{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "262a5ec8-2553-4237-ab62-319b6ca22089",
   "metadata": {},
   "source": [
    "# Example-51: Advance (Optics correction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9adad6f6-23af-436b-8788-776b811abb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example model response matrices of normal and chromatic phase advances are used for correction\n",
    "# ML style optimization is also performed for optics correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "742d1651-a94e-4b29-b003-9b8fafdb3bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "import torch\n",
    "from torch import Tensor\n",
    "from torch.utils.data import TensorDataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "\n",
    "from model.library.line import Line\n",
    "\n",
    "from model.command.util import select\n",
    "\n",
    "from model.command.external import load_sdds\n",
    "from model.command.external import load_lattice\n",
    "\n",
    "from model.command.build import build\n",
    "\n",
    "from model.command.wrapper import group\n",
    "from model.command.wrapper import forward\n",
    "from model.command.wrapper import inverse\n",
    "from model.command.wrapper import normalize\n",
    "from model.command.wrapper import Wrapper\n",
    "\n",
    "from model.command.tune import tune\n",
    "from model.command.twiss import twiss\n",
    "from model.command.twiss import chromatic_twiss\n",
    "from model.command.advance import advance\n",
    "from model.command.advance import chromatic_advance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3e1eab6-70fc-4952-9c07-6a531bafa099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load ELEGANT twiss\n",
    "\n",
    "path = Path('ic.twiss')\n",
    "parameters, columns = load_sdds(path)\n",
    "\n",
    "nu_qx:Tensor = torch.tensor(parameters['nux'] % 1, dtype=torch.float64)\n",
    "nu_qy:Tensor = torch.tensor(parameters['nuy'] % 1, dtype=torch.float64)\n",
    "\n",
    "# Set twiss parameters at BPMs\n",
    "\n",
    "kinds = select(columns, 'ElementType', keep=False)\n",
    "\n",
    "a_qx = select(columns, 'alphax', keep=False)\n",
    "b_qx = select(columns, 'betax' , keep=False)\n",
    "a_qy = select(columns, 'alphay', keep=False)\n",
    "b_qy = select(columns, 'betay' , keep=False)\n",
    "\n",
    "a_qx:Tensor = torch.tensor([value for (key, value), kind in zip(a_qx.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n",
    "b_qx:Tensor = torch.tensor([value for (key, value), kind in zip(b_qx.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n",
    "a_qy:Tensor = torch.tensor([value for (key, value), kind in zip(a_qy.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n",
    "b_qy:Tensor = torch.tensor([value for (key, value), kind in zip(b_qy.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n",
    "\n",
    "positions = select(columns, 's', keep=False).items()\n",
    "positions = [value for (key, value), kind in zip(positions, kinds.values()) if kind == 'MONI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b55f6bfd-9e4c-4078-af50-b7397981babb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build and setup lattice\n",
    "\n",
    "# Load ELEGANT table\n",
    "\n",
    "path = Path('ic.lte')\n",
    "data = load_lattice(path)\n",
    "\n",
    "# Build ELEGANT table\n",
    "\n",
    "ring:Line = build('RING', 'ELEGANT', data)\n",
    "ring.flatten()\n",
    "\n",
    "# Merge drifts\n",
    "\n",
    "ring.merge()\n",
    "\n",
    "# Split BPMs\n",
    "\n",
    "ring.split((None, ['BPM'], None, None))\n",
    "\n",
    "# Roll lattice start\n",
    "\n",
    "ring.roll(1)\n",
    "\n",
    "# Set linear dipoles\n",
    "\n",
    "for element in ring:\n",
    "    if element.__class__.__name__ == 'Dipole':\n",
    "        element.linear = True\n",
    "\n",
    "# Split lattice into lines by BPMs\n",
    "\n",
    "ring.splice()\n",
    "\n",
    "# Set number of elements of different kinds\n",
    "\n",
    "nb = ring.describe['BPM']\n",
    "nq = ring.describe['Quadrupole']\n",
    "ns = ring.describe['Sextupole']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91bfe929-11a9-4382-93c0-cf1644d2a1f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Compare tunes\n",
    "\n",
    "nuqx, nuqy = tune(ring, [], alignment=False, matched=True)\n",
    "\n",
    "print(torch.allclose(nu_qx, nuqx))\n",
    "print(torch.allclose(nu_qy, nuqy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49f1285f-5a43-481f-84c2-00e1512a4eec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Compare twiss\n",
    "\n",
    "aqx, bqx, aqy, bqy = twiss(ring, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "\n",
    "print(torch.allclose(a_qx, aqx))\n",
    "print(torch.allclose(b_qx, bqx))\n",
    "print(torch.allclose(a_qy, aqy))\n",
    "print(torch.allclose(b_qy, bqy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f351b74-a3e0-4a25-ba66-6c3f6d8da342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.1062,  0.1321,  0.2508,  0.1047, -0.2323, -0.1218,  0.0207,  0.0901,\n",
      "         -0.2828, -0.1534, -0.1003,  0.1049,  0.3543,  0.2371,  0.0391,  0.0137,\n",
      "          0.1178, -0.2663, -0.1903, -0.1382, -0.1483, -0.2131, -0.2389,  0.1460,\n",
      "          0.0278,  0.0292,  0.2080,  0.3302],\n",
      "        [-0.0703,  0.0397, -0.0028, -0.0334,  0.0909,  0.1122, -0.0339, -0.0639,\n",
      "          0.0021,  0.0591,  0.0098, -0.2945, -0.1397,  0.0792,  0.3057,  0.1834,\n",
      "          0.0682, -0.0502,  0.0598,  0.1259,  0.0753,  0.0062, -0.0379,  0.0883,\n",
      "          0.1483,  0.0521, -0.0259, -0.0698]], dtype=torch.float64)\n",
      "\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0.]], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Test derivatives with respect kn and ks at the lattice start\n",
    "\n",
    "kn = torch.zeros(nq, dtype=torch.float64)\n",
    "ks = torch.zeros(nq, dtype=torch.float64)\n",
    "\n",
    "result, *_ = torch.func.jacrev(lambda kn: advance(ring, [kn], ('kn', ['Quadrupole'], None, None), matched=True))(kn)\n",
    "pprint(result)\n",
    "print()\n",
    "\n",
    "result, *_ = torch.func.jacrev(lambda ks: advance(ring, [ks], ('ks', ['Quadrupole'], None, None), matched=True))(ks)\n",
    "pprint(result)\n",
    "print()\n",
    "\n",
    "# Note, first order derivatives with respect to ks are identicaly equal to zero as expected\n",
    "# Second order derivative is not identicaly equal to zero in general\n",
    "# In the following, only first order derivatives are used for optics correctios (lattice without coupling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3928b01-e3b7-42a5-9dad-e6faa37b3ec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 2, 28])\n",
      "torch.Size([16, 2, 28])\n"
     ]
    }
   ],
   "source": [
    "# Compute advance derivatives with respect to quadrupole settings (normal and chromatic)\n",
    "\n",
    "def fn_dtwiss_dkn(kn):\n",
    "    return advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)\n",
    "\n",
    "def fn_dtwiss_dp_dkn(kn):\n",
    "    return chromatic_advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)\n",
    "\n",
    "kn = torch.zeros(nq, dtype=torch.float64)\n",
    "\n",
    "dtwiss_dkn = torch.func.jacrev(fn_dtwiss_dkn)(kn)\n",
    "dtwiss_dp_dkn = torch.func.jacrev(fn_dtwiss_dp_dkn)(kn)\n",
    "\n",
    "print(dtwiss_dkn.shape)\n",
    "print(dtwiss_dp_dkn.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1cde0de8-70cc-4ad9-97af-6396753149d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set lattice with focusing errors (no coupling)\n",
    "\n",
    "error:Line = ring.clone()\n",
    "\n",
    "nq = error.describe['Quadrupole']\n",
    "\n",
    "error_kn = 0.1*torch.randn(nq, dtype=torch.float64)\n",
    "\n",
    "index = 0\n",
    "label = ''\n",
    "\n",
    "for line in error.sequence:\n",
    "    for element in line:\n",
    "        if element.__class__.__name__ == 'Quadrupole':\n",
    "            if label != element.name:\n",
    "                index +=1\n",
    "            label = element.name\n",
    "            element.kn = (element.kn + error_kn[index - 1]).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35dc3845-d34b-40b7-8539-54b27b964336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.6391, dtype=torch.float64)\n",
      "tensor(0.9940, dtype=torch.float64)\n",
      "tensor(0.4831, dtype=torch.float64)\n",
      "tensor(0.2865, dtype=torch.float64)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute twiss and plot beta beating\n",
    "\n",
    "ax_model, bx_model, ay_model, by_model = twiss(ring, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_error, bx_error, ay_error, by_error = twiss(error, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "\n",
    "# Compare twiss\n",
    "\n",
    "print((ax_model - ax_error).norm())\n",
    "print((bx_model - bx_error).norm())\n",
    "print((ay_model - ay_error).norm())\n",
    "print((by_model - by_error).norm())\n",
    "print()\n",
    "\n",
    "# Plot beta beating\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_error)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_error)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='o')\n",
    "plt.xticks(ticks=positions, labels=['BPM05', 'BPM07', 'BPM08', 'BPM09', 'BPM10', 'BPM11', 'BPM12', 'BPM13', 'BPM14', 'BPM15', 'BPM16', 'BPM17', 'BPM01', 'BPM02', 'BPM03', 'BPM04'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da6dcae5-2dbb-45b7-bf8b-5935dd1af771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.3569, dtype=torch.float64)\n",
      "tensor(0.0325, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test Twiss response\n",
    "\n",
    "twiss_error = advance(error, [], alignment=False, matched=True)\n",
    "twiss_model = advance(ring, [], alignment=False, matched=True)\n",
    "\n",
    "print((twiss_error - (twiss_model + 0.0*(dtwiss_dkn @ error_kn))).norm())\n",
    "print((twiss_error - (twiss_model + 1.0*(dtwiss_dkn @ error_kn))).norm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d652ad69-1bf9-4298-8cd6-9c0806a119cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.3569, dtype=torch.float64)\n",
      "tensor(0.3533, dtype=torch.float64)\n",
      "tensor(0.3499, dtype=torch.float64)\n",
      "tensor(0.3464, dtype=torch.float64)\n",
      "tensor(0.3431, dtype=torch.float64)\n",
      "tensor(0.3398, dtype=torch.float64)\n",
      "tensor(0.3366, dtype=torch.float64)\n",
      "tensor(0.3334, dtype=torch.float64)\n",
      "tensor(0.3303, dtype=torch.float64)\n",
      "tensor(0.3272, dtype=torch.float64)\n",
      "tensor(0.3242, dtype=torch.float64)\n",
      "tensor(0.3212, dtype=torch.float64)\n",
      "tensor(0.3183, dtype=torch.float64)\n",
      "tensor(0.3154, dtype=torch.float64)\n",
      "tensor(0.3126, dtype=torch.float64)\n",
      "tensor(0.3098, dtype=torch.float64)\n",
      "tensor(0.3071, dtype=torch.float64)\n",
      "tensor(0.3044, dtype=torch.float64)\n",
      "tensor(0.3018, dtype=torch.float64)\n",
      "tensor(0.2991, dtype=torch.float64)\n",
      "tensor(0.2966, dtype=torch.float64)\n",
      "tensor(0.2940, dtype=torch.float64)\n",
      "tensor(0.2915, dtype=torch.float64)\n",
      "tensor(0.2890, dtype=torch.float64)\n",
      "tensor(0.2865, dtype=torch.float64)\n",
      "tensor(0.2841, dtype=torch.float64)\n",
      "tensor(0.2817, dtype=torch.float64)\n",
      "tensor(0.2793, dtype=torch.float64)\n",
      "tensor(0.2770, dtype=torch.float64)\n",
      "tensor(0.2746, dtype=torch.float64)\n",
      "tensor(0.2723, dtype=torch.float64)\n",
      "tensor(0.2700, dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform correction (model to experiment)\n",
    "\n",
    "# Set response matrix\n",
    "\n",
    "matrix = dtwiss_dkn.reshape(-1, nq)\n",
    "\n",
    "# Set target twiss parameters\n",
    "\n",
    "twiss_error = advance(error, [], alignment=False, matched=True)\n",
    "\n",
    "# Set learning rate\n",
    "\n",
    "lr = 0.01\n",
    "\n",
    "# Set initial values\n",
    "\n",
    "kn = torch.zeros_like(error_kn)\n",
    "\n",
    "# Fit\n",
    "\n",
    "for _ in range(32):\n",
    "    twiss_model = advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)\n",
    "    dkn = - lr*torch.linalg.lstsq(matrix, (twiss_model - twiss_error).flatten(), driver='gelsd').solution\n",
    "    kn += dkn\n",
    "    print((twiss_model - twiss_error).norm())\n",
    "\n",
    "# Plot final quadrupole settings\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.bar(range(len(error_kn)), error_kn.cpu().numpy(), color='red', alpha=0.75, width=1)\n",
    "plt.bar(range(len(kn)), +kn.cpu().numpy(), color='blue', alpha=0.75, width=0.75)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96e75206-0c39-4ef7-a4f5-41f929deabe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply corrections\n",
    "\n",
    "lattice:Line = error.clone()\n",
    "\n",
    "index = 0\n",
    "label = ''\n",
    "\n",
    "for line in lattice.sequence:\n",
    "    for element in line:\n",
    "        if element.__class__.__name__ == 'Quadrupole':\n",
    "            if label != element.name:\n",
    "                index +=1\n",
    "            label = element.name\n",
    "            element.kn = (element.kn - kn[index - 1]).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab8ecaf9-7de6-4cff-b23f-1ad0431b418a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute twiss and plot beta beating\n",
    "\n",
    "ax_model, bx_model, ay_model, by_model = twiss(ring, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_error, bx_error, ay_error, by_error = twiss(error, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_final, bx_final, ay_final, by_final = twiss(lattice, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "\n",
    "# Plot beta beating\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_error)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_error)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_final)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='x')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_final)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='x')\n",
    "plt.xticks(ticks=positions, labels=['BPM05', 'BPM07', 'BPM08', 'BPM09', 'BPM10', 'BPM11', 'BPM12', 'BPM13', 'BPM14', 'BPM15', 'BPM16', 'BPM17', 'BPM01', 'BPM02', 'BPM03', 'BPM04'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a1d51f90-bf09-4607-af75-3c4cd4b8f984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(13.0000, dtype=torch.float64)\n",
      "tensor(1.2593, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test Twiss response (chromatic)\n",
    "\n",
    "twiss_error = chromatic_advance(error, [], alignment=False, matched=True)\n",
    "twiss_model = chromatic_advance(ring, [], alignment=False, matched=True)\n",
    "\n",
    "print((twiss_error - (twiss_model + 0.0*(dtwiss_dp_dkn @ error_kn))).norm())\n",
    "print((twiss_error - (twiss_model + 1.0*(dtwiss_dp_dkn @ error_kn))).norm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72b8a353-40a8-48c3-9577-35286021ca11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(13.0049, dtype=torch.float64)\n",
      "tensor(12.8748, dtype=torch.float64)\n",
      "tensor(12.7459, dtype=torch.float64)\n",
      "tensor(12.6181, dtype=torch.float64)\n",
      "tensor(12.4914, dtype=torch.float64)\n",
      "tensor(12.3658, dtype=torch.float64)\n",
      "tensor(12.2412, dtype=torch.float64)\n",
      "tensor(12.1178, dtype=torch.float64)\n",
      "tensor(11.9954, dtype=torch.float64)\n",
      "tensor(11.8742, dtype=torch.float64)\n",
      "tensor(11.7539, dtype=torch.float64)\n",
      "tensor(11.6348, dtype=torch.float64)\n",
      "tensor(11.5167, dtype=torch.float64)\n",
      "tensor(11.3996, dtype=torch.float64)\n",
      "tensor(11.2835, dtype=torch.float64)\n",
      "tensor(11.1685, dtype=torch.float64)\n",
      "tensor(11.0546, dtype=torch.float64)\n",
      "tensor(10.9416, dtype=torch.float64)\n",
      "tensor(10.8296, dtype=torch.float64)\n",
      "tensor(10.7187, dtype=torch.float64)\n",
      "tensor(10.6087, dtype=torch.float64)\n",
      "tensor(10.4997, dtype=torch.float64)\n",
      "tensor(10.3917, dtype=torch.float64)\n",
      "tensor(10.2847, dtype=torch.float64)\n",
      "tensor(10.1786, dtype=torch.float64)\n",
      "tensor(10.0735, dtype=torch.float64)\n",
      "tensor(9.9694, dtype=torch.float64)\n",
      "tensor(9.8661, dtype=torch.float64)\n",
      "tensor(9.7639, dtype=torch.float64)\n",
      "tensor(9.6625, dtype=torch.float64)\n",
      "tensor(9.5621, dtype=torch.float64)\n",
      "tensor(9.4626, dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform correction (model to experiment) including chromatic twiss\n",
    "\n",
    "# Set response matrix\n",
    "\n",
    "matrix = torch.vstack([dtwiss_dkn.reshape(-1, nq), dtwiss_dp_dkn.reshape(-1, nq)])\n",
    "\n",
    "# Set target twiss parameters\n",
    "\n",
    "twiss_error = advance(error, [], alignment=False, matched=True)\n",
    "chromatic_twiss_error = chromatic_advance(error, [], alignment=False, matched=True)\n",
    "\n",
    "# Set learning rate\n",
    "\n",
    "lr = 0.01\n",
    "\n",
    "# Set initial values\n",
    "\n",
    "kn = torch.zeros_like(error_kn)\n",
    "\n",
    "# Fit\n",
    "\n",
    "for _ in range(32):\n",
    "    twiss_model = advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)\n",
    "    chromatic_twiss_model = chromatic_advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)\n",
    "    dkn = - lr*torch.linalg.lstsq(matrix, torch.stack([twiss_model - twiss_error, chromatic_twiss_model - chromatic_twiss_error]).flatten(), driver='gelsd').solution\n",
    "    kn += dkn\n",
    "    print(torch.stack([twiss_model - twiss_error, chromatic_twiss_model - chromatic_twiss_error]).norm())\n",
    "\n",
    "# Plot final quadrupole settings\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.bar(range(len(error_kn)), error_kn.cpu().numpy(), color='red', alpha=0.75, width=1)\n",
    "plt.bar(range(len(kn)), +kn.cpu().numpy(), color='blue', alpha=0.75, width=0.75)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf90d235-9bbc-4e52-99b9-bdaba0c6b559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply corrections\n",
    "\n",
    "lattice:Line = error.clone()\n",
    "\n",
    "index = 0\n",
    "label = ''\n",
    "\n",
    "for line in lattice.sequence:\n",
    "    for element in line:\n",
    "        if element.__class__.__name__ == 'Quadrupole':\n",
    "            if label != element.name:\n",
    "                index +=1\n",
    "            label = element.name\n",
    "            element.kn = (element.kn - kn[index - 1]).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6ab3811-9178-49b1-88e4-fce60f4ed706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute twiss and plot beta beating\n",
    "\n",
    "ax_model, bx_model, ay_model, by_model = twiss(ring, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_error, bx_error, ay_error, by_error = twiss(error, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_final, bx_final, ay_final, by_final = twiss(lattice, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "\n",
    "# Plot beta beating\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_error)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_error)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_final)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='x')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_final)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='x')\n",
    "plt.xticks(ticks=positions, labels=['BPM05', 'BPM07', 'BPM08', 'BPM09', 'BPM10', 'BPM11', 'BPM12', 'BPM13', 'BPM14', 'BPM15', 'BPM16', 'BPM17', 'BPM01', 'BPM02', 'BPM03', 'BPM04'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4fb4e632-2bc6-4fae-950d-b9fc05f6d54e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.3569, dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n",
      "\n",
      "tensor(0.3569, dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n",
      "\n",
      "tensor(0.3569, dtype=torch.float64)\n",
      "tensor(0.3282, dtype=torch.float64)\n",
      "tensor(0.3006, dtype=torch.float64)\n",
      "tensor(0.2745, dtype=torch.float64)\n",
      "tensor(0.2504, dtype=torch.float64)\n",
      "tensor(0.2285, dtype=torch.float64)\n",
      "tensor(0.2089, dtype=torch.float64)\n",
      "tensor(0.1917, dtype=torch.float64)\n",
      "tensor(0.1769, dtype=torch.float64)\n",
      "tensor(0.1645, dtype=torch.float64)\n",
      "tensor(0.1540, dtype=torch.float64)\n",
      "tensor(0.1446, dtype=torch.float64)\n",
      "tensor(0.1356, dtype=torch.float64)\n",
      "tensor(0.1264, dtype=torch.float64)\n",
      "tensor(0.1172, dtype=torch.float64)\n",
      "tensor(0.1082, dtype=torch.float64)\n",
      "tensor(0.0999, dtype=torch.float64)\n",
      "tensor(0.0927, dtype=torch.float64)\n",
      "tensor(0.0875, dtype=torch.float64)\n",
      "tensor(0.0845, dtype=torch.float64)\n",
      "tensor(0.0832, dtype=torch.float64)\n",
      "tensor(0.0822, dtype=torch.float64)\n",
      "tensor(0.0806, dtype=torch.float64)\n",
      "tensor(0.0776, dtype=torch.float64)\n",
      "tensor(0.0733, dtype=torch.float64)\n",
      "tensor(0.0685, dtype=torch.float64)\n",
      "tensor(0.0639, dtype=torch.float64)\n",
      "tensor(0.0601, dtype=torch.float64)\n",
      "tensor(0.0572, dtype=torch.float64)\n",
      "tensor(0.0547, dtype=torch.float64)\n",
      "tensor(0.0521, dtype=torch.float64)\n",
      "tensor(0.0494, dtype=torch.float64)\n",
      "tensor(0.0469, dtype=torch.float64)\n",
      "tensor(0.0452, dtype=torch.float64)\n",
      "tensor(0.0443, dtype=torch.float64)\n",
      "tensor(0.0441, dtype=torch.float64)\n",
      "tensor(0.0442, dtype=torch.float64)\n",
      "tensor(0.0441, dtype=torch.float64)\n",
      "tensor(0.0431, dtype=torch.float64)\n",
      "tensor(0.0412, dtype=torch.float64)\n",
      "tensor(0.0389, dtype=torch.float64)\n",
      "tensor(0.0368, dtype=torch.float64)\n",
      "tensor(0.0350, dtype=torch.float64)\n",
      "tensor(0.0336, dtype=torch.float64)\n",
      "tensor(0.0325, dtype=torch.float64)\n",
      "tensor(0.0315, dtype=torch.float64)\n",
      "tensor(0.0308, dtype=torch.float64)\n",
      "tensor(0.0302, dtype=torch.float64)\n",
      "tensor(0.0297, dtype=torch.float64)\n",
      "tensor(0.0292, dtype=torch.float64)\n",
      "tensor(0.0284, dtype=torch.float64)\n",
      "tensor(0.0275, dtype=torch.float64)\n",
      "tensor(0.0268, dtype=torch.float64)\n",
      "tensor(0.0265, dtype=torch.float64)\n",
      "tensor(0.0263, dtype=torch.float64)\n",
      "tensor(0.0261, dtype=torch.float64)\n",
      "tensor(0.0258, dtype=torch.float64)\n",
      "tensor(0.0254, dtype=torch.float64)\n",
      "tensor(0.0249, dtype=torch.float64)\n",
      "tensor(0.0245, dtype=torch.float64)\n",
      "tensor(0.0239, dtype=torch.float64)\n",
      "tensor(0.0234, dtype=torch.float64)\n",
      "tensor(0.0231, dtype=torch.float64)\n",
      "tensor(0.0229, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# ML style correction (model to experiment)\n",
    "\n",
    "# Set target twiss parameters\n",
    "\n",
    "twiss_error = advance(error, [], alignment=False, matched=True)\n",
    "\n",
    "# Set learning rate\n",
    "\n",
    "lr = 0.005\n",
    "\n",
    "# Set parametric twiss\n",
    "\n",
    "def twiss_model(kn):\n",
    "    return advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)\n",
    "\n",
    "# Set objective function\n",
    "\n",
    "def objective(kn):\n",
    "    return (twiss_error - twiss_model(kn)).norm()\n",
    "\n",
    "# Set initial values\n",
    "\n",
    "kn = torch.zeros_like(error_kn)\n",
    "\n",
    "# Test objective function\n",
    "\n",
    "print(objective(0.0*error_kn))\n",
    "print(objective(1.0*error_kn))\n",
    "print()\n",
    "\n",
    "# Set normalized objective\n",
    "\n",
    "objective = normalize(objective, [(-0.5, 0.5)])\n",
    "\n",
    "# Test normalized objective\n",
    "\n",
    "print(objective(*forward([0.0*error_kn], [(-0.5, 0.5)])))\n",
    "print(objective(*forward([1.0*error_kn], [(-0.5, 0.5)])))\n",
    "print()\n",
    "\n",
    "# Normalize initial settings\n",
    "\n",
    "kn, *_ = forward([kn], [(-0.5, 0.5)])\n",
    "\n",
    "# Set model (forward returns evaluated objective)\n",
    "\n",
    "model = Wrapper(objective, kn)\n",
    "\n",
    "# Set optimizer\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "# Perform optimization\n",
    "\n",
    "for epoch in range(64):\n",
    "    value = model()\n",
    "    value.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    print(value.detach())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea165cfe-ca26-4c5b-975e-b7de3c09e16a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply corrections\n",
    "\n",
    "kn, *_ = inverse([kn], [(-0.5, 0.5)])\n",
    "\n",
    "lattice:Line = error.clone()\n",
    "\n",
    "index = 0\n",
    "label = ''\n",
    "\n",
    "for line in lattice.sequence:\n",
    "    for element in line:\n",
    "        if element.__class__.__name__ == 'Quadrupole':\n",
    "            if label != element.name:\n",
    "                index +=1\n",
    "            label = element.name\n",
    "            element.kn = (element.kn - kn[index - 1]).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11fcde78-835b-4fd7-9ce0-4b8b88f8669f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute twiss and plot beta beating\n",
    "\n",
    "ax_model, bx_model, ay_model, by_model = twiss(ring, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_error, bx_error, ay_error, by_error = twiss(error, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_final, bx_final, ay_final, by_final = twiss(lattice, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "\n",
    "# Plot beta beating\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_error)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_error)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_final)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='x')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_final)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='x')\n",
    "plt.xticks(ticks=positions, labels=['BPM05', 'BPM07', 'BPM08', 'BPM09', 'BPM10', 'BPM11', 'BPM12', 'BPM13', 'BPM14', 'BPM15', 'BPM16', 'BPM17', 'BPM01', 'BPM02', 'BPM03', 'BPM04'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8eb4f4a7-ab67-409c-a465-6adc0ae95ee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0040, dtype=torch.float64)\n",
      "tensor(0.0034, dtype=torch.float64)\n",
      "tensor(0.0028, dtype=torch.float64)\n",
      "tensor(0.0024, dtype=torch.float64)\n",
      "tensor(0.0020, dtype=torch.float64)\n",
      "tensor(0.0016, dtype=torch.float64)\n",
      "tensor(0.0014, dtype=torch.float64)\n",
      "tensor(0.0012, dtype=torch.float64)\n",
      "tensor(0.0010, dtype=torch.float64)\n",
      "tensor(0.0009, dtype=torch.float64)\n",
      "tensor(0.0008, dtype=torch.float64)\n",
      "tensor(0.0007, dtype=torch.float64)\n",
      "tensor(0.0006, dtype=torch.float64)\n",
      "tensor(0.0006, dtype=torch.float64)\n",
      "tensor(0.0005, dtype=torch.float64)\n",
      "tensor(0.0005, dtype=torch.float64)\n",
      "tensor(0.0004, dtype=torch.float64)\n",
      "tensor(0.0004, dtype=torch.float64)\n",
      "tensor(0.0003, dtype=torch.float64)\n",
      "tensor(0.0003, dtype=torch.float64)\n",
      "tensor(0.0003, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0001, dtype=torch.float64)\n",
      "tensor(0.0001, dtype=torch.float64)\n",
      "tensor(0.0001, dtype=torch.float64)\n",
      "tensor(0.0001, dtype=torch.float64)\n",
      "tensor(9.6877e-05, dtype=torch.float64)\n",
      "tensor(9.1666e-05, dtype=torch.float64)\n",
      "tensor(8.7230e-05, dtype=torch.float64)\n",
      "tensor(8.3082e-05, dtype=torch.float64)\n",
      "tensor(7.8988e-05, dtype=torch.float64)\n",
      "tensor(7.4955e-05, dtype=torch.float64)\n",
      "tensor(7.1150e-05, dtype=torch.float64)\n",
      "tensor(6.7778e-05, dtype=torch.float64)\n",
      "tensor(6.4986e-05, dtype=torch.float64)\n",
      "tensor(6.2814e-05, dtype=torch.float64)\n",
      "tensor(6.1198e-05, dtype=torch.float64)\n",
      "tensor(6.0007e-05, dtype=torch.float64)\n",
      "tensor(5.9105e-05, dtype=torch.float64)\n",
      "tensor(5.8375e-05, dtype=torch.float64)\n",
      "tensor(5.7739e-05, dtype=torch.float64)\n",
      "tensor(5.7134e-05, dtype=torch.float64)\n",
      "tensor(5.6496e-05, dtype=torch.float64)\n",
      "tensor(5.5748e-05, dtype=torch.float64)\n",
      "tensor(5.4812e-05, dtype=torch.float64)\n",
      "tensor(5.3630e-05, dtype=torch.float64)\n",
      "tensor(5.2191e-05, dtype=torch.float64)\n",
      "tensor(5.0546e-05, dtype=torch.float64)\n",
      "tensor(4.8795e-05, dtype=torch.float64)\n",
      "tensor(4.7061e-05, dtype=torch.float64)\n",
      "tensor(4.5458e-05, dtype=torch.float64)\n",
      "tensor(4.4060e-05, dtype=torch.float64)\n",
      "tensor(4.2890e-05, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# ML style correction (batched)\n",
    "\n",
    "# Set target twiss parameters\n",
    "\n",
    "twiss_error = advance(error, [], alignment=False, matched=True)\n",
    "\n",
    "# Set learning rate\n",
    "\n",
    "lr = 0.005\n",
    "\n",
    "\n",
    "# Set batched function\n",
    "\n",
    "def task(Is, kn):\n",
    "    return advance(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True)[Is]\n",
    "\n",
    "# Set initial values\n",
    "\n",
    "kn = torch.zeros_like(error_kn)\n",
    "\n",
    "# Normalize objective\n",
    "\n",
    "task = normalize(task, [(None, None), (-0.5, 0.5)])\n",
    "\n",
    "# Normalize initial settings\n",
    "\n",
    "kn, *_ = forward([kn], [(-0.5, 0.5)])\n",
    "\n",
    "# Set model\n",
    "\n",
    "model = Wrapper(task, kn)\n",
    "\n",
    "# Set optimizer\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "# Set features and labels \n",
    "\n",
    "X = torch.arange(len(ring))\n",
    "y = twiss_error.clone()\n",
    "\n",
    "# Set dataset\n",
    "\n",
    "batch_size = 16\n",
    "dataset = TensorDataset(X.clone(), y.clone())\n",
    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# Set loss funtion\n",
    "\n",
    "lf = torch.nn.MSELoss()\n",
    "\n",
    "# Perfom optimization\n",
    "\n",
    "for epoch in range(64):\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        y_hat = model(X)\n",
    "        value = lf(y_hat, y)\n",
    "        value.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "    with torch.no_grad():\n",
    "        print(value.detach())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de930db2-51ae-44be-97b8-359560a5d841",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply corrections\n",
    "\n",
    "kn, *_ = inverse([kn], [(-0.5, 0.5)])\n",
    "\n",
    "lattice:Line = error.clone()\n",
    "\n",
    "index = 0\n",
    "label = ''\n",
    "\n",
    "for line in lattice.sequence:\n",
    "    for element in line:\n",
    "        if element.__class__.__name__ == 'Quadrupole':\n",
    "            if label != element.name:\n",
    "                index +=1\n",
    "            label = element.name\n",
    "            element.kn = (element.kn - kn[index - 1]).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d483db0-e5df-4e6e-8fa8-ca8fee27da9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute twiss and plot beta beating\n",
    "\n",
    "ax_model, bx_model, ay_model, by_model = twiss(ring, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_error, bx_error, ay_error, by_error = twiss(error, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "ax_final, bx_final, ay_final, by_final = twiss(lattice, [], alignment=False, matched=True, advance=True, full=False, convert=True).T\n",
    "\n",
    "# Plot beta beating\n",
    "\n",
    "plt.figure(figsize=(16, 2))\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_error)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_error)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='o')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((bx_model - bx_final)/bx_model).cpu().numpy(), color='red', alpha=0.75, marker='x')\n",
    "plt.plot(ring.locations().cpu().numpy(), 100*((by_model - by_final)/by_model).cpu().numpy(), color='blue', alpha=0.75, marker='x')\n",
    "plt.xticks(ticks=positions, labels=['BPM05', 'BPM07', 'BPM08', 'BPM09', 'BPM10', 'BPM11', 'BPM12', 'BPM13', 'BPM14', 'BPM15', 'BPM16', 'BPM17', 'BPM01', 'BPM02', 'BPM03', 'BPM04'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
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  "colab": {
   "collapsed_sections": [
    "myt0_gMIOq7b",
    "5d97819c"
   ],
   "name": "03_frequency.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
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  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
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 "nbformat": 4,
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}