{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling CX and tracing neutrals\n",
    "\n",
    "This example shows how to include atomic reactions that change the charge state of the markers, and how to model neutrals.\n",
    "\n",
    "> **_NOTE:_**  This tutorial requires ADAS data to run.\n",
    "  Therefore the results won't be displayed on the online version.\n",
    "\n",
    "At its current state, the atomic physics in ASCOT5 enables simulation of neutrals and singly charged ions.\n",
    "Neutral can become ionized and ion can become neutral but it is not currently possible for ion to remain ion when its charge state changes.\n",
    "\n",
    "We begin by creating some test data that is not directly relevant for this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:08:56.228081Z",
     "iopub.status.busy": "2025-04-14T13:08:56.227910Z",
     "iopub.status.idle": "2025-04-14T13:08:57.939660Z",
     "shell.execute_reply": "2025-04-14T13:08:57.939140Z"
    }
   },
   "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",
    "a5.data.create_input(\"bfield analytical iter circular\")\n",
    "a5.data.create_input(\"wall rectangular\")\n",
    "a5.data.create_input(\"E_TC\")\n",
    "a5.data.create_input(\"Boozer\")\n",
    "a5.data.create_input(\"MHD_STAT\")\n",
    "\n",
    "print(\"Inputs created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for the plasma and neutral data, pay attention to the following:\n",
    "\n",
    "- Plasma species must be fully ionized, i.e. `charge` and `znum` must match (partially ionized species have not been implemented yet).\n",
    "- The neutral species must be in the same order as the species in the plasma input (neutral data does not yet contain `anum` and `znum` fields so the ones in the plasma input are used instead).\n",
    "\n",
    "In this tutorial we have $^1_1$H plasma and neutrals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:08:57.941647Z",
     "iopub.status.busy": "2025-04-14T13:08:57.941230Z",
     "iopub.status.idle": "2025-04-14T13:08:57.986997Z",
     "shell.execute_reply": "2025-04-14T13:08:57.986437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N0_1D_3183578115'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plasma data\n",
    "nrho   = 11\n",
    "rho    = np.linspace(0, 2, nrho).T\n",
    "vtor   = np.zeros((nrho, 1))\n",
    "edens  = 1e18 * np.ones((nrho, 1))\n",
    "etemp  = 1e3  * np.ones((nrho, 1))\n",
    "idens  = 1e18 * np.ones((nrho, 1))\n",
    "itemp  = 1e3  * np.ones((nrho, 1))\n",
    "\n",
    "pls = {\n",
    "    \"nrho\" : nrho, \"nion\" : 1, \"rho\" : rho, \"vtor\": vtor,\n",
    "    \"anum\" : np.array([2]), \"znum\" : np.array([1]),\n",
    "    \"mass\" : np.array([1.007]), \"charge\" : np.array([1]),\n",
    "    \"edensity\" : edens, \"etemperature\" : etemp,\n",
    "    \"idensity\" : idens, \"itemperature\" : itemp}\n",
    "a5.data.create_input(\"plasma_1D\", **pls, desc=\"FLAT\")\n",
    "\n",
    "# Neutral data\n",
    "density = np.ones((11,1)) * 1e17\n",
    "temperature = np.ones((11,1)) * 1e3\n",
    "ntr = {\"rhomin\" : 0, \"rhomax\" : 10, \"nrho\" : 11, \"nspecies\" : 1,\n",
    "       \"anum\" : np.array([1]), \"znum\" : np.array([1]),\n",
    "       \"density\" : density, \"temperature\" : temperature,\n",
    "       \"maxwellian\" : 1}\n",
    "a5.data.create_input(\"N0_1D\", **ntr, desc=\"FLAT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When generating the marker input, note that `anum` and `znum` fields specify the particle species whereas `charge` specifies the charge state.\n",
    "One can assume that the `mass` is same for all charge states and it stays fixed in the simulation.\n",
    "For this tutorial we create equal amounts of ions and neutrals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:08:57.988545Z",
     "iopub.status.busy": "2025-04-14T13:08:57.988388Z",
     "iopub.status.idle": "2025-04-14T13:08:58.054685Z",
     "shell.execute_reply": "2025-04-14T13:08:58.054090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gc_3664367445'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from a5py.ascot5io.marker import Marker\n",
    "mrk = Marker.generate(\"gc\", n=100, species=\"deuterium\")\n",
    "mrk[\"energy\"][:] = 1.0e4\n",
    "mrk[\"pitch\"][:]  = 0.99 - 1.98 * np.random.rand(100,)\n",
    "mrk[\"r\"][:]      = np.linspace(6.2, 7.2, 100)\n",
    "mrk[\"charge\"][:50] = 0\n",
    "mrk[\"charge\"][50:] = 1\n",
    "a5.data.create_input(\"gc\", **mrk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atomic data is created using ADAS or Open-ADAS datasets if those are available.\n",
    "Since these tutorials are run on the GitHub server, we don't have ADAS available and we have to resort to analytical models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:08:58.056391Z",
     "iopub.status.busy": "2025-04-14T13:08:58.056212Z",
     "iopub.status.idle": "2025-04-14T13:10:20.523176Z",
     "shell.execute_reply": "2025-04-14T13:10:20.522531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not import adas package."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using analytical model instead."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a5.data.create_input(\"import_adas\")\n",
    "except Exception as err:\n",
    "    print(err)\n",
    "    print(\"Using analytical model instead.\")\n",
    "    a5.data.create_input(\"asigma_chebyshev_cx_hh0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once generated, the atomic data can be interpolated via `ascotpy`.\n",
    "Now we can calculate the mean-free-time which in turn gives us a good estimate on what the simulation time-step should be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:10:20.525298Z",
     "iopub.status.busy": "2025-04-14T13:10:20.524942Z",
     "iopub.status.idle": "2025-04-14T13:10:20.540930Z",
     "shell.execute_reply": "2025-04-14T13:10:20.539343Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7376/481609561.py:20: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  print(\"Mean free time: %.3e (CX) %.3e (BMS)\" % (mft_cx[0], mft_bms[0]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean free time: 8.556e-05 (CX) 9.330e-06 (BMS)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# First calculate velocity with physlib\n",
    "#import a5py.physlib as physlib\n",
    "from a5py import physlib\n",
    "gamma = physlib.gamma_energy(mrk[\"mass\"][0], mrk[\"energy\"][0])\n",
    "vnorm = physlib.vnorm_gamma(gamma)\n",
    "\n",
    "a5.input_init(bfield=True, plasma=True, neutral=True, asigma=True)\n",
    "sigmacx  = a5.input_eval_atomiccoefs(\n",
    "    mrk[\"mass\"][0], mrk[\"anum\"][0], mrk[\"znum\"][0],\n",
    "    mrk[\"r\"][0], mrk[\"phi\"][0], mrk[\"z\"][0], mrk[\"time\"][0], vnorm,\n",
    "    reaction=\"charge-exchange\")\n",
    "sigmabms = a5.input_eval_atomiccoefs(\n",
    "    mrk[\"mass\"][0], mrk[\"anum\"][0], mrk[\"znum\"][0],\n",
    "    mrk[\"r\"][0], mrk[\"phi\"][0], mrk[\"z\"][0], mrk[\"time\"][0], vnorm,\n",
    "    reaction=\"beamstopping\")\n",
    "a5.input_free()\n",
    "mft_cx  = 1/sigmacx\n",
    "mft_bms = 1/sigmabms\n",
    "\n",
    "print(\"Mean free time: %.3e (CX) %.3e (BMS)\" % (mft_cx[0], mft_bms[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once atomic input data has been created, the physics are enabled from options with `ENABLE_ATOMIC=1`.\n",
    "However, consider setting `ENABLE_ATOMIC=2` when there is a possibility that the marker orbits are outside the separatrix where the temperature and density can be outside the data range in which the atomic data is given.\n",
    "When using `ENABLE_ATOMIC=2`, the cross sections are set to zero when extrapolating the data.\n",
    "Otherwise the simulation would terminate with an error.\n",
    "\n",
    "If you wish to terminate the simulation when a marker ionizes or when it becomes neutral, you can enable the end conditions `ENDCOND_IONIZED` and `ENDCOND_NEUTRAL`, respectively.\n",
    "\n",
    "When collecting distributions it is important to set the charge abscissa properly.\n",
    "The abscissa should cover all expected charge states and there should be exactly one bin for each charge state.\n",
    "\n",
    "Here we set simulation options for tracing markers for a fixed time with atomic reactions and Coulomb collisions enabled.\n",
    "We also collect distribution and orbit data.\n",
    "\n",
    "**Finally, it is important to note that the atomic reactions are implemented only for the gyro-orbit mode,** `SIM_MODE=1`, **and it is not possible at all to simulate neutrals in guiding center mode.**\n",
    "In `SIM_MODE=1`, the neutrals are traced according to their ballistic trajectories.\n",
    "However, the marker input can still be either particles or guiding centers as in the latter case the guiding center is transformed to particle coordinates using the zeroth order transformation (where the gyroradius $\\rho_g=0$) if the marker is a neutral.\n",
    "Same is done for the ini- and endstate but it is recommended to use the particle coordinates nevertheless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:10:20.542994Z",
     "iopub.status.busy": "2025-04-14T13:10:20.542808Z",
     "iopub.status.idle": "2025-04-14T13:10:20.591332Z",
     "shell.execute_reply": "2025-04-14T13:10:20.590809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'opt_2989511098'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from a5py.ascot5io.options import Opt\n",
    "\n",
    "opt = Opt.get_default()\n",
    "opt.update({\n",
    "    # Simulation mode and end condition (use rho max as otherwise neutral escape from\n",
    "    # the plasma and become aborted since we don't have a proper wall)\n",
    "    \"SIM_MODE\":1, \"FIXEDSTEP_USE_USERDEFINED\":1, \"FIXEDSTEP_USERDEFINED\":1e-8,\n",
    "    \"ENDCOND_SIMTIMELIM\":1, \"ENDCOND_MAX_MILEAGE\":1e-4,\n",
    "    \"ENDCOND_RHOLIM\":1, \"ENDCOND_MAX_RHO\":1.0,\n",
    "    # Physics\n",
    "    \"ENABLE_ORBIT_FOLLOWING\":1, \"ENABLE_COULOMB_COLLISIONS\":1, \"ENABLE_ATOMIC\":1,\n",
    "    # Distribution output\n",
    "    \"ENABLE_DIST_RHO5D\":1,\n",
    "    \"DIST_MIN_RHO\":0.0,      \"DIST_MAX_RHO\":1.0,     \"DIST_NBIN_RHO\":50,\n",
    "    \"DIST_MIN_PHI\":0,        \"DIST_MAX_PHI\":360,     \"DIST_NBIN_PHI\":1,\n",
    "    \"DIST_MIN_THETA\":0.0,    \"DIST_MAX_THETA\":360,   \"DIST_NBIN_THETA\":1,\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\":2,    \"DIST_NBIN_CHARGE\":2,\n",
    "    # Orbit output\n",
    "    \"ENABLE_ORBITWRITE\":1, \"ORBITWRITE_MODE\":1,\n",
    "    \"ORBITWRITE_INTERVAL\":1e-7, \"ORBITWRITE_NPOINT\":10**4,\n",
    "})\n",
    "a5.data.create_input(\"opt\", **opt, desc=\"TUTORIAL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us run the simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:10:20.592991Z",
     "iopub.status.busy": "2025-04-14T13:10:20.592654Z",
     "iopub.status.idle": "2025-04-14T13:10:20.882925Z",
     "shell.execute_reply": "2025-04-14T13:10:20.882378Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASCOT5_MAIN\n",
      "Tag 37c8483\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 2989511098\n",
      "Options read and initialized.\n",
      "\n",
      "Reading magnetic field input.\n",
      "Active QID is 3805326181\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 1910822713\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 4091602546\n",
      "\n",
      "1D plasma profiles (P_1D)\n",
      "Min rho = 0.00e+00, Max rho = 2.00e+00, Number of rho grid points = 11, Number of ion species = 1\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  /  1.007             1.00e+18/1.00e+18                1.00e+03/1.00e+03       \n",
      "[electrons]   -1  /  0.001             1.00e+18/1.00e+18                1.00e+03/1.00e+03       \n",
      "Toroidal rotation [rad/s] at Min/Max rho: 0.00e+00/0.00e+00\n",
      "Quasi-neutrality is (electron / ion charge density) 1.00\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 3183578115\n",
      "\n",
      "1D neutral density and temperature (N0_1D)\n",
      "Grid:  nrho =   11   rhomin = 0.000   rhomax = 10.000\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 0986700793\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 4096780862\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 3479287822\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 3518541417\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     = 1 / 2, 1 / 2\n",
      "  Min/Max energy                            = 5.00e+01 / 1.50e+05\n",
      "  Min/Max density                           = 1.00e+00 / 1.00e+30\n",
      "  Min/Max temperature                       = 1.00e+02 / 1.00e+04\n",
      "  Number of energy grid points              = 200\n",
      "  Number of density grid points             = 1\n",
      "  Number of temperature grid points         = 20\n",
      "Atomic reaction data read and initialized.\n",
      "\n",
      "Reading marker input.\n",
      "Active QID is 3664367445\n",
      "\n",
      "Loaded 100 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 0400904259\n",
      "\n",
      "Inistate written.\n",
      "Simulation begins; 4 threads.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation complete.\n",
      "Simulation finished in 0.192823 s\n",
      "Endstate written.\n",
      "\n",
      "Combining and writing diagnostics.\n",
      "\n",
      "Writing diagnostics output.\n",
      "\n",
      "Writing rho 5D distribution.\n",
      "Writing orbit diagnostics.\n",
      "\n",
      "Diagnostics output written.\n",
      "Diagnostics written.\n",
      "\n",
      "Summary of results:\n",
      "       78 markers had end condition Max rho\n",
      "       22 markers had end condition Sim time limit\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\"])\n",
    "print(\"Simulation completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In post-processing we can plot the final charge distribution as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:10:20.884833Z",
     "iopub.status.busy": "2025-04-14T13:10:20.884414Z",
     "iopub.status.idle": "2025-04-14T13:10:21.041739Z",
     "shell.execute_reply": "2025-04-14T13:10:21.041052Z"
    }
   },
   "outputs": [
    {
     "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\")\n",
    "a5.data.active.plotstate_histogram(\"end charge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The orbit data contain the marker charge at each time step.\n",
    "Here we verify that the energy has not changed while the marker was neutral since Coulomb collisions only affect charged particles (how well the plot below demonstrates this point depends on RNG)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:10:21.043428Z",
     "iopub.status.busy": "2025-04-14T13:10:21.043261Z",
     "iopub.status.idle": "2025-04-14T13:10:21.312975Z",
     "shell.execute_reply": "2025-04-14T13:10:21.312452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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.data.active.plotorbit_trajectory(\"mileage\", \"charge\", ids=[1, 51])\n",
    "a5.data.active.plotorbit_trajectory(\"mileage\", \"diff ekin\", ids=[1, 51])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distributions and moments can be obtained for each charge state separately when the charge abscissa was set properly.\n",
    "Here we plot the neutral and ion densities separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-14T13:10:21.314614Z",
     "iopub.status.busy": "2025-04-14T13:10:21.314449Z",
     "iopub.status.idle": "2025-04-14T13:10:21.451679Z",
     "shell.execute_reply": "2025-04-14T13:10:21.451125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = a5.data.active.getdist(\"rho5d\")\n",
    "# Integrate over all dimensions except rho and charge\n",
    "dist.integrate(phi=np.s_[:], theta=np.s_[:], ppar=np.s_[:], pperp=np.s_[:], time=np.s_[:])\n",
    "\n",
    "# Copy and slice charge at 0 and +1. Then integrate charge so that we can plot\n",
    "neutraldist = dist.slice(copy=True, charge=0)\n",
    "dist.slice(charge=1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "neutraldist.plot(axes=ax)\n",
    "dist.plot(axes=ax)\n",
    "plt.show()"
   ]
  },
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   "source": []
  }
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