{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2171719e-a8a3-41b5-ac06-dbd43a28d0b8",
   "metadata": {},
   "source": [
    "## SimFrame Beams Module examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18403e61-08c4-4b8d-9bed-0ebf217bebab",
   "metadata": {},
   "source": [
    "The `beam` module in SimFrame 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), [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 SimFrame `Beams` module and the plotting functions.\n",
    "\n",
    "The test file is loaded from the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90159295-fa5e-423c-99fe-d9292c1b2be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import SimulationFramework.Modules.Beams as rbf  # noqa E402\n",
    "from SimulationFramework.Modules.Beams.plot import (\n",
    "    plotScreenImage,\n",
    "    density_plot,\n",
    "    marginal_plot,\n",
    "    slice_plot,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f61864f4-2acf-4650-a65f-ebc8acc183d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "testbeamlocation = '../../tests/CLARA/Basefiles/Base_250pC/CLA-S02-SIM-APER-01.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": 3,
   "id": "77bbb4e4-81b8-4f7f-96ac-966ccc6a83a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 5,
   "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": 6,
   "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": 8,
   "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=500)"
   ]
  },
  {
   "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": 14,
   "id": "f6b319ee-e85a-4b34-bc50-c0a3cc05ba62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Q': -249.99904632567237 pC,\n",
       " 'x': array([ 5.51101429e-07, -6.50362433e-05, -2.54837562e-04, ...,\n",
       "         4.33454182e-04,  4.25720265e-04, -5.71904628e-04], units='m'),\n",
       " 'y': array([-1.49411997e-06,  2.54326650e-04, -6.59195007e-05, ...,\n",
       "        -3.35637164e-04, -1.45399671e-04, -1.67661723e-04], units='m'),\n",
       " 'z': array([3.37146439, 3.37146054, 3.3714605 , ..., 3.37226684, 3.37182739,\n",
       "        3.37151151], units='m'),\n",
       " 'cpx': array([    4.00533305,  -227.35835643,  -884.67755086, ...,\n",
       "         1967.51902427,  1013.42351584, -1785.75700152], units='eV/c'),\n",
       " 'cpy': array([  -13.10264798,   880.77217307,  -231.4252551 , ...,\n",
       "        -2019.46124709,  -650.02971922,  -606.29400726], units='eV/c'),\n",
       " 'cpz': array([35239721.98261   , 35241031.27186141, 35241046.23069784, ...,\n",
       "        34961040.3273581 , 35103161.1710736 , 35223046.05993564], units='eV/c'),\n",
       " 'emittance': {'ex': 6.085569542900601 nm*rad,\n",
       "  'ey': 6.096951369790895 nm*rad,\n",
       "  'enx': 419.5296118918665 nm*rad,\n",
       "  'eny': 420.31425717185937 nm*rad,\n",
       "  'ecx': 6.085564599083844 nm*rad,\n",
       "  'ecy': 6.096937375117689 nm*rad,\n",
       "  'ecnx': 419.529272031375 nm*rad,\n",
       "  'ecny': 420.3132933613225 nm*rad,\n",
       "  'normalized_horizontal_emittance': 419.5296128503428 nm*rad,\n",
       "  'normalized_vertical_emittance': 420.3142581321283 nm*rad,\n",
       "  'horizontal_emittance': 6.085569542900601 nm*rad,\n",
       "  'vertical_emittance': 6.096951369790895 nm*rad,\n",
       "  'horizontal_emittance_90': 26.77955808473015 nm*rad,\n",
       "  'normalized_horizontal_emittance_90': 1.8459464492811317 µm*rad,\n",
       "  'vertical_emittance_90': 26.76232912109781 nm*rad,\n",
       "  'normalized_vertical_emittance_90': 1.84475883654528 µm*rad,\n",
       "  'horizontal_emittance_corrected': 6.085564599083844 nm*rad,\n",
       "  'vertical_emittance_corrected': 6.096937375117689 nm*rad,\n",
       "  'normalised_horizontal_emittance_corrected': 419.529272031375 nm*rad,\n",
       "  'normalised_vertical_emittance_corrected': 420.3132933613225 nm*rad},\n",
       " 'twiss': {'normalized_horizontal_emittance': 419.5296128503428 nm*rad,\n",
       "  'normalized_vertical_emittance': 420.3142581321283 nm*rad,\n",
       "  'horizontal_emittance': 6.085569542900601 nm*rad,\n",
       "  'vertical_emittance': 6.096951369790895 nm*rad,\n",
       "  'horizontal_emittance_corrected': 6.085564599083844 nm*rad,\n",
       "  'vertical_emittance_corrected': 6.096937375117689 nm*rad,\n",
       "  'beta_x': 32.05020978021645 m/rad,\n",
       "  'alpha_x': -3.3212154062213517 m^-1/rad,\n",
       "  'gamma_x': 0.3753632770899389 rad/m,\n",
       "  'beta_y': 31.976841557655998 m/rad,\n",
       "  'alpha_y': -3.3160807576647953 m^-1/rad,\n",
       "  'gamma_y': 0.375158740106479 rad/m,\n",
       "  'beta_x_corrected': 32.050219635126446 m/rad,\n",
       "  'alpha_x_corrected': -3.3212156754949214 m^-1/rad,\n",
       "  'gamma_x_corrected': 0.37536321747910933 rad/m,\n",
       "  'beta_y_corrected': 31.97687646175686 m/rad,\n",
       "  'alpha_y_corrected': -3.316082358099601 m^-1/rad,\n",
       "  'gamma_y_corrected': 0.375158662543124 rad/m,\n",
       "  'eta_x': 70.85083726242118 µm,\n",
       "  'eta_xp': 10.634193345543734 µrad},\n",
       " 'sigmas': {'sigma_x': 441.6376121686877 µm,\n",
       "  'sigma_y': 441.5441630647349 µm,\n",
       "  'sigma_t': 1.6386590592345411 ps,\n",
       "  'sigma_z': 491.2058865500386 µm,\n",
       "  'sigma_cp': 156.01265890540358 keV/c,\n",
       "  'sigma_cp_eV': 156.01265890540358 keV/c,\n",
       "  'Sx': 441.6376121686877 µm,\n",
       "  'Sy': 441.5441630647349 µm,\n",
       "  'Sz': 491.2058865500386 µm,\n",
       "  'St': 1.6386590592345411 ps,\n",
       "  'momentum_spread': 156.01265890540358 keV/c,\n",
       "  'linear_chirp_t_pz': -5.118464776079278 Gs/m/kg,\n",
       "  'linear_chirp_z': -31.484941869066564 p},\n",
       " 'centroids': {'mean_x': 82.91555735978675 nm,\n",
       "  'mean_y': -558.26953533303 nm,\n",
       "  'mean_t': -18.70884225084076 fs,\n",
       "  'mean_z': 3.3714699999998476 m,\n",
       "  'mean_cpx': -1.6822938497155069 eV/c,\n",
       "  'mean_cpy': -4.6192358672362035 eV/c,\n",
       "  'mean_cpz': 35.22375881604315 MeV/c,\n",
       "  'mean_energy': 2.8838063994312573 µJ*eV/c,\n",
       "  'mean_gamma': 68.93843003511505 ,\n",
       "  'mean_cp': 35.22375889651695 MeV/c,\n",
       "  'Cx': 82.91555735978675 nm,\n",
       "  'Cy': -558.26953533303 nm,\n",
       "  'Cz': 3.3714699999998476 m,\n",
       "  'Ct': -18.70884225084076 fs,\n",
       "  'Cp': 35.22375889651695 MeV/c,\n",
       "  'Cpx': -1.6822938497155069 eV/c,\n",
       "  'Cpy': -4.6192358672362035 eV/c,\n",
       "  'Cpz': 35.22375881604315 MeV/c,\n",
       "  'Cxp': -47.96882077938042 nrad,\n",
       "  'Cyp': -130.8047034777232 nrad,\n",
       "  'Cgamma': 68.93843003511505 ,\n",
       "  'Ccp': 35.22375889651695 MeV/c,\n",
       "  'CEn': 2.8838063994312573 µJ*eV/c}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "de97c280-db60-421b-b69d-97ed9f18101f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ex': 6.085569542900601 nm*rad,\n",
       " 'ey': 6.096951369790895 nm*rad,\n",
       " 'enx': 419.5296118918665 nm*rad,\n",
       " 'eny': 420.31425717185937 nm*rad,\n",
       " 'ecx': 6.085564599083844 nm*rad,\n",
       " 'ecy': 6.096937375117689 nm*rad,\n",
       " 'ecnx': 419.529272031375 nm*rad,\n",
       " 'ecny': 420.3132933613225 nm*rad,\n",
       " 'normalized_horizontal_emittance': 419.5296128503428 nm*rad,\n",
       " 'normalized_vertical_emittance': 420.3142581321283 nm*rad,\n",
       " 'horizontal_emittance': 6.085569542900601 nm*rad,\n",
       " 'vertical_emittance': 6.096951369790895 nm*rad,\n",
       " 'horizontal_emittance_90': 26.77955808473015 nm*rad,\n",
       " 'normalized_horizontal_emittance_90': 1.8459464492811317 µm*rad,\n",
       " 'vertical_emittance_90': 26.76232912109781 nm*rad,\n",
       " 'normalized_vertical_emittance_90': 1.84475883654528 µm*rad,\n",
       " 'horizontal_emittance_corrected': 6.085564599083844 nm*rad,\n",
       " 'vertical_emittance_corrected': 6.096937375117689 nm*rad,\n",
       " 'normalised_horizontal_emittance_corrected': 419.529272031375 nm*rad,\n",
       " 'normalised_vertical_emittance_corrected': 420.3132933613225 nm*rad}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.emittance.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0312fb7-3488-441a-b581-a0b6ef8d3627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'normalized_horizontal_emittance': 419.5296128503428 nm*rad,\n",
       " 'normalized_vertical_emittance': 420.3142581321283 nm*rad,\n",
       " 'horizontal_emittance': 6.085569542900601 nm*rad,\n",
       " 'vertical_emittance': 6.096951369790895 nm*rad,\n",
       " 'horizontal_emittance_corrected': 6.085564599083844 nm*rad,\n",
       " 'vertical_emittance_corrected': 6.096937375117689 nm*rad,\n",
       " 'beta_x': 32.05020978021645 m/rad,\n",
       " 'alpha_x': -3.3212154062213517 m^-1/rad,\n",
       " 'gamma_x': 0.3753632770899389 rad/m,\n",
       " 'beta_y': 31.976841557655998 m/rad,\n",
       " 'alpha_y': -3.3160807576647953 m^-1/rad,\n",
       " 'gamma_y': 0.375158740106479 rad/m,\n",
       " 'beta_x_corrected': 32.050219635126446 m/rad,\n",
       " 'alpha_x_corrected': -3.3212156754949214 m^-1/rad,\n",
       " 'gamma_x_corrected': 0.37536321747910933 rad/m,\n",
       " 'beta_y_corrected': 31.97687646175686 m/rad,\n",
       " 'alpha_y_corrected': -3.316082358099601 m^-1/rad,\n",
       " 'gamma_y_corrected': 0.375158662543124 rad/m,\n",
       " 'eta_x': 70.85083726242118 µm,\n",
       " 'eta_xp': 10.634193345543734 µrad}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.twiss.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4ec6c81-ef48-420e-9ac0-4a64c0759ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sigma_x': 441.6376121686877 µm,\n",
       " 'sigma_y': 441.5441630647349 µm,\n",
       " 'sigma_t': 1.6386590592345411 ps,\n",
       " 'sigma_z': 491.2058865500386 µm,\n",
       " 'sigma_cp': 156.01265890540358 keV/c,\n",
       " 'sigma_cp_eV': 156.01265890540358 keV/c,\n",
       " 'Sx': 441.6376121686877 µm,\n",
       " 'Sy': 441.5441630647349 µm,\n",
       " 'Sz': 491.2058865500386 µm,\n",
       " 'St': 1.6386590592345411 ps,\n",
       " 'momentum_spread': 156.01265890540358 keV/c,\n",
       " 'linear_chirp_t_pz': -5.118464776079278 Gs/m/kg,\n",
       " 'linear_chirp_z': -31.484941869066564 p}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beam.Particles.sigmas.model_dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "34e902f2-8385-4bda-a664-ee9884a2befe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_x': 82.91555735978675 nm,\n",
       " 'mean_y': -558.26953533303 nm,\n",
       " 'mean_t': -18.70884225084076 fs,\n",
       " 'mean_z': 3.3714699999998476 m,\n",
       " 'mean_cpx': -1.6822938497155069 eV/c,\n",
       " 'mean_cpy': -4.6192358672362035 eV/c,\n",
       " 'mean_cpz': 35.22375881604315 MeV/c,\n",
       " 'mean_energy': 2.8838063994312573 µeV*J/c,\n",
       " 'mean_gamma': 68.93843003511505 ,\n",
       " 'mean_cp': 35.22375889651695 MeV/c,\n",
       " 'Cx': 82.91555735978675 nm,\n",
       " 'Cy': -558.26953533303 nm,\n",
       " 'Cz': 3.3714699999998476 m,\n",
       " 'Ct': -18.70884225084076 fs,\n",
       " 'Cp': 35.22375889651695 MeV/c,\n",
       " 'Cpx': -1.6822938497155069 eV/c,\n",
       " 'Cpy': -4.6192358672362035 eV/c,\n",
       " 'Cpz': 35.22375881604315 MeV/c,\n",
       " 'Cxp': -47.96882077938042 nrad,\n",
       " 'Cyp': -130.8047034777232 nrad,\n",
       " 'Cgamma': 68.93843003511505 ,\n",
       " 'Ccp': 35.22375889651695 MeV/c,\n",
       " 'CEn': 2.8838063994312573 µeV*J/c}"
      ]
     },
     "execution_count": 14,
     "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": 4,
   "id": "6bcc6441-6824-46c9-b553-649ff32608fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initializing ocelot...\n"
     ]
    }
   ],
   "source": [
    "astrabeamlocation = testbeamlocation.replace('hdf5', 'astra')\n",
    "sddsbeamlocation = testbeamlocation.replace('hdf5', 'sdds')\n",
    "ocelotbeamlocation = testbeamlocation.replace('hdf5', 'ocelot.npz')\n",
    "gptbeamlocation = testbeamlocation.replace('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": 5,
   "id": "806b4592-2a0e-43dd-8468-9e1debd8e5fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.51101429e-07 -6.50362433e-05 -2.54837562e-04 ...  4.33454182e-04\n",
      "  4.25720265e-04 -5.71904628e-04]\n"
     ]
    }
   ],
   "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": 11,
   "id": "d4a089ae-e451-48c3-9914-e5a5b66edd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScreenImage(sddsbeam, keys=[\"z\", \"cpz\"], subtract_mean=[True, False])"
   ]
  }
 ],
 "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
}