{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "262a5ec8-2553-4237-ab62-319b6ca22089", "metadata": {}, "source": [ "# Example-52: Normalized dispersion" ] }, { "cell_type": "code", "execution_count": 1, "id": "c889727b-a5f3-40d7-b590-ab38f5f350a6", "metadata": {}, "outputs": [], "source": [ "# In this example normalized dispersion is used for optics correction along with CS twiss parameters" ] }, { "cell_type": "code", "execution_count": 2, "id": "d5e87b7a-ac95-41d4-9cef-e3f94b287c44", "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.orbit import dispersion\n", "from model.command.tune import tune\n", "from model.command.twiss import twiss" ] }, { "cell_type": "code", "execution_count": 3, "id": "33a5c46f-4d9b-403c-8d55-c7de925a7a4f", "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", "eta_qx = select(columns, 'etax' , keep=False)\n", "eta_px = select(columns, 'etaxp', keep=False)\n", "eta_qy = select(columns, 'etay' , keep=False)\n", "eta_py = select(columns, 'etayp', keep=False)\n", "\n", "eta_qx:Tensor = torch.tensor([value for (key, value), kind in zip(eta_qx.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n", "eta_px:Tensor = torch.tensor([value for (key, value), kind in zip(eta_px.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n", "eta_qy:Tensor = torch.tensor([value for (key, value), kind in zip(eta_qy.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n", "eta_py:Tensor = torch.tensor([value for (key, value), kind in zip(eta_py.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": "2f1a9e24-452a-4b0a-8e76-89f4588d9fff", "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": "2f3c18b8-99e3-469b-9874-003b16217045", "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": "7f69c280-9063-4f73-8682-398aa4fac2b9", "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": "8c8dedfd-32b9-4318-a743-18564b3b78f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "True\n", "True\n", "True\n" ] } ], "source": [ "# Compare dispersion\n", "\n", "guess = torch.tensor(4*[0.0], dtype=torch.float64)\n", "\n", "etaqx, etapx, etaqy, etapy = dispersion(ring, guess, [], alignment=False)\n", "\n", "print(torch.allclose(eta_qx, etaqx))\n", "print(torch.allclose(eta_px, etapx))\n", "print(torch.allclose(eta_qy, etapy))\n", "print(torch.allclose(eta_py, etaqy))" ] }, { "cell_type": "code", "execution_count": 8, "id": "8e33a4cc-47f6-4be6-9588-75f55c068b66", "metadata": {}, "outputs": [], "source": [ "# Define parametric normalized dispersion\n", "\n", "def normalized_dispersion(kn, line=ring):\n", " guess = torch.tensor(4*[0.0], dtype=torch.float64)\n", " etaqx, _, etaqy, _ = dispersion(line, guess, [kn], ('kn', ['Quadrupole'], None, None), alignment=False)\n", " _, bqx, _, bqy = twiss(line, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True, advance=True, full=False, convert=True).T \n", " return torch.stack([etaqx/bqx.sqrt(), etaqy/bqy.sqrt()])" ] }, { "cell_type": "code", "execution_count": 9, "id": "83409fc7-85ad-4a6e-a74f-f88fed490aac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([16, 4, 28])\n", "torch.Size([16, 2, 28])\n" ] } ], "source": [ "# Compute twiss and normalized dispersion derivatives\n", "\n", "kn = torch.zeros(nq, dtype=torch.float64)\n", "\n", "dtwiss_dkn = torch.func.jacrev(lambda kn: twiss(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True, advance=True, full=False, convert=True))(kn)\n", "dnormal_dkn = torch.func.jacrev(normalized_dispersion)(kn).swapaxes(0, 1)\n", "\n", "print(dtwiss_dkn.shape)\n", "print(dnormal_dkn.shape)" ] }, { "cell_type": "code", "execution_count": 10, "id": "08c35c8f-80d7-41f0-996e-6aee17f9bd5b", "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": 11, "id": "b71b5cbf-140f-4904-ad85-910b70d4e483", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(2.4390, dtype=torch.float64)\n", "tensor(1.4076, dtype=torch.float64)\n", "tensor(2.3071, dtype=torch.float64)\n", "tensor(1.4153, dtype=torch.float64)\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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": 12, "id": "25971ca5-25f1-47a2-9fd3-94a0d6b68ea6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0782, dtype=torch.float64)\n", "tensor(0., dtype=torch.float64)\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compute and plot normalized dispersion\n", "\n", "netaqx_model, netaqy_model = normalized_dispersion(kn, ring)\n", "netaqx_error, netaqy_error = normalized_dispersion(kn, error)\n", "\n", "print((netaqx_model - netaqx_error).norm())\n", "print((netaqy_model - netaqy_error).norm())\n", "print()\n", "\n", "plt.figure(figsize=(16, 2))\n", "plt.plot(ring.locations().cpu().numpy(), (netaqx_model - netaqx_error).cpu().numpy(), color='red', alpha=0.75, marker='o')\n", "plt.plot(ring.locations().cpu().numpy(), (netaqy_model - netaqy_error).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": 13, "id": "2044cf11-6239-4797-b1cd-79a6c0b8027d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([3.9058, 0.0782], dtype=torch.float64)\n", "tensor([3.5081, 0.0731], dtype=torch.float64)\n", "tensor([3.1346, 0.0685], dtype=torch.float64)\n", "tensor([2.7876, 0.0644], dtype=torch.float64)\n", "tensor([2.4690, 0.0608], dtype=torch.float64)\n", "tensor([2.1800, 0.0576], dtype=torch.float64)\n", "tensor([1.9211, 0.0548], dtype=torch.float64)\n", "tensor([1.6919, 0.0523], dtype=torch.float64)\n", "tensor([1.4908, 0.0501], dtype=torch.float64)\n", "tensor([1.3159, 0.0481], dtype=torch.float64)\n", "tensor([1.1645, 0.0462], dtype=torch.float64)\n", "tensor([1.0341, 0.0445], dtype=torch.float64)\n", "tensor([0.9219, 0.0429], dtype=torch.float64)\n", "tensor([0.8253, 0.0414], dtype=torch.float64)\n", "tensor([0.7420, 0.0400], dtype=torch.float64)\n", "tensor([0.6700, 0.0386], dtype=torch.float64)\n", "tensor([0.6076, 0.0372], dtype=torch.float64)\n", "tensor([0.5531, 0.0359], dtype=torch.float64)\n", "tensor([0.5054, 0.0346], dtype=torch.float64)\n", "tensor([0.4634, 0.0334], dtype=torch.float64)\n", "tensor([0.4263, 0.0322], dtype=torch.float64)\n", "tensor([0.3932, 0.0310], dtype=torch.float64)\n", "tensor([0.3636, 0.0299], dtype=torch.float64)\n", "tensor([0.3371, 0.0287], dtype=torch.float64)\n", "tensor([0.3131, 0.0277], dtype=torch.float64)\n", "tensor([0.2914, 0.0266], dtype=torch.float64)\n", "tensor([0.2716, 0.0256], dtype=torch.float64)\n", "tensor([0.2535, 0.0246], dtype=torch.float64)\n", "tensor([0.2370, 0.0236], dtype=torch.float64)\n", "tensor([0.2217, 0.0227], dtype=torch.float64)\n", "tensor([0.2077, 0.0218], dtype=torch.float64)\n", "tensor([0.1948, 0.0210], dtype=torch.float64)\n", "tensor([0.1828, 0.0201], dtype=torch.float64)\n", "tensor([0.1716, 0.0193], dtype=torch.float64)\n", "tensor([0.1613, 0.0185], dtype=torch.float64)\n", "tensor([0.1517, 0.0178], dtype=torch.float64)\n", "tensor([0.1427, 0.0171], dtype=torch.float64)\n", "tensor([0.1343, 0.0164], dtype=torch.float64)\n", "tensor([0.1265, 0.0157], dtype=torch.float64)\n", "tensor([0.1192, 0.0151], dtype=torch.float64)\n", "tensor([0.1123, 0.0144], dtype=torch.float64)\n", "tensor([0.1059, 0.0138], dtype=torch.float64)\n", "tensor([0.0999, 0.0133], dtype=torch.float64)\n", "tensor([0.0942, 0.0127], dtype=torch.float64)\n", "tensor([0.0889, 0.0122], dtype=torch.float64)\n", "tensor([0.0839, 0.0117], dtype=torch.float64)\n", "tensor([0.0792, 0.0112], dtype=torch.float64)\n", "tensor([0.0748, 0.0107], dtype=torch.float64)\n", "tensor([0.0706, 0.0102], dtype=torch.float64)\n", "tensor([0.0667, 0.0098], dtype=torch.float64)\n", "tensor([0.0630, 0.0094], dtype=torch.float64)\n", "tensor([0.0595, 0.0090], dtype=torch.float64)\n", "tensor([0.0563, 0.0086], dtype=torch.float64)\n", "tensor([0.0532, 0.0082], dtype=torch.float64)\n", "tensor([0.0503, 0.0079], dtype=torch.float64)\n", "tensor([0.0475, 0.0075], dtype=torch.float64)\n", "tensor([0.0449, 0.0072], dtype=torch.float64)\n", "tensor([0.0425, 0.0069], dtype=torch.float64)\n", "tensor([0.0402, 0.0066], dtype=torch.float64)\n", "tensor([0.0380, 0.0063], dtype=torch.float64)\n", "tensor([0.0359, 0.0060], dtype=torch.float64)\n", "tensor([0.0340, 0.0058], dtype=torch.float64)\n", "tensor([0.0322, 0.0055], dtype=torch.float64)\n", "tensor([0.0304, 0.0053], dtype=torch.float64)\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Perform correction (model to experiment)\n", "\n", "# Set response matrix\n", "\n", "matrix = torch.vstack([dtwiss_dkn.reshape(-1, nq), dnormal_dkn.reshape(-1, nq)])\n", "\n", "# Set target twiss parameters\n", "\n", "twiss_error = twiss(error, [], alignment=False, matched=True, advance=True, full=False, convert=True)\n", "\n", "# Set target normalized dispesion\n", "\n", "normal_error = normalized_dispersion(0*kn, error)\n", "\n", "# Set learning rate\n", "\n", "lr = 0.1\n", "\n", "# Set initial values\n", "\n", "kn = torch.zeros_like(error_kn)\n", "\n", "# Fit\n", "\n", "for _ in range(64):\n", " twiss_model = twiss(ring, [kn], ('kn', ['Quadrupole'], None, None), alignment=False, matched=True, advance=True, full=False, convert=True)\n", " normal_model = normalized_dispersion(kn, ring)\n", " dkn = - lr*torch.linalg.lstsq(matrix, torch.cat([(twiss_model - twiss_error).flatten(), (normal_model - normal_error).flatten()]), driver='gelsd').solution\n", " kn += dkn\n", " print(torch.stack([(twiss_model - twiss_error).norm(), (normal_model - normal_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": 14, "id": "896cc36e-cd1f-4830-a51f-375b19931281", "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": 15, "id": "0c811568-bb3c-4a8c-8971-359b3c28dcf6", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "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()" ] } ], "metadata": { "colab": { "collapsed_sections": [ "myt0_gMIOq7b", "5d97819c" ], "name": "03_frequency.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.1" }, "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 } }, "nbformat": 4, "nbformat_minor": 5 }