finesse-simulation-O4/high order mode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6ba3eb0-8f27-4ebd-b407-3f25f449c6bf",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd9299aa-a531-468c-b04b-798b06315f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pyright: reportUnknownArgumentType=false, reportCallIssue=false, reportAttributeAccessIssue=false, reportOptionalSubscript=false, reportArgumentType=false\n",
    "from rich.console import Console\n",
    "from rich.table import Table\n",
    "from rich.theme import Theme\n",
    "from rich.logging import RichHandler\n",
    "\n",
    "from finesse.model import Model\n",
    "from finesse.analysis.actions.axes import Noxaxis, Xaxis\n",
    "from finesse.solutions import SeriesSolution\n",
    "from finesse.detectors import PowerDetector\n",
    "from finesse.analysis.actions import (\n",
    "    TemporaryParameters,\n",
    "    Change,\n",
    "    Maximize,\n",
    "    Minimize,\n",
    "    Series,\n",
    "    FrequencyResponse,\n",
    "    Noxaxis,\n",
    ")\n",
    "from finesse.components import Mirror, SignalGenerator\n",
    "from finesse.detectors import QuantumNoiseDetector\n",
    "from finesse.exceptions import ModelMissingAttributeError\n",
    "\n",
    "from pathlib import Path\n",
    "from typing import NamedTuple\n",
    "import re\n",
    "\n",
    "from matplotlib.axes import Axes\n",
    "from matplotlib.pyplot import figure, show\n",
    "\n",
    "\n",
    "from numpy import (\n",
    "    linspace,\n",
    "    geomspace,\n",
    "    pi,\n",
    "    angle,\n",
    "    diff,\n",
    "    loadtxt,\n",
    "    load,\n",
    "    sqrt,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c038d40-1d01-49cb-9182-a9a0e94d0d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gettext import install\n",
    "from logging import getLogger, basicConfig, INFO, WARNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12fc3934-0929-479f-9431-95bd8788d7ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import (\n",
    "    compute_solutions,\n",
    "    process_solution,\n",
    "    DisplayData,\n",
    "    display_displaydata,\n",
    "    fix_dark_fringe,\n",
    "    get_QNLS,\n",
    ")\n",
    "from MaskedReadoutDC import MaskedReadoutDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d4f2612-c5ea-4b21-a326-7074022966bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "install(__name__)\n",
    "theme = Theme(\n",
    "    {\n",
    "        \"strong\": \"cyan underline\",\n",
    "        \"result\": \"red bold\",\n",
    "    }\n",
    ")\n",
    "console = Console(theme=theme)\n",
    "basicConfig(\n",
    "    level=WARNING,\n",
    "    format=\"%(message)s\",\n",
    "    datefmt=\"[%X]\",\n",
    "    handlers=[RichHandler(console=console, rich_tracebacks=True)],\n",
    ")\n",
    "logger = getLogger(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7d2340-c817-4309-9599-6d58070ff4ab",
   "metadata": {},
   "source": [
    "## Paramètres généraux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8fc23eea-145e-4641-93e9-6f8989edca96",
   "metadata": {},
   "outputs": [],
   "source": [
    "C_POWER = 25  # en Whatt\n",
    "C_DARK_FRINGE = 8e-3  # en Whatt\n",
    "C_PRECISION = 100  # number of points in simulation\n",
    "C_DEBUG = False  # if some figure should be displayed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3052aa2b-350e-4eb3-b31c-a4204ab84dac",
   "metadata": {},
   "source": [
    "## Modèle simplifié de Virgo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d32480d0-8525-478a-9af5-b1a1c8b30f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model: Model = Model()\n",
    "model.phase_config(zero_k00=False, zero_tem00_gouy=True)\n",
    "\n",
    "model.parse(Path(\"model.kat\").read_text())\n",
    "model.lambda0 = model.get(\"wavelength\")\n",
    "model.SR.xbeta = 2e-6  # yaw rotation of SR\n",
    "model.laser.P = C_POWER\n",
    "\n",
    "if C_DEBUG:\n",
    "    # Show model elements\n",
    "    graph = model.plot_graph()\n",
    "    show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "370c1750-922a-4235-954f-dd8116a7a8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[17:34:02] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Adding B1 to the model                                                         <a href=\"file:///tmp/ipykernel_35177/2923683328.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2923683328.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_35177/2923683328.py#12\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">12</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[17:34:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Adding B1 to the model                                                         \u001b]8;id=576034;file:///tmp/ipykernel_35177/2923683328.py\u001b\\\u001b[2m2923683328.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=441493;file:///tmp/ipykernel_35177/2923683328.py#12\u001b\\\u001b[2m12\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    model.get(\"B1\")\n",
    "    logger.info(\"B1 already exists\")\n",
    "except ModelMissingAttributeError:\n",
    "    model.add(\n",
    "        MaskedReadoutDC(\n",
    "            \"B1\",\n",
    "            output_detectors=True,\n",
    "            optical_node=model.SDB1.p2.o,\n",
    "        )\n",
    "    )\n",
    "    logger.info(\"Adding B1 to the model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b742cd14-2149-437b-9194-249cf40849b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.run(\n",
    "    TemporaryParameters(\n",
    "        Series(\n",
    "            Change(\n",
    "                {\n",
    "                    \"SR.misaligned\": True,\n",
    "                    \"PR.misaligned\": True,\n",
    "                }\n",
    "            ),\n",
    "            Maximize(\n",
    "                model.get(\"NE_p1\"),\n",
    "                model.get(\"NORTH_ARM.DC\"),\n",
    "                bounds=[-180, 180],\n",
    "                tol=1e-14,\n",
    "            ),\n",
    "            Maximize(\n",
    "                model.get(\"WE_p1\"),\n",
    "                model.get(\"WEST_ARM.DC\"),\n",
    "                bounds=[-180, 180],\n",
    "                tol=1e-14,\n",
    "            ),\n",
    "            Minimize(\n",
    "                model.get(\"SR_p2\"),\n",
    "                model.get(\"MICH.DC\"),\n",
    "                bounds=[-180, 180],\n",
    "                tol=1e-14,\n",
    "            ),\n",
    "            Change(\n",
    "                {\n",
    "                    \"PR.misaligned\": False,\n",
    "                }\n",
    "            ),\n",
    "            Maximize(\n",
    "                model.get(\"PR_p2\"),\n",
    "                model.get(\"PRCL.DC\"),\n",
    "                bounds=[-180, 180],\n",
    "                tol=1e-14,\n",
    "            ),\n",
    "            Change(\n",
    "                {\n",
    "                    \"SR.misaligned\": False,\n",
    "                }\n",
    "            ),\n",
    "            Maximize(\n",
    "                model.get(\"B1_DC\"),\n",
    "                model.get(\"SRCL.DC\"),\n",
    "                bounds=[-180, 180],\n",
    "                tol=1e-14,\n",
    "            ),\n",
    "            Change(\n",
    "                {\n",
    "                    \"SRCL.DC\": -90,\n",
    "                },\n",
    "                relative=True,\n",
    "            ),\n",
    "        ),\n",
    "        exclude=[\n",
    "            \"NE.phi\",\n",
    "            \"NI.phi\",\n",
    "            \"WE.phi\",\n",
    "            \"WI.phi\",\n",
    "            \"SR.phi\",\n",
    "            \"PR.phi\",\n",
    "            \"NORTH_ARM.DC\",\n",
    "            \"WEST_ARM.DC\",\n",
    "            \"DARM.DC\",\n",
    "            \"MICH.DC\",\n",
    "            \"PRCL.DC\",\n",
    "            \"SRCL.DC\",\n",
    "            \"SR.misaligned\",\n",
    "        ],\n",
    "    ),\n",
    ")\n",
    "model.B1.select_mask(exclude=(0, 0))\n",
    "model.modes(maxtem=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "14c711bb-820e-4ee1-91e9-b1d29350513d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">0.00109100341796875</span> with <span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">17</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> steps</span> give <span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">0.007989</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> mW</span> on the <span style=\"color: #008080; text-decoration-color: #008080; text-decoration: underline\">dark fringe</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;31m0.00109100341796875\u001b[0m with \u001b[1;31m17\u001b[0m\u001b[1;31m steps\u001b[0m give \u001b[1;31m0.007989\u001b[0m\u001b[1;31m mW\u001b[0m on the \u001b[4;36mdark fringe\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logger.setLevel(WARNING)\n",
    "for handler in logger.handlers:\n",
    "    handler.setLevel(WARNING)\n",
    "display_displaydata(\n",
    "    model,\n",
    "    [\n",
    "        DisplayData(\"NORTH_ARM\", 10),\n",
    "        DisplayData(\"WEST_ARM\", 10),\n",
    "        DisplayData(\"PRCL\", 10),\n",
    "        DisplayData(\"MICH\", 10),\n",
    "        DisplayData(\"DARM\", 10),\n",
    "        DisplayData(\"CARM\", 10),\n",
    "    ],\n",
    ")\n",
    "number, power = fix_dark_fringe(model, C_DARK_FRINGE)\n",
    "console.print(\n",
    "    \"[result]{dof}[/result] with [result]{number} steps[/result] give [result]{power:.6f} mW[/result] on the [strong]dark fringe[/strong]\".format(\n",
    "        number=number,\n",
    "        dof=model.DARM.DC,\n",
    "        power=power,\n",
    "    )\n",
    ")\n",
    "logger.setLevel(INFO)\n",
    "for handler in logger.handlers:\n",
    "    handler.setLevel(INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66c9be03-cedc-4d07-acbd-3269f3143cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">             Puissances dans l'interferomètre              </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> position                         </span>┃<span style=\"font-weight: bold\"> puissance (W)        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Injection                        </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 25.0                 </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> PR                               </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 3.529110765817165    </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> cavité de recyclage de puissance </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 965.3742229980147    </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> cavité ouest                     </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 135259.4179445027    </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> cavité nord                      </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 133191.31054722168   </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> frange noire                     </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.007990521602279099 </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SNEB                             </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.5978209233893383   </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SWEB                             </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.5815451496990748   </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SDB1                             </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.007989394938733177 </span>│\n",
       "└──────────────────────────────────┴──────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m             Puissances dans l'interferomètre              \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mposition                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mpuissance (W)       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[37m \u001b[0m\u001b[37mInjection                       \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m25.0                \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mPR                              \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m3.529110765817165   \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mcavité de recyclage de puissance\u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m965.3742229980147   \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mcavité ouest                    \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m135259.4179445027   \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mcavité nord                     \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m133191.31054722168  \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mfrange noire                    \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.007990521602279099\u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mSNEB                            \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.5978209233893383  \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mSWEB                            \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.5815451496990748  \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mSDB1                            \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.007989394938733177\u001b[0m\u001b[36m \u001b[0m│\n",
       "└──────────────────────────────────┴──────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">       DOF dans l'interferomètre        </span>\n",
       "┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> nom        </span>┃<span style=\"font-weight: bold\"> valeur                  </span>┃\n",
       "┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Bras nord  </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> 6.592588499188424e-06   </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Bras ouest </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -5.8479547500610346e-05 </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> PR         </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -44.946681098699614     </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SR         </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -135.0960161170923      </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> MICH       </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -89.79029219055182      </span>│\n",
       "└────────────┴─────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m       DOF dans l'interferomètre        \u001b[0m\n",
       "┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mnom       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mvaleur                 \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[37m \u001b[0m\u001b[37mBras nord \u001b[0m\u001b[37m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m6.592588499188424e-06  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mBras ouest\u001b[0m\u001b[37m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m-5.8479547500610346e-05\u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mPR        \u001b[0m\u001b[37m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m-44.946681098699614    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mSR        \u001b[0m\u001b[37m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m-135.0960161170923     \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mMICH      \u001b[0m\u001b[37m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m-89.79029219055182     \u001b[0m\u001b[35m \u001b[0m│\n",
       "└────────────┴─────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> nom                   </span>┃<span style=\"font-weight: bold\"> valeur                          </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> FSR                   </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 49968.74091606107               </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Loss                  </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.013848722846499961            </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Finesse               </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 450.5516190359727               </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> FWHM                  </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 110.90569605093683              </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Storage time          </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.0028700950223295757           </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Pole                  </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 55.45284802546841               </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Round trip length     </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 5999.6                          </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Waist size            </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [0.00968668 0.00968668]         </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Waist position        </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [-1363.71492094 -1363.71492094] </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Stability (m-factor)  </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [0.7409226 0.7409226]           </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Stability (g-factor)  </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [0.8704613 0.8704613]           </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Round trip gouy phase </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [317.81006617 317.81006617]     </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Mode separation       </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [5856.04964666 5856.04964666]   </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Resolution            </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [52.80206387 52.80206387]       </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Stable                </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> True                            </span>│\n",
       "│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Critically stable     </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> False                           </span>│\n",
       "└───────────────────────┴─────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mnom                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mvaleur                         \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[37m \u001b[0m\u001b[37mFSR                  \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m49968.74091606107              \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mLoss                 \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.013848722846499961           \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mFinesse              \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m450.5516190359727              \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mFWHM                 \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m110.90569605093683             \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mStorage time         \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.0028700950223295757          \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mPole                 \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m55.45284802546841              \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mRound trip length    \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m5999.6                         \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mWaist size           \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[0.00968668 0.00968668]        \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mWaist position       \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[-1363.71492094 -1363.71492094]\u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mStability (m-factor) \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[0.7409226 0.7409226]          \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mStability (g-factor) \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[0.8704613 0.8704613]          \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mRound trip gouy phase\u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[317.81006617 317.81006617]    \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mMode separation      \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[5856.04964666 5856.04964666]  \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mResolution           \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m[52.80206387 52.80206387]      \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mStable               \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36mTrue                           \u001b[0m\u001b[36m \u001b[0m│\n",
       "│\u001b[37m \u001b[0m\u001b[37mCritically stable    \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36mFalse                          \u001b[0m\u001b[36m \u001b[0m│\n",
       "└───────────────────────┴─────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution = model.run(Noxaxis())\n",
    "console = Console()\n",
    "table = Table(title=\"Puissances dans l'interferomètre\")\n",
    "table.add_column(\"position\", justify=\"left\", style=\"white\")\n",
    "table.add_column(\"puissance (W)\", justify=\"left\", style=\"cyan\")\n",
    "\n",
    "table.add_row(\"Injection\", str(model.get(\"laser\").P.eval()))\n",
    "table.add_row(\"PR\", str(solution[\"PR_p1\"]))\n",
    "table.add_row(\n",
    "    \"cavité de recyclage de puissance\", str(solution[\"PR_p2\"])\n",
    ")\n",
    "table.add_row(\"cavité ouest\", str(solution[\"WE_p1\"]))\n",
    "table.add_row(\"cavité nord\", str(solution[\"NE_p1\"]))\n",
    "table.add_row(\"frange noire\", str(solution[\"SR_p2\"]))\n",
    "table.add_row(\"SNEB\", str(solution[\"SNEB_DC\"]))\n",
    "table.add_row(\"SWEB\", str(solution[\"SWEB_DC\"]))\n",
    "table.add_row(\"SDB1\", str(solution[\"SDB1_DC\"]))\n",
    "\n",
    "console.print(table)\n",
    "\n",
    "table = Table(title=\"DOF dans l'interferomètre\")\n",
    "table.add_column(\"nom\", justify=\"left\", style=\"white\")\n",
    "table.add_column(\"valeur\", justify=\"left\", style=\"magenta\")\n",
    "\n",
    "table.add_row(\"Bras nord\", str(model.get(\"NORTH_ARM.DC\")))\n",
    "table.add_row(\"Bras ouest\", str(model.get(\"WEST_ARM.DC\")))\n",
    "table.add_row(\"PR\", str(model.get(\"PRCL.DC\")))\n",
    "table.add_row(\"SR\", str(model.get(\"SRCL.DC\")))\n",
    "table.add_row(\"MICH\", str(model.get(\"MICH.DC\")))\n",
    "\n",
    "console.print(table)\n",
    "\n",
    "console = Console(theme=theme)\n",
    "table = Table(title=\"\")\n",
    "table.add_column(\"nom\", justify=\"left\", style=\"white\")\n",
    "table.add_column(\"valeur\", justify=\"left\", style=\"cyan\")\n",
    "for i in range(1, model.west_arm.info_parameter_table().table.shape[0]):\n",
    "    table.add_row(\n",
    "        str(model.west_arm.info_parameter_table().table[i, 0]),\n",
    "        str(model.west_arm.info_parameter_table().table[i, 1]),\n",
    "    )\n",
    "console.print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7fde067a-52b7-4798-8bd6-0890e27f33e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/demagny/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/base.py:157: UserWarning: Signal frequency (fsig) was set to None but simulation needs it. Setting default value of 1 Hz\n",
      "  self.__sim.__enter__()\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'B1.DC.o'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[30]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m     25\u001b[39m Figure.gca().grid(\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mboth\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mboth\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     26\u001b[39m show()\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m solution = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     29\u001b[39m \u001b[43m    \u001b[49m\u001b[43mFrequencyResponse\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     30\u001b[39m \u001b[43m        \u001b[49m\u001b[43mgeomspace\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m10000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mC_PRECISION\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDARM\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mB1_detector\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m     31\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     32\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m     33\u001b[39m maximum_amplitude_step: \u001b[38;5;28mfloat\u001b[39m = \u001b[38;5;28mmax\u001b[39m(\n\u001b[32m     34\u001b[39m     \u001b[38;5;28mabs\u001b[39m(diff(angle(solution[B1_detector, \u001b[33m\"\u001b[39m\u001b[33mDARM\u001b[39m\u001b[33m\"\u001b[39m])))\n\u001b[32m     35\u001b[39m )\n\u001b[32m     37\u001b[39m pole_index = \u001b[38;5;28mround\u001b[39m(\n\u001b[32m     38\u001b[39m     mean(\n\u001b[32m     39\u001b[39m         where(\n\u001b[32m   (...)\u001b[39m\u001b[32m     43\u001b[39m     )\n\u001b[32m     44\u001b[39m )  \u001b[38;5;66;03m# find the index where the curve is the closest to -45°\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/model.py:108\u001b[39m, in \u001b[36mlocked_when_built.<locals>.wrapper\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m    104\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.is_built:\n\u001b[32m    105\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[32m    106\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mModel has been built for a simulation, cannot use \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m here\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    107\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m108\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/model.py:3277\u001b[39m, in \u001b[36mModel.run\u001b[39m\u001b[34m(self, analysis, return_state, progress_bar, simulation_type, simulation_options)\u001b[39m\n\u001b[32m   3274\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   3275\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCannot handle analysis input `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00manalysis\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m`\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m3277\u001b[39m rtn = \u001b[43m_analysis\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3278\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3279\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreturn_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreturn_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3280\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3281\u001b[39m \u001b[43m    \u001b[49m\u001b[43msimulation_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43msimulation_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3282\u001b[39m \u001b[43m    \u001b[49m\u001b[43msimulation_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43msimulation_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3283\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3285\u001b[39m \u001b[38;5;28mself\u001b[39m.analysis = curr_analysis\n\u001b[32m   3287\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_state:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/base.py:239\u001b[39m, in \u001b[36mAction._run\u001b[39m\u001b[34m(self, model, return_state, progress_bar, simulation_type, simulation_options)\u001b[39m\n\u001b[32m    236\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    237\u001b[39m     action = \u001b[38;5;28mself\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m239\u001b[39m result = \u001b[43mstate\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    241\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(result) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m:\n\u001b[32m    242\u001b[39m     sol = BaseSolution(\u001b[33m\"\u001b[39m\u001b[33mroot\u001b[39m\u001b[33m\"\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/base.py:105\u001b[39m, in \u001b[36mAnalysisState.apply\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    103\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, action):\n\u001b[32m    104\u001b[39m     start = time.time_ns()\n\u001b[32m--> \u001b[39m\u001b[32m105\u001b[39m     sol = \u001b[43maction\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_do\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    106\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m sol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    107\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(sol, BaseSolution):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/series.py:70\u001b[39m, in \u001b[36mSeries._do\u001b[39m\u001b[34m(self, state)\u001b[39m\n\u001b[32m     68\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m pbar:\n\u001b[32m     69\u001b[39m     pbar.set_description_str(\u001b[38;5;28mself\u001b[39m.name)\n\u001b[32m---> \u001b[39m\u001b[32m70\u001b[39m     next_sol = \u001b[43mstate\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     71\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.flatten \u001b[38;5;129;01mand\u001b[39;00m next_sol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m     72\u001b[39m         first.add(next_sol)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/base.py:105\u001b[39m, in \u001b[36mAnalysisState.apply\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    103\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, action):\n\u001b[32m    104\u001b[39m     start = time.time_ns()\n\u001b[32m--> \u001b[39m\u001b[32m105\u001b[39m     sol = \u001b[43maction\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_do\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    106\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m sol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    107\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(sol, BaseSolution):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/lti.py:442\u001b[39m, in \u001b[36mFrequencyResponse._do\u001b[39m\u001b[34m(self, state, fsig_independant_outputs, fsig_dependant_outputs)\u001b[39m\n\u001b[32m    439\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m node.type \u001b[38;5;129;01mis\u001b[39;00m NodeType.MECHANICAL:\n\u001b[32m    440\u001b[39m             output_scaling[i] *= state.sim.model_settings.x_scale\n\u001b[32m--> \u001b[39m\u001b[32m442\u001b[39m         output_node_indices[i] = \u001b[43mstate\u001b[49m\u001b[43m.\u001b[49m\u001b[43msim\u001b[49m\u001b[43m.\u001b[49m\u001b[43msignal\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnode_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    444\u001b[39m sol = FrequencyResponseSolution(\u001b[38;5;28mself\u001b[39m.name)\n\u001b[32m    445\u001b[39m sol.f = \u001b[38;5;28mself\u001b[39m.f\n",
      "\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/simulations/basesolver.pyx:321\u001b[39m, in \u001b[36mfinesse.simulations.basesolver.BaseSolver.node_id\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/simulations/basesolver.pyx:325\u001b[39m, in \u001b[36mfinesse.simulations.basesolver.BaseSolver.node_id\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mKeyError\u001b[39m: 'B1.DC.o'"
     ]
    }
   ],
   "source": [
    "solution = get_QNLS(model, 5, 5000, C_PRECISION)\n",
    "\n",
    "B1_detector = model.B1.DC\n",
    "\n",
    "QNLS = load(\"sensitivities/finesse-virgo.npy\")\n",
    "current_O4_sensitivity_ASD = loadtxt(\n",
    "    \"sensitivities/O4_nominal_reference.txt\"\n",
    ")\n",
    "\n",
    "Figure = figure(figsize=(14, 5))\n",
    "_ = Figure.gca().loglog(\n",
    "    solution.x1, abs(solution[\"NSR_with_RP\"]), label=\"this lock process\"\n",
    ")\n",
    "_ = Figure.gca().loglog(\n",
    "    QNLS[0],\n",
    "    QNLS[1],\n",
    "    label=\"packaged lock process\",\n",
    ")\n",
    "_ = Figure.gca().loglog(\n",
    "    current_O4_sensitivity_ASD[0],\n",
    "    abs(current_O4_sensitivity_ASD[1]),\n",
    "    label=\"current nominal sensitivity during O4\",\n",
    ")\n",
    "_ = Figure.gca().legend()\n",
    "Figure.gca().grid(True, \"both\", \"both\")\n",
    "show()\n",
    "\n",
    "solution = model.run(\n",
    "    FrequencyResponse(\n",
    "        geomspace(5, 10000, C_PRECISION), [\"DARM\"], [B1_detector]\n",
    "    )\n",
    ")\n",
    "maximum_amplitude_step: float = max(\n",
    "    abs(diff(angle(solution[B1_detector, \"DARM\"])))\n",
    ")\n",
    "\n",
    "pole_index = round(\n",
    "    mean(\n",
    "        where(\n",
    "            abs(angle(solution[B1_detector, \"DARM\"]) + pi / 4)\n",
    "            < maximum_amplitude_step * 2\n",
    "        )\n",
    "    )\n",
    ")  # find the index where the curve is the closest to -45°\n",
    "console.print(\n",
    "    \"Le [strong]pôle[/strong] de la fonction de transfert [strong]DARM[/strong] est à [result]{:.1f}[/result] Hz\".format(\n",
    "        solution.f[pole_index]\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "table = Table(title=\"Position des différents miroirs\")\n",
    "table.add_column(\"miroir\", justify=\"left\", style=\"white\")\n",
    "table.add_column(\"offset (°)\", justify=\"left\", style=\"white\")\n",
    "table.add_column(\"offset (m)\", justify=\"left\", style=\"white\")\n",
    "\n",
    "for name in [\n",
    "    \"NE\",\n",
    "    \"NE_AR\",\n",
    "    \"NI\",\n",
    "    \"NI_AR\",\n",
    "    \"WE\",\n",
    "    \"WE_AR\",\n",
    "    \"WI\",\n",
    "    \"WI_AR\",\n",
    "    \"PR\",\n",
    "    \"PR_AR\",\n",
    "    \"SR\",\n",
    "    \"SR_AR\",\n",
    "]:\n",
    "    element: Mirror = model.get(name)\n",
    "    table.add_row(\n",
    "        str(element.name),\n",
    "        str(element.phi.eval()),\n",
    "        str(element.phi.eval() * model.lambda0 / 180),\n",
    "    )\n",
    "\n",
    "console.print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5ff122-8d97-43f2-982a-084ee984b827",
   "metadata": {},
   "source": [
    "## Comparaison avec Optickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd3b1078-0fd6-4e1b-9e55-b231d9464bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.SNEB.phi = model.NE.phi - 45\n",
    "model.SWEB.phi = model.WE.phi - 45\n",
    "model.SDB1.phi = model.SR.phi + 45\n",
    "\n",
    "quad_tf: dict[str, SeriesSolution] = dict()\n",
    "in_tf: dict[str, SeriesSolution] = dict()\n",
    "\n",
    "for bench_name in [\"SNEB\", \"SWEB\", \"SDB1\"]:\n",
    "    quad_tf[bench_name] = model.run(\n",
    "        FrequencyResponse(\n",
    "            geomspace(5, 10000, C_PRECISION),\n",
    "            [\"{}_z\".format(bench_name)],\n",
    "            [B1_detector],\n",
    "        )\n",
    "    )\n",
    "\n",
    "quad_tf[\"DARM\"] = model.run(\n",
    "    FrequencyResponse(\n",
    "        geomspace(5, 10000, C_PRECISION), [\"DARM\"], [B1_detector]\n",
    "    )\n",
    ")\n",
    "\n",
    "model.SNEB.phi = model.NE.phi\n",
    "model.SWEB.phi = model.WE.phi\n",
    "model.SDB1.phi = model.SR.phi\n",
    "\n",
    "for bench_name in [\"SNEB\", \"SWEB\", \"SDB1\"]:\n",
    "    in_tf[bench_name] = model.run(\n",
    "        FrequencyResponse(\n",
    "            geomspace(5, 10000, C_PRECISION),\n",
    "            [\"{}_z\".format(bench_name)],\n",
    "            [B1_detector],\n",
    "        )\n",
    "    )\n",
    "\n",
    "in_tf[\"DARM\"] = model.run(\n",
    "    FrequencyResponse(\n",
    "        geomspace(5, 10000, C_PRECISION), [\"DARM\"], [B1_detector]\n",
    "    )\n",
    ")\n",
    "\n",
    "modelisation_file = Path(\"TF results/TEM00_2025-05-22.npy\")\n",
    "\n",
    "TEM00_TFs = load(modelisation_file, allow_pickle=True)\n",
    "TEM00_TF_in = TEM00_TFs[0]\n",
    "TEM00_TF_qu = TEM00_TFs[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be43f8b2-eddf-4dfc-a342-a54017b571f6",
   "metadata": {},
   "source": [
    "### En fonction de la phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c3d4e0-b8bc-48e0-83cd-5675c62feba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Figure = figure(figsize=(7, 5))\n",
    "ax = Figure.gca()\n",
    "_ = ax.loglog(\n",
    "    quad_tf[\"DARM\"].f,\n",
    "    abs(quad_tf[\"DARM\"][B1_detector, \"DARM\"]),\n",
    "    label=\"High order mode\",\n",
    ")\n",
    "_ = ax.loglog(\n",
    "    TEM00_TF_qu[\"DARM\"].f,\n",
    "    abs(TEM00_TF_qu[\"DARM\"][B1_detector, \"DARM\"]),\n",
    "    label=\"TEM00\",\n",
    ")\n",
    "_ = ax.set_ylabel(\"$\\\\frac{W}{\\\\sqrt{Hz}}$\")\n",
    "_ = ax.set_xlabel(\"Frequencies (Hz)\")\n",
    "_ = ax.set_title(\"Transfer function module comparison for DARM\")\n",
    "_ = ax.legend()\n",
    "ax.grid(True, \"both\", \"both\")\n",
    "\n",
    "Figure = figure(figsize=(7, 5))\n",
    "ax = Figure.gca()\n",
    "_ = ax.semilogx(\n",
    "    quad_tf[\"DARM\"].f,\n",
    "    angle(quad_tf[\"DARM\"][B1_detector, \"DARM\"]) * 180 / pi,\n",
    "    label=\"High order mode\",\n",
    ")\n",
    "_ = ax.semilogx(\n",
    "    TEM00_TF_qu[\"DARM\"].f,\n",
    "    angle(TEM00_TF_qu[\"DARM\"][B1_detector, \"DARM\"]) * 180 / pi,\n",
    "    label=\"TEM00\",\n",
    ")\n",
    "_ = ax.set_title(\"Comparison of transfer function phase for DARM\")\n",
    "_ = ax.set_ylabel(\"phase (°)\")\n",
    "_ = ax.set_xlabel(\"Frequencies (Hz)\")\n",
    "_ = ax.hlines(\n",
    "    [-45],\n",
    "    min(quad_tf[\"DARM\"].f),\n",
    "    max(quad_tf[\"DARM\"].f),\n",
    "    colors=\"red\",\n",
    "    label=\"45°\",\n",
    ")\n",
    "_ = ax.legend()\n",
    "ax.grid(True, \"both\", \"both\")\n",
    "\n",
    "for bench_name in [\"SNEB\", \"SWEB\", \"SDB1\"]:\n",
    "    in_index = 0\n",
    "    if bench_name == \"SDB1\":\n",
    "        in_index = 0\n",
    "    quad_index = (1 + in_index) % 2\n",
    "    Figure = figure(figsize=(14, 5))\n",
    "    _ = Figure.suptitle(\n",
    "        \"Comparison of transfer function module for {}\".format(\n",
    "            bench_name\n",
    "        )\n",
    "    )\n",
    "    ax = Figure.add_subplot(1, 2, 1)\n",
    "    _ = ax.loglog(\n",
    "        quad_tf[bench_name].f,\n",
    "        abs(quad_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        / abs(quad_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"High order mode\",\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        TEM00_TF_qu[bench_name].f,\n",
    "        abs(\n",
    "            TEM00_TF_qu[bench_name][\n",
    "                B1_detector, \"{}_z\".format(bench_name)\n",
    "            ]\n",
    "        )\n",
    "        / abs(TEM00_TF_qu[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"TEM00\",\n",
    "    )\n",
    "    _ = ax.set_ylabel(\"$\\\\frac{m}{m}$\")\n",
    "    _ = ax.set_xlabel(\"Frequencies (Hz)\")\n",
    "    _ = ax.set_title(\"$K_P$\")\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")\n",
    "    ax = Figure.add_subplot(1, 2, 2)\n",
    "    _ = ax.loglog(\n",
    "        in_tf[bench_name].f,\n",
    "        abs(in_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        / abs(in_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"High order mode\",\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        TEM00_TF_in[bench_name].f,\n",
    "        TEM00_TF_in[bench_name][B1_detector, \"{}_z\".format(bench_name)]\n",
    "        / abs(TEM00_TF_in[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"TEM00\",\n",
    "    )\n",
    "    _ = ax.set_ylabel(\"$\\\\frac{m}{m}$\")\n",
    "    _ = ax.set_xlabel(\"Frequencies (Hz)\")\n",
    "    _ = ax.set_title(\"$K_n$\")\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")\n",
    "    console.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "480e8cfe-181d-41b1-8416-ab5dcbf398a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for bench_name in [\"SNEB\", \"SWEB\", \"SDB1\"]:\n",
    "    Figure = figure(figsize=(7, 5))\n",
    "    ax = Figure.gca()\n",
    "    _ = ax.set_title(\n",
    "        \"Comparison of transfer function module for {}\".format(\n",
    "            bench_name\n",
    "        )\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        quad_tf[bench_name].f,\n",
    "        sqrt(\n",
    "            (\n",
    "                abs(\n",
    "                    quad_tf[bench_name][\n",
    "                        B1_detector, \"{}_z\".format(bench_name)\n",
    "                    ]\n",
    "                )\n",
    "                / abs(quad_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "                / model.space_NI_NE.L.eval()\n",
    "            )\n",
    "            ** 2\n",
    "            + (\n",
    "                abs(\n",
    "                    in_tf[bench_name][\n",
    "                        B1_detector, \"{}_z\".format(bench_name)\n",
    "                    ]\n",
    "                )\n",
    "                / abs(in_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "                / model.space_NI_NE.L.eval()\n",
    "            )\n",
    "            ** 2\n",
    "        ),\n",
    "        label=\"High order mode\",\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        TEM00_TF_qu[bench_name].f,\n",
    "        sqrt(\n",
    "            (\n",
    "                abs(\n",
    "                    TEM00_TF_qu[bench_name][\n",
    "                        B1_detector, \"{}_z\".format(bench_name)\n",
    "                    ]\n",
    "                )\n",
    "                / abs(TEM00_TF_qu[\"DARM\"][B1_detector, \"DARM\"])\n",
    "                / model.space_NI_NE.L.eval()\n",
    "            )\n",
    "            ** 2\n",
    "            + (\n",
    "                abs(\n",
    "                    TEM00_TF_in[bench_name][\n",
    "                        B1_detector, \"{}_z\".format(bench_name)\n",
    "                    ]\n",
    "                )\n",
    "                / abs(TEM00_TF_in[\"DARM\"][B1_detector, \"DARM\"])\n",
    "                / model.space_NI_NE.L.eval()\n",
    "            )\n",
    "            ** 2\n",
    "        ),\n",
    "        label=\"TEM00\",\n",
    "    )\n",
    "    _ = ax.set_ylabel(\"$\\\\frac{m}{m}$\")\n",
    "    _ = ax.set_xlabel(\"Frequencies (Hz)\")\n",
    "    _ = ax.set_title(\n",
    "        \"Sum of the module of the transfer function for {}\".format(\n",
    "            bench_name\n",
    "        )\n",
    "    )\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11606546-2606-404c-a75e-6e434d19084b",
   "metadata": {},
   "source": [
    "### En fonction de la simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88002141-6a42-4ae0-a614-4c2b8dd336ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "for bench_name in [\"SNEB\", \"SWEB\", \"SDB1\"]:\n",
    "    in_index = 0\n",
    "    if bench_name == \"SDB1\":\n",
    "        in_index = 0\n",
    "    quad_index = (1 + in_index) % 2\n",
    "    Figure = figure(figsize=(14, 5))\n",
    "    _ = Figure.suptitle(\n",
    "        \"Comparaison des fonctions de transfert pour {}\".format(\n",
    "            bench_name\n",
    "        )\n",
    "    )\n",
    "    ax = Figure.add_subplot(1, 2, 1)\n",
    "    _ = ax.loglog(\n",
    "        quad_tf[bench_name].f,\n",
    "        abs(quad_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        / abs(quad_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"Quadrature de phase\",\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        in_tf[bench_name].f,\n",
    "        abs(in_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        / abs(in_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"En phase\",\n",
    "    )\n",
    "    _ = ax.set_title(\"High order mode\")\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")\n",
    "    ax = Figure.add_subplot(1, 2, 2)\n",
    "    _ = ax.loglog(\n",
    "        TEM00_TF_qu[bench_name].f,\n",
    "        abs(\n",
    "            TEM00_TF_qu[bench_name][\n",
    "                B1_detector, \"{}_z\".format(bench_name)\n",
    "            ]\n",
    "        )\n",
    "        / abs(TEM00_TF_qu[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"Quadrature de phase\",\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        TEM00_TF_in[bench_name].f,\n",
    "        abs(\n",
    "            TEM00_TF_in[bench_name][\n",
    "                B1_detector, \"{}_z\".format(bench_name)\n",
    "            ]\n",
    "        )\n",
    "        / abs(TEM00_TF_in[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"En phase\",\n",
    "    )\n",
    "    _ = ax.set_title(\"TEM00\")\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")\n",
    "    show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c70d12b-b5ae-44b7-b0d3-6f054b697300",
   "metadata": {},
   "source": [
    "### En fonction du module/phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb1fd40c-83bb-4dc3-b259-7dd3a08d3b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for bench_name in [\"SNEB\", \"SWEB\", \"SDB1\"]:\n",
    "    Figure = figure(figsize=(14, 5))\n",
    "    _ = Figure.suptitle(\n",
    "        \"Comparaison des fonctions de transfert pour {}\".format(\n",
    "            bench_name\n",
    "        )\n",
    "    )\n",
    "    ax = Figure.add_subplot(1, 2, 1)\n",
    "    _ = ax.loglog(\n",
    "        quad_tf[bench_name].f,\n",
    "        abs(quad_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        / abs(quad_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"Quadrature de phase\",\n",
    "    )\n",
    "    _ = ax.loglog(\n",
    "        in_tf[bench_name].f,\n",
    "        abs(in_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        / abs(in_tf[\"DARM\"][B1_detector, \"DARM\"])\n",
    "        / model.space_NI_NE.L.eval(),\n",
    "        label=\"En phase\",\n",
    "    )\n",
    "    _ = ax.set_title(\"Module\")\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")\n",
    "    ax = Figure.add_subplot(1, 2, 2)\n",
    "    _ = ax.semilogx(\n",
    "        quad_tf[bench_name].f,\n",
    "        angle(\n",
    "            quad_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)]\n",
    "        )\n",
    "        * 180\n",
    "        / pi,\n",
    "        label=\"Quadrature de phase\",\n",
    "    )\n",
    "    _ = ax.semilogx(\n",
    "        in_tf[bench_name].f,\n",
    "        angle(in_tf[bench_name][B1_detector, \"{}_z\".format(bench_name)])\n",
    "        * 180\n",
    "        / pi,\n",
    "        label=\"En phase\",\n",
    "    )\n",
    "    _ = ax.set_title(\"Finesse\")\n",
    "    _ = ax.legend()\n",
    "    ax.grid(True, \"both\", \"both\")\n",
    "    show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99dbfa5a-df5c-4d48-ae76-546b7bae7092",
   "metadata": {},
   "outputs": [],
   "source": [
    "modes = model.modes()\n",
    "for mode in modes:\n",
    "    print(mode)\n",
    "    name = \"B1_power_{}_{}\".format(*mode)\n",
    "    model.add(PowerDetector(name, model.SDB1.p2.o))\n",
    "    temp_modes = [list(mode) for mode in modes]\n",
    "    temp_modes.remove(list(mode))\n",
    "    model.get(name).select_mask(temp_modes)\n",
    "\n",
    "result = model.run(Noxaxis())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d5e48d-81b5-48ad-9391-70e0fc471779",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = Table()\n",
    "table.add_column(\"Mode\")\n",
    "table.add_column(\"Power (W)\")\n",
    "\n",
    "somme = 0\n",
    "outputs: list[str] = []\n",
    "for mode in modes:\n",
    "    name = \"B1_power_{}_{}\".format(*mode)\n",
    "    table.add_row(\"{}, {}\".format(*mode), \"{}\".format(result[name]))\n",
    "    somme += result[name]\n",
    "\n",
    "table.add_row(\"Total\", \"{}\".format(somme))\n",
    "console.print(table)\n",
    "console.print(result[\"SDB1_DC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcdb1608-5815-4c90-b16a-bd2504148e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.B1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d96e810-6c21-4081-b425-f35accfe3fdf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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