{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating fast ion slowing-down process\n",
    "\n",
    "<img src='../../figures/slowingdown.png' alt='thumbnail' width='200'/>\n",
    "\n",
    "This example shows how to run a fast-ion slowing-down simulation which is the bread-and-butter for ASCOT5.\n",
    "\n",
    "We begin this example by generating some general input. For slowing-down simulations we need at least bfield and plasma inputs to contain real data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:58:25.397346Z",
     "iopub.status.busy": "2025-04-01T08:58:25.397159Z",
     "iopub.status.idle": "2025-04-01T08:58:27.224375Z",
     "shell.execute_reply": "2025-04-01T08:58:27.223743Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs created"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import unyt\n",
    "import matplotlib.pyplot as plt\n",
    "from a5py import Ascot\n",
    "\n",
    "a5 = Ascot(\"ascot.h5\", create=True)\n",
    "\n",
    "# Use analytical field since it is fast to interpolate\n",
    "a5.data.create_input(\"bfield analytical iter circular\", desc=\"ANALYTICAL\")\n",
    "\n",
    "# DT-plasma\n",
    "nrho   = 101\n",
    "rho    = np.linspace(0, 2, nrho).T\n",
    "vtor   = np.zeros((nrho, 1))\n",
    "edens  = 2e21 * np.ones((nrho, 1))\n",
    "etemp  = 1e4  * np.ones((nrho, 1))\n",
    "idens  = 1e21 * np.ones((nrho, 2))\n",
    "itemp  = 1e4  * np.ones((nrho, 1))\n",
    "\n",
    "edens[rho>1, :] = 1\n",
    "idens[rho>1, :] = 1\n",
    "\n",
    "pls = {\n",
    "    \"nrho\" : nrho, \"nion\" : 2, \"rho\" : rho, \"vtor\": vtor,\n",
    "    \"anum\" : np.array([2, 3]), \"znum\" : np.array([1, 1]),\n",
    "    \"mass\" : np.array([2.014, 3.016]), \"charge\" : np.array([1, 1]),\n",
    "    \"edensity\" : edens, \"etemperature\" : etemp,\n",
    "    \"idensity\" : idens, \"itemperature\" : itemp}\n",
    "a5.data.create_input(\"plasma_1D\", **pls, desc=\"FLATDT\")\n",
    "\n",
    "# These inputs are not relevant for this tutorial\n",
    "a5.data.create_input(\"wall rectangular\")\n",
    "a5.data.create_input(\"E_TC\")\n",
    "a5.data.create_input(\"N0_1D\")\n",
    "a5.data.create_input(\"Boozer\")\n",
    "a5.data.create_input(\"MHD_STAT\")\n",
    "a5.data.create_input(\"asigma_loc\")\n",
    "print(\"Inputs created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create alpha source distribution using AFSI, but one could use BBNBI5 to generate beam ions or some other tool.\n",
    "This and the next step, where we sample markers from the alpha distribution, are covered in separate tutorials.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:58:27.226375Z",
     "iopub.status.busy": "2025-04-01T08:58:27.226054Z",
     "iopub.status.idle": "2025-04-01T08:58:28.161770Z",
     "shell.execute_reply": "2025-04-01T08:58:28.161157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grid spanning the whole plasma\n",
    "rmin =  4.2; rmax = 8.2; nr = 50\n",
    "zmin = -2.0; zmax = 2.0; nz = 50\n",
    "\n",
    "# Run AFSI which creates a fusion alpha distribution\n",
    "a5.afsi.thermal(\n",
    "    \"DT_He4n\", nmc=100,\n",
    "    r=np.linspace(rmin, rmax, nr), phi=np.linspace(0, 360, 2),\n",
    "    z=np.linspace(zmin, zmax, nz),\n",
    "    ppar1=np.linspace(-1.3e-19, 1.3e-19, 80),\n",
    "    ppar2=np.linspace(-1.3e-19, 1.3e-19, 80),\n",
    "    pperp1=np.linspace(0, 1.3e-19, 40),\n",
    "    pperp2=np.linspace(0, 1.3e-19, 40),\n",
    "    )\n",
    "\n",
    "# Convert to E-xi as then we can make dist more compact in momentum space\n",
    "alphadist = a5.data.active.getdist(\"prod1\", exi=True, ekin_edges=20, pitch_edges=10)\n",
    "alphadist.integrate(time=np.s_[:], charge=np.s_[:]) # Distribution must be 5D\n",
    "\n",
    "\n",
    "# Plot the distribution\n",
    "rzdist  = alphadist.integrate(copy=True, phi=np.s_[:], ekin=np.s_[:], pitch=np.s_[:])\n",
    "exidist = alphadist.integrate(copy=True, phi=np.s_[:], r=np.s_[:], z=np.s_[:])\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "\n",
    "rzdist.plot(axes=ax1)\n",
    "exidist.plot(axes=ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Markers are created from the fusion source distribution by first initializing marker distribution from a flat radial profile, and then assigning weights from the alpha birth distribution.\n",
    "In this tutorial we only initialize markers at the core to avoid losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:58:28.163878Z",
     "iopub.status.busy": "2025-04-01T08:58:28.163522Z",
     "iopub.status.idle": "2025-04-01T08:58:28.726622Z",
     "shell.execute_reply": "2025-04-01T08:58:28.725960Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AFSI5\n",
      "Tag e37fd83\n",
      "Branch docs\n",
      "\n",
      "Note: Output file /home/runner/work/ascot5/ascot5/doc/tutorials/ascot.h5 is already present.\n",
      "\n",
      "The qid of this run is 2063624950\n",
      "\n",
      "Done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/ascot5/ascot5/a5py/ascot5io/__init__.py:246: AscotUnitWarning: Argument(s) zeta given without dimensions (assumed rad)\n",
      "  name = HDF5TOOBJ[inp].write_hdf5(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create marker distribution. Here we just initialize markers evenly in rho for convenience\n",
    "rho  = np.linspace(0, 0.5, 2)\n",
    "prob = 0.5 * np.ones((2,))\n",
    "a5.input_init(bfield=True)\n",
    "markerdist = a5.markergen.rhoto5d(\n",
    "    rho, prob, alphadist.abscissa_edges(\"r\"),\n",
    "    alphadist.abscissa_edges(\"phi\"),  alphadist.abscissa_edges(\"z\"),\n",
    "    alphadist.abscissa_edges(\"ekin\"), alphadist.abscissa_edges(\"pitch\"))\n",
    "a5.input_free()\n",
    "\n",
    "# Create markers and take weights from the alpha distribution\n",
    "mrk, mrkdist, prtdist = a5.markergen.generate(\n",
    "    10**3, 4.002*unyt.amu, 2.0*unyt.e, 4, 2, alphadist, markerdist=markerdist,\n",
    "    minweight=1e-10, return_dists=True)\n",
    "a5.data.create_input(\"gc\", **mrk, desc=\"ALPHAS\")\n",
    "\n",
    "# Plot the marker and particle distributions\n",
    "rzmrk  = mrkdist.integrate(copy=True, phi=np.s_[:], ekin=np.s_[:], pitch=np.s_[:])\n",
    "eximrk = mrkdist.integrate(copy=True, phi=np.s_[:], r=np.s_[:], z=np.s_[:])\n",
    "rzprt  = prtdist.integrate(copy=True, phi=np.s_[:], ekin=np.s_[:], pitch=np.s_[:])\n",
    "exiprt = prtdist.integrate(copy=True, phi=np.s_[:], r=np.s_[:], z=np.s_[:])\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    "\n",
    "rzmrk.plot(axes=ax1)\n",
    "eximrk.plot(axes=ax2)\n",
    "rzprt.plot(axes=ax3)\n",
    "exiprt.plot(axes=ax4)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now only options are missing.\n",
    "If the guiding-center approximation is valid, then using adaptive step is strongly recommended to accelerate the simulation.\n",
    "For other options, it is important to include energy limit as an end condition and activate orbit-following and Coulomb collisions in physics.\n",
    "Diagnostics can be whatever you like, though usually it makes little sense to collect orbit data.\n",
    "Here we collect 5D distribution which can be used to produce *the slowing-down distribution*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:58:28.728736Z",
     "iopub.status.busy": "2025-04-01T08:58:28.728535Z",
     "iopub.status.idle": "2025-04-01T08:58:28.783296Z",
     "shell.execute_reply": "2025-04-01T08:58:28.782756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'opt_1653026328'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from a5py.ascot5io.options import Opt\n",
    "\n",
    "opt = Opt.get_default()\n",
    "opt.update({\n",
    "    # Simulation mode\n",
    "    \"SIM_MODE\":2, \"ENABLE_ADAPTIVE\":1,\n",
    "    # Setting max mileage above slowing-down time is a good safeguard to ensure\n",
    "    # simulation finishes even with faulty inputs. Same with the CPU time limit.\n",
    "    \"ENDCOND_SIMTIMELIM\":1, \"ENDCOND_MAX_MILEAGE\":0.5,\n",
    "    \"ENDCOND_CPUTIMELIM\":1, \"ENDCOND_MAX_CPUTIME\":1.0e1,\n",
    "    # The energy limit which separates a fast ion from thermal bulk is not well defined,\n",
    "    # but we usually use Emin = 2 x Tion as the limit. Setting also a fixed minimum energy\n",
    "    # is advised sine plasma temperature at the pedestal can be low.\n",
    "    \"ENDCOND_ENERGYLIM\":1, \"ENDCOND_MIN_ENERGY\":2.0e3, \"ENDCOND_MIN_THERMAL\":2.0,\n",
    "    # Physics\n",
    "    \"ENABLE_ORBIT_FOLLOWING\":1, \"ENABLE_COULOMB_COLLISIONS\":1,\n",
    "    # Distribution output\n",
    "    \"ENABLE_DIST_5D\":1,\n",
    "    \"DIST_MIN_R\":4.3,        \"DIST_MAX_R\":8.3,       \"DIST_NBIN_R\":50,\n",
    "    \"DIST_MIN_PHI\":0,        \"DIST_MAX_PHI\":360,     \"DIST_NBIN_PHI\":1,\n",
    "    \"DIST_MIN_Z\":-2.0,       \"DIST_MAX_Z\":2.0,       \"DIST_NBIN_Z\":50,\n",
    "    \"DIST_MIN_PPA\":-1.3e-19, \"DIST_MAX_PPA\":1.3e-19, \"DIST_NBIN_PPA\":100,\n",
    "    \"DIST_MIN_PPE\":0,        \"DIST_MAX_PPE\":1.3e-19, \"DIST_NBIN_PPE\":50,\n",
    "    \"DIST_MIN_TIME\":0,       \"DIST_MAX_TIME\":1.0,    \"DIST_NBIN_TIME\":1,\n",
    "    \"DIST_MIN_CHARGE\":1,     \"DIST_MAX_CHARGE\":3,    \"DIST_NBIN_CHARGE\":1,\n",
    "})\n",
    "a5.data.create_input(\"opt\", **opt, desc=\"SLOWINGDOWN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to simulate.\n",
    "Usually slowing-down simulations are expensive (especially with 3D magnetic field) but this one runs in a PC in a few minutes or so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:58:28.785153Z",
     "iopub.status.busy": "2025-04-01T08:58:28.784956Z",
     "iopub.status.idle": "2025-04-01T08:59:26.319390Z",
     "shell.execute_reply": "2025-04-01T08:59:26.318851Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASCOT5_MAIN\n",
      "Tag e37fd83\n",
      "Branch docs\n",
      "\n",
      "Initialized MPI, rank 0, size 1.\n",
      "\n",
      "Reading and initializing input.\n",
      "\n",
      "Input file is ascot.h5.\n",
      "\n",
      "Reading options input.\n",
      "Active QID is 1653026328\n",
      "Options read and initialized.\n",
      "\n",
      "Reading magnetic field input.\n",
      "Active QID is 0497939220\n",
      "\n",
      "Analytical tokamak magnetic field (B_GS)\n",
      "Psi at magnetic axis (6.618 m, -0.000 m)\n",
      "-5.410 (evaluated)\n",
      "-5.410 (given)\n",
      "Magnetic field on axis:\n",
      "B_R = 0.000 B_phi = 4.965 B_z = -0.000\n",
      "Number of toroidal field coils 0\n",
      "Magnetic field read and initialized.\n",
      "\n",
      "Reading electric field input.\n",
      "Active QID is 2902664925\n",
      "\n",
      "Trivial Cartesian electric field (E_TC)\n",
      "E_x = 0.000000e+00, E_y = 0.000000e+00, E_z = 0.000000e+00\n",
      "Electric field read and initialized.\n",
      "\n",
      "Reading plasma input.\n",
      "Active QID is 2712560782\n",
      "\n",
      "1D plasma profiles (P_1D)\n",
      "Min rho = 0.00e+00, Max rho = 2.00e+00, Number of rho grid points = 101, Number of ion species = 2\n",
      "Species Z/A  charge [e]/mass [amu] Density [m^-3] at Min/Max rho    Temperature [eV] at Min/Max rho\n",
      "   1  /  2     1  /  2.014             1.00e+21/1.00e+00                1.00e+04/1.00e+04       \n",
      "   1  /  3     1  /  3.016             1.00e+21/1.00e+00                1.00e+04/1.00e+04       \n",
      "[electrons]   -1  /  0.001             2.00e+21/1.00e+00                1.00e+04/1.00e+04       \n",
      "Toroidal rotation [rad/s] at Min/Max rho: 0.00e+00/0.00e+00\n",
      "Quasi-neutrality is (electron / ion charge density) 2.00\n",
      "Toroidal rotation [rad/s] at Min/Max rho: 0.00e+00/0.00e+00\n",
      "Plasma data read and initialized.\n",
      "\n",
      "Reading neutral input.\n",
      "Active QID is 3474801297\n",
      "\n",
      "1D neutral density and temperature (N0_1D)\n",
      "Grid:  nrho =    3   rhomin = 0.000   rhomax = 2.000\n",
      " Number of neutral species = 1\n",
      "Species Z/A   (Maxwellian)\n",
      "        1/  1 (1)    \n",
      "Neutral data read and initialized.\n",
      "\n",
      "Reading wall input.\n",
      "Active QID is 0440933518\n",
      "\n",
      "2D wall model (wall_2D)\n",
      "Number of wall elements = 20, R extend = [4.10, 8.40], z extend = [-3.90, 3.90]\n",
      "Wall data read and initialized.\n",
      "\n",
      "Reading boozer input.\n",
      "Active QID is 0141807599\n",
      "\n",
      "Boozer input\n",
      "psi grid: n =    6 min = 0.000 max = 1.000\n",
      "thetageo grid: n =   18\n",
      "thetabzr grid: n =   10\n",
      "Boozer data read and initialized.\n",
      "\n",
      "Reading MHD input.\n",
      "Active QID is 1979577652\n",
      "\n",
      "MHD (stationary) input\n",
      "Grid: nrho =    6 rhomin = 0.000 rhomax = 1.000\n",
      "\n",
      "Modes:\n",
      "(n,m) = ( 1, 3) Amplitude = 0.1 Frequency =   1 Phase =   0\n",
      "(n,m) = ( 2, 4) Amplitude =   2 Frequency = 1.5 Phase = 0.785\n",
      "MHD data read and initialized.\n",
      "\n",
      "Reading atomic reaction input.\n",
      "Active QID is 1130451398\n",
      "\n",
      "Found data for 1 atomic reactions:\n",
      "Reaction number / Total number of reactions = 1 / 1\n",
      "  Reactant species Z_1 / A_1, Z_2 / A_2     = 0 / 0, 0 / 0\n",
      "  Min/Max energy                            = 1.00e+03 / 1.00e+04\n",
      "  Min/Max density                           = 1.00e+18 / 1.00e+20\n",
      "  Min/Max temperature                       = 1.00e+03 / 1.00e+04\n",
      "  Number of energy grid points              = 3\n",
      "  Number of density grid points             = 4\n",
      "  Number of temperature grid points         = 5\n",
      "Atomic reaction data read and initialized.\n",
      "\n",
      "Reading marker input.\n",
      "Active QID is 4153218164\n",
      "\n",
      "Loaded 1000 guiding centers.\n",
      "Marker data read and initialized.\n",
      "\n",
      "All input read and initialized.\n",
      "\n",
      "Initializing marker states.\n",
      "Estimated memory usage 0.0 MB.\n",
      "Marker states initialized.\n",
      "\n",
      "Preparing output.\n",
      "Note: Output file ascot.h5 is already present.\n",
      "\n",
      "The qid of this run is 1401724026\n",
      "\n",
      "Inistate written.\n",
      "Simulation begins; 4 threads.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation complete.\n",
      "Simulation finished in 57.395030 s\n",
      "Endstate written.\n",
      "\n",
      "Combining and writing diagnostics.\n",
      "\n",
      "Writing diagnostics output.\n",
      "\n",
      "Writing 5D distribution.\n",
      "\n",
      "Diagnostics output written.\n",
      "Diagnostics written.\n",
      "\n",
      "Summary of results:\n",
      "     1000 markers had end condition Thermalization\n",
      "\n",
      "          No markers were aborted.\n",
      "\n",
      "Done.\n",
      "Simulation completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "subprocess.run([\"./../../build/ascot5_main\", \"--d=\\\"SDALPHA\\\"\"])\n",
    "print(\"Simulation completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the first thing to check in the results is that the markers actually slowed-down.\n",
    "Therefore we look at the end conditions, final energy, and mileage which gives us the slowing-down time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:59:26.321356Z",
     "iopub.status.busy": "2025-04-01T08:59:26.321020Z",
     "iopub.status.idle": "2025-04-01T08:59:26.569466Z",
     "shell.execute_reply": "2025-04-01T08:59:26.568844Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([(np.int64(1000), 'THERMAL')], [])"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a5 = Ascot(\"ascot.h5\") # Re-read the data\n",
    "print(a5.data.active.getstate_markersummary())\n",
    "a5.data.active.plotstate_histogram(\"end ekin\")\n",
    "a5.data.active.plotstate_histogram(\"end mileage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks reasonable!\n",
    "Now let us look at the slowing-down distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:59:26.571415Z",
     "iopub.status.busy": "2025-04-01T08:59:26.571074Z",
     "iopub.status.idle": "2025-04-01T08:59:27.095374Z",
     "shell.execute_reply": "2025-04-01T08:59:27.094760Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = a5.data.active.getdist(\"5d\", exi=True, ekin_edges=np.linspace(0,5e6,40), pitch_edges=2)\n",
    "d.integrate(time=np.s_[:], charge=np.s_[:], phi=np.s_[:], r=np.s_[:], z=np.s_[:], pitch=np.s_[:])\n",
    "d.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can note that there might be markers above the initial energy, i.e. some markers have actually gained energy!\n",
    "However, this \"up-scattering\" is fine because the collision operator contains a stochastic component and the main thing is that the whole population cools on average.\n",
    "And, as we checked earlier, there were no energetic markers left at the end of the simulation so all markers either slowed-down or were lost.\n",
    "\n",
    "Finally, it is a good idea to check the energy-pitch distribution as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:59:27.097332Z",
     "iopub.status.busy": "2025-04-01T08:59:27.096963Z",
     "iopub.status.idle": "2025-04-01T08:59:27.700543Z",
     "shell.execute_reply": "2025-04-01T08:59:27.699972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = a5.data.active.getdist(\"5d\", exi=True, ekin_edges=20, pitch_edges=10)\n",
    "d.integrate(time=np.s_[:], charge=np.s_[:], phi=np.s_[:], r=np.s_[:], z=np.s_[:])\n",
    "d.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we want to verify that marker pitch has not accumulated in some point because the physics governs that the pitch scattering should make the pitch distribution close to even in the end.\n",
    "Initially there is little pitch scattering, since fast particles are usually born above the critical energy,\n",
    "$$v_\\mathrm{crit} = v_\\mathrm{th} \\left(\\frac{3 \\sqrt{\\pi}}{4}\\frac{m_e}{m_\\mathrm{ion}}\\right)^{1/3}.$$\n",
    "\n",
    "Above the critical energy we should see little pitch scattering as the fast ions mostly collide with electrons, but this changes below the critical energy as now the ion-ion collision frequency is higher than the electron-ion collision frequency.\n",
    "\n",
    "This concludes the tutorial.\n",
    "Here's a complementary fast-ion slowing-down density to brighten your day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-01T08:59:27.702311Z",
     "iopub.status.busy": "2025-04-01T08:59:27.702134Z",
     "iopub.status.idle": "2025-04-01T08:59:28.074961Z",
     "shell.execute_reply": "2025-04-01T08:59:28.074313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = a5.data.active.getdist(\"5d\")\n",
    "d.integrate(time=np.s_[:], charge=np.s_[:], phi=np.s_[:], ppar=np.s_[:], pperp=np.s_[:])\n",
    "d.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}