{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0191e93b-d2e4-43d2-817b-7e9479d0eb8b",
   "metadata": {},
   "source": [
    "## ELETTRA-47: EU100 (local/global correction & short section IDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee1e5fd2-64c8-4738-bde9-f9080dd12243",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import\n",
    "\n",
    "import torch\n",
    "from torch import Tensor\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "\n",
    "from model.library.element import Element\n",
    "from model.library.line import Line\n",
    "from model.library.quadrupole import Quadrupole\n",
    "from model.library.matrix import Matrix\n",
    "\n",
    "from model.command.external import load_lattice\n",
    "from model.command.build import build\n",
    "from model.command.tune import tune\n",
    "from model.command.tune import chromaticity\n",
    "from model.command.orbit import dispersion\n",
    "from model.command.twiss import twiss\n",
    "from model.command.advance import advance\n",
    "from model.command.coupling import coupling\n",
    "\n",
    "from model.command.wrapper import Wrapper\n",
    "from model.command.wrapper import forward\n",
    "from model.command.wrapper import inverse\n",
    "from model.command.wrapper import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb5d54ca-9fc7-4e5d-9768-d14a78317455",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "Element.dtype = dtype = torch.float64\n",
    "Element.device = device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4664ed4b-a421-4043-a9b6-ee3b7838a601",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load lattice (ELEGANT table)\n",
    "# Note, lattice is allowed to have repeated elements\n",
    "\n",
    "path = Path('elettra.lte')\n",
    "data = load_lattice(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e7f3d08-cc34-4ebb-be30-864d54c55abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168, 'Drift': 720, 'Dipole': 156, 'Quadrupole': 360, 'Marker': 24}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build and setup lattice\n",
    "\n",
    "ring:Line = build('RING', 'ELEGANT', data)\n",
    "\n",
    "# Flatten sublines\n",
    "\n",
    "ring.flatten()\n",
    "\n",
    "# Remove all marker elements but the ones starting with MLL (long straight section centers)\n",
    "\n",
    "ring.remove_group(pattern=r'^(?!MSS_)(?!MLL_).*', kinds=['Marker'])\n",
    "\n",
    "# Replace all sextupoles with quadrupoles\n",
    "\n",
    "def factory(element:Element) -> None:\n",
    "    table = element.serialize\n",
    "    table.pop('ms', None)\n",
    "    return Quadrupole(**table)\n",
    "\n",
    "ring.replace_group(pattern=r'', factory=factory, kinds=['Sextupole'])\n",
    "\n",
    "# Set linear dipoles\n",
    "\n",
    "def apply(element:Element) -> None:\n",
    "    element.linear = True\n",
    "\n",
    "ring.apply(apply, kinds=['Dipole'])\n",
    "\n",
    "# Merge drifts\n",
    "\n",
    "ring.merge()\n",
    "\n",
    "# Change lattice start\n",
    "\n",
    "ring.start = \"BPM_S01_01\"\n",
    "\n",
    "# Split BPMs\n",
    "\n",
    "ring.split((None, ['BPM'], None, None))\n",
    "\n",
    "# Roll lattice\n",
    "\n",
    "ring.roll(1)\n",
    "\n",
    "# Splice lattice\n",
    "\n",
    "ring.splice()\n",
    "\n",
    "# Describe\n",
    "\n",
    "ring.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3283351-80ca-4bd1-ad97-947f4a25449f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux, nuy = tune(ring, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4cf0f044-44dd-436c-a16b-ee2d994c38f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx, etapx, etaqy, etapy = dispersion(ring, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f926b3b-dd29-461f-bdb6-e87e94f843c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax, bx, ay, by = twiss(ring, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07cebe09-c37b-4426-8732-f3cdccc54502",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux, muy = advance(ring, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c597fb0-eea2-4a64-be32-3a7e85ded8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c = coupling(ring, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfcc83c3-a332-45bf-bbb9-d580062384af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi = chromaticity(ring, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6fda8e9-ba89-4444-b412-d24df51b917e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quadrupole names for global tune correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32148c01-9bc9-4bda-be6c-bba280b17155",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.2994, 0.1608], dtype=torch.float64)\n",
      "tensor([[ 5.8543,  2.0964],\n",
      "        [-2.9918, -1.2602]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Global tune responce matrix\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(ring, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "knobs = torch.tensor([0.0, 0.0], dtype=dtype)\n",
    "global_target = global_observable(knobs)\n",
    "global_matrix = torch.func.jacfwd(global_observable)(knobs)\n",
    "\n",
    "print(global_target)\n",
    "print(global_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40b79381-9b00-4c00-963f-14aadd76a449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Several local knobs can be used to correct ID effects\n",
    "\n",
    "# Normal quadrupole correctors\n",
    "\n",
    "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n",
    "\n",
    "# Skew quadrupole correctors\n",
    "\n",
    "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "139a150b-dee4-4456-b5e7-90a05dc40162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 0., 0.],\n",
      "        [0., 1., 0.],\n",
      "        [0., 0., 1.],\n",
      "        [0., 0., 1.],\n",
      "        [0., 1., 0.],\n",
      "        [1., 0., 0.]], dtype=torch.float64)\n",
      "\n",
      "tensor([[ 1.,  0.],\n",
      "        [ 0.,  1.],\n",
      "        [ 0., -1.],\n",
      "        [-1.,  0.]], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define knobs to magnets mixing matrices\n",
    "\n",
    "Sn = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]], dtype=dtype)\n",
    "\n",
    "print(Sn)\n",
    "print()\n",
    "\n",
    "Ss = torch.tensor([[+1.0, 0.0], [0.0, +1.0], [0.0, -1.0], [-1.0, 0.0]], dtype=dtype)\n",
    "\n",
    "print(Ss)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16c0a393-e02f-48cf-ba69-437c4fd4e578",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define twiss observable (full coupled twiss)\n",
    "\n",
    "def observable_twiss(kn, ks):\n",
    "    return twiss(ring, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b082f268-95e6-4178-8c8f-50b9cfd866f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define dispersion observable\n",
    "\n",
    "def observable_dispersion(kn, ks):  \n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(ring, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b6cfed78-242b-4869-ab07-1ae1d8447c2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5712])\n",
      "torch.Size([5712, 5])\n"
     ]
    }
   ],
   "source": [
    "# Construct full target observable vector and corresponding responce matrix\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [3, 2])\n",
    "    kn = Sn @ kn\n",
    "    ks = Ss @ ks    \n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])\n",
    "\n",
    "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n",
    "print((target := observable(knobs)).shape)\n",
    "print((matrix := torch.func.jacfwd(observable)(knobs)).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b3a9663-639d-409f-90d4-0610b4c026a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define ID model\n",
    "# Note, only the flattened triangular part of the A and B matrices is passed\n",
    "\n",
    "A = torch.tensor([[-0.03484222052711237, 1.0272120741819959E-7, -4.698931299341201E-9, 0.0015923185492594811],\n",
    "                  [1.0272120579834892E-7, -0.046082787920135176, 0.0017792061173117564, 3.3551298301095784E-8],\n",
    "                  [-4.6989312853101E-9, 0.0017792061173117072, 0.056853750760983084, -1.5929605363332683E-7],\n",
    "                  [0.0015923185492594336, 3.3551298348653296E-8, -1.5929605261642905E-7, 0.08311631737263032]], dtype=dtype)\n",
    "\n",
    "B = torch.tensor([[0.03649353186115209, 0.0015448347221877217, 0.00002719892025520868, -0.0033681183134964482],\n",
    "                  [0.0015448347221877217, 0.13683886657005795, -0.0033198692682377406, 0.00006140578258682469],\n",
    "                  [0.00002719892025520868, -0.0033198692682377406, -0.05260095308967722, 0.005019907688182885],\n",
    "                  [-0.0033681183134964482, 0.00006140578258682469, 0.005019907688182885, -0.2531573249456863]], dtype=dtype)\n",
    "\n",
    "ID = Matrix('ID', \n",
    "            length=0.0, \n",
    "            A=A[torch.triu(torch.ones_like(A, dtype=torch.bool))].tolist(), \n",
    "            B=B[torch.triu(torch.ones_like(B, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea79960f-b32e-4a48-9eb2-91024a3ab1aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# W96_S05\n",
    "\n",
    "ARK = torch.tensor([[-0.000248802, 0.0,        0.0,      0.0],\n",
    "                    [ 0.0,        -9.53447e-6, 0.0,      0.0],\n",
    "                    [ 0.0,         0.0,        0.0176392,0.0],\n",
    "                    [ 0.0,         0.0,        0.0,      0.000856368]], dtype=dtype)\n",
    "\n",
    "\n",
    "W96_POS = 0.0\n",
    "W96_S05 = Matrix('W96_S05', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a388cca-750d-4919-88c1-219cf5683b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# W96_S06\n",
    "\n",
    "ARK = torch.tensor([[-0.000248802, 0.0,        0.0,      0.0],\n",
    "                    [ 0.0,        -9.53447e-6, 0.0,      0.0],\n",
    "                    [ 0.0,         0.0,        0.0176392,0.0],\n",
    "                    [ 0.0,         0.0,        0.0,      0.000856368]], dtype=dtype)\n",
    "\n",
    "W96_POS = 0.0\n",
    "W96_S06 = Matrix('W96_S06', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7082f3a8-1a75-41dc-98de-e2aeb4d930c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(W96_S05, error.next('MSS_S05').name, position=W96_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e4f9cc8-4789-4067-96dd-d5a44d152d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0999a690-088b-4eb7-8500-e41d51782851",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "99e5a99a-6966-46e6-94b9-1075ae32b863",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "87e0b94f-3ad0-4f7f-9b22-753f80924e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c368e344-8cfc-44b3-8fe1-1f89c1148b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7afa7132-0a2e-4d18-b375-af3753f84e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "be6108ad-2f09-4b1a-b13e-1fb6afb7add7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.8934e-05, dtype=torch.float64)\n",
      "tensor(-0.0028, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f605b0e9-94ae-4bbd-875d-fa9daa397071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca56615f-7fe0-4402-83a4-34e4546aaaa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2073, -66.6162], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4bf54523-e888-48fb-a050-7eddf3d8573a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "71ff923f-3b6e-42df-93cf-bec44a75cd30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "79c4c4a4-850c-47db-b756-600e754fa612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c89294db-5398-45ed-854c-cd7ca53ebc96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c67618c-29ca-4c3e-932d-29ee1c7db41e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "da890205-8899-406c-90a1-9e0b83902368",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "da99b2a3-e64e-452a-b981-b1a66bfec90b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cff59348-1072-4f6e-8784-f0c85837749b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0260, dtype=torch.float64)\n",
      "tensor(-0.0114, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b834efe0-9d80-4288-aaea-42ae4a483f7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0.0004, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1a30551d-fe8e-412e-a3d4-0d5e8712f6d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-72.2597, -66.6681], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8a89e726-3695-4d76-93e1-033846a1bf43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 722,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 2}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n",
    "error.insert(W96_S05, error.next('MSS_S05').name, position=W96_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7ce9a7be-16dc-4c5d-bdc1-6a08a18bc083",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_c, nuy_id_c = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "70303588-7008-4564-ac4e-f5ed49b9c8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_c, etapx_id_c, etaqy_id_c, etapy_id_c = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "07065a23-bc62-4e99-9444-bf2fa97cd5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_c, bx_id_c, ay_id_c, by_id_c = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1a6fa5b4-f278-43f9-b883-d2e55aac24cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_c, muy_id_c = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "379447fb-e18e-4f5a-abcd-911f84d1a9ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_c = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e45b9c93-bd61-43fb-9ff1-b61be9259cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_c = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3b3f03f6-fffb-4c41-a441-077807b951b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0260, dtype=torch.float64)\n",
      "tensor(-0.0142, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_c))\n",
    "print((nuy - nuy_id_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "71245331-b39c-415a-89db-891f413e1e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0.0004, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "45a9264e-ec15-432c-bb23-15b4fa6734f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-72.2592, -66.6015], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "12ddb2ac-c726-4d16-b488-e166a915cf55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n",
      "tensor(-71.2073, dtype=torch.float64) tensor(-66.6162, dtype=torch.float64)\n",
      "tensor(-72.2597, dtype=torch.float64) tensor(-66.6681, dtype=torch.float64)\n",
      "tensor(-72.2592, dtype=torch.float64) tensor(-66.6015, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "(psi_x, psi_y) = psi\n",
    "(psi_id_a_x, psi_id_a_y) = psi_id_a\n",
    "(psi_id_b_x, psi_id_b_y) = psi_id_b\n",
    "(psi_id_c_x, psi_id_c_y) = psi_id_c\n",
    "\n",
    "print(psi_x, psi_y)\n",
    "print(psi_id_a_x, psi_id_a_y)\n",
    "print(psi_id_b_x, psi_id_b_y)\n",
    "print(psi_id_c_x, psi_id_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "608c02b3-3e34-4696-949c-3d5d27f7cb8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "bx_c_bb = 100.0*(bx - bx_id_c) / bx\n",
    "by_c_bb = 100.0*(by - by_id_c) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "rms_x_c = rms(bx_c_bb).item()\n",
    "ptp_x_c = (bx_c_bb.max() - bx_c_bb.min()).item()\n",
    "rms_y_c = rms(by_c_bb).item()\n",
    "ptp_y_c = (by_c_bb.max() - by_c_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "bx_c_np = bx_c_bb.cpu().numpy()\n",
    "by_c_np = by_c_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (W96)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (W96)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (EU100)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (EU100)')\n",
    "ax.errorbar(s, bx_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (EU100 \\& W96)')\n",
    "ax.errorbar(s, by_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (EU100 \\& W96)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    r'W96~~~~~~~~~~~~~:~'\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.25, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    r'EU100~~~~~~~~~~~:~'\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    r'EU100 \\& W96~:~'\n",
    "    rf'RMS$_x$={rms_x_c:05.2f}\\% \\quad RMS$_y$={rms_y_c:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_c:05.2f}\\% \\quad PTP$_y$={ptp_y_c:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_c)}{(nux - nux_id_c).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_c)}{(nuy - nuy_id_c).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_c.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "error.flatten()\n",
    "for position in error.locations()[[error.position(name) for name in ['ID', 'W96_S05']]]:\n",
    "    ax.axvline(position, -1, 1, linestyle='dashed', color='black')\n",
    "error.splice()\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-20, 20)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e980b86e-0f6a-4730-b204-13b3a98c9cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3e75ba6e-17ea-4a3b-ab92-651a07937a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id, nuy_id = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9665db7b-c937-4f0f-b4f1-93ab2ff9df28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id, etapx_id, etaqy_id, etapy_id = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3cf7bcf8-abcb-47b9-9b3a-7f030d8fb6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id, bx_id, ay_id, by_id = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "63720406-0133-4f33-8ed7-55bce2a4612d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "835695db-a01a-41f6-b1f2-2810fe276cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bc34065e-471d-48db-96d9-e2fd24de8c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "76048e1f-791e-41cd-b344-452696f43f12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0260, dtype=torch.float64)\n",
      "tensor(-0.0114, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id))\n",
    "print((nuy - nuy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c9817f39-5aed-4154-81ad-dedf45f9787e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0.0004, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f704dfb0-3f57-405e-a4ee-a81a779045ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-72.2597, -66.6681], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e98ba9d0-9e99-4012-9f9f-0bd2b1b31340",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric observable vector\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(error, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "\n",
    "def observable_twiss(kn, ks):\n",
    "    return twiss(error, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)\n",
    "\n",
    "\n",
    "def observable_dispersion(kn, ks):\n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(error, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T\n",
    "\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [3, 2])\n",
    "    kn = Sn @ kn\n",
    "    ks = Ss @ ks  \n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a1b5b75b-3b75-470a-8cc9-4197d3932697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0008, dtype=torch.float64)\n",
      "tensor(212.9162, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check the residual vector norm\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n",
    "\n",
    "print(((global_observable(global_knobs) - global_target)**2).sum())\n",
    "print(((observable(knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6bc7f83b-61a1-4085-bbf5-e0310b5e1dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(212.9162, dtype=torch.float64)\n",
      "tensor(12.5009, dtype=torch.float64)\n",
      "tensor(1.1080, dtype=torch.float64)\n",
      "tensor(0.3135, dtype=torch.float64)\n",
      "tensor(0.2637, dtype=torch.float64)\n",
      "tensor(0.2607, dtype=torch.float64)\n",
      "tensor(0.2605, dtype=torch.float64)\n",
      "tensor(0.2605, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (local)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(8):\n",
    "    value = observable(knobs)\n",
    "    residual = target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    knobs += lr*delta\n",
    "    print(((value - target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6bc94fe7-5f96-48b1-9464-62b51c4e4d99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.06037695 -0.00614317  0.20562899  0.20562899 -0.00614317 -0.06037695]\n",
      "[-0.00051896 -0.00251065  0.00251065  0.00051896]\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kn, ks = torch.split(knobs, [3, 2])\n",
    "\n",
    "kn = Sn @ kn\n",
    "ks = Ss @ ks\n",
    "\n",
    "print(kn.numpy())\n",
    "print(ks.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "69054875-2f5c-4ab9-bd35-31d6a4d91331",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name, knob in zip(nkn, kn):\n",
    "    error[name].kn = (error[name].kn + knob).item()\n",
    "\n",
    "for name, knob in zip(nks, ks):\n",
    "    error[name].ks = (error[name].ks + knob).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6d30eed7-32d8-44a5-9cc1-aa3318272a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0001, dtype=torch.float64)\n",
      "tensor(0.2605, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check \n",
    "\n",
    "print(((global_observable(0.0*global_knobs) - global_target)**2).sum())\n",
    "print(((observable(0.0*knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d358bfb9-e3f5-4f91-8a04-f7402f3502e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8425fc6f-6aa9-4315-a3b6-e7462bdbc0d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "158a15cd-c476-4255-829b-8b6f5004bfa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7342c825-d7ec-4b82-9fb3-c112a2e35d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "130c9067-261a-41d4-bb65-2dd04bdb6729",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "67a1e35a-313f-4f84-bd0f-e956e8e7b346",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9b9ed26b-68ec-49d9-a38b-2ff79f5711e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0031, dtype=torch.float64)\n",
      "tensor(-0.0106, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e11c4fb4-f4e9-4b66-b55a-5a00cb13fb4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.0686e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "087926cd-16e8-4082-b719-5218d0fe0c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1952, -66.7068], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "baf8fd9b-b814-445d-9c76-48da2a98ac67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0001, dtype=torch.float64)\n",
      "tensor(9.0535e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5676e5e2-24a8-4080-8611-0b3d8c9f1dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.061527614111642676\n",
      "-0.022554709001744343\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bbe4d566-2215-43da-ac79-58d1e110de83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "de8a353a-0b7b-4b44-a5b4-a70555017b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cb3c7d76-fcc4-46bd-9ef6-669b9ab90b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f9d4f478-1010-4229-b3d1-d8406d8c19bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "995f378e-11d9-42f1-a02c-c41654601f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e313e5c1-908a-4b4a-bc67-4be917170109",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "399c0f30-baa3-48f0-b22b-52594215954d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ef21eef9-e37c-4c98-8750-2ab46bf1c690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0004, dtype=torch.float64)\n",
      "tensor(-0.0008, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7047c355-2bb2-4687-ba10-9fefa45046f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.5885e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0b13ac60-536c-498c-a7df-595c78a4e0b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1487, -66.7459], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6def178b-4c22-4458-bbb9-cd90bcc75dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "error.flatten()\n",
    "for position in error.locations()[[error.position(name) for name in ['ID']]]:\n",
    "    ax.axvline(position, -1, 1, linestyle='dashed', color='black')\n",
    "error.splice()\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-3, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4fe12210-531e-4c8e-aaac-18a1fbac06cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "\n",
    "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n",
    "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']\n",
    "\n",
    "ring.flatten()\n",
    "error.flatten()\n",
    "\n",
    "dkn = {name: (error[name].kn - ring[name].kn).item() for name in nkn}\n",
    "dks = {name: (error[name].ks - ring[name].ks).item() for name in nks}\n",
    "dkf = {name: (error[name].kn - ring[name].kn).item() for name in QF}\n",
    "dkd = {name: (error[name].kn - ring[name].kn).item() for name in QD}\n",
    "\n",
    "ring.splice()\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8fe45351-d702-4cb1-a314-8451e107c0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 722,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 2}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n",
    "error.insert(W96_S05, error.next('MSS_S05').name, position=W96_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "819309f4-c14a-4494-8a2c-82a15c907377",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply correction\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in nkn:\n",
    "    error[name].kn = error[name].kn.item() + dkn[name]\n",
    "    \n",
    "for name in nks:\n",
    "    error[name].ks = error[name].ks.item() + dks[name]\n",
    "\n",
    "for name in QD:\n",
    "    if name not in nkn:\n",
    "        error[name].kn = error[name].kn.item() + dkd[name]\n",
    "\n",
    "for name in QF:\n",
    "    if name not in nkn:\n",
    "        error[name].kn = error[name].kn.item() + dkf[name]\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9d206d30-9a0e-461e-a98d-a01dc7ef0ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f3cb5c54-40e2-47c9-84f2-b2c9c2d651dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e3f4e170-7cb4-49f5-b20f-daf7d744c786",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9b3439e1-a8fc-4cc5-9468-1d31afb64355",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a548c600-4dec-4b1c-99b2-c7f6fe99180c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dc5be3fc-48e2-4e5d-84ec-595d118ccbeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7f48695c-63ab-49b3-8efb-c9ac4b99121c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0003, dtype=torch.float64)\n",
      "tensor(-0.0036, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4d2ce2ec-2c5f-4312-9109-dfbc9b01b904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.3666e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c05cd532-d9bc-4fd4-8daf-a37702c00ee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1466, -66.6850], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2600e3af-a399-41af-a124-e7f1d7e958e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.3087e-05, dtype=torch.float64)\n",
      "tensor(1.1039e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e4f2da79-9ab9-430c-98da-149572bb48dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.019468586966671107\n",
      "-0.007040292415693757\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "23fa714d-b47e-4b59-b27e-66b0259394cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "234cefb7-9980-4312-8c2d-f60a7b70e04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_c, nuy_id_c = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7f5a9587-68f3-4a7f-b927-7602a89ea608",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_c, etapx_id_c, etaqy_id_c, etapy_id_c = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5571ad0c-02dd-42fa-ad4d-c2e545e23430",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_c, bx_id_c, ay_id_c, by_id_c = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "656b79ee-df46-4320-9539-d12700c9d671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_c, muy_id_c = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0ec43b07-e9fa-4c36-9d69-bb296f09d73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_c = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "147e5fb8-fd3e-4480-aabf-6c20b373c649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_c = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c0a107e4-c63e-4220-befe-48a4acc3a0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0001, dtype=torch.float64)\n",
      "tensor(-0.0003, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_c))\n",
    "print((nuy - nuy_id_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d10a283d-11c0-4f38-b6da-cfb96246a1cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.6028e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "edb7f716-bcec-4c41-893f-7a3126412428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1294, -66.6959], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "18579460-8347-4150-9fe5-2fd80ea61104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n",
      "tensor(-71.1466, dtype=torch.float64) tensor(-66.6850, dtype=torch.float64)\n",
      "tensor(-71.1294, dtype=torch.float64) tensor(-66.6959, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "(psi_x, psi_y) = psi\n",
    "(psi_id_b_x, psi_id_b_y) = psi_id_b\n",
    "(psi_id_c_x, psi_id_c_y) = psi_id_c\n",
    "\n",
    "print(psi_x, psi_y)\n",
    "print(psi_id_b_x, psi_id_b_y)\n",
    "print(psi_id_c_x, psi_id_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9fea5a48-f24b-47ff-a589-29661942ba8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "bx_c_bb = 100.0*(bx - bx_id_c) / bx\n",
    "by_c_bb = 100.0*(by - by_id_c) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "rms_x_c = rms(bx_c_bb).item()\n",
    "ptp_x_c = (bx_c_bb.max() - bx_c_bb.min()).item()\n",
    "rms_y_c = rms(by_c_bb).item()\n",
    "ptp_y_c = (by_c_bb.max() - by_c_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "bx_c_np = bx_c_bb.cpu().numpy()\n",
    "by_c_np = by_c_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (EU100)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (EU100)')\n",
    "ax.errorbar(s, bx_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (EU100 \\& TUNE)')\n",
    "ax.errorbar(s, by_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (EU100 \\& TUNE)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_c:05.2f}\\% \\quad RMS$_y$={rms_y_c:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_c:05.2f}\\% \\quad PTP$_y$={ptp_y_c:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_c)}{(nux - nux_id_c).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_c)}{(nuy - nuy_id_c).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_c.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "error.flatten()\n",
    "for position in error.locations()[[error.position(name) for name in ['ID', 'W96_S05']]]:\n",
    "    ax.axvline(position, -1, 1, linestyle='dashed', color='black')\n",
    "error.splice()\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-3, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7e798a8a-b28d-48db-ba42-bf4b7546bf23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 723,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 3}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n",
    "error.insert(W96_S05, error.next('MSS_S05').name, position=W96_POS*1.0E-3)\n",
    "error.insert(W96_S06, error.next('MSS_S06').name, position=W96_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "dee35d93-842f-4b74-acf1-ca23a9977b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply correction\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in nkn:\n",
    "    error[name].kn = error[name].kn.item() + dkn[name]\n",
    "    \n",
    "for name in nks:\n",
    "    error[name].ks = error[name].ks.item() + dks[name]\n",
    "\n",
    "for name in QD:\n",
    "    if name not in nkn:\n",
    "        error[name].kn = error[name].kn.item() + dkd[name]\n",
    "\n",
    "for name in QF:\n",
    "    if name not in nkn:\n",
    "        error[name].kn = error[name].kn.item() + dkf[name]\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ed00178c-7cbb-4ca9-af08-cb6755f97b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5c7c94f1-6ccd-4fa7-a22a-c8529b08dde4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d3a50837-6be1-4b26-b341-3225881c52af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e82f0edb-44e3-4148-9cb7-c2197e4503cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "082136e2-0c3d-45a0-90bd-47fc22979240",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "06589b76-7483-4303-b3b3-3803c3dbc510",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5c4daeca-ea6d-413d-8db3-b610dab489f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0002, dtype=torch.float64)\n",
      "tensor(-0.0064, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "651d366d-4955-4e5c-a4b6-9dd370ec21bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.1968e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2a2be562-eb59-4a0c-a440-78668b1cafdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1445, -66.5964], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4f642c21-19cd-41f4-815f-520955754456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(4.1593e-05, dtype=torch.float64)\n",
      "tensor(3.5005e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7a1417c5-a8f1-47ec-bf85-66815723f1b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03407559744334108\n",
      "-0.012277688972451763\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7fad4f10-af8f-4640-bb09-992cf74b9698",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "cf675a10-1a18-444e-96dd-01988baf92da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_c, nuy_id_c = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "dfd119f0-eeba-4be7-9dfd-63fb9a6ce85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_c, etapx_id_c, etaqy_id_c, etapy_id_c = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "a0f6f221-baee-4889-91b9-bf402ad4b0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_c, bx_id_c, ay_id_c, by_id_c = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2b48dd9f-cf83-4450-a86b-72034e3286ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_c, muy_id_c = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "74528677-c81d-4121-b4ca-18a22c07550c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_c = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "708ce550-dad4-4449-8e29-b3fc9b0936df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_c = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6d977b4b-f8fb-4722-8933-476e674ebf87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0002, dtype=torch.float64)\n",
      "tensor(-0.0005, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_c))\n",
    "print((nuy - nuy_id_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "8245d1c9-f030-472d-9e46-46ea04669c99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.6421e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "e5d9663b-c4f4-4bf3-b629-aaacbac4bcfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1133, -66.6140], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "9ba4e27d-4bd2-4f06-9046-b74c1fed7807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n",
      "tensor(-71.1445, dtype=torch.float64) tensor(-66.5964, dtype=torch.float64)\n",
      "tensor(-71.1133, dtype=torch.float64) tensor(-66.6140, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "(psi_x, psi_y) = psi\n",
    "(psi_id_b_x, psi_id_b_y) = psi_id_b\n",
    "(psi_id_c_x, psi_id_c_y) = psi_id_c\n",
    "\n",
    "print(psi_x, psi_y)\n",
    "print(psi_id_b_x, psi_id_b_y)\n",
    "print(psi_id_c_x, psi_id_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "7094649a-6c05-4bf0-987d-f4c677265f3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "bx_c_bb = 100.0*(bx - bx_id_c) / bx\n",
    "by_c_bb = 100.0*(by - by_id_c) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "rms_x_c = rms(bx_c_bb).item()\n",
    "ptp_x_c = (bx_c_bb.max() - bx_c_bb.min()).item()\n",
    "rms_y_c = rms(by_c_bb).item()\n",
    "ptp_y_c = (by_c_bb.max() - by_c_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "bx_c_np = bx_c_bb.cpu().numpy()\n",
    "by_c_np = by_c_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (EU100)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (EU100)')\n",
    "ax.errorbar(s, bx_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (EU100 \\& TUNE)')\n",
    "ax.errorbar(s, by_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (EU100 \\& TUNE)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_c:05.2f}\\% \\quad RMS$_y$={rms_y_c:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_c:05.2f}\\% \\quad PTP$_y$={ptp_y_c:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_c)}{(nux - nux_id_c).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_c)}{(nuy - nuy_id_c).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_c.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "error.flatten()\n",
    "for position in error.locations()[[error.position(name) for name in ['ID', 'W96_S05']]]:\n",
    "    ax.axvline(position, -1, 1, linestyle='dashed', color='black')\n",
    "error.splice()\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-3, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "myt0_gMIOq7b",
    "5d97819c"
   ],
   "name": "03_frequency.ipynb",
   "provenance": []
  },
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   "display_name": "Python 3 (ipykernel)",
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