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"# Dealing with distributions\n",
"\n",
"\n",
"\n",
"## Notes about distributions and moments in ASCOT5\n",
"\n",
"ASCOT5 is a kinetic code that solves the Fokker-Planck equation indirectly by tracing weighted markers that form the test particle distribution function.\n",
"Therefore distribution (histograms), which ASCOT5 collects and which approximate the distribution function, can be considered as the main output of the code.\n",
"In this example we show how to collect and post-process the distributions.\n",
"\n",
"The distribution function in ASCOT5 is represented by N-dimensional histograms that divide the entire (preferably) possible phase-space into a finite grid.\n",
"Every time a marker is advanced in a simulation, the code finds the correct bin in the histogram and increments that by marker weight times time-step length.\n",
"We implicitly assume that the marker weight is in units of ``particles/s`` meaning that the markers represent a constant particle source e.g. alpha particle birth rate.\n",
"The collected distribution therefore is *a steady-state distribution* and it has units of ``particles``.\n",
"\n",
"The distribution function contains all information there is to know about a physical system.\n",
"Often the distribution function itself is of little interest, and the interesting quantities are the moments of the distribution.\n",
"While the distribution function is collected during the simulation, the moments are calculated in post-processing.\n",
"\n",
"> **_NOTE:_** The resolution of the distribution histogram directly affects the accuracy of the calculated moment(s).\n",
"> It is therefore advised to allocate as much memory for the distribution that is available and can be reasonably post-processed.\n",
"\n",
"ASCOT5 can be used to collect following distributions, which can be collected simultaneously in a simulation:\n",
"\n",
"- ``5D`` $(R,\\phi,z,p_\\parallel,p_\\perp)$\n",
"- ``rho5D`` $(\\rho,\\theta,\\phi,p_\\parallel,p_\\perp)$\n",
"- ``6D`` $(R,\\phi,z,p_R,p_\\phi,p_z)$\n",
"- ``rho6D`` $(\\rho,\\theta,\\phi,p_R,p_\\phi,p_z)$\n",
"- ``COM`` $(E_\\mathrm{kin},P_\\mathrm{ctor},\\mu)$\n",
"\n",
"``5D`` and ``rho5D`` are the default distributions and also the ones that should be used in all guiding-center and hybrid simulations.\n",
"Both distributions can be used in post-processing to evaluate moments of the distribution, but the difference between these two is that only ``rho5D`` can be used to evaluate 1D quantities.\n",
"\n",
"``6D`` and ``rho6D`` cannot be used to evaluate moments.\n",
"They are to be used only in gyro-orbit simulations where the particle distribution on itself is of interest: e.g. for marker sampling.\n",
"\n",
"``COM`` (constant-of-motion) distribution collects the data in kinetic energy, canonical toroidal angular momentum, and magnetic moment coordinates.\n",
"It cannot be used to compute moments and it is used to provide input from ASCOT5 to other codes.\n",
"\n",
"All distributions except for ``COM`` also have additional abscissae in time and charge.\n",
"Usually these are of little intereset, but make sure to set the charge abscissa properly so that it has a single bin for each expected charge state as otherwise the moments cannot be computed properly.\n",
"\n",
"> **_Summary:_** Use ``5D`` or ``rho5D`` distribution for guiding center simulations and in any simulations where you wish to calculate moments in post-processing.\n",
"> For computing 1D profiles and moments, use ``rho5D``.\n",
"> Use ``6D`` or ``rho6D`` in gyro-orbit simulations when the particle distribution is of specific interest.\n",
"> If you need the ``COM`` distribution, you'll know it.\n",
"\n",
"## Collecting and plotting distributions\n",
"\n",
"Let's begin by setting up a test case that mimics alpha particle slowing down:"
]
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"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(\"plasma flat\", density=1e21)\n",
"a5.data.create_input(\"wall_2D\")\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",
"from a5py.ascot5io.marker import Marker\n",
"nmrk = 1000\n",
"mrk = Marker.generate(\"gc\", n=nmrk, species=\"alpha\")\n",
"mrk[\"energy\"][:] = 3.5e6\n",
"mrk[\"pitch\"][:] = 0.99 - 1.98 * np.random.rand(nmrk,)\n",
"mrk[\"r\"][:] = 4.5 + 3 * np.random.rand(nmrk,)\n",
"a5.data.create_input(\"gc\", **mrk)\n",
"\n",
"from a5py.ascot5io.options import Opt\n",
"opt = Opt.get_default()\n",
"opt.update({\n",
" \"SIM_MODE\":2, \"ENABLE_ADAPTIVE\":1,\n",
" \"ENDCOND_ENERGYLIM\":1, \"ENDCOND_MIN_ENERGY\":2.0e3, \"ENDCOND_MIN_THERMAL\":2.0,\n",
" \"ENABLE_ORBIT_FOLLOWING\":1, \"ENABLE_COULOMB_COLLISIONS\":1,\n",
"})\n",
"\n",
"print(\"Inputs created\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The principle of setting up distribution data collection is the same for all distributions, so here we only collect ``5D`` and ``rho5D`` as those will be used to calculate moments later on.\n",
"Distributions are very memory intensive so while multiple distributions can be collected simultaneously, it is not always feasible to do so."
]
},
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"outputs": [
{
"data": {
"text/plain": [
"'opt_4034039336'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"opt.update({\n",
" # Distribution output\n",
" \"ENABLE_DIST_5D\":1, \"ENABLE_DIST_RHO5D\":1,\n",
" # (R,z) abscissae for the 5D distribution\n",
" \"DIST_MIN_R\":4.3, \"DIST_MAX_R\":8.3, \"DIST_NBIN_R\":50,\n",
" \"DIST_MIN_Z\":-2.0, \"DIST_MAX_Z\":2.0, \"DIST_NBIN_Z\":50,\n",
" # (rho, theta) abscissae for the rho5D distribution. Most of the time a single\n",
" # theta slot is sufficient but please verify it in your case.\n",
" \"DIST_MIN_RHO\" :0, \"DIST_MAX_RHO\" :1.0, \"DIST_NBIN_RHO\" :100,\n",
" \"DIST_MIN_THETA\":0, \"DIST_MAX_THETA\":360, \"DIST_NBIN_THETA\":1,\n",
" # Single phi slot since this is not a stellarator.\n",
" # These values are shared between other distributions\n",
" \"DIST_MIN_PHI\":0, \"DIST_MAX_PHI\":360, \"DIST_NBIN_PHI\":1,\n",
" # The momentum abscissae are shared by 5D distributions\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",
" # One time slot, the span doesn't matter as long as it covers the whole simulation time\n",
" \"DIST_MIN_TIME\":0, \"DIST_MAX_TIME\":1.0, \"DIST_NBIN_TIME\":1,\n",
" # One charge slot exactly at q=2 since we are simulating alphas\n",
" \"DIST_MIN_CHARGE\":1, \"DIST_MAX_CHARGE\":3, \"DIST_NBIN_CHARGE\":1,\n",
"})\n",
"a5.data.create_input(\"opt\", **opt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now run the simulation."
]
},
{
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"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 4034039336\n",
"Options read and initialized.\n",
"\n",
"Reading magnetic field input.\n",
"Active QID is 1265683846\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 1743450117\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 1433629039\n",
"\n",
"1D plasma profiles (P_1D)\n",
"Min rho = 0.00e+00, Max rho = 1.00e+01, Number of rho grid points = 100, 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 / 1 1 / 1.000 1.00e+21/1.00e+00 1.00e+04/1.00e+04 \n",
"[electrons] -1 / 0.001 1.00e+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) 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 3768291983\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 2596397265\n",
"\n",
"2D wall model (wall_2D)\n",
"Number of wall elements = 4, R extend = [0.01, 100.00], z extend = [-100.00, 100.00]\n",
"Wall data read and initialized.\n",
"\n",
"Reading boozer input.\n",
"Active QID is 0278327143\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 0788080151\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 3197853540\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 2327281192\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 0129884723\n",
"\n",
"Inistate written.\n",
"Simulation begins; 4 threads.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulation complete.\n",
"Simulation finished in 99.069442 s\n",
"Endstate written.\n",
"\n",
"Combining and writing diagnostics.\n",
"\n",
"Writing diagnostics output.\n",
"\n",
"Writing 5D distribution.\n",
"\n",
"Writing rho 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": [
"Distributions are accessed via ``getdist`` method that returns a ``DistData`` object which contains methods for additional processing and plotting.\n",
"The data can be sliced, integrated, and interpolated.\n",
"For plotting, the distribution must be reduced to 2D or 1D first."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
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"iopub.status.idle": "2025-04-14T13:12:32.334222Z",
"shell.execute_reply": "2025-04-14T13:12:32.333698Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"List of abscissae:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['r', 'phi', 'z', 'ppar', 'pperp', 'time', 'charge']"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a5 = Ascot(\"ascot.h5\")\n",
"dist = a5.data.active.getdist(\"5d\")\n",
"\n",
"print(\"List of abscissae:\")\n",
"print(dist.abscissae)\n",
"\n",
"# Abscissa values (bin center points)\n",
"dist.abscissa(\"r\");\n",
"# Bin edges\n",
"dist.abscissa_edges(\"r\");\n",
"# Value of the distribution function\n",
"dist.distribution();\n",
"# Phase-space volume of bin elements\n",
"dist.phasespacevolume();\n",
"# Number of particles per bin (= distribution x phasespacevolume)\n",
"dist.histogram();\n",
"\n",
"# Perform linear interpolation on R at the given value\n",
"dist.interpolate(r=6.6*unyt.m);\n",
"\n",
"# Take a slice by giving =\n",
"dist.slice(r=np.s_[1:-1])\n",
"\n",
"# Slicing and integrating modifies the distribution object, unless copy=True is given\n",
"dist.integrate(phi=np.s_[:], charge=np.s_[:], time=np.s_[:])\n",
"\n",
"rzdist = dist.integrate(copy=True, ppar=np.s_[:], pperp=np.s_[:])\n",
"dist.integrate(r=np.s_[:], z=np.s_[:], pperp=np.s_[:])\n",
"\n",
"rzdist.plot()\n",
"dist.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the units of the distribution, $f$, remain consistent when the operations above are used.\n",
"\n",
"The momentum space in 5D distributions can also be converted to energy and pitch, $(E_\\mathrm{kin},\\xi)$, which can be more convenient to use.\n",
"The resulting ``DistData`` object is no different, except that it can't be used to evaluate moments.\n",
"Setting ``plotexi=True`` visualizes the transformation: the top plot shows the original momentum space with the new grid overlayed, the second plot shows the distribution after rebinning, and the third shows the final distribution in the new basis."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-14T13:12:32.335936Z",
"iopub.status.busy": "2025-04-14T13:12:32.335736Z",
"iopub.status.idle": "2025-04-14T13:12:33.500064Z",
"shell.execute_reply": "2025-04-14T13:12:33.499553Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Particle numbers are 3.302169e+01 and 3.302169e+01"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"distexi = a5.data.active.getdist(\"5d\", ekin_edges=10, pitch_edges=10, exi=True, plotexi=True)\n",
"\n",
"# Integrate over all dimensions to get the total particle number\n",
"distexi.integrate(r=np.s_[:],phi=np.s_[:],z=np.s_[:],ekin=np.s_[:],pitch=np.s_[:],\n",
" charge=np.s_[:],time=np.s_[:])\n",
"dist = a5.data.active.getdist(\"5d\")\n",
"dist.integrate(r=np.s_[:],phi=np.s_[:],z=np.s_[:],ppar=np.s_[:],pperp=np.s_[:],\n",
" charge=np.s_[:],time=np.s_[:])\n",
"print(\"Particle numbers are %e and %e\" % (dist.histogram(), distexi.histogram()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The conversion basically \"re-bins\" the histogram thus preserving the total particle number.\n",
"The resolution of the new momentum space should be similar to the one in $(p_\\parallel,p_\\perp)$.\n",
"If it is significantly higher, \"holes\" can appear in the distribution."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-14T13:12:33.502011Z",
"iopub.status.busy": "2025-04-14T13:12:33.501530Z",
"iopub.status.idle": "2025-04-14T13:12:34.178294Z",
"shell.execute_reply": "2025-04-14T13:12:34.177662Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"distexi = a5.data.active.getdist(\"5d\", ekin_edges=50, pitch_edges=50, exi=True)\n",
"distexi.integrate(r=np.s_[:],phi=np.s_[:],z=np.s_[:],charge=np.s_[:],time=np.s_[:])\n",
"distexi.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Moments\n",
"\n",
"Moments are computed from the ``DistData`` object, so it is possible to process the data before it is passed to ``getdist_moments`` which computes the moments.\n",
"The result is a ``DistMoment`` object:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-14T13:12:34.180087Z",
"iopub.status.busy": "2025-04-14T13:12:34.179910Z",
"iopub.status.idle": "2025-04-14T13:12:35.174204Z",
"shell.execute_reply": "2025-04-14T13:12:35.173612Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored moments"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['density', 'chargedensity']"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available distributions:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"5d"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"rho5d"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available Moments:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"density : Particle number density"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"chargedensity : Charge density"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"energydensity : Energy density"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"toroidalcurrent : Toroidal current"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"parallelcurrent : Parallel current"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pressure : Pressure"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"powerdep : Total deposited power"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ionpowerdep : Power deposited to ions"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"electronpowerdep : Power deposited to electrons"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Evaluate moment by giving the distribution and specifying what moment is to be evaluated\n",
"dist = a5.data.active.getdist(\"5d\")\n",
"mom2d = a5.data.active.getdist_moments(dist, \"density\")\n",
"\n",
"# Moment object integrates over all momentum space (and time and charge) so that in the end\n",
"# it stores the data in 3D spatial grid and it records physical volume and poloidal area of each bin\n",
"mom2d.volume;\n",
"mom2d.area;\n",
"\n",
"# Grid center (R,phi,z coordinates)\n",
"mom2d.rc;\n",
"mom2d.phic;\n",
"mom2d.zc;\n",
"\n",
"# The value is stored as an ordinate and accessed like this\n",
"# This returns (nr,nphi,nz) matrix.\n",
"mom2d.ordinate(\"density\");\n",
"\n",
"# Multiple moments can be evaluated simultaneously\n",
"mom2d = a5.data.active.getdist_moments(dist, \"density\", \"chargedensity\")\n",
"\n",
"# Single object can store multiple moments, which can be listed like this\n",
"print(\"Stored moments\")\n",
"print(mom2d.list_ordinates())\n",
"\n",
"# All available distributions and moments that can be calculated\n",
"a5.data.active.getdist_list();\n",
"\n",
"# Evaluating some distributions requires interpolating input data\n",
"a5.input_init(bfield=True)\n",
"a5.data.active.getdist_moments(dist, \"parallelcurrent\")\n",
"a5.input_free()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``DistMoment`` behaves differently depending on whether it was calculated from ``5d`` or ``rho5d`` distribution.\n",
"The abscissae are different and for ``5d`` one can take a toroidal average of the moment and for ``rho5d`` both toroidal and poloidal averages are possible.\n",
"The average values are used when plotting the moments."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-14T13:12:35.176256Z",
"iopub.status.busy": "2025-04-14T13:12:35.175899Z",
"iopub.status.idle": "2025-04-14T13:12:35.886819Z",
"shell.execute_reply": "2025-04-14T13:12:35.886236Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dist = a5.data.active.getdist(\"5d\")\n",
"mom2d = a5.data.active.getdist_moments(dist, \"density\")\n",
"\n",
"# Evaluating moments from rho5d always requires bfield initialization so\n",
"# that (rho,theta) can be mapped to (r,z)\n",
"dist = a5.data.active.getdist(\"rho5d\")\n",
"a5.input_init(bfield=True)\n",
"mom1d = a5.data.active.getdist_moments(dist, \"density\")\n",
"a5.input_free()\n",
"\n",
"# Moment objects have different abscissae\n",
"mom1d.rho;\n",
"mom1d.theta;\n",
"mom1d.phi;\n",
"mom2d.r;\n",
"mom2d.phi;\n",
"mom2d.z;\n",
"\n",
"# Poloidal average only valid for moments calculated from rho5d\n",
"mom2d.ordinate(\"density\", toravg=True);\n",
"mom1d.ordinate(\"density\", toravg=True, polavg=True);\n",
"\n",
"fig = plt.figure(figsize=(8,4))\n",
"ax1 = fig.add_subplot(1,2,1)\n",
"ax2 = fig.add_subplot(1,2,2)\n",
"\n",
"mom2d.plot(\"density\", axes=ax1)\n",
"mom1d.plot(\"density\", axes=ax2)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Distribution resolution must be chosen carefully since moments are calculated in post-processing and there is a loss of information when the markers are binned to the histogram.\n",
"Running even a short simulation with different distribution settings helps finding the correct values.\n",
"\n",
"Other possible source of error is the volume calculation, for moments computed from ``rho5d``, which is also done in post-processing.\n",
"There are two methods to compute the volume and it is always good to check that those agree."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-14T13:12:35.888540Z",
"iopub.status.busy": "2025-04-14T13:12:35.888362Z",
"iopub.status.idle": "2025-04-14T13:12:36.194794Z",
"shell.execute_reply": "2025-04-14T13:12:36.194292Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/runner/work/ascot5/ascot5/a5py/ascotpy/__init__.py:803: AscotUnitWarning: Argument(s) theta given without dimensions (assumed rad)\n",
" rc, zc = self.input_rhotheta2rz(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"489.52942053550026 m**3"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"489.6613806880541 m**3"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"a5.input_init(bfield=True)\n",
"# Divide volume into prisms and sum them\n",
"mom1 = a5.data.active.getdist_moments(dist, \"density\", volmethod=\"prism\")\n",
"# Calculate volume using Monte Carlo method\n",
"mom2 = a5.data.active.getdist_moments(dist, \"density\", volmethod=\"mc\")\n",
"a5.input_free()\n",
"\n",
"print(np.sum(mom1.volume))\n",
"print(np.sum(mom2.volume))"
]
},
{
"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
}