{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOm5fHBtug/d3SMEK1DtfNi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GamelinAl/mbtrack2_examples/blob/main/mbtrack2_cavity_resonator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction\n",
        "\n",
        "This notebook introduces different classes for **mbtrack2** dealing with RF cavities and longitudinal beam dynamics:\n",
        "\n",
        "* The `RFCavity` class is a very simple class using in tracking to model RF cavities using a perfect cosine wave.\n",
        "\n",
        "* The `CavityResonator` class is the main class which can be used to model RF cavities self-consistenly considering beam loading. It can be used in tracking to model:\n",
        "\n",
        "  *   Active RF cavity\n",
        "  *   Passive RF cavity\n",
        "  *   Cavity HOM\n",
        "\n",
        "  The cavity physics is based on the phasor formalism developped in [1], details of the implementation and benchmark can be found in [2,3].\n",
        "\n",
        "* The `BeamLoadingEquilibrium` is used to compute analytically the beam equilibrium profile for a given storage ring and a list of RF cavities of any harmonic. The class assumes an uniform filling of the storage ring.\n",
        "\n",
        "  The implementation is based on an extention of [4] which is detailed in [3].\n",
        "\n",
        "More advanced features of `CavityResonator`, including different kinds of RF loops and feedbacks, are described in this notebook:\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GamelinAl/mbtrack2_examples/blob/main/mbtrack2_cavity_resonator.ipynb)\n",
        "\n",
        "## Convention\n",
        "mbtrack2 uses the cosine convention for RF voltage.\n",
        "\n",
        "## References\n",
        "\n",
        "[1]  Wilson, P. B. (1994). Fundamental-mode rf design in e+ e− storage ring factories. In Frontiers of Particle Beams: Factories with e+ e-Rings (pp. 293-311). Springer, Berlin, Heidelberg.\n",
        "\n",
        "[2] Yamamoto, Naoto, Alexis Gamelin, and Ryutaro Nagaoka. \"Investigation of Longitudinal Beam Dynamics With Harmonic Cavities by Using the Code Mbtrack.\" Proc. 10th International Partile Accelerator Conference (IPAC’19), Melbourne, Australia, 19-24 May 2019. 2019.\n",
        "\n",
        "[3] Gamelin, Alexis, and Naoto Yamamoto. \"Equilibrium Bunch Density Distribution with Multiple Active and Passive RF Cavities.\" 12th International Particle Accelerator Conference. 2021.\n",
        "\n",
        "[4] Venturini, M. (2018). Passive higher-harmonic rf cavities with general settings and multibunch instabilities in electron storage rings. Physical Review Accelerators and Beams, 21(11), 114404.\n",
        "\n"
      ],
      "metadata": {
        "id": "IKih_9pcq9Ui"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialization"
      ],
      "metadata": {
        "id": "MvPLW4R_Td8L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## mbtrack2 set-up"
      ],
      "metadata": {
        "id": "O5fColz-q2EI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BKGTZA_EvtF6",
        "outputId": "d2d1c5c2-8293-4ae6-9db0-3f57f4909268"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'mbtrack2'...\n",
            "remote: Enumerating objects: 1629, done.\u001b[K\n",
            "remote: Counting objects: 100% (311/311), done.\u001b[K\n",
            "remote: Compressing objects: 100% (181/181), done.\u001b[K\n",
            "remote: Total 1629 (delta 182), reused 212 (delta 130), pack-reused 1318\u001b[K\n",
            "Receiving objects: 100% (1629/1629), 1.88 MiB | 2.24 MiB/s, done.\n",
            "Resolving deltas: 100% (1091/1091), done.\n"
          ]
        }
      ],
      "source": [
        "! git clone https://gitlab.synchrotron-soleil.fr/PA/collective-effects/mbtrack2.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd mbtrack2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aIwNdoXlVDus",
        "outputId": "6cfb7ede-b04e-46ad-856b-93b94deb22e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mbtrack2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define a Synchrotron object"
      ],
      "metadata": {
        "id": "-CHO7pYlsa76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from mbtrack2.tracking import Synchrotron, Electron\n",
        "from mbtrack2.utilities import Optics"
      ],
      "metadata": {
        "id": "ewMLyHDOslSD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h = 20 # Harmonic number of the accelerator.\n",
        "L = 100 # Ring circumference in [m].\n",
        "E0 = 1.5e9 # Nominal (total) energy of the ring in [eV].\n",
        "particle = Electron() # Particle considered.\n",
        "ac = 1e-3 # Momentum compaction factor.\n",
        "U0 = 200e3 # Energy loss per turn in [eV].\n",
        "tau = np.array([1e-3, 1e-3, 2e-3]) # Horizontal, vertical and longitudinal damping times in [s].\n",
        "tune = np.array([12.2, 15.3]) # Horizontal and vertical tunes.\n",
        "emit = np.array([10e-9, 10e-12]) # Horizontal and vertical equilibrium emittance in [m.rad].\n",
        "sigma_0 = 15e-12 # Natural bunch length in [s].\n",
        "sigma_delta = 1e-3 # Equilibrium energy spread.\n",
        "chro = [2.0, 3.0] # Horizontal and vertical (non-normalized) chromaticities."
      ],
      "metadata": {
        "id": "GH7V8wmmxH4i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "local_beta = np.array([3, 2]) # Beta function at the tracking location.\n",
        "local_alpha = np.array([0, 0]) # Alpha function at the tracking location.\n",
        "local_dispersion = np.array([0, 0, 0, 0]) # Dispersion function and its derivative at the tracking location.\n",
        "optics = Optics(local_beta=local_beta, local_alpha=local_alpha,\n",
        "                  local_dispersion=local_dispersion)"
      ],
      "metadata": {
        "id": "yY9Fh3JR1rQy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ring = Synchrotron(h=h, optics=optics, particle=particle, L=L, E0=E0, ac=ac,\n",
        "                   U0=U0, tau=tau, emit=emit, tune=tune,\n",
        "                   sigma_delta=sigma_delta, sigma_0=sigma_0, chro=chro)"
      ],
      "metadata": {
        "id": "WiPq6SDLvbjC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define a Beam object"
      ],
      "metadata": {
        "id": "xETy3HQMTql_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mbtrack2.tracking import Beam"
      ],
      "metadata": {
        "id": "Vcy-pUOYTwp4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filling_pattern = np.ones(ring.h)*0.025\n",
        "filling_pattern[5:7] = 0.05\n",
        "filling_pattern[10:12] = 0\n",
        "mybeam = Beam(ring)\n",
        "mybeam.init_beam(filling_pattern, mp_per_bunch=1e3)\n",
        "fig = mybeam.plot(\"bunch_current\")\n",
        "print(mybeam.current)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "T2Gu7M1XTzSc",
        "outputId": "cba53b56-05d1-477c-bbd2-36299129201d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5000000000000001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RFCavity class"
      ],
      "metadata": {
        "id": "IvqUpee5OXBF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `RFCavity` class is a very simple class to model RF cavities using a perfect cosine wave."
      ],
      "metadata": {
        "id": "EaCxRIgzOfZn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mbtrack2.tracking import RFCavity"
      ],
      "metadata": {
        "id": "_Un3rKy7PFWT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m = 1 # Harmonic number of the cavity\n",
        "Vc = 1e6 # Total cavity voltage in [V].\n",
        "theta = np.arccos(ring.U0/Vc) # Total cavity phase in [rad].\n",
        "RF = RFCavity(ring, m, Vc, theta)"
      ],
      "metadata": {
        "id": "W3yPFAXfPJUr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `track` method of the `RFCavity` class can be called for both `Bunch` and `Beam` elements and simply applies:\n",
        "\n",
        "$\\delta = \\delta +  \\frac{V_c}{E_0} \\cos(m \\omega_1 \\tau + \\theta)$"
      ],
      "metadata": {
        "id": "DrGl4KxsSX9n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(mybeam[0][\"delta\"][:5])\n",
        "RF.track(mybeam)\n",
        "print(mybeam[0][\"delta\"][:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnUmubbnUyGJ",
        "outputId": "d956f844-891a-4da6-ff46-ecb92e696273"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 6.30935783e-04  1.85770969e-05  1.04912102e-03 -1.62874235e-03\n",
            "  4.16169410e-04]\n",
            "[ 0.00076549  0.00014476  0.00118558 -0.00149344  0.00054759]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CavityResonator class\n",
        "The `CavityResonator` can be used to model:\n",
        "\n",
        "*   Active RF cavities\n",
        "*   Passive RF cavities\n",
        "*   Cavity HOMs\n",
        "\n",
        "The cavity physics is based on the phasor formalism developped in [1], details of the implementation and benchmark can be found in [2,3]."
      ],
      "metadata": {
        "id": "ffXq_OnXoFXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mbtrack2.tracking import CavityResonator"
      ],
      "metadata": {
        "id": "f2hjNnrzoYi8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us define a first `CavityResonator` element, for example to describe the fundamental RF cavity needed for our synchrotron."
      ],
      "metadata": {
        "id": "OtAksc5Bqf6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m = 1 # Harmonic number of the cavity\n",
        "Rs = 5e6 # Shunt impedance of the cavity in [Ohm], defined as 0.5*Vc*Vc/Pc.\n",
        "         # If Ncav = 1, used for the total shunt impedance.\n",
        "         # If Ncav > 1, used for the shunt impedance per cavity.\n",
        "Q = 35e3 # Quality factor of the cavity.\n",
        "QL = 5e3 # Loaded quality factor of the cavity.\n",
        "detune = -100e3 # Detuing of the cavity in [Hz], defined as (fr - m*ring.f1).\n",
        "Ncav = 4 # Number of cavities.\n",
        "MC = CavityResonator(ring, m, Rs, Q, QL, detune, Ncav=Ncav)"
      ],
      "metadata": {
        "id": "ebAW1aY_rCR0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this first input, usual quantities are computed:"
      ],
      "metadata": {
        "id": "EFCTmCKVtmsG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(MC.beta) # Coupling coefficient of the cavity.\n",
        "print(MC.fr) # Resonance frequency of the cavity in [Hz].\n",
        "print(MC.psi) # Tuning angle in [rad].\n",
        "print(MC.filling_time) # Cavity filling time in [s].\n",
        "print(MC.loss_factor) # Cavity loss factor in [V/C]."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_XRdEeutedr",
        "outputId": "51bb3ad7-865f-4354-c691-1692fea54e24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6.0\n",
            "59858491.6\n",
            "-1.5109593939048\n",
            "2.6588532192798413e-05\n",
            "107457712837.44363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The full list of parameter, attribute and method are listed in the class docstring and can be accessed by calling:\n",
        "\n",
        "```\n",
        "help(CavityResonator)\n",
        "```"
      ],
      "metadata": {
        "id": "OgBGRud4sNbS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As this cavity is intented to be an active one, the total voltage and phase must be declared:"
      ],
      "metadata": {
        "id": "7C-P4zzYuPBE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MC.Vc = 1e6 # Total cavity voltage in [V].\n",
        "MC.theta = np.arccos(ring.U0/MC.Vc) # Total cavity phase in [rad]."
      ],
      "metadata": {
        "id": "oCn2lTk6rUtr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then class methods can be used to set the optimal tuning point and computing the generator parameters for a given beam current $I_0$:"
      ],
      "metadata": {
        "id": "2fwKN1vDuOJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "I0 = 0.5 # Total multi-bunch current in [A].\n",
        "MC.set_optimal_detune(I0) # Set detuning to optimal conditions.\n",
        "print(MC.detune) # Cavity detuning in [Hz] at optimal condition.\n",
        "MC.set_generator(I0) # Set generator parameters (Pg, Vgr, theta_gr, Vg and theta_g) for a given current and set of parameters.\n",
        "print(MC.Pg) # Generator power in [W].\n",
        "print(MC.Vgr) # Generator voltage at resonance in [V].\n",
        "print(MC.theta_gr) # Generator phase at resonance in [rad].\n",
        "print(MC.Vg) # Generator voltage in [V].\n",
        "print(MC.theta_g) # Generator phase in [rad]."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqbtSTJ6s4Ge",
        "outputId": "4ca6d5ba-1e3c-410c-e45a-5aaf7387c3cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-16782.53176803142\n",
            "126041.66666666666\n",
            "1571428.5714285714\n",
            "1.369438406005134\n",
            "528626.2644652845\n",
            "0.14173199913728252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The phasor diagram showing the phasor addition of the beam voltage $V_b$ and generator voltage $V_g$ giving the total cavity voltage $V_c$ can be plotted:"
      ],
      "metadata": {
        "id": "DihPkQmzFymv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = MC.plot_phasor(I0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "Ypdhc4McFwXa",
        "outputId": "1530c97c-6e2d-49e8-b1c9-a444beda9477"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The power dissipated in the walls, transmitted to the beam and reflected can be computed:"
      ],
      "metadata": {
        "id": "3pvaDaiSIDgH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(MC.Pc) # Power dissipated in the cavity walls in [W].\n",
        "print(MC.Pb(I0)) # Return power transmitted to the beam in [W].\n",
        "print(MC.Pr(I0)) # Return power reflected back to the generator in [W]."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQNoawSrIDyA",
        "outputId": "fa936c10-5a9b-4f36-d616-deb4f6a8daee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25000.0\n",
            "99999.99999999997\n",
            "1041.666666666686\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The reflected power can be minimized if the cavity coupling is set to the optimal value using the `set_optimal_coupling` method:\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Z8D6mvrdJPYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MC.set_optimal_coupling(I0) # Set coupling to optimal value.\n",
        "print(MC.beta) # Coupling coefficient of the cavity.\n",
        "MC.set_optimal_detune(I0)\n",
        "MC.set_generator(I0)\n",
        "print(MC.Pr(I0)) # Return power reflected back to the generator in [W]."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCeGDP9eJOWT",
        "outputId": "97d6bb4e-dee9-43ef-eda5-b77088ac133f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.999999999999998\n",
            "-1.4551915228366852e-11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The DC Robinson stability (for single RF system) can be checked using the methods `is_DC_Robinson_stable` or `plot_DC_Robinson_stability`:"
      ],
      "metadata": {
        "id": "Ahr1lJIuLazM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MC.is_DC_Robinson_stable(I0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdZ8GmWxLrVi",
        "outputId": "207e8971-15d6-4b30-a1e5-7e22fa43e817"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = MC.plot_DC_Robinson_stability([-50e3,-5e3])\n",
        "plt.scatter(MC.detune,I0,c=\"g\")\n",
        "print(MC.is_DC_Robinson_stable(0.6))\n",
        "plt.scatter(MC.detune,0.6,c=\"r\")\n",
        "plt.legend([\"Threshold\",\"stable\",\"unstable\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "yOhFgjdFLrSi",
        "outputId": "b79d7e8e-c547-4b36-b913-3c920a876431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f86cf0196f0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tracking"
      ],
      "metadata": {
        "id": "bT_CpT4F_0tv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tracking using the `CavityResonator` class is based on the `cavity_phasor` which is the phasor sum of:\n",
        "+ the `generator_phasor` which is fixed by the generator voltage `Vg` and phase `theta_g` attributes:\n",
        "\n",
        "$$\\tilde{V}_{g} = V_{g} e^{j(m \\omega_{1} \\tau + \\theta_{g})}$$\n",
        "\n",
        "+ the `beam_phasor` which evolves dynamically at each call of the `track` method depending on the macro-particle positions and charges.\n",
        "\n",
        "The beam phasor $\\tilde{V}_b$ is built up by the successive passages of the different particles inside the cavity. Each bunch is binned longitudinally and when a bin of charged particle goes through the RF cavity, it induces a voltage\n",
        "\n",
        "$$\\tilde{V}_0 = -2 k_l q_{mp} N_{mp}$$\n",
        "\n",
        "Where $k_l$ is the cavity loss factor, $q_{mp}$ is the macroparticle charge, and $N_{mp}$ is the number of macropartiles in the bin.\n",
        "\n",
        "The voltage induced by the different particles crossing the cavity between time $t$ and time $t + \\Delta t$ is added to the voltage $\\tilde{V}_{b} (t)$ already present in the cavity at time $t$:\n",
        "\n",
        "$$\n",
        "\\tilde{V}_{b} (t + \\Delta t) = \\tilde{V}_{b} (t) e^{-\\frac{\\Delta t}{\\tau_l}}  e^{j\\delta_l \\Delta t} + \\tilde{V}_0 = \\tilde{V}_{b} (t) e^{-\\frac{\\Delta t}{\\tau_l}}  e^{j\\delta_l \\Delta t} -2 k_l q_M\n",
        "$$\n",
        "\n",
        "Where $\\tau_l$ is the cavity filling time and $\\delta_l$ is the phase slippage factor.\n",
        "\n",
        "As a particle see only half of its wake, the energy change felt by the particles in the bin is:\n",
        "\n",
        "$$\\delta = \\delta + \\frac{q}{E_0} \\left [ Re[\\tilde{V}_{g}] + Re[\\tilde{V}_{b}] - q_M k_l \\right]$$\n",
        "\n",
        "At the initialization of the `CavityResonator`, the `beam_phasor` attribute is set to zero:"
      ],
      "metadata": {
        "id": "KeXH-A3WWGyH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(MC.beam_phasor) # Beam phasor for tracking in [V]\n",
        "print(MC.cavity_phasor) # Cavity phasor for tracking in [V]\n",
        "print(MC.cavity_voltage) # Cavity voltage for tracking in [V]\n",
        "print(MC.cavity_phase) # Cavity phase for tracking in [rad]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwnJdmllhV1h",
        "outputId": "9ace34d2-aba9-43e6-bb66-dc6ad6d3395c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.+0.j]\n",
            "[485714.28571412+46656.94748183j]\n",
            "[487950.03647412]\n",
            "[0.0957646]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Depending on the cavity parameters, it can take a long time (especially for super conducting cavities) to fill the cavity and reach the equilibrium beam loading.\n",
        "\n",
        "To speed-up the cavity filling, one should use the `init_phasor` method before starting the tracking."
      ],
      "metadata": {
        "id": "Y5kCnNBjhjZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MC.init_phasor(mybeam)\n",
        "print(MC.beam_phasor) # Beam phasor for tracking in [V]\n",
        "print(MC.cavity_phasor) # Cavity phasor for tracking in [V]\n",
        "print(MC.cavity_voltage) # Cavity voltage for tracking in [V]\n",
        "print(MC.cavity_phase) # Cavity phase for tracking in [rad]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLyRKsWxhiWP",
        "outputId": "8272b139-b93d-4230-ac6c-93202f4a8968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-283883.46892953+933000.63156325j]\n",
            "[201830.81678459+979657.57904508j]\n",
            "[1000232.29841092]\n",
            "[1.36761734]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After the `beam_phasor` initialization, the cavity is filled and the `cavity_voltage` and `cavity_phase` attributes match the objective values `Vc` and `theta` which were set previously."
      ],
      "metadata": {
        "id": "AGi1mcHSlPul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(MC.Vc) # Total cavity voltage in [V]. Objective value used in calculations but not in tracking.\n",
        "print(MC.theta) # Total cavity phase in [rad]. Objective value used in calculations but not in tracking."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4IpK8W4mnPz",
        "outputId": "545a3716-612e-4abd-ae4f-404a10113743"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000000.0\n",
            "1.369438406004566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the `beam_phasor` initialization is done, just call the `track` method to update both the beam particle energy deviation $\\delta$ and the `beam_phasor`:"
      ],
      "metadata": {
        "id": "Skr9SAQRnA1b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(mybeam[0][\"delta\"][:5])\n",
        "print(MC.beam_phasor)\n",
        "MC.track(mybeam)\n",
        "print(mybeam[0][\"delta\"][:5])\n",
        "print(MC.beam_phasor)"
      ],
      "metadata": {
        "id": "uFca7rCTpVWU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "417891de-d6de-4d8b-a9ef-b0d235b83fe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.00076549  0.00014476  0.00118558 -0.00149344  0.00054759]\n",
            "[-283883.46892953+933000.63156325j]\n",
            "[ 0.00090082  0.00027107  0.0013232  -0.00135713  0.00067958]\n",
            "[-283883.56168717+932993.66729763j]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The value stored in `beam_phasor` corresponds to the last value of the beam phasor at t=0 (synchronous particle) of the first non empty bunch.\n",
        "\n",
        "The last value of the beam phasor at t=0 (synchronous particle) of each bunch in stored in the `cavity_phasor_record` attribute."
      ],
      "metadata": {
        "id": "-qa6lLrKojo6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MC.cavity_phasor_record"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDPJPsHHWY94",
        "outputId": "7aedb0a7-f06a-43cb-d793-a4017d8f76a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([200036.15208512+979657.38497534j, 200035.66739687+979655.84112697j,\n",
              "       200035.17760907+979653.80141957j, 200034.68246341+979652.31199819j,\n",
              "       200034.18718513+979650.29995926j, 198239.02581783+979647.78612658j,\n",
              "       196444.82944756+979648.55556845j, 196446.26204388+979652.3585456j ,\n",
              "       196447.70261996+979657.0909942j , 196449.15126411+979661.20675296j,\n",
              "       198245.27031205+979666.01342313j, 200040.42824809+979667.66040315j,\n",
              "       200039.95505622+979666.04782213j, 200039.48039337+979664.5148787j ,\n",
              "       200039.00555869+979662.21766757j, 200038.52380292+979659.4067355j ,\n",
              "       200038.03692367+979656.92114933j, 200037.54507254+979655.36000134j,\n",
              "       200037.05063212+979653.79945364j, 200036.55748377+979652.10889599j])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Passive cavity or HOM\n",
        "\n"
      ],
      "metadata": {
        "id": "VTTme9ogyDrF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A passive (harmonic) cavity or a cavity HOM can be defined in the same way as an active cavity:"
      ],
      "metadata": {
        "id": "I889WwKa3Aak"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m = 4 # Harmonic number of the cavity\n",
        "Rs = 4.5e9 # Shunt impedance of the cavity in [Ohm], defined as 0.5*Vc*Vc/Pc.\n",
        "           # If Ncav = 1, used for the total shunt impedance.\n",
        "           # If Ncav > 1, used for the shunt impedance per cavity.\n",
        "Q = 1e8 # Quality factor of the cavity.\n",
        "QL = 1e8 # Loaded quality factor of the cavity.\n",
        "detune = 25e3 # Detuing of the cavity in [Hz], defined as (fr - m*ring.f1).\n",
        "HC = CavityResonator(ring, m, Rs, Q, QL, detune)"
      ],
      "metadata": {
        "id": "_qCp81Zo1n-u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The induced voltage can be estimated:"
      ],
      "metadata": {
        "id": "ohllR8eG3Jj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HC.Vb(I0) # Beam voltage in [V]."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nWBy0b42FpF",
        "outputId": "1500a99d-b6ee-4619-d573-973a614a649a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "215861.81892505847"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If the `CavityResonator` is passive, the **generator voltage** `Vg` and **phase** `theta_g` should **explicitly be set to zero before tracking**:"
      ],
      "metadata": {
        "id": "CSITLGxv3Tf0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HC.Vg = 0\n",
        "HC.theta_g = 0"
      ],
      "metadata": {
        "id": "QiSFXSAZ2B4u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tracking using MPI"
      ],
      "metadata": {
        "id": "YqJtFL_OIvSq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "MPI can be used to speed up the tracking when using a `Beam` object by distributing the different `Bunch` objects in different cores.\n",
        "\n",
        "To be able to use this feature, **the python code must be run with as many core as there is of `Bunch` objects in the `Beam`.**"
      ],
      "metadata": {
        "id": "KmnutEIbrZgf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "MPI parallelisation can be enabled during ``Beam`` initialization by setting the ``mpi`` option to ``True``:"
      ],
      "metadata": {
        "id": "dQLd4_Rym5oz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "mybeam = Beam(ring)\n",
        "mybeam.init_beam(filling_pattern, mp_per_bunch=1e3, mpi=True)\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "Le0RaqmJIeaO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compared to the tracking without using MPI, the method `Beam.mpi.share_distributions` must be called before each call of `CavityResonator.track` to compute the bunch profiles and share it between the different cores using MPI."
      ],
      "metadata": {
        "id": "Ha14JRYIssAI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A typical tracking loop using MPI would then be:\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "MC.init_phasor(beam)\n",
        "HC.init_phasor(beam)\n",
        "\n",
        "for i in range(turns):\n",
        "    \n",
        "    long.track(beam) # Longitudinal map\n",
        "    rad.track(beam) # Synchrotron radiation element\n",
        "    beam.mpi.share_distributions(beam) # Using MPI, this line is needed\n",
        "    MC.track(beam) # CavityResonator element\n",
        "    HC.track(beam) # CavityResonator element\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "sUO8Nj4wzLCM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CavityMonitor"
      ],
      "metadata": {
        "id": "a_B5YgHFHaIA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `CavityMonitor` can be used to save data from a `CavityResonator` during the tracking.\n",
        "\n",
        "The follwing attributes are saved:\n",
        "\n",
        "\n",
        "*   Cavity and Beam phasor for at each bunch\n",
        "*   Cavity detuning and angle\n",
        "*   Generator voltage, phase and power\n",
        "*   Shunt impedance, loaded and unload quality factor\n",
        "\n"
      ],
      "metadata": {
        "id": "vXI4RF7thfdw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mbtrack2.tracking.monitors import CavityMonitor, plot_cavitydata"
      ],
      "metadata": {
        "id": "CHF_GzVC5KQD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Like the other monitors, the `CavityMonitor` must be initialized before the tracking:"
      ],
      "metadata": {
        "id": "pz6oiLwWKx59"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MCmon = CavityMonitor(\"MC\", ring, file_name=\"tracking_test\", save_every=1, buffer_size=10, total_size=100, mpi_mode=False)"
      ],
      "metadata": {
        "id": "yN07pCXd5G6q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `track` method of `CavityMonitor` takes the `Beam` object as first agrument and the saved `CavityResonator` as second agrument."
      ],
      "metadata": {
        "id": "QfvJPLzhLEfu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mbtrack2.tracking import LongitudinalMap, SynchrotronRadiation\n",
        "long = LongitudinalMap(ring)\n",
        "rad = SynchrotronRadiation(ring)\n",
        "\n",
        "for i in range(100):\n",
        "    long.track(mybeam)\n",
        "    rad.track(mybeam)\n",
        "    MC.track(mybeam)\n",
        "    MCmon.track(mybeam, MC)"
      ],
      "metadata": {
        "id": "tywlUGCoKguV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The saved data can be plotted using the `plot_cavitydata` function:"
      ],
      "metadata": {
        "id": "tYptR7efLVar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plot_cavitydata(\"tracking_test.hdf5\",\"MC\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "wL7jVkvMLVzL",
        "outputId": "9a8e8ff0-945c-4605-9343-9b85b1d465b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plot_cavitydata(\"tracking_test.hdf5\",\"MC\",plot_type=\"turn\",turn=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "fRGflKdSLuvk",
        "outputId": "dd824d08-3c06-414c-9fd2-54e7fbe2b2f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plot_cavitydata(\"tracking_test.hdf5\",\"MC\",plot_type=\"streak_amplitude\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "15fd1864-b200-4591-d42d-ee76e40ee8ba",
        "id": "srURo9xxMqqy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plot_cavitydata(\"tracking_test.hdf5\",\"MC\",plot_type=\"psi\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "5d60b6f7-9380-43d4-9e5f-e91c8e8ab2aa",
        "id": "vI20crgCMTkj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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77rkbOjgBAIDrW6WfMF5SUqKDBw+qqKhIHTp0UEBAgKtq81hceQIAwPNUyZWniRMnVrjtW2+9ZaZrAAAAj2AqPO3cubNC7SwWi1PFAAAAuDtT4SktLa2q6gAAAPAIN+y77QAAAJzh9K/trjb/yWKxqE6dOmrbtq0GDhyoBg0aOF0cAACAu3H613a9e/dWRkaGysrKdPPNN0uS9u/fL29vb0VERCgrK0sWi0WbN292eFfcjYBf2wEA4Hkq+v3t9G27gQMHKi4uTrm5uUpPT1d6erqOHz+ue+65R0OHDlVOTo569eqlCRMmOHsIAAAAt+P0laebbrpJKSkpl11V2rt3r/r27aucnBxlZGSob9++OnXqlEuK9RRceQIAwPNU+ZUnm82m/Pz8y9afPHlSBQUFkqTg4GD7S3cBAACuB5W6bTdy5EglJyfr+PHjOn78uJKTkzVq1CgNGjRIkrRt2za1b9/eVbUCAADUOKdv2xUVFWnChAn65z//qYsXL0qSfHx8NHz4cM2fP1/+/v6yWq2SpMjISFfV6xG4bQcAgOep6Pd3pd9tV1RUpEOHDkmSWrduzbvtRHgCAMATVcm77a4kICBAnTt3rmw3AAAAHqFS4WnDhg3asGGD8vPzVV5e7rDt/fffr1RhAAAA7sjp8DRr1izNnj1b0dHRatq0KS8DBgAANwSnw9OSJUu0fPlyDRs2zJX1AAAAuDWnH1VQUlKimJgYV9YCAADg9pwOT3/961/14YcfurIWAAAAt+f0bbsLFy5o6dKlWr9+vTp37qxatWo5bH/rrbcqXRwAAIC7cTo87d692/7wyz179jhsY/I4AAC4XjkdntLS0lxZBwAAgEdwes4TAADAjajSTxjft2+fsrOzVVJS4rB+wIABle0aAADA7Tgdng4dOqTBgwfrxx9/lMVi0aVX5F2a71RWVuaaCgEAANyI07ftxo0bp1atWik/P19+fn7au3evNm3apOjoaP373/92YYkAAADuw+krT1u2bFFqaqoaNmwoLy8veXl56c4771RSUpKeffZZ7dy505V13vAMw9BvpVzNAwBAkurW8q6xX/c7HZ7KyspUr149SVLDhg2Vm5urm2++WS1atFBWVpbLCsTvfistU4fp/1vTZQAA4Bb2ze4nv9qVnrrtFKePeuutt2rXrl1q1aqVunfvrtdee021a9fW0qVL1bp1a1fWCAAA4DacDk8vvfSSzp07J0maPXu2HnjgAd11110KCQnRJ5984rIC8bu6tby1b3a/mi4DAAC3ULeWd40d22Jc+pmcC5w+fVr169e/4Z8wXlBQoKCgINlsNgUGBtZ0OQAAoAIq+v3t0puFDRo0cGV3AAAAbocnjAMAAJhAeAIAADCB8AQAAGAC4QkAAMAEpyeMT5w48YrrLRaL6tSpo7Zt22rgwIFMIgcAANcVpx9V0Lt3b2VkZKisrEw333yzJGn//v3y9vZWRESEsrKyZLFYtHnzZnXo0MGlRbs7HlUAAIDnqej3t9O37QYOHKi4uDjl5uYqPT1d6enpOn78uO655x4NHTpUOTk56tWrlyZMmODsIQAAANyO01eebrrpJqWkpFx2VWnv3r3q27evcnJylJGRob59++rUqVMuKdZTcOUJAADPU+VXnmw2m/Lz8y9bf/LkSRUUFEiSgoODVVJS4uwhAAAA3E6lbtuNHDlSycnJOn78uI4fP67k5GSNGjVKgwYNkiRt27ZN7du3d1WtDubMmaOYmBj5+fkpODj4T9uXlpZq8uTJ6tSpk/z9/RUWFqbHH39cubm5Du3279+vgQMHqmHDhgoMDNSdd96ptLS0KjkHAADgeZwOT++884769OmjRx99VC1atFCLFi306KOPqk+fPlqyZIkkKSIiQu+++67Liv2jkpISxcfHa8yYMRVqf/78eWVkZGjatGnKyMjQqlWrlJWVpQEDBji0e+CBB3Tx4kWlpqYqPT1dXbp00QMPPKC8vLyqOA0AAOBhKv1i4KKiIh06dEiS1Lp1awUEBLiksIpavny5xo8fr7Nnz5red/v27erWrZuOHj2q5s2b69SpUwoNDdWmTZt01113SZIKCwsVGBiolJQUxcXFVahf5jwBAOB5qu3FwAEBAercuXNlu6kRNptNFovFftsvJCREN998s/75z3/qtttuk6+vr9555x01atRIXbt2vWo/xcXFKi4utn++NOcLAABcfyoVnjZs2KANGzYoPz9f5eXlDtvef//9ShVW1S5cuKDJkydr6NCh9nRpsVi0fv16DRo0SPXq1ZOXl5caNWqkdevWqX79+lftKykpSbNmzaqu0gEAQA1yes7TrFmz1LdvX23YsEGnTp3SmTNnHBZnTJkyRRaL5ZpLZmamsyXblZaWasiQITIMQ4sXL7avNwxDY8eOVaNGjfTtt99q27ZtGjRokB588EGdOHHiqv0lJibKZrPZl2PHjlW6RgAA4J6cnvPUtGlTvfbaaxo2bJjLijl58qR+/fXXa7Zp3bq1ateubf9sds7TpeB06NAhpaamKiQkxL5tw4YN6tu3r86cOeNwr7Ndu3YaNWqUpkyZUqFjMOcJAADPU+VznkpKShQTE+Ps7lcUGhqq0NBQl/b5R5eC04EDB5SWluYQnKTff5EnSV5ejhfkvLy8LrstCQAAbkxO37b761//qg8//NCVtZiSnZ0tq9Wq7OxslZWVyWq1ymq1qqioyN4mIiJCycnJkn4PTo888oh27NihlStXqqysTHl5ecrLy7M/yLNHjx6qX7++hg8frl27dmn//v16/vnndfjwYd1///01cp4AAMC9OH3l6cKFC1q6dKnWr1+vzp07q1atWg7b33rrrUoXdy3Tp0/XihUr7J+joqIkSWlpaYqNjZUkZWVlyWazSZJycnK0evVqSVJkZKRDX5f2adiwodatW6cXX3xRf/nLX1RaWqqOHTvqyy+/VJcuXar0fAAAgGdwes5T7969r7n9Rn4qN3OeAADwPFU+5+lGDkcAAODGZSo8TZw4US+//LL8/f01ceLEq7azWCx68803K10cAACAuzEVnnbu3KnS0lL7v6/GYrFUrioAAAA3Vel32+FyzHkCAMDzVPT72+lHFQAAANyIbth32wEAADjD6fA0a9YszZ49W9HR0WratCnznAAAwA3B6fC0ZMkSLV++3KXvtgMAAHB3Ts95qop32wEAALg7j323HQAAQE3w2HfbAQAA1ASnw9Pu3bvtL9jds2ePwzYmjwMAgOsV77YDAAAwgYdkAgAAmOD0lafZs2dfc/v06dOd7RoAAMBtOR2ekpOTHT6Xlpbq8OHD8vHxUZs2bQhPAADguuR0eNq5c+dl6woKCjRixAgNHjy4UkUBAAC4K5fOeQoMDNSsWbM0bdo0V3YLAADgNlw+Ydxms8lms7m6WwAAALfg9G27BQsWOHw2DEMnTpzQv/71L917772VLgwAAMAdOR2e5s+f7/DZy8tLoaGhGj58uBITEytdGAAAgDsyHZ5mz56t5557TocPH66KegAAANya6TlPs2bNUlFRUVXUAgAA4PZMhyfDMKqiDgAAAI/g1K/tePEvAAC4UTk1Ybx9+/Z/GqBOnz7tVEEAAADuzKnwNGvWLAUFBbm6FgAAALfnVHh69NFH1ahRI1fXAgAA4PZMz3livhMAALiR8Ws7AAAAE0zftisvL6+KOgAAADyCy18MDAAAcD0jPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwwWPD05w5cxQTEyM/Pz8FBwdXaJ+ZM2cqIiJC/v7+ql+/vuLi4rR161aHNqdPn1ZCQoICAwMVHBysUaNGqaioqArOAAAAeCKPDU8lJSWKj4/XmDFjKrxP+/bt9fbbb+vHH3/U5s2b1bJlS/Xt21cnT560t0lISNDevXuVkpKiNWvWaNOmTXryySer4hQAAIAHshiGYdR0EZWxfPlyjR8/XmfPnjW9b0FBgYKCgrR+/Xr16dNHP/30kzp06KDt27crOjpakrRu3Trdd999On78uMLCwq7YT3FxsYqLix36DQ8Pl81mU2BgoFPnBQAAqtelXPBn398ee+WpskpKSrR06VIFBQWpS5cukqQtW7YoODjYHpwkKS4uTl5eXpfd3vujpKQkBQUF2Zfw8PAqrx8AANSMGy48rVmzRgEBAapTp47mz5+vlJQUNWzYUJKUl5enRo0aObT38fFRgwYNlJeXd9U+ExMTZbPZ7MuxY8eq9BwAAEDNcavwNGXKFFkslmsumZmZlTpG7969ZbVa9f3336t///4aMmSI8vPzK9Wnr6+vAgMDHRYAAHB98qnpAv5o0qRJGjFixDXbtG7dulLH8Pf3V9u2bdW2bVvdcccdateund577z0lJiaqSZMmlwWpixcv6vTp02rSpEmljgsAAK4PbhWeQkNDFRoaWq3HLC8vt0/27tGjh86ePav09HR17dpVkpSamqry8nJ17969WusCAADuya1u25mRnZ0tq9Wq7OxslZWVyWq1ymq1OjyTKSIiQsnJyZKkc+fOaerUqfrhhx909OhRpaena+TIkcrJyVF8fLwk6ZZbblH//v01evRobdu2Td99952eeeYZPfroo1f9pR0AALixuNWVJzOmT5+uFStW2D9HRUVJktLS0hQbGytJysrKks1mkyR5e3srMzNTK1as0KlTpxQSEqLbb79d3377rTp27GjvZ+XKlXrmmWfUp08feXl56eGHH9aCBQuq78QAAIBb8/jnPLmjij4nAgAAuA+e8wQAAFAFCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACZ4bHiaM2eOYmJi5Ofnp+Dg4ArtM3PmTEVERMjf31/169dXXFyctm7dat9+5MgRjRo1Sq1atVLdunXVpk0bzZgxQyUlJVV0FgAAwNN4bHgqKSlRfHy8xowZU+F92rdvr7fffls//vijNm/erJYtW6pv3746efKkJCkzM1Pl5eV65513tHfvXs2fP19LlizR1KlTq+o0AACAh7EYhmHUdBGVsXz5co0fP15nz541vW9BQYGCgoK0fv169enT54ptXn/9dS1evFiHDh26aj/FxcUqLi526Dc8PFw2m02BgYGm6wIAANXvUi74s+9vj73yVFklJSVaunSpgoKC1KVLl6u2s9lsatCgwTX7SkpKUlBQkH0JDw93dbkAAMBN3HDhac2aNQoICFCdOnU0f/58paSkqGHDhldse/DgQS1cuFBPPfXUNftMTEyUzWazL8eOHauK0gEAgBtwq/A0ZcoUWSyWay6ZmZmVOkbv3r1ltVr1/fffq3///hoyZIjy8/Mva5eTk6P+/fsrPj5eo0ePvmafvr6+CgwMdFgAAMD1yaemC/ijSZMmacSIEdds07p160odw9/fX23btlXbtm11xx13qF27dnrvvfeUmJhob5Obm6vevXsrJiZGS5curdTxAADA9cWtwlNoaKhCQ0Or9Zjl5eUOk71zcnLUu3dvde3aVcuWLZOXl1tdnAMAADXMY5NBdna2rFarsrOzVVZWJqvVKqvVqqKiInubiIgIJScnS5LOnTunqVOn6ocfftDRo0eVnp6ukSNHKicnR/Hx8ZJ+D06xsbFq3ry53njjDZ08eVJ5eXnKy8urkXMEAADux62uPJkxffp0rVixwv45KipKkpSWlqbY2FhJUlZWlmw2myTJ29tbmZmZWrFihU6dOqWQkBDdfvvt+vbbb9WxY0dJUkpKig4ePKiDBw+qWbNmDsfz8Cc6AAAAF/H45zy5o4o+JwIAALgPnvMEAABQBQhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATPDY8zZkzRzExMfLz81NwcHCF9pk5c6YiIiLk7++v+vXrKy4uTlu3br1i2+LiYkVGRspischqtbqucAAA4NE8NjyVlJQoPj5eY8aMqfA+7du319tvv60ff/xRmzdvVsuWLdW3b1+dPHnysrYvvPCCwsLCXFkyAAC4DlgMwzBquojKWL58ucaPH6+zZ8+a3regoEBBQUFav369+vTpY1+/du1aTZw4UZ9//rk6duyonTt3KjIy0nS/NptNgYGBpusCAADVr6Lf3z7VWJNbKSkp0dKlSxUUFKQuXbrY1//yyy8aPXq0vvjiC/n5+VWor+LiYhUXF9s/FxQUuLxeAADgHjz2tp2z1qxZo4CAANWpU0fz589XSkqKGjZsKEkyDEMjRozQ008/rejo6Ar3mZSUpKCgIPsSHh5eVeUDAIAa5lbhacqUKbJYLNdcMjMzK3WM3r17y2q16vvvv1f//v01ZMgQ5efnS5IWLlyowsJCJSYmmuozMTFRNpvNvhw7dqxSNQIAAPflVrftJk2apBEjRlyzTevWrSt1DH9/f7Vt21Zt27bVHXfcoXbt2um9995TYmKiUlNTtWXLFvn6+jrsEx0drYSEBK1YseKKffr6+l62DwAAuD65VXgKDQ1VaGhotR6zvLzcPl9pwYIFeuWVV+zbcnNz1a9fP33yySfq3r17tdYFAADck1uFJzOys7N1+vRpZWdnq6yszP4sprZt2yogIECSFBERoaSkJA0ePFjnzp3TnDlzNGDAADVt2lSnTp3SokWLlJOTo/j4eElS8+bNHY5xqZ82bdqoWbNm1XdyAADAbXlseJo+fbrDbbSoqChJUlpammJjYyVJWVlZstlskiRvb29lZmZqxYoVOnXqlEJCQnT77bfr22+/VceOHau9fgAA4Jk8/jlP7ojnPAEA4Hkq+v3tVr+2AwAAcHeEJwAAABM8ds6TO7t0J5QnjQMA4DkufW//2YwmwlMVKCwslCSeNA4AgAcqLCxUUFDQVbczYbwKlJeXKzc3V/Xq1ZPFYnFZvwUFBQoPD9exY8eYiF7FGOvqxXhXH8a6+jDW1cdVY20YhgoLCxUWFiYvr6vPbOLKUxXw8vKq0udCBQYG8h+xmjDW1Yvxrj6MdfVhrKuPK8b6WlecLmHCOAAAgAmEJwAAABMITx7E19dXM2bM4CXE1YCxrl6Md/VhrKsPY119qnusmTAOAABgAleeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhyYMsWrRILVu2VJ06ddS9e3dt27atpkvyeElJSbr99ttVr149NWrUSIMGDVJWVpZDmwsXLmjs2LEKCQlRQECAHn74Yf3yyy81VPH1Ye7cubJYLBo/frx9HePsWjk5OXrssccUEhKiunXrqlOnTtqxY4d9u2EYmj59upo2baq6desqLi5OBw4cqMGKPVNZWZmmTZumVq1aqW7dumrTpo1efvllh3ejMdbO2bRpkx588EGFhYXJYrHoiy++cNhekXE9ffq0EhISFBgYqODgYI0aNUpFRUWVro3w5CE++eQTTZw4UTNmzFBGRoa6dOmifv36KT8/v6ZL82gbN27U2LFj9cMPPyglJUWlpaXq27evzp07Z28zYcIEffXVV/rss8+0ceNG5ebm6qGHHqrBqj3b9u3b9c4776hz584O6xln1zlz5ox69uypWrVqae3atdq3b5/efPNN1a9f397mtdde04IFC7RkyRJt3bpV/v7+6tevny5cuFCDlXueefPmafHixXr77bf1008/ad68eXrttde0cOFCexvG2jnnzp1Tly5dtGjRoitur8i4JiQkaO/evUpJSdGaNWu0adMmPfnkk5UvzoBH6NatmzF27Fj757KyMiMsLMxISkqqwaquP/n5+YYkY+PGjYZhGMbZs2eNWrVqGZ999pm9zU8//WRIMrZs2VJTZXqswsJCo127dkZKSopx9913G+PGjTMMg3F2tcmTJxt33nnnVbeXl5cbTZo0MV5//XX7urNnzxq+vr7GRx99VB0lXjfuv/9+Y+TIkQ7rHnroISMhIcEwDMbaVSQZycnJ9s8VGdd9+/YZkozt27fb26xdu9awWCxGTk5OperhypMHKCkpUXp6uuLi4uzrvLy8FBcXpy1bttRgZdcfm80mSWrQoIEkKT09XaWlpQ5jHxERoebNmzP2Thg7dqzuv/9+h/GUGGdXW716taKjoxUfH69GjRopKipK//3f/23ffvjwYeXl5TmMd1BQkLp37854mxQTE6MNGzZo//79kqRdu3Zp8+bNuvfeeyUx1lWlIuO6ZcsWBQcHKzo62t4mLi5OXl5e2rp1a6WOz4uBPcCpU6dUVlamxo0bO6xv3LixMjMza6iq6095ebnGjx+vnj176tZbb5Uk5eXlqXbt2goODnZo27hxY+Xl5dVAlZ7r448/VkZGhrZv337ZNsbZtQ4dOqTFixdr4sSJmjp1qrZv365nn31WtWvX1vDhw+1jeqW/KYy3OVOmTFFBQYEiIiLk7e2tsrIyzZkzRwkJCZLEWFeRioxrXl6eGjVq5LDdx8dHDRo0qPTYE56A/2fs2LHas2ePNm/eXNOlXHeOHTumcePGKSUlRXXq1Knpcq575eXlio6O1quvvipJioqK0p49e7RkyRINHz68hqu7vnz66adauXKlPvzwQ3Xs2FFWq1Xjx49XWFgYY30d47adB2jYsKG8vb0v++XRL7/8oiZNmtRQVdeXZ555RmvWrFFaWpqaNWtmX9+kSROVlJTo7NmzDu0Ze3PS09OVn5+v2267TT4+PvLx8dHGjRu1YMEC+fj4qHHjxoyzCzVt2lQdOnRwWHfLLbcoOztbkuxjyt+Uynv++ec1ZcoUPfroo+rUqZOGDRumCRMmKCkpSRJjXVUqMq5NmjS57EdVFy9e1OnTpys99oQnD1C7dm117dpVGzZssK8rLy/Xhg0b1KNHjxqszPMZhqFnnnlGycnJSk1NVatWrRy2d+3aVbVq1XIY+6ysLGVnZzP2JvTp00c//vijrFarfYmOjlZCQoL934yz6/Ts2fOyR27s379fLVq0kCS1atVKTZo0cRjvgoICbd26lfE26fz58/Lycvwq9fb2Vnl5uSTGuqpUZFx79Oihs2fPKj093d4mNTVV5eXl6t69e+UKqNR0c1Sbjz/+2PD19TWWL19u7Nu3z3jyySeN4OBgIy8vr6ZL82hjxowxgoKCjH//+9/GiRMn7Mv58+ftbZ5++mmjefPmRmpqqrFjxw6jR48eRo8ePWqw6uvDH39tZxiMsytt27bN8PHxMebMmWMcOHDAWLlypeHn52d88MEH9jZz5841goODjS+//NLYvXu3MXDgQKNVq1bGb7/9VoOVe57hw4cbN910k7FmzRrj8OHDxqpVq4yGDRsaL7zwgr0NY+2cwsJCY+fOncbOnTsNScZbb71l7Ny50zh69KhhGBUb1/79+xtRUVHG1q1bjc2bNxvt2rUzhg4dWunaCE8eZOHChUbz5s2N2rVrG926dTN++OGHmi7J40m64rJs2TJ7m99++83429/+ZtSvX9/w8/MzBg8ebJw4caLmir5O/N/wxDi71ldffWXceuuthq+vrxEREWEsXbrUYXt5ebkxbdo0o3Hjxoavr6/Rp08fIysrq4aq9VwFBQXGuHHjjObNmxt16tQxWrdubbz44otGcXGxvQ1j7Zy0tLQr/n0ePny4YRgVG9dff/3VGDp0qBEQEGAEBgYaTzzxhFFYWFjp2iyG8YfHoAIAAOCamPMEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBOC6c+TIEVksFlmt1pouxS4zM1N33HGH6tSpo8jIyJouB0AlEJ4AuNyIESNksVg0d+5ch/VffPGFLBZLDVVVs2bMmCF/f39lZWU5vMz0j2JjYzV+/PjqLQyAaYQnAFWiTp06mjdvns6cOVPTpbhMSUmJ0/v+/PPPuvPOO9WiRQuFhIS4sKrLVaZOAH+O8ASgSsTFxalJkyZKSkq6apuZM2dedgvr73//u1q2bGn/PGLECA0aNEivvvqqGjdurODgYM2ePVsXL17U888/rwYNGqhZs2ZatmzZZf1nZmYqJiZGderU0a233qqNGzc6bN+zZ4/uvfdeBQQEqHHjxho2bJhOnTpl3x4bG6tnnnlG48ePV8OGDdWvX78rnkd5eblmz56tZs2aydfXV5GRkVq3bp19u8ViUXp6umbPni2LxaKZM2de1seIESO0ceNG/eMf/5DFYpHFYtGRI0e0fPlyBQcHO7T9v1fwLo3ju+++q1atWqlOnTr247777rsaPHiw/Pz81K5dO61evdq+35kzZ5SQkKDQ0FDVrVtX7dq1u+I4AnBEeAJQJby9vfXqq69q4cKFOn78eKX6Sk1NVW5urjZt2qS33npLM2bM0AMPPKD69etr69atevrpp/XUU09ddpznn39ekyZN0s6dO9WjRw89+OCD+vXXXyVJZ8+e1V/+8hdFRUVpx44dWrdunX755RcNGTLEoY8VK1aodu3a+u6777RkyZIr1vePf/xDb775pt544w3t3r1b/fr104ABA3TgwAFJ0okTJ9SxY0dNmjRJJ06c0HPPPXfFPnr06KHRo0frxIkTOnHihMLDwys8RgcPHtTnn3+uVatWOcz1mjVrloYMGaLdu3frvvvuU0JCgk6fPi1JmjZtmvbt26e1a9fqp59+0uLFi9WwYcMKHxO4YRkA4GLDhw83Bg4caBiGYdxxxx3GyJEjDcMwjOTkZOOPf3ZmzJhhdOnSxWHf+fPnGy1atHDoq0WLFkZZWZl93c0332zcdddd9s8XL140/P39jY8++sgwDMM4fPiwIcmYO3euvU1paanRrFkzY968eYZhGMbLL79s9O3b1+HYx44dMyQZWVlZhmEYxt13321ERUX96fmGhYUZc+bMcVh3++23G3/729/sn7t06WLMmDHjmv3cfffdxrhx4xzWLVu2zAgKCnJYd6VxrFWrlpGfn+/QTpLx0ksv2T8XFRUZkoy1a9cahmEYDz74oPHEE0/82ekB+D+48gSgSs2bN08rVqzQTz/95HQfHTt2lJfX///nqnHjxurUqZP9s7e3t0JCQpSfn++wX48ePez/9vHxUXR0tL2OXbt2KS0tTQEBAfYlIiJC0u/zky7p2rXrNWsrKChQbm6uevbs6bC+Z8+elTpns1q0aKHQ0NDL1nfu3Nn+b39/fwUGBtrHacyYMfr4448VGRmpF154Qd9//3211Qt4MsITgCrVq1cv9evXT4mJiZdt8/LykmEYDutKS0sva1erVi2HzxaL5YrrysvLK1xXUVGRHnzwQVmtVoflwIED6tWrl72dv79/hfusChUdo6vVea1xuvfee3X06FFNmDBBubm56tOnzxVvKQJwRHgCUOXmzp2rr776Slu2bHFYHxoaqry8PIdw4MpnM/3www/2f1+8eFHp6em65ZZbJEm33Xab9u7dq5YtW6pt27YOi5nAFBgYqLCwMH333XcO67/77jt16NDBVL21a9dWWVmZw7rQ0FAVFhbq3Llz9nWuHKPQ0FANHz5cH3zwgf7+979r6dKlLusbuF4RngBUuU6dOikhIUELFixwWB8bG6uTJ0/qtdde088//6xFixZp7dq1LjvuokWLlJycrMzMTI0dO1ZnzpzRyJEjJUljx47V6dOnNXToUG3fvl0///yz/vd//1dPPPHEZQHmzzz//POaN2+ePvnkE2VlZWnKlCmyWq0aN26cqX5atmyprVu36siRIzp16pTKy8vVvXt3+fn5aerUqfr555/14Ycfavny5ab6vZrp06fryy+/1MGDB7V3716tWbPGHi4BXB3hCUC1mD179mW31W655Rb913/9lxYtWqQuXbpo27ZtLr1tNHfuXM2dO1ddunTR5s2btXr1avuvyS5dLSorK1Pfvn3VqVMnjR8/XsHBwQ7zqyri2Wef1cSJEzVp0iR16tRJ69at0+rVq9WuXTtT/Tz33HPy9vZWhw4dFBoaquzsbDVo0EAffPCBvv76a3Xq1EkfffTRFR914IzatWsrMTFRnTt3Vq9eveTt7a2PP/7YJX0D1zOL8X9vpgMAAOCquPIEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAn/H7TYCmAl1efkAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BeamLoadingEquilibrium"
      ],
      "metadata": {
        "id": "FCcSoOCRxtGo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `BeamLoadingEquilibrium` class is used to compute beam equilibrium profile for a given storage ring and a list of RF cavities of any harmonic.\n",
        "\n",
        "The class assumes an uniform filling of the storage ring and is based on [3,4]."
      ],
      "metadata": {
        "id": "994_v-dON3Kn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mbtrack2.utilities import BeamLoadingEquilibrium"
      ],
      "metadata": {
        "id": "aYfAxnr3Pe9V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For example, we can define a $4^{th}$ harmonic passive cavity and compute the resulting bunch profile from the addition of this new cavity with the active fundamental cavity which was defined earlier from tracking.\n",
        "\n",
        "To do that, we reuse the same `CavityResonator` class which was used for tracking:"
      ],
      "metadata": {
        "id": "U0vGsmpj0u_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m = 4\n",
        "Rs = 90e8\n",
        "Q = 1e8\n",
        "QL = 1e8\n",
        "detune = 60e3\n",
        "HC = CavityResonator(ring, m, Rs, Q, QL,detune)\n",
        "HC.Vg = 0\n",
        "HC.theta_g = 0"
      ],
      "metadata": {
        "id": "hBQxYlQvN2f5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then it is possible to define a `BeamLoadingEquilibrium` object to solve using the `beam_equilibrium` method for different harmonic cavity detuning:"
      ],
      "metadata": {
        "id": "VfWbDnTp2Jwn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "values = np.array([100e3, 60e3, 45e3, 42e3, 41e3])\n",
        "leg = [\"Detune = \" + str(val*1e-3) + \" kHz\" for val in values]\n",
        "for det in values:\n",
        "  HC.detune = det\n",
        "  V = BeamLoadingEquilibrium(ring, [MC,HC], I0, auto_set_MC_theta=False)\n",
        "  sol = V.beam_equilibrium(plot=False)\n",
        "  fig = V.plot_rho(z1=-0.2, z2=0.2)\n",
        "plt.legend(leg)"
      ],
      "metadata": {
        "id": "xAlbfHmoPk4G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "d6b113e7-40b3-4dea-a7b4-d04b3f1d50fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The initial center of mass offset is -0.17202964722127753 ps\n",
            "The final center of mass offset is 0.7603234535161224 ps\n",
            "The algorithm has converged: True\n",
            "The initial center of mass offset is -0.7480023322567962 ps\n",
            "The final center of mass offset is 1.507668642882292 ps\n",
            "The algorithm has converged: True\n",
            "The initial center of mass offset is -21.12365937281321 ps\n",
            "The final center of mass offset is 6.987443148551981 ps\n",
            "The algorithm has converged: True\n",
            "The initial center of mass offset is -178.42158504142026 ps\n",
            "The final center of mass offset is 13.77731893548217 ps\n",
            "The algorithm has converged: True\n",
            "The initial center of mass offset is -325.2644871748486 ps\n",
            "The final center of mass offset is 17.165812866907483 ps\n",
            "The algorithm has converged: True\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f86ceb5e8c0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The equilibrium bunch profile computed analytically using `BeamLoadingEquilibrium` can be compared to the tracking results:"
      ],
      "metadata": {
        "id": "cjL-np8paYEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HC.detune = 42e3\n",
        "\n",
        "# Tracking using a uniform beam filling pattern\n",
        "filling_pattern = np.ones(ring.h)*I0/ring.h\n",
        "mybeam = Beam(ring)\n",
        "mybeam.init_beam(filling_pattern, mp_per_bunch=1e4, track_alive=False)\n",
        "\n",
        "for i in range(2000):\n",
        "    if i%100 == 0:\n",
        "      print(i)\n",
        "    long.track(mybeam)\n",
        "    rad.track(mybeam)\n",
        "    MC.track(mybeam)\n",
        "    HC.track(mybeam)\n",
        "\n",
        "\n",
        "# Plot the equilibrium bunch profile\n",
        "V = BeamLoadingEquilibrium(ring, [MC,HC], I0, auto_set_MC_theta=False)\n",
        "sol = V.beam_equilibrium(plot=False)\n",
        "z0 = np.linspace(-0.2, 0.2, 1000)\n",
        "plt.plot(z0, V.rho(z0)/np.max(V.rho(z0)))\n",
        "plt.xlabel(\"z [m]\")\n",
        "plt.title(\"Equilibrium bunch profile\")\n",
        "\n",
        "# Plot the bunch profile of bunch 0 from tracking\n",
        "bins, sorted_index, profile, center = mybeam[0].binning(\"tau\", 75)\n",
        "plt.plot(center*3e8, profile/max(profile))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        },
        "id": "xvg8zezzV4f_",
        "outputId": "85f9d757-1e1a-4ec0-83c3-5785a8521721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "100\n",
            "200\n",
            "300\n",
            "400\n",
            "500\n",
            "600\n",
            "700\n",
            "800\n",
            "900\n",
            "1000\n",
            "1100\n",
            "1200\n",
            "1300\n",
            "1400\n",
            "1500\n",
            "1600\n",
            "1700\n",
            "1800\n",
            "1900\n",
            "The initial center of mass offset is -178.42158504142026 ps\n",
            "The final center of mass offset is 13.77731893548217 ps\n",
            "The algorithm has converged: True\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f86cea53460>]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}