the evolution of the distribution:\n",
    "\n",
    "$[a_0, 0.4 * a_1, a_1, 0.4 * a_{max}, a_{max}]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.00097461 0.01991676 0.04979191 0.0545901  0.13647524]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.a[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this means all the dust quantities either have shape $(N_r, 2)$ or $(N_r, 5)$ depending if they are defined for the mass bins or the particle sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data files\n",
    "\n",
    "If we want to read data files, we can use the read/writer module provided by `simframe` that is used to write the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import hdf5writer\n",
    "\n",
    "wrtr = hdf5writer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should make sure that the correct data directory is assigned to the writer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Writer (HDF5 file format using h5py)\n",
       "------------------------------------\n",
       "    Data directory : data\n",
       "    File names     : data/data0000.hdf5\n",
       "    Overwrite      : \u001b[93mFalse\u001b[0m\n",
       "    Dumping        : True\n",
       "    Options        : {'com': 'lzf', 'comopts': None}\n",
       "    Verbosity      : 1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrtr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now read a single data file with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data0003 = wrtr.read.output(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns a namespace and the data can simply be accessed in the same way as for the `Simulation` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.11399371e+003, 1.03936331e+003, 9.69418292e+002, 9.03901717e+002,\n",
       "       8.42577816e+002, 7.85223246e+002, 7.31620090e+002, 6.81552405e+002,\n",
       "       6.34806398e+002, 5.91172929e+002, 5.50450541e+002, 5.12447758e+002,\n",
       "       4.76984260e+002, 4.43891110e+002, 4.13010428e+002, 3.84194816e+002,\n",
       "       3.57306712e+002, 3.32217749e+002, 3.08808143e+002, 2.86966128e+002,\n",
       "       2.66587413e+002, 2.47574692e+002, 2.29837176e+002, 2.13290157e+002,\n",
       "       1.97854607e+002, 1.83456798e+002, 1.70027947e+002, 1.57503892e+002,\n",
       "       1.45824781e+002, 1.34934785e+002, 1.24781832e+002, 1.15317357e+002,\n",
       "       1.06496066e+002, 9.82757223e+001, 9.06169385e+001, 8.34829889e+001,\n",
       "       7.68396309e+001, 7.06549389e+001, 6.48991486e+001, 5.95445117e+001,\n",
       "       5.45651595e+001, 4.99369755e+001, 4.56374756e+001, 4.16456957e+001,\n",
       "       3.79420864e+001, 3.45084138e+001, 3.13276659e+001, 2.83839651e+001,\n",
       "       2.56624852e+001, 2.31493726e+001, 2.08316729e+001, 1.86972608e+001,\n",
       "       1.67347738e+001, 1.49335495e+001, 1.32835669e+001, 1.17753902e+001,\n",
       "       1.04001165e+001, 9.14932670e+000, 8.01503936e+000, 6.98966853e+000,\n",
       "       6.06598484e+000, 5.23708046e+000, 4.49633800e+000, 3.83740365e+000,\n",
       "       3.25416470e+000, 2.74073163e+000, 2.29142506e+000, 1.90076741e+000,\n",
       "       1.56347942e+000, 1.27448129e+000, 1.02889813e+000, 8.22069157e-001,\n",
       "       6.49560116e-001, 5.07177865e-001, 3.90986192e-001, 2.97321654e-001,\n",
       "       2.22808174e-001, 1.64369134e-001, 1.19235769e-001, 8.49508478e-002,\n",
       "       5.93668651e-002, 4.06383541e-002, 2.72082855e-002, 1.77889719e-002,\n",
       "       1.13382867e-002, 7.03235028e-003, 4.23606956e-003, 2.47302040e-003,\n",
       "       1.39611750e-003, 7.60333317e-004, 3.98435021e-004, 2.00348591e-004,\n",
       "       9.63848531e-005, 4.42233694e-005, 1.92860885e-005, 7.96543382e-006,\n",
       "       3.10355024e-006, 1.13600681e-006, 3.87860290e-007, 1.00000000e-100])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0003.gas.Sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also read the entire data directory with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = wrtr.read.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has now an additional dimension for time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 100)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.gas.Sigma.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the simulation data can also be comfortably plotted from the data read, for example we can reproduce the last plot form the panel function as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time step we want to plot\n",
    "it = 10\n",
    "plt.figure()\n",
    "plt.plot(data.grid.r[it,:]/c.au, data.gas.Sigma[it, :], label='Gas Surface Density')\n",
    "#total dust surface density\n",
    "plt.plot(data.grid.r[it,:]/c.au, data.dust.Sigma[it,...].sum(-1), label='Dust Surface Density')\n",
    "plt.xlabel('Radius [AU]')\n",
    "plt.ylabel('Surface Density $[g/cm^2]$')\n",
    "plt.title(f'Surface Densities at Time Step {it}')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e-2, 1e4)\n",
    "plt.xlim(1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data files can be quite large and reading the entire data set can consequently take some time. Instead, it is more efficient to only read single fields from the data files. We can do so via e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "SigmaGas = wrtr.read.sequence(\"gas.Sigma\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 100)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SigmaGas.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to exclude certain fields from being written into the data files to save memory by setting their `save` attribute to `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.save = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Dump Files\n",
    "\n",
    "The data files merely contain the pure data, but no information about the operations `TriPoDPy` has to perform, e.g. customized functions. Hence, it is not possible to directly restart a simulation from data files.\n",
    "\n",
    "`simframe` is saving by default the most recent dump file, from which a simulation can be restarted.\n",
    "\n",
    "**Attention:** Malware can be injected with dump files, which are pickled objects. One should only read dump files that were created by oneself or from trusted sources! Dump files have to be read with the same version of `TriPoDPy` as they were written. Otherwise, it is not guaranteed to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import readdump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_restart = readdump(\"data/frame.dmp\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is now a simulation frame that should be identical to our previous object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.Sigma == sim_restart.gas.Sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now, for example, add more snapshots and restart the simulation. Here we just want to extend the run by one year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import constants as c\n",
    "\n",
    "sim_restart.t.snapshots = np.concatenate((sim_restart.t.snapshots, [100001.*c.year]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The current time is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_restart.t / c.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now restart the simulation for another year after the time of the dumpfile (which is automatically used)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "tripodpy v1.0.0\n",
      "\n",
      "Writing file \u001b[94mdata/data0021.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94mdata/frame.dmp\u001b[0m\n",
      "Execution time: \u001b[94m0:00:00\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim_restart.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another file was written and the current time is now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100001.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_restart.t / c.year"
   ]
  }
 ],
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