{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2171719e-a8a3-41b5-ac06-dbd43a28d0b8",
   "metadata": {},
   "source": [
    "## SIMBA Beams Module examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18403e61-08c4-4b8d-9bed-0ebf217bebab",
   "metadata": {},
   "source": [
    "The `beam` module in SIMBA is a container for arbitrary 6-dimensional particle distributions, providing functions for reading and writing to and from various file formats for simulation codes, including [ASTRA](https://www.desy.de/~mpyflo/Astra_manual/Astra-Manual_V3.2.pdf), [GPT](https://pulsar.nl/) (via [easyGDF](https://github.com/electronsandstuff/easygdf)), [SDDS](https://ops.aps.anl.gov/manuals/SDDStoolkit/SDDStoolkit.html) (via [soliday.sdds](https://pypi.org/project/soliday.sdds/)), [Ocelot](https://github.com/ocelot-collab/ocelot), [Xsuite](https://github.com/xsuite), [OpenPMD](https://github.com/ChristopherMayes/openPMD-beamphysics) and an in-house HDF5 distribution.\n",
    "\n",
    "Functions and properties are provided for obtaining:\n",
    "* slice properties\n",
    "* Twiss parameters\n",
    "* beam centroids\n",
    "* beam sigmas\n",
    "* beam emittances\n",
    "* kernel density estimator\n",
    "* minimum volume ellipse\n",
    "\n",
    "This notebook will show some of the functionality for loading in distributions, writing them to files, and plotting beam distributions and analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f147bb4a-ca5b-4fc8-ad34-7bd5363d57c5",
   "metadata": {},
   "source": [
    "First import the SIMBA `Beams` module and the plotting functions.\n",
    "\n",
    "The test file is loaded from the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90159295-fa5e-423c-99fe-d9292c1b2be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import simba.Modules.Beams as rbf  # noqa E402\n",
    "from simba.Modules.Beams.plot import (\n",
    "    plotScreenImage,\n",
    "    density_plot,\n",
    "    marginal_plot,\n",
    "    slice_plot,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f61864f4-2acf-4650-a65f-ebc8acc183d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "testbeamlocation = './CLA-S02-DIA-SCR-03.openpmd.hdf5'\n",
    "beam = rbf.beam(testbeamlocation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0d0a68-d33d-402b-be8a-9bb7bf742b91",
   "metadata": {},
   "source": [
    "In addition to the 6D phase space distribution [`x` (m), `y` (m), `z` (m) / `t` (s), `px` (kg*m/s), `py` (kg*m/s), `pz` (kg*m/s)] , the following properties are derived:\n",
    "- `fullbeam` -- the transpose of the 6D array.\n",
    "- [`xp`, `yp`] -- horizontal and vertical angular distributions.\n",
    "- [`xc`, `xpc`, `yc`, `ypc`] -- horizontal and vertical positions and angular distributions, corrected for dispersion.\n",
    "- [`cpx`, `cpy`, `cpz`] -- the beam momenta in eV/c.\n",
    "- `deltap` -- fractional momentum deviation from the mean.\n",
    "- [`p`, `cp`] -- total beam momentum in kg*m/s and eV/c, respectively.\n",
    "- [`Ex`, `Ey`, `Ez`] -- beam energies in eV.\n",
    "- [`Bx`, `By`, `Bz`] -- relativistic betas.\n",
    "- `gamma` -- relativistic Lorentz factor.\n",
    "- `Brho` -- magnetic rigidity.\n",
    "- `BetaGamma` -- beam momentum as beta*gamma.\n",
    "- [`kinetic_energy`, `mean_energy`] -- kinetic energy in J and its mean.\n",
    "- `E0_eV` -- rest energy of the particles in eV.\n",
    "- `Q` -- total charge of the bunch in C.\n",
    "\n",
    "Note that only the base-level 6D distribution can be modified (along with attributes such as `toffset` and `total_charge`); all of the properties above are derived and cannot be set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2396a6-dd38-4ca8-a926-b0c5712c159d",
   "metadata": {},
   "source": [
    "### Plotting beam parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4f8e3b-3987-4a29-a1ce-552749fe0f18",
   "metadata": {},
   "source": [
    "Any of the array-like properties of the `beam` object can be plotted using the three functions defined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77bbb4e4-81b8-4f7f-96ac-966ccc6a83a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='z (mm)', ylabel='cpz (keV/c)'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScreenImage(beam, keys=[\"z\", \"cpz\"], subtract_mean=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f40eb22-8dee-491b-adf0-361d7b8ee983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "density_plot(beam, key=\"x\", bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df10bd9e-2144-46c4-b525-2ab56dd558d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marginal_plot(beam, key1=\"t\", key2=\"cpz\", bins=50, subtract_mean=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93f0cca2-9dcc-4eeb-a7fa-a8c66f069071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice_plot(beam, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfa7a20-0e29-4612-83ff-d30e149179bd",
   "metadata": {},
   "source": [
    "### Derived objects from the distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdfa8c91-645b-4dc9-b2ed-65a20cbbd783",
   "metadata": {},
   "source": [
    "Once a distribution is loaded, the `beam` object will calculate the emittance, Twiss, and so on, with the following properties defined therein."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6b319ee-e85a-4b34-bc50-c0a3cc05ba62",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Q': 250.0000000000001 pC,\n",
       " 'x': array([ 3.84012607e-06,  1.24709072e-04, -6.53576034e-04,  3.63362210e-04,\n",
       "        -2.17527257e-03,  4.08700903e-04, -2.56052705e-03,  1.42263885e-03,\n",
       "         4.23819774e-03,  1.49548872e-03,  3.43928836e-03,  2.75204847e-05,\n",
       "        -4.60962054e-04,  2.97270433e-03, -1.47440009e-03, -9.73810902e-04,\n",
       "         2.03861975e-03, -4.02725043e-04,  2.21416480e-05, -1.37148719e-03,\n",
       "        -3.66777700e-03,  3.09407193e-03,  1.14202147e-03,  9.23852830e-04,\n",
       "         1.85029882e-03, -1.53588540e-03, -2.35215119e-03, -2.18535575e-03,\n",
       "        -7.52776809e-04,  2.77055096e-03, -9.05148287e-04,  1.52677663e-04,\n",
       "        -2.61296122e-03,  3.27244012e-03,  2.96092847e-03,  1.78582148e-03,\n",
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       " 'cpy': array([   103.16433471,   6059.89700081,   1185.56764183,  20222.93362448,\n",
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       "        35028099.24891921, 35074513.85350901, 35054178.99556862,\n",
       "        34996006.73468602, 35021074.64037486, 34910312.31599438,\n",
       "        34925259.57007505, 34959285.10047764, 35066669.02205967,\n",
       "        35079085.44666639, 35071962.50326843, 35029073.35705302,\n",
       "        34999036.58113389, 34938846.99086028, 35010250.11738656,\n",
       "        35099814.53218973, 34968588.34853601, 35060719.74378131,\n",
       "        34945254.66838232, 34907281.93538673, 35067015.69642039,\n",
       "        35095190.17495715, 34994196.76454587, 34934667.39433554,\n",
       "        34959943.97632324, 35036331.40423876, 34969173.4623411 ,\n",
       "        35010292.24013184, 35013236.20881191, 34958546.7851454 ,\n",
       "        34992452.10856816, 34956257.52509311, 35033844.29942864,\n",
       "        35059083.31338143, 34935821.9006018 , 34948614.66774028,\n",
       "        35047980.23864172, 35089234.9735786 , 35075282.83288906,\n",
       "        34983861.89113148, 35040776.58594171, 34965685.57389542,\n",
       "        34959389.31918765, 35101889.44400427, 34984585.6149902 ,\n",
       "        34952786.02389792, 35038187.07572615, 35063967.85899214,\n",
       "        35027821.94853441, 34906823.92519353, 34907545.00057886,\n",
       "        34968636.08685114, 34979071.26364787], units='eV/c'),\n",
       " 'emittance': {'ex': 174.09858753209383 nrad*m,\n",
       "  'ey': 196.1049044674848 nrad*m,\n",
       "  'enx': 11.928836982710356 µrad*m,\n",
       "  'eny': 13.436659481636406 µrad*m,\n",
       "  'ecx': 174.08724977827632 nrad*m,\n",
       "  'ecy': 194.84363413737034 nrad*m,\n",
       "  'ecnx': 11.928061562116598 µrad*m,\n",
       "  'ecny': 13.350241708896782 µrad*m,\n",
       "  'normalized_horizontal_emittance': 11.928838399166315 µrad*m,\n",
       "  'normalized_vertical_emittance': 13.436661077134476 µrad*m,\n",
       "  'horizontal_emittance': 174.09858753209383 nrad*m,\n",
       "  'vertical_emittance': 196.1049044674848 nrad*m,\n",
       "  'horizontal_emittance_90': 906.3832287336161 nrad*m,\n",
       "  'normalized_horizontal_emittance_90': 62.096690935635905 µrad*m,\n",
       "  'vertical_emittance_90': 1.0341899610614655 µrad*m,\n",
       "  'normalized_vertical_emittance_90': 70.85278317704308 µrad*m,\n",
       "  'horizontal_emittance_corrected': 174.08724977827632 nrad*m,\n",
       "  'vertical_emittance_corrected': 194.84363413737034 nrad*m,\n",
       "  'normalised_horizontal_emittance_corrected': 11.928061562116598 µrad*m,\n",
       "  'normalised_vertical_emittance_corrected': 13.350241708896782 µrad*m},\n",
       " 'twiss': {'normalized_horizontal_emittance': 11.928838399166315 µrad*m,\n",
       "  'normalized_vertical_emittance': 13.436661077134476 µrad*m,\n",
       "  'horizontal_emittance': 174.09858753209383 nrad*m,\n",
       "  'vertical_emittance': 196.1049044674848 nrad*m,\n",
       "  'horizontal_emittance_corrected': 174.08724977827632 nrad*m,\n",
       "  'vertical_emittance_corrected': 194.84363413737034 nrad*m,\n",
       "  'beta_x': 18.84877195823276 m/rad,\n",
       "  'alpha_x': 1.193974461783896 rad^-1/m,\n",
       "  'gamma_x': 0.1286861033051388 rad/m,\n",
       "  'beta_y': 52.747359809450934 m/rad,\n",
       "  'alpha_y': -7.543884036036961 rad^-1/m,\n",
       "  'gamma_y': 1.097878387816433 rad/m,\n",
       "  'beta_x_corrected': 18.847571321953417 m/rad,\n",
       "  'alpha_x_corrected': 1.1938848622097344 rad^-1/m,\n",
       "  'gamma_x_corrected': 0.12868294926617554 rad/m,\n",
       "  'beta_y_corrected': 53.08380763573523 m/rad,\n",
       "  'alpha_y_corrected': -7.593104330038315 rad^-1/m,\n",
       "  'gamma_y_corrected': 1.1049552769338418 rad/m,\n",
       "  'eta_x': 0.013340593680719525 m,\n",
       "  'eta_xp': -919.4879600646053 µrad,\n",
       "  'eta_y': 0.020277140256491067 m,\n",
       "  'eta_yp': -1.5620659992864943 mrad},\n",
       " 'sigmas': {'sigma_x': 1.8115034017751281 mm,\n",
       "  'sigma_y': 3.216211429048846 mm,\n",
       "  'sigma_t': 1.9002308373657044 ps,\n",
       "  'sigma_z': 569.6141711291078 µm,\n",
       "  'sigma_px': 2.801888062189512 ykg*m/s,\n",
       "  'sigma_py': 8.679082455484542 ykg*m/s,\n",
       "  'sigma_pz': 28.844075405565082 ykg*m/s,\n",
       "  'sigma_cp': 53.918563091267316 keV/c,\n",
       "  'sigma_cp_eV': 53.918563091267316 keV/c,\n",
       "  'Sx': 1.8115034017751281 mm,\n",
       "  'Sy': 3.216211429048846 mm,\n",
       "  'Sz': 569.6141711291078 µm,\n",
       "  'St': 1.9002308373657044 ps,\n",
       "  'momentum_spread': 53.918563091267316 keV/c,\n",
       "  'linear_chirp_t_cpz': -8.781475645772638 ac/eV,\n",
       "  'linear_chirp_t_pz': -16.43152267263281 Gs/kg/m,\n",
       "  'linear_chirp_z': -105.9598271810107 p},\n",
       " 'centroids': {'mean_x': -9.469452800676454 µm,\n",
       "  'mean_y': -79.31028735907547 µm,\n",
       "  'mean_t': 21.644981711285823 ns,\n",
       "  'mean_z': 6.489002270393042 m,\n",
       "  'mean_cpx': -30.324421765187047 eV/c,\n",
       "  'mean_cpy': -595.7111651581272 eV/c,\n",
       "  'mean_cpz': 35.00874585117013 MeV/c,\n",
       "  'mean_px': -1.6206238247575812e-26 kg*m/s,\n",
       "  'mean_py': -3.1836508356366553e-25 kg*m/s,\n",
       "  'mean_pz': 18.709675007364343 zkg*m/s,\n",
       "  'mean_energy': 35.51974895818066 MeV,\n",
       "  'mean_gamma': 68.51771255304351 ,\n",
       "  'mean_cp': 35.00875000818449 MeV/c,\n",
       "  'Cx': -9.469452800676454 µm,\n",
       "  'Cy': -79.31028735907547 µm,\n",
       "  'Cz': 6.489002270393042 m,\n",
       "  'Ct': 21.644981711285823 ns,\n",
       "  'Cp': 35.00875000818449 MeV/c,\n",
       "  'Cpx': -30.324421765187047 eV/c,\n",
       "  'Cpy': -595.7111651581272 eV/c,\n",
       "  'Cpz': 35.00874585117013 MeV/c,\n",
       "  'Cxp': -864.0144434704091 nrad,\n",
       "  'Cyp': -17.01235281354686 µrad,\n",
       "  'Cgamma': 68.51771255304351 ,\n",
       "  'Ccp': 35.00875000818449 MeV/c,\n",
       "  'CEn': 35.51974895818066 MeV}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de97c280-db60-421b-b69d-97ed9f18101f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ex': 174.09858753209383 nrad*m,\n",
       " 'ey': 196.1049044674848 nrad*m,\n",
       " 'enx': 11.928836982710356 µrad*m,\n",
       " 'eny': 13.436659481636406 µrad*m,\n",
       " 'ecx': 174.08724977827632 nrad*m,\n",
       " 'ecy': 194.84363413737034 nrad*m,\n",
       " 'ecnx': 11.928061562116598 µrad*m,\n",
       " 'ecny': 13.350241708896782 µrad*m,\n",
       " 'normalized_horizontal_emittance': 11.928838399166315 µrad*m,\n",
       " 'normalized_vertical_emittance': 13.436661077134476 µrad*m,\n",
       " 'horizontal_emittance': 174.09858753209383 nrad*m,\n",
       " 'vertical_emittance': 196.1049044674848 nrad*m,\n",
       " 'horizontal_emittance_90': 906.3832287336161 nrad*m,\n",
       " 'normalized_horizontal_emittance_90': 62.096690935635905 µrad*m,\n",
       " 'vertical_emittance_90': 1.0341899610614655 µrad*m,\n",
       " 'normalized_vertical_emittance_90': 70.85278317704308 µrad*m,\n",
       " 'horizontal_emittance_corrected': 174.08724977827632 nrad*m,\n",
       " 'vertical_emittance_corrected': 194.84363413737034 nrad*m,\n",
       " 'normalised_horizontal_emittance_corrected': 11.928061562116598 µrad*m,\n",
       " 'normalised_vertical_emittance_corrected': 13.350241708896782 µrad*m}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.emittance.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0312fb7-3488-441a-b581-a0b6ef8d3627",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'normalized_horizontal_emittance': 11.928838399166315 µrad*m,\n",
       " 'normalized_vertical_emittance': 13.436661077134476 µrad*m,\n",
       " 'horizontal_emittance': 174.09858753209383 nrad*m,\n",
       " 'vertical_emittance': 196.1049044674848 nrad*m,\n",
       " 'horizontal_emittance_corrected': 174.08724977827632 nrad*m,\n",
       " 'vertical_emittance_corrected': 194.84363413737034 nrad*m,\n",
       " 'beta_x': 18.84877195823276 m/rad,\n",
       " 'alpha_x': 1.193974461783896 rad^-1/m,\n",
       " 'gamma_x': 0.1286861033051388 rad/m,\n",
       " 'beta_y': 52.747359809450934 m/rad,\n",
       " 'alpha_y': -7.543884036036961 rad^-1/m,\n",
       " 'gamma_y': 1.097878387816433 rad/m,\n",
       " 'beta_x_corrected': 18.847571321953417 m/rad,\n",
       " 'alpha_x_corrected': 1.1938848622097344 rad^-1/m,\n",
       " 'gamma_x_corrected': 0.12868294926617554 rad/m,\n",
       " 'beta_y_corrected': 53.08380763573523 m/rad,\n",
       " 'alpha_y_corrected': -7.593104330038315 rad^-1/m,\n",
       " 'gamma_y_corrected': 1.1049552769338418 rad/m,\n",
       " 'eta_x': 0.013340593680719525 m,\n",
       " 'eta_xp': -919.4879600646053 µrad,\n",
       " 'eta_y': 0.020277140256491067 m,\n",
       " 'eta_yp': -1.5620659992864943 mrad}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.twiss.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4ec6c81-ef48-420e-9ac0-4a64c0759ea0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sigma_x': 1.8115034017751281 mm,\n",
       " 'sigma_y': 3.216211429048846 mm,\n",
       " 'sigma_t': 1.9002308373657044 ps,\n",
       " 'sigma_z': 569.6141711291078 µm,\n",
       " 'sigma_px': 2.801888062189512 ykg*m/s,\n",
       " 'sigma_py': 8.679082455484542 ykg*m/s,\n",
       " 'sigma_pz': 28.844075405565082 ykg*m/s,\n",
       " 'sigma_cp': 53.918563091267316 keV/c,\n",
       " 'sigma_cp_eV': 53.918563091267316 keV/c,\n",
       " 'Sx': 1.8115034017751281 mm,\n",
       " 'Sy': 3.216211429048846 mm,\n",
       " 'Sz': 569.6141711291078 µm,\n",
       " 'St': 1.9002308373657044 ps,\n",
       " 'momentum_spread': 53.918563091267316 keV/c,\n",
       " 'linear_chirp_t_cpz': -8.781475645772638 ac/eV,\n",
       " 'linear_chirp_t_pz': -16.43152267263281 Gs/kg/m,\n",
       " 'linear_chirp_z': -105.9598271810107 p}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.sigmas.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34e902f2-8385-4bda-a664-ee9884a2befe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_x': -9.469452800676454 µm,\n",
       " 'mean_y': -79.31028735907547 µm,\n",
       " 'mean_t': 21.644981711285823 ns,\n",
       " 'mean_z': 6.489002270393042 m,\n",
       " 'mean_cpx': -30.324421765187047 eV/c,\n",
       " 'mean_cpy': -595.7111651581272 eV/c,\n",
       " 'mean_cpz': 35.00874585117013 MeV/c,\n",
       " 'mean_px': -1.6206238247575812e-26 kg*m/s,\n",
       " 'mean_py': -3.1836508356366553e-25 kg*m/s,\n",
       " 'mean_pz': 18.709675007364343 zkg*m/s,\n",
       " 'mean_energy': 35.51974895818066 MeV,\n",
       " 'mean_gamma': 68.51771255304351 ,\n",
       " 'mean_cp': 35.00875000818449 MeV/c,\n",
       " 'Cx': -9.469452800676454 µm,\n",
       " 'Cy': -79.31028735907547 µm,\n",
       " 'Cz': 6.489002270393042 m,\n",
       " 'Ct': 21.644981711285823 ns,\n",
       " 'Cp': 35.00875000818449 MeV/c,\n",
       " 'Cpx': -30.324421765187047 eV/c,\n",
       " 'Cpy': -595.7111651581272 eV/c,\n",
       " 'Cpz': 35.00874585117013 MeV/c,\n",
       " 'Cxp': -864.0144434704091 nrad,\n",
       " 'Cyp': -17.01235281354686 µrad,\n",
       " 'Cgamma': 68.51771255304351 ,\n",
       " 'Ccp': 35.00875000818449 MeV/c,\n",
       " 'CEn': 35.51974895818066 MeV}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.centroids.model_dump()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248619a5-a5c5-4ef1-924c-1f039cf0d40b",
   "metadata": {},
   "source": [
    "### Reading and writing to and from different simulation code formats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66abb132-b8e6-4911-9876-504a3035667f",
   "metadata": {},
   "source": [
    "The `beam` object can load in and write to different file formats. The examples below show how to do this, and as a sanity check we can plot the beam distribution to check that it is the same after transforming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6bcc6441-6824-46c9-b553-649ff32608fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "astrabeamlocation = testbeamlocation.replace('openpmd.hdf5', 'astra')\n",
    "sddsbeamlocation = testbeamlocation.replace('openpmd.hdf5', 'sdds')\n",
    "ocelotbeamlocation = testbeamlocation.replace('openpmd.hdf5', 'ocelot.npz')\n",
    "gptbeamlocation = testbeamlocation.replace('openpmd.hdf5', 'gdf')\n",
    "beam.write_astra_beam_file(filename=astrabeamlocation)\n",
    "beam.write_gdf_beam_file(filename=gptbeamlocation)\n",
    "beam.write_ocelot_beam_file(filename=ocelotbeamlocation)\n",
    "beam.write_SDDS_beam_file(filename=sddsbeamlocation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "806b4592-2a0e-43dd-8468-9e1debd8e5fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "astrabeam = rbf.beam(astrabeamlocation)\n",
    "sddsbeam = rbf.beam(sddsbeamlocation)\n",
    "ocelotbeam = rbf.beam(ocelotbeamlocation)\n",
    "gptbeam = rbf.beam(gptbeamlocation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d4a089ae-e451-48c3-9914-e5a5b66edd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='z (mm)', ylabel='cpz (keV/c)'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScreenImage(gptbeam, keys=[\"z\", \"cpz\"], subtract_mean=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1a9101b8-0434-4eee-977b-4de4d6e065d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "for ext in [\"astra\", \"ocelot.npz\", \"sdds\", \"gdf\"]:\n",
    "    os.remove(testbeamlocation.replace('openpmd.hdf5', ext))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "544219fd-dcd2-4cd0-8b9a-9fd01e8f6487",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}