examples/3_updating.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/stammler/simframe/HEAD?labpath=examples%2F3_updating.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This notebook teaches how to:**  \n",
    "update groups and fields and use heartbeats.\n",
    "\n",
    "In addition to the `simframe` requirements, the following packages are needed for this notebook: `astropy`, `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Updating Groups and Fields\n",
    "\n",
    "In this example we revisit the orbital integration from the previous tutorial. But this time we also want to calculate the equilibrium temperature on Earth. That is not a quantity that we need to integrate. We can simply calculate it from the other quantities. But we have to tell `simframe` how to do it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Setting up the problem**\n",
    "\n",
    "The problem setup follows the previous example. Please have a look there for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Frame(description=\"Earth-Sun system\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addgroup(\"Sun\")\n",
    "sim.addgroup(\"Earth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy.constants as c\n",
    "import astropy.units as u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "AU   = (1.*u.au).si.value\n",
    "day  = (1.*u.day).si.value\n",
    "G    = c.G.si.value\n",
    "year = (1.*u.year).si.value\n",
    "\n",
    "M_earth = c.M_earth.si.value\n",
    "M_sun   = c.M_sun.si.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Earth**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.addfield(\"M\", M_earth, description=\"Mass [kg]\", constant=True)\n",
    "sim.Earth.addfield(\"r\", np.zeros(3), description=\"Position [m]\")\n",
    "sim.Earth.addfield(\"v\", np.zeros(3), description=\"Velocity [m/s]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sun**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Sun.addfield(\"M\", c.M_sun.si.value, description=\"Mass [kg]\", constant=True)\n",
    "sim.Sun.addfield(\"r\", np.zeros(3), description=\"Position [m]\")\n",
    "sim.Sun.addfield(\"v\", np.zeros(3), description=\"Velocity [m/s]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Integration variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addintegrationvariable(\"t\", 0., description=\"Time [s]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 1.*day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_dt(frame):\n",
    "    return dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.updater = f_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "snapwidth = 5.*day\n",
    "tmax = 2.*year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.arange(snapwidth, tmax+1, snapwidth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Writer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import writers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer = writers.namespacewriter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer.verbosity = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Differential equations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dr_Earth(frame, x, Y):\n",
    "    return frame.Earth.v\n",
    "\n",
    "def dr_Sun(frame, x, Y):\n",
    "    return frame.Sun.v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gravitational acceleration\n",
    "def ag(M, r, R):\n",
    "    direction = r-R\n",
    "    distance = np.linalg.norm(direction)\n",
    "    return -G * M * direction / distance**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dv_Earth(frame, x, Y):\n",
    "    return ag(frame.Sun.M, frame.Earth.r, frame.Sun.r)\n",
    "\n",
    "def dv_Sun(frame, x, Y):\n",
    "    return ag(frame.Earth.M, frame.Sun.r, frame.Earth.r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.v.differentiator = dv_Earth\n",
    "sim.Earth.r.differentiator = dr_Earth\n",
    "sim.Sun.v.differentiator   = dv_Sun\n",
    "sim.Sun.r.differentiator   = dr_Sun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Integrator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import Integrator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "integrator = Integrator(sim.t, description=\"Leapfrog integratior\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import schemes\n",
    "from simframe.integration import Instruction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Leapfrog integrator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "instructions = [Instruction(schemes.expl_1_euler, sim.Sun.v,   fstep=0.5),\n",
    "                Instruction(schemes.expl_1_euler, sim.Earth.v, fstep=0.5),\n",
    "                Instruction(schemes.update,       sim.Sun.v             ),\n",
    "                Instruction(schemes.update,       sim.Earth.v           ),\n",
    "                Instruction(schemes.expl_1_euler, sim.Sun.r,   fstep=1.0),\n",
    "                Instruction(schemes.expl_1_euler, sim.Earth.r, fstep=1.0),\n",
    "                Instruction(schemes.update,       sim.Sun.r             ),\n",
    "                Instruction(schemes.update,       sim.Earth.r           ),\n",
    "                Instruction(schemes.expl_1_euler, sim.Sun.v,   fstep=0.5),\n",
    "                Instruction(schemes.expl_1_euler, sim.Earth.v, fstep=0.5),\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "integrator.instructions = instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.integrator = integrator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Initial conditions**\n",
    "\n",
    "In this example we want to send the Earth on an eccentric orbit to have a seasonal change in the equilibrium temperature. For this we simply incline the initial velocities of Earth an Sun by an angle `alpha`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_Earth_ini = np.array([AU, 0., 0.])\n",
    "r_Sun_ini = np.zeros(3)\n",
    "# Center of mass\n",
    "COM_ini = (M_earth*r_Earth_ini + M_sun*r_Sun_ini) / (M_earth+M_sun)\n",
    "# Offset both positions\n",
    "r_Earth_ini -= COM_ini\n",
    "r_Sun_ini -= COM_ini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = M_earth*M_sun / (M_earth+M_sun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 30. * u.deg\n",
    "v_Earth_ini = np.sqrt(G*M_sun/M_earth*mu/AU)\n",
    "v_Earth_ini = np.array([-np.sin(alpha)*v_Earth_ini, np.cos(alpha)*v_Earth_ini, 0.])\n",
    "v_Sun_ini   = np.sqrt(G*M_earth/M_sun*mu/AU)\n",
    "v_Sun_ini   = np.array([np.sin(alpha)*v_Sun_ini, -np.cos(alpha)*v_Sun_ini, 0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.r = r_Earth_ini\n",
    "sim.Earth.v = v_Earth_ini\n",
    "sim.Sun.r   = r_Sun_ini\n",
    "sim.Sun.v   = v_Sun_ini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Starting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: \u001b[94m0:00:00\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reading and plotting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_orbits(data):\n",
    "    fig, ax = plt.subplots(dpi=150)\n",
    "    ax.set_aspect(1)\n",
    "    ax.axis(\"off\")\n",
    "    fig.set_facecolor(\"#000000\")\n",
    "    imax = data.t.shape[0]\n",
    "    for i in range(imax):\n",
    "        alpha = np.maximum(i/imax-0.1, 0.5)\n",
    "        ax.plot(data.Sun.r[i, 0], data.Sun.r[i, 1], \"o\", c=\"#FFFF00\", markersize=4, alpha=alpha)\n",
    "        ax.plot(data.Earth.r[i, 0], data.Earth.r[i, 1], \"o\", c=\"#0000FF\", markersize=1, alpha=alpha)\n",
    "    ax.plot(data.Sun.r[-1, 0], data.Sun.r[-1, 1], \"o\", c=\"#FFFF00\", markersize=16)\n",
    "    ax.plot(data.Earth.r[-1, 0], data.Earth.r[-1, 1], \"o\", c=\"#0000FF\", markersize=4)\n",
    "    ax.set_xlim(-1.5*AU, 1.5*AU)\n",
    "    ax.set_ylim(-1.5*AU, 1.5*AU)\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_orbits(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Earth is now on an eccentric orbit around the sound. By the spacing of the snapshots, which are constant in time, you can even tell that the Earth is faster around the perihelion and slower around the aphelion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding Temperature**\n",
    "\n",
    "To calculate the temperature Earth and Sun need additional fields for their temperatures and their radii. In additional to that, we give the Earth an additional field for the Earth-Sun distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_Earth = c.R_earth.si.value\n",
    "R_Sun   = c.R_sun.si.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initialize the Earth's temperature with zero and update it as soon as we have a function for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.addfield(\"d\", AU, description=\"Earth-Sun distance [m]\")\n",
    "sim.Earth.addfield(\"R\", R_Earth, description=\"Radius [m]\")\n",
    "sim.Earth.addfield(\"T\", 0., description=\"Temperature [K]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group\n",
       "-----\n",
       "    d            : Field (Earth-Sun distance [m])\n",
       "    M            : Field (Mass [kg]), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Position [m])\n",
       "    R            : Field (Radius [m])\n",
       "    T            : Field (Temperature [K])\n",
       "    v            : Field (Velocity [m/s])\n",
       "  -----"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Sun.addfield(\"R\", R_Sun, description=\"Radius [m]\", constant=True)\n",
    "sim.Sun.addfield(\"T\", 5778, description=\"Effective temperature [K]\", constant=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group\n",
       "-----\n",
       "    M            : Field (Mass [kg]), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Position [m])\n",
       "    R            : Field (Radius [m]), \u001b[95mconstant\u001b[0m\n",
       "    T            : Field (Effective temperature [K]), \u001b[95mconstant\u001b[0m\n",
       "    v            : Field (Velocity [m/s])\n",
       "  -----"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Sun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Skipping Fields in Output Files\n",
    "\n",
    "It is possible to not write certain fields into the output. This can be especially useful if you have large fields, that consume a significant amount of memory, that you don't need for your data analysis. Simply the the `save` attribute to `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Sun.T.save = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Updating fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The energy the sun is emitting per time, i.e., it's luminosity $L$, is given by\n",
    "\n",
    "$L_\\odot = 4\\pi R_\\odot^2 \\sigma_\\mathrm{SB} T_\\mathrm{eff}^4$.\n",
    "\n",
    "The energy flux arriving at Earth, i.e., the Solar constant $S$ is then given by\n",
    "\n",
    "$S = \\frac{L}{4\\pi d^2}$.\n",
    "\n",
    "Only one hemisphere of the Earth is illuminated at any time, so the energy that the Earth is receiving per unit time, neglecting any albedo effects, is given by\n",
    "\n",
    "$P_\\mathrm{in} = \\pi R_\\oplus^2 S$.\n",
    "\n",
    "We assume that the temperature on Earth is in euilibrium, i.e., there is no difference between day and night. The energy the Earth is emitting per unit time is then given by\n",
    "\n",
    "$P_\\mathrm{out} = 4\\pi R_\\oplus^2 \\sigma_\\mathrm{SB} T^4$.\n",
    "\n",
    "In equilibrium both are equal $\\left( P_\\mathrm{in} = P_\\mathrm{out} \\right)$ and we can solve for $T$\n",
    "\n",
    "$T = T_\\mathrm{eff} \\sqrt[4]{\\frac{R_\\odot^2}{4d^2}}$.\n",
    "\n",
    "So the equilibrium temperature on Earth depends on the Sun's effective temperature $T_\\mathrm{eff}$, the Sun's radius $R_\\odot$, and the Earth-Sun distance $d$. We can now write a function that takes the frame object as argument and returns Earth's temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma_sb = c.sigma_sb.si.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T(frame):\n",
    "    \"\"\"Function computes the equilibrium temperature of the Earth\"\"\"\n",
    "    return frame.Sun.T * (frame.Sun.R**2 / (4.*frame.Earth.d**2))**0.25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now assign this function to the updater of the temperature field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.T.updater = T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function for calculating the Earth-Sun distance is pretty simple and can be simply assigned to the updater of the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d(sim):\n",
    "    \"\"\"Function computed the Sun-Earth distance\"\"\"\n",
    "    return np.linalg.norm(sim.Earth.r - sim.Sun.r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.d.updater = d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now the temperature is zero as we initialized it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use our the updater of the temperature field to calculate it's initial value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.T.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278.6190681198289"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Updating groups\n",
    "\n",
    "But before we can rerun the simulation we have to tell `simframe` how to update groups. This is not done automatically, because for one, not all fields and groups need to be updated, and second, the order of update matters. In our case, we do not need to update the group of the Sun, because it's temperature is not changing, and we need to update the Earth-Sun distance before we update the Earth's temperature, because we need the distance for it.\n",
    "\n",
    "The only update operation that is performed once per time step is the update of the frame object `Frame.update()`. From here we have to delegate tasks down the tree structure.\n",
    "\n",
    "There are in principle two methods of updating groups. One is by writing a function and assigning it to the group's updater. Here we write a function that is calling Earth's updater and assign it to theupdater of the frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_Earth(frame):\n",
    "    frame.Earth.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.updater = update_Earth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second method is by assigning a list of group attributes to an updater. The updater is then calling the updaters of these attributes in exactly that order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.updater = [\"d\", \"T\"]    # Earth-Sun distance first, then temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the updater of a group has been set with a list, the order can be displayed. The attribute will return `None`, if no updater is set or if the updater was set with a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['d', 'T']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Resetting to initial conditions**\n",
    "\n",
    "We can now reset to the initial conditions we have saved earlier, reset the namespace writer, and run the simulation again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.r = r_Earth_ini\n",
    "sim.Earth.v = v_Earth_ini\n",
    "sim.Sun.r   = r_Sun_ini\n",
    "sim.Sun.v   = v_Sun_ini\n",
    "sim.t       = 0.\n",
    "sim.writer.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: \u001b[94m0:00:00\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reading and plotting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_T = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_temperature(data):\n",
    "    fig, ax = plt.subplots(dpi=150)\n",
    "    ax.plot(data.t/year, (data.Earth.T*u.K).to(u.Celsius, equivalencies=u.temperature()), c=\"C3\", label=\"Temperature\")\n",
    "    ax.plot(-2., 0., label=\"Earth-Sun distance\", c=\"C0\")\n",
    "    ax.set_xlabel(\"Time [years]\")\n",
    "    ax.set_ylabel(\"Temperature [°C]\")\n",
    "    ax.set_xlim(data.t[0]/year, data.t[-1]/year)\n",
    "    ax.set_ylim(-100., 150.)\n",
    "    axr = ax.twinx()\n",
    "    axr.plot(data.t/year, data.Earth.d/AU, c=\"C0\")\n",
    "    axr.set_ylabel(\"Distance [AU]\")\n",
    "    axr.set_ylim(0., 2.0)\n",
    "    ax.legend()\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temperature(data_T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The heartbeat concept\n",
    "\n",
    "Every updater is actually performing three operations. The updater operation itself, a **\"systole\"**, that is executed before the update operation and a **\"diastole\"** that is executed after the update operation.\n",
    "\n",
    "In this simple case we want to print Earth's temperature before and after it's calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_sys(frame):\n",
    "    \"\"\"This function prints the temperature before the update process.\"\"\"\n",
    "    msg = \"{:6s}: T = {:5.1f} K\".format(\"Before\", sim.Earth.T)\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T_dia(frame):\n",
    "    \"\"\"This function prints the temperature after the update process.\"\"\"\n",
    "    msg = \"{:6s}: T = {:5.1f} K\".format(\"After\", sim.Earth.T)\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can assign these functions to systole and diastole of the temperature updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.T.updater.systole = T_sys\n",
    "sim.Earth.T.updater.diastole = T_dia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Earth.T = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: T =   0.0 K\n",
      "After : T = 278.0 K\n"
     ]
    }
   ],
   "source": [
    "sim.Earth.T.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The updater of an integration variable is not directly setting its new value. Only the integrator is setting the new value of the integration variable after all integration instructions have been executed successfully. This has the consequence, that the diastole of an integration variable _DOES NOT_ have access to the updated value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Retrieving updater information**\n",
    "\n",
    "It is possible to retrieve information about the currently assigned updater functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Heartbeat\n",
       "---------\n",
       "\n",
       "\u001b[91mSystole: \u001b[0m <function T_sys at 0x7f951a0a4720>\n",
       "\u001b[91mUpdater: \u001b[0m <function T at 0x7f9519454fe0>\n",
       "\u001b[91mDiastole:\u001b[0m <function T_dia at 0x7f951a0a45e0>\n",
       "\n",
       "Docstrings\n",
       "----------\n",
       "\n",
       "\u001b[91mSystole:\u001b[0m\n",
       "This function prints the temperature before the update process.\n",
       "\n",
       "\u001b[91mUpdater:\u001b[0m\n",
       "Function computes the equilibrium temperature of the Earth\n",
       "\n",
       "\u001b[91mDiastole:\u001b[0m\n",
       "This function prints the temperature after the update process."
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.T.updater"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The source code of the assigned functions can also be displayed by adressing the respective sub-level. For example the systole or the actual updater. This may not work if the assigned function is not a Python function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Updater\n",
       "-------\n",
       "\n",
       "\u001b[91mSignature:\u001b[0m T_sys(frame)\n",
       "\u001b[91mSource:\u001b[0m\n",
       "def T_sys(frame):\n",
       "    \"\"\"This function prints the temperature before the update process.\"\"\"\n",
       "    msg = \"{:6s}: T = {:5.1f} K\".format(\"Before\", sim.Earth.T)\n",
       "    print(msg)\n",
       "\n",
       "\u001b[91mFile:\u001b[0m /tmp/ipykernel_24064/2382827243.py\n",
       "\u001b[91mType:\u001b[0m function"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.T.updater.systole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Updater\n",
       "-------\n",
       "\n",
       "\u001b[91mSignature:\u001b[0m T(frame)\n",
       "\u001b[91mSource:\u001b[0m\n",
       "def T(frame):\n",
       "    \"\"\"Function computes the equilibrium temperature of the Earth\"\"\"\n",
       "    return frame.Sun.T * (frame.Sun.R**2 / (4.*frame.Earth.d**2))**0.25\n",
       "\n",
       "\u001b[91mFile:\u001b[0m /tmp/ipykernel_24064/4057753429.py\n",
       "\u001b[91mType:\u001b[0m function"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.T.updater.updater"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the updater of a group was defined by assigning a list as in this example, this will be shown as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Heartbeat\n",
       "---------\n",
       "\n",
       "\u001b[91mSystole: \u001b[0m None\n",
       "\u001b[91mUpdater: \u001b[0m list_updater\n",
       "\u001b[91mDiastole:\u001b[0m None\n",
       "\n",
       "Docstrings\n",
       "----------\n",
       "\n",
       "\u001b[91mSystole:\u001b[0m\n",
       "None\n",
       "\n",
       "\u001b[91mUpdater:\u001b[0m\n",
       "The attributes in this group are updated in the order: \n",
       "['d', 'T'].\n",
       "\n",
       "\u001b[91mDiastole:\u001b[0m\n",
       "None"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.Earth.updater"
   ]
  }
 ],
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