{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating markers\n", "\n", "This tutorial illustrates the different ways marker input is created.\n", "\n", "## Generating markers explicitly\n", "\n", "The straight-forward way to initialize markers is to just fill the marker dictionary with proper data.\n", "Marker input can either consist of particles, guiding centers, or field lines, but the input does not necessarily have to correspond to the simulation mode.\n", "It is perfectly fine to use particle input for a guiding-center simulation and vice-versa.\n", "The only real limitation is that field line input can only be used in field line simulations.\n", "\n", "> **_NOTE:_** When particle or guiding center input is used in a field line simulation, the field lines to be traced are initialized at the (particle's) guiding center position.\n", "\n", "You can find the information on what data is required to initialize a specific type of marker in the description of the corresponding ``write_hdf5`` function.\n", "A more convenient way is to use ``a5py.ascot5io.marker.Marker.generate`` to create an empty template that already contains the relevant arrays where you only need to replace the dummy values with actual data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:32.142068Z", "iopub.status.busy": "2025-04-14T13:08:32.141891Z", "iopub.status.idle": "2025-04-14T13:08:33.914244Z", "shell.execute_reply": "2025-04-14T13:08:33.913630Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import unyt\n", "from a5py.ascot5io.marker import Marker\n", "mrk = Marker.generate(\"gc\", n=100)\n", "mrk[\"energy\"][:] = 3.5e6\n", "mrk[\"pitch\"][:] = 0.99 - 1.98 * np.random.rand(100,)\n", "mrk[\"r\"][:] = np.linspace(6.2, 8.2, 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is strongly recommended that the marker input consists of a single species, i.e., ``mass``, ``anum``, and ``znum`` should be identical for all markers.\n", "``generate`` can be used to fill these fields automatically by specifying the particle species.\n", "By default the markers are assumed to be fully ionized, but you can change the charge at will." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:33.916301Z", "iopub.status.busy": "2025-04-14T13:08:33.915996Z", "iopub.status.idle": "2025-04-14T13:08:33.924764Z", "shell.execute_reply": "2025-04-14T13:08:33.924311Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4" ] }, { "name": "stdout", "output_type": "stream", "text": [ " " ] }, { "name": "stdout", "output_type": "stream", "text": [ "2" ] }, { "name": "stdout", "output_type": "stream", "text": [ " " ] }, { "name": "stdout", "output_type": "stream", "text": [ "4.003 amu" ] }, { "name": "stdout", "output_type": "stream", "text": [ " " ] }, { "name": "stdout", "output_type": "stream", "text": [ "2 e" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "mrk = Marker.generate(\"gc\", n=100, species=\"alpha\")\n", "print(mrk[\"anum\"][0], mrk[\"znum\"][0], mrk[\"mass\"][0], mrk[\"charge\"][0])\n", "mrk[\"energy\"][:] = 3.5e6\n", "mrk[\"pitch\"][:] = 0.99 - 1.98 * np.random.rand(100,)\n", "mrk[\"r\"][:] = np.linspace(6.2, 8.2, 100)\n", "mrk[\"charge\"][:] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating markers from end state\n", "\n", "You may want to create a new marker input from the endstate of a previous simulation.\n", "Common cases where this is done is when the markers met the CPUMAX end condition or they were traced as guiding centers to separatrix and the simulation is continued in gyro-orbit mode to model wall loads accurately.\n", "\n", "To demonstrate how this can be done, we first run a simple simulation with the previously generated markers:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:33.926386Z", "iopub.status.busy": "2025-04-14T13:08:33.926222Z", "iopub.status.idle": "2025-04-14T13:08:34.153884Z", "shell.execute_reply": "2025-04-14T13:08:34.153370Z" } }, "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 4265172167\n", "Options read and initialized.\n", "\n", "Reading magnetic field input.\n", "Active QID is 3553263594\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 1402621183\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 2586751633\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 3601268653\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 0249733714\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 1745283661\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 2797463103\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 0158107728\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 0774729818\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 1608977864\n", "\n", "Inistate written.\n", "Simulation begins; 4 threads.\n", "Simulation complete.\n", "Simulation finished in 0.007340 s\n", "Endstate written.\n", "\n", "Combining and writing diagnostics.\n", "\n", "Writing diagnostics output.\n", "\n", "Diagnostics output written.\n", "Diagnostics written.\n", "\n", "Summary of results:\n", " 82 markers had end condition Sim time limit\n", " 18 markers had end condition Max rho\n", "\n", " No markers were aborted.\n", "\n", "Done.\n", "Simulation complete" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from a5py import Ascot\n", "\n", "a5 = Ascot(\"ascot.h5\", create=True)\n", "\n", "a5.data.create_input(\"gc\", **mrk)\n", "a5.data.create_input(\"bfield analytical iter circular\", desc=\"ANALYTICAL\")\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", "\n", "# DT-plasma will be needed later in this tutorial\n", "nrho = 101\n", "rho = np.linspace(0, 2, nrho).T\n", "prof = (1.0 - rho**(3.0/2))**3\n", "prof = (rho<=1)*(1.0 - rho**(3.0/2))**3 + 1e-6\n", "vtor = np.zeros((nrho, 1))\n", "edens = 2e21 * prof\n", "etemp = 1e4 * np.ones((nrho, 1))\n", "idens = 1e21 * np.tile(prof,(2,1)).T\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=\"DT\")\n", "\n", "from a5py.ascot5io.options import Opt\n", "opt = Opt.get_default()\n", "opt.update({\n", " \"SIM_MODE\":2, \"ENABLE_ADAPTIVE\":1, \"ENABLE_ORBIT_FOLLOWING\":1,\n", " \"ENDCOND_SIMTIMELIM\":1, \"ENDCOND_MAX_MILEAGE\":1e-5,\n", " \"ENDCOND_RHOLIM\":1, \"ENDCOND_MAX_RHO\":0.9,\n", "})\n", "a5.data.create_input(\"opt\", **opt, desc=\"SHORT\")\n", "\n", "import subprocess\n", "subprocess.run([\"./../../build/ascot5_main\", \"--d=\\\"BEAMSD\\\"\"])\n", "print(\"Simulation complete\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the simulation is complete, we can read the data and generate the input with simply:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:34.155659Z", "iopub.status.busy": "2025-04-14T13:08:34.155473Z", "iopub.status.idle": "2025-04-14T13:08:34.221860Z", "shell.execute_reply": "2025-04-14T13:08:34.221344Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "a5 = Ascot(\"ascot.h5\")\n", "mrk = a5.data.active.getstate_markers(\"gc\")\n", "print(mrk[\"n\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since endstate contains both particle and guiding center coordinates, one can freely choose which type of input to create.\n", "Note that this function supports pruning markers by their ID and, hence, their end condition:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:34.223571Z", "iopub.status.busy": "2025-04-14T13:08:34.223390Z", "iopub.status.idle": "2025-04-14T13:08:34.256507Z", "shell.execute_reply": "2025-04-14T13:08:34.255949Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ 59 64 77 78 80 81 83 88 91 92 93 94 95 96 97 98 99 100]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "ids = a5.data.active.getstate(\"ids\", endcond=\"RHOMAX\")\n", "mrk = a5.data.active.getstate_markers(\"gc\", ids=ids)\n", "print(mrk[\"n\"])\n", "print(mrk[\"ids\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating markers that represent physical populations\n", "\n", "The supporting tools AFSI and BBNBI can generate a particle source for slowing-down simulations of fast ions.\n", "These tools produce 5D distributions from which markers can be sampled using ``MarkerGenerator``.\n", "To illustrate how it is used, we first create test data with AFSI:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:34.258210Z", "iopub.status.busy": "2025-04-14T13:08:34.258047Z", "iopub.status.idle": "2025-04-14T13:08:34.485317Z", "shell.execute_reply": "2025-04-14T13:08:34.484799Z" } }, "outputs": [ { "data": { "text/plain": [ "(,\n", " )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "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", "# AFSI run\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", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A ``MarkerGenerator`` can be found in ``markergen`` attribute in ``Ascot``.\n", "Supplying the 5D distribution, which can also be from BBNBI or from any other source, to its ``generate`` method along with the species` information creates a marker input with requested amount of markers:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:34.487039Z", "iopub.status.busy": "2025-04-14T13:08:34.486858Z", "iopub.status.idle": "2025-04-14T13:08:37.335255Z", "shell.execute_reply": "2025-04-14T13:08:37.334659Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The input must be a 5D distribution (which is what AFSI calculates)\n", "alphadist = a5.data.active.getdist(\"prod1\")\n", "\n", "# \"Extra\" dimensions (time and charge) must be removed first\n", "alphadist.integrate(time=np.s_[:], charge=np.s_[:])\n", "\n", "# Generate markers\n", "nmrk = 10**6\n", "anum = 4\n", "znum = 2\n", "mass = 4.014*unyt.amu\n", "charge = 2.0*unyt.e\n", "mrk = a5.markergen.generate(nmrk, mass, charge, anum, znum, alphadist)\n", "\n", "# Plot\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(1,2,1)\n", "ax1.scatter(mrk[\"r\"], mrk[\"z\"])\n", "\n", "ax2 = fig.add_subplot(1,2,2)\n", "ax2.scatter(mrk[\"pitch\"], mrk[\"energy\"])\n", "ax2.set_xlim(-1, 1)\n", "ax2.set_ylim(0, 6e6)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The generator works also when the momentum distribution is in $(E,\\xi)$ basis.\n", "It also calculates the distribution of generated markers (with and without weights) so that these can be compared to the inputs." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:37.336981Z", "iopub.status.busy": "2025-04-14T13:08:37.336808Z", "iopub.status.idle": "2025-04-14T13:08:38.382371Z", "shell.execute_reply": "2025-04-14T13:08:38.381770Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Repeat the above example but with Exi distribution\n", "alphadist = a5.data.active.getdist(\n", " \"prod1\", exi=True, ekin_edges=20, pitch_edges=10)\n", "alphadist.integrate(time=np.s_[:], charge=np.s_[:])\n", "\n", "# Generate markers\n", "nmrk = 10**6\n", "anum = 4\n", "znum = 2\n", "mass = 4.014*unyt.amu\n", "charge = 2.0*unyt.e\n", "mrk, mrkdist, prtdist = a5.markergen.generate(\n", " nmrk, mass, charge, anum, znum, alphadist, return_dists=True)\n", "\n", "# Plot\n", "mrkdist_rz = mrkdist.integrate(ekin=np.s_[:], pitch=np.s_[:], phi=np.s_[:], copy=True)\n", "mrkdist_exi = mrkdist.integrate(r=np.s_[:], z=np.s_[:], phi=np.s_[:], copy=True)\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(1,2,1)\n", "mrkdist_rz.plot(axes=ax1)\n", "\n", "ax2 = fig.add_subplot(1,2,2)\n", "mrkdist_exi.plot(axes=ax2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, the markers that are created this way have all (almost) equal weights, i.e. the markers themselves have the same distribution as the alpha particles that are born.\n", "This might be undesirable if for example one is interested in wall loads: most alphas are born in the core and thus simulating those is just a waste of CPU time since they are unlikely to contribute to the losses.\n", "\n", "A better way is to have a separate distribution from which markers are sampled, and use the alpha particle birth distribution (or any other particle distribution) only to assign correct weights for the markers.\n", "To make a crude illustration of this idea, we define a marker distribution that is uniform in space, and reject markers whose weight is below some threshold.\n", "This way we get a distribution of markers that is uniform in space (inside the separatrix) and still represents the alpha particle population as a whole when weights (now non-identical) are included." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:38.384349Z", "iopub.status.busy": "2025-04-14T13:08:38.384011Z", "iopub.status.idle": "2025-04-14T13:08:47.456712Z", "shell.execute_reply": "2025-04-14T13:08:47.456122Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5D (Exi) distribution of alphas\n", "alphadist = a5.data.active.getdist(\n", " \"prod1\", exi=True, ekin_edges=20, pitch_edges=10)\n", "alphadist.integrate(time=np.s_[:], charge=np.s_[:])\n", "\n", "# Construct a 5D distribution that is uniform\n", "markerdist = alphadist._copy()\n", "markerdist._distribution[:] = 1\n", "\n", "# Generate markers\n", "nmrk = 10**6\n", "anum = 4\n", "znum = 2\n", "mass = 4.014*unyt.amu\n", "charge = 2.0*unyt.e\n", "mrk, mrkdist, prtdist = a5.markergen.generate(\n", " nmrk, mass, charge, anum, znum, alphadist, markerdist=markerdist,\n", " return_dists=True, minweight=1)\n", "\n", "# Plot both the distribution of markers and distribution of\n", "# particles (markers with weights)\n", "mrkdist_rz = mrkdist.integrate(ekin=np.s_[:], pitch=np.s_[:], phi=np.s_[:], copy=True)\n", "mrkdist_exi = mrkdist.integrate(r=np.s_[:], z=np.s_[:], phi=np.s_[:], copy=True)\n", "prtdist_rz = prtdist.integrate(ekin=np.s_[:], pitch=np.s_[:], phi=np.s_[:], copy=True)\n", "prtdist_exi = prtdist.integrate(r=np.s_[:], z=np.s_[:], phi=np.s_[:], copy=True)\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(2,2,1)\n", "mrkdist_rz.plot(axes=ax1)\n", "\n", "ax2 = fig.add_subplot(2,2,3)\n", "prtdist_rz.plot(axes=ax2)\n", "\n", "ax3 = fig.add_subplot(2,2,2)\n", "mrkdist_exi.plot(axes=ax3)\n", "\n", "ax4 = fig.add_subplot(2,2,4)\n", "prtdist_exi.plot(axes=ax4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Specifying the marker distribution explicitly is not convenient, which is why there is a tool that converts a 1D profile to a 3D distribution.\n", "This still leaves one with the freedom to choose the momentum distribution as they wish.\n", "Here we use a rho profile that peaks towards the edge:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T13:08:47.458618Z", "iopub.status.busy": "2025-04-14T13:08:47.458228Z", "iopub.status.idle": "2025-04-14T13:08:54.899574Z", "shell.execute_reply": "2025-04-14T13:08:54.898954Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AFSI5\n", "Tag 37c8483\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 1252771609\n", "\n", "Done\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5D (Exi) distribution of alphas\n", "alphadist = a5.data.active.getdist(\n", " \"prod1\", exi=True, ekin_edges=20, pitch_edges=10)\n", "alphadist.integrate(time=np.s_[:], charge=np.s_[:])\n", "\n", "# Generate markerdist from a rho profile\n", "rho = np.linspace(0, 1, 100)\n", "prob = np.ones((100,))\n", "prob = (1.0+rho)**3\n", "\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", "# Generate markers\n", "nmrk = 10**6\n", "anum = 4\n", "znum = 2\n", "mass = 4.014*unyt.amu\n", "charge = 2.0*unyt.e\n", "mrk, mrkdist, prtdist = a5.markergen.generate(\n", " nmrk, mass, charge, anum, znum, alphadist, markerdist=markerdist,\n", " return_dists=True, minweight=1)\n", "\n", "# Plot both the distribution of markers and distribution of\n", "# particles (markers with weights)\n", "mrkdist_rz = mrkdist.integrate(ekin=np.s_[:], pitch=np.s_[:], phi=np.s_[:], copy=True)\n", "mrkdist_exi = mrkdist.integrate(r=np.s_[:], z=np.s_[:], phi=np.s_[:], copy=True)\n", "prtdist_rz = prtdist.integrate(ekin=np.s_[:], pitch=np.s_[:], phi=np.s_[:], copy=True)\n", "prtdist_exi = prtdist.integrate(r=np.s_[:], z=np.s_[:], phi=np.s_[:], copy=True)\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(2,2,1)\n", "mrkdist_rz.plot(axes=ax1)\n", "\n", "ax2 = fig.add_subplot(2,2,3)\n", "prtdist_rz.plot(axes=ax2)\n", "\n", "ax3 = fig.add_subplot(2,2,2)\n", "mrkdist_exi.plot(axes=ax3)\n", "\n", "ax4 = fig.add_subplot(2,2,4)\n", "prtdist_exi.plot(axes=ax4)\n", "plt.show()" ] }, { "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": "3.10.17" } }, "nbformat": 4, "nbformat_minor": 4 }